Handbook of Child Language Disorders [2nd ed.]

Table of contents :
Cover......Page 1
Title......Page 4
Copyright......Page 5
Dedication......Page 6
CONTENTS......Page 8
About the Editor......Page 11
Contributors......Page 12
Preface......Page 17
PART I Typology of Child Language Disorders......Page 20
1 Specific Language Impairment......Page 22
2 Language Disorders in Children with Intellectual Disability of Genetic Origin......Page 71
3 Autism Spectrum Disorders......Page 101
4 Hearing Loss......Page 128
5 Dyslexia......Page 149
PART II Bases of Child Language Disorders......Page 168
6 Linguistics in Child Language Disorders......Page 170
7 Neurobiology of Child Language Disorders......Page 203
8 Working Memory in Child Language Disorders......Page 232
9 Perception and Production in Child Language Disorders......Page 257
10 Genetics of Child Language Disorders......Page 273
11 Model-Based Approaches to Child Language Disorders......Page 293
PART III Language Contexts of Child Language Disorders......Page 314
12 Bilingualism and Child Language Disorders......Page 316
13 Cross-Linguistic Studies of Child Language Disorders......Page 347
14 African American English and Child Language Disorders......Page 364
PART IV Deficits, Assessment, and Intervention in Child Language Disorders......Page 382
15 Morphosyntax in Child Language Disorders......Page 384
16 Semantics in Child Language Disorders......Page 411
17 Syntax in Child Language Disorders......Page 435
18 Pragmatics and Social Communication in Child Language Disorders......Page 460
19 Reading and Writing in Child Language Disorders......Page 480
20 Processing Speed, Attention, and Perception in Child Language Disorders......Page 500
PART V Research Methods in Child Language Disorders......Page 518
21 Language Production Approaches to Child Language Disorders......Page 520
22 Language Comprehension Approaches to Child Language Disorders......Page 548
23 Translational and Implementation Research in Child Language Disorders......Page 580
24 Neuroscience Approaches to Child Language Disorders......Page 596
Author Index......Page 620
Subject Index......Page 662

Citation preview

HANDBOOK OF CHILD LANGUAGE DISORDERS

The acquisition of language is one of the most remarkable human achievements. When language acquisition fails to occur as expected, the impact can be far-reaching, affecting all aspects of the child’s life and the child’s family. Thus, research into the nature, causes, and remediation of children’s language disorders provides important insights into the nature of language acquisition and its underlying bases and leads to innovative clinical approaches to these disorders. This second edition of the Handbook of Child Language Disorders brings together a distinguished group of clinical and academic researchers who present novel perspectives on researching the nature of language disorders in children. The handbook is divided into five sections: Typology; Bases; Language Contexts; Deficits, Assessment, and Intervention; and Research Methods. Topics addressed include autism, specific language impairment, dyslexia, hearing impairment, and genetic syndromes and their deficits, along with introductions to genetics, speech production and perception, neurobiology, linguistics, cognitive science, and research methods. With its global context, this handbook also includes studies concerning children acquiring more than one language and variations within and across languages. Thoroughly revised, this edition offers state-of-the-art information in child language disorders together in a single volume for advanced undergraduate students and graduate students. It will also serve as a valuable resource for researchers and practitioners in speech-language pathology, audiology, special education, and neuropsychology, as well as for individuals interested in any aspect of language acquisition and its disorders. Richard G. Schwartz is a Presidential Professor in the Ph.D. Program in Speech-LanguageHearing Sciences at the Graduate Center of the City University of New York.

HANDBOOK OF CHILD LANGUAGE DISORDERS Second Edition

Edited by Richard G. Schwartz

Second edition published 2017 by Routledge 711 Third Avenue, New York, NY 10017 and by Routledge 2 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN Routledge is an imprint of the Taylor & Francis Group, an informa business © 2017 Taylor & Francis The right of Richard G. Schwartz to be identified as the author of the editorial material, and of the authors for their individual chapters, has been asserted in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. First edition published 2009 by Psychology Press Library of Congress Cataloging-in-Publication Data Names: Schwartz, Richard G., editor. Title: Handbook of child language disorders / edited by Richard G. Schwartz. Description: New York, NY : Routledge, 2017. | Includes bibliographical references and index. Identifiers: LCCN 2016033080 | ISBN 9781848725959 (hbk : alk. paper) | ISBN 9781848725966 (pbk : alk. paper) | ISBN 9781315283531 (ebk) Subjects: LCSH: Language disorders in children—Handbooks, manuals, etc. Classification: LCC RJ496.L35 H334 2017 | DDC 618.92/855—dc23 LC record available at https://lccn.loc.gov/2016033080 ISBN: 978-1-84872-595-9 (hbk) ISBN: 978-1-84872-596-6 (pbk) ISBN: 978-1-315-28353-1 (ebk) Typeset in Bembo by Apex CoVantage, LLC

This book is dedicated to my daughters, Lindsay, Brandi, and Helene.

CONTENTS

About the Editor Contributors Preface

x xi xvi

PART I

Typology of Child Language Disorders

1

1 Specific Language Impairment Richard G. Schwartz

3

2 Language Disorders in Children with Intellectual Disability of Genetic Origin Andrea McDuffie, Angela John Thurman, Marie Moore Channell, and Leonard Abbeduto 3 Autism Spectrum Disorders Joanne Gerenser and Karece Lopez

52

82

4 Hearing Loss Zara Waldman DeLuca and Miranda Cleary

109

5 Dyslexia Sally E. Shaywitz and Bennett A. Shaywitz

130

PART II

Bases of Child Language Disorders

149

6 Linguistics in Child Language Disorders Richard G. Schwartz, Irena Botwinik-Rotem, and Naama Friedmann vii

151

Contents

7 Neurobiology of Child Language Disorders Baila Epstein and Richard G. Schwartz

184

8 Working Memory in Child Language Disorders Ronald B. Gillam, James W. Montgomery, Sandra L. Gillam, and Julia L. Evans

213

9 Perception and Production in Child Language Disorders Jan Edwards and Benjamin Munson

238

10 Genetics of Child Language Disorders J. Bruce Tomblin

254

11 Model-Based Approaches to Child Language Disorders Marc F. Joanisse

274

PART III

Language Contexts of Child Language Disorders

295

12 Bilingualism and Child Language Disorders Elizabeth D. Peña, Lisa M. Bedore, and Alisa Baron

297

13 Cross-Linguistic Studies of Child Language Disorders Laurence B. Leonard

328

14 African American English and Child Language Disorders Brandi L. Newkirk-Turner and Lisa Green

345

PART IV

Deficits, Assessment, and Intervention in Child Language Disorders

363

15 Morphosyntax in Child Language Disorders Janna B. Oetting and Pamela A. Hadley

365

16 Semantics in Child Language Disorders Karla K. McGregor

392

17 Syntax in Child Language Disorders Paul Fletcher and Pauline Frizelle

416

18 Pragmatics and Social Communication in Child Language Disorders Martin Fujiki and Bonnie Brinton

441

19 Reading and Writing in Child Language Disorders Pamela E. Hook and Charles W. Haynes

461

viii

Contents

20 Processing Speed, Attention, and Perception in Child Language Disorders Jennifer Windsor

481

PART V

Research Methods in Child Language Disorders

499

21 Language Production Approaches to Child Language Disorders Liat Seiger-Gardner and Diana Almodovar

501

22 Language Comprehension Approaches to Child Language Disorders Patricia Deevy

529

23 Translational and Implementation Research in Child Language Disorders Lizbeth H. Finestack and Marc E. Fey

561

24 Neuroscience Approaches to Child Language Disorders Valerie L. Shafer, Emily R. Zane, and Nathan D. Maxfield

577

Author Index Subject Index

601 643

ix

ABOUT THE EDITOR

Richard G. Schwartz is a Presidential Professor in the Ph.D. Program in Speech-LanguageHearing Sciences at the Graduate Center of the City University of New York. He has written numerous research articles and book chapters concerning speech and language disorders in children. Dr. Schwartz’s research has been supported by grants from the National Institute on Deafness and Other Communication Disorders of the National Institutes of Health. He has served as an editor of the Journal of Speech, Language, and Hearing Research.

x

CONTRIBUTORS

Leonard Abbeduto Department of Psychiatry and Behavioral Sciences MIND Institute University of California, Davis School of Medicine Davis, CA, USA Diana Almodovar Department of Speech-Language-Hearing Sciences Lehman College, City University of New York Bronx, NY, USA Alisa Baron Communication Sciences and Disorders Moody College of Communication University of Texas at Austin Austin, TX, USA Lisa M. Bedore Communication Sciences and Disorders Moody College of Communication University of Texas at Austin Austin, TX, USA Irena Botwinik-Rotem Department of Linguistics Tel Aviv University Tel Aviv, Israel Bonnie Brinton Department of Communication Disorders McKay School of Education Brigham Young University Provo, UT, USA

xi

Contributors

Marie Moore Channell Department of Speech and Hearing Science University of Illinois at Urbana-Champaign Champaign, IL, USA Miranda Cleary Department of Hearing and Speech Sciences University of Maryland College Park, MD, USA Patricia Deevy Department of Speech, Language, and Hearing Sciences Purdue University West Lafayette, IN, USA Zara Waldman DeLuca Center for Hearing and Communication The Graduate Center of the City University of New York New York, NY, USA Jan Edwards Hearing and Speech Sciences Department and the Language Science Center University of Maryland-College Park College Park, MD, USA Baila Epstein Department of Speech Communication Arts and Sciences Brooklyn College, City University of New York Brooklyn, NY, USA Julia L. Evans Child Language and Cognitive Processes Laboratory School of Behavioral and Brain Sciences The University of Texas at Dallas Richardson, TX, USA Marc E. Fey Department of Hearing and Speech University of Kansas Medical Center Kansas City, KS, USA Lizbeth H. Finestack Department of Speech-Language-Hearing Sciences University of Minnesota Minneapolis, MN, USA Paul Fletcher Speech and Hearing Sciences Brookfield Health Sciences Centre University College Cork Cork, Ireland

xii

Contributors

Naama Friedmann Language and Brain Laboratory School of Education and Sagol School of Neuroscience Tel Aviv University Tel Aviv, Israel Pauline Frizelle Speech and Hearing Sciences Brookfield Health Sciences Centre University College Cork Cork, Ireland Martin Fujiki Department of Communication Disorders McKay School of Education Brigham Young University Provo, UT, USA Joanne Gerenser The Eden II/Genesis Programs Staten Island, NY, USA Ronald B. Gillam Department of Communicative Disorders and Deaf Education Utah State University Logan, UT, USA Sandra L. Gillam Department of Communicative Disorders and Deaf Education Utah State University Logan, UT, USA Lisa Green Department of Linguistics University of Massachusetts at Amherst Amherst, MA, USA Pamela A. Hadley The Department of Speech and Hearing Science College of Applied Health Sciences University of Illinois Champaign, IL, USA Charles W. Haynes Department of Communication Sciences and Disorders School of Health and Rehabilitation Sciences MGH Institute of Health Professions Boston, MA, USA Pamela E. Hook Department of Communication Sciences and Disorders School of Health and Rehabilitation Sciences MGH Institute of Health Professions Boston, MA, USA

xiii

Contributors

Marc F. Joanisse Brain and Mind Institute Department of Psychology The University of Western Ontario London, Ontaria, Canada Laurence B. Leonard Department of Speech, Language, and Hearing Sciences College of Health and Human Sciences Purdue University West Lafayette, IN, USA Karece Lopez Department of Communication Sciences and Disorders St. John’s University Staten Island, NY, USA Nathan D. Maxfield Department of Communication Sciences and Disorders College of Behavioral and Community Sciences University of South Florida Tampa, FL, USA Andrea McDuffie Laboratory on Language Development in Neurodevelopmental Disorders MIND Institute University of California, Davis School of Medicine Davis, CA, USA Karla K. McGregor Department of Communication Sciences and Disorders The University of Iowa Iowa City, IA, USA James W. Montgomery Department of Communication Sciences and Disorders School of Rehabilitation and Communication Sciences Ohio University Athens, OH, USA Benjamin Munson Department of Speech-Language-Hearing Sciences University of Minnesota Minneapolis, MN, USA Brandi L. Newkirk-Turner Department of Communicative Disorders Jackson State University Jackson, MS, USA Janna B. Oetting Department of Communication Sciences and Disorders

xiv

Contributors

Louisiana State University Baton Rouge, LA, USA Elizabeth D. Peña Communication Sciences and Disorders Moody College of Communication The University of Texas at Austin Austin, TX, USA Liat Seiger-Gardner Department of Speech-Language-Hearing Sciences Lehman College, City University of New York Bronx, NY, USA Valerie L. Shafer Speech-Language-Hearing Sciences The Graduate Center of the City University of New York New York, NY, USA Bennett A. Shaywitz Yale Center for Dyslexia and Creativity Department of Pediatrics Yale School of Medicine New Haven, CT, USA Sally E. Shaywitz Yale Center for Dyslexia and Creativity Department of Pediatrics Yale University School of Medicine New Haven, CT, USA Angela John Thurman Laboratory on Language Development in Neurodevelopmental Disorders MIND Institute University of California, Davis School of Medicine Davis, CA, USA J. Bruce Tomblin Department of Communication Sciences and Disorders The University of Iowa Iowa City, IA, USA Jennifer Windsor Faculty of Humanities and Social Sciences Victoria University of Wellington Wellington, New Zealand Emily R. Zane FACELab Department of Communication Sciences and Disorders Emerson College Boston, MA, USA

xv

PREFACE

My own interests in developmental processes and what can go awry began in high school with a focus on biology. Although my specific focus changed throughout my undergraduate and graduate studies, this core interest remained the same. I became interested in language and ultimately in the mechanisms of language acquisition and disorders of that process. Although I suspect the authors of the following chapters came to this interest along various paths, we all have arrived at the same destination in our focus on language impairments that affect children. This handbook is intended to bring our interests in these different groups of children together, for the first time in a single volume. As has often been noted, the acquisition of language is one of the most remarkable human achievements. It is achieved without effort or direct teaching for the vast majority of children, a remarkable interaction of biology and environment that occurs with seemingly wide individual variation, yet with remarkable consistency. Besides its intimate relationship with human cognition, language is also the thread that binds our social lives. When language acquisition fails to occur as expected, the impact can be far-reaching, affecting all aspects of the child’s life and the child’s family. Impairments in language affect social development, academic performance, employment, and quality of life. Research into the nature, causes, and remediation of children’s language disorders provides important insights into the nature of language acquisition and its underlying bases and leads to innovative clinical approaches to these disorders. In this second edition we sought to update the information in this field. The book is still organized into four sections: Typology; Bases; Language Contexts; Deficits, Assessment, and Intervention; and Research Methods. Because the focus is the children, we begin with the general diagnostic categories of children’s language disorders in Part I, Typology. In Part II, Bases, the authors provide overviews of linguistics, cognitive science, neurobiology, memory and attention, speech perception and production, genetics, and cognitive science that underlie these disorders. Part III, Language Contexts, considers the implications of variation for children’s language disorders when children acquire more than one language, across languages, and in other dialects. The chapters in Part IV, Deficits, Assessment, and Intervention, examine the deficits in specific areas such as pragmatics, syntax, semantics, morphosyntax, reading and writing, as well as in processing speed, attention, and perception. The final section, Part V, explores the Research Methods used in the study of production, comprehension, translational and implementation research, and neuroscience in children with language disorders.

xvi

Preface

Determining the most appropriate level for the book continues to be a challenge. We wanted to bring state-of-the-art information in child language disorders together in a single volume for advanced undergraduate students and graduate students in speech language pathology, special education, and neuropsychology, as well as for clinicians and active researchers in these disciplines. We believe we have accomplished this balancing act by including introductory-level information as well as advanced, state-of-the-art reviews of current theories and research. I want to acknowledge the generous and outstanding contributions of my fellow chapter authors, who all took time from their busy research and writing lives to contribute to this volume. I also want to thank my teachers, colleagues, students, and the children from whom I first learned about language disorders and from whom I continue to learn about the nature and impact of these disorders. The National Institutes of Health, particularly the National Institute on Deafness and Other Communicative Disorders (NIDCD), has funded my research for almost 40 years and has funded the research of my colleagues who have written chapters. Other authors in the volume have received support from the National Institute on Child Health and Development. The preparation of this volume was supported by a grant from the NIDCD. I also want to acknowledge the important role played by the Symposium on Research on Child Language Disorders (SRCLD) at the University of Wisconsin, Madison, founded by my friend and colleague Jon F. Miller, which has provided a home for research in child language disorders for 40 years. A portion of the royalties from this book will be donated to the SRCLD. Richard G. Schwartz Brooklyn 2016

xvii

PART I

Typology of Child Language Disorders

1 SPECIFIC LANGUAGE IMPAIRMENT Richard G. Schwartz

Terminology: “How Shall a Thing Be Called?” (Brown, 1958) Roger Brown considered how children come to attach a word to things and categories of things in the world. Attaching words or names to things and categories is basic to human language—the label Specific Language Impairment (SLI) is one example. Our field came to this terminology through a long history, but recently some researchers have raised questions about its use and have suggested alternatives. I will consider some of those issues before describing this clinical category of child language disorders. Reports of language learning disabilities in the absence of other developmental disabilities first appeared in the 19th century and grew exponentially beginning in the second half of the 20th century (see a recent review in Reilly et al., 2015). These children have been varyingly described as having congenital aphasia, congenital word deafness, congenital auditory agnosia, and congenital developmental aphasia, among other terms. Many of these earlier labels were based on inferred etiology and, to some degree, reflect parts of the elephant as described by the proverbial blind men. More recent terminology has included language disorder, delayed language, developmental language disorder, specific language deficit, specific language impairment, and, most recently, primary language impairment. Consistent among these terms is the assumption that these children have a language disorder in the absence of autism, general developmental/cognitive delays, identified genetic syndromes, hearing impairments, and seizures or other neurological conditions. These disorders are only specific in that exclusionary or idiopathic sense. Two recent papers (Bishop, 2015; Reilly et al., 2015) have approached this issue of terminology in different ways. Capturing all of the carefully considered perspectives of these two keynote papers and all of the commentaries is beyond the scope of the discussion here, but I will briefly summarize some of the key points. Bishop considered the many advantages and disadvantages of labels and found that the former far outweigh the latter. Among the benefits of clinical labels can be in defining research populations, identifying children clinically, identifying strengths as well as weaknesses, and providing needed services including assessment, accommodations, and intervention. She noted that the wide variety of Language Learning Impairment (LLI) terms have divided the field and that although SLI has not been adopted outside of the research community, it is the most widely used term in the research literature. Reilly et al. critically evaluated exclusionary and inclusionary criteria, revealing the weaknesses in the definition along with the quantitative criteria. All are certainly good points regarding the

3

Richard G. Schwartz

potential heterogeneity of deficits, language and non-language, and use this heterogeneity to argue for terminological change. I share the perspective offered by Leonard (2015), Rice (2015), and other commenters, that none of the other terms offered solve all the problems of SLI. There is good reason to suspect that, at some point in the future, SLI will no longer be viewed as idiopathic; we will identify neurobiological and genetic bases for SLI, along with their specific and universal cognitive-linguistic manifestations. An alternate term at this point would further divide the research literature as it already has been by terms like Language Impairment (LI—also the same as the abbreviation for Long Island) and Primary Language Impairment (PLI—also the abbreviation for Pragmatic Language Impairment). It is not clear what LLI would add. As several of the commenters noted, one of the most critical issues is the lack of use of SLI outside of research environments and the general lack of public awareness. Few efforts, other than the Raise Awareness of Language Learning Impairments campaign (RALLIcampaign, 2012) in the United Kingdom, have been made by researchers or by state, local, and national organizations to raise public awareness of not only the term but the impairment. It is this situation that must change, not the terminology. None of the other terms suggested seem to be any more palatable than SLI, and there is the nowestablished history. Studies of specific language impairment (SLI) have become ubiquitous over the last four decades (Bishop, 1997; Leonard, 2014). A Google Scholar search (July 12, 2016) yielded 1,520,000 results for SLI, far more than for any other term (Bishop, 2015). This large body of research has significantly enhanced our general understanding of these impairments, while leaving us still uncertain about important aspects of their exact nature. We still do not know their cause(s), their range of manifestation, the course of their development, or the most effective remediation approaches. Our knowledge base has increased exponentially, allowing investigators to propose better-informed models of SLI, links to other childhood language disorders, and approaches to assessment and intervention. SLI affects approximately 7% of the population, with boys affected slightly more often than girls (Tomblin et al., 1997). SLI may occur at the same rate in other populations of children with language disorders. If this is true, subgroups of children with autism, children with genetic syndromes, and children with hearing impairments may have SLI co-morbidly to their primary impairment. There is mounting evidence that SLI is genetically transmitted, and thus we expect to see familial patterns (see Chapter 10 by Tomblin). Siblings of children who have already been diagnosed with SLI are approximately four times as likely to have SLI as are children without a family history. The definition continues to be primarily one of exclusion. SLI is an impairment of language comprehension, language production, or both in the absence of hearing impairment, the absence of a general developmental delay (i.e., a normal performance IQ), the absence of any neurological impairment (e.g., perinatal bleeds, seizure disorders), and no diagnosis of autism. It is only in this singular sense that this language impairment is specific. Despite these definitional exclusions, there is evidence that children may have co-occurring deficits. The SLI criterion for deficits in production and comprehension varies widely across research studies and schools. Cutoffs have included 1.00, 1.25, 1.3, or 1.5 standard deviations below the mean on one or more measures of language production and comprehension or performance in the lowest 10th percentile on such measures. There is no universal agreement on the quantitative criteria that identify children who are at risk for communication failures, academic failure, or social disvalue due to limitations in one or more components of language production or comprehension. A recent article argued that the cutoff should be 1.25 SDs below the mean, but of course this is arbitrary, and this area requires further research. Children with SLI may have various limitations in general auditory and speech perception; limitations in central cognitive domains such as memory, attention, and executive functions; deficits

4

Specific Language Impairment

in other cognitive functions such as problem solving, mental rotation, and mathematics; and deviations in neurological structure and function. They also have a relatively high incidence of dyslexia and other, more global, reading and writing disabilities, along with attention deficit disorders. The nature of these limitations and their relation to SLI remain controversial. In this chapter, I provide a review of theoretical proposals concerning the bases of SLI, an overview of the language and related cognitive deficits common to SLI, and the relation of SLI to other language disorders in children. The threads that run through the chapter are the identification and subcategorization of SLI, the biology of SLI, the role that underlying cognitive deficits may play in the origins and maintenance of language deficits, and the relationships between SLI and other disorders.

Theories of SLI Theories of SLI can be divided into two general groups: (1) those that explain SLI as a result of deficits in linguistic knowledge, typically attributed to delayed maturation or a deficient representation of language, and (2) those that explain SLI in terms of domain-general (with respect to language) or domain-specific deficits in cognitive or cognitive-linguistic processes. A number of proposals have emerged over the last several decades. The greatest limitation of many of these theories is that they are not sufficiently comprehensive to account for all of the deficits associated with SLI. Other proposals are, as yet, too vague. Finally, others still lack convincing evidence or have been demonstrated to be untrue. Accurate or not, these proposals are important for the research direction they provide and for their potential implications for assessment and intervention.

Linguistic Knowledge and Computational Explanations Among the earliest proposals of linguistic knowledge deficits in children with SLI is the extended optional infinitive (EOI) account (Rice & Wexler, 1996a, 1996b; Rice, Wexler, & Cleave, 1995). This proposal maintains that children with SLI extend a period that occurs in typically developing children during which tense is optionally marked on verbs that occur in main clauses. The result is that finite verbs are produced without markers such as tense and number. The extended unique checking constraint (EUCC) account is an elaboration of the EOI account (Wexler, 1998, 2003). In the required linguistic operation of checking, a feature in a phrase must check all of the relevant functional categories in order for an element to be produced. According to this proposal, children with SLI experience an extended period in which they are limited to checking a single functional category. For example, for the third-person singular and for auxiliary and copula forms, both tense (TNS) and agreement (AGRS) must be checked, but a child with SLI can check only one of these functional categories, and thus production is blocked (see Chapter 13 by Leonard for a detailed discussion of this proposal). Although this proposal better accounts for morphosyntactic deficits in SLI across languages than the original EOI proposal does, other, processing-based explanations (described in following section) have also been offered for these deficits (e.g., see (Chapter 11 by Joanisse and Chapter 13 by Leonard)). The Representational Deficit for Dependent Relations (RDDR) proposal (van der Lely, 1998; van der Lely & Stollwerck, 1997) suggests that children with SLI have a limitation in building long-distance dependencies that include any kind of syntactic movement affecting passives, whquestions, object relative clauses, and pronoun or reflexive antecedent relations (referred to as anaphoric dependencies), as they are governed by binding principles. Movement is characterized as optional in children with SLI, which leads to deficient production of sentences with these structures as well as their interpretation. Simply put, the various versions of RDDR propose that children

5

Richard G. Schwartz

with SLI lack the linguistic structural knowledge necessary to establish anaphoric relations between pronouns and their antecedents, or long-distance relations between nouns or pronouns, or as gaps in relative clauses and in wh-questions. Van der Lely (2005) revised this proposal and renamed it the computational grammatical complexity (CGC) hypothesis. According to this view, children with SLI are impaired in the linguistic representation or computations that underlie hierarchical, structurally complex forms in one or more components of language (i.e., syntax, morphology, phonology). For syntax, the proposal implicates the optionality of an obligatory linguistic operation called Move that increases complexity with each application. Complexity is the result of one or more applications of this operation, with each adding to the complexity of the sentence. Although the same level of detail is not provided for morphology and phonology, this makes the proposal more general, and thus it is more capable of explaining deficits in language domains other than syntax. The notion of optionality and the distinction between a representation versus linguistic operation deficits have yet to be specified. A related proposal provides additional focus to this notion that children with SLI have a deficient grammar affecting certain complex sentences with long-distance grammatical relations (e.g., Friedmann & Novogrodsky, 2004, 2007; Novogrodsky & Friedmann, 2006; see also Chapter 6 by Schwartz, Botwinik-Rotem, & Friedmann and Chapter 17 by Fletcher & Frizelle). Although children with SLI appear to have the same general structural linguistic knowledge as their typically developing peers, their grammar seems to be deficient in the syntactic process of phrasal movement affecting reversible passives (Adams, 1990; Bishop, 1997; Leonard, Wong, Deevy, Stokes, & Fletcher, 2006; van der Lely & Harris, 1990; van der Lely & Stollwerck, 1996), relative clauses (Adams, 1990; Friedmann & Novogrodsky, 2004, 2007; Novogrodsky & Friedmann, 2006), and wh-questions (Deevy & Leonard, 2004; Ebbels & van der Lely, 2001; van der Lely & Battell, 2003). Notably, these same deficits have been reported in children with hearing impairment (see Chapter 6 by Schwartz, Botwinik-Rotem, & Friedmann and Chapter 4 by Waldman DeLuca & Cleary). According to this proposal, the challenge presented by these sentences does not lie in establishing long-distance dependencies but, rather, in the underlying phrasal movement and, even more specifically, in the assignment of thematic roles (e.g., agent, patient) to noun phrases that appear in noncanonical or atypical locations because of phrasal movement (Friedmann & Novogrodsky, 2007; Novogrodsky & Friedmann, 2006). These latter proposals and the studies on which they were based focused on children with SLI who exclusively have grammatical deficits, a subgroup I discuss later. The strength of these proposals lies in their detailed theoretical underpinnings (Fletcher, 1999; Chapter 17 by Fletcher & Frizelle) and their focus on the language deficits of a narrowly defined and infrequently occurring subgroup of children with SLI. Their overall weakness is that they are not intended to address the full range of language deficits in children with SLI.

Process-Based Explanations As mentioned earlier, a large body of evidence has revealed limitations in speech perception, working memory, and slowed reaction times, as well as suggestions that children with SLI have deficits in attention and in various executive functions. These deficits in psychological processes have formed the basis for several accounts of SLI. One central question concerning these accounts is whether these deficits are general (domain-general), affecting both linguistic and nonlinguistic cognitive processing, or whether they are specific to language (domain-specific). Domain-specific and domaingeneral (e.g., Marinis & van der Lely, 2007) have been used to differentiate views that propose underlying deficits in linguistic knowledge or operations, such as movement from those that propose

6

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deficits in general or language-specific related cognitive processes (e.g., general auditory perception, speech perception, phonological working memory, processing speed, etc.). Here these terms are used to distinguish general deficits in language-related cognitive processes (e.g., working memory, auditory perception) and deficits in these same processes that are specific to language (e.g., phonological working memory, speech perception).

Speech Perception Beginning with a series of seminal studies in the 1970s (e.g., Tallal & Piercy, 1973, 1974), Tallal and colleagues found that, as a group, children with language impairments (some children in the initial studies had mild hearing impairments) exhibited poorer performance on temporal order judgments, discrimination, and categorization of tones and sounds. It is worth noting that there were individuals in the two groups whose perception performance overlapped. These deficits have been varyingly characterized over years and across a number of studies as impairments in the ability to perceive stimuli that are presented rapidly, stimuli that are brief in duration, and stimuli that have components (e.g., formant transitions) that change rapidly. This deficit has also been characterized more generally as a deficit in temporal processing. The interpretation of this deficit has varied over the years from being a general processing deficit affecting all modalities to a general auditory deficit and to a deficit specific to speech processing. It has led to the development of an intervention program, Fast ForWord, designed to improve the speech perception and, consequently, the language abilities of children with SLI. Several findings (e.g., Bishop et al., 1999; Rosen & Eva, 2001) argued against a direct causal relationship between auditory perception deficits and the language deficits seen in these children. Furthermore, the identical deficits Tallal and colleagues (Tallal, 1984) had reported in children who were poor readers were more aptly characterized (Mody, StuddertKennedy, & Brady, 1997; Studdert-Kennedy & Mody, 1995) as impairments in differentiating less discriminable sounds (e.g., fricatives such as /f/ and /th/). Other studies (e.g., Sussman, 1993) have indicated that children with SLI discriminate accurately (e.g., /ba/ vs. /da/), but have different boundaries in categorization tasks and appear to have more uncertainty than their age-matched peers at the category boundary. More recently, a study (Burlingame, Sussman, Gillam, & Hay, 2005) directly examined sensitivity to formant transition durations along two continua (/ba/ to /wa/ and /da/ to /ja/). On the first continuum, the children with SLI were less sensitive to phonetic changes and made more identification errors, whereas on the second continuum, the children with SLI were similar to their typically developing peers in identification at the longer formant transitions but poorer on the short transitions. Some investigators have suggested that task effects such as the stimuli employed or the memory demands may affect the performance of children with SLI. For example, in a series of tasks involving categorical perception of words (e.g., bowl/ pole) and nonword syllables (ba/pa), children with SLI performed comparably to age-matched peers on word perception but more poorly on identification for syllables, whether they were synthetic or natural speech (Coady, Evans, Mainela-Arnold, & Kluender, 2007; Coady, Kluender, & Evans, 2005). A recent study (Schwartz, Scheffler, & Lopez, 2013), relying on the Ganong effect (Ganong, 1980), sheds some light on the relation between speech perception and language processes. This effect occurs when a continuum of a phonemic contrast (e.g., [d] vs. [t]) is embedded in a word-nonword pair (e.g., dish vs. tish). Listeners identify more of the tokens as having a “d” because of the influence of lexical knowledge. The t-d category boundary shifts from the one found with non-meaningful syllables. Children with SLI differed from their age-matched peers in that they exhibited a great deal of uncertainty at category boundaries, and some children never actually established a clear boundary. Children with SLI relied more heavily on their lexical knowledge, perhaps attempting to compensate for a deficit in categorical perception. Thus, deficits in categorical perception appear to

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alter the overall process of lexical access, forcing children with SLI to place greater reliance on existing phonological representations in making categorical decisions. Coupled with evidence of weaker phonological representations of lexical items (see Lexical and Semantic Deficits section), deficits in perception may affect lexical access both directly and indirectly. Event-related potentials (ERPs) measuring electrical brain responses (see Chapter 7 by Epstein & Schwartz and Chapter 24 by Shafer, Zane, & Maxfield) have revealed more detailed information about the nature of these perceptual deficits. Two of these studies used ERP and behavioral methods to study vowel perception in children with and without SLI. Children with SLI exhibited poor categorization of long (250-millisecond [msec]) and short (50-msec) vowels. Their discrimination of short vowels was also less accurate than that of their peers, and ERP data revealed the absence of a left anterior discriminative response. Importantly, there were two conditions in the ERP study: one in which the children’s attention was directed toward the auditory stimulus by asking them to report embedded tones and a second in which their attention was directed toward a silent video. In the latter condition, the children without SLI exhibited an ERP discriminative response that was not seen in the children with SLI. These findings suggest that typically developing children continue to process speech automatically even when their attention is focused elsewhere. A follow-up study reanalyzing these data provides further evidence that these perceptual deficits distinguish children with SLI from their age-matched peers on the basis of their overall brain response to these vowel distinctions (Shafer, Ponton, Datta, Morr, & Schwartz, 2007). Another pair of studies examined brain responses in a backward-masking task to tones differing in frequency and followed up with the same subjects 18 months later (Bishop & McArthur, 2005; McArthur & Bishop, 2004). One-third of the individuals with SLI had poorer behavioral frequency discrimination thresholds, but the majority had age-inappropriate late ERP components. At follow-up, these individuals exhibited ERPs that were improved but were still outside the range of those of their typical language controls. In some cases, the ERPs were simply immature, whereas in other cases ERPs were unlike those of younger typically developing individuals. Although these latter studies are limited by the wide age range of a relatively small number of subjects, most of the children with SLI had immature brain responses to tones differing in frequency. McArthur and colleagues (McArthur, Atkinson, & Ellis, 2009) found that regardless of the auditory stimuli (tones, rapid tones, vowels, or consonant-vowels), only one-third of children (6;0–12;0) with SLI or children with specific reading disability (SRD) exhibited atypical, lower amplitude N1-P2 auditory brain responses compared to their typically developing peers. These researchers (McArthur, Atkinson, & Ellis, 2010) then examined the effect of customized and individualized auditory training on one or more of four auditory discrimination tasks (tones, backward-masked tones, vowels, and consonantvowels) on ERPs. Although the children’s behavioral performance improved, their ERPs remained atypical. The fact remains that some, but not all, children with SLI have a deficit in the underlying neurophysiology of perception. Thus, the nature of this deficit and its relation to the language impairments in these children remains undetermined. Auditory perceptual deficits seem unlikely to be a primary causal factor for SLI. For the children who do exhibit these deficits, training does not change their brains’ responses to these stimuli. Two studies have examined the synchrony of auditory and visual processing in older children with a history of SLI (Kaganovich, Schumaker, Macias, & Gustafson, 2015; Kaganovich, Schumaker, Gustafson, & Macias, 2015). In the first study, event-related potentials (ERPs) and behavioral responses were recorded in response to visual stimuli that depicted a flash and a tone that occurred simultaneously, preceding, or following the picture; children had to judge whether the picture and tone occurred simultaneously. As a group, the children with H-SLI (history of SLI) were far less sensitive behaviorally to temporal asymmetry than were their typically developing peers, who in turn were less accurate than young adults. Children with H-SLI who had higher

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language test scores were more accurate, and attentional abilities, measured by a scale, predicted performance but did not fully explain the group differences. Inspection of the figures suggests that, despite the group differences, there is overlap in performance for some children with H-SLI and some children with typical development, again suggesting the possibility of subgroups. The ERPs indicated that the children with H-SLI had typical early auditory encoding responses, but atypical visual responses, which could lead to auditory-visual integration deficits, but the range of individual differences was unspecified. The second study examined the McGurk effect in these children. This effect involves a mismatch between the sound produced by a speaker’s face (ka) and the sound on the audio track (pa); listeners report hearing a third sound (ta) reflecting auditory-visual integration. One-third of the children with H-SLI were more likely to report hearing pa than ka. The fact that all of the children with H-SLI performed well in conditions where the stimuli matched was interpreted as evidence of intact early auditory-visual integration, whereas the absence of the McGurk effect reflects deficits in later stages of integration for some children. One of the more controversial aspects of the perceptual account of SLI is the relation between the presumed perceptual deficits and the various language deficits exhibited by these children. One view is that of Tallal and her colleagues (e.g., Merzenich et al., 1996; Tallal, Miller, Bedi, Wang, & Nagarajan, 1996). They have fashioned an intervention approach called Fast ForWord, in which children are exposed to speech and language stimuli that have been altered temporally and spectrally in a variety of tasks with feedback. Although the initial reports suggested that this approach was effective in improving language performance on several standardized measures, subsequent research questioned the effectiveness of this method in improving language performance. An important proposal growing out of this research that relates perceptual deficits to language acquisition is the Surface Account of the morphosyntactic deficits in SLI (Leonard, 1989; see also Chapter 15 by Oetting & Hadley and Chapter 13 by Leonard). It suggests that these deficits result from the relative (to surrounding syllables) lack of perceptual salience of morphological markers (Leonard, McGregor, & Allen, 1992), in combination with the processing demands of establishing morphological paradigms. Specifically, for children with SLI, markers that have low phonetic substance require more exposure to become established because of the processing demands required by their poor perception. This view is supported by extensive evidence from English and by the varying patterns of morphosyntactic deficits in children across languages, reflecting the variations in the phonetic substance of certain morphosyntactic markers (see Chapter 13 by Leonard). One specific characterization of these deficits is that children have particular difficulty perceiving brief syllables when they are embedded between two longer syllables (Leonard, Bortolini, Caselli, & McGregor, 1992). In summary, it seems clear that only a subset of children with SLI have deficits in auditory or speech perception. The specific nature of these deficits and, more critically, their relation to the language deficits observed remain unresolved. One promising suggestion is that these deficits may be related to some more general deficit in attention (e.g., Dispaldro et al., 2013), which may also affect other aspects of language. Their perceptual deficits may also reflect more general task demands (Coady et al., 2005), including attention, working memory, or attentional control. Children with SLI who have deficits in auditory or speech perception may represent a subgroup of SLI, as I will discuss later.

Memory Children with SLI have deficits in working memory that may underlie their language deficits (see Chapter 8 by Gillam, Montgomery, Gillam, & Evans). Verbal working memory was the largest

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contributor to statistical models of SLI language performance (Leonard, Davis, & Deevy, 2007). These working memory deficits, however, are only meaningful in their direct relation to the language deficits observed in these children. Working memory models vary widely, including those that emphasize capacity and forgetting attributable to decay (lack of rehearsal or time elapsed), those based on focus of attention and more limited capacity, those based on content addressable memory (much like computers), and those that emphasize interference, binding of information. These models of working memory, along with views of language acquisition and language processing, characterize the relationship in different ways, from domain-general working memory that is assumed to affect language in various ways, to working memory that is unique to and inherent in language processing (e.g., MacDonald & Christiansen, 2002). A large body of evidence for working memory deficits in SLI comes from a task called nonword repetition (NWR), which is the most widely used means of assessing phonological working memory. In this task, children are asked to repeat nonwords of increasing syllable length. Typically, children repeat nonwords ranging in length from one to four or five syllables (Dollaghan, Biber, & Campbell, 1995; Dollaghan & Campbell, 1998; Gathercole & Baddeley, 1990; Weismer et al., 2000). These productions are typically scored as the number of nonwords produced correctly and, in some studies, the number of consonants and vowels produced correctly. Children with SLI diverge from their typically developing peers (age-matched and younger) once the nonwords reach three syllables in length (Archibald & Gathercole, 2006; Botting & Conti-Ramsden, 2001; Dollaghan & Campbell, 1998; Ellis Weismer et al., 2000; Gathercole & Baddeley, 1990; Montgomery, 1995). This is true for children with SLI ranging from preschool age through adolescence. It holds true across languages, as well as in bilingual children. This deficit also notably appears to occur more frequently across monozygotic than across dizygotic twins (Bishop et al., 1999). Although the deficit is characterized as severe (Gathercole, 2006), because age-matched children typically perform at or near ceiling, the quantitative differences between the groups are quite small when the scores are the number of nonwords repeated correctly. The quantitative differences are magnified somewhat when the number of correct consonants or segments is compared. The groups do not differ in the production of oneand two-syllable nonwords. Several of these studies have demonstrated clearly that this task very successfully distinguishes children with SLI from their typically developing peers. Nonword repetition may be a potentially useful clinical marker for SLI (e.g., Redmond, 2016), though not necessarily a good measure of working memory. It also appears to be culturally unbiased (Ellis Weismer et al., 2000) in that it is unrelated to maternal education level (Alloway, Gathercole, Willis, & Adams, 2004) or race (Campbell, Dollaghan, Needleman, & Janosky, 1997). Although still controversial, nonword repetition is assumed to reflect a deficit in the capacity of working memory that is most closely related to vocabulary growth and development. It is not clear that the working memory capacity deficit revealed by children’s partially inaccurate repetition of nonwords of three, four, and five syllables could feasibly account for the range of language deficits of these children. To some extent, this deficit may reflect their familiarity with less frequent, multisyllabic words. Evidence comes from a study (Kohnert, 2002) in which bilingual Spanish-English children with SLI did not exhibit poorer performance than their typically developing peers on longer nonwords. Multisyllabic words are much more frequent in Spanish than in English. Although nonword repetition may not be an ideal measure of working memory, it may reveal information about lexical production abilities and about phonological knowledge (e.g., Danahy Ebert, Pham, & Kohnert, 2014). A number of other tasks have been used to examine working memory in children with SLI. They are similarly impaired on tasks such as scanning, which involves deciding whether a target item was heard in a previous list; serial list recall; and listening span tasks, in which children are asked to repeat the sentence-final words for a series of sentences (e.g., Gillam, Cowan, & Day, 1995; Henry, Messer, & Nash, 2012; Marton & Schwartz, 2003; Montgomery, 2000a, 2000b; Sininger,

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Klatzky, & Kirchner, 1989; Weismer & Evans, 1999). One particularly interesting set of findings has emerged from a series of listening span studies by Marton and colleagues. In English, memory limitations were a function of syntactic complexity in the sets of sentences, not the sheer amount of material being held in working memory. The listening span task was also administered to Hungarian-speaking children with SLI (Marton, Schwartz, Farkas, & Katsnelson, 2006). Because, in contrast to English, Hungarian is a very highly inflected language with relatively free word order, structural language complexity resides in the morphology, not in the syntax. The children with SLI performed more poorly when the sentences were morphologically complex than when they were longer. Thus, one way of characterizing the working memory limitations of these children is that their working memory is challenged by linguistic complexity, regardless of how it is reflected in a given language, in comparison to their typically developing peers. Phonological working memory is reported to be most closely related to vocabulary acquisition, whereas other measures of working memory may be more closely related to language comprehension and syntactic processing. Working memory plays a role in language acquisition because it allows children to analyze and to determine the structural properties of the language to which they are exposed. Early in development a short working memory span may be developmentally adaptive because it enables children to focus on short-distance grammatical relations (e.g., subjectverb in canonical sentences). As memory span increases, children are assumed to be increasingly able to determine and establish longer distance relations such as pronouns and antecedents or displaced elements such as object relative clauses. Once language has been acquired, working memory is critical for processing language because, in at least one view, building syntactic and discourse structures requires relating linguistic units across a number of intervening words and syllables and a lengthy time-span. A continuing question in the psycholinguistic literature has been the specifics of the relation between working memory and language. Caplan and Waters (1999, 2013) have proposed a model in which working memory for language is divided into short-term and longterm components. They argue that interference effects occur in short-term working memory but that sentence processing depends more on long-term working memory. Although this idea is intriguing, the empirical evidence remains limited for typically and atypically developing children. The direct relationship between working memory and syntactic processing has not been extensively studied in children with SLI. Most of the studies (e.g., Deevy & Leonard, 2004) examined off-line sentence comprehension and, thus, do not reveal how children manage working memory demands while language is being processed. Several studies that examined working memory demands in off-line complex sentence comprehension more directly (e.g., Deevy & Leonard, 2004; Montgomery, 1995, 2000a, 2000b) initially concluded that sentence length, not complexity, was the key factor in the poor performance of children with SLI. A re-analysis of Montgomery’s data indicated that sentence complexity, not length, was the key factor. In more recent studies, Montgomery and colleagues (e.g., Montgomery, Evans, & Gillam, 2009) examined correlations between off-line sentence comprehension and a sentence span task and a nonword repetition (NWR) task. NWR was highly correlated with simple sentence comprehension but not with complex sentence comprehension. The span task, not surprisingly, was correlated moderately with complex sentence comprehension. This research has certainly pointed the way to the relationship between working memory and the comprehension of complex sentences, but it has provided little definitive information about this relationship for several reasons. The range of syntactic structures has been limited, and at times the sentences have been poorly manipulated and motivated. Even with some manipulations meant to vary the memory or processing load, the bulk of this research relies on correlations between working memory tasks and sentence comprehension. Finally, the tasks and the models of memory upon which they are based are not consistent with current views of memory and its relations to sentence comprehension.

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Recent advances in working memory (e.g., Lewandowsky, Oberauer, & Brown, 2009; Martin & McElree, 2008; McElree, 2000, 2001; Oberauer, 2005a, 2005b; Oberauer & Lange, 2009; Oberauer & Lewandowsky, 2008) have led to models in which forgetting is not the result of decay but rather is due to interference and failures of information binding. These models have also proposed new ways of considering capacity, including a very narrow (one item) focus of attention and the view that capacity is not adequately assessed by single measures because of task-induced effects. Also, there is evidence that memory for sentence comprehension is not a matter of remembering lists of words or syllables but rather is specialized and content addressable. These changes have led to tasks that better assess working memory in general and working memory as it is related to language comprehension (e.g., Glaser, Martin, van Dyke, Hamilton, & Tan, 2013; Lewandowsky, Oberauer, Yang, & Ecker, 2010; van Dyke & Johns, 2012; van Dyke & McElree, 2006, 2011). Marton, Campanelli, Eichorn, Scheuer, and Yoon (2014) demonstrated that children with SLI exhibit greater susceptibility to proactive interference than do their age-matched or language-matched peers. This finding might have been attributed to differences in relative activation levels for children with SLI. Item repetition (practice) revealed that children with SLI needed more repetitions than their typically developing peers to strengthen representations, and once those representations were strengthened, performance on the immediately following item was negatively affected. The authors suggested this finding might reflect a deficiency in these children’s ability to bind content and context, with potentially important implications for language processing deficits and language intervention. Another, markedly different, proposal concerning a causal underlying memory deficit in children with SLI as well as deficits in other populations (agrammatic aphasia, Parkinson’s disease) relies on a distinction between two types of memory: procedural and declarative (Ullman & Pierpont, 2005; Ullman & Pullman, 2015). Procedural memory includes motor and cognitive abilities that involve a series of steps generated by a set of rules (i.e., procedures) that govern these steps (e.g., playing solitaire, folding origami, forming the regular past tense of verbs). Declarative memory includes facts or items that are stored and recalled individually and cannot be generated by rule (e.g., Mickey Mantle’s jersey number, words in vocabulary, irregular past tense forms of verbs, etc.). It should be noted that this view of regular and irregular past tense is not uncontroversial (see Chapter 11 by Joanisse), and the same is true for the general distinction between procedural and declarative knowledge. That said, this proposal maintains that children with SLI (and other clinical populations) have deficits in procedural memory that affect their linguistic and nonlinguistic abilities to form and execute such rule-based behavior. The proposal offers a detailed description of the neurobiology of the proposed deficit and cites supporting evidence from structural brain studies of SLI. When procedural memory is deficient, the declarative memory system is believed to compensate. This means that aspects of language typically generated by rules (e.g., regular past tense) will, in children with SLI, be learned and produced instead on an instance-by-instance basis via declarative memory. Ullman and Pullman have extended this proposal beyond SLI to include dyslexia, autism spectrum disorder, Tourette syndrome, and obsessive-compulsive disorder. Language evidence for this deficit continues to come from reports of regular past tense deficits in children with SLI, from the apparent preservation of declarative memory (e.g., Lum, Ullman, & Conti-Ramsden, 2015) and from other declarative and nondeclarative memory measures. Specifically, declarative memory performance appeared to be more closely related to overall working memory scores than to language abilities, providing some support for this model. There is, as yet, no strong evidence from syntax or phonology suggesting that children with SLI rely on declarative memory for language production or comprehension. Though this proposal continues to be intriguing, empirical support is limited.

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Processing Speed There is a long history of using reaction time (RT) to measure cognitive processing globally, and, when complex cognitive tasks can be subdivided into additive processes, the component operations can be inferred from the additive relation among the time each takes (Donders, 1969). RT decreases with age, particularly during adolescence and through early adulthood (Kail, 1991; Kail & Miller, 2006), and then begins to increase again later in the life-span, reflecting a gradual decline in processing efficiency (e.g., Cerella & Hale, 1994). A meta-analysis revealed slower RTs across a number of studies on a variety of tasks (Kail, 1994), leading to the claim that children with SLI have cognitive slowing, which might account for their language impairments (see Chapter 20 by Windsor). The slowing hypothesis posits that children with SLI differ from their age-matched and even language-matched peers in their overall speed of processing. Subsequent meta-analyses (Windsor, 1999; Windsor & Hwang, 1999; Windsor, Milbrath, Carney, & Rakowski, 2001) also found evidence of slowing in children with SLI but raised issues concerning the way in which RT data are analyzed. Although one analysis supported the slowing hypothesis, the other indicated slower RTs in children with SLI that were not significantly different from typically developing peers and were highly variable. A more extensive study of RT in children with SLI across a number of linguistic and nonlinguistic tasks generally supported the slowing hypothesis (Leonard, Weismer et al., 2007; Miller, Kail, Leonard, & Tomblin, 2001). Taken as groups of tasks, the linguistic and nonlinguistic tasks each yielded slower reaction times for the SLI children than for their age-matched typically developing peers. However, when the tasks were further subdivided, motor and lexical tasks did not yield slower RTs for the children with SLI. Furthermore, individual analyses revealed that not all children with SLI exhibited slowing. A follow-up study five years later at age 14 (Miller, Leonard, & Kail, 2006) revealed similar findings. In general, children with SLI were slower than their age-matched peers, but some of these children did not exhibit slowing. Reaction times (RTs) at age 9 did not predict their RTs at age 14, and although the children with SLI were consistent across domains as a group, individual children were not. The investigators concluded that other factors may play a role in RT. If processing speed were a causal factor in SLI, it should be related to the severity of the impairment, but that does not seem to be the case (Lahey, Edwards, & Munson, 2001). A more recent study (Leonard, Weismer et al., 2007) paints a different and more complex picture in which predictive models suggest that working memory and speed measures separately are related to language performance scores, accounting for almost two-thirds of the variance in these scores. Reaction time may reflect global cognitive developments such as speed of processing, speed of response generation, or derivative developments such as automaticity or linguistic complexity. Although the slowing hypothesis is intriguing and seems to fit well with the notion that children with SLI have deficits in processing and in their processing resource capacity, it has some limitations. For example, reaction time on linguistic versus nonlinguistic tasks may reflect very different cognitive processes. Even within the language domain, detection tasks (e.g., monitoring, matchto-sample or same-different, simple lexical decision or word/nonword tasks) and on-line language processing tasks (e.g., lexical priming, cross-modal word interference, sentence processing with cross-modal priming, eye tracking) tap the speed of some overlapping low-level processes, but an otherwise very different set of cognitive-linguistic processes and knowledge. A novel perspective concerning processing speed has emerged from some recent, but as yet unpublished, work (Swinney, personal communication, 2000) and receives some support from several studies of children with SLI as well as with adults who have agrammatic aphasia. According to this view, the “slowing” in SLI directly reflects an impairment in the rate at which language can be processed. Thus, by slowing the rate of presentation, performance improves in clinical

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populations (see also Montgomery, 2005; Weismer & Hesketh, 1996), yet the slowed rate of presentation impairs sentence processing in nonclinical populations. For example, in two studies, children with SLI did not exhibit priming for the filler (first) noun in the “gap” (*) of an object relative sentence (e.g., The zebra that the hippo kissed *ran far away) or for antecedents at pronouns or reflexives(*) (e.g., The leopard that chased the tiger washed himself*) at a normal rate, but did exhibit priming when these sentences were presented at a slower rate (Love et al., 2007). The typically developing, age-matched children exhibited priming at normal rates but did not when the rate was slowed. The specific mechanism underlying these findings has yet to be explicated.

Executive Functions and Attention Executive functions (EFs) include a wide range of abilities that permit the control, monitoring, and planning of other, more basic cognitive functions. The category at times becomes unwieldy and difficult to manage, define, and measure. Using a factor analysis and structural equation modeling, Miyake et al. (2000) found three correlated but separable functions that emerged in the tasks: Shifting (Wisconsin Card Sorting Task), Inhibition (Tower of Hanoi), and Updating (Operation Span). Though each of these EFs can be examined in far greater detail, this is an important study in the focus it provides for work in this area. It is important to note that working memory in the form of operation span, inhibition (i.e., competition/interference), and shifting all may be part of working memory. Henry et al. (2012) found that children with SLI or children with low language functioning (low nonverbal IQ or limited language abilities) performed more poorly than typically developing peers on six out of ten executive function areas: verbal and nonverbal executive-loaded working memory, verbal and nonverbal fluency, nonverbal inhibition, and nonverbal planning. IQ and verbal abilities did not account for the group findings. Because all EF tasks engage more than one EF, closer examination of these abilities and deficits seems warranted. Attention is a basic component of cognitive and perceptual processing (see Chapter 20 by Windsor). It is often treated as a unitary phenomenon when, in fact, it can be subdivided into at least orienting, selective attention, divided attention, and sustained attention. Executive functions refer to control of attention and other cognitive processes such as shifting attention, inhibition, planning, and so on. Attention and executive processes are closely intertwined with working memory. Individual and developmental differences and variations in working memory and executive functions within and across groups of children have led to controversy concerning the control and allocation of processing resources. A variety of models (e.g., Conway & Engle, 1996; Cowan, 1997; Just, Carpenter, & Keller, 1996) have challenged Baddeley’s (1986) model in which a phonological memory store does not directly interact with the central executive. In these alternative models, working memory capacity is tied more directly to attentional control in explaining performance on tasks that involve distraction or interference (Barrett, Tugade, & Engle, 2004). Individual differences in working memory capacity appear to be related to performance reflecting more general executive functions (e.g., Conway & Engle, 1994). Working memory span reflects attentional control (Engle, Kane, & Tuholski, 1999) in task-switching ability (Towse, Hitch, & Hutton, 1998) and in the ability to inhibit irrelevant information (Hasher, Stoltzfus, Zacks, & Rypma, 1991). Working memory performance improves with greater abilities to control attention, to suppress irrelevant information, to avoid distraction, to focus on task-relevant thoughts, and to coordinate simultaneous processing and storage (Engle et al., 1999; Lustig, May, & Hasher, 2001; Miyake, 2001). To date, few studies have examined attention in children with SLI. Hanson and Montgomery (2002) used the Auditory Continuous Performance Test (Keith, 1994), in which the children listened to 600 monosyllabic words and indicated when they heard the word dog. The children

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with SLI did not differ from their typically developing peers in their identification accuracy (hits). Although this task is characterized as examining sustained selective attention, it actually confounds sustained and selective attention. Armstrong (1997) found that an auditory version of the Continuous Performance Test (Mirsky & Cardon, 1962) failed to differentiate sustained from selective attention. Therefore, this particular task may not be sensitive to the attentional deficits that may occur in children with SLI. Limiting the dependent measure to accuracy may also have concealed deficits in attentional processes. More recent studies focused on visual sustained attention (Finneran, Francis, & Leonard, 2009) and on temporal visual attention masking Dispaldro et al., 2013). In the first case, they found sustained visual attention deficits in children with SLI, and in the latter case, there were temporally conditioned visual attention deficits that predicted language abilities. Although these findings are intriguing, deficits in the control of attention as inadvertently first observed in working memory tasks may be more directly related to language processing. Several studies of working memory have incidentally revealed that children with SLI have poor cognitive control. Children with SLI have exaggerated (i.e., better recall) recency effects compared to their typically developing peers in the recall of one-syllable words following a set of digits (Gillam & McFadden, 1994). In working memory studies that require the recall of words and sentences, these children frequently provide irrelevant items from other sentence positions when the required response is the final word from previous items (Marton & Schwartz, 2003; Weismer & Evans, 1999). These findings suggest that children with SLI have difficulty inhibiting linguistic information that is not relevant to the required response. Despite these findings, few studies have directly examined attentional control in children with SLI. In a sentence processing and memory task, these children had greater difficulty than typically developing peers in inhibiting irrelevant information (Lorsbach, Wilson, & Reimer, 1996). Similarly, Norbury (2005) found that children with SLI had slower reaction times and made more errors than did typically developing children in inhibiting secondary word meanings in ambiguous contexts (e.g., John stole from the bank.—picture of a river). However, this finding was influenced by more limited knowledge of secondary word meanings in the children with SLI. There is a similarly limited finding concerning the nonverbal control abilities of children with SLI (Noterdaeme, Amorosa, Mildenberger, Sitter, & Minow, 2001). Their inhibition of predominant responses (interference task) and motor responses when presented with irrelevant stimuli (go/no-go task) was similar to that of typically developing peers. Both of these tasks had low levels of cognitive conflict, because there were equal numbers of the go/no-go and compatible/incompatible stimuli. In such tasks, the goal is generally to provide a higher level of conflict by manipulating the relative percentage of the two stimulus types. Bishop and Norbury (2005) provided clearer evidence of cognitive verbal and nonverbal control deficits in children with SLI on a task requiring inhibition of a verbal response and on an inhibition task requiring sustained attention but no verbal response. A large battery of verbal and nonverbal tasks (Im-Bolter, Johnson, & Pascual-Leone, 2006) revealed that children with SLI perform more poorly than typically developing children on verbal and nonverbal tasks requiring the activation or inhibition of task-relevant information and in working memory updating. Epstein and colleagues (Epstein, Shafer, Melara, & Schwartz, 2014) found that children with SLI exhibit immature ERP and behavioral responses to conflict. In attempt to examine attentional control in the context of language, specifically lexical access, Victorino and Schwartz (2015) combined auditory cross-modal lexical decision (match/mismatch the picture shown) and dichotic listening with direction to an attended ear to examine selective attention in children with SLI (9;0–12;0) and age-matched typically developing peers. Although accuracy was similar across groups, reaction time differences indicated that the children with SLI had difficulty controlling their auditory attention in all conditions, with particular difficulty inhibiting distractors of all types.

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These studies provide evidence of domain-general and domain-specific deficits in executive functions. Such deficits may be related to findings concerning processing speed, speech perception, working memory, and the deficits in language acquisition and processing that have been identified in children with SLI. These cognitive control abilities must be directly examined in language comprehension or production tasks before we can conclude that they are directly related to the language deficits associated with SLI.

Emergentist Perspective A final proposal concerning the nature, origins, and maintenance of SLI is perhaps the broadest of those discussed so far. It is in the general category of an emergentist view, as discussed in Chapter 11 by Joanisse and Chapter 6 by Schwartz, Botwinik-Rotem, and Friedmann. According to this view, typical language development depends heavily on the regularities of language input, and patterns such as morphosyntax and syntax, along with phonology and the lexicon, can be extracted from the input by the child. Thus, what are characterized as linguistic rules and representations emerge from an interaction of the child’s general cognitive or learning processes with the input (e.g., Goldberg, 2006; Leonard, 2014; Tomasello, 2003). Proposals in this framework are sometimes instantiated in connectionist models (see Chapter 11 by Joanisse). Briefly, these computer models consist of multiple levels of units that are fully connected with adjustable weights reflecting the strength of connection and are sometimes presented as metaphors for neural networks. These networks take input of various sorts (e.g., a present tense verb) and produce outputs (e.g., past tense verb form). One of their most interesting characteristics is that they are capable of learning (i.e., becoming more accurate) with feedback. Connectionist models have been developed for lexical access in word production, subject-verb agreement, and past tense formation, among other aspects of language and language learning. Another interesting aspect of these models and of an emergentist view is that they offer a different perspective of SLI and other childhood language disorders related to dynamical systems or general systems theory. Many views of childhood language impairments entail an assumption that there is an impaired or deficient underlying developmental mechanism (e.g., general or specific linguistic knowledge, working memory, etc.). In this framework, a disorder may arise from more peripheral deficits (e.g., speech perception, attention), which may, downstream, manifest themselves as broader deficits (e.g., Thomas & Karmiloff-Smith, 2003). A recent study has applied the emergentist or construction-based perspective to morphological errors seen in children with SLI. Leonard and Deevy (2011) examined the extent to which input can account for morphological deficits observed in children with SLI. The study was based on numerous observations that typically developing children’s language productions (e.g., Tomasello, 2003; see Chapter 6 by Schwartz, Botwinik-Rotem, & Friedmann) can be attributed to input characteristics. Based on this, Leonard and Deevy surmised that nonfinite utterances (e.g., The clown laughing) seen in young typically developing children and more persistently in children with SLI may reflect adult utterances such as We saw the clown laughing. In the first of two experiments, they found that after hearing sentences with novel verbs preceded by the auxiliary was (e.g., Just now the horse was channing) and sentences with other novel verbs in grammatical nonfinite contexts (e.g., We saw the horse channing), a production probe focusing on obligatory contexts for is revealed that children with SLI were less accurate in general and more likely to produce ungrammatical nonfinite verbs if the verb had been heard in a grammatical nonfinite context. In the second experiment, the children with SLI made more errors comprehending sentences with real verbs such as The pig sees the chicken running, and were more affected than their typically developing peers by the nonfinite clause. Thus, children with SLI may be unduly influenced by certain input characteristics that lead them to ungrammatical productions in other similar contexts. Unlike their typically developing

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peers, they cannot easily distinguish when nonfinite contexts (or other such structures) presented grammatically in input lead them to ungrammatical utterances in other contexts. There may be other similar input conditions that can explain production and comprehension deficits in SLI. The specific deficit that leads to this incorrect generalization of clausal patterns in input remains undertermined.

Subgroups of Children with SLI Although the definition of children with SLI is relatively specific and can be quantified, the specific profiles of language deficits vary widely. This magnifies the typical variation we encounter in the course of normal language acquisition. In typically developing children, production performance seems to lag behind comprehension performance—though comprehension is often more difficult to test, and even production may not always fully reflect the children’s underlying knowledge—and components of language develop at different rates across and within children. When we consider variations across children or groups of children with SLI, it is important to recognize the limitations of our measurements, the variation that occurs in and across typically developing children, and the extent to which these variations fit an explanatory framework. One of the first groupings of children with SLI was a distinction between children who have expressive deficits only and those who have expressive and receptive deficits (Edwards & Lahey, 1996). Such a distinction should be viewed with some caution because of the limitations of our comprehension instruments. These standardized tasks typically ask children to point to one of four pictures in response to a word or a sentence containing critical contrastive elements. Most language comprehension tests do not examine the semantics of lexical comprehension in depth, the comprehension of contrastive morphosyntactic features in detail, or the comprehension of sentences with complex syntactic structures. The pointing response occurs at the end of comprehension; thus, the tests reveal little about the processes leading to the pointing response. Even the production data we obtain may have some limitations. Although some of the data in the literature come from systematically elicited productions, particularly focusing on morphosyntax, most production data come from spontaneous language samples. A number of studies have revealed that typically developing children’s syntactic knowledge may be revealed through production priming and more sensitive elicitation tasks (Crain & Thornton, 1998; Shimpi, Gámez, Huttenlocher, & Vasilyeva, 2007). Leonard (2009) has argued that language production deficits occur in the context of language knowledge deficits and deficits in the processing of language input. As a result of his extensive review, it seems unlikely that any children with SLI could ever have a focal and exclusive deficit in language production. Another approach to subgrouping children with SLI recognizes that some children have deficits across language components, whereas other children have deficits focused primarily in a single component (Bishop, 1997; Leonard, 2014). One such group appears to have deficits that are specific to syntax, grammatical SLI (GSLI). This is an outgrowth of a proposal mentioned earlier (van der Lely, 2005), in which these children were first characterized as having difficulty establishing long-distance grammatical relations and subsequently as having a broader structural deficit in knowledge or processes that affect hierarchical syntactic, morphosyntactic, and structural knowledge or processes. Although this is an interesting proposal, there are some reasons to question the status of this subgroup. In a rather large-scale study of children with SLI, only a very small number met the criteria for GSLI (Bishop, Bright, James, Bishop, & van der Lely, 2000). Specifically, out of 37 same-sex twin pairs with at least one member identified as SLI, and of 104 pairs selected generally, only 2 children met all five criteria, and 9 met four criteria for GSLI. Most of the children who made grammatical

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errors exhibited deficits in other areas of language. This is not surprising, given that studies involving these children are spread over a very wide age range. The speed of response and the priming effects may be highly variable in the group because they develop with age. Even if these chronologically heterogenous children with SLI are individually age-matched to controls, both groups will have high variability. A more critical limitation is the fact that some of the tasks employed may not accurately reflect the deficit. The assumption is that these children fail to establish grammatical relations at a distance in complex sentences or may do so inconsistently. Experimental tasks that involve answering questions about pictures or pointing to pictures in comprehension tasks provide valuable information, but they do not provide information about the automatic processes of sentence processing for production or comprehension. Adults with agrammatic aphasia exhibit slower activation and slower decay of information during sentence processing in online tasks (Prather, Shapiro, Zurif, & Swinney, 1991). Even when such online sentence processing methods are applied, they need to be designed in a way that permits the observation of processes that may be delayed compared to typically developing controls. In an online study with GLSI children, Marinis and van der Lely (2007) examined question processing to determine whether the filler noun (Who/Matt) is reactivated at the gap (*) (Lindsay gives Matt a thick book in the office. Who did Lindsay give a thick book to* in the class?) using a cross-modal picture priming task. Children with SLI did not reactivate at the gap, but it is possible that they may do so later. There is evidence from a study of pronouns, reflexives, and antecedents that children with SLI do activate such information later (Schwartz et al., 2005). When presentation rate is slowed, children with SLI show normal reactivation at gaps (Love et al., 2007). It is not that they fail to establish certain long-distance grammatical relations, but, rather, that they fail to do so in a timely fashion and that their brains process this linguistic information atypically (Hestvik, Tropper, Schwartz, & Shafer, 2007). Despite these concerns, Friedmann and Novogrodsky (2004; Novogrodsky & Friedmann, 2006) have provided supporting evidence for a subgroup of syntactically impaired children with SLI (S–SLI) who have been identified in greater numbers by a relative clause probe. Similarly, investigators, including Friedmann and colleagues, have identified groups of children with SLI who seem to have lexical deficits as their primary impairment (Dockrell & Messer, 2007; German & Newman, 2004; McGregor & Waxman, 1998; Messer & Dockrell, 2006). Another subgroup of children with SLI are characterized as having pragmatic impairments (Bishop, 2000; Botting & Conti-Ramsden, 2003). These are children who exhibit atypical social behaviors, irrelevant utterances, atypical interests (e.g., obsessive focus on a particular topic), atypical conversational behaviors (e.g., misses nonverbal facial or intonational cues, poor coherence), poor use of conversation context (e.g., misses social cues such as politeness), and other communication limitations. This characterization is based on the Children’s Communication Checklist (CCC, Bishop, 1998, 2006). The Diagnostic and Statistical Manual of Mental Disorders (DSM-5, American Psychiatric Association, 2013) includes a category of Social (Pragmatic) Communication Disorder (SPCD) that involves persistent difficulties in verbal and nonverbal communication for the purposes of social interaction. This occurs in the absence of the repetitive and restrictive behaviors characteristic of autism. Some children with SLI exhibit these characteristics. Most of the CCC items that identify these children address nonlinguistic issues in social interaction and the use of language for social purposes (see Chapter 18 by Fujiki & Brinton), but some of the items address the ability to produce and comprehend structural and prosodic aspects of discourse. Many of the former characteristics define children with pervasive developmental disability, autism, or Asperger’s syndrome (see Chapter 3 by Gerenser & Lopez). Typically, such children are excluded from research studies on SLI. The question remains whether at least some of these children might be

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better characterized as children with autism who have SLI. Bishop’s (1998) solution to this issue was to distinguish between children with primary pragmatic language impairment (PLI) without autistic-like behaviors (PLI pure) and those with such behaviors (PLI plus). A battery of standardized and nonstandardized tasks successfully discriminated with a high degree of accuracy among PLI pure, PLI plus, autism spectrum disorders (ASD), and SLI (Botting & Conti-Ramsden, 2003). I will return to children with ASD and language impairments in the later section on co-morbidity. Although the profiles and severity of language impairments vary across subjects, we have yet to identify, with certainty, subgroups of children with SLI that have clear implications for theories or for differential approaches to intervention. Even in the subgroups defined thus far, no one claims that children have exclusive deficits in a given component of language. Instead, claims are made regarding primary deficits. Clinicians can certainly respond to varying profiles in how they select and prioritize goals in intervention, but researchers continue to face a challenge in the heterogeneity of children with SLI. One solution in research may be to abandon group-driven statistical analyses in favor of analyses that permit the examination of multiple factors nested within subjects in relation to the outcome of experimental tasks. Hierarchical linear modeling (Bryk & Raudenbush, 1992; Schonfeld & Rindskopf, 2007), also called multilevel modeling, is an approach that has been frequently used for growth curve monitoring, but it has not yet been widely used for this purpose (e.g., Jacobson & Schwartz, 2002, 2005). With the use of this and other related approaches, we may be better able to determine how varying profiles of linguistic and nonlinguistic abilities are related to a child’s classification as SLI and to the child’s specific pattern of language deficits.

Language Deficits The various areas of language deficits that characterize SLI are summarized briefly here; they are discussed in great detail in other chapters in this volume. These deficits may be more prominent in some language domains than in others; the profiles of deficits vary across children with SLI, and in given children all domains may be affected.

Lexical and Semantic Deficits Children with SLI are delayed in the emergence of first words, exhibit limited vocabularies, appear to have incomplete or underspecified phonological representations of words, have limited elaboration of the semantic information underlying words, and atypical organization or access to their mental lexicon (see Chapter 16 by McGregor). Verbs seem to present particular problems for these children. Finally, lexical access for production and comprehension appears to be atypical in children with SLI. The general course and speed of lexical development is delayed in children with SLI. Their first words emerge much later than in their typically developing peers, and their word comprehension is also delayed (e.g., Clarke & Leonard, 1996). Children who are late talkers are variously identified as having fewer than 50 words and no word combinations at 24 months (Rescorla, 1989), as children who, on the MacArthur-Bates Communicative Development Inventory (Fenson et al., 1996), score below the 10th percentile at 24 and 30 months of age (e.g., Irwin, Carter, & Briggs-Gowan, 2002; Moyle, Weismer, Evans, & Lindstrom, 2007; Weismer & Evans, 2002), or the 15th percentile on the Communicative Development Inventory (CDI; Thal, Reilly, Seibert, Jeffries, & Fenson, 2004). Late talkers who exhibit receptive delays are more often identified as having SLI than are late talkers who seem to have normal receptive vocabulary development (Thal et al., 2004). The outcomes for these children in language abilities at age 13 are predicted by their language abilities at age 2

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(Rescorla, 2005). Those children who appear to catch up may actually have an illusory recovery in that they reach a plateau that masks continued deficits not apparent in standardized testing (Scarborough & Dobrich, 1990). Preschool children with SLI continue to exhibit delays in receptive (e.g., Clarke & Leonard, 1996) and expressive (Thal, O’Hanlon, Clemmons, & Fralin, 1999; Watkins, Kelly, Harbers, & Hollis, 1995) vocabulary. Older school-aged children with SLI may have even more apparent deficits in vocabulary (Haynes, 1992; Stothard, Snowling, Bishop, Chipchase, & Kaplan, 1998). These children seem to have sparse lexical semantic representations (McGregor, Friedman, & Reilly, 2002) and deficits in semantic category knowledge (Kail & Leonard, 1986). Some measures of lexical diversity in language samples (number of different words, total number of words) suggest that children with SLI have less lexical diversity than their age-matched peers, but they may be similar to mean length of utterance (MLU)-matched peers (Goffman & Leonard, 2000; Klee, 1992; Leonard, Miller, & Gerber, 1999; Watkins et al., 1995). A more recently developed lexical diversity measure, D (Malvern & Richards, 2002)—a repeated calculation of the type-token ratio (TTR) over a range of tokens (35–50) related to sample size that is then compared to a mathematical model of TTR—may provide a more accurate picture of lexical diversity in SLI. Owen and Leonard (Owen & Leonard, 2002) found no difference in D between children with SLI and their age-matched peers, although within both groups, older children had higher scores than younger children. Wong, Klee, Stokes, Fletcher, and Leonard (2010) found that a composite score of D, MLU, and age did not successfully differentiate Cantonese-speaking children with and without SLI. Some children have apparent word-finding problems not unlike those associated with adultacquired anomia (Dockrell & Messer, 2007; German & Newman, 2004; Lahey & Edwards, 1999; Leonard, Nippold, Kail, & Hale, 1983; McGregor et al., 2002; Seiger-Gardner & Schwartz, 2008). These children have difficulty in naming-on-demand tasks, use circumlocutions, exhibit pauses and hesitations, and have limitations in production vocabulary. Vocabulary skills and the growth of vocabulary appear to be the aspects of language development that are most closely correlated with phonological working memory (Gathercole, 2006). However, as noted above, when the measure D is used, children with SLI do not differ from their age-matched peers (Owen & Leonard, 2002). Furthermore, children with SLI rarely have difficulty with phonological working memory when the nonwords to be repeated are one or two syllables in length. In English and a number of other languages, the vast majority of words are no more than two syllables in length. A number of experimental studies conducted by Leonard and Schwartz and their colleagues (e.g., Leonard, 1982; Schwartz, 1988; Schwartz, Leonard, Messick, & Chapman, 1987) have examined word learning in young children with SLI. These were novel or unfamiliar real words for objects and actions that were presented in 10 sessions over a month or so with comprehension and production testing. In general, the groups were similar, but children with SLI were less likely to extend the learned words to novel exemplars in a comprehension test. They were also more likely to make errors on experimental words that differed from their errors on those target sounds in their spontaneous language. This suggests that children with SLI do not relate novel words to existing phonological representations of word production. Several studies have used fast mapping (short-term limited exposure word learning) to examine early lexical abilities (Dollaghan, 1987; Rice, Buhr, & Nemeth, 1990; Rice, Buhr, & Oetting, 1992; Rice, Oetting, Marquis, & Bode, 1994). The findings vary somewhat, but children with SLI acquired a novel object word in comprehension, but not in production, with a single presentation; with five presentations embedded in a video story, children with SLI did more poorly than their peers; children with SLI did not learn object and action names with

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only three presentations; and even after 10 presentations they did not maintain their word learning. A fast mapping study (Alt & Plante, 2006) revealed that children with SLI perform more poorly overall and that their performance is particularly impaired when they only receive visual information, when the task complexity increases, and when they are asked to learn words with low phonotactic (sound and sound sequence) probability. It is difficult to dissociate the syntactic and semantic (argument structure vs. thematic role) bases for these children’s difficulties with verbs (Conti-Ramsden & Jones, 1997; Ingham, Fletcher, Schelleter, & Sinka, 1998; Loeb, Pye, Richardson, & Redmond, 1998; Oetting, Rice, & Swank, 1995; Watkins & Rice, 1991). However, it is clear that verbs pose a significant challenge for these children, in particular a special category of verbs—those that encode mental states (Johnston, Miller, & Tallal, 2001). The word-finding difficulties mentioned earlier may well reflect issues in lexical access for production or spoken word recognition. A variety of tasks have been used to examine lexical access. Auditory lexical list priming with a lexical animacy decision (Velez & Schwartz, 2010) revealed priming for children with SLI, but only in a repetition condition, unlike their typically developing peers who exhibited phonological and semantic priming as well. This suggests deficits in access or the organization of the mental lexicon in children with SLI. Eye tracking provides continuous data on spoken word acquisition. McMurray, Samelson, Lee, and Tomblin (2010) examined lexical access in adolescents with SLI using an auditory word and four pictures (the target, a picture representing a word with the same beginning consonant-cohort, a picture representing a word that rhymes, and an unrelated foil). The adolescents with poor language scores exhibited fewer looks to the target and more looks to the cohort and rhyme than did children with stronger language scores, regardless of IQ. Exploration of the findings using modeling revealed that this atypical eye gaze behavior and the inferred lexical access patterns is attributable to lexical decay, particularly as it applies to the target, allowing higher continuing activation for the cohort and rhyme. A more recent eye tracking study (Aharodnik et al., 2016) found that for semantic and phonological priming, children with SLI (7;0–11;0) did not differ from their peers in looks to target, but the typically developing children exhibited phonological cohort and semantic interference effects from pictures representing words related to the target, whereas the children with SLI did not exhibit these effects, suggesting a deficit in lexical organization or access. Similarly, children with SLI exhibit both typical and atypical lexical access in production. This study used a task called Picture-Word Interference, which requires the child to name a picture when the picture is presented after, simultaneously with, or before an auditory word (interfering stimulus) that is related (semantically or phonologically) or unrelated to the word represented by the picture. By comparing the reaction times for related and unrelated conditions, it is possible to infer what information is active. The children with SLI exhibited typical phonological facilitation but atypical lingering semantic inhibition and a late semantic inhibition effect. The disparity between phonological and semantic effects in processing for lexical production and lexical access/ comprehension does not have an obvious explanation. A within-child comparison of processing for production and recognition might contribute to an understanding of when and why this may happen.

Morphosyntactic Deficits The morphosyntactic deficits associated with SLI have been studied extensively in English (see Chapter 15 by Oetting & Hadley) and in other languages (see Chapter 13 by Leonard). It is the most studied language deficit in children with SLI. In English, children with SLI have particular difficulty with verb morphology, functional morphemes that mark finiteness (i.e., tense, agreement),

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often producing bare stem verbs (e.g., jump) without third-person singular or past tense endings. These deficits are part of a more general pattern of morphosyntactic deficits in English during the preschool years, with deficits in finite verb morphology becoming more pronounced when MLU reaches 3.50 and continuing to be prominent up to 8 years of age. Notably, measures of finite verb morphology are remarkably sensitive (97% accuracy) in distinguishing children with and without SLI. In general, children with SLI perform more poorly than age-matched and language (MLU)-matched typically developing peers and exhibit distinct growth curves in development of these morphosyntactic markers. The patterns hold true across regional dialects of English and for children who speak African American Vernacular (see Chapter 14 by Newkirk-Turner & Green). There is behavioral evidence from twins (Bishop, Adams, & Norbury, 2006) that these specific deficits are heritable. In older children with SLI, morphosyntactic deficits may persist (e.g., Marshall & van der Lely, 2006), but they are no longer a reliable indicator of the language status (Conti-Ramsden, Botting, Simkin, & Knox, 2001). Studies of other verb-related morphological forms such as past participles have yielded mixed findings. Some indicated that children with SLI produce participles comparably to languagematched controls (e.g., Redmond & Rice, 2001), whereas others (Leonard et al., 2003) revealed deficits. Children with SLI were more likely to mark participles correctly than simple past tense. The extent to which these deficits affect noun-related morphology (i.e., plurals, articles) is still unknown. Although some studies revealed deficits in noun plurals (Leonard et al., 1992; Leonard, Eyer, Bedore, & Grela, 1997), others revealed minimal deficits (Oetting & Rice, 1993; Rice & Wexler, 1996b). McGregor and Leonard (1994) and Rice and Wexler (1996b) found lower degrees of article use by children with SLI than by TD-MLU-matched children, but another study (le Normand, Leonard, & McGregor, 1993) did not find a difference. Case marking (subject versus object) for pronouns in English is also impaired in children with SLI compared to language-matched controls (Loeb & Leonard, 1991; Loeb et al., 1998). However, not all children with SLI make these errors, and the error rates differ between he and she (Pine, Joseph, & Conti-Ramsden, 2004; Wexler, Schütze, & Rice, 1998). Thus, the nature or underlying cause of this particular deficit remains unknown. Similar patterns have been observed in bilingual children with SLI. Bilingual French-English children with SLI omitted tense markings in both languages (Paradis, Crago, Genesee, & Rice, 2003). Sequential Spanish-English bilinguals perform more poorly than typically developing bilingual children on past tense marking in English (Jacobson & Schwartz, 2005). Young typically developing children produced these forms correctly or, at least, demonstrated knowledge of rules for regular past tense in overregularizations (e.g., goed for went). The children with SLI overregularized infrequently, but more frequently they produced bare stem infinitive forms (e.g., jump for jumped). In Spanish, bilingual children exhibited verb tense errors as well as article and clitic errors in number and gender (Bedore & Leonard, 2001; Gutiérrez-Clellen, Restrepo, & Simón-Cereijido, 2006; Gutiérrez-Clellen & Simon-Cereijido, 2007). Patterns of morphological deficits in languages reflect the prosodic (Demuth & Tomas, 2016) and structural characteristics of the given language (see Chapter 13 by Leonard). Whereas Englishspeaking children with SLI omit unstressed past tense markers and produce a bare stem infinitive form, in many other languages infinitives are different forms of the verb, not bare stems, and thus the specific errors manifest themselves differently. Even in languages that are similar, the error patterns seem to differ. For example, Italian-speaking children with SLI tend to omit object clitic pronouns, whereas Spanish-speaking children with SLI tend to produce substitute forms that have errors in gender or number. The nature of SLI in languages other than English (both similar and dissimilar) is critical to our understanding of the underlying deficits characteristic of SLI (e.g., Krok & Leonard, 2015).

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Phonological Deficits Children with phonological disorders are routinely excluded from studies of SLI in order to avoid including children whose speech production limitations might be the result of apparent nonlinguistic limitations in language production. However, it is clear that a significant number of children with SLI have phonological impairments in production, perception, and phonological awareness. Furthermore, deficits in other areas of language, such as morphosyntax, may be conditioned by phonological factors. There are several ways to consider phonological deficits in children. One is the extent to which children with phonological disorders and children with language impairments overlap. One-third of the children with speech delays of unknown origin had significant deficits in language comprehension, and language-production abilities were deficient in almost 80% of these children (Shriberg & Kwiatkowski, 1994). Furthermore, cognitive-linguistic status is strongly associated with short-term and long-term normalization of phonological disorders (Shriberg, Gruber, & Kwiatkowski, 1994; Shriberg, Kwiatkowski, & Gruber, 1994). An additional study revealed that 11–15% of 6-year-old children with speech delay had SLI, and 5–8% of children with SLI had speech delay (Shriberg, Tomblin, & McSweeny, 1999). There are a number of other ways to consider phonological deficits in children. As discussed earlier, children with SLI have deficits in speech perception—notably, in categorical perception. Nonword repetition may also reflect phonological deficits and may, in some respects, be a more accurate measure of phonological abilities than working memory. Findings from a lexical decision task (Edwards & Lahey, 1996) have been interpreted as indicating deficits in phonological representations. In contrast, a cross-modal interference task requiring children to name pictures while they heard phonologically related and unrelated words revealed a similar time course for the availability of phonological information in naming for children with SLI and their peers for highly familiar words (Seiger-Gardner & Schwartz, 2008). Less familiar words may have revealed group differences. There is substantial evidence that deficits in the production of morphosyntax and function words may be attributed to phonological factors (see Chapter 11 by Joanisse; see also Gallon, Harris, & van der Lely, 2007; Leonard, Davis et al., 2007; Marshall & van der Lely, 2006, 2007). Children with SLI are less likely to produce past tense -ed overall in novel words but were even less likely to do so when the word stem was low in its phonotactic (sound sequence) probability, whereas typically developing MLU-matched peers were not influenced by phonotactic probability (Leonard, Davis et al., 2007). Children with SLI were also less likely to produce the past tense when the addition of -ed formed a consonant cluster that does not occur in uninflected English words (Marshall & van der Lely, 2006). The production of inflections and function words also may be influenced by the prosodic structure of words and phrases (McGregor & Leonard, 1994). For example, unstressed syllables are more likely to be omitted when they don’t fit the trochaic (strongweak) syllable pattern of English. Another aspect of phonological deficits concerns phonological awareness. This includes a variety of metalinguistic abilities that have been related to dyslexia and reading disabilities (see Chapter 5 by Shaywitz & Shaywitz and Chapter 19 by Hook & Haynes). It includes tasks such as identifying the number of syllables or identifying the word that is formed when a segment is omitted (e.g., bat/at) or added, providing rhymes. Children with SLI exhibit mild deficits in phonological awareness, whereas children with dyslexia and SLI exhibit more severe deficits (Catts, Adlof, Hogan, & Weismer, 2005). An important line of research has examined motor aspects of speech production in children and related motor deficits (e.g., Brumbach & Goffman, 2014). Children with SLI exhibit speech motor and general motor performance deficits. These speech motor deficits may impact various

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aspects of segmental and prosodic phonology, and they highlight the embodiment of language in a physical world.

Syntactic Deficits Early in development, children with SLI exhibit delayed growth in the syntactic complexity, beginning as early as the onset of syntactic comprehension and production. They also exhibit persistent difficulty producing and comprehending syntactically complex sentences. We now know a great deal about specific syntactic deficits of children with SLI (see Chapter 17 by Fletcher & Frizelle). The vast majority of what we know comes from studies of language samples, although some more recent studies have used targeted elicitation, and a small number of studies have tested comprehension using off-line and on-line tasks. Children with SLI have difficulties comprehending and producing sentences that involve long-distance dependencies, such as wh-questions (Deevy & Leonard, 2004; Hansson & Nettelbladt, 2006; Marinis & van der Lely, 2007; Stavrakaki, 2006) or relative clauses (Friedmann & Novogrodsky, 2004, 2007; Håkansson & Hansson, 2000; Novogrodsky & Friedmann, 2006; Schuele & Tolbert, 2001). It should be noted that some of these studies included children with SLI who speak languages other than English, and thus, it appears to be a more global deficit. One view is that children with SLI construct grammars in acquisition where long-distance dependencies are optionally represented. Thus, in a sentence with a relative clause (e.g., The zebra that the hippo kissed t on the nose ran far away), the relationship between the zebra and its trace position (t) may not be established. The deficit in establishing long-distance relations or in a more recent view is specific to a grammatical operation called Move. A related proposal from Friedmann and colleagues is that children with SLI have a problem in movement, which, in turn, causes a problem with the assignment of thematic roles. An alternative view is that for children with SLI, the challenge of these complex syntactic structures lies in the processing of these sentences for comprehension affecting acquisition and the continuing comprehension of these structures and, perhaps, in production as well. Among the candidate deficits that might explain these difficulties are working memory (Deevy & Leonard, 2004; Marton et al., 2006; Montgomery, 2000a, 2000b, 2003), attention, control of attention, and processing speed (Leonard, Weismer et al., 2007). As discussed earlier in the chapter, deficits in these cognitive processes may be general, affecting domains other than language, or specific to language processing. One proposal in line with current views of working memory is that inference occurs between elements (e.g., nouns) in a sentence, particularly in long-distance relationships (e.g., van Dyke & Johns, 2012). A recent study (Leonard, Deevy, Fey, & Bredin-Oja, 2013) explored sentence comprehension when included adjectives were contrastive with respect to the picture array and when the adjectives did not serve to distinguish the picture choices. Children with SLI and younger typically developing children performed more poorly when the adjectives mattered. Leonard et al. assumed this to be the result of increased processing demands, which might include the interference of one of the two adjectives matching a referent in the foils. Further exploration of this and other types of potential interference in sentence processing would be informative. There is also evidence of deficits in other structures with complex syntax such as passives (e.g., Leonard et al., 2006; Marshall, Marinis, & van der Lely, 2007) that may be due to factors other than syntactic complexity. Sentences with finite complement clauses also seem to pose problems for children with SLI (e.g., Owen & Leonard, 2006). Children with SLI also exhibited atypical, non-asymmetrical behavioral responses to wh-subject and wh-object questions and exhibited generally poor comprehension of both types (Epstein, Hestvik, Shafer, & Schwartz, 2013). Their ERP responses suggested again that children with SLI did not exhibit the asymmetry between question types and exhibited attenuated responses. In two experiments using a picture-pointing paradigm,

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Fortunato-Tavares et al. (2012) found that children with SLI exhibit deficits in the interpretation of long-distance adjective attachment and reflexives, suggesting a lack of hierarchical structure for these sentence types. A working memory manipulation to increase the distance (noun-adjective or antecedent-reflexive) negatively affected performance for the children with SLI and their typically developing peers. Finally, children with SLI have syntactic deficits in argument structure that affect production and comprehension (Grela & Leonard, 2000; Loeb et al., 1998; Thordardottir & Weismer, 2002). Many of these deficits persist into adolescence (Nippold, Mansfield, Billow, & Tomblin, 2009). Processing studies of these and the preceding deficits are still limited in number and need to be the subject of future research.

Pragmatics Children with SLI have deficits in the social use of language, overlapping to some degree and apart from the deficits seen in other populations of children with language disorders (see Chapter 18 by Fujiki & Brinton and Chapter 3 by Gerenser & Lopez). Pragmatics is a heterogeneous category of language abilities including presuppositions about the knowledge and social status of the listener, the communicative intent or function of utterances, the structure of narratives and discourse and conversation, as well as the more global use of language and nonlinguistic means of communication (e.g., tone of voice, facial expression, and gesture for and in social interaction). One of the challenges posed by this category is that it combines social behavior with aspects of language that are truly structural. In the heyday of pragmatics, investigators initially focused on identifying and categorizing the communicative functions of children’s utterances. Children with SLI performed similarly to their language-matched peers in the communication functions expressed and in their relative frequencies (Fey, 2006; Leonard, 1986), but they may do so less appropriately or efficiently (Brinton, Fujiki, & Sonnenberg, 1988; Conti-Ramsden & Friel-Patti, 1983). These deficits have been taken as indications of structural language deficits rather than a lack of pragmatic knowledge (Craig, 1985). Children with SLI also have deficits in conversation that may reflect either social deficits or structural language deficits. Children with SLI produced fewer adequate responses to adult requests for information (Bishop, Chan, Adams, Hartley, & Weir, 2000). Within the group of children with SLI, those defined as having pragmatic SLI were more likely to give no response (not even nonverbal) to such requests. A child who does not even acknowledge the obligation to respond clearly has a more general deficit with conversational turn-taking and social interaction than a child who gives an inadequate response due, perhaps, to a comprehension deficit. Brinton and colleagues (Brinton, Fujiki, & Powell, 1997) reported a similar observation. There is further evidence that children with SLI have structural deficits in conversational interaction, particularly as it affects the contingency and coherence (structural or semantic relatedness) of successive utterances (e.g., Craig & Evans, 1993). Children with expressive and receptive deficits exhibited fewer conversational interruptions and relied more on lexical ties than on conjunction connective, and more on incomplete cohesive ties that were ambiguous or incorrect, than did children with just expressive deficits. There were a small number of children in this study, and it would be worthwhile to have more information on this structural aspect of pragmatics. Several studies have revealed deficits in the narratives of children with SLI. In general, children with SLI produce narratives that are less structurally complex and less cohesive, include morphosyntactic errors, are syntactically less complex, have omitted information, and exhibit poor event sequencing (e.g., Botting, 2002; Liles, 1993; Norbury & Bishop, 2003; Reilly, Losh, Bellugi, & Wulfeck, 2004). One study examined story-telling and conversation in adolescents with SLI (Wetherell, Botting, & Conti-Ramsden, 2007). The children with SLI performed more poorly than

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their typically developing peers on both narrative types, with story-telling being more difficult in terms of productivity (total number of morphemes and number of different words), syntactic complexity (number of different syntactic units and number of complex sentences), syntactic errors, and performance (amount of examiner support and prompts, total number of fillers, and total number of dysfluencies). This confirms previous findings concerning these kinds of deficits and indicates that these deficits continue into adolescence. Although there is a large body of literature on discourse processing and comprehension, including the establishment of inferences across sentences, this has not yet been applied to children with SLI. It seems likely to be a significant area of deficit for older children and may reveal deficits that have not been apparent in studies of narrative production. The area of social interaction and its use has also received relatively limited attention, even though it is apparent that language deficits pose social problems for these children as well as for other groups of children with language impairments. Children with SLI have early difficulties in establishing peer relationships that extend into adolescence (e.g., Conti-Ramsden & Botting, 2004; Conti-Ramsden et al., 2001). Pragmatic abilities such as initiating conversations, contributing to conversations, communicating intentions clearly, addressing each child as part of joining a group, and adjusting to listeners’ needs are critical to establishing positive peer interactions (Brinton & Fujiki, 1999; McCabe, 2005). Children with SLI have deficits in social initiation (e.g., Craig & Washington, 1993), in participation in social interactions (Hadley & Rice, 1991; Rice, Sell, & Hadley, 1991), in conflict resolution (Brinton, Fujiki, & McKee, 1998), and with appropriate responses to social bids (Brinton & Fujiki, 1982). Besides observations of these deficits, parent responses to questionnaires such as the Child Behavior Checklist reveal deficits across all social skills and in some internalizing behaviors, but not in externalizing behaviors (Stanton-Chapman, Justice, Skibbe, & Grant, 2007). These questionnaires revealed clinically significant problems in socialization, but not in behavior. A broad range of pragmatic deficits, including structural discourse deficits, deficits in the use of language for social interaction, and deficits in social skills affect children with SLI. Although experimental pragmatics is a burgeoning field in language acquisition and psycholinguistics, a number of such areas remain unexplored in SLI.

Genetics The first hint that SLI might be genetically transmitted (see Chapter 10 by Tomblin) came from interview studies of families with affected children. These were followed by studies in which family members were evaluated directly. As a whole, these studies provided convincing evidence that SLI is a heritable disorder (Beitchman, Hood, & Inglis, 1992; Choudhury, Leppanen, Leevers, & Benasich, 2007; Neils & Aram, 1986; Rice, Haney, & Wexler, 1998; Tallal, Ross, & Curtiss, 1989; Tomblin, 1989; Whitehurst, Arnold, Smith, & Fischel, 1991). With the exception of one, in all of these studies some increased rates of speech, language, or reading problems were reported for family members of children with SLI in comparison to children without SLI. The frequency of this varied because these were reports and because the history questions were asked in widely different ways. Tomblin (Chapter 10) indicates that having a first-degree relative with SLI increases your chances of being affected by approximately four times (the typical rate of occurrence is approximately 7% in the general population). This has strong implications for early assessment and intervention for children of parents who are affected and for children with affected siblings. Of course, family patterns do not conclusively demonstrate heritability. The next step in the accumulation of evidence for heritability was a series of twin studies (e.g., Bishop, North, & Donlan, 1995; Bishop et al., 2006; Lewis & Thompson, 1992; Tomblin & Buckwalter, 1998). Comparing monozygotic (100% shared genes) to dizygotic (50% shared genes) twins provided further evidence for those

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aspects of development that are heritable versus those that are attributable to environmental factors. These studies have revealed a greater degree of occurrence for SLI in general, phonological working memory deficits. Some studies have also revealed some more specific information about the relation between heritability and the discrepancy between IQ and language scores: there is greater heritability of SLI when no discrepancy is required (Bishop et al., 1995; Eley, Bishop, Dale, Price, & Plomin, 2001; Hayiou-Thomas, Oliver, & Plomin, 2005; Newbury, Bishop, & Monaco, 2005). More recently, Bishop et al. (2006) found that both grammar and grammar deficits are heritable and some evidence that these deficits arise from different genes. One of the greatest leaps in our understanding of the genetics of SLI has come from the study of a single family in the United Kingdom, known as the KE family, with 15 family members who have severe speech and language impairments across three generations and 37 living members (Vargha-Khadem et al., 1998). It is important to note that although these affected family members do have expressive and receptive language deficits, they have apraxia of speech or oral facial apraxia (Hurst, Baraitser, Auger, Graham, & Norell, 1990; Vargha-Khadem et al., 1998). Crago and colleagues (Crago & Gopnik, 1994; Gopnik, 1990; Gopnik & Crago, 1991) omitted any description of the apraxia and described these individuals as having a morphosyntactic deficit that reflected missing underlying features of morphosyntax. Because of the apraxia, these individuals would not fit the commonly used definitions of SLI. Nevertheless, this family has revealed a great deal about the genetic bases of language impairments. Molecular geneticists have identified the FOXP2 as a location of anomaly that was consistent across the 15 affected members and a single case study of speech and language impairment (Lai et al., 2000; Lai, Fisher, Hurst, Vargha-Khadem, & Monaco, 2001). Follow-up studies revealed that the affected family members were differentiated from unimpaired members in intelligence, language, and limb and oral facial findings (Watkins, Dronkers, & Vargha-Khadem, 2002). Nonword repetition was the strongest predictor for being affected. These deficits were then associated with brain structure (Watkins, Vargha-Khadem, et al., 2002) and functional imaging findings (Liégeois et al., 2003). Among the structural findings were abnormalities in the caudate nucleus, putamen, cerebellum, temporal cortex, inferior frontal gyrus, motor cortex, and the inferior frontal gyrus. Functionally, affected individuals exhibited lower activation during language tasks in Broca’s area, the right inferior frontal gyri, and the putamen. They exhibited higher activation in traditionally nonlanguage areas such as posterior parietal, occipital, and postcentral regions. These findings were interpreted as indicating that the genetic abnormality interfered with the caudate development and results in procedural learning deficits, consistent with a proposal by Ullman and Pierpont (2005), as mentioned earlier. Despite the KE family findings, several research groups (Meaburn, Dale, Craig, & Plomin, 2002; Newbury et al., 2002; O’Brien, Xuyang, Nishimura, Tomblin, & Murray, 2003) have not found FOXP2 abnormalities in children with SLI, but suggestions of other gene associations have emerged. Now that genome-wide analysis is more readily available, further rapid progress seems likely (e.g., Evans et al., 2015; Simpson et al., 2015; see Chapter 10 by Tomblin).

Neurobiology Developmental cognitive neurosciences is still very much in its infancy, particularly as it has been applied to children with SLI, but new research is now emerging at a rapid pace (see Chapter 7 by Epstein & Schwartz). Some of the reasons this research has emerged more slowly than behavioral research is the challenges of employing some of these methods with children (see Chapter 24 by Shafer, Zane, & Maxfield). The research to date has examined the underlying neurobiology of SLI using magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and electrophysiology (ERPs). These studies have revealed

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structural and functional differences between the brains of children with SLI and their typically developing peers. The characterization of the neurobiology underlying SLI begins with autopsy studies of adults who had histories of reading disabilities and a girl who had a history of a language disorder (e.g., Cohen, Campbell, & Yaghmai, 1989; Galaburda, 1985; Humphreys, Kaufmann, & Galaburda, 1990). The primary finding of interest was that these individuals seemed to lack hemispheric asymmetry in an area called the planum temporale (PT). The PT is an area defined by landmarks on the inferior portion of the Sylvian fissure. It is considered to be an area involved in receptive language that roughly corresponds to Wernicke’s area. In previous studies, autopsies revealed that in adults with a history of normal language status, the planum temporale was larger in the left hemisphere than in the right (e.g., Geschwind & Levitsky, 1968). MRI has been used to examine the relative size and volume of various brain areas and structures in living subjects. Plante and her colleagues have reported findings from a pair of dizygotic twins involving a boy with SLI and his twin sister with typical language development (Plante, Swisher, & Vance, 1989), a group of boys with SLI (4;2 to 9;6), and controls with typical language development (Plante, Swisher, Vance, & Rapcsak, 1991), as well as the parents and siblings of a subset of these children (Plante, 1991). Overall, these studies suggest that children with SLI, their siblings, and their parents tend to lack asymmetry or have atypical asymmetry (right hemisphere larger than left) in the perisylvian area, which includes the planum temporale. All of these findings should be considered against the finding that the presence of this asymmetry may vary with gender, with males being more likely to show asymmetry (Lane, Foundas, & Leonard, 2001). A more extensive MRI study (Jernigan, Hesselink, Sowell, & Tallal, 1991) was conducted of 20 children (8;0–10;0) with substantial receptive and expressive language delays and severe learning disabilities, along with 12 age-matched children with typical language development. The language-impaired children had leftward asymmetry in the superior parietal region and rightward asymmetry of the inferior frontal region, whereas asymmetry was reversed in the typically developing children. The languageimpaired children had lower volumes for most of the structures measured and for their overall left hemispheres, particularly for posterior perisylvian regions, which include the planum temporale. Subcortical structures, including the caudate nucleus, had bilaterally smaller volumes. Similar findings regarding subcortical structural abnormalities have been reported in studies of the KE family discussed above (Belton, Salmond, Watkins, Vargha-Khadem, & Gadian, 2003; Liégeois et al., 2003; Watkins, Vargha-Khadem et al., 2002). Such findings are consistent with the proposal that deficits in procedural memory underlie SLI and that motor deficits may be related. Only a small number of studies have employed MRI to examine the structural neurological basis of SLI. In the first of these studies, Weismer and colleagues found that children with SLI exhibit atypical brain activation patterns during a working memory task. A final MRI study (Gauger, Lombardino, & Leonard, 1997) focused on the planum temporale (in Wernicke’s area) and the pars triangularis (in Broca’s area). In the children with SLI, there was atypical rightward asymmetry of the planum temporale and the poster ascending ramus, a smaller left pars triangularis, and a narrower right hemisphere. A recent MRI study (Girbau-Massana, Garcia-Martí, Martí-Bonmatí, & Schwartz, 2014) used a relatively new technique called optimized voxel-based morphometry. The children with SLI had a lower volume of gray matter (neuronal cell bodies, dendrites, myelinated and unmyelinated axons, glial cells, synapses, and capillaries) overall and specifically lower gray matter volume in the right postcentral parietal gyrus (BA4), and in the left and right medialoccipital gyri. They also had a greater volume of gray matter in the right superior occipital gyrus, which may reflect a compensatory re-organization. They also had great cerebrospinal fluid volume. Children with SLI and reading disability had a greater volume of white matter (myelinated nerve cell projections that connect

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areas of gray matter) in the right inferior longitudinal fasciculus. The origins of these differences and whether they change over time remains unknown. To date only two studies have employed fMRI to investigate SLI. In the first, Weismer and colleagues (Weismer, Plante, Jones, & Tomblin, 2005) examined brain differences during a modified listening span task focusing on sentence encoding and final word recognition for previous sentence sets. The adolescents with SLI exhibited lower activation during encoding in the left parietal region, associated with attentional control, and in the precentral sulcus, a region associated with memory processes, and lower activation during recognition in language processing regions, compared to their typically developing peers. They also exhibited different patterns of coordinating activation among brain regions during encoding and recognition compared to the typically developing adolescents, suggesting that their brains have a less well-established functional network for such tasks. Another fMRI study (Niemi, Gunderson, Leppäsaari, & Hugdahl, 2003) compared the brain response of five family members with SLI and six control subjects to isolated vowel sounds, pseudowords, and real words. The family members with SLI exhibited reduced brain activation in areas associated with speech processing and phonological awareness located in the temporal and frontal lobes, most notably in the middle temporal gyrus bordering the superior temporal sulcus. Electrophysiology is the most widely used method to date that has been applied to children with SLI. Event-related potentials (ERPs) have been used to examine speech perception, lexicalsemantic processing, and syntactic processing in these children and in family members of these children (see Chapter 7 by Epstein & Schwartz). ERP studies have revealed that children with SLI exhibit atypical responses, such as immature N1-P2-N2 responses, on a backward-masking frequency discrimination task (Bishop & McArthur, 2005; McArthur & Bishop, 2004, 2005); smaller MisMatched Negativity discrimination responses to syllables and vowels (e.g., Shafer, Morr, Datta, Kurtzberg, & Schwartz, 2005; Uwer, Albrecht, & von Suchodoletz, 2002); absent left hemisphere responses or rightward asymmetry to speech, tones, and the word the in discourse (Bishop, Hardiman, Uwer, & von Suchodoletz, 2007; Shafer, Schwartz, Morr, Kessler, & Kurtzberg, 2000; Shafer et al., 2005); larger N400 to semantic anomalies (Neville, Coffey, Holcomb, & Tallal, 1993); lack of the typical leftward asymmetrical response to function words (Neville et al., 1993); and very delayed responses to gaps in sentences with relative clauses (Hestvik, Tropper, Schwartz, Shafer, & Tornyova, 2007). Some of the most interesting ERP findings regarding SLI involve the absence of N400 responses at 19 months of age in children who at 2;6 exhibited poor expressive language abilities (Friedrich & Friederici, 2006), as well as delayed positive mismatch response in 2-monthold infants from families with a history of SLI (Friedrich, Weber, & Friederici, 2004). Shafer et al. (2007) used a global field power analysis to determine attention allocation in speech perception tasks where the child had to attend to a visual stimulus and ignore the speech or attend to the speech. The children with SLI reached an attentional peak later than their peers with typical language development (TLD), and when attention was directed towards the visual stimuli, the children with TLD still directed some attention resources to the speech, whereas the children with SLI did not. Evidence of deficits in selectional attention during story processing not apparent in a behavioral task was revealed by ERPs (Stevens, Sanders, & Neville, 2006). Although imaging and ERPs have been used to examine the outcomes of intervention in adults with aphasia, few studies have done this in children with SLI. Popescu, Fey, Lewine, Finestack, and Popescu (2009) employed a classic N400 paradigm using sentences with and without semantic anomalies. There was no difference in response to the two sentence types before intervention but a significant difference afterwards. The difference was due to a decrease in the N400 to the final word in the non-anomalous sentences. As noted elsewhere throughout the chapter and in Chapter 7, we are only beginning to tap the potential of this method in examining the neurobiology of SLI and using it to examine the effects of intervention.

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The great challenges remaining in the study of the neurobiology of SLI include the continuing establishment of relations between neurological findings and behavior, determining the specific cognitive and linguistic implications of anatomical and functional differences between children with SLI and their typically developing peers, the use of these methods to provide early identification of children who are at risk for SLI, as well as their use to measure changes following intervention.

Assessment Clinical assessment of SLI predominantly relies on the use of standardized tests of syntax, semantics, vocabulary, and phonology. These can be supplemented by tests of cognitive abilities, including performance IQ and working memory. Researchers use the same tools to identify children for research studies, but there are some serious concerns about the psychometric value of such tests (e.g., Fidler, Plante, & Vance, 2011). Although Fidler and colleagues focused on adults with language disorders in this particular paper, the questions about reliability and validity, as well as sensitivity and specificity, they raise apply more widely to language tests. Many standardized language tests have limitations in sensitivity and specificity, reliability, and validity and are not amenable to examinations of language use in context (pragmatics) or of language processing. Furthermore, they often do not provide sufficient information to plan therapy because they are designed to survey various language abilities rather than to provide in-depth testing on any given aspect of language. Despite all of these limitations, standard tests remain the pillar of language assessment for SLI. At the very least, researchers and clinicians need to ask questions about these things for any test or battery of tests they use to identify children with SLI. Language samples have been an important supplement to standardized testing for some time. They have the advantage of permitting assessment of some pragmatic features and providing data about children’s use of language structure (syntax and morphosyntax) and vocabulary in a more natural, communicative context. Several computer programs are available to analyze language samples. The programs that are most widely used are Systematic Analysis of Language Transcripts (SALT, Miller & Iglesias, 2008), Computerized Language Analysis (CLAN, MacWhinney, 2000), and Computerized Profiling (Long, Fey, & Channell, 2004). All permit calculation of mean length of utterance (MLU) and other syntactic, morphosyntactic, and lexical analyses. One issue that has been addressed by a number of investigators (e.g., Plante, 1998) and by clinicians is whether children are judged to be SLI by reference to their performance IQ (MA referencing) or to the mean language score(s) for their chronological age (CA referencing). MA referencing was intended to ensure that there is a language impairment rather than a more general developmental delay, but this may be affected by issues with MA (e.g., one year below chronological age means something very different for a 3-year-old than for a 12-year-old). CA referencing compares children to their age-matched peers, assuming that the normative data have been collected from a representative sample. Most research studies use a single omnibus language test with supplemental tasks (performance IQ, working memory, etc.) and set a criterion (e.g., 1.25 standard deviations [SD] below the mean on two or more subtests of a standardized language test). In many studies, the children have had performance/nonverbal IQs within normal limits (i.e., 85 or above). Some years ago, compelling arguments, along with some evidence, led to the suggestion that the nonverbal IQ criterion for identifying children with SLI is, at best, ill-advised (Plante, 1998; Tomblin & Zhang, 1999). Tomblin and Zhang found no differences on omnibus test score patterns between groups of children above (SLI) and below (nonspecific language impairment [NLI]) the 85 cutoff, but further studies examining more specific measures have revealed a mix of similarities and differences (tense marking and narrative measures) in performance (e.g., Nippold et al., 2009;

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Rice et al., 2004). In research studies IQ should be treated as a potential predictor, without using a cutoff. Children with SLI have generally intact speech production abilities (but see DiDonato, Brumbach, & Goffman, 2014 for evidence of co-occurring speech-motor and generalized motor deficits), normal hearing, an absence of diagnosed neurological issues (e.g., no perinatal bleeds, seizure activity, etc.), and no evidence of autism spectrum disorders. However, despite the seeming clarity of these definitions, researchers and clinicians encounter some difficulties identifying these children, especially across ages, when the specific deficits associated with SLI may vary in severity and the available tests may vary in their sensitivity to subtle deficits in complex language. Some alternative measures such as nonword repetition and verb morphosyntax may add sensitivity (identifying all or most children with SLI) and specificity (accurately labeling a child as SLI) to omnibus language tests. Clinical definitions used to determine eligibility for services also vary widely. Many school districts or government regulations permit some latitude in the means for identifying children with SLI. Generally, standardized tests are required, but language samples and, particularly for younger children, other observational and structured measures may be used. Alternatives to published omnibus tests include published and standardized tests that focus on a single language domain (e.g., morphosyntax—Rice & Wexler, 2001), language samples (Miller, 1981), and nonstandardized language probes (Leonard, Prutting, Perozzi, & Berkley, 1978; Miller, 1981). In recent years, researchers have found that a battery including measures of tense marking, nonword repetition, and sentence recall appear to be sensitive and specific clinical markers of SLI (see Pawlowska, 2014, for a review; Redmond, 2016). Using a battery designed by Tomblin, Freese, and Records (1992) as a starting point, Fidler and colleagues (Fidler et al., 2011) found that three measures—the Modified Token Test, a 15-word spelling test, and the Word Definition subtest of the Clinical Evaluation of Language Fundamentals, Fourth Edition (CELF-4)—consistently contributed to accurate identification. Each of these approaches has great potential to add to the assessment information for identifying SLI and for planning intervention. One challenge that faces researchers and clinicians is the identification of SLI in children who speak African American English (AAE) and in children who are bilingual. Children who are speakers of AAE are overidentified as having language impairments because some dialect and SLI features overlap (see Chapter 14 by Newkirk-Turner & Green). Some language tests include procedures for distinguishing dialect features from SLI patterns. Only one test, the Diagnostic Evaluation of Language Variation (DELV; Seymour, Roeper, & de Villiers, 2004), provides information on dialect use in children. Analyses of language samples and nonstandardized probes may be more useful in identifying SLI in these children (e.g., Craig & Washington, 2006). Some alternative approaches such as nonword repetition (Campbell et al., 1997) or reaction-time-based tasks (see Chapter 20 by Windsor) are less affected by cultural, linguistic, or dialect factors and, thus, may serve as useful approaches to the identification of children with SLI from these groups. Behavioral computer-based tasks, eye tracking, and event-related potentials (see Chapter 21 by Seiger-Gardner & Almodovar, Chapter 22 by Deevy, and Chapter 24 by Shafer, Zane, & Maxfield) have become increasingly well established as methods for measuring language production and language comprehension in research studies and may have a future role in the clinical assessment of language.

Intervention Intervention remains among the least studied aspects of SLI. Fewer intervention studies have been published to date than other types of investigations, in part because of publication limitations and because of the general challenges of intervention research (see Chapter 23 by Finestack & Fey). There has been sufficient research published to demonstrate that language intervention is effective

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and has the best outcome when it begins early in development. Children with SLI are at risk for undertreatment. The majority of individuals in longitudinal studies did not receive intervention during their school years (Tomblin, 2014). Intervention for SLI can be described by the specific method, the activity, the physical context, and the social context using a framework initially proposed by Fey (1986). The specific methods were divided into trainer-oriented, child-oriented, and hybrid approaches. The activity, physical, and social contexts can be characterized on a continua of naturalness (e.g., drill to organized games to daily activities; clinic to school to home; clinician to teacher to parents). This brief overview of research on intervention for SLI focuses on some selected methods of intervention and some of the variables that have been examined to determine their effect on outcomes of intervention. Trainer-oriented approaches include methods such as operant procedures (e.g., Gray & Ryan, 1973) and social learning approaches (Leonard, 1975). Although these procedures are effective in establishing the production of new language forms, the extent to which these gains are maintained and generalized to communicative situations is limited (Fey, 1986). Child-oriented approaches include facilitative play involving self-talk (the adult talks about her/his activities) and parallel-talk (the adult describes the child’s activities) without requiring a response from the child (Van Riper, 1947). Expansions (the adult repeats the child’s preceding utterance, adding grammatical and semantic information). Recasting is a form of expansion in which the adult takes the child’s utterance and changes it into a different form (e.g., I’m a scary monster. You’re a scary monster, aren’t you?). Recasting has been extensively researched by Camarata, Nelson, and colleagues (e.g., Camarata & Nelson, 2006; Camarata, Nelson, & Camarata, 1994; Nelson, Camarata, Welsh, & Butkovsky, 1996) as well as other investigators (e.g., Proctor-Williams, Fey, & Loeb, 2001). Across these studies, recasting was demonstrated to be a successful procedure for establishing new syntactic structures in children with language impairments that generalize to language samples. Recently, Proctor-Williams and Fey (2007) examined the effects of recast density in teaching novel irregular verbs over five sessions to children with SLI and to a younger group of children with TLD. They presented recasts at three frequency levels: none, conversational level, and intervention level. The children with TLD were more successful at producing the novel verbs presented with conversational density than those presented without recasting, but this was not true for the children with SLI. The children with SLI did not produce the verbs more accurately at the intervention-density level, and the children with TLD also performed more poorly in this condition. The authors suggest that one explanation for the findings is that the short period of intervention with high recast density is not efficient for word learning. Thus, dosage is an important variable in intervention. Hybrid approaches include planned activities that modify the environment to motivate the use of certain linguistic forms (Lucas, 1980), focused stimulation (Fey, 1986), and incidental milieu teaching (e.g., Finestack, Fey, & Catts, 2006; Hancock & Kaiser, 2006; Hart & Risley, 1980). The latter two have been studied extensively. Fey, Cleave, Long, and Hughes (1993) employed focused stimulation in which the intervention agents—clinicians or parents—frequently modeled grammatical targets, provided recasts that included the target forms, and created activities designed to maximize opportunities and obligate the production of these forms. One purpose of this study was to examine whether the less costly approach using parents as primary intervention agents with support from clinicians would be as effective in establishing language target structures in spontaneous speech as an approach that only involved clinicians as intervention agents. The more costly clinician-only approach appeared to be more effective. A follow-up study (Fey, Cleave, & Long, 1997) with 18 of the participants confirmed the results and led to fewer gains than the first five-month intervention. It was successful in establishing recasting in the parents, especially for the younger children. This does not mean that parents are not effective intervention agents alone or

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in conjunction with clinicians, particularly for younger children (e.g., Girolametto, Weitzman, & Greenberg, 2006; Kaiser & Hancock, 2003). Most of the preceding research has focused on preschool and young school-aged children. Two recent studies have examined intervention for more complex syntax in older children with SLI. The first study (Ebbels, van der Lely, & Dockrell, 2007) examined intervention for argument structure deficits using syntactic-semantic, semantic, and a control therapy to which they were randomly assigned. The semantic-syntactic therapy used shapes and positions to illustrate constructing syntactic structures and provided semantic information in terms of the category/function of verbs (change of location vs. change of state) along with unique association to question words (where vs. how). Based on video probes, both approaches led to improvements, but the syntactic-semantic therapy led to increased use of optional arguments. In a single-subject study, Levy and Friedmann (2009) taught syntactic movement to a 12-year-old child with SLI who had deficits in this area using targeted comprehension, repetition, and elicitation of semantically reversible sentences. Performance improved on a probe compared to baseline and, in some cases, reached that of agematched typically developing children. Generalization was noted, and the performance was maintained when reassessed 10 months later. Together these studies demonstrate that complex sentence structures can be taught to older children with SLI. Very often such children are no longer enrolled in speech-language therapy in public schools. The outcome of these syntactic interventions also has a role in evaluating theories of the syntactic deficits in SLI. Two other disparate but widely used intervention methods warrant some attention: Fast ForWord and sensory integration. Fast ForWord is a commercially available program (Scientific Learning Corporation, 1998) based on the notions that perceptual deficits underlie SLI and that the brain is sufficiently plastic to be changed by relatively short-term participation in a computer-based intervention administered at a clinic or at home (Merzenich et al., 1996, 1999; Tallal et al., 1996). There are seven components: three sound tasks involving discrimination and identification and four word tasks in isolation or in sentence contexts. The sounds, words, and sentences used are lengthened, and selective frequencies are amplified in a way that is assumed to facilitate the child’s perception of speech. These modifications are reduced adaptively as the child successfully proceeds through the program. Merzenich, Tallal, and colleagues (Merzenich et al., 1996; Tallal et al., 1996) provided initial evidence for the effectiveness of this approach. The claim is that children’s language age scores may increase by as much as three years. These initial studies were conducted by researchers who are the founders or are connected with the Scientific Learning Corporation (SLC). A more recent review by individuals associated with the SLC (Agocs, Burns, De Ley, Miller, & Calhoun, 2006) presented data that have been collected from a national field trial, a school pilot study, and more recent users, all of which suggest a more positive outcome. There were a number of methodological limitations in these initial studies, including a rather mixed group of subjects and measurement instruments that mirrored the intervention tasks too closely. Studies by independent investigators have revealed a much more mixed efficacy story. For example, in a randomized control trial of children with severe receptive-expressive language disorder, they found no difference in outcome among children who received Fast ForWord, children who received other commercially available programs to enhance language, and children who received no treatment (Cohen et al., 2005). All the children made gains, but there was no difference among the groups, suggesting that this approach is not effective for these children. Similar concerns have been raised in case studies about the lack of or inconsistent outcomes from this approach (e.g., Friel-Patti, DesBarres, & Thibodeau, 2001; Loeb, Stoke, & Fey, 2001; Troia & Whitney, 2003). A largescale randomized controlled trial revealed that children with poor backward-masking scores assigned to a Fast ForWord Language condition did not make any more improvement in language or temporal processing than children assigned to a general academic enrichment program or to a language intervention program without acoustically modified speech (Gillam et al., 2008).

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Sensory integration was initially proposed to explain the relationship between learning disabilities and motor learning deficits in children who exhibit sensory processing disorders (Ayers, 1979). Clinical observations suggest that children with language impairments may also have motor planning deficits, poor attention, or difficulties with emotional or behavior regulation that are characterized as sensory processing deficits. Although sensory integration approaches do not directly address language, they appear to have some positive impact on reading scores for children with auditory-language learning disabilities (Ayers, 1979). There is no direct evidence of the effectiveness of this approach in facilitating language development in children with SLI. Given the proposal (Ullman & Pierpont, 2005; Ullman & Pullman, 2015) concerning the relationship between procedural learning (in motor and other domains) and SLI, such an approach may be worth further investigation. This proposal assumes that children with SLI will have motor deficits that reflect the limitation in procedural memory. An important line of research by Goffman and colleagues (e.g., DiDonato Brumbach & Goffman, 2014) has revealed motor speech and general motor deficits in children with SLI. We need to think of language as embodied and that such an approach could lead to an evidence-based approach to intervention that includes consideration of speech motor and motor abilities. This general typology of intervention approaches aside, a group of variables appears to influence intervention and should be taken into account by treatment researchers and clinicians alike. One general approach is to structure intervention around well-established principles of learning (e.g., Alt, Meyers, & Ancharski, 2012). These include variables such as the positive effect of variability in input, the advantage of distributed versus massed practice, and sleep or time consolidation of learning. For example, Plante et al. (2014) found that introducing a high variability of verbs in recasts led to more successful outcomes for children with language impairment. The same may be true for other types of variability, such as talker variability as well as other types of linguistic and nonlinguistic context variability, all harkening back to long-established principles of intervention. In typically developing children, there is evidence that distributed presentations (over sessions) of novel words leads to greater acquisition than the same number of presentations condensed into a small number of sessions (Childers & Tomasello, 2002; Schwartz, 2015; Schwartz & Terrell, 1983). Consolidation time, whether sleep or over time, in general is also critical to learning. Evidence that working on more complex linguistic elements or structures can lead to the acquisition of less complex elements or structures suggests an alternative approach to intervention sequencing. Finally, methods from research literature with typically and atypically developing children, such as production priming (e.g., Leonard, recasting, etc.), can all point to potential intervention methods. Given the current emphasis on evidence-based practice, it seems critical that we continue to evaluate our current approaches to intervention as well as novel approaches before they are widely adopted. Although we often bemoan the paucity of intervention research, there is a great deal of evidence in the literature concerning variables, language learning principles, input conditions, and effects that can be adapted to intervention.

SLI and Other Disorders With a still small number of exceptions, researchers have tended to focus on single clinical groups. However, it is apparent that groups of children with language impairments share certain deficits. This is even true in comparing deficits for children with developmental language disorders and adults with acquired language disorders. For example, adults with agrammatism appear to share deficits in morphosyntax and syntax with children who have SLI. Some of these apparently shared deficits may simply reflect weak points in the language that are affected by any general limitation in language

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production or comprehension or by deficits in related cognitive abilities. Another consideration is that SLI may occur in children from other groups with language disorders at the same rate as it does in the general population. For example, one proposal has suggested that the relationship between dyslexia and SLI can be characterized as quadrants: (1) children with normal language and no dyslexia, (2) children with dyslexia only, (3) children with SLI only, and (4) children with dyslexia and SLI (Bishop & Snowling, 2004). In general, these appear to be nonadditive disorders when they cooccur, but the evidence has largely been limited to nonword repetition, tense marking, and omnibus language tests. It is possible that if more detailed on-line or off-line language measures were used, we might see additive effects. Even with this apparent association/disassociation, there may be commonalities across these groups in language deficits and language-related deficits such as working memory, processing speed, neurobiological findings, and genetics. The same may hold true for autism (Rice, Warren, & Betz, 2005; Warren et al., 2006). The DSM-5 category of Social (Pragmatic) Communication Disorder (SPCD) might have simplified the earlier discussed confusion regarding Pragmatic Language Impairment and how it relates to SLI, but that doesn’t appear to be the case. A diagnosis of SPCD involves impairments in all of the following: “using communication for social exchange, adapting communication style to the context, following rules of conversation or narrative convention and understanding implicit or ambiguous language” (Norbury, 2014, p. 209). Norbury (2014) argued that our current assessment instruments lack validity and reliability. Her review also raises questions about the extent to which this is a coherent and self-contained diagnostic category. As she noted, these are deficits that might be better considered as symptoms across a number of developmental disorders, including SLI, ASD, and ADHD, among others. Finally, conjoining social communication and pragmatics belies an unsophisticated view of pragmatics, which overlaps with structural knowledge of language (e.g., syntax and narrative structure) and with semantics. It would be useful to distinguish further among the pragmatic deficits associated with autism those that involve the structure and prosody of discourse, narrative structure, and semantics (e.g., the comprehension of scalar implicatures such as some/all) and those that represent the proficient use of language in social interaction. A more careful delineation will better enable us to understand the language and communication deficits associated with autism. Attention-deficit hyperactivity disorder (ADHD) has an expected prevalence of 5–7% with regional variation (American Psychiatric Association, 2013; Redmond, 2016). In a review, Redmond (2016) noted that two-thirds of individuals diagnosed with ADHD have co-morbid disorders, with SLI being a common associated disorder. ADHD receives far more attention than SLI, by government agencies (e.g., the Centers for Disease Control), in public awareness, and in research (Bishop, 2010). Redmond cogently argued that studying the co-morbidity of SLI and ADHD can inform the search for stable markers of SLI, inform theoretical accounts with respect to nonlinguistic and linguistic deficits, impact clinical decisions regarding the mutual effect of co-morbidity on individuals, and direct public health care in the form of access to services. The identification of these disorders distinctly or co-morbidly is complicated and has varied widely in the literature, in part due to the instruments used and in part due to changes in the DSM definitions. Redmond, Thompson, and Goldstein (2011) found that focused measures of tense marking, nonword repetition, and sentence recall, and a standardized measure of narrative abilities, were highly successful in differentiating children with SLI from children with ADHD only, who performed similarly to typically developing controls. Perhaps most importantly, Redmond, Ash, and Hogan (2015) found that children with ADHD + (S)LI did not differ from children with SLI only in their production of tense markers, sentence recall, and nonword repetition. Furthermore, the children with AD + (S)LI with higher levels of ADHD symptoms performed slightly better than the SLI-only children, suggesting that there are no interactive or additive effects of these disorders.

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Auditory processing disorders (APDs) are defined as deficits in one or more of the following: “sound localization and lateralization; auditory discrimination; auditory pattern recognition; temporal aspects of audition; auditory performance in competing acoustic signals; and auditory performance with degraded acoustic signals” (ASHA, 2005, p. 2). Individuals with APD have difficulties listening to background noise, following oral directions, and understanding rapid or degraded speech (Bamiou, Musiek, & Luxon, 2001), all in the presence of normal hearing thresholds. APD is often co-morbid with other developmental disorders such as SLI, reading disabilities, ADHD, or ASD (Miller & Wagstaf, 2011). Seventy-two percent of 68 children suspected had APD and almost half (47%) had APD in conjunction with language impairment and reading disorders. Of sixtyeight 7- to 12-year-old children who were either suspected of having APD by a parent or teacher or had received a diagnosis of APD, 47% had APD in conjunction with language impairment and reading disorders (Sharma, Purdy, & Kelly, 2009). Two recent dissertations at the Graduate Center have examined children with APD and/or SLI (Rota-Donahue, 2014; Rota-Donahue, Schwartz, Shafer, & Sussman, 2016; Sylvia, 2016). In the first study, children with SLI and APD were found to have additive negative effects on behavioral and ERP response to small auditory frequency differences. Sylvia examined picture naming with auditory or visual interfering stimuli and found that in children with SLI, with and without APD, derived measures of temporal resolution and frequency resolution predicted reaction time when there was an auditory interfering stimulus but not when there was a picture interfering stimulus. This represents just a beginning to our investigation to the co-morbidity of APD and SLI, but this may lead to a better understanding of auditory abilities in child language disorders, as well as better focused approaches to assessment and intervention. The challenge of further defining these commonalities and differences, as well as assessing their implications for theories, for phenotype, and for clinical considerations will certainly engage researchers in the coming decade.

Future Directions Clearly, we know far more about the nature of SLI, its origins, and the scope and details of the deficits seen in these children than we did in the 1970s, when the modern era of this research began. Although we now know something about the neurobiology and genetics of SLI, the next decade will bring us many more details. We still know relatively little about basic cognitive processes such as procedural memory, attention and executive functions, and the role they play in the language deficits of SLI. There is a clear need for further research concerning the relative efficacy of various approaches to interventions and the variables that may facilitate language learning in these children (see Chapter 23 by Finestack & Fey). Finally, we need additional information about the relationships between SLI and other groups of childhood language disorders and possible subgroups of these children, so we have a fully integrated picture of childhood language disorders. Furthermore, researchers, clinicians, state and local associations, and families of children with SLI and with other child language disorders must engage in active advocacy. The following chapters are one step in this direction.

Note Preparation of this chapter was supported by Grant DC011041 from the NIDCD

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2 LANGUAGE DISORDERS IN CHILDREN WITH INTELLECTUAL DISABILITY OF GENETIC ORIGIN Andrea McDuffie, Angela John Thurman, Marie Moore Channell, and Leonard Abbeduto

Examining language development in syndromes with identifiable genetic causes is particularly helpful for identifying the ways in which language and cognition can influence one another. By examining relationships between various aspects of language ability and other domains of psychological and behavioral functioning within a particular syndrome, we can learn about the behavioral consequences of particular genetic variations. In addition, cross-syndrome comparisons can help us to determine whether challenges to language development are due to intellectual disability and cognitive delay, more generally, or are syndrome-specific (i.e., a direct or indirect consequence of the genetic anomalies in question). In this chapter, we review the literature on several genetic syndromes associated with intellectual disability. We emphasize three genetic syndromes that are relatively frequent in terms of prevalence, have been the focus of considerable empirical research on language, and that present with language phenotypes that contrast in interesting ways: Down syndrome (DS), fragile X syndrome (FXS), and Williams syndrome (WS). In a concluding section, we briefly consider several other genetic syndromes that have been less well-studied but that we believe are worthy of more intense empirical study. In the 1970s, WS was advanced as the prototypical example of modularity in the organization of the brain, as demonstrated by the reported independence of language from cognition (Bellugi, Bihrle, Jernigan, Trauner, & Doherty, 1990). More specifically, the common characterization of WS was that of excellent language skills in the presence of severe intellectual disability (Mervis, Robinson, Rowe, Becerra, & Klein-Tasman, 2003). As will be described in the following paragraphs, more recent characterizations have revealed substantial links between the language and cognitive profiles of individuals with WS, in direct opposition to modularity proposals. Just as specific associations between cognition and language have been identified for WS, these associations are different or of different magnitudes for individuals with DS and FXS. Overall, current research supports not the notion of modularity of brain organization but the interdependence of language and its acquisition with patterns of strengths and weaknesses in other cognitive domains. In the next section, we provide a brief overview of the cognitive and behavioral characteristics of each syndrome. The chapter then considers the language profiles of the three genetic disorders that provide our main focus. We review research findings relevant to the development of language comprehension and production, and highlight the ways in which the relationships between language and cognition differ across the three syndromes. We then briefly consider several other

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syndromes that offer interesting avenues for future study. We conclude by summarizing the overarching themes that emerge during the review.

Syndrome-Specific Profiles of Cognition and Behavior Down Syndrome DS has an estimated prevalence of approximately 1 in 691 births and is the leading genetic cause of intellectual disability (Presson et al., 2013). Most cases (95%) are caused by nondisjunction, an error during meiotic cell division prior to fertilization, resulting in three copies of all or part of chromosome 21. As the embryo with trisomy 21 develops, the extra chromosome is replicated in virtually every cell of the body. Thus, the genetic abnormality in DS is quantitative, as the genes involved are normal, and involves the increased production of the gene products of chromosome 21 (Antonarakis & Epstein, 2006). Recent genomic sequencing has identified more than 300 genes on the long arm of chromosome 21, and scientists are now working to identify specific characteristics of the DS cognitive and behavioral phenotype that are the result of particular gene dosage effects (Gardiner & Costa, 2006; Sturgeon, Le, Ahmed, & Gardiner, 2012). Advanced maternal age is the most common risk factor for DS caused by nondisjunction (Roizen, 1997). The likelihood of having a child with DS rises from less than 1 in 1,000 in mothers under age 30 to 1 in 12 by age 40. An additional 2% of cases of DS are caused when nondisjunction of chromosome 21 takes place in one of the early cell divisions after fertilization. In this condition, termed mosaicism, there is a mixture of cells, some containing 46 and some containing 47 chromosomes. Individuals with mosaic DS are less impaired cognitively, on average, although the degree of affectedness in any individual depends on the proportion of affected cells within the nervous system. Finally, the remaining 2% of cases of DS are caused by translocation, in which part of chromosome 21 breaks off during cell division and attaches to another chromosome, usually chromosome 14. Although the total number of chromosomes in the cells of an individual with a translocation is 46, extra genetic material is present, due to the extra segment of chromosome 21, resulting in the characteristic features of DS. As is the case for all genetic syndromes, DS produces both structural and functional abnormalities in multiple organ systems, and a characteristic phenotype emerges across the lifespan of the individual (Antonarakis & Epstein, 2006). The physical features of DS include dysmorphologies of the face, hands, and feet; congenital heart disease and duodenal stenosis; hearing loss and vision problems; and hypotonia (Korenberg et al., 1994; Sherman, Allen, Bean, & Freeman, 2007). Most individuals with DS function in the mild to moderate range of intellectual disability, displaying an average IQ of 50, with a range between 30 and 70 (Chapman, 1999). Individuals with DS often experience a loss of cognitive ability by early adulthood. DS confers a 100% risk of developing early neuropathology associated with Alzheimer’s disease, with the full pathology present by age 35–40 (Rumble et al., 1989), leading to a high incidence of early-onset dementia in individuals with DS (Lott, 2012). Many learning impairments in DS are thought to be related to cognitive processes that rely heavily on the hippocampus (Pennington, Moon, Edgin, Stedron, & Nadel, 2003), although evidence also exists for involvement of the prefrontal cortex and cerebellum (Nadel, 2003). The brains of the Ts65Dn strain of mice, which provides a mouse model of DS, exhibit problems with longterm potentiation and long-term depression in hippocampal neurons (Siarey et al., 2006). Several genes have been suggested as associated with the behavioral and psychological features of DS. These genes, located on chromosome 21, include superoxide dismutase (SOD-1), associated with oxidative stress and rapid aging (Gulesserian, Seidl, Hardmeier, Cairns, & Lubec, 2001; Harman,

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2006), and the beta-amyloid precursor protein gene (APP), associated with Alzheimer’s disease (Golaz, Charnay, Vallet, & Bouras, 1991). Recent work has shown that one deleterious effect of APP, in the mouse model of DS, is to decrease neural growth factor (NGF), resulting in degeneration of the cholinergic neurons of the basal forebrain (Salahi et al., 2006). Findings such as these contribute to the growing understanding of the genetic pathways influencing the cognitive and behavioral phenotype of DS and provide an avenue for the development of treatments targeting the regulation of such pathways. The cognitive profile of individuals with DS is predominantly characterized by a weakness in auditory short-term memory relative to both visual short-term memory and nonverbal cognitive ability level (Chapman, 2003; Conners, Moore, Loveall, & Merrill, 2011; Jarrold, Baddeley, & Phillips, 2002; Merrill, Lookadoo, & Rilea, 2003). Backward memory for both verbal and visual information also is impaired (Vicari, Carlesimo, & Caltagirone, 1995). By adolescence, dissociations are observed within the domain of nonverbal visual cognition (Yang, Conners, & Merrill, 2014). For example, visual short-term memory, as measured with the Bead Memory subtest of the Stanford-Binet Intelligence Test (SB-IV; Thorndike, Hagen, & Sattler, 1986), falls behind visual ability, as measured with the Pattern Analysis subtest of the SB-IV (Chapman, Hesketh, & Kistler, 2002). Hearing is mildly impaired in 60% of individuals with DS (Chapman, Seung, Schwartz, & Kay-Raining Bird, 2000). Finally, symptoms of Alzheimer’s-related dementia may be observed in approximately 50% of individuals with DS over the age of 50 (Lott, 2012; Menendez, 2005). The result of this phenotypical pattern of skills is that individuals with DS often have better comprehension and problem-solving behavior than their spoken communication would indicate (Chapman, 2003). Individual differences in hearing status, auditory short-term memory, and visual short-term memory may contribute to the levels of language development achieved.

Fragile X Syndrome FXS is less prevalent than DS, affecting 1 in 4,000 males and 1 in 6,000 females (Sherman, 1996). As the most common inherited form of intellectual disability (Coffee et al., 2010) and autism (Wang et al., 2010), FXS accounts for 40% of all X-linked intellectual disability (Hagerman, 1999). FXS results from the mutation of a single gene (FMR1) on the X chromosome (Brown, 2002). In the full mutation, a repetitive sequence of trinucleotides (i.e., the CGG repeats), which in typically developing individuals consists of 54 or fewer repeats, expands to 200 or more. This expansion leads to methylation and subsequent transcriptional silencing of the FMR1 gene, reducing or completely eliminating the production of its associated protein, FMRP (Oostra & Willemsen, 2003). FMRP is an RNA binding protein that is highly expressed in neurons and largely functions to downregulate protein synthesis at the synapse (Krueger et al., 2011). FMRP has been shown, in both animal and human studies, to be critical for experience-dependent neural development, affecting both the maturation and pruning of synapses (Klintsova & Greenough, 1999). The mGluR theory of FXS posits that many symptoms of the disease are secondary to exaggerated activation of metabotropic glutamate receptors in the brain arising from lack of inhibition normally provided by FMRP (Bear et al., 2004). This theory has been supported by recent animal studies demonstrating that pharmacological manipulations that inhibit mGluR receptors rescue some aspects of the affected phenotype (Bhakar, Dolan, & Bear, 2012). In contrast to the behavioral phenotype in DS, which arises from too much genetic material and consequent gene overexpression, the phenotype in FXS results from gene protein underexpression (Abbeduto & Chapman, 2005). Although the range of cognitive impairment is broader in FXS relative to DS, much of this variability is related to gender and the fact that females have two X chromosomes. As only one X chromosome in an affected female contains the CGG expansion, X inactivation determines the

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proportion of cells contributing to the phenotype (Brown, 2002). As with other X-linked disorders, males with FXS are more likely than females to be affected and to be affected more severely (Hagerman, 1999; Keysor & Mazzocco, 2002). Nearly all males with the FXS full mutation meet diagnostic criteria for intellectual disability, with an IQ range similar to that observed for DS (Dykens, Hodapp, & Finucane, 2000). In contrast, females with the FXS full mutation may have intellectual disability, a learning disability, or social adjustment difficulties with normal-range IQs. However, subtle impairments in cognition may still exist for females with FXS (Keysor & Mazzocco, 2002). Whereas individuals with DS and WS, as a group, are highly sociable (Bellugi, Lichtenberger, Mills, Galaburda, & Korenberg, 1999; Kasari & Bauminger, 1998), the behavioral profile of FXS is characterized by problems with social avoidance and social anxiety. Other defining characteristics of the FXS behavioral phenotype include eye gaze aversion, inattention, hyperactivity, and abnormal responses to sensory stimulation (Abbeduto & Murphy, 2004; Bailey, Hatton, & Skinner, 1998; Belser & Sudhalter, 1995, 2001; Cornish, Sudhalter, & Turk, 2004; Dykens et al., 2000; Ferrier, Bashir, Meryash, Johnston, & Wolff, 1991; Keysor & Mazzocco, 2002; Murphy & Abbeduto, 2003; Murphy, Abbeduto, Schroeder, & Serlin, 2007; Sudhalter, Cohen, Silverman, & Wolf-Schein, 1990; Wisbeck et al., 2000). Many behaviors observed in individuals with FXS resemble the characteristics of individuals with diagnoses on the autism spectrum. Conservative estimates place the prevalence of autism in individuals with FXS at 25%; however, recent studies utilizing standardized diagnostic criteria representing the broader autism spectrum have documented prevalence rates as high as 47% in young children with FXS (Demark, Feldman, & Holden, 2003; Kaufmann et al., 2004; Philofsky, Hepburn, Hayes, Hagerman, & Rogers, 2004; Rogers, Wehner, & Hagerman, 2001). Autistic-like behaviors observed in individuals with FXS include avoidance of eye gaze, unusual reactions to sensory stimuli, hand and finger stereotypies, hand biting, and spinning and repetitive object use (Levitas et al., 1983). Poor social use of language, and other expressive language characteristics, such as unusual prosody and tangential and repetitive speech, add to the clinical impression of autism in FXS. Importantly, males with co-morbid diagnoses of FXS and ASD demonstrate lower levels of nonverbal cognitive ability than do males with diagnoses of nonsyndromic ASD (i.e., an autism spectrum disorder for which a genetic cause cannot be determined). Additionally, recent studies have revealed that males with FXS and co-morbid ASD demonstrate less severe levels of autism symptomatology, on average, than do males with nonsyndromic ASD who are matched on nonverbal cognitive level. Thus, several lines of current research are aimed at characterizing the symptoms of ASD observed in individuals with FXS, examining within-syndrome variation and between-syndrome differences in autism symptoms, and determining the association between symptoms of autism and levels of nonverbal cognition. There is also ongoing consideration of whether symptoms of ASD in FXS represent the same underlying psychological mechanisms and problems as in nonsyndromic ASD.

Williams Syndrome Williams syndrome (WS), a neurodevelopmental disorder caused by a deletion of approximately ~25 genes on one copy of chromosome 7q11.23, is estimated to have a prevalence rate of 1 in 7,500 live births (Hillier et al., 2003; Strømme et al., 2002). More than 98% of individuals with WS have the same deletion breakpoints, referred to as classic Williams syndrome (Morris & Mervis, 2000). WS affects multiple organ systems and is characterized by a distinctive pattern of dysmorphic facial features, cardiovascular disease, growth deficiency, connective tissue abnormalities, and intellectual disability (Mervis et al., 2003; Morris, 2004). WS is associated with a range of intellectual ability, with most individuals demonstrating overall IQs in the borderline to moderate

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intellectual disability range (Mervis & John, 2010). A specific cognitive profile is associated with WS. As a group, scores on verbal, nonverbal reasoning, and verbal short-term memory measures are usually comparable, whereas performance on measures of spatial ability are ~20 points lower (Mervis & John, 2010; Mervis et al., 2000). Weaknesses in visual-spatial abilities characteristic of WS may be the consequence of the absence of the LIM-Kinase 1 gene, rather than the absence of the Elastin gene (Frangiskakis et al., 1996). The WS cognitive profile differs from the profile of individuals with DS who show weaknesses in auditory memory and language relative to nonverbal visual cognition (Miolo, Chapman, & Sindberg, 2005), as well as from the profile of individuals with FXS, which is characterized by relative synchrony between nonverbal cognition and some aspects of language (e.g., vocabulary; Abbeduto et al., 2003) and by deficits relative to nonverbal cognition in other facets of language (e.g., verbal perseveration; Murphy & Abbeduto, 2007). Individuals with WS are socially disinhibited, as they are frequently described to approach others and be gregarious and overfriendly (Gosch & Pankau, 1997; Jones et al., 2000). Despite these seemingly positive characteristics, children with WS demonstrate significant difficulty interacting with others (Davies, Udwin, & Howlin, 1998; John, Dobson, Thomas, & Mervis, 2012) and high levels of anxiety (Leyfer, Woodruff-Borden, KleinTasman, Fricke, & Mervis, 2006).

Syndrome-Specific Profiles of Language Ability Down Syndrome Language Comprehension Language, including both comprehension and production, may be the most affected domain of behavioral development for individuals with DS (Chapman & Hesketh, 2000; Næss, Lyster, Hulme, & Melby-Lervåg, 2011; Roberts, Price, & Malkin, 2007). In general, language comprehension is less problematic than production, with most young children with DS displaying levels of receptive language that are commensurate with levels of nonverbal cognition (Miller, 1999). By adolescence, however, syntax comprehension lags behind nonverbal cognitive ability level (Chapman, Schwartz, & Kay-Raining Bird, 1991; Finestack, Sterling, & Abbeduto, 2013; Phillips et al., 2014; Rosin, Swift, Bless, & Vetter, 1988). Vocabulary comprehension, in contrast, continues to keep pace with or exceed nonverbal cognition during adolescence (Chapman et al., 1991; Finestack et al., 2013; Phillips et al., 2014; Rosin et al., 1988). The uneven profile of performance observed among vocabulary, syntax comprehension, and nonverbal cognition depends, to some extent, on the measures used to assess these domains. When measured with the Peabody Picture Vocabulary Test-Revised (PPVT; Dunn & Dunn, 1981), vocabulary comprehension has a stronger relationship to chronological than mental age (Chapman et al., 1991). Miolo et al. (2005) were interested in whether a discrepancy between vocabulary and syntax comprehension continued to be observed when a more conceptual measure was used to assess vocabulary comprehension. Participants were 19 adolescents and young adults matched pairwise for syntax comprehension with a group of typically developing 3- to 5-year-olds. The group with DS, but not the comparison group, achieved a significantly higher score on the PPVT-Third Edition (Dunn & Dunn, 1997) than they did in response to the vocabulary subtest of the Test for Auditory Comprehension of Language-Third Edition (TACL-3; Carrow-Woolfolk, 1999). This pattern of performance may be attributable to the cumulative age-related experiences of the individuals with DS (Facon, Facon-Bollengier, & Grubar, 2002; Fazio, Johnston, & Brandl, 1993), which are likely to have a positive impact on a frequency-based measure of vocabulary

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ability (Miolo et al., 2005). Thus, when tested with a measure based on frequency of occurrence (e.g., PPVT), vocabulary comprehension appears in advance of nonverbal developmental levels for adolescents with DS. When tested with a measure based on conceptual difficulty (e.g., TACL-3), vocabulary comprehension is commensurate with nonverbal ability levels (Miolo et al., 2005). In contrast to vocabulary comprehension, syntax comprehension represents a domain of greater challenge for individuals with DS, at least in early adolescence and beyond. Chapman et al. (2002) used hierarchical linear modeling to identify longitudinal predictors of syntax comprehension, across a six-year period, in 31 participants with DS. Participants were between 5 and 20 years of age at the onset of the study. Syntax comprehension, as measured by the TACL-R, was best predicted by three variables at the start of the study: auditory verbal short-term memory, visual short-term memory, and chronological age. Age at study start also predicted the change in slope for growth in comprehension. For a child of 7.5 years at study start, the change in comprehension growth rate was positive; for a child of 12.5 years, the slope was shallower; and the predicted growth rate was negative for participants who entered the study at age 17.5 years. Miolo et al. (2005) also examined predictors of performance on different components of a sentence comprehension task requiring participants to act out the intended meaning of each tested sentence by manipulating objects. The experimental task, composed of information-dense sentences, was designed to place greater demands on auditory short-term memory than does a traditional comprehension measure (e.g., the TACL), which requires a picture-pointing response. The two syntax comprehension subtests of the TACL-3 were also used to provide a more traditional measure of sentence comprehension. The group with DS showed a larger discrepancy, relative to a syntax comprehension-matched typically developing group, between syntax comprehension and production as well as between nonverbal visual cognition and auditory memory (as measured by number recall and nonword repetition). For the participants with DS, auditory short-term memory accounted for a significant portion of the variance in performance on both the TACL-3 and the experimental comprehension task. In contrast to previous findings (Chapman et al., 2002), however, visual short-term memory did not contribute significant variance to any of the outcome measures. Results of the Miolo et al. (2005) study reveal the contribution of auditory memory to syntax comprehension skills and provide evidence regarding the interdependence of language with other areas of cognitive development in adolescents and young adults with DS. Abbeduto et al. (2003) examined associations between syntax comprehension, vocabulary comprehension, and nonverbal cognitive ability level in 25 adolescents and young adults with DS, matched group-wise for nonverbal mental age with 19 adolescent and young adult participants with FXS and 24 typically developing 3- to 6-year olds. Participants with DS and FXS were also matched group-wise for IQ and chronological age. Language comprehension was measured using the three subtests of the TACL-R (Carrow-Woolfolk, 1985). On average, age-equivalent scores for overall language comprehension were significantly lower for participants with DS than for the mental age-matched participants with FXS. In addition, participants with DS demonstrated a significant difference among TACL-R subtest scores, attributable to a significantly higher score for vocabulary relative to syntax comprehension. In comparison, the mental age-matched groups with FXS or typical development demonstrated a relatively flat performance profile for the TACLR subtests. All three participant groups demonstrated significant associations between nonverbal mental age and each of the TACL-R subtests, consistent with theories proposing the importance of cognitive contributions to language development. A more fine-tuned examination of syntax comprehension in individuals with DS, FXS, or typical development was conducted by Oakes, Kover, and Abbeduto (2013). They examined error patterns associated with syntax comprehension, measured by the Test for Reception of GrammarVersion 2 (TROG-2; Bishop, 2003) in 22 children and adolescents with DS, 23 children and

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adolescents with FXS, and 22 typically developing children matched by nonverbal cognitive ability level. As expected, participants with DS exhibited lower performance overall, resulting in more errors than participants with FXS or TD had. In addition, the analyses of error patterns revealed that the group with DS was more likely to make grammatical errors, such as selecting a picture that reflected a misunderstanding of word-order constraints, than either of the other participant groups, suggesting that syntax comprehension may develop differently for individuals with DS than for other individuals of a similar cognitive ability level.

Language Production Chapman, Seung, Schwartz, and Kay-Raining Bird (1998) suggested that the expressive language skills of children and adolescents with DS are more delayed than would be expected based on levels of nonverbal visual cognition. This phenotypic profile of especially severe expressive language delay emerges early in development for children with DS. Prelinguistic communicative gestures are less likely to be accompanied by vocalizations (Greenwald & Leonard, 1979), and delays are observed in the emergence of nonverbal requesting behaviors (Mundy, Kasari, Sigman, & Ruskin, 1995). A delay in nonverbal requesting is a concurrent correlate of goal-directed problem-solving in toddlers with DS, relative to mental age-matched typically developing children and chronological and mental age-matched children with developmental delays (Fidler, Philofsky, Hepburn, & Rogers, 2005). Prelinguistic requesting also serves as a longitudinal predictor of subsequent expressive language development (Mundy et al., 1995). The appearance of first words is substantially delayed in young children with DS (Bergland, Eriksson, & Johansson, 2001), varying from 8 to 45 months’ delay (Berry, Gunn, Andrews, & Price, 1981; Miller, Leddy, Miolo, & Sedey, 1995). On average, both first words and multiword combinations emerge at the same developmental ages as reported for typically developing children (Cardoso-Martins & Mervis, 1985, Miller et al., 1995), with early spoken vocabulary displaying a slower than typical rate of development (Beeghly & Cicchetti, 1987). Dykens, Hodapp, and Evans (1994) examined profiles of language development in 80 children with DS, ages 1–11 years. Expressive language, as measured by the Vineland Adaptive Behavior Scales-Interview Edition (Sparrow, Balla, & Cicchetti, 1984), was delayed relative to receptive language by the time overall communication performance exceeded a 24-month level. By adolescence, expressive language in individuals with DS is characterized by deficits in syntax, vocabulary, and speech intelligibility (Chapman & Hesketh, 2000). Language production is delayed relative to comprehension as well as in comparison to the production skills of typically developing children (Chapman et al., 1998) and youth with FXS (Finestack et al., 2013; Martin, Losh, Estigarribia, Sideris, & Roberts, 2013) of similar nonverbal cognitive developmental levels. Furthermore, problems with speech intelligibility are widespread in children with DS and are frequently reported as an area of parental concern (Kumin, 1994). Chapman et al. (1998) examined a variety of measures of language production in 47 children and adolescents with DS, ranging in age from 5 to 20 years. Participants were matched groupwise for nonverbal mental age to 47 typically developing children (2 to 6 years old). Measures of production, including mean length of utterance (MLU, a measure of expressive syntax), total words, and number of different words, were significantly lower in the group with DS relative to the typically developing comparison group, although rate of speaking (i.e., utterances per minute) was higher for the participants with DS. In both groups, all three measures of language production were higher during narration than during conversation with an examiner. However, participants with DS also had poorer intelligibility during narration, a pattern not observed in the typically developing children. It is possible that individuals with DS may self-select more familiar words

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when speaking in a conversational context, whereas the vocabulary used in narration is determined largely by the stimuli used to elicit the narrative sample (e.g., film, book, or pictures). Using the same participant samples described in their 1998 study, Chapman et al. (2000) examined predictors of language production in youth with DS compared to younger typically developing children. Number of different word roots and MLU in morphemes (both derived from a narrative sample) as well as intelligibility were used as criterion measures of production. Hearing status, chronological age, and nonverbal cognition emerged as significant predictors of the number of different words, accounting for 8%, 35%, and 13% of the variance in narrative production, respectively, for participants with DS. Hearing status, chronological age, and nonverbal cognition were also significant predictors of MLU, accounting for 7%, 35%, and 24% of the variance, respectively. Furthermore, after controlling for the variance associated with language comprehension, hearing status remained a unique predictor of MLU. Finally, hearing status and chronological age accounted for 8% and 24% of the variance in predicting speech intelligibility, with comprehension failing to contribute significant additional variance to the regression model. Although these findings emphasize the importance of language comprehension for the prediction of expressive vocabulary and syntax, they also confirm the contribution of hearing status to the ability of individuals with DS to produce utterances that are intelligible and syntactically complete. An important developmental issue is whether language learning plateaus at the level of simple syntax for individuals with DS. Chapman and colleagues (Chapman et al., 2002) investigated this issue by conducting a longitudinal study of 31 individuals with DS, ages 5–20. These researchers used hierarchical linear modeling to predict change in MLU for spontaneous utterances in the context of narratives over the course of six years. Results demonstrated that individuals with DS continued to make progress in expressive language, with average spontaneous MLU increasing from 3.48 words (standard deviation = 1.76) to 4.93 words (standard deviation = 2.14) across the six-year observation period. In contrast to improvement in expressive language, slopes for growth in syntax comprehension, as measured by age-equivalent scores on the TACL-R, slowed and became negative for the older adolescent participants. Syntax production improved most for the individuals who demonstrated the least decline in syntax comprehension. There is broad consensus that sampling context has an important influence on the characteristics of productive language (Abbeduto, Benton, Short, & Dolish, 1995; Evans & Craig, 1992; Kover, McDuffie, Abbeduto, & Brown, 2012; Miller, 1996). Narrative performance, in particular, is believed to rely upon the integration of linguistic and cognitive abilities (Hemphill, Picardi, & Tager-Flusberg, 1991). Given the discrepancy between expressive language and nonverbal cognition observed in DS, examination of narrative samples may be especially informative of the way these two domains contribute to the ability of individuals with DS to produce a narrative account. To this end, Chapman and colleagues examined the narrative performance of 31 individuals with DS in response to a wordless film (Boudreau & Chapman, 2000) and to a wordless picture book (Miles & Chapman, 2002). These studies included the same individuals who participated in the longitudinal studies reported by Chapman et al. (1991, 1998), in which participants with DS were matched with three different groups of typically developing children, based on either nonverbal mental age, syntax comprehension, or MLU. Boudreau and Chapman (2000) found that when recounting the wordless film, The Pear Story, participants with DS produced longer narratives, recalled more events overall, and expressed more inferential relationships than did MLU-matched comparison participants. Thus, despite their deficits in expressive syntax, individuals with DS mentioned more of the content of the film than did typically developing children with the same MLU. Presumably, participants with DS compensated for challenges in the area of syntax by using more utterances to tell their stories. As expected, the mental age-matched comparison group produced a greater number of different words in their

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narratives, and both the mental age-matched and comprehension-matched comparison groups had a longer average number of utterances than the group with DS. Participants with DS also produced more mazes in their narratives, demonstrating greater dysfluency than the MLU-matched group. In response to the wordless picture book, Frog, Where Are You?, individuals with DS produced significantly more plot line components and made more mentions of both the search theme and the protagonist’s misadventures than did the MLU-matched comparison group (Miles & Chapman, 2002). However, they produced fewer mentions of the search theme than the mental agematched comparison group. Overall, the group with DS performed most like the comparison group matched for syntax comprehension. In summary, both the Boudreau and Chapman (2000) study and the Miles and Chapman (2002) study suggest that narrative performance of individuals with DS is more similar to that of comparison participants matched for nonverbal cognition or syntax comprehension, rather than those matched for expressive language ability (i.e., MLU). These findings, once again, inform theories regarding the interdependence of language and cognition in individuals with DS. In a more recent study, Kover et al. (2012) examined differences between several measures of expressive language derived from two different sampling contexts in individuals with DS, FXS, or typical development. They found that children and adolescents with DS (ages 10–17 years) attempted to produce fewer utterances per minute and demonstrated greater dysfluency during narration of a wordless picture book (i.e., Frog Goes to Dinner or Frog On His Own) than during conversation with an examiner, a pattern that was not observed in participants with FXS or typical development matched by nonverbal developmental level. The idea that narrative skill, in particular, requires the integration of linguistic and cognitive abilities (Hemphill et al., 1991) suggests that such integration may be particularly challenging for individuals with DS.

Summary Individuals with DS show a phenotypic pattern of greater impairment in expressive language relative to nonverbal cognition, social skills, and comprehension of vocabulary and syntax. Within the domain of comprehension, vocabulary skills are commensurate with, or in advance of, levels of nonverbal cognition depending on the measures used to assess both domains. In addition, vocabulary is in advance of syntax, and individual differences in syntax comprehension predict individual differences in the complexity of expressive utterances. Levels of syntax comprehension are predicted by auditory, but not visual, short-term working memory and are likely to decline across adolescence for individuals with DS. Higher levels of expressive language are revealed when expressive language measures are based on lexical content rather than lexical diversity or utterance length. Hearing status seems particularly important for syntactic production. Finally, chronological age and hearing status predict the intelligibility of expressive utterances for individuals with DS.

Fragile X Syndrome Language Comprehension Existing data indicate that males with FXS achieve well below chronological age expectations on measures of vocabulary and syntax comprehension (Abbeduto, Brady, & Kover, 2007; Madison, George, & Moeschler, 1986; Paul et al., 1987; Sudhalter, Maranion, & Brooks, 1992). Although vocabulary comprehension appears to keep pace with nonverbal cognition in affected males, at least in adolescence and young adulthood (Abbeduto et al., 2003; Paul et al., 1987), the picture for syntax comprehension is less clear.

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Abbeduto et al. (2003) used the TACL-R (Carrow-Woolfolk, 1985) to examine patterns of receptive language in participants with FXS, relative to participants with either DS or typical development. Participants with FXS and DS were matched group-wise based on chronological age and nonverbal IQ, and all three participant groups were matched on nonverbal mental age. Participants with FXS, who included both males and females, achieved significantly higher total age-equivalent scores on the TACL-R than did participants with DS. In addition, females with FXS achieved higher total scores than males with FXS, but this gender difference was not significant after controlling for differences in nonverbal mental age. A significant difference in total age-equivalent scores was not observed between participants with FXS and MA-matched typically developing children. It should be noted, however, that receptive language may pose more of a challenge for individuals with FXS during early childhood (Price, Roberts, Vandergrift, & Martin, 2007), and thus, receptive language may only catch up with nonverbal cognition during adolescence. A closer look at performance for the individual TACL-R subtests in the Abbeduto et al. (2003) study reveals additional differences among the groups. All three groups achieved vocabulary comprehension scores commensurate with their nonverbal mental ages, replicating previous findings for individuals with DS. However, participants with FXS and MA-matched, typically developing children achieved vocabulary scores commensurate with their scores on the two TACL-R subtests measuring grammar and syntax. This flat profile of performance across language domains contrasts with the profile that emerged for participants with DS, who performed less well on the subtests measuring syntactical skills relative to vocabulary. In contrast to individuals with DS, therefore, adolescents and young adults with FXS demonstrate neither an asynchrony in individual domains of language comprehension nor an asynchrony between language comprehension and nonverbal cognition. In contrast, Oakes et al. (2013) have found differences in comprehension skills of adolescents with FXS relative to typically developing and DS peers. These investigators conducted an in-depth analysis of error patterns that emerged for adolescents with FXS or DS and younger TD children matched on nonverbal cognitive level in response to administration of the Test for Reception of Grammar (TROG-2), a standardized test measuring comprehension of a variety of grammatical structures. Participants with FXS had overall scores that were lower than those for TD comparison children but higher than participants with DS. This between-groups difference was not observed when females were excluded from the FXS group, indicating similar overall levels of grammar comprehension across FXS and DS. In terms of specific grammatical constructions, there were no differences between TD and FXS participants for either reversible in/ on constructions or four-element sentences, although participants with FXS outperformed those with DS on reversible in/on constructions. Participants with TD did outperform participants with FXS on both reversible SVO sentences and subject relative clauses, although there were no differences between FXS and DS for these constructions. Errors committed on the TROG-2 can be either lexical or grammatical in type. In a series of follow-up analyses, Oakes et al. (2013) demonstrated that participants with FXS made significantly more errors overall relative to TD participants and also made more grammatical relative to lexical errors. Participants with DS made a similar overall number of errors relative to participants with FXS but made significantly more grammatical errors. Taken together, the results of Oakes et al. (2013) indicate that children with FXS do show a weakness in grammatical comprehension relative to TD comparison children matched on levels of nonverbal cognition but not relative to children with DS. Additionally, these authors propose that adolescents with FXS may have particular difficulty in processing sentences that recruit auditory sequential memory and that do not provide lexical supports for grammatical processing.

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Language Production Most studies of language in FXS have focused on males, given the potential causal links between the behavioral characteristics of males with FXS syndrome (e.g., overarousal and inattention) and the ability to use language for interpersonal communication (Abbeduto et al., 2007). Existing data indicate that males with FXS achieve well below chronological age-expectations on measures of expressive vocabulary (Madison et al., 1986; Paul et al., 1987; Sudhalter et al., 1992). Studies using summary measures that collapse across vocabulary and other domains of language also have reported more difficulty with expressive than receptive language skills for males with FXS (Roberts, Mirrett, & Burchinal, 2001). However, there is little consensus as to whether receptive and expressive vocabularies are delayed to a similar extent in males with FXS. Few studies have provided data on expressive syntax for individuals with FXS. Madison et al. (1986) examined conversational samples of male participants from a single extended family and found that they generally displayed MLUs that were at or above nonverbal mental age expectations. In contrast, Paul, Cohen, Breg, Watson, and Herman (1984) found no differences on several measures of expressive syntax in conversational language between institutionalized adult males with FXS and age- and IQ-matched individuals with other etiologies of intellectual disability. Similarly, Ferrier et al. (1991) found that males with FXS did not differ from cognitively matched individuals with DS or younger typically developing children on measures of syntax derived from conversational samples. It is difficult to generalize from studies such as these, however, given methodological and sampling limitations. Using the Oral Expression subtest of the Oral and Written Language Scales (OWLS; CarrowWoolfolk, 1995), Abbeduto et al. (2001) demonstrated that adolescents and young adults with FXS did not differ in age-equivalent scores from typically developing 3- to 6-year-olds matched groupwise on nonverbal MA and did not show a discrepancy between receptive language (as measured by the TACL-R) and expressive language (as measured by the OWLS). Finally, participants with FXS demonstrated significantly better expressive language performance than did individuals with DS who were matched on nonverbal cognition. These findings add support to the notion of a specific expressive language impairment in individuals with DS. Studies of language production in FXS typically have derived measures of expressive syntax (e.g., MLU) from conversational language samples (Murphy & Abbeduto, 2003). In fact, standardized procedures for collecting expressive language samples using conversation and wordless picture book narrative contexts are currently being used to supplement the assessment of expressive language skills derived from standardized, norm-referenced assessments (Berry-Kravis et al., 2013). As is the case for individuals with DS and other etiologies of intellectual disability, there is evidence that variations in sampling context can impact the length and complexity of expressive language (Abbeduto et al., 1995; Chapman et al., 1998). Kover et al. (2012) examined five aspects of expressive language derived from the narrative and conversational language sampling contexts: syntactic complexity, lexical diversity, talkativeness, dysfluency, and unintelligibility. Participants with FXS who did not meet criteria for autism and participants with DS were matched on nonverbal IQ (all participants scored at floor levels on three nonverbal subtests of the Stanford Binet 4), nonverbal mental age, and chronological age. (Also included were participants with FXS who also met criteria for autism, but their findings are discussed in a subsequent section.) All participants displayed significantly higher levels of syntactic complexity and lower levels of lexical diversity in narration relative to conversation. For talkativeness, there was a significant main effect of group and of context in that participants with FXS talked more than those with DS, and all participants talked more during conversation than narration. All participants were more dysfluent during the conversational context. Thus, although the narrative context seems to provide the structure needed

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to support grammatical complexity, it may limit the diversity of vocabulary words that participants may use to describe the illustrations in the book. Similarly, although participants may produce more utterances during conversation, increased dysfluency within this context may be attributable to the more open-ended and less constrained nature of the conversational interaction. There are numerous other ways in which the behavioral phenotype of FXS may affect the use of language in social interactions. Perseverative language, which is considered a unique and defining feature of the expressive language of males with FXS (Abbeduto & Hagerman, 1997; Bennetto & Pennington, 1996), may result directly from the hyperarousal and social anxiety characteristic of this syndrome (Cornish et al., 2004). In particular, males with FXS display especially high rates of both self-repetition and off-topic or tangential utterances (Belser & Sudhalter, 2001; Ferrier et al., 1991; Sudhalter & Belser, 2001; Sudhalter et al., 1990). Both strategies may allow males with FXS to escape the linguistic, social, and cognitive demands of participating in a conversational interaction. Abbeduto et al. (2006) used a laboratory-based barrier task to examine the ability of adolescents and young adults with FXS to make the intended referents of their expressive utterances clear to other people. In this task, the participant assumed the role of speaker, with a researcher taking the role of listener. The task required the speaker to describe a novel target shape so that the listener could select the corresponding shape from a set of potential referents. Participants with FXS were less likely than nonverbal MA-matched typically developing children to create unique mappings between their descriptions and the target shapes; instead, they often extended the same description to multiple shapes. Participants with FXS were also less likely to use the same description each time a shape appeared as a target, even if the previous description of that shape had been comprehended by the listener. Thus, individuals with FXS produced talk that was less comprehensible than expected based on their nonverbal mental ages. Although there has been little study of the pragmatic skills of females with FXS, Simon, Keenan, Pennington, Taylor, and Hagerman (2001) did examine the ability of high-functioning females with the full FXS mutation to complete a discourse task in a coherent manner. These participants had difficulty, relative to IQ-matched comparison females, in selecting humorous endings for stories that they read. Their inability to select cohesive endings to stories, while possibly attributable to memory constraints, is suggestive of a need to conduct more thorough investigations into the pragmatic ability of females with FXS.

Gender Difference in Language in FXS Direct comparisons of males and females with FXS within the same study, using the same measures, or under the same experimental conditions have been surprisingly rare in the literature, making it difficult to understand the role that gender plays in shaping the language characteristics of individuals with FXS (Abbeduto et al., 2007; Murphy & Abbeduto, 2003; Pavetto & Abbeduto, 2002). Dykens et al. (2000) have suggested that, despite differences in the severity of affectedness, the profile of strengths and weaknesses associated with FXS does not vary by gender. This conclusion, however, is based largely on a synthesis of results from studies employing different methodologies with samples differing along many dimensions in addition to gender. Nevertheless, preliminary data collected by Abbeduto et al. (2003) supports this conclusion, at least with respect to language comprehension. Abbeduto et al. (2003) examined gender differences in receptive vocabulary and syntax using age-equivalent scores from the three subtests of the TACL-R administered to adolescents and young adults with FXS. Although female participants with FXS had higher TACL-R scores than the males, on average, the magnitude of the male-female differences was constant across the three

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TACL-R subtests. In addition to these flat profiles of language comprehension, both males and females achieved language scores commensurate with a measure of nonverbal cognition. These results are consistent with a pattern of quantitative, but not qualitative, differences in receptive language ability between males and females with FXS. A recent longitudinal study provides insights into the ways in which gender impacts language for adolescents with FXS and additionally suggests that the role of verbal working memory skills may differ by gender for these individuals. Within both genders, Pierpont, Richmond, Abbeduto, Kover, and Brown (2011) observed a wide variation in performance on four standardized tests of language ability (two each for comprehension and production), although, on average, girls outperformed boys on tests of language as well as nonverbal cognitive development. In fact, performance on all four language tests was significantly and positively correlated with growth scores from a test of nonverbal cognition, reinforcing the strong association between language development and intellectual ability for individuals of both genders with FXS. Not only did adolescent girls with FXS demonstrate higher levels of language skills, but they also exhibited significantly more growth over a two-year period than did boys, especially in the domain of receptive syntax, for which boys demonstrated little improvement over the course of the study. Identifying the psychological mechanisms that underlie the ongoing development of language skills has important implications for intervention planning and for the development of targeted pharmacological treatment approaches. In this regard, Pierpont et al. (2011) found that phonological working memory, the ability to maintain the phonological representations of words and sentences in short-term memory, was related to growth over time in both receptive and expressive language for adolescent males with FXS, but not females with FXS, even after controlling for intellectual ability and symptoms of autism. Additionally, these authors found that verbal working memory, which involves the ability to plan, organize, and manipulate language information, accounted for growth in vocabulary ability over time for these same males with FXS. Pavetto and Abbeduto (2002) compared the language produced by males and females in conversation and narration on talkativeness, fluency, lexical diversity, and syntactic complexity. Although no gender differences were observed for fluency or lexical diversity, two aspects of production were influenced by gender: males were more talkative than females, but females produced utterances of greater syntactic complexity. In addition, both males and females produced utterances of longer MLU in narration than in conversation; however, the magnitude of the gender difference in syntactic complexity was greater for narration than for conversation. This is consistent with previous findings demonstrating that narration elicits language with greater syntactic complexity than conversation (Abbeduto et al., 1995) and suggests that the language characteristics of males and females are influenced in fairly similar ways by sampling context. Murphy and Abbeduto (2006) examined the relationship between perseverative language and both gender and sampling context in a group of adolescent males and females with FXS. Language samples were coded for utterance-level repetitions, topic repetitions, and conversational device repetitions (i.e., repetition of rote phrases or expressions used to manage the interaction). Males produced more conversational device repetitions than did females, and these gender differences were not explained by differences in nonverbal cognitive or expressive language ability. In addition, more topic repetitions occurred in conversation than in narration regardless of gender. The use of utterance-level repetition also was marginally greater during conversation. The observed gender differences in verbal perseveration among adolescents with FXS suggest that, relative to females, males with FXS may rely more heavily on rote phrases in expressive language and that speakers of both genders are influenced by the context of the talk, with less structure leading to an increase in maladaptive verbal behavior.

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Language in Individuals with Co-morbid FXS and Autism An association between FXS and autism is well documented (Bailey, Hatton, Skinner, & Mesibov, 2001), but a number of critical issues remain to be clarified. There is not yet agreement on the exact proportion of individuals with FXS who are likely to be diagnosed with an autism spectrum disorder. It is not clear whether the characteristics of autism are distributed on a continuum throughout the population of individuals with FXS or whether the co-morbid diagnosis of an autism spectrum disorder represents a qualitatively distinct subtype of the FXS behavioral phenotype. Finally, it has yet to be determined whether autism within FXS has a different etiology or arises from different neuropsychological and neurobiological mechanisms compared with nonsyndromic autism (e.g., social anxiety versus social indifference). Lewis and colleagues examined language profiles of adolescents and young adults with comorbid FXS and autism (Lewis et al., 2006). As expected from previous research, adolescents with co-morbid FXS and autism (N = 10) scored significantly lower than adolescents with FXS only (N = 44) on a measure of nonverbal cognition; all 10 participants with both FXS and autism achieved the lowest possible standard score on the Stanford-Binet, 4th edition, in contrast to 48% of participants with FXS only. When participants with both FXS and autism were compared to age-matched participants with FXS only (N = 21), who had also achieved the lowest standard score on the Stanford-Binet, there were no significant between-group differences in expressive language, as measured with the expressive subscale of the OWLS (Carrow-Woolfolk, 1995). With regard to receptive language, however, the group with co-morbid FXS and autism performed more poorly on all three subtests of the TACL-R or TACL-3 (i.e., Vocabulary, Grammatical Morphemes, and Elaborated Phrases) than did participants with FXS only. This study replicates the greater impairment in cognitive ability observed in younger children with co-morbid FXS and autism (Rogers et al., 2001, but see Price et al., 2007, for contrary evidence) and suggests that this impairment persists into adolescence. In addition, the findings suggest that, while youth with FXS only typically display a flat profile of language and nonverbal cognitive skills, those with comorbid FXS and autism display an asynchronous profile, with receptive language more impaired than either expressive language or nonverbal cognition; however, more fine-grained analyses have uncovered areas of substantial impairment in expressive language that are associated with autism symptoms in FXS. In the study by Kover et al. (2012), the analysis of conversational and narrative samples demonstrated high rates of unintelligible speech by males with FXS who met criteria for autism. Moreover, their rates were higher than nonverbal mental age-matched males with FXS who did not meet criteria for autism and as high as the participants with DS, also matched for nonverbal mental age. This difference may reflect the involvement of an oral motor component in a subset of FXS cases. At the same time, the lack of an association between autism status and other measures of expressive language could also reflect reliance on a categorical diagnosis. Recent studies have suggested that symptoms of ASD are distributed as a continuum within FXS and that dichotomous categorization approaches may not be the most informative approach to characterizing the presence and severity of symptoms of ASD in FXS. In a more recent study, Kover et al. (2013) utilized the two expressive language sampling procedures to examine grammatical complexity, lexical diversity, fluency, and intelligibility in males with FXS relative to comparison groups of individuals with DS and TD who were matched group-wise on nonverbal mental age. This study is important in that co-morbid autism in participants with FXS was quantified along a continuum of impairment rather than using a dichotomous classification approach. For participants with FXS, when assessed in conversation and after controlling for nonverbal cognition, severity of autism symptoms was significantly related to the number of

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complete and intelligible utterances and to the number of utterances attempted per minute. Thus, both Kover et al. (2013) and Kover et al. (2012) suggest that males with FXS and symptoms of ASD are likely to demonstrate lower levels of intelligibility in conversational settings. McDuffie et al. (2010) used scores derived from the Autism Diagnostic Interview-Revised (ADI-R) to capture the nature of the differences between individuals with FXS relative to autism status. After controlling for nonverbal IQ, significant between-group differences were observed in the domains of Communication and Restricted Repetitive Behaviors, but not for the Reciprocal Social Interaction domain. As deficits in social reciprocity are core to the diagnosis of ASD in individuals with nonsyndromic autism diagnosis, the finding that scores in this domain did not distinguish between individuals with FXS is thought provoking and suggests that impairments in communication and the presence of restricted and repetitive behaviors may lead to the diagnosis of autism in FXS. McDuffie, Kover, Abbeduto, Lewis, and Brown (2012) examined receptive and expressive language profiles, using standardized language tests, for a group of verbal male children and adolescents with FXS who had varying degrees of autism symptoms. Two separate sets of analyses utilized either a categorical or continuous approach for assigning autism diagnostic classification. The categorical approach to diagnosis was based on the combined use of the ADI-R and the Autism Diagnostic Observation Schedule (ADOS), and the continuous approach for representing autism symptom severity was based on ADOS severity scores. All analyses controlled for nonverbal IQ and chronological age. Nonverbal IQ accounted for significant variance in all language outcomes with large effect sizes. The categorical analyses failed to reveal an effect of diagnostic group (FXS only vs. FXS + ASD) on standardized test performance. The analysis using the continuous severity metric revealed a negative relationship between autism symptom severity and all of the standardized language measures. This study suggests that representing autism affectedness along a continuum of impairment in FXS may reveal more subtle information about the way symptoms of ASD influence other domains of functioning than would be revealed using a dichotomous classification approach. One limitation of much previous research seeking to characterize autism symptoms in FXS has been the lack of direct comparisons between individuals with FXS and those with nonsyndromic ASD. For example, all of the studies reviewed in this section compared individuals with FXS only to those with a co-morbid diagnosis of FXS and ASD. A more nuanced picture of how symptoms of autism affect those with FXS must rely on direct between-disorders comparisons, which have been rare in the research literature to date. In one of the few published studies comparing FXS and nonsyndromic ASD directly, McDuffie, Thurman, Hagerman, and Abbeduto (2015) compared symptoms of autism in boys with FXS to those with nonsyndromic ASD using individual item scores from the ADI-R. This study compared several different matched subsamples of participants in an attempt to determine which analysis approach would yield the most information relative to how symptoms of ASD are distributed in FXS. As a group, boys with FXS had fewer symptoms of ASD than boys with nonsyndromic ASD when matched on chronological age and when matched on both chronological age and autism diagnosis. This finding suggests that, even when they meet the categorical cutoffs for a diagnosis of ASD, boys with co-morbid FXS and ASD are less affected than age-matched boys with nonsyndromic ASD. Even when matched on severity of autism symptoms, between-group comparisons revealed that age-matched boys with FXS were significantly less impaired in Social Smiling, Offering to Share, and Communicative Gesture Use but more impaired in Complex Mannerisms than were boys with nonsyndromic ASD. These findings suggest that important differences do exist between individuals with FXS and those with nonsyndromic ASD. It seems possible that the use of measures that assess other aspects of the behavioral phenotypes of both disorders may reveal other

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important differences between the two disorders. This will be of critical importance in understanding the mapping between overt behaviors and underlying psychological mechanisms and in designing both pharmacological and behavioral treatment approaches. In an initial attempt to understand the nature of the neuropsychological problems at the heart of autism symptomatology in FXS, Thurman, McDuffie, Hagerman, and Abbeduto (2014) recently demonstrated that symptoms of manic/hyperactive behaviors and general anxiety were more frequently reported for boys with FXS than for age-, IQ-, and autism severity–matched boys with nonsyndromic ASD. Additionally, the positive association observed between general anxiety and social avoidance for individuals with FXS was significantly stronger than that observed in nonsyndromic ASD. The findings of Thurman et al. (2014) suggest that inattention, hyperactivity, and anxiety are significantly more likely to characterize the behavioral phenotype of boys with FXS relative to boys with nonsyndromic ASD who have the same level of nonverbal cognitive ability and severity of autism symptoms. These authors speculate on the downstream developmental effects of these characteristics, which are likely to adversely affect the ways in which boys with FXS interact with objects and people and learn from their social and physical environments over time.

Summary In contrast to individuals with DS, individuals with FXS demonstrate a relatively flat profile of language comprehension and production skills, and these language abilities are, on average, commensurate with levels of nonverbal cognition. The language of males and females with FXS is affected similarly, with quantitative differences that are related to differences in nonverbal cognition. The language produced within a naturalistic context may be more revealing of the syndrome-specific behavioral challenges faced by individuals with FXS (e.g., social anxiety, hyperarousal, and inattention). A significant proportion of individuals with FXS also meet diagnostic criteria for an autism spectrum disorder. However, we do know that males with FXS are more affected in the domain of nonverbal cognition and less affected in the domain of autism symptoms than age-matched males with nonsyndromic ASD, at least during childhood and adolescence. Although much remains to be learned, initial research suggests that an autism diagnosis in males with FXS may reflect impairments in the ability to communicate verbally and in the presence of repetitive behaviors and complex mannerisms, rather than reflecting core impairments in the motivation to approach or relate to other people (i.e., social reciprocity).

Williams Syndrome Language Comprehension Comprehension of concrete vocabulary (e.g., labels for objects, actions, descriptors) is a relative strength for individuals with WS (Mervis & Beccera, 2007). Mervis and John (2010) reported that for individuals with WS, performance is highest on the Peabody Picture Vocabulary Test (PPVT4; Dunn & Dunn, 1997) when compared to virtually any other standardized language measure. Of their participants, 83% earned standard scores within the normal range (70 or higher) and 8% scored above the average standard score for the general population (100). In contrast to their relative strength in concrete vocabulary, individuals with WS demonstrate considerable difficulty with relational/conceptual vocabulary (e.g., terms for spatial, temporal, and dimensional concepts). In a sample of children with WS between 5 and 7 years of age, Mervis and John (2010) found that relational vocabulary ability was comparable with the significant weakness individuals with WS

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have in spatial ability, with standard scores for relational vocabulary ~30 points lower than standard scores for concrete vocabulary. Mervis and colleagues compared vocabulary comprehension, as measured by the PPVT, with comprehension of abstract relational vocabulary, as measured with the Test of Relational Concepts (TRC; Edmonston & Litchfield Thane, 1988), in a group of 5- to 7-year-olds with WS (mean CA 6–3) (Mervis et al., 2003). A group of typically developing 4- to 7-year-olds, matched on PPVT raw scores, provided the comparison group. The typically developing group demonstrated a significantly larger abstract vocabulary size than did the group with WS (i.e., an average raw score of 31 words compared with 22 words). However, the typically developing group did not display a significant difference in standard scores on the PPVT relative to the TRC, while the WS group showed a difference of almost two standard deviations between the two measures. The same type of discrepancy between measures of concrete and abstract vocabulary comprehension has been reported for individuals with DS (Miolo et al., 2005). In contrast to individuals with DS, comprehension of syntax is at a level relatively similar to comprehension of concrete vocabulary for individuals with WS. Mean performance on the Test for Reception of Grammar (TROG; Bishop, 2003) is in the borderline normal range (with an average standard score of 74), comparable to performance on the PPVT (with an average score of 81) (Mervis & John, 2010). Grant and colleagues found that performance on the TROG was significantly correlated with nonword repetition performance for a group of participants with WS ranging in age from 8 to 35 years (Grant et al., 1997). Receptive grammatical ability has also been shown to relate strongly to backward digits recall, with this association stronger in individuals with WS than in ability-matched typically developing children (Robinson, Mervis, & Robinson, 2003). These findings support the view that auditory working memory is a characteristic strength for individuals with WS and contributes to relatively strong levels of performance in the areas of vocabulary and syntax.

Language Production Expressive concrete vocabulary is also considered an area of relative strength in individuals with WS. Administration of the Mullen Scales of Early Learning (MSEL; Mullen, 1995) confirms that the William Syndrome cognitive profile has already emerged in preschoolers and toddlers with WS, with performance weakest in the fine motor domain and considerably stronger for receptive and expressive language (Mervis & Beccera, 2007; Mervis et al., 2003). In addition, a group of 2-year-olds with WS, while scoring below the 10th percentile in vocabulary acquisition on the Words and Sentences version of the MacArthur Communicative Development Inventory (CDI; Fenson et al., 1994), displayed larger average expressive vocabulary sizes relative to a matched group of 2-year-olds with DS; mean vocabulary size was 133 words (range 3–391) for the toddlers with WS compared with 66 words (range 0–324) for the toddlers with DS (Mervis & Robinson, 2000). Mervis and Bertrand (1997) followed 10 children with WS longitudinally from 3–5 years beginning when the children ranged in age from 4–26 months. Nine of these 10 children began to produce words several months before they first understood or produced a referential pointing gesture, in contrast to the pattern observed for children with either typical development or DS. This delay in the emergence of referential pointing may be the consequence of a deficit in fine motor skill development for toddlers with WS and suggests a different pattern in the emergence of early language skills for these children. Recent data indicates an average standard score of 79 in response to the Expressive Vocabulary Test (EVT-2; Williams, 1997) for individuals with WS, which is relatively comparable to scores for vocabulary comprehension, as measured by the PPVT-4 (average score = 81). Data from previous

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editions of these measures revealed that average scores for expressive vocabulary were lower (mean = 65) than average scores for vocabulary comprehension (mean = 80). Mervis et al. (2003) attribute this discrepancy to the response structure of the EVT. The task of providing a synonym for some of the EVT test items may be more challenging conceptually than simply pointing to a picture in response to PPVT items. Consistent with this hypothesis, the EVT-2 includes fewer items pressing for synonyms than its predecessor the EVT. Individuals with WS are characterized in the literature as having a particular strength in grammatical abilities. This generalization forms the primary basis for Bellugi’s controversial claim that the case of WS provides evidence for the independence, or modularity, of language from cognition (Bellugi, Poizner, & Klima, 1989, 1990, 1999). On average, the spontaneous language of individuals with WS is more syntactically complex than that of CA- and IQ- matched individuals with DS (Klein & Mervis, 1999). Children with WS are also more proficient at tense marking than younger children with specific language impairment who are matched for MLU (Mervis & Klein-Tasman, 2000; Rice, 1999). However, as Mervis et al. (2003) pointed out, these two comparison groups have deficits in morphosyntax relative to their levels of nonverbal cognition. A more nuanced picture emerges when individuals with WS are matched with typically developing children based on chronological age or mental age. Volterra, Capirci, Pezzini, Sabbadini, and Vicari (1996) compared the spontaneous expressive language of Italian children with WS to that of younger, MA-matched typically developing children and found that MLU and other measures of syntax were similar for the two groups. In addition, Zukowski (2001) found that the ability of older children and adolescents with WS to produce complex sentence constructions (e.g., embedded relative clauses) was similar to that of a typically developing comparison group matched for MA. These findings suggest that, on average, the grammatical abilities of children with WS are commensurate with, rather than in advance of, their level of cognitive development. Mervis and colleagues conducted an extensive examination of the associations among MLU, cognitive ability, and grammatical complexity in children with WS (Mervis & Klein-Tasman, 2000; Mervis, Morris, Bertrand, & Robinson, 1999). These investigators collected spontaneous language samples during play from 39 participants with WS, ranging in age from 2–12 years. Two measures of grammatical complexity—MLU in morphemes and IPSyn scores (Scarborough, 1990)—were lower than scores reported for typically developing children at ages 3–6 years in Scarborough’s (1990) sample. Thus, rather than being advanced in grammatical development, children with WS were substantially delayed in the development of both syntax and grammar when compared to CA-matched children with typical development, rather than to children with other types of language delay. Moreover, for the children with WS, both MLU and IPSyn scores were commensurate with cognitive ability but lower than expected based on auditory short-term memory and vocabulary comprehension. Morris and Mervis (2000) compared the morphological abilities of this sample of children with WS to a group of younger typically developing 3-year-olds matched for MLU in morphemes. Use of noun plurals, determiners, and verb tense was similar for the two groups, indicating that the morphological abilities of the children with WS were at the level expected for the length of their productive utterances. However, the children with WS had larger receptive vocabularies than the MLU-matched, typically developing children, suggesting that utterance length and grammatical complexity are lower than expected relative to vocabulary size. This discrepancy between vocabulary and morphology was observed despite the fact that English is a relatively uninflected language. Children with WS who are learning languages that are morphologically more complex than English (e.g., French, Italian, and Hebrew) perform less well than younger, MA-matched, typically developing children (Karmiloff-Smith et al., 1997; Levy & Hermon, 2003; Volterra et al., 1996). Thus, these studies support the notion that grammatical morphology is not advanced in individuals

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with WS relative to their levels of cognitive ability or vocabulary comprehension. Similarly, studies such as these refute the notion that language is “intact,” or even advanced, for individuals with WS and do not support claims that language in WS is independent of other cognitive domains. In contrast to DS, auditory short-term or working memory is a relative strength for individuals with WS and may provide an important mechanism for language learning in this population (Mervis et al., 1999). Individuals with WS perform at developmentally appropriate levels on tasks involving forward and backward digit span (Mervis et al., 1999), as well as nonword repetition (Grant et al., 1997). Individuals with DS, in contrast, show exceptional difficulty on tests of backward memory for both verbal and visual information (Vicari, Caselli, & Tonucci, 2000). It has been proposed that speech in individuals with WS may appear to be intact due to the use of unanalyzed, stereotyped phrases and sentences learned by rote memorization (Gosch, Stading, & Pankau, 1994; Udwin & Yule, 1990). Robinson et al. (2003) examined the association between verbal short-term memory and language in 39 children with WS, ages 4–16 years. Participants with WS were matched to a comparison group of younger typically developing children based on performance on the TROG. After controlling for chronological age, measures of forward digit span, backward digit span, and nonword repetition showed significant associations with TROG performance for the children with WS. After controlling for CA and forward digit span, nonword repetition and backward digit span both accounted for unique variance in TROG performance. Robinson et al. (2003) proposed that phonological working memory, represented by nonword repetition performance, and verbal working memory, represented by backward digit span, likely make a strong contribution to the ability of individuals with WS to comprehend and produce vocabulary and grammar. In fact, the group with WS showed a significantly stronger association between backward digit span and TROG performance than did children with typical development. These findings suggest that individuals with WS may use a basic cognitive strength, in the form of verbal working memory, to overcome challenges to language learning that result from relative weaknesses in nonverbal cognitive ability (Mervis et al., 2003). In a direct comparison between the two syndrome groups, Klein and Mervis (1999) examined a group of 9- and 10-year-olds with WS matched on chronological age with a group of children with DS. Nineteen of the 23 WS participants spoke in complete and grammatical sentences, whereas only 4 of the 25 participants with DS did so. The children with WS outperformed the CA-matched children with DS on the McCarthy Scales of Children’s Abilities (McCarthy, 1972), the PPVT, and a test of verbal working memory. When a subset of each group was individually matched for both CA and MA, the DS group performed better on measures of visual spatial skills (i.e., block building, draw-a-person, and draw-a design), while the WS group performed better on measures of verbal working memory. There were no significant between-group differences for the PPVT or for the McCarthy subtests measuring verbal ability.

Summary In contrast to initial descriptions of language in WS as being unaffected by the syndrome, and even advanced, few individuals with WS have language skills at levels commensurate with their chronological ages. Instead, mean levels of performance on standardized tests of language ability are consistently in the borderline to mildly impaired range, commensurate with the language skills of cognitively matched, but younger, typically developing children. Both vocabulary and grammar are more advanced than overall levels of nonverbal cognition. This finding is due to the fact that visual spatial ability is an area of particular weakness for individuals with WS and considerably more impaired than nonverbal reasoning ability, which is relatively

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comparable to vocabulary and grammatical ability in individuals with WS. In contrast, verbal working memory is an area of particular strength and may account for the ability of individuals with WS to acquire vocabulary and syntax. This syndrome-specific profile is in direct contrast to individuals with DS who show a relative strength in visual-spatial skills, a relative weakness in verbal working memory (especially for backward information), and a specific expressive language impairment.

Other Genetic Syndromes Associated with Language Impairments In this final section, we briefly consider several other genetic syndromes associated with intellectual disability. Although language impairments appear to be a feature of their phenotypes, the data on those impairments are quite sparse relative to DS, FXS, and WS. Nevertheless, we describe these syndromes in the hopes of encouraging more research.

22q11.2 Deletion Syndrome This syndrome, also known as velo-cardio-facial syndrome, results from a deletion of approximately 40 genes on chromosome 22, although the size of the deletion is variable across individuals (Morrow et al., 1995). In most cases, the deletion is de novo rather than inherited (Shprintzen et al., 2005). The prevalence of 22q11.2DS is 1 in 2,000 (Antshel, Marrinan, Kates, Fremont, & Shprintzen, 2009). Congenital heart defects and cleft palate are common features of the physical phenotype (Shprintzen et al., 2005), as are hearing loss and velopharyngeal insufficiency (Antshel et al., 2009). IQs typically fall in the range of 70 to 75, although there is considerable variability across individuals (Bearden et al., 2001). There also are differences in the severity of impairments across cognitive domains; for example, auditory memory is less impaired than visuospatial skills, on average (Antshel et al., 2009; Simon, Bearden, Mc-Ginn, & Zackai, 2005). Language is impaired in most individuals with 22q11.2DS (Antshel et al., 2009). Rates of unintelligible utterances are quite high early in development, and expressive language is generally found to be more impaired than receptive language (Golding-Kushner, Weller, & Shprintzen, 1985; Persson, Lohmander, Jonsson, Oskarsdottir, & Soderpalm, 2003; Swillen et al., 1997). Pragmatic impairments are common (Antshel et al., 2007), although this domain has not been well-studied or fully characterized (Antshel et al., 2009). The language impairments associated with 22q11.2DS may play a role in the emergence of schizophrenia and psychotic symptoms, which are highly prevalent in adolescents and adults with the syndrome (Antshel et al., 2009). In particular, individuals with lower verbal IQs or who display a decline in verbal IQ are at elevated risk for developing psychosis (Gothelf et al., 2005, 2007). Whether language skills play a causal role in the transition to psychosis or are symptomatic of a process that both limits verbal IQ and produces psychosis (Antshel et al., 2009) is unknown. In either case, language and related verbal skills are important to monitor as indices of risk (Antshel et al., 2009). Social impairments and anxiety also are characteristic of individuals with 22q11.2DS (Angkustsiri et al., 2014), and these co-morbid problems may contribute to the language impairments associated with the syndrome. Autism also has been thought to be a frequent co-morbid condition (Fine et al., 2005), but more recent research using the ADOS, considered to be the gold standard diagnostic tool, suggests that autism may be misdiagnosed in this population (Angkustsiri et al., 2014). In any event, there is a need for more longitudinal data tracking the ways in which language shapes, and is shaped, by anxiety, social limitations, and experience.

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Cornelia de Lange Syndrome (CdLS) This syndrome is caused by deletions or mutations in genes that regulate a specific set of processes involved in neural maintenance and repair, with loci on chromosome 5, chromosome 10, and the X chromosome (Deardorff et al., 2012; Musio et al., 2006). CdLS is quite rare, with a prevalence of 1 in 50,000. IQs typically fall in the range of severe to profound intellectual disability (Moss et al., 2013a, 2013b), although cases of average-range IQs have been observed (Ajmone et al., 2014). The phenotype includes problems in mood, negative affect, hyperactivity, and impulsivity (Nelson, Moss, & Oliver, 2014). The phenotype also includes symptoms of autism, although these symptoms may reflect different underlying problems relative to the nonsyndromic case (Moss et al., 2013a,b). Many of these behavioral problems worsen with age during adolescence and beyond (Nelson et al., 2014). In addition, physical symptoms of premature aging are also common (Kline et al., 2007). In light of the cognitive impairments and autistic-like symptoms, it is not surprising that language and communication impairments also are common, although these impairments and their developmental trajectory have not been described in detail. Interestingly, Ajmone et al. (2014) observed substantial receptive language impairments in a small sample of children and young adolescents with CdLS, with those impairments being most pronounced in participants with more autism symptoms. Longitudinal data on the trajectory of language relative to other aspects of the phenotype would be useful in identifying the targets and timing of language intervention for this population.

Smith-Magenis Syndrome Smith-Magenis syndrome results from a de novo deletion of genes on chromosome 17. Like CdLS, it is relative rare, with some estimates placing the prevalence at 1 in 50,000 (Colley, Leversha, Voullaire, & Rogers, 1990). The syndrome is typically associated with intellectual disability, although there have been reports of individuals with IQs in the typical range (Dykens et al., 2000). Among the behavioral features associated with the syndrome are high rates of self-injurious behavior and high thresholds for pain (Dykens et al., 2000). In the cognitive realm, sequential processing is an area of relative weakness (Dykens, Finucane, & Gayley 1997), which raises the possibility that some aspects of language learning will be especially impaired in the population. Moreover, sleep problems are frequent among individuals with Smith-Magenis (Smith, Dykens, & Greenberg, 1998), which is likely to contribute to a variety of learning problems given the memory consolidation function of sleep. In fact, other than a few studies focusing on speech (Dykens et al., 2000) and analyses of clinical data from very small samples of participants (Wolters et al., 2009), little is known about language in this population and, thus, there is a desperate need for research on this topic.

Conclusion Four general themes emerge from the literature on language in the genetic syndromes considered here. First, the profile of language development and strengths and weaknesses varies dramatically across syndromes, not only in terms of degree of impairment but also in the profile or character of the impairments. Broadly speaking, language is more challenging for DS and less challenging for WS, although even the latter individuals do less well in language, on average, than do their typically developing chronological-age peers. In WS and DS, vocabulary acquisition appears to be especially strong relative to other language domains, although this appears true more for comprehension of concrete than abstract or relational vocabulary. In DS, syntax, especially expressive syntax, is more impaired than vocabulary, whereas the two domains are more synchronous and more highly

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developed in WS and FXS relative to DS. In FXS, the form and content of language (i.e., syntax and vocabulary) are less impaired, on average, than are the skills involved in using language for social ends. Approaches to assessment and intervention will need to be informed by and tailored to these syndrome-specific profiles (Fidler, Philofsky, & Hepburn, 2007). Moreover, it is important to recognize that language does not represent a unitary ability or set of skills, but rather a set of separate but inter-related skills and domains of knowledge. The profile of linguistic impairments associated with each syndrome emerges gradually and, thus, changes with age. In DS, for example, positive growth in expressive syntax continues, whereas syntax comprehension becomes increasingly delayed during adolescence and may even be characterized by regression in young adulthood. In FXS, the use of language for social ends appears to lag further behind age expectation during adolescence. In WS, the sequence of achievements in language and communication does not consistently follow the typical path, suggesting that there are various routes to language competence. From a clinical perspective, it is important to recognize the dynamic nature of the syndrome-specific phenotype and of the need to anchor assessment and interpretation of language and neurocognitive profiles to specific points in development. These syndrome-related differences in language appear to reflect not a direct effect of the genes on behavior but the influence of nonlanguage dimensions of the behavioral phenotypes and, thus, the indirect effects of individual or combinations of genes. In FXS, for example, problems in attention and arousal regulation may account for the verbal perseveration that places limits on social interaction. The overlay of autistic-like symptoms in FXS has rather dramatic effects on language in general, but especially on receptive language. The contextual variation in expressive language skills (e.g., higher MLU in narration than in conversation) is also likely to be attributable to the interaction of context with language skills, nonlanguage skills, and profiles of impairment. From a clinical perspective, such variations suggest the need to embed language assessment and intervention within a more integrated framework that recognizes the profile defining the whole child. Social-interactionist theories of language development are likely to be useful in this regard. The linguistic profile of each syndrome is closely tied to its cognitive profile. Thus, all of the syndromes, even WS, are associated with language delays relative to chronological-age peers. Indeed, correlations between summary measures of broad language domains (e.g., receptive syntax) and of nonverbal cognitive domains (e.g., nonverbal problem-solving) are substantial. Moreover, more subtle variations across cognitive domains appear to have a dramatic impact on the course of language development. For example, the relatively weak auditory short-term memory skills of individuals with DS contributes in important ways to their delays in syntax, whereas the relative strength in auditory short-term memory evidenced by individuals with WS appears to account, at least in part, for their stronger (relative to DS) syntactic skills. Such findings serve as a reminder that, in the case of individuals with intellectual disabilities, language learning and use are embedded in, and intimately connected to, other aspects of the psychological and behavioral life of the child, which means that assessment and treatment will need to be equally broad based. Finally, as noted, many other genetic syndromes produce language impairments, along with a host of other cognitive and behavioral phenotypes. It will be important to learn more about the language impairments associated with 22q11.2DS, CdLS, Smith-Magenis, and other syndromes, because individuals with these syndromes are likely to be in the case loads of speech-language clinicians.

Acknowledgements Preparation of this chapter was supported by NIH grants R01 HD024356, R01 HD054764, R01 HD055345, and U54 HD079125.

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3 AUTISM SPECTRUM DISORDERS Joanne Gerenser and Karece Lopez

Introduction Autism spectrum disorders (ASD) are a group of developmental disorders that are characterized by deficits in social communication and restricted or repetitive patterns of behaviors or interests (American Psychiatric Association [APA], 2013). Currently, prevalence estimates of ASD in children under the age of 8 are between 1 in 88 (Wingate et al., 2012) to as high as 1 in 68 (Wingate et al., 2014). It is clear that the prevalence of ASD has increased. What is not clear is the reason for this increase. Expanded diagnostic criteria, improved diagnostic tools and instruments, better awareness, and possible environmental and genetic factors have all been discussed as possible contributing factors (Blaxill, 2004; Rutter, 2005). Prior to the release of the new Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5, APA, 2013), there were five specific diagnoses within the autism spectrum disorders (see Table 3.1). The DSM-5, however, eliminated these individual diagnoses. The diagnoses of autism, Asperger’s syndrome, and PDD-NOS have been merged into an umbrella diagnosis of Autism Spectrum Disorder (ASD) (see Table 3.2). Rett Syndrome and Childhood Disintegrative Disorder have been removed as individual diagnoses. In addition to the merging of the diagnostic categories, the DSM-5 made a number of other changes. For example, the three primary symptom areas of Social, Communication, and Restricted and Repetitive Behaviors were reduced to two by merging the Social and Communication Domains into one, the Social Communication Domain. Although the DSM-5 has reduced the diagnoses from five distinct diagnoses to one, additional diagnostic specifiers capture the individual variability common in ASD. For example, there are three distinct levels of ASD within the DSM5, based on the severity of autism symptoms (see Table 3.3). In addition, the manual suggests that information on cognitive functioning, language abilities, known etiologic factors, as well as associated conditions, should be included in a diagnosis (APA, 2013). ASD can occur at all levels of intelligence, although 40% of people with ASD function in the range of intellectual disability (Bailey, Phillips, & Rutter, 1996; Fombonne, 2005). It is four times more common in boys than girls (Fombonne, 2005). There is a higher incidence of autism in siblings and family members, suggesting a strong genetic component (Bailey et al., 1995; Rutter, 2005). ASD is commonly associated with other developmental disabilities, such as Fragile X syndrome and tuberous sclerosis (Rutter, Silberg, O’Connor, & Simonoff, 1999;

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Table 3.1 Overview of Diagnostic Criteria (adapted from DSM-5, APA, 2013) Diagnosis

Characteristics

Autistic Disorder

1. A. B. C. D. 2. A. B.

Qualitative impairment in social interaction Impairment in use of nonverbal behaviors to regulate behavior Failure to develop age-appropriate peer relationships Lack of spontaneous seeking to share enjoyment Lack of social or emotional reciprocity Qualitative impairment in communication Delay or total lack of the development of spoken language Marked inability to initiate or sustain conversation, even with adequate speech C. Stereotyped or repetitive use of language D. Lack of spontaneous make-believe play 3. Restrictive, repetitive, and stereotyped patterns of behaviors, interests, and activities A. Preoccupation with one or more patterns of interests B. Inflexible adherence to nonfunctional routines C. Stereotyped and repetitive motor mannerisms D. Persistent preoccupation with parts of objects 4. Onset of abnormal functioning prior to 3 years of age

Rett’s Disorder

1. All of the following: A. Apparently normal prenatal and perinatal development B. Apparently normal psychomotor development through the first 5 months after birth C. Normal head circumference at birth 2. Onset of all of the following after the period of normal development: A. Deceleration of head growth between ages 5 and 48 months B. Loss of previously acquired purposeful hand skills between 5 and 30 months with subsequent development of stereotyped hand movements C. Loss of social engagement early D. Appearance of poorly coordinated gait or trunk movements E. Severely impaired expressive and receptive language development with severe psychomotor retardation

Childhood Disintegrative Disorder

1. Apparently normal development for at least the first 2 years after birth as manifested by age appropriate verbal and nonverbal communication, social relationships, play and adaptive behavior 2. Clinically significant loss of previously acquired skills (before age 10 years in at least two of the following areas: A. Expressive or receptive language B. Social skills or adaptive behavior C. Bowel or bladder control D. Play E. Motor skills 3. Abnormal functioning in at least two of the following areas: A. Qualitative impairment in social interaction (see Autistic Disorder) B. Qualitative impairment in communication (see Autistic Disorder) C. Restricted, repetitive, and stereotyped patterns of behavior, interests, and activities (Continued)

Table 3.1 (Continued) Diagnosis

Characteristics

Asperger’s Syndrome

1. A. B. C. D. 2. A. B. C. D. 3.

4. 5.

Pervasive Developmental Disorder-Not Otherwise Specified (PDD-NOS)

Qualitative impairment in social interaction Impairment in use of nonverbal behaviors to regulate behavior Failure to develop age-appropriate peer relationships Lack of spontaneous seeking to share enjoyment Lack of social or emotional reciprocity Restrictive, repetitive, and stereotyped patterns of behaviors, interests, and activities Preoccupation with one or more patterns of interests Inflexible adherence to nonfunctional routines Stereotyped and repetitive motor mannerisms Persistent preoccupation with parts of objects The disturbance causes a clinically significant impairment in social, occupational, or other important areas of functioning. There is no clinically significant general delay of language. There is no clinically significant delay in cognitive development or in the development of age-appropriate self-help skills or adaptive behavior.

This category is used when there is a severe and pervasive impairment in the development of reciprocal social interaction or verbal and nonverbal communication; or when stereotyped behavior, interests, and activities are present, but the criteria are not met for specific pervasive developmental disorder, schizophrenia, schizotypal personality disorder, or avoidant personality disorder.

Table 3.2 Overview of DSM-5 Criteria for Autism Spectrum Disorder Currently, or by history, must meet criteria A, B, C, and D: A. Persistent deficits in social communication and social interaction across contexts, not accounted for by general developmental delays, and manifest by all three of the following: 1. Deficits in social-emotional reciprocity 2. Deficits in nonverbal communicative behaviors used for social interaction 3. Deficits in developing and maintaining relationships B. Restricted, repetitive patterns of behavior, interests, or activities as manifested by at least two of the following: 1. Stereotyped or repetitive speech, motor movements, or use of objects 2. Excessive adherence to routines, ritualized patterns of verbal or nonverbal behavior, or excessive resistance to change 3. Highly restricted, fixated interests that are abnormal in intensity or focus 4. Hyper- or hypo-reactivity to sensory input or unusual interest in sensory aspects of environment C. Symptoms must be present in early childhood (but may not become fully manifest until social demands exceed limited capacities). D. Symptoms together limit and impair everyday functioning.

Autism Spectrum Disorders Table 3.3 Severity Levels Severity level

Social communication

Restrictive, repetitive behaviors

Level 3 “Requiring very substantial support”

Severe deficits in verbal and nonverbal social communication skills cause severe impairments in functioning, very limited initiation of social interactions, and minimal response to social overtures from others. For example, a person with few words of intelligible speech who rarely initiates interaction and, when he or she does, makes unusual approaches to meet needs only and responds to only very direct social approaches.

Inflexibility of behavior, extreme difficulty coping with change, or other restricted/repetitive behaviors markedly interfere with functioning in all spheres. Great distress/difficulty changing focus or action.

Level 2 “Requiring substantial support”

Marked deficits in verbal and nonverbal social communication skills; social impairments apparent even with supports in place; limited initiation of social interactions; and reduced or abnormal responses to social overtures from others. For example, a person who speaks simple sentences, whose interaction is limited to narrow special interests, and who has markedly odd nonverbal communication.

Inflexibility of behavior, difficulty coping with change, or other restricted/repetitive behaviors appear frequently enough to be obvious to the casual observer and interfere with functioning in a variety of contexts. Distress and/or difficulty changing focus or action.

Level 3 “Requiring support”

Without supports in place, deficits in social communication cause noticeable impairments. Difficulty initiating social interactions, and clear examples of atypical or unsuccessful response to social overtures of others. May appear to have decreased interest in social interactions. For example, a person who is able to speak in full sentences and engages in communication but whose to- andfro conversation with others fails, and whose attempts to make friends are odd and typically unsuccessful.

Inflexibility of behavior causes significant interference with functioning in one or more contexts. Difficulty switching between activities. Problems of organization and planning hamper independence.

Wiznitzer, 2004). Almost one-third of people with autism will develop epilepsy by adolescence (McDermott et al., 2005). Recent advances in early indicators of autism have revealed behaviors that can distinguish infants with autism from typically developing infants as early as 1 year of age (Tierney, GabardDurnam, Vogel-Farley, Tager-Flusberg, & Nelson, 2012; Werner & Dawson, 2005). Researchers have employed three types of studies to identify early warning signs of ASD. Two of the methods for identifying early signs are behavioral in nature: retrospective studies and prospective studies (Boyd, Odom, Humphreys, & Sam, 2010). Retrospective studies involve reviewing videotapes of

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children as infants and toddlers who later receive a diagnosis of ASD. Prospective studies track the development of infant siblings of children diagnosed with ASD. As a result of these studies, a number of early warning signs have been identified. These include (1) a delay or disorder in early social behaviors such as looking at faces, social smiling, or responding to name (Osterling & Dawson, 1994; Werner & Dawson, 2005); (2) a delay or disorder in vocalizations or use of gestures (Colgan et al., 2006; Maestro et al., 2002; Patten et al., 2014); and (3) difficulty coordinating verbal and nonverbal behaviors (Hubbard et al., 2012; Yoder, Stone, Walden, & Malesa, 2009). More recently, researchers have begun to examine endophenotypes and how these can lead to earlier identification (Tierney et al., 2012). Endophenotypes are biological markers associated with a given disorder that provide insight into the origin of the disorder. Endophenotypes that have been identified in infants who are determined to be at high risk for ASD include patterns of head growth during the first year of development (Redcay & Courchesne, 2005) and differences in hemispheric electroencephalogram (EEG) activity as early as 6 months of age (Tierney et al., 2012). Instruments used to diagnose autism have evolved and developed over the past 20 years, contributing to more effective differential diagnosis. The most used instruments today include the Autism Diagnostic Interview-Revised (ADI-R; Lord, Rutter, & LeCouter, 1994), the Autism Diagnostic Observation Schedule-Generic (Lord et al., 2000), and the Diagnostic Interview for Social and Communication Disorders (Wing, Leekman, Libby, Gould, & Larcombe, 2002). In addition, several screening tools are available for use. The Checklist for Autism in Toddlers (CHAT; BaronCohen, Allen, & Gillberg, 1992) has been the most rigorously researched and validated of those available (Chakrabarti, Haubus, Dugmore, Orgill, & Devine, 2005). Despite advances in early identifying behaviors and the development of sophisticated diagnostic and screening instruments, accurate differential diagnosis of ASD remains difficult and is often delayed. Many children continue to go undiagnosed or misdiagnosed until they are 3 or 4 years old (Brogan & Knussen, 2003; Zwaigenbaum et al., 2009). Some clinicians are hesitant to discuss the possibility of autism because they anticipate family distress and assume adverse side effects of labeling the child (Filipek et al., 1999). However, families universally prefer to be informed about a diagnosis of autism as early as possible (Marcus & Stone, 1993). Furthermore, it has been well established that early intervention yields the most successful outcomes (Crais & Watson, 2014; Dawson & Osterling, 1997; Filipek et al., 2000). In addition to the changes described above, the DSM-5 also contains a new diagnostic category called Social (Pragmatic) Communication Disorder (SPCD). This diagnosis is meant for children who meet the criteria for a disorder in social communication but do not have the restrictive and repetitive behaviors and interests seen in ASD (see Norbury, 2014, for a detailed review of SPCD). There remains a great deal of controversy regarding the validity of the SPCD diagnosis (Skuse, 2012). Some feel that there is insufficient evidence to consider SPCD an etiologically distinct diagnosis and are concerned it will simply serve as the fall-back diagnosis for those children who do not quite meet the diagnosis of ASD, similar to what PDD-NOS was in the DSM-IV (Huerta, Bishop, Duncan, Hus, & Lord, 2012; Skuse, 2012). Norbury (2014) suggested several important activities that will need to take place in order to establish SPCD as a useful and reliable diagnostic category, including developing valid assessment tools to evaluate social communication and pragmatic language deficits, establishing pragmatic profiles across typical children, and charting the developmental trajectories of children over time to monitor the stability of the diagnosis.

Early Regression When Leo Kanner first described autism, he suggested it was a disorder present from birth (Kanner, 1943). Until recently, knowledge about early development was limited because diagnosis was

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usually made after the child turned 3 years old. Clinicians and researchers were forced to rely upon parent recall for information about the years before diagnosis. Many parents reported being aware of developmental differences from infancy and noted that these problems gradually became more severe (Hoshino et al., 1987). Other parents described symptom onset as occurring late; there was normal or even precocious development followed by a sudden and dramatic loss of skills (Hoshino et al., 1987; Rogers & DiLalla, 1990). This regression occurs more frequently in ASD than in other disorders (Baird et al., 2008). These two distinct patterns of development (late onset—children who regress and early onset— children who do not regress) have been validated using detailed parent interview protocols and tools, review of early home videos, as well as tracking at-risk infants (Osterling & Dawson, 1994; Werner, Dawson, Munson, & Osterling, 2005). Anywhere from 20–47% of individuals with autism exhibited the late-onset pattern (Davidovitch, Glick, Holtsman, Tirosh, & Safir, 2000; Parr et al., 2011). Late onset of symptoms was associated with earlier acquisition of first words (Baird et al., 2008). There is conflicting evidence that skills are subsequently regained to pre-regression levels (Parr et al., 2011; Rapin & Katzman, 1998). These children may represent two distinct subgroups of autism with potentially very different underlying etiologies. (Werner et al., 2005). Some studies found that the late-onset or regression group was more severely impaired in speech and language as well as in social skills compared to the early-onset or nonregression group (Kalb, Law, Landa, & Law, 2010; Parr et al., 2011). However, children with mild-to-moderate regression and children who lost motor skills developed language at least to the point of sentence production. Furthermore, children who were more developmentally advanced prior to regression had less severe autism (Kalb et al., 2010). Davidovitch et al. (2000) also found that more children in the regression group developed verbal communication skills than did those in the nonregression group. Impairment was greater for the early-onset group prior to 24 months (Landa, Gross, Stuart, & Faherty, 2013). Other data, however, suggest that there is no relation between regression and the development of sentence production (Baird et al., 2008) and other skills (Werner et al., 2005). Further research on the factors underlying regression in autism may lead to better prognostic indicators for speech and language development.

Speech, Language, and Communication Skills Although the cognitive, linguistic, and behavioral characteristics of autism and related disorders vary considerably, one consistent problem area is in the acquisition and use of language (Lord & Paul, 1997; Rutter & Schopler, 1987). The unique speech and language problems present in children with autism have attracted significant attention from developmental psycholinguists. However, the precise nature of these deficits has not yet been delineated. The following sections will discuss variables and features of the process of language acquisition in ASD. This is followed by a review of the literature on language function (speech, semantics, syntax and morphology, pragmatics, and hyperlexia).

Nonverbal IQ and Language Acquisition Anywhere from 40–75% of individuals diagnosed with autism have IQ scores consistent with a diagnosis of mental retardation (Frith, 1989; Zelazo, 2001). Children with IQs in this range have language deficits that vary widely in profile and severity. The same range of severity and profiles of language deficits is seen in children with austism who have nonverbal IQs within normal limits (Gluer & Pagin, 2003; Wetherby, Prizant, & Schuler, 2000). Therefore, these language

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deficits cannot be simply accounted for by deficits in general intelligence (Chan, Cheung, Leung, Cheung, & Cheung, 2005). The best approach for determining cognitive and linguistic deficits specific to autism is to focus on the higher-functioning subgroup of individuals with autism spectrum disorders (Lord & Paul, 1997; Rutter, 1983; Tager-Flusberg, 1985b). This subgroup of individuals provides researchers with an opportunity to identify those deficits that are unique to autism and are not the result of more general cognitive impairments. Although these findings may not be generalizable to the entire population of individuals diagnosed with autism and related disorders, this is a starting point for research with the other more severely impaired subgroups.

Joint Attention and Language Acquisition Joint attention, the ability to use eye contact and pointing for the social purpose of sharing experiences with others, plays a critical role in the development of language, communication, and social interaction (Baldwin, 1991). Typical infants demonstrate a predisposition for focusing on eye gaze, facial expression, gestures, and caregivers’ voices from very early in life (Bushnell, Sai, & Mullin, 1989; Hains & Muir, 1996; Mundy & Neal, 2001). There is an abundance of research demonstrating a breakdown or failure to develop joint attention skills in children with ASD (e.g., Gillespie-Lynch et al., 2013; Paparella, Goods, Freeman, & Kasari, 2011; Werner, Dawson, Osterling, & Dinno, 2000). Joint attention begins with face-to-face affective exchanges between the infant and caregiver. By 6 months of age, the infant becomes interested in objects in the environment. This is followed by the coordination of the child’s and the caregiver’s attention to a third object or event between 6 and 18 months of age (Bakeman & Adamson, 1984). A typical infant is able to follow a caregiver’s point or eye gaze by 9 months of age. By 1 year old, the child will begin to demonstrate protoimperative pointing (pointing to get an object). Proto-declarative pointing, directing an adult’s attention to an object or event simply for the purpose of sharing interest, follows a few months later (Corkum & Moore, 1998). By 16–19 months of age, a typically developing child is sensitive to a caregiver’s nonverbal cues (e.g., gaze, gestures) as a source of information about the referents novel words (Baldwin, 1991). This allows the young language learner to accurately map new words onto novel objects, given the infinite possibilities in the environment. Joint attention is also important for appreciating a speaker’s intention and perspective. Reading facial expressions and intonation in others helps the young child perceive emotional states, which in turn allows the child to perceive new events with fear or happiness. More importantly, it contributes to the development of social communication, such as sharing experiences and expressing empathy (Vander Zanden, 1993). Measures of joint attention behaviors now serve as powerful prognostic indicators, allowing for earlier identification of children with ASD (Baron-Cohen, Cox, Baird, Swettenham, & Nighingale, 1996; Veness et al., 2012). Children with ASD may be delayed or display joint attention skills in an atypical sequence (Gillespie-Lynch et al., 2013; Paparella et al., 2011). Differences in early joint attention behaviors become apparent in young children with ASD by approximately 1 year of age. Children with ASD fail to attend to attentional cues such as eye gaze, pointing, and gestures (Klin, Chawarska, Ruben, & Volkmar, 2004). They do not respond preferentially to a caregiver’s voice (Dawson, Meltzoff, Osterling, Rinaldi, & Brown, 1998; Lord, 1995). Children with ASD have difficulty shifting gaze between people and objects and demonstrate little or no proto-declarative pointing (Werner et al., 2000). In a study examining word learning, BaronCohen, Baldwin, and Crowson (1997) found that children with autism made frequent mapping errors due to their inability to follow the eye gaze or pointing of others. Even older children

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with ASD who have IQ within normal limits demonstrate atypical neurological responses to gestures accompanying speech (Hubbard et al., 2012).

Autism-Specific Issues in Language Acquisition There is also great heterogeneity across behavioral and linguistic abilities within and across individuals with autism. There are two aspects of language and language deficits that often characterize the language of people with ASD, the presence of echolalia and problems with deixis.

Echolalia More than 75% of verbal children with autism demonstrate echolalia, the repetition of what has been said by someone else (Prizant, 1983). This is a significantly higher incidence than in any other population of people who also demonstrate echolalia (e.g., those with intellectual disability and schizophrenia). Echolalia can be immediate or it can be delayed by hours or even days. As the child with autism develops more language, there is typically a reduction in the use of echolalia (McEvoy, Loveland, & Landry, 1988). Echolalia was once seen as nonfunctional and problematic (Lovaas, 1977). More recently, however, some aspects of echolalia have been shown to be quite functional and can actually play an important role in language and communication development (Prizant & Rydell, 1984; Sterponi & Shankey, 2014). Echolalic utterances may actually be used with communicative intent (e.g., to request) before the individual with autism has sufficient language abilities to make a request. Although some of the child’s echolalic utterances may be functional and communicative, others may be nonfunctional and self-stimulatory in nature (Frith, 1998). It is essential, especially as the child gets older, to distinguish between echolalic utterances that are functional and communicative from those that are self-stimulatory and nonfunctional. There is an inverse relationship between the presence of high rates of self-stimulatory behavior and learning (Lovaas & Smith, 1989). Therefore, those behaviors determined to be self-stimulatory, including immediate and delayed echolalia, must be addressed programmatically to ensure the learning and development of more appropriate behaviors.

Deixis Another unique aspect of the language of verbal individuals with autism is difficulty with deictic language. Deixis is the aspect of language that codes shifting reference. In a conversation, it is used to refer to places, times, or other participants from the speaker’s or another person’s point of view. Examples of deictic terms include place terms (e.g., here and there) and temporal terms (e.g., now and then). The most noted example of difficulty with deictic terms in autism is atypical use of personal pronouns. Kanner (1943) was the first to describe the challenges with the use of personal pronouns in children with autism. He wrote that children with autism would simply repeat what was heard but not adapt or change it at all to suit the situation. For example, a child might say “You want a cookie?”, when asking for a cookie. Typically developing children are sensitive to deixis as young as 2 and 3 years of age (Wales, 1986). Speakers with autism have a lot of difficulty with deictic terms from very early in development continuing through adulthood (Hobson, Garcia-Perez, & Lee, 2010; Le Couteur et al., 1989). The underlying reason for these difficulties is far from clear. Hobson, Garcia-Perez et al. (2010) suggest the reason relates to problems with interpersonal engagement. In their review of available research, they point to two key findings: the correct use of personal pronouns was related to the

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number of spontaneous joint attention initiations with an investigator, and atypical personal pronoun use was accompanied by atypical attention and engagement with a communicative partner (Hobson, Lee, & Hobson, 2010; Loveland & Landry, 1986). Problems with deixis may be part of a more general deficit in reference, including shifting semantic roles for certain verbs.

Articulation, Phonology, and Prosody Higher verbal IQ, nonverbal IQ, and less severe symptoms of autism predict the development of fluent speech in children with autism (Wodka, Mathy, & Kalb, 2013). However, delayed onset of speech and higher numbers of speech errors are typical in children with autism (Patten et al., 2014; Shriberg, Paul, Black, & van Santen, 2011). Despite this delay, children with autism seem to demonstrate normal speech acquisition patterns with typical phonological errors (Cleland, Gibbon, Peppé, O’Hare, & Rutherford, 2010; Gluer & Pagin, 2003). Children with autism and language impairment demonstrated phonological errors similar to children with SLI (Tager-Flusberg, 2006). More recent research, however, questions the findings that the phonetic and phonological development of children with autism is appropriate for their developmental levels. It now appears that speech development is not only delayed but is atypical in many children with autism. Early vocalizations are atypical in that they contain more squeals and non-native consonant blends than the vocalizations of either younger language-matched or same-aged typically developing children (Schoen, Paul, & Chawarska, 2011). These atypical aspects of speech production in children with autism may be linked to atypical speech perception and social communication (Kujala, Lepistö, & Näätänen, 2013; Schoen et al., 2011; Shriberg et al., 2011; Wodka et al., 2013). One of the problems with some of the earlier findings was that researchers focused exclusively on children in preschool and elementary school (Flipsen, 1999; Shriberg et al., 2001). Flipsen (1999) found that 33% of high-functioning adolescents and adults with autism and Asperger syndrome exhibited distortion errors. This is compared to estimates of 1–2% in the typical adult population. Furthermore, if phonetic and phonological impairments in ASD were developmental, there would be a correlation between speech and language delays (Cleland et al., 2010; Shriberg et al., 2011). As with many aspects of language in children with autism, speech development is highly variable. Although infants who are later identified with autism produced fewer vocalizations and babbled later than their typically developing peers, a few infants in the clinical group actually displayed precocious speech development (Patten et al., 2014). Acoustic analysis revealed that typical productions were more like those of typically developing children than like those of children without autism and with speech delay (Shriberg et al., 2011). The speech of verbal children with autism is often perceived as being too loud and variable in pitch (Shriberg et al., 2011). Many individuals with autism also demonstrate deficits in the comprehension and use of prosody (Baltaxe & Simmons, 1992; Shriberg et al., 2001). These deficits typically persist over time despite improvements in other aspects of speech and language (DePape, Chen, Hall, & Trainor, 2012). Abnormal prosody has been shown to negatively affect the perception of social and communicative competence of a speaker (Paul, Augustyn, Klin, & Volkmar, 2005; Shriberg et al., 2001). Furthermore, it has been reported that the atypical prosody of individuals with autism is the factor that most immediately creates an impression of oddness (Mesibov, 1992). Prosody can be examined in three general categories: (1) grammatical prosody, used to mark syntactic information within a sentence; (2) pragmatic prosody, used to carry social information beyond what is conveyed in the sentence; and (3) affective prosody, the change in register

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conveying a speaker’s general feelings. Studies looking at specific types of prosody within the three categories suggest greater variability in the comprehension and production of prosody by individuals with ASD. Diehl and Paul (2013) found that given multiple opportunities and models, school-aged children with autism acceptably produced the prosodic types despite some slight acoustic abnormalities. The children’s comprehension of grammatical prosody was also good, but their affective and pragmatic prosody comprehension was very poor. Older children and adults with ASD also demonstrated significant problems with pragmatic and affective prosody, although problems with grammatical prosody were also noted (Paul et al., 2005). Colluding neuroimaging findings suggest that children with autism require extra cognitive resources to attend to affective and grammatical prosody (e.g., Eigsti, Schuh, Mencl, Schultz, & Paul, 2012).

Semantics Semantic problems have been noted in children with autism at the earliest stages of language acquisition. The first words acquired by children with autism are generally names for concrete objects such as cookie and car. Noticeably absent from their early vocabularies are words such as up, more, and all gone (Menyuk & Quill, 1985). Receptive vocabulary is impaired compared to expressive vocabulary, unlike in typical development (Hudry et al., 2014; Kover, McDuffie, Hagerman, & Abbeduto, 2013). Even children with high-functioning autism and adolescents who no longer meet the criteria for autism continue to have difficulty understanding some word categories such as mental state verbs (Kelley, Paul, Fein, & Naigles, 2006). Although it is clear that children with autism demonstrate semantic deficits, there are conflicting views as to the nature of these deficits. In a series of experiments examining naming and categorization skills, children with autism performed similarly to mental age-matched control groups (Tager-Flusberg, 1985b; Ungerer & Sigman, 1987). Children with autism also performed similarly to children with SLI on word association and definition tasks when syntactic ability was controlled (McGregor et al., 2012). These results suggest that the semantic deficits present in children with autism are a result of cognitive deficits and are not unique to autism. Other findings indicate that these semantic deficits cannot be accounted for by cognitive deficits. The acquisition and maintenance of novel words, in a social context, was compromised by decreased attention to social cues (Norbury, Griffiths, & Nation, 2010). There are signs of atypical language processing for known words as well. Children with autism fail to use semantic information to aid in encoding verbal information and to recall information (Bowler, Matthews & Gardiner, 1997; TagerFlusberg, 1991) They are not able to recall words from a list of related word any better than from a list of unrelated words in free recall tasks (Tager-Flusberg, 1991). This inability to use semantic information to recall lists persists into adulthood even under explicit instruction (Gaigg, Gardiner, & Bowler, 2008; Smith, Gardiner, & Bowler, 2007). In addition, children with autism appear to rely on syntactic as opposed to semantic comprehension strategies when interpreting sentences (Paul, Fischer, & Cohen, 1988). Many children with autism demonstrate age-appropriate vocabulary skills as measured by standardized tests, but there is compelling evidence that the underlying organization of their lexicons may be atypical and impoverished (Dunn & Bates, 2005; Gerenser, 2004; Kjelgaard & TagerFlusberg, 2001). Dunn, Gomes, and Sebastian (1996) found that children with autism provided significantly less prototypical exemplars of categories in a word fluency task when compared to typically developing children and to language-impaired children matched on mental age. The depth of the lexicon, based on word association and definition tasks, is negatively correlated to the number and degree of autism symptoms (McGregor et al., 2012). A lack of organization within lexical categories could limit access to more prototypical exemplars and create a less robust lexicon.

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Children with autism also appear unable to extract the common features of items to form prototypes, instead relying on inflexible rule-based strategies to form novel categories (Klinger & Dawson, 1995, 2001). Children with autism and typically developing peers were given pictures of nonsense objects to sort into categories. In one condition, the children were given the rules for membership (e.g., big head, three eyes, yellow), and in the other condition, they were not given any rules. When given the rules for category membership, both groups of children were able to categorize novel items. Only typical children were also able to form novel categories using a prototype strategy in the no-rule condition. More recent findings suggest that task ambiguity, stimuli (social versus nonsocial), and subject selection are major factors of prototype formation in children with autism (Froehlich et al., 2012; Gastgeb, Rump, Best, Minshew, & Strauss, 2009; Molesworth, Bowler, & Hampton, 2008). Difficulty with category induction, however, is a persistent feature of semantic deficits in children with autism and in children who no longer meet the criteria for having autism (Naigles, Kelley, Troyb, & Fein, 2013). Children with autism who also had semantic or syntactic impairments made their categories too broad (McGregor & Bean, 2012). Findings from more controlled on-line tasks also provide evidence of atypical lexical organization in children with autism. Gerenser (2004) measured naming reaction times within a picture naming task that included association primes (e.g., hat-head), category primes (e.g., nose-head), and identity primes (e.g., head-head). Children with autism did not demonstrate the robust priming effect within the association prime condition that was found in the typically developing control group. Children with ASD had no priming effect for near-semantically related words (Kamio, Robins, Kelley, Swainson, & Fein, 2007). Adolescents with high-functioning autism also demonstrate atypical lexicons in a written task where they responded equally to identity and orthographically similar prime words (Speirs, Yelland, Rinehart, & Tonge, 2011). Recent advances in electrophysiological research provide further support for these behavioral findings. Dunn and Bates (2005) found that children with autism consistently failed to show the typical differentiation response to context-dependent words in a single-word semantic classification task. This lack of response is also present with cross-model cues suggestive of general semantic processing delays in autism (Ribeiro, Valasek, Minati, & Boggio, 2013). The typical ERP effect was absent, but there was evidence of atypical extensive semantic processing in adults with high-functioning autism on a sentence context task (Pijnacker, Geurts, van Lambalgen, Buitelaar, & Hagoort, 2010). Functional magnetic resonance imaging (fMRI) revealed that adults with high-functioning autism have less activation of Broca’s area and greater activation in Wernicke’s area to words than do their verbal IQ age–matched peers (Harris et al., 2006). They also lacked neurological differentiation between concrete and abstract words. Subsequent fMRI research in ASD children with language impairment found poorer connectivity between regions of the brain (Verly et al., 2014). Semantic development and processing is a complex and critical aspect of language (see Chapter 16 by McGregor). Anecdotal information as well as recent research suggests that children with autism demonstrate unique deficits in semantic development and lexical processing. Future behavioral, electrophysiological, and imaging research will be essential to delineate the specific aspects of these deficits and guide future intervention.

Morphology and Syntax There are conflicting findings in the area of morphological and syntactic development in children with autism. Early researchers concluded that children with autism had no specific deficits in the comprehension and production of syntax (Gluer & Pagin, 2003; Tager-Flusberg, 1994;

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Waterhouse & Fein, 1982). However, the large part of that older research examined only the most basic aspects of syntax (see Chapter 17 by Fletcher & Frizelle). More recent research suggests that there are specific deficits in syntactic processing and development in children with autism (Boucher, 2003; Kjelgaard & Tager-Flusberg, 2001; Park, Yelland, Taffe, & Gray, 2012). These conflicting results may reflect that some children with autism also have language impairment. There are several differences between typically developing children and children with autism in the comprehension and production of inflectional morphology and syntax. The rate of acquisition and use of morphology and syntax in early childhood was faster for typically developing children than for children with autism (Tek, Mesite, Fein, & Naigles, 2014). In addition, children with autism used fewer grammatical morphemes than did typically developing children, although the order of morpheme acquisition was similar in both groups (Bartolucci, Pierce, & Streiner, 1980; Park et al., 2012). Typically developing children were also significantly better than children with autism at recalling syntactically well-formed utterances, regardless of degree of semantic relatedness (Ramando & Milech, 1984). Even when IQ scores and vocabulary scores are within normal limits, many children with autism demonstrate specific deficits in syntax on standardized language tests and during structured play (Kjelgaard & Tager-Flusberg, 2001; Tek et al., 2014). Young children with autism were also more impaired in expressive morphology and syntax than were their cognitive age-matched peers (Park et al., 2012). These data suggest that some aspects of syntax development in children with autism are atypical. These more recent findings demonstrating deficits in syntax and morphology that could not be accounted for by cognitive deficits have led to speculation about an overlap between specific language impairment (SLI) (see Chapter 1 by Schwartz) and autism (Kjelgaard & Tager-Flusberg, 2001; Tager-Flusberg, 2006). However, more recent findings question the relation between SLI and ASD (e.g., Riches, Loucas, Baird, Charman, & Simonoff, 2010; Taylor, Maybery, Grayndler, & Whitehouse, 2014). More research is necessary to determine whether similar language deficit profiles in some children with ASD and in children with SLI reflect aspects of language that are simply vulnerable to any developmental deficit or whether language impairment in autism is distinct from impairment in SLI. It may be that the genetic anomalies (see Chapter 10 by Tomblin) associated with SLI are also present in this subgroup of children with ASD (Tager-Flusberg, 2006). There remain many unresolved questions regarding the development and use of syntax in children with autism.

Pragmatics Deficits in social skills are one of the hallmark features defining autism. Thus, it is not surprising that those individuals with ASD would demonstrate significant problems in pragmatics. Pragmatics can be defined as the appropriate use of language in context (see Chapter 18 by Fujiki & Brinton). More specifically, pragmatics refers to the conventions that govern language within social interactions (Prutting & Kirchner, 1987). Any comprehensive language intervention program for individuals with ASD must address the challenges of social communication. In addition, careful evaluation of the child must involve the whole clinical picture and not just the communication impairment alone (Bishop & Norbury, 2002). Deficits in pragmatics are seen across the entire autism spectrum. Even those individuals who develop advanced vocabulary skills and sophisticated grammar will have problems with the use of language in social situations (Klin & Volkmar, 1997). These social communication deficits often create a discrepancy between IQ and adaptive behavior (Kenworthy, Case, Harms, Martin, & Wallace, 2010). For example, an individual may perform within normal limits on an IQ test but be unable to participate appropriately in a social conversation. He or she may get a college degree but not be able to keep a job due to the inability to respond to social cues.

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Deficits in nonverbal communication skills are prominent in ASD. These deficits include problems with comprehension and use of gestures and intonation, an inability to read facial expressions, as well as qualitative issues with the use of eye contact (Lewy & Dawson, 1992; Mundy & Crowson, 1997). Much of what is communicated in social situations is done so nonverbally. Thus, individuals on the autism spectrum often miss key information within social interactions. Eye contact issues typically involve a failure to make appropriate eye contact during conversations and other social situations. In some cases, the individual who has been directly taught to make eye contact overcompensates for the lack of eye contact by staring intently during a conversation. Individuals with ASD have significant deficits in conversation skills (Loveland & Tunali, 1993). They demonstrate fewer initiations, frequent empty turns, as well as an inability to follow the topic or content of a conversation. This often leads to noncontextual or socially inappropriate comments (Klin & Volkmar, 1997). In addition, many individuals with ASD have problems with turn-taking and perseveration of topics. They often have a difficult time recognizing and repairing breakdowns in communicative exchanges (Prizant & Rydell, 1993). Prior to the DSM-5, the term Pragmatic Language Impairment (PLI), previously referred to as Semantic Pragmatic Disorder, was used to refer to a subgroup of children who demonstrated fluent expressive language skills with clear articulation but failed to use their language appropriately (Bishop, 2000). Researchers and clinicians continue to question the relationship among ASD, PLI, and SLI (Bishop & Norbury, 2002). It has been suggested that there is a closer relationship between PLI and autism than between PLI and SLI. More specifically, PLI was assumed to be a subgroup of autism, typically described as high-functioning autism (Shields, Varley, Broks, & Simpson, 1996). The changes in the DSM-5 described earlier, especially the addition of the new diagnostic category of Social (Pragmatic) Communication Disorder (SPCD), reflect an alternative perspective that children with PLI may actually fall between the classifications of SLI and ASD (Bishop, 1998, 2000). These children demonstrate the pragmatic and social communication deficits but none of the nonsocial deficits present in ASD. Research exploring the relationship between SLI, PLI, and ASD, although limited, supports the notion that there is a social-pragmatic language impairment distinguishable from ASD. Most recently, Gibson, Adams, Lockton, and Green (2013) compared measures of social interaction and restrictive repetitive behaviors and interests in a group of 65 children with diagnoses of PLI, SLI, or high-functioning autism. The results revealed a clear distinction among the three groups. Despite this evidence, others continue to question SPCD as a distinct diagnosis and believe a great deal more research is needed in this area.

Hyperlexia A person with hyperlexia has word recognition skills that are far above his or her reading comprehension skills (Nation, 1999; Pennington, Johnson, & Welsh, 1987; Silberberg & Silberberg, 1967). Kanner (1943) was the first to observe exceptional reading skills in individuals with austim. More than 70 years later, we still do not have a explanation of hyperlexia. (see review in Grigorenko, Klin, & Volkmar, 2003). Some investigators have proposed that hyperlexia is a subtype of dyslexia (Benton, 1978; Cohen, Campbell, & Gelardo, 1987). Others, however, believe it is a subtype of language impairment not specific to ASD (Healy, Aram, Horowitz, & Kessler, 1982; Seymor & Evans, 1992). Although hyperlexia is not exclusive to autism, it occurs at a higher frequency than in other groups (Grigorenko et al., 2003). The prevalence of hyperlexia in ASD is estimated to be between 5–10% (Burd & Kerkeshian, 1985). Children with ASD who have hyperlexia demonstrate an early, and often obsessive, interest in letters and printed material in general (Nation, 1999). There is, however, a significant gap between their decoding abilities and reading comprehension.

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Some speculate that hyperlexia in autism is just one of many obsessive interests not tied directly to cognitive or linguistic factors (Klin et al., 2004). Instead, it reflects a reduced ability to make sense of social stimuli and a preference for unchanging, constantly interpretable stimuli, such as print. As the child develops better social skills and capacity for social stimuli, the obsessive interest in letters and print diminishes.

Theories Underlying Core Deficits in Autism Extensive research has been conducted over the past three decades examining the genetic, neurological, behavioral, and cognitive foundations of autism. There are four important cognitive theories that attempt to explain the speech, language, and communication deficits in individuals with ASD. These theories have some overlap with one another, and a popular current view is that one of these cognitive theories will not adequately nor specifically explain the entire spectrum of autism based on the current data (Noens & Berckelaer-Onnes, 2005; Williams & Bowler, 2014). One of the most well-known perspectives, referred to as the mindblindness theory or theory of mind model (TOM model), was first described by Baron-Cohen, Leslie, and Frith (1985). This model suggests that the social communication deficits in autism reflect a fundamental impairment in the ability to understand the thoughts or intentions of others (for a comprehensive overview of this model, see Baron-Cohen et al., 2005). Typically developing children demonstrate theory of mind—or the ability to understand the complex mental states of others—by 4–5 years of age (Leslie, 1987). There is considerable experimental evidence that children with autism fail to develop theory of mind, even at the most basic level (Baron-Cohen et al., 1985; Baron-Cohen, Tager-Flusberg, & Cohen, 1993). This failure to develop theory of mind, in turn, is thought to lead to the social and language impairments present in autism (Tager-Flusberg, 1999). Although the model is compelling, some problems must be considered. Some children with autism demonstrate language deficits that go beyond the inability to use language in social contexts. The TOM model fails to account for the grammatical, phonological, and semantic problems or the other cognitive deficits found in many individuals with autism (Burnette et al., 2005; Tager-Flusberg, 1999). Furthermore, traditional TOM tasks have been criticized because they are complex and performance can be attributed to other factors besides TOM. Despite the limitations, the TOM account has provided both theoretical as well as practical benefits in understanding and treating the social and communication deficits in people with ASD (Happe, 1997). The executive functions (EF) theory proposes that a general cognitive disturbance in executive function is central to autism (Ozonoff, South, & Provencal, 2005; Pennington & Ozonoff, 1996). Specific executive functions include the ability to initiate behaviors while inhibiting competing responses that may interfere, the ability to regulate attention and filter distraction, intentional control, and the ability to shift attention across relevant stimuli (Goldknopf, 2013). According to EF theory proponents, children fail TOM tasks because of a more general EF deficit as opposed to a disturbance in theory of mind (Frye, Zelazo, & Palfai, 1995; Pennington & Ozonoff, 1996). There are a few problems with this conclusion. The first is that EF theory fails to explain the individual with autism’s ability to understand false photographs and maps while being unable to perform similar false belief tasks (Leslie & Thaiss, 1992). Deficits in executive functions should result in similar performance across both tasks. Second, EF theory fails to offer an account for why some children with ASD demonstrate significant executive function deficits yet perform well on TOM tasks (Ozonoff, 1995). These problems may stem from the fact that the executive functions are a broad group of cognitive processes and that many of the tasks used to assess them are not component specific. Although the EF theory is not sufficient to account for all of the core deficits in autism, it is an important area of research that does effectively address some of the

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complex behaviors in autism. Future research in intentional control, cognitive flexibility, working memory, and inhibition strategies for addressing these areas clinically are already underway (Ozonoff et al., 2005; Poljac & Bekkering, 2011). The weak central coherence theory (WCC; Happe & Booth, 2008) is a third model that attempts to account for the core deficits present in autism (Frith, 1989; Frith & Happe, 1994). Typically developing children are able to interpret information rapidly because of automatic and implicit coherent processing (Frith, 1989). Children with autism demonstrate weak central coherence, resulting in a focus on individual pieces of information as opposed to more holistic processing. There is considerable evidence for piecemeal processing in this population (Happe, 1997; Joliffe & Baron-Cohen, 2001; Plaisted, Swettenham, & Rees, 1999). For example, children with autism demonstrate superior performance on tasks that favor processing (e.g., block design and embedded figure tasks) when compared to mental age-matched peers (Morgan, Maybery, & Durkin, 2003). In addition, children with autism do poorly on tasks that require more holistic processing, such as semantic disambiguation tasks or drawing inferences (Booth & Happé, 2010; Filippello, Marino, & Oliva, 2013). The WCC theory provides insight into the learning styles of individuals with autism and in turn could help inform teachers and clinicians working with this population. Related theories suggest that people with autism have enhanced perceptual processing, also referred to as reduced generalization, as opposed to WCC (O’Riordan & Plaisted, 2001). As with each of the theories presented, the WCC theory is insufficient to account for all of the core deficits present in ASD. It is very likely that the WCC theory explains one component of a complex set of cognitive neural mechanisms or systems working together. For example, some individuals with autism appear to have a theory of mind, pretend play skills, and joint attention skills, yet they demonstrate the inability to process information holistically (Happe, 2005). The social orienting model (Mundy & Burnette, 2005) is based on two key assumptions regarding early development. The primary assumption is that typically developing children are predisposed to attend preferentially to social stimuli over nonsocial stimuli (Blass, 1999). The second assumption is that the early and pervasive deficits in joint attention in children with autism reflect a basic disturbance in this preference for social information (Mundy & Burnette, 2005; Mundy, Gwaltney, & Henderson, 2010). As noted previously in this chapter, there is considerable evidence of joint attention deficits in young children with autism (e.g., Paparella et al., 2011). In addition, there is evidence that children with autism do not attend preferentially to social information. For example, children with autism do not show a preference for speech over nonspeech (Klin, 1991) or for social stimuli such as clapping over nonsocial stimuli such as shaking a rattle (Dawson et al., 1998). These social orienting and joint attention deficits lead to secondary neurological disturbances, which may be linked to impaired social motivation (Kim et al., 2014). Over time, the child moves farther and farther off the path of normal development. As with the first three theories, this proposal provides a compelling explanation for some, but not all, of the language and social deficits in autism. However, it fails to account for some of the more unique learning characteristics described within the WCC model. Genetic research has significantly advanced within the last decade to uncover neurological endophenotypes for the autism phenotype (Rutter, 2013). Genetic research to date suggests a polygenetic mode of inheritance, with as many as 200 to 1,000 genes suspected to be involved in autism (Berg & Geschwind, 2012; Rosti, Sadek, Vaux, & Gleeson, 2014). Neurological research in autism has been greatly advanced in the past decade through the use of structural imaging, electrophysiology, functional imaging, and autopsy studies (Jeste & Nelson, 2009; Minshew & Keller, 2010; Mody & Belliveau, 2013). Some of the most significant findings include evidence of early

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abnormalities in brain growth that coincide with the onset of many clinical symptoms, as well as evidence of underconnectivity of neocortical neural systems involved in social, communication, and reasoning abilities (Damarla et al., 2010; Minshew, Sweeney, Rauman, & Webb, 2005). It seems unlikely that any single cognitive theory will explain the complex and multifaceted disorder of autism. Several independent cognitive deficits may collectively account for the core deficits present in individuals with autism, but the answer is probably somewhere in all of these models. Researchers should continue to examine these different models, as well as the relationships among them, to derive a more comprehensive picture of the autism spectrum disorders.

Intervention A great deal has been written over the past decade regarding the treatment of speech and language skills of individuals with autism (for an in-depth review of communication intervention, see Corsello, 2005; Goldstein, 2002). Interventions range from behavioral approaches to developmental and social pragmatic models. A thorough review of the underlying theoretical foundations, as well as an in-depth overview of the actual approaches, is beyond the scope of this chapter. The reader is referred to Prelock and McCauley (2012) for a more detailed review of evidence-based intervention strategies for communication and social interactions. There is evidence supporting the use of more traditional behavioral approaches such as discrete trial instruction and more naturalistic behavioral interventions such as natural learning paradigm to successfully address the speech language deficits in individuals with autism (Buffington, Krantz, McClannahan, & Poulson, 1998; Koegel, O’Dell, & Dunlop, 1988; Lasky, Charlop, & Schreibman, 1988; Lovaas, 1987). The developmental model known as DIR (Developmental, Individual Difference, Relationship; based or floortime, Greenspan, 1997; Greenspan & Wieder, 1998) and the SCERTS model (Social-Communication, Emotional Regulation and Transactional Support; Prizant, Wetherby, & Rydell, 2000) are frequently considered as interventions for the communication deficits in individuals with autism. Both intervention models are comprehensive and focus on the range of challenges present in learners on the autism spectrum. Although little scientific evidence is available for either model, both have anecdotal support. Greenspan and Wieder (1997) reviewed charts of a large number of children who had received DIR and found progress across the majority of participants. The SCERTS model has evolved over the years in response to ongoing research focusing on the learning characteristics of children with autism. However, as with the DIR approach, support for SCERTS remains anecdotal in nature. The Early Start Denver Model (ESDM), which integrates both Applied Behavior Analysis (ABA) and developmental approaches, has been shown to be highly effective in promoting speech and language in young children with autism (Rogers & Dawson, 2009). In a randomized control trial, children who received 20 hours a week of the ESDM showed significantly more improvement in language abilities as well as adaptive behavior when compared to the control group, who received community-based special education services (Dawson & Rogers, 2010). The use of augmentative/alternative communication (AAC) to support the speech-language development in autism has also been found to be effective. There is evidence that the use of the Picture Exchange Communication System (PECS; Bondy & Frost, 1994), sign language, and other visual systems can enhance the speech, language, and communication of individuals with autism (Charlop-Christy et al., 2003; Konstantantareas, 1984; Layton & Baker, 1981). Facilitated communication (FC) has no empirical support. FC, first identified in Australia and later popularized in the U.S. (Biklen, 1990; Crossley, 1992), involves a facilitator providing physical support on the hand, arm, or shoulder of a person with autism as he or she types on a keyboard. Proponents of FC have made remarkable claims of extraordinary literacy and cognitive abilities in

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people with ASD and that their failure to express themselves was due largely to motor limitations (Biklen & Schubert, 1991; Biklen et al., 1992). Due to the widespread and controversial claims made by the FC community, as well as the weak theoretical underpinnings (Hudson, 1995), there has been considerable experimental evaluation. More than 15 well-controlled evaluations conducted over the past decade have failed to find any support for the efficacy of FC (Mostert, 2001). Despite the lack of any credible evidence, FC continues to find support in the autism community. Auditory integration therapy (AIT) is another intervention that does not meet the standards of being an evidence-based approach. AIT involves listening to electronically modified music via headphones for a prescribed period of time, typically done over the course of two weeks (Dawson & Watling, 2000). The two most common forms of AIT are the Berard method and the Tomatis method. The American Speech-Language-Hearing Association (ASHA) reviewed the existing literature on the efficacy of AIT and recommended that it be considered experimental in nature (ASHA, 1994). Almost a decade later, ASHA conducted a new review of the evidence, given AIT’s continued use and practice, most notably by speech-language pathologists and audiologists (ASHA, 2004). Despite a number of new studies and many more publications, ASHA’s conclusion was that AIT (most notably the Berard method) did not meet scientific standards for efficacy and safety. Similar conclusions were made with regard to a review of the literature on the Tomatis method (Corbett, Schickman, & Ferrer, 2008). Due to the great variability in the language profiles within ASD, careful evaluation of each individual is essential. In addition, understanding the variables that may underlie some of the unique deficits is critical. For example, many individuals with ASD have difficulty processing transient input such as speech (Frith, 1989; Quill, 1997). This can play a significant role in the development of both receptive and expressive language. Problems with the development of joint attention adversely affects language development (Baron-Cohen et al., 1997; Mundy & Crowson, 1997). Other learning characteristics that must be considered include issues of stimulus overselectivity (Lovaas, Koegel, & Schreibman, 1979), problems with motivational variables and social contingencies (Lovaas & Smith, 1989), as well as reduced observation learning and imitation skills (Rogers & Pennington, 1991). There remains considerable debate and controversy over which interventions should be used for individuals with autism. Although some models of intervention have more empirical evidence supporting their efficacy, to date no evidence indicates that one approach is superior (Corsello, 2005). Prelock and McCauley (2012) provided an overview of the current interventions and indicated those that are evidence-based and those that are emerging. No one treatment is appropriate for all individuals. The individual’s strengths, deficits, and unique learning profile should guide the practitioner to select the intervention strategies. The only two consistent findings regarding intervention and the attainment of the best outcomes are that the intervention must begin early and it must be intensive (Dawson & Osterling, 1997).

Conclusion Speech and language, communication, and social deficits are the defining characteristics of individuals with ASD. As we make progress in understanding these deficits, we will also make progress in treatment and intervention. Many questions remain about the relative effectiveness of the current intervention models. As we enter the era of evidence-based practice, it will be imperative that interventions with little formal empirical support be critically evaluated. It is very probable that specific interventions will work better with specific types of children with autism. It will be important that applied research in treatment efficacy delineate or identify the specific types of children and their cognitive and behavioral profiles that benefit from the intervention. The one

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aspect of intervention we do know with certainty is that it must begin as early as possible and it must be delivered with a high level of intensity. Another important area for future research will be to better understand the exact nature of the core deficits in autism. Although at least four compelling cognitive theories attempt to account for the core deficits, none are yet sufficient to account for the complex behaviors in autism. In fact, it is much more likely that these theories are inter-related and all contribute in some way to the multifaceted disorder of autism. Understanding early-appearing markers of autism, especially in the area of joint attention, will be critical to early identification and intervention. Because we know that early intervention is essential to achieving best outcomes for this population, any research that would allow earlier identification would be important. To date, most children are not identified until at least 18–24 months of age, allowing valuable time to pass before intervention is initiated. Individuals with autism spectrum disorders represent a diverse and heterogeneous group. It is quite likely that there are distinct subgroups with different etiologies and behavioral characteristics (Kjelgaard & Tager-Flusberg, 2001). Research into the possible subgroups of ASD will be very important. In addition to helping delineate possible genetic and neurological influences, defining clear subgroups would contribute to subject definition within research and possibly provide insights into intervention strategies. Individuals with ASD will continue to challenge researchers and clinicians with their complex profiles and diverse characteristics. Only through continued investigation of this population will we gain the knowledge needed to provide optimal and effective interventions for all individuals on the autism spectrum.

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4 HEARING LOSS Zara Waldman DeLuca and Miranda Cleary

Factors in Language Development Many factors contribute to language development in children with hearing loss (HL). For the purpose of this chapter, we will use this as an umbrella term to include congenital hearing impairment, deafness, as well as acquired or progressive hearing loss. These factors include the age of the child at the identification of HL, the severity of HL, etiology of the HL, the timing and type of devices used to augment hearing ability, additional disorders or deficits, and the nature of the communication environment. This chapter reviews these factors, their effect on language development, and the speech, language, and cognitive characteristics of children with HL. We will focus primarily on children with early acquired or congenital severe to profound HL, which is diagnosed in approximately 30% of the ~6,000 U.S. newborns identified each year with some degree of HL (ASHA, 2014; U.S. CDC, 2014).

Age at Hearing Loss Identification Age at HL identification can play a major role in shaping a child’s speech and language development. Auditory deprivation, or lack of auditory input for a prolonged period, is linked to less favorable spoken language outcomes in children. While in the past, HL diagnoses may not have occurred until age 2 or later (Mace, Wallace, Whan, & Stelmachowicz, 1991), the advent of newborn hearing screenings has dropped the average age of identification to 2–3 months in the United States (ASHA, 2014; Connolly, Carron, & Roark, 2005). As of 2014, at least 47 U.S. states have adopted laws establishing newborn and infant hearing screening programs (ASHA, 2014). This screening generally consists of measuring otoacoustic emissions (OAEs) or the auditory brainstem response (ABR). However, newborn hearing screenings cannot predict post-natal-onset HL or progressive losses that cause hearing to deteriorate over months and years of early childhood. Thus, it is important to conduct hearing screenings throughout childhood and following events for which HL might be a secondary consequence. Recent advances in the identification of genes associated with hearing losses, including typically progressive losses, have also helped identify children who are at risk for HL (Nance, 2003).

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The age at identification typically drives the age at which both audiological and languagerelated intervention begins. Early amplification and auditory/oral therapies are widely believed to have profound effects on later levels of spoken language attainment. Later identification does not necessarily preclude a child from developing good spoken language, but the probability of such success is reduced. In many countries, infants of only a few months are now routinely fit with conventional hearing aids and infants of 6–12 months with more severe HL can receive cochlear implants.

Degree of Hearing Loss The nature of HL can be quite varied, but most often it is quantified through a pure tone average (PTA) threshold. The PTA threshold is usually defined as the average of the threshold for pure tones at 500, 1,000, and 2,000 Hertz (Hz) presented through headphones. This range of frequencies is particularly crucial for speech perception, as many of the acoustic cues to speech sound identity lie within this range. The reference point for the degree of loss is typically taken as the average intensity threshold of detection under normal hearing, labeled as 0 decibel (dB) hearing level. Thresholds between -10 to +15 dB hearing level are considered normal. For a person with HL, the severity of their loss is labeled by the dB hearing level at which they are capable of perceiving these pure tones. The level of loss and its label, according to PTA, is listed in Table 4.1. Hearing loss is also characterized in terms of its physiological origin. Conductive HL involves problems in the outer and middle ear. In contrast, sensorineural HL typically involves dysfunction of the hair cells in the cochlea, which are responsible for converting sound information from fluid-mechanical motion to electrical signals. Partial degeneration of the auditory nerve may also underlie sensorineural HL. Hearing losses can be bilateral or unilateral. Bilateral losses may be relatively symmetric across the two ears in terms of the sound frequencies affected, or asymmetric. The impact of unilateral hearing losses on language and behavior as a function of degree of loss has been an area of debate (e.g., Bess & Tharpe, 1986; Kiese-Himmel, 2002). Recent data suggest increased probability of language processing and academic difficulties in children with unilateral HL (Lieu, Tye-Murray, Karzon, & Piccirillo, 2010). The buildup of extraneous fluid with or without infection in the middle ear cavity, referred to as otitis media with effusion (OME), can also result in a transient (or chronic and fluctuating) mild conductive HL. The effect of OME on language development has been controversial. Research with large epidemiological samples indicates that contrary to earlier suggestion, a history of OME in the first 2 years of life (in otherwise healthy children) does not correlate strongly with speech and language development later in childhood (Roberts, Rosenfeld, & Zeisel, 2004; also Zumach, Gerrits, Chenault, & Anteunis, 2010). In recent years, evidence favoring a more-conservative Table 4.1 PTA Hearing Losses and Their Labels (Clark, 1981) Severity of Hearing Loss

PTA Threshold

Profound

Above 90 dB

Severe

90–71 dB

Moderate-Severe

70–56 dB

Moderate

55–41 dB

Slight/Mild

40–15 dB

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approach to tympanostomy tube insertion and antibiotic use in otherwise healthy children has emerged (Paradise, Campbell, Dollaghan et al., 2005). As Roberts, Hunter, Gravel et al. (2004) suggest, however, additional research specifically considering resulting hearing levels, other risk factors, and involving special clinical populations of children may be needed. Additionally, only a subset of language behaviors has been assessed, with areas such as morphological and syntactic processing having received less study.

Etiology and Issues of Syndrome-Related Conditions For many children with HL, expectations for language acquisition cannot be based on the HL alone. About 30% of children with moderate to profound HL have additional disabilities (Fortnum, Marshall, & Summerfield, 2002). Some medical conditions and HL etiologies, such as cytomegalovirus, have been associated with lower-than-average performance on tests of nonverbal intelligence and developmental motor-skill milestones. In many cases, partial corrective measures can be implemented to aid the HL, unlike other aspects of the child’s medical condition, which may be more difficult to address but which also affect language acquisition.

Amplification Conventional amplification in the form of externally worn hearing aids (HAs) is routinely used with children having mild to severe sensorineural HL. The fitting process for HAs is important, often requiring a series of refinements over time in order to provide sufficient amplification without exceeding comfort thresholds. Most sensorineural losses involve reduced dynamic range and abnormal increases in perceived loudness as signal intensity is increased. Thus, ceilings for amplification levels must be carefully selected. Ideally, aided hearing thresholds should be within or close to the normal range. Fitting usually involves verifying that the amplified signal is as desired as measured near the tympanic membrane, followed by validation of the fitting through testing in free-field conditions using standardized auditory materials. Programmable digital HAs allow for different device settings under different listening situations (Dillon, 2001). But for many children with HL, not enough benefit is received from the use of HAs with regards to spoken language development. In 1992, the U.S. Food and Drug Administration approved the use of cochlear implants (CIs) for children with severe to profound, bilateral sensorineural HL who did not benefit from HA use. The CI consists, in part, of an externally worn microphone and electronic signal processing system that analyzes the energy content of the acoustic signal across a series of frequency bandwidths. This information is then sent via radio-range frequencies across the scalp to a surgically implanted receiver, which passes the information to a series of electrical contacts contained in an electrode array threaded into the scala tympani of the tonotopically arranged cochlea. High-frequency information is coded through minute electrical pulses transmitted to the base of the cochlea and progressively lower-frequency information to more apical regions of the cochlea. Over the years, small variations made to the electronic strategies by which the acoustic signal is converted to electrical pulses have steadily improved average expected speech perception levels. Today, average openset monosyllabic word recognition scores for pediatric CI users tend to fall in the range of 40–90% of words correct, albeit with many individual differences (Davidson, Geers, & Brener, 2010; Leigh, Dettman, Dowell, & Briggs, 2013; Ruffin, Kronenberger, Colson, Henning, & Pisoni, 2013). With appropriate device fitting and language intervention services, some children with CIs can achieve scores in quiet listening conditions comparable to those of normal hearing (NH) children.

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Although HA and CI technologies have become increasingly sophisticated, currently available devices continue to have some marked limitations. Abnormal growth of loudness with increased signal continues to be a challenging issue in sensorineural HL. Various mechanisms that the normal human auditory system has evolved to deal with background noise and localizing sound are also not fully accessible using currently available HAs and CIs. A growing number of children use two CIs, or a combination of a CI on one ear and an HA on the opposite ear (bimodal). The best way to fit such patients is an active area of research, as there may be significant benefits of bilateral input for spoken language development (Boons, Brokx, Frijns, et al., 2012; Sarant, Harris, Bennet, & Bant, 2014).

Communication Methods A variety of communication approaches for children with HAs and CIs currently exist in the United States and elsewhere, with an emphasis on developing skills that will allow them to communicate with their primary caregivers. Auditory/oral approaches involve reliance on listening and speaking while using HAs/CIs. Some traditional oral approaches emphasize the use of lipreading, while other oral methods, such as the auditory-verbal approach, encourage using only the auditory signal and argue that nonreliance on lip-reading cues should be taught. Cued speech, though not very widely used today, encourages use of spoken language and lip-reading, but incorporates a set of manual gestures to disambiguate certain sound contrasts that are difficult to distinguish through lip-reading alone. In contrast, adequate exposure to fully fluent adult users of American Sign Language (ASL) or other language variants can set the child on a path to acquire a manual form of language with its own lexical and grammatical conventions. Signed languages do not necessarily match the lexicon and syntax of the corresponding spoken language and have their own nonliteral usage. Sign language is also an integral part of the deaf community culture. Another communication strategy, termed total communication (TC), incorporates elements of both manual and spoken language. TC methods that place emphasis on correspondence with spoken English require that gestures follow the word order and grammatical and morphological conventions of spoken English. These methods fall under the umbrella of Signed Exact English (SEE). Such systems may aid in the development of English phonics, spelling, and literacy skills. More often, TC strategies incorporate the use of spoken English with support from ASL. Bilingualism in signed and spoken languages is quite possible under the right circumstances. Approaches to communication that embrace the goal of fluency in both modalities are often referred to as bilingual/bicultural (Metzger, 2000).

Spoken Language Development Early Vocal Development and Babbling Infants with NH display a series of pre-lexical vocalizations before uttering their first words. From about 2 months of age, NH infants begin experimenting with interrupted phonation and vocal exploration. Between 6–12 months of age, babbling with recognizably consonant-vowel (CV) sequences appears. In NH children, canonical babbling involving reduplicated CV syllables begins around 7 months. Children with severe to profound HL typically begin babbling later than NH children (Oller & Eilers, 1988; Koopmans-van Beinum, Clement, & van den Dikkenberg-Pot, 2001; Moeller et al., 2007). They appear to produce about the same quantity of non-speech-like vocalizations (Iyer & Oller, 2008; Moeller et al., 2007) and many eventually produce babbling, even without significant access to auditory information. Although there are individual differences,

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infants who receive little auditory input begin producing canonical babbling at an average age of 18 months (Oller & Eilers, 1988). Even after such children reach this milestone, well-formed canonical babbling is often a small proportion of their vocalizations, unlike NH children. Children without adequate access to sound have atypical sound inventories with limited and non-native sounds, and atypical distributions of sound and syllable types (e.g., a higher proportion of glottal sequences involving a glottal stop or fricative (Oller & Eilers, 1988; Stoel-Gammon, 1988). Additionally, their early vocal quality exhibits restricted formant frequency ranges, inappropriate nasality, and unusually elongated utterances. When auditory access is provided, the development of babbling in children with HL is very different. Infants receiving CIs before the age of 2 years have reported an onset of canonical babbling within several months following implant activation (Ertmer & Mellon, 2001; Schauwers, Gillis, Daemers, DeBeukelaer, & Govaerts, 2004; Sharma, Tobey, Dorman et al., 2004). More detailed reports of infants and toddlers, a few of whom already exhibited canonical babbling prior to activation, suggest that CIs provide the input necessary to develop more mature and advanced syllable forms, such as those involving a coda, consonant cluster onsets, diphthongs, etc. (Ertmer, Young, & Nathani, 2007). The babbling of Dutch infants and toddlers implanted between 5–20 months of age has been found to display only subtle structural differences from that of their NH peers (Schauwers, Gillis, & Govaerts, 2008). Moreover, once implanted, some toddlers with HL appear to progress more quickly through the stages of vocal development than do younger, typical, NH infants. This suggests that the vocalization development gap typically associated with a period of auditory deprivation can be narrowed (Ertmer et al., 2007).

Phonetics and Phonology The transition from babbling to first words relies on development of a phonological inventory of different speech sounds (Stoel-Gammon, 1998; Vihman, 2014). Although variability across children is typically quite large, even among NH toddlers and preschoolers, some general patterns of acquisition exist. For the vast majority of English-speaking American children, the labial sounds /p/, /m/, and /b/ as well as /n/, /h/, and /w/ emerge in adult-like forms early and well before age 3, followed by the other stop/plosive sounds. The /r/ and /l/ sounds, as well as fricatives (other than /h/), emerge in their mature form relatively late (Goldman & Fristoe, 2000; Sander, 1972). By age 8, the vast majority of NH American children are producing all of these sounds, in addition to affricates and consonant clusters, in an adult-like manner. The frequency of the individual sounds in the ambient language, articulatory complexity, utterance position, audibility, and confusability (both auditorily and visually) all may contribute to order of emergence. These general patterns of acquisition are also characteristic of children with HL with HAs or CIs, although the rate of acquisition has typically been slower (Osberger & McGarr, 1982; Serry & Blamey, 1999). Later-acquired sounds (e.g., fricatives, liquids, affricates), in particular, appear to remain delayed (Ertmer & Goffman, 2011; Moeller et al., 2007). Children with HL who cannot benefit from a HA or CI (due to poor auditory nerve survival, or inaccessible services, for example) continue to demonstrate many of the speech characteristics first extensively described by Hudgins and Numbers (1942). For consonants, these include segment substitutions and deletions, cluster reductions, voicing errors, and extra nasality. For vowels, loss of vowel quality, atypical diphthongizations, and extra nasality are observed. Some studies have additionally noted relatively better production of consonants produced with more visible places of articulation (e.g., Geffner, 1980). Sounds that are not part of the ambient language may also be present (Chin, 2003), and overall speaking rate is typically slower (Osberger & McGarr, 1982). Children with long periods of auditory deprivation tend to score quite poorly on standard measures of phonological ability (Chin & Kaiser, 2002). However, as CIs and HAs have been improved

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and provided at earlier ages, fewer children with HL display substantial delays in speech sound acquisition. Recent research finds about 50% of studied preschoolers implanted before 2.5 years to score within or above 1 standard deviation (SD) of age-matched children on the Goldman-Fristoe Test of Articulation, 2nd Edition (GFTA-2; Goldman & Fristoe, 2000) after several years of device use (Spencer & Guo, 2013). Consonant production scores approximate those of NH children matched for number of years of access to auditory input, also known as hearing-age (see also Connor, Craig, Raudenbush, Heavner, & Zwolan, 2006). Similarly, Schorr, Roth, and Fox (2008) reported a mean standard score of 93.5 on the GFTA-2 for 39 children with CIs already judged to have a language proficiency of 5 years. Other measures have also revealed high proportions of correct consonants produced in elicited and spontaneous speech by early-implanted children (Ertmer & Goffman, 2011; Tobey, Geers, Sundarrajan, & Shin, 2011). By age 2;0 about 50% of an NH child’s words will be understood by an unfamiliar listener (Coplan & Gleason, 1988). By 3;0 this proportion will rise to about 75%, and by age 4, an NH child will be mostly intelligible, even though many sounds are not yet mastered. In the past, schoolage children with profound HL typically demonstrated very low speech intelligibility, even those immersed in oral-language environments. Estimates of word intelligibility in sentences approached 20–40%, with some variability associated with factors such as listener experience and sentence content (Markides, 1970; McGarr, 1983; Smith, 1975; Svirsky, Chin, Miyamoto, Sloan, & Caldwell, 2002). Far better results have been reported for large samples of children receiving CIs before age 5;0, with keyword intelligibility in sentence context reaching 64% after several years of device use and 80% after 10 years of use (Tobey et al., 2011). Among earlier implanted children, speech intelligibility appears to be even less of an issue; in children implanted before age 2;0, intelligibility of keywords in simple sentences after age 5 has been reported to reach 93% (Habib, Waltzman, Tajudeen, & Svirsky, 2010).

First Words and Early Vocabulary Typically developing children often utter their first words around one year of age. Evidenced by parental report, most typically developing children reach the 50-word stage in production between 15–19 months of age and the 100-word stage between 17–22 months (Fenson, Dale, Reznick et al., 1991; Nott, Cowan, Brown, & Wigglesworth, 2009). Although there are relatively few studies of children with HL that span the time period between onset of canonical babbling and the point at which the child has 50 to 100 words in his/her productive vocabulary, children with moderate to profound losses exhibit delays in reaching these milestones (Gregory & Mogford, 1981; Shafer & Lynch, 1981). With earlier fitting of CIs or HAs, these delays can typically be reduced (Nott et al., 2009). An early study by Gregory and Mogford (1981) longitudinally examined the early productions of eight toddlers with moderate to profound HL using HAs. They discovered that two of the eight toddlers, who had more profound HL, had not reached the 10-word stage by 4 years. The children with lesser degrees of HL reached the 10-word stage at a mean age of 23 months and the 50-word stage at 29 months. The children reached the 100-word stage at 34 months (2;10), on average, as compared to 20 months for an NH comparison group. This comparison displayed a delay of over a year, with a larger interval between the 50- and 100-word milestones. These results are in close agreement with data reported for implanted children (e.g., Ertmer & Inniger, 2009; Ertmer & Mellon, 2001). In a more recent study, Nott et al. (2009) tracked the progress of 24 children with profound HL using a diary-style parental report. Half the HL group had been fitted with HAs and/or CIs before 12 months of age and half the group was fitted after 12 months, but before 2.5 years. Whereas the NH comparison group reached their first and 100th words at 12 months and 21 months, on

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average, the earlier-fit HL group reached these milestones at 14 months and 27 months, and the later-fit group at 21 months and 34 months. Nott et al.’s (2009) later-fit group performed very similarly to the older cohort of children from Gregory and Mogford (1981). The data from their earlier-fit group approximated the development of the NH children. However, whereas the NH children displayed a nine-month interval between their first and 100th words, both of the HL groups showed a somewhat longer 13-month interval between these milestones. These studies suggest similar early acquisition patterns for 50- and 100-word milestones, as well as two-word combinations, for NH children and children with HL who receive HAs and/or CIs prior to 12 months. Later device fitting widened the gap between the early lexical acquisition of children with HL and typically developing children. These early lexicons set the stage for subsequent vocabulary acquisition.

Lexical Development One of the most compelling aspects of language acquisition in NH children is the apparent ease with which new words are added to their vocabularies. However, for many children with HL, the acquisition of new vocabulary poses a challenge. As noted in the previous section, many children with HAs and CIs are delayed in reaching earlier lexical milestones (Nott et al., 2009). Many studies of vocabulary acquisition have employed structured experimental situations in which children are exposed to novel words labeling new objects, actions, or attributes, and are then tested on their memory for these novel associations. A small number of analogous studies have been carried out with children with HL. Several studies have found that children with HL perform more poorly than age-matched NH children on word-learning tasks, but very similarly to younger, receptive-vocabulary-matched NH children (e.g., Gilbertson & Kamhi, 1995; Walker & McGregor, 2013). Children with HL typically make more labeling errors and require more training to attain correct novel word production. Multisyllabic novel words, in particular, pose a challenge. In novel word-learning tasks, children are typically given the task of mapping an unfamiliar label to an unfamiliar object. Impaired performance implies that some aspect of the mapping process is delayed or atypical. One possibility is that children with HL may fail to make the inference that novel words or signs should attach to an unfamiliar item or action (as opposed to one that already possesses a familiar label). NH children generally exhibit this inferential behavior by age 2. In a relevant study, Lederberg, Prezbindowski, and Spencer (2000) found the majority of a small group of 3–6-year-old children with moderate to profound HL demonstrated this inferential behavior. Some of the children with HL, however, required explicit linking of the novel word and novel object, and did not demonstrate the inferential behavior until one year later. In a larger follow-up study, Lederberg and Spencer (2009) were able to report that individual differences in the ability to learn new vocabulary could reliably be identified in children with HL, and that this ability is correlated with teacher-generated estimates of a student’s vocabulary size. An extension of these word mapping skills involves learning to extend already familiar words to new instances of the category. In one study of such extension, Houston, Carter, Pisoni, Kirk, and Ying (2005) assessed the ability of children 2–5 years of age using CIs to learn associations between already familiar adjectives used as proper names and small stuffed animal toys (e.g., “Fuzzy” the Bear). After a play session used to train the associations, children were tested on their ability to select the correct referent when given the name, and to produce the name when given the referent. The children with CIs performed more poorly on average than did aged-matched NH children. In the same vein of examining new associations between already familiar words and objects, GriecoCalub, Saffran, and Litovsky (2009) have further shown that toddlers implanted with a CI before

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2.5 years with at least one year of CI experience take longer than age-matched NH toddlers to appropriately orient their gaze toward a familiar object image when hearing the familiar object name. Such findings highlight one possible area of deficit in lexical acquisition and emphasize the potential usefulness of explicit vocabulary instruction in this population. One complicating factor, however, is that many of the studies reviewed utilize spoken responses, and that in children with HL, articulation delays may impact the scoring of the child’s productions. Thus, it is useful to assess word learning also using tasks that do not rely on interpreting a verbal response by the child. The Preferential Looking Paradigm (PLP) has been used to study infants with HL and their recognition of novel and familiar words. In PLP, the infant’s preference for looking at one object referent over another object referent is observed after the infant is exposed to a spoken stimulus. Toddlers with NH and toddlers with one year of cochlear implant experience (18–21 months of age) were presnted with nonsense words paired with pictures of novel objects (Houston, Stewart, Moberly, Hollich, & Miyamoto, 2012). NH toddlers looked preferentially toward the appropriate referent image upon hearing the nonsensel word for that object. The looking behavior of CI users implanted before 15 months of age resembled that of NH age-mates, but children implanted later did not demonstrate word learning. Together, earlier implantation and better residual hearing prior to implantation positively impact novel word learning (Houston et al., 2012; Tomblin, Barker, & Hubbs, 2007). These indicators are also supported in statistical growth-trajectory models of vocabulary development of children with CI (Nicholas & Geers, 2006; Niparko et al., 2010; Tomblin, Barker, Spencer, Zhang, & Gantz, 2005). These results show the importance of auditory experience as it pertains to vocabulary development, as mapping skill is positively affected by earlier implantation. Earlier implantation allows earlier and more consistent language exposure. This research also indicates that exposure to sound very early in infancy, even if that sound is somewhat degraded, has a positive influence on language development (Schwartz, Steinman, Ying, Ying Mystal, & Houston, 2013).

Morphology, Morphosyntax, and Syntax Normal hearing children begin to combine words into multiword utterances at about 18 months of age. However, as mentioned earlier in the chapter, production of word combinations are often delayed by at least three months in children with HL (Nott et al., 2009). Shortly thereafter, children begin to produce the grammatical morphemes of their language.

Early MLU Children with NH reach a mean length of utterance (MLU) of 3.0 between 2;6 and 3;0 years of age (Miller & Chapman, 1981). Toddlers and preschoolers with HL who are acquiring spoken language through HAs tend to display shorter MLU and make slower gains in MLU than their NH peers (Geffner, 1987; Ramkalawan & Davis, 1992). More recent research on children with CIs acquiring spoken German has supported these findings. Szagun (2001) found implanted children to progress from an MLU of 1.0 to an MLU of approximately 2.4 in 18 months. Meanwhile, their NH peers who began with the same MLU level progressed to an MLU of approximately 4.5 in the same time period. Therefore, the CI group showed markedly slower average gains than the NH group in MLU. While nearly half of the CI group displayed gains in MLU that fell within the range of gains seen in the NH group, the remaining children with CIs showed much slower rates of progress. Akin to word mapping abilities, children who had received their CIs earlier and had more residual hearing featured gains in MLU comparable to that of their NH peers. Analogous findings

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showing slower gains in MLU compared to NH children initially matched for MLU have been reported for implanted children acquiring spoken French (LeNormand, Ouellet, & Cohen, 2003).

Morphology The acquisition of spoken language syntax and morphology poses a particular challenge for children with HL. Generally, although children with HL acquire the morphology of spoken English in a similar pattern to NH children, age at emergence is substantially later. As is the case for NH children, patterns of acquisition appear to be related to the structure of the individual language, the frequency of word/morpheme usage, and issues of perceptual salience and semantic/conceptual complexity. Nouns typically dominate the early vocabularies of NH children, with some cross-linguistic exceptions. This is also true of children with HL (Nott et al., 2009). Their content-word vocabulary develops over a longer period of time than that of their NH peers. Many language samples from English-speaking children with HL have been shown to feature fewer function words. When function words are employed, a disproportionate amount of those function words are determiners. Additionally, many English-speaking children with HL have struggled with accurately choosing between definite and indefinite determiners (Caselli, Rinaldi, Varuzza, Giuliani, & Burdo, 2012; Levitt, 1987; McAfee, Kelly, & Samar, 1990; Szagun, 2004a, 2004b; Wilbur, 1977). Further study of language samples taken from pediatric CI users in Szagun’s research (Szagun, 2000, 2001, 2004a, 2004b) found German-speaking children with CIs had similar difficulties with plurals and inflectional endings. When comparing morphological acquisition in children with CIs to children with similar audiological profiles using HAs, Spencer, Tye-Murray, and Tomblin (1998) found that inclusion of bound morphemes where required was more common for the CI users than for the HA users for all of the inflectional morphemes examined in children aged from 5–16 years. Spencer et al. (1998) found that both groups of children with HL were most accurate on the present progressive and regular plural markers, mirroring NH children’s earliest acquisition of these forms. In addition, regular past tense posed a problem for both groups of children with HL. No child in the HA group accurately produced the regular past tense marker, and CI users did so only half of the time. But not all research suggests that morphological acquisition is simply delayed. A recent study examined whether children with CIs follow an atypical course of morphological acquisition. To do so, the experimenters employed a sentence completion task to test production of uncontracted copulas, noun plurals, and the regular past tense in children with CIs (Svirsky, Stallings, Lento, Ying, & Leonard, 2002). The children performed better on uncontracted than on contracted copulas and on regular than irregular plurals. The children performed most poorly on regular past tense. This pattern of acquisition does not match the order of acquisition present in NH children, where plural and past tense forms emerge prior to uncontracted copular verbs. Additional data from Ruder (2004) revealed earlier acquisition of is as an uncontracted copula verb and more difficulty with third-person singular conjugation of a verb in children with CIs. Szagun (2000) also found that children with CIs were more likely to produce pronouns than determiners. This pattern of acquisition and errors may indicate a general preference for perceptually salient morphosyntactic markers (e.g., copula or identical segmental forms used as pronouns versus articles). Because English has limited morphosyntax (e.g., limited or absent gender or case marking), studies of morphosyntax in other languages can contribute to our understanding of morphology in children with HL. For children with HL who are acquiring spoken Hebrew, Tur-Kaspa and Dromi (1999, 2001) reported that grammatical agreement of the verb form with the head noun, as well as the adjectives and the nouns they modify, was highly prone to error. Similarly, German

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children with CIs aged 1–7 years have been reported to omit case and gender markings more often than do NH children matched on MLU (Szagun, 2004a). These children also made substitution errors of gender, unlike their NH peers (Szagun, 2004a, 2004b). Not long ago, researchers may have simply interpreted these difficulties in morphosyntax as an issue of perception, as determiners and other morphosyntactic endings are less perceptually salient. However, error analysis of Szagun’s (2004a, 2004b) language samples revealed that while the errors of typical, NH children were often attributable to perceptual confusion, this was not true of CI users and children with HL. The most predominant error in children with CI was total omission of case endings and determiners (Szagun, 2004a, 2004b). This is intriguing, as it suggests that perception may not be wholly at fault for these errors and that broader deficits in morphology and syntax exist (see Chapter 15 by Oetting & Hadley).

Syntax While many aspects of their syntax development are understudied, the difficulties in syntax faced by children with HL are very apparent. As early as 1974, children with HL were noted to take nearly twice as long as their NH peers to master early grammatical constructions like irregular verbs and questions (Quigley, Wilbur, & Montanelli, 1974). More recent research in CIs has found that only 36% of 7–9-year-olds implanted prior to age 4 reached age-appropriate scores on standardized assessments of grammar (Nikolopoulos, Dyar, Archbold, & O’Donoghue, 2004). Children with CIs have also been found to perform significantly poorer than NH children on most standardized measures of syntax, typically only reaching 75–80% accuracy on receptive, expressive, and repetition tasks of syntax (Caselli et al., 2012; Geren, 2010). Many researchers have commented on the tendency of children with HL, unlike NH children, to omit obligatory syntactic elements of sentences despite many years of spoken language exposure. Tur-Kaspa and Dromi (1999, 2001), for example, reported that children with severe to profound HL who are acquiring spoken Hebrew using HAs or CIs omitted the subject or main verb in ~15% of their spoken clauses. Levitt (1987) highlighted that main verbs were frequently omitted when placed before a prepositional phrase. Levitt (1987) also noted that frequently used irregular verbs were often omitted. These errors can remain uncorrected until the middle school years. Some of the earliest syntactic developments are negation and question formation. Levitt (1987) found negation to be a relative strength in the syntax of children with HL but still prone to errors even in early forms. Question formation is also an early syntactic acquisition, generally beginning in NH children with the creation of yes/no question forms marked by rising intonation contours. In spoken English, the acquisition of rising intonation is followed by the acquisition of wh-questions, gradually incorporating an auxiliary verb in the correct location. The progress of children with HL mirrors this typical pattern of emergence in both production and comprehension, but with much later ages of acquisition. The type of wh-question (e.g., subject questions vs. object questions) has also been found to affect ease of processing in ways analogous to young NH children (Levitt, 1987; Quigley et al., 1974). Children with HL using HAs or CIs demonstrate the greatest difficulty with “which” questions and perform significantly better on subject questions over object questions (Friedmann & Szterman, 2011; Haddad-Hanna & Friedmann, 2009), similar to their NH peers. Later-acquired syntactic structures include combined clauses and syntactic movement. These structures have attracted a great deal of research, as their implications for syntax processing theories and relation to cognition are quite intriguing. Information on real-time processing of these structures would inform more abstract theories of syntax, while the attention and memory demands of these structures might provide insight into the interaction between cognition and language. One

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example of this complex syntactic structure is the relative clause, which has been studied in children with HL. Children with HL using HAs or CIs often perform significantly poorer than their NH peers on tasks of comprehension and production of relative clauses (Friedmann & Szterman, 2006, 2011; Haddad-Hanna & Friedmann, 2009). When prompted to create embedded clauses, Hebrew-speaking children with HL tended to produce more complement-type clauses (e.g., I know (that) you are visiting), rather than relative clauses (e.g., you are reading the book (that) I brought), modifying the verb instead of the noun (Friedmann & Szterman, 2011; Tur-Kaspa & Dromi, 1999, 2001). In contrast, NH children of similar age produced more relative clauses than complement clauses. Additionally, many children with HL relied upon the use of resumptive pronouns, a sort of restating of the subject or object of the sentence, to ease syntactic complexity (Friedmann & Szterman, 2006). More recent and related research has indicated a probable deficit in the processing of syntactic movement by children with HL. Friedmann and colleagues (Friedmann & Szterman, 2006, 2011; Haddad-Hanna & Friedmann, 2009) found that children with moderate to severe HL who are acquiring spoken Hebrew via HAs or CIs demonstrated poor comprehension and production of sentence structures argued to involve syntactic movement. The structures studied included objectrelative sentences, topicalized sentences that deviated from the basic Hebrew word order (subjectverb-object, SVO), and wh-questions. Many of the children with HL preferred to duplicate the previously used noun phrase, replace the noun phrase with a less descript pronoun, reverse the two noun phrases, or omit a second noun phrase entirely (Friedmann & Szterman, 2006, 2011). Bever (1970) proposed a hypothesis that a default SVO interpretation of sentences is imposed in childhood acquisition. A preference for interpreting sentences through this canonical structure has been identified in children with HL by Quigley et al. (1974, 1976) and Bishop (1982). In keeping with this hypothesis, Friedmann & Szterman (2006, 2011) argued that children with HL have difficulty resolving syntactic movement because of difficulties with assigning subject and object roles in less canonical sentences, like wh-questions. This would make it difficult to restore the canonical order of the sentence and, subsequently, comprehend the sentence. However, this proposal may be insufficient, as chance levels of performance were found for many individual children with HL, rather than a consistent preference for canonical-form-based interpretations (Friedmann & Szterman, 2006, 2011). Thus, more research is needed to explore the true cause of these syntactic deficits found in children with HL.

Global Measures of Language Performance on individual aspects of language is the most revealing type of data for understanding the effects of HL on language acquisition, but it is also useful to consider the overall level of spoken language skills attained. This information can be gleaned through studies that directly compare measures of general language competence for children with HL relative to normative data from age-matched NH children. Many studies of this kind have reported results for a receptive vocabulary measure and general language measure. Although these measures are often highly correlated in typical children (Dunn & Dunn, 2013), in the case of children with HL, for whom vocabulary acquisition tends to be an area of strength relative to morphological and syntactic processing, comparing these two types of scores is informative. Expectations for spoken language acquisition in children with severe to profound HL have been rather dramatically revised in the past decade. In the past, norms for NH children were often not useful in assessing this clinical population because there was little overlap in scores. Because of early identification and intervention, there is now considerable overlap between the two distributions and what is debated is the extent of this overlap (and the source of remaining differences).

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One recent data set suggests that children who receive early audiological intervention can attain overall language levels that very closely approach those of NH children. Geers and Nicholas (2013) reported that 4.5-year-olds implanted either between 6–12 months (n = 27) or 13–18 months (n = 42) exhibited receptive vocabulary (Peabody Picture Vocabulary Test, 4th edition [PPVT-4; Dunn & Dunn, 2007]) standard scores of 103 (57th percentile) and 94 (35th percentile), respectively. Language performance assessed using the Preschool Language Scale, 3rd edition (PLS; Zimmerman, Steiner, & Pond, 1992) yielded average standard scores of 100 (50th percentile) for the earlier-implanted group and 90 (25th percentile) for the children implanted between 12–18 months. These data suggest that a child implanted by 12 months can be expected to score about equivalently to a typically developing child on these measures at 4.5 years. These results imply that a language delay proportional to the length of auditory deprivation is not necessarily a given, if implantation takes place before the end of the first year of life. While these results are reassuring of average language performance post-implantation, it is important to note that the PPVT-4 (Dunn & Dunn, 2007) is only a measure of the child’s ability to recognize vocabulary as it applies to specific pictures. In a similar and related study with slightly later implanted children (12–38 months), Geers and Nicholas (2013) tracked the progress of 60 children at 4.5 years of age and again at 10.5 years. These children exhibited an improvement in their standardized language scores that implied a language gain beyond that expected for NH children in those six intervening years. The mean standard score for the CI children on the PPVT increased from 84 to 96 (14th to 40th percentile). On the general language assessments, standardized scores rose from 80 (9th percentile on the PLS) to ~90 (25th percentile using the Clinical Evaluation of Language Fundamentals, 4th edition (CELF4; Semel, Wiig & Secord, 2003). These results suggest that an initial delay can be lessened over time, with language gains per year sometimes exceeding those expected of an NH child. Other research has yielded less encouraging results. Boons, Brokx, Frijns et al. (2012) compared Dutch-speaking preschool-age children who were either bilaterally (n=25) or unilaterally implanted (n=25), but otherwise closely matched in patient and device characteristics. The children had been implanted before 28 months; mean age at first CI was 12 months. All children had at least three years of CI use. On both receptive and expressive language measures (Dutch analogue of the Reynell Developmental Language Scales [Edwards, Fletcher, Gurman, Hughes, & Letts, 1997]), the bilateral group average was ~1 SD below age-matched NH norms (standard scores around 80, 9th percentile), and the unilateral group average was almost 2 SDs below (standard scores around 75, ~5th percentile). While suggesting an advantage for bilateral implantation, these standard scores are clearly markedly lower than others reported (see also Leigh et al., 2013; Niparko et al., 2010). Recent large-scale studies of children implanted as preschoolers have yielded fairly similar results. Such groups appear, on average, to obtain receptive vocabulary scores of about 85–95 (16th–37th percentile) and 80–93 (9th–32nd percentile) on standardized general language tests such as the CELF-4 after several years of device use (Geers, Moog, Biedenstein, Brenner, & Hayes, 2009; Sarant et al., 2014). Early generations of pediatric CI users (who tended to be older, 5–7 years, at implantation) continue to show language delays as teenagers, but a subset of these CI recipients show skills typical of NH peers. Two recent studies of teens with over a decade of CI experience have reported mean scores of ~91 (27th percentile) on PPVT vocabulary and 90 to 92 (25th–30th percentile) on the CELF-4 (Geers & Sedey, 2011; Ruffin et al., 2013). Thus, well over 50% of these adolescents with HL scored above 85 (or above -1 SD of the mean for NH children). While this section focuses on average performance, it is critical to note the considerable withingroup variability in the language performance of children with HL. Many studies have attempted to account for this variability. Although dependent on the sample and language measures utilized,

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approximately 35–75% of the variance found in children using CIs can be accounted for by known patient and device factors (Boons et al., 2012; Geers, Nicholas, & Sedey, 2003; Geers & Sedey, 2011; Sarant et al., 2014). A portion of the remaining variability may be due to individual differences in central cognitive functions. Individual differences in cognition are believed to play a role in typical language acquisition, thus it is probable that similar relationships may exist in children with HL.

Cognition and Language When discussing cognitive processes, it is hard to avoid the controversial construct of the intelligence quotient, or IQ. IQ scores are thought to reflect an individual’s capacity to reason and learn (Mackintosh, 2011). Measures of IQ have been specifically designed to predict educational achievement in hearing children by creating a single composite score out of scores on various tests designed to assess individual differences in skills such as memory, attention, pattern recognition, and conceptual relations (Sternberg & Kaufman, 2011). It is therefore not surprising that such scores also correlate with educational attainment in children with HL (Paal, Skinner, & Reddig, 1988). On average, children with HL and no additional handicaps display no difference in nonverbal IQ from NH children on the most widely used assessment tools (Maller & Braden, 1993; Schildroth, 1976; see Mayberry, 1992; Vernon, 1968). Within this distribution, children who have HL with higher nonverbal IQ scores tend to demonstrate better spoken language skills for their age (Dawson, Busby, McKay, & Clark, 2002; Geers et al., 2003; Geers & Sedey, 2011; Watson, Sullivan, Moeller, & Jensen, 1982), as is also found with typically developing children (Anastasi & Urbina, 1997; see discussion in DeThorne & Schaefer, 2004). Verbal IQ scores, in contrast, tend to be lower for children with HL than for NH children, on average. However, these differences appear to be diminishing with advances in technology and treatment (Geers & Moog, 1989; Geers & Sedey, 2011). Short-term memory is one skill typically included in IQ measures. Children with HL tend to display poorer short-term memory for lists of auditory items than do their NH, age-matched peers, even when the items to be recalled are reliably identified in isolation, or when identification during list presentation is assured to be correct (Burkholder & Pisoni, 2003; Cleary, Schwartz, Wechsler-Kashi, & Madell, 2006; Conrad, 1972, 1979; Dawson et al., 2002; Nittrouer et al., 2014). Additionally, poorer recall is typically also observed for sequences of visual stimuli such as familiar, easily labelled images, shapes, or colored lights (Cleary, Pisoni, & Geers, 2001; Dawson et al., 2002). These results for familiar visual stimuli underscore the tendency for humans to use linguistically based information-encoding strategies when possible. Studies of children’s behaviors during list recall demonstrate the ubiquity of simple strategies such as verbal repetition to aid short-term memory in the school-age years. Children with HL often exhibit different memory maintenance strategies than NH children of comparable age, with some of their strategies resembling those of younger NH children (Bebko & McKinnon, 1990). Working memory (WM) is also believed to be a factor in how children with HL process linguistic information. Working memory tasks involve the use of short-term memory while shifting attention across more than one task (in addition to disregarding irrelevant information) over relatively short periods of time. Confusingly, some literature uses the terms working memory and short-term memory interchangeably. In this chapter, only tasks that involve a secondary component will be referred to as working memory (see Chapter 8 by Gillam et al.). Child language researchers have become particularly interested in whether the comprehension of complex syntactic constructions and pronoun reference relations relates to individual differences in WM in NH children (Magimairaj & Montgomery, 2012; Montgomery, 2003; Montgomery & Evans, 2009). This notion emerged from data suggesting that individual differences in WM performance among adults were

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related to language processing skills (Just & Carpenter, 1992; MacDonald, Just, & Carpenter, 1992), although other research disputes this (e.g., Caplan & Waters, 2013). As a result, WM has been studied in children with HL using tasks that tax attention via dualtask or task-switching demands. Such attention-demanding tasks commonly involve repeating or making a truth judgment about a series of spoken or printed sentences while holding the final word of each sentence in memory for later recall. In an early study, Daneman, Nemeth, Stainton, and Huelsmann (1995) reported that verbal WM, but not level of HL, was correlated with reading skills among school-age children in oral-education environments. More recently, Geers, Pisoni, and Brenner (2013) found that adolescent children with CIs were capable of performing as well as their NH peers on a reading-based WM task, and that variability within the HL group was uniquely related to variability in spoken language abilities. Willstedt-Svensson, Löfqvist, Almqvist, and Sahlén (2004) have also reported auditory WM to be correlated with receptive and expressive grammar skills in children using CIs (see also Lyxell et al., 2008). There is also a small amount of data suggesting that subtests of popular language assessment measures that are believed to draw most heavily on auditory memory pose a particular problem for children with HL. Average scores for children with HL on subtests of the CELF-4 (Semel et al., 2003) that require accurate recall of long spoken sentences, or require following a set of spoken directions after a brief delay, appear to reflect these particular weaknesses (Geers et al., 2009; Geers & Nicholas, 2013; Spencer, Barker, & Tomblin, 2003; Young & Killen, 2002). Some researchers have argued that individual differences in comprehending complex sentences in NH populations relate not simply to variability in WM capacity, but also to individual differences in working memory and memory for lexical items (Caplan & Waters, 2013; Kidd, 2013; MacDonald & Christiansen, 2002; Ullman, 2008; see Chapter 8 by Gillam et al.). Spoken language input and experience tends to be less extensive in children with HL than in NH children of the same age, presumably resulting in less-well-established lexical and syntactic representations in memory. Further examination of these relationships may help us to better understand how general cognition influences language processing in children with HL.

Conclusion With many advances in technology and accessibility, more children with HL are receiving their education in mainstream, oral language classrooms. For many early-identified children with severeto-profound sensorineural HL, CIs and HAs have made age-appropriate spoken language skills attainable. While these developments are significant, there is, nevertheless, still room for improvement in the spoken language skills attained by children with HL. In particular, a substantial number of children still do more poorly than expected despite otherwise propitious circumstances. A number of researchers have raised the issue of whether a subgroup of children exists that have language-learning deficits beyond those anticipated with their HL alone (e.g., Bunch & Melnyk, 1989; Gilbertson & Kamhi, 1995; Young & Killen, 2002). Such a group may exist, but identifying these children represents a challenge. Understanding remaining outcome variability may require more focused study of specific areas of language use, such as morphological and syntactic processing. Further study of cognitive development, particularly in the areas of verbal short-term and working memory, as well as attention, may prove helpful as well. New ways of assessing individual differences in the surviving auditory system also holds potential. In pursuing these new directions, it will continue to be vital to take into account the heterogeneity of this clinical population. To aid in future attempts at synthesis of the literature, language researchers should continue to be conscientious about describing their participant samples in detail with respect to the variables

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discussed in the first third of this chapter. Such research will enable parents, clinicians, and educators to generate appropriate expectations for the language development of children with HL. A clear understanding of expected benefit, together with sustained efforts to ensure that each child has access to the services (medical and rehabilitative) needed for optimal outcome, has the potential to dramatically minimize the language delays historically associated with this clinical population.

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Hearing Loss Wilbur, R. B. (1977). An explanation of deaf children’s difficulty with certain syntactic structures of English. Volta Review, 79(2), 85–92. Willstedt-Svensson, U., Löfqvist, A., Almqvist, B., & Sahlén, B. (2004). Is age at implant the only factor that counts? The influence of working memory on lexical and grammatical development in children with cochlear implants. International Journal of Audiology, 43(9), 506–515. Young, G. A., & Killen, D. H. (2002). Receptive and expressive language skills of children with five years of experience using a cochlear implant. The Annals of Otology, Rhinology, & Laryngology, 111(9), 802–810. Zimmerman, I. L., Steiner, V. G., & Pond, R. E. (1992). PLS-3: Preschool Language Scale-3. San Antonio, TX: Psych Corp. Zumach, A., Gerrits, E., Chenault, M., & Anteunis, L. (2010). Long-term effects of early-life otitis media on language development. Journal of Speech, Language, & Hearing Research, 53, 34–43.

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5 DYSLEXIA Sally E. Shaywitz and Bennett A. Shaywitz

Dyslexia (or specific reading disability) is the most common and most carefully studied of the learning disabilities, affecting 80% of all individuals identified as learning disabled. Not only is dyslexia the best characterized of all learning disabilities, but it is historically the oldest. In fact, the first description of dyslexia in children preceded the first mention of learning disability by over 60 years—dyslexia was first described in 1896, while the term learning disability was not used until 1962! This chapter reviews recent advances in our knowledge of the epidemiology, etiology, cognitive influences, neurobiology, clinical manifestations, and management of dyslexia in children and adults. Dyslexia first came to attention in the latter part of the 19th century, when physicians began to report on a puzzling group of children who seemed to have all the factors present to be good readers: they were bright and motivated, their parents were caring and concerned, they had received intensive reading instruction and tutoring, and yet, they continued to struggle to learn to read. This paradox was captured by Dr. W. Pringle Morgan in his report about 14-year-old Percy F. in the British Medical Journal on November 7, 1896 (Morgan, 1896, p. 1378): “He has always been a bright and intelligent boy, quick at games, and in no way inferior to others his age. His great difficulty has been—and is now—his inability to read.” Dr. Morgan labeled this condition congenital word blindness and emphasized the lack of obvious contributing factors, concluding that the disorder is congenital and, in a highly prescient statement, ascribes the problem to a malfunction in the brain. He comments that the boy has good vision (“His eyes are normal . . . and his eyesight is good.”) and he is intelligent (“The schoolmaster who has taught him for some years says that he would be the smartest lad in the school if the instruction were entirely oral.”).

Definition The definition of dyslexia as an unexpected difficulty in reading (Critchley, 1970; Lyon, Shaywitz, & Shaywitz, 2003), has remained invariant over the century since its first description (Morgan, 1896), and this concept of an unexpected difficulty has also been the hallmark of learning disability from its origins in 1962 continuing to the present day. Thus, in the earliest descriptions of learning disability, authors noted a “discrepancy between the capacity for reading and actual achievement” (Kirk & Bateman, 1962, p. 73) and “there is a discrepancy between

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potential and actual success in learning” (Myklebust, 1968, p. 1). The most current definition of dyslexia is that proposed by the U.S. Senate in Resolution 576: (1) defined as an unexpected difficulty in reading for an individual who has the intelligence to be a much better reader; and (2) most commonly due to a difficulty in phonological processing (the appreciation of the individual sounds of spoken language), which affects the ability of an individual to speak, read, spell, and often, learn a second language; (Senate Resolution. 114TH CONGRESS, 2ND SESSION ed, 2016.) In dyslexia, unexpected refers to the presence of a reading difficulty in a child (or adult) who appears to have all of the factors (intelligence, motivation, exposure to reasonable reading instruction) present to be a good reader but who continues to struggle (S. Shaywitz, 1998). There is now empiric support for defining dyslexia (and by extension, other learning disabilities) as an unexpected difficulty in reading. Using data from the Connecticut Longitudinal Study, we (E. Ferrer, B. Shaywitz, J. Holahan, K. Marchione, & S. Shaywitz, 2010) demonstrated that in typical readers, reading and IQ development are dynamically linked over time. Not only do reading and IQ track together over time, but they also influence one another. In contrast, such mutual inter-relationships are not perceptible in dyslexic readers, suggesting that reading and cognition develop more independently in these individuals (Figure 5.1).

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Ferrer et al., 2010 Figure 5.1 Uncoupling of reading and IQ over time: Empirical evidence for a definition of dyslexia. Left panel shows that in typical readers, reading and IQ development are dynamically linked over time. In contrast, right panel, dyslexic readers, shows that reading and IQ development are dissociated and one can be highly intelligent and still struggle with reading. Source: Data adapted from (Ferrer et al., 2010).

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These findings of an uncoupling between IQ and reading in dyslexia, and the influence of this uncoupling on the developmental trajectory of reading, provide evidence to support the conceptual basis of dyslexia as an unexpected difficulty in reading in children who otherwise have the intelligence to learn to read, but who struggle to read fluently. Based on dynamic models, the uncoupling of reading and cognition demonstrates that in the special case of dyslexia a child or adult can be both bright and accomplished along with a much lower level of reading than expected for a person of that level of intelligence, education, or professional status. They also demonstrate that in dyslexia, the reading difficulty is unexpected for an individual’s level of intelligence or education; that is, the difficulty is defined as a disparity existing within the individual. The implication is that for individuals who are dyslexic, the appropriate comparison is between a person’s ability and his/her reading. Thus, in dyslexia, a highly intelligent person may read at a level above average, but below that expected, based on his/her intelligence, education, or accomplishments. These new findings provide an explanation for the “unexpected” nature of dyslexia and provide the long-sought empirical evidence for the seeming paradox involving cognition and reading in individuals with dyslexia.

Epidemiology Epidemiological data indicate that, like hypertension and obesity, dyslexia occurs in gradations and fits a dimensional model. In other words, within the population, reading ability and reading disability occur along a continuum, with reading disability representing the lower tail of a normal distribution of reading ability (Gilger, Borecki, Smith, DeFries, & Pennington, 1996; S. Shaywitz, Escobar, B. Shaywitz, Fletcher, & Makuch, 1992). Dyslexia is perhaps the most common neurobehavioral disorder affecting children, with prevalence rates ranging from 5–17.5% (Interagency Committee on Learning Disabilities, 1987; S. Shaywitz, 1998). Data from the 2013 National Assessment of Educational Progress (http://nationsreportcard.gov/reading_math_2013) indicate that overall, only about one in three students is proficient in fourth-grade or eighth-grade reading. And among some groups of students, the numbers are far worse. About one in five African American, Latino, and Native American students are proficient in fourth-grade and eighth-grade reading. Longitudinal studies, both prospective (D. Francis, S. Shaywitz, K. Stuebing, B. Shaywitz, & J. Fletcher, 1996; B. Shaywitz et al., 1995) and retrospective (M. Bruck, 1992; R. Felton, C. Naylor, & F. Wood, 1990; H. Scarborough, 1990), indicate that dyslexia is a persistent, chronic condition; it does not represent a transient developmental lag (Figure 5.2). Over time, poor readers and good readers tend to maintain their relative positions along the spectrum of reading ability—children who early on function at the 10th percentile for reading and those who function at the 90th percentile and all those in-between tend to maintain their positions. Dyslexia is found in readers of all languages, including both alphabetic and logographic scripts. A recent study demonstrates that the achievement gap in reading between typical and dyslexic readers is evident as early as first grade and persists (Ferrer et al., 2015).

Etiology Dyslexia is both familial and heritable (Pennington & Gilger, 1996). Family history is one of the most important risk factors, with 23% to as much as 65% of children who have a parent with dyslexia reported to be dyslexic (Scarborough, 1990). A rate among siblings of affected persons of approximately 40% and among parents ranging from 27–49% (Pennington & Gilger, 1996) implies that it should be possible to identify affected siblings early and also to identify affected adults, such as a parent of the child known to be dyslexic. Yet, despite the strong familial nature, within a single family both recessive and dominant transmission is frequently observed. These data are consistent with a complex etiology; studies of heritability show that between 44–75% of 132

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© Sally Shaywitz, Overcoming Dyslexia, 2003, after Francis et al., 1996 Figure 5.2 Trajectory of reading skills over time in nonimpaired and dyslexic readers. Ordinate is Rasch scores (W scores) from the Woodcock-Johnson reading test (R. Woodcock & M. Johnson, 1989) and abscissa is age in years. Both dyslexic and nonimpaired readers improve their reading scores as they get older, but the gap between the dyslexic and nonimpaired readers remains. Thus, dyslexia is a deficit and not a developmental lag. Source: Figure derived from data in (Francis et al., 1996) and reprinted from (S. Shaywitz et al., 2003) with permission.

the variance is explained by genetic factors and the remaining by environmental factors (DeFries, Olson, Pennington, & Smith, 1991). Given that dyslexia is familial and heritable, initial hope that dyslexia would be explained by one or by just a few genes has been disappointing. Thus, along with a great many common diseases, genome-wide association studies (GWAS) in dyslexia have so far identified genetic variants that account for only a very small percentage of the risk, less than 1% (Meaburn, 2008). Current evidence suggests “that common diseases involve thousands of genes and proteins interacting on complex pathways” (Duncan, August 24, 2009) and that, similar to experience with other complex disorders (e.g., heart disease, diabetes), it is unlikely that a single gene or even a few genes will identify people with dyslexia. Rather, dyslexia is best explained by multiple genes, each contributing a small amount of the variance. Thus, current evidence suggests that the etiology of dyslexia is best conceptualized within a multifactorial model, with multiple genetic and environmental risk and protective factors leading to dyslexia.

Cognitive Influences: Theories of Dyslexia Among investigators in the field, there is now a strong consensus supporting the phonological theory. This theory recognizes that speech and language are acquired naturally, whereas reading must be taught. To read, the beginning reader must recognize that the letters and letter strings 133

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(the orthography) represent the sounds of spoken language. In order to read, a child has to develop the insight that spoken words can be pulled apart into the elemental particles of speech (phonemes) and that the letters in a written word represent these sounds (S. Shaywitz et al., 2003); such awareness is largely missing in dyslexic children and adults (Bruck, 1992; Fletcher et al., 1994; Liberman & Shankweiler, 1991). Results from large and well-studied populations with dyslexia confirm that in young school-age children (Fletcher et al., 1994; Stanovich & Siegel, 1994), as well as in adolescents (S. Shaywitz et al., 1999), a deficit in phonology represents the most robust and specific correlate of dyslexia. Difficulties with phonological processing represent the most commonly reported problem in dyslexia (Morris et al., 1998). Phonological processing includes phonological awareness, a component of oral language ability that encompasses the abilities to attend to, discriminate, and manipulate individual speech sounds. This metacognitive understanding involves the realization that spoken language is composed of a series of discrete speech sounds (phonemes) that are arranged in a particular sequence (Clark & Uhry, 1995). Phonemic awareness refers to the ability to discern and identify the smallest individual speech sounds or phonemes, whereas phonological awareness is a broader term that includes phonemes, as well as all types of larger elements of speech that can be assessed by asking the child to rhyme words or count the number of syllables in a word. Both types of awareness involve the understanding that speech can be divided into sounds, and these sounds can then be sequenced into a series to form syllables and words. Weaknesses in phonological processing play a critical role in dyslexia (S. Shaywitz, 2003; Willcutt, Pennington, Olson, Chhabildas, & Hulslander, 2005). While spoken language is natural and instinctive, print or written language is artificial and must be learned (S. Shaywitz, 2003). Brain mechanisms are in place to process the sounds of language automatically, but not the letters and words that make up print. Accordingly, these printed elements must link to something that is accepted by the neural machinery and has inherent meaning—the sounds of spoken language. To read, a child first must pull apart the written words into their individual sounds, link the letters to their appropriate sound (phonics), and then blend the sounds together. Thus, the awareness that spoken words come apart and the ability to notice and identify phonemes, these smallest elements of sound, allow the child to link letters to sound. In order to read, a child first must master what is referred to as “the alphabetic principle” (i.e., to develop the awareness that the printed word has the same number and sequence of sounds as the spoken word). Phonemic awareness abilities have their primary impact on the development of phonics skills, or knowledge of the ways that letters represent the sounds in printed words (Torgesen & Mathes, 2000), as well as on encoding or spelling development (Bailet, 2001). Reflecting the core phonological deficit, a range of downstream effects is observed in spoken as well as in written language (S. E. Shaywitz & B. A. Shaywitz, 2008). Phonological processing is critical to both spoken and written language. While most attention has centered on print difficulties, the ability to notice, manipulate, and retrieve phonological elements has an important function in speaking. For example, uttering a spoken word requires a two-step mechanism involving (1) semantic and (2) phonological components (Levelt, Roelofs, & Meyer, 1999). In dyslexia, the second step involving phonology is affected (Hanley & Vandenberg, 2010). First, one must generate the concept of what one wants to communicate; this in turn triggers activation of the semantic or meaning-based representation of the word in the speaker’s lexicon. However, in order to speak the word, once the concept and associated semantic form are activated, the lexical representations must be transformed into their phonological codes. To accomplish this, in the second step, the speaker accesses and retrieves the phonological representations (phonological codes) that link to the semantic structures, a necessary step in order to generate the articulatory (motor) patterns that are ultimately put into action by the articulatory

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muscles, resulting in the production of the spoken word. In dyslexia, activation of the concept and its semantic representation proceeds smoothly, but the second step, the transformation of the semantic (meaning) into the phonological (sound) code, is disrupted. A feedback mechanism enables the speaker to monitor his or her own speech and exercise some output control to correct errors. However, if the individual is anxious, as often is the case in dyslexia, word retrieval is further negatively impacted.

Neurobiological Studies in Dyslexia Though brain imaging studies of dyslexia are relatively recent, neural systems influencing reading were first proposed over a century ago by Dejerine in studies of adults who suffered a stroke with subsequent acquired alexia, the sudden loss of the ability to read (Dejerine, 1891). Dejerine proposed at least two brain regions in the left hemisphere, one in the parieto-temporal region, the other more inferior in the occipito-temporal region. It has only been within the last two decades that neuroscientists using noninvasive brain imaging, particularly functional magnetic resonance imaging (fMRI), have been able to confirm the importance for dyslexia of the posterior brain regions proposed by Dejerine. FMRI measures changes in metabolic activity and blood flow in specific brain regions while subjects are engaged in cognitive tasks and depends on the principle of autoregulation of cerebral blood flow (Anderson & Gore, 1997; Frackowiak et al., 2004; Jezzard, Matthews, & Smith, 2001). While a number of studies using structural imaging modalities [e.g., measuring gray matter volume (GMV)] have reported relatively less GMV in dyslexia in bilateral temporoparietal and left occipito-temporal regions, some have suggested that these GMV differences reflect the outcome of experience rather than the effects of dyslexia (Krafnick, Flowers, Luetje, Napoliello, & Eden, 2014). It is not surprising, then, that structural brain imaging has not provided as consistent results in dyslexia as fMRI studies.

The Reading Systems in Dyslexia Using fMRI, converging evidence from many laboratories around the world has demonstrated a neural signature for dyslexia—that is, an inefficient functioning of posterior reading systems during reading real words and pseudowords (see Figure 5.3). This evidence from fMRI has for the first time made visible what previously was a hidden disability. For example, in one of the first studies of fMRI in dyslexia, we (B. Shaywitz et al., 2002) used fMRI to study 144 children, approximately half of whom had dyslexia and half were typical readers. Our results indicated significantly greater activation in typical readers than in dyslexic readers in posterior reading systems during a task tapping phonologic analysis. These data from fMRI studies in children with dyslexia reported by our group have been replicated in reports from many investigators and show a failure of left hemisphere posterior brain systems to function properly during reading, particularly the systems in the left hemisphere occipito-temporal region (see Peterson & Pennington, 2012; Price & Mechelli, 2005; Richlan, Kronbichler, & Wimmer, 2009, 2011; S. Shaywitz & B. Shaywitz, 2005 for reviews). Similar findings have been reported in German (Kronbichler et al., 2006) and Italian (Brambati et al., 2006) readers with dyslexia. Studies in Chinese readers with dyslexia show some differences, though the systems are generally the same. For example, in both typical Chinese readers and Chinese readers with dyslexia, there is more involvement of the left middle frontal, superior parietal, and bilateral posterior visual regions and less for the inferior frontal and superior parietal regions (Perfetti, 2011).

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Figure 5.3 Neural signature for dyslexia. A neural signature for dyslexia is illustrated in this schematic view of left hemisphere brain systems in (left) nonimpaired and (right) dyslexic readers. In dyslexic readers, the anterior system is slightly overactivated compared with systems of typical readers; in contrast, the two posterior systems are underactivated. This pattern of underactivation in left posterior reading systems is referred to as the neural signature for dyslexia. Source: Adapted from Overcoming Dyslexia: A New and Complete Science-Based Program for Reading Problems at Any Level, by S. Shaywitz, 2003. New York: Alfred A. Knopf. Copyright 2003 by S. Shaywitz. Adapted with permission.)

Although readers with dyslexia exhibit an inefficiency of functioning in the left occipito-temporal word form area, they appear to develop ancillary systems in other brain regions (B. Shaywitz et al., 2002). While these ancillary systems allow the reader to read accurately, readers with dyslexia continue to read dysfluently. Inefficient functioning in this essential system for skilled reading has very important practical implications for individuals with dyslexia—it provides the neurobiological evidence for the biologic necessity for the accommodation of additional time on high-stakes tests (see Figure 5.4).

Development of Reading Systems in Dyslexia and the Visual Word Form Area (VWFA) In a cross-sectional study of 232 children, comprising a group with dyslexia and a group of typical readers (B. Shaywitz et al., 2007), brain regions in dyslexic readers differed developmentally from those in nonimpaired readers, primarily in being localized to a more left posterior and medial,

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Figure 5.4 Compensatory neural systems and the neural basis for the requirement for extended time for dyslexic students on high-stakes testing. The image is a cutaway view of the brain showing the left and right hemispheres. Typical readers activate three left hemisphere neural systems for reading: an anterior system and two posterior systems. Dyslexic readers have an inefficient functioning in the left hemisphere posterior neural systems for reading but compensate by developing anterior systems in the left and right hemispheres and the posterior homolog of the visual word form area in the right hemisphere. Source: Adapted from Overcoming Dyslexia: A New and Complete Science-Based Program for Reading Problems at Any Level, by S. Shaywitz, 2003. New York: Alfred A. Knopf. Copyright 2003 by S. Shaywitz. Adapted with permission.

rather than a more left anterior and lateral, occipito-temporal region. This difference in activation patterns between dyslexic and nonimpaired readers has parallels to brain activation differences observed during reading of two Japanese writing systems: Kana and Kanji. Kana script employs symbols that are linked to the sound (comparable to English and other alphabetic scripts); Kanji script uses ideographs where each character must be memorized. In the imaging study of these writing systems, activation, similar to that developing in nonimpaired readers, occurred during reading Kana. In contrast, activation, comparable to that developing in dyslexic readers, was noted during reading of Kanji script, suggesting that the portion of the word form region developing in dyslexic readers functions as part of a memory-based system (Nakamura et al., 2005). It is reasonable to suppose that as children with dyslexia mature, this posterior medial system supports memorization rather than the progressive sound-symbol linkages observed in nonimpaired readers. These findings are consonant with other evidence that readers with dyslexia are unable to make good use of sound-symbol linkages as they mature and, instead, come to rely on memorized words as they enter adolescence and adult life (Bruck, 1992; S. Shaywitz et al., 1999).

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These results support and now extend previous findings to indicate that the system responsible for the fluent, automatic integration of letters and sounds, in the anterior lateral occipitotemporal region, known as the visual word form area (VWFA), is the neural circuit that develops with age in typical readers. Just how the VWFA functions is the subject of intense investigation. Dejaeme and associates (Cohen, Jobert, Le Bihan, & Dejaeme, 2004; Henry et al., 2005; Nakamura et al., 2005) have suggested a systematic sensitivity to coding within the left occipito-temporal region, with more posterior regions coding for letters and letter fragments and more anterior regions coding for bigrams and words. In contrast, Price and Devlin (2011), in what they term the Integrative Account, suggest that the VWFA acts to integrate phonologic, orthographic, and semantic information. Readers with dyslexia who struggle to read new or unfamiliar words come to rely on an alternate system, the posterior medial occipito-temporal system that functions via memory networks.

Connectivity Connectivity analyses of fMRI data represent the most recent evolution in characterizing brain networks in dyslexia. Measures of functional connectivity are designed to detect differences in brain regions with similar magnitudes of activation, but whose activity is differentially synchronized with other brain systems across subject groups and/or types of stimuli. In fact, this synchrony between anatomically distinct regions might be equally or more important for cognitive performance than the magnitude of activation in any single region (Van den Heuvel & Hulshoff, 2010). Although there have been some functional connectivity studies of dyslexia (Horwitz, Rumsey, & Donohue, 1998; Koyama et al., 2013; Stanberry et al., 2006; van der Mark et al., 2011; Vogel, Miezin, Petersen, & Schlaggar, 2012), these have examined connections between specific regions chosen a priori and potentially fail to provide a complete picture of the connectivity profiles of dyslexic compared to typical readers. For example, one recent study (S. van der Mark et al., 2011) examined differences between readers with dyslexia and typical readers in the connectivity of the VWFA and other components of the reading system. In typical readers, the VWFA was connected to distant as well as adjacent reading systems in the left and right hemispheres. In contrast, functional connectivity in readers with dyslexia was significantly reduced to primarily adjacent areas in the left VWFA. In a recent report, we (Finn et al., 2014) presented the first whole-brain functional connectivity study of dyslexia. Compared to nonimpaired readers, dyslexic readers showed divergent connectivity within the visual pathway and between visual association areas and prefrontal attention areas, increased right-hemisphere connectivity, reduced connectivity in the VWFA (part of the left fusiform gyrus specialized for printed words), and persistent connectivity to anterior language regions around the inferior frontal gyrus. These findings suggest that nonimpaired readers are better able to integrate visual information and modulate their attention to visual stimuli, allowing them to recognize words on the basis of their visual properties, whereas dyslexic readers recruit altered reading circuits and rely on laborious phonology-based “sounding out” strategies into adulthood. Diffusion tensor imaging (DTI) offers another MRI technique to assess brain connectivity, providing a quantitative measure of brain white matter, the fiber tracts connecting the neurons themselves. Fractional anisotropy (FA) is one of the most commonly measured DTI parameters, with higher FA values often interpreted to indicate more consistent ordering of axons, greater myelination, and/or denser axon packing (for review, see Beaulieu, 2002). A number of studies have reported significant positive correlations between reading ability and FA in a region of left temporal-parietal white matter in children (Beaulieu et al., 2005; Carter et al., 2009; Deutsch, 2005; Nagy, 2004; Niogi, 2006; Odegard, 2009; Qiu, 2008;

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Rimrodt, 2010) and adults (Klingberg, 2000). Tractography studies have also demonstrated relationships between the arcuate fasciculus, a frontal-parietal-temporal white matter pathway, and cognitive scores reflective of reading ability, particularly emphasizing the role of the left arcuate fasciculus (Lebel & Beaulieu, 2009; Qui, Tan, Siok, K. Zhou, & Khong, 2011; Vandermosten et al., 2012; Yeatman et al., 2011). In a recent report (Lebel et al., 2013), we examined correlations between reading and white matter structure (as measured by the diffusion parameter FA) in a large sample of healthy adolescents and young adults (n = 136) with a wide range of reading ability. Three complementary reading scores (word reading, decoding, and reading fluency) yielded positive correlations with FA that spanned bilateral brain regions, particularly in the frontal lobes, but also included the thalamus and parietal and temporal areas. Most of the variance in FA values could be attributed to sight word reading ability.

Attentional Mechanisms in Reading and Dyslexia For almost two decades, the central dogma in reading research has been that the generation of the phonological code from print is modular—that is, automatic and not attention demanding and not requiring any other cognitive process. Recent findings now suggest that attentional mechanisms may also play an important role in reading and dyslexia (Reynolds & Besner, 2006). Recent imaging studies, too, support the role of attentional systems in dyslexia. For example, compared to typical readers, children with dyslexia failed to recruit the left dorsolateral prefrontal cortex (DLPFC) during a phonological task. The DLPFC is long known to play an important role in attention, and this study suggests it plays a role in reading and dyslexia as well (I. Kovelman et al., 2012). Stimulant medications such as amphetamine and methylphenidate have been used for more than 75 years to improve symptoms of attention-deficit hyperactivity disorder (ADHD), probably through mechanisms that influence catecholaminergic systems affecting prefrontal attention systems. Several reports now indicate that stimulants may have beneficial effects on reading in children with both ADHD and dyslexia (Grizenko, Bhat, Schwartz, Ter-Stiepanian, & Joober, 2006; E. Keulers et al., 2007; E. Richardson, S. Kupietz, Winsberg, Maitinsky, & Mendell, 1988). Most recently, we have reported that the norepinephrine uptake inhibitor, atomoxetine, significantly improved reading scores in patients with dyslexia only and ADHD + Dyslexia. (S. Shaywitz et al., in press).

Clinical Manifestations Overview From the very first descriptions over a century ago, dyslexia has always been a paradox: a child or adult with problems in reading yet smart in every other way. In Overcoming Dyslexia, Sally Shaywitz conceptualized this as a weakness in phonology (getting to the sounds of spoken words) surrounded by a sea of strengths in higher-order thinking (S. Shaywitz, 2003). In younger children, there is an encapsulated weakness in decoding surrounded by strengths in, for example, problem solving, critical thinking, concept formation, and reasoning. In older children, adolescents, and adults, it may be thought of as an encapsulated weakness in fluent reading surrounded by these strengths in higher-order thinking. Emphasizing the strengths, some have proposed that dyslexia is a gift. We believe it is best to conceptualize dyslexia as a paradox—individuals who have difficulty getting to the sounds of spoken language that are unexpected given the person’s intelligence, age, and professional accomplishments. Considering what they have had to experience throughout school, for nearly everyone with dyslexia, having dyslexia can in no way be considered a gift or an

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advantage. Recently, lecturing at a dyslexia conference in New Jersey, one of the other speakers told the audience what her 10-year-old daughter with dyslexia told her: “If dyslexia is a gift, can I please give it back?”

Diagnosis In dyslexia, a deficit at the level of the phonologic module impairs the ability to segment the spoken word into its underlying phonologic elements and then link each letter(s) to its corresponding sound(s). As a result, the reader experiences difficulty, first in decoding the word, and then, in identifying it. The phonological deficit is domain-specific; it is independent of other, nonphonological, abilities. In particular, the higher-order cognitive and linguistic functions involved in comprehension are generally intact. As a consequence of this sound-based difficulty in accessing and retrieving the phonological codes, the individual with dyslexia, no matter how intelligent or well educated, will often exhibit difficulties in word retrieval, so that his/her verbal output does not represent his/her abilities and knowledge. Thus, it should not be surprising that problems with spoken language are often observed. These include late speaking, mispronunciations, difficulties with word retrieval, needing time to summon an oral response, and confusing words that sound alike, such as saying recession when the individual meant to say reception (Faust, Dimitrovsky, & Shacht, 2003; Faust & Scharfstein-Friedman, 2003). The clinician seeks to determine through history, observation, and psychometric assessment, if there are (1) unexpected difficulties in reading (i.e., difficulties in reading that are unexpected for the person’s age, intelligence, or level of education or professional status) and (2) associated linguistic problems at the level of phonologic processing. No single test score is diagnostic for dyslexia or, conversely, rules out a diagnosis of dyslexia. In the preschool child, important risk factors for dyslexia include: a history of language delay or early signs of phonological difficulties manifested as poor attention to the sounds of words (trouble learning nursery rhymes or playing rhyming games with words, confusing words that sound alike, mispronouncing words), trouble learning to recognize and to name the letters of the alphabet, and a positive family history of such difficulties or diagnosed dyslexia. In the school-aged child, presenting complaints most commonly center around school performance (“she’s not doing well in school”), and often parents (and teachers) do not appreciate that the reason for this is a reading difficulty. A typical picture is that of a child who may have had a delay in speaking his or her first words, does not learn letters by kindergarten, has not begun to learn to read by first grade, and has difficulty consistently sounding out words. The child progressively falls behind, with teachers and parents puzzled as to why such an intelligent child who seems to grasp concepts so easily may have difficulty learning to read. The reading difficulty is unexpected with respect to the child’s ability, age, or grade. Even after acquiring decoding skills, the child typically remains a slow reader. Thus, dyslexic children, who are often very bright children, may laboriously learn how to read words accurately, but do not become fast or automatic readers. A child’s difficulties become apparent when he or she is asked to read aloud in class, where mispronunciations, omissions of words that are present or, conversely, inserting words that are not on the page, reading with a lack of prosody, and frequent pauses, hesitations, or loss of place are noted. Difficulties involving not only decoding, but also deficits in spelling are also frequently observed. Together, poor spelling and messy handwriting result in difficulties in note-taking in class. As children progress in school, other problems are noted: seeming inability to learn a second language and an avoidance of reading. At all ages, spoken language difficulties are evident (e.g., speech that is not fluent and is replete with hesitations, um’s, and mispronunciations, difficulties with word retrieval, circumlocutions, and the need for time to summon an oral response). Listening comprehension is typically robust (Bruck, 1992; Bryant, MacLean, Bradley, & Crossland, 1990; Catts, 1989; Gallagher, Frith, & Snowling, 2000; Scarborough, 1990; S. Shaywitz, 2003; S. Shaywitz et al., 1999).

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In several studies, older children have been found to improve their reading accuracy over time, albeit without commensurate gains in reading fluency (the ability to read words accurately, rapidly, and with good intonation); they remain slow readers (Bruck, 1990; Bruck, 1998; Lefly & Pennington, 1991; S. Shaywitz et al., 2003). In particular, the refractory difficulty in being able to read fluently is often the hallmark of dyslexia in even the brightest of older children and adults. For example, in an accomplished adolescent or young adult, dyslexia is often reflected by slowness in reading or choppy reading aloud that is unexpected in relation to the level of education or professional status (e.g., graduation from a competitive college or completion of graduate, law, or medical school and a residency). Thus, in bright adolescents and young adults, a history of phonologically based reading difficulties, requirements for extra time on tests and current slow and effortful reading, and signs of a lack of automaticity in reading are the sine qua non of a diagnosis of dyslexia. Self-esteem is frequently affected, particularly if the disorder has gone undetected for a long period of time (S. Shaywitz, 2003). In summary, at all ages, a history of difficulties getting to the basic sounds of spoken language, of mispronunciations, of confusing words that sound alike, of difficulties in word retrieval and a lack of glibness, of laborious and slow reading and writing, of poor spelling, and of requiring additional time in reading and in taking tests provide indisputable evidence of a deficiency in phonological processing, which, in turn, serves as the basis for and the signature of dyslexia.

Assessment Even prior to the time a child is expected to read, a child’s phonological abilities can be evaluated beginning at about age 4 to 5 years. Such tests are centered on a child’s ability to focus on syllables and then phonemes. In general, as a child develops, he or she gains the ability to notice and to manipulate smaller and smaller parts of spoken words (Snow, Burns, & Griffin, 1998; Uhry, 2005). Tests of phonological capabilities and reading readiness are becoming increasingly available, for example, the Comprehensive Test of Phonological Processing, Second Edition (CTOPP-2; Wagner, Torgesen, Rashotte, & Pearson, 2013), which is normed from age 4 through adult. We and other researchers have begun to recognize that one source of potentially powerful and highly accessible screening information that has thus far been ignored is the teacher’s judgment about the child’s reading and reading-related skills. Remarkably, we found that kindergarten and first grade teachers’ response to a small set of questions predict children at risk for dyslexia with a high degree of accuracy, with good sensitivity and specificity. (S. Shaywitz, 2016). These children will then have further assessment and, and if diagnosed as dyslexic, should receive evidence-based intervention. Reading is assessed by measuring accuracy, fluency, and comprehension. In the school-age child, one important element of the evaluation is how accurately the child can decode words (i.e., read single words). This is measured with standardized tests of single real word and pseudoword reading, such as the Woodcock-Johnson IV (WJ-IV; Schrank, McGrew, Mather, & Woodcock, 2014). Because pseudowords are unfamiliar and cannot be memorized, each nonsense word must be sounded out. Tests of nonsense word reading are referred to as word attack. Reading (passage) comprehension is also assessed by the Woodcock-Johnson test. Reading fluency, an often overlooked component of reading, is of critical importance because it allows for the automatic, attention-free recognition of words. Fluency is generally assessed by asking the child to read aloud using the Gray Oral Reading Test-Fifth Edition (GORT-5; Wiederholt & Bryant, 2001). This test consists of increasingly difficult passages, each followed by comprehension questions; scores for accuracy, rate, fluency, and comprehension are provided. Such tests of oral reading are particularly helpful in identifying a child who is dyslexic; by its nature, oral reading forces a child to pronounce each word. Listening to a struggling reader attempt to pronounce each word leaves no doubt about the child’s reading difficulty. In addition to reading passages aloud, single word reading efficiency may be assessed using, for example, the Test of Word Reading Efficiency, 2nd edition (TOWRE-2;

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Torgesen, Wagner, & Rashotte, 2013), a test of speeded oral reading of individual words. Children who struggle with reading often have trouble spelling; spelling may be assessed with the WJ-IV spelling test (Schrank et al., 2014). Because dyslexia is defined as an unexpected difficulty in a child or adult, unexpected in relation to intelligence, it is not surprising that a measure of intelligence such as the Wechsler Intelligence Scale for Children—Fifth Edition (WISC-V) is an important component of a comprehensive assessment. Very often an IQ test can reveal areas of strength, particularly in areas of abstract thinking and reasoning, which are very reassuring to parents and especially to the child. They also indicate that the reading difficulty is isolated and not reflective of a general lack of learning ability. If the clinical picture, including history of spoken language difficulties, difficulties getting to the sounds of written words, problems reading words accurately or fluently, slow reading, trouble with spelling words, despite good intelligence, motivation, and appropriate reading instruction converge with indications of reading and phonological weaknesses in a school-age child, by all measures that child is dyslexic. Because they are able to read words accurately (albeit very slowly), dyslexic adolescents and young adults may mistakenly be assumed to have outgrown their dyslexia (Bruck, 1998; Lefly & Pennington, 1991; Shaywitz, 2003). Data from studies of children with dyslexia who have been followed prospectively support the notion that in adolescents, the rate of reading as well as facility with spelling may be most useful clinically in differentiating typical from dyslexic readers in students in secondary school, in college, and even in graduate school. It is important to remember that these older dyslexic students may be similar to their nonimpaired peers on untimed measures of word recognition yet continue to suffer from the phonologic deficit that makes reading less automatic, more effortful, and slower. Thus, the most consistent and telling sign of a reading disability in an accomplished young adult is slow and laborious reading and writing. The failure either to recognize or to measure the lack of automaticity in reading is perhaps the most common error in the diagnosis of dyslexia in older children and in accomplished young adults. Simple word identification tasks will not detect a dyslexic who is accomplished enough to be in honors high school classes or to graduate from college and attend law, medical, or any other graduate degree school. Tests relying on the accuracy of word identification alone are inappropriate to use to diagnose dyslexia in accomplished young adults; tests of word identification reveal little to nothing of their struggles to read. It is important to recognize that, since they assess reading accuracy, but not automaticity, the kinds of reading tests commonly used for school-age children may provide misleading data on bright adolescents and young adults. The most critical tests are those that are timed; they are the most sensitive to a phonologic deficit in a bright adult. However, very few standardized tests for young adult readers are administered under timed and untimed conditions; the Nelson-Denny Reading Test (Brown, Fishco, & Hanna, 1993) represents an exception. Any scores obtained on testing should be considered relative to peers with the same degree of education or professional training.

Management The management of dyslexia demands a life-span perspective. Early on, the focus is on remediation of the reading problem. As a child matures and enters the more time-demanding setting of secondary school, the emphasis shifts to incorporate the important role of providing accommodations. Based on a prior consensus report from the National Research Council (Snow et al., 1998) and on the results of its own evidence-based analysis, the National Reading Panel (Report of the National Reading Panel, 2000) reported that five critical elements were necessary to effectively teach reading: (1) phonemic awareness (i.e., ability to focus on and manipulate phonemes and

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speech sounds in spoken syllables and words); (2) phonics (i.e., understanding how letters are linked to sounds to form letter-sound correspondences and spelling patterns); (3) fluency (i.e., ability to read accurately, rapidly, and with good intonation); (4) vocabulary; and (5) comprehension. We briefly discuss each of these elements in the following sections.

Decoding Numerous studies over the last two decades have demonstrated that systematic, explicit instruction that focuses on the sound structure of words is more effective than whole word instruction, which teaches little or no phonics or teaches phonics haphazardly or in a by-the-way approach. Largescale studies have focused on younger children, and there are few or no data available on the effect of these training programs on older children. The data on younger children are more encouraging (B. Foorman, J. Brier, & J. Fletcher, 2003; S. Shaywitz et al., 2003; J. Torgesen et al., 1999). Despite such successes, investigators have begun to question whether the current dogma needs to be reevaluated. As reviewed most recently by Compton et al. (Compton, Miller, Elleman, & Steacy, 2014, p. 61), the plethora of intervention studies in dyslexic readers show “significant and lasting improvements in nonword decoding but much less so in real word identification.”

Fluency Fluency represents the next developmental stage in the reading process and bridges word identification and reading comprehension (Zimmerman & Rasinski, 2012, pp. 172–184). It is considered to comprise three components: decoding words accurately, decoding words automatically (rapidly), and oral reading of connected text with appropriate prosody (appropriate expression). The critical importance of fluency is that it enables the reader to deploy attentional and other cognitive resources to comprehend what he or she has read. As noted in the previous section, in diagnosis it is critical to listen to the child read aloud to determine if he or she is reading automatically and with expression. Encouraging children to read more seems to be the most critical element in improving reading fluency.

Comprehension Teaching comprehension has not been as easily implemented as teaching decoding skills and teaching fluency. But just what is reading comprehension? As quoted in Compton et al. (2014) and attributed to Kamhi (2009), reading comprehension is not a skill, but rather “comprises a set of complex higher level mental processes that include thinking, reasoning, imagining, and interpreting.”

Accommodations An essential component of the management of dyslexia in students in secondary school, college, and graduate school incorporates the provision of accommodations. High school and college students with a history of childhood dyslexia often present a paradoxical picture; they are similar to their unimpaired peers on measures of word recognition and comprehension, but they continue to suffer from the phonologic deficit that makes reading less automatic, more effortful, and slower. Neurobiologic data provide strong evidence for the necessity of extra time for readers with dyslexia (see Figure 5.4). Functional MRI data demonstrate that in the word-form area, the region supporting rapid reading functions inefficiently. Readers compensate by developing anterior systems bilaterally and

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the right homolog of the left word-form area. Such compensation allows for accurate reading, but it does not support fluent or rapid reading (B. Shaywitz et al., 2002). For these readers with dyslexia, the provision of extra time is an essential accommodation; it allows them the time to decode each word and to apply their unimpaired higher-order cognitive and linguistic skills to the surrounding context to get at the meaning of words that they cannot entirely or rapidly decode. While readers who are dyslexic improve greatly with additional time, providing additional time to nondyslexic readers results in very minimal or no improvement in scores. Although providing extra time for reading is by far the most common accommodation for people with dyslexia, other helpful accommodations include allowing the use of computers for writing essay answers on tests, access to recorded books (from organizations such as Learning Ally and Bookshare) and text-to-voice software from a number of vendors including Kurzweil, apps such as Pdf to Speech, Go Read, and many others. Other helpful accommodations include providing access to syllabi and lecture notes, tutors to talk through and review the content of reading material, alternatives to multiple-choice tests (e.g., reports or projects), foreign language waivers, and a separate, quiet room for taking tests (S. Shaywitz et al., 2003). With such accommodations, many students with dyslexia are successfully completing studies in a range of disciplines, including science, law, and medicine.

Acknowledgements Sally Shaywitz is the Audrey Ratner Professor in Learning Development and Co-Director of the Yale Center for Dyslexia and Creativity at the Yale University School of Medicine. Bennett Shaywitz is the Charles and Helen Schwab Professor in Dyslexia and Learning Development and CoDirector of the Yale Center for Dyslexia and Creativity at the Yale University School of Medicine. Portions of this chapter are similar to other chapters by us and have appeared whole or in part in Shaywitz and Shaywitz (2014).

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Sally E. Shaywitz and Bennett A. Shaywitz Shaywitz, S. E., Shaywitz, B. A., Wietecha, L., Wigal, S., McBurnett, K., Williams, D., Kronenberger, W. G., & Hooper, S.R. (in press). Effect of atomoxetine treatment on reading and phonological skills in children with dyslexia or attention-deficit/hyperactivity disorder and comorbid dyslexia in a randomized, placebocontrolled trial. Journal of Child and Adolescent Psychopharmacology. Snow, C., Burns, M., & Griffin, P. (Eds.). (1998). Preventing reading difficulties in young children. Washington, DC: National Academy Press. Stanberry, L., Richards, T., Berninger, V., Nandym, R., Aylward, E., Maravilla, K., Stock, P.S., & Cordes, D. (2006). Low-frequency signal changes reflect differences in functional connectivity between good readers and dyslexics during continuous phoneme mapping. Magnetic Resonance Imaging, 24, 217–229. Stanovich, K., & Siegel, L. (1994). Phenotypic performance profile of children with reading disabilities: A regression-based test of the phonological-core variable-difference model. Journal of Educational Psychology, 86(1), 24–53. Torgesen, J., & Mathes, P. (2000). A basic guide to understanding, assessing and teaching phonological awareness. Austin, TX: PRO-ED. Torgesen, J., Wagner, R., & Rashotte, C. (2013). TOWRE-2: Test of word reading efficiency. (2nd ed.). Austin, TX: PRO-ED. Torgesen, J., Wagner, R., Rashotte, C., Rose, E., Lindamood, P., & Conway, T. (1999). Preventing reading failure in young children with phonological processing disabilities. Journal of Educational Psychology, 91, 579–593. Uhry, J. (2005). Phonemic awareness and reading. In J. Birsh (Ed.), Multisensory teaching of basic language skills (pp. 83–111). Baltimore, MD: Paul H. Brookes. Van den Heuvel, M., & Hulshoff, P. H. E. (2010). Exploring the brain network: A review on resting-state fMRI functional connectivity. Eur Neuropsychopharmacol, 20, 519–534. van der Mark, S., Klaver, P., Bucher, K., Maurer, U., Schulz, E., Brem, S., . . . Brandeis, D. (2011). The left occipitotemporal system in reading: Disruption of focal fMRI connectivity to left inferior frontal and inferior parietal language areas in children with dyslexia. Neuroimage, 54, 2426–2436. Vandermosten, M., Boets, B., Poelmans, H., Sunaert, S., Wouters, J., & Ghesquiere, P. (2012). A tractography study in dyslexia: Neuroanatomic correlates of orthographic, phonological and speech processing. Brain: A Journal of Neurology, 135(Pt3), 935–948. Vogel, A., Miezin, F., Petersen, S., & Schlaggar, B. (2012). The putative visual word form area is functionally connected to the dorsal attention network. Cereb Cortex, 22, 537–549. Wagner, R., Torgesen, J., Rashotte, C., & Pearson, N. (2013). CTOPP-2: Comprehensive test of phonological processing. (2nd ed.). Austin, TX: PRO-ED. Wiederholt, J., & Bryant, B. (2001). GORT-4 examiner’s manual. Austin, TX: PRO-ED, Inc. Willcutt, E., Pennington, B., Olson, R., Chhabildas, N., & Hulslander, J. (2005). Neuropsychological analyses of comorbidity between reading disability and attention deficit hyperactivity disorder: In search of the common deficit. Developmental Neuropsychology, 27, 35–78. Woodcock, R., & Johnson, M. (1989). Woodcock-Johnson psycho-educational battery—revised (WJ-R). Allen, TX: Developmental Learning Materials. Yeatman, J., Dougherty, R., Rykhlevskaia, E., Sherbondy, A., Deutsch, G., & Wandell, B. (2011). Anatomical properties of the arcuate fasciculus predict phonological and reading skills in children. Journal of Cognitive Neuroscience, 23(11), 3304–3317. Zimmerman, B., & Rasinski, T. (2012). The fluency development lesson: A model of authentic and effective fluency instruction. (2nd ed.). New York: The Guilford Press.

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PART II

Bases of Child Language Disorders

6 LINGUISTICS IN CHILD LANGUAGE DISORDERS Richard G. Schwartz, Irena Botwinik-Rotem, and Naama Friedmann

What does a person know when she speaks and understands her native language? Traditional linguistics described inventories of sounds, words, and sentences observed in a language. Generative Linguistics (Chomsky, 1957, 1965, 1981, 1986b, 1995) shifted the focus to the description of the native speaker’s intuitive knowledge of language. Linguists conceive of grammar as a set of rules that reflects the ideal native speakers’ ability (i.e., competence) to utter or understand all the possible elements in their language. Linguistic theory addresses four major questions (the first three are adapted from Chomsky, 1986b) alone or in concert with psycholinguistics, developmental psycholinguistics, or clinical psycholinguistics: 1. 2. 3. 4.

What constitutes knowledge of language? How is knowledge of language used? How is knowledge of language acquired? How does language develop atypically or break down?

Linguistic theories address a number of language domains, including phonology, morphology, semantics, pragmatics, and syntax. We will first consider what is meant by language knowledge, then briefly review the domains of language, along with some of the relevant linguistic theories in each of these areas, with a greater focus on the generative characterization of syntax. We will also consider various views of language acquisition and development. Throughout, we will consider how linguistics has been used to describe or explain language disorders.

Knowledge of Language Knowledge of language is not the explicit grammar taught in school. Rather, it is the largely unconscious knowledge used by the native speakers of a language to judge, for example, that sentence (1) is a grammatical sentence of English while (2) is not (an * indicates ungrammatical sentences); that despite being very similar, sentence (3) is ambiguous (who holds the binoculars?), whereas (4) is not, and that in (5) the pronoun he can refer to the same individual as Homer, though the same is impossible in (6). In sentence examples, the intended reference is usually symbolized by a letter or a number subscript, and identical indices indicate co-reference. In (5) and (6) the

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co-reference between the pronoun he and the intended antecedent Homer is symbolized by the shared subscript index1; it is possible in (5), but not in (6). (1) (2) (3) (4) (5) (6)

Lisa is sitting in the garden. *Lisa are sitting in the garden. Marge saw the girl with the binoculars. Marge loved the girl with the binoculars. When he1 is tired Homer1 takes a nap. *He1 thinks that Homer1 is clever. (Intended meaning: Homer1 thinks that he1 is clever.)

Given the unconscious nature of this knowledge (i.e., competence), it cannot be observed or studied directly. Thus, judging sentences (5) correct and (6) incorrect, while a conscious behavior, may not be based on conscious knowledge of why they are correct or incorrect. We can, however, observe speaker-listeners’ linguistic performance and infer their underlying linguistic competence, the knowledge that guides their linguistic behavior. Performance is influenced by various nonlinguistic factors (e.g., attention or working memory limitations, production errors such as slips of the tongue), giving rise to a variety of ungrammatical sentences, speech or lexical errors, as well as perception or comprehension errors. Such errors can reveal information about underlying knowledge. The number of grammatical sentences in any human language is infinite (Chomsky, 1957, 1965). It seems unlikely that humans are capable of storing all potential sentences of the language. Thus, linguistic theory posits a finite system of principles or rules that enables humans to construct and interpret a potentially infinite number of sentences. This finite system of principles is the internal grammar of a language or its knowledge.

Phonology Phonology is concerned with the sounds of language and the knowledge that underlies their systematic occurrence. Historically, phonology focused on the distribution of consonants and vowels that did (phonemes) or did not (allophones) change meaning in a given language. And phonology was distinct from phonetics. Theoretical phonology has come to encompass larger units such as syllables, morae (i.e., units that determine the weight of a syllable, which in turn determines timing or stress depending on the language), other prosodic units (e.g., phonological words or phrases), and smaller units such as syllable components (onset and rhyme) or phonetic characteristics (features). An early theory of phonology proposed by linguists from Prague (Trubetzkoy, 1969) laid the groundwork for many of the theories that followed in proposing the phoneme as the smallest unit composed of phonological features separate from phonetics as well as an interface with morphology. In 1968, Chomsky and Halle (Chomsky & Halle, 1968) presented a generative theory of phonology in which underlying phonological forms (segments composed of distinctive features) were transformed into surface forms via a limited set of rules along with a system of distinctive features. The model, called the Sound Pattern of English, was soon joined by a number of theories that addressed perceived deficits in the Sound Pattern of English. For example, in Natural Phonology (Stampe, 1973), a universal set of processes, which reflect natural tendencies (e.g., final consonant devoicing), are applied to underlying forms. The active processes are language specific. In the 1970s and 1980s, Natural Phonology was widely used to describe phonological development and disorders using processes to describe errors (e.g., Ingram, 1976; Shriberg & Kwiatkowski, 1980). In autosegemental or nonlinear phonology (e.g., Goldsmith, 1979, 1990), rather than a single linear sequence of segments, features are represented on various levels (tiers), with some parallel

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sequences of features. This model has also been applied to children’s phonological development and disorders (e.g., Bernhardt & Stemberger, 1998). Optimality theory (OT; Prince & Smolensky, 1993) was another major step in the evolution of phonological theories. OT posits a universal set of potentially conflicting constraints, which are ranked in order of importance for each language. Phonology is thought to take an input (underlying representation), generate all possible outputs, compare the outputs to the language-specific ordered set of universal constraints, and then evaluate which output is optimal (violates the fewest and least important constraints). It bears some relation to connectionist models (see Chapter 11 by Joanisse). OT entails a proposal regarding phonological acquisition; acquisition, at least in part, involves adjusting the hierarchy of constraints. Traditionally, phonetics and phonology have been viewed as separate, with phonology representing abstract underlying knowledge and rules, and phonetics being the motor or acoustic instantiation of this knowledge. New approaches, such as laboratory phonology (Pierrehumbert, Beckman, & Ladd, 2000), reject a dichotomy of abstract underlying knowledge and human performance in speech perception and production. Theoretical phonologists have also examined the interface between phonology and morphology (e.g., Kiparsky, 1982; McCarthy & Prince, 1986) as well as syntax (e.g., Selkirk, 1984). Some of the prosodic features of this interface may explain some of the phonological and morphosyntactic errors in children with language impairments (e.g., Gerken & McGregor, 1998).

Morphology Morphology is the branch of linguistics concerned with the smallest meaningful units of language including words, which are free-standing morphemes, as well as inflectional affixes and derivational affixes, which are bound morphemes. Morphology is particularly important because it serves as a structural interface between words and syntax and between phonology and syntax. Words can be grouped into two general classes, content words (sometimes referred to as open class) and function words (sometimes referred to as closed class). Content words include nouns and verbs. Function words have grammatical roles (e.g., prepositions, articles, conjunctions, pronouns, etc.). They are often prosodically weaker than open class words and are a more fixed set in that there are rarely innovations as languages change and add new content words. Morphologically complex words consist of a root (or base) and one or more affixes (e.g., teach/teacher, act/react, wash/washable, pragmatic/pragmatically, depict/depiction). In some languages (e.g., Hebrew), roots consisting of three consonants and vowels are inserted as infixes to create related word forms. Some roots are bound and, thus, cannot occur without their affixes (e.g., function). There are rules for word formation that lead to systematic changes in grammatical class (e.g., verb to noun, teach/teacher). The other type of bound morphology is inflectional morphology, sometimes referred to as morphosyntax. This too involves formation rules. In English, inflectional morphology includes verb tense, person, number; past participle markers on verbs; possessive and plural markers on nouns; and comparative and superlative on adjectives. English has very limited inflectional morphology compared to many languages that have a larger number of verb tense markers as well as gender, number, and case markings on nouns and, in some cases, on adjectives and pronouns. In rich morphological languages, such as Hungarian or Turkish, much of the structural information in sentences is encoded in inflectional markings, and many inflections may be added. Inflections can be regular (i.e., follow consistent rules such as the English past tense -ed with phonetic modifications depending on the last sound of the root—jump/jumped) or irregular (e.g., run/ran), which may fall into groups of words that follow certain patterns, but don’t follow a consistent rule across words. Inflectional morphology is particularly relevant for the current volume as morphosyntactic deficits are a core occurrence in the profile of childhood language impairments. These impairments vary depending

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on the characteristics of the language (see Chapter 11 by Joanisse; Chapter 12 by Peña, Bedore, & Baron; and Chapter 13 by Leonard). Like other areas of linguistics, theories of morphology have evolved, including a triad that were respectively, morpheme-based, lexeme-based, and word-based. Morpheme-based theories arose from early descriptive approaches to linguistics in which morphologically complex words (and sentences for that matter) were viewed simply as sequential combinations of morphemes that included meaning, regardless of whether they were roots or affixes. Lexeme-based morphology assumed that the morpheme is not actually the basic unit. Instead, lexemes (all the forms of a given word—e.g., run/runs) were the basic unit, whether they were derived or not, and affixes or any other phonological operations simply mark the processes undergone by that item. Variants of this view (e.g., Distributed Morphology) maintained the focus on morphemes, but eliminated the distinction between morphology and syntax (Halle & Marantz, 1993), so that similar generative rules were applied. Notably, it shifted the focus from the word to other parts of the grammar. This perspective is particularly relevant to aspects of language that are often impaired in children with language disorders. For example, in English-speaking children with SLI, the issue may not simply be the failure to produce a morpheme such as third-person singular, but a failure to maintain subject-verb agreement or appropriately marked tense in the context of sentences (see Chapter 13 by Leonard and Chapter 15 by Oetting & Hadley). The final group of morphology theories (e.g., Aronoff, 1976) proposed that all regular word-formation rules are word-based. Nonword stems (e.g., receive, conceive) are not formed by rules. A variant of this proposal is termed word and paradigm, adopting a method that was long present in morphological description, in which word-based paradigms are formed for activation. This approach was adopted by Pinker (1984) to characterize morphosyntactic development and was further adapted by Leonard (1989) to explain morphosyntactic deficits in children with SLI.

Semantics Semantics is concerned with meaning in language at various levels including words, phrases, sentence, or discourse (texts or narratives). Lexical semantics considers word meaning and scope, relations among items in the lexicon (synonyms, antonyms, homonyms), hierarchical relationships (hyponymy) among words (e.g., animals/dog, cat, cow, etc.), and relations among items in a category (e.g., birds) organized around a prototype (most typical member, e.g., bird). Although these are all important considerations, the greatest challenge in semantics is the description of how these word meanings combine in sentences, compositional semantics. One approach has been to conceptualize compositional meanings in terms of set theory. For example, the noun phrase brown dog is characterized as the intersection of the set of things that are brown and things that are dogs. These set theory-based descriptions can become more complex in phrases such as some girls play baseball and even more complex as more sentence elements are added. This approach focuses on one aspect of meaning, termed referential or extensional meaning. This general approach is termed Montague Grammar. This approach does not consider context, which is another important part of meaning, sense, or intention. This aspect of semantics focuses on the truth value of sentences in relation to context, as well as what is termed possible worlds semantics. This can also be modelled mathematically. Thus, compositional semantic theory in its present form can be quite abstract. Nevertheless, there are practical consequences for certain categories of words referring to time, quantity, and qualities that can be characterized as scalar (e.g., never, always sometimes; all, some; tall, short). Scalars are understood both in terms of their meaning and their implicature in relation to a context and to a listener. This latter aspect of meaning is the concern of pragmatics.

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Though not initially, generative grammar sought to incorporate aspects of semantics into the grammar, first with a set of innate features that coded the presence or absence of basic qualities (e.g., ± animate, ± human). In more recent generative models, aspects of semantics are addressed as thematic roles (e.g., agent, experiencer, location), which then correspond to argument structure of verbs (e.g., how many noun roles a verb takes on) and are in turn related to subcategorization frames that specify the general sentence structure for given verbs. These topics are discussed in the section on syntax.

Pragmatics Pragmatics is concerned with language usage in context and the contribution of context to meaning. As such, it includes speech act theory (Austin, 1962; Searle, 1969), rules of conversation (Grice, 1989), and meaning beyond what is stated (implicature). Speech acts can be characterized as having three general levels: (1) locutionary, (2) illocutionary, and (3) perlocutionary. The locutionary act is simply the utterance itself, its ostensible meaning, its phonetics, phonology, syntactic structure, and semantic meaning. The illocutionary act is the intended pragmatic function of the utterance (e.g., making an assertion, directive, etc.). One notable illocutionary act is the performative; this includes utterances that on their own perform an act (e.g., I sentence you to 10 years; I promise you. . .; I nominate you to serve. . .). Finally, many utterances involve a perlocutionary act, which is the intended or unintended effect of the utterance on the listener (e.g., persuade, embarrass, scare). These effects are external to the utterance Searle (1969). In general, conversation is governed by a cooperative principle, which states: “Make your contribution such as it is required, at the stage at which it occurs, by the accepted purpose or direction of the talk exchange in which you are engaged” (Grice, 1989, p. 26). Grice also proposed four categories of rules or maxims for conversation: Quantity (Information) Make your contribution as informative as is required for the current purposes of the exchange. Do not make your contribution more informative than is required. Quality (Truth) Do not say what you believe to be false. Do not say that for which you lack adequate evidence. Relation (Relevance) Be relevant. Manner (Clarity) Avoid obscurity of expression. Avoid ambiguity. Be brief. Be orderly. (Grice, 1989, pp. 26–27) Grice also explored various kinds of implicatures, meaning beyond what is stated that can be interpreted accurately only in terms of the context. Conversational implicatures generally involve some violation of the above maxims. An often-cited example from Grice is a letter of recommendation for an academic position in which the recommender comments on qualities that are not relevant to the position (e.g., John is an excellent speaker of English, who comes to class on time), implying that John is not a good candidate without stating that explicitly. Similarly, irony and sarcasm violate

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maxims, and listeners must use prosody and context to interpret the meaning beyond what is actually stated. Implicatures can also occur as an interface between semantics and pragmatics for scalar or temporal terms. Pragmatic deficits occur across populations of children with language disorders (see Chapters 1, 2, 3, and 4).

Syntax Syntax examines the formation of sentences across languages, defining the workings of the Computational System. In the following sections we will focus on the generative description of syntax, present some central aspects of the syntactic theory, and illustrate their contribution to the research of language acquisition and language impairments. Sentences are produced and perceived as linear strings of words from our mental lexicon, but words in a sentence are organized into larger units, referred to as phrases. Consider sentence (7): (7) A very talented actress liked the movie with Tim Robbins. We have a clear intuition that the words very and talented belong together, a very talented actress is a unit, as is the movie with Tim Robbins. Compare (7) with a slightly different (8): (8) A very talented actress went to the movie with Tim Robbins. Sentence (8) is ambiguous. Either Tim Robbins is in the movie or the actress went with Tim Robbins to see some movie. In the second reading, with Tim Robbins is related to went but not to movie. This fact cannot be expressed by a linear description, as with Tim Robbins is not adjacent to the verb. This is exemplified in the structures in (9) and (10), which describe the relations between the parts of the sentence.

(1) went to the movie

with Tim Robbins

(2) went to the movie

with Tim Robbins

What enables us to interpret (8) in two different ways—to break it into different kinds of phrases, and why is this impossible in (7)? Put differently, what guides the formation of the phrases in a given sentence? In order to answer this question, it is necessary to specify the lexical information associated with words that is relevant for the syntactic component.

Syntactic-Semantic Information in the Lexicon Categories of Words The category N(oun) includes words denoting objects or events (e.g., table, child, Italy, destruction, examination); members of the category V(erb) denotes events and states (e.g., drink, run, know, love); words modifying nouns (e.g., tall, beautiful, red) belong to the lexical category A(djective); and

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those modifying verbs and sentences are classified as Adv(erb) (e.g., happily, peacefully, yesterday, often, fortunately). These categories (N, A, V, Adv) each include many members and can admit new formations (e.g., downloadable, emailed, online, etc.). The number of grammatical words is limited in each of the various categories. Grammatical words specify the function or the status of the appropriate lexical categories, and therefore their categories are referred to as functional lexical categories. The category D(eterminer) is composed of definite and indefinite articles, demonstratives (e.g., this, these), and quantifiers (e.g., the, some, every, three), namely elements performing some function with respect to Ns. (Pronouns like he, they, our, etc., are also analyzed as members of the D category.) Another functional category is I(nflection), which includes auxiliary verbs (do, is, have), modals (can, will, shall), and the infinitival marker to, namely elements associating the verbs with tense or aspectual specification (e.g., past, future, present, perfective, progressive), and agreement features (Person, Number, and Gender). The category Deg(ree) includes words like very, too, enough determining the status of As and Advs. P(reposition)s, including on, in, near, before, above, and during, express a relation between two entities (e.g., a book about elephants, drinking before driving). The classification of P as a functional category is controversial; some linguists view the category P as a lexical category, on a par with N and V, whereas others consider it to be composed of both lexical and functional members. Conj(unction) (and, or) combines and groups elements. Members of C(omplementizer), such as that, whether, and if specify the force of sentences (e.g., declarative, interrogative, subjunctive, etc.). In some linguistic texts, the category I is termed AUX(iliary), and C is COMP. In the process of language acquisition a child learns the words of her/his language and the category to which they belong.

Thematic Structure and Subcategorization Frames In addition to their lexical category, the lexical entry of verbs (and certain nouns) includes another kind of information that dictates their incorporation into sentence structure. Specifically, verbs are associated with their subcategorization frame and thematic structure. The traditional division of verbs into intransitive, transitive, and ditransitive relates to the number of complements a verb takes. Some verbs, like sneeze, take no complement, as shown in (11). These are the intransitive verbs. Others, the transitive verbs, take one complement, like found in example (12), and there are verbs that take two complements, like gave in (13), referred to as ditransitive verbs. The examples illustrate that lexical entries include, in addition to the lexical category (V, in these cases), the number of phrasal categories that the verb takes as complements, and their type: NP (noun phrase), PP (prepositional phrase), etc. The square brackets include an underscore (_),which symbolizes the verb’s location and the types of complements that follow it, if any. The information regarding the number of complements and their type is called the subcategorization frame of the verb. Lexical entries

(11) Esther sneezed. (12) Esther found an elephant. (13) Esther gave an apple to the elephant.

sneeze: [V] [ _ ] found: [V] [ _ NP] gave: [V] [_NP PP]

There are transitive or ditransitive verbs, like eat or sell, respectively, that optionally realize one of their complements (14). Optional complements are specified in the lexical entry of the verbs in parentheses (e.g., eat: [V] [_ (NP)]):

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(14) a. Dan ate (ice cream). b. Lisa sold her car (to Bart). Complements, thus, define the various classes of verbs and are considered as part of the speaker’s/ listener’s lexical knowledge of verbs as part of the verb’s lexical entry. However, other types of phrases, like those denoting place or time, can be added to (almost) any verb (in square brackets in the examples under (15)). Because lexical entries include only unpredictable, entry-specific information, such phrases, termed adjuncts, are not part of the lexical entry of verbs. Thus, in (15b), an elephant is a complement and therefore part of the lexical entry of found, whereas in the kitchen is an adjunct that can be added to any verb and therefore, it does not need to be specified in the lexical entry of found. (15) a. Esther sneezed [in the kitchen]/[before breakfast]. b. Esther found an elephant [in the kitchen]/[before breakfast]. c. Esther gave an apple to the elephant [in the kitchen]/[before breakfast]. The subcategorization frames of verbs are related to the nature of the events associated with the verb. This determines the necessary number of the participants, as well as their role in the event. Verbs can be characterized by their predicate-argument structure (PAS). Intransitive verbs (e.g., sneeze) denote events with one participant (the “sneezer”) and are referred to as one-place predicates. In the events denoted by transitive verbs (e.g., found), there are two necessary participants (e.g., “finder” and “the thing being found”), hence these are two-place predicates. Finally, ditransitive verbs like give describe events with three necessary participants (“the giver”, “the thing given”, “the recipient”) and are referred to as three-place predicates. The roles performed by the various participants in an event are called thematic roles (also known as theta roles, or θ-roles), and they include the following types: Agent, Cause, Theme, Goal, Experiencer, Location, Source. The required participants are referred to as the arguments of the verb. Agent is the role of the participant that performs some action or brings about some change (e.g., Esther in (16–18)). A participant serving as the Theme undergoes the action or the change (e.g., an elephant and an apple in (17) and (18), respectively). In an event denoted by a verb of motion or transfer (e.g., give), the participant that is the target of the transfer or motion receives the role of a Goal (e.g., (to) the elephant in (18)). (16) Esther[Agent] sneezed. (17) Esther[Agent] found an elephant[Theme]. (18) Esther[Agent] gave an apple[Theme] to the elephant[Goal]. The verb loved in (19) denotes a state, one of whose participants (Mary) experiences an emotion (of love), rather than performing some action. This participant’s role is the Experiencer. In the event expressed by a verb like put (20), the locative phrase on the table serves the required thematic role of Location (*Lisa put the book). These location phrases are complements, rather than adjuncts for verbs like put or lived. The verb bring in (21) includes the Source participant (from the office). Finally, as shown in (22), the participant bringing about some change does not have to be conscious (the storm). Such a participant is termed the Cause, rather than the Agent. (19) (20) (21) (22)

Mary[Experiencer] loved her dog[Theme]. Lisa[Agent] put the book[Theme] on the table[Location]. Homer[Agent] brought a pen[Theme] from the office[Source]. The storm[Cause] destroyed the town[Theme].

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An example of the theta-structure (or theta-grid) of the verb give is shown in (23): (23) give: [V] 1 | 2 | 3 (necessary arguments/participants) Agent| Theme| Goal (specification of the thematic roles of the arguments) The theta-grid of a verb subsumes its subcategorization frame in most cases. Typically, there is no need to specify the phrases of its complements once the theta-grid is given. In the above examples, Theme is realized as a nominal phrase (NP), whereas Goal, Source, and Location are realized as prepositional phrases (PPs), but this is not always the case. For example, verbs like trust, depend, and claim assign a Theme. Trust takes an NP complement (She trusts Mary), but depend takes a PP complement (She depends on Mary), and claim takes an embedded sentence (She claims that Dave plays beautifully). Note also that the Goal argument of give can be an NP, as in (24), rather than a PP. Thus, at least for some verbs, the syntactic specification of their complement is necessary, as it is not predictable from the thematic role of the relevant argument (as shown in (25) for give). Verbs like depend on, believe in, or look at, which have an obligatorily prepositional complement, present a further complication. Because the identity of the preposition is verb-specific, it has to be included in the lexical entry of these verbs (Botwinik-Rotem, 2004). (24) Mary[Agent] gave the elephant[Goal] an apple[Theme] (25) give: [V] 1 | 2 | 3 (necessary arguments) Agent| Theme| Goal (specification of the thematic roles of the arguments) NP | NP | NP/PP (realization of the arguments) Finally, some verbs are associated with more than one theta-grid or subcategorization frame. Take, for instance, a verb like know. It can occur with an NP complement (26a), a PP complement (26b), a clausal complement denoting an embedded question (26c), or a clausal propositional (declarative) complement (26d). Assuming that the second thematic role of know is invariably Theme, its various syntactic realizations should be specified in the lexical entry of know, yielding multiple subcategorization frames. Alternatively, the different syntactic realizations may indicate that the internal role of this verb is not uniformly Theme. For example, when it is an NP, the role is Theme, and when it is a clause, its role is a Proposition (Grimshaw, 1979, 2005). Consequently, the lexical entry of a verb may include several different theta-grids. (26) a. Claudio knew [the answer]. [__NP] b. Claudio knew [about the corruption of the government]. [__PP] c. Claudio knew [where Broca’s area is]. [__CP[+Q]] d. Claudio knew [that Raffaella loves red clothes]. [__CP[proposition]]

Predicate Argument Structure: Psychological Reality and Language Disorders The complexity of the predicate argument structure and of the subcategorization frame has an effect on the speed of access to the lexical entry of a verb (Shapiro, Brookins, Gordon, & Nagel, 1991; Shapiro, Gordon, Hack, & Killackey, 1993; Shapiro & Levine, 1990; Shapiro, Zurif, & Grimshaw, 1987). The complexity of the verb’s argument structure, defined by the number of thematic options, affects lexical access. The more argument structure options a verb has, the longer it takes to access it in the mental lexicon. The number of complements (0, 1, or 2 complements, see (19–21)), unlike the number of options for complements, did not show an effect on lexical access

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to the verb. This effect was found even when subjects listened to sentences that were structurally biased towards one particular argument structure. For example, even when presented with the sentence I met the girl that Claudio knew, in which the verb clearly takes an NP Theme, the subjects still activated all possible argument structure options for knew (Shapiro, Zurif, & Grimshaw, 1989). This occurs regardless of sentence type (Shapiro, Gordon, Hack, & Killackey, 1993), and only at the vicinity of the verb (Shapiro & Levine, 1990). Some adults who have language impairments, such as individuals with Broca’s aphasia, appear to have intact predicate argument structure knowledge. They show the same effect of verb complexity as typical adults, although their syntactic abilities are impaired. The same is true for children with SLI (Kenan, 2007). However, adults with Wernicke’s aphasia do show impairment in predicate argument structure; they do not exhibit the normal predicate argument structure complexity effects in lexical access (Shapiro & Levine, 1990). Data from brain imaging also support the psychological reality of predicate argument structure representation, the complexity of multiple thematic roles, and the relation of predicate argument structure to Wernicke’s area. Shetreet, Palti, Friedmann, and Hadar (2006) investigated the patterns of brain activation associated with the increasing number of subcategorization and argument structure options. They report graded activation for increasing number of argument structure/ subcategorization options in three brain locations in the left hemisphere, in the left-superior temporal gyrus, a part of Wernicke’s area, and in two areas in the left-inferior frontal gyrus: in BA 47 and in BA 9, areas that are believed to be involved in semantic memory. The thematic structure and the subcategorization frame that are specified in the lexical entry of the verb are the basis for syntactic structures. Specifically, syntactic structures are formed according to the Projection Principle (Chomsky, 1981), which states that the lexical properties of the verb have to be reflected in the sentence structure. A more explicit version of the Projection Principle is the Theta Criterion (Chomsky, 1981), which specifies how this is achieved; the verb assigns all of its thematic roles to the appropriate phrases in the sentence, and all of the appropriate phrases in the sentence receive thematic roles from the verb.

Hierarchical Sentence Structure As mentioned, sentences are hierarchically organized combinations of phrases. Consider the simple sentence in (27). In the tree diagram (28), the noun rain and the D the form a noun phrase (NP) the rain. The preposition about forms a prepositional phrase (PP), about the rain. The noun song forms another NP, a song about the rain. Rain can form an NP with the determiner the, and song can form a noun phrase with the PP about the rain. The N Jane forms a noun phrase (NP) by itself (29). (27) Jane sang a song about the rain. (28)

NP

D

N’

a PP

N

P’

song

P

NP

NP

PP P’

P

about

NP

about

D

N’

D

N’

D

the

N

the

N

the

rain

rain

N’ N

rain

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(29)

NP N’ N

Jane The lexical categories (e.g., N, P, V) that form the phrasal categories (e.g., NP, PP, VP) are referred to as the heads of the phrases. They also determine the phrase: a head noun creates a noun phrase; a head verb creates a verb phrase. Put differently, the phrases are projections of their heads. Thus, phrases are built around a head from the same lexical category. They also have a uniform inner structure referred to as the X-bar (Chomsky, 1981, 1986a; Jackendoff, 1977). (X stands for any of the potential heads: N, V, P, A, I, C.) The complements of the head are attached (or merged) to the head and form with it an intermediate phrasal level, referred to as the bar-level (symbolized as N’, V’, P’, etc.) (e.g., the PP about the rain in (27) is the complement of the N song). Phrases or elements that are not the complements of the head occupy the specifier position (spec), whose attachment to the intermediate level completes the formation of the phrase (e.g., the determiner the in the rain in 28). This is a rough summary of the X-bar theory (Radford, 1988). XP

Specifier

X’ intermediate phrase

X head

(complement)

Although the hierarchical structure of the phrases is assumed to be universal, the linear ordering between the head and its complement, as well as between the intermediate level and the specifier position, is language-specific. The latter is viewed as an option in a parameter that determines word order across languages (e.g., Does the head precede its complement? Yes/No). For example, sentences like John ate an apple occur in languages in which the head (here, the verb ate) precedes the complement (here, the NP the apple), whereas languages in which the head follows the complement have sentences like John an apple ate. To complete the structure of (28), Jane sang a song about the rain, both NPs are the arguments of the verb sang (i.e., they represent the necessary participants in the event denoted by the verb). The NP a song about the rain is merged as the complement of V and is assigned the thematic role Theme. Arguments that are complements of the verb are referred to as the internal argument of the verb. The NP Jane is combined with the V’ (i.e., it is inserted in spec of VP), it is assigned the thematic role Agent, and it is referred to as the external argument of the verb. This is not the final position of this NP. Importantly, the association of the verb with its arguments, namely the assignment of

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the verb’s thematic roles to the relevant syntactic phrases, is what underlies the formation of the VP. The assignment of some thematic roles can take place within the VP. In English, French, or Hebrew, the verb precedes its complement, and the internal theta-role is assigned to a position after the verb. In languages like Japanese or German, the verb follows its complements. Accordingly, the internal theta-role of the verb is assigned to a position before the verb. For simplicity’s sake, we will not specify the details of every phrase. When the inner structure of some phrase is not relevant, a triangle is used instead of specifying its exact structure (like the rain in (30)). (30) The formation of VP VP

NP

V’

N’ N

V

Jane

sang

NP

D

N’

a N

PP

song

P’

P

NP

about

the rain

The syntactic tree includes two additional functional layers above the VP: the inflectional layer (IP), which is immediately above VP, and the complementizer layer (CP), which is the highest layer in the tree. The head of the IP, I(nflection), is related to the tense inflection of the verb and its agreement to the subject (in person, gender, and number). It carries the tense of the sentence, either by hosting the modals and auxiliaries (e.g., will, has), the particle to, or, in sentences where no overt tenseelement is present, the functional head I is assumed to carry abstract tense and agreement features (in our case, [+past], [3rd person, feminine, singular]). The specifier of IP is the structural position of the subject of a sentence (31). To reach this position, the subject moves from its original position within the VP into spec-IP (Movement is discussed in the following section.). The claim that the argument serving as the subject of the clause is merged in the VP, rather than being merged directly into the subject position, spec-IP, is known as The VP-internal Subject Hypothesis (Koopman & Sportiche, 1991).

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(31) The formation of IP

The formation of CP CP

C’

IP

C

IP

I’

Jane1

I’

Jane1 I

I

VP

V’

NP

V’

NP

VP

N’

N’ N

V

t1

sang

NP

D

N

V

t1

sang

NP

D

N’

N’

a

a N

PP

N

PP

song

P’

song

P’

P

about

NP

P

NP

the rain

about

the rain

The structure of a sentence is not complete until its force is specified (i.e., whether it is an interrogative ([+Q(uestion)]) or a declarative sentence ([-Q])). This function is performed by the functional head C, which assigns the feature [-Q] in (33) to the IP. C also enables the embedding of the sentence in other sentences. Like the functional head I, C can be realized by overt morphemes, as in embedded English clauses: John said that Lisa won; John asked whether Lisa won. As seen in (31), the combination of the IP with C forms a CP, the phrasal category of a sentence. The CP plays an important role in various constructions: in addition to embedding markers like that and whether, it also hosts Wh-morphemes in Wh-questions (like “Who sneezed?”), verbs and auxiliary verbs when they move to the beginning of the sentence (“When did Bill sneeze?”), and noun phrases that move to the beginning of the sentence in topicalized sentences (like “This boy, the doctor will see right away”), as will be discussed in detail. The syntactic tree, its hierarchical structure, and the ability to project it all the way up to the functional categories IP and CP are all key parts of generative explanations of language acquisition and syntactic deficits in adults and children. A number of researchers Guilfoyle and Noonan (1988), Lebeaux (1988), Platzack (1990), and Radford (1990, 1992, 1995), have argued that the syntactic tree of young children does not include any functional categories, and that the tree is composed only of VP. Others, like Clahsen (1990–1991), have suggested a single functional projection (which they term FP) above VP. Yet others suggest that the whole tree, with IP and CP, already exists very early on (Boser, Lust, Santelmann, & Whitman, 1992; Déprez & Pierce, 1993; Poeppel & Wexler, 1993; Weissenborn, 1990). Rizzi’s (1994) truncation hypothesis (also called the root infinitive hypothesis), suggested that children have knowledge of the whole tree, but that they still do not know that it should be obligatorily projected all the way up to CP, and therefore sometimes project the tree only partially. Explanations for the syntactic deficits in SLI vary (see Chapter 1 by Schwartz), but none of the main theories ascribes the deficit to the inability to project the high nodes of the syntactic tree.

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Leonard (1995, 2015) presented convincing evidence showing that the syntactic tree is present in children with SLI. In deaf children, a detailed description of syntactic abilities (de Villiers, de Villiers, & Hoban, 1994) suggested that the central syntactic deficits in orally trained children with hearing impairment are the functional categories IP and CP.

Movement Dislocation of the various syntactic elements (i.e., phrases or heads) is a basic and universal property of human languages (Chomsky, 1986b). Consider the pairs in (32) and (33): (32) a. John will clean [the orange house]Theme b. [Which house]Theme will John clean _? (33) a. John cleaned [the room]Theme b. [The room]Theme was cleaned __ (by John) What is common to the constructions in (32b) and (33b) is that the sentence-initial phrases (which house, the room) act as the Theme of the verb clean, as they do in the corresponding (a) sentences. Given that the verb can assign its thematic roles only within the VP, and that Theme is assigned to the complement of the verb, the question arose of how the Theme is associated with the sentenceinitial phrases in (32b) and 33b). This led linguists to suggest that such sentences involve movement of these phrases from their original position, the complement of V, to another position in the syntactic structure. We describe the mechanism that associates the moved phrases with their thematic role, as well as the positions to which they move.

Movement Types There are several types of syntactic movement. The first distinguishes between movement of phrases (e.g., NPs or PPs) and movement of heads (e.g., V or N). The former is called phrasal movement and the latter head movement. Phrasal movement can be further classified by the destination of the movement: movement to spec-VP and spec-IP or movement to spec-CP. In a variety of languages, among them English, the formation of Wh-questions, for instance, involves movement of a wh-phrase to spec-CP. This position is termed non-argument position, and this type of phrasal movement is termed A’-movement (A bar movement) or wh-movement. When phrases move to spec-VP or spec-IP, which are argument positions, as in passive sentences for example, this kind of phrasal movement is referred to as A(rgument)-movement. Head movement occurs in languages where the association of the verb with tense (and agreement) features involves movement of the verb from its base position as the head of the VP (V) to a higher head position, namely to the functional head I. Verbs raised to I, as well as elements originating in I (e.g., English modals), can move further up to C. In the following subsections, we present and discuss briefly the most typical sentence types involving these three types of movement.

Phrasal Movement Constructions Involving A’-Movement A very familiar construction involving A’-movement in a variety of languages is the Wh-question. The derivation of such a question is given in (34). Because the thematic role assigned to the

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wh-phrase which house is Theme, and since Theme can be assigned only to the complement of the verb, the surface position of the wh-phrase is assumed to result from movement of the wh-operator from its original position to spec-CP (the dislocation of the modal will to pre-subject position will be addressed in the section on head movement). The position from which movement takes place is marked with t (trace, a silent copy of the moved element; Chomsky, 1995). The trace of a moved element and the element itself form a chain. The chain formed by A’-movement is called an A’-chain, and it connects the moved phrase with its original position, allowing it to receive its thematic role. Specifically, the thematic role is assigned to the trace, and the trace transfers the thematic role to the moved element (the antecedent) via a chain of movement. (34) [Which house]1 will John clean t1?

Note that in English, for example, wh-movement is obligatory, and failure to do so results in an ungrammatical sentence (35): (35) a. *John cleaned which house? b. *Will John clean which house? Another instance of an obligatory A’-movement is illustrated by the relative clause formation shown in (36). (36)

I met the boy [who Lisa likes __ ]

The clause who Lisa likes modifies the NP the boy. The modified NP is termed the head of the relative (not to be confused with the head of a phrase), and the embedded clause is the relative clause. Who in the relative clause is not a question marker (i.e., who Lisa likes is not an embedded question), rather, it is a relative operator. Similar to the movement of the wh-phrases in questions, exemplified in (37), the relative operator who moves from its original position within the VP to the beginning of the relative clause, spec-CP 0. (37) I met [ NP[NP the boy i] [CP who i [IP Lisa likes t i]]]

Although the Theme of likes (who) refers to the same individual as the boy, the head of the relative, these are two distinct syntactic entities, each receives its thematic role from a different verb (likes and met, respectively). Thus, the co-indexing of the head of the relative with the relative operator is a means of conveying co-reference (identical reference), rather than membership in a chain of movement. (Kayne, 1994; Vergnaud, 1974, among others, suggest a radically different analysis of relative clauses; the relative head itself moves.) Relative clauses in English can also have the forms such as (38), which can include the complementizer that, but do not include a wh-phrase. These are assumed to involve movement of a phonetically null operator (Op), as shown in (39). (38) a. I met the boy that Lisa likes __ b. I met the boy Lisa likes __

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(39) a. I met the boyi [Opi that [IP Lisa likes ti]]

b. I met the boyi [Opi

[IP Lisa likes ti]]

Topicalization, moving of a phrase to the beginning of the clause, is another instance of A’movement (40). (40) a. John will clean the orange house tomorrow. b. The orange house, John will clean tomorrow (and the blue house he will leave for Friday). Like Wh-questions and relative clauses, topicalization involves movement of a phrase to specCP. However, topicalization is usually motivated by discourse considerations, and therefore, not obligatory. Hence, both sentences, with and without the topicalization of the Theme, are grammatical. (In some languages, topicalization appears to be syntactically motivated; in German main clauses, spec-CP has to be filled by some phrase, and failure to do so results in ungrammaticality.) Subject relative clauses and questions are created when the subject moves to spec-CP, whereas object relatives and object questions are created by the movement of the object, as seen in (41). Object relatives are more difficult than subject in comprehension because of complexity or in processing. For individuals with syntactic deficits (individuals with agrammatic aphasia, children with SLI and children with hearing impairment), object relatives and object questions are usually harder to understand than their subject relative and subject question counterparts (Friedmann & Novogrodsky, 2004; Friedmann & Shapiro, 2003; Friedmann & Szterman, 2006; Grodzinsky, 2000; Grodzinsky, Piñango, Zurif, & Drai, 1999). Deevy and Leonard (2004) found that young children with SLI were able to comprehend short subject and object Wh-questions, but had more difficulty with slight longer object Wh-questions compared to their typically developing peers. In more complex sentences with subject and object relative clauses, Hestvik, Schwartz, and Tornyova (2010) found that children with SLI failed to exhibit activation of the filler noun at the gap in object relative sentences. (41) Subject relative: I met the boy who __ likes Lisa. Object relative: I met the boy who Lisa likes__. Subject question: Who __ likes Lisa? Object question: Who does Lisa like __? Another classification of relative clauses that was found to affect child language as well as the language of typical adults is the distinction between center-embedding and right-branching relative clause. In sentences like (42), the relative clause is on the right-hand side, the end of the sentence, whereas in sentence (43) it is embedded in the center of the main clause, between the subject and its predicate. For both children and adults, center-embedded relative clauses are harder to understand than right-branching relatives, and center-embedded relatives are acquired later than right-branching ones (Correa, 1995; de Villiers, Tager-Flusberg, Hakuta, & Cohen, 1979; Kidd & Bavin, 2002; Sheldon, 1974). (40) I met the boy that Lisa likes.

(41) The boy that Lisa likes plays the saxophone.

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Constructions Involving A-Movement A-movement is a short movement of a phrase to spec-IP (or within VP from the complement position to spec-VP). As already mentioned, spec-IP is the subject position. Since almost all subjects originate within VP and then move to IP, the derivation of a large variety of clauses involves movement to spec-IP. There are two other types of A-movement: movement of unaccusative verb Themes and movement of the passive verb Themes. Unaccusative verbs. There are two main subgroups of intransitive verbs, which take only one NP argument. These include unaccusative verbs like sank and fell, and unergative verbs like jumped and laughed. (Reflexive verbs, like washed in Dan washed are also considered unergatives; see Reinhart & Siloni, 2004.) Both unaccusatives and unergatives take one participant and assign one thematic role. The difference between them is the thematic role they assign to their participant. Unaccusative verbs assign Theme, whereas unergatives assign Agent. For example, in The leaf fell, the leaf is not actively responsible for the action of the unaccusative verb fell, but rather undergoes the action (i.e., it has the role of Theme). In contrast, the bird in the sentence The bird chirped, which includes an unergative verb, is the Agent. The difference between these two types of intransitive verbs led researchers to assume different structural analyses. The single argument of unaccusatives is base-generated in object position, after the verb, whereas in unergatives the argument is base-generated before the verb (in spec-VP). Thus, although the sentences The leaf fell (44) and The bird chirped (45) both have an NP-V word order, their derivations are different. The NP-V order of (44) is the result of NP-movement from object to subject position, and it therefore includes a trace in object position. The NP-V order of (45) involves no movement because the NP is base-generated pre-verbally. This is the Unaccusativity Hypothesis (Levin & Rappaport-Hovav, 1995; Perlmutter, 1978; Perlmutter & Postal, 1984). The moved NP is linked to its initial position via an argument chain (A-chain). Similar to A’-chains, A-chains enable thematic role assignment despite the movement. The verb assigns the thematic role of Theme to the position where the NP was generated (i.e., after the verb), and the role is transferred via the A-chain to the new position, before the verb. In English, sentences with unaccusatives require that the NP argument, which is generated in object position as the complement of the verb, moves to subject position. In other languages, like Hebrew, this movement is optional, and both V-NP and NP-V orders are grammatical with unaccusative verbs. fell the leaf

(44) The leafi fell ti (45) The bird chirped The Unaccusativity Hypothesis is also supported by online processing studies. A study that tested the online processing of the moved NP shows that in an English sentence like The coffee spilled, the coffee is first accessed when it is heard at the position before the verb, but then it is also re-accessed after the verb, at the original position of the Theme. In sentences with the same word order (NP-V) but with unergative verbs, no such re-accessing occurs (Friedmann, Taranto, Shapiro, & Swinney, 2008). The passive construction also involves A-movement of the Theme argument to spec-IP. Passive verbs. Passive verbs are derived from their active counterparts, the corresponding transitive verbs. For instance, a transitive verb like clean, which assigns Agent and Theme thematic roles (46a), is passivized as was cleaned (46b). In (46b), which includes the passive verb, the argument bearing the internal thematic role (Theme) appears in subject position. Because the internal argument can be assigned only to the verb’s complement, the occurrence of the Theme in subject

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position must be the result of movement. The passive verb does not assign the external (Agent) role; at the semantic level, this role clearly exists and can be realized in a by-phrase.

(46) a. John[Agent] cleaned the room [Theme]. b. The room [Theme] was cleaned ti (by John).

Head Movement V-to-I Movement As we mentioned in an earlier section, the IP layer is responsible for the tense and agreement inflection of the verb. The head I hosts the appropriate inflectional features. In many richly inflected languages, the verb has to move to I to collect its inflectional features or to check the features of the I head. This movement, referred to as verb-raising, is reflected in word order changes in certain sentences (e.g., Pollock, 1989). For example, in the French sentence (47), the verb precedes the adverb. Importantly, an adverb like often resides in a fixed position on the syntactic tree in English and French. It is located above VP, before the VP. Therefore, if the inflected verb in (48) precedes the adverb in French, it indicates that the verb has moved outside of VP, to a position above (and before) the adverb as shown in (49). (47) Je mange souvent de chocolat. I eat often of+the chocolate. (48) Je [I mange [AdvP souvent [VP ___ de chocolat]]] chocolat

(49) I often eat apples. In English, the verb follows the adverb (49). This indicates that lexical verbs in English do not raise overtly to I, and the feature-checking is achieved in a different way. According to Chomsky (2000, 2001), the checking procedure can be achieved via an operation termed Agree, which allows feature checking without movement. There are many languages like French, with overt movement of the verb, as well as languages like English, where the lexical verb remains in its base position. It is customary to split the functional head I into two functional heads, T(ense) and Agr(eement) (The Split-Infl Hypothesis). Instead of assuming that the functional head I carries both the tense and the agreement verb features of the verb, the tense specification of the verb is associated with the head T, projecting a tense phrase (TP), whereas the agreement features are carried by a distinct functional head Agr that forms an Agreement Phrase (AgrP). Overt verb-raising (e.g., in French) is movement of the verb from V to both T and Agr. According to Pollock (1989, 1993), TP is above AgrP, whereas Belletti (1990) advocates the order AgrP above TP. Chomsky (1995) has dispensed with Agr nodes and suggested that the agreement of the subject is checked in TP. For simplicity, we will assume that tense and agreement features are carried by a single functional head, I.

I-to-C Movement A lexical verb raised overtly to I, or an element originating in I (e.g., auxiliary verbs, modals) can move further up the tree to C. Movement of the verb from I to C is obligatory in some languages

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for question formation, as seen in (50) and (51), but head movement can take place only to the closest head position. Thus, the verb cannot move directly from V to C, but rather has to move from V to I, and then from I to C. English verbs do not raise to I and, thus, English cannot have a question like *Eat you the apples? But in languages like German and Dutch, the verb in the main clause always has to move to C, and thus, it occurs after the first constituent in the sentence (which is in spec CP). This is called V2 because the verb ends up in second sentential position (see (52) for a German example). (50) a. John will come. b. Willv John tv come? (51) a. John will clean the orange house. b. Which housei willv John tv clean ti ? (52) Gestern tanzte Dani mit Marko. Yesterday danced Dani with Marko. In a variety of languages, I-to-C movement is not obligatory, occurring mainly when spec CP is filled by an adverb or wh-phrase (Shlonsky & Doron, 1992). This is illustrated in the Hebrew example in (53), translated into English. Children acquire V-to-I movement earlier than I-to-C movement (Déprez & Pierce, 1993). (53)

CP

C’

yesterday

IP

C

atev

I’

Dani

I

VP

V’

V

NP

tv

N’ N

hummus To summarize, elements of a sentence can appear at points in the sentence (or in the tree) that are different from their original position. Analyzing displacement via movement (chain formation)

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provides an explanation of the association of a moved phrase with the relevant thematic role and accounts for word-order alternations within and across languages. Movement also has an important role in explaining a long line of phenomena from language acquisition, language processing, and language disorders. It is important to note that there are a number of other theoretical approaches to syntax, including other generative approaches (e.g., lexical functional grammar, head-driven phrase structure grammar). Approaches that are more cognitively and functionally based have received a great deal of attention in recent years and have been invoked as theories of language development. Cognitive linguists reject the notion that language is modular or different from other cognitive abilities, view semantics as conceptual rather than truth value, and consider syntax as a combination of symbols (semantic/conceptual structure-phonological label pairings). Grammar then is the set of constraints on how such units can be combined into larger pairings of semantics and phonology (see Langacker, 1987). Another instantiation of cognitive linguistics, termed Construction Grammar (Fillmore & Kay, 1999; Goldberg, 1995, 2006), considers language as form and function pairings that are evaluated by their plausibility and are part of a broader network of knowledge. Additional core ideas of this approach are statistical pre-emption, which attributes ungrammatically to competition between forms in context. Goldberg has made extensive use of an artificial construction learning paradigm to demonstrate that input frequency and functions influence structure learning. Among the key structures that have been examined in detail include argument structure island constraints (e.g., Goldberg, 2006), syntactic categories (Ambridge, unpublished and the ensuing discussion on Info-CHILDES; Tomasello, 2000, 2003), subject-auxiliary inversions (e.g., Goldberg, 2006), and sentences with movement (Goldberg, 2006). At their core, these approaches differ radically in the characterization of how patterns and instances are related. Generativists have characterized varyingly as rules or structural relations that are applied underlyingly to generate individual instances, whereas constructionists characterize these as generalizations from instances. In each case, convincing alternative descriptions and evidence for this alternative view of these linguistic constructs have been offered. This approach has important implications for a view of language development that differs radically from that proposed by nativists. Although there have been great strides in cognitive linguistics in recent years, few of these alternatives yet offer as complete a description of sentence structure as the versions of generative syntax presented here. In the last 20 years, generative syntax has shifted to the Minimalist Program (Chomsky, 1995), which eliminates virtually all of the mechanisms of earlier versions. This approach may be far less amenable to psycholinguistic modeling and descriptions of typical and atypical language acquisition than previous versions of the theory were.

Syntactic Movement: Psychological Reality and Language Disorders Many psycholinguistic studies have demonstrated the psychological reality of syntactic movement, using paradigms such as Cross-Modal Lexical Priming (CMLP) to detect the activation of constituents in the sentence during on-line processing. These studies show that in the course of processing of movement-derived sentences, the moved constituent appears to be activated twice in the sentence—once when first encountered and again at the trace, where it is not phonetically present (Love & Swinney, 1996; Nicol & Swinney, 1989; Tanenhaus, Boland, Garnsey, & Carlson, 1989; Zurif, Swinney, Prather, Wingfield, & Brownell, 1995). When the hearer reaches the trace position, the antecedent (the relocated constituent) is reactivated. This finding indicates that the NP is semantically processed in its original position—the trace position. This has been found both for traces of Wh-movement and for traces of A-movement. Another demonstration of the reality

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of movement, in this case its neurological reality, is the finding that sentences that include movement of NPs activate certain areas in the brain, specifically left Broca’s and Wernicke’s areas (BenShachar, Hendler, Kahn, Ben-Bashat, & Grodzinsky, 2003; Ben-Shachar, Palti, & Grodzinsky, 2004). The trace, therefore, is psychologically real, and its existence as well as the existence of the chain that connects it to the antecedent allows for the understanding of who is doing what to whom in the sentence. Movement also plays a role in the explanation of the order of syntactic acquisition in typically developing children (e.g., Guasti, 2002), as well as syntactic impairments in children (see Chapter 1 by Schwartz and Chapter 4 by Waldman DeLuca & Cleary) and in adults. For example, children with hearing impairment show impaired comprehension and production of passive sentences (Power & Quigley, 1973), Wh-questions (de Villiers, de Villiers, & Hoban, 1994; DeLuca, 2015; Geers & Moog, 1978; Quigley, Wilbur & Montanelli, 1974), topicalization structures (Friedmann & Szterman, 2006), and object relative sentences (see Berent, 1988; de Villiers, 1988; Friedmann & Szterman, 2006; Quigley, Smith, & Wilbur, 1974). The acquisition of passives, Wh-questions, topicalized sentences, and relative clauses is significantly delayed in the language development of children with hearing loss, and in many cases, these structures are not mastered even at older ages. These four syntactic structures have a common characteristic that might be the source of the deficit: they all involve phrasal movement. Similarly, children with SLI who have a syntactic deficit have difficulty producing and comprehending sentences that are derived by phrasal movement, such as reversible passives in English (Adams, 1990; Bishop, 1979; van der Lely, 1996; van der Lely & Harris, 1990); relative clauses in English, Hebrew, and Greek (Adams, 1990; Friedmann & Novogrodsky, 2004; Hestvik, Schwartz, & Tornyova, 2010; Stavrakaki, 2001); and Wh-questions (Ebbels & van der Lely, 2001; van der Lely & Battell, 2003). Deficits in the comprehension of sentences with movement have also been widely reported in individuals with agrammatic aphasia (Grodzinsky, 1990, 2000). These individuals have significant difficulties in the comprehension of object relative clauses, object Wh-questions, and topicalized structures, all involving A’-movement (Friedmann & Shapiro, 2003; Grodzinsky, 1989, 2000; Grodzinsky, Pierce, & Marakovitz, 1991; Schwartz, Linebarger, Saffran, & Pate, 1987; Schwartz, Saffran, & Marin, 1980; Zurif & Caramazza, 1976; see Grodzinsky, Piñango, Zurif, & Drai, 1999 for a review). Structures with V-to-C movement are also impaired in adults with agrammatism; they have difficulties producing and understanding sentences with verbs in C (Friedmann, Gvion, Biran, & Novogrodsky, 2006; Zuckerman, Bastiaanse, & van Zonneveld, 2001). Similar deficits have been reported in children with SLI (see Chapter 1 by Schwartz). In these clinical populations, there is disagreement about whether these deficits are due to movement, thematic role assignment coupled with movement, or processing deficits. Treatment studies of these deficits in adults (Thompson & Shapiro, 1994, 1995; Thompson et al., 1997) ameliorated deficits in movement-derived structures using an intervention program directed at various types of movement, and also found that the treatment of one structure with a specific type of movement generalizes to other structures derived by the same movement. Treatment using similar principles also proved effective in children with SLI (Ebbels & van der Lely, 2001; Levy & Friedmann, 2009). To summarize, syntactic movement is not only psychologically real, but more importantly, it is a useful tool in understanding and treating the syntactic impairments in children and adults. Syntactic movement (i.e., chain formation), thus, is a central mechanism employed by a human language giving rise to long-distance dependencies. Not all long-distance dependencies involve movement. Long-distance dependencies that do not involve movement are discussed in the following section.

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Binding Language includes three kinds of nominal expressions (noun phrases—NPs): R-expressions (referential NPs), pronouns (pronominal NPs), and anaphors (reflexive and reciprocal NPs). Referential NPs (R-expressions), which are the vast majority of NPs, include common nouns and proper nouns (54). Pronouns can refer to individuals (I, you, we). However, third-person pronouns can also refer to another NP in the sentence. Here, we will focus on these pronouns. Sentence (55) exemplifies both options: either him refers to John, symbolized by having the same index (i) as John, or it can refer to some other individual discourse, symbolized by having an index distinct from that of the NP John. Here we will focus only on the meaning where the pronoun is coindexed, namely co-referential, with some NP in a sentence. Anaphors cannot refer by themselves to an individual in the world. Rather, they have to refer to some NP in the sentence; see (56) for a reflexive and (57) for a reciprocal. (54) (55) (56) (57)

John saw the movie. Johni thinks that Mary likes himi/j. Johni likes himselfi. [John and Mary]i like each other i.

These NP types have different distributions (i.e., they cannot occur freely in any position in a sentence). Conditions A, B, and C of the Binding Theory (Chomsky, 1981) are the principles that define the distribution of anaphors, pronouns, and referential NPs, respectively.

Condition A: The Distribution of Anaphors (58) Johni likes himselfi. (59) *Johni thinks [CP that Mary likes himselfi]. (60) *Himselfi likes Johni The ungrammatical sentence (59) differs only from the grammatical sentence (58) in that the reflexive anaphor himself does not occur in the same clause as its antecedent, the NP to which it refers (John). The antecedent is in the main clause, whereas the anaphor is in the embedded clause. Thus, an anaphor has to occur in the same clause as its antecedent. This, however, is not sufficient, as shown by (60), where the anaphor (himself) and the antecedent (John) are clause-mates, and it is still ungrammatical; (60) differs from the grammatical (58) in the hierarchical relations between the antecedent and the anaphor. In (58), the antecedent is the subject of the sentence and therefore in the highest clausal position (spec-IP), whereas the anaphor is in the object position (inside the VP). In (60), the hierarchical relation is reversed. The anaphor (himself) is higher than the referential NP, it is the subject of the sentence, and the referential NP, the antecedent of the anaphor, is inside the VP. Thus, an anaphor has to have an antecedent in the same clause, and the antecedent has to be higher in the sentence structure than the anaphor. An anaphor whose antecedent is in the appropriate structural relation and is in the same clause is called bound. Otherwise, it is free. This is true for reflexive anaphors as well as for reciprocal anaphors. (For the precise formulation of the relevant structural relation, referred to as c-command, see Chomsky, 1981.) Condition A is summarized as: An anaphor has to be bound in the local domain (i.e., in its clause).

Condition B: The Distribution of Pronouns In (62), the pronoun him has to refer to some sentence external antecedent in the discourse; otherwise, it is ungrammatical. Thus, a pronoun cannot be bound to an antecedent in the same clause. 172

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Being bound by an antecedent in another clause is fine (61). The distribution of pronouns, thus, is complementary to that of the anaphors (Reinhart & Reuland, 1993). (61) Johni thinks that Mary likes himi. (62) *Johni likes himi. Consequently, the distribution of pronouns is defined as follows: Condition B: A pronoun has to be free in its local domain. A potential antecedent in the same clause with the pronoun does not necessarily lead to a condition B violation. For example (63), where the antecedent of the pronoun, John, is embedded (serves as a possessive modifier) inside the subject of the sentence, and thus is not the subject of the sentence (i.e., it does not c-command the pronoun), Condition B states that a pronoun cannot have a local antecedent and the antecedent cannot c-command the pronoun. (63) [John’si mother]j likes himi.

Condition C: The Distribution of Referential NPs A referential NP simply cannot have a binder (antecedent), in the local domain (64) or outside its local domain (65). It just has to be free as (66) where the pronoun refers to some noun outside the sentence: (64) *Hei likes Johni. (65) *Hei thinks that Mary likes Johni. (66) Johni thinks that Mary likes himi. Condition C states that an R-expression has to be free. Most young children reliably follow Principles A (reflexives) and C (names and nouns), but appear to violate Principle B (pronouns) with referential NPs (The girl splashed her), but not with quantified NPs (e.g., Every girl splashed her) until they achieve full mastery, somewhat later in development. The findings and the explanations offered for this pattern have been and continue to be quite mixed. A number of studies, however, have demonstrated that task issues have been largely responsible for these reported patterns (see Schwartz, Hestvik, Seiger-Gardner, & Almodovar, in press, for a review). Individuals with agrammatic aphasia have a selective deficit in the interpretation of pronominal dependencies, whereas interpretation of reflexives is unimpaired (Grodzinsky, Wexler, Chien, Marakovitz, & Solomon, 1993; Ruigendijk, Vasič, & Avrutin, 2006). Only a small number of studies have examined binding in children with language impairments. In one off-line study using picture and truth-value judgments (van der Lely & Stollwerck, 1997), the researchers concluded that children with grammatical SLI were unable to interpret sentences with quantifiers and were more generally unable to use syntactic information to resolve co-reference. A more recent study using cross-modal priming revealed that children with SLI activate the appropriate referent at the pronoun or reflexive similar to their age-matched, typically developing peers (e.g,, Schwartz, Hestvik, SeigerGardner, & Almodovar, in press). The children with SLI were slower in general, which suggests that any deficit may be in processing rather than in grammatical knowledge of binding relations.

Language Acquisition: The Nativist View Nativists believe that the process of language acquisition can be represented roughly as: (67) Linguistic data  Initial state of linguistic knowledge  Final state of linguistic knowledge 173

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Because language is a property unique to the humans, nativists assume that this initial state of linguistic knowledge is part of the biological endowment of the species that is genetically encoded (Chomsky, 1986b). They also assume that language acquisition has a critical period. The idea of the critical period is that to be fully acquired, first (and perhaps even second language) language must be acquired before puberty. This notion gained great popularity in the 1960s. Some of the evidence came from deaf children, reports of feral children, and a well-known case of an isolated child named Genie, and from second-language learners. But in each case, issues concerning inadequate input and developmental factors led to late language-learning failures. Although the notion of a critical period continues to have proponents, abundant evidence suggests that second and even third languages can be fully acquired well after puberty. For children who do not have language, cognitive, hearing, or other impairments, the general time course of first language acquisition is very similar across children and across languages. Moreover, there are linguistic phenomena attested in the process of language acquisition, regardless of the language acquired. For instance, children acquiring a variety of languages go through a stage where they do not necessarily produce the subject of the sentence. Although there are languages where the subject does not have to be produced (e.g., Italian, Spanish, etc.), many other languages require a subject (e.g., French, English); see the Italian example in (68) and its English counterpart in (69). Subject omission in early language acquisition occurs regardless of the language acquired and the initial input. (68) a. Lei è malata. b. È malata. (69) a. She is ill. b. *Is ill. Further support for the claim that children come to the world endowed with some linguistic knowledge is based on assumptions that the input available to them is partial (including performance mistakes), not presented in any systematic way, and does not include negative evidence (Brown & Hanlon, 1970). Children are not directly taught what is ungrammatical in their language (e.g., Hornstein & Lightfoot, 1981; Lightfoot, 1982). Nativists argue that if there were no initial language knowledge, a great variety of mistakes would be expected in the course of language acquisition, contrary to what has often been observed. Children do make mistakes, but they make fewer than expected given the assumed deficiency of the data and lack of negative evidence. Even more significantly, they simply never make some mistakes. In (70), for example, the question word refers to the object of the verb kissed in the embedded sentence that Lisa kissed (i.e., a gap). This embedded sentence complements the verb say in (70a) but the noun the rumor in (70b). When a question word corresponds to a gap in a clause complementing the verb (say), the sentence is grammatical. If the gap is in the clause complementing the noun (the rumor), the sentence is ungrammatical (this is also called a violation of island constraints). Children do not make errors like (70b). The only difference between these sentences is their syntactic structure and the constraint on movement for Wh-questions in English. (70) a. Who did Bart say that Lisa kissed? b. *Who did Bart spread the rumor that Lisa kissed? Another example of nonexisting mistakes involves Subject-Aux(iliary) inversion in English questions (61–62). Crain (Crain, 1991; Crain & Nakayama, 1987) reported that even complex sentences including two instances of the auxiliary verb is (61a) do not lead to an incorrect inversion

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(61b), but only to the correct one (61c), indicating children’s knowledge of the hierarchical structure of the sentences. Children could assume that a sentence is a linear string of words, and that the first is encountered should be inverted. However, they appear to always invert is in the main clause, even if it occurs after an embedded is. (71) a. John is tall. b. Is John tall? (72) a. The man who is running is bald. b. *Is the man who __ running is bald? c. Is the man who is running __bald? The Universal Grammar (UG) proposed to be the initial state for the child is not intended to be the grammar of any one language. Rather, it is a general blueprint underlying all human language. Thus, it includes the principles that are common to all human languages (e.g., movement, which is realized in different ways across languages). Given that languages differ from each other, the UG should allow for the cross-linguistic variation. The variation attested across languages is limited and systematic. Languages do not differ from each other infinitely, but rather only along certain dimensions. For instance, in many languages the verb agrees in number, gender, and person with the subject, and in others it can agree with both the subject and the object, but there are no languages where a verb agrees with the adjacent noun that is neither the subject nor the object, see (73) and (74). (73) The girlfriends of my grandfather adore him. (74) *The girlfriends of my grandfather adores him. The observation that human languages share certain properties, and that they differ from each other in limited ways, led to the Principles and Parameters (P&P) approach to UG (Chomsky, 1981). Principles encode the invariant properties of languages—the universal properties that make the languages similar. For example, the language-specific rule that governs the interpretation of pronouns and proper names is a principle. Parameters are the part of UG that encodes the properties that vary from one language to another. For instance, languages can vary in word order, in the way they form questions, or, as already mentioned, in the realization of the subject. In English, the object follows the verb (75a), whereas in Japanese it precedes it (75b). The question element in English occurs in sentence initial position (76a), whereas in Japanese and in Israeli and American Sign Languages it does not (76b). Italian allows the subjects to be omitted (77a), but English does not (77b). (75) a. John hit Bill. English b. John-ga Bill-o but-ta. Japanese John-subject Bill-object hit (adapted from Kuno, 1973) (76) a. Why was John fired? English b. John-wa naze kubi-ni natta no? Japanese John-topic why was fired question marker (Lasnik & Saito, 1984) (77) a. Lei è malata. (She) is ill. b. È malata. *Is ill.

Italian Italian

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A parameter is a binary setting. Thus, the parameter for optional subjects could be stated as: (78) Are subjects required to be present? Yes/No If a child is exposed to Italian, the answer is yes; if the child is exposed to English, the subject is required and the answer is no. Because under the P&P approach both the principles and the parameters are given by UG, children are innately endowed with them. The task of the child acquiring a language, thus, is to set the parameters to the value of the language environment.

Language Development: Emergentist Views There are alternative views of language development (e.g., usage-based, constructivist, functionalist, emergentist, dynamical, among others) that are consistent with the perspective of cognitive and constructionist linguistics described earlier. Emergentist will serve here as an umbrella term for these views. This general perspective differs radically from a nativist perspective in several important respects. Emergentist views assume a more limited (i.e., no pre-specified UG as described by nativists) and a more general biological endowment for the development of cognition and language. Associated nativist claims of a critical period for language acquisition, which have historically been supported by evidence that is equivocal at best, also represent the nativist biological endowment for language. The notion of critical period is rejected because of a lack of compelling evidence, and in light of evidence that under non-clinical circumstances languages can be learned across the lifespan, given adequate input. Proficiency is dependent on exposure (e.g., Steinhauer, White, & Drury, 2009). The primary driver of language development is input, which has been demonstrated to be far richer than has been assumed by nativists. Whereas nativists assume that input is incomplete and flawed, emergentists present compelling evidence of rich input and ways in which this input can readily account for the facts of language development without assuming an innate endowment for grammar. Rather than native language structures generated from a broader set of innate rules or parameters, emergentists assume that children infer these rules or patterns from instances in input and then apply them to instances yielding patterns resembling rules. Language development is attributed to language input coupled with general learning and cognitive mechanisms. Nativists have often argued against emergentist approaches by invoking the traditional empiricist (i.e., infants are born as blank slates) versus rationalist (i.e., nativist) debate. As Goldberg (2006) noted, the traditional empiricist view of infants as blank slates with no learning predispositions does not accurately characterize emergentist views (Elman et al.,1996; Lakoff,1987; MacWhinney, 1999; Tomasello, 2003). Numerous proposals posit some type of underlying, possibly innate, cognitive, perceptual, or conceptual constraints or abilities, potentially specific to humans, that underlie the development of language. Such underlying constraints may be thought of as an interaction among human perception, cognition, and the constraints of the physical environment. Whereas nativists view input as simply a means of triggering underlying, pre-existing rules or parameter settings (poverty of the stimulus), emergentists (e.g., Tomasello, 1992) have provided extensive evidence that language input is quite rich and, coupled with general cognitive and conceptual learning abilities, can lead to language development. Nativists claim that children do not receive negative input. Although adults are more likely to correct the truth value of children’s utterances than their structure, negative input may take other forms such as zero frequency or semantic constraints and may be widely available (e.g., Pullum & Scholtz, 2002). A number of linguistic structures and types of linguistic knowledge that have been assumed to be unlearnable from input alone, do appear to be learnable from input characteristics (e.g.,

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Goldberg, 2003, 2006). Statistical regularities and other cues are widespread in language, including transitional probabilities between words (e.g., Saffran, 2001a, 2001b; Saffran, Aslin, & Newport, 1996), word categories (e.g., Elman, 1990), morphophonological patterns (e.g., Gerken, Wilson, & Lewis, 2005), phrase boundaries using articles as cues to nouns, and word order rules (Saffran & Wilson, 2003). According to one view, language development consists of constructing structure meaning pairs. Goldberg and colleagues (see reviews in Goldberg, 2003, 2006) have provided compelling evidence form corpora and from experiments that argument structure (meaning/structure pairings) generalizations are learnable using general categorization strategies applied to skewed input that provides cues by limiting the occurrence of frequently used verbs to specific structures. Nativists have assumed that argument structure cannot be learned from input alone. An example provided earlier of an error (72) that nativists assume children do not make involves complex yes/no questions (e.g., Is the boy who is spinning is dizzy?). In fact, there is evidence that children do occasionally produce such questions and that such auxiliary doubling errors reflect the children’s sensitivity to the surface co-occurrence patterns of input (Ambridge, Rowland, & Pine, 2008). Goldberg has provided a compelling analysis of movement constraints (also called island constraints) and scope in terms of statistical regularities and information structures that can potentially account for the acquisition of complex structures that nativists assume are not available in input. Input effects are also seen in children with SLI. Leonard and Deevy (2011) found that all children were more likely to omit auxiliaries on novel verbs in obligatory contexts when the input sentences presented the verbs in contexts such as We saw the dog relling, but the effect was stronger for children with SLI. The children with SLI also did more poorly than the children with typical language development in comprehending sentences such as The elephant sees the giraffe eating. More recently, Leonard and colleagues (Leonard, Fey, Deevy, & Bredin-Oja, 2015) found experimental evidence that input (Does the boy jump? Let’s watch the boy jump.) leads to erroneous optional infinitives (e.g., The boy run), with even stronger effects in children with SLI than in their typically developing peers. Findings supporting the construction of language without recourse to innate knowledge have burgeoned in the last 15 years, and this brief summary does not adequately cover all of the research in this area. It seems clear that the extreme view of a fully formed innate grammar with minimal triggering input is unnecessary to explain language acquisition. Although input frequency appears to play a large role in acquisition (Ambridge, Kidd, Rowland, & Theakston, 2015), a great deal remains unknown regarding the mechanisms of input frequency (e.g., Schwartz, 2015), regarding the general conceptual and cognitive processes, and whether any of these processes might be specifically adapted to language learning. Despite this apparently mounting evidence, controversies continue. For example, there is a continuing controversy regarding the origins (innate versus learned) of syntactic categories (Pine, Freudenthal, Krajewski, & Gobet, 2013; Valian, Solt, & Stewart, 2009; Yang, 2013). The focus is on the articles in English a and the, which can be used interchangeably by adults to mark nouns. The question is whether young children use these articles interchangeably; if they do, it suggests they come to language acquisition with a pre-existing rule, and if they don’t, it suggests that they have learned the articles individually for each noun. The argument has been based on two seemingly contradictory analysis of the same language samples from children and their parents. Yang and Valian et al. found that children produce the two articles at a rate comparable to adults, but Pine et al. found that adults use a much wider range of nouns and thus have fewer opportunities for alternation, whereas children use a small set of nouns repeatedly yet don’t exhibit the alternation. But the argument continues concerning article alternation, along with other types of evidence for syntactic categories that are innate or learned.

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Compromise positions have been offered (e.g., Lidz & Gagliardi, 2015) in which inferential and deductive components of the language learning mechanism are treated distinctly. Similarly, Ambridge, Kidd, Rowland, and Theakston (2015) have considered ways in which frequency could be accommodated in nativist models of acquisition. These proposals seem unlikely to satisfy anyone engaged in this debate, but it will be interesting to see how theories of acquisition develop in the coming years.

Note The version of this chapter that appeared in the first edition was authored by Botwinik-Rotem and Friedmann. This chapter was revised by Schwartz. This edition retains their now-edited discussions of generative syntax and nativist view of language acquisition, as well as their applications to child and adult language disorders, with substantive additions by Schwartz. The revisions and additions were supported by a grant to Schwartz from NIDCD, DC011041.

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7 NEUROBIOLOGY OF CHILD LANGUAGE DISORDERS Baila Epstein and Richard G. Schwartz

Introduction The scientific mapping between brain and language behavior is now proceeding at an exceptionally rapid pace. This venture, though, is far from new; it has deep roots planted in the early 19th century (Dax, 1865; Harlow, 1868). In its infancy, research on linking brain and language function was limited to neuroanatomical studies. Early investigations focused on postmortem brains of adults who exhibited language disorders due to acquired brain damage. In recent years, this research has been dramatically expanded within the field of cognitive neuroscience. This branch of study concentrates on the structure and function of the brain underlying cognitive behavior, including perception, attention, memory, executive functions, and language. From among these topics of interest, the study of brain-language relations is the least developed (Brown & Hagoort, 1999). In the area of child language, research concerning brain and language functions is particularly sparse. Even so, there is a growing body of literature on this subject that appears promising. Readers might ask: What is the benefit of knowing which brain structures are responsible for typical and atypical language performance? The present chapter addresses this question by considering a number of critical and multifaceted aspects of neurobiology in normal and disordered child language development. The study of neurobiology has been propelled by recent technological progress in two areas: imaging the anatomy and ongoing activity of the brain and recording the brain’s electrical activity. These advancements have afforded researchers fine-grained, on-line techniques to examine the cortical structures and activities implicated in language processing. In this chapter, we examine studies that have applied these methods in exploring the neurobiology of typical and atypical language development. First, we describe research that has employed neuroimaging techniques, including structural and functional magnetic resonance imaging (MRI and fMRI, respectively) and positron emission tomography (PET). We then proceed to discuss studies that have used neurophysiological methods, such as event-related potentials (ERP). Our principal goal is to review major findings on the neurobiology of child language disorders. To accomplish this task, we consider studies that have used the aforementioned methods in children from normal and clinical populations. Regrettably, the complete range of developmental language disorders is far too broad to address within the confines of one chapter. We therefore devote the emphasis of our discussion to the relevant issues in specific language impairment (SLI). The

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scope of this review, though, is broadened by considering the neurobiological profile of SLI relative to other child language disorders, among them dyslexia, autism spectrum disorder (ASD), and Williams syndrome (WS). We conclude by offering suggestions for future research aimed at unveiling the neural correlates of normal and disordered language in children. To begin, a rudimentary background of some essential concepts in neurobiology is provided.

Neurobiology Primer At the neurobiological level, the two main categories of interest are brain structure and function. The natural assumption is that these variables bear a one-to-one correspondence, but this has yet to be established. There are, in fact, multiple regions that are anatomically, but not functionally, distinct, at least given our current state of knowledge. The challenge of linking structure and function is complicated by the extensive interconnectedness of the human brain. Because multiple structures typically contribute to a given neural function, it is difficult to identify which brain regions are active in association with particular cognitive processes. Moreover, the structure-function relationship varies across normal individuals because it is determined by factors that differ from person to person. These factors may be categorized as either genetic or environmental. Both types of factors are instrumental in shaping neurobiology, consequently altering cognition. It follows then, that a discussion of neurobiology requires consideration of these influences on neurodevelopment. To abide by the gold standard, we evaluate abnormal neurodevelopment in the context of normal brain development.

Normal Brain Development Genetic and Environmental Effects on Neurodevelopment Contributions to brain development have traditionally been characterized as either genetic or environmental. Genetic influences are those that involve the innate pre-specification of brain structure that is provided by genes. Genes encode structural information to produce proteins that build the central nervous system, including the cortex of the brain. The approximately 105 genes possessed by the human are not nearly sufficient to specify the far greater number of neural connections that it accommodates. Therefore, some aspects of brain organization must depend on external factors. Moreover, during early phases of neurodevelopment, any area of cortex can support an assortment of cortical representations, which are manifested as intricate differences in synaptic connections and dendritic branching. External input helps mold the configuration of cortical representations, thereby exploiting the initial plasticity of the cortex. Therefore, in this context, cortical plasticity is a natural and defining aspect of neurodevelopment (de Haan & Johnson, 2003), and the cortex is an outgrowth of a complex interplay between genetic and environmental factors. We follow with a simplified account of this relationship. The interested reader is referred to Chapter 10 by Tomblin for a more comprehensive description of genetics in language development.

Cortical Differentiation The growth of the human cortex begins prenatally and continues well into postnatal life. Over this time, the cortex differentiates into areas that support different functions, among them language processing. In most normal adults, the same approximate areas of cortex are responsible for specific linguistic functions. This would lead one to assume that the areal differentiation of cortex is genetically predetermined, and it is, but only partially. One prevailing view in cognitive neuroscience is

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that patterns of gene expression form large-scale areas in the cerebral cortex according to properties that make each area suitable for particular computations. Small-scale regions within these areas develop, or become specialized, through activity-dependent processes that are triggered by environmental experience. The representations that emerge in these smaller-scale regions, however, are not strictly determined by environmental input. They are, rather, constrained by certain architectural properties of the cortex. For example, each laminae, or layer of the cortex, supports specific cell types and patterns of inputs and outputs. Specific connectional and neurochemical features of each large-scale region also limit the representations that emerge within their boundaries (Johnson, 2005). Thus, both genetic and environmental effects are interwoven into the cortical tapestry. A more specific understanding of genetic-environmental effects on neurodevelopment may be gained by considering the postnatal process of synaptic pruning. Synapses are genetically predetermined, but are labile during early stages of development. The labile nature of these connections permits them to either stabilize or regress. Counterintuitively, the selective loss of synapses is actually a gain that occurs through the process of learning because in this context, “to learn is to eliminate” (Changeux, 1985; Changeux, Courrege, & Danchin, 1973). This synaptic loss is enabled by two factors: sensory input (environmental) and spontaneous activity in the neural network (genetic). Both of these factors may affect the activity of post-synaptic cells, resulting in selective synaptic loss that fine-tunes neural connectivity. Thus, environmental input may contribute to genetically programmed processes, leading to synaptic elimination. Consequently, brain anatomy is refined, allowing for increased cognitive capabilities. Although the aforementioned account of genetic-environmental influences is well-grounded, it leaves several questions unanswered. One question that remains is, to what extent are environmental influences constrained by genetic specifications? This question may be best explored in studies of cortical plasticity. The central aim of these studies is to reveal what optimally induces cortical changes to result in improved cognitive capacity. This objective has clear implications for intervention in childhood language disorders.

Language Areas in the Brain Having provided a basic outline of neurodevelopment, we now focus on the following question: Where is language processed in the developing brain? Dividing the brain into areas, hinges on the inter-related concepts of localization and specialization. Here, localization refers to ascribing a particular cognitive function to a specific region of the cerebral cortex. Areas that support particular functions are said to be specialized for those functions. Cortical specialization is a maturational process. During infancy, functional segregation of the cortex is generally poor. For this reason, many areas become partially activated in response to a wide range of sensory information. This has been demonstrated by fMRI studies that have shown a greater degree of brain activation in attention and memory tasks in children, as compared with adults (see Casey, Geidd, & Thomas, 2000; Luna, Padmanabhan, & O’Hearn, 2010, for a review). Over the course of development, the specificity of neural connections increases through processes such as synaptic loss. With changes in intra-regional connectivity (large-scale), neural activity becomes more narrowed, or finely tuned (small-scale), resulting in functional specialization. The development of functional specialization may enable processing within different areas of the brain to become increasingly segregated. This change, referred to as parcellation, may encapsulate, or modularize, information processing streams within their corresponding structures. As a result, there should be less informational exchange, and also less interference, between certain brain regions over the course of neurodevelopment. Presumably, the emergence of partial modularity

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should increase the efficiency of language processing in the brain (Johnson, 2005). This appealing notion of modularity, though, must be curbed by current knowledge of the interconnected nature of language processing in both the developing and fully developed brain. At this point, there is still strong disagreement over the extent to which language is modular and about whether modularity is the starting point or end product of brain development (Karmiloff-Smith, 1993). Segregating the neuroanatomical landscape according to language functions is misleading because it implies that language is entirely modular. Even so, it is helpful to classify brain regions in relation to their basic language roles if only to provide a framework for understanding the neurobiology of language. Several brain areas have been the historical focus of research in the neurological bases of language. These areas are directly associated with the inter-related tasks of speech perception and production and language comprehension and production. In most individuals, these language functions are dominant, or specialized, in the left hemisphere. In navigating the language areas within this hemisphere, we traverse three anatomical landmarks of the cortex: convolutions, called gyri; depressions between gyri, called sulci; and deeper grooves, called fissures. These landmarks span the four primary lobes of the cerebral cortex, two of which predominate in language: the frontal and temporal lobes (see Figure 7.1). These two lobes are separated by the lateral sulcus, commonly referred to as the Sylvian fissure. This fissure serves as an inferior boundary for the frontal lobe and a medial boundary for the temporal lobe. Surrounding the Sylvian fissure are the perisylvian areas of the frontal and temporal lobes that are most crucial for language. Within the frontal lobe, the inferior frontal gyrus includes a critical language region called Broca’s area. This area comprises two sections: pars opercularis and pars triangularis, also identified as Brodmann’s areas (BA) 44 and 45, respectively. Below BA 45 is another area involved in language functioning, called pars orbitalis (BA 47). Notably, Broca’s area lies anterior to the section of the motor strip dedicated to the organs of speech production. Accordingly, this area plays a pivotal role in speech motor planning for the oral production of language. Rogalsky and Hickok (2011) recently reviewed the functional organization of Broca’s area in regard to sentence comprehension, verbal working memory, and other cognitive processes. They suggested that the highly debated contribution of Broca’s area to sentence comprehension can partially be attributed to its function as a phonological short-term memory resource. Parietal Lobe

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Figure 7.1 Primary language-related areas in the left hemisphere of the human brain.

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The temporal lobe serves as the station for auditory perception and is thus crucial for receptive language processing. The upper surface of the superior temporal lobe, called Heschl’s gyrus, is the site to which all auditory information is projected in the brain. This area comprises part of the superior temporal gyrus, situated along the Sylvian fissure. The area on Heschl’s gyrus on the superior portion of the Sylvian fissure is the primary auditory cortex. This area is connected via ascending projections to the medial geniculate nucleus of the thalamus, where auditory information is relayed. The primary auditory cortex is therefore of profound importance for speech perception. Posterior to Heschl’s gyrus, on the inferior portion of the Sylvian fissure, is the auditory association area, also called the planum temporale. Encircling the auditory cortex, on the posterior part of the superior temporal gyrus, is Wernicke’s area, commonly referred to as BA 22. This area consists of the planum temporale, along with some additional lateral and inferior portions of the superior temporal gyrus. Wernicke’s area is most closely associated with the comprehension of spoken language. A neural pathway formed by a fiber bundle, called the arcuate fasciculus, connects Wernicke’s area to Broca’s area. This connection thus links the primary brain areas involved in the comprehension and generation of language, respectively. Although descriptions of these primary language areas often seem quite precise, there is substantial uncertainty about their exact locations and boundaries (Uylings, Malofeeva, Bogolepova, Amunts, & Zilles, 1999). Findings of wide inter-individual variability suggest the need for further exploration of these language territories. Also, although language is mostly attributed to the perisylvian areas, additional brain regions are of paramount importance in language processes. Other left frontal and temporoparietal areas, as well as areas in the right hemisphere, play important roles in language and related cognitive abilities. Subcortical regions that house structures such as the caudate nucleus (a type of basal ganglion), the cerebellum, and portions of the thalamus, are also commonly engaged in language functions. An account of language areas in the brain gives rise to a fundamental question: How do we know which brain structures subserve language functions? The earliest evidence of brain localization for language came from combining clinical and postmortem examinations of language-impaired adults. These studies led investigators to conclude that the primary brain areas for language are those defined above as Broca’s and Wernicke’s areas. As mentioned, we now know that the initial and long-standing definitions of these areas are not consistent across individuals and that these brain regions are not the sole ones responsible for language. This refinement of our knowledge in neurobiology is owed to several methodological advancements, which will soon be discussed. First, though, let us consider how the neurobiological bases of language differ in children with developmental language disorders.

Abnormal Brain Development Like the maturation of other bodily organs, brain development does not always proceed normally. When it doesn’t, there are often adverse consequences on the brain’s greatest enterprise—language development. Despite the great strides made in the study of language acquisition, we still know incredibly little about the neurobiology of developmental language disorders. What we do know is that there are at least some neural correlates of impaired language. We review several of these here, with particular emphasis on those thought to be closely associated with specific language impairment. To begin, let us present a sketch of the distinctive features of this disorder.

Specific Language Impairment Children who fail to acquire age-appropriate language skills, despite normal non-verbal intelligence and adequate educational opportunities, fit the diagnostic category of SLI (see Chapter 1

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by Schwartz). This disorder is characterized by syntactic and morphological deficits, poor vocabulary, and impaired language comprehension. It is also common to observe poor working memory, attentional deficits, as well as reading problems in individuals with SLI. Notably, there is a great deal of behavioral heterogeneity among affected individuals. Therefore, this inclusive list of deficits lacks the specificity needed to clearly define SLI. This problem is exacerbated by controversy over whether SLI is a disorder specific to language, as its name denotes. By convention, performance on non-linguistic cognitive tests must be within the average range to meet the criteria for SLI. However, there is bountiful evidence of non-linguistic deficits in individuals with SLI, observed, for example, in areas of perception, motor abilities, and mental representation (see Leonard, 2014, for a review). These findings have prompted reconsideration of whether language processes play an exclusive role in this disorder. The opening chapter of this volume provides a detailed account of the predominant theories of SLI. This account includes proposals of information processing deficits, auditory perceptual deficits, impaired grammatical representations, and abnormal procedural memory in individuals with SLI. The fact that there are a plethora of hypotheses to explain SLI is actually unsurprising, in view of the clinical diversity in this population. Unfortunately, though, this lack of theoretical coherence has wide-ranging repercussions. For researchers, it confounds subject selection, lends to manifold possibilities in data interpretation, and generates piecemeal research that often remains fragmented. For clinicians, the absence of theoretical conformity in defining SLI engenders uncertainty. This may lead to intervention that is more discretionary and less evidence-based than is desirable. In an effort to resolve this issue, some researchers have asked: Could knowledge of the neurobiology of SLI demystify this disorder?

Neurobiology of Specific Language Impairment By definition, SLI is a language disorder that is not accompanied by major signs of neurological impairment, such as focal lesions. However, there must be neurobiological complements to the behavioral differences in this disorder. Linking deviant behavior to deviations in brain structure and function should bring us closer to a coherent account of SLI and other childhood language disorders. The consensus is that SLI is associated with early neurodevelopmental abnormalities, rather than with acquired insults to the brain. To date, though, the specific origin of these abnormalities has yet to be empirically ascertained. As discussed earlier, neurodevelopment is molded by both genetic and environmental factors. Wide disparities in linguistic input impose relatively minor effects on ultimate linguistic capacity (Bishop, 1997). Therefore, environmental influences are not likely to be the chief culprit in SLI. Instead, it seems that genetic factors impacting early stages of neurodevelopment are largely accountable for this disorder. It is thought that developmental language disorders evolve due to genes that prescribe abnormal timing of prenatal neural migration, leading to deviations in the construction of the cerebral cortex (Galaburda, Sherman, Rosen, Aboitz, & Geschwind, 1985). These deviations are thought to compromise patterns of neural connectivity and as a result, the brain is not optimally configured to support language learning from birth. This critical concept is the outgrowth of multiple studies on the neurobiology of language, some of which we present in the following sections.

Postmortem Studies of Language Impairment Some of the earliest evidence for neurobiological correlates of language was drawn from postmortem studies of adult brains. In an historic study, Geschwind and Levitsky (1968) documented

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findings of asymmetry in the length of the plana temporale (PT) in a sample of 100 postmortem adult brains. As described above, the PT is a landmark (not a structure) that is located on the inferior portion of the Sylvian fissure—an area that is critical for receptive language. In this study, leftward asymmetry was observed in 65% of the specimens, whereas PT symmetry was found in 24% of the sample and rightward asymmetry was seen in 11% of the brains examined. This distribution was compared with the patterns of asymmetry in left hemisphere dominance for language, as determined by localization studies. Close similarities between the two distributions were noted. As a result, the asymmetries observed in this study were considered possible morphological markers for variations in language functioning. Subsequent findings of similar distributions in children and fetuses bolstered this hypothesis (Chi, Dooling, & Gilles, 1977; Witelson & Pallie, 1973). Years later, structural asymmetries of the cortex were directly linked to developmental language disorders. Galaburda et al. (1985) reported neuropathological findings in four postmortem brains of adult males who exhibited lifelong reading disability, accompanied by minimally low-average IQ scores. Three of the four individuals also had documented delays in oral language development, which persisted into adulthood for at least one member of the group. Autopsy results revealed two main findings. One observation was that all four individuals had symmetrical PT. This symmetry was attributable to a normal-sized left and an abnormally large right PT. As reported by the aforementioned study (Geschwind et al., 1968), the PT of the left hemisphere is typically larger than that of the right hemisphere. This asymmetry is presumably tied to the critical role of the left hemisphere-PT region in language performance. The second key finding was that all four brains had numerous architectural anomalies in both the right and left hemispheres. For example, all four individuals showed disruptions in laminar structure, called cerebral dysplasias, as well as clusters of malpositioned cells, called cortical ectopias. Notably, some of these anomalies were preponderant in the left hemisphere, surrounding the Sylvian fissure. Recall that the left hemisphere is typically dominant for language and that the areas neighboring the Sylvian fissure (the perisylvian areas) are those conventionally linked with language functioning. The findings by Galaburda et al. thus offered preliminary evidence of a correlation between language-related areas of the brain and developmental reading impairment. The results of this study also shed light on the origin of developmental reading disorders. We know that the observed structural anomalies are rooted in abnormal brain development. Asymmetries of the brain, for example, are established during the third prenatal trimester (Chi et al., 1977). Effects on this period of brain development could thus result in the emergence of atypical PT symmetry. Likewise, the cortical ectopias found in these autopsies arise during prenatal neurodevelopment when clusters of neurons are displaced during neural migration. The course of brain development is also implicated by the bilateral feature of these aberrations. In contrast to localized brain damage that typifies acquired language disorders, the more diffuse brain anomalies in this study concur with the global nature of brain maturation (Plante, 1996). A methodologically similar study was conducted with three postmortem brains of females who had lifelong reading disability (Humphreys, Kaufmann, & Galaburda, 1990). All three brains were found to have symmetrical PT and cortical ectopias, similar to the male brains discussed above. However, the neuropathological anomalies between the male and female brains of these two studies differed in type, frequency, and distribution, suggesting that cortical structure may vary by gender (Lane, Foundas, & Leonard, 2001). Findings of atypical symmetry in reading-impaired individuals provided the impetus to examine brain structure in related language disorders. To date, we know of only one documented study of the PT in the postmortem brain of a language-impaired child (Cohen, Campbell, & Yaghmai, 1989). This study examined the brain of a 7-year-old girl with a history of

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language delay and attention deficit disorder with hyperactivity. Akin to findings for reading impairment (Galaburda et al., 1985), symmetry of the PT was observed in the brain of this language-impaired child. To recap, early postmortem studies suggest that developmental reading impairment, and perhaps language delay, is associated with symmetry of the PT. In addition, these studies point to abnormal brain development as the genesis of developmental language disorders. These conclusions, however, are constrained by the limitations inherent in postmortem studies. One drawback of postmortem studies is that they rely on visual inspection and may therefore lend to subjectivity in the analysis and description of findings. Obviously, these investigations are also limited by the small number of specimens included. It is also critical to acknowledge that research based on postmortem brains of adults can only comment on the outcome of language development in adults, not on the process of language development in children. Along these lines, it has been well-established that autopsy studies of adults with acquired language disorders cannot be generalized to normal language populations (Thomas & Karmiloff-Smith, 2002). Finally, we note that both the development and functioning of language involve a dynamic progression of events, which cannot be captured by examination of static, postmortem specimens. Fortunately, the drawbacks of postmortem analysis have been mitigated by technological advances in brain imaging techniques. In the following section, we describe an assortment of studies that have applied these methods to the study of neurobiology in child language disorders. We focus here on the knowledge gleaned from this body of research and refer the reader to Chapter 24 by Shafer, Zane, and Maxfield for a description of some of the methods discussed.

Neuroimaging Studies of Childhood Language Since the 1980s, progress in technology has made us remarkably sophisticated at examining neurobiology. Combined advances in MRI, fMRI, and PET have dramatically enhanced our understanding of brain-language relations. The refinement of MRI, in particular, has facilitated the search for brain correlates of language. This tool allows investigators to measure brain structure within an anatomical region using a method called morphometric analysis. In contrast to autopsy studies, MRI permits the study of live brains and, thus, confers the benefit of relating neuroanatomical data to behavioral data collected from individuals. In the next section, we review research that has employed MRI in the quest to identify linguistic and non-linguistic markers in the brains of individuals with SLI. First, the search for linguistic correlates is divided into four sections that discuss brain asymmetry, subcortical structures, gyral morphology, and the relationship between brain structure and language functions in SLI.

Neuroanatomy of SLI: In Search of Linguistic Correlates Is Brain Asymmetry Typical in SLI? Using MRI, Plante and colleagues (Plante, Swisher, & Vance, 1989) studied a 4.9-year-old pair of dizygotic twins, a male with SLI and a female with no evidence of language impairment. Given the difficulties in using MRI scans to measure the planum temporale (PT), a larger area that includes the PT, the perisylvian area, was measured instead. Typically, this area is larger in the left hemisphere, similar to the normal pattern of leftward PT asymmetry. The male twin with SLI exhibited an atypical configuration: symmetrical perisylvian structures, consistent with the abovementioned findings for individuals with reading disability (Galaburda et al., 1985) and language impairment

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(Cohen et al., 1989). Interestingly, his non-impaired twin sister had a right perisylvian area that was larger than the left perisylvian area. A subsequent MRI study by Plante et al. (Plante, Swisher, Vance, & Rapcsak, 1991) further investigated perisylvian symmetry in SLI. The perisylvian brain regions of eight males with SLI (ages 4.2–9.6) were compared with those of male controls matched for handedness. Most of the SLI subjects (six out of eight), as compared with only two of the eight control children, deviated from the normal left > right pattern. A hasty interpretation of the studies by Plante and colleagues might lead one to conclude that SLI is characterized by atypical patterns of the left-right perisylvian areas, but what of the non-impaired twin who also showed unusual asymmetry of this region (Plante et al., 1989)? This finding suggests that normally developing siblings of SLI children may also show deviant structural patterns. Plante (1991) probed this supposition by examining the parents and siblings of four out of the eight SLI subjects from the study by Plante et al. (1991). Some of these parents reported a history of language problems, and several of the siblings were found to have language difficulties at the time of the investigation. Analysis of MRI scans revealed that most parents and siblings of the SLI children had deviant symmetry in the perisylvian region. However, findings of structural configurations did not neatly correspond with the language abilities of the subjects. This factor cast doubt on the behavioral relevance of the observed structural patterns. Moreover, the presence of atypical configurations in non-impaired individuals indicates that asymmetry of the perisylvian area does not necessarily reflect language impairment. Thus, at most, the findings of Plante et al. suggest that aberrant asymmetry of the perisylvian area may bear some association with language abilities. Along with their equivocal findings, these MRI studies of children with SLI and their family members have some significant methodological limitations. For example, the region of interest (ROI) in these studies was liberally defined such that we do not know whether the entire perisylvian area or only a section of it contributed to the observed atypical patterns. We also do not have detailed information on how the groups in Plante’s (1991) study were matched. Nonetheless, these investigations are valuable because they expanded the limited database on neuroanatomical correlates of developmental language disorders. Having designated the perisylvian area as the only ROI, the aforementioned studies (Plante, 1991; Plante et al., 1989, 1991) may have overlooked deviations in other cortical areas in individuals with SLI. Jernigan and colleagues (Jernigan, Hesselink, Sowell, & Tallal, 1991) employed MRI to examine a wider range of cortex in 8–10-year-old children with significant receptive and expressive language delays and severe learning disabilities. Scans of 20 children with this diagnosis were compared with the scans of 12 normally developing controls, matched for age, gender, and handedness. Significant differences in structural symmetry were observed between the groups. The language-learning-impaired group exhibited leftward asymmetry of the superior parietal region and rightward asymmetry of the inferior frontal region. Controls, on the other hand, showed reverse patterns of asymmetry. Smaller volumes for most of the measured structures in the language-learning-impaired subjects was also reported. In particular, these subjects demonstrated bilaterally reduced volume in posterior perisylvian regions, which include the PT. Notably, volume was especially reduced in these regions of the left hemisphere in the language-learning-impaired subjects. These observations are in line with findings by Plante et al. (1989, 1991) of abnormal configurations of the perisylvian area in children with SLI. A novel observation in this study was that volume was bilaterally reduced in subcortical structures, including the caudate nucleus. This finding highlighted the importance of using morphometric analysis to examine deeper structures than those previously associated with developmental language disorders.

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Are Subcortical Structures Typical in SLI? In a case study of a 10-year-old boy with a history of SLI, articulation difficulties, and behavioral problems, Tallal, Jernigan, and Trauner (1994) used MRI to examine subcortical structures. The head of the caudate nucleus was found to be aberrant bilaterally. Notably, though, the child had been severely impaired in expressive language and articulation at 4 years of age, but improved significantly in these areas by age 8. Relating his neural profile to a discrete diagnosis of SLI therefore seems questionable. Evidence for an association between subcortical anomalies and language impairment has accumulated from studies of a unique pedigree named the KE family. Among three generations of this family, 15 (6 male, 9 female) of its 37 members have a verbal dyspraxia that is transmitted as an autosomal-dominant mutation. Extensive behavioral testing has revealed significantly impaired abilities in affected, relative to unaffected, members in articulation, grammar, semantics, and both verbal and non-verbal IQ. A clear finding is that language production is generally more deficient than language comprehension in the affected group (Belton, Gadian, & Vargha-Khadem, 2003; Vargha-Khadem, Watkins, Alcock, Fletcher, & Passingham, 1995; Vargha-Khadem et al., 1998). Given these deficits and their severity, there is debate as to whether this family can be classified as specifically language impaired (Bishop, 2003). Several neuroimaging studies have shown deviations in subcortical structures in impaired members of the KE family. This finding was initially discovered in a functional PET investigation by Vargha-Khadem and her research team (Vargha-Khadem et al., 1998). The two affected KE members that were studied displayed significantly more activation in the left caudate nucleus than did four normal controls. The structural counterpart of this anomaly was found by comparing affected and unaffected family members using MRI with a method called voxel-based morphometry. This technique is used to detect minor regional differences in gray or white matter. Here, results showed two bilateral subcortical anomalies in affected, but not unaffected, members: less gray matter in the caudate nuclei and more gray matter in the putamen. These findings were replicated in later studies that examined subcortical structures, some of which employed more detailed volumetric analysis (Belton, Salmond, Watkins, Vargha-Khadem, & Gadian, 2003; Liegeois et al., 2003; Watkins et al., 2002). Two key interpretations have been posited to explain the observed subcortical anomalies in affected family members. One strong proposal is that atypical development of the caudate nucleus is related to deficits in oromotor control and articulation abilities in this family. This suggestion was derived from the significant correlations found between the volume of the nuclei and scores of affected members on a test of oral praxis and a test of non-word repetition (Watkins et al., 2002). This may bear implications for the caudate nuclei abnormalities in language-impaired children that have been reported by Jernigan et al. (1991) and Tallal et al. (1994). It has also been suggested that abnormalities of the caudate nuclei and putamen may be related to the language deficits in affected members. This is based on an extrapolation from the literature on aphasia. Several studies have reported a link between acquired damage to the striatum (caudate nuclei and putamen) and language deficits in adult aphasics that are comparable to those of the KE family (e.g., Pickett, Kuniholm, Protopapas, Friedman, & Lieberman, 1998). However, the developmental nature of the KE family’s impairment versus the acquired nature of aphasia limits the explanatory power of this proposal. More conclusive information on brain-language relations in the KE family may be gained by comparing the neural phenotype of affected members with those of other developmental language disorders. To return to the question of interest, subcortical anomalies have been observed in children with SLI, although the findings require replication. Moreover, preliminary evidence suggests that atypical structure and function of the striatum may be associated with speech and language impairments of a developmental nature. A more definitive interpretation of subcortical anomalies in SLI

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was forwarded as part of the procedural deficit hypothesis (Ullman & Pierpont, 2005). This hypothesis claims that SLI can be largely accounted for by abnormalities of the brain structures that subserve the procedural memory system—the system that underlies the learning and control of motor and cognitive skills and procedures. These structures are embedded in circuits of frontal cortex and the basal ganglia, both of which have been found deviant in SLI. Although additional data are needed to support this hypothesis, it is valuable because it attempts to bridge the behavioral data on SLI with a neural basis. The information presented thus far conveys that SLI may be neurobiologically expressed as atypical brain asymmetry and, perhaps, as subcortical anomalies. This may not be the full picture, though, because the studies discussed above were limited in their scope of analysis. A more detailed investigation of cortical structure could assess, for example, whether the morphology, or structural form, of gyri differs in SLI.

Are Gyral Morphology and Volume Typical in SLI? Gyral morphology in SLI was addressed by Clark and Plante (1995) in a study examining the inferior frontal gyrus, which includes one of the classical language regions, Broca’s area. Upon examination of MRI scans, an additional sulcus was more commonly observed in parents of SLI children with documented language problems, relative to parents without this background and relative to adult controls with normal language functioning. Jackson and Plante (1997) further examined gyral morphology in SLI in an MRI study of 10 affected school-age children, each set of their parents, 10 siblings, as well as 20 adult controls. Among the family members, 15 of the 20 parents and 4 of the 10 siblings demonstrated language deficits, whereas the controls evidenced normal language skills. Analysis of gyral morphology in bilateral posterior perisylvian areas revealed distinct differences between the groups. The typical finding of one gyrus pattern was shown in 75% of the hemispheres in controls, but in only 58% of the hemispheres in family members. An intermediate gyrus was observed in 23% of the hemispheres in controls, relative to 41% of the hemispheres in family members. Interestingly, an intermediate gyrus was more commonly shown in the left, rather than right, hemisphere in both groups of subjects. Additionally, no clear correspondence was found between the presence of an intermediate gyrus and language functioning in the family members. This result echoes Plante’s (1991) finding of inconsistency between asymmetry in perisylvian areas and language status. Thus, here too, one may question whether the observed differences in brain structure in SLI carry behavioral consequences. A few recent studies have used voxel-based morphometry as a neuroimaging technique to examine gray-white matter gyral volume, as well as cerebrospinal fluid volume, in children with SLI (Badcock et al., 2012) or language impairment (Jancke et al., 2007; Soriano-Mas et al., 2009). Findings revealed subtle brain structural alterations, with mixed findings that were difficult to reconcile due to varying clinical criteria for diagnosing language impairment and failure to control for intelligence scores and total intracranial volume, which are associated with volumetric measures of gray-white matter. Girbau-Massana, Garcia-Marti, Marti-Bonmati, and Schwartz (2014) controlled for these factors and found lower overall gray matter volume, particularly at the right postcentral parietal gyrus, and greater cerebrospinal fluid volume in children with SLI.

Is Atypical Brain Structure Accompanied by Atypical Brain Function in SLI? Gauger and colleagues (Gauger, Lombardino, & Leonard, 1997) searched for a structure-function relationship by acquiring surface area measurements of the classical language regions in children

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with SLI. High-resolution volumetric MRI scans were used to measure the brains of 11 children with SLI and 19 normal-language controls, matched for age and gender. The planum temporale in Wernicke’s area and the pars triangularis in Broca’s area were identified as the regions of interest. Quantitative comparisons of these perisylvian areas revealed three group differences: (1) greater rightward asymmetry of the total planum (planum temporale + the posterior ascending ramus, which is the terminal branch of the Sylvian fissure), (2) a significantly smaller left pars triangularis, and (3) a narrower right hemisphere in the SLI group. The first two findings contrast with the typical leftward asymmetry of the planum temporale and pars triangularis in normal individuals (Foundas, Leonard, & Heilman, 1995). Given the well-established roles of these areas in language, the present findings for the SLI group were taken as evidence for a correspondence between brain structure and function. This interpretation thus posits that SLI results from neurobiological defects in the perisylvian areas of the brain.

Neuroanatomy of SLI: In Search of Non-Linguistic Correlates At this point, it is important to recall that children with SLI show weaknesses in many tasks that fall outside the domain of language. In view of this factor, we would expect the neurobiology of SLI to reflect, not only linguistic limitations, but non-linguistic deficits as well. A study by Trauner, Wulfeck, Tallal, and Hesselink (2000) tested this assumption. Thirty-five children with SLI and 27 controls with normal language (ages 5–14 years) were assessed via a neurological battery and MRI. Results of the study showed neurological abnormalities (e.g., fine motor impairment) in 70% of the SLI children, as compared with only 22% of the control children. Similar to the neurological battery, MRI scans also revealed significant group differences. Structural deviations were found in the scans of 12 out of the 35 children with SLI—about one-third of the group—whereas none of the 27 controls showed abnormal scans. Interestingly, the observed abnormalities ranged in type, including ventricular enlargement, central volume loss, and white matter aberrations. This diversity in structural profile demonstrates that children with SLI are neurobiologically heterogeneous. A correlation between these MRI findings and the observed neurological abnormalities would attest to a structure-function relationship for nonlanguage abilities. This association, however, was not borne out. The probability of showing an abnormal MRI scan was apparently no greater if the child had neurological abnormalities than if the child presented as neurologically normal. Despite this factor, there is a key message in the neurological results. The finding of atypical neurology in many members of the SLI group supports the notion that SLI is not restricted to the sphere of language, but instead, is cognitively pervasive. This conclusion underscores the importance of accounting for non-linguistic abilities in the neurobiology of SLI. In pursuit of this goal, Ellis Weismer, Plante, Jones, and Tomblin (2005) used functional MRI (fMRI) to examine whether SLI is characterized by a general limitation in processing capacity that affects both linguistic and non-linguistic performance (Kail, 1994; Leonard, McGregor, & Allen, 1992). To test this claim, verbal working memory was measured in adolescents with SLI and in normal language controls. Subjects were required to perform a listening span task that involved sentence encoding and recognition of final words from prior groups of sentences. The utility of this task was its ability to provide information about two types of neural systems: those implicated in language processing and those systems involved in more general processing for functions such as working memory and attention. Neuroimaging revealed several group differences. During encoding, the SLI group showed hypoactivation of two regions implicated in general cognitive functions: the left parietal region, associated with attentional control mechanisms, and the precentral sulcus, a region associated with

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memory processes (e.g., Braver et al., 1997; Rypma, Berger, & D’Esposito, 2002; Shaywitz et al., 2001). In the recognition phase, the group with SLI displayed hypoactivation of the insular portion of the inferior frontal gyrus, which includes Broca’s area. Thus, atypical neural activation in the SLI group was observed not only in a language area but also in regions associated with general cognitive processing. This finding was taken as support for a general processing limitation in adolescents with SLI. To summarize, neuroimaging studies have revealed differences in brain structure and function in individuals with SLI as compared with normal language controls. Major structural differences in SLI include deviant brain asymmetry in perisylvian areas, reduced volume of cortical and subcortical structures, and aberrant gyral morphology. Functional differences involve abnormal patterns of activation in brain areas associated with both linguistic and non-linguistic functions. Some of the studies described above considered whether the neurobiology of SLI is concentrated in families. In the following section, we examine the significance of this inquiry.

Neuroanatomy of SLI: In Search of an Etiology Earlier in this chapter, we noted that both genetic and environmental factors shape brain development, and thus, underlie developmental language disorders. Because family members typically share their genes and environment, family studies cater to a vital goal of research in SLI: identification of its etiology. Ultimately, these studies should inform us of the extent to which various genetic and environmental factors place a child at risk for SLI. At this time, though, the evidence accrued mostly pertains to genetic influences, the probable origin of child language disorders. Support for genetic contributions to SLI is garnered from at least three types of investigations: twin studies, familial aggregation studies, and pedigree analyses (see Bishop, 2003, for a review). Generally, these investigations involve assessing language status among family members. Some of these studies have focused on examining neurobiology in families with and without a positive history of SLI.

Twin Studies The principal advantage of twin studies lies in the existence of two types of twins: monozygotic twins (MZ), who are genetically identical and typically share a common environment, and dizygotic twins (DZ), who share an average of 50% of their genes and generally share a common environment. In theory, there should be higher concordance (shared effects) for MZ twins than for DZ twins if SLI is genetically determined. Earlier, in reviewing the research of Plante et al. (1989), we discussed the outcomes of an MRI study of 4.9-year-old DZ twins discordant for language status. In a later study, Preis, Engelbrecht, Huang, and Steinmetz (1998) used MRI to examine a pair of 9.2-year-old MZ twin boys concordant for SLI. Results showed bilateral heterotopias (displacements of gray matter from neuronal migration) in the parieto-temporal white matter in both twins. The heterotopias were more prominent on the left side in both subjects and were more pronounced in the twin with poorer language abilities. It was therefore concluded that the observed heterotopias were likely to have a causal link with the language impairment in both twins. Reflecting on these two investigations, it is apparent that twin studies of SLI neurobiology have been limited in their analysis of only one type of twins (MZ or DZ pairs), as well as in their small sample sizes. Future research should capitalize on the potential for twin studies to elucidate the role of genes in the neurobiology of SLI.

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Familial Aggregation The hereditary nature of SLI has been corroborated by numerous studies (see Stromswold, 1998, 2000; Tallal et al., 2001; Chapter 10 by Tomblin, for reviews), only several of which have examined neurobiological data. In an earlier section, we reviewed two reports of familial aggregation in SLI: deviant asymmetry in the perisylvian region (Plante, 1991) and atypical gyral morphology (Jackson & Plante, 1997). Hugdahl and colleagues (Hugdhal et al., 2004) followed up on studies of brain structure in families with a positive history of SLI by examining brain function in such families. Using fMRI, the frontal and temporal lobes of five Finnish family members with SLI (one grandmother, two daughters, and two grandsons) and six normal language controls were measured to detect changes in neuronal activation. During data acquisition, the participants listened to strings of isolated vowel sounds, pseudowords, and real words. Group differences in activation were observed in both the frontal and temporal lobes. Within these territories, the SLI family showed reduced activation that was most distinct in two areas: in the dorsal inferior frontal gyrus bordering the premotor area (frontal lobe) and in the medial temporal gyrus bordering the superior temporal sulcus (temporal lobe). These areas are known to be crucial for speech processing and phonological awareness. Thus, a positive family history of SLI was found to co-occur with reduced activation in speech-language areas of the brain. This finding dovetails with those of MRI studies that revealed structural abnormalities in the same or approximate regions of the brain. Evidence for familial concentration, however, does not translate into proof for genetic effects. It is reasonable to surmise that sharing an environment with language-impaired relatives may breed learning of faulty language patterns by genetically intact relatives (Bishop, 2003). These deficient patterns may, in theory, alter the neurobiological substrates of language in individuals with SLI.

Pedigree Analyses Exploring the phenotype of a developmental disorder within an extended family is referred to as pedigree analysis. The most acclaimed one in the relevant literature is, by far, the study of the KE family. Above, we described this family as highly heritable for a severe speech and language disorder, which some investigators classify as SLI. The cause of this disorder has been localized to a point mutation of the FOXP2 gene in affected members (Lai, Fisher, Hurst, Vargha-Khadem, & Monaco, 2001). With this knowledge, researchers have worked toward relating this genetic abnormality to behavioral and neural phenotypes, the latter of which is our present focus (Vargha-Khadem, Gadian, Copp, & Mishkin, 2005). As would be expected for a neurodevelopmental disorder, the affected members of the KE family have no frank focal lesions on standard neuroradiological measures of MRI. However, using more sensitive methods, structural and functional imaging studies have uncovered multiple brain abnormalities in these individuals. An MRI study (Belton et al., 2003) that used voxel-based morphometry found the following in affected members: levels of gray matter were abnormally low in the inferior frontal gyrus (Broca’s area), the precentral gyrus, the temporal pole, the head of the caudate nucleus, and in parts of the ventral cerebellum. Levels of gray matter were abnormally high in the posterior portion of the superior temporal gyrus (Wernicke’s area), the angular gyrus, and in the putamen. Note here, the prevalence of abnormalities in speech-language areas of the brain. An fMRI study with two language experiments, one of covert (silent) verb generation and the other of overt (spoken) verb generation and word repetition, also revealed brain anomalies in affected members (Liegeois et al., 2003). In the verb generation tasks, the unaffected group showed a typical left-dominant pattern of activation in Broca’s area; in the repetition task, they showed a

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more bilateral pattern. By contrast, the affected group displayed activation that was more posterior and more widely bilateral in all tasks. Moreover, the affected members showed less activation than the unaffected members in Broca’s area, its right homologue, and in the putamen. However, the affected members also displayed overactivation in areas that are not commonly engaged in language, such as the postcentral, posterior parietal, and occipital regions. Three possibilities were put forth to explain this overactivation: the enlisting of compensatory circuits, the use of atypical strategies, and increased cognitive effort or attention. Together, these results describe the neural phenotype of a speech and language disorder that is prototypically genetic in nature. Debates persist as to whether this phenotype represents SLI and, if so, whether it can be generalized to all cases of the disorder. Even if the gene-behavior pathway of SLI differs from that of the KE family, as we suspect, this pedigree analysis is helpful for the study of child language disorders. Tracing the effects of the mutated FOXP2 gene on brain structure and function across development may increase our ability to define the language roles played by particular brain areas at different stages of life. To conclude this section, neurobiological studies of familial aggregation, the KE pedigree, and to a lesser extent, of twins, provide converging evidence for a genetic origin of SLI. Genetic factors are manifested in brain structures that are both language- and non-language specific. This widespread outcome is consistent with the fact that genes expressed in the cortex appear to be expressed throughout most cortical regions (Johnson, 2005). Therefore, early genetic abnormalities may impact multiple areas of the brain, although certain functions, such as language, may be differentially vulnerable to these effects. Because genetic outcomes evolve over time, the etiology of SLI will remain obscure until research focuses on the course of neurodevelopment as a function of both genes and environment. The dearth of studies on brain development in SLI is, in part, due to the practical limitations of applying MRI and PET to young, healthy children. Besides, these techniques have poor temporal resolution and are thus of little utility in charting the essential time differences in neural processing across development. Fortunately, though, these inadequacies may be surmounted by complementing neuroimaging data with measures of neurophysiology. Following is a brief description of these measures, with a synopsis of their main findings for the SLI population (see also Chapter 24 by Shafer, Zane, & Maxfield).

Neurophysiological Studies of Childhood Language Two methods have been developed to examine neurophysiology, or neural activity, in a non-invasive manner. The more common methodology is called electroencephalography (EEG). This technique uses scalp electrodes to record the voltages at the surface of the skull that are generated by neuronal currents in the brain. By time-locking a stimulus to a point in the ongoing EEG and repeating that stimulus, voltage fluctuations that are not tied to the stimulus become eliminated in an averaging process. What remains is the electrical brain activity that is directly related to the specific stimulus, or event, called an event-related potential (ERP). The ERP technique offers remarkable temporal precision and is therefore beneficial for examining rapid neural activity, which is common to speech and language processes. Because the ERP method shows maturational changes and does not require an overt response by the subject, the technique is particularly well-suited for studies of child language development. ERPs are displayed as waveforms that are characterized by their components. These components represent demarcated scalp distributions of electrical activity that are presumably tied to stimulus variables. The analysis of ERP waveforms involves classification of specific components according to their polarity, the latency of their initial or peak occurrence, and their topographical distribution

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over the scalp (Otten & Rugg, 2005). At least some ERP components reveal developmental changes in these characteristics from childhood to adulthood (Neville, 1995). In fact, various studies have reported marked developmental changes in ERPs (Albrecht, von Suchodoletz, & Uwer, 2000; Daruna & Rau, 1987; Morr, Shafer, Kreuzer, & Kurtzberg, 2002; Pang & Taylor, 2000; Ponton, Eggermont, Kwong, & Don, 2000; Shafer, Morr, Kreuzer, & Kurtzberg, 2000; Sharma, Kraus, McGee, & Nicol, 1997). Assuming that these changes reflect neurobiological development, ERPs should be especially useful for examining the maturation of skills in children with and without language impairment (Bishop & McArthur, 2005). ERP studies in the SLI population are relatively sparse, but informative. Mirroring our division of neuroimaging data into linguistic and nonlinguistic categories, we present neurophysiological data on children with SLI as follows.

Neurophysiology of SLI: In Search of Linguistic Correlates Semantic abilities in individuals with SLI have been examined via an ERP component referred to as the N400. This component is a negative-going wave that occurs between 250–500 ms poststimulus onset, peaking at approximately 400 ms (Kutas & Hillyard, 1980). The N400 is typically observed in the centro-parietal region of the brain and has been shown to vary systematically with semantic processing (see Kutas & Federmeier, 2000, for a review). The prevailing view is that the N400 indexes on-line semantic integration processes, with larger N400s suggestive of more effortful integration (e.g., Holcomb, 1993; but see Besson, Kutas, & Van Petten, 1992; Kellenbach, Wijers, & Mulder, 2000; Kutas & Federmeier, 2000; Kutas & Hillyard, 1989). Neville and colleagues (Neville, Coffey, Holcomb, & Tallal, 1993) observed that school-age children with SLI had abnormally large N400 amplitudes in a task of judging the semantic plausibility of sentences with congruent and incongruent word endings. These results contrasted with those of age controls with normal language abilities. In a more recent study, children with SLI showed a delayed N400 effect in a task of identifying whether picture-word pairs matched or mismatched (Cummings & Čeponienė, 2010). This finding supports the notion of a semantic integration deficit in SLI, possibly resulting from weaker or less efficient connections in their language networks. Another ERP study of semantic abilities (Ors et al., 2001) employed a visual semantic priming task in testing parents of children with SLI and parents serving as controls. Despite comparable behavioral performance, the ERPs of fathers (but not of mothers) of SLI children had lessdifferentiated responses between congruent and incongruent sentences as compared with those of controls. Furthermore, the fathers of SLI children displayed larger N400 amplitudes than did the controls. These findings were considered residual markers of language impairment in fathers of children with SLI. The ERP methodology has also shed light on grammatical skills in children with SLI. In the study by Neville et al. (1993), mentioned above, an SLI subgroup with significant morphosyntactic deficits, as opposed to one with auditory processing problems, did not demonstrate the normal asymmetry of larger N400 amplitudes for closed-class words in the anterior left hemisphere than in the anterior right hemisphere. This finding corresponds with evidence from MRI studies that children with SLI fail to show the typical leftward asymmetry in brain areas associated with language functions (e.g., Gauger et al., 1997; Jernigan et al., 1991; Plante et al., 1991). Also, the ERP differences between subgroups of children with SLI in this study suggest that their behavioral differences in grammatical and auditory processing skills might reflect neurophysiological differences. In a recent study of grammatical processing, SLI children demonstrated an attenuated and non-significant sustained anterior positivity in processing the wh-dependency of object relative to subject questions as compared with typically developing children (Epstein, Hestvik, Shafer, & Schwartz, 2013). In view of the greater working memory demand in processing object

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wh-questions, this group difference may reflect poor working memory skills in SLI. Purdy, Leonard, Weber-Fox, and Kaganovich (2014) observed that children with a history of SLI, relative to typically developing children, showed a delayed, attenuated, and shorter P600 effect (a positive-going deflection elicited by syntactic anomalies) in response to long-distance finiteness errors in an auditory grammaticality judgment task. The children’s grammaticality judgments were consistent with the ERP outcomes. Children with grammatical SLI (G-SLI), a subgroup identified based on marked grammatical deficits, elicited ERP brain responses that revealed a selective impairment to neural circuitry dedicated to grammatical processing. Neural circuitry involved in semantic processing and low-level auditory neural responses that were non-grammar-specific, by contrast, were normal (Fonteneau & van der Lely, 2008). This finding indicates that different neural signatures may index particular instantiations of SLI. Discourse processing is another language ability that has been examined in individuals with SLI. Shafer, Schwartz, Morr, Kessler, and Kurtzberg (2000) studied the ERP correlates of processing the grammatical function word “the” in story context and followed by nonsense syllables in children with SLI and typical language development (TLD). On both tasks, the SLI group showed reduced activation (indexed by a reduced positivity) at left temporal sites and increased activation at right temporal sites relative to children with TLD. Shafer and colleagues suggested that children with SLI were compensating for poor processing at a structural level (shown as reduced left hemisphere processing) by depending on discourse information (shown as increased right hemisphere processing). Similar to the aforementioned study, this reversed asymmetry in neurophysiology parallels the deviant asymmetry in neuroanatomy of the perisylvian areas in children with SLI. In sum, the ERP waveforms of individuals with SLI (and those of their fathers) have shown deviations in linguistic tasks. The differences include (1) abnormally large and delayed N400 amplitudes, suggesting deficient semantic integration; (2) attenuated waveforms in processing whdependencies of object questions, possibly related to working memory deficits; and (3) deviant asymmetry in discourse processing, which is consistent with anatomical evidence. These preliminary findings suggest that ERPs may enable us to read out a neural code for language abilities in impaired and non-impaired individuals. This prompts us to ask: Are there ERP correlates for nonlinguistic abilities in SLI?

Neurophysiology of SLI: In Search of Non-Linguistic Correlates One line of ERP research on SLI is concerned with assessing the auditory processing skills of affected individuals. This interest is based on the theory that SLI is a downstream consequence of low-level auditory perceptual deficits (Miller, 2011; Tallal & Piercy, 1973, 1974). ERP studies of tone detection, frequency discrimination, and automatic discrimination in individuals with SLI have tested the validity of this hypothesis. The standard ERP waveform in response to a brief auditory stimulus has peaks and troughs that are characterized by their polarity and order. For example, the first positive, first negative, and second positive deflections are labeled P1, N1, and P2, respectively. In general, these early components are highly replicable across sessions within an individual (Halliday, 1982) and have low variability across individuals (e.g., Sandman & Patterson, 2000). Furthermore, these components vary predictably with systematic physical changes in the eliciting stimulus (e.g., frequency), thereby signifying a low-level change in the activation of sensory pathways (see Key, Dove, & Maguire, 2005, for a review). Studies that have recorded auditory ERPs to tones in language-impaired children have reported mixed findings for the early components. Mason and Mellor (1984) examined responses to a 1000-Hz tone with a duration of 200 ms. No differences were observed between typically

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developing children and children with SLI in the latency or amplitude of the N1 or P2 responses. Several investigations, though, did not replicate these findings. In one case, a group of five language-impaired children showed a larger P2 than normally developing peers (Adams, Courchesne, Elmasian, & Lincoln, 1987). A later study (Lincoln, Courchesne, Harms, & Allen, 1995) did not find deviant P2 amplitudes in children with SLI, but did observe atypically large amplitudes and long latencies of the N1 in impaired subjects in one of their experiments. Additionally, longer latencies of the N1, P2, and N2 in 20 children with severe language impairment (but with questionable diagnoses), as compared with controls, were reported by Tonnquist-Uhlén (1996). Intriguingly, Neville et al. (1993) found an abnormally small and delayed N1 at right frontal sites in a subset of children with SLI who performed poorly on a test of rapid auditory processing. A number of ERP studies on auditory processing in SLI have focused on frequency discrimination. In two studies of this nature (Bishop & McArthur, 2005; McArthur & Bishop, 2004a, 2004b), the N1-P2-N2 region of average waveforms in children and young adults with SLI was immature relative to age-matched controls with normal language skills. This was the case, however, for several SLI subjects who showed normal performance on a frequency discrimination task. Automatic discrimination of acoustic stimuli has also been examined in ERP studies of language-impaired individuals. These investigations have focused on a discriminative response called the mismatch negativity (MMN), a sharp negative shift between 100 to 300 ms at fronto-central scalp sites following the onset of an acoustic change. This component is elicited by a stimulus that is ‘deviant’ in a train of ‘standard’ stimuli that are presented frequently. Each new stimulus is compared with a memory trace of preceding auditory information, and an MMN occurs if the new stimulus violates the expectation of the auditory cortex. Thus, the MMN indexes the automatic detection of a stimulus change (Näätänen, Gaillard, & Mantysalo, 1978) and reflects the encoding of a memory trace to a pattern in the environment (Näätänen, Paavilainen, Alho, Reinikainen, & Sams, 1989). A handful of studies have reported deviant MMNs in children with developmental language disorders (see Leppänen & Lyytinen, 1997, for a review). Kraus and colleagues (Kraus et al., 1996) observed smaller MMN amplitudes to synthesized [da] versus [ga] speech sounds with 40-ms transition durations in children with learning problems (learning disability or attention-deficit disorder) relative to children with typical learning skills. Similarly, Uwer, Albrecht, and von Suchodoletz (2002) found smaller MMNs to natural consonant-vowel syllables differing in place of articulation (/ba/, /da/, and /ga/), but not to tones, in children with SLI (also see Shafer, Morr, Datta, Kurtzberg, & Schwartz, 2005). These findings suggest that deviant MMNs in SLI reflect an auditory processing deficit that is specific to speech stimuli. Anomalies in the MMN, though, are not unique to SLI. For example, there is evidence of attenuation in the area of the late MMN in children diagnosed with dyslexia, as well as their unaffected siblings, who had a genetic risk for dyslexia (Neuhoff et al., 2012). Bishop’s (2007) review of the MMN literature on SLI and dyslexia highlights the limitations of these studies, including methodological inconsistencies, low reliability in the measures used, and low statistical power. As noted above, there is inconsistent evidence describing the nature of early components evoked during auditory processing in individuals with SLI. Moreover, the results of various studies do not consistently locate a deficit at the same level of auditory processing. For example, a study that examined ERPs in discriminating CV syllables and consonant-to-vowel transitions (spectral sweeps) isolated from the syllables found diminished spectral processing in language-impaired children. This finding was suggestive of a deficit in acoustic feature integration, which occurs at higher levels of sensory processing (Čeponienė, Cummings, Wulfeck, Ballantyne, & Townsend, 2009). Another example is a study (Ors et al., 2002) that involved discrimination of deviant tone and speech stimuli. The N1-P2 components in a group of SLI children matched those of controls,

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but the SLI group showed deviations at a later stage of auditory processing: delayed latencies of the P3 (a positive potential that occurs between 300–800 ms post-stimulus onset) to tone and speech stimuli and smaller P3 amplitudes to speech stimuli were observed in the SLI group relative to controls. In a subsequent study, delayed P3 latencies to speech stimuli were exhibited by parents of children with SLI, as compared with parents serving as controls (Ors, Lindgren, Blennow, & Rosen, 2002). Thus, whereas some results point to a deficit at low levels of cortical processing (indexed by deviant early potentials), others suggest a higher-order auditory processing deficit (indexed by deviant P3 responses) in SLI. Notably, though, the nature of the children’s disorders in these studies differed. The disordered populations included severe language impairments (e.g., TonnquistUhlén, 1996), various learning disabilities (e.g., Kraus et al., 1996), and SLI (e.g., Ors et al., 2002). Thus, it is likely that different auditory processing deficits underlie different types of language/ learning impairment. Accordingly, Bishop and colleagues observed that subsets of children with language impairment with auditory perceptual deficits were often younger children with receptive rather than expressive language deficits or children with concomitant reading problems (McArthur & Bishop, 2004a, 2004b, 2005). Overall, then, ERP research suggests that there are neurophysiological correlates of auditory processing deficits in SLI. These markers include waveform differences in the early components, in the MMN response, and in the P3. We have yet to determine, though, if these differences support the claim that SLI stems from a global auditory impairment that is not specific to language. Furthermore, it is essential that researchers track the correlates of auditory processing over the course of development, beginning in infants with and without risk for language impairment.

Neurophysiology of SLI: Can ERPs Predict SLI? A number of studies have attempted to identify neural risk markers for language impairment that are manifested prior to overt behavioral symptoms (see Luyster, Seery, Talbott, & Tager-Flusberg, 2011, for a review). These neural markers are subtle traits related to an elevated risk for a disorder and serve as endophenotypes, or intermediate links between genotypic risk and diagnosis. Neural markers are present in both affected and unaffected individuals who are at risk (Gottesman & Gould, 2003). This area of research has generally been conducted using neurophysiological measures in infants and toddlers at risk for language impairment. A prime example is a series of investigations by Molfese and colleagues, which sparked an interest in using ERPs to predict later-developing language abilities (see Molfese, Narter, Van Matre, Ellefson, & Modglin, 2001, for a review). Collectively, these studies showed that electrophysiological recordings at birth could be used to predict later language skills through 5 years of age. Prospective ERP studies of early neural processing of auditory stimuli found that infants with a family history of language impairment, relative to infants without risk, showed a delayed mismatch response to changes in syllable length at 2 months of age (Friedrich, Weber, & Friederici, 2004) and an attenuated mismatch response to tone pairs at 6 months of age (Benasich et al., 2006). The high-risk infants also demonstrated atypical lateralization of response to tone pairs between 6–12 months (Choudhury & Benasich, 2011), lower resting gamma power at frontal sites between 16–36 months (Benasich et al., 2008), as well as an immature developmental trajectory in response to tone pairs across the first three years of life (Choudhury & Benasich, 2011). In a retrospective study, 2.5-year-olds with age-appropriate expressive language skills showed an N400 at 19 months of age; those with poor expressive language skills who were at risk for SLI did not show an N400 at this age (Friedrich & Friederici, 2006). The identification of auditory processing deficits could potentially be a useful predictive measure of later language impairment if prospective studies of auditory processing are systematically

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conducted across populations of child language disorders, including SLI, ASD, dyslexia, and WS. This endeavor would enable us to determine if particular neural markers detected before the onset of language signal specific neurodevelopmental disorders. To date, there is a paucity of comprehensive studies that have systematically explored endophenotypes across neurodevelopmental disorders. Findings across studies, though, indicate overlap between neural risk markers in different populations with neurodevelopmental disorders. For example, the aforementioned finding of a delayed mismatch in infants at risk for language impairment appears to match that of newborns at risk for dyslexia: Leppänen and his associates (Leppänen, Pihko, Eklund, & Lyytinen, 1999) observed delayed and enhanced ERP responses to the short deviant Finnish syllable /ka/, relative to the long standard syllable /ka:/, in a newborn group at-risk for dyslexia compared to a not-at-risk control group. Similarities between the infant ERPs of these at-risk groups suggest that SLI and dyslexia may share a neurobiological substrate. Similarly, the atypical lateralization of response to tone pairs in infants at high risk for language impairment mirrors findings of atypical lateralization to speech in infants at risk for ASD (Seery, Vogel-Farley, TagerFlusberg, & Nelson, 2013). Further research should explore whether the presence of several neural markers, rather than individual ones, in an infant is predictive of a diagnosis for a particular language impairment. To conclude, the predictive use of ERPs is still at an embryonic stage, but research shows its potential for the prognosis of later language skills. If we can identify electrophysiological precursors of language deficits, we may be able to remediate the source of impaired language by providing early intervention. Environmental enrichment might influence brain structure to overcome cortical disruptions, thereby exploiting neural plasticity (Elbert, Heim, & Rockstroh, 2001). Moreover, longitudinal ERP data might allow us to developmentally track the outcomes of intervention.

Neurophysiology of SLI: Can ERPs Track Intervention Outcomes? Investigations that use ERPs to assess intervention outcomes are appealing because they may reveal neurophysiological changes resulting from treatment that are not readily apparent behaviorally. For example, Popescu and colleagues (Popescu, Fey, Lewine, Finestack, & Popescu, 2009) assessed treatment outcomes for 6–8-year-old children with primary language disorder (PLD; a larger diagnostic category comprising SLI) who completed a narrative-based language intervention program that heavily taxed semantic integration skills. In comparison with typical controls, children with PLD did not show pre-treatment differences in their N400 responses to congruous and incongruous sentence-final words. Following intervention, children with PLD showed the expected difference due to a decrease in the N400 response to congruous words. These brain response changes were interpreted as potential signals of improved lexical-semantic processing in PLD. ERP studies may also be used to assess the impact of treatment on cognitive skills that are critical for language processing, such as selective attention. Stevens, Fanning, Coch, Sanders, and Neville (2008) examined whether a six-week, highly intensive computerized language intervention program for 7-year-old children with SLI would impact their neural mechanisms of auditory selective attention, which has been found deficient in this population. Children completed standardized language tests and an ERP assessment of selective auditory attention before and after the intervention (or following a delay period for the no-treatment control group). Results revealed improvements on standardized language measures, as well as a large signal enhancement to attended stimuli, in the SLI treatment group, relative to the no-treatment control group (Stevens et al., 2008). In summary, the ERP method has the potential to measure intervention outcomes in children with developmental language disorders. Further research is needed to identify specific and reliable electrophysiological markers for this purpose.

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Neurobiology within and between Child Language Disorders Two key questions in the neurobiological study of language disorders are as follows: (1) Is a given finding of brain structure or function consistent within the impaired population? and (2) Is it specific to the population of interest? There are, in fact, contradictory results among anatomical and functional studies of SLI. There is also mounting evidence of overlap between neurobiological features of SLI and other developmental language disorders. Consider, for example, the phenotypes of SLI and dyslexia. Deficits in auditory and phonological processing are common to both of these disorders (Kamhi & Catts, 1986; Tallal, 2000). Researchers have therefore asked whether the neural profiles of SLI and dyslexia are distinct. Anatomical studies have shown that both disorders are characterized by reduced or reversed asymmetry of the plana temporale (Hynd, Semrud-Clikeman, Lorys, Novey, & Eliopulos, 1990; Kushch et al., 1993; Plante et al., 1991). Several investigations, though, have not replicated this finding and have even reported greater than typical leftward asymmetry in both populations (C.M. Leonard et al., 2002; Preis, Jäncke, Schittler, Huang, & Steinmetz, 1998). These inconsistencies may be due to the inclusion of various language and literacy deficits as SLI or dyslexia (Bishop & Snowling, 2004). Functional MRI investigations of SLI and dyslexia have revealed another similarity between the two disorders. Resting levels of brain activity in SLI have shown atypical function of the left hemisphere temporoparietal area (Denays et al., 1989). Dyslexic individuals have likewise shown reduced activity in the left temporoparietal cortex relative to controls (e.g., Paulesu et al., 1996). Despite these similarities, C.M. Leonard et al. (2002) succeeded in identifying three anatomical markers that distinguished groups of children with SLI and reading disability: plana temporale asymmetry, normalized cerebral volume, and surface area of left Heschl’s gyrus. These anatomical differences support the view that SLI and dyslexia are qualitatively distinct (Bishop & Snowling , 2004). Overlapping language deficits in SLI and dyslexia have been investigated using the ERP method, but the populations have been examined in separate studies. For example, Sabisch and colleagues investigated whether German-speaking school-age children with dyslexia (2006) and with SLI (2009) process syntactic information differently than normally developing children and the extent to which this relates to the processing of prosodic information. In one study (2006), children with dyslexia completed an auditory comprehension task that required processing sentences with syntactic violations. Relative to unimpaired controls, children with dyslexia demonstrated a delayed left lateralized anterior negativity, suggesting impaired automatic phrase structure building. Because the right hemisphere is responsible for processing prosodic information, the absence of a right hemisphere negativity in the children with dyslexia suggests that prosodic cues for the acquisition of syntactic information are not processed normally in this population. This finding supports the notion that dyslexia is rooted in a phonological deficit that might hinder the development of syntactic processing (Sabisch, Hahne, Glass, von Suchodoletz, & Friederici, 2006). In a similar study (2009), children with SLI heard sentences with a syntactic violation and a joined prosodic incongruity and responded by displaying a late, left lateralized anterior negativity, relative to typically developing children. Here too, the absence of a right anterior negativity suggests that children with SLI may not access prosodic information normally, which might hamper their syntactic development. A less obvious comparison between the neural profiles of two child language disorders has recently been drawn between SLI and ASD. Although diagnoses of SLI and ASD are mutually exclusive in the same individual, evidence suggests that a subgroup of autistic children has a language profile that resembles that of SLI (Kjelgaard & Tager-Flusberg, 2001; Roberts, Rice, & TagerFlusberg, 2000). Accordingly, the expected reversed asymmetry in frontal language cortex in a group of boys with SLI was also found in a group of boys with ASD and language impairment, but

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not in controls (De Fossé et al., 2004). This finding challenges the sharp divide between the neurocognitive phenotypes of SLI and ASD. There are, however, multiple neuroanatomical deviations found in ASD, implicating the cerebellum, brainstem, thalamus, frontal lobes, and limbic system, that have not been thoroughly examined in SLI (see Filipek, 1999, for a review). Finally, let us examine the neurobiology of a rare genetic disorder with a unique linguistic profile, namely WS. Interest in WS has largely been driven by the uneven relationship between deficits in several areas of cognition and the relatively preserved linguistic performance in affected individuals. This non-linearity constitutes the argument for a dissociation between language abilities and other cognitive skills, and thus, supports the modularity debate (see Stojanovik, Perkins, & Howard, 2004, for a review). Neurobiological evidence from studies of WS, however, weakens this claim. The superior temporal gyrus, which includes the auditory association areas involved in language, was found to be relatively intact in WS. However, deviant asymmetry of the plana temporale has also been observed in this population (Eckert et al., 2006; Galaburda & Bellugi, 2000; Reiss et al., 2000), comparable to findings in individuals with SLI and dyslexia. In addition, ERP responses to auditorily presented words in sentences have revealed two intriguing findings in WS (Mills, Neville, Appelbaum, Prat, & Bellugi, 1997). Individuals with WS showed a greater scalp distribution for the N400 response to semantic anomalies (e.g., “I have five fingers on my moon”) relative to normal controls. The typical leftward asymmetry for grammatical function words was also lacking in the group with WS. Together, these results add to the accumulating evidence that language functioning in WS is, in fact, abnormal. Another brain atypicality in WS and other developmental language disorders involves the cerebellum. An abnormal cerebellum has been documented in WS, ASD, dyslexia, and Fragile X syndrome. (see Johnson, 2005, for a review). Individuals with WS, for example, show a relative volumetric increase in certain lobules on the cerebellum. In ASD, these lobules are relatively smaller than typical (Jernigan & Bellugi, 1994). Shared brain abnormalities among disorders as diverse as SLI, dyslexia, ASD, and WS highlight an important fact: most neurobiological anomalies in child language disorders are not specific to one disorder (Johnson, 2005). This overlap suggests that the current division of developmental language disorders into discrete categories may not reflect reality, but is rather a convenience for clinicians and scientists. Future research should consider sub-classifying language disorders according to a broad range of linguistic and non-linguistic deficits, allowing for partial overlap between categories. Furthermore, neurobiological similarities across disorders indicate that the quest to discover one particular neural marker for any given impairment is misguided. Instead, a host of studies within and across clinical populations will be needed to construct sets of neural markers that capture the characteristics of each type of child language impairment.

Conclusions and Future Directions An array of neuroimaging and neurophysiological techniques has enabled us to study the brain structure and function of child language disorders. In SLI, for example, we now have evidence of abnormal asymmetry in perisylvian areas, subcortical anomalies, atypical gyral morphology, and deviant linguistic and non-linguistic processing. These findings, however, have not been entirely consistent, which could be due to several factors. Discrepancies among criteria for subject classification, comparison groups, methodological protocols, and anatomical definitions may account for the bulk of this variability. Then, there is the expected phenotypic heterogeneity that is prevalent in SLI, which is commonly invoked to dismiss unexpected findings. We propose that these heterogeneous data can be more accurately interpreted by sub-categorizing SLI into a range of behavioral and neural phenotypes. Also, there are presumably different neurobiological pathways

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leading to the same behavioral outcome of SLI. Therefore, if we want to draw inferences about cognitive processes from language behavior, matching subjects on the basis of behavioral measures, rather than by neural phenotype, should be reconsidered. The relevant areas that warrant further research are plentiful. For example, neurobiological methods require refinement to permit the reliable study of individual data. In addition, the behavioral paradigms used to elicit brain activity should be improved to increase the specificity of processes being measured. Furthermore, results collected through various behavioral and neurobiological methods should be converged to allow comparisons across methodologies. We also propose that longitudinal ERP studies be conducted within and across clinical populations for two key purposes: to establish benchmarks for the prediction of language deficits and to track neurodevelopmental changes in association with intervention. Finally, neurobiological research on child language disorders should be aimed at revealing how neuroanatomical and brain processing differences relate to behavioral functioning and cognitive processing within a disorder. Overlap in the neurobiological profiles of SLI, dyslexia, ASD, and WS underscores the importance of sub-classifying these disorders in a manner that acknowledges commonalities among them. Moreover, the goal of neurobiological research should be to identify sets of neural markers that typify distinct subgroups within a given disorder. This novel approach presents a formidable challenge because it opposes traditional research and may require collaboration among laboratories that focus on individual clinical populations. This approach, though, also stands to cultivate our knowledge of child language disorders and the means by which they may be remediated.

Acknowledgements Preparation of this chapter was supported by a grant from the National Institute on Deafness and Other Communication Disorders, 5R01DC011041(R. Schwartz, P.I.).

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8 WORKING MEMORY IN CHILD LANGUAGE DISORDERS Ronald B. Gillam, James W. Montgomery, Sandra L. Gillam, and Julia L. Evans

Children with specific language impairment have language-learning difficulties that cannot be attributed to clinically significant medical, neurological, sensory, or environmental factors (see Chapter 1 by Schwartz). However, various risk factors related to heredity and neural development are likely to contribute to their developmental lags in language development (Bishop, 2009, Law, Garrett, & Nye, 2004, Rice et al., 1994). A number of memory limitations have been observed in children with SLI. They tend to perform more poorly than their age-matched, typically developing peers on memory tasks, whether the stimuli consist of tones (Marler, Champlin, & Gillam, 2002), phonemes (Tallal & Piercy, 1974), syllables (Stark & Tallal, 1988), numbers (Gillam, Cowan, & Marler, 1998), words (Majerus, Van der Linden, Ve’ rane, & Eliez, 2007), nonwords (Campbell, Dollaghan, Needleman, & Janosky, 1997), sentences (Montgomery, 2000a, 2000b), or stories (Gillam & Carlile, 1997; Kaderavek & Sulzby, 2000). A number of authors have hypothesized that deficits in working memory contribute to disorders in expressive and receptive language (e.g., Adams & Gathercole, 2000; Montgomery, 2000a, 2000b, 2002). With respect to language production, working memory may provide short-term storage for an intended utterance until the motor processes involved in speech production can be executed (Adams & Gathercole, 1995). Restrictions on the amount of verbal information that can be stored in memory could limit the length of the speaker’s utterances. With respect to language comprehension, Montgomery (2004) argued that working memory constraints limit the capacity to fully extract and represent the meaning of longer sentences that the child hears. Because language learning requires some degree of memory for word meanings and word order, it is likely that limitations in attention and memory contribute to the difficulties that children with SLI have with language comprehension and production (Johnston, 1999). This chapter summarizes what is known about the attention and memory abilities of children with SLI. We use the term working memory to refer to the processes involved in holding and manipulating information in order to perform a task (Baddeley, 2012; Cowan, 2011, 2014; Engle, 2001; Gathercole & Alloway, 2006). The primary difference between working memory (WM) and short-term memory (STM) is that WM refers to moment-by-moment storage and manipulation of information while STM refers only to the temporary storage of information (Baddeley, 2012). Long-term memory (LTM) refers to a more permanent storage system that retains information for periods of time that range from minutes to years.

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We examine the findings related to verbal working memory in children with SLI from the perspective of Cowan’s embedded-processes model, which posits dynamic relationships between attention, STM, WM, and LTM within a cognitive processing system. This model differs from storage-specific representations of memory like Baddeley’s (2000, 2012) multicomponent model, which represents WM as a system of stores and buffers. According to Baddeley, WM consists of a phonological loop that specializes in holding the serial order of verbal information, a visuo-spatial sketchpad that holds visual, spatial, and kinesthetic information, a central executive that controls attention, and an episodic buffer that links WM to LTM through the use of multidimensional (phonological, visual, spatial, and kinesthetic) codes. We chose to organize our summary of WM findings in SLI according to Cowan’s embedded processes perspective because his model includes many of the mechanisms and relationships that are important for understanding the role that attention, WM, and LTM play in normal and impaired language development, and it has been supported by a wide range of neuroimaging and behavioral findings (Chein, Ravizza, & Fiez, 2003; Cowan, 2001; Cowan et al., 2005).

A Model of Attention and Memory Cowan’s (1999) embedded-processes model of information processing (Figure 8.1) illustrates the critical elements of attending to and remembering spoken language. The model depicted in Figure 8.1 assumes a general capacity that limits the amount of encoding, storage, and retrieval that can be accomplished at any given time. In this model, working memory is viewed as a dynamic process, where general memory capacity emerges as a result of interactions between the momentby-moment ability to selectively bring information into mind and the current state of available knowledge (Cowan, 2010). From Cowan’s perspective, memory capacity is thought to vary depending upon the nature of the information to be remembered, the processing characteristics of the task, familiarity with the information to be remembered, and basic processing and storage capabilities. The center of Cowan’s (2014) model is an unlimited LTM system that consists of cognitive strategies called schemas, which are mental models for organizing information into sets of interrelated units of information. These schemas assist children in regrouping or recoding multiple pieces of information together (Ericsson & Kintsch, 1995) into coherent packages called chunks that become increasingly large and complex as new knowledge is added. For example, if you were asked to reverse the letters of the last word in the previous sentence mentally, you could probably do so. A schema that includes the spelling of the word added along with a schema for letter order would make the task relatively easy . However, the task would become harder if you do not speak Spanish and you were asked to reverse the letters of the last words in the sentence, La verdad no se me da una idea qué era exactamente (The truth is that I didn’t have any idea what it was exactly). Without a chunk of the word exactamente, the task would be more difficult and would place greater demands on working memory capacity. Most people who are literate could come pretty close to the correct answer if they realized that exactamente was a near cognate to exactly. The task would still require some degree of dependence on working memory, requiring individuals to repeat the final word to themselves often, holding the sounds that they had already spelled backward in mind while applying their schema for reversing letter order. Capacity limitations in WM are understood in this model to be a combination of attention processes, storage processes, and various aspects of knowledge in LTM (especially implicit learning strategies and phonological knowledge, but also lexical, syntactic, and discursive language knowledge). These knowledge stores are built into cognitive schemas that increase in complexity over time. Some children may have deficiencies in one or two of these areas of knowledge, whereas

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Top-Down Processes

Long-Term Memory World Knowledge

Central Executive Processes

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Figure 8.1 A basic model of working memory.

others may have deficiencies in nearly all of these areas. When they do, their cognitive schemas may be less precise and/or less automatized, which places a larger load on working memory (as was demonstrated in the previous example of spelling a word backwards). Some researchers have considered capacity limitations to be fixed and unalterable. However, an evolutionary account of educational psychology suggests that complex schemas for biologically primary knowledge may function automatically, thereby circumventing the capacity limitations of working memory. This would enable humans to attend to and process the large amounts of information necessary to attend to, process, understand, and use language with minimal cognitive effort. However, biologically secondary knowledge, such as academic knowledge, that requires task-specific communication, coordination, and problem-solving processes would impose a substantial load on working memory.

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In the next sections of this chapter we will summarize research on SLI that is relevant to the three principal components of Cowan’s embedded-processes model of memory: LTM, WM (with a focus on processing spoken language), and controlled attention.

Long-Term Memory Long-term memory is often divided into two systems: declarative knowledge of facts and events and nondeclarative knowledge of how to do things (Anderson, 1995). The declarative memory system is characterized by the rapid learning of arbitrarily related information. According to Tulving (1991), it includes memory for events or experiences (episodic memory) and memory for meanings (semantic memory). Knowledge learned by the declarative system can be expressed verbally and is readily applied to novel situations (cf. Squire, 1992, 1994; Squire & Knowlton, 2000). Nondeclarative (procedural) memory occurs on an ongoing basis, across multiple trials or exemplars, is largely unconscious, and is not easily verbalized. One example of procedural knowledge is typing. Even people who can type quickly and accurately find it difficult to name the keys on a keyboard aloud without moving their fingers. Other examples of procedural knowledge include habits, motor skills, and, most importantly for this chapter, grammar (Squire, 1992; Ullman, 2001c). Procedural memory is sometimes tested with implicit learning tasks in which learning is expressed through performance and is not available to conscious access. A variety of learning capacities are considered to be implicit, including probabilistic learning of categories, prototype abstraction, statistical learning, and artificial grammar learning (cf. Ashby & Ell, 2001; Perruchet & Pacton, 2006; Reber, 1989; Reber, Stark, & Squire, 1998; Squire & Knowlton, 2000; Squire & Zola, 1996). These implicit learning tasks are considered to be examples of procedural memory because they yield incremental and unconscious changes in the behavioral responses that demonstrate memory for sequences of stimuli. It has been proposed that declarative and procedural memory play different roles in language development. According to Ullman’s Declarative-Procedural (DP) model of language (Ullman, 2001a, 2001b, 2004), the lexicon and grammar are separable cognitive systems (Chomsky, 1981, 1995; Pinker, 1994, 1999). In the DP model, the acquisition and use of form-meaning associated aspects of language (e.g., the lexicon) are thought to be supported primarily by the declarative memory system, whereas the acquisition and use of grammar is supported primarily by the procedural memory system (cf. Squire & Knowlton, 2000). According to the DP model, declarative memory is hypothesized to play an important role in the acquisition, representation, and use of knowledge about facts and events, as well as wordspecific knowledge, which includes word meanings and representations of semantic categories. Procedural memory is hypothesized to have an important role in the sequential concatenation of stored forms and abstract representations into complex structures such as syntax and morphology. Specifically, procedural memory includes a set of cognitive strategies (sometimes referred to as schema) for deducing the statistical probability and regularity of the order of phonemes within words and words within sentences. These schema are thought to be very important for learning word boundaries, word order within clauses, and grammatical morphology.

Hypotheses about LTM and SLI Ullman extended his Declarative-Procedural (DP) model of language to explain the mechanisms underlying SLI. According to Ullman’s Procedural Deficit Hypothesis (PDH), SLI is an impairment of procedural memory, resulting from a dysfunction of the brain structures underlying this system (Ullman, 2004; Ullman & Gopnik, 1999; Ullman & Pierpont, 2005). Based upon a review

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of SLI research and investigations of the speech and language abilities of the KE family—a family with inherited speech, language, and motor impairments (Vargha-Khadem, Watkins, Alcock, Fletcher, & Passingham, 1995, 2005), Ullman and colleagues proposed that the pattern of cooccurring syntactic (van der Lely, 1996a, 1996b), morphological (Rice & Oetting, 1993; van der Lely & Ullman, 2001), and phonological (Gathercole & Baddeley, 1993) deficits in SLI, along with poor motor sequencing abilities (Bishop, 2002; Hill, 1998, 2001), reduced working memory (Ellis Weismer, Evans, & Hesketh, 1999), and poor mental rotation abilities (Johnston & Ellis Weismer, 1983) is consistent with abnormalities in the brain structures that support procedural sequential learning and memory. They also proposed that lexical knowledge, which is a relative strength in SLI, is supported by the brain systems related to declarative memory, which is relatively spared and can be used as a compensatory learning mechanism for these children (Ullman, 2004; Ullman & Pierpont, 2005). Other researchers have also entertained the notion that SLI is related to a procedural learning deficit (e.g., Ellis Weismer, 1991; Kamhi et al., 1984, 1985).

LTM Research on Children with SLI Recently, Lum and colleagues (Lum, Conti-Ramsden, Morgan, & Ullman, 2014; Lum, ContiRamsden, Page, & Ullman, 2012) published a meta-analysis of studies of declarative and procedural memory in children with SLI. Their literature search yielded 11 studies of declarative memory and four studies of procedural learning that met their inclusion criteria. Nine of the 11 studies of declarative memory used list learning and list retrieval tasks to determine how well participants could learn word pairs after multiple exposures. In all nine studies, children with SLI recalled fewer words than the age-matched controls during the learning trials, with an average effect size difference of .899. In some of the studies, the primary differences between the impaired and nonimpaired groups occurred on the first trial; after that, the two groups did not differ on the rate at which new words were learned. One problem with the studies of declarative memory is that the learning tasks that have been used have large attention, working memory, and language requirements. Once researchers accounted for differences in basic language knowledge and working memory, the declarative memory differences between children with and without language impairments were no longer significant. It would appear that the declarative memory difficulties of children with SLI are likely a result of their language deficiencies. Recent studies of procedural memory in children with SLI have employed a variety of implicit memory tasks. In one of the first studies of implicit learning in individuals with SLI, Tomblin, Mainela-Arnold, and Zhang (2007) administered the SRT task to 85 adolescents diagnosed with SLI in kindergarten and 47 normal language (NL) peers. In this task, participants press a button corresponding to a spatial location of a visual stimulus trial by trial. Blocks of trials where the spatial location occurs in a random order are followed by blocks of trials where the order is either deterministic or probabilistic. If children are deducing the pattern (presumably through the use of the cognitive systems underlying procedural memory), their response times should decrease. Tomblin et al. observed a decrease in reaction times (RTs) for correct trials over the period of trials for all of the participants. The NL group also demonstrated the classic learning pattern—initial rapid learning followed by gradual slowing towards an asymptote. However, learning was significantly slower for the SLI group as compared to the NL group. In addition, the learning curve for the SLI group was characterized by slowed responses initially followed by the onset of rapid learning, with no evidence of an asymptote by the last trial block. It is especially interesting that the RTs for the children with SLI with primarily grammatical deficits in kindergarten were significantly slower than the NL group, but RTs for the SLI group with low vocabulary scores but better grammatical

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abilities in kindergarten were no different from the NL group’s RTs. This finding is consistent with Ullman’s PDH, which posits different roles of declarative and procedural memory systems for language development and a procedural memory deficit that interferes with the grammatical abilities of children with SLI. Evans, Saffran, and Robe-Torres (2009) obtained similar results for a younger group of children using a verbal implicit learning task in which participants listened to a continuous stream of nonwords for 21 minutes as they drew a picture. The stimuli were constructed so that the adjacent probabilities between the syllables within the nonwords were much higher than the transitional probabilities across the nonword boundaries. After listening to the stimuli, children completed a recognition task in which they were asked to choose nonwords that “sounded” like the ones they had heard. Thirty children repeated the study 6 months later. This time, they listened to the stimuli twice (a total of 42 minutes) and then completed a nonverbal implicit memory task similar to the one administered by Tomblin et al. (2007). Evans et al. (2009) found that children with SLI performed more poorly than their age-matched controls on both the verbal and nonverbal implicit learning tasks at the shorter exposure time, but not the longer exposure time. Consistent with the predictions of the PDH, children with SLI had difficulty learning verbal and nonverbal material implicitly, but they were able to perform the task when they were given more opportunities to hear the phonological sequences. However, a careful analysis of the pattern of results suggested that group differences in working memory and attentional resources influenced the outcomes. These latter findings were inconsistent with the PDH, which holds that the procedural memory system supports the learning and use of grammar independently of other attention and working memory systems. Other research has also supported the role of basic attention and working memory mechanisms in implicit learning., For example, Hsu and Bishop (2014) administered two nonverbal procedural learning tasks and an implicit verbal sequence learning task to children with and without SLI. In their investigation, children with SLI performed more poorly than controls on a verbal implicit memory task that required them to learn sequential relationships, but they performed similarly to the typically developing controls on motor procedural learning tasks that did not require sequential learning. Hsu and Bishop believed their findings pointed to deficits in basic attention and memory mechanisms that support the ability to learn sequence-specific information rather than generally weak procedural learning. Lum et al. (2012) administered standardized tests of working memory and declarative memory together with a nonstandardized visual-spatial procedural memory task to a relatively large sample of children with and without SLI. The children with SLI performed poorer than the controls on the working memory measures, even when their language skills were accounted for. As noted earlier, the declarative memory results were not significant when working memory and language were controlled for. Similarly, the SLI and control groups did not perform differently on the accuracy with which they performed the visual-spatial procedural memory task. Consistent with Tomblin et al. (2007), there were group differences in reaction time, with the children in the SLI group performing slower on the later blocks even when working memory was controlled for. The grammatical abilities of children with SLI were moderately related to declarative memory, but they were not related to their measures of phonological working memory or procedural memory. These results were reversed for the typically developing children, with grammatical abilities being related to procedural memory to a somewhat greater degree than to declarative memory. However, the differences between the correlations were not statistically significant. Lum et al. (2012) concluded that children with SLI had a generalized procedural memory deficit in the nonverbal domain that affected verbal learning, but the evidence supporting their conclusion was far from compelling.

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Summary Clearly, there is much yet to be learned about the roles that declarative and procedural knowledge play in language development and language disorders. Even though the research base is still quite small, there are a few consistent findings. First, it does not appear that declarative memory, per se, is an important contributor to language impairment. Across multiple studies, group differences in language ability account for a great deal of the variance in performance on the declarative tasks. The results of procedural memory studies are somewhat more promising, with a consistent pattern of deficits on verbal and nonverbal implicit learning tasks that are not explained solely by language differences. However, there are limits to the explanatory power of the procedural memory deficit. Ullman proposed that only the procedural memory system, which includes the frontal/basal ganglia circuits, parietal cortex, superior temporal cortex, and the cerebellum (Mishkin, Malamut, & Bachevalier, 1984; Schacter & Tulving, 1994) is impaired in children with SLI. However, research suggests that a procedural learning impairment in SLI may encompass brain systems in addition to those supporting the procedural nondeclarative memory system. Specifically, some studies suggest that problems with attention and working memory (especially memory for sequence) may account for the procedural memory findings (Evans et al., 2009; Hsu & Bishop, 2014). Additionally, in studies of implicit memory, the correlations between verbal and nonverbal memory measures and grammatical measures that have been reported in a few papers have been small. It does not appear that procedural memory deficits alone can explain the grammatical deficits of children with SLI. It is possible that the correlations between procedural memory and grammar are relatively small because the association is indirect. There may be multiple associations among dynamic interactions among long-term memory (both declarative and procedural systems), working memory, and attention. Research that carefully assesses all three components of a language processing system may clarify the nature of the relationships among LTM, WM, and attention and the parts of the system that play the largest role in language development and disorders in children with SLI. In the following section, we will consider the role of WM in language more directly.

Working Memory A number of authors (Adams & Gathercole, 1995; Gathercole & Baddeley, 1990; Montgomery, 1995, 2004) have hypothesized that a specific WM deficit related to remembering and storing phonological information may play an important role in the limited capacity of working memory in children with language disorders. With respect to expressive language, phonological memory is thought to provide short-term storage for an intended utterance until the motor processes involved in speech production can be executed (Adams & Gathercole, 1995). There are strong associations between children’s phonological memory and their vocabulary knowledge (Gathercole, Willis, Emslie, & Baddeley, 1992), with better phonological memory being associated with the ease of acquiring new words in both first and second languages (Papagno & Vallar, 1995; Service, 1992). Gathercole and Baddeley (1990) have also suggested that difficulty forming and maintaining phonological representations in phonological memory could lead to problems establishing clear phonological boundaries between word stems and their inflections, resulting in deficits in grammatical morphology. However, deficits in phonological memory have not been shown to affect general syntactic abilities. Other investigators have hypothesized that deficits in WM play an important role in SLI sentence comprehension deficits (Montgomery & Evans, 2009; Norbury, Bishop, & Briscoe, 2002). Some investigators have attempted to distinguish the potential role of the storage and controlled attention components of working memory in these children’s sentence comprehension. Two

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sources of evidence have been used to support the claim that working memory may play a role in comprehension. First, most children with SLI exhibit sentence comprehension deficits coupled with poor memory storage and attentional control. Second, significant correlations have been observed in children with SLI between sentence comprehension and memory (Montgomery & Evans, 2009; Norbury et al., 2002) and attention (Montgomery, Evans, & Gillam, 2009). Here, we focus on the role of memory storage. We will return to a consideration of controlled attention in the next section. Researchers have primarily studied WM with three types of memory span tasks: simple span, complex span, and running span. Span refers to the amount of information that can be stored and recalled within a particular time frame. In simple span tasks, lists of sounds, words, or sentences are presented auditorily or visually, and participants attempt to recall as many items as possible immediately after the last item in the list. For example, a simple span task known as the nonword repetition task has been used by many investigators to assess phonological working memory. In this task, an examiner presents a multisyllable nonword (e.g., pembictocade), and children repeat it aloud immediately after hearing it. Successful repetition requires children to invoke a variety of phonological and memory-related processes such as perceiving a sequence of sounds and syllables, mentally encoding them, storing them, retrieving them, and then repeating them back in the same order. In complex span tasks, individuals complete a processing task between the time they store information and the time they retrieve it. For example, in the well-known reading span task (Daneman & Carpenter, 1980), individuals read lists of sentences. After reading each sentence, the individual judges whether it is truthful or not. After a set of sentences have been presented and judged, the individual is asked to recall the last word from each sentence in the set. The Competing Language Processing Task (CLPT), based on Just and Carpenter’s reading span task, has been used to study WM in children. In the CLPT, investigators present sets of simple sentences (Pumpkins are purple; Fish can swim). The child’s task is to judge the truthfulness of each sentence (i.e., answer Y/N question) as they are presented. After a set of sentences has been presented, children recall as many sentence-final words as possible (Gaulin & Campbell, 1994). This task requires children to divide their attentional resources between language processing (i.e., comprehending each sentence) and verbal storage (i.e., remembering words at the end of each sentence in a list). In the third type of working memory task, known as a running span task, participants must recall items from the end of word lists with varying and unpredictable lengths. In a variation of the traditional running span, known as the n-back task (Jaeggi, Buschkuehl, Jonides, & Perrig, 2008), individuals listen to and/or see streams of letters or numbers and must decide quickly whether each item that is presented matches one that occurred a certain number of items (represented by the “n” in n-back) before it. For example, in a 2-back task, individuals listen to the following stream of numbers and indicate which numbers were heard 2 steps back: 1–4—5–7—5–2—7– 2—7–3—6–8—6. They press a button as soon as the underlined items are heard because each of these items was presented 2 steps back. The dual n-back task requires individuals to look and listen to sequences of items presented at the same time. The dual n-back task is more difficult than the simple n-back task because it requires individuals to keep the visual and auditory information active in working memory while making decisions about when items were presented. Jaeggi et al. (2008) studied whether performance on dual n-back tasks could be improved by adaptive training. In their study, college students practiced dual n-back tasks over a period of 8 days, 12 days, 17 days, or 19 days. Students started training on 1-back, then increased to 2-back, then 3-back, etc., as their performance improved. If their performance worsened, n was decreased by one. Students who participated in the adaptive training for a longer period of time (e.g., 19 days)

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made greater gains on their n-back performance and on a measure of fluid intelligence than did participants who received shorter training (e.g., 8, 12, and 17 days). One potential explanation for this finding is that the dual n-back task improved students’ abilities to efficiently and effectively control their attention. The three kinds of tasks appear to be measuring different aspects of working memory. In a recent meta-analysis, Ridick and Lindsey (2013) found a consistent pattern of small correlations between performance on n-back tasks, simple span tasks, and complex span tasks. Simple span is often interpreted as a representation of the basic capacity to store a particular kind of information (Baddeley, 2012). Complex span tasks are thought to require higher-effort updating and rehearsal strategies that involve the ability to hold multiple forms of information in a temporarily active state before retrieving them. Running span tasks, on the other hand, appear to involve low-effort strategies that may be equivalent to the scope of attention (Cowan et al., 2005). Bunting, Cowan, and Saults (2005) have hypothesized that, in running span tasks, participants perceive the items passively as they are presented, letting them enter into a type of storage that does not require special mnemonic processing. When the list ends, participants use attention mechanisms to access as many items as possible from passive storage. The mechanism is important since running span yields substantially higher correlations with intellectual aptitudes than simple span task or complex span tasks (Cowan et al., 2005; Mukunda & Hall, 1992).

WM in Children with SLI With respect to SLI, the results of studies employing simple span tasks have been interpreted as demonstrations that children with SLI have smaller memory stores than their typically developing, age-matched peers for a variety of nonverbal and verbal stimuli including tones (Marler et al., 2002), phonemes (Tallal & Piercy, 1974), syllables (Stark & Tallal, 1988), numbers (Gillam et al., 1998), words (Brock & Jarrold, 2004; Majerus et al., 2007), nonwords (Campbell et al., 1997), sentences (Montgomery, 2000a, 2000b), or stories (Gillam & Carlile, 1997; Kaderavek & Sulzby, 2000). Clearly, simple span tasks have yielded a robust pattern of deficiencies for the immediate recall of information by children with SLI. These findings are consistent with the idea that these children have working memory limitations related to the ability to store nearly all aspects of language. One particular simple span task, the nonword repetition task, has received a great deal of attention in the SLI literature. The finding that children with specific language impairments have difficulties with longer (three- and four-syllable) nonword stimuli has been replicated extensively (Dollaghan, Biber, & Campbell, 1993; Dollaghan & Campbell, 1998; Gathercole & Baddeley, 1990; Weismer et al., 2000) and has been interpreted as showing that children with SLI have special problems with phonological representation. In fact, poor performance on nonword repetition tasks is so prevalent in children with SLI that the task has been proposed as a diagnostic marker of SLI (Bishop, North, & Donlan, 1996; Dollaghan & Campbell, 1998; Weismer et al., 2000). In support of this idea, Conti-Ramsden (2003) found that a combination of past tense production (a language marker) and nonword repetition (a processing marker) yielded very good classification of 5-yearolds with and without language impairment. The interpretation of the meaning of a deficiency in nonword repetition has become a matter of some controversy. It has become clear that nonword recall is not a pure test of phonological representation and WM capacity. Rather, there are components of LTM that contribute to nonword repetition performance. Previously constructed phonological knowledge in LTM contributes to nonword repetition (e.g., Gathercole, 2006; Munson, Kurtz, & Windsor, 2005b). Nonwords containing phonemes that appear frequently in a language are repeated more accurately than nonwords that comprise low-frequency phonemes. For example, Edwards, Beckman, and Munson

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(2004) showed that the relative probability of phoneme co-occurrence is a significant mediator of children’s nonword repetition performance. Nonwords that contain phonemes that are likely to occur together in a language are easier to remember than nonwords containing phoneme sequences that appear less often in a language. Children as young as two and a half years of age are sensitive to the phonotactic probability of phoneme sequences during nonword imitation tasks (Coady & Aslin, 2004). Consistent with the theory of attention and memory that has been proposed in this chapter, inter-relationships among prior phonological knowledge in LTM, phonological representations that are activated in WM, and attention to phonological features all play important roles in nonword repetition. Therefore, it is possible that nonword repetition is a marker of SLI not because it measures poor phonological representation, but because it assesses the limits of the entire WM system (focus of attention and LTM activation) in the service of recalling long sequences of unknown phonological information. Said differently, the nonword repetition task may prove to be a reasonably good way to assess the general information processing system that supports language development. Children with SLI perform poorly on multisyllable nonwords, but does that skill explain the nature of SLI or is it simply associated with the complex of information processing underlying language? In one of the first studies to assess the relation between phonological memory and sentence comprehension, Montgomery (1995) compared the performance of children with SLI and their age-matched peers on a nonword repetition task and an off-line, picture-pointing sentence comprehension task. Both the short and long sentences included SVO-like structures containing either extra verbiage (e.g., The short fat clown is holding the little yellow balloons; The tall skinny girl is chasing the little brown horse) or dependent clausal material (e.g., The furry cat standing is biting the brown mouse; The fat clown laughing is hugging the girl crying). Sentence length was thus the variable of interest. Children heard a sentence and then from an array of pictures pointed to one picture corresponding to the sentence. On the nonword repetition task, children with SLI performed worse than controls on the three- and four-syllable items. On the comprehension task, the children with SLI comprehended fewer long sentences versus short sentences and fewer long sentences, but not short sentences than the control children. Combining the groups, there was a positive correlation between nonword repetition and sentence comprehension. Norbury et al. (2002) also explored the intersection of phonological memory storage and sentence comprehension in children with SLI. However, these investigators examined the role of storage (indexed by nonword repetition) in the comprehension of passives requiring movement (The man is eaten by the fish.) and pronominal sentences requiring binding (Mowgli says Baloo Bear is tickling himself; Baloo Bear says Mowgli is tickling him.). Children with SLI and two typically developing control groups were studied. Predictably, the SLI group performed significantly worse than controls on the nonword task and on both the passive and pronominal sentences. Nonword repetition and sentence comprehension were significantly correlated in all of the children combined. Additional studies have provided further evidence of the impact of reduced memory storage in the sentence comprehension deficits of SLI (e.g., Montgomery & Evans, 2009). Montgomery and Evans examined the association of memory storage (indexed by nonword repetition) with complex sentence (e.g., The woman is painted by the girl; Winnie the Pooh says Christopher Robin is touching him) and simple sentence (e.g., The old man is touching the blue-haired woman) comprehension in an SLI group and two control groups, one matched on age and the other a younger group matched on vocabulary and memory storage. As expected, the SLI group performed worse than their age peers on nonword repetition and comprehending the complex sentences, but not the simple ones. The SLI and younger groups performed similarly across tasks. Correlation analyses showed that nonword repetition correlated with simple sentence comprehension, but only in the SLI group. Taking all of the results from these off-line studies together, it appears that the sentence comprehension

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of children with SLI is negatively impacted by a reduced WM capacity for attending to, storing, manipulating, and recalling phonological information. Another source of evidence implicating a wider WM-based account of SLI sentence comprehension comes from recent online studies with children with SLI (Epstein, Hestvik, Shafer, & Schwartz, 2013; Hestvik, Schwartz, & Tornyova, 2010). Hestvik et al. examined the immediate gap-filling (NP1 reactivation) abilities of children with SLI and an age-matched group of children as they listened to object relative sentences (The camel that the rhino in the mud had kissed [probe] on the nose ran far away). To assess NP1 reactivation, children performed a cross-modal picture priming task in which they listened to a sentence (object relative or filler) and saw a probe picture occur at the gap or a pre-gap location. The probe picture was either a picture of NP1 (primed probe) or another animal not mentioned in the sentence (control probe). NP1 reactivation was implied by a speed advantage of the primed probe over the control probe at the gap, but not at the pre-gap. Immediately following each sentence, children were asked a short comprehension question. The online results showed that the SLI group showed no speed advantage for the primed probe at the gap, whereas the control group did. Interestingly, at the pre-gap location the SLI group showed a probe effect, but the control group did not. On the comprehension questions, the SLI and control groups did not differ. The authors interpreted their findings to suggest that SLI children are delayed in NP1 reactivation during sentence processing, but are not impaired in their syntactic knowledge. The authors speculated that an attenuated active filler strategy may have been responsible for the delayed reactivation in the children with SLI. That is, children with SLI were slower than age-matched peers to establish a filler-gap dependency. Finally, findings from recent studies by Leonard, Deevy, Fey, and Bredin-Oja (2013) and Robertson and Joanisse (2010) suggest another possible memory-related factor affecting SLI sentence comprehension. The poorer storage of previously processed linguistic material during sentence comprehension may in part be a reflection of linguistic interference. Recent memory-based models of adult sentence comprehension (e.g., Lewis, Vasishth, & Van Dyke, 2006; McElree, Foraker, & Dyer, 2003; Van Dyke & McElree, 2006) propose that an interference factor (i.e., similarity-based retrieval interference) may play an important role in complex sentence comprehension, especially as it relates to forgetting representations that were built earlier in the sentence. According to this view, a miscomprehension of who did what to whom may occur in sentences such as The criminal that the lawyer defended was running past the courthouse (i.e., the lawyer defended the criminal) if the listener retrieves noun phrase (NP) 1 (the criminal) from memory and then associates it with the embedded verb (defended). The semantic similarity between NP1 and NP2 can interfere with the listener’s retrieval/movement of NP1. Applying this proposal to children with SLI, it may be that the poorer comprehension of even SVO constructions that happen to include NP-modifying adjectival material (e.g., The little brown cat is walking under the old white fence.) may induce broad lexical interference. By the time these children hear the end of the sentence, the adjective combination old white could interfere with the reactivation of the stored adjective combination little brown (modifying NP1), leading to miscomprehension. Working memory capacity can also be assessed using complex span tasks (e.g., listening span task, backwards digit task), in which children are asked to engage in simultaneous information processing and storage. Recall that Gaulin and Campbell (1994) adapted Daneman and Carpenter’s reading span task into an auditory presentation mode with simpler sentences, known as the Competing Language Processing Task (CLPT), so that it could be administered to young children who were not yet proficient readers. Children with SLI do not perform tasks like the CLPT as well as their nonimpaired peers. For example, Ellis Weismer et al. (1999) examined the relationship between working memory capacity using the CLPT and performance on standardized nonverbal cognitive and language tests in a group of children with language disorders and age-matched

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nonimpaired peers. Although both groups had similar comprehension scores (more than 96% correct), the children with language disorders showed significantly poorer word recall than did the nonimpaired children. The children with language disorders had a 40% recall accuracy score, whereas the nonimpaired children had an accuracy score of 60%. The performance of the children with language disorders suggested that they had less overall working memory capacity than their nonimpaired peers, thus leading to their poorer dual task performances (Ellis Weismer et al., 1999). In a more recent study, Mainela-Arnold and Evans (2005), also using the CLPT, reported very similar findings for their children with SLI. The third type of measure of working memory capacity is often referred to as a running span task. We know of only one running span study of children with SLI. Evans, Sellinger, and Pollak (2011) recorded event-related potentials (ERPs) as children with SLI and their age-matched controls completed visual and auditory 1-back and 2-back tasks. Children with SLI were significantly less accurate and responded significantly slower than the controls on the 2-back tasks in the auditory modality. They also had lower amplitudes compared to the controls, which could be due to lexical encoding deficits and/or deficits in the ability to maintain target items in a “moving window” in memory as new items were continually added. Even though the behavioral responses of the children with SLI were similar in accuracy to the controls during the 1-back tasks, the ERP data indicated that their accuracy came at a higher processing cost. Evans et al. (2011) interpreted their results as suggesting that the children with SLI had degraded phonological representations, which created a greater burden on cognitive resources during the n-back tasks.

Summary Research on WM is much more advanced than research on LTM. Results from studies employing simple span measures, complex span measures, and most recently, a running span measure converge on the fact that children with SLI present limitations in storing and retrieving many different kinds of language information, from nonwords to complex sentences. There can be no doubt that children with SLI have difficulty forming and maintaining phonological representations in phonological memory, and these deficits impact language learning negatively. However, the interpretation of that finding as demonstrating that poor phonological representation is the nexus of SLI may have been premature. Perhaps it is better to think of the nonword repetition task as representing the capacity of the entire WM system as it is applied to unfamiliar phonological stimuli. It is likely that a number of factors related to working memory, including phonological representation, decay, interference, updating, and reactivation, play a role in language development and language disorders.

Attention Cowan’s embedded processes theory of memory involves a limited-capacity attentional focus that serves to activate information in LTM that is the most relevant to the task at hand. According to Cowan (2005, 2014), all incoming information activates long-term memory to some degree. As information is encountered, the mind forms neural models of what is being processed within the focus of attention. Moment-by-moment, incoming information that is consistent with the current neural model is habituated. However, perceived discrepancies between the incoming information and the current neural model result in involuntary attentional orienting responses toward the new stimuli and voluntary attention toward rebuilding of the neural model. Cowan’s conceptualization of the role of attention is consistent with that of Barrouillet and colleagues (e.g., Barrouillet et al., 2011; Vergauwe, Barrouillet, & Camos, 2010) who emphasized that cognitive load, the proportion of time that is taken up by perceiving discrepancies between mental models and incoming information, interferes with memory span.

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There is no doubt that staying on task (sustaining attention) is important for learning. Individuals with lower spans appear to have more problems attending in daily life (Kane et al., 2007). In that study, participants carried devices that allowed them to report what they were doing and what they wanted to be doing during the day. Low-span individuals were more likely to report that their minds were wandering away from the tasks on which they were trying to focus attention. This study was conducted with adults, but it has implications for children. Gathercole and Alloway (2006) found that children who were identified as not trying to follow directions often had difficulties remembering instructions or paying attention for the duration of a task. The ease with which individuals perceive discrepancies between incoming information and mental models and then adjust those models (attention switching) and the vigilance with which they search for meaningful connections between information and mental models in LTM (sustaining attention) may play important roles in academic performance and language development.

Attention in Children with SLI There is an ever-growing literature on the role of attention in SLI. Noterdaeme, Amorosa, Mildenberger, Sitter, and Minow (2001) administered tasks measuring reaction time, attention, and executive functions to children with SLI, children with autism spectrum disorder (ASD), and their age, sex, and nonverbal IQ matched controls. The visual and auditory attention tasks required children to respond only when a specific pattern or tone sequence was presented. Sustained visual and auditory attention tasks required children to indicate when patterns of dots or tones changed. All children performed similarly in terms of reaction times, but children with SLI were significantly slower and made more errors on the measure of sustained auditory attention. They also made more errors than children with autism or controls on the selective auditory attention task. The children with autism and SLI demonstrated significant deficits in sustained attention that were not present in the control group. An event-related potential study (Marler et al., 2002) used mismatch negativity (MMN) along with behavioral measures of simultaneous and backward masking in children with SLI and their age-matched peers. The MMN tasks included blocks with a ratio of many targets to few foils (to measure impulsivity) and blocks with a ratio of few targets to many foils (to measure inattention). Although a commercially available test of attention (Test of Variables of Attention) did not discriminate between the SLI and control groups, the SLI group performed more poorly on the behavioral backward-masking task. The MMN latency and amplitude data were consistent with impaired attention-switching mechanisms responsible for processing new auditory information, and these impaired mechanisms occurred prior to memory storage and retrieval processes. Recall that Evans, Selinger, and Pollak (2011) observed that children with SLI are more vulnerable to lexical interference than CA peers when trying to maintain lexical items in WM. The authors used the P3b waveform—a positive component that peaks 300 ms or more after a stimulus onset—with an n-back WM task to determine if difficulty maintaining and updating lexical items in WM is due to slower speed of processing or reduced WM storage in children with SLI. Children heard a series of high-frequency common words and were asked to determine whether the word they heard matched a word n-items back in the sequence—in this case 1-back and 2-back. In the 1-back condition, the SLI group’s accuracy and RT data were comparable to CA peers. In the 2-back condition, behavioral RTs and accuracy and P3b amplitudes decreased significantly for the SLI group relative to the CA group; however, P3b latencies did not differ for the two groups. Results indicated that for children with SLI, the requirement to hold even two lexical items in the

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focus of attention resulted in a significant reduction in behavioral decision speed and item retention. Evans and colleagues interpreted these results to suggest that the children with SLI in their study had significantly reduced attentional/WM resources available to rapidly switch between lexical encoding and retrieval. With respect to continuous attention, Spaulding, Plante, and Vance (2008) provided behavioral evidence showing that preschoolers with SLI had significant difficulties sustaining auditory attention (but not visual attention) when the tasks were more cognitively demanding. In a related study, Montgomery (2000a, 2000b) showed that children with SLI could manage their working memory resources (i.e., storage and attentional allocation) when the task is easy enough. He asked children with SLI and age-matched peers to recall lists of familiar words under three conditions: (1) free recall (requiring no processing), (2) words arranged according to the physical size of the word referents (requiring semantic knowledge of size), and (3) words arranged according to semantic category and word referent size (requiring semantic categorization plus semantic knowledge of the size of the word referents within each category). Both groups performed similarly in the first two conditions. Children with SLI had unusual difficulty only when they had to recall lists that were organized according to category and size of the referent—a task requiring a greater number of mental processes. Such findings indicate that children with SLI can allocate their resources to both verbal processing and storage. Their recall suffers only when the processing demands exceed their overall cognitive capacity. These findings may reflect independent problems with attentional control. Like the nonword repetition test, word recall tasks are likely to measure a general memory capacity that includes interactions among attention, WM, and LTM. A few studies have examined the relationship between attention control and sentence comprehension in children with SLI more directly. Montgomery and Evans (2009) examined the association between performance on the CLPT and comprehension of complex sentences and simple sentences in a group of children with SLI, a group of age-matched children, and a younger group of children matched on vocabulary and memory storage. Recall that the CLPT involves children listening to sets of simple sentences (Pumpkins are purple), judging the truth of the sentences, and recalling as many sentence-final words as possible. The task requires controlled attention allocation in that children must rapidly switch their attentional focus between item storage and sentence comprehension (e.g., Barrouillet & Camos, 2001; Barrouillet, Gavens, Vergauwe, Gaillard, & Camos, 2009). As expected, the SLI group performed more poorly than age-matched peers on the CLPT and in comprehending the complex sentences, but not the simple sentences, whereas the SLI and younger groups performed similarly across tasks. CLPT and complex sentence comprehension were correlated in the SLI and in the younger groups, but not in the age-matched group. The comprehension of passive sentences involving syntactic movement, sentences with reflexives, and sentences with pronouns appeared to require more attentional control in children with SLI, but not in age-matched peers, implying that children with SLI expend cognitive resources at a rate comparable to younger children during sentence comprehension. A study by Montgomery et al. (2009) provides converging evidence for this claim. These investigators examined the relationship of two aspects of controlled auditory attention, sustained and allocation, and sentence comprehension in children with SLI and an age-matched group of children. Children completed a 10-minute auditory continuous performance task in which they listened to a stream of familiar words and tapped the table each time they hear the target word dog. They also completed a concurrent verbal processing-storage task (index of allocation) in which they listened to lists of familiar words (e.g., nut, car, train) and were to recall the items in one of two conditions: (1) no-load, recall items in serial order and (2) dual-load, recall items according to semantic category and perceptual size of each referent within each category

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(e.g., nut, tree, car, plane). In the dual-load condition, children must rapidly alternate/allocate their attentional focus between item storage and item categorization. The comprehension task included simple SVO sentences (e.g., Point to the picture of the three cats; The dirty little boy climbed the big tall green tree) and complex SVOs containing one or two dependent clauses (The girl smiling is pushing the little boy; The boy standing is kissing the little girl sitting). For the SLI group, simple sentence comprehension was correlated with sustained attention, whereas complex sentence comprehension was related to attentional allocation. By contrast, for the control children, neither attention ability was correlated with comprehension. Such results suggest that, compared with age-matched peers, SVO sentence comprehension in children with SLI is more closely related to attentional resources.

Summary Children with language disorders exhibit a limited capacity for sustaining their focus of attention. These children may not be able to allocate the specific attention resources that are needed to sustain their focus, and they may need longer periods of sustained stimulation or repeated stimulation in order to trigger important attention-focusing mechanisms. Whether children with language disorders have a diminished involuntary ability to perceive differences between mental models and incoming information, a limited capacity for voluntarily rebuilding mental models, sustaining the focus of attention, or both, limitations in attention are likely to contribute to their language learning difficulties. The ability to sustain the focus of attention could be critical for processing complex sentences. Minimally, listeners should be able to (1) retrieve linguistic properties of the input words and to activate language processing schemes that generate appropriate linguistic representations of the word sequences (e.g., NP, VP) from LTM, (2) sustain a focus of attention in WM long enough to store partial products of linguistic processing while ongoing processing is occurring, and (3) retrieve processes responsible for reactivating partial products of processing (i.e., representations) being temporarily stored in memory (Just & Carpenter, 1992) and integrate them with newly created representations from ongoing processing (e.g., McElree et al., 2003). Comprehension may suffer in children with reduced attentional control because they may have too few executive resources to devote to the storage and processing demands of such sentences (Van der Molen, Van Luit, Jongmans, & Van der Molen, 2007). Based on our review, we hypothesize that atypical interactions between WM processes and the language knowledge and procedural memory strategies in LTM could disrupt the child’s focus of attention. The inefficient interactions between deficits in LTM and WM systems are likely to result in an increased cognitive load during language learning that could have negative implications for language development. We said earlier that some researchers have considered capacity limitations to be fixed and unalterable. However, as noted above, the evolutionary account of educational psychology suggests that complex schemas for biologically primary knowledge may function automatically, thereby circumventing the capacity limitations of working memory. This would enable humans to attend to and process the large amounts of information necessary to attend to, process, understand, and use language with minimal cognitive effort. However, biologically secondary knowledge, such as academic knowledge, that requires task-specific communication, coordination, and problemsolving processes would impose a substantial load on working memory. We believe that language may function more as secondary knowledge than primary knowledge for children with SLI. This conceptualization of a dynamic memory system could have important implications not only for hypothesized accounts of the disorder but for treatment models as well.

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Implications for Treatment It has been hypothesized that limitations on the amount of verbal information that can be stored in working memory restrict the length of speakers’ utterances (Adams & Gathercole, 1995) and their ability to comprehend longer sentences (Montgomery, 2004). A subsequent re-analysis of these data suggest that sentence complexity rather than length is the key factor in sentence comprehension (Montgomery & Evans, 2009). If working memory deficits play a causal role in specific language impairment, then training working memory should improve language skills. Two examples of memory-training programs are CogMed (2012) and n-back (Jaeggi et al., 2008).

CogMed CogMed is a computer-based program that purports to improve working memory using an adaptive training system designed to increase the level of difficulty of tasks relative to an individual’s performance on each of 25 modules. The modules range in length from 30–45 minutes and target various verbal and visuo-spatial memory tasks to be delivered five times weekly for 5 weeks. Several studies have examined the impact of CogMed on memory skills in children. For example, Klingberg et al. (2005) compared the performance of 7–12-year-olds on adaptive and nonadaptive versions of CogMed. Children who had participated in the adaptive version of CogMed made significantly greater gains on measures of visuo-spatial memory, digit span, and attention than did children who received the nonadaptive version. CogMed training also appeared to generalize to nonverbal measures of fluid intelligence. In a similar study, Holmes, Gathercole, and Dunning (2009) assigned children with poor memory skills to an adaptive or nonadaptive version of CogMed for twenty 35-minute sessions. Students in the adaptive training condition demonstrated greater skill than children in the nonadaptive version of CogMed on measures of working memory and following directions. However, training on CogMed did not generalize to measures of verbal IQ, performance IQ, or word reading. CogMed has also been studied in other populations, including children with intellectual deficits and children with cochlear implants (Kronenberger et al., 2011; Van der Molen et al., 2010). Findings in both studies suggested that training improved aspects of working memory. Training generalized to improved sentence imitation for the children with cochlear implants, but it did not generalize to a measure of story retelling in children with intellectual disabilities. In theory, CogMed may also be associated with improved attention skills. The embedded processes model in Figure 8.1 suggests that attention is fully integrated into working memory. If that is true, improvements on working memory tasks should generalize to attention tasks. In Cowan’s model, the ability to control and extend the scope of attention may be improved by some working memory tasks. There is some support for this claim. In a study of children with ADHD and/or learning disabilities, Beck, Hanson, Puffenberger, Benninger, and Benninger (2010) examined the impact of home-based CogMed training for improving ADHD symptoms in 52 children ages 7–17. The investigators collected parent and teacher ratings of ADHD symptoms and executive functioning. Parents reported improvements in both ADHD symptoms and executive functioning. However, teachers, who were unaware of the timing of instruction, did not report improvements in either ADHD symptoms or executive functioning to the same degree as parents. It is possible that children who participated in training demonstrated improvements in their ADHD symptoms, but the method by which outcomes were measured (i.e., parents knew their children were receiving treatment, leading to expectation of improvement) make it difficult to know for sure.

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Some preliminary data suggest that CogMed training may have the potential to impact performance on working memory measures. These results have been obtained in children with ADHD, low IQ, hearing impairments, and poor working memory. Despite these findings, a critical evaluation of CogMed, its claims, and evidence of its effectiveness (Shipstead, Redick, & Engle, 2012) has raised serious questions regarding its efficacy. Furthermore, there is no experimental evidence that CogMed training generalizes to changes to language comprehension or production.

N-Back Training Recall that in the n-back task, participants are asked to determine whether a new stimulus item matches one that was encountered a certain number of items before it. Few studies have examined n-back training with children. Jaeggi, Buschkuehl, Jonides, and Shah (2011) applied an adaptive n-back training paradigm to elementary and middle school children. The participants who received the n-back training had greater gains on measures of working memory and measures of fluid intelligence than children who received knowledge-based training that did not engage working memory. The children who did not make improvements on the n-back training and those who only made slight improvements on the training did not show growth on the fluid intelligence measures. Thus, generalization to fluid intelligence only occurred when n-back training was successful. Wener and Archibald (2011) administered serial recall tasks and n-back training tasks to two children with working memory impairment and no identified language impairment and to four children with language and working memory impairment. Children participated in four weeks of intervention that targeted verbal or visual memory. In the verbal memory training, children worked on an n-back task that contained object pictures and completed memory tasks involving recalling sentences and retelling stories. In the visuo-spatial memory training, children worked on an n-back task that contained pictures of spatial relations (dots on a grid) and were trained to use visual strategies to support sentence and story recall. In both conditions, children were asked to recall sentences and to retell stories after their training. The children who received verbal memory training showed greater improvements on a picture recall task and the CELF-IV word structure subtest, while the children who received visual memory training made more improvement on a spatially based puzzle task and the CELF-IV concepts and following directions subtest. These preliminary findings suggest differential effects forms of verbal versus visual training (verbal vs. visual). The extent to which these findings may reflect training-to-task remains undetermined. As mentioned, a critical review of this literature (Shipstead et al., 2012) has cast serious doubt on the claim that WM training really improves WM capacity. The main problem with both the n-back and CogMed findings is that the tasks that have been used to assess WM are too similar to the tasks that were used in training, suggesting that training may improve task-dependent skills rather than increase memory capacity or selective attention. Shipstead et al. (2012) point out that n-back tasks and running span tasks share many features, while n-back and complex span tasks have less in common. Most n-back studies measure change in memory with running span tasks rather than complex span tasks. Similarly, in most of the CogMed studies, in which simple and complex span tasks are trained, change is measured with simple and complex span tasks that simply contain different stimuli than the ones used in training. Change in memory after CogMed training has never been measured with an n-back or a running span task. Furthermore, in claiming that CogMed changes selective attention, the Stroop test has been misused by using only one of the conditions. To date, there is no compelling evidence that memory training has an effect on memory capacity, as it is currently defined in the working memory literature, or on selective attention.

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Conclusion Drawing from Cowan’s embedded processes theory of working memory, we have suggested that general memory capacity emerges as a result of the dynamic interaction between biologically endowed language knowledge in long-term memory, working memory storage and retrieval processes, and attentional control. The main idea is that the cognitive schemas for processing, organizing, and chunking language interact automatically with WM and attentional control mechanisms to allow language users to quickly and efficiently process sounds in ways that enable them to implicitly deduce their statistical regularity. Children with SLI impairments have deficits in LTM and WM that may interfere with processing efficiency. We believe children with SLI may process language in much the same way that typically developing children do for biologically secondary knowledge (e.g., academic content). This processing requires extra attention, storage, coordination, and problem solving that imposes a substantial load on working memory. Our review of the literature demonstrates that children with SLI have difficulties with procedural memory, working memory, phonological representation, and attentional control. The information processing theories of SLI, such as the Procedural Deficit Hypothesis (Ullman & Pierpont, 2005), the Capacity Limitation Hypothesis (Ellis Weismer & Evans, 2002), and the Phonological Memory Deficit Hypothesis (Gathercole & Baddeley, 1990), all posit ways in which single aspects (e.g., implicit learning, attention switching, phonological WM) of the information processing system are affected. Difficulties in one of these systems, in isolation, may account for the wide variety of language impairments expressed by children with language disorders. However, an embedded processes account of SLI binds these separate theories together into a coherent model of the relationship between LTM, WM, and language learning. To our way of thinking, children with SLI do not appear to apply cognitive schemas for processing, organizing, and chunking language automatically within WM and attentional control mechanisms. That could explain why children with SLI work harder to learn less than same-age children who are developing language typically. There has been a great deal of interest recently in the benefits of explicitly training working memory skills. We reviewed the available evidence on n-back training and CogMed, which are two frequently studied memory-training programs. We have learned some valuable lessons about approaching these intervention programs with some degree of caution. Carefully conducted research will better inform researchers and clinicians as to the efficacy and effectiveness of interventions touted to improve memory, attention, learning, and academic skills by focusing on training various aspects of memory. Until such time, we recommend that clinicians focus on improving language skills in children with SLI using procedures with greater evidence for promoting efficient cognitive processing, such as contextualized language instruction with a focus on organizational frameworks.

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9 PERCEPTION AND PRODUCTION IN CHILD LANGUAGE DISORDERS Jan Edwards and Benjamin Munson

Introduction If one was to ask a lay person to describe a symptom of a language disorder, the typical answer would probably focus on a phonological error (“wabbit” for rabbit); on a problem related to academic performance, such as difficulty in learning to read; or on a specific named disorder that is also associated with deficits in areas other than language, such as autism spectrum disorder, or attention-deficit hyperactivity disorder. The general public’s knowledge of language disorders of an unknown origin is generally quite limited, although such language impairments are relatively common, with recent prevalence estimates of approximately 7.42% from a population-based sample in the Midwestern United States (Tomblin, Records, Buckwalter, Zhang, Smith, & O’Brien, 1997). Even within the study of functional language impairments, there are great discrepancies in the specific topics that have been studied. Within this research area, a much larger proportion of research has examined morphology, syntax, and academic problems of children with specific language impairment relative to their difficulties with speech perception and speech production or their knowledge of higher-level aspects of the sound structure of language. Yet the latter are arguably the foundations on which knowledge of more abstract aspects of language, such as syntax, are based. Sounds are one of the media through which language is conveyed. Deficits in knowledge of sounds may be a contributing, maintaining, or even a causal factor in language impairments. Thus, the topic of this chapter—a review of studies of what children with specific language impairment know about the knowledge of sounds—is both understudied and poorly understood, as well as a topic that can explain much about the nature of a commonly occurring childhood communication disorder. There is ample evidence that the task of acquiring knowledge of the sound structure of language is highly protracted and begins quite early in life. Children begin to recognize some familiar words, such as their names, by 4 to 5 months of age (Mandel, Jusczyk, & Pisoni, 1995) and begin to produce words around their first birthday. While most typically developing children produce most or all of the sounds in their native language in a way that listeners perceive to be accurate by about 5 or 6 years of age (Smit, Freilinger, Bernthal, Hand, & Bird, 1990), more subtle measures of speech perception and production suggest that the phonological system is not adult-like until about age 10 or 12 (e.g., Goffman, 2004; Hazan & Barrett, 2000; Kent & Forner, 1980; Romeo, Hazan, & Pettinato, 2013; Smith, 1978).

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A discussion of the protracted nature of phonological development must begin with a description of phonological knowledge. Phonological knowledge is far from monolithic. Knowledge of the sound structure of language includes many different sub-types of knowledge. This includes knowledge of the physical instantiation of phonological categories: perceptual knowledge of the acoustic characteristics of speech sounds and their perceptual consequences, and articulatory knowledge of the motoric, tactile-kinesthetic, and proprioceptive characteristics of speech sounds. Phonological knowledge also involves more abstract higher-level knowledge of the ways that words can be divided into sounds, and sounds can be combined into meaningful sequences in words. In our previous work (e.g., Munson, Edwards, & Beckman, 2005b), we have referred to this as higher-level phonological knowledge. Perceptual, articulatory, and higher-level knowledge all refer to people’s knowledge of the way that sounds are used to convey linguistic meaning. One last kind of phonological knowledge, social-indexical knowledge, refers to individuals’ knowledge of the way that variation in speech production is used to convey social identity and social-group membership. The different types of phonological knowledge can be illustrated by the knowledge that people have of the sound /r/. People have perceptual knowledge that /r/ is characterized by a low thirdformant frequency, as illustrated in studies in which people identify synthetic stimuli varying in third-formant frequency as /r/ if the stimuli have a low F3 and /l/ if the stimuli have a high F3 (e.g., Munson & Nelson, 2005). People also have articulatory knowledge that /r/ can be produced either with a bunched tongue root or with a retroflex movement of the tongue tip, and that different configurations can be used to reduce acoustic variability in this sound (Guenther et al., 1999). People also have higher-level knowledge that /r/ does not occur in any word-initial clusters following /v/, and that /vræm/ is not a possible word of English. Finally, people have knowledge of the ways that variation in /r/ production can be used to convey social-group membership. For example, British English speakers presumably have tacit knowledge that labiodental variants of /r/ are more likely to be produced by middle-class women than middle-class men or working-class people (Foulkes & Docherty, 2000). A full characterization of knowledge of /r/ includes all of these different types of knowledge. This chapter will focus on the development of the first three kinds of phonological knowledge in children with language impairment relative to their typically developing peers, simply because there is little or no research on the acquisition of social-indexical knowledge in children with language impairments relative to their typically developing peers. However, in our conclusions we will speculate on how deficits in the acquisition of social-indexical knowledge might interact with the pragmatic problems frequently observed in children with language impairment. In this chapter, we will consider primarily the phonological knowledge of children with specific language impairment (SLI) and, to a lesser extent, children with a related and sometimes co-occurring disorder, dyslexia. There are several reasons for this. First, there is a well-established, though small, body of research on phonological acquisition for these children. In contrast, there has been relatively little research on this subject in children with other genetically or neurologically based language impairments, such as autism, Williams syndrome, or Fragile X. This discussion will exclude children with broad cognitive deficits, such as developmental disability or Down syndrome (see Chapter 2 by McDuffie et al.). It will also exclude children with hearing impairment (see Chapter 4 by Waldman DeLuca & Cleary). Our motivation for this is twofold. First, the prevalence of many of these disorders is considerably lower than that of SLI. Second, children with language problems associated with cognitive deficits often have concomitant hearing deficits and speech motor deficits, and therefore a discussion of phonological acquisition for these children is considerably more complex. To illustrate, consider two recent findings. Seung and Chapman (2000) found that 11 of their 33 participants with Down syndrome failed a hearing screening, but they did not differ from the 22 individuals who passed the screening on a psycholinguistic measure

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closely related to language performance, digit span. Marler, Elfenbein, Ryals, Urban, and Netzloff (2005) found a high rate of sensorineural hearing loss in individuals with Williams syndrome, who are generally characterized as having relatively good language abilities in the absence of low full-scale IQ scores.

Perceptual Knowledge Speech Perception In this first section, we focus on speech perception in children with language impairments relative to their typically developing peers, an area of research that goes back 30 years. The interest in speech perception began with the early work of Tallal and colleagues (e.g., Tallal & Piercy, 1973, 1974, 1975; Tallal & Stark, 1981). These researchers found that school-age children with language and/or learning disorders had more difficulty than typically developing age peers in the discrimination of nonspeech tones and in the discrimination of both synthetic speech consonants embedded in CV syllables and in brief synthetic vowels. Crucially, children with language impairment performed more poorly than their typically developing age peers when the distinction hinged on brief acoustic cues, such as formant transitions, voice onset time, or even steady-state formants for vowels if they were of sufficiently brief duration. This finding has since been replicated by a number of researchers using a variety of experimental paradigms and a variety of stimulus types (e.g., Leonard, McGregor, & Allen, 1992; Stark & Heinz, 1996; Sussman, 2001; Tallal & Piercy, 1974, 1975; Tallal & Stark, 1981). A number of researchers have hypothesized that these observed auditory processing deficits are causally related to language impairment. For example, Leonard et al. (1992) suggested that the inability to perceive rapidly changing acoustic information might underlie the grammatical deficits of children with SLI, as these acoustic parameters are potentially the cues used to perceive some grammatical morphemes. For example, two commonly occurring allomorphs of the English past-tense morpheme are word-final /t/ and /d/, the perception of which would be based primarily on perception of formant transitions. This view has been challenged in other studies. Recent research has suggested that the observed speech perception deficits of children with SLI may have more to do with the nature of the stimulus and the memory demands of the task than on the perception of brief acoustic cues. Coady, Evans, Mainela-Arnold, and Kluender (2007) and Coady, Kluender, and Evans (2005) found that children with SLI performed similarly to typically developing age peers when natural speech rather than synthetic speech was used, when the stimuli were real words rather than nonsense words, and when the memory demands of the task were lessened. This finding is consistent with the claim of Gillam, Hoffman, Marler, and Wynn-Dancy (2002) that the performance of children with SLI is disproportionately affected by task difficulty relative to the performance of chronological-age peers. McMurray, Munson, and Tomblin (2014) examined sensitivity to small changes in voice onset time (a primary cue used to differentiate voiced and voiceless stop consonants in English), both within and across phoneme categories. They measured eye gaze patterns in the visual world paradigm, rather than the more traditional two-alternative forced-choice paradigm, in which participants must make a decision about a stimulus and initiate a response by either pointing or vocalizing. McMurray and colleagues found that phoneme discrimination of children with SLI and their typically developing peers was similar; their modeling results suggest that children with SLI had a deficit in lexical processing rather than in speech perception. Nevertheless, research on speech perception has served as an impetus to understand how speech perception deficits are related to language impairment, whether these deficits are considered as an underlying cause of the language impairment, as in the work of Tallal and colleagues (e.g., Merzenich et al., 1996), or

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as a symptom of a more general processing problem, as in the work of Miller, Kail, Leonard, and Tomblin (2001) and Windsor and Kohnert (2004), among others. While some researchers have suggested that differences in speech perception observed in school-age children with language impairment relative to typically developing peers might be a consequence of the language disorder, rather than a cause, other research suggests that such differences in speech perception are evident as early as the first year of life, even before children begin to produce words. Two longitudinal studies are relevant here. Tsao, Liu, and Kuhl (2004) followed 28 infants and found a correlation between performance on a vowel discrimination task at 6 months of age and word production and comprehension at 13 months and at 16 months. Speech perception performance at 6 months was also correlated with production of irregular forms and grammatical complexity at 24 months. In another prospective study, Benasich and Tallal (2002) examined younger siblings of children with SLI relative to siblings of children with typical language development. Across the two groups of children, they found that performance on a nonspeech auditory discrimination task at 7.5 months predicted subsequent language performance at 16 and 24 months for measures of both language comprehension and production.

Nonspeech Auditory Processing In addition to differences in speech perception relative to their typically developing peers, studies have suggested that children with SLI may differ from their typically developing peers on a range of auditory perception tasks that do not utilize speech signals. The general focus of psychophysical studies since the foundational study of Tallal and Piercy (1973) has been to identify possible difficulties in the perception of acoustic parameters that carry crucial acoustic cues to speech sounds. A finding that children with SLI have difficulty perceiving acoustic parameters in nonspeech stimuli considerably strengthens the hypothesis that general perceptual difficulties may underlie language difficulties. Wright et al. (1997) examined auditory temporal processing in eight children with SLI, to examine whether the deficits in rapid auditory processing for speech found by Tallal and colleagues could also be demonstrated for nonspeech stimuli. Wright measured detection thresholds for pure-tone stimuli presented simultaneous with, prior to, or after broad-band noise with different spectral characteristics. The crucial condition in this study was the backward-masking condition. In this condition, a tone is presented immediately prior to an interval of noise. A large, statistically significant group difference was found for detection thresholds in the backward-masking condition: the tone needed to be louder in this condition for the SLI children to detect it. This difference was not present when the spectral characteristics of the noise and those of the tone were considerably different. Wright et al. claimed that these findings supported Tallal’s earlier conjecture that the perception of brief auditory stimuli is impaired in children with SLI. Wright and Zecker (2004) expanded on this finding with a larger, more heterogeneous group of children, including children with SLI (including the eight children in Wright et al., 1997), children with dyslexia, and children with central auditory processing disorder, as well as age-matched peers with normal language and academic achievement. Again, typically developing children could detect a less-intense tone in the backward-masked condition better than the children in any of the other groups. Wright and colleagues’ result has been replicated in a number of studies. Marler and Champlin (2005) and Marler, Champlin, and Gillam (2002) further showed that children with SLI have abnormal neurophysiologic responses in the backward-masking condition. Marler, Champlin, and Gillam (2001) examined detection thresholds in backward-masking conditions by children with SLI undergoing computer-based auditory training programs (either Fast ForWord or Laureate Learning Systems software) and typically developing controls not receiving treatment. They found

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no association between participation in these programs and improvement in backward-masking thresholds. Thresholds decreased on successive trials, for both groups of children, suggesting that performance on the backward-masking task is at least partly dependent on task familiarity. Basu, Krishnan, and Weber-Fox (2010) found that frequency-perception deficits in children with SLI were present not only in behavioral data but also in brainstem encoding of frequency changes. The auditory perception problems of children with SLI may extend beyond temporal perception. McArthur and Bishop (2004) examined frequency discrimination in teenagers and young adults with SLI and peers with typical language achievement. McArthur and Bishop argued that many previous findings regarding the purported auditory temporal processing deficit in children with SLI may be due to their decreased ability to perceive fine differences in frequency. They demonstrated that frequency detection thresholds were lower for people with typical language achievement than for a subgroup of people with SLI who had poor phonemic awareness. In a subsequent study, Bishop and McArthur (2005) showed that some of the children with SLI in the McArthur and Bishop (2004) study had atypical neurophysiologic responses to auditory stimuli, though this didn’t coincide perfectly with the subset who demonstrated poor frequency discrimination. In follow-up measures taken 18 months later, the frequency discrimination of many of the children with SLI improved, though a large proportion of the group continued to have atypical neurophysiologic responses to stimuli. Bishop, Adams, Nation, and Rosen (2005) examined the perception of brief glide stimuli (i.e., pure-tone stimuli that change in frequency). They found that duration and frequency-range thresholds did not differ significantly between the two groups, though they did differ in a linguistic task, perceiving words in noise. Research by Goswami and colleagues has examined the perception of amplitude rise times by children with SLI. Amplitude rise time refers to the duration of the interval between silence and the peak amplitude of a sound. Sounds can have very short amplitude rise times, corresponding to a percept of a sudden onset, or very gradual ones. Corriveau, Pasquini, and Goswami (2007) found that children with SLI have poorer ability than their typically developing peers to perceive changes in amplitude rise time. Performance on that task was found to uniquely predict measures of language beyond what is predicted by other measures of nonlinguistic auditory processing. Goswami and colleagues interpreted this as evidence that children with SLI (as well as children with developmental dyslexia, who also show this deficit) have impaired ability to perceive the rhythmic structure of language, and that this impairment has a cascading effect on their ability to learn those aspects of language that purportedly relate to its rhythmic structure. In general, the studies reviewed in this section seem to converge on the notion that at least some children with SLI have deficits in at least some aspects of auditory perception. The interpretation of this finding is qualified, however, by a number of factors. First, not all findings have been replicable across studies, suggesting that small differences in identification criteria used for SLI, or the inclusion of children with a variety of different language impairments (i.e., both SLI and dyslexia), may lead to different results. Second, as discussed by McArthur and Bishop (2004), it is not clear that the tasks that have been used in the classic studies on the psychophysical abilities measure what they purport to measure. For example, McArthur and Bishop argue that tasks that have been purported to measure temporal-processing abilities may in fact have been measuring frequency perception. Finally, and perhaps most importantly, is the possibility that the group differences may reflect task learning rather than psychophysical abilities. Classical research on the psychophysical abilities of adults has examined asymptotic performance on listening tasks. This requires that individual listeners participate in numerous listening sessions to determine threshold performance. The long times required to determine these thresholds in these studies make them inappropriate for children with language impairment, who often show decreased attention. Consequently, thresholds

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are often determined using procedures that are relatively quick and potentially affected by lapses in attention. The group differences may reflect attention or task learning, rather than differences in psychophysical abilities. This possibility is underscored by Marler et al.’s (2001) finding that backward-masked thresholds in children decreased with successive trials. It is possible that, with increased familiarity with a task, the auditory perception of children with SLI may reach levels that are comparable to those of children with typical development. Only one study has examined this possibility systematically. McArthur and Bishop (2004) examined the association between performance on a frequency-detection task and performance on tasks that measure basic-level cognitive processes involved in their task: attention, perception, and temporal-order perception. Though some group differences were found on these measures, McArthur and Bishop argued that “although temporal order and paired association may account for some variance in [frequency detection] thresholds, this amount is too small to explain the poor [frequency detection] thresholds of the [children with SLI demonstrating poor frequency detection]” (p. 537). A challenge for future studies is to further delimit the extent to which group differences in auditory perception are related to task familiarity and other basic-level cognitive processes. Another challenge for future research is to identify the extent to which individual differences in auditory processing contribute to the heterogeneity in language abilities that is characteristic of the population of children with SLI, beyond what can be predicted by basic-level cognitive processes. Few studies have examined this, and the results of these studies do not find a consistent relationship between psychophysical abilities and language performance, as measured with standardized instruments. For example, Bishop et al. (1999) found that a measure of auditory perception, performance on the Tallal Auditory Repetition Test, did not predict scores on a standardized language test as well as scores on another task, nonword repetition. Much previous research has shown that children with SLI perform more poorly than their typically developing peers on nonword repetition tasks (e.g., Ellis Weismer, Tomblin, Zhang, Buckwalter, Chynoweth, & Jones, 2000). It is well documented that children with SLI very often have lower nonverbal IQ scores than age- and languagematched peers with typical language development, even when children with scores below a cutoff (i.e., 85) are excluded. As argued by Rosen (2003), these subtle, sub-clinical differences in nonverbal IQ may account for auditory-processing differences between children with SLI and typically developing children, rather than the differences in language abilities. In short, the findings reviewed in this paragraph support Rosen’s (2003) argument that the auditory processing deficits observed in some children with SLI may be co-occurring deficits, rather than a causal deficit.

Relating Speech Perception to Language Skills Two hallmark symptoms of SLI in English-acquiring preschool children are (1) vocabulary problems, as exemplified by late talking (a delay in when first words are produced), difficulties with word learning, and a smaller productive vocabulary size than typically developing peers at any age (e.g., Dollaghan, 1987; Oetting, Rice, & Swank, 1995) and (2) morphological deficits, as exemplified by a protracted period for morphological acquisition, especially for morphemes related to verb tense (for a summary of this work, see Leonard, 1998). In this section, we consider how these difficulties in word learning and morphological acquisition might be related to early problems in speech perception, such as those observed by Benasich and Tallal (2002) and Tsao et al. (2004). A large body of research on infant speech perception provides some insight into why early deficits in speech perception might lead to delays in word learning. One of the primary language-learning problems that children must solve in their first year of life is how to pick out words—which they don’t yet know—from the continuous stream of speech. This task is made easier by child-directed speech with its larger pitch range, shorter utterances, and simpler syntactic structure, but the problem

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still remains. Research on speech perception in the first year of life provides much insight into how infants gradually develop the abilities they need in order to delimit words from running speech. By about 9 to 10 months of age, children prefer listening to (1) words with the preferred English strongweak stress pattern (Jusczyk, Cutler, & Redanz, 1993), (2) words that contain sequences with permissible phonotactic sequences (Jusczyk, Friederici, Wessels, Svenkerud, & Jusczyk, 1993), and sequences with high rather than low phonotactic probabilities (Jusczyk, Luce, & Charles-Luce, 1994). Children are able to exploit these preferences so that they can segment continuous speech into words sometime between 6 and 9 months of age, using phonotactic information (Friederici & Wessels, 1993; Mattys, Jusczyk, Luce, & Morgan, 1999; Saffran, 2001). If children have difficulty with speech perception in their first year of life, then these difficulties might make it more difficult for them to segment out words from running speech and this difficulty, in turn, could lead to a delay in word learning. There exist any number of deficits in speech perception that might make word learning problematic, and relatively few of these deficits have been studied in children with SLI. Several recent studies suggest that children with language impairment may have early difficulty in segmenting the continuous speech stream into words. In a retrospective study, Newman, Bernstein Ratner, Jusczyk, Jusczyk, and Dow (2006) found that infants’ ability to segment speech at 1 year of age was related to their expressive vocabulary size one year later. Evans and colleagues (Evans, Saffran, & Robe-Torres, 2009; Mainela-Arnold & Evans, 2014) found that older (6 to 14 years of age) children with SLI performed more poorly than typically developing age peers on a task that involved segmenting continuous speech into words based on phonotactic probability (Saffran, 2001); furthermore, the performance of children with SLI was related to their receptive vocabulary size. Werker, Fennell, Corcoran, and Stager (2002) provide some experimental evidence relevant to the prediction of a relationship between speech perception and word learning. They examined the auditory word discrimination skills of children in their second year of life, at a time when there is a wide range in vocabulary size, even for typically developing children. Werker et al. (2002) found that most 14-month-old infants were unable to distinguish between minimal pairs such as /bɪ/ and /dɪ/ in a word-learning task, although they were able to do so in a simpler speech perception task and in a word-learning task using phonetically dissimilar nonwords. Werker et al. (2002) found that across the whole age range of 14 to 20 months, productive vocabulary size was correlated with the ability to distinguish between minimal pair words on the word-learning task. In addition, they found that infants with a productive vocabulary of at least 25 words or a receptive vocabulary of at least 200 words were successful on this task. Werker and Curtin (2005) interpreted these results within their model of infants’ and toddlers’ speech perception and word learning (PRIMIR). In the PRIMIR model, three multidimensional planes underlie speech perception and word learning: a general perceptual plane, a word form plane, and a phoneme plane. Information on the phoneme plane develops gradually, based on regularities that emerge from the multidimensional clusters on the word form plane. This model predicts an interaction between word learning and phonological development, as observed by Werker et al. (2002). As predicted by the PRIMIR model, children who knew more words have a more highly developed phoneme plane and children with a more highly developed phoneme plane were better word learners. Thus, this model provides an explanation of the first hallmark symptom of SLI, namely, difficulties in word learning. The second hallmark symptom of SLI in English-speaking children is difficulty in the acquisition of morphology. The deficits in word learning that are observed for English-speaking children with SLI may be related to their deficits in morphological acquisition. There is some evidence that at least some aspects of morphological acquisition are related to vocabulary size. For example, Marchman (1995) found that the best predictor of when English-speaking children begin to overgeneralize the regular past tense (goed instead of went) is the number of verbs in productive vocabulary. Even for regular morphemes, there is evidence that children need a critical mass of lexical

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forms to robustly abstract the appropriate allomorphic alternation. The past tense morpheme /əd/ is much lower in frequency than its allomorphs /d/ and /t/, and it is the last of the regular past tense allomorphs to be acquired (Marchman, Wulfeck, & Ellis Weismer, 1999). Similarly, the plural morpheme /əz/ is lower in frequency than its allomorphs /s/ and /z/, and it is the last of the regular plural allomorphs to be acquired (Derwing & Baker, 1980). One interpretation of these findings is that children need a critical mass of lexical items in order to make a morphological generalization (Marchman & Bates, 1994). Marchman and Bates simulated these results with a connectionist model in which learning shifts qualitatively from memorization to systematic generalization as a function of vocabulary size. This view of morphological learning predicts that children with smaller vocabularies will have difficulties with the acquisition of morphology, and this is precisely what is observed for English-speaking children with SLI. In short, deficits in building a lexicon may mediate the causal relationship between speech perception deficits and morphological deficits in children with SLI: early speech perception deficits make the task of acquiring words challenging, and the resulting smaller-sized vocabulary may limit the robustness of the morphological generalizations that children with SLI can make. The second area in which speech perception deficits may relate causally to language impairment concerns children with dyslexia. Dyslexia is defined broadly as a deficit in comprehending and producing written language (see Chapter 5 by Shaywitz & Shaywitz; Chapter 19 by Hook & Haynes; and Chapter 11 by Joanisse). Like SLI, it is often diagnosed using exclusionary criteria (i.e., poor reading ability in the absence of a deficit that would otherwise compromise reading). It is commonly observed that SLI and dyslexia overlap, though the estimates are higher in clinically referenced samples (e.g., Catts, 1993) than in a population-based sample in the Midwestern United States (Catts, Adolf, Hogan, & Ellis Weismer, 2005). The question of whether children with dyslexia have deficits in speech perception was examined by Joanisse, Manis, Keating, and Seidenberg (2000), who found that speech perception deficits occurred in children with dyslexia only if they had a concomitant oral-language impairment. Joanisse et al.’s study provides further evidence for a link between speech perception and language abilities. It further suggests that speech perception deficits in children with dyslexia may be mediated by oral language abilities, rather than directly attributable to the reading impairment.

Articulatory Knowledge The studies reviewed thus far all deal with only one of the four types of phonological knowledge, perceptual knowledge. Another type of phonological knowledge is articulatory knowledge. Relatively few studies have examined speech production directly in children with SLI. This general lack of research may reflect a long-standing belief that language is distinct from general cognition, and that we should therefore expect language acquisition to be distinct from the development of other skills (e.g., Lenneberg, 1967). In contrast, more recent theorizing suggests that language should be viewed in the context of the body in which the developing language system is embedded. In infancy, there are significant changes in the ways in which the body moves in and interacts with the environment; and these may in turn impact the development of skills and experiences that play a role in the emergence of communication and language. (Iverson, 2010, p. 230) This paradigm shift invites a more rigorous investigation of the relationship between motor performance and language skills across different levels of language ability.

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The earliest studies of articulatory abilities in children with SLI used phonetic transcription as a measure of production ability. Studies using this method found that there is a low comorbidity rate between primary language impairment and phonological disorder (Shriberg, Tomblin, & McSweeny, 1999) at kindergarten entry, at least when a relatively restrictive definition of phonological disorder is used. However, other studies have observed that children with SLI have speech production deficits relative to their typically developing peers. One example of this is given by McGregor and Leonard (1994), who showed that children with SLI repeated initial unstressed pronouns and articles less accurately than initial stressed content words. That is, initial syllables were more likely to be deleted in weak-strong sequences like they RUN than in strong-strong sequences like DOGS RUN (where words in caps indicate stressed words). This is consistent with the behavior of younger, typically developing children (as reviewed in Gerken, 1996) and may indicate that the well-established tendency for children with SLI to omit articles and function-word subjects has a basis in difficulties with speech production, rather than in deficits in abstract grammatical knowledge. More recent research has utilized finer-grained measures of articulation. One example of this is studies of articulatory variability in the speech of children with SLI. Goffman (1999, 2004) showed that children with language impairments have greater kinematic variability in lip movement than typically developing age-matched children when producing nonsense sequences. Both groups of children produced greater kinematic variability in weak-strong sequences than in strong-weak ones. This finding was replicated in a recent study by Goffman, Heisler, and Chakraborty (2006), who further showed that these differences are maintained in productions embedded in different positions within larger phrases. Heisler, Goffman, and Younger (2004) also found that children with SLI show more kinematic variability in word-learning tasks. DiDonato, Brumbach, and Goffman (2014) showed that measures of articulatory variability were significantly correlated with performance on a standardized measure of motor performance. Crucially, the children with SLI in Goffman’s studies did not have frank pronunciation problems; their increased kinematic variability does not appear to be secondary to categorical phonological errors of the type seen in children with articulation and phonological impairments. Goffman’s findings suggest that children with SLI have less mature motor control than their typically developing peers. Finally, some research has shown that children with SLI differ from their typically developing peers in general motor skills. Bishop (1990) found that children with SLI performed more slowly than typically developing children on a timed peg-moving task. Johnston, Stark, Mellits, and Tallal (1981) showed that children with SLI were slower than typically developing peers in executing rapid finger movements. However, not all studies of motor ability in children with SLI find group differences. Zelaznik and Goffman (2010) found that children with SLI did not differ from typically developing children on experimental tasks of rhythmic tapping and drawing. Children with SLI did perform more poorly than typically developing children on a standardized measure of motor performance. However, both groups performed within normal limits, and the SLI children’s poorer performance may be attributable to the cognitive-linguistic demands of that test, rather than to motor performance. The relationship between the language and motor deficits in children with SLI has been a matter of active debate, as it is theoretically possible that the observed motor deficits are comorbid with SLI rather than a causal factor. Brookman, McDonald, McDonald, and Bishop (2013) found that children with SLI perform more poorly on a task of gestural imitation and on a motor task requiring control of hand grip. These deficits appeared to be uniquely related to SLI, as a group of children with reading difficulties performed similarly to typically developing peers. Archibald, Joanisse, and Munson (2013) found that children with SLI repeated phonologically complex nonwords like perplisteronk disproportionately less accurately than their peers in an articulatorily challenging task where they held a bite-block between their molars. Children who demonstrated

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deficits in working memory but no evidence of SLI did not perform disproportionately poorly in the bite-block condition. The results of both Brookman et al. and Archibald et al. suggest that at least some of the motor deficits seen in children with SLI are uniquely related to their language abilities and not to comorbid factors.

Higher-Level Phonological Knowledge In the PRIMIR model of speech perception and word learning (Werker & Curtin, 2005), higherlevel phonological knowledge is highly inter-related with word learning. One prediction that this model makes is that children with smaller vocabularies will have difficulty with higher-level phonological knowledge, such as being able to robustly abstract consonant and vowel categories from their usual consonant-vowel, vowel-consonant, and consonant-consonant contexts. Previous work (Edwards, Beckman, & Munson, 2004; Munson, Edwards, & Beckman, 2005a) argued that the difference in repetition between high- and low-frequency sequences of phonemes is related to the robustness of children’s higher-level phonological knowledge. High-frequency sequences of phonemes (e.g., /mp/) can be repeated accurately by resorting to knowledge in existing lexical items (as in simple, camper, etc.). In contrast, low-frequency sequences like /mk/, which occur in no known lexical items, can be repeated accurately only if the child’s higher-level phonological knowledge includes knowledge of individual phonemes, like /m/ and /k/, in addition to knowledge of the sound structure of known words. Munson, Kurtz, and Windsor (2005) examined the accuracy of production of high- and lowprobability nonwords (i.e., nonwords that contained either all high-frequency or all low-frequency two-phoneme sequences) in children with SLI and two control groups. One control group was matched on chronological age (CA group) and the other was matched on an estimate of expressive vocabulary size (the VS group). All three groups of participants produced the high-probability nonwords more accurately than the low-probability ones. Interestingly, the effect of probability was larger for the children with SLI relative to their CA controls and was not significantly different for the children with SLI relative to their VS controls. Thus, children with language impairments have less-robust higher-level phonological knowledge than their peers with typical development. These deficits appear to be due entirely to the smaller size of their vocabularies. Munson, Kurtz, and Windsor conjectured that the larger phonotactic-probability effect seen in children with language impairments is related to their word-learning difficulties: children with SLI may experience more difficulty than their age peers in learning higher-level phonological knowledge from lexical items. Consequently, the robust scaffold that phonological representations serve in word learning is not available to them, and their subsequent word learning suffers. Further evidence for higher-level phonological knowledge deficits can be seen in studies of spoken-word recognition by children with SLI. Dollaghan (1998) examined the ability of children with SLI to recognize spoken words from which acoustic information had been removed using a technique called gating. Word recognition with information removed is facilitated if children have higher-level phonological knowledge that words consist of phoneme strings, as this allows them to relate a partial input to a lexical representation in memory (Garlock, Walley, & Metsala, 2001; Walley, 1988). Typically developing children with larger lexicons recognize gated words more accurately than do those with smaller lexicons (Edwards, Fox, & Rogers, 2002; Munson, 2001). Children with SLI did not require more acoustic information than their typically developing peers to recognize familiar words, but did require significantly more information to recognize unfamiliar words. This finding complements Munson, Kurtz, and Windsor’s (2005) finding by providing further evidence that children with SLI have a deficit in higher-level knowledge of the phonemic structure of words.

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An alternative explanation of these difficulties with spoken word recognition by children with SLI has been proposed by McMurray et al. (2014). McMurray and colleagues suggest that the deficit is due to a problem with inhibition—difficulty with suppressing competing words—rather than to deficits in speech perception or higher-level phonological knowledge. In an eye-tracking study using the visual world paradigm, McMurray et al. found that children with SLI and their typically developing age peers were equally sensitive to within-category differences in voice onset time, but that children with SLI were more likely to look at a competitor word that differed by only a single phoneme. The work of Mainela-Arnold, Evans, and Coady (2008) also supports their proposal. Mainela-Arnold et al. found exactly the opposite of Dollaghan (1998). They found that children with SLI did not require more acoustic information than their peers to recognize either high- or low-frequency words, but they did vacillate more between possible words at longer gates. However, a later study by these same researchers (Mainela-Arnold & Evans, 2014) found that children with SLI performed more poorly than age peers on word recognition in a gating task. These conflicting reports in the literature make it difficult to determine whether children with SLI have deficits in higher-order phonological knowledge without further research.

Conclusions With the notable exception of work on speech perception in children with SLI, there is a paucity of research on other aspects of phonological knowledge. Even in the area of speech perception, research has focused on whether children with SLI can perceive lower-level phonetic contrasts, rather than on whether they also have difficulty in abstracting higher-level phonological knowledge, such as phonotactic information. In addition, there are relatively few studies on articulatory and higher-level phonological knowledge in children with SLI relative to age controls or vocabulary-size controls. Furthermore, to our knowledge, there is virtually no research on the acquisition of socialindexical knowledge in children with SLI. Social-indexical knowledge refers to knowledge of how linguistic variation is used to convey and perceive membership in different social groups. Socialindexical knowledge encompasses a variety of different factors, including social class, race, gender, and regional dialect, as well as the ability to identify individual talkers. Social-indexical variation can relate to any aspect of linguistic structure, including phonology, syntax, morphology, and the lexicon. Though previous studies have shown the pervasive influence of social-indexical variability on speech production and perception in adults and children (see Foulkes, 2005, for a review), very little research has examined how social-indexical knowledge may be impaired in children with SLI. One noteworthy exception is Levi and Schwartz (2013), who found that children with SLI were no worse than their typically developing peers at discriminating talkers in either a familiar language (English) or an unfamiliar language (German). There is also evidence that children with other commonly occurring communication disorders have deficits in socio-indexical knowledge. Children with phonological disorders have deficits in knowledge of social-indexical variability (Nathan & Wells, 2001), as illustrated by their decreased ability to perceptually accommodate dialect variation in a spoken word recognition task. Young adults with mild autism do not display the same subjective attitudes about dialects as adults without autism, despite their intact ability to perceive and classify dialects based on phonological information alone (Clopper, Rohrbeck, & Wagner, 2012). It is well established that children with SLI show a host of deficits in social skills and social communication (e.g., Brinton, Fujiki, & McKee, 1998, Brinton, Fujiki, Spencer, & Robinson, 1997; Marton, Abramoff, & Rosenzweig, 2004; Chapter 18 by Fujiki & Brinton). It is possible that a causal or maintaining factor for these concomitant impairments is a decreased ability to perceive and convey social roles and social-group membership through variation in speech production. This is a potentially rich area for future research.

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To summarize, while there are many critical gaps in our understanding of phonological knowledge in children with SLI, the research to date suggests that children with SLI have deficits in perceptual knowledge, articulatory knowledge, and higher-level phonological knowledge relative to their typically developing age peers. In this chapter, we have suggested that the observed deficits in perceptual knowledge, in particular, could lead to difficulties with word learning that, in turn, could lead to difficulties in the acquisition of morphology. Our account of the relationship between speech perception and language acquisition differs from that of others (e.g., Merzenich et al., 1996; Sussman, 2001; Wright et al., 1997). These other accounts have generally proposed a fairly direct link between deficits in speech perception and deficits in language acquisition (for example, children with SLI will have difficulty learning grammatical morphemes if they have difficulty processing rapidly changing temporal information). In contrast, we have proposed that the lexicon plays a crucial role in the acquisition of both phonological knowledge and morphological knowledge. In this account, deficits in speech perception will result in difficulties in word learning, which, in turn, will make the acquisition of robust phonological representations more difficult. This proposal is consistent with theories that posit continuity between processing and knowledge of language (e.g., MacDonald & Christiansen, 2002). Such an account of SLI has many implications, both theoretically and clinically.

Acknowledgements The preparation of this chapter was supported by NIH grant R01 DC02932 to Jan Edwards, Mary E. Beckman, and Benjamin Munson. We especially thank Mary E. Beckman, who has been crucially involved in the development of the model of phonological acquisition described in this chapter.

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Jan Edwards and Benjamin Munson McGregor, K., & Leonard, L. B. (1994). Subject pronoun and article omissions in the speech of children with specific language impairment: A phonological interpretation. Journal of Speech and Hearing Disorders, 37, 171–181. McMurray, B., Munson, C., & Tomblin, J. B. (2014). Individual differences in language ability are related to variation in word recognition, not speech perception: Evidence from eye-movements. Journal of Speech, Language, and Hearing Research, 57, 1344–1362. Merzenich, M. M., Jenkins, W., Johnston, P., Schreiner, C., Miller, S., & Tallal, P. (1996). Temporal processing deficits in language-learning impaired children ameliorated by training. Science, 271, 77–81. Miller, C. A., Kail, R., Leonard, L. B., & Tomblin, J. B. (2001). Speed of processing in children with specific language impairment. Journal of Speech and Hearing Research, 44, 416–433. Munson, B. (2001). Relationships between vocabulary size and spoken word recognition in children aged 3–7. Contemporary Issues in Communication Disorders and Sciences, 28, 20–29. Munson, B., Edwards, J., & Beckman, M. E. (2005a). Relationships between nonword repetition accuracy and other measures of linguistic development in children with phonological disorders. Journal of Speech, Language, and Hearing Research, 48, 61–78. Munson, B., Edwards, J., & Beckman, M. E. (2005b). Phonological knowledge in typical and atypical speechsound development. Topics in Language Disorders, 25, 190–206. Munson, B., Kurtz, B. A., & Windsor, J. (2005). The influence of vocabulary size, phonotactic probability, and wordlikeness on nonword repetitions of children with and without specific language impairment. Journal of Speech, Language, and Hearing Research, 48, 1033–1047. Munson, B., & Nelson, P. B. (2005). Phonetic identification in quiet and in noise by listeners with cochlear implants. Journal of the Acoustical Society of America, 118, 2607–2617. Nathan, L., & Wells, B. (2001). Can children with speech difficulties process an unfamiliar accent? Applied Psycholinguistics, 22, 343–361. Newman, R., Ratner, N. B., Jusczyk, A. M., Jusczyk, P. W., & Dow, K. A. (2006). Infants’ early ability to segment the conversational speech signal predicts later language development: A retrospective analysis. Developmental Psychology, 42, 643–655. Oetting, J., Rice, M., & Swank, L. (1995). Quick incidental learning (QUIL) of words by school-age children with and without PLI. Journal of Speech, Language, and Hearing Research, 23, 434–445. Romeo, R., Hazan, V., & Pettinato, M. (2013). Developmental and gender-related trends of intra-talker variability in consonant production. Journal of the Acoustical Society of America, 134, 3781–3792. Rosen, S. (2003). Auditory processing in dyslexia and specific language impairment: Is there a deficit? What is its nature? Does it explain anything? Journal of Phonetics, 31, 509–527. Saffran, J. (2001). Words in a sea of sounds: The output of statistical learning. Cognition, 81, 149–169. Seung, H.-K., & Chapman, R. (2000). Digit span in individuals with Down syndrome and in typically developing peers: Temporal aspects. Journal of Speech, Language, and Hearing Research, 43, 609–620. Shriberg, L. D., Tomblin, J. B., & McSweeny, J. L. (1999). Prevalence of speech delay in 6-year-old children and comorbidity with language impairment. Journal of Speech, Language, and Hearing Research, 42, 1461–1481. Smit, A. B., Freilinger, J. J., Bernthal, J. E., Hand, L., & Bird, A. (1990). The Iowa articulation norms project and its Nebraska replication. Journal of Speech and Hearing Disorders, 55, 779–798. Smith, B. (1978). Temporal aspects of English speech production: A developmental perspective. Journal of Phonetics, 6, 37–67. Stark, R. E., & Heinz, J. M. (1996). Vowel perception in children with and without language impairment. Journal of Speech and Hearing Disorders, 39, 860–869. Sussman, J. E. (1993). Perception of formant transition cues to place of articulation in children with language impairments. Journal of Speech and Hearing Disorders, 26, 1286–1299. Sussman, J. E. (2001). Vowel perception by adults and children with normal language and specific language impairment: Based on steady states or transitions? Journal of the Acoustical Society of America, 109, 1173–1180. Tallal, P., & Piercy, M. (1973). Defects of nonverbal auditory perception in children with developmental aphasia. Nature, 241, 468–469. Tallal, P., & Piercy, M. (1974). Developmental aphasia: Rate of auditory processing and selective impairment of consonant perception. Neuropsychologia, 12, 83–93. Tallal, P., & Piercy, M. (1975). Developmental aphasia: The perception of brief vowels and extended stop consonants. Neuropsychologia, 13, 69–74. Tallal, P., & Stark, R. E. (1981). Speech acoustic cue discrimination abilities of normally developing and language impaired children. Journal of the Acoustical Society of America, 69, 568–574.

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Perception and Production Tallal, P., Stark, R., Kallman, C., & Mellits, D. (1981). A reexamination of some nonverbal perceptual abilities of language-impaired and normal children as a function of age and sensory modality. Journal of Speech and Hearing Disorders, 24, 351–357. Tomblin, J. B., Records, N. L., Buckwalter, P., Zhang, X., Smith, E., & O’Brien, M. (1997). Prevalence of specific language impairment in kindergarten children. Journal of Speech, Language, and Hearing Research, 40, 1245–1260. Tsao, F.-M., Liu, H.-M., & Kuhl, P. K. (2004). Speech perception in infancy predicts language development in the second year of life: A longitudinal study. Child Development, 75, 1067–1084. Walley, A. C. (1988). Spoken word recognition by young children and adults. Cognitive Development, 3, 137–165. Werker, J. F., & Curtin, S. (2005). PRIMIR: A developmental model of speech processing. Language Learning and Development, 1, 197–234. Werker, J. F., Fennell, C. T., Corcoran, K. M., & Stager, C. L. (2002). Infants’ ability to learn phonetically similar words: Effects of age and vocabulary size. Infancy, 3, 1–30. Werker, J. F., & Tees, R. C. (1984). Cross-language speech perception: Evidence for perceptual reorganization during the first year of life. Infant Behavior and Development, 7, 49–63. Windsor, J., & Kohnert, K. (2004). The search for common ground: Part 1. Lexical performance by linguistically diverse learners. Journal of Speech, Language, and Hearing Research, 47, 877–890. Wright, B., Lombardino, L., King, W., Puranik, C., Leonard, C., & Merzenich, J. (1997). Deficits in auditory temporal and spectral resolution in language-impaired children. Nature, 387, 176–178. Wright, B., & Zecker, S. (2004). Learning problems, delayed development, and puberty. Proceedings of the National Academy of Sciences, 101, 9942–9946. Zelaznik, H. N., & Goffman, L. (2010). Motor abilities and timing behavior in children with specific language impairment. Journal of Speech, Language, and Hearing Research, 53, 383–393.

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10 GENETICS OF CHILD LANGUAGE DISORDERS J. Bruce Tomblin

Whether we are working in the clinic or in the laboratory, we are bound to ask why some children find language development easy and others are challenged by it. Despite decades of research, we still do not have satisfactory answers to this question, although progress has been made. We know from children with hearing loss and from children from low socioeconomic status (SES) homes that language input matters. Children who have more limited opportunities to have access to spoken language are less likely to fare well in language learning. We also know that this experience requires an elegant neurobiological system that can take advantage of this experience to form enduring memories that, in the case of language, contain abstractions about the form and content of language. How these abstractions are computed remains a central point of theoretical debate, but it is clear this process requires a complex set of neural systems that arise from networks of neurons formed early in development and shaped by experience. Throughout this process we can be quite certain that genetic processes play a role, since there is very little biology that is not tied in one way or another to genetic function. Thus, the key questions we will address in this chapter are not so much whether there is a place for genetics in the study of developmental language disorders, but rather the degree to which individual differences in learners is influenced by genetic sources and the manner in which this influence is conveyed. In fact, the latter issue—how genes influence language development—provides the strongest rationale for the study of genetics in language development and disorders. As I suggested above, genetic mechanisms participate in complex neurobiological pathways that involve the formation of basic brain architecture and the dynamic structuring of neural connectivity and activity from which cognition and language arises. Furthermore, this neural activity feeds back onto neural activity, which can then influence genetic expression. Rather than viewing genetics as an ultimate or initial cause of this developmental process, the study of genetics of language should be viewed as one element that participates in these complex pathways. Thus, any insights regarding genetic factors involved with genetics must integrate into the broader understanding the multiple systems that give rise to language.

Models of Genetic Influence on Language A simple view of genetics is that, through DNA, heritable information is stored in the form of genes that code for proteins. This heritable information has been viewed as being packed into islands of information units called genes that are scattered along chromosomes. Each gene contains

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Protein

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Figure 10.1 A schematic of the steps involved in transcribing information from DNA to a protein. DNA sequence in exons and introns are transcribed into pre-mRNA via an RNA polymerase (RNApol). The premRNA transcript is shown to include noncoding information in the form of an intron. The pre-mRNA is converted into mRNA through RNA splicing, during which the intronic information is deleted. The mRNA then moves out of the nucleus, where it enters into the process of protein synthesis performed by the ribosomes.

information coded in the DNA (see Figure 10.1) that is used by the cell to first convert this information (transcription) into RNA, which then is used to guide the assembly of a particular protein (transcription). This principle that information flows from DNA in genes to RNA and then proteins has been termed the central dogma of genetics. The information coded in the DNA of a gene is in duplicate form in that it is found on both chromosomes—one from the paternal line and the other from the maternal line. Each copy of a gene is called an allele, and the copies can differ somewhat in the DNA content of the gene. This variation (allelic variation) may or may not result in differences in the protein product of the gene. When these allelic differences result in differences in the form of the protein, subsequent differences in biological function can be reflected in variation in a phenotype. If a particular allele determines the phenotype regardless of the nature of the other allele, it is called a dominant allele. Alleles that have no effect on the phenotype when paired with a dominant allele are called recessive alleles. Alleles whose effect adds together are said to be additive. The term phenotype is often used in genetics and refers to any characteristic or trait in the organism and as such may or may not be genetically influenced. Genetic variants can affect phenotypes in different ways. The simplest way is for there to be a simple one-to-one matching between a gene and a phenotype. This is often termed monogenic. Some of the earliest gene discoveries involved this relationship. A mutation of the CFTR gene on chromosome 7 was found to be responsible for cystic fibrosis in individuals who were homozygotic for the mutation. This is an example of a monogenetic trait and one where the effect size of the gene is large and the pattern of phenotypic variation is qualitative (affected/unaffected). Phenotypic variations arising from large single-gene monogenic effects have served as the leading edge of genetic discoveries, and later an example of such an effect for speech and language will be given in the case of FOXP2. These instances in which a single gene is sufficient to cause a disease, such as the CFTR gene, are usually rare because they are selected against in evolution.

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Many phenotypes, particularly those involving complex behaviors or functions, are likely to be influenced by more than one gene and thus are referred to as being polygenic. In this case, the variation in the phenotype arises from the combinations and interactions among several genes, and the distributions of these polygenic traits move toward a normal distribution in accord with the law of large numbers. In such a case, as the number of genes involved in the polygenic effect increases, the amount of variance in the phenotype associated with any single gene is likely to decline. Because the effect size of a polygenetic allele is small, it is less likely to be selected against and thus, these allelic variants can remain in the population at higher levels, so these genes can be fairly common. We can see that while single genes of large effect may contribute to some instances of language impairment (LI), it is more likely that polygenic effects contribute to the individual differences in language that extend over the whole range of abilities. These genes of small effect that contribute to individual differences in general can lead to something like LI when several deleterious alleles of small effect come together in a way that leads to poor language ability. In this way we can see that genetic variation can result in poor language ability in different ways.

The SLI/LI Phenotype As noted above, a dominant question in genetic research involves the relationship between variance in a phenotype and variance in a genotype. Within this chapter, the phenotype of interest will be poor language abilities in children who have normal hearing and no other neurodevelopmental disorders. Simply put, these are children with unexplained language disorders. These children have often been referred to as children with specific language impairment (SLI). The conceptualization of SLI has also incorporated the notion that the poor language is not due to a generalized deficit in intelligence, such as might be the case in children with intellectual disability. Thus, it became common practice to require that children with SLI had poorer language than would be expected given their nonverbal IQ; there was a discrepancy between language and nonverbal IQ. It is not uncommon, however, for children to have poor language abilities that are accompanied with similarly poor nonverbal IQs, although not sufficiently poor to be viewed as intellectually disabled (sometimes referred to as nonspecific language impaired, NLI). For more than two decades, there has been a debate as to whether this IQ discrepancy criterion was warranted, and we will see that this issue has been addressed in some of the genetic research. There is an increasing consensus that the requirement of a nonverbal IQ discrepancy for SLI can be relaxed or eliminated and thus include children with NLI within the same diagnostic category. But then the question has become what to call this diagnostic category (Bishop, 2014; Reilly et al., 2014)? Within genetics research, the diagnostic criteria for SLI has varied from more stringent, narrow phenotypic definitions of SLI to broader, more relaxed definitions. For simplicity, in this chapter, both NLI and SLI will usually be referred to as LI, unless this distinction between SLI narrowly defined and NLI is the actual focus of the research question.

Nonmolecular Evidence of Genetic Influences on LI The questions asked regarding the genetic basis of any phenotype begin with whether the phenotype is genetically influenced. Initially, we are not likely to look for specific genes influencing the phenotype, but rather we ask if there is indirect evidence of a genetic influence on the phenotype. To answer this question, we can test whether there is co-variation between the phenotypic similarity and genetic similarity within sets of individuals who vary in their biological relationship. Therefore, investigators use the natural variation of gene sharing among relatives to be indirect reflections of genetic sources of variance and couple this with variance in phenotypic resemblance.

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Several types of studies can be conducted to provide converging evidence that genes may influence the phenotype. The study designs differ with respect to the specific family members in the study. Many of these study designs have been used with children with developmental language disorders and, therefore, we will discuss the methods in conjunction with the findings for these designs.

Family Aggregation If a phenotype is genetically influenced, it should run in biological families. Evidence for the familial aggregation of LI can be found dating back to Ingram (1959), who noted that specific disorders of speech and language presented a familial character suggestive of a genetic etiology. Systematic studies directed toward testing such genetic hypotheses are of more recent origin. Much of this work used a family history method to test whether SLI aggregates in families. Because it is difficult to ask family members to be particularly specific with regard to spoken language, some of these studies asked about speech and reading problems as well as language (Beitchman, Hood, & Inglis, 1992; Lewis, Ekelman, & Aram, 1989; Neils & Aram, 1986; Rice, Haney, & Wexler, 1998; Tallal, Ross, & Curtiss, 1989; Tomblin, 1989; van der Lely & Stollwerck, 1996; Whitehurst et al., 1991). Figure 10.2 summarizes the findings of these studies. There is quite a bit of variability in the rates of these disorders, but this might be expected in studies where the phenotype varies and is a reported history. In all cases but the Whitehurst study, the differences between the rates of LI in the families of the LI probands were significantly higher than that of the control probands who had normal speech and language. It would appear from these data that having a first-degree relative with a specific speech and language impairment increases your chances of also being affected by around a factor of four. In the studies mentioned previously, the language status of the probands was established and then the rates of affectedness in the family members was determined. In contrast, Choudhury and Benasich (2003) sampled a cohort of infants from families with or without a positive history of LI. These infants were then followed until 3 years of age, at which time their language status was examined. The language abilities of the children with positive family histories were significantly

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Figure 10.2 The rate of language impairment in family members of control probands without language impairment contrasted with this rate in probands with SLI.

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lower than those of the children with negative family histories. Also, when the children were categorized into low- and normal-language groups, this grouping was significantly associated with family history of LI. A feature of these studies is that language status of the family members was based on historical report rather than direct testing of the family members. In contrast, Tomblin (1996) tested the language status of family members of SLI probands and reported that 21% of first-degree family members also had LI. This study did not include a control group, but if the population prevalence for SLI is around 7% (Tomblin et al., 1997), then SLI is about three times more common in firstdegree relatives than would be expected among unrelated individuals. Tallal and colleagues (Tallal et al., 2001) also tested family members of LI probands and probands with normal language status. They reported a rate of 31% LI in the families of the LI probands and 7.1% in the control families. Thus, LI in a family increases the risk for LI in first-degree relatives by a factor of 4.4. Collectively, these data provide consistent support for a familial pattern of occurrence for LI.

Twin Design Although family aggregation studies provide suggestive evidence that genes may play a role in a phenotype, it is very likely that the environment can cause aggregation as well. A special type of family relationship, twinning, provides a means of separating out the environmental and genetic effects. Twins come in two forms: in one case, dizygotic (DZ) twins are genetically like any other sibship in that, on average, they share 50% of their genes. In the other case, monozygotic twins (MZ) are genetic clones in that they share all of their genes. In both cases, twins share the same environment, both in utero and to a large degree after birth. This pattern of differential genetic variation between MZ and DZ among pairs across twin set types and similarity of environmental exposure for both twin types provides a natural experiment. Thus, if a phenotype is genetically influenced, it should be more similar among MZ twins than DZ twins. If the phenotype being studied is categorical, as in the case of clinical diagnosis (+/- affected) or qualitative trait (straight vs. curly hair), then this between-twin similarity can be represented as concordance. Concordance represents the proportion of twins who have the same phenotype. If a qualitative trait is genetically influenced, then the concordance rate in MZ twins should be higher than in DZ twins. When the phenotype is quantitative, the similarity can be represented by regression-based statistics such as correlation. The magnitude of the difference in phenotypic correlation between twins as a function of twin type is called heritability and provides an index of the proportion of the phenotype variance across individuals that is attributable to genetic factors. For quantitative traits from a broad sample of twins, heritability (h2) can be represented by doubling the difference between the correlation of MZ twins and DZ twins [h2 = 2*(rMZ - rDZ)]. We need to understand that this method represents what is called broad sense heritability because it represents the total effects of genes on the phenotype, including those instances where alleles have a dominant relationship between each other. Using this method with children drawn from the general population has shown that the h2 for vocabulary ability in school-age twins is about .50 (see Stromswold, 2001). About half of the variance in vocabulary abilities in twins can be explained by differential degrees of gene sharing. It is important to emphasize that h2 values cannot be generalized beyond the population studied. Heritability will be greater in populations where environmental sources of variance are constrained, such as in populations where exposure to language is similar across children, than in populations where individuals may have great differences in learning opportunities that would be environmental. Thus, if the total variance is increased via nongenetic factors, heritability will drop, even though the absolute gene effect may be constant.

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We can see that in the general population, individual differences in language do seem to be, at least in part, genetically influenced. In order to ask about heritability in children with poor language scores, it is necessary to use a different approach than correlations between twins. Commonly in LI research, a quantitative language phenotype and often a nonverbal IQ measure are used to create an impaired group by selecting individuals at the low end of the distribution of language ability and in some cases at least in the average range with regard to nonverbal IQ. This creates a challenge if we were to use simple correlational methods as these assume a bivariate normal distribution. If twin sets are selected because at least one child has LI, the language scores will not be normally distributed, at least among the LI probands. An alternative measure of heritability, called group differences heritability (h2g ), has been developed to deal with this issue. In this case, twins containing an LI proband with low language are sampled, and then the co-twin is measured on the language phenotype. If gene sharing influences language ability, the MZ co-twins will be similar to the proband and thus also have low language scores, whereas the DZ co-twins should have scores much closer to the mean of the population. This differential amount of shift in the co-twin scores that is associated with gene sharing provides an estimate of heritability.

Twin Studies of LI The twin method has been used to test several questions regarding the nature of SLI. To begin with, the hypothesis that specific language disorders are genetically influenced has been tested using the twin design. Beyond this, we can ask whether the different approaches to diagnosis make a difference in the heritability of LI, and along with this it is also possible to ask whether there is evidence that poor language or LI in particular arises from unique genetic sources that do not contribute to individual differences in the general population. Concordance rates for LI were reported in several studies. For example, Lewis and Thompson (1992) reported concordance rates of 86% for MZ and 48% for DZ twins for a phenotype of clinical identification of speech or language impairment. Bishop, North, and Donlan (1995) reported the results of a twin study of specific speech and language impairments. Using the diagnostic standard of the DSM-III-R for SLI, they reported a probandwise concordance of .46 and .70 for the DZ and MZ twins, respectively. Tomblin and Buckwalter (1998) looked at twins with a languageimpaired proband and found a concordance of .96 for the MZ pairs and .69 for the DZ pairs. Stromswold (2001) performed a meta-analysis of the twin studies published up to that time and found 10 studies that had reported concordance rates for either LI by report or LI based on direct testing. Across these studies, the overall concordance of language impairment was 83.6% for MZ twins, significantly greater than the 50.2% for DZ twins. More recently, DeThorne and colleagues (DeThorne et al., 2006) reported similar concordance rates for parental report of expressive language (MZ = .89; DZ = .53) and receptive language (MZ = .67; DZ = .20) problems. This pattern of higher concordance for MZ than DZ provides evidence suggesting a genetic influence on LI. Although concordance provides evidence for heritability, computation of h2g is more interpretable with regard to the magnitude of the heritability and respects the fact that the phenotype is quantitative. One of the first studies to report this index for LI was by Bishop et al. (1995). In this case heritability was estimated for four language measures, and h2g values in the range of .56 to above 1.0 were obtained for each language measure. Thus, from around 50% to as much as 100% of the variation in language abilities across twins in these studies is tied to genetic differences. These same authors reported an h2g of greater than 1.0 for these twins on a nonword repetition task, suggesting that phonological memory (PM) may be strongly genetically influenced. PM is often viewed as a measure of a memory system that contributes to language learning (Baddeley, Gathercole, & Papagno, 1998; Gupta & Tisdale, 2009). Thus, PM can be viewed as an endophenotype of

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language and language disorders. We will see later that PM has surfaced as one of the most robust phenotypes in the search for specific molecular genetic bases of LI. Shortly after this, Tomblin and Buckwalter (1998) reported an h2g of .45 for LI in school-age children. Toward the end of the 1990s, results of a large-scale longitudinal study of twins in the UK (Twins Early Development Study; TEDS) began to be reported. This study has provided an extensive amount of information on the heritability of language and related skills over the past two decades. Dale et al. (1998) reported an h2g of .73 for delays in parent report of vocabulary development of 2-year-olds in the TEDS. When these twins were 4 years of age, a subsample of these children were tested directly in their homes. Viding et al. (2004) reported that h2g for poor language in this subgroup of twins ranged from .38 when LI was determined by a 15th percentile cutoff, but increased to .48 when the cutoff was dropped to the second percentile. Thus, a decade of research had produced clear evidence of heritability for poor language. This consistent pattern of moderate heritability for poor language was broken when HayiouThomas et al. (Hayiou-Thomas, Oliver, & Plomin, 2005) recast the twins in the Viding study into children who had language skills that were discrepant from their nonverbal IQ (SLI) and children with low language and nonverbal IQ—nonspecific language impaired (NLI). When SLI was defined as language below -1 SD and nonverbal IQ greater than -1 SD, a low (h2g=.18) nonsignificant heritability was found. When SLI was defined by a regression-based discrepancy, the heritability was even lower. NLI, however, remained heritable (h2g=.52). Later, Bishop and Hayiou-Thomas (2008) hypothesized that the low heritability for SLI in the Hayiou-Thomas study could have been due to the fact that these children were sampled from the general population rather than from a clinical sample, as had been done in prior studies of SLI. These clinical samples were likely to have a higher rate of speech sound disorder. Thus, they examined the heritability of enrollment in clinical services and found it to be high. They argued that studies that sample children with SLI from clinical services may have an excess of co-morbid speech sound disorder, which may inflate the estimate of heritability of SLI. It is also possible that the low heritability of SLI as defined in the Hayiou-Thomas studies was due to the use of a discrepancy criterion between language abilities and nonverbal IQ. Recently, Dennis et al. (2009) have presented an argument that this practice of controlling intelligence in research on developmental disorders was ill founded on theoretical and measurement grounds. Researchers using the twin design have also questioned the value and impact of the use of nonverbal intelligence in the phenotype of SLI. Bishop et al. (1995) showed that heritability of SLI increased as the nonverbal IQ requirements for SLI were diminished. Several studies coming out of the TEDS project have shown that, in the general population, most of the heritability of language overlapped with nonverbal IQ. Within the general population, Colledge et al. (2002) found that 63% of the heritability of nonverbal IQ was common with the heritability of language. As noted earlier, when children with poor language are required to have language-nonverbal IQ discrepancies, heritability is often low (Bishop et al., 1995; Eley et al., 2001; Viding et al., 2003). These findings suggest that the use of a nonverbal IQ requirement for LI may be a factor in the low heritability for LI in the Hayiou-Thomas et al. (2005) study. These findings also support the notion that there are not likely to be language genes per se, but rather genes that influence pathways for development related to language, but not exclusive to language development.

Molecular Genetic Studies of LI The research from family studies and twin studies point to an involvement of genes in LI. These research methods share one thing in common: none of them actually studied the genotype of the individuals being studied. Instead, the genetic influences in these studies are manipulated

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indirectly via biological relationships. Advances in molecular genetics have now enabled researchers to directly measure the genotypes of large numbers of individuals and at many locations on the genome.

The FOXP2 Gene and LI A discussion of the molecular genetics of developmental language disorders needs to begin with one gene, the FOXP2 gene, which has been associated with developmental speech and language impairment. The discovery of the FOXP2 gene provides an example of how both positional cloning and candidate gene methods are used to understand a genetically based disorder. Much of what is known about the phenotype of FOXP2 has come from a three-generation family (KE) consisting of 37 members, 15 of whom had severe speech and language impairment (Vargha-Khadem et al., 1998). After preliminary work, linking this trait to the 7q31 region, Lai, Fisher, Hurst, Vargha-Khadem, and Monaco (2001) found that all affected family members had a mutation in the FOXP2 gene. The FOX genes encode a family of transcription factors with a characteristic winged-helix—or forkhead box (“fox”) DNA-binding domain (Shu, Yang, Zhang, Lu, & Morrisey, 2001). FOXP2 was found to code for a transcription factor. Transcription factors are proteins that bind to regulatory regions of genes and influence when and how much genes are being expressed. Thus, the effect of a mutation in a transcription factor gene comes from the alterations of its effects on downstream genes. Later, the gene CNTNAP2 will be discussed as a gene associated with language impairment that is regulated by FOXP2. FOXP2 is highly conserved across species, with only three amino acid changes between mice and humans, two of which have occurred in the human lineage since diverging from the chimpanzee (Enard, 2011; Enard et al., 2002). Studies of FOXP2 expression in the brain of mice, rats, and humans show it is expressed in several brain structures, including the cortical plate, basal ganglia including the striatum, thalamus, inferior olives, and cerebellum (Lai, Gerrelli, Monaco, Fisher, & Copp, 2003; Takahashi, Liu, Hirokawa, & Takahashi, 2003). Studies of the behavioral and neurological characteristics of the members of the KE family with the FOXP2 mutation have shown impairment of the same brain systems where FOXP2 expression was found. Vargha-Khadem’s group (Watkins, Donkers, & Vargha-Khadem, 2002a) compared the affected family members with the unaffected family members with regard to intelligence, language, and limb and oral facial praxis. The affected family members performed more poorly than the unaffected family members in all but limb praxis. A discriminant analysis indicated that nonword repetition of complex words was the strongest predictor of group membership. Structural brain imaging by this same group (Watkins et al., 2002b) revealed abnormalities of either elevated or reduced grey matter density in the caudate nucleus, putamen, cerebellum, temporal cortex, inferior frontal gyrus, motor cortex, and the inferior frontal gyrus. Furthermore, several of these regions, particularly the caudate, were shown to be bilaterally abnormal (Belton et al., 2003). Functional imaging showed underactivation of the left (Broca’s area) and right inferior frontal gyri and in the putamen. Overactivation during language tasks was also observed in the posterior parietal, occipital, and postcentral regions, which are not normally associated with language (Liegeois et al., 2003). The involvement of these brain systems has led Watkins et al. (2002a) to conclude that with regard to FOXP2, “the gene product interferes with the normal development of the caudate nucleus (and possibly other components of the motor system) and that this in turn impairs procedural learning” (p. 463). Soon after reports of the KE family, additional cases of speech and language impairment in individuals and families that were associated with a FOXP2 abnormality were presented. The first was a child (CS), whose balanced 5;7 translocation involving a breakpoint in FOXP2 aided in

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locating the FOXP2 in the KE family. This child was described as having a severe oral facial apraxia that also involved speech and language impairment (Lai et al., 2000). Several additional cases of individuals or family members have been reported with genetic abnormalities in FOXP2. Often these abnormalities involved either a deletion of genetic material in the 7q31 region that included a loss of FOXP2 (Lennon et al., 2007; Liegeois et al., 2003; Reimers-Kipping, Hevers, Pääbo, & Enard, 2011; Zeesman et al., 2004) or a translocation where a segment of DNA on chromosome 7 was exchanged with DNA from another region (Tomblin, Shriberg, Murray, Patil, & Williams, 2004). Further evidence of the association of FOXP2 abnormalities with apraxia of speech come from studies where individuals with apraxia of speech were recruited and tested for mutations of FOXP2. MacDermot et al. (2005) reported that three individuals out of 49 with signs of apraxia of speech had mutations. Feuk et al. (2006) also described 13 individuals with FOXP2 mutations who had diagnoses of apraxia of speech. Three instances of point mutations were found, and one of these mutations was also found in a sibling and in the mother of the children. These three individuals had noteworthy speech and language problems. Few details were provided regarding the speech and language characteristics of these individuals, but the developmental verbal apraxia coincided with reports of late talking and articulation disorder. Across these studies, some common phenotypic features can be seen, and these features are similar to those reported for the KE family. In all cases, a motor speech problem is reported. In most cases, this problem has been described as apraxia of speech. Also, language problems are often described. These common features suggest that the genes affected by the FOXP2 transcription factor are responsible for brain systems that affect motor speech and language abilities, with grammar possibly more affected than vocabulary. As more instances of FOXP2 impairment are found, the details of this phenotype should become clearer. These isolated cases of FOXP2 abnormalities associated with speech and language impairment raise questions over whether variation in FOXP2 is a common contributor to language impairment. The research group studying the KE family did find mutations of FOXP2 in a large sample of children with SLI (Newbury et al., 2002). Likewise, Meaburn, Dale, Craig, and Plomin (2002) searched for the KE family FOXP2 mutation in a large twin sample and did not find this particular mutation. Recently, my lab found no evidence of an association between the FOXP2 markers and SLI, nor did we find evidence of mutations in exon 14 of FOXP2 in a group of children with SLI (O’Brien, Zhang, Nishimura, Tomblin, & Murray, 2003). Thus, although FOXP2 appears to affect neural systems that are important for normal speech and language development, it does not appear to be a very common cause for SLI. MacDermot et al. (2005) have cautioned that few of these studies have sequenced all coding regions of FOXP2. Mutations in other regions of FOXP2 may be more common and may contribute to a greater proportion of speech and language disorders than these studies imply. So far, FOXP2 represents the singular success case of molecular genetic research into developmental speech and language disorder. Given that there were reasonable doubts as to whether genes with such strong effects on speech and language could be found, we should view this singular result as encouragement to keep looking for more genes associated with language. For one thing, we know that FOXP2 regulates other genes, and mutations of these genes could be much more common in the populations of LI individuals. The primary way that genetic research searches for new genetic loci has been to look across the genome—a genome-wide scan—using markers that are informative with regard to genetic variation in that region. These markers are not likely to actually serve as the causal variant or even be in the causal gene. By being in the genetic neighborhood they are likely to co-vary with the causal variant (referred to as being in linkage disequilibrium). This approach is not hypothesis driven, but rather throws out a large net in hopes of finding a potential variant. Because this strategy is largely a blind search, replication of findings is viewed as

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an important way to confirm initial discoveries. An ideal replication will be one done by a separate research group. This research strategy requires considerable resources and the collaboration across labs that are studying the same phenotype. Progress in the genetics of language disorder has been handicapped by this strategy due to the limited number of labs engaged in this research and of course the limited funding for such work. We will find two forms of genome-wide scans or studies: linkage scans and association scans. In a linkage scan, a panel of several thousand genetic markers that are indicative of genetic variation in a region is used to discover whether transmission of variants of the marker is associated with variation in the phenotype. This method seeks to determine if there is a locus in the genome where variations in the genetic makeup travel across generations with the phenotype. In fact, a linkage analysis was performed in an early stage of research on FOXP2 that identified a region on chromosome 7q. Genome-wide linkage analysis is best suited to the discovery of genetic loci that have relatively large effects on the phenotype, as shown in the case of FOXP2. Because linkage is performed within families, it is able to detect uncommon genetic variants that arise and run within families. Association scans test for a relationship between variation in a genetic marker and variation in the phenotype; however, this is performed across individuals who are not family members, but may share a functional genetic variation (polymorphism) that influences individual differences in language and is in linkage disequilibrium with an informative marker. Because association is computed across unrelated individuals, it is better suited to detect the effects of genetic variants that are fairly frequent (> 5%) in the population. Furthermore, these variants are likely to have smaller effects than those revealed by linkage analysis. Similar to genome-wide linkage studies, it is possible to perform genome-wide association studies (GWAS) to discover possible loci of interest. In the case of GWAS, it is necessary to have a set of markers that densely cover the genome. Such marker panels containing a million or more markers became available in the early 2000s. The fact that many markers are being tested and the effect (signal strength) is small means that GWAS demand large sample sizes, whereas linkage studies can be performed on much smaller samples, as was the case in the KE family.

Genome-Wide Linkage Studies Within genetics, linkage analysis refers to testing for patterns of co-transmission of genes and phenotypes within biological relatives. Thus, testing for linkage requires that related individuals serve as research participants. Typically, these are either multigeneration families or siblings. Bartlett et al. (2002) performed a linkage analysis on five families originally ascertained as part of a study on schizophrenia (Brzustowicz, Hodgkinson, Chow, Honer, & Bassett, 2000). Each family contained at least two individuals with spoken language abilities more than 1 SD below the mean. In this study, LI was defined as a categorical, as opposed to a quantitative, variable. In linkage studies, a pattern of transmission can be modeled as either dominant, where individuals who are heterozygotic (A/a) show the trait, or recessive, where heterozygotes do not show the trait. Because the mode of transmission for LI was unknown, analyses were conducted under both dominant and recessive models. Affected cases had poor language scores and nonverbal IQs greater than 80 with normal hearing and without any psychiatric diagnoses. Under the recessive model, significant evidence for linkage was found to 13q21 (SLI3) and suggestive evidence to 2p22 for LI. A follow-up study of 22 nuclear and extended families, which targeted chromosomes 2, 7, and 13, replicated findings for linkage to 13q21 with the same phenotype and genetic model (Bartlett et al., 2004). Logarithm of odds (LOD) are used to reflect the extent to which the data reflecting the pattern of transmission exceeds chance. A LOD score of 1 would be no evidence of linkage and LOD scores higher than 3 are considered significant. Combining both samples resulted in a maximum LOD score of

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6.18 at the same location. However, most of the linkage effect in the replication sample came from four pedigrees, suggesting this locus may not be indicative as a risk factor for LI in the general population. In the Bartlett study, linkage was examined across generations. Cross-generation linkage requires establishing a phenotype in at least parents and their children, which is challenging in the case of developmental language disorder, where adult diagnosis is rare and few diagnostic tools are available. Linkage can be studied within one generation using siblings. In this case, examining the association of genetic marker sharing between siblings with phenotype sharing can provide a test of linkage. The SLI Consortium (2002) used such sib-pair linkage methods in a combined clinical/ epidemiological sample. The original sample comprised 98 families selected via a proband with past or current receptive and/or expressive language abilities more than 1.5 SD below the mean for age. Three quantitative language phenotypes—the receptive and expressive language scales of the Clinical Evaluation of Language Fundamentals–Revised (CELF-R; Semel, Wiig, & Secord, 1987) and the Children’s Test of Nonword Repetition (CNRep; Gathercole, Willis, Baddeley, & Emslie, 1994) measuring phonological memory (PM)—were used as phenotypes. All individuals in this study (siblings, as well as probands) had performance (nonverbal) IQ (PIQ) higher than 80 and normal hearing. Individuals were excluded from the study on the basis of autism spectrum disorder, known neurological difficulties, as well as for English as a second language. Findings from the initial screen showed that two loci, 16q24 (SLI1) and 19q13 (SLI2), exceeded the threshold that is considered as evidence of linkage. The language measure found to be linked to SLI1 was the CNRep (Gathercole et al., 1994) measuring phonological memory (PM). Linkage to SLI2 was found for the expressive language phenotype. The SLI Consortium (2004) followed this study with a new replication sample. Eighty-six additional families were ascertained clinically using the same language and cognitive measures as their first study (denoted Wave 1). However, in the second study (Wave 2), markers were targeted to the SLI1 and SLI2 regions on chromosomes 16 and 19 only. Both regions showed evidence of suggestive linkage to the PM phenotype. In contrast to Wave 1, however, the expressive language phenotype was not linked to either chromosome. When the data from the two waves were combined, the linkage of phonological memory to chromosome 16 yielded strong evidence for linkage. In contrast, the strength of association to chromosome 19 (SLI2) decreased, leading to nonsignificant linkage for phonological memory or expressive language. This loss in signal was attributed to the fact that, in each wave of the study, linkage to chromosome 19 came from different phenotypes (i.e., subgroups), and these were not additive. Monaco (2007) reanalyzed data from the SLI Consortium using a multivariate approach in which the language measures were combined. Although an initial multivariate screen using subjects from Wave 1 found seven loci on chromosomes 1, 4, 5, 7, 10, 16, and 19 yielding significant levels of linkage, only the SLI1 locus on chromosome 16 remained significant once Wave 2 was added to the analysis. Further investigation of the SLI1 linkage peak showed new evidence for linkage to reading and spelling phenotypes, as well as to PM as before. Loci at SLI2 on chromosome 19, and a previously unreported locus on chromosome 10, showed borderline significance in this analysis. In an attempt to replicate findings from the SLI Consortium, Falcaro et al. (2008) ascertained an additional 93 probands with LI in an independent study. Two measures of language ability—phonological memory and past tense marking—were treated quantitatively and dichotomously in a family-based analysis, with markers targeted to chromosomes 16 and 19. Both loci yielded LOD scores necessary for replication: PM at the SLI1 locus and expressive morphology at the SLI2 locus. In addition to the linkage studies by Bartlett and the SLI Consortium, a linkage study was performed in Chile on a small, isolated population with high rates of SLI (Villanueva, Jara, &

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Palomino, 2010; Villanueva et al., 2011). This study examined four large pedigrees and found strong evidence of linkage to a region on chromosome 7q, as well as regions on chromosomes 13, 17, 6q, and 12. The finding to 7q is important because candidate genes for language impairment, FOXP2 and CNTNAP2, reside in this region; however, linkage was not to regions containing these genes. The linkage peak to chromosome 13 overlapped with the SLI3 region identified previously by Bartlett et al. (2002, 2004). The findings from the SLI consortium linkage studies have provided indications that gene variation linked to poor language and in particular the endophenotype of phonological memory might be found in the SLI1 region of chromosome 16. Unfortunately, no evidence for this locus was found in the Bartlett study or the Villanueva study, nor did the SLI Consortium replicate the linkage findings on chromosome 13. Thus, the linkage studies to date provided one replicated candidate locus SLI1 within the SLI Consortium studies and one replication of the SLI3 region across the Bartlett and Villanueva studies. Furthermore, the phenotype that is linked to SLI1 locus is the phonological memory measure. Recall that PM may be viewed as an endophenotype for LI and as such taps into a narrow domain of language function. In part, because of this narrowness it may have stronger ties to certain specific underlying neurological systems that are in turn more specifically genetically influenced. We will return to the SLI1 locus again in the section below on Candidate Loci and Genes, and we will see that promising findings have emerged in recent years again, particularly with regard to the PM.

Genome-Wide Association Studies As noted earlier, research using GWAS methods require the use of high-density marker panels, which became available in 2005. The first GWAS concerned with language was reported by Luciano et al. (2013). This study used two large cohorts with measures of phonological memory (PM) using nonword repetition and measures of reading. As we have seen, PM has been shown to be heritable and linked to the SLI1 locus. One of the cohorts consisted of children followed in the Avon Longitudinal Study of Parents and their Children (ALSPAC) (Team, 2001) and the other comprised twins and siblings in the Brisbane Adolescent Twin Sample (Wright & Martin, 2004). One marker in a gene (ABCC13) was found to be associated with PM. This gene lies in chromosome 21 and, within humans, it is believed to produce a nonfunctional protein and, thus, is called a pseudogene. Pseudogenes are genes that within a species have mutated to become nonfunctional. This gene or region may contain regulatory mechanisms that still have a function. Another GWAS was reported by Eicher and colleagues (Eicher et al., 2013), who also used the ALSPAC sample to search for genes that might contribute to the co-morbidity of reading disorder (RD) and LI. Association of markers to LI, RD, or co-morbid cases were computed. Genome-wide significant associations were found to markers in two genes (ZNF385D and COL4A2) in the comorbid cases and with LI in one gene (NDST4). A replication sample from the Pediatric Imaging Neurocognition and Genetics Consortium (PING; Akshoomoff et al., 2014) was then examined, and LI without RD was found to be associated with ZNF385D. Thus, ZNF385D appears to be a good candidate for LI and perhaps for co-morbid instances of LI and RD. ZNF385D is found on chromosome 3 and has been associated with ADHD and schizophrenia. Furthermore, Eicher et al. reported that brain imaging measures within the PING sample showed that ZNF385 was associated with fiber track volume and overall brain size. These measures were also associated with reading and language scores. An additional GWAS using the children in the TEDS sample has been performed by Harlaar and colleagues (Harlaar et al., 2014). In this case, the language phenotype was based on several measures of receptive language, and the GWAS was performed on one member of the twin pairs.

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Thus, this study looked for association of markers with the full range of individual differences on receptive language. One marker on chromosome 2 was significantly associated with receptive language in the discovery sample. The DZ co-twins that were not used in the discovery study served as a replication sample, and this marker was not associated in this sample. Thus, this GWAS did not yield replicable findings. The GWAS method used in the studies was based on information coming from marker variations in individual children. These markers reflect the characteristic of each of the two alleles comprising the marker locus. One of these alleles comes from the child’s father and the other from the mother. Traditional GWAS methods do not consider which parent the allele came from. A genetic mechanism called imprinting can result in either the parental or maternal allele being expressed more than the other. This parent of origin effect adds an additional variable into the manner in which a gene might influence the expression of the phenotype. In order to test for this parent of origin effect, it is necessary to have DNA from the parents. Recently, the family data from the SLIC cohort were used to test for parent of origin effects in a GWAS (Nudel et al., 2014). This study revealed evidence of an association of SLI with markers in regions of chromosomes 5p13 and 14q12 that demonstrated maternal or paternal effects, respectively. The findings for the 5p13 locus were replicated using data from the ALSPAC study; however, the direction of the effect was in the opposite direction. The ALSPAC sample did not allow for a test of the chromosome 14q12 findings, as paternal DNA was not available. The findings from the recent GWAS reports provide some additional regions and genes that may contain new genes that influence language disorder. Again, as in the genome-wide linkage analyses, the candidate loci were supported by within-study replications, but replication across studies has not occurred. Also, we continue to see that phonological memory seems to be a sensitive phenotype for genetic research in LI.

Candidate Locus/Gene Studies and LI The genome-wide approaches above begin without any hypotheses regarding the location in the genome that may contain a genetic mechanism (gene or functional noncoding DNA) that contributes to the neurobiology of language. As research has progressed, we have gained sufficient information to begin to generate hypotheses about possible specific loci in the genome where we can focus our efforts. These hypotheses are often generated by the findings from the discovery studies using genome-wide methods. Alternatively, they can be motivated by genetic discoveries in similar conditions where the phenotype is similar. In the case of language disorders, hypotheses have often come from findings in autism and reading disorder. Once a candidate site is established, the research strategy involves similar methods as those used in linkage and association, but now the panel of markers is much more fine grained, and as such this work is sometimes referred to as fine-mapping.

Candidate Genes: CMIP and ATP2C2 As was noted earlier, linkage studies have pointed to the SLI1 locus on chromosome 16 as a candidate region for genes involved in language impairment and in particular PM. Following their initial finding regarding the SLI1 locus, the SLI Consortium has conducted finemapping studies of the SLI1 locus. This has resulted in the identification of two candidate genes—CMIP and ATP2C2—that were associated with phonological memory abilities in children with SLI in the SLIC sample and replicated in children with poor PM in a replication sample from children in the ALSPAC sample (Newbury et al., 2009, 2011). Examination

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of the association of these genes to measures of language other than phonological memory showed that some measures were nominally associated with ATP2C2 within the SLIC sample. In these studies the children had poor language abilities. These authors asked whether these genetic variants were associated with individual differences across the ability spectrum. When they examined these genes in the full ALSPAC sample of 3,612 children, they did not find a significant association (Newbury et al., 2009). Thus, these data suggest that the associations of ATP2C2 and CMIP with phonological memory are restricted to children with SLI or to children with poor phonological memory. Subsequently, the SLI Consortium has shown that the CMIP gene is also associated with reading abilities in SLI, although not with dyslexia (Newbury et al., 2011). These genes therefore continue to be interesting candidates for liability for poor language and phonological memory, but now we need to have a better understanding of the biology of this effect.

Candidate Gene: CNTNAP2 Delays in language onset and development are an important diagnostic feature of autism spectrum disorder (ASD), along with repetitive behaviors and poor social skills. Alarcon and colleagues (Alarcon, Cantor, Liu, Gilliam, & Geschwind, 2002) performed a linkage analysis on three component features of autism (age at first words, age at first phrase, and repetitive and stereotyped behavior) and found that the measures of late onset of language were linked to a region of chromosome 7q. This region then served as a candidate region for fine-mapping. In 2008, two published papers each reported association of autism to the gene CNTNAP2 in this region (Alarcón et al., 2008; Arking et al., 2008). In the Alarcon et al. study, the phenotype was a quantitative measure of individual differences in language onset in children with ASD. In the Arking study, the phenotype was a global, qualitative diagnosis of ASD. Although the samples used for these studies overlapped, each study revealed a different region of CNTNAP2 for the likely causal locus. Alarcon et al. also reported follow-up studies revealing that CNTNAP2 was expressed in the frontal subcortical regions of fetal brains that supported executive function. CNTNAP2 codes for the protein CASPR2, which is a cell adhesion molecule that is important to normal action potential function in myelinated nerve fibers (Brophy, 2003). CASPR2 has been shown to influence cortical development in humans. Vernes et al. (2008) reported that CNTNAP2 was a downstream target of FOXP2. Recall that FOXP2 is a regulatory gene and, thus, CNTNAP2 is within this regulatory pathway. Following from this, these researchers examined whether the children in the SLIC sample who had SLI but not autism also showed an association of genetic variation in CNTNAP2 with language. As hypothesized, variations in alleles within the region containing exons 13–15 in CNTNAP2 were found to be clearly associated with phonological memory, and also this study provided weaker support for association with expressive language. Whitehouse, Bishop, Ang, Pennell, and Fisher (2011) reported a significant association of communication and language abilities at 2 years of age in infants who were in the Western Australian Pregnancy Cohort with markers in the same region of CNTNAP2 that were found to be associated in the Vernes study. Peter et al. (2010) also has reported that within families selected because of a dyslexic proband, measures of phonological memory were associated with CNTNAP2. Thus, across these studies the evidence is strong that the gene product of CNTNAP2 is likely to contribute to neurodevelopmental differences in children with LI, ASD, and dyslexia. We must be watchful at this point that these findings are not simply the result of publication biases that favor positive findings but work against publication of negative findings. It is important that all valid findings find their way to press.

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Candidate Gene: DCDC2/KIA0319 Dyslexia has been the focus of genetic research for quite some time. One of the most robust findings from this research has been the association of markers in a region (6p21; DYX2) on chromosome 6 (Fisher et al., 1999; Gayan et al., 1999; Grigorenko et al., 1997; Kaplan et al., 2002; Petryshen, Kaplan, & Field, 1999). Several genes in the DYX2 region (VMP, DCDC2, KIAA0319, TTRAP, and THEM2) have been proposed as candidate genes for dyslexia due to the strong linkage evidence to this region. There is particularly strong evidence that two of these genes play a role in dyslexia. One is KIAA0319 and the other is DCDC2, which is located near KIAA0319. Both DCDC2 and KIAA0319 disrupt neural migration and, therefore, may play a role in dyslexia. LI and dyslexia have been found to be co-morbid, and thus these genes could also contribute to SLI. Note that in the genome-wide scans, these loci did not surface, but this could have been due to methodological or power issues. Rice, Smith, and Gayan (2009) performed association analyses of DCDC2 and KIA0319 in a sample selected for SLI. They found nominal associations (0.05 > p > 0.01) with several single nucleotide polymorphisms (SNPs) across KIAA0319 using several measures of reading, articulation, vocabulary, and an omnibus test of language ability. No association was found between SLI and DCDC2. Scerri et al. (2011) also examined several genes, including DCDC2 and KIAA0319, that are associated with reading disorder to determine if they were also associated with LI. They also found that LI was associated with KIAA0319 but not DCDC2. Scerri et al. (2011) also examined whether SLI found in the ALSPAC sample was associated with these candidate reading genes, but they did not find an association with SLI for either gene. These authors hypothesized that this failure may have been due to the language measures used in the ALSPAC study. Although these studies showed inconsistent findings for an association of LI and KIAA0319, they were consistent in showing no association between DCDC2 and language. However, recently, Powers et al. (2013) reported that genetic variants in a region within DCDC2 were associated with LI in children within the ALSPAC sample. This same group (Eicher et al., 2013) also performed a fine-mapping association in the ALSPAC sample for reading and LI in a region containing both KIAA0319 and DCDC2 as well as some additional genes. Associations of LI with markers in DCDC2 and KIAA0319 in the ALSPAC sample were revealed and were also replicated in independent samples. These authors note that because of the proximity of KIAA0319 and DCDC2 to each other, genetic variation in one is often linked with variation in the other, and thus it is difficult to sort out the causal effects. However, the evidence shows that this region is not just a reading locus, but rather it also influences pathways associated with language. In this regard, this locus may account for some of the co-morbidity between dyslexia and language impairment.

Conclusions Most of the research that has been surveyed in this chapter was conducted over a span of 20 years. Initially, this work used quantitative methods to examine behavioral data obtained from families with LI and in twins. This work has shown that LI, particularly when nonverbal IQ is not highly constrained, is likely to be genetically influenced. Although rare mutations in one gene—FOXP2— can result in poor language, it is more likely that most of this genetic influence is likely to be grounded in common allelic variants of small effects. So far a handful of genes (e.g., CNTNAP2, CMIP, and ATP2C2) have been associated with poor language abilities, most particularly with poor phonological memory. These genes are known to have effects on brain development, and thus the pathway from these genes through brain development and function to phonological memory and language stands as an important future research agenda. In this regard, we can see that insights into the biological bases of language are being made via genetic research.

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A skeptic could also argue that the progress is slow, and having at best a handful of genes seems small given that perhaps as much as one-half of the variance in language ability appears to be heritable. This disappointment in the yield of genetic findings for complex traits has been voiced across a number of areas of human genetics and has come to be called “missing heritability.” This missing heritability could be evidence that the estimates of the influence of genetic variation on language via measures of heritability are incorrect, and we must consider this as a viable hypothesis. However, it is becoming clear that much of this missing heritability is the result of current research methods that have focused on allelic variation in genes that code for proteins (coding variants), as was shown in Figure 10.1. Currently, research in genetics is moving into a new era where the interest is in how the information in the genome is read out and in particular the systems that regulate this process of information extraction—that is, genetic expression. It is now becoming clear that a large amount of the genome is devoted to these control systems, which engage in very complex patterns of action and control of expression. Essentially, this regulation is concerned with when and how much a gene is expressed, which contrasts with qualitative variants in the protein product. Thus, the genome is not just a place where information is stored, but rather it is also a place where a great deal of information processing occurs. We are now learning that much of the human genome contains noncoding DNA that is concerned with this regulatory control (Gerstein et al., 2007). Furthermore, variants in these regulatory control systems have been shown to be associated with complex traits (Gusev et al., 2014; Nicolae et al., 2010). Given that language is clearly a complex trait, these control systems may offer new opportunities for discovery of genetic factors that influence individual differences in language. Thus, it seems quite likely that insights into the genetics of language and language disorders will see considerable advancement in the next decade.

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11 MODEL-BASED APPROACHES TO CHILD LANGUAGE DISORDERS Marc F. Joanisse

A popular idea held in the 20th century was that the mind is parceled into discrete and semiindependent processing modules (e.g., Coltheart, 1999; Fodor, 1983). This view shaped the study of the neurocognitive bases of typical language, and also the way in which one might conceptualize developmental language disorders. One could hypothesize that developmental language impairments represent a failure in the development of a certain language module, while sparing others altogether. On this account, disorders that target specific language abilities might actually represent evidence for the existence of independent (or domain-specific) language processing modules (Marshall & van der Lely, 2012; van der Lely, 2005). An alternative hypothesis exists, however, which I would argue is equally interesting: language disorders occur not because of a domain-specific deficit, but are instead the result of domaingeneral impairments. As I discuss in this chapter, such hypotheses have been put forward to explain a range of seemingly specific language deficits in both developmental and acquired cases. These theories focus on the idea that language disorders occur as a result of problems with perception, processing speed, and working memory, among others (Basu, Krishnan, & Weber-Fox, 2010; Gathercole & Baddeley, 1990; Kail, 1994; Tallal, Miller, & Fitch, 1993). Such a hypothesis is compelling because it suggests that difficulties that are domain-general (i.e., not specifically linguistic in nature) can affect different abilities in an uneven fashion, such that language processing might be disproportionately impaired compared to other capacities.

Connectionist Modeling This chapter focuses on how connectionist models have been used to simulate normal and impaired development. The connectionist, or parallel distributed processing (PDP), framework uses computer simulations of artificial neural networks to model cognitive processes (Rumelhart, McClelland, & The PDP Research Group, 1986). The key assumptions of this framework can be summarized as follows (Smolensky, 1999): 1. Knowledge is encoded as patterns of numerical activity distributed across large numbers of simple processing units. Information is represented as the activation of many neurons distributed across many brain regions. In connectionist models, this is represented as patterns of

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activity across many simple processing units, or nodes. These are functionally similar to neurons, but simplified in a way that allows us to more easily implement them as computer simulations. 2. Processing occurs as the transformation of this activity across massively parallel sets of connections. Mental processes happen when neurons pass information along synaptic connections to other neurons. Connectionist networks capture this using weighted connections that link nodes to each other. The activity of a unit is a direct reflection of the activity of other units that are connected to it, scaled by the strength of the connections between units. 3. Learning occurs through changes to these connections. A network’s behavior is modified by changing the strength (or weight) of connections among units, which yields changes in the way that the network processes information. Three mechanisms influence this: (1) experience, (2) the innate architecture of the system, and (3) the innate learning rule used to modify connections with experience. Thus, much like biological organisms, we assume that how a network learns and behaves is a function of both its inherent structure and the input that is provided to it.

Predecessors of Connectionism Before the connectionist enterprise came along, a predominant view of cognition was one of a computer analogy, which envisioned the mind as a software program used to perform mental computations, and the brain as the hardware that this program runs on (Block, 1990). This analogy also promoted the view that mental representations are symbols, and that mental processes are rule-like operations that are performed on these symbols. This symbolicist view has profoundly influenced modern theories of normal and intact language processing. For example, such theories describe our knowledge of language as a set of symbols and rules that operate on them (Chomsky, 1986); learning is seen as the process of acquiring new symbols and rules, and impaired language development is consequently seen as a deficit in specific symbol processing mechanisms (Berwick, 1997). The connectionist perspective I put forward in this chapter is part of a larger tradition of functionalist/constructivist perspectives on language development, which assume that language does not have an innate component to it in any useful sense of the term (Ambridge & Lieven, 2011). Rather, language development occurs as a consequence of domain-general properties of the child’s learning system in conjunction with equally domain-general social biases (Tomasello, 2009). So, for instance, children are able to learn the words and rules of their language thanks to the domaingeneral statistical learning mechanisms (Saffran, 2003; Saffran & Sahni, 2012). Likewise, language input contains a (sometimes hidden) structure that enhances its learnability through domaingeneral means, and which is in fact reflected in its natural structure (Ambridge, Kidd, Rowland, & Theakston, 2015). What sets the connectionist framework apart from these theories is the use of specific types of computational models to illustrate how such learning can take place. Connectionism also takes a view of cognition that does not draw a strong distinction between mind and brain. This is because the mechanisms of mental representations, processes, and learning are intended to directly reflect what we know about neural systems, notwithstanding some simplifying assumptions, which I will return to later in this chapter. This has a certain reductionist appeal, since it suggests that our mental software and neural hardware are two in the same. In addition, because this approach is model-based, it forces researchers to be explicit in specifying the types of processes and

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knowledge that participate in a cognitive ability such as language learning. This, unfortunately, is also the largest drawback of connectionism: because the behavior of a model is a direct reflection of how it is implemented, an improperly specified system leads to incorrect or inconsistent results (Marcus, 1998). Whether this represents a failure of the connectionist enterprise or a local problem with how a model is implemented is another matter, of course. In the following sections I discuss connectionist approaches to understanding three types of language processing impairments: reading, grammatical morphology, and syntax. Throughout, I emphasize the similarity in the approaches that have been taken to explaining these disorders within a connectionist framework, how these explanations fit with existing behavioral data, and ways in which this approach can be extended to other types of data or populations.

Reading Disorders Perhaps the most closely studied and best understood developmental language disorder is developmental dyslexia (see Chapter 5 by Shaywitz & Shaywitz and Chapter 19 by Hook & Haynes). Dyslexia is marked by poor reading development despite normal sensory, cognitive, and emotional abilities. It is typically associated with very poor decoding, which is the process of sounding out a word using letter-to-sound correspondences, especially in the case of nonwords like MAVE, STOOK, and PLINDER. Since decoding is a fundamental process of word recognition, difficulties with reading fluency and comprehension are seen as the consequence of this more basic difficulty. The prevailing hypothesis holds that a phonological deficit impairs the ability to accurately acquire letter-sound relationships, which in turn leads to delayed development of fluent reading skills. Consistent with this, affected children have difficulties with how they perceive the spoken forms of words across a number of tasks (Desroches, Newman, Robertson, & Joanisse, 2013; Godfrey, Syrdal-Lasky, Millay, & Knox, 1981; Goswami, 1999; Wagner, Torgesen, & Rashotte, 1994; Werker & Tees, 1987). The phonological deficit theory of dyslexia is a popular one, but it is also most likely an oversimplification, in the sense that some instances of dyslexia do not appear to involve problems with phonological awareness and decoding. These children instead appear to have problems with reading words with irregular spellings. For instance, although the spelling-sound relationship is usually consistent in English (e.g., SAVE, RAVE, PAVE, GAVE), there are a number of irregular words (or exception words) that violate this rule (e.g., HAVE). Accordingly, it has been suggested that there are in fact two subtypes of dyslexia: phonological dyslexia, which involves poor nonword decoding, and surface dyslexia, which instead involves poor irregular word reading (Castles & Coltheart, 1993; Manis, Seidenberg, Doi, McBride-Chang, & Petersen, 1996; Sprenger-Charolles, Colé, Lacert, & Serniclaes, 2000; Stanovich, Siegel, & Gottardo, 1997). As I discuss below, models of reading have given us some useful ways of thinking about how a complex linguistic task like reading is accomplished and why reading disorders occur. They also appear to address more specific concerns about the direction of causation of phonological difficulties and the nature of dyslexia subtypes.

Dual-Route Models of Reading and Dyslexia Reading a word involves two processes: recognizing its sound by translating orthography to phonology, and recognizing its meaning by translating orthography to semantics. Coltheart and colleagues sought to capture this idea within their dual-route model of reading (Coltheart, Curtis, Atkins, & Haller, 1993; Perry, Ziegler, & Zorzi, 2007). This model suggests that

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Visual Input

Lexical Route Sublexical Route

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GraphemePhoneme Correspondence Rules (GPCs)

Semantic Lexicon Phonological Lexicon

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The Seidenberg and McClelland (1989) Model

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Figure 11.1 Models of reading. Top: Coltheart’s dual-route model of reading. The model integrates both a lexical route (left) that identifies words holistically and a sublexical route that decomposes words via grapheme-phoneme correspondence (GPC) rules. Bottom: the Seidenberg and McClelland (1989) connectionist reading model. This model encodes words as patterns of activation across groups of artificial neurons (ellipses) that represent orthographic, phonological, and semantic information. Mappings between these codes are accomplished via weighted connections (arrows). Portions in bold represent the usual implementation of the model, as a network that maps orthography to phonology.

both processes in reading occur via discrete processing streams (Figure 11.1, top). The lexical route involves a holistic lookup process in which a visual word is mapped onto orthographic, semantic, and phonological lexical entries that are organized in a dictionary-like fashion. The nonlexical route involves using grapheme-to-phoneme correspondence rules (GPCs) to determine a word’s phonological form, which can then be used to access other lexical information

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indirectly. Recent versions of the dual-route model have modified the ways in which lexical and nonlexical information are computed (i.e., the dual-route cascaded model, DRC; Coltheart, Rastle, Perry, Langdon, & Ziegler, 2001), but maintains the strict division between the two routes. As such, each word must be recognized using one route or the other. This division plays out most strongly with respect to irregular words and nonwords. Because spelling-sound correspondences in irregular words are, by definition, arbitrary, these words must be read via the lexical route. In contrast, nonwords, by definition, do not have lexical representations; they must be read via the nonlexical or GPC route. The DRC framework models dyslexia as resulting from damage to, or the absence of, a specific component of the model, with subtypes of dyslexia reflecting different types of impaired or absent components. Phonological dyslexia occurs because the nonlexical GPC component of the model is damaged, which impairs the ability to decode nonwords but preserves the ability to read familiar words. In contrast, damage to the lexical route specifically impairs the ability to read irregular words since they cannot be decoded using GPCs, thereby simulating surface dyslexia (Castles & Coltheart, 1993). This yields three interesting claims about reading impairment. The first is that because decoding processes in reading are not intrinsically phonological, one need not posit a causal relationship between a phonological awareness and reading deficit. Instead, one need only posit a reading-specific deficit in GPCs. Any other observed phonological deficit in dyslexia is seen as either epiphenomenal or perhaps caused by affected individuals’ reading difficulties (rather than the other way around). Second, surface dyslexia is conceived of as the breakdown of the lexical route, such that the sublexical (GPC) route is intact and available for reading all words. Finally, it suggests that since one complete reading route is spared in either subtype, there should be a strong division in the reading deficits observed in these two forms of dyslexia, such that phonological dyslexics should read irregular words relatively well, and surface dyslexics should be good nonword readers.

Connectionist Approaches to Reading Impairment The connectionist theory of word recognition builds on Seidenberg and McClelland’s (1989) model of reading (SM89; see Figure 11.1, bottom). This model simulates word reading as taking a word’s orthographic form as input (i.e., the orthographic units corresponding to a word’s spelling are activated) and computing its phonological and semantic forms. The mappings between orthography, semantics, and phonology are encoded as the weights of connections between units in each of these layers. Additional layers of units, called hidden layers, are also used in this network and others like it (Rumelhart et al., 1986). Their purpose is to increase the network’s computational capacity by allowing it to encode higher-level mappings between units that are not possible in simpler architectures. Because of limitations in technology, some earlier implementations of the SM89 model included only orthography and phonology, as indicated by the grayed-out portions of the model in Figure 11.1. Note, however, that more recent studies have presented a full implementation of this model that includes a semantics layer (e.g., Harm & Seidenberg, 2004). The original SM89 network learned a corpus of English monosyllables, both with regular and irregular spellings, to a good degree of proficiency. Because the network was able to learn both regular and irregular spellings within a single architecture, there is no need for two distinct routes for reading. Whereas the DRC model computes a word’s meaning either via a lexical or sublexical mechanism, the SM89 model uses all connections in parallel to recognize a given word. In addition, its accuracy in producing different forms respected frequency and regularity distinctions known to influence reading times in skilled readers (Jared, 1997). Finally, it was able

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to generalize to novel words, marked by accurate phonological outputs for orthographic forms like NUST. One question about the SM89 architecture is whether the elimination of dissociable routes limits its applicability to dyslexia. That doesn’t appear to be the case; Plaut and colleagues (Plaut, McClelland, Seidenberg, & Patterson, 1996) presented an updated network that included enhancements to the network’s architecture, training regime, and phonological and orthographic representations. Among other things, they investigated whether lesions to a fully trained network would lead to a pattern of impairment consistent with acquired dyslexia following brain damage. Removing either connections between orthography and phonology, or removing some of the hidden units entirely, had a greater impact on the network’s ability to recognize irregular words compared to regulars, very similar to what has been observed in patients with acquired surface dyslexia. Next, Harm and Seidenberg (1999) tested whether the same logic could be applied to understanding developmental dyslexia, and whether different subtypes of dyslexia could be induced by impairments to different portions of the model. One interesting finding from their simulations of normal reading development was that the network acquired the reading task much more quickly and accurately when it was given prior experience with phonology. This prior knowledge was provided to the network by pre-training it on a phonological task, in which it learned to encode only the phonological form of English words, prior to any experience with orthography. This simulated how children develop spoken-word phonology before learning to read, and appeared to boost subsequent reading development in the network compared to a network that did not receive this pre-training. Moreover, a careful analysis of the network’s connection strengths and activation patterns also revealed that learning to read resulted in significant changes in how it encoded the phonological forms of words, something that seems consistent with the observation that print exposure can enhance phonological abilities (Morais, Cary, Alegria, & Bertelson, 1979). Next, Harm and Seidenberg (1999) examined how introducing different types of damage to the network prior to training would simulate developmental dyslexia. Phonological dyslexia was simulated by weakening the network’s phonological representations. This was achieved by adding random noise to the units in the phonological layer and severing feedback connections within this layer (implemented by reciprocal connections to and from a separate ‘cleanup’ hidden layer). The subsequent model showed difficulty with encoding and processing phonological information, even before it was trained to read. For instance, it was less accurate at encoding the phonology of words presented to it (marked by noisier activation values in the phonology layer units). When this phonologically impaired network was trained on the reading task, it demonstrated a general slowing in its ability to recognize all words in the training corpus, but it was especially poor at nonword reading compared to the intact network. Surface dyslexia was simulated by reducing the number of hidden units between the orthographic and semantic layers. Hidden units increase a network’s computational capacity, in part because the number of connections in a network scales in proportion to the number of hidden units. It was observed that reducing the number of hidden units resulted in generally slower learning, but especially poor performance of exception words, even compared to the phonological dyslexia simulation. Note that reducing the network’s computational capacity should not be construed as simulating a general reduction in cognitive capacity in surface dyslexic children. The authors suggested that it could instead reflect a reduction in neural capacity in brain regions responsible for learning to map orthographic to phonological information, which leads to a general delay in reading acquisition. It is also possible that this deficit is due to environmental factors related to impoverished exposure to print, which would again lead to generally delayed reading skills. Different types of damage to the SM89 model architecture yielded patterns of performance

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consistent with different subtypes of reading impairment, supporting the theory that a single type of mechanism can be used for reading all word types.

How Do the Connectionist and Dual-Route Models Differ? The DRC model of reading suggests that dyslexia occurs as a result of impairments to one of its routes. The connectionist view of dyslexia differs in several important ways. The first has to do with the extent to which one tends to observe pure instances of developmental phonological or surface dyslexia. Studies of dyslexic subtypes have tended to find that a large proportion of children are poor on both nonword and exception word reading, and very few show a pure deficit on one but not the other (Castles & Coltheart, 1993; Manis et al., 1997; Peterson, Pennington, & Olson, 2013; Stanovich et al., 1997). This seems to fit well with Harm and Seidenberg’s simulations, which illustrated that such dissociations tend to be partial rather than wholesale. This is because all words are being processed within the same architecture. Differences between phonological and surface dyslexia occur as a result of damage to different subcomponents of the model, which results in different patterns of difficulties. However, all words are impaired to some extent by any damage type. In contrast, the DRC model seems to predict that mixed subtypes will occur as a result of both routes being impaired, with purer subtypes occurring in cases where one route is disproportionately more damaged than the other (Ziegler et al., 2008). A second point of divergence between dual-route and connectionist models has to do with how they envision the proximal causes of dyslexia. In the dual-route model perspective, dyslexia is seen as a reading-specific disorder that occurs due to damage to one or both routes to reading. In contrast, the connectionist model suggests that dyslexia occurs as an epiphenomenon of other types of impairments. This is clearest in the case of phonological dyslexia, where these individuals’ deficits are due to a phonological impairment that leads to problems with learning spelling-sound correspondences. This view seems much more in keeping with the current view that dyslexia is the result of a phonological processing deficit (Desroches, Joanisse, & Robertson, 2006; Goswami, 1999; Stanovich, 1988; Wagner et al., 1994), rather than the other way around. It also seems consistent with the observation that children with phonological dyslexia have appreciably greater phonological processing problems than do children with surface dyslexia (Joanisse, Manis, Keating, & Seidenberg, 2000; Manis et al., 1996; Stanovich et al., 1997). In the case of surface dyslexia, the connectionist theory suggests this disorder could be the result of a reading delay, due to either endogenous factors such as restricted or delayed processing capacities or exogenous factors such as environmental effects leading to impoverished reading experience. Consistent with this, Manis and colleagues have found that children with surface dyslexia have nonword and exception word reading abilities that are very similar to those of younger, typically developing children (Joanisse et al., 2000; Manis et al., 1996). In addition, surface dyslexic children show slower reaction times on speeded tasks, compared to both phonological dyslexics and typically developing children (Manis et al., 1999). Finally, behavioral genetics data suggest differences in the extent to which heritable and shared environmental factors account for reading deficits in surface and phonology dyslexic subtypes (Castles, Datta, Gayan, & Olson, 1999). Phonological dyslexia shows very strong heritability with a relatively weaker (but still significant) contribution of environment, whereas the opposite was true in surface dyslexia, where only a relatively weak genetic component was found, with environment playing a much larger role in these individuals. This again seems consistent with the Harm and Seidenberg (1999) model, although it also suggests that in both cases there are tradeoffs between endogenous and exogenous risk factors. Because connectionist models address the roles that both types of information play in learning to read, they have played a central role in current thinking about dyslexia.

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Grammatical Morphology Modeling efforts have also been applied to developmental impairments to oral language in children, and in particular whether affected children have difficulties with the rules of grammar. The focus is on specific language impairment (SLI; see Chapter 1 by Schwartz), a developmental language impairment that impacts oral language development while sparing other cognitive abilities (Bishop, 1997; Leonard, 2014). Children with SLI have impairments in the development of many areas of language, including phonology, morphology, syntax, and vocabulary, and that these do not coincide with significant nonlinguistic disabilities such as sensory, neural, or pervasive developmental deficits (see Chapter 2 by McDuffie et al.). A modular interpretation has held that cognitive mechanisms supporting normal language development are independent of those involved in other cognitive processes and that SLI is caused by a deficit that is specific to the language domain (Gopnik & Crago, 1991; Marshall & van der Lely, 2007; Pinker, 1991; Rice & Wexler, 1996; van der Lely & Ullman, 2001). This contrasts with domain-general theories, which propose that language deficits can occur as a consequence of a nonlinguistic impairment, such as in perception, short-term memory, or processing speed (Gathercole & Baddeley, 1990; Kail, 1994; Leonard, Eyer, Bedore, & Grela, 1997; Tallal, Miller, & Fitch, 1995). Perhaps the most closely studied aspect of SLI has been the area of English past tense morphology (Gopnik & Goad, 1997; Oetting & Horohov, 1997; Rice & Wexler, 1996; van der Lely, 2005). Past tense in English is usually marked by adding the -ed suffix to the ends of verb stems (talk—talked, glue—glued; see Chapter 15 by Oetting & Hadley). This pattern is also usually applied to new verbs entering English (unfriended) and nonce words (wug—wugged, blick—blicked) (Berko, 1958). Given this, it is possible to describe past tense formation as following a grammatical rule, bearing in mind that some English verbs are irregular (e.g., slept, went, blew) and do not follow a rule-like pattern, as discussed in further sections. Children with SLI have marked difficulty learning morphological patterns such as past tense, as evidenced in spontaneous elicitation tasks (e.g., generating a verb’s past tense by completing sentences such as “Bobby likes to play. He did that yesterday: he ____”) and in speech samples (Marchman, Wulfeck, & Ellis Weismer, 1999; Oetting & Horohov, 1997; van der Lely & Ullman, 2001; Vargha-Khadem, Watkins, Alcock, Fletcher, & Passingham, 1995). Children with SLI have especially great difficulty with generating nonword past tenses. Unlike familiar past tenses, which could arguably be recalled from memory wholesale, nonword past tenses must be produced by generalization from known forms, either via a rule or through some other type of analogy process. From a domain-specific point of view, one could assume that this difficulty in SLI reflects a problem in mechanisms supporting rule learning. In contrast, domain-general theories suggest that this problem is due to peripheral processing difficulties that interfere with learning past tenses and applying the pattern to novel forms. Finally, the errors that SLI children make are also somewhat distinct from those of typically developing children, as they are less likely to overapply the regular past tense ending to irregulars (e.g., eated, goed; Marchman et al., 1999). As it turns out, the debate about past tense deficits in SLI echoes a broader one in the cognitive science literature concerning how children normally acquire and use the rules of language and how irregular cases are handled. As mentioned earlier, one can conceptualize past tense generation as the process of taking a verb stem (walkV) and using a rule to concatenate the suffix that marks its past tense (walkV + edPAST). Because the rule acts blindly on any given symbol, it can apply to any form including a nonword. It has been suggested that this mechanism is insufficient on its own, however, since it cannot be used to generate irregular past tenses like is—was and take—took. Instead, it has been suggested that a second mechanism is also necessary, one that encodes the past

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tenses of irregulars in an associative memory system (Pinker, 1991, 1999). Because irregular forms are already marked for past tense, the rule is blocked in these cases. The connectionist view of past tense is different. It suggests that one can implement both regular and irregular past tense learning within a single connectionist mechanism that learns patterns of present-past similarity via probabilistic learning (Rumelhart & McClelland, 1986). In such a model, the relationship between a word’s present and past tense form is encoded within the distributed connection weights. A number of connectionist models of past tense have been developed as part of an ongoing dialogue between dual-mechanism and connectionist proponents (e.g., Daugherty & Seidenberg, 1992; Joanisse & Seidenberg, 1999; MacWhinney & Leinbach, 1991; Plunkett & Marchman, 1993). However, they all capture the assumption that regular and irregular morphological forms can be encoded within a single distributed system. The regular default pattern is learned as a statistical generalization across many different forms, with coarser-grained statistics being similarly used to encode idiosyncratic forms (take—took). The same process can also capture pools of subregularities among irregulars (swept, kept, slept; sang, rang, sank). Can these theories of past tense help us understand why children with SLI have difficulty with past tense? Proponents of a dual-mechanism model have suggested that affected children have an impaired rule mechanism (Clahsen & Almazan, 1998; Pinker & Ullman, 2002; van der Lely & Ullman, 2001; Ullman & Pierpont, 2005). On this theory, SLI involves a domain-specific deficit that impairs the cognitive mechanism responsible for encoding grammatical rules. Instead, these children only have access to an associative memory system for learning both regular and irregular past tenses. The prediction of this theory seems to be that children with SLI will tend to have significant problems with regular and nonword forms, since these require the use of rules, but they will not have difficulty with irregular forms since these are processed within a separate (and presumably intact) cognitive mechanism. So, for instance, Ullman and Pierpont (2005) proposed a model of SLI that builds on a procedural/declarative memory explanation of language processing. Briefly, rules are encoded using a procedural memory system, whereas irregulars are memorized within declarative memory. With that in mind, they suggested that children with SLI have a procedural deficit that leads to poor rule learning in addition to a host of other linguistic and nonlinguistic deficits. However, their declarative memory system is intact, allowing them to still learn irregulars along with high-frequency regulars. The connectionist theory takes a different view, that SLI involves a domain-general deficit. Models of past tense deficits in SLI have focused on the idea that these children have perceptual or phonological difficulties that impair the ability to accurately perceive or manipulate phonological information. For example, Hoeffner and McClelland (1993) simulated verb production in a model that took a verb’s semantics as an input and output its phonological form. Because present and past tense verbs overlap significantly in their semantic form (i.e., their meanings presumably differ only with respect to ‘past tense-ness’), similar connections are used for both forms. Similarly, the [PAST TENSE] semantic feature tends to map consistently onto features signaling word-final alveolar stops on the output layer, because all regular forms end in ‘d’ or ‘t’ sounds (as do many irregulars). The reduced salience of word-final stops was simulated by weakening the connections to units that encoded these features. This in itself did not impair the network’s ability to learn to produce either past tenses or other words with past-like endings (e.g., blast, most). However, when they simulated a speech perception deficit by also weakening inputs to the phonological layer as a whole, the network showed specific deficits in generating past tenses like walked. Interestingly, the impaired network was still able to accurately generate other words ending in alveolar stops (e.g., blast), because in these cases there was no phonological and semantic competition with an existing present tense form (i.e, blas is not a word in English). Thus, this model represents an interesting illustration of how a perceptual deficit can lead to specific problems with morphology rather than weakening representations of all words equally.

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semantic form “WALK”

Semantics

hidden layer

phonological form /wak/

Phonology

Figure 11.2 The Joanisse (2000, 2004) model of past tense. The model encodes a verb’s phonological and semantic forms within discrete layers that are connected bidirectionally. A verb is presenting with a semantic form as input and provides a phonological form as output, or vice versa, simulating the generation and recognition of present and past tenses.

More recent work has followed up on this by looking at how a speech perception deficit might impair different types of past tenses, including nonwords, and whether connectionist models can simulate the types of errors that are observed in SLI when such an impairment is implemented (Joanisse, 2000, 2004; Thomas & Karmiloff-Smith, 2003). Joanisse (2000, 2004; Figure 11.2) simulated past tense acquisition as the process of learning to map a word’s phonological representation to its semantic form (simulating hearing a verb), as well as the reverse process of mapping semantics to phonology (simulating speaking a verb). The network was trained on a corpus of regular and irregular verbs in their present and past tense forms, where the activation of the [PAST TENSE] unit in the semantic layer signaled its tense. In addition to being trained on recognition and production trials, the network was also occasionally given experience with transformation trials, in which a present tense phonological form was presented, and the [PAST TENSE] unit was also set to on. The network learned to transform the present tense form to its correct past tense, roughly simulating what occurs in a past tense elicitation task. This network showed several important similarities to normally developing children and adults. When the network was fully trained, it was able to accurately produce, recognize, and transform the verbs in the training corpus at a high degree of proficiency (more than 98% correct on all tasks). It was also able to transform a nonword form to its past tense, providing a regular -ed ending for these forms in nearly every case. Over the course of training, the acquisition of irregular verbs tended to lag behind that of regulars, which is what is typically observed in children (Brown, 1973). Nonword generalization also tended to be acquired later in training than were familiar forms, again consistent with what is seen in normal development (Bybee & Slobin, 1982). Finally, during training, the network occasionally produced overregularization errors on irregulars that it had not yet learned correctly (Marcus et al., 1992). Next, an identical model was trained in which a speech perception deficit was implemented by adding small amounts of random noise to the phonological units. This caused the network to be slightly less accurate at categorizing phonemes in words. The effect of this impairment led to generally slower learning, such that performance on all verb types lagged behind the control simulation, and asymptotic performance also never reached the same level of accuracy as the intact network. Nonword generalization was especially poor in the impaired model (10–15% correct depending on the point in training). As it turns out, this pattern of deficit closely resembles what is seen in SLI. A number of studies have found that affected children

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have delayed development of irregular past tenses relative to regulars of a similar frequency, as well as very poor nonword generalization (e.g., Robertson, Joanisse, Desroches, & Terry, 2013; van der Lely & Ullman, 2001). The network also produced fewer overregularization errors and more zero marking errors than the intact network, which is again consistent with SLI. Overall, then, this model illustrated how a deficit in learning morphology can result from an impairment in perceiving phonological information about words, and that the pattern of deficits that results from this impairment is consistent with what is observed in languageimpaired children.

Past Tense Deficits in Other Populations As with the reading impairment model discussed in the previous section, implementing different types of impairments in the connectionist model of past tense will tend to yield different types of deficits. Specifically, when the network was re-trained with a semantic impairment (Joanisse, 2004), a different pattern of impairment was observed. Adding random noise to the network’s semantics units during training led to very poor performance on irregulars, while performance on regulars and nonwords was somewhat less impaired. The semantically impaired network also produced many more overregularization errors than did the phonologically impaired network. The fact that a semantic deficit leads to a different pattern of errors in the connectionist past tense model suggests that phonology and semantics make different contributions to morphology processing. Phonological information is important in generalizing morphological patterns to nonwords, due to the role that phonological similarity plays in discovering the compositional structure of words during learning. For instance, it is important for identifying the phonological overlap between a verb’s past and present tense form (i.e., the overlap between walk and walked, between heal and healed, and between waste and wasted ), along with the overlap among past tense forms (e.g., walked, healed, and wasted share critical phonological features that correspond to the concept of IN THE PAST). This information can apply with minimal recourse to idiosyncratic information such as a word’s semantic form, permitting it to be generalized to unfamiliar forms (e.g., wugged). Notably, however, this information is not as helpful for learning the past tenses of irregulars, where the phonological relationship between present and past tense forms is less consistent. In those cases, semantic information becomes critically important, since it is used to uniquely identify a word and the idiosyncratic patterns that are specific to it. One question that arises from this is whether the pattern of deficit predicted by the semantics of impaired network actually occurs in any known population of children. To test this, Nation and colleagues have investigated past tense deficits in children with specific comprehension deficits, or poor comprehenders (Nation, Snowling, & Clarke, 2005). These children tend to exhibit very poor language comprehension, especially with respect to reading, even though they have relatively fluent single-word reading and grammatical abilities. In this study, it was found that poor comprehenders had significant problems with tasks that tap semantic processing, compared to normal controls of the same age. However, their phonological processing abilities were intact. The fact that poor comprehenders have poor semantics seems to predict that they will have problems with irregular past tenses. Consistent with this, they observed a greater proportion of errors on irregular past tenses in poor comprehenders compared to controls, as well as a larger than expected number of overregularization errors. There is also a growing literature on past tense impairments in children with Williams syndrome (WS; see also Chapter 2 by McDuffie et al.). WS is a relatively rare developmental disorder that results in a range of cognitive problems, including visuospatial deficits, problem solving, and numerical deficits (Donnai & Karmiloff-Smith, 2000). Despite their profound cognitive problems, individuals with

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WS have been assumed to have relatively good language skills, most famously with respect to their verbal fluency. That said, there is some evidence that these individuals do have language difficulties, especially in the areas of semantics and vocabulary; for instance, affected individuals tend to develop shallower semantic representations of concepts that tend to overemphasize perceptual features while underemphasizing abstract knowledge (Thomas & Karmiloff-Smith, 2003). There is some question about how to characterize the morphological deficits in WS. An earlier study of four individuals with WS found that these individuals had greater difficulties with irregular past tenses compared to regular and nonword items (Clahsen & Almazan, 1998). This pattern of deficit would seem to suggest that affected individuals have difficulty encoding idiosyncratic forms while having a preserved ability to learn rule-like forms. This seems to mesh well with the theory that different processing systems are responsible for learning regular and irregular forms, and that the irregular mechanism can be independently impaired in some individuals. However, this characterization has been disputed in a more recent study that involved a larger WS sample and tested these individuals on items more closely matched for factors such as phonological complexity and frequency (Thomas et al., 2001). This study found that individuals with WS had an equal-sized delay on regulars and irregulars, such that they performed slightly worse than vocabulary-matched controls on both types of past tense. They also showed significantly poorer performance on nonword generalization compared to controls. Thomas et al. suggested that this deficit pattern does not correspond to the theory that individuals with WS have a deficit with irregulars, while showing preserved ability to apply rules. They argued instead that it could arise from a more subtle cognitive impairment that affects different aspects of morphological processing to different degrees. This was examined more closely using a connectionist model of past tense (Thomas & Karmiloff-Smith, 2003). The network architecture they used was similar to the one presented in Figure 11.1, though it also provided an additional input layer that specified the present tense verb’s phonological form. They examined a wide array of deficit types in the model in order to fully flesh out how different patterns of developmental impairment can occur as a result of different underlying deficit types. The manipulations included strengthening and weakening the degree of detail provided by the phonological and semantics layers, slowing the rate with which connection weights could be adjusted during training, and adjusting the number of hidden units available to the network. As expected, different impairment types led to different patterns of problems in producing past tenses and different error types such as overgeneralization errors. Most deficit types resulted in slower general learning of past tenses, and typically also impaired the network’s ability to fully learn the training vocabulary. With respect to WS, they found that only two specific deficits closely simulated the behavioral pattern observed in affected individuals: (1) reducing the similarity and redundancy of the phonological features being input to the network, and (2) a weakening of the ability of hidden units to integrate phonology and semantics information within the hidden layer. This first deficit was achieved by manipulating the number of units that are being used to uniquely identify phonemes in words in a way that makes it more difficult to specify overlapping features across words that are phonologically similar. The second deficit involved manipulating the activation function of units in the hidden layer such that these units could not respond as effectively to multiple inputs simultaneously. Thomas and KarmiloffSmith (2003) have suggested various ways in which these deficits could map onto the actual cognitive abilities of individuals with WS, but also acknowledge that this remains an open question. Importantly, their study indicates quite nicely that the past tense deficit in WS is likely the result of a relatively general processing impairment—rather than a deficit to a distinct functional module subserving lexical coding, as Clahsen and colleagues have suggested (Clahsen & Almazan, 1998)—and that such a deficit can have nonobvious consequences for the development of

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morphology. It is also not the case that any type of impairment that slows language development will yield precisely the types of difficulties that are observed in impaired individuals. Also notable is the fact that the Thomas and Karmiloff-Smith model could also simulate past tense deficits in SLI. They observed that weakening the degree to which the networks’ phonological units could accurately represent phonemes resulted in an SLI-like pattern of deficit. This has more recently been extended to examining deficits in the morphology of nouns, verbs, and adjectives, both in English and Modern Greek (Karaminis, 2011; Karaminis & Thomas, 2010). Importantly, the results of the intact simulations capture key facts about typical learning patterns of both languages in unimpaired children. Likewise, implementing weaker phonological representations in this simulation yielded the patterns of deficits that one sees in SLI in either language.

Summary: Past Tense Deficits Theories of past tense impairments that build on the symbols-and-rules model of language have suggested that different subtypes of impairment occur due to deficits in different processing modules. In this section I have summarized some of the evidence from the competing connectionist approach that suggests that a single neural architecture is responsible for learning both a generalizable morphological rule and also exceptional cases. This approach also suggests that impairments to different aspects of past tense processing can occur due to problems with different aspects of this neural architecture. Given a sufficiently sophisticated model of past tense processing, one can test the nature of behavioral impairments by testing the consequences of fine-grained deficits in the model. Notably, these deficits are seen as domain-general, rather than language-specific. In the case of SLI, it is hypothesized that a deficit in processing perceptual or phonological information results in a delayed acquisition of past tenses, marked by problems with nonword generalization and fewer than expected overregularization errors. In contrast, other types of impairments can yield different constellations of deficits, as observed in poor comprehenders and individuals with Williams syndrome. There is no need to appeal to either grammatical rules or to a discrete lexicon to explain any of these phenomena.

Syntactic Deficits In addition to morphology deficits, children with SLI also have significant problems with processing syntactic relationships in sentences. For instance, they have difficulty assigning the correct thematic roles in syntactically complex sentences (van der Lely & Harris, 1990), such as reversible passives (e.g., The boy is pushed by the girl), datives (e.g., Give the girl the boy), and locatives (e.g., The book is on the paper). Notably, children with SLI are much better on syntactically similar sentences in which the thematic roles are not reversible; for instance, they are more accurate at choosing the correct meaning of a sentence such as The ball is thrown by the boy, or The cat is on the table, where the reverse interpretation (a ball throwing a boy, a table is on top of a cat) is nonsensical. This would suggest that their difficulty with these sentences is not simply one of lexical semantics or pragmatics. Instead, they appear to have problems understanding how word order specifically modulates a sentence’s meaning. One interesting example of this is the interpretation of bound pronouns by children with SLI. In the sentence Kathy thinks Marni likes her, the referent of her can be Kathy, but it cannot be Marni. Likewise, the reflexive pronoun at the end of the sentence Kathy thinks Marni likes herself must refer to Marni and cannot refer to Kathy. The grammatical principles that determine the possible relationships between a pronoun and its referent are known as binding principles (Chomsky,

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1981). Children with SLI perform much more poorly than controls on tests designed to assess this knowledge (van der Lely & Stollwerck, 1997). For instance, they score significantly more poorly than control children at matching a sentence such as Peter Pan is tickling him to a picture that depicts the correct relationship (i.e., Peter Pan tickling another individual vs. Peter Pan tickling himself). In contrast, these same children perform significantly better on control sentences in which semantic information can also be used to resolve pronoun reference. For example, when given the sentence Peter Pan is tickling her, they tend not to choose the picture of Peter Pan tickling himself. However, a more recent study using cross-modal picture priming (Schwartz, Hestvik, Seiger-Gardner, & Almodovar, 2016) indicates that children with SLI do apply binding principles in processing sentences with pronouns and reflexives, but they have substantially slower responses than do typically developing age-matched peers. The source of these slower responses remains undetermined but suggests a domain-general deficit affecting processing.

Are Syntax Deficits Domain Specific? It is difficult to imagine how syntactic difficulties such as this could be related to a domain-general processing deficit. If children have difficulty simply understanding that pronouns refer to other individuals or objects in a given context, then they would tend to perform just as poorly in the semantically constrained conditions as in the purely syntactic conditions. Similarly, it could be argued that pronouns and reflexives such as him, herself, and it are perceptually salient in at least some syntactic contexts. (Note that in some cases, such as clitics, the salience of pronouns is reduced.) In any case, it is unclear that language-impaired children are delayed in their acquisition of pronouns because they are not perceiving them properly. It is still possible that a domain-general processing difficulty might explain these problems, however. In addition to the grammatical and phonological processing difficulties discussed above, children with SLI also appear to have problems with phonological short-term memory, most notably indexed by nonword repetition (Archibald & Gathercole, 2006). Although children with SLI tend to be as accurate as controls at repeating one- and two-syllable nonwords, they are much poorer when the length is increased to three and four syllables. As such, it appears that their difficulty with this task stems from the ability to maintain these nonwords in working memory for the purpose of then repeating them (Archibald & Gathercole, 2006; Gathercole & Baddeley, 1990). One possibility is that limited phonological working memory also weakens sentence comprehension ability, on the theory that one must use phonological codes to hold a sentence in memory during syntactic parsing (Just & Carpenter, 1992; MacDonald & Christiansen, 2002; see Chapter 8 by Gillam et al.).

A Connectionist Model of Pronoun Deficits Taking this a step further, it is also possible that problems with phonological short-term memory or working memory more generally could be caused by a more basic phonological or perceptual problem, similar to what is suggested in the models of reading and past tense impairments discussed above. On this account, problems with perceiving or categorizing the phonemes in words can weaken the phonological codes used for holding sentences in memory, which in turn limits the ability to accurately learn and process syntactic relationships within a sentence. Indeed, many studies have identified subtle perceptual deficits in language-impaired children using sensitive measures of speech perception (Joanisse et al., 2000; Robertson, Joanisse, Desroches, & Ng, 2009; Sussman, 1993; Tallal & Piercy, 1974; see Chapter 9 by Edwards & Munson). Such findings suggest that subtle perceptual deficits do tend to occur in SLI.

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Semantic Features [person] [male] [Bob]

Semantics

Hidden Units

Phonology

Hidden Units

‘Working Memory’

Phonological form /bab/

Figure 11.3 The Joanisse and Seidenberg (2003) model of sentence comprehension. The model receives the phonological form of each word in a sentence, in sequence, and outputs the semantic form of each word. Pronoun recognition is simulated by generating the semantic form of the pronoun’s referent.

The theory that a low-level perceptual deficit could lead to problems with sentence comprehension was tested in a connectionist model of sentence comprehension that learned to map a sentence’s phonological form to its meaning (Joanisse & Seidenberg, 2003). In this model (Figure 11.3), the input consisted of the phonological forms of each word in a sentence, presented in sequence, and the output layer encoded a simplified semantic representation of a given word. The network’s task was to recognize each word in the sequence (e.g., Harry danced with Mary) by activating the correct semantic output. This was complicated somewhat by the fact that some sentences contained bound pronouns and reflexives such as Harry says Mary danced with her or Mary says Harry likes himself; in these cases, the network was also required to output the semantics of the word that the pronoun or reflexive referred to. The dynamic nature of the sentence comprehension task required the network to draw on representations of previous words in a given sequence. To accomplish this, the network had a type of working memory, in the form of two sets of hidden units recurrently interconnected. The network was trained on a corpus of sentences with and without pronouns. At the end of training, it showed good generalization to novel sentences (containing familiar words) and was able to resolve pronouns and reflexives accurately. Next, this network was trained with a simulated speech perception deficit by adding random noise to the phonological input units. This speechimpaired network required more time to train, but it did eventually learn the training corpus with a good degree of accuracy. In addition, at the conclusion of training it was able to accurately recognize words in sentences, with one notable exception: the network showed marked difficulty with resolving pronouns and reflexives. In those cases, it produced significantly more errors than did the control network. Interestingly, however, these errors were limited to semantically unconstrained cases (Harry says Bob slapped him/himself); performance was near ceiling on sentences where semantic information could help resolve pronouns (e.g., Harry says Mary slapped him/herself). The Joanisse and Seidenberg (2003) model stands as an illustration of how specific syntactic problems can be explained as resulting from a domain-general processing deficit. In the network, a phonological or perceptual deficit (the two were conflated in this simulation) resulted in a specific problem with syntactic pronoun resolution. Other abilities, such as single word recognition and the use of semantic context to resolve pronoun mechanisms, remained intact. This deficit seems to closely match what is known about syntax in SLI. The mechanism by which this occurs appears to be a working memory limitation. Even though the impaired and intact networks were

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architecturally identical, we hypothesized that a capacity limitation was being induced by weakening the quality of the phonological input the network received. Consequently, it was unable to maintain accurate working memory representations of previous words in a sentence, which limited its ability to learn or use syntactic relationships. The results of the network also suggest that one need not instantiate a domain-specific deficit to explain syntactic problems. For instance, while children with SLI clearly have difficulty understanding structural complexities in sentences (e.g., wh-questions, passive voice), a deficit in core syntax (van der Lely, 2005) is not required to explain this deficit.

Challenges for Model-Based Accounts Simplifying Assumptions By definition, models are simplifications of an actual system. These simplifying assumptions are necessary for implementational reasons having to do with keeping the computational complexity of a model within the parameters of what existing technology permits. They also make it easier to understand and analyze a model’s behavior. One consequence of simplifying assumptions is that there is always the risk that they will lead to an inaccurate portrayal of the system that is being simulated. However, just as in any other type of principled scientific simulation, such a breakdown is not an inevitable consequence of model-based approaches to cognition. For instance, implementational constraints related to computer storage space and processor speed have meant that early visual word recognition models were limited to smaller sets of monosyllabic words. Based on this, these models were not able to handle certain types of words (Besner, Twilley, McCann, & Seergobin, 1990). However, subsequent simulations have indicated that the model does in fact scale-up to larger corpora, allowing it to address a broader range of topics (Plaut et al., 1996). Thus, it is important to discern narrow implementational shortcomings of a specific simulation from more serious theoretical shortcomings of a model. I would also argue that, because models are explicit by nature, it is always an empirical question whether simplifying assumptions have hopelessly corrupted a model’s generalizability to actual cognitive processes. One must be cautious about concluding that a model is incompatible with empirical data based on how it has been implemented in a specific instance.

Concerns with the Phonological Deficit Theory Phonological deficits represent a central theme of the modeling work in phonological dyslexia and SLI. However, there is considerable debate as to whether such a deficit can fully explain the range of problems observed in these children. A central controversy in the literature on SLI and (to a somewhat lesser degree) dyslexia concerns the extent to which these children also have perceptual deficits (Bishop, Carlyon, Deeks, & Bishop, 1999; Mody, Studdert-Kennedy, & Brady, 1997; Ramus & Szenkovits, 2008; Rosen, 2003). There is no doubt that many studies have identified clear perceptual deficits in dyslexia (e.g., Chiappe, Chiappe, & Siegel, 2001; Godfrey et al., 1981; Werker & Tees, 1987) and SLI (e.g., Sussman, 1993; Tallal et al., 1996; Thibodeau & Sussman, 1979). However, some studies have reported more mixed results, for instance, finding perceptual deficits in only a subset of these impaired children (Joanisse et al., 2000; Robertson et al., 2009; Rosen & Manganari, 2001). Such findings tend to be problematic for perceptual deficit theories, since they suggest that problems with perception are not a necessary component of broader language disorders. One response to this is that perceptual deficits represent a delayed maturation of neural mechanisms supporting sensory processes (Wright et al., 1997). There is some limited support for

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this theory: in one study, children with SLI who exhibited clear perceptual deficits were re-tested four years later (Bernstein & Stark, 1985). At that point, many of these same children no longer showed perceptual problems. Notably, however, these children continued to exhibit significant language impairments. This suggests that an early perceptual deficit could interfere with language learning but also be undetectable at a later stage even while its impact on language development persists. Similarly, Bishop and McArthur (2004) found that abnormal event-related electroencephalographic responses to auditory stimuli in SLI tend to resemble normal waveforms observed in younger controls. This again seems to support the delayed maturation hypothesis and could explain why it is sometimes difficult to observe perceptual deficits in impaired individuals. Perhaps questions about perceptual deficits in SLI and dyslexia are moot; speech perception and phonological processing are, after all, independent abilities, and so a perceptual deficit might not be a necessary component of a phonological deficit. Some approaches to phonological deficits have not made strong distinctions between perceptual and phonological deficits (Tallal et al., 1993). Nevertheless, this distinction could be important for understanding subtle differences between reading and language impairments. For instance, there is evidence that many children with phonological dyslexia have very good speech perception abilities, compared to language-impaired children of a similar age (Robertson et al., 2009). This might mean that, although dyslexia and SLI involve similar problems with phonology, the nature of this deficit is subtly different in both disorders. Indeed, this could also explain why dyslexia and SLI exist as separate diagnostic categories. Although they involve similar types of problems, subtle differences between the two lead to slightly different types of weaknesses. Models of reading and language impairments have incorporated simplifying assumptions about phonological representations that make it difficult to independently study perception and phonology. For instance, in the reading, morphology, and syntax models discussed previously, the network perceives a word by activating its phonological features. There is no discrete mechanism by which auditory information is translated into these features, and thus no straightforward way to simulate more specific perceptual vs. phonological deficits. One possibility is that the phonology/perception distinction is necessary to fully capture the behavioral data. It is possible that future modeling efforts will help uncover these differences and why they occur. For instance, Plaut and Kello (1999) have developed a model of normal phonological development that contains perceptual and articulatory components, as well as an intermediary level that is presumed to represent phonological knowledge. Adapting such a model to the development of higher-level language abilities such as reading and morphosyntax might help shed light on this issue.

Conclusion Language is an enormously complex cognitive ability and one that surely integrates a broad range of processes, ranging from low-level sensory abilities (e.g., auditory and visual perception) all the way to higher-level morphosyntactic, syntactic, semantic, contextual, and pragmatic knowledge. Thus, it seems reasonable to assume that a deficit at any point in this processing chain can lead to problems with acquiring language. There has been a great deal of interest in whether one can identify deficits that specifically target domain-specific language abilities, since such a finding would attest to the cognitive and neural reality of language-specific processing mechanisms. This domain-specific deficit hypothesis is an intriguing one, and it has received considerable attention over the past few decades of language research. In this chapter I have suggested that the alternative possibility is just as interesting: the development of specialized language abilities such as reading, morphology, and syntax depend critically on more basic abilities related to the sound and meanings of words—that is, phonology and semantics. This perspective

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can be implemented and tested using connectionist models. These models are explicit and testable instantiations of theories of language, and as a result, they have the potential to provide us with helpful insights into the cognitive bases of language impairments.

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Marc F. Joanisse Smolensky, P. (1999). Grammar-based connectionist approaches to language. Cognitive Science, 23(4), 589–613. Sprenger-Charolles, L., Colé, P., Lacert, P., & Serniclaes, W. (2000). Canadian Journal of Experimental Psychology, 54(2), 87–104. Stanovich, K. E. (1988). The right and wrong places to look for the cognitive locus of reading disability. Annals of Dyslexia, 38, 154–177. Stanovich, K. E., Siegel, L. S., & Gottardo, A. (1997). Converging evidence for phonological and surface subtypes of reading disability. Journal of Educational Psychology, 89(1), 114–127. Sussman, J. E. (1993). Perception of formant transition cues to place of articulation in children with language impairments. Journal of Speech and Hearing Disorders, 36, 1286–1299. Tallal, P., Miller, S., & Fitch, R. H. (1993). Neurobiological basis of speech: A case for the preeminence of temporal processing. Annals of the New York Academy of Sciences, 682, 27–47. Tallal, P., Miller, S., & Fitch, R. H. (1995). Neurobiological basics of speech: A case for the preeminence of temporal processing. Irish Journal of Psychology, 16, 194–219. Tallal, P., Miller, S. L., Bedi, G., Vyma, G., Wang, X., Nagarajan, S. S., . . . Merzenich, M. M. (1996). Language comprehension in language-learning impaired children improved with acoustically modified speech. Science, 272, 81–84. Tallal, P., & Piercy, M. (1974). Developmental aphasia: Rate of auditory processing and selective impairment of consonant perception. Neuropsychologia, 12, 83–94. Thibodeau, L. M., & Sussman, H. M. (1979). Performance on a test of categorical perception of speech in normal and communication disordered children. Journal of Phonetics, 7, 379–391. Thomas, M. S. C., Grant, J., Barham, Z., Gsödl, M., Laing, E., Lakusta, L., . . . Karmiloff-Smith, A. (2001). Past tense formation in Williams syndrome. Language and Cognitive Processes, 16, 143–176. Thomas, M. S. C., & Karmiloff-Smith, A. (2003). Modeling language acquisition in atypical phenotypes. Psychological Review, 110(4), 647–682. Tomasello, M. (2009). Constructing a Language: A Usage Based Theory of Acquisition. Cambridge, MA: Harvard University Press. Ullman, M. T., & Pierpont, E. I. (2005). Specific language impairment is not specific to language: The procedural deficit hypothesis. Cortex, 41(3), 399–433. van der Lely, H. K. J. (2005). Domain-specific cognitive systems: Insight from grammatical-SLI. Trends in Cognitive Sciences, 9(2), 53–59. van der Lely, H. K. J., & Harris, M. (1990). Comprehension of reversible sentences in specifically language impaired children. Journal of Speech and Hearing Disorders, 5, 101–117. van der Lely, H. K. J., & Stollwerck, L. (1997). Binding theory and grammatical specific language impairment in children. Cognition, 62(1), 245–290. van der Lely, H. K. J., & Ullman, M. T. (2001). Past tense morphology in specifically language impaired and normally developing children. Language and Cognitive Processes, 16, 177–217. Vargha-Khadem, F., Watkins, K., Alcock, K., Fletcher, P., & Passingham, R. (1995). Praxic and nonverbal cognitive deficits in a large family with a genetically transmitted speech and language disorder. Proceedings of the National Academy of Science, U.S.A., 92, 930–933. Wagner, R. K., Torgesen, J. K., & Rashotte, C. A. (1994). Development of reading-related phonological processing abilities: New evidence of bi-directional causality from a latent variable longitudinal study. Developmental Psychology, 30, 73–87. Werker, J., & Tees, R. (1987). Speech perception in severely disabled and average reading children. Canadian Journal of Experimental Psychology, 41(1), 48–61. Wright, B. A., Lombardino, L. J., King, W. M., Puranik, C. S., Leonard, C. M., & Merzenich, M. M. (1997). Deficits in auditory temporal and spectral resolution in language-impaired children. Nature, 387, 176–178. Ziegler, J. C., Castel, C., Pech-Georgel, C., George, F., Alario, F. X., & Perry, C. (2008). Developmental dyslexia and the dual route model of reading: Simulating individual differences and subtypes. Cognition, 107(1), 151–178.

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PART III

Language Contexts of Child Language Disorders

12 BILINGUALISM AND CHILD LANGUAGE DISORDERS Elizabeth D. Peña, Lisa M. Bedore, and Alisa Baron

In the United States, an increasing number of children learn language in a bilingual environment. Simultaneous bilinguals are those who are exposed to two languages from birth or from a very young age (typically before 2 years old). Sequential bilinguals learn a second language after they master the first. Many of these bilingual children are known as early sequential bilinguals—learning a home language first, then learning English at the time of initial formal schooling (i.e., preschool or kindergarten). They typically learn a second language after age 2 but before mastering their home language. When sequential bilinguals are in the early phase of learning language, it is difficult to differentiate language impairment (LI) from language differences. Until very recently, there have been few systematic studies of LI in bilingual children. Now a growing database of research informs practice in this area of study. In this chapter, we first review some of the prevailing theories about bilingual language acquisition; next, we discuss the social factors influencing bilingualism in the U.S.; then, studies of LI in bilinguals are reviewed with particular attention to similarities and differences in markers of impairment across languages; finally, we discuss current research and practices informing language assessment and intervention for bilinguals. For these two latter points we draw on our own research examining language acquisition and LI in Spanish-English bilinguals—one of the largest bilingual populations in the U.S.

Theoretical Perspectives on Bilingualism in Children Much of the early research on bilingualism focused on whether the two languages were independent or interdependent (e.g., Genesee, 1978; Volterra & Taeschner, 1978). At present, most scholars agree that bilinguals master the linguistic rule systems for each of their two languages and are able to keep them separate (Greene, Peña, & Bedore, 2013; Marchman, Martínez-Sussmann, & Dale, 2004; Meisel, 1983). Important current questions are to what degree, in what domains, and at what point in development do the two languages influence each other? There are two main hypotheses about bilingual development emphasizing independence or interdependence. These hypotheses, in part, reflect the manner in which children become bilingual. A contemporary statement of the independence hypothesis is de Houwer’s (2005; De Houwer, 2011) Separate Development Hypothesis. This model suggests that when there is bilingual input (defined as a body of dual-language input containing lexical and syntactic input in one language at a time) from birth onwards, children will acquire both of the languages. Because children can form

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language-specific utterances and development follows the same general patterns and timeframe as monolingual development, separate development is assumed. Most of the work evaluating this perspective focuses on morphology (Castro & Gavruseva, 2003; Thordardottir, 2014) and syntax (Almgren & Barreña, 2001; Flynn, 1996). More recent work has additionally focused on semantics (Álvarez, 2003; Thordardottir, 2005), phonology (Eckman, 2004; Fabiano-Smith & Goldstein, 2010; Parra, Hoff, & Core, 2011), and language choice (Greene et al., 2013; Wickström, 2005). From this perspective, linguistic errors are attributed to insufficient input, and it is emphasized that even when speakers produce errors, these errors reflect natural language constraints. Much of the work on the Separate Development Hypothesis focuses on simultaneous bilingual speakers and has not been systematically applied to sequential bilingual learners. Reflecting an interdependence perspective, some researchers suggest that sequential bilingualism is more inter-related in the early stages of second language learning because the stronger, established language mediates or guides development in the second language (Meisel, 1983). Interdependence emerges because children use the syntactic or lexical structure of the stronger language as the foundation for second language utterances (e.g., Bernardini & Schlyter, 2004; GawlitzekMaiwald & Tracy, 1996). Another reason that two languages develop interdependently is that children learn their second language through the filter of their first language. MacWhinney’s (2005, 2012) unified model, based on Bates and MacWhinney’s (1987) earlier versions of the competition model, addresses this issue. He characterized language learning as competition between cues across arenas (i.e., phonology, lexicon, morphosyntax, and pragmatics). For learners to process information they must store, chunk, and decode information. For example, children may retain certain chunks of information or language and associate them based on the language learning strategies they already have available. Young, early sequential bilinguals can exploit their experience as language learners by using first-language knowledge and rules to learn the second language. But, they are limited in their ability to do so because they are still in the process of learning the first language when they are introduced to the second language. Finally, usage-based approaches to learning emphasize the role of language experience. The constructions children hear influence the acquisition of grammatical forms in both languages. When grammatical concepts are expressed in similar ways across languages, the acquisition of forms is facilitated. In contrast, where different linguistic concepts are expressed grammatically, children will show error patterns in one or both languages (e.g., Blom & Paradis, 2013; Bybee, 2010). A good example of this is the acquisition of articles. When languages employ grammatical elements that have converging grammatical characteristics, bilingual children are more likely to make similar errors in both languages. Teaching would focus on the common features across the languages. Where there are differences in the language (i.e., one language has articles and the other does not, as in English Chinese bilinguals), teaching would focus on how the grammatical contrasts represented by the forms of interest facilitate attention to grammatical features. Typological differences between language pairs are thought to play a role in the degree to which languages influence each other. Based on the competition model, children who are exposed to two languages are likely to use high-frequency forms correctly, but they will make errors on lowerfrequency forms. Forms that have a high degree of similarity between two languages (e.g., plural -s in English and Spanish) will be mastered sooner. A languages-in-contact perspective (Döpke, 2000) predicts that unambiguously different forms such as a gender-neutral system of determiners in English are less likely to be vulnerable to changes. But, differences between languages that are more subtle may be more difficult to tease apart. For example, in French and Spanish gender is clearly marked on all determiners (matching the phonological characteristics of the noun) when the noun starts with a consonant: le toit, el techo (‘the roof ’, masculine in French and Spanish, respectively), la pomme, la manzana (‘the apple’, feminine in French and Spanish, respectively). But,

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in French when the noun starts with a vowel, the determiner is unmarked for gender (l’aigle ‘the eagle’). In Spanish, if the noun starts with a vowel, the masculine determiner (el) is used (e.g., el águila ‘the eagle’, masculine), even when the phonological form of the word would otherwise predict the use of a feminine article. Languages with similar features may give rise to more merging of forms, but languages that are more distant in structure will not (Döpke, 2000; Tracy, 1995). This languages-in-contact perspective is consistent with one of interdependence. From an independence perspective, Hulk and Müller (2000) propose that when language structures overlap, but one of the languages appears to have more than one interpretation for the same structure (due to the child’s language input), there is the potential for mutual influence between the two. An example is that a child exposed to languages with subject drop (a Romance language) and topic drop (German) will make different assumptions about the structure than will a child exposed to a pair of languages that does not permit topic drop (e.g., a Romance language and English). They point out that mutual influence occurs at the intersection between pragmatics and syntax and is a language internal phenomenon, rather than one of language dominance. Another consideration is whether different linguistic domains are differentially influenced by bilingualism. There is evidence that languages influence each other in phonology (Gildersleeve-Neumann, Kester, Davis, & Peña, 2008), semantics (Ameel, Storms, Malt, & Sloman, 2005), and syntax (Döpke, 2000; Volterra & Taeschner, 1978). Children must acquire the form of each of their languages, and this has been the area best explained by the independent development models such as the Separate Development Hypothesis. Semantic knowledge is different; children have an underlying set of concepts and may have words for those concepts in one or both of their languages. The argument we will make here is that the independent and interdependent hypotheses both are useful in predicting language development patterns in young sequential bilingual learners. Bilingual children’s competence will vary by domain and over time. At some points in time, bilinguals will look more like their monolingual peers, and at other points in time their mixed knowledge will be more apparent. Rule-based systems are more likely to appear independent and meaning-based systems are more likely to appear interdependent. Configurations of first-language (L1) and second-language (L2) fluency may reflect the degree to which the two languages are used. Time spent hearing and interacting in one language necessarily influences the time available to hear and interact in the other language. The question of amount of exposure has important implications for examining language abilities of bilinguals and understanding how LI may be manifested. With respect to both assessment and intervention, it is important to consider whether both languages should be assessed and how findings in one language or domain might be related to findings in the other language or other domains.

Social and Linguistic Influences on Bilingualism in the U.S. The largest group of bilingual children in the U.S. consists of children of immigrants (Grieco, 2004). Generally, these children are exposed to their parents’ language at home and have their first exposure to English by preschool or kindergarten age. Bilingualism in this population emerges from the need to communicate in two languages (see Bialystok, 2001; Grosjean, 1989, 1998). Variation in the timing, contexts, and amount of exposure to L1 and L2 impacts their degree of fluency in each language across contexts and guides when and how children learn English as a second language. Timing of English language exposure may initially be influenced by birth order. First-born children will necessarily learn the language of their parents (Brodie, Steffenson, Vasquez, Levin, & Suro, 2002). Levels of proficiency in each language will influence the extent to which children choose to use each of their languages (Paradis & Nicoladis, 2007). Older children, however, may use English at home with later-born children, thus exposing them to the second

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language at a younger age (i.e., before school entry). Bilingual and English as a Second Language (ESL) school programs vary in the grade levels at which they are implemented as well as the total amounts of English and other language input that they provide. Social interaction and neighborhood demographics play a part as well. In neighborhoods with high immigrant populations, it is more likely that children will hear their home language spoken by other adults and children in the community, by personnel in retail stores and restaurants, and by medical service providers. These situations provide a variety of contexts in which the child may hear and use the home language as well as how much exposure the child has to it. Parental choice in language use also comes into play. Some parents may choose to maintain the home language, by deliberately providing language, literacy, and cultural experiences in the form of after-school programs and weekend schools. Other parents may choose to limit use of the home language and select English-only school programs for their children. Over time, the configuration of L1 and L2 contexts and degree of exposure to each language change. As children progress in school, many have increasing exposure to English and to Englishspeaking peers. The contexts in which bilingual children are exposed to each of their two languages influence the degree of exposure and results in different profiles of knowledge and fluency of L1 and L2 for different children (Bedore et al., 2012; Collins, O’Connor, Suárez-Orozco, NietoCastañon, & Toppelberg, 2014; Pease-Alvarez, 2002). Although we might picture all bilinguals as communicating in two languages, it is useful to envision bilingualism as a continuum. Most bilinguals are able to communicate to some degree in each of their languages. At the ends of the continuum there are individuals who are exposed to bilingual input but use only one language (Valdes & Figueroa, 1994). These individuals are sometimes referred to as passive bilinguals. Early sequential bilingual children growing up in the U.S. present a challenge because, as a group, they tend to begin to be regularly exposed to English before they master their first language. But, they begin learning the second language later than the cut-off of about 2 years of age that is usually established for considering a child to be a simultaneous bilingual. Thus, these children do not fully fit the definition of either sequential or simultaneous bilinguals. At preschool entry, these children have a significant body of first-language knowledge when they start to learn English. As such, predictions about their language knowledge and development may be based on interdependent models. Because second-language learning starts while these children are still acquiring their first language, development may also follow some of the predictions of the independent models. In addition to these social context considerations, linguistic considerations such as typology and domain (as discussed above) may additionally influence the degree of convergence or divergence in a languages-in-contact situation.

Language Development in Bilingual Children Children learning language in dual-language environments need to learn and use linguistic forms to meet everyday, language-specific discourse demands. Thus, they need to master the phonological patterns, vocabulary, semantics, morphology, syntax, and pragmatics of each language. Research demonstrates that children can separate their languages at the level of speech perception (Bosch & Sebastián-Galles, 2001), speech production (Gildersleeve-Neumann et al., 2008), vocabulary (Bedore, Peña, García, & Cortez, 2005; Sheng, McGregor, & Marian, 2006), morphosyntax (De Houwer, 1990), and pragmatics (Genesee, Boivin, & Nicoladis, 1996; Iluz-Cohen & Walters, 2012). Each of these domains of language, however, may allow different degrees of mixing between the two systems. We propose that the intersection of pragmatics with each of these domains serves to constrain or to allow transfer between the two languages. Language transfer may lead to a greater or lesser degree of mixed knowledge or integration between the two languages.

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Very often, bilingual children demonstrate mixed profiles of language development. Specifically, they may be stronger in one domain of language in their L1 yet stronger in another domain of language in their L2 (Kohnert & Bates, 2002; Kohnert, Bates, & Hernández, 1999; Ordóñez, Carlo, Snow, & McLaughlin, 2002). In a recent study of Spanish-English bilingual children’s profiles, Bedore, Peña, Gillam, and Ho (2010) report that approximately 40% of the bilinguals in their study demonstrated these kinds of mixed profiles across morphosyntax and semantics. Thus, it is important to keep in mind that many bilingual children may not demonstrate the same level of performance across domains. In the following sections, we discuss vocabulary learning, morphology, syntax, and discourse in more detail, as these areas have been studied in bilingual children with LI.

Semantics in Typical Bilingual Children Vocabulary acquisition in bilinguals has been the focus of study for a number of years, particularly in young bilingual children. Generally, findings indicate that trajectories of word learning in bilinguals are similar to that of monolinguals if words across both languages are counted (Holowka, Brosseau-Lapré, & Petitto, 2002; Patterson, 1998, 2000), suggesting distributed vocabulary knowledge. Distributed knowledge has been demonstrated for bilingual toddlers (Conboy & Thal, 2006; Holowka et al., 2002; Patterson, 2000; Pearson, Fernández, & Oller, 1995), preschoolers (Peña, Bedore, & Zlatic-Guinta, 2002; Thordardottir, 2005; Vermeer, 2001), and school-age children (Umbel, Pearson, Fernández, & Oller, 1992). Even when children are clearly dominant in one language or the other, they often know some items in the weaker language that they do not use in the stronger language (Bedore et al., 2005; Peña et al., 2002). Distributed knowledge occurs because objects and ideas can be encoded in one or both of a bilingual’s languages. Because language is learned in context, children learn vocabulary items in the language in which they hear and use them (Pearson, Fernández, Lewedeg, & Oller, 1997; Peña et al., 2002). Bilinguals may therefore exhibit performance differences in comparison to their monolingual peers related to language context. Thus, it is likely that bilinguals and monolinguals have different profiles of word knowledge reflecting language exposure and the social context of language learning. Cross-language associations in vocabulary knowledge may point to common underlying conceptualization of lexical entries as well as to common cognitive resources used in vocabulary acquisition. Yet, there is a degree of independence between children’s two languages. A number of studies demonstrate significant positive correlation in vocabulary knowledge and semantic performance across bilinguals’ two languages. For example, Kohnert, Kan, and Conboy (2010) found significant correlations in young sequential bilinguals for number of different words (NDW) in HmongEnglish bilinguals. Conboy and Thal (2006) reported significant associations in Spanish-English toddler vocabularies based on parent report. In a study of kindergarten bilingual children (Bedore et al., 2010), there were significant correlations for MLU between children’s Spanish and English narratives but not for NDW. Marchman et al. (2004) reported nonsignificant cross-language associations for bilingual toddlers but strong positive correlations within language between vocabulary and grammar. The language in which children are tested may also affect performance, even when responses are accepted in either language. Bedore et al. (2005) examined the performance of 40 bilingual children ages 4;0 to 6;11 on an experimental version of the Bilingual English Spanish Assessment (BESA; Peña, Gutiérrez-Clellen, Iglesias, Goldstein, & Bedore, 2014). When tested in English, children’s conceptual and monolingual scores were not significantly different. When tested in Spanish, however, children’s conceptual scores were higher than their monolingual scores. Specifically, during administration of the Spanish subtest, children were likely to add information by switching to English, but they did not switch to Spanish during English testing. Thordardottir, Rothenberg,

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Rivard, and Naves (2006) found similar patterns when they compared bilingual French-English children’s performance in both languages. Expressively, bilingual children scored similarly to monolingual French speakers on the French (Frank, Poulin-Dubois, & Trudeau, 1997; Trudeau, Frank, & Poulin-Dubois, 1999) version of the MacArthur Bates Communicative Developmental Inventories (CDI). But, in English they scored below monolinguals, even when conceptual scores were used on the CDI (Fenson et al., 1993). Receptively, on the Peabody Picture Vocabulary Test (PPVT) (Dunn & Dunn, 1997) and its French adaptation (Dunn, Thériault-Whalen, & Dunn, 1993), bilingual children’s receptive scores were comparable to that of monolinguals. Group studies indicate that bilingual children demonstrate similar levels of performance in each of their two languages on semantic productivity tasks. For example, on a category generation task, young (ages 4;0 to 6;11) bilingual children generated similar numbers of items in each language (Peña et al., 2002). On a repeated associations task in which children respond by producing a word related to a stimulus item over three trials, Sheng et al. (2006) found that bilingual children demonstrated similar patterns of response in Mandarin and English. These effects are moderated by age and level of exposure. In a group of Spanish-English bilinguals ages 7 through 9, Sheng, Bedore, Peña, and Fiestas (2013) reported that older children produced more semantic responses on a repeated associations task compared to younger children and that children produced more responses in the language for which they had more exposure. Early sequential bilingual children appear to lexicalize items specific to language context. Peña et al. (2002) reported that on the category generation task, comparisons between the two languages indicated a large proportion (68.4%) of items were produced in only one language. Thus, children who are learning language in bilingual environments may not encode the same items in each of their two languages. From a pragmatic perspective, familiarity was constrained by the linguistic context. For example, children generated frijoles, arroz, and pastel (beans, rice, and cake) most frequently when asked to generate names of foods eaten at a birthday party in Spanish, and hamburgers, hotdogs, and cake when asked the same question in English. Two follow-up studies with early sequential bilinguals yielded similar patterns. Peña, Bedore, and Rappazzo (2003) compared three groups of bilingual (Spanish-English) and functionally monolingual (predominantly Spanishspeaking or predominantly English-speaking) children ages 4 to 6 on a battery of semantic tasks (categorization, functions, analogies, comparisons, descriptions, and linguistic concepts). While the three language groups had similar total scores, there were differences in patterns of performance related to task type between languages. For example, English speakers correctly responded to more linguistic concept items (e.g., locatives such as Show me the gift in front of the TV.) than did Spanish speakers. Spanish speakers responded to more function items (e.g., Which picture shows what a party hat is for?) correctly than did English speakers. An analysis of 7- to 9-year-old children’s repeated associations (Sheng, Bedore, Peña, & Taliancich-Klinger, 2013) provides the five most common responses to each of 12 stimulus items in Spanish and English. The typically developing bilingual children produced 66% of the most common items in both languages, and 34% of the items were generated in only one language. Bilingual children may demonstrate different code-switching (nontarget language) patterns depending on language context (Bedore et al., 2005). A study of 606 bilingual children’s expressive responses on a semantics screener demonstrated that approximately half of the children code-mixed (Greene et al., 2013), but consistent with previous studies (Gutiérrez-Clellen, Simon-Cereijido, & Loene, 2009), they did so rarely. When they provided responses in the nontarget language, they did so to add new information rather than to provide translations of responses they had already given. Also, children tended to switch unilaterally—that is, either from Spanish to English or English to Spanish, consistent with their stronger language. Only balanced bilingual children were more likely to switch bidirectionally.

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Morphology and Syntax in Typical Bilingual Children Opportunities to hear and use each of a bilingual’s languages support the acquisition of morphology and syntax. For example, Spanish-English bilingual toddlers’ language exposure accounts for a significant amount of the variance in children’s grammatical knowledge (Parra et al., 2011; Place & Hoff, 2011). In preschool-age children, amount of use and exposure are both correlated to grammatical accuracy (Bohman, Bedore, Peña, Mendez-Pérez, & Gillam, 2010). The relationship between morphosyntactic performance and the amount of use and exposure is also observed in preschool and early school-age children (Bedore et al., 2012; Hammer et al., 2012; Thordardottir, 2014) through the preteen years and young adulthood (Unsworth, 2013, 2014). Variability in knowledge relative to monolingual children is a hallmark of typical language development in bilingual children. Children’s performance on measures such as MLU—a general index of syntactic complexity—is typically lower than that observed for monolingual children. This is of course most likely to be observed for sequential bilinguals early in their second-language acquisition process. Eventually, children perform within the low average to average range (e.g., Fiestas & Peña, 2004; Paradis & Genesee, 1997). For young simultaneous bilingual children, measures that tap language-specific knowledge, such as MLU and grammatical complexity, are significantly poorer their monolingual peers, even when their total vocabulary is comparable to monolinguals and bilinguals (Hoff et al., 2012). Consistent with theories of independent development, morphological development generally proceeds in the expected developmental sequence relative to that observed for monolingual children. Studies of Spanish-English bilinguals (Davison & Hammer, 2012), as well as studies of bilinguals from diverse language backgrounds (e.g., Greek, Turkish, and Cantonese; Nicholls, Eadie, & Reilly, 2011), demonstrate similarities in the order of acquisition of grammatical forms. For example, forms such as progressives, locatives, and regular plurals were all acquired early in acquisition by monolingual and bilingual children. Possessive forms and irregular past were later acquired by bilingual children as well as their monolingual peers. Finally, children acquiring English as a second language produce many of the same error types as do their monolingual peers. For example, Jacobson and colleagues (Jacobson & Livert, 2010; Jacobson & Schwartz, 2005) have shown that children omit and overregularize past tense forms (e.g., jump or jumpeded for jumped, respectively). Despite these general similarities, some of children’s productions indicate that knowledge of one language’s grammar influences production of grammatical forms or sentence structures in the other language, as would be predicted by interdependent models. This is evident when the acquisition of a form is accelerated because of convergence or delayed due to competition between the two languages. Some forms emerge earlier and sometimes more accurately than expected, such as a and the in Spanish-English bilingual children (Davison & Hammer, 2012) and the overuse of definite articles by Arabic-English and Spanish-English bilingual 5–6-year-olds (Zdorenko & Paradis, 2012). In contrast, bilingual children from Chinese or Hindi/Urdu/Punjabi backgrounds omitted articles more often than expected. The authors attributed the differences in performance to differences in the ways that the children’s home languages grammaticalize the linguistic concept of definiteness. At the sentence level, interdependence yields cross-language influenced productions such as a young French-English bilingual’s use of the house of the teddy bear instead of the teddy bear’s house (Paradis, Nicoladis, & Genesee, 2000) or an older Spanish-English bilingual’s use of the past progressive (e.g., was running) in English where she might use an imperfect form (e.g., corría—she used to run) in Spanish; and an English monolingual might be expected to use the preterite ran (unpublished data). An example of an influenced syntactic structure is the use of subject dislocation (e.g., The frog, he is escaping from the boat) in the English of Spanish-speaking children and in Filipino English. In this

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example, subject dislocation is carried into English as a means of emphasizing the subject. In a story-telling task, a bilingual Spanish-English-speaking child’s use of postverbal subject and verb + object configurations was higher in Spanish than in English, as would be predicted by monolingual data (Álvarez, 2003). His use of these forms was higher than expected in English but lower than expected for Spanish, demonstrating a convergence of these two strategies. Children who are exposed to two languages may be more variable in their production of the forms they know. For example, school-age Spanish-dominant bilingual children seem to plateau at about 80% accuracy of Spanish forms such as subjective, conditionals, and direct object clitics. In contrast, children from English-dominant backgrounds are only 50–60% accurate for this set of forms (Bedore, Peña, & Fiestas, unpublished ms). Variability in production seems to result in linguistic trade-offs in which the task demand interacts with the speaker’s linguistic knowledge in one or both of his or her languages. For example, in a series of narrative samples, several of the Filipino-English-speaking children studied by LoFranco, Peña, and Bedore (2006) produced high levels of mazes (up to 60% with a group average of 25%). This high proportion of mazes was most likely to occur on the first story when children had to tell a story based on a wordless picture book with no model of the task demands from the examiner. In contrast, Spanish-English bilinguals (4;0–6;0 years) and functionally monolingual Spanish- and English-speaking children produced comparable percentages of mazes in English and Spanish in narratives (Bedore, Fiestas, Peña, & Nagy, 2006). Each utterance was coded as grammatical or ungrammatical to examine the percentage of grammatical utterances. The bilinguals’ narratives had a slightly lower percentage of grammatical utterances than their functionally monolingual age-matched peers in both languages (i.e., 79% vs. 83% for English and 83% vs. 89% for Spanish). Bilingual and functionally monolingual children made more grammatical errors in Spanish than in English. In Spanish, MLU-words and the number of different words were significantly correlated with the use of grammatical revisions. In English, MLU-words were associated with the repetition of connectors and filled pauses. This pattern suggests a complexity trade-off across languages. In English, children may demonstrate more hesitation phenomena when they attempt to produce longer utterances, whereas in Spanish they exhibit more grammatical revisions. There are also trade-offs in the ways that bilingual children achieve grammatical complexity. Fiestas and Peña (2004), for example, observed that early sequential, bilingual (Spanish-English) 5and 6-year-old children attained similar levels of productivity (number of C-units, MLU-words, and number of words) and grammaticality on narrative and picture description tasks. In English, children achieved this by using Spanish-influenced utterances, especially during the production of complex language tasks. More recently, Greene, Bedore, and Peña (2014) analyzed children’s English production across monologue and dialogue conditions. Increased sentence length was associated with increased use of all-purpose words (thing instead of a specific label), word approximations (circle in place of ring), and literal translation of constructions (e.g., making a party from the Spanish hacer una fiesta).

Narrative Discourse At the level of discourse, patterns for bilinguals are also similar to reports in other language domains (Fiestas & Peña, 2004; Fusté-Herrmann, Silliman, Bahr, Fasnacht, & Federico, 2006; GutiérrezClellen, 2002), and findings support both independence and interdependence. Bilingual children use their knowledge of each language in discourse. In narratives, this knowledge influences the components of the stories that children emphasize as well as the grammatical structures they use to convey their ideas. Young bilingual school-age children (ages 4;0 to 6;11) produced narratives of equal complexity in Spanish and English (Fiestas & Peña, 2004) as well as in Hebrew and English

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(Iluz-Cohen & Walters, 2012), which is suggestive of interdependence. At the same time, bilingual children also vary their use of story-grammar propositions by language, consistent with the notion of independence (Fiestas & Peña, 2004). Silliman, Huntley Bahr, Brea, Hnath-Chisolm, and Mahecha (2001) found that Spanish-English bilingual children used more adverbial than nominal clauses in Spanish but more nominal than adverbial clauses in English. While children use languagespecific structures in narration, they also demonstrate cross-language influences. For instance, in the Fiestas and Peña (2004) study, children used Spanish-influenced utterances in English, which may have allowed them the flexibility to perform at a complex level in English (their second language) as well as in Spanish. As in other domains, amount of language exposure is related to children’s performance. Hipfner-Boucher et al. (2014) compared English narrative skills in preschool-age bilingual and monolingual children. Among the bilingual children, some used more English at home and others used the home language more. There were no differences on productivity measures (number of different words, sentence length, and grammaticality) for the monolingual English and bilingual English users overall. Children who used the home language at home performed lower on these measures in English compared to both groups. Continued L1/L2 exposure is also related to performance over time, and it seems that some aspects of narrative structure transfer between languages. Schwartz and Shaul (2013) studied narrative script knowledge in Russian (L1)-Hebrew (L2) bilingual children attending bilingual or monolingual preschool programs. Children who attended Russian-Hebrew bilingual preschools demonstrated growth in their stories in both languages, with greater growth in Russian compared to children who attended Hebrew-monolingual schools. Children in the monolingual schools demonstrated higher Hebrew-narrative knowledge at time 1, but the differences between their narrative skills and those of children who attended bilingual schools disappeared by mid-year. Similarly, Squires et al. (2014) report on growth in narrative macro- and micro-story structures in a group of Spanish-English bilinguals from kindergarten to first grade. In this study, children who attended predominantly English schools demonstrated growth in both languages on macrostructure and greater gains in Spanish microstructure. Performance in the language of testing was significantly correlated to the amount of exposure children had to Spanish versus English. Productivity and grammaticality have strong within-language associations indicating independence, and across-language associations may indicate interdependence. Simon-Cereijido and Gutiérrez-Clellen (2009) studied the narrative production of 196 preschool and school-age children in Spanish and English. There were strong, significant associations between number of different words and number of different verbs with MLU and use of ditransitives within language. Among the children who produced narratives in both languages, correlations were similar within language to those who produced narratives in only one language. However, there were no significant cross-linguistic associations across domains (e.g., number of different words and MLU). In contrast, Bedore et al. (2010) identified both within-language and cross-language correlations in children of the same age. In a study of 170 kindergarten-age Spanish-English bilinguals, there were strong within-language associations among measures derived from narratives including mean length of utterance, number of different words, and grammaticality consistent with independence. In addition, there were significant positive correlations between Spanish and English MLU, Spanish grammaticality, and English MLU, as well as a negative correlation between English grammaticality and Spanish number of utterances. These cross-linguistic correlations are suggestive of interdependence. Language-specific demands and proficiency in each language affect the composition of bilingual children’s narratives. When children are bilingual, it is important to know that language performance in each language may not be equivalent, even if they are judged to be fluent in both

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(Gutiérrez-Clellen, 2002). From these accounts, we know that bilingual children who are typically developing are able to use each language in appropriate ways and with few overt errors. Typical errors are more likely to be substitutions rather than omissions (Jacobson & Walden, 2013). Furthermore, the amount of mixing that occurs across the two languages has pragmatic constraints. Language essentially must be understandable to the listener. Switching or mixing by using a translation equivalent is less likely to interfere with the intended meaning and thus to be easily parsed. Grammatical violations may inadvertently affect the meaning of the sentence (Gutiérrez-Clellen et al., 2009) and thus interfere with an accurate interpretation of the message. Monolingual children as young as 2 years of age seem to have a good understanding of how the language works. When they make errors, communication breakdowns are rare because the errors do not change the pragmatic intent or global meaning of the child’s utterance (Joseph & Pine, 2002; Wexler, 1996). Children with LI, in contrast, have difficulty learning and using language efficiently and effectively, and they persist in making errors for a longer period of time than do their peers (Rice, Wexler, & Hershberger, 1998). Based on the studies reviewed thus far, we see similar patterns of development for typically developing bilingual children. Bilinguals vary in the frequency and persistence of errors and in use of other-language influenced forms. These errors and influenced changes are similar to those made by monolingual children but may differ in frequency at a given point in development. In semantics, children may demonstrate mixed knowledge, selecting an appropriate word from the nontarget language or selecting a closely related word in the target language demonstrating knowledge constraints in that language. In syntax, children differentiate their two languages from a young age (Nicoladis & Genesee, 1997). Yet, contact between the two languages results in mutual syntactic-pragmatic influences one on the other. This influence may be seen in the frequency and distributions of forms used. In discourse, children again are able to separate their two languages, but there seem to be trade-offs between the amount of influenced utterances they use and complexity. The use of nonstandard and influenced forms allows emerging bilinguals to convey complex ideas in discourse. How then is language impairment (LI) manifested in bilingual children? Do children with LI demonstrate learning patterns that are predicted by hypotheses of independent bilingual language acquisition, such as the Separate Development Hypothesis (De Houwer, 2005), interdependent models such as usage-based models (Blom & Paradis, 2013; Bybee, 2010), or the unified model proposed by MacWhinney (2005, 2012)?

Language Impairment in Bilinguals SLI is defined as a delay in language development in the absence of frank neurological, cognitive, or physical impairment (see Chapter 1 by Schwartz). The incidence of SLI in the mainstream, English-speaking U.S. population is 7.4% according to the most recent epidemiological studies (Tomblin et al., 1997). There are no such studies for bilingual populations. A question that is often asked is whether bilingualism increases the risk for language impairment. A number of studies indicate that there are no differences in the morphosyntactic features of bilingual French-English children and Spanish-English children with SLI compared to their monolingual peers (Paradis, Crago, Genesee, & Rice, 2003; Windsor, Kohnert, Lobitz, & Pham, 2010) or phonological impairment in Spanish-English bilinguals versus their monolingual peers (Fabiano-Smith & Barlow, 2010). In a study of 1,200 children exposed to different levels of Spanish and English, Peña, Gillam, Bedore, and Bohman (2011) found that balanced bilingual, bilingual dominant in Spanish or in English were no more likely than functional monolinguals to score in the risk range on a language screener. Given this evidence, there is reason to assume that the percentage of the bilingual population that is affected by LI is about the same as for the general population.

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Similar findings are reported for bilingual children with developmental disabilities. For example, children with autism whose overall patterns of communication were measured by functional communication skills or receptive expressive abilities were no more impaired than their monolingual peers (Hambly & Fombonne, 2012; Petersen, Marinova-Todd, & Mirenda, 2012). Specifically, bilingual children with autism did not differ from their monolingual peers in the onset of first words and word combinations (Ohashi et al., 2012). Bilingual children with Down syndrome have been compared to their monolingual peers with Down syndrome. These two groups do not seem to differ in general indices of language such as number of total words and MLU. Cleave, Kay-Raining Bird, Trudeau, and Sutton (2014) conducted a syntactic bootstrapping task with monolingual and bilingual children with and without DS. Across groups, children were able to use syntactic cues, although they performed better on nouns than verbs. The patterns of performance of the bilingual and monolingual children did not differ, suggesting that bilingualism did not have a negative impact on their ability to identify new words on this task. These studies have been conducted on a relatively small scale, but the findings are thought provoking. In at least several cases, the bilingual children with autism or Down syndrome have had somewhat higher IQs than their monolingual peers. This raises a question of directionality. Were these children better able to learn two languages because of their higher IQs or did learning two languages enhance their cognitive control leading to higher IQ? It is well established that IQ is significantly correlated with measures of language. Even nonverbal IQ is significantly related to language. For example, Tomblin and Nippold (2014) report a correlation of .48 between nonverbal IQ and language in a group of 500 kindergarten children. Consistent with the bilingual advantage hypothesis (Bialystok, Craik, & Luk, 2012), it may be that learning two languages enhances children’s attention and cognitive control, leading to higher IQ. As we continue to study bilingual children with developmental disabilities, it is important to study children prospectively to disambiguate this question. It is particularly difficult to identify SLI or LI in bilingual children. Many of the linguistic errors or language changes that mark LI in monolinguals are also made by typical children who are learning language in bilingual environments. Therefore, it is important to identify markers that distinguish typical bilingual learners from bilingual learners with LI who show promise for development of measures to help clinicians make these distinctions. In the U.S., most of this effort has been focused on Spanish-English bilinguals (Dollaghan & Horner, 2011), though other language pairs have been studied as well. The European Concerted Research Action, Language impairment in a multilingual society is one group that has taken a systematic approach to study language impairment across a number of language pairs (Armon-Lotem, 2012; Armon-Lotem & Walters, 2011; Chondrogianni & Marinis, 2012). The Bangor University Bilingualism Centre has also focused on bilingual development and on patterns of language impairment in bilingual populations (Gathercole, 2013a, 2013b; Gathercole, Thomas, & Hughes, 2008; Thomas, Thomas, & Mennen, 2014). Current studies of language in bilinguals with and without LI reveal some potential characteristics of LI in bilingual children. Here, we review available literature in semantics, morphosyntax, syntax, and discourse.

Semantic Performance in Bilingual Children with Language Impairment Monolingual children with LI typically demonstrate a delayed onset of first words, slower rate of vocabulary acquisition, and decreased lexical diversity in their connected speech in comparison to their typical age peers during early language learning (Duchan & Erickson, 1976; Gray, 2004; see Chapter 1 by Schwartz and Chapter 16 by McGregor). These delays have been noted across multiple languages, including Dutch (van Daal, Verhoeven, & van Balkom, 2004), German (Höhle, van de Vijver, & Weissenborn, 2006), and Cantonese (Klee, Stokes, Wong, Fletcher, & Gavin, 2004).

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By early school-age, these children appear to catch up to their age peers (Hick, Joseph, ContiRamsden, Serratrice, & Faragher, 2002; Leonard, Camarata, Rowan, & Chapman, 1982). Typically, their performance on standardized tests of vocabulary is within the normal age range but below that of their typically developing peers. At school-age, children with LI may have word-finding difficulties and naming errors in addition to vocabulary test scores below that of their age peers (Lahey & Edwards, 1999; van der Lely & Howard, 1993; Windsor, 1999). This has also been noted in languages other than English, including Swedish (Hansson & Bruce, 2002) and German (Eisenbeiss, Bartke, & Clahsen, 2006). Available data on semantic performance of bilingual children with LI demonstrates that they make more naming errors and need more processing time to respond in comparison to their typical bilingual peers. Consistent with findings for monolingual children (Alt & Plante, 2006; Botting & Adams, 2005; Gray, 2004; Haosheng, 2004; Mainela-Arnold, Evans, & Coady, 2010; Nash & Donaldson, 2005), bilingual children with LI seem to have deficits in retrieval and organizational aspects of semantic use rather than in vocabulary size. Lack of breadth in lexical entries, inadequate or weak links between lexical entries, or limited depth of lexical entries may further compromise performance (Dollaghan, 1992; McGregor, Oleson, Bahnsen, & Duff, 2013). Bilingual children with LI, like monolingual children with LI (Gray, 2004; Nash & Donaldson, 2005), demonstrate difficulty with learning new words. Word-learning tasks using a dynamic assessment paradigm demonstrate that both groups have difficulty learning and using naming strategies (Peña, Iglesias, & Lidz, 2001; Peña, Quinn, & Iglesias, 1992). Across this set of studies, children with and without LI were taught strategies for learning single real or nonsense words. At post-test, findings demonstrated differential responses to the short-term intervention. Children with typical development demonstrated use of the strategies they were taught, while children with LI had difficulty employing the skills they were taught. Bilingual children with LI appear to have weak semantic representations (Sheng et al., 2013; Sheng, Peña, Bedore, & Fiestas, 2012; Simonsen, 2002), consistent with proposals for monolingual children with LI (McGregor & Appel, 2002; McGregor, Friedman, Reilly, & Newman, 2002; Sheng & McGregor, 2010a, 2010b). Both monolingual and bilingual children with LI exhibit vulnerable semantic network organization, and they appear to have fewer links in their semantic networks within and across languages when compared to typically developing children. Sheng et al. (2012) examined Spanish-English bilingual children’s semantic depth using a repeated associations task. Children were asked to respond with a related word in response to a stimulus word three times. The task was repeated in Spanish and English, eliciting associations for a list of 12 translated words. Conceptual scores were used to derive a total semantic depth score. More than two-thirds of the children with LI had scores more than 1 SD below the mean for the typical children. Children with LI provided significantly fewer paradigmatic responses compared to typical controls and compared to use of syntagmatic responses. Despite difficulties with semantic breadth and depth, English-speaking children with LI seem to be sensitive to input frequency (e.g., Plante, Bahl, Vance, & Gerken, 2011). Bilingual children with LI also appear to follow these patterns. In a follow-up study to Sheng et al. (2012), Sheng et al. (2013) explored the typicality of responses made by bilingual Spanish-English school-age children with and without language impairment. While the children with LI used fewer normative responses compared to the children with typical development, the frequency of responses in both groups was significantly correlated with rate of occurrence. In children’s second language, English, there were stronger correlations with the normative response rate for the typically developing group compared to those with LI. Similarly, Robillard, Mayer-Crittenden, Minor-Corriveau, and Bélanger (2014) studied core words in French-English bilingual and monolingual children with and without LI. Core words were defined as the set of words that occur frequently within a child’s

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repertoire and that have a high degree of commonality among speakers—not unlike the typicality ratings in the previous study. The authors examined kindergarten children’s French core words in spontaneous language samples while children interacted in the classroom setting. Findings indicated a high degree of commonality among the core words used by children with and without LI and among monolingual and bilingual children. Children with LI appear to have more difficulty on production tasks compared to receptive tasks (Leonard, 2014). In the area of vocabulary, however, typical bilingual children also have weaker expressive skills than comprehension skills (Gibson, Oller, Jarmulowicz, & Ethington, 2012; Gibson, Peña, & Bedore, 2014b), even when differences between the two tasks are controlled. These differences may have to do with children’s relative exposure to the home or mainstream language or to the timing of exposure to the second language. Gibson, Peña, and Bedore (2014a) explored the differences in expressive and receptive semantic performance on an experimental test, the Bilingual English Spanish Assessment-Middle Elementary (Peña, Bedore, Iglesias, GutiérrezClellen, & Goldstein, 2010). Children were between 7;0 and 9;11 years of age. Those with LI were compared to typically developing children matched by age, sex, current exposure to Spanish and English, and age of first exposure to English. Results demonstrated both lower performance for children with LI in both languages and a larger receptive-expressive gap in comparison to typically developing children. Consistent with previous studies of LI in monolinguals, these children seemed to have particular difficulty in the expressive domain. The research conducted up to this point on bilingual children with LI is consistent with the findings for monolingual children. Children with LI have semantic systems that are impoverished in their breadth and depth (Dollaghan, 1992; McGregor, 1997; McGregor & Appel, 2002; McGregor et al., 2013; McGregor & Windsor, 1996). Furthermore, children with LI demonstrate inefficiencies that affect word learning (Gray, 2004; Nash & Donaldson, 2005), retrieval, and lexical organization (Montgomery, 1999, 2002). At the same time, because their time is distributed across two situated languages, it is important to document lexical-semantic knowledge in both languages. This documentation can be done through the use of conceptual scoring. With respect to studies of bilingual children with developmental disabilities, there are some studies of bilingual children with Down syndrome (DS). Feltmate and Kay-Raining Bird (2008) explored the vocabulary of monolingual and bilingual children with DS. Both groups displayed similar receptive vocabulary in their L1. The authors encountered significant variability in the L2 abilities of children with DS, suggesting that some children with DS may have more difficulty than others in acquiring two languages. In the L2 of children with DS, chronological age, mental age, and L2 vocabulary comprehension were all significantly related to MLU. Compared to the language samples of typically developing children, children with DS generally had lower MLUs, used more bare nouns in noun phrases, fewer verbs overall, fewer total words, and fewer number of different words. Bilingual typically developing children and children with DS performed similarly on several receptive measures in English. When receptive vocabularies in both languages as measured by the PPVT-R (Dunn & Dunn, 1981) and its French adaptation (Dunn et al., 1993) were compared, bilinguals with DS had higher scores than monolinguals with DS in three out of four cases.

Morphology and Syntax in Bilingual Children with Language Impairment Typically, bilinguals with LI or at risk for LI have shorter, less complex utterances, relative to L1- or L2-speaking monolingual peers (Bedore et al., 2010; Paradis, Schneider, & Duncan, 2013). Additionally, their language skills progress more slowly than do those of their typically developing peers (Jacobson & Schwartz, 2002; Restrepo & Gutierrez-Clellen, 2001). Their difficulties are observable in their difficulties with nonword repetition (Gutiérrez-Clellen & Simon-Cereijido, 2010; Windsor

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et al., 2010) and other information processing abilities (Kohnert & Ebert, 2010). Morphology and syntax difficulties are evident in deficits in overall grammaticality, low MLU, verb tense, and article errors (Anderson & Marquez, 2009; Bedore & Leonard, 2005; Morgan, Restrepo, & Auza, 2009; Morgan, Restrepo, & Auza, 2013; Simon-Cereijido & Gutiérrez-Clellen, 2007), clitic errors (Bedore & Leonard, 2005; Gutiérrez-Clellen, Restrepo, & Simon-Cereijido, 2006; Morgan et al., 2009; Morgan et al., 2013), subjunctive verb errors (Sánchez-Naranjo & Pérez-Leroux, 2010), derivational morphemes (Auza & Hernández, 2005; Morgan et al., 2009), and missing grammatical arguments (Gutiérrez-Clellen et al., 2006; Simon-Cereijido & Gutiérrez-Clellen, 2007). This lack of morphological productivity is consistent with observations of the grammatical errors observed in children with SLI who speak English only (Leonard, 2014). Consistent with the independent development hypothesis, bilingual children with LI exhibit morphological error patterns that are analogous to those of monolingual children with LI. A recent example of this is Morgan et al.’s (2013) comparison of production of Spanish morphology in monolingual and bilingual Spanish-English children with and without LI. Fifty-seven children completed cloze task items that specifically targeted articles, clitics, subjunctives, and derivational morphemes. Significant differences were noted between children with the typically developing and impaired language skills on all morphemes. However, differences between typically developing bilinguals and language-impaired monolinguals were not observed. For articles, omission errors were the most frequent across all groups. For clitics, the most frequent errors for monolinguals were omission errors, while for bilinguals, both omission errors and substitution errors were common. For both articles and clitics, bilinguals made more gender and number substitutions. Monolinguals, on the other hand, made slightly more gender and number substitutions. Across all groups, the most errors were made on plural articles and clitics. For subjunctives, infinite use was the most common error across groups, but the LI groups made more indicative errors than the TD groups. Another example of independent development is that production of tense-marking forms is especially challenging for children across languages; for example, Italian (Bortolini et al., 2006), Spanish (Anderson, 2001), and Swedish and German (Clahsen, Bartke, & Göllner, 1997; Hansson & Leonard, 2003) show tense-marking errors. Several studies of bilingual children show that these difficulties are observed in each of their languages. For example, Salameh, Håkansson, and Nettelbladt (2004) observed tense-marking difficulties in both languages of Swedish-Arabic-learning children with LI. These findings were considered to be consistent with processability accounts that suggest that grammatical performance is related to sentence complexity. Paradis et al. (2003) documented poor performance on tense-bearing (e.g., present and past tense verb forms) and nontense-bearing morphemes (e.g., articles) produced by English-French balanced bilingual schoolage children with LI and their monolingual peers, consistent with predictions of the Extended Optional Infinitive Hypothesis (Leonard, 2014; Liceras, Bel, & Perales, 2006; Rice, Noll, & Grimm, 1997; see Chapter 13 by Leonard). Blom and Paradis (2013) investigated past tense use in children with language impairment with various L1 and English L2 combinations from the perspective of usage-based accounts. They noted that the LI group used fewer tense-marked verbs than did the typically developing group. Vocabulary size and word frequency predicted accuracy with all verbs. A few examples of error types that are frequently seen in Spanish for they ate include person errors—he/she ate, other tense errors—they are eating, person and tense errors—he/she is eating, infinitive/gerund substitutions—eat or eating, and regularization of an irregular verb—eated (Jacobson, 2012). Similarly, school-age Spanish-English sequential bilinguals with LI performed differently than their typically developing peers on tasks requiring them to produce past tense marking in English (Jacobson & Schwartz, 2005). Some examples of interdependence are also evident in the grammatical profile of a child with LI. For example, French-English bilinguals with LI have difficulty with French direct object

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pronouns but do not have the analogous difficulties in English (Paradis, 2005). Restrepo and Kruth (2000) identified error patterns not typically observed in monolingual children with LI in their case study of a 7-year-old sequential bilingual child with a history of language learning difficulties. Her mother reported that, in Spanish, the child produced words and sentences incorrectly and that she made more such errors than did her younger sibling. She participated in an ESL program from kindergarten through the time of the study. Her progress was compared to that of a classmate who also began to learn English at the time of kindergarten entry. In Spanish, the child with LI produced articles with 6–33% accuracy and produced regular present and past tense verbs with 20–50% accuracy. Her classmate produced these forms with 80–100% accuracy. Her MLU was low and, only 1% of her utterances were complex relative to 30% complex utterances observed in the narrative sample of her classmate. The error types were consistent with observations for Spanish and English monolingual children and are consistent with the findings discussed earlier. This child, however, produced a more limited set of prepositions than is usually observed in second-language learners or in children with LI. For example, the only preposition she produced in English was on. Furthermore, she inappropriately used the preposition con (with) in contexts that required other prepositions such as a (to) or en (in/on) or omitted them. Finally, the child with LI demonstrated a decrement in the complexity of her Spanish that was not observed in her typically developing classmate, suggesting that with more of her input shifting to English, she had more difficulty maintaining her Spanish skills. The last two findings point to some difficulty integrating input from two languages and are more consistent with predictions from interdependent models. More work focusing on both languages of bilingual children is needed to further understand the extent of interdependence in the grammatical production in SLI.

Narrative Performance in Bilingual Children with Language Impairment In the domain of narrative discourse, children with LI have difficulty taking the listener’s perspective and, thus, make assumptions that are inaccurate (Westby, Van Dongen, & Maggart, 1989). They also suggest that children with LI have difficulty with the overall organization of the narrative, keeping track of the time elements and cause-effect relationships (Miranda, McCabe, & Bliss, 1998; Thomson, 2005). Patterns of narrative performance for children with LI include difficulty with use of mental state predicates (Johnston, Miller, & Tallal, 2001; Norbury & Bishop, 2003), limited use of story structure elements (Manolitsi & Botting, 2011; Merritt & Liles, 1987), difficulty using cohesive devices (Klop et al., 2013; Liles, Duffy, Merritt, & Purcell, 1995), and overall organization (Colozzo, Gillam, Wood, Schnell, & Johnston, 2011; Kaderavek & Sulzby, 2000; Merritt & Liles, 1987) when compared to their age-matched peers. Pearce, McCormack, and James (2003) compared children with SLI, LI with low nonverbal IQ, and typical children. Children with SLI told stories that were less complex than those told by typical children. Children with low nonverbal IQs told the simplest stories. The last 10 years have seen an increase of research on narrative performance in bilingual children with and without language impairment. Overall, bilingual children with LI have the same kinds of difficulties that monolingual children have with narrative story telling. Analyses are typically conducted at the macro-level, focusing on story structure and productivity, and at the microlevel, focusing on literate, semantic, and grammatical aspects of the story. Iluz-Cohen and Walters (2012) studied macrostructure (setting, initiating event, goal, attempt consequence, internal response, and ending) in English-Hebrew bilingual 5- and 6-year-old children. Examination of children’s narrative structure demonstrated no differences between group or language. Squires et al. (2014) examined the quality of children’s use of macrostructure elements (character, setting, initiating event, plan, action, consequence, and internal response) in

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Spanish-English bilingual children using story retell. Children told stories in both of their languages in kindergarten and first grade. Each of the story elements was scored from 0 to 3 and summed. Children with LI scored lower on macrostructure in both languages at both time points. Children with typical development made greater gains in use of macrostructure elements over time in both languages. It is important to note that Spanish macrostructure scores at kindergarten predicted English macrostructure scores at first grade. This result supports the notion of interdependence between languages. Bilingual children with and without LI appear to have difficulties with microstructure, including lexical variability, grammatical complexity, and use of literate language. Bedore et al. (2010) examined the degree to which MLU, grammaticality, NDW, and number of utterances together predicted language ability in a group of 170 kindergarten children. Language ability was determined using a composite score of grammatical cloze, sentence repetition, and semantics from the 2008 experimental version of the Bilingual English Spanish Assessment (Peña, Gutiérrez-Clellen et al., 2014). Predictors were derived from children’s narrative samples for both Spanish and English. Results indicate that a combination of predictors, including English MLU, Spanish grammaticality, and English grammaticality, best accounted for language ability as determined by the BESA composite. In the Squires et al. (2014) study, microstructure (coordinating conjunctions, subordinating conjunctions, mental and linguistic verbs, adverbs, and elaborated noun phrases) was analyzed in each language at the two time points. Consistent with macrostructure findings, children with LI performed lower than their typical peers at both time points. In contrast with the macrostructure findings, children with LI made no significant gains in microstructure in either language over the two time points. Microstructure scores were not cross-linguistically associated. This finding is consistent with language independence.

Implications for Assessment and Intervention Identification of LI in bilinguals and planning appropriate language intervention is challenging. Currently, there are some measures designed to assess children’s skills in languages other than English but fewer still specifically designed for bilinguals. Among the handful of available standardized tests, all focus on Spanish and English bilinguals. Additionally, there are also some emerging data on which to base effective interventions for bilingual children. A unique challenge to the needs of bilingual children concerns determination of the most effective language of instruction (for intervention and the classroom) to facilitate language growth and transfer. Our approach to assessment and intervention combines both interdependence and independence perspectives. An interdependence approach focuses on underlying cognitive linguistic mechanisms (Kohnert & Windsor, 2004; Kohnert, Yim, Nett, Kan, & Duran, 2005), on forms and uses that are common across the language pairs (e.g., gender marking in Catalan and Spanish; Costa, Miozzo, & Caramazza, 1999), and on vocabulary needed in home and school (Kohnert & Derr, 2004; Kohnert et al., 2005; Peña & Stubbe Kester, 2004). From this viewpoint, children all have the same set of general cognitive tools for learning language (Bates, Devescovi, & D’Amico, 1999; Bialystok & Barac, 2012; Haywood, Brooks, & Burns, 1992; Kohnert et al., 1999; Kohnert & Medina, 2009; Magliano, Trabasso, & Graesser, 1999; Naglieri & Das, 1989; Stein & Albro, 1997; Stubbe Kester, Peña, & Gillam, 2001; Tomasello, 2003). Thus, interdependent approaches focus on identification and remediation of inefficient cognitive functions that give rise to difficulty learning and using language. At the same time, specific surface structures must be learned in order for children to communicate effectively within their social and academic context. An independence approach focuses on specific linguistic structures that might be identified as potential markers of impairment and targets for intervention (e.g., Bedore & Leonard, 2001, 2008;

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Dollaghan & Horner, 2011; Gutiérrez-Clellen, 2002, 2004; Gutiérrez-Clellen et al., 2006; Jacobson, 2012; Jacobson & Schwartz, 2002; Morgan et al., 2009, 2013; Restrepo & Gutierrez-Clellen, 2001; Restrepo & Kruth, 2000). The evidence of cross-language influences thus far suggests different degrees of language mixing, dependent on the language domain. The difference in mixing has implications for how language dominance is viewed and assessed and means that there is a role for both interdependence and independence in assessment and treatment of language disorders. We present three inter-related principles for the identification and remediation of LI in bilinguals. First, focus on manifestations of difficulty that are consistent with general cognitive functions associated with language impairment. Second, take an interdependent approach that allows for mixed knowledge with a focus on pragmatic use of language. Third, focus on aspects of LI that are characteristic of the target language while allowing for changes that arise from languages-in-contact.

Assessment of Language in Bilinguals Applied to assessment, a focus on underlying cognitive skills associated with LI closely examines potential difficulties with underlying strategies including attention, memory, storage, processing, organization, retrieval, and self-regulation (Peña, Reséndiz, & Gillam, 2007) and may be observed in vocabulary size and retrieval (Newman & German, 2002; Peña et al., 2002; Schiff-Myers & Mikulajova, 1997; Sheng & McGregor, 2010a) as well as in on-line performance of semantic tasks. Children with LI across monolingual and bilingual language environments have difficulty with efficient learning and use of language. Thus, we would expect them to make more off-target errors in word choice, be less effective in code-switching to add new information, and be less efficient in making conversational repairs (Greene et al., 2013; Sheng et al., 2013). Assessment strategies such as dynamic assessment focus on underlying cognitive skills (Allal & Ducrey, 2000; Bain & Olswang, 1995; Budoff, 1987; Lidz, 2002; Lidz & Peña, 2009; Tzuriel, 2001) and have been applied to assessment of language in areas of vocabulary learning (Kapantzoglou, Restrepo, & Thompson, 2012; Peña et al., 2001), categorization (Ukrainetz, Harpell, Walsh, & Coyle, 2000), and narrative development (Gutierrez-Clellen & Quinn, 1993; Peña, Gillam, & Bedore, 2014; Peña et al., 2006) in children from culturally and linguistically diverse backgrounds. Consistent with the notion of examining cognitive skills, findings across a number of studies demonstrate that modifiability measures (e.g., child responsivity and examiner effort) differentiate between children with and without impairment with a high rate of accuracy. Follow-up analyses indicate that with preschool children, motivation and persistence were related to language ability (Peña, 2000), and with school-age children, metacognition and flexibility together best differentiated children with and without LI across African American, Latino American, and European American groups (Peña et al., 2007). Recent work examining use of dynamic assessment in bilingual children demonstrates that this approach can effectively differentiate children with and without language impairment (Hasson, Camilleri, Jones, Smith, & Dodd, 2013; Kapantzoglou et al., 2012; Peña, Gillam et al., 2014). A bilingual or interdependent approach allows for mixed knowledge within and across domains. With respect to dominance, an interdependent approach acknowledges that for children, as well as for adults, dominance at a single point in time may vary, depending on the specific context (Grosjean, 1998). We have found it helpful to use a questionnaire that reports on the amount of exposure and use of each language hour-by-hour (Gutiérrez-Clellen & Kreiter, 2003; Restrepo, 1998) in order to have an accurate picture of the percentage of time the child is exposed to and uses L1 and L2. Moreover, the questionnaire provides information about the communicative demands specific to each language (Bedore, Peña, Joyner, & Macken, 2011). Children in a bilingual environment may have mixed or distributed knowledge across their two languages and thus may not exhibit all skills

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subsumed in a given language. This mixed knowledge is apparent in the semantic domain where children learning language in bilingual environments lexicalize concepts in a distributed manner across two languages (Holowka et al., 2002; Patterson, 1998, 2000; Pearson et al., 1995; Umbel et al., 1992). So, it is not appropriate to test only in one of the languages or to compare the child to a monolingual standard (Pearson, 2010). Thus, examination of vocabulary scores using an approach such as conceptual scoring (Pearson & Fernández, 1994), accepting each lexicalized concept across languages, is recommended. Our work demonstrates that children’s performance across different tasks varies, depending on both the language of testing and linguistic status (bilingual vs. monolingual; Bedore et al., 2005; Bedore et al., 2012). In the semantic domain, we examined how children with and without LI performed on the semantic tasks from the Bilingual English-Spanish Assessment (BESA; Peña, Gutiérrez-Clellen et al., 2014). Findings thus far suggest that across Spanish and English there are both similarities and differences in how well different semantic tasks discriminate language impairment. Specifically, consistent with Peña et al. (2003), more function (e.g., What is a crayon used for?) items discriminated LI and typical development in Spanish, but more similarities-and-differences (e.g., What is different about these two invitations?) and characteristic properties (e.g., Tell me three things about this girl) items discriminated impairment better in English. Category generation items worked equally well in the two languages. Given that bilingual children with LI have comparable difficulties to their monolingual peers in the morphosyntactic domain, findings indicate that clinical markers of LI for monolingual children often characterize the performance of bilingual children with LI. It is important to consider the difficulties that are indicative of impairment in the child’s first or stronger language. Thus, a clinician might examine article and clitic difficulties in Spanish or Italian (Bedore & Leonard, 2001, 2005; Leonard, Sabbadini, Leonard, & Volterra, 1987; Restrepo & Gutierrez-Clellen, 2001), auxiliary, clitic pronoun, copula, and present tense marking in French (Restrepo & GutierrezClellen, 2001), or perfective and imperfective aspect marking in Chinese (Stokes & Fletcher, 2003), while recognizing that LI influences how children learn their second language as well (Jacobson & Schwartz, 2005). For example, the identified markers of LI in Spanish described above are useful for Spanish-speaking children exposed to English and bilingual children dominant in Spanish (Gutiérrez-Clellen et al., 2006; Simon-Cereijido & Gutiérrez-Clellen, 2007). Note that the decision about the language of evaluation should be made on the basis of domain rather than on the strength of the language as a whole. Over the past few years, a handful of standardized tests focusing on identification of language impairment in Spanish-English bilinguals or in Spanish speakers have been published. Examples of these include the Preschool Language Scale-5 Spanish Edition (PLS-5; Zimmerman, Steiner, & Pond, 2012), the Clinical Evaluation of Language Fundamentals Preschool-2 English and Spanish Editions (CELF-P2; Wiig, Secord, & Semel, 2009), the Clinical Evaluation of Language Fundamentals-4 English and Spanish Editions (CELF-4; Wiig, Secord, & Semel, 2006), and the Bilingual English Spanish Assessment (BESA; Peña, Gutiérrez-Clellen et al., 2014). A challenge in the development of language assessment tests for bilingual children has been in how to combine scores to best differentiate between children with and without LI and how to represent both languages. One approach is the use of conceptual scores. This approach is used for the dual-language administration on the PLS-5, where responses in the other language are incorporated into the total receptive and expressive language scores. The CELF-4, on the other hand, has two separate tests, one in English and one in Spanish. The subtests are very similar, so abilities can be observed in both languages to make a clinical diagnosis. While there are no instructions for how to combine results from the two languages, researchers have used scores below the cut-off in both languages to verify LI (Ebert, Pham, & Kohnert, 2014). BESA allows for conceptual scoring and pulls the higher score from each

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subtest to combine into a composite language ability score. This approach has been verified to yield a high degree of sensitivity and specificity (Peña, Bedore, & Kester, 2016).

Intervention with Bilingual Children with Language Impairment Children’s language knowledge emerges from their use of each of their languages (Bedore et al., 2012; Cummins, 1989; Hammer et al., 2012). Consistent with best practices in the field, the focus should be on children’s learning and using meaningful language, and when morphosyntactic forms are taught, they should be taught in contexts that support the development of meaningful communication (e.g., Fey, Long, & Finestack, 2003). As before, interdependence and independence perspectives can be used in a complementary way to help children meet the daily demands in each of their languages. Goals for intervention need to build from knowledge of L2 vocabulary and take into account expected patterns of cross-language influence. From a monolingual or independence perspective, we expect that many of the goals for bilingual children will be analogous to those for monolingual children. To target these forms, it is important to use themes and vocabulary available to the child in the language of intervention to facilitate attention on the grammatical targets. However, as children learn these forms, they will likely use the main sentence structure of the stronger language but make changes related to the second or weaker language consistent with principles of interdependence. As they learn more of the second language, they may use more of the words from the second language but may still preserve the word order from the first or stronger language. As they master syntax in L2, they may continue to have difficulties in organization of discourse in L2 and thus demonstrate more intrusions from the other language. A focus on general learning principles needed for language production and use may provide children with basic skills for improving language performance in each of their languages. Cognitive approaches such as those used in mediated learning have been demonstrated to be effective in increasing language performance in the short term in children from culturally diverse backgrounds (Peña & Gillam, 2000; Peña et al., 2006; Peña et al., 2007; Stubbe Kester et al., 2001). For example, in a study following the French and English language recovery of a 17-year-old girl who had undergone a partial left hemispherectomy, Trudeau, Colozzo, Sylvestre, and Ska (2004), it was hypothesized that bilateral cortical representation of language laid the cognitive foundation for her recovery. Relatively greater improvements in English, however, reflected differences in the amount of input she received in each language. Helping children to use language in a functional and meaningful way is consistent with this approach. For example, the principles of paying attention to what people say, focusing on meaning, and responding to communicative intent are skills that children will be likely to use across situations and language contexts. A goal for children from bilingual backgrounds might be to improve their vocabulary learning strategies, which would in turn lead to more efficient vocabulary learning in context, such as categorization and recognition of similarities and differences (Stubbe Kester et al., 2001; Ukrainetz et al., 2000). Consistent with this approach is support of both the home and school language (Kohnert et al., 2005). Language input can be structured in ways that maximize the likelihood that they will use all of the cues available to derive the meaning of morphological and syntactic structures (Bedore & Leonard, 1995; Peters, 1985). Furthermore, making explicit connections between children’s two languages may further facilitate cross-linguistic transfer. To facilitate bilingual transfer, comparable forms can be modeled in salient positions. For example, to model a plural form, a clinician might use it in sentence-final position where it can be lengthened (I need two marbles; Necesito dos canicas). Approaches that allow for mixed knowledge across two languages may facilitate children’s learning new information. A question that we are frequently asked is whether children with

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impairment should be limited to one language. Thordardottir (2010) reviewed available studies examining the evidence for providing intervention for bilingual children. While there continue to be few studies, findings indicate that bilingual (L1 and L2 together) or primary (L1) instruction was as effective as or slightly more effective than second language (L2) instruction alone. Studies of language of intervention for bilingual children with LI are consistent with those for typical bilingual children. These studies demonstrate that instruction in the first or primary language facilitates learning in the second language (when the second language is also taught). For example, Perozzi (1985) explored whether teaching children receptive vocabulary in L1 or L2 first had differential outcomes for L2 learning. Findings for both impaired and typical English-speaking and Spanish-speaking children suggested that teaching L1 receptive vocabulary first had a facilitating effect on learning receptive vocabulary when taught in L2. In a follow-up study, Perozzi and Sanchez (1992) found that children in a bilingual condition, where words were first taught in Spanish followed by English, demonstrated faster gains compared to children in the English-only condition. Similarly, Thordardottir, Ellis Weismer, and Smith (1997) compared a bilingual and monolingual treatment for teaching English vocabulary, in a single subject alternating treatment design. The child increased production of the target words in both conditions. Follow-up analysis of home versus school words demonstrated an advantage for the bilingual condition for production of home words. Several studies have been published on the Vocabulary, Oral Language and Academic Readiness (VOLAR) program (Gutiérrez-Clellen, Simon-Cereijido, & Restrepo, 2013). A two-site study compared bilingual (Spanish-English) and English-only implementation of the program in comparison to bilingual and English-only control groups, which focused on math skills. The VOLAR program was implemented in bilingual and English-only conditions. The bilingual treatment groups showed significant improvements on measures of Spanish receptive and expressive vocabulary and English receptive vocabulary, while the English-only group demonstrated significant growth on English receptive vocabulary (Restrepo, Morgan, & Thompson, 2013). GutiérrezClellen, Simon-Cereijido, and Sweet (2012) and Simon-Cereijido, Gutierrez-Clellen, and Sweet (2013) examined the effects of the program on children’s grammar and measures derived from children’s narratives. Results favored the VOLAR program, with both English-only and bilingual approaches demonstrating growth on English oral language. Those in the bilingual program demonstrated greater gains in Spanish as well. Together, these studies indicate that, for children with LI, bilingual approaches have at least the same outcome for learning words in the second language as a monolingual (second language) approach alone. These studies demonstrate that transfer between the two languages can occur, but only when deliberately planned. A question clinicians might ask is whether children need to learn the same vocabulary items in each of their languages. Bilingual children may use vocabulary in context-specific ways. This is reflected in the lack of difference between the conceptual and total scores in our own work and in the use of some items from the second language for all of the bilingual children. A specific recommendation that takes mixed knowledge into account is to focus on targets that are related across the two languages, while including strategies for transferring these targets between languages. Similarly, Peña and Stubbe Kester (2004) suggest that targeted vocabulary should be that which is needed for a given situation. Thus, some vocabulary may need to be targeted in both the home language and second language, but other vocabulary words may initially be most needed in only one language. For example, words such as kinship terms may be most functional at home, whereas color, size, and shape words would be most functional at school. Functionally overlapping, commonly used words would provide a basis for transfer across the two languages. This does not mean that the child should not learn home words in the second language or school words in the first language, but that home versus school and overlapped words would be a starting point for targeting vocabulary teaching. The unified model emphasizes that children learn language through

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the filter of their home or stronger language. Thus, to begin, intervention should be in the home language (assuming it is the stronger language) but should also help the child build vocabulary in the second language. Morphology and syntactic forms should be targeted in the service of meaning. Targets for intervention should be forms that are of special difficulty for children, not just those that are difficult for second-language learners. For example, in Spanish, articles and clitics are often difficult for children with LI, but in English, forms related to tense marking are most difficult. Articles mark new and old information in discourse and clitic pronouns permit the speaker to pronominalize old information. Tense marking permits the speaker to reference events temporally. These are all meaningful targets because they help the child make reference in discourse. These can be targeted in activities in which children make functional use of these forms to establish reference or maintain concordance since these are cues that native listeners rely on. It is also important to focus on structures in each language that are problematic for a particular child, not necessarily those that are simply the result of second-language changes. Overuse of definite articles is an example of influence that does not have a large negative impact on the child’s ability to communicate effectively. Targeting such differences will not effectively improve children’s language skills and, thus, should be a low priority. Intervention should also focus on linguistic features or behaviors that are not shared across the bilingual’s languages. In the area of morphology and syntax, many rules and forms are language specific and are unlikely to be learned without specific attention. Examples include number agreement for verb marking in Spanish or do insertion for the formation of negative and question construction in English. In the semantic domains, words are likely to share a common underlying meaning that will support acquisition in both languages. Figurative language constructions and idioms often do not translate directly across languages and thus would require teaching in each language.

Conclusion Bilingual children with LI present a unique challenge for researchers in child language and language disorders as well as for those who provide direct services to this population. Here, we have provided a review of the available research describing the language performance of bilingual children with typical development and children with language impairment. Although their performance is similar in some respects to monolinguals in each of their languages consistent with an independent approach, they also make changes in ways that are predictable from an interdependence perspective. In contrast, bilingual children with language impairments will demonstrate many of the same difficulties as monolingual children with LI in each of their two languages. As a group, they are likely to make more errors and to use their languages in less productive ways. The similarities and differences between bilinguals and monolingual children with and without LI should be taken into consideration when working with these children.

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13 CROSS-LINGUISTIC STUDIES OF CHILD LANGUAGE DISORDERS Laurence B. Leonard

Introduction As noted in Chapter 1, children with specific language impairment (SLI) exhibit significant deficits in their language ability, yet do not display the symptoms associated with other clinical populations. These children earn age-appropriate scores on nonverbal tests of intelligence, they pass screening tests for hearing acuity and oral-motor structure and function, and they do not show clear evidence of neurological damage or disease. Although the term “specific language impairment” is by far the most frequently used in the research literature, it is not the only label that has been adopted; other terms include “developmental dysphasia,” “primary language impairment,” and even the quite general term “language impairment.” However, regardless of the term, the existence of language disorders without significant deficits in other areas has been widely recognized by researchers and clinicians alike. Not surprisingly, this kind of disorder has been reported in many different languages. Yet, despite the apparently universal nature of this disorder, there are striking differences in how SLI manifests itself across languages. In this chapter, we shall review some of these systematic differences across groups of languages. Initially, we focus on the common SLI grammatical profile for English, placing emphasis on grammatical morphology. We then turn to the grammatical profile reflected in other Germanic languages (German, Dutch, Swedish), followed by the profile seen in Romance languages (Italian, Spanish, French) and two languages differing markedly from Germanic and Romance languages, Hungarian and Cantonese. Following a review of some prominent accounts of the grammatical deficits of SLI and how they might explain some of the cross-linguistic findings, we conclude with a discussion of some emerging issues that need resolution in this area of study.

The SLI Grammatical Profile for English Probably the best documented problem in English-speaking children with SLI is an especially serious deficit in the use of grammatical morphemes that mark tense and agreement. These morphemes include the inflections, past tense -ed, third-person singular -s, and the copula and auxiliary forms of be (is, are, am, was, were). As a group, children with SLI make less use of these morphemes than do young typically developing children matched for mean length of utterance (MLU) (e.g., Hoover, Storkel, & Rice, 2012; Joseph, Serratrice, & Conti-Ramsden, 2002; Leonard, Eyer, Bedore, & Grela, 1997; Oetting & Horohov, 1997; Rice & Wexler, 1996). These limitations can continue

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into the school years (Marchman, Wulfeck, & Ellis Weismer, 1999; Norbury, Bishop, & Briscoe, 2001; Rice, Tomblin, Hoffman, Richman, & Marquis, 2004). Case studies of individuals reveal that these problems can persist into adolescence and beyond (e.g., van der Lely, 1997). Composite measures of tense and agreement morphemes show good sensitivity and specificity in distinguishing children with SLI from their typically developing age mates during the preschool years (Bedore & Leonard, 1998; Rice & Wexler, 2001). The differences between English-speaking children with SLI, younger typically developing children matched for MLU (TD-MLU children), and typically developing children matched for age (TD-A children) are differences in degree of use. It is rarely the case that children with SLI fail to use these forms altogether. Furthermore, when these morphemes are used, they are nearly always applied in the appropriate contexts. Productions such as They runs fast are rare. In addition, occasional overgeneralizations are seen in the speech of these children, such as throwed for threw (see review in Leonard, 2014). Such findings suggest that despite their limited use of tense and agreement morphemes in obligatory contexts, children with SLI possess knowledge of their grammatical function and where not to use them. Tense and agreement morphemes are not the only elements of grammatical morphology that have been studied in the speech of English-speaking children with SLI. Differences favoring TD-MLU (and TD-A) children have also been found for possessive ’s (e.g., Kate’s car), the infinitival complementizer to (e.g., They like to eat grapes), and nonthematic of (e.g., cup of tea) (e.g., Leonard, 1995; Owen & Leonard, 2006; see also Schuele & Dykes, 2005 for a detailed case study). There has been somewhat mixed evidence for three other morpheme types. Initial reports suggested that children with SLI are less proficient than TD-MLU children in the use of noun plural inflections (Leonard, Bortolini, Caselli, McGregor, & Sabbadini, 1992; Leonard et al., 1997; Rice & Oetting, 1993), but other studies report either no group differences or group differences with percentages sufficiently high for the SLI group not to be considered clinically significant (Oetting & Rice, 1993; Rice & Wexler, 1996). McGregor and Leonard (1994), Polite, Leonard, and Roberts (2011), and Rice and Wexler (1996) found lower degrees of article use by children with SLI than by TD-MLU children, but Leonard et al. (1992) found a numerical difference that did not reach statistical significance. An additional morpheme type yielding somewhat mixed findings is the passive participle -ed (e.g., The girl got kissed by the boy). Although two studies reported less consistent use of this inflection by children with SLI than by TD-MLU children (Leonard et al., 2003; Leonard, Wong, Deevy, Stokes, & Fletcher, 2006), Redmond (2003) found no such group difference. In all of these studies, the children were more successful with the participle -ed than with the phonetically comparable past tense -ed.

The SLI Grammatical Profile for German, Dutch, and Swedish In languages such as German and Dutch, verb inflections that mark tense and agreement are more abundant than is the case for English. For example, whereas English has only one inflection in the present tense paradigm (third-person singular), German possesses an inflection for each person and number combination (e.g., lerne “I learn,” lernst “you learn,” lernt “he/she learns”). Swedish verb inflections do not make a distinction according to person or number, but unlike English, Swedish employs an overt inflection for present as well as past tense. Despite their greater abundance of verb inflections, in all of these languages, differences between children with SLI and their younger MLU-matched compatriots are nevertheless observed (e.g., Bartke, 1994; de Jong, 1999; Hansson, Nettelbladt, & Leonard, 2000; Rice, Noll, & Grimm, 1997). When children fail to use tense and agreement inflections, they are quite likely to use nonfinite forms such as infinitives in their place. Unlike English, in these languages, infinitives have overt inflections (e.g., German “to learn” is lernen, not the bare stem lern). However, in both German and Dutch, bare stems are also produced at

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times in contexts requiring a tense and agreement inflection (e.g., Blom, de Jong, Orgassa, Baker, & Weerman, 2013; de Jong, 1999; Rice et al., 1997; Roberts & Leonard, 1997; Spoelman & Bol, 2012). German, Dutch, and Swedish are viewed as “verb-second” languages. In these languages, the verb expressing agreement and/or tense must appear as the second constituent in the sentence. For utterances with sentence-initial subjects, the word order matches that of English, as in die Frau fand die Kinder “The woman found the children.” However, when a constituent other than the subject appears in sentence-initial position, the finite verb rather than the subject appears next, as in gestern fand die Frau die Kinder that has the meaning of “Yesterday the woman found the children” but is literally translated as “Yesterday found the woman the children.” Children with SLI sometimes fail to use the proper verb-second word order. (As we shall see, this difficulty may not be independent of their problems with tense and agreement morphology.) In German and Dutch, when children with SLI use nonfinite verbs in place of verbs with tense and agreement inflections, they often produce them in sentence-final position (Rice et al., 1997; Wexler, Schaeffer, & Bol, 2004). Indeed, sentence-final position is the usual position for nonfinite verb forms, as in the German utterance Chris kann das kochen “Chris can that cook” (= “Chris can cook that”) where kochen is the infinitive “to cook.” However, children with SLI may inappropriately produce the infinitival form in sentence-final position even when an auxiliary such as “can” is not included, as in Chris das kochen “Chris that cook” (= “Chris cook that”). Swedish differs from German and Dutch in the placement of the infinitive form; instead of placing the finite verb in second position and the infinitive form in sentence-final position, Swedish places the infinitive form immediately after the finite form that occupies second position. Predictably, then, when children with SLI produce infinitives in finite contexts, these infinitive forms appear in second, rather than final, position. Another common word order error seen in German-speaking children with SLI is the use of a finite verb in sentence-final position (e.g., Lindner, 2002). This is the proper position for finite verbs that appear in subordinate clauses. Although Swedish does not have this requirement, there are word order differences between main and subordinate clauses in this language as well. For example, the negative particle inte follows the finite verb in main clauses but precedes it in subordinate clauses. Swedish-speaking children with SLI seem to have difficulty with this distinction, as they often use inte both before and after the finite verb in main clauses (Hansson et al., 2000).

The SLI Grammatical Profile for Italian, Spanish, and French In recent years, children with SLI who speak the Romance languages of Italian, Spanish, and French have been the focus of increasing investigative attention. In Italian and Spanish, the subject of the sentence can be omitted when either the physical context or the discourse context makes the referent of the subject quite clear. Thus, for example, in Italian, corrono “[they] run” would be a very natural response when asked “Tell me about the boys in this picture.” In such “null-subject” languages, the verb inflection paradigms are rich. In present tense, for example, there is a distinct inflection for each person and number combination. In these languages, bare stems do not appear. It is therefore more accurate to say that a stem appears with a variety of inflections than to say that inflections are actually added to the stems. Although the most frequent word order in these languages is subject-verb-object, their rich verb morphology permits considerable variation in word order as a function of pragmatic context. French differs from these other Romance languages in key respects. Most notably, subjects are obligatory, and many of the verb inflections that are distinguishable in the orthography of the language are often homophonous in speech. For example, the present tense forms for “give”—donne, donnes, and donnent—are all pronounced as [don].

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The evidence from Italian and Spanish differs markedly from that of English and other Germanic languages. Relatively few differences are found between children with SLI and younger TD children matched for MLU, and for many tense and agreement inflections, the children with SLI are as proficient as their TD same-age peers (Bedore & Leonard, 2001, 2005; Bortolini, Caselli, & Leonard, 1997). When errors occur, they rarely take the form of infinitives. Instead, children with SLI produce substitute inflections that usually differ from the appropriate inflections by only one feature. For example, in Italian, the most likely substitute for a third-person plural inflection is the third-person singular inflection; the most likely replacement for a first-person plural inflection is the first-person singular inflection (Bortolini et al., 1997). There are exceptions to this pattern. Although such “near-miss” errors are evident in Spanish, the third-person singular form appears to be the most frequent substitute (see Grinstead et al., 2013), an observation that has some theoretical importance, as we shall see later in this chapter. One tense and agreement inflection that has consistently produced differences between SLI and TD-MLU groups is the third-person plural inflection of Italian (e.g., dormono “[they] sleep”). This inflection seems to be especially difficult for children with SLI. The corresponding morpheme in Spanish (e.g., duermen “[they] sleep”) is also somewhat difficult for Spanish-speaking children with SLI, yet they do not differ from TD-MLU children in their success in supplying this inflection in appropriate contexts. The evidence regarding verb inflections in French-speaking children with SLI has been interpreted differently by different investigators. According to Paradis and Crago (2001), children with SLI in this language produce what might pass as the correct inflected forms, but these forms actually lack tense. (Their reasoning is based in part on the children’s very limited use of auxiliary forms, discussed in the subsequent descriptions of SLI across languages.) In contrast, Thordardottir and Namazi (2007) regarded the superficially accurate verb inflections of French-speaking children with SLI as accurate, as did Parisse and Maillart (2007). The advantage that Romance languages hold for inflections does not apply to function words. Italian- and Spanish-speaking children with SLI use several different types of function words with significantly lower percentages than younger TD-MLU children. For Italian, differences of this type have been documented for definite articles, direct object clitics, and certain auxiliary verbs (Bortolini et al., 2006; Bortolini & Leonard, 1996; Dispaldro, Leonard, & Deevy, 2013; Leonard & Bortolini, 1998). For Spanish, differences of this type have been documented for definite articles and direct object clitics (Bedore & Leonard, 2001, 2005; Restrepo & Gutierrez-Clellan, 2001). Some studies on Spanish have employed only comparisons between SLI and TD-A groups; in these studies, children with SLI have made less use of these forms than their same-age peers (Eng & O’Connor, 2000; Jacobson & Schwartz, 2002). Although Italian and Spanish have produced similar function word differences between children with SLI and their typically developing peers, the results for the two languages are not identical. In Italian, omissions of function words predominate; substitution errors are relatively infrequent. For example, in tasks requiring children to complete a sentence with the direct object clitic followed by the finite verb, children with SLI often provide the finite verb without the clitic. Such a production lacks an obligatory element of the sentence, the direct object. An example from Leonard et al. (1992) is shown in (1). (1) Examiner: Appropriate response: Child’s response:

Qui la ragazza prende il piatto, e qui. . . . Here the girl takes the plate, and here. . . . (lo lava) ([she] washes it) (lava) ([she] washes)

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In Spanish, omissions of function words are also quite frequent in children with SLI (Anderson & Souto, 2005). However, in this language, substitutions are also common (Restrepo & Gutierrez-Clellan, 2001), in contrast to the case for Italian. Analyses of the substitution errors for both definite articles and direct object clitics reveal that singular forms are somewhat more likely to replace plural forms than the reverse. In addition, errors might be best characterized as “near-miss” errors, because the substitute form often differs from the appropriate form by only one feature. For example, feminine plural definite articles are more likely to replace masculine plural articles than masculine singular articles. When masculine singular articles are replaced, the substitute is usually a feminine singular article (Bedore & Leonard, 2005). In French, auxiliary verbs, articles, and clitics are also problematic for children with SLI. Paradis and Crago (2001) found that children with SLI were more likely than younger typically developing children to omit auxiliaries. For this reason, these investigators were reluctant to assume that the same children’s use of present tense verb inflections actually marked tense. Probably the significant homophony in French verb inflections influenced this interpretation, because, in the Romance languages of Italian and Spanish (where homophony is minimal), verb inflections are in fact used with greater consistency than function words by children with SLI. Object clitics are a significant obstacle for French-speaking children with SLI (Grüter, 2005; Hamann et al., 2003). Jakubowicz, Nash, Rigaut, and Gérard (1998) found that omissions of object clitics by these children were more abundant than the same children’s omissions of definite article forms (notably, le) that were phonetically identical to the clitics. However, both Parisse and Maillart (2007) and Pizzioli and Schelstraete (2008) found that articles, too, were used in fewer obligatory contexts than was the case for younger control children.

The SLI Grammatical Profile for Hungarian Hungarian is a language in which many of the grammatical morphemes are agglutinating. That is, certain morphemes mark a single feature and appear in a string with other morphemes attached to the stem. For example, a plural noun serving the direct object function would be produced as the stem, followed by the noun plural inflection, followed in turn by the accusative case inflection. Verb inflections, too, have an agglutinating characteristic, in that the verb stem is followed by a tense inflection and then an agreement inflection. However, agreement is fusional, with the agreement inflection simultaneously marking person and number agreement with the subject and definiteness agreement with the object. Thus, not only is a different agreement inflection used for the Hungarian equivalent of She pushes the car versus I push the car, but the agreement inflections also differ in She pushes the car and She pushes a car. Along with tense, Hungarian marks aspect. Specifically, perfective aspect, used to express completion of an event, is marked with a prefix. Verbs without a prefix are interpreted as imperfective with no implication that an event has been completed. Although these characteristics of Hungarian make it quite distinct from other richmorphology languages such as Italian and Spanish, Hungarian shares one important property with these languages—it is a null-subject language. Lukács, Leonard, Kas, and Pléh (2009) examined the use of tense/agreement verb inflections by Hungarian-speaking children with SLI. The children were tested on the 24 different tenseagreement inflection combinations that were possible (present, past tense x first, second, third person x singular, plural x definite, indefinite). The children were less accurate than both same-age and younger control children. However, the types of errors they made followed the same pattern as that of their typically developing compatriots. Most errors differed from the correct form by only a single feature, either in tense only, person only, number only, or definiteness only. No single feature was consistently problematic, and no inflected form served as the primary substitute. This

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pattern of frequently erring on only a single feature was noteworthy given that only five of the 23 possible substitute forms differed from the correct form by only one feature. The use of perfective and imperfective aspect by Hungarian-speaking children with SLI was examined by Leonard, Lukács, and Kas (2012). Children with SLI were less accurate than younger control children as well as same-age peers in the use of both types of aspect. Recall that the expression of imperfective aspect requires no prefix; thus, an error in this instance constituted the production of a (perfective) prefix where one was not needed. The challenge for these children seemed not to be the prefix itself but rather when to use it.

The SLI Grammatical Profile for Cantonese Cantonese stands in dramatic contrast to the languages discussed thus far, in part, of course, because it is a tone language. However, its grammatical properties are also quite unlike those of the other languages. Cantonese has no inflections, objects as well as subjects can be omitted when the referents are clear from the discourse context, and neither tense nor agreement is used. Aspect, however, is employed in the language, through syllabic morphemes that follow the verb. Fletcher, Leonard, Stokes, and Wong (2005) examined the use of both perfective and imperfective aspect markers in Cantonese-speaking children with SLI, same-as controls, and younger control children. The children with SLI used both types of aspect markers less reliably than either of the typically developing groups. In many languages, modal verbs that express notions such as ability (as in “can”) and permission (as in “may”) are marked for agreement and/or tense. However, in Cantonese, modals express neither, only the core modal meaning itself. Leonard, Deevy, Wong, Stokes, and Fletcher (2007) asked how Cantonese-speaking children with SLI might compare to typically developing children in their use of modals when tense and agreement are not involved. The results indicated that these children used modals at the same level as younger control children and differed from age controls only on modals that convey permission.

Prominent Accounts of Grammatical Deficits and the Cross-Linguistic Findings Checking Constraints and Grammatical Deficits in SLI Some attempts to explain the tense and agreement morpheme limitations seen in SLI use the minimalist framework of Chomsky (1995) as a foundation. One such account is the Extended Unique Checking Constraint (EUCC) account of Wexler (1998, 2003), an important elaboration of the Extended Optional Infinitive approach (e.g., Rice & Wexler, 1996) and the Agreement/ Tense Omission Model (Wexler, Schütze, & Rice, 1998). Wexler proposed that children with SLI go through a protracted period during which a Determiner (D) feature in a Determiner Phrase (DP) can only check the noninterpretable D feature of one functional category. For English, this means that the subject DP can check the D feature at Tense (TNS) only or at Subject Agreement (AGRS) only. This constraint has major implications for English grammatical morphemes that express tense and agreement. For example, the morphemes third-person singular -s and the copula and auxiliary forms is/are/am/was/were mark both tense and agreement. Therefore, when checking occurs at only one of these functional categories (TNS or AGRS), the remaining category is not projected and the use of the morpheme is blocked. Less obviously, past tense -ed use can also be adversely affected. Use of this inflection will not be blocked if checking occurs at TNS only. However,

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checking might instead occur at AGRS only. In this instance, TNS will not be projected and past tense -ed will not be used. An example of an utterance of this type would be Yesterday she walk all the way home, where the nominative case pronoun she is assumed to result from checking at AGRS and the absence of -ed is taken to mean that checking did not occur at TNS. The appearance of the bare stem form walk in the above example should not be interpreted as a failed attempt to include the -ed inflection. Rather, Wexler made the assumption that when the checking constraint applies and the inflection cannot be expressed, a nonfinite form is used in its place. Thus, walk is comparable to the nonfinite verb walk in the adult utterance We saw the girl walk all the way home. For contexts requiring a copula or auxiliary be form, application of the constraint results in the absence of the tense/agreement morpheme. Here again, there are parallels in the adult grammar, as can be seen by comparing the (constraint-driven) utterance The boy running and the adult production We saw the boy running. According to Wexler (1998, 2003), the grammars of languages such as German and Dutch render them vulnerable to the same kind of inconsistent expression of tense and agreement by children with SLI that is seen in English. In both of these languages, for example, present tense inflections also mark agreement. Therefore, checking at TNS only or at AGRS only will prevent the use of the inflection. Because children presumably select a nonfinite form in place of the finite form in such instances, an infinitive can result (e.g., lernen “to learn” in place of lernt “learns”). Children with SLI acquiring Swedish show greater use of present and past tense inflections than do their German- and Dutch-speaking counterparts. Within the checking constraint framework, this difference has a ready explanation. In Swedish, neither present tense inflections nor past tense inflections express agreement. Thus, the same present tense inflection -er is used for “I play” ( jag leker) and “she plays” (hon leker); similarly, the same past tense inflection -te is used for “I played” ( jag lekte) and “she played” (hon lekte). If checking occurs at TNS only, the inflection used for “she plays,” for example, will not be blocked, whereas the corresponding inflection in German will be blocked, as this inflection marks agreement as well as tense. Although German and Dutch require the same checking operations as English, it is also true that children with SLI acquiring these two languages show greater use of tense and agreement inflections than do their English counterparts. Wexler et al. (2004) have proposed some possible explanations for this cross-linguistic difference. For example, they propose that certain details in Dutch morphology will lead to an infinitive production only if both TNS and AGRS are underspecified. Leonard, Hansson, Nettelbladt, and Deevy (2005) have provided a plausible account to handle another seemingly paradoxical finding—the observation that Swedish-speaking children with SLI make greater use of past tense inflections than do children with SLI acquiring English. In both languages, past tense inflections do not involve agreement. The Leonard et al. solution involves interactions between the verb-second characteristic of Swedish and feature checking. A major reason for Wexler’s (1998, 2003) EUCC proposal was that the Extended Optional Infinitive account and the Agreement-Tense Omission Model do not capture the fact that children acquiring null-subject languages such as Italian and Spanish do not exhibit the difficulty with tense and agreement inflections that is seen in languages such as English and German. Rather than assuming that children with SLI differ across languages, Wexler assumed that the same constraint, the EUCC, manifests itself differently in different languages. Specifically, he assumes that in nullsubject languages such as Italian and Spanish, only TNS carries a noninterpretable D feature; AGRS has no noninterpretable D feature to check. Checking, then, is limited to the D feature in TNS, and such checking at a single functional category obeys the EUCC. Thus, the use of tense and agreement inflections in these languages should not be adversely affected. Given the findings of very few differences between SLI and TD-MLU groups in the use of tense and agreement inflections in these null-subject languages, the EUCC account seems to hold considerable promise.

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This account also provides a possible explanation for some of the difficulties with function words seen in null-subject languages. Wexler (1998, 2003) assumes that in languages such as Italian and Spanish, a functional category Auxiliary (AUX) exists, which has a noninterpretable D feature. Thus, utterances employing the present perfect (commonly used in Italian in past contexts), such as Anna ha visto Gina “Anna has seen Gina,” requires checking of the D feature, first at AUX and then at TNS. Such checking at two functional categories violates the EUCC and should result in an utterance that lacks the auxiliary (Anna visto Gina “Anna seen Gina”). Given that auxiliaries such as è and ha are frequently omitted by Italian-speaking children with SLI, Wexler’s account seems very plausible. Two-syllable auxiliaries appear less prone to omission, though the evidence in this case is limited to small samples from spontaneous speech. The difficulty with clitics experienced by Italian- and Spanish-speaking children with SLI might also be handled by this checking constraint account. Wexler (1998, 2003) assumes that direct object clitics originate inside the VP, either as the clitic itself or as a phonetically null Noun Phrase. This element is assumed to have a D feature. Presumably, the clitic first passes through an intermediate position, the functional category Object Agreement, where it must be checked for accusative case, and then proceeds to the functional category Clitic, to occupy its pre-verbal position. According to Wexler, these movements can be viewed as checking the D feature twice, which runs counter to the EUCC. Consequently, the utterance might be produced without the clitic, as shown in the earlier example in (1). This account does not address the clitic substitution errors seen in Spanish but does provide a possible explanation for the omissions that occur in both Spanish and Italian. Problems with other types of function words, such as articles, do not seem to be addressed by the EUCC.

Phonology/Prosody Contributions to Grammatical Deficits in SLI Other accounts of the difficulties with grammatical morphology hold that the phonotactic or prosodic demands of grammatical morphemes are especially troublesome for children with SLI. For example, Marshall and van der Lely (2007) found that English-speaking children with SLI had greater difficulty with past tense -ed inflections that form word-final consonant clusters that do not exist in monomorphemic words (e.g., rushed) than with past tense -ed inflections that form wordfinal consonant clusters that do exist in monomorphemic words as well as in past tense forms (e.g., crossed; note the existence of words such as cost, frost, last, test). This difference was not found in the productions of the TD children in the comparison groups, who performed at a significantly higher level on both types of past tense forms. Such a finding suggests that the past tense use of children with SLI may benefit from the practice gained in the production of word-final consonant clusters when grammatical morpheme use is not involved. Leonard, Davis, and Deevy (2007) reported a similar finding; verbs whose final segment + past tense -ed sequence had high phonotactic probability in English were produced more consistently by children with SLI, whereas younger control children used past tense -ed to a similar degree in low and high phonotactic probability sequences. There are other context effects as well. For example, Polite (2011) found that children with SLI were more successful in using the noun plural -s inflection when the stem ended in a vowel than when it ended in either a single consonant or consonant cluster. An especially intriguing finding was reported by Owen and Goffman (2007), who looked for possible differences between omissions of third-person singular -s from verbs and appropriate bare stem productions of the same verbs. If the children’s omissions were selections of infinitive forms (which are bare stems in English), then the two types of productions should not differ. However, Owen and Goffman found that the verbs inappropriately lacking the inflection were actually longer in duration than the appropriate bare stem forms. This finding suggests that phonological/

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prosodic factors might have interfered with the children’s attempts at producing the third-person singular -s inflection. Hoover et al. (2012) examined a somewhat different factor relating to phonology. They examined neighborhood density—the number of words in the language that differ from a target word by one phoneme. These investigators found that children with SLI were no more likely to use third-person singular -s in words from dense neighborhoods than in words from sparse neighborhoods. In contrast, younger TD children used this inflection more accurately in words from dense neighborhoods. Difficulties with grammatical morphology are not limited to inflections; they are seen as well in function words. McGregor and Leonard (1994) found that English-speaking children had more difficulty than TD children in producing function words (e.g., articles) that took the form of weak, nonfinal monosyllables. Bortolini and Leonard (1996) extended this finding to uncontractible copula forms. They found that children with SLI differed most from younger TD-MLU controls in their use of these nonfinal weak-syllable function words, but differed as well even in their ability to use nonfinal weak syllables in monomorphemic words (e.g., the first syllable of banana). Such a finding suggests that prosodic factors pose problems for children with SLI that are then exacerbated when the prosodically challenging elements have morpheme status. The reach of phonology/prosody accounts extends beyond morphemes that mark tense and agreement, including function words such as articles and the infinitival complementizer to, and inflections such as noun plural -s and possessive ’s. However, as noted earlier, for both articles and noun plurals, the evidence of special difficulties has been somewhat mixed. Studies of children with SLI in Germanic languages have not placed emphasis on possible prosodic influences on the use of grammatical morphology. Nevertheless, phonology/prosody accounts might well be applied to these languages. The clearest cases of difficulty with tense and agreement inflections occurs with those inflections that involve word-final consonants or consonant clusters (e.g., Dutch -t, German -st, -t). In German, the copula forms and the auxiliary forms used in the present perfect—the most common means of referring to past events (e.g., er hat gegangen “he has gone”)—are weak monosyllables, and these are deleted to a greater extent by children with SLI than by younger TD children (e.g., Rice et al., 1997; Roberts & Leonard, 1997). Definite articles are also omitted quite frequently by German-speaking children with SLI (Roberts & Leonard, 1997), and these, too, are weak monosyllabic forms. Hansson, Nettelbladt, and Leonard (2003) attempted to separate the effects of prosody in children’s article use from any effects that might be attributable to the particular grammatical function of these morphemes. These investigators examined the article use of children with SLI who were acquiring Swedish. In this language, indefinite articles are pre-verbal monosyllabic function words (e.g., en bil “a car,” ett hus “a house”), but definite forms often take the form of a syllabic suffix (e.g., bilen “the car,” huset “the house”). Hansson et al. found that Swedish-speaking children with SLI used definite suffixes as accurately as TD same-age peers but used indefinite articles less accurately than both younger TD-MLU children and TD-A peers. Because these two morpheme types differ in definiteness as well as in prosody, these investigators also examined the children’s use of definite forms in constructions with an adjective preceding the noun. Such constructions require an article in pre-adjective position, for both definite and indefinite reference (e.g., den svarta katten “the black cat”). The children with SLI produced definite as well as indefinite articles in fewer obligatory contexts than both TD-MLU and TD-A children. These findings suggest that the prosodic position of a definite form probably has a bearing on these children’s production success, independent of grammatical function. In Italian and Spanish, present tense and agreement inflections are usually word-final syllables (e.g., Italian dormo “[I] sleep,” dorme “[he/she] sleeps”) or word-final two-syllable inflections with stress on the first of these syllables (e.g., Spanish corremos “[we] run”). In (Mexican) Spanish, the preterite is the most frequent means of expressing past tense, and monosyllabic inflections in the

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preterite place primary stress on the final syllable (the inflection), not on the penultimate syllable (e.g., corrí “[I] ran”). It can be seen, then, that in each of these cases, the tense and agreement inflection is syllabic and appears in stressed or word-final position, precisely the prosodic properties that should render them less problematic for children with SLI. The findings of similar production levels by children with SLI and MLU-matched TD children seem consistent with this expectation. Recall that one of the Italian tense and agreement inflections, present third-person plural, has been the exception to the usual pattern. This inflection is less likely to be produced by children with SLI than by TD children matched for MLU. It is perhaps no coincidence that this inflection is also an exception to the typical prosodic pattern of Italian. Verbs inflected for third-person plural carry stress on the initial syllable (e.g., the first syllable of dormono “[they] sleep”), resulting in an inflection consisting of two consecutive weak syllables. It should also be pointed out that there are a handful of verbs with short stems whose third-person plural forms consist of only two syllables (e.g., fanno “[they] do/make”, danno “[they] give”). Therefore, these forms have a strong syllable– weak syllable pattern, in keeping with the penultimate stress pattern of Italian. These forms are not problematic for children with SLI (Leonard et al., 1992). Phonology/prosody accounts also offer a possible explanation for the function word difficulties of Italian- and Spanish-speaking children with SLI. In studies on Italian, all of the function words that have produced differences favoring TD-MLU and TD-A children over children with SLI have been weak monosyllables in nonfinal position. The great majority of errors by the children with SLI have been omissions. Included among the function words yielding group differences are the Italian auxiliary forms è and ha (e.g., è andata “[she] has gone” produced as andata “[she] gone”; ha mangiato “[he] has eaten” produced as mangiato “[he] eaten”). It should be noted that these data come from spontaneous speech samples (e.g., Leonard & Bortolini, 1998), and obligatory contexts for multisyllabic auxiliary forms (e.g., abbiamo mangiato “[we] have eaten”) have been relatively infrequent. Nevertheless, based on the limited number of contexts identified, multisyllabic auxiliary forms do not appear to show group differences between children with SLI and TD-MLU children. Italian direct object clitics are also weak monosyllables. When assessed in their most frequent, pre-verbal position, as shown earlier in (1), these morphemes also reveal lower percentages of use by children with SLI than by TD-MLU children. When these morphemes have been studied in their less frequent postverbal position (e.g., lo in ha smesso di farlo “[she] has stopped to do it”), children with SLI have been as proficient as TD-MLU children. This combination of findings suggests that when clitics appear in nonfinal position, they are more vulnerable to omission by children with SLI. As noted earlier, definite articles are also omitted more frequently by Italian-speaking SLI groups than by TD-MLU groups. These morphemes are weak monosyllables that can never appear in final position. The data for Spanish are generally consistent with the data for Italian. Definite articles and direct object clitics—weak monosyllabic forms appearing in nonfinal position—are more difficult for Spanish-speaking children with SLI than for TD-MLU children. Omissions are more likely to be seen in the SLI data than in the TD-MLU data. However, one finding is not handled well by phonology/prosody accounts: substitution errors are almost as prevalent as omission errors. An example of a clitic substitution error from Bedore and Leonard (2001) is shown in (2). (2) Examiner:

El niño compra el halado y luego. . . . The boy buys the ice cream, and then. . . . Appropriate response: (lo come) ([he] eats it [masculine singular]) Child’s response: (la come) ([he] eats it [feminine singular])

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Why would errors of this type (a feminine singular clitic replacing a masculine singular clitic) occur if the children’s chief difficulty was the production of morphemes that take the form of nonfinal weak syllables? The only explanation that would be compatible with a phonology/prosody account would be that the prosodic characteristics of Spanish are not, in fact, comparable to those of Italian, at least with respect to nonfinal weak syllables. Indeed, there is reason to suspect that there are differences between the two languages. Unlike Italian, Spanish is traditionally classified as a syllable-timed language, and the duration differences between stressed and unstressed syllables in Spanish are smaller than those seen in other languages studied (Delattre, 1966). As a result, the duration of the syllables represented in definite articles and direct object clitics are not as short relative to phonetically similar stressed syllables as they are in other languages, including Italian (Farnetani & Kori, 1982). In addition, Demuth, Patrolia, Song, and Masapollo (2012) and Gennari and Demuth (1997) have proposed that unstressed monosyllabic morphemes in Spanish, such as definite articles and direct object clitics, are attached to a higher node of the prosodic phrase than the comparable morphemes in other languages. This higher attachment might make these morphemes less vulnerable to the omission of nonfinal weak syllables. This proposal warrants consideration, especially considering that nonfinal weak syllables that are in monomorphemic words (e.g., the first syllable of banana) are not omitted to the same degree as monosyllabic morphemes such as articles. These two types of nonfinal weak syllables occupy different locations in prosodic structure. It is not implausible, then, that the same morpheme type (e.g., definite articles) could show cross-linguistic differences in degree of omission if its placement in prosodic structure differed across the languages. Demuth and McCullough (2009) and Demuth and Tremblay (2008) have made a similar proposal for French; recall that some studies have found no differences between French-speaking children with SLI and younger controls in the use of articles.

SLI across Languages: Some Emerging Issues Both checking constraint and phonology/prosody approaches to the grammatical deficits of SLI have been able to account for many of the salient symptoms of children across languages. However, there are numerous details for which these accounts have not yet provided an explanation. For example, although Hungarian is a null-subject language, children with SLI have more difficulty than MLU controls in using inflections pertaining to tense and agreement. It is not yet clear if the checking assumptions made for other null-subject languages should apply to Hungarian, given its agglutinating characteristics and the fact that verbs must agree not only with the subject (for person and number) but also with the object (for definiteness). Phonology/prosody accounts have no ready explanation for the finding that aspect markers are used less consistently by children with SLI acquiring Cantonese. In this language, all syllables including aspect markers carry a full tone and seem to present no prosodic obstacles. These are but a few of the details that existing accounts must address.

Beyond Infinitives Along with details that existing accounts must handle, there are several new issues emerging in the cross-linguistic literature on SLI that will pose additional challenges to any attempt at explaining the grammatical deficits of children with this disorder. There is a great need to account for the many instances in which children with SLI diverge from producing either infinitive forms or correctly inflected verb forms. We saw that, in Dutch, errors seemed to vary from infinitives to bare stem forms. The latter have been treated as default forms that lack the status of true finite forms. Thus, the reasons for both types of errors are not yet clear.

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Recent evidence from young typically developing English-speaking children render this general issue even more intriguing. Räsänen, Ambridge, and Pine (2013) have presented evidence that verbs that frequently (and appropriately) appear in bare stem form in the input are more likely to be produced by children as bare stems in contexts that obligate use of third-person singular -s. This suggests that, even in English, children may sometimes be using a bare stem as a phonologically simple default rather than as a true infinitive. In Spanish, third-person singular forms seem to be the most frequent substitute, and therefore it might be reasonable to treat these as default forms lacking true finite status. However, errors are not only of this type. Many errors differ from the correct form by only a single feature (e.g., number). In Hungarian, another null-subject language, the errors are so varied that the notion of a default form does not seem applicable. Languages with a great deal of homophony (as in French) also complicate resolution of this issue. Some investigators who find weaknesses in the use of function words that express tense and agreement have been reluctant to credit children’s superficially correct use of inflections as adultlike. However, in most languages, tense and agreement forms do not emerge simultaneously; some trail others in development by a number of months. Therefore, a weakness in one type of tense and agreement form (e.g., an auxiliary verb) does not mean that another (e.g., a verb inflection) lacks tense and agreement even though it appears adult-like.

A New Look at the Contributions of Input There is increasing evidence that the degree to which young children produce infinitives in contexts requiring tense and agreement inflections is in part a function of the nature of their linguistic input. Studies operating from very different theoretical frameworks have shown that children are slower to acquire consistent use of tense and agreement inflections when their input contains a large proportion of ambiguous forms, such as zero-marked forms as in I go (Legate & Yang, 2007), or the input contains a large proportion of infinitive forms in or near utterance-final position (e.g., Freudenthal, Pine, Aguado-Orea, & Gobet, 2007; Freudenthal, Pine, & Gobet, 2010). Such differences can even be seen within a given language. For example, Hadley, Rispoli, Fitzgerald, and Bahnsen (2011) found that the degree to which parents used verb forms with unambiguous tense and agreement marking predicted the rate of their children’s development of tense and agreement use. Leonard and Deevy (2011) applied the notion of input in a different way. They asked whether children with SLI might be especially weak in recognizing that nonfinite subject-verb sequences in input utterances (e.g., Did the boy fall down?, Is the car going fast?, Let’s watch the girl dance, We saw him eating ice cream) were dependent on an earlier-appearing verb form (did, let’s watch, saw). They hypothesized that if children failed to recognize that nonfinite verbs depend on information appearing earlier in the same sentence, they may be more likely to extract nonfinite subject-verb sequences for use as stand-alone utterances in their own speech (e.g., The girl dance; Him eating ice cream) and even use them as the basis for generating novel nonfinite utterances (e.g., The boy dance, Him eating hamburger). Using a novel verb paradigm adapted from Theakston, Lieven, and Tomasello (2003), Leonard and Deevy showed that English-speaking children with SLI were, in fact, especially influenced by nonfinite subject-verb sequences that appeared in larger structures, such as the examples provided above. An inspection of the SLI literature on German and Swedish suggests that a similar weakness in understanding dependency relations could be the basis for the errors seen in these languages. In German, children with SLI commit two types of errors. They sometimes produce an utterancefinal nonfinite verb as the only verb in the sentence and/or they produce a finite verb form in utterance-final position. Consider the nonfinite subject-verb sequences in the following input

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sentences: Kann Karl Spaghetti essen? “Can Karl spaghetti eat?” = “Can Karl eat spaghetti?” and Sie sagt, dass Karl Spaghetti isst “She says that Karl spaghetti eats” = “She says that Karl eats spaghetti.” If children do not grasp that the verb forms and word order of the utterance-final subjectverb sequence were dependent on the earlier appearing information in the sentence, they could easily produce errors such as Karl Spaghetti essen and Karl Spaghetti isst. In Swedish, a common error is an inappropriate variation of using the negative particle inte either after the verb (as is appropriate) or before the verb. In subordinate clauses, the latter order is correct. Thus, the error Karl inte äter spagetti could have its origins in an input sentence such as Jag vet att Karl inte äter spagetti “I know that Karl not eats spaghetti” = “I know that Karl does not eat spaghetti.” It is likewise noteworthy that for languages showing very little use of infinitives in place of finite forms, there are very few adult sentences in which an infinitive immediately follows a subject. For example, in Italian, the question “Can Carlo eat spaghetti?” is just as likely to be expressed as Carlo può mangiare gli spaghetti? “Carlo can eat spaghetti?” or Può mangiare gli spaghetti Carlo? “Can eat spaghetti Carlo?” as it is to be produced as Può Carlo mangiare gli spaghetti? “Can Carlo eat spaghetti?” These examples suggest that the study of input factors in the errors of children with SLI could prove quite productive. It may be that a more general problem with understanding structural dependencies is leading to what appear to be diverse errors across languages.

Conclusion In this chapter, we have concentrated on grammatical morphology and word order problems in children with SLI. Even within this relatively narrow area of focus, we have seen a wide variety of symptoms across languages. Current accounts of these deficits have been successful in providing a reasonable explanation for some of these symptoms, but these accounts were not designed to handle the wide range of symptoms that we have seen here. Even with the refinement of existing accounts, it is likely that wholly new proposals will be necessary. Two areas in particular need of coherent proposals have been identified here. The first is the fact that, in some languages, children with SLI might produce errors that alternate between infinitives with overt inflections, bare stem forms, and finite inflections that differ from the correct form by only a single feature. The second is the fact that the nonfinite forms and word order errors seen in some languages bear a striking resemblance to portions of larger syntactic structures that could have occurred in the children’s input. Only select languages have been discussed in this chapter. Many other types of languages are now receiving investigative attention, and some are the focus of relatively intense study, including languages as varied as Greek (Stavrakaki, 2006), Hebrew (Dromi, Leonard, Adam, & ZadunaiskyEhrlich, 1999; Friedmann & Novogrodsky, 2011), Japanese (Tanaka Welty, Watanabe, & Menn, 2002), and British Sign Language (Marshall, Mason, Rowley, Herman, & Morgan, 2011). With the addition of newly studied languages, fresh perspectives will likely emerge, which will gradually move us toward a greater understanding of this perplexing disorder.

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C., & Leonard, L. (1997). Grammatical deficits in Italian-speaking children with specific language impairment. Journal of Speech, Language, and Hearing Research, 40, 809–820. Bortolini, U., & Leonard, L. (1996). Phonology and grammatical morphology in specific language impairment: Accounting for individual variation in English and Italian. Applied Psycholinguistics, 17, 85–104. Chomsky, N. (1995). The minimalist program. Cambridge, MA: MIT Press. de Jong, J. (1999). Specific language impairment in Dutch: Inflectional morphology and argument structure. Groningen Dissertations in Linguistics. Groningen, The Netherlands: University of Groningen. Delattre, P. (1966). A comparison of syllable length conditioning among languages. International Review of Applied Linguistics in Language Teaching, 4, 183–198. Demuth, K., & McCullough, E. (2009). The prosodic (re)organization of children’s early articles. Journal of Child Language, 36, 173–200. Demuth, K., Patrolia, M., Song, J. Y., & Masapollo, M. (2012). The development of articles in children’s early Spanish: Prosodic interactions between lexical and grammatical forms. First Language, 32, 17–37. Demuth, K., & Tremblay, A. (2008). Prosodically-conditioned variability in children’s production of French determiners. Journal of Child Language, 35, 99–127. Dispaldro, M., Leonard, L., & Deevy, P. (2013). Clinical markers in Italian-speaking children with and without specific language impairment: A study of nonword and real word repetition as predictors of grammatical ability. International Journal of Language and Communication Disorders, 48, 554–564. Dromi, E., Leonard, L., Adam, G., & Zadunaisky-Ehrlich, S. (1999). Verb agreement morphology in Hebrewspeaking children with specific language impairment. Journal of Speech, Language, and Hearing Research, 42, 1414–1431. Eng, N., & O’Connor, B. (2000). Acquisition of definite article + noun agreement of Spanish-English bilingual children with specific language impairment. Communication Disorders Quarterly, 21, 114–124. Farnetani, E., & Kori, S. (1982). Lexical stress in spoken sentences: A study on duration and vowel formant pattern. Quaderni del Centro di Studio per le Ricerche di Fonetica, 1, 104–133. Fletcher, P., Leonard, L., Stokes, S., & Wong, A. M.-Y. (2005). The expression of aspect in Cantonese-speaking children with specific language impairment. Journal of Speech, Language, and Hearing Research, 48, 621–634. Freudenthal, D., Pine, J., Aguado-Orea, J., & Gobet, F. (2007). Modeling the developmental patterning of finiteness marking in English, Dutch, German, and Spanish suing MOSAIC. Cognitive Science, 31, 311–341. Freudenthal, D., Pine, J., & Gobet, F. (2010). Explaining quantitative variation in the rate of Optional Infinitive errors across languages: A comparison of MOSAIC and the Variational Learning Model. Journal of Child Language, 37, 643–669. Friedmann, N., & Novogrodsky, R. (2011). Which questions are most difficult to understand? The comprehension of Wh questions in three subtypes of SLI. Lingua, 121, 367–382. Gennari, S., & Demuth, K. (1997). Syllable omission in the acquisition of Spanish. In E. Hughes, M. Hughes, & A. Greenhill (Eds.), Proceedings of the 21st annual Boston University conference on language development (Volume 1, pp. 182–193). Somerville, MA: Cascadilla Press. Grinstead, J., Baron, A., Vega-Mendoza, M., De la Mora, J., Cantú-Sánchez, M., & Flores, B. (2013). Tense marking and spontaneous speech measures in Spanish SLI: A discriminant function analysis. Journal of Speech, Language, and Hearing Research, 56, 352–363. Grüter, T. (2005). Comprehension and production of French object clitics by child second language learners and children with specific language impairment. Applied Psycholinguistics, 26, 363–391. Hadley, P., Rispoli, M., Fitzgerald, C., & Bahnsen, A. (2011). Predictors of morphosyntactic growth in typically developing toddlers: Contributions of parent input and child sex. Journal of Speech, Language, and Hearing Research, 54, 549–566. Hamann, C., Ohayon, S., Dubé, S., Frauenfelder, U., Rizzi, L., Starke, M., & Zesiger, P. (2003). Aspects of grammatical development in young French children with SLI. Developmental Science, 6, 151–158.

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Laurence B. Leonard Hansson, K., Nettelbladt, U., & Leonard, L. (2000). Specific language impairment in Swedish: The status of verb morphology and word order. Journal of Speech, Language, and Hearing Research, 43, 848–864. Hansson, K., Nettelbladt, U., & Leonard, L. (2003). Indefinite articles and definite forms in Swedish children with specific language impairment. First Language, 23, 343–362. Hoover, J., Storkel, H., & Rice, M. (2012). The interface between neighborhood density and optional infinitives: Normal development and specific language impairment. Journal of Child Language, 39, 835–862. Jacobson, P., & Schwartz, R. (2002). Morphology in incipient bilingual Spanish-speaking preschool children with specific language impairment. Applied Psycholinguistics, 23, 23–41. Jakubowicz, C., Nash, L., Rigaut, C., & Gérard, C.-L. (1998). Determiners and clitic pronouns in Frenchspeaking children with SLI. Language Acquisition, 7, 116–160. Joseph, K., Serratrice, L., & Conti-Ramsden, G. (2002). Development of copula and auxiliary BE in children with specific language impairment and unaffected controls. First Language, 22, 137–172. Legate, J., & Yang, C. (2007). Morphosyntactic learning and the development of tense. Language Acquisition, 14, 315–344. Leonard, L. (1995). Functional categories in the grammars of children with specific language impairment. Journal of Speech and Hearing Disorders, 38, 1270–1283. Leonard, L. (2014). Children with specific language impairment. Second Edition. Cambridge, MA: MIT Press. Leonard, L., & Bortolini, U. (1998). Grammatical morphology and the role of weak syllables in the speech of Italian-speaking children with specific language impairment. Journal of Speech, Language, and Hearing Research, 41, 1363–1374. Leonard, L., Bortolini, U., Caselli, M. C., McGregor, K., & Sabbadini, L. (1992). Morphological deficits in children with specific language impairment: The status of features in the underlying grammar. Language Acquisition, 2, 151–179. Leonard, L., Davis, J., & Deevy, P. (2007). Phonotactic probability and past tense use by children with specific language impairment and their typically developing peers. Clinical Linguistics and Phonetics, 21, 747–758. Leonard, L., & Deevy, P. (2011). Input distribution influences degree of auxiliary use by children with specific language impairment. Cognitive Linguistics, 22, 247–273. Leonard, L., Deevy, P., Miller, C., Charest, M., Kurtz, R., & Rauf, L. (2003). The use of grammatical morphemes reflecting aspect and modality by children with specific language impairment. Journal of Child Language, 30, 769–795. Leonard, L., Deevy, P., Miller, C., Rauf, L., Charest, M., & Kurtz, R. (2003). Surface forms and grammatical functions: Past tense and passive participle use by children with specific language impairment. Journal of Speech, Language, and Hearing Research, 46, 43–55. Leonard, L., Deevy, P., Wong, A., Stokes, S., & Fletcher, P. (2007). Modal verbs with and without tense: A study of English- and Cantonese-speaking children with specific language impairment. International Journal of Language and Communication Disorders, 42, 209–228. Leonard, L., Eyer, J., Bedore, L., & Grela, B. (1997). Three accounts of the grammatical morpheme difficulties of English-speaking children with specific language impairment. Journal of Speech, Language, and Hearing Research, 40, 741–753. Leonard, L., Hansson, K., Nettelbladt, U., & Deevy, P. (2005). Specific language impairment in children: A comparison of English and Swedish. Language Acquisition, 12, 219–246. Leonard, L., Lukács, Á., & Kas, B. (2012). Tense and aspect in childhood language impairment: Contributions from Hungarian. Applied Psycholinguistics, 33, 305–328. Leonard, L., Wong, A. M.-Y., Deevy, P., Stokes, S., & Fletcher, P. (2006). The production of passives by children with specific language impairment acquiring English or Cantonese. Applied Psycholinguistics, 27, 267–299. Lindner, K. (2002). Finiteness and children with specific language impairment: An exploratory study. Linguistics, 40, 797–847. Lukács, Á., Leonard, L., Kas, B., & Pléh, C. (2009). The use of tense and agreement by Hungarian-speaking children with language impairment. Journal of Speech, Language, and Hearing Research, 52, 98–117. Marchman, V., Wulfeck, B., & Ellis Weismer, S. (1999). Morphological productivity in children with normal language and SLI: A study of the English past tense. Journal of Speech, Language, and Hearing Research, 42, 206–219. Marshall, C., Mason, K., Rowley, K., Herman, R., & Morgan, G. (2011). Sentence repetition as a proxy for language development and impairment: Insights from deaf signers. Poster presented at the Meeting of the European Group on Child Language Disorders, Thesslaoniki, Greece. Marshall, C., & van der Lely, H. (2007). The impact of phonological complexity on past tense inflection in children with grammatical-SLI. International Journal of Speech-Language Pathology, 9, 191–203.

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Cross-Linguistic Studies McGregor, K., & Leonard, L. (1994). Subject pronoun and article omissions in the speech of children with specific language impairment: A phonological interpretation. Journal of Speech and Hearing Disorders, 37, 171–181. Norbury, C. F., Bishop, D. V. M., & Briscoe, J. (2001). Production of English finite verb morphology: A comparison of SLI and mild-moderate hearing impairment. Journal of Speech, Language, and Hearing Research, 44, 165–178. Oetting, J., & Horohov, J. (1997). Past tense marking by children with and without specific language impairment. Journal of Speech, Language, and Hearing Research, 40, 62–74. Oetting, J., & Rice, M. (1993). Plural acquisition in children with specific language impairment. Journal of Speech and Hearing Disorders, 36, 1241–1253. Owen, A., & Goffman, L. (2007). Acoustic correlates of inflectional morphology in the speech of children with specific language impairment and their typically developing peers. Clinical Linguistics and Phonetics, 21, 501–522. Owen, A., & Leonard, L. (2006). The production of finite and nonfinite complement clauses by children with specific language impairment and their typically developing peers. Journal of Speech, Language, and Hearing Research, 26, 548–571. Paradis, J., & Crago, M. (2001). The morphosyntax of specific language impairment in French: An extended optional default account. Language Acquisition, 9, 269–300. Parisse, C., & Maillart, C. (2007). Phonology and syntax in French children with SLI: A longitudinal study. Clinical Linguistics and Phonetics, 21, 945–951. Pizzioli, F., & Schelstraete, M.-A. (2008). The argument-structure complexity effect in children with specific language impairment: Evidence from the use of grammatical morphemes in French. Journal of Speech, Language, and Hearing Research, 51, 706–721. Polite, E. (2011). The contribution of part-word phonological factors to the production of regular noun plural—s by children with and without specific language impairment. First Language, 31, 425–441. Polite, E., Leonard, L., & Roberts, F. (2011). The use of definite and indefinite articles by children with specific language impairment. International Journal of Speech-Language Pathology, 13, 291–300. Räsänen, S., Ambridge, B., & Pine, J. (2013). Infinitives or bare stems? Are English-speaking children defaulting to the highest frequency form? Journal of Child Language, 41, 756–779. doi: 10.1017/ S0305000913000159 Redmond, S. (2003). Children’s productions of the affix—ed in past tense and past participle contexts. Journal of Speech, Language, and Hearing Research, 46, 1095–1109. Restrepo, M. A., & Gutierrez-Clellan, V. (2001). Article use in Spanish-speaking children with SLI. Journal of Child Language, 28, 433–452. Rice, M., Noll, K. R., & Grimm, H. (1997). An extended optional infinitive stage in German-speaking children with specific language impairment. Language Acquisition, 6, 255–295. Rice, M., & Oetting, J. (1993). Morphological deficits in children with SLI: Evaluation of number marking and agreement. Journal of Speech and Hearing Disorders, 36, 1249–1257. Rice, M., Tomblin, J. B., Hoffman, L., Richman, W., & Marquis, J. (2004). Grammatical tense deficits in children with SLI and nonspecific language impairment: Relationships with nonverbal IQ over time. Journal of Speech, Language, and Hearing Research, 47, 816–834. Rice, M., & Wexler, K. (1996). Toward tense as a clinical marker of specific language impairment in Englishspeaking children. Journal of Speech, Language, and Hearing Disorders, 39, 1239–1257. Rice, M., & Wexler, K. (2001). Rice/Wexler test of early grammar impairment. San Antonio, TX: Psychological Corporation. Roberts, S. S., & Leonard, L. (1997). Grammatical deficits in German and English: A cross-linguistic study of children with specific language impairment. First Language, 17, 131–150. Schuele, C. M., & Dykes, J. (2005). Complex syntax acquisition: A longitudinal case study of a child with specific language impairment. Clinical Linguistics and Phonetics, 19, 295–318. Spoelman, M., & Bol, G. (2012). The use of subject-verb agreement and verb argument structure in monolingual and bilingual children with specific language impairment. Clinical Linguistics and Phonetics, 26, 357–379. Stavrakaki, S. (2001). Comprehension of reversible relative clauses in specifically language impaired and normally developing Greek children. Brain and Language, 77, 419–431. Stavrakaki, S. (2006). Developmental perspectives on Specific Language Impairment: Evidence from the production of wh-questions by Greek SLI children over time. Advances in Speech Language Pathology, 8, 384–396.

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Laurence B. Leonard Tanaka Welty, Y., Watanabe, J., & Menn, L. (2002). Language production in Japanese preschoolers with specific language impairment: Testing theories. In E. Fava (Ed.), Clinical linguistics: Theory and applications in speech pathology and therapy (pp. 175–193). Amsterdam, The Netherlands: John Benjamins. Theakston, A., Lieven, E., & Tomasello, M. (2003). The role of input in the acquisition of third person singular verbs in English. Journal of Speech, Language, and Hearing Research, 46, 863–877. Thordardottir, E., & Namazi, M. (2007). Specific language impairment in French-speaking children: Beyond grammatical morphology. Journal of Speech, Language, and Hearing Research, 50, 698–715. van der Lely, H. (1997). Language and cognitive development in a grammatical SLI boy: Modularity and innateness. Journal of Neurolinguistics, 10, 75–107. Wexler, K. (1998). Very early parameter setting and the unique checking constraint. Lingua, 106, 23–79. Wexler, K. (2003). Lenneberg’s dream: Learning, normal language development, and specific language impairment. In Y. Levy & J. Schaeffer (Eds.), Language competence across populations: Toward a definition of specific language impairment (pp. 11–61). Mahwah, NJ: Lawrence Erlbaum. Wexler, K., Schaeffer, J., & Bol, G. (2004). Verbal syntax and morphology in typically developing Dutch children and children with SLI: How developmental data can play an important role in morphological theory. Syntax, 7, 148–198. Wexler, K., Schütze, C., & Rice, M. (1998). Subject case in children with SLI and unaffected controls: Evidence for the Arg/Tns omission model. Language Acquisition, 7, 317–344.

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14 AFRICAN AMERICAN ENGLISH AND CHILD LANGUAGE DISORDERS Brandi L. Newkirk-Turner and Lisa Green

Introduction Phases of research in African American English (AAE) reflect changes and developments in linguistic, educational, and socio-political views about the variety that have taken place over the past 50 years. Developments on the study of AAE in these three areas are evident in the communication disorders literature in reports ranging from descriptions of properties of child AAE to assessment and intervention. In early descriptions of AAE, the weak morphological properties and instantiations of pre-verbal markers as a means of indicating information about the constituency of events, for instance, were viewed as linguistic deficits. Over the years, the description of AAE has evolved, and the characterizations of linguistic patterns that occur in the language of developing AAE-speaking children have changed. That is, now it is recognized that the children are developing a variety of English that is different from standard or classroom English—not a deficient form or variety of English. A number of studies in the area of AAE and language development have included children with and without language impairments for the purpose of distinguishing dialect from disorder in language assessment activities. These studies are grounded in the premise that language variation is not a disorder; it is possible for a child to speak a variation of English, such as AAE, and also have a disorder. Although researchers have acknowledged that AAE is a rule-governed system in which variation plays a major role, the line of demarcation between language variation and disorder remains blurred. One of the factors related to the diagnostic conundrum is what appears to be an overlap between clinical markers of specific language impairment, as determined by research on child speakers of general American English (GAE), and contrastive features of AAE. Considerable gains have been made in the assessment arena, so there is ongoing discussion about the types of evaluation that are better suited for assessing patterns in the AAE grammar and not penalizing AAE speakers for using structures that are part of their native language system, although they are markers of deficiency on standardized assessments for English speakers. This chapter considers AAE and childhood language disorders from the perspective of phases of research, approaches to the study of AAE, and questions about the effects of disorders on the grammar of the linguistic system.

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Phases of Research on AAE Research on AAE in general has been consistent since the first major wave of studies on the variety in the 1960s and 1970s (e.g., Dillard, 1972; Fickett, 1975; Labov, 1969a, 1972; Labov, Cohen, Robins, & Lewis, 1968; Loflin, 1972, 1975; Stewart, 1966/1971), and it should be noted that observations about the linguistic variety used by Blacks were made much earlier than the 1960s (Krapp, 1924). Some of the early work on child AAE, along the lines of research on AAE in general, was presented to respond to socio-political issues, so proving that AAE was indeed systematic and not deficient GAE was a major goal of research on AAE. As a means of countering the deficit claim, especially the erroneous view that AAE was simply GAE with mistakes, issues related to identifying the developmental path for the acquisition of AAE and ways of distinguishing disorders from developmental AAE received limited attention.

Deficit Hypothesis: Deviant Language and Intervention Research on African American children who were presumed to be speakers of AAE began in the 1960s. The work at that time proposed that unlike other children, preschool African American children were essentially mute or only capable of producing incomprehensible gibberish for communication. These claims were reported by deficit theorists such as Bereiter and Englemann (1966) and Deutch (1967), who advanced the major claim that African American children were incapable of learning language, like other children, without formal language intervention. According to deficit theorists, young African American children’s model for language learning was deficient because of the major limitations in input received from their parents and family members. Deutch (1967) argued that owing to the low cognitive abilities of these families, the language input that African American children received was primitively non-verbal (e.g., a head nod), absent of distinct usable words, and when words were distinguishable enough to form sentences, the structures were typically void of complete thoughts and instead were characterized by one-word utterances that were grammatically simple, often unfinished, syntactically incorrect, run-on, and not cohesive. Bereiter and Englemann (1966) and Deutch (1967) argued that the speech and language modeled to culturally deprived African American children by their parents was so limited that it prohibited them from naturally acquiring the generative rules of syntax without mandatory formal intervention and instruction within a preschool curriculum. Although not supported by empirical data, the claims of the deficit theory were widely spread throughout multiple disciplines (e.g., education) and were perpetuated for many years throughout the scientific and scholarly literature. See the references in this section for a discussion about and samples of language used by young African American children that were labeled as culturally deprived. Also, see Labov (1969b, 1972, “The Logic of Nonstandard English”) for a rebuttal of the type of verbal deprivation claims on which the views about children’s language were based.

The Comparative Paradigm: Major Differences between AAE and GAE Reports like those of Bereiter and Englemann (1966) and Deutch (1967) sparked research in the 1970s on African American children’s language conducted in a comparative framework. African American children were studied in comparison to other children—typically middle-class European American children—with the goal of revealing differences between the groups, not similarities. This research on African American children’s language was focused on revealing deficient language and debating the legitimacy of AAE. The differences that were revealed were explained as deficits or in terms of African American children’s acquisition of an underdeveloped form of English. This led to many research reports that had the primary goal of answering the question:

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Do African American children have an adequate communication system? This type of work dominated the study of African American children’s language throughout the late 1960s and 1970s.

The Language of AAE-Speaking Children: Typical Development The 1980s ushered in research about the normal language development of African American children. Scholars began conducting studies that focused exclusively on African American children without a comparison group of European American children (e.g., Bridgeforth, 1984; Cole, 1980; Reveron, 1978; Steffensen, 1974; Wolfram, 1989). For example, Fay Vaughn-Cooke and Ida Stockman began a research program in 1980 that involved the collection of longitudinal language data on African American children who were being raised in AAE-speaking homes. The goal of their work was to answer basic questions about the language of typically developing preschoolers who acquire AAE. More specifically, their research asked questions about the kinds of meaning, grammatical, and pronunciation patterns that the children’s words coded over time. Stockman (2007) recounted that the goals of Vaughn-Cooke’s and her original work were guided by three basic assumptions about children in AAE-speaking communities: (1) they acquire a spoken language for social communication that is rule governed and functionally adequate for social communication within their own linguistic community; (2) they acquire the spoken language for their communities without formal instruction unless a pathological condition prevents them from doing so; and (3) an adequate description of their language use requires a description of AAE patterns that are like GAE varieties in addition to those that are not. (p. 306) Therefore, the studies of Stockman and Vaughn-Cooke and others (e.g., Bridgeforth, 1984; Cole, 1980; Craig & Washington, 1986; Haynes & Moran, 1989; Wolfram, 1989) in the 1980s directly challenged the unfounded claims that deficit theorists advanced in the prior decade. Over time, various factors merged to create a new paradigm for the study of African American children’s language in the 1980s and 1990s (Stockman, 2010). These factors included key litigation, such as the Martin Luther King, Jr. Elementary School Children v. Ann Arbor School District Board case [1979], which brought to the forefront the finding that children’s use of AAE in the classroom can be a barrier to education if it is not taken into consideration in instruction. In addition, the socio-political climate in America (e.g., the Civil Rights movement), professional recognition of language differences, which led to the erection of multicultural offices in professional organizations, stimulated interest in AAE by up-and-coming scholars, the birth of new fields (e.g., sociolinguistics, developmental psycholinguistics), and the increased diversity of the U.S. population (Stockman, 2010) effected changes in the study of language in African American communities. In 1983, the American Speech-Language Hearing Association (ASHA) officially recognized social dialects such as AAE as being fundamentally different from disordered speech and language and issued a position statement that likely contributed to the increase of research on assessment in AAE and language impairment. The position statement, which promulgated the fact that linguistic disorders can be manifested in dialects, also made clear that “an essential step toward making accurate assessments of communicative disorders is to distinguish between those aspects of linguistic variation that represent the diversity of the English language from those that represent speech, language and hearing disorders” (p. 3). The end result was a paradigm shift from a deficit framework to a difference framework, one that considers child AAE as being different from other varieties of English, including GAE, and not deficient.

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Being guided by the ASHA position statement, a host of studies that began examining the language of African American children through a difference perspective characterized the communication disorders research program on AAE in the 1980s and 1990s. Generally, the goal of this research was to study and describe the speech and language patterns of groups of African American children, mostly morphosyntactic and phonological patterns, in light of the adult AAE literature (e.g., Washington & Craig, 1994, p. 818; Oetting & McDonald, 2001, p. 211). Thus, a main research question of this phase of research was the following: Are phonological and morphosyntactic features of adult AAE found in the speech and language of AAE-speaking children? The general finding of this work was that school-age children used linguistic patterns associated with adult AAE. These studies further supported the claims of the difference theory and added to the descriptive picture of AAE. However, most of these studies conducted from the difference framework did not shed much light on the acquisition of the linguistic system of AAE in that they did not, with few exceptions, focus on the development of language over time or the process by which young children acquire the AAE system.

AAE and Assessment The 1980s and 1990s also saw a surge in studies conducted on AAE-speaking children’s performance on standardized tests of speech and language. The highly publicized black-white achievement gap and the overrepresentation of African Americans among failing students on standardized achievement tests and in special education classes was a catalyst for this type of research. Studies conducted in this phase of research raised questions about the appropriateness of the commonly used standardized speech and language tests for AAE-speaking children (e.g., Cole & Taylor, 1990; Seymour & Seymour, 1981; Washington & Craig, 1992). Children’s results on these tests were presented in a comparative framework, in which they were measured against those of European American children. Generally, the results of these studies showed that African American children performed lower on standardized speech and language tests that were designed to elicit productions of GAE and were normed on standardization samples that were majority European American. Research on assessment continued throughout the 1990s and into the 2000s up to the present. Studies in the 1990s and 2000s considered language data from children with impaired language with the aim of distinguishing systematic language variation from language disorder (e.g., McGregor, Williams, Hearst, & Johnson, 1997; Seymour, Bland-Stewart, & Green, 1998). With the increasing focus on specific language impairment (SLI) in the GAE literature during the late 1990s and the 2000s (e.g., Grela & Leonard, 2000; Leonard, 1998, 2014; Leonard et al., 2003; Redmond & Rice, 2001; Rice, Cleave, & Oetting, 2000; Rice, Redmond, & Hoffman, 2006; Rice, Tomblin, Hoffman, Richman, & Marquis, 2004; Rice, Wexler, Marquis, & Hershberger, 2000; Oetting, 1999; Schwartz, this volume; Owen & Leonard, 2006; Tomblin et al., 1997), AAE researchers followed the methodological trend and began conducting studies that included a group of AAE-speaking children who were diagnosed as having SLI (e.g., Cleveland & Oetting, 2013; Garrity & Oetting, 2010; Oetting & McDonald, 2001; Oetting & Newkirk, 2008). These studies shared research questions such as: Are the linguistic structures that were shown in the child GAE literature to be clinical markers for SLI also clinical markers for SLI in AAE?

Disorders and Approaches to the Study of AAE A recurring theme in early research on AAE, especially in the context of school-age children, was distinguishing systematic AAE from disordered language. In light of seeming overlap between markers of AAE and disorders, a number of approaches were taken to avoid confusing a feature of AAE with a disorder in GAE. For instance, zero morphological marking in tense/agreement

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marking (e.g., She run) is associated with language disorders in GAE, but it is a pattern in some linguistic contexts in the typical AAE system. Assessments that do not take instantiations of the zero morphological marking “feature” into consideration as part of systematic AAE could very easily lead to evaluation of AAE as atypical development. Owing to a better understanding of patterns of language use in AAE, different steps have been taken to avoid erroneous classification of grammatical utterances in the linguistic variety as evidence of disordered or delayed speech. For instance, the publication of the Diagnostic Evaluation of Language Variation-Norm Referenced test (DELV-NR; Seymour, Roeper, & deVilliers, 2005) and the research leading to that assessment tool represent significant strides in the development of items that serve to evaluate language use without placing considerable emphasis on features of the grammars of AAE-speaking children that are also associated with non-standard language varieties. In this way, any features in non-standard English that overlap with those that might count as developmental delay or disorder do not enter into the evaluation. The point that systematic AAE is not disordered speech continues to be underscored; however, questions about the effects of disorders on the AAE system remain underexplored. The next section presents an overview of research on AAE in communication disorders from the perspective of characterizations of AAE. It highlights the strides that have been made in this area as well as questions for further research in relation to the effects of disorders on the AAE system.

Disorders A language disorder refers to “persistent difficulties in the acquisition and use of language across modalities due to deficits in comprehension or production” that include reduced vocabulary, limited sentence structure, and impairments in discourse (American Psychiatric Association, 2013, p. 42). The diagnosis of language impairment must take into consideration “regional, social, and cultural/ethnic variations of language” (American Psychiatric Association, 2013, p. 43) and should consider if and how a child’s language deviates “from the average level of ability achieved by a similar group of people” (Paul & Norbury, 2012, p. 2). That means that, for children, the benchmark for the assessment of language disorders is same-age children from linguistically similar communities. This is an important point for all children, including children from AAE-speaking backgrounds. A more specific type of language impairment, SLI, has been defined in the research literature as a significant deficit in language ability that cannot be attributed to hearing loss, oral structure and function, non-verbal intelligence, or neurological damage. A hallmark feature of English-speaking children with SLI is inconsistency using grammatical tense markers in obligatory contexts (i.e., third-person singular -s, past tense -ed, copula BE, auxiliary BE, auxiliary DO) (Leonard, 2014). These grammatical morphemes have become recognized as clinical markers of SLI.

Characterizations of AAE and Disorders At least three approaches to the study of AAE should be considered in the context of language disorders: features approach, dual components approach, and patterns and systems approach. (For an extended discussion of these approaches, see Green, 2011.) The different ways of characterizing AAE may emphasize certain groups or types of features, similarities that AAE shares with GAE, or general patterns of language use. In some cases, AAE is linked to a group of people, so it is not uncommon to find that AAE is defined as the language of African Americans or that it is the variety used by working-class African Americans, a definition that does not shed much light on the actual linguistic patterns of the variety. AAE was associated more closely with the working class in early analyses because the view was that that group spoke the most vernacular form, and

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the goal was to describe the variety that was farthest from GAE. However, more current research has been conducted on AAE and middle-class African Americans (e.g., Horton-Ikard & Miller, 2004; Britt & Weldon, 2013).

Features Approach Under the features-based approach, which has a long history in the study of AAE, features from the morphological, syntactic, and phonological components, or from the morphology and syntax interface (morphosyntactic) or morphology and phonology interface (morphophonological) that are maximally different from those that are acceptable in GAE are argued to characterize AAE. Semantics and lexical components must also be involved, but in general, the commonly cited features lists do not make reference to semantic and lexical patterns. Rickford’s (1999) list is an exception. It is a descriptive list, which consists of 25 features that are categorized according to components of the grammar. Features 1–18 are under the category of distinctive phonological (pronunciation) features of African American Vernacular English (AAVE), another name for AAE. Features such as “realization of syllable initial str as skr are included in this category especially before high vowels” (p. 5). The remaining seven features (19–25) are under the category distinctive grammatical (morphological and syntactic) features of AAVE. Feature 19, pre-verbal markers of tense, mood, and aspect is divided into 13 subcategories, and Feature 20, other aspects of verbal tense marking, has six subcategories. The subcategories for Feature 19 include references to quasi-modals, such as liketa and poseta, and markers such as be, BIN, and done, for example. Feature 21, nouns and pronouns, is divided into seven categories, and Feature 22, negation, includes four subcategories. Feature 23, questions, includes two subcategories. The final two features, Feature 24, existential and locative constructions, and Feature 25, complementizer/ quotative say, are separated into three and one subcategories, respectively. The first point to note is that other feature lists for AAE are not descriptive along the lines of Rickford’s list. For instance, other lists group together fitna, sposeta, and bouta for unexplained reasons, and tense/aspect related markers are randomly interspersed among other features (e.g., Washington & Craig, 1994). Rickford’s list is organized in such a way as to reflect components of the grammar of AAE; however, other lists on which analyses of AAE are based seem to be composed of unrelated isolated features. Furthermore, the lists that are associated with child AAE studies are often based on mature AAE grammars and give no indication about developmental trends. GAE is implicit in the conceptualization of the features-based characterization in that it guides the type of features that are included on the lists of AAE features, which are maximally different from grammatical structures in that standard variety. Research on AAE in communication disorders has been presented in frameworks that take the features-based approach, and this might be the case for a number of reasons. One reason is that the features-based approach provides a way to characterize AAE such that it is clearly represented as deviating from GAE, so the approach is suitable for very basic descriptions in which the goal is to enumerate the way differences from GAE are reflected in the language of AAE speakers. Another reason is that the features can be isolated and then aggregated as representation of how much or little AAE a speaker uses. Along similar lines, a single feature that is instantiated a number of times in speech can be counted to draw various types of conclusions about dialect density or prevalence of the use of a certain structure. Finally, under this approach, it is possible to compare similarities and differences between AAE and other non-standard varieties of English, at least on superficial dimensions. Some questions about disorders and AAE have been raised from the perspective of the featuresbased characterization. One approach to the study of child AAE and assessment of language disorders that is in line with the features-based approach is to focus on the contrastive features

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between AAE and GAE. In focusing on contrastive features within analyses that are concerned with differentiating typically developing AAE-speaking children from atypically developing AAEspeaking children, it is possible to keep child AAE research in line with theoretical studies of SLI that focus on five morphological structures that are documented to be variable (or absent) in AAE. According to research such as Seymour (1986), Seymour and Bland (1991), Seymour et al. (1998), and Stockman (1996), analyses based solely on contrastive features are insufficient in avoiding the diagnostic conundrum regarding the deficit/difference distinction. That is, structures that are grammatical in AAE (e.g., zero BE, zero DO) might also be produced by children with language impairment, so it might be difficult to pinpoint the source of the structure that is produced by AAE SLI: the AAE grammar or impairment. The contrastive approach to the study of AAE is best illustrated by the work of Oetting and colleagues (e.g., Cleveland & Oetting, 2013; Garrity & Oetting, 2010; Oetting & McDonald, 2001). Generally, this work has examined features of AAE that are considered to be contrastive with GAE. For example, Oetting and McDonald (2001) examined the production of 35 features that are associated with AAE using the three-group model that is common in SLI research. Two of the groups of AAE-speaking children were typically developing; one group consisted of 4-year-old children and the other group of 6-year-old children. The third group consisted of 6-year-olds who were specifically language impaired. When the individual patterns were analyzed, Oetting and McDonald (2001) found that three patterns distinguished the SLI group from their age-matched peers: zero marking of irregular past, zero marking of irregular third, and non-inversion of wh-questions. The children with SLI produced zero marking of irregular past and non-inversion of wh-question features more frequently than did the typically developing children. They were less likely to zero mark irregular third-person present forms. Oetting and McDonald (2001) interpreted these findings to mean that AAE-speaking children with SLI can be distinguished from those who are typically developing through analyses completed with contrastive features. Other studies have been conducted using the contrastive features approach and data from AAE-speaking children with and without language impairment. For example, the production of auxiliary BE, a contrastive feature between AAE and GAE, was examined by Garrity and Oetting (2010). Spontaneous language samples were collected and average percentages of overt marking of auxiliary BE were computed. Although statistical tests failed to indicate a significant group difference, the authors noted that through visual inspection of the data, they observed that the children in the SLI group overtly marked auxiliary BE less often than their typically developing peers. When the forms were analyzed separately, it was reported that the rate of overt marking of is was less (at a level of statistical significance) for the SLI group compared to the typically developing 4-year-old group (21% vs. 5%, respectively). For are, the same was reported, although tests failed to indicate statistical significance (7% for the SLI group and 39% for the typically developing 4-yearold group). The authors interpret the findings to suggest that in AAE, the grammar deficit involves auxiliary BE and specifically is and are. The final example comes from Cleveland and Oetting (2013), in which AAE-speaking children’s production of verbal -s as a function of clinical status was examined. The spontaneous language sample data of two groups of children were analyzed: a group of typically developing 6-year-olds and a group of 6-year-old children who were diagnosed as having SLI. When the average rate of overt production of verbal -s was calculated for both groups, it was shown that the production rate was lower for the group of children with SLI (14.07, SD = 16.69) compared to their same-age typically developing peers (21.42, SD = 16.44). The rates of production were not statistically different, and the authors argued that these results suggest that verbal -s should not be used within the language assessment of AAE-speaking children when the goal is to identify children who have language impairments.

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Dual Components The dual components approach explicitly includes GAE in the characterization in that AAE is taken to be a variety that consists of two components: a GAE grammar, which is the substantial component that can be used alone, and an African American (AA) component. Although the AA component is not a complete grammar that can be used alone, it has an additive effect in contributing properties that are not part of the GAE grammar and that distinguish AAE from GAE and other varieties of English. The dual components characterization shares some similarities with the features-based characterization of AAE. For instance, both the features-based and dual components characterizations operate on the view that a linguistic construction can be categorized as belonging to a single component. In the case of the features-based approach, all of the patterns that are produced by AAE speakers that could also pass as patterns of GAE are automatically assumed to be standard, thus not part of AAE. It appears that when AAE speakers produce any construction that is also found in GAE, that is an instance of code-shifting. Given the observations of the dual components approach in Labov’s (1998) discussion, it is clear that a line of demarcation is drawn between structures that could be part of the AA component and those that are part of the GAE component. Tense/Aspect markers (e.g., be, BIN, done—often represented as dǝn) and, it is assumed, information about their uses are included in the AA component. One approach to child AAE in communication disorders that is in line with the dual components characterization is the use of non-contrastive features, or those features that are not characterized as non-standard but that could also be part of GAE, in the assessment of language disorders. Non-contrastive features in Seymour et al. (1998) are those such as articles, complex sentences, conjunctions, demonstratives, locatives, modals, and present progressive. According to Seymour (2004), the underlying assumptions of using a non-contrastive approach are that (1) AAE and GAE “are more similar than they are different; (2) specific AAE features should be avoided because they represent patterns that appear similar to disordered features, and thus would be ambiguous in the diagnostic process; and (3) an impaired language system also will reflect itself in the similarities” between AAE and GAE (pp. 8–9). On the other hand, Oetting and McDonald (2001) argue that such an approach significantly limits the number of linguistic structures that can be examined, and by excluding contrastive features, the researcher is left with the types of structures that are not useful for testing existing models of language impairment (e.g., extended optional infinitive account of specific language impairment). The work of Stockman and colleagues (2010, 2013) that is related to the minimal competency core (MCC), a tool designed to identify delayed spoken grammar in African American children, is relevant here because it aligns with the dual components approach. The MCC uses empirically derived norms to identify speech and language delay. According to Stockman (2010), the MCC concept assumes that all typically developing speakers in a given age range within a linguistic community share basic core communication skills despite individual variability. The MCC, in principle, should reflect the least level of linguistic competence that a speaker should demonstrate with consideration of his/her age, situational context, and linguistic community. Stockman, Guillory, Seibert, and Boult (2013) analyzed the language samples of 120 African American children (those who were presumed to be typically developing because they were not targeted for clinical referrals, those who had received or were scheduled to receive a speech-language evaluation, and those who were likely referrals because of teacher- or teacher-suspected delay) using an MCC that consisted of five linguistic competencies common to both AAE and GAE: mean length of utterance calculated in morphemes, elaborated sentences, multiclausal sentences, grammatical elaboration of sentences, and sentence ellipsis. Utterances that reflected contrastive or non-contrastive

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features were counted as evidence of the competencies. For example, both He don’t got no candy and He doesn’t have candy were counted as evidence for the clausal elaboration competency (negative sentences). Results of the study showed that the majority of the children (86%) passed the MCC. Of 11 variables examined, the two that best predicted passing the MCC were clinical status (i.e., typically developing, past or present speech-language evaluation referral, likely referral) and the number of different words produced in the language sample. The two measures of AAE feature use (i.e., dialect density in AAE morphosyntactic + phonological features, dialect density in AAE morphosyntactic features only) did not discriminate those who passed the MCC-MS from those who failed. What these findings suggest is that when non-contrastive and contrastive aspects are considered, contrastive ones (i.e., rate of AAE features) are less able to predict performance on the MCC, a tool designed to identify delayed spoken grammar in African American children.

Patterns and Systems The final characterization of AAE is the patterns and systems approach, in which AAE is defined as a linguistic variety with syntactic, phonological, morphological, semantic, lexical, and pragmatic systems. The goal is to describe the properties of the grammar of AAE and document what speakers know when they know AAE. Under this approach, patterns in AAE are also intertwined with patterns in GAE, so there are overlapping constructions, properties, and patterns in these varieties. That is to say that constructions such as I’m singing and I’m happy are certainly part of AAE as well as the GAE grammar. However, it is clear that, as in the other characterizations, patterns in one variety might not be in the other. For instance, the extent to which third-person singular -s is part of the AAE grammar is not clear, although the marker is used in some third-person singular contexts. Nevertheless, there is no assumption that AAE only includes what have been referred to as distinctively AAE features and not the corresponding features that are associated with the standard, because one pattern is in one variety, it cannot be in the other. That is, although in many contexts or instances auxiliary BE might occur in its zero form (a pattern undeniably associated with AAE), the overt form of BE is also part of the AAE grammar. In this way, both utterances He running and He IS running are part of AAE. This approach differs from the features-based approach in that it moves beyond defining AAE as a list of isolated non-standard features that are maximally different from (standard) GAE. In addition, taking the patterns and systems approach differs from appealing to the dual components view in that in the former there is no assumption that two separate linguistic components make up AAE. The analysis of wh-constructions in child AAE data in de Villiers, de Villiers, and Roeper (2010) is in line with the patterns and systems approach. In that paper, de Villiers et al. consider children’s wh-movement patterns in sentences such as the following: 1. What did the mother say she bought? 2. How did the woman learn what to bake? The authors did not find a difference in the responses to the question in (1) between the 4- to 5-yearold AAE-speaking and GAE-speaking children. They found that both groups of children had difficulty answering appropriately when the lower clause involved a false statement. That is, the question (1) that is embedded under say asks what the mother bought instead of what the mother said she bought. Onethird of the 4- and 5-year-old children answered the lower verb only (bought, second clause) (What the mother bought or What did the mother buy? instead of say, as in what the mother said she bought or What did the mother say she bought?). Answering the lower verb only represented an error. On the other hand, onequarter of the children from the SLI group continued to answer inappropriately to age 6 or 7.

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De Villiers et al. did find a difference between the AAE-speaking and GAE-speaking children in their responses to the question in (2). They found a link between children who answered the long-distance question (How did the woman learn what to bake?) and those who produced embedded inversion constructions, such as the following: 3. The girl asked [can she have some cake]. (‘The girl asked if she could have some cake’) In the indirect question (3), yes/no question formation applies in the embedded clause, and subject auxiliary inversion results in can preceding the embedded subject she. Embedded questions can also be produced with a complementizer (e.g., if or whether) in the position occupied by the inverted auxiliary (e.g., could), as in The girl asked if she could have some cake. In the complementizer option, the auxiliary (could) stays in a lower position. The complementizer construction is possible in AAE, of course, because inversion is not required, or even possible, in all contexts with all verbs. When the inversion option does not apply, speakers must use the complementizer option. AAE-speaking children who produced embedded inversion constructions were more likely to answer the longdistance question (How did the woman learn what to bake?) than the medial question (What to bake?) as in (2). In consideration of this finding, the dialectal pattern of embedded inversion is argued to be beneficial in answering long-distance wh-questions. The patterns-based approach reveals some properties that help to account for AAE-speaking children’s performance on wh-questions. De Villiers et al. explain how the AAE grammar might work to help AAE-speaking children avoid erroneous medial interpretation of the wh-word more often than their GAE peers. Clark’s (2006) investigation of the language of AAE-speaking children with SLI incorporated techniques of analyzing grammatical and ungrammatical constructions, which is also a strategy of the patterns-based approach. Clark noted that the AAE speakers with SLI did produce grammatical structures, but they also produced ungrammatical structures owing to language impairment. Her analysis of the system of use of copula/auxiliary BE led to the following observation: “The most common error observed with the zero be construction was the omission of the copula when it follows the pronouns I or that” (p. 72). In essence, Clark showed that AAE SLI participants used Ø copula/auxiliary BE in environments that have traditionally been labeled as don’t count cases, a use that is ungrammatical in AAE beyond developmental stages. These don’t count forms were identified in early research (e.g., Labov, 1972; Labov et al., 1968), and Blake (1997) expanded the analysis of them. They were labeled don’t count because, given that they are always virtually overt, the forms are not factored into statistical analyses to avoid skewing the results. Clark also noted some discrepancies in the uses of past marking (-ed) in that children used the marking in non-past contexts. Clark explains the results in the following way: Instead of marking the regular third person present on the surface as is required in ME [Mainstream English] or omitting the marking which is common in AAE, study participants often chose to use an irregular verb or past tense forms. For example, a participant might produce the following utterance, “It spinded around” in response to the question “What does the spinning top do when you push the button?” (p. 73) The patterns-based approach makes it possible to detect patterns that are unacceptable in certain contexts in AAE because it requires that different linguistic constraints and properties, such as those from the syntactic and semantic components, be analyzed.

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The AAE Grammar and Locus of Impairment: Beyond Contrastive/Non-Contrastive Features The instantiation of features has been a constant topic in the linguistic study of AAE in general and in child AAE research in communicative disorders in particular. One question on this topic that has been raised in the literature is about the extent to which contrastive features and non-contrastive features can be used to identify impairment in child AAE. Research from the perspective of contrastive and non-contrastive features has yielded important findings that have led to insights into different properties of child AAE, and it has been particularly revealing in underscoring the need to use methods that distinguish disorders and typically developing AAE. Along these lines, it has been noted that contrastive features are precisely the types of features that are considered in extended optional infinitive accounts of SLI, so including them in research makes it possible to extend existing models of language impairment to AAE. In short, optional infinitive refers to stages in which children produce uninflected verb forms in contexts in which inflection is required in the target adult grammar. There is substantial literature on the topic (e.g., Hoekstra & Hyams, 1998; Rice, Wexler, & Cleave, 1995; Rice & Wexler, 1996; Deen, 1997; Wexler, Schütze, & Rice, 1996). Without a doubt, it is clear that there is overlap in features of AAE and features of SLI. There has been some exploration into the superficial similarities, especially from the perspective of nonstandard features; however, many questions remain about the extent to which these non-standard features are altered or reconfigured by SLI. For typically developing AAE speakers, the features are part of the AAE grammar. Some of the basic features identified in AAE SLI are part of the target AAE grammar, at least superficially. It is clear that the research from the angle of contrastive and non-contrastive features has helped to advance the study of typically developing and impaired AAE; however, a number of issues underscore the point that other information and approaches must be brought to bear on analyses of features of AAE. The unanswered questions related to criteria for categorization, meaning of frequency of use of features, and patterns of use underscore the need for further investigation of a number of areas that can provide crucial information about contrastive and non-contrastive features.

Contrastive/Non-Contrastive Features: What Are the Criteria for Categorization? In reading literature associated with contrastive and non-contrastive features, and arguments for appealing to one over the other, it becomes clear that including a feature in one category or the other is not straightforward. That is, what are the criteria for labeling a feature contrastive or non-contrastive? Over the years, different lists of (contrastive) features observed in the language of child AAE have been published. There is certainly variation within and among varieties of AAE, but the criteria for including certain features on some lists and not on others are not clear. For instance, Oetting and McDonald (2001) included prepositions on their list as a contrastive feature, but Seymour et al. (1998) included prepositions on the list of non-contrastive features. Seymour et al. included modals as a non-contrastive feature; however, for Washington and Craig (1994) modals are categorized as a contrastive feature. Another example of a feature that is categorized differently in different studies is the progressive marker -ing. For Oetting and McDonald (2001), this marker is a contrastive feature but non-contrastive for Seymour et al. (1998) and Stockman (1996, 2010). Another case in point is the feature complex sentences, which is placed in the non-contrastive category in Seymour et al. Certainly there is overlap between some properties of complex sentences in AAE and other varieties of English but not all, so further description would be useful in clarifying which complex sentences and what properties of them are involved. More explanation is

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needed in the description of these features, especially in interpretation of the results that are related to them. If there are claims about different outcomes based on the classification of contrastive and non-contrastive features, it is necessary to be clear about what criteria are used for assigning a particular feature to one category or the other.

Contrastive/Non-Contrastive Features: What Do Frequency and Rate of Occurrence Reveal? Another issue that arises in observations about contrastive and non-contrastive features and disorders in AAE is the nature of frequency and the information it provides. This line of research has led to different conceptualizations of the conclusions about child AAE that are based on frequency rates. Frequency of occurrence of contrastive features has also been implicated in studies to distinguish impaired and grammatical AAE. Owing to the sociolinguistic framework of variation, in which AAE data have been analyzed, there is a long tradition of reporting frequency and rate of production of AAE features. In discussing the study of variation, Labov (1972) noted that it “is necessarily quantitative, and quantitative analysis necessarily involves counting. At first glance, counting would seem to be a simple operation, but even the simplest type of counting raises a number of subtle and difficult problems” (pp. 82–83). Labov goes on to discuss the importance of considering constraints on variation, identification of variants, and subcategories that are necessary for determining frequency. Some of the features that are analyzed in child AAE have already been addressed on lists referring to adult AAE, so the assumption is that the types of constraints relevant in the adult data also apply to the child data. However, there is generally very little discussion about the environments that are considered, and data sets are often excluded from the presentation, so it is not clear what contexts and environments are being considered in establishing frequency or rate of occurrence. Is the frequency intended to correlate with how well the pattern is established (or not) in the child’s grammar? For instance, on the one hand, when contrastive features have a high frequency rate or rate of occurrence, the child might be characterized as being a heavy dialect user or as having high dialect density. On the other hand, it appears that a high rate of occurrence of contrastive features can also signal disorder, as in Oetting and McDonald (2001). They noted that S[Southern]AAE SLI produced lower rates of zero irregular third-person singular than their peers from the SAAE control groups. However, in calculation in which the number of zero irregular third-person forms were divided by obligatory contexts rather than number of utterances, Oetting and McDonald found that SAAE SLI zero marking of irregular third-person singular was 73% and SAAE zero marking was 69%, p > .05. They observed: the most pronounced difference between these two groups of SAAE speakers is not in their rates of zero marking, but in the frequency at which obligatory contexts for this structure are produced. We speculate that this finding is related to the normally developing SAAE speakers’ superior ability to use narrative discourse genre and their use of historical present tense within these narrative contexts. (p. 219) A number of factors seem to enter the picture here. One is the use of obligatory contexts. It appears that the authors take historical present to be an obligatory context for third-person singular marking in AAE. Is this the case, or is historical present simply an obligatory context for thirdperson singular overt morphological marking in GAE and that requirement is imposed on SAAE in the study? Nevertheless, Oetting and McDonald observed that typically developing child AAE

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speakers use zero marking somewhat less frequently due to their skills in use of discourse genre and morphological marking in historical present contexts. This is an interesting observation, especially because there have not been any reports on the correlation between narrative skill and overt third-person singular marking in child AAE. Furthermore, given the status of overt third-person singular marking in the grammar of AAE, it would be unexpected for there to be a requirement on its use as a tense marker in any contexts for superior narrative strategies in AAE. However, the point in Oetting and McDonald may be that the typically developing AAE speakers have been in environments—possibly school—in which overt third-person singular morphological marking is used more consistently, and thus they have been able to use it in some contexts in which it occurs in GAE. On the other hand, the AAE SLI children may not be able to appropriate the feature in the same way or to the same extent. The question that remains is what do these results reveal about the AAE grammar, and to what extent is the children’s language susceptible to influence from another variety. The Oetting and Newkirk (2008) study on relative clauses highlights the importance of raising questions about frequency and considering patterns of use in the AAE grammar. In that study, not much attention was paid to the small number of zero relatives in subject position in the data by typically developing AAE speakers, such as That’s the teacher __ let us leave early. The zero relative (which is not overtly specified) is indicated by ‘__’. It was noted that a smaller number of AAE SLI speakers produced overt relative markers (e.g., that) than typically developing AAE-speaking children. Oetting and Newkirk concluded that there was little difference between GAE and AAE relative clauses, and the deviation from GAE is attributable to impairment. This is insightful from the perspective of the comparison to GAE, a goal of the paper, but it is not clear what the variable production says about the developing AAE grammar. Variable production refers to the pattern in which the children produce some overt relatives in subject position (possibly most of the time) and zero relatives in that position (only occasionally). In some cases, the target for AAE is variable production of one form or another—although variation is not allowed in all contexts—so constant comparison to GAE without acknowledgment of variation is misleading. One question here is about how to understand the variable production of zero relatives, which is argued to be part of the AAE grammar (Sistrunk, 2012). More specifically, in AAE zero relatives are allowed in subject position—not required, so even a small percentage of zero relatives is noteworthy for at least two reasons. One reason is that the information sheds light on the developmental stages for zero relatives: at what point do children start to acquire them? The second is that it provides insight into different environments for zero subject relatives, as described in Sistrunk’s research on relative clauses and AAE. Oetting and McDonald (2001) found that AAE SLI children produced more wh-question noninversion than their typically developing age-matched peers. One implication of the results is that a certain frequency of production of non-mainstream features is encoded in the AAE grammar, and any production beyond that is excessive production, which indicates impairment. Another way to understand the observation that children who speak AAE with SLI produce more non-inversion is from a developmental perspective; SLI participants’ somewhat more frequent production of noninversion of the auxiliary in wh-questions is similar to younger developing AAE speakers. As such, non-inversion should also be discussed in light of stages of development of AAE. Finally, understanding frequency in relation to non-inversion in constructions with particular wh-words (e.g., Is non-inversion more likely to occur with why than with what?) would also shed more light on what information observations about frequency provide, especially given what is already known about wh-words and auxiliary inversion in the literature. Here, it should also be noted that Craig and Washington (2000) found no difference in the dialect density or the frequency of contrastive AAE features between typically developing AAE-speaking children and those identified as SLI.

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However, they did note differences in performance on wh-construction responses between the two groups. For instance, the typically developing children made factual errors and gave imprecise responses, whereas the children identified as language impaired made all error types, including high frequency of no response and unrelated response. Their results strongly suggest that whquestion performance can distinguish typically developing AAE-speaking children and those with SLI, although the specific questions and the responses that were produced by the children were not provided. Thus, it is unclear whether the distinguishing characteristics are syntactic, semantic, pragmatic, or a combination of these different components.

Contrastive/Non-Contrastive Features: What Are Patterns of Use in the Context of the System of AAE? Another issue that is implicit in the contrastive and non-contrastive discussion in relation to AAE and disorders is patterns of use. Patterns of use refer to the linguistic environments, such as syntactic, semantic, phonological, lexical, and pragmatic, in which the targeted construction occurs. Studies such as Clark (2006) and de Villiers et al. (2010) help to underscore the importance of moving beyond a simple contrastive/non-contrastive distinction in analyses of child AAE that focuses on superficial instantiations of features. For instance, in analyzing patterns associated with the copula/ auxiliary BE, Clark considered what could be contrastive and non-contrastive patterns. Clark considered contrastive features or those that differ from GAE (e.g., covert forms of the copula/ auxiliary BE) as well as non-contrastive features or those that overlap with GAE (e.g., obligatory overt instantiations of the copula/auxiliary BE), an approach that begins to consider the copula system of AAE, not just the extent to which the marker is absent. For example, Clark investigated the extent to which children produced overt and covert copular forms in the context of firstperson singular subjects, an environment in which copular forms would be predicted to be overt in typically developing language. The benefit of taking such a systematic approach is that it reveals which patterns are part of the grammar of AAE and which are not. Clark’s research underscores at least two important points: (1) Frequency counts alone may not give much information about the effects that impairment has on the AAE system, and (2) it is important to consider patterns of use of features in environments in which they occur in order to determine whether the features are used in grammatical or ungrammatical contexts. Describing patterns of use is a step in characterizing the system of AAE, which leads to being able to identify systematic as well as aberrant uses of structures. Unfortunately, an approach with just a focus on contrastive features does not provide much insight into the grammar to which the features belong. De Villiers et al. (2010) factored properties of AAE into their analysis of whquestions. Although they considered wh-questions, an area of complex syntax in which properties of the movement and interpretation of the wh-words (e.g., what, why, how) are shared between AAE and GAE, they did not limit their discussion to non-contrastive properties. In a detailed examination of language use patterns, it was difficult to consider only contrastive or non-contrastive features. They linked a property of AAE embedded questions to the general developmental path that children take in acquiring and interpreting wh-questions. The data revealed that, in the system of questions reflected in child AAE, contrastive and non-contrastive features interact such that the contrastive feature of embedded inversion in AAE gives the children an advantage in interpreting wh-questions that are common to all varieties of English. The children are claimed to have an advantage in interpreting wh-questions because inversion only occurs with indirect questions, which are introduced with question words such as ask, as in “The girl asked [can she go outside]” (de Villiers et al., 2010, p. 360). De Villiers et al. (2010) link the AAE-speaking children’s success in resisting medial wh-answers in elicitation tasks to their development of embedded inversion.

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They did not discuss this property in the language of AAE-speaking children with SLI, so it is not clear whether knowledge of embedded inversion in AAE helps the AAE SLI participants with wh-questions.

Conclusion Some important challenges have presented themselves in the study of child AAE. One is that given the focus on “features” of AAE, child AAE is often analyzed with the expectation that children will have acquired the adult AAE grammar at the earliest stages of development, so the features that are based on language produced by adolescents and adults are expected to appear in child AAE. Not much attention is paid to developmental stages of AAE and what feature production might look like in those stages. Another challenge is the comparison of structures in child AAE to GAE. General comparison has been extremely beneficial in that such an approach has helped to underscore differences and move the discussion away from deficits. However, in some cases, it is tempting to evaluate use of child AAE in terms of GAE and not in terms of the grammatical system of AAE. It appears that, in some cases, the findings are interpreted to suggest that greater use (frequency or rate of use) of AAE features might indicate disorder. An evaluation of AAE-speaking children for the diagnosis of language impairment should be comprehensive enough to cover a range of skills (Owen & Leonard, 2006) as well as comprehensive enough to align with a patterns and systems conceptualization of AAE. Certainly using assessment tools that have acceptable measures of diagnostic accuracy (e.g., sensitivity, specificity, positive and negative likelihood ratios) is necessary. Studies in which the goal is to identify such tests for AAEspeaking children continue to be needed. Language samples are a promising approach and a potential gold standard to corroborate diagnostic decisions (Dollaghan & Horner, 2011; Hadley, 1998; Pearson, Jackson, & Wu, 2014; Stockman, 1996, 2010). To be informative, language sample analyses should reflect consideration of patterns of language use in linguistic environments, developmental properties, and possible variation. The analyses would provide more insight into what the developing AAE speakers know about their language. In addition to language samples, research should continue to explore the clinical utility of elicitation and experimental tasks that are designed to help in assessing production and comprehension (McDaniel, McKee, & Cairns, 1996). Language samples give clear indications of children’s production and performance, but they do have some limitations in conveying the type of meaning and interpretation children associate with agreement, tense/aspect marking, and semantic scope, for example. Experimental tasks (e.g., de Villiers & Johnson, 2007; Green, 2011) could supplement language samples to assess receptive language and provide cues to the meaning of children’s production. A number of advances have been made in child AAE over the years through phases of research moving from a deficit view and comparative approaches to linguistic descriptions of structures in the variety and principles governing them. Approaches that bring together linguistics and research in communication disorders contribute to the discussion about disorders and the AAE grammar.

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PART IV

Deficits, Assessment, and Intervention in Child Language Disorders

15 MORPHOSYNTAX IN CHILD LANGUAGE DISORDERS Janna B. Oetting and Pamela A. Hadley

The morphological deficits of children with specific language impairment (SLI) have been extensively documented, and there is a growing literature base of comparative studies and cross-etiology studies investigating the structural systems of children with other developmental disabilities. Much of this work was initially grounded in methods and constructs first developed by Brown (1973). These include the use of spontaneous language samples as a method of data collection and mean length of utterance (MLU), Brown’s 14 morphemes, and 90% mastery as constructs by which to assess a child’s morphological profile. In the current chapter, we review these studies as they relate to the broad categories of deficits, assessment tools, and intervention methods while also highlighting a number of scientific advances within the field. Some of the advances have been empirical in nature. Researchers have identified limits to the use of Brown’s approach as it has been applied to clinical populations (for MLU, see DeThorne, Johnson, & Loeb, 2005; Eisenberg, Fersko, & Lundgre, 2001; Johnston, 2001; Klee, Stokes, Wong, Fletcher, & Gavin, 2004; for Brown’s 14 morphemes, see Balason & Dollaghan, 2002; for percent use, see Hadley & Short, 2005; Oetting & McDonald, 2001). Since the 1970s, researchers have also developed new methods to study children’s morphological systems. These include a wide range of elicitation, sentence recall, and grammatical judgment tasks, various measures to assess older children’s sensitivity to grammatical and ungrammatical stimuli, diverse indices to measure young children’s emergence and early productivity of grammatical structure, and sophisticated statistical programs that allow for tests of grammar onset and growth within and across children. Other advances have been theoretical in nature. Scholars working within a generative linguistic framework often recognize a distinction between the emergence of structures associated with lexical and functional categories (Chomsky, 1995). Lexical categories are characterized as Noun, Verb, Adjective, and Preposition, those categories associated with both syntactic and semantic information. Functional categories do not contain semantic information; they are purely responsible for realizing grammatical features such as Tense, Agreement, Definiteness, and so forth. Figure 15.1 organizes the morphemes originally studied by Brown within this framework. From this categorical perspective, the surface forms once studied as distinct elements of morphology and syntax are unified (hence the term morphosyntax). Recent investigations of childhood language impairment have focused most explicitly on functional morphemes that mark finiteness (i.e., tense and/or agreement).

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Grammatical Morphemes

Lexical Morphemes plural –s progressive –ing in/on

Functional Morphemes

Determiner articles a/the possessive –s

Tense/Agreement regular 3rd person –s irregular 3rd person regular past –ed irregular past copula and auxiliary BE auxiliary DO

Figure 15.1 The morphemes originally studied by Brown (1973) in a morphosyntactic framework.

Scholars working out of nonlinguistic frameworks do not necessarily recognize abstract grammatical structure, including a functional category classification system, within their studies. Yet recently, they too have focused their efforts on developing explanations for the functional morpheme deficits that have been repeatedly documented in childhood language impairment. Examples of nonlinguistic accounts of impairment include domain-general processing models and domain-specific models involving working memory, procedural learning, and/or statistical learning mechanisms (see Chapter 8 by Gilliam et al.; for two example studies, see Hedenius et al., 2011; Owen, 2010).

Morphosyntactic Deficits across Populations Specific Language Impairment Children with specific language impairment (SLI) present delayed language growth in the absence of other developmental conditions. During the early preschool years, a lower than expected MLU with limited use of grammatical morphology is a salient indicator of delayed growth. Young preschoolers with SLI often produce a number of morphemes at rates that are lower than age-matched

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and language-matched controls. These morphemes cut across lexical and functional categories; however, group differences are most consistent and dramatic for functional morphemes that mark finiteness, especially when children’s MLUs are above 3.50 and language matching is incorporated into a study (for review, see Leonard, 2014; for example studies, Bedore & Leonard, 1998; Leonard, Miller, & Gerber, 1999; and for studies involving MLUs below 3.0, see Beverly & Williams, 2004; Ingram & Morehead, 2002; Paul & Alford, 1993). By age 5, and up to at least age 8, difficulties with finite verb morphology become even more pronounced for children with SLI (Bishop, 1994; Conti-Ramsden, 2003; Krantz & Leonard, 2007; Leonard, Bortolini, Caselli, McGregor, & Sabbadini, 1992; Leonard et al., 2003; Marchman, Wulfeck, & Ellis Weismer, 1999; Oetting & Horohov, 1997; Owen & Leonard, 2006; Redmond, 2003; Rice & Wexler, 1996; Rice, Wexler, & Cleave, 1995; Rice, Wexler, & Hershberger, 1998). In Rice and Wexler (1996), children with SLI produced lower rates of finite verb morphemes when compared to both age- and language-matched controls, with effect sizes ranging from .40 to .52. Moreover, using a composite measure of finite marking and 80% as a clinical cutoff, more than 97% of the children were correctly classified as either SLI or typically developing. Compelling evidence for the difficulties children with SLI have with morphemes marking finiteness comes from studies examining homophonous surface forms that do and do not mark finiteness. Structures examined thus far include homophonous regular past tense (finite) and passive participle (nonfinite) morphemes (Grace walked the dog vs. The dog was walked by Grace) and homophonous auxiliary DO (finite) and lexical DO (nonfinite) morphemes (Where does Olivia swim? vs. Can Olivia do a dive?). In each of these cases, children with SLI demonstrate more difficulty relative to controls with the morphemes marking finiteness than those that do not, even though the surface forms of the structures are identical (Leonard et al., 2003; Redmond, 2003; Rice & Blossom, 2013; Smith-Locke, 1993). As an example, Rice and Blossom (2013) showed that children with SLI marked auxiliary DO less often than both age- and language-matched controls (SLI mean = 37% vs. age- and language-matched mean = 82–92%), with rates of marking for lexical DO near ceiling (SLI mean = 97%). Almost all of the morphosyntactic studies of SLI have been conducted with children who speak General American English, yet similar weaknesses with morphemes marking finiteness have also been documented in children with SLI who speak other dialects of English (Conti-Ramsden, 2003; Oetting & McDonald, 2001). In Conti-Ramsden (2003), children spoke British English, and measures included productivity probes of past tense and plural and two probes unrelated to morphosyntax (i.e., nonword repetition, digit recall). For all tasks, children with SLI earned lower scores than did controls, but the children’s past tense scores, together with their nonword repetition scores, yielded the highest level of diagnostic accuracy (sensitivity = .81 and specificity = .91). The children’s marking of past tense alone yielded sensitivity and specificity rates of .81. In Oetting and McDonald (2001), children produced a rural American dialect of either Southern White English or African American English, and measures included frequency counts of 35 different morphosyntactic structures within language samples. Although all 35 structures were entered into a discriminate function analysis, a number of structures related to finite verb morphology best separated the groups according to language ability. In at least five other studies, reliable differences involving functional morphemes, many of which relate to verb finite marking, have also been documented between children with and without SLI within the context of one or both of these nonmainstream dialects of English (Cleveland & Oetting, 2013; Garrity & Oetting, 2010; Oetting, Cantrell, & Horohov, 1999; Oetting & Garrity, 2006; Oetting & Newkirk, 2008; but see also Seymour, Bland-Steward, & Green, 1998). After age 8, it is more difficult to distinguish children with and without SLI using finite verb morphology, as evidenced by Conti-Ramsden, Botting, and Faragher’s (2001) study of 11-year-olds.

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Measures were from probes and included regular and irregular marking of past tense, regular thirdperson marking, sentence imitation, and nonword repetition. Again on all four tasks, children with SLI scored lower than controls. Using the 16th percentile as a clinical cutoff, all four measures also showed fair to good specificity at .87 or higher. Only sentence recall, however, demonstrated good sensitivity at .90. For the two finite verb morphemes, sensitivity was too low at this age to be clinically useful (past tense = .74, third person = .63) (for other studies of older children, see Betz, 2005; Mackie & Dockrell, 2004; Moyle, Karasinski, Ellis Weismer, & Gorman, 2011; Poll, Betz, & Miller, 2010; Windsor, Scott, & Street, 2000). The nature of children’s difficulties with finite verb morphology has been studied longitudinally with a clinical sample and a large, epidemiologically ascertained sample (Rice, 2009, 2012; Rice et al., 1998; Rice, Tomblin, Hoffman, Richman, & Marquis, 2004; Rice, Wexler, Marquis, & Hershberger, 2000; Rice, Wexler, & Redmond, 1999). Results indicate that the acquisition of these morphemes by children with SLI lags behind and is not predicted by their general delays in vocabulary and MLU or by IQ and maternal education. Nevertheless, children with SLI present growth trajectories for individual finite morphemes that are similar to each other and in parallel to those observed for typically developing controls. For both groups, a combination of linear and quadratic growth (i.e., early linear growth, with overall slowing) occurs even though rates of marking by those with SLI are protracted and reach a lower asymptote. Children’s difficulties with finite verb morphology have not been found to be strongly related to their scores on tasks involving nonword repetition (Bishop, Adams, & Norbury, 2005), sentence recall (Conti-Ramsden et al., 2001), and perception of natural speech stimuli (Evans, Viele, Kass, & Tang, 2002; van der Lely, Rosen, & Adlard, 2004). Evidence from pedigree analyses, twin studies, and genetic linkage and association studies also suggests that deficits with finite verb morphology is heritable, with KIAA0319 identified as a possible regulatory gene within genetic and/or epigenetic models of language impairment (Rice, 2012, see also Bishop, 2005; Bishop et al., 2005; Falcaro et al., 2008; Newbury et al., 2011; Rice, Haney, & Wexler, 1998; Rice, Smith, & Gayán, 2009). Children’s difficulties with finite verb morphology primarily involve errors of omission (e.g., He walking and Everyday she dance). Errors of commission, such as He am walking and I dances, are comparable in frequency to rates produced by language-matched controls (Cleave & Rice, 1997; Eadie, Fey, Douglas, & Parsons, 2002; Leonard et al., 1992; Rice & Blossom, 2013; Rice et al., 1995; but also see Pine, Joseph, & Conti-Ramsden, 2004). What are not included in these counts of errors are overregularizations of regular affixes (e.g., he felled, two mices, got hanged up). Children with SLI overregularize past, plural, and passive participle markers, but the frequency at which they make these errors do not typically exceed that of controls (Leonard et al., 2003; Leonard, Eyer, Bedore, & Grela, 1997; Oetting & Horohov, 1997; Oetting & Rice, 1993). Arguably, these types of errors also involve the surface realization of the marked form (regular vs. irregular) rather than an omission or misapplication of a marker (Redmond & Rice, 2001; Rice et al., 2000). Children with SLI are also less able than age-matched controls (and often language-matched controls) to detect errors involving omissions of finite verb markers within sentences during grammatical judgment tasks. At the same time, children with SLI can detect errors of commission (i.e., he am falling), overregularization (i.e., he felled), irregularization (i.e., leck for looked), and other types of omission errors (i.e., he is walk where the progressive -ing is omitted; Montgomery & Leonard, 1998; Redmond & Rice, 2001; Rice, Hoffman, & Wexler, 2009; Rice et al., 1999, 2000; van der Lely & Ullman; 1996). In Rice et al.’s (2009) longitudinal study of children’s grammaticality judgment of finite verb markers, those with SLI, aged 8 to 15 years, earned lower scores than language controls at each of nine annual or semi-annual testing sessions. Moreover, the controls reached ceiling on the task early in the study, whereas those with SLI never did. Leonard, Miller, and Finneran (2009) also examined older children’s sensitivity to grammatical structure but used a

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word monitoring task that required the children, aged 16 years, to press a button when they heard a target word within sentences that were either grammatical or contained a grammatical error before the target. Both grammatical judgment data and reaction time data revealed that the children with SLI (and a group with language impairments and borderline nonverbal IQ scores) were less able than controls to detect omissions of finite verb markers, even though they detected omissions of nonfinite morphemes and inappropriate insertions of finite markers at rates that were no different than the controls’ rates. Although children with SLI omit finite verb morphemes for a longer period than do age- and language-matched controls, and they struggle with grammaticality judgments of finite verb marking through adolescence and likely adulthood, other aspects of their morphological systems appear intact. Oetting and Horohov (1997) and Oetting and Rice (1993) demonstrated this with regular and irregular markers of plural and past tense. In the former, children were asked to produce novel noun compounds with regular and irregular plurals (i.e., he is a X-eater with targets such as mice and dogs), and in the latter, they were asked to produce past tense expressions for pairs of homophonous verbs; one item in the pair depicted an action that could be described with an irregular past tense form (e.g., meet—met), whereas the other was a newly created action that required a child to express the past using a verb that had been derived from a noun (meat—meated). For reasons that are not critical to the presentation here, typically developing children and adults produce past and plural irregular forms differently than regulars within these tasks. For example, irregular nouns can be expressed in the singular or plural form within compounds (e.g., mouse-eater and mice-eater), but nouns taking regular plural markers are only felicitous in the singular form (dog-eater but not dogs-eater). On these tasks, children with SLI perform like controls because their responses show clear distinctions between items requiring regular and irregular markings (see also Grela, Synder, & Hiramatsu, 2005; Jacobson & Schwartz, 2005). There are also other ways in which the morphosyntactic systems of children with SLI are robust. Hadley and Rice (1996) showed that children with SLI understand the distributive properties of auxiliary and main verb BE and DO forms as well as the properties of subject-verb agreement from the earliest appearance of these forms. Cleave and Rice (1997) also noted parallel findings between children with and without SLI for BE forms relative to grammatical function (auxiliary vs. copula) and contractibility (both phonetic as in she’s vs. Bess is and syntactic as in she is vs. is she). Finally, Ogiela, Casby, and Schmitt (2005) examined the types of verbs and verb predicates that children with SLI produce with present progressive -ing, regular and irregular past, and third-person singular -s. Consistent with patterns reported for typically developing children, those with SLI produced -ing most frequently with activity predicates, regular and irregular past forms most frequently with event predicates, and third-person forms most frequently with state and activity predicates combined. Children with SLI have difficulties with other morphosyntactic structures that are not necessarily related to markers of finiteness, but the nature and scope of these difficulties are not yet fully known. For example, mixed findings have been reported for articles and plurals (Eadie et al., 2002; Leonard et al., 1997; McGregor & Leonard, 1994; Oetting & Rice, 1993; Rice & Oetting, 1993; Rice & Wexler, 1996). Findings have been more consistent for subject case marking of pronouns, with group differences favoring language-matched controls (Loeb & Leonard, 1988, 1991; but also see Moore, 1995). Nevertheless, the nature of children’s difficulties with this structure remains elusive because errors are not apparent for all children and error rates differ across forms such as he versus she (Grinstead, Donnellan, Barajas, & Johnson, 2014; Pine et al., 2004; Pine, Rowland, Lieven, & Theakston, 2005; Rispoli, 1998, 1999; Schűtze, 1999; Wexler, Schűtze, & Rice, 1998). Other structures that have been identified as difficult for children with SLI include the possessive ’s, dative preposition to, subject relativer that, optional complementizer that, nonthematic of, infinitival

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complementizer to, and modals could and should (Eyer & Leonard, 1995; Grela, Rashiti, & Soares, 2004; Leonard, 1995; Leonard et al., 1992, 1997; Oetting & Newkirk, 2008; Owen & Leonard, 2006; Schuele & Dykes, 2005; Schuele & Tolbert, 2001). It is important that these areas of potential weaknesses be further pursued so that we can learn whether these deficits are characteristic of most children with SLI or with particular subgroups. Within future studies, it will also be important to determine if weaknesses with any of these structures co-occur with others and/or with other deficits in finite marking and the age at which these weaknesses are most salient. In some but not all cases, the morphosyntactic skills of children with SLI are more affected by experimental manipulations of the stimuli than those of controls. Although manipulations involving the allomorphic characteristics of affixes (e.g., -t vs. -d of past tense and -s vs. -z vs. -əz of plurals) affect children with and without SLI similarly, the inflectional frequency of word roots and the phonotactic properties of the resulting affixed clusters (whether or not the cluster is legal in the final positions of monomorphemic words) do not (Marshall & van der Lely, 2012; Oetting & Horohov, 1997; Oetting & Rice, 1993; van der Lely & Ullman, 1996). For both of these latter cases, marking by children with SLI is influenced by the properties of the stimuli, whereas marking by language-matched controls is not. Leonard et al. (2000) also found that structural priming affected the auxiliary BE productions of children with SLI to a greater degree than language-matched controls, but this finding was not replicated in Leonard et al. (2002; see also Leonard, 2011). Other manipulations of linguistic complexity (e.g., varying the number and type of argument structures) within sentences have yielded similar effects for children with and without SLI, especially when language matching is employed (Grela & Leonard, 2000). In fact, using an elicitation task targeting past tense marking, Owen (2010) varied the argument structure, clause location, and sentence type of the stimuli to determine if these manipulations would impact children with SLI to a greater extent than age- and language-matched controls. Although the children with SLI marked past tense at lower rates than both control groups, and all three groups produced lower rates of marking with increased linguistic complexity, the language-matched control group, not the SLI group, was most affected by the manipulations. Results are more consistent for grammaticality judgment tasks, with experimental manipulations repeatedly showing children with SLI to be more affected than controls (Lum & Bavin, 2007; Miller, Leonard, & Finneran, 2008; Montgomery & Leonard, 2006; Pawłowska, Robinson, & Seddoh, 2014; Purdy, Leonard, Weber-Fox, & Kaganovich, 2014; Redmond & Rice, 2001). For example, Redmond and Rice (2001) found that children with SLI were less able than controls to detect grammatical errors within sentences involving complex syntax, even though they perform similarly to the controls when the stimuli involved simple syntax. Miller et al. (2008) found that when grammaticality judgment tasks involved utterances with many words (i.e., 6–12 as compared to 3–6, which have been used in early studies), children with SLI were less able than controls to detect a number of different types of grammatical errors (i.e., omissions of finite and nonfinite markers and inappropriate insertions of finite makers). Finally, recent SLI studies have begun to include electrophysiological measures to examine children’s sensitivity to the grammaticality of sentences (Fonteneau & van der Lely, 2008; Neville, Coffey, Holcomb, & Tallal, 1993; Purdy et al., 2014; Sabisch, Hahne, Glass, von Suchodeletz, & Friederici, 2009; Weber-Fox, Leonard, Hampton Wray, & Tomblin, 2010). Some of these studies have focused on two event-related potential (ERP) components, anterior negativity and P600, both of which are thought to index morphosyntactic processing. In Purdy et al. (2014), both of these components were measured in children, aged 7 to 11 years, with and without a history of SLI during a grammaticality judgment task involving inappropriate insertion of the finite, third-person marker in local (They talks) and long-distance sentence contexts (She makes Ellen talks). Whereas the controls demonstrated the expected anterior negativity and positive P600 shift when judging

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the grammaticality of both sentence contexts, the children with SLI demonstrated the expected pattern when judging the local sentence contexts only.

Autism and Fragile X Syndrome The morphosyntactic profiles of children with autism and/or fragile X syndrome (FXS) are highly variable, with some children showing relatively intact systems, others showing delays and/or weaknesses relative to their general level of language ability, and still others presenting limits in language that are so severe as to preclude even cursory study of this area. Children with autism and/or FXS are considered together here given that 25–45% of boys with FXS meet the criteria for a codiagnosis of autism, and an even greater percentage (up to 90%) demonstrate autistic symptoms such as hand waving and poor eye contact (Philofsky, Hepburn, Hayes, Hagerman, & Rogers, 2004). Recent studies have also begun to evaluate differences between children as a function of the co-occurrence of these two conditions. Of the studies that have examined morphosyntax (e.g., Bartolucci & Albers, 1974; Bartolucci, Pierce, & Streiner, 1980; Botting & Conti-Ramsden, 2003; Finestack & Abbeduto, 2010; Howlin, 1984; Kjelgaard & Tager-Flusberg, 2001; McDuffie, Kover, Abbeduto, & Lewis, 2012; Perovic, Modyanova, & Wexler, 2013a; Price et al., 2008; Roberts, Rice, & Tager-Flusberg, 2004; Sterling, Rice, & Warren, 2012), research by Roberts et al. and Sterling et al. most closely aligns with other studies that have been completed on children with SLI. Roberts et al.’s (2004) study included 62 children with autism, and the target morphemes were three markers of finiteness (i.e., regular third person and regular and irregular past tense). The children’s use of these structures was examined as a function of their vocabulary abilities (Group 1 = vocabulary score above -1 SD of normative mean; Group 2 = vocabulary score between -1 and -2 SD; Group 3 = vocabulary score below -2 SD). The three groups of children with autism presented different rates of finite marking. Those with vocabulary scores in the normal range (Group 1) had relatively high rates of finite marking, especially when idiosyncratic responses were excluded from the calculations (rates ranged from 81–86%), whereas rates of marking by the children with very low vocabulary scores (Group 3) were much lower (65–68%). Rates of use for Group 2 fell in the middle and are harder to interpret. When all errors were considered, their rates of use were lower but not significantly different from those in Group 1. When idiosyncratic errors were removed, their rate of marking for third person was low (69%), but for past tense, it was relatively high and similar to that of Group 1 (86%). Also, although the children with autism produced a number of error types not seen in children with SLI, morpheme omissions were common and overregularizations were rare (averaged < 1%). Sterling et al.’s study (2012) included 21 boys with FXS. Following the methods of Roberts et al. (2004), finite marking was examined, and the participants were grouped by their vocabulary abilities, with two in Group 1 (i.e., vocabulary score above -1 SD of normative mean), 10 in Group 2 (i.e., vocabulary score between -1 and -2 SD), and nine in Group 3 (i.e., vocabulary score below -2 SD). Results for the children with FXS were consistent with those of Roberts et al. (2004). Rates of finite marking correlated with the children’s vocabulary scores, with children presenting very low vocabulary scores (Group 3) producing lower rates of marking than those with low vocabulary scores (Group 2), and with children presenting vocabulary scores in the normal range (Group 1) producing rates of marking that were near ceiling (96–100%). Interesting and unlike what has been documented for children with SLI, the MLUs of the children with FXS were lower than what one would expect for their observed rates of finite marking and for their vocabulary abilities. Findings from the above studies have been interpreted by Roberts et al., Sterling et al., and many others as indicating overlapping profiles between children with SLI and some children with autism and/or FXS (for review, see Tager-Flusberg, 2004). Family pedigree studies and genetic linkage

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and association studies also support the notion of overlapping profiles between children with SLI and children with autism (cf. Tomblin, Hafeman, & O’Brien, 2003). Nevertheless, the study of morphosyntax in children with autism and/or FXS is complicated by a number of issues. Two of these issues arise from the semantic-pragmatic difficulties of children with autism and/or FXS and their tendencies to perseverate and to produce echolalia (Klusek, Martin, & Losh, 2014). Other issues relate to the measurement of autism, which can be categorical (+/-) or continuous to reflect the severity of the condition, the matching criteria (i.e., nonverbal IQ, receptive vocabulary, MLU, etc.) used to compare different groups of children to each other, and developmental changes in the relation between the language skills and nonverbal cognitive skills of children with autism and/or FXS (McDuffie et al., 2010, 2012; see also Tager-Flusberg, 2000).

Down Syndrome Children with Down syndrome (DS), like children with SLI, present more difficulties with morphosyntax than is expected given their chronological age, nonverbal mental age, vocabulary ability, and MLU; their grammatical errors also primarily involve omissions (Abbeduto & Murphy, 2004; Chapman, Schwartz, & Kay-Raining Bird, 1991; Chapman, Seung, Schwartz, & Kay-Raining Bird, 1998; Eadie et al., 2002; Estigarribia, Martin, & Roberts, 2012; Finestack & Abbeduto, 2010; Laws & Bishop, 2003; Lázaro, Garayzábal, & Moraleda, 2013; Polišenská & Kapalková, 2014; Price et al., 2008; Price, Roberts, Vandergrift, & Martin, 2007; Rutter & Buckley, 1994; Thordardottir, Chapman, & Wagner, 2002; Volterra, Caselli, Caprici, Tonucci, & Vicari, 2003; Zampini & D’Odorico, 2011). However, compared to children with SLI, those with DS present a wider spread between their scores on vocabulary and morphosyntactic tests (Laws & Bishop, 2003) and present stronger morphosyntactic skills when tested in comprehension than production (Chapman et al., 1998; Chapman, Seung, Schwartz, & Kay-Raining Bird, 2000). After controlling for maternal education and nonverbal IQ, children with DS have also been found to present with lower receptive and expressive morphosyntactic skills than children with autism and/or FXS (Estigarribia et al., 2012; Price et al., 2007, 2008). Importantly, the specific nature of the DS morphosyntactic profile is still not well understood, and the study of DS is complicated by these children’s severe deficits in auditory working memory, decreased rates of intelligibility, and fluctuating hearing loss (cf. Chapman & Hesketh, 2000). Nevertheless, the expressive grammatical deficits of DS appear to be more expansive than those with SLI, autism, and/or FXS, with omissions of modals, articles, prepositions, pronouns, conjunctions, adverbial adjuncts, progressive -ing, possessive ’s, and regular plural co-occurring with omissions of finite verb morphemes (Chapman et al., 1998). In a morpheme elicitation task, Laws and Bishop (2003) found that children with DS were also more likely than children with SLI to refuse to reply or to respond with a word that didn’t require a target affix (DS = 30% vs. SLI = 7%). However, when these errors were excluded, the children with DS produced higher rates of past tense marking than did children with SLI. Similarly, using a parent questionnaire and two control groups (one matched for expressive vocabulary and another matched for receptive vocabulary), Lázaro et al. (2013) found that the children with SLI but not those with DS presented morphosyntactic skills below that of the controls. Yet, in Eadie et al.’s (2002) study, children with DS and children with SLI omitted a number of morphemes at rates that were indistinguishable from one another. Findings from these studies are difficult to reconcile because of differences in the matching criteria (mental age vs. MLU vs. receptive and expressive vocabulary) and the age of the children studied (Chapman et al., age range = 5 to 20 years; Laws & Bishop, age range = 10 to 19 years; Eadie et al., age = 7 years; Lázaro et al., age = 2 to 4 years). Type of task (elicitation vs. language sample vs. parent questionnaire), type of language sampling context (narrative vs. conversational), and type of analysis (parametric vs. nonparametric) also varied across these studies.

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Williams Syndrome Children with Williams syndrome (WS) present difficulties with morphosyntax even though the language profiles of these children are often described as precocious in light of their general cognitive deficits and their severe weaknesses in visual-spatial, number concept, and motor skill learning (Karmiloff-Smith et al., 1997; Mervis, 2003; Mervis, Morris, Bertrand, & Robinson, 1999; Van Herwegen, Rundblad, Davelaar, & Annaz, 2011). Emerging findings indicate that these children’s morphosyntactic difficulties differ from those documented for SLI (Bartke & Siegmüller, 2004; Clahsen & Almazan, 1998, 2001; Clahsen & Temple, 2003; Krause & Penke, 2002; Levy, 2002; Levy & Herman, 2003; Perovic, Modyanova, & Wexler, 2013b; Thomas et al., 2001; Volterra et al., 2003; Zukowski, 2005). Children with WS mark many finite verb morphemes (i.e., regular past, third-person singular, and copular and auxiliary be) at rates commensurate to those of mental agematched controls, and their correct use of these structures can be close to ceiling levels by 7 years of age. Children with WS also do not seem to have difficulties with regular plural marking, prepositions in and on, and adjectival -er or the use of various regular affixes on nonce words that exceed their general delays in language and cognition. For children with WS, however, use of irregular morphosyntax (and use of other types of constructions that require access to the mental lexicon) often lags behind that of mental age-matched controls. Interestingly, data collected by Clahsen and colleagues show children with WS producing rates of overregularization that are higher than mental age controls in a variety of tasks that require irregular productions (Clahsen & Almazan, 1998, 2001; Clahsen & Temple, 2003). In contrast, Zukowski (2005) found that children with WS produced rates of overregularization that were lower than those of mental age controls. Moreover, when prompts to highlight the contrasting nature of the referents (e.g., the singular versus plural nature of the stimuli) were provided, the children with WS increased their rate of correct irregular productions to a level that was comparable to that of the controls. Reconciling the findings of these studies is complicated by the fact that the control groups performed very differently across the studies. Like other children with developmental disorders, the most common type of morphosyntactic error children with WS make involves omissions. Nevertheless, Volterra et al. (2003) showed that children with WS are 10 times more likely than controls to add morphemes to sentences when they are asked to repeat them (e.g., on a task with 51 sentences, six children with WS made 44 of these errors; controls made three), and often these added morphemes are relics from previous utterances within the stimuli. On these tasks, children with WS also make some substitutions and word order errors that disrupt the meaning of the sentence; these types of errors are rarely made by controls. Some have suggested that the unique morphosyntactic profile of WS (and the specific error patterns observed during elicitation tasks) is directly related to these children’s particular strengths in, or particular dependency upon, short-term phonological memory for learning language (Lukács, Racsmány, & Pléh, 2001; Robinson, Mervis, & Robinson, 2003; but also see Brock, 2007).

Other Childhood Developmental Disabilities The search for clinical markers and precise behavioral descriptions (i.e., phenotypes) of different types of childhood developmental disabilities has led to a number of cross-etiology comparisons in the area of morphosyntax. These studies have included children with mild to moderate sensorineural hearing loss (Norbury, Bishop, & Briscoe, 2001), developmental coordination disorder (Archibald & Alloway, 2008), severe speech and physical impairments (Redmond & Johnston, 2001), and attention-deficit/hyperactivity disorder (ADHD; Parigger, 2012; Redmond, 2004, 2005; Redmond, Ash, & Hogan, 2015; Redmond, Thompson, & Goldstein, 2011). Emerging findings

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from these studies suggest that, despite significant limitations in important neurodevelopmental areas, these groups do not display morphosyntactic profiles that mirror those of SLI, autism, FXS, WS, or DS. Furthermore, observed variability among these children’s proficiencies with finite marking has not been associated with the severity of their intellectual, perceptual, motor, attention, and executive functioning deficits. As was seen for studies that included children with autism and/or FXS, recent studies have also begun to compare the morphosyntactic profiles of children who present with different combinations of co-occurring disorders, such as deficits in nonverbal intelligence and/or language impairment (Rice et al., 2004), deficits in working memory and/or language impairment (Noonan, Redmond, & Archibald, 2014), and deficits in ADHD and/or language impairment (Redmond et al., 2015). As an example, Redmond et al. (2015) compared the language profiles of children with SLI to children with co-occurring ADHD and language impairment. The purpose of the study was to determine if the co-occurrence of ADHD presented an additive effect on children’s morphosyntactic profiles such that the language abilities of children with ADHD and language impairment would be weaker than those with SLI only. Results did not show an additive effect of ADHD because relative to those with SLI only, children with ADHD and language impairment performed consistently better on measures of finite marking. This study extends work by Redmond et al. (2011), which showed deficits in finite marking and other areas of language to be more pronounced in children with SLI than in typical controls or children with ADHD only. In this study, all four measures of language (i.e., finite marking, sentence recall, nonword repetition, and a standardized test of narrative structure) together differentiated those with and without SLI with 95% accuracy, and finite marking alone differentiated those with and without SLI with 88% accuracy.

Assessment of Morphosyntactic Deficits Documentation of the morphosyntactic deficits of children plays an important role in assessment because grammatical ability is essential for effective spoken and written forms of communication. As shown in the deficits section, fine-grained analyses of morphological productivity and error patterns, as well as new methods (e.g., grammaticality judgment) and measures (e.g., reaction time and ERP measures), may eventually help professionals differentially diagnosis and explain different types of morphosyntactic profiles.

Norm-Referenced Tests Standardized tests have long been used as a time-efficient means of assessing morphosyntax. Examples of norm-referenced tools used in research and clinical practice include receptive tools such as the Test for Auditory Comprehension of Language-4 (Carrow-Woolfolk, 2014) and the Test of Reception of Grammar-2 (Bishop, 2003), expressive tools such as the Structured Photographic Expressive Language Tests (SPELT:Preschool-2: Dawson et al., 2005; SPELT-3: Dawson, Stout, & Eyer, 2003), and subtests of omnibus tests such as the Clinical Evaluation of Language Fundamentals-5 (Semel, Wiig, & Secord, 2013) and Test of Language Development-Primary 4 (Newcomer & Hammill, 2008). Typically, drawings or photographs and questions or cloze prompts are used to assess children’s production of morphosyntactic structures (e.g., The girl spilled the juice. Then what did she do? She_____; Dawson et al., 2005). Although such tasks are generally viewed as measures of expressive morphosyntax, children with stronger receptive abilities may be better able to use the structured prompts to supply target responses (Perona, Plante, & Vance, 2005). A central assumption of norm-referenced test interpretation is that children with language impairments will demonstrate below-average performance. Yet, in a comprehensive, empirical review of commercially available test manuals, Spaulding, Plante, and Farinella (2006) documented

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the inadequacy of this assumption for accurate identification of children with language impairments. Many tests failed to reveal large group differences between children with and without language impairments; however, tests of morphosyntax were generally most robust. Given the limited sensitivity of many tools, Dollaghan (2007) called for the systematic appraisal of diagnostic evidence when selecting assessment tools. Plante and colleagues have argued for the use of data-driven, alternative cutoff points that discriminate maximally between children with language impairments and those with typically developing language (Perona et al., 2005; Plante & Vance, 1994, 1995; Spaulding et al., 2006). For example, in a study exploring the diagnostic accuracy of the SPELT-3, Perona et al. (2005) demonstrated that a standard score cutoff of 95 provided the best classification accuracy, identifying 92% of the children with SLI as affected and 100% of the typically developing children as unaffected. Although the establishment of data-driven cutoffs provides an empirically derived solution to the problem of under-identification, clinicians should also ask why a test has poor sensitivity in the first place. Under-identification is more likely for children with relatively selective impairments, such as children with SLI as compared to children with more global developmental disabilities. To improve identification for children with more selective impairments, content validity should be examined. Sound content validity depends upon sufficient opportunities to measure a particular construct (i.e., content coverage) while avoiding extraneous items unrelated to the construct (i.e., content relevance; McCauley, 2001). On many tools, only a few items may assess structures known to be especially difficult (i.e., finite verb morphology), which are then collapsed with other structures not known to be diagnostically sensitive (e.g., progressive -ing, plural -s) to derive total scores. Table 15.1 illustrates a content analysis of the items contained on the SPELT: Preschool-2 (Dawson et al., 2005) and SPELT-3 (Dawson et al., 2003). As can be seen, on both versions, children’s overall performance on these tests will be heavily influenced by their proficiency with finite verb morphology. However, the way in which these structures are collapsed with less diagnostically sensitive structures may provide some explanation for this tool’s limited sensitivity, as documented by Perona et al. (2005). Table 15.1 Item Analysis by Categorical Distinction Category

Structure

SPELT-P2 Items

SPELT-3 Items

Lexical

Prepositions Plural -s Progressive -ing

1 3, 4, 5 7, 11, 25

1, 2, 3, 4 5, 6, 7 8

Functional Non-tense/ agreement

Articles Possessive ’s Possessive Pronoun

2 8 9

— 29, 30 31, 32, 33, 34

Functional Finite Verb Morphology

Third Singular Present -s Past regular Past irregular Copula Auxiliary BE Modal will Tense + nondeclarative syntactic contexts

19, 20 14, 15 26, 27 16, 17, 18 6, 10, 24 — 22, 23, 33, 34, 35, 36, 37

9, 24, 25, 26 11, 16 14, 23 20, 21, 22, 50 13, 15, 17, 18, 19 10, 12 38, 39, 42, 43, 44, 45, 46, 47, 49

Complex Syntax

Infinitival to Dependent clauses

31, 32 21, 30, 38, 39, 40

24, 25, 26, 49 28, 51, 52, 53, 35, 36, 37, 49

12, 13, 28, 29

27, 40, 41, 48

Other

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Criterion-Referenced Tools Although it is possible to identify broad deficits in morphosyntactic abilities using norm-referenced tools, these tools were not designed to provide descriptive characterizations of children’s progress towards mastery of individual structures or composite constructs such as finite verb morphology. To establish pre-intervention abilities and measure progress over time, criterion-referenced approaches are necessary (McCauley, 2001). Criterion-referenced tools are designed to differentiate individuals who have mastered a particular body of knowledge or set of skills from those who have not. Given that typically developing children master nearly all elements of morphosyntax before kindergarten entry, criterion-referenced approaches provide an appropriate means of assessing progress in morphosyntactic development during the preschool years and beyond. The Rice-Wexler Test of Early Grammatical Impairment (TEGI; Rice & Wexler, 2001) is an example of a standardized, criterion-referenced tool. The TEGI is made up of several elicitation probes designed to assess production of regular past tense, irregular past tense, third-person singular, auxiliary and copula BE in questions and statements, and auxiliary DO in questions. Multiple opportunities are provided to estimate children’s progress towards mastery for each structure as well as for a combined Tense composite. Empirically derived cutoff scores are provided for children, ages 3;0 to 8;11, along with full documentation of the associated levels of sensitivity and specificity for alternative cutoff values. School-age children with performance levels below the criterion have not mastered this knowledge. For preschool children, performance below the criterion indicates those who are unlikely to master tense by kindergarten entry. Thus, the TEGI can be used to identify deficits in finite verb morphology, as well as to document progress towards mastery over time. The TEGI also provides a unique format for eliciting grammaticality judgments from children. In this task, children are asked to judge the statements of two robots, who sometimes say things right and sometimes say things that are not so good. This format reveals whether or not children will accept omissions or errors on morphosyntactic structures as permissible or not. This focus differs considerably from the format of other receptive measures of grammar that determine whether children are able to match morphosyntactic structures to events in the world from a set of contrasting pictures (e.g., Show me The boy climbed the tree.). Training items ask children to judge the use of two lexical morphemes, plural -s and progressive -ing, each used correctly and incorrectly on five occasions. Only if children are able to complete the judgment task with the lexical morphemes do they proceed to items designed to assess knowledge of obligatory tense morpheme use and subject-verb agreement in simple clauses. Children with selective deficits would be expected to perform poorly on items where tense is omitted but would be expected to make more accurate judgments on items involving subject-verb agreement errors and omission of progressive -ing. In contrast, poor performance across all three judgment probes would indicate more general limitations in grammatical development or metalinguistic ability (cf. Rice & Wexler, 2001).

Language Samples Despite its time-consuming nature, language sampling remains the mainstay of authentic, ecologically valid assessment. Certainly, if children do not possess the knowledge to perform well on structured tasks, then deficits are also likely in discourse. However, other children may have sufficient knowledge for constructing adult-like sentences in structured probes yet be unable to deploy this knowledge rapidly and automatically in spontaneous speech (Lahey, 1990). Language sampling is often necessary for young children who do not readily participate in highly structured formal assessment tasks. Clinicians should think consciously about creating

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opportunities for children to use specific morphosyntactic structures when selecting toy sets and structuring the discourse while gathering language samples; otherwise, too few obligatory contexts will be available for analysis (Balason & Dollaghan, 2002; Hadley, Rispoli, Holt, Fitzgerald, & Bahnsen, 2014). Because contexts for third person singular (-3s) are rare in naturally occurring conversation, clinicians must use play activities that support the use of state verbs (e.g., want, need, fit, look, like, love), such as puzzles and construction toys (e.g., The lion fits here. Mr. Potatohead needs a hat. He looks silly). Play with bubbles or games with well-defined endpoints are needed to assess past tense, although low-frequency regular verbs must be introduced into the discourse (e.g., land, stomp, step) to create opportunities for regular -ed. Adult conversational partners should also try to create opportunities for children to use more low-frequency, lexical nouns as grammatical subjects (e.g., The baby is tired/sleeping.) to better assess the productivity of copula and auxiliary BE with low-frequency grammatical subjects (Hadley et al., 2014; Hadley & Walsh, 2014). To create opportunities for copula and auxiliary use in question and negative forms, planned probes and false assertions can be used strategically (see Cleave & Fey, 1997; Cleave & Rice, 1997). To properly identify children with morphosyntactic deficits, diagnostically sensitive measures must be used. Although computation of MLU is widely used in clinical practice, MLU must be interpreted cautiously. Because MLU has a very good positive predictive value, a clinician can be relatively confident of language-impaired status when a positive result is observed (i.e., very low MLU). Therefore, if MLU is very low relative to age expectations, children’s morphosyntactic abilities are likely to be poor relative to age expectations, too (Eisenberg et al., 2001). However, because MLU has a poor negative predictive value, it often overestimates children’s finite verb morphology, in particular, and grammatical complexity, in general, in samples of children with developmental language disorders (e.g., Eisenberg et al., 2001; Goffman & Leonard, 2000; Scarborough, Rescorla, Tager-Flusberg, Fowler, & Sudhalter, 1991; but also see Sterling et al., 2012 for unique MLU profile of children with FXS). Thus, when a negative result is obtained (i.e., age-appropriate MLU), a clinician cannot be confident of a child’s typical language status. Recent research has focused on examining the diagnostic accuracy of specific measures of morphosyntax and global measures of grammaticality from language samples (Eisenberg & Guo, 2013; Gladfelter & Leonard, 2013; Guo & Eisenberg, 2014; Hadley & Short, 2005; Souto, Leonard, & Deevy, 2014). Hadley and Short (2005) and Rispoli, Hadley, and Holt (2009, 2012) proposed that assessment of children with MLUs under 3.00 should focus on the emergence and productivity of morphosyntactic structures (see also Hadley, 1998; Miller & Deevy, 2003; Miller & Leonard, 1998; Scarborough, 1990). They also raised concerns about composite measures of accuracy (i.e., percent correct in obligatory contexts) when applied to children in the earliest period of grammatical development. Composite measures can be disproportionately influenced by the frequency of structures in language samples insofar as opportunities for copula is occur frequently, whereas opportunities for past -ed are comparatively rare. They also noted that children may produce highfrequency constructions (e.g., it’s, that’s) by rote, resulting in an overestimate of morphosyntactic abilities. To minimize these possibilities, Hadley and Short (2005) introduced measures of the variety and productivity of tense and agreement morphemes (i.e., third-person singular present, past tense, copula BE, auxiliary BE, and auxiliary DO) for assessing younger children at risk for SLI. Hadley and Short demonstrated that a cumulative measure of tense/agreement productivity was moderately correlated with a composite measure of accuracy. In addition, fair differentiation of children at risk for SLI from those with low-average language abilities was observed, whereas overlapping distributions resulted for the composite measure of tense accuracy. More recent investigations have demonstrated the predictive validity of tense/agreement productivity growth with outcomes on the TEGI at age 3 (Hadley et al., 2014) and the diagnostic accuracy of tense/agreement productivity for differentiating young 3-year-olds with and without

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language impairments (Guo & Eisenberg, 2014). For children with MLUs above 3.00, composite measure of accuracy are well established as highly sensitive and specific measures for differentiating preschool and early school-age children with and without language impairment (e.g., Bedore & Leonard, 1998; Gladfelter & Leonard, 2013; Rice & Wexler, 1996; Souto et al., 2014). The diagnostic accuracy of global measures of grammaticality obtained from language samples with preschool children have also been examined. Eisenberg and Guo (2013) found the Percentage of Grammatical Utterances to be a diagnostically accurate measure for differentiating 3-year-olds with and without language impairments. Children with typical language were 72% grammatical (range 46–89%), whereas children with language impairments were 38% grammatical (range 17–57%), resulting in 100% sensitivity and 88% specificity. This global measure included a broader range of grammatical errors, such as omission of plural -s, progressive -ing, and articles, pronoun case errors, and verb argument structure errors, as well as omission of tense/agreement morphemes. Souto et al. (2014) examined the diagnostic accuracy of grammatically correct utterances in a 50-utterance sample, following the sentence point procedures of Developmental Sentence Scoring (DSS; Lee, 1974) in 4- and 5-year-olds. Only 60–70% of sentences were well-formed for the children with SLI, whereas over 90% of sentences were well-formed for the typically developing children, with measures of sensitivity and specificity exceeding 90% for both ages. However, when standard DSS scoring was applied, levels of sensitivity and specificity were unacceptable. Souto et al. proposed that standard DSS scoring allows children to earn points in a variety of categories, even when they may earn no points for the Main Verb category or the Sentence Point, thus narrowing the differences between the children with and without SLI. This finding may also have resulted from the collapsing diagnostically sensitive and insensitive items found on the DSS. MLU referencing can also be used to recognize more pronounced deficits in morphosyntax. Goffman and Leonard (2000) provide a detailed discussion of this procedure, illustrating longitudinal change for children with SLI on a composite measure of finite verb morphology relative to expectations for MLU. As expected, pronounced weakness in mastery of finite verb morphology, exceeding expectations based on MLU, was a hallmark characteristic of the children with SLI. Hadley and Holt (2006) examined evidence for individual differences in tense onset between the ages of 2 and 3. This study revealed robust individual differences in the linear growth of tense morpheme productivity at 30 months of age, even after average levels of MLU and MLU growth were controlled (i.e., MLU-referenced in a statistical sense). Further research is needed to determine if individual differences in the rate of tense onset will explain the pronounced deficits observed in accuracy of tense/agreement marking, relative to MLU expectations, among older children with SLI.

Parent Report Tools Although parent report tools have been most widely developed and used to assess young children’s vocabulary knowledge (see Chapter 16 by McGregor), this methodology holds promise for assessing early grammatical abilities as well. To date, only two studies have examined the validity of parent report for assessing early syntax, in general (Dale, 1991; Thal, O’Hanlon, Clemmons, & Fralin, 1999), and only one has focused upon the emergence of finite verb morphology (Bryant, 2003). All three studies have used the MacArthur-Bates Communicative Development Inventories (Fenson et al., 2003). The CDI: Words and Sentences (CDI:WS) includes a section designed to evaluate emerging grammatical complexity for children 16 to 30 months of age. A similar section can be found on the CDI: Level 3 (Dale, 2001) for children 30 to 37 months of age. To explore the validity of parent report for assessing tense onset, Bryant (2003) examined the two different reporting formats on the CDI:WS for obtaining information about finite verb

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morphology. As part of the Helping Verb section, parents reported on the presence/absence of tense/agreement morphemes in their children’s spontaneous speech. Of particular interest were is, am, are, was, and were. As part of the Complexity section, parents were asked to mark one member of sentence pairs that sounds MOST like the way their child talks (e.g., Baby crying versus Baby is crying). Six items focused on past tense -ed, copula BE, auxiliary BE, and auxiliary DO. Parent report for these items was then compared to the children’s frequency and productivity of these forms in spontaneous language samples. In general, parents were better reporters when sentence contexts were provided on the Complexity Section than for the words in isolation listed in the Helping Verb section (see Hadley, 2006, for further discussion). More recently, Rice, Zubrik, Taylor, Gayán, and Bontempo (2014) used a subset of finiteness items from the Complexity section to evaluate the heritability of late onset of finiteness, revealing significant heritability for the onset of this grammatical system in a sample of 473 24-month-old twin pairs.

Intervention for Morphosyntactic Deficits Given the numerous deficits children with developmental language disorders experience, clinicians must consider the extent to which morphosyntax is a priority for a given child. Such targets are likely to be of low priority until children demonstrate social engagement, participate easily in the event structure of everyday activities, and are familiar with the concepts and vocabulary associated with those events. These prerequisites should be followed by intervention activities designed to increase the diversity of basic sentences (Fletcher, Klee, & Gavin, 2012; Hadley, 2006, 2014). Intervention activities that address combinations of different grammatical subjects and lexical verbs simultaneously create opportunities for a range of morphemes, especially those marking tense and agreement, to be used in more variable contexts. Subsequent intervention focused explicitly on morphosyntax should foster children’s participation in activities of daily living and more effective communication within those activities, consistent with Fey, Long, and Finestack’s (2003) first principle of grammar facilitation. When grammar is targeted within a functional communication context, clinicians will be addressing communicative effectiveness simultaneously (Bunce & Watkins, 1995; Johnston, 1985; Rice, 1991). Finally, the priority placed upon morphosyntax may also be influenced by a child’s anticipated future needs for literacy and participation within an academically oriented curriculum. Three decades ago, Johnston (1985) claimed that language intervention should use “focused linguistic input, to narrow the child’s search for order, and simplify his rule formation task” (p. 130). Over the years, evidence documenting the effectiveness of this principle has mounted (e.g., Fey, Cleave, & Long, 1997; Fey, Cleave, Long, & Hughes, 1993; Leonard, Camarata, Brown, & Camarata, 2004; Leonard, Camarata, Pawłowska, Brown, & Camarata, 2006, 2008; Plante et al., 2014; Tyler, Lewis, Haskill, & Tolbert, 2002, 2003). Most programs have been implemented by clinicians. In one study involving evaluation of both clinician- and parent-implemented programs, Fey et al. (1993) found no difference in the average gains between clinician- and parentimplemented programs; however, more consistent results were observed in the clinician-implemented program. Most intervention packages have used focused stimulation as an intervention technique. In this approach, specific targets are selected and then a high density of target forms is produced in a variety of discourse appropriate contexts. Opportunities are also created for children to produce the target by manipulating the non-linguistic environment or discourse context (see Cleave & Fey, 1997; Proctor-Williams, 2009). Other studies have explicitly compared the relative efficiency of specific techniques. For example, conversational recasting has been identified as a more efficient technique than imitation for promoting generalization to spontaneous speech (Camarata, Nelson, & Camarata,

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1994; Nelson, Camarata, Welsh, Butkovsky, & Camarata, 1996; see Chapter 23 by Finestack & Fey), at least when these techniques are used in isolation. The use of imitation, when used to contrast grammatical forms or to practice accessing the phonological form of a target morpheme, remains an issue worthy of further investigation (cf. Fey et al., 2003). Additional research is also needed to advance our understanding of the most efficient goal attack strategies, especially for children with many areas of language deficits. Tyler and colleagues (Tyler et al., 2003) revealed that greater morphosyntactic gains were observed for children with co-occurring phonological and morphosyntactic deficits when intervention targets alternated between these two domains from week to week as opposed to targeting these areas sequentially in 12-week blocks or simultaneously for 24 weeks. More recent research efforts have begun to examine specific characteristics of treatment intensity following the operational definitions advanced by Warren, Fey, and Yoder (2007), such as the effects of dose, dose frequency, dose form, and treatment duration on child outcomes. For example, Bellon-Harn (2012) and Smith et al. (2013) investigated the effect of dose frequency, comparing the same treatment delivered in concentrated conditions (e.g., four times per week) versus distributed conditions (e.g., one or two times per week). Although Bellon-Harn found no differences as a result of condition, Smith et al. observed greater benefit for children who received treatment weekly as compared to daily. Plante et al. (2014) evaluated the effects of conversational recasting in two different input conditions, holding dose and dose frequency constant. While targeting several different grammatical structures (e.g., auxiliary is, past -ed), children in the low variability condition heard 12 different verbs recasted twice each, whereas children in the high variability group heard 24 different verbs recasted once each. Plante and colleagues documented greater intervention progress in the high variability condition, as expected based on principles of statistical learning theory. Despite advances in the knowledge base, practicing clinicians are still faced with the task of selecting and prioritizing morphosyntactic targets. Several important questions remain. For example, is there an ideal time to initiate intervention on morphosyntactic targets? Pawłowska, Leonard, Camarata, Brown, and Camarata (2008) investigated readiness indicators for intervention on agreement morphemes. They found children’s pretreatment abilities on plural -s and subject-verb constructions predicted their progress in intervention. As more is learned about the nature of morphosyntactic growth trajectories, interventions may be better timed to children’s underlying biological propensities and readiness for morphosyntactic learning (Leonard et al., 2006, 2008; Rice, 2004, 2012). Which targets should be prioritized? Initially, intervention should focus on the emergence and productivity of structures within a system, contrasting how related grammatical structures function as a system. Grammatical contrasts have been used to teach novel derivational morphemes in short-term learning paradigms (e.g., Connell, 1987; Kiernan & Snow, 1999; Swisher, Restrepo, Plante, & Lowell, 1995; Swisher & Snow, 1994) as well as in more naturalistic settings (i.e., focused contrasts, Bunce & Watkins, 1995). Will acquisition of one structure facilitate the acquisition of related structures? Recent input and intervention studies have demonstrated this type of crossmorpheme facilitation in young typically developing children (Rispoli & Hadley, 2014) and children with SLI (Leonard et al., 2004, 2006). In Leonard et al. (2004, 2006), children received intervention on either third-person singular present tense or forms of auxiliary BE. Children in each group showed significantly greater use of their targeted morphemes as well as cross-morpheme generalization to the other form. However, cross-morpheme generalization to past tense -ed (which shares tense but not agreement features with the other morphemes) was not observed. The facilitation of morphosyntax also requires clinicians to be knowledgeable about the developmental relationships between the acquisition of grammatical systems, semantic and phonological representation, and discourse. To begin, detection of morphosyntactic forms and analysis of their function will be simplified when the content words in the sentence are already known. Modeling

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and production practice with grammatical contrasts appear to be helpful. Explicit metalinguistic instruction on the rules governing the use of bound morphemes does not seem beneficial for children younger than age 6 (Connell, 1987; Swisher et al., 1995) but may be appropriate for older school-age children (Finestack & Fey, 2009). Clinicians must also know the semantic contexts in which a particular morphosyntactic form is most likely to appear (Johnson & Fey, 2006; Ogiela et al., 2005). Typically developing children produce past -ed morphology with greater accuracy in accomplishment predicates compared to activities, particularly for verbs with more phonologically complex codas (Johnson & Fey, 2006; Johnson & Morris, 2007). Therefore, when targeting past tense morphology, clinicians can assist children by selecting verbs with phonologically simple codas (e.g., showed) and targeting these in semantically facilitating contexts with clear results or endpoints (e.g., He showed me the picture). Similarly, clinicians must know the discourse contexts in which forms are felicitous. If a clinician intends to target third-person singular present, it is crucial to recognize that the simple present is only marked on state verbs (e.g., goes, fits, needs, wants). When this morpheme marks action verbs, its use reflects habitual aspect rather than simple present (e.g., She rides the bus to school [everyday] vs. She is riding the bus to school [right now]. Finally, recent evidence has shown that children with SLI show more progress on this morpheme when intervention focused on its use with verbs from sparse phonological neighborhoods as compared to dense phonological neighborhoods (Hoover & Storkel, 2013). Thus, knowledge of these factors will allow clinicians to modify the task complexity of grammatical interventions more effectively.

Conclusions A large literature base of studies have examined the morphosyntactic deficits of children with language impairments, and comparison studies across etiologies and co-occurring disorders have begun to appear in the literature. Findings from these studies suggest that there are both similarities and differences in the manifestation of morphosyntactic deficits across disorders as well as subsets of children across clinical populations who present with similar difficulties. Research is needed to explain the patterns of relative strengths, weaknesses, and error types observed across these different groups of language learners. The study of children’s morphosyntactic deficits has also led to several assessment approaches that can be used to improve the identification of children with morphosyntactic deficits in different clinical populations. Research is needed to refine assessment tools for use with younger children in particular. Finally, several comprehensive treatment programs have been developed to facilitate children’s development of morphosyntax. Research is needed to determine the optimal timing, intensity, and duration of these interventions to maximize children’s successful participation in their social and academic settings.

Acknowledgements The authors would like to thank Matthew Rispoli and Christy Seidel for numerous discussions about morphosyntax. Appreciation is also extended to Sean Redmond for his expertise in attention deficit/hyperactivity disorders and Audra Sterling for her expertise in children with FXS and other intellectual deficits.

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16 SEMANTICS IN CHILD LANGUAGE DISORDERS Karla K. McGregor

Children with deficits in semantics have difficulty understanding and conveying the meanings of words, sentences, and extended discourse. The nature of these deficits varies from population to population and, of course, from child to child. This chapter is an overview of semantic deficits that characterize selected pediatric populations as well as state-of-the-art assessments and interventions for addressing these deficits.

Semantic Deficits across Populations Specific Language Impairment One of the first and most persistent signs of specific language impairment (SLI) is a vocabulary marked by limited breadth and depth. As a group, children with SLI begin expressing meaning with conventional words 11 months later than do typical children (Trauner, Wulfeck, Tallal, & Hesselink, 1995). Delays in receptive vocabulary may occur as well, and late talkers who exhibit receptive delays are more likely to be diagnosed with SLI during the preschool years than their late-talking peers with intact receptive abilities (Thal, Reilly, Seibert, Jeffries, & Fenson, 2004). Relative to age-mates, children with SLI continue to exhibit deficits in novel word learning (Kan & Windsor, 2010), receptive vocabulary (Bishop, 1997; Clarke & Leonard, 1996), and expressive vocabulary (Leonard, Miller, & Gerber, 1999; Thal, O’Hanlon, Clemmons, & Fralin, 1999; Watkins, Kelly, Harbers, & Hollis, 1995) throughout the preschool period. Those who present with SLI in kindergarten demonstrate consistent gaps in their vocabulary knowledge into their high school years (McGregor, Oleson, Bahnsen, & Duff, 2013). During the school years, the vocabulary deficits of children affected by SLI may present as word-finding problems (Dockrell, Messer, George, & Wilson, 1998; Lahey & Edwards, 1999; Leonard, Nippold, Kail, & Hale, 1983), sparse lexical semantic representations (McGregor & Appel, 2002; McGregor, Newman, Reilly, & Capone, 2002; Munro & Lee, 2005), or sparse semantic category knowledge (Kail & Leonard, 1986). Comprehending the meaning of connected speech may be problematic as well. For example, when asked to identify agents in NVN, NNV, and VNN constructions, the comprehension strategies applied by school-aged children with SLI were easily disrupted by increases in external processing demands (Evans, 2002). Schoolchildren with SLI have difficulty comprehending the meaning of stories, whether the meanings are explicit or implicit and

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whether the stories are presented verbally or nonverbally (Bishop & Adams, 1992). Adolescents with SLI continue to present with difficulties in communicating meanings, especially via figurative language and extended discourse (Norbury, 2004; Rinaldi, 2000). Semantic deficits of children with SLI may stem from a number of causes. First, these children have poor short-term memory (Bishop, North, & Donlan, 1995; Dollaghan & Campbell, 1998; Gathercole & Baddeley, 1990; Montgomery, 1995). Given that mapping words to meanings requires holding novel word forms in short-term memory while hypothesizing the meaning of the word from contextual cues, this limitation could reduce successful word mappings. In fact, Gathercole and Baddeley (1990) found children with SLI to lag 20 months behind typical children in receptive vocabulary development but four years behind these same children in short-term memory performance, leading them to conclude that the word-learning lag was a consequence of the extraordinary memory deficit. Moreover, the short-term memory deficit could reduce the processing of meaning in sentences and connected discourse. Children with SLI do have difficulty comprehending sentences, especially as length, syntactic complexity, and simultaneous processing demands increase (Marton & Schwartz, 2003). One can look to the grammar for another potential source of the semantic deficit. The grammatical impairment that is considered a hallmark of SLI (Leonard, 1998) may have knock-on effects on the semantic system. Children who are normally developing use syntactic cues such as word order, function words, and inflections to “bootstrap” the semantics of words. For example, one can infer from a sentence like “the girl glimmed some pov” that glim is an action that is carried out on objects and that pov is an object of a particular kind, a substance. Given the grammatical impairments of children with SLI, they should be less able to make such inferences. Indeed, syntactic bootstrapping limitations among children with SLI are well documented (Eyer, Leonard, Bedore, McGregor, Anderson, et al., 2002; Johnson & de Villiers, 2009; O’Hara & Johnston, 1997; Rice, Cleave, & Oetting, 2000; Shulman & Guberman, 2007; van der Lely, 1994). Syntactic bootstrapping is thought to play a more important role in verb learning than in noun learning (Gentner & Boroditski, 2001; Gillette, Gleitman, Gleitman, & Lederer, 1999; Gleitman, 1990); therefore, the syntactic bootstrapping limitations of children with SLI may be one basis for their problems with verbs (Conti-Ramsden & Jones, 1997; Fletcher & Peters, 1984; Kan & Windsor, 2010; Loeb, Pye, Redmond, & Richardson, 1996; Oetting, Rice, & Swank, 1995; Watkins, Rice, & Moltz, 1993; Windfuhr, Faragher, & Conti-Ramsden, 2002). Mental state predicates may present challenges for similar reasons (Johnston, Miller, & Tallal, 2001). Finally, consider the implicit learning deficits that characterize SLI. People with SLI are poor at using probabilistic information in the input to learn visual-motor procedures (e.g., Lum, Gelnic, & Conti-Ramsden, 2010; Tomblin, Mainela-Arnold, & Zhang, 2007) and grammar (Plante, Gomez, & Gerken, 2002). They also need to double the exposure that their age-mates need to use probabilistic information to parse the speech stream into words (Evans, Saffran, & Robe-Torres, 2009). Given that tracking of word and referent co-occurrences across situations is one route into the learning of word meanings (Smith & Yu, 2008), this deficit could play a role in the problems of vocabulary breadth and depth that characterize SLI.

Developmental Delay Children with developmental delays present with language impairment secondary to intellectual ability; however, their semantic systems are not necessarily commensurate with their IQ (see Chapter 2 by McDuffie et al. for additional information about developmental delays). Scores on intelligence tests account for only 29% of the variability in vocabulary scores earned by children and adolescents with developmental delays of mixed etiology (Facon,

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Facon-Bollengier, & Grubar, 2002). Furthermore, the relationship between intelligence and semantic development depends on which aspects of semantic development are being measured. For example, the mental age scores of schoolchildren with mild intellectual disabilities correlate more strongly with their knowledge of relational labels (e.g., size, direction, quantity, time) than with their knowledge of labels for objects and events (Fazio, Johnston, & Brandl, 1993). Finally, the relation between mental age and semantic development varies with etiology. Children with Down syndrome (DS) and Williams syndrome (WS) will be compared to illustrate this point. In some respects, children with DS present profiles of overall language development that parallel those of children with SLI (see Laws & Bishop, 2004 for a review); in particular, semantic development is often stronger than grammatical development but is still delayed relative to nonverbal mental age (Kumin, 1996). A recent meta-analysis of 15 studies of language skills in children with DS revealed no differences relative to mental age controls in receptive vocabulary but performances more than .5 SD lower on measures of expressive vocabulary (Naess, Lyster, Hulme, & MelbyLervag, 2011). Like children with SLI, children with DS may experience semantic deficits because of limitations in short-term memory (Chapman, 1995; Hick, Botting, & Conti-Ramsden, 2005; Jarrold & Baddeley, 1997; Jarrold, Thorn, & Stephens, 2009; Kay-Raining Bird & Chapman, 1994; Mervis, 1990; Wang & Bellugi, 1994, but see Mosse & Jarrold, 2011 for an alternative view). Hick et al. (2005) compared the vocabulary development, on both receptive and expressive levels, of children with DS or SLI and their normally developing peers over the course of a year, beginning when participants in all three groups presented with nonverbal mental ages between 42 and 60 months. The DS and SLI groups had similar vocabulary scores at the final test, and both groups were significantly lower than their normal peers. However, their patterns of vocabulary growth varied over the course of the year. The children with DS began at a higher level, but their vocabulary growth plateaued, whereas the children with SLI made slow but steady progress. Miller (1995) reported deficits in the rate of vocabulary learning in children with DS relative to mentalage peers, with these deficits increasing with age, again suggesting a plateau. In contrast to children with DS, the receptive vocabularies of children with WS are higher than mental age expectations and sometimes higher than chronological age expectations as well (Brock, 2007). This observation must be qualified in several ways. First, superior vocabulary skills are not yet apparent in younger children affected by WS (Thal, Bates, & Bellugi, 1989); in fact, emergence of first words is late and often commensurate with mental age (Mervis, Robinson, Rowe, Becerra, & Klein-Tasman, 2003). Second, a number of studies have revealed qualitative differences in semantic knowledge among children with WS. For example, their understanding of the meaning of relational terms (e.g., before/after; many/few; under/over) (Mervis & John, 2008) and metaphors (e.g., a flood of people) (Annaz, Van Herwegen, Thomas, Fishman, Karmiloff-Smith, et al., 2009) is particularly weak relative to their general receptive vocabulary levels. Also, their ability to define words, retrieve words for naming, and identify names in contexts that require fine-grained semantic knowledge are weaker than those of their typically developing mental-age peers (Brock, 2007). Finally, children with WS do not abide by the same word-learning heuristics as normally developing children, indicating atypical developmental pathways (Laing, Butterworth, Ansari, Gsödl, Longhi, et al., 2002; Stevens & Karmiloff-Smith, 1997). In summary, it is difficult to characterize semantic deficits associated with developmental delay. Degree of intellectual disability accounts for the deficit to some extent, but patterns of deficit vary with etiology and the aspect of semantic development under consideration.

Autism Spectrum Disorders As a group, children with autism spectrum disorders (ASD) are heterogeneous. Their language abilities range from normal to nonverbal (for additional information on autism, see Chapter 3 by 394

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Gerenser & Lopez). Semantic development is a concern for many of these children. Among those who are verbal, loss of meaningful words in the expressive vocabulary is a unique, but not universal, symptom of ASD during the second year of life (Lord, Shulman, & DiLavore, 2004), thus the potential usefulness of word-learning abnormalities as a clinical warning sign of autism. Even when children with ASD do add words to their lexicons, they may do so via different mechanisms and strategies than other children. Consider, for example, that typical children tend to operate with a shape bias; that is, when they learn a new word for an object, they infer that other objects that are similarly shaped are called by that same word. Tek, Jaffery, Fein, and Naigles (2008) demonstrated that children with ASD were not shape biased even though they could discern shape and even though they knew as many words as typical children who were shape biased. They conclude that “even though children with ASD can learn and produce words, they do not seem to organize them into abstract conceptual units” (Tek et al., 2008, p. 220). Another qualitative difference was reported by Norbury, Griffiths, and Nation (2010). They trained children with ASD but average receptive vocabulary scores in a task that required mapping a novel word form to one of three unusual objects. The correct mapping required paying attention to the eye gaze of a woman seated behind the objects. The children with ASD were as accurate at using the gaze cue as their typical vocabulary-mates, but they did not fixate on the woman’s face as long. Moreover, immediately after training, they were better at recalling the word forms than at describing the semantic features of the referents, whereas the reverse was true of the typical peers. The authors conclude that children with ASD privilege form over meaning. This finding is especially interesting in light of a report that children with ASD have greater receptive than expressive vocabulary deficits (Hudry, Leadbitter, Temple, Slonims, McConachie, et al., 2010). Among children with ASD, the frequency of social bids toward communicative partners (e.g., verbal imitation and use of gesture to initiate joint attention) is positively associated with expressive vocabulary growth (Smith, Mirenda, & Zaidman-Zait, 2007), and interventions that teach joint attention facilitate expressive vocabulary growth (Kasari, Gulsrud, Freeman, Paparella, & Hellemann, 2012). Because most children with ASD find it challenging to attend to and process social information, they may miss important cues to word meaning. For example, children with ASD are less able than their peers with developmental delays to infer an intended referent from a speaker’s eyegaze (Baron-Cohen, Baldwin, & Crowson, 1997), and children with ASD who are highfunctioning are less able than age-mates to infer the correct semantic category boundaries from social-communicative context (McGregor & Bean, 2012). Perhaps the mismappings that result from the inability to fully utilize social cues to meaning explain, in part, the use of neologisms and idiosyncratic words on the part of speakers with ASD (Volden & Lord, 1991). For children with ASD, deficits in social cognition disrupt development of a theory of mind (Baron-Cohen, Tager-Flusberg, & Cohen, 1993). Theory of mind refers to the awareness that others have mental and emotional states and that these states may differ from one’s own or from reality. Theory of mind deficits are reflected in the difficulty that children with ASD have in comprehending and using mental state terms such as “imagine” and “pretend” (Baron-Cohen, 1991; Baron-Cohen, Ring, Moriarty, Schmitz, Costa, et al., 1994). Theory of mind deficits also lead to problems sustaining conversations and constructing coherent narratives (Bruner & Feldman, 1993) and, potentially, to problems with story retelling (Gabig, 2008). Recent research reveals the existence of a subgroup of children that is particularly relevant to discussions of semantics and ASD. These children have a profile of language deficits similar to those of children with SLI (Kjelgaard & Tager-Flusberg, 2001; Tager-Flusberg & Joseph, 2003). This profile includes some weaknesses in semantic processing. For example, Norbury (2005) compared two groups of 9- to 17-year-olds with ASD, one group with low verbal performance and one with verbal abilities in the normal range, with a group of children with SLI and a group of normally developing age-mates. These groups participated in tasks requiring word-to-picture matching for 395

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words with both dominant meanings (e.g., bank to mean a place to keep money) and secondary meanings (e.g., bank to mean the edge of a river). The children with ASD and low verbal abilities performed as poorly as the children with SLI. These children were less able than the other two groups to identify secondary word meanings correctly and were less efficient in using semantic context to facilitate this identification. Another similarity emerges by comparing the work of McGregor and Waxman (1998) and Tager-Flusberg (1985). These investigators found typical organization of the semantic lexicons of children with ASD and SLI, respectively, but in both cases lexicons that were less developed and more prone to error. A final similarity is that children with ASD and those with SLI have difficulty using sentences and discourse to aid interpretation of meaning (López & Leekam, 2003; Montgomery, 1995; Norbury, 2004). Despite these overlapping profiles, the similarities between children with SLI and children with ASD plus low verbal abilities are not without limit. Of the two, children with SLI are poorer at using syntax to bootstrap the referents of new words (Shulman & Guberman, 2007).

Hearing Impairment Children with hearing impairment (HI) face some obvious challenges in learning language via audition (for a more detailed summary of hearing impairment, see Chapter 4 by Waldman DeLuca & Cleary). There is an inverse relationship between the severity of HI and outcomes on measures of receptive vocabulary (Wake, Poulakis, Hughes, Carey-Sargeant, & Rickards, 2005) and expressive vocabulary (Vohr, Jodoin-Krauzyk, Tucker, Topol, Johnson, et al., 2011). Age at enrollment in intervention is also predictive of outcome. Among a group of 5-year-olds with congenital HI, children enrolled in an intervention by 11 months of age demonstrated significantly better receptive vocabulary than did later-enrolled children, and the scores of the early-enrolled children were similar to those of their normal-hearing age-mates (Moeller, 2000). In a prospective study of infants identified via a newborn screening program, those who enrolled in an intervention at or before age 3 months had better expressive vocabulary outcomes than later-enrolled children. Moreover, only those who had mild losses and were enrolled at or before 3 months performed as well as hearing peers (Vohr et al., 2011). Older children with HI, those already receiving intervention, continue to struggle with word learning. For example, only half of children with profound losses who are implanted early and who receive early intervention and consistent audiological management achieve age-appropriate vocabulary by school entry (Hayes, Geers, Treiman, & Moog, 2009). Walker and McGregor (2013) trained children with cochlear implants as well as age-matched and vocabulary-matched hearing children on eight novel words and their referents over the course of two sessions. The children with cochlear implants and the younger vocabulary-matched peers did not differ on any outcome measure, suggesting a strong influence of existing vocabulary on new vocabulary learning. When compared to age-mates, the children with cochlear implants were poorer at comprehending the new words immediately after training, and they were poorer at both comprehending and producing the new words one day later. Only the hearing children improved over the one-day interval, suggesting less effective memory consolidation mechanisms on the part of the children with cochlear implants. Slow word learning also characterizes children with milder losses. Stelmachowicz, Pittman, Hoover, and Lewis (2004) examined word learning in schoolchildren with mild-to-moderate HI and their normal-hearing age-mates. The tasks involved exposure to eight novel words in an animated story and a post-test that required selecting the correct pictured referent from an array upon hearing each of the newly exposed words. The children with HI identified fewer referents. Significant predictors of performance were existing vocabulary knowledge, hearing status, dB level

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at which the words were presented, and number of exposures to the target words in the story. Beyond hearing the words, why might children with HI have any difficulties with word learning? One hypothesis is that, because of limited acuity, their language development slows, and, without the foundation of a strong linguistic system, bootstrapping and integration of new words into the semantic lexicon is difficult. This hypothesis is supported indirectly by the fact that extant vocabulary size predicts word learning in children with HI (Stelmachowicz et al., 2004; Walker & McGregor, 2013). Semantic deficits associated with HI are not limited to the lexicon—rather, several studies reveal broader deficits. As a group, children with HI are late to produce two-term semantic relations (Kiese-Himmel & Ohlwein, 2003). Schoolchildren who are deaf are less able to identify semantic coordinates (e.g., bee and ant) and superordinate semantic relations (e.g., bee and insect) than their hearing peers, even when the task involves picture matching rather than words (Ormel, Gijsel, Hermans, Bosman, Knoors, et al., 2010). Also, children and adolescents with HI demonstrate delays in theory of mind. Children ranging from 6 to 18 years (mean age = 15.28 years), with hearing losses ranging from mild to profound, comprehended emotion labels at a level comparable to that of their much younger hearing peers (mean age = 8.77 years) matched on verbal ability (Dyck, Farrugia, Shochet, & Holmes-Brown, 2004). Depressed language levels and reduced exposure to talking about mental states may limit access to such labels (Peterson & Siegal, 2000). Finally, adolescents with HI have great difficulty applying top-down, contextual strategies to pull meaning from written text (Banks, Fraser, Fyfe, Grant, Gray, et al., 1989), especially when those meanings are implicit (Doran & Anderson, 2003).

Reading Impairment By definition, children with dyslexia (for more information on dyslexia, see Chapter 5 by Shaywitz & Shaywitz) do not have semantic deficits: rather, their reading problems involve difficulty decoding print, and that difficulty is generally attributed to deficits in phonological processing (e.g., Shankweiler et al., 1995). However, there are important reasons to pay attention to semantics when considering children with reading impairments (see review in McGregor, 2004). First, not all children with reading impairments have dyslexia. A subgroup of reading-impaired children is composed of “poor comprehenders.” Unlike children with dyslexia, these children can decode print, but they have difficulty attaching meaning to it (Nation & Snowling, 1998; Oakhill, 1982; Stothard & Hulme, 1995). These children are particularly challenged when they must depend on semantic context for correct interpretation, as required for low-frequency exception words (Nation & Snowling, 1998). Their semantic deficits extend to oral language as well. Nation and Snowling (1998) found these children to be weaker than normal age-mates on both receptive and expressive subtests of the Test of Word Knowledge (Wiig & Secord, 1992). In addition, on probes of oral language ability, they were poorer at synonym judgment (e.g., fast–quick) but not rhyme judgment (e.g., joke–coke) and poorer at semantic fluency (e.g., say all of the animals you can think of) but not rhyme fluency (e.g., say all the rhymes you can think of for the word “plate”). Though these children can learn orally presented word forms as well as their normal age-mates, they are particularly weak in learning the meanings of those words (Nation, Snowling, & Clarke, 2007). Deficits in both verbal short-term memory and inhibition may underlie both the learning of meaning and the extraction of meaning from texts (Borella, Carretti, & Pelegrina, 2010; Pimperton & Nation, 2010). A second population of impaired readers is also relevant to discussions of semantics. Children with hyperlexia decode print at grade levels above their general functioning; however, this skill is sharply dissociated from their ability to attach meaning to print (Nation, 1999; Silberberg &

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Silberberg, 1967). Children with hyperlexia typically have receptive and expressive oral language deficits as well. These deficits include problems with single-word meanings (Aram, Rose, & Horwitz, 1984; Nation, Clarke, Wright, & Williams, 2006) and meanings expressed by sentences and narrative discourse (Snowling & Frith, 1986). These children may learn to read words before they can speak them (Elliot & Needleman, 1976). Unlike children with dyslexia, children with hyperlexia typically have some degree of intellectual disability (Nation, 1999). They also have opposite strengths and weakness: as compared to children with dyslexia, their decoding is superior but their comprehension is much poorer (Snowling, 1987). A third reason to focus on semantics and reading impairment is that people who have difficulty reading read more slowly, less often, and with less enjoyment, thereby limiting important opportunities for word learning throughout the life-span. These missed opportunities tend to exacerbate any existing deficiencies in the semantic system, creating a downward spiral of skills relative to developmental expectations, a phenomenon dubbed the “Matthew effect” (Stanovich, 1986). Evidence for the reading–semantics connection is that, among children (Cunningham & Stanovich, 1991; Echols, Stanovich, West, & Zehr, 1996) and adults (Stanovich & Cunningham, 1992), exposure to print accounts for significant variance in vocabulary size, even when age, IQ, reading comprehension, and phonological decoding skills are factored out. More direct evidence of Matthew effects is that the size of vocabulary gaps between good and poor readers grows over time (Cain & Oakhill, 2011; Serniclaes, Sprenger-Charolles, Carré, & Demonet, 2001; White, Graves, & Slater, 1990; but see Bast & Reitsma, 1998). Matthew effects are generally thought of in connection with reading impairment; however, children from each of the populations considered in this chapter may be prone to these effects. In particular, slow reading development is frequently associated with SLI (Catts, 1993; Catts, Fey, Zhang, & Tomblin, 1999; Stothard, Snowling, Bishop, Chipchase, & Kaplan, 1998) and HI (Conrad, 1977; DiFrancesca, 1972; Holt, Traxler, & Allen, 1997). Furthermore, a number of children with ASD exhibit hyperlexia (Burd & Kerbeshian, 1985; Grigorenko, Klin, Pauls, Senft, Hooper, et al., 2002; Whitehouse & Harris, 1984).

Summary Semantic deficits are characteristic of a variety of developmental language disorders. Regardless of diagnosis, some symptoms are common. These include slow word learning, difficulties with semantic content that relies upon theory of mind, problems using nonliteral and secondary meanings, and limitations in use of sentence and discourse contexts to infer meaning. Despite commonalities across diagnostic groups, the nature and severity of semantic deficits will vary greatly from child to child. Therefore, careful assessment will be necessary to guide clinical management.

Assessment Assessment of semantics is trickier than it may first appear. Beyond the very earliest stages of semantic development, the vocabulary is too large to measure in its entirety. Also, because children find themselves in very different learning contexts, vocabulary is extremely individual in its content. The child with a bedtime prayer ritual has a meaning attached to the word pray; the child whose parents are dairy farmers knows the difference between calf, cow, and bull. Furthermore, though we can determine that a child does or does not know a given word, it is more difficult to determine the depth of knowledge for any given word. The dairy farmer’s child is certainly not alone in knowing the meaning of cow at a young age, but she may well have a deeper knowledge of cow than the child of the accountant who lives down the street. Finally, meaning is not conveyed by single words alone but by combinations of those words into phrases, sentences, and textual

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discourse. Measuring semantic development at each of these levels is important, but it is also difficult, as the complete disentangling of semantic and grammatical contributions to any observed deficits may not be possible. The following sections summarize the primary approaches to assessment of semantics, and the best uses of each approach are emphasized.

Parent Report For the child who is very young or very low functioning, parent report can be a valid, reliable, and efficient means of determining the approximate size of the lexicon. Checklists (e.g., Fenson, Dale, Reznick, Bates, Hartung, et al., 1993; Rescorla, 1989) have largely replaced diary methods as a means of gathering parents’ reports of their children’s vocabulary knowledge. Checklists are preferred to diaries because they are more efficient, involving a one-time tally as opposed to daily notes, and more reliable, involving recognition rather than free recall. The MacArthur-Bates Communicative Development Inventory (MBCDI; Fenson et al., 1993) is a widely used checklist. The Words and Gestures form of the MBCDI assesses the receptive and expressive vocabulary of children functioning between 8 and 16 months of age by asking parents to endorse object, action, and description words that their child understands and says. The Words and Sentences form of the MBCDI is a 680-word checklist designed to assess the expressive vocabularies of children functioning in the 16–30-month age range. A large literature supports the reliability and validity of the MBCDI (see Fenson, Marchman, Thal, Dale, Bates, et al., 2007, for a review). The MBCDI allows an estimation of vocabulary size and comparison to normative expectations via percentile and age equivalency scores. Normative data are also available for many different language communities (Fenson et al., 2007), including deaf and hard-of-hearing children who use English (Mayne, Yoshinaga-Itano, Sedey, & Carey, 2000a, 2000b) and American Sign Language (ASL; Anderson & Reilly, 2002). A unique aspect of the MBCDI is that it includes normative data for individual words for both English and Spanish speakers (Dale & Fenson, 1996; http://www. sci.sdsu.edu/cdi/). With this database, one can determine at what age the majority of children in the normative sample were reported to know, for example, the verb tickle or the preposition beside. Therefore, the MBCDI may be effective not only as an assessment instrument but also as one means of selecting appropriate vocabulary to target in early intervention. Finally, several recent or pending improvements to the MBCDI stand to increase its clinical utility. These include a short form for maximum efficiency in educational settings, a specialized form that is maximally sensitive to the performance of lower-functioning children, and a database on MBCDI performance from a number of clinical populations, including children with DS, cleft palate, and drug exposure (Fenson et al., 2007).

Standardized Tests To measure receptive or expressive semantics at the word, phrase, sentence, or text levels in the older or higher-functioning child, standardized tests are a frequently employed option. A sample of standardized tests designed in part or full to measure semantics appears in Table 16.1, together with target ages. Most of these tests estimate the child’s semantic knowledge and compare that knowledge to that of the normative sample via standard scores, percentiles, and age equivalencies. These tests do not attempt to document the entire semantic system as this becomes an impossible goal once vocabulary size increases beyond roughly 500 or 600 words and word combining is well underway (Fenson, Dale, Reznick, Bates, Thal, et al., 1994). Rather, these tests estimate receptive or expressive development by sampling performance on what is meant to be a representative subset of the system. They do so efficiently and they are widely used; however, some caution is warranted.

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Table 16.1 Some Standardized Tests That Measure Semantic Development Test

Receptive/Expressive

Age

Adolescent Language Screening Test (ALST; Morgan & Guilford, 1984)

receptive, expressive

11;0–17;11

Assessing Semantic Skills Through Everyday Themes

receptive, expressive

3;0–9;1

Bankson Language Test (BLT–2; Bankson, 1990)

receptive, expressive

3;0–6;11

Boehm Test of Basic Concepts–3 (Boehm, 2000)

receptive, expressive

5;0–7;11

Boehm–3 Preschool (Boehm, 2001)

receptive, expressive

3;0–5;11

Carolina Picture Vocabulary Test (CPVT; Layton & Holmes, 1985)

receptive

4;0–11;6

Clinical Evaluation of Language Fundamentals (CELF–5; Wiig, Semel, & Secord, 2013)

receptive, expressive

5;0–21;0

Clinical Evaluation of Language Fundamentals: Preschool–2 (CELF: P–2; Wiig, Secord, & Semel, 2005)

receptive, expressive

3;0–6;11

Clinical Evaluation of Language Fundamentals (CELF–4 Spanish)

receptive, expressive

5;0–21;0

Comprehensive Assessment of Spoken Language (CASL; CarrowWoolfolk,1999)

expressive

3;0–21;11

Comprehensive Receptive and Expressive Vocabulary Test (CREVT–3; Wallace & Hammill, 2013)

receptive, expressive

4;0–89;0

Detroit Test of Learning Aptitude Primary–3 (DTLA–P:3; Hammill & Bryant, 2005)

expressive

3; 0–9;11

Developmental Indicators for the Assessment of Learning (DIAL–3; Mardell-Czudnowski & Goldenberg, 1998)

receptive, expressive

3;0–6;11

Expressive One-Word Picture Vocabulary Test (EOWPVT–4; Brownell, 2010)

expressive

2;0–80+

Expressive One-Word Picture Vocabulary Test: Spanish-Bilingual Edition (EOWPVT–SBE; Brownell, 2000a)

expressive

4;0–12;0

Expressive Vocabulary Test (EVT–2; Williams, 2007)

expressive

2;6–90 +

Language Processing Test 3: Elementary (LPT 3: Elementary; Richard & Hanner, 2005)

receptive, expressive

5;0–11;11

Oral and Written Language Scales (OWLS; Carrow-Woolfolk, 1996)

receptive, expressive

5;0–21;11

Peabody Picture Vocabulary Test (PPVT–4; Dunn & Dunn, 2007)

receptive

2;6–90 +

Preschool Language Assessment Instrument (PLAI–2; Blank, Rose, & Berlin, 2003)

receptive, expressive

3;0–5;11

Preschool Language Scale (PLS–5; Zimmerman, Steiner, & Pond, 2011)

receptive, expressive

Birth–7;11

Preschool Language Scale, Spanish Edition (PLS–5 Spanish; Zimmerman, Steiner, & Pond, 2012)

receptive, expressive

Birth-7;11

Receptive-Expressive Emergent Language Test (REEL–3; Bzoch, League, & Brown, 2003)

receptive, expressive

Birth–3;0

Receptive One-Word Picture Vocabulary Test (ROWPVT–4; Brownell, 2010)

receptive

2;0–80+

Receptive One-Word Picture Vocabulary Test: Spanish-Bilingual Edition (ROWPVT–SBE; Brownell, 2010)

receptive

4;0–12;0

New Reynell Developmental Language Scales (RDLS–IV; Edwards et al., 2011)

receptive, expressive

3;0–7;6

Rossetti Infant-Toddler Language Scale (RITLS; Rosetti, 2006)

receptive, expressive

Birth–3;0

Test of Adolescent and Adult Language (TOAL–4; Hammill, Brown, Larsen, & Wiederholt, 2007)

receptive, expressive

12;0–24;11

Semantics in Child Language Disorders

Test

Receptive/Expressive

Age

Test of Adolescent/Adult Word Finding (TAWF; German, 1990)

expressive

12;0–80;0

Test for Auditory Comprehension of Language (TACL–4; CarrowWoolfolk, 2014)

receptive

3;0–12;11

Test of Early Language Development (TELD–3; Hresko, Reid, & Hammill, 1999)

receptive, expressive

2;0–7;11

Test of Early Language Development: Spanish (TELD–3 Spanish; Ramos & Ramos, 2007)

receptive, expressive

2;0–7;11

Test of Language Development: Intermediate (TOLD: I–4; Hammill & Newcomer, 2008a)

receptive, expressive

8;0–17;11

Test of Language Development: Primary (TOLD: 4P; Hammill & Newcomer, 2008b)

receptive, expressive

4;0–8;11

Test of Problem Solving-2 Adolescent (TOPS–2; Bowers, Barret, Huisingh, Orman, LoGiudice , 2007)

receptive, expressive

12;0–17;11

Test of Semantic Skills: Intermediate (TOSS: I; Bowers, Huisingh, LoGuidice, & Orman, 2004a)

receptive, expressive

9;0–13;11

Test of Semantic Skills: Primary (TOSS: P; Huisingh, Bowers, LoGuidice, & Orman, 2004)

receptive, expressive

4;0–8;11

Test of Word Finding (TWF–2; German, 2000)

expressive

4;0–12;11

Test of Word Knowledge (ToWK; Wiig & Secord, 1992)

receptive, expressive

5;0–17;11

Structured Photographic Expressive Language Test-Preschool 2 (SPELT–P2; Dawson, Stout, Eyer, Tattersall, Fonkalsrud, et al., 2004)

receptive, expressive

3;0–5;11

WORD Test–2: Elementary (Bowers, Huisingh, LoGiudice, & Orman, 2004b)

expressive

6;0–11;11

WORD Test–2: Adolescent (Huisingh, Bowers, LoGiudice, & Orman, 2005)

expressive

12;0–17;11

First, tests should be selected carefully. Because semantic knowledge is heavily influenced by cultural experiences, cultural bias may lead to an underestimation of a child’s developmental level. No standardized test is perfect in this regard, but some are better than others (see Washington & Craig, 1999). Second, because only a subsample of age-appropriate words and phrases can be included in any one test, these tests are not particularly helpful in guiding selection of intervention goals. Third, empirical results do not support the use of standardized vocabulary tests for screening or diagnosis of primary language disorders (Gray, Plante, Vance, & Henrichsen, 1999). Finally, the clinician should be cognizant that a majority of tests approach the measurement of word knowledge in a binary fashion: the child gets the item right or wrong. Therefore, most standardized tests do not measure depth of semantic knowledge.

Probes Nonstandardized probes offer solutions to the limitations of standardized tests. Take, for example, their limitations in measuring depth, as opposed to breadth, of semantic knowledge. The sensitivity of tests to depth of knowledge may be improved by presenting target words in forced-choice formats with a large number of close semantic (and phonological) distractors (Chiat, 2000). Parsons, Law, and Gascoigne (2005) employed this method successfully as a means of goal selection in a case

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study of two children with SLI. Another approach is illustrated by McGregor and Appel (2002), who asked children to draw and to define objects that they could and could not name. Both the drawings and the definitions were rated for the amount of information they contained, not just whether they were accurate or inaccurate, thereby yielding an estimate of depth of knowledge. The drawing and defining ratings correlated with each other, and both ratings were lower for misnamed objects than for correctly named objects. These findings suggest the validity of the probes as a means of exploring depth of word knowledge. These are only two examples. Miller and Paul (1995) provide a variety of suggestions for additional probes of extant semantic knowledge for all levels of language learners. Probes may also be designed to measure a child’s ability to learn new semantic information. As an example of such a dynamic assessment, consider Lederberg, Prezbindowski, and Spencer (2000), who observed children with HI during a rapid word-learning task and a novel mapping task. In the rapid word-learning task, the examiner pointed to a new object while naming it three times. The novel mapping task was similar, except that the examiner did not point out the object; instead, the child had to infer that the new name must refer to the new object because the other objects in the environment had familiar names. The child passed either task if he or she could later identify the object in an array upon hearing its name and could also identify a new example of the object (i.e., one that was of a different color or size than the trained object). Performance on these tasks was indicative of the child’s extant vocabulary development. Those with the largest vocabularies passed both tasks; those with moderately sized vocabularies passed the rapid word-learning but not the novel mapping task; those with the smallest vocabularies passed neither task. Camilleri and Botting (2013) developed the Dynamic Assessment of Word Learning, a probe of word-learning ability for preschoolers. During the procedure, the amount of assistance the child needs to link an unknown word to a referent and to extend that word to a second exemplar is determined. Higher levels of assistance indicate poorer word-learning ability. The details of the procedure, along with preliminary evidence of its strong psychometric characteristics, are available in Camilleri and Botting (2013). Dynamic assessments may be particularly useful when determining whether a child’s poor vocabulary development is the result of a true impairment or an environmental difference. Because vocabulary learning is highly dependent upon input and children from impoverished families are exposed to less input (Hart & Risley, 1995), standardized tests tend to overidentify these children (Dollaghan, Campbell, Paradise, Feldman, Janosky, et al., 1999; Whitehurst, 1997). Likewise, because multilingual children are necessarily exposed to less input in any one of their languages than monolingual children who are exposed to that language alone, they too are often overidentified. Kapantzoglou, Restrepo, and Thompson (2012) devised a dynamic assessment to improve identification of primary language impairment among children who are bilingual. After a teaching activity involving nine exposures to each of three new words and referents, the child’s ability to identify the newly trained referents when named combined with the examiner’s rating of the child’s learning strategies (e.g., planning, motivation, self-awareness) resulted in accurate identification 78.6% of the time (76.9% sensitivity and 80% specificity).

Language Samples Language samples are a good source of data on semantic development. One widely used and easily calculated measure is the number of different words (NDW) in the sample. NDW in a 50- or 100-utterance sample reliably differentiates children with SLI from their normally developing age-mates (Klee, 1992; Watkins et al., 1995). Furthermore, NDW reflects vocabulary breadth. For example, the number of different words (signs) produced by 4-year-olds who were deaf correlated with their receptive vocabulary scores on standardized measures (Everhart, 1993). Finally, NDW

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is readily interpretable, as norms are available for children ages 3–13 (Miller & Chapman, 2000; Miller & Leadholm, 1992). One limitation of NDW is that it is not independent of utterance length or total sample length; therefore, it reflects not only lexical diversity but also grammatical ability and overall volubility. An alternative measure, D, is less vulnerable to sample size variations and is, therefore, a purer measure of lexical diversity (Owen & Leonard, 2002). Essentially, the D procedure involves multiple calculations of word type–to–word token ratios based on random samples of 35–50 tokens from a given language transcript (McKee, Malvern, & Richards, 2000). D calculations may be accomplished with VOCD, a program option within CLAN software (MacWhinney, 2000). D scores distinguish younger from older language learners (Owen & Leonard, 2002) and correlate positively with scores on expressive vocabulary tests (Silverman & Ratner, 2002). Moreover, a combination of D, MLU, and age accurately differentiates children with SLI from those with normal language development (Klee, Stokes, Wong, Fletcher, & Gavin, 2004). Language samples can aid in the identification of word-finding deficits. Circumlocutions, reformulations, nonspecific words, and wrong word usages may be relevant signs (German & Simon, 1991). The rate of circumlocutions and reformulations can be interpreted in comparison to normative expectations if these are coded as mazes in the Systematic Analysis of Language Transcripts and the corresponding database is used for the normative comparison (Miller & Iglesias, 2012). Finally, language samples can be used to monitor generalization of treatment effects, especially when collected in contexts that are apt to elicit relevant targets. For example, asking the child to compare and contrast may elicit description words; asking for a fictional story may elicit temporal conjunctions; and asking for a summary of a chapter from a science book may elicit relevant academic vocabulary. One caution about the use of language samples should be noted. The lexical semantic development of very young or low-functioning children may be underestimated by language samples because their word productions occur infrequently and may be highly context-dependent (Fenson et al., 1994). In these cases, parent report is a better alternative.

Summary The breadth and depth of semantic development may be measured at varying levels of complexity, word, phrase, sentence, or text, and with a variety of tools, parent reports, standardized tests, and probes. Choosing the correct levels and the correct battery of measures will be a highly individual decision; however, the recommendations set forth by Watkins and DeThorne (2000) are generally applicable: 1. Integrate multiple types of assessment tools in the evaluation of vocabulary abilities 2. Use measures with demonstrated validity for appropriate purposes 3. Recognize that vocabulary comprehension and production are heavily dependent on life experience 4. Incorporate word-learning measures in vocabulary assessment practices (Watkins & DeThorne, 2000, pp. 240–242)

Intervention Goals Semantic interventions may be focused on development of the single-word lexicon or the integration of words into meaningful phrases, sentences, and text. At the most general level, goals may

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involve increasing the breadth or depth of the semantic lexicon. To achieve increased breadth, the child will learn new words and idiomatic phrases or, perhaps, less common or less literal meanings of old words. Also, for the higher-functioning child, learning to create new meanings via word formation (i.e., compounding and derivation) or word combination (e.g., sentence construction and text building) may be an appropriate way to expand breadth of the semantic system. To increase depth, the child will enrich understanding of old words and phrases, perhaps by learning new semantic relationships between these words, relationships that might include coordination (e.g., beetle, cricket), collocation (e.g., butter, knife), superordination (e.g., tool, compass), synonymy (e.g., thirsty, dehydrated), or antonymy (e.g., starving, satiated). Functionality is key in selecting target content. For example, the target vocabulary for children who are not yet communicating with conventional words or other symbols often includes names of important people (e.g., mama, papa) and favorite objects (e.g., juice, bear) as well as ways to express needs and desires (e.g., help, more). Functionality continues to guide goal selection for older, more sophisticated children. For example, a focus on vocabulary associated with academic themes or social situations may be functional for the adolescent with semantic deficits. In particular, Beck, McKeown, and Kucan (2008) recommend targeting “Tier Two” words. These are words like “logical” and “justify” that are used infrequently in oral communication with children but that are used with regularity in texts across numerous academic subjects. In contrast, Tier One words like “happy” and “boy” are so frequent in oral language that they typically do not require direct teaching, whereas Tier Three words like “isotope” and “tributary” are so rare or domain specific that they are unlikely to be highly functional targets. Biemiller (2010) offers a searchable CD that aids in determining which Tier Two words are appropriate at given grade levels.

Techniques Successful interventions for addressing semantic goals provide opportunities for the child to participate as an active learner in meaningful, interesting contexts that involve multiple exposures to the target content. This section is an overview of training and intervention research that illustrates the utility of these general principles and demonstrates some specific techniques. For a more detailed exploration, see McGregor and Duff (2015). For some learners, increasing exposure to words in meaningful oral or written contexts may be enough to facilitate their semantic development; however, meta-analyses reveal that vocabulary interventions that involve didactic instruction are associated with larger gains than those that depend solely on implicit opportunities for learning (Marulis & Neuman, 2010; National Reading Panel, 2000). One popular didactic approach is Robust Vocabulary Instruction (Beck, McKeown, & Kucan, 2002), with which children and teachers engage in numerous activities that promote deep processing of word meanings. These might include giving examples of contexts in which the word would be used, acting out word meanings, and relating one word meaning to others. Semantic relations between target words may be emphasized via activities such as “which of these things belong together,” “odd-man out,” and quiz games with semantic clues (see Norbury & Chiat, 2000, Appendix 2, p. 162). Clarke and colleagues (Clarke, Snowling, Truelove, & Hulme, 2010) conducted a randomized controlled trial comparing three interventions designed to improve reading comprehension. Gains were largest for the children who received an oral language intervention based on the principals of Robust Vocabulary Instruction. Children functioning at a preschool level can also benefit from didactic interventions. Consider, for example, the work of Yoder, Kaiser, Alpert, and Fischer (1993), who taught preschoolers with developmental delays new object labels in play contexts using a milieu method (Kaiser, Hendrickson, & Alpert, 1991). In this case, the milieu method involved asking the child to imitate the name

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of a new object and then answer a question that obligated an additional production of the name. Correct names were expanded into longer utterances by the teacher and rewarded by access to the object. Importantly, this didactic approach did not ignore the child’s interest. The teaching took place in a motivating play context. Furthermore, half of the words were taught at moments when the child expressed interest in the objects and half were taught at moments when the child’s attention had to be directed to the object. Word learning occurred in both conditions but more often in the condition that respected the child’s interests. Gray and colleagues (Gray, 2003, 2004; Kiernan & Gray, 1998) have completed a series of studies with the general aim of determining how best to facilitate word learning among children affected by SLI. Their approach involved the use of a didactic “supported-learning context” in which the instructor provided repeated models of new words in daily sessions, prompted the child to produce the words, and provided feedback to the child about accuracy of these productions during interactive play with toys. In such supported learning contexts, children with SLI did acquire new words. However, normally developing age-mates, the comparison group in these studies, typically learned to comprehend and to produce significantly more words and to achieve this learning in fewer trials. Presenting phonological cues (i.e., initial sound, syllable, or rhyme) or semantic cues (i.e., category, function, physical features) immediately after new words were modeled to the child with SLI enhanced their expressive learning and receptive learning, respectively (Gray, 2005). The single best way to remediate semantic deficits may be to teach a child to be a successful and avid reader (Nagy, Herman, & Anderson, 1985). Although this goal is not achievable for all children affected by pediatric language disorders, reading does figure prominently as a context for intervention in the semantic domain (see review in Kaderavek & Justice, 2002). Because written text is thematic, new words and concepts are naturally introduced together with related words and concepts, aiding both inferences about meaning and integration into the child’s existing semantic network. Joint book reading (i.e., child and caregiver reading together) promotes word learning (e.g., Dale, Crain-Thoreson, Notari-Syverson, & Cole, 1996) and increases word combinations (e.g., Yoder, Spruytenburg, Edwards, & Davies, 1995) among children affected by language disorders. A child who actively participates during joint book reading learns more words than does a child who participates passively (Ewers & Brownson, 1999). One method for stimulating a child’s active role is dialogic reading (Whitehurst, Falco, Lonigan, Fischel, DeBaryshe, et al., 1988). In dialogic reading, the caregiver scaffolds the child’s experience with storybooks so that the child eventually becomes the storyteller. The caregiver administers a sequence of techniques known as PEER, beginning with a prompt for the child to say something about a book, followed by an evaluation of the child’s comment and an expansion of the comment, and ending with a request to the child for repetition to ensure comprehension and aid learning. In other words, dialogic reading provides a didactic scaffold for the already rich incidental learning opportunities provided by storybooks. As an example of its effectiveness, consider that children with HI whose parents received 20 minutes of PEER training and storybooks supplemented with prompt questions made significantly greater gains after eight weeks of twice-weekly story reading than did children of untrained parents who used the same storybooks on the same time schedule (Fung, Chow, & McBride-Chang, 2005). Dialogic reading techniques implemented in day-care classrooms have also been successful in improving the vocabulary of hearing children from impoverished families (Hargrave & Sénéchal, 2000; Whitehurst, Arnold, Epstein, Angell, Smith, et al., 1994). Older children who can read independently may benefit from instruction on how to derive word meaning from context. Nash and Snowling (2006) provided a group intervention to 7- and 8-year-olds with low vocabulary. The children were taught to look for known words to use as clues to discover the meaning of unknown words. The children then mapped the word and its clues.

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With guidance from the teacher, they came up with a definition and then took turns imagining a context where they could use the word. Three months later, children who learned in this way were better able than a comparison group who learned words via definitions alone to infer new word meanings from context. They also had a better understanding of the texts that they read. Given the size of the lexicon and the vast number of possible meaningful relations expressed by combinations of lexical items, didactic instruction of content alone is, arguably, a losing battle. Therefore, it is important to supplement content goals with didactic instruction of strategies that aid future independent learning.

Summary There is a wide variety of interventions for enhancing semantic development, and this chapter includes only a small sample of the possibilities. Nonetheless, some themes emerged. Whether via didactic or incidental teaching, frequent exposure to targets in meaningful contexts is important. Those contexts may involve play, storybooks, or structured games and activities, depending on which of these maintains the child’s interest and promotes active participation. Interventions that introduce or enhance semantic content as well as those that focus on strategies for learning and communicating meaning may be valuable.

Conclusions Efforts to enhance a child’s communication of meaning are no less than efforts to enhance a child’s life. The status of the semantic system, particularly the lexicon, is a strong predictor of academic and social success (e.g., Gertner, Rice, & Hadley, 1994; Walker, Greenwood, Hart, & Carta, 1994). Fortunately, the semantic system is plastic and capable of change throughout the life-span (Neville & Bruer, 2001). Therefore, enhancing the semantic development of children affected by developmental language disorders is not only a valuable goal but an attainable one. Continued attention to this goal in the home, the classroom, the clinic, and the laboratory is essential. Future work on semantic deficits associated with developmental language disorders will build on the current knowledge base. This chapter, a broad overview of the extant literature, elaborates seven themes from this knowledge base: (1) semantic deficits are characteristic of a variety of developmental language disorders; (2) these deficits may affect comprehension or expression of meaning at the word, phrase, sentence, or text levels; (3) assessment tools that aid in identification of semantic deficits include parent report surveys, standardized tests, nonstandardized probes of static or dynamic knowledge, and language samples; (4) increased breadth and depth of semantic content as well as strategies for learning, remembering, and using such content are useful goal areas; (5) some helpful components of semantic interventions include repeated exposure to targets, meaningful contexts, and active engagement on the part of the learner; (6) both incidental and didactic interventions can stimulate semantic development, though for some goals, didactic approaches may be more effective; and (7) reading provides excellent opportunities for incidental learning as well as contexts to support didactic interactions.

Acknowledgements I thank Richard Schwartz for giving me the opportunity to synthesize this information, Hannah Zimmerman for assistance with Table 16.1, Ellen Heywood for careful proofreading, and the National Institutes of Health for supporting my research via NIH-NIDCD 5R01DC011742–03.

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17 SYNTAX IN CHILD LANGUAGE DISORDERS Paul Fletcher and Pauline Frizelle

Introduction Phonology and inflectional morphology in English present the analyst with relatively constrained systems within which impairment can be defined. This is not true of syntax, although definitions of this level can be beguilingly straightforward. Trask (1993, p. 273) provides this succinct description: “[t]he branch of grammar dealing with the organisation of words into larger structures, particularly into sentences.” Such a broad sweep is necessary to embrace constructions ranging from simple noun phrases to complex sentences with recursion, but it does underplay the complexity of the problem space. It also misses the psychological dimension resulting from Chomsky’s view of grammar (Chomsky, 1965) as the description both of a language and of the linguistic knowledge of the native speaker (see Chapter 6 by Schwartz, Botwinik-Rotem, & Friedmann). As inflectional morphology—a paradigmatic dimension of sentence structure—is being dealt with elsewhere in this volume (see Chapter 15 by Oetting & Hadley), here we will consider exclusively the syntagmatic dimension that Trask’s definition encapsulates. To address the intrinsic complexity of syntax, linguists have developed distinct theoretical approaches and a variety of descriptive frameworks. In facing the considerable problems posed by the nature of the system and linguists’ solutions to dealing with it, those working on syntax in language impairment have adopted different strategies. They have shone the spotlight of a theory, usually Chomskyan, onto data of particular interest for the theory: or they have applied a descriptive framework to some limited area of children’s syntactic abilities and deficits. In this light, it is perhaps not surprising that, in contrast to phonology and inflectional morphology, no comprehensive picture of syntactic impairment in English-speaking children emerges. After three decades of research, we have a reasonable grasp of what phonological impairment looks like. And there is ample evidence that morphology in English, in the form of tense and agreement markers, poses a particular problem to children with language impairment. So far, comparable clarity is not available for syntax. In what follows we will review the work that has been accomplished on syntactic problems in children with specific language impairment (SLI), primarily, but also in those with other developmental disabilities. To explore the research systematically, it will be helpful if we organize the syntactic problem space under three headings—the simple declarative sentence; what Brown (1973) called modalities of the simple sentence; and complex sentences—possible combinations of simple sentences in English.

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Syntax

Simple Declarative Sentences The descriptive shorthand for the canonical English simple sentence is SVO: subject–transitive verb–object. The verb is central to the organization of this construction. Sentences have to have verbs, and transitive verbs have complementation—one or more arguments that they mandate, usually in the form of noun phrases (NP) but sometimes plus prepositional phrases (PP). If a child understands that the subject of a sentence—the external argument—is obligatory, and is also aware of the obligatory internal argument requirements for a particular verb, then the basic syntactic structure of an SVO sentence falls out automatically. Appreciating that push has to be immediately followed by a direct object noun or noun phrase gets the child directly to a syntactic sequence such as push car. At which point the child only has to add the obligatory subject argument (e.g., a personal pronoun)—to achieve the SVO sequence me push car. There is of course more to simple declarative sentences than this—the elaboration of noun phrases, the organization of auxiliary verbs in the pre-modification of lexical verbs, the provision of adverbials—but learning lexical verbs and the syntactic company they keep provides the child with the basic scaffolding for simple declarative sentences.

The Modalities of the Simple Sentence For Brown (1973) these modalities involved interrogative, negative, and imperative constructions. All three involve structural modifications of the simple declarative structure. However, little is known about negatives and imperatives in children with language impairment, as research has concentrated on interrogatives. There are two types, wh-interrogatives (e.g., Where did you put it? What can you see?), which are introduced by an interrogative word, and where an auxiliary verb precedes the subject noun phrase, and yes/no interrogatives. These are signalled by a pre-subject auxiliary verb only: Can you see him? Is he coming home?

Complex Sentences English grammar also offers mechanisms for combining sentences, in the form of constructions that involve two or more clauses linked in specific ways. They include complement constructions (I want Daddy to come home; Pretend you’re a pirate), relative clause constructions (That’s the girl who helped me; I found the book that you’re looking for), coordinate constructions (You push it and it goes up; Daddy’s at work and mummy’s home), and adverbial constructions, in which the linking connectives relate the two clauses semantically. The most common linkages are temporal, as in We sleep on the floor when we take naps, or causal, as with I can’t get them out because my hand is too big. We examine what is known about syntax first in children with specific language impairment (see Chapter 1) and then in children with other developmental disabilities that impact on their language development.

Specific Language Impairment The Simple Sentence: Verbs and Argument Structure Central to the organization of the simple sentence is the main or lexical verb. Sentences have to have verbs, and verbs have arguments that they mandate. A distinction is made between internal arguments and external arguments. Internal arguments are those that are within the verb phrase (VP) and are subcategorized for by the verb. So the verbs move and give differ in their internal

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arguments, with transitive move requiring only a single NP (move the cupboard), while give requires either an NP PP sequence or an NP NP (give the bone to the dog/give the dog the bone). The subject NP, obligatory in the vast majority of English sentences, is an external argument, as in the SV structure with intransitive verbs (e.g., the cupboard moved). This intimate relationship between lexis and syntax implies that deficiencies in verb learning in children with SLI will result in “collateral damage” (Leonard & Deevy, 2004, p. 224ff)—that is, syntactic consequences. Children with SLI do seem to have problems learning verbs. They are not as adept as age controls or language-matched controls in fast-mapping names for actions, and they have problems retaining information about verbs that they have initially acquired after a limited number of exposures (Rice, Oetting, Marquis, Bode, & Pae, 1994; Oetting, Rice & Swank, 1995; O’Hara & Johnston, 1997; Johnson & de Villiers, 2009). Chiat (2000, p. 147) suggests reasons why verbs present a particular challenge for inefficient language learners. She points out that verbs rarely occur in isolation and that they typically receive less stress than the words around them, which may affect the integrity or specificity of verbs’ phonological representations and hence their identification and recognition (see also Leonard & Deevy, 2004, p. 223). Further issues may result from limited understanding of the perspectives verbs impose on the events they characterize, especially as these events are often of relatively brief duration. The verb learning problems highlighted by experimental studies using novel verbs seem to be reflected in two features of production data. Some studies have found that the verb lexicon of children with SLI appears to be less diverse than that of typically developing age peers (e.g., Fletcher & Peters, 1984). The claim has also been made that children with SLI will prefer to use verbs like put—referred to as General All Purpose (GAP) verbs (Rice & Bode, 1993)—rather than verbs that are more precisely specified semantically. In a cross-linguistic project on English and Dutch children (Ingham, Fletcher, Schelletter, & King, 1998; de Jong, 1999; de Jong & Fletcher, 2014), this claim was tested by asking children with SLI between 6 and 8 years old (average age 6;8) to describe video clips portraying scenes that could be described using put verbs (Levin, 1993). These are verbs relating to an action that could be described using the verb put but that specify more precisely the manner of ‘putting’ involved (e.g., hang, stick, pin, sew). The clip for pin shows a man using a drawing pin to fix a piece of paper onto a notice board. The clip for sew shows a woman sewing a button onto a coat. In describing these scenes, the children with SLI were less likely than typically developing (TD) controls to use the relevant manner verb. Rather than describing the action in the video clip with the specific target verb, they were prone to use the GAP verb put, or to provide an inappropriate response. There was also a potential effect on syntax of the way in which verbs were deployed: arguments were less consistently used with manner verbs (see also Ebbels, van der Lely, & Dockrell, 2007). There are also reports of difficulties with obligatory arguments in children with language impairment. Fletcher (1991), in a report on a group of 15 school-age children with SLI, observed some errors of omission of internal arguments (e.g., he puts webs), and also incorrect ordering of arguments (e.g., my mum was take me a picture). Elin Thordadottir and Ellis Weismer (2002) examined verb complementation in 50 children with SLI between 5 and 9 years of age (average age 7;9), and 50 TD children. In the narrative language samples they examined, they found that frank argument structure errors in the children with SLI were very infrequent, and when they did occur most often involved the external, obligatory subject argument (see also Grela, 2000). But the children with SLI used fewer argument structure types than did age- and language-matched controls. They also used fewer verb alternations than did age-matched controls. The term alternation refers to the potential for a verb to have more than one argument structure. Children’s knowledge of alternations was also assessed in the joint English/Dutch project, using a video elicitation technique (de Jong & Fletcher, 2014). For each verb tested, two scenes

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were prepared, designed to elicit a particular argument structure. Three types of alternation were explored (King, Schelletter, Sinka, Fletcher, & Ingham, 1995): 1. causative, as exemplified by verbs like move or bounce, where the verb can appear with just one external (subject) argument in an SV construction, or in an SVO construction with an internal argument as well as the external one: the ball is bouncing ~ the boy is bouncing the ball; 2. dative, where in a verb like give, both what is given and the recipient of the gift have to be expressed, either in an NP PP sequence or an NP NP sequence—he gave a flower to the girl ~ he gave the girl a flower; 3. locatives with verbs like load, which have a consistent NP PP complementation, involving the goal (G) of the verb and the theme (T), but with different orders possible for these roles: she loaded the truck (G) with bricks (T) ~ she loaded bricks (T) onto the truck (G). In describing the scenes with which they were faced, the English-speaking children with SLI in the study generally preferred one alternant. In contrast to younger TD children (matched on vocabulary test score), across all alternations the children with SLI tended to prefer one description for both scenes. For causatives, the preferred version was SVO, with agent as subject and theme as direct object (but for findings that suggest more control of this alternation on the part of children with SLI, see Loeb, Pye, Richardson, & Redmond, 1998). In the dative alternation, children with SLI were likely to omit one of the arguments, most often the goal/recipient. When they did— infrequently—supply both arguments, they tended to prefer the NP PP sequence, like their TD peers (see also, Ebbels, 2007). In describing the scenes involving locative verbs, children with SLI were limited in their ability to use both alternants and tended not to realize both arguments. Their preferred argument tended to be the direct object as goal—she loaded the truck. Across all the alternations then, it seems as if the canonical SVO simple sentence structure is still a strong influence on early school-age children with SLI, and that some of the subtler ramifications of verb-related syntactic knowledge are under weak control. This conclusion is reinforced by findings on complex verb complements known as resultatives, like the man kicked the ball under the car. In describing scenes like these, the children with SLI produced VPs with both arguments at only one-third of the frequency of their peers matched for vocabulary age (Ingham et al., 1998). These findings on verb alternations and resultative verb phrases, elicited via video description from children with SLI, resonate with those observed in narrative samples by Elin Thordaottir and Ellis Weismer (2002), mentioned above. Both data sets suggest that basic simple sentence constructions will be under control in school-age children with SLI. But for these children, their weak or partial control of verbs with more complex complementation will continue to present problems (see also Murphy, 2012, p. 197, and the following section on verbs + clausal complements).

The Simple Sentence: Noun Phrases and Verb Pre-Modification Noun Phrases Mastering verbs and the syntactic company they keep will facilitate the organization of simple sentence structures, but English noun phrase structure also presents a test for the learner. An example, from Huddleston and Pullum’s comprehensive treatment of the grammar of English (2003, p. 332), with extensive pre- and post-modification of the head noun, salary, underlines the extent of the challenge: even all the preposterous salary from the bank that Bill gets. It is true that this sentence would not be expected from a child, but analogous structures, using noun pre- and post-modification to specify a referent or referents for a hearer, would be expected at least in the early school years

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(e.g., I can name [all the boys in the class]). Little research attention has been paid to how TD children or children with SLI learn to organize the syntax of noun phrases and to integrate them into clauses. For young TD children, however, we can use a reference database constructed by Klee and Gavin (2010), which applied grammatical categories based on the Language Assessment, Remediation and Screening Procedure (LARSP; Crystal, Fletcher, & Garman, 1989) to spontaneous language samples obtained from children in play situations. Their sample involved 152 TD children from 24–47 months, half from the U.S. and half from the UK. The data revealed: • By 3 years of age, 100% of children in the sample are using determiner + noun (DN) structures: the boy, a car. • At 3 years of age, 50% of children are using determiner + adjective + noun (DAdjN) structures: the red ball. This increases to 64% at close to 4 years old. • There is limited postmodification at 3 years old, with 14% of children providing structures like the boys in the class. This rises to 43% at close to 4 years old. • Structured NPs (i.e., those not consisting solely of a pronoun) occur more frequently in object position in sentences than in subject position. At 3 years of age, for example, only 21% of subjects in utterances in the Klee-Gavin (KG) database consist of structured noun phrases (DN, DAdjN, etc.), while the figure for object position is 70%. (Fletcher, Klee, & Gavin, 2012, p. 21) A study of older TD children appears to confirm these developmental trends. Eisenberg, Ukrainetz, Gillam, Kadaravek, and Justice (2009) reported on noun phrase elaboration in narrative samples drawn from 5-, 8-, and 11-year-old TD children. If we compare the figures in their data to those from the KG database, we see that, unsurprisingly, 100% of the 5-year-olds are using DN noun phrases. The proportion of 5-year-olds using DAdjN structures has increased to 81%, and that of 5-year-olds providing postmodification of nouns to 58%. Generally, object position was strongly preferred to subject position for noun phrase elaboration. When noun phrases do appear in subject position, they tend to be of the simplest type, determiner + noun. What are the clinical implications of this developmental progression in NP control? There is limited direct evidence of NP use by children with SLI. One study, by Gavin, Klee, and Membrino (1993), does suggest that noun phrase elaboration may be a discriminator between TD children and those with SLI. In a study based on spontaneous language samples, Gavin et al. found that three element noun phrases (such as DAdjN) were one of three structural categories that distinguished children with SLI, whose average age was 37.9 months, from their age peers. Eisenberg et al. suggest that the data from their study (and we could presumably extend this to the KG data as well) could be used for clinical decision making, based on the percentage of TD children demonstrating each NP type and appropriate cutoff points for determining possible impairment.

Verb Pre-Modification English has an intricate system of lexical verb pre-modification. Auxiliary verbs be, have, and do, along with modal verbs (can, could, shall, should, will, would, may, might), signal three syntactic systems: tense—present or past; aspect—progressive or perfective; and mood. Aside from do, whose role is limited to its syntactic function in interrogatives, negatives, and tag questions, each auxiliary or modal can appear either as the sole auxiliary or as the first element in a sequence of auxiliaries (he might sing; he might have been working). The full range of tense, aspect, and mood combinations for declarative sentences is exemplified in the verb paradigm in Table 17.1.

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Syntax Table 17.1 The English Verb Paradigm walk

present tense except third-person singular

walks

present tense third-person singular

walked

past tense

is walking

present tense, progressive

was walking

past tense, progressive

has walked

present tense, perfective

had walked

past tense, perfective

has been walking

present tense, perfective, progressive

had been walking

past tense, perfective, progressive

might walk

modal

might be walking

modal, progressive

might have walked

modal, perfective

might have been walking

modal, perfective, progressive

Verb inflections and the auxiliaries together realize three categories of meaning—time, aspectuality, and modality (Huddleston & Pullum, 2003, p. 118). And the auxiliaries and modals play a crucial role in signaling interrogative structures—yes/no questions, wh-questions, and tag questions. Researchers have recognized the gradual and piecemeal nature of the development of auxiliaries via the longitudinal study of development in preschool TD children (e.g., Lieven, 2008). Rispoli, Hadley, and Holt (2012) examined language samples from 18 children aged 21–33 months over a 12-month period. They report on the use of a range of forms and the productivity of these forms over this period. By 33 months, copula be is the form most used and most productive (where productivity is measured according to the number of unique contexts the full form of the copula occurs in). Auxiliary be and do were used at about one-fifth the rate of copula be, but do was somewhat more productive. In a study reported in Rowland and Theakston (2009) and Theakston and Rowland (2009), TD children between 2;10 and 3;6 participated in a series of elicitation tasks designed to elicit forms of auxiliary be and of can, will, and do in positive and negative declarative and interrogative sentences. We can track development via the results they report, as well as differences between forms. So over this eight-month time period, the provision of will in declaratives improves overall from 54% to 71%. The provision of can in questions goes from 26% to 70%. However, the overall figures hide variation in positive and negative forms. For instance, can’t in questions starts out at 13% and goes to 55%, but can in questions starts out higher at 41% at 2;11 and grows to 82% at 3;5. Some of these points are echoed in the information we have on the development of auxiliaries and modals in children with language impairment: • The average age of emergence of auxiliaries do and be is much later in children with SLI than in their TD peers. Hadley and Rice (1996) followed 11 children with SLI between 3;4 and 4;11 and found that auxiliary do emerged at 44.2 months and auxiliary be at 46.6. For TD children, the ages of emergence were 25.2 months and 26.5 months, respectively. • Children with SLI with an average age of 5;3 have just over a 30% correct use of auxiliary do in questions, significantly below the level of both age peers, and younger children of a similar language level. They are more successful at using don’t in negative statements (Rice & Blossom, 2013). • Children with SLI, with an average age of 5;2, omitted both present and past forms of be (is/are/was/were) in approximately 50% of cases, in a task designed to elicit present and

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past progressive forms (she is dancing, he was running; Leonard, Deevy, Miller, Charest, Kurtz, et al., 2003). • Children with SLI performed at a lower level than age peers on modal can in the context of expressing ability (Leonard et al., 2003). A later study (Leonard, Deevy, Wong, Stokes, & Fletcher, 2007) examined use of can in both its ability and permission meanings and also included could as a past tense of can signaling ability. Comparisons were made with both age-matched TD children and a group of younger TD children. So far as can is concerned, in both ability and permission meanings, the children with SLI performed as well as the TD children but were not as proficient as their age peers. However, both groups of TD children outperformed the children with SLI on the could task. We do not have studies of other modals in children with SLI. Nor do we have any clear sense, in TD children or those with language impairment, how the complex sequencing of lexical verb pre-modification in English, as exemplified in Table 17.1, unfolds.

Modalities of the Simple Sentence Working out the argument structure potential of lexical verbs does not guarantee all aspects of the syntactic structure of simple sentences. The child also has to come to grips with what Brown (1973) refers to as the modalities of the simple sentence, particularly interrogatives. Problems with the syntactic organization of interrogatives, attributed to difficulties with wh-movement, are found by van der Lely and Battell (2003) in a group of G-SLI children G-SLI children are those with a “relatively rare form of SLI” (van der Lely, 2005, p. 53), who present with grammatical difficulties but intact sensory and nonverbal abilities. In another study of children with SLI, not restricted to the G-SLI subtype, Deevy and Leonard (2004) attribute the difficulties these children have with the comprehension of wh-questions, which are more marked in longer sentences, to processing difficulties rather than a particular grammatical operation. There are two major types of wh-questions, referred to as subject and object questions. In subject questions, such as Who ate my porridge?, the wh-word is the subject of the main verb and is in the syntactic position relative to that verb which the subject would have in any English sentence. In object questions, such as What did Goldilocks eat?, the wh-word is the direct object of the verb, but instead of appearing after the verb, it has been moved to the initial position in the sentence. Understanding this syntactic relationship—one of non-local dependency—appears to present a problem for G-SLI children (van der Lely & Battell, 2003). In the study by Deevy and Leonard (2004), the group of children with SLI that they tested did show more problems with wh-questions than a group of TD children, but only in one of their experimental conditions. Deevy and Leonard (2004) manipulated the length of the questions subjects were required to understand. Children with SLI performed as well as TD children on short questions, whether subject or object type, but they were less accurate on long object questions than on long subject questions, and less accurate on long object questions than the TD group. These results suggest that for children with SLI who do not fall into the restricted G-SLI set, demands on processing abilities could be a significant part of their syntactic problems.

Combining Sentences Typically Developing Children As Barako Arndt and Schuele (2013) remind us, complex, or multiclausal, utterances are “not just for big kids.” Studies of these constructions in TD children confirm that their development is well underway by the end of the preschool period.

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Coordinate Structures These are the most frequent structures to appear in the samples examined by Diessel (2004). The total data set for this study was 160,000 utterances, culled from longitudinal data from five children, with an age range covering the preschool period, from 1;8–5;1. A little over 7,000 of these utterances—around 4%—consisted of two or more clauses. Early in development we see antecedents of coordinate clauses—semantically related clauses that are sequential but that have no overt link: Hit ball. Get it. is an example from Adam at 2;3. Another precursor may be a single clause introduced by and, which is pragmatically linked to a previous independent utterance from the child or from an adult. Diessel (2004, p. 159) gives these examples, the first from Naomi at 2;7 and the second from Sarah at 3;8: (1) Child: Adult: Child: (2) Adult: Child:

Piggy went to market Yes. And Piggy had none. Flipper’s on tv, yeah. And Shaggy’s not on tv.

Diessel reported that up to the age of 3;0, close to 85% of clauses headed by and are of the type found in (1) and (2). Only 15% consist of two clauses linked by and within the same utterance by the child. But after the age of 4;0, this proportion doubles. A similar pattern applies to the use of clauses in which but is the connective. Early in development but is commonly used, like and, across speaker turns, linking clauses produced by different speakers. Later, it is integrated into a two-clause sequence by the child.

Verb + Clausal Complement These structures are the next most frequent in Diessel’s data. Relevant issues for the development of these forms include the frequency of complement-taking verbs and the type of complement that the introducing verb is followed by. If we consider only finite complement clauses, we find 81% of these introduced by just six verbs. In frequency order they are know, see, think, say, look, and tell (Diessel, 2004, p. 91). S-complements are by far the most numerous, with 60% of the total (e.g., I think I don’t know that one (finite), tell him to come home (non-finite)). Some verbs (35% of the total) can also be followed by a wh-complement (e.g., she knows what I like). A third and infrequent type is the if-complement, around 3% of the total, seen in examples like see if I can make a kite. S-complements emerge at a mean age of 2;4, wh-complements at 2;7, and if-complements at 3;5.

Adverbial Constructions This structure is the third most frequent type in the data reported in Diessel (2004). The main developmental issue here is the order in which children learn the meanings of causal, temporal, and other connectives in order to appropriately relate two clauses. It seems because is the earliest item to appear, appearing on average at 2;5. This is followed by so (2;7), when (2;10), if (3;0), while (3;2), before (3;2), and after (3;4).

Relative Clause (RC) Constructions These structures are the least frequent to be found in the preschool TD data examined by Diessel (see also Givón, 2008), but they have been of particular interest to researchers because of their

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theoretical significance and the linguistic controversies surrounding them (Hauser, Chomsky, & Fitch, 2002; Everett, 2005; Kidd, 2011). Consequently, for good or ill, we know more about these structures in typical development and child language impairment than about any other complex construction. Relative clauses are subordinate clauses that modify a noun phrase in the matrix clause (Diessel, 2004, p. 127). RC constructions are a family of structures that can vary along four dimensions: (1) the kind of matrix clause the relative clause is subordinate to, (2) the position of the relative clause in the sentence of which they are a part, (3) the syntactic role which the head of the relative clause fulfills, and (4) whether or not a relative pronoun begins the relative clause: • Matrix clause types: Relative clauses can occur in what are called presentational constructions (e.g., that’s the car that I saw), where the matrix clause that’s the car simply introduces the NP to be relativized. Diessel and Tomasello (2000) found that more than 90% of children’s early RC constructions (based on the first 10 relative constructions produced in their corpus data) and 70% of all relatives were of the presentational type. Less frequent in child data are dual proposition RC constructions. Here the NP that is relativized is either the external argument (subject) of a lexical verb in the matrix clause or one of the internal arguments of the verb. So in, for example, the boy who stole your bike ran away, the subject of ran (the boy) is the head of the relative clause who stole your bike. In I saw the man who stole your bike, the man, which is the direct object of saw, is the head of the relative clause who stole your bike. • The position of the relative clause in the sentence: A strong preference for relative clauses that post-modify matrix clause objects, as in I saw the man who stole your bike, rather than matrix clause subjects, as in the boy who stole your bike ran away, is evidenced in data from TD children (Diessel, 2004). • The syntactic role of the head of the relative clause: In the main this issue has been addressed in relation to RC constructions where the relativized item (the boy) is the subject of the relative clause as in (3), and those where it fulfills the syntactic role of object in the relative clause— the bike in (4): (3) That’s the man who stole your bike. (4) That’s the bike that he stole. The head of the relative clause can however fulfill syntactic roles other than subject and direct object. In (5), the girl is head of the relative clause but is functioning as an indirect object. (5) That’s the girl that he gave the flowers to. And other syntactic roles are possible (see Diessel and Tomasello (2000). • Optionality of the relative pronoun: The relative pronoun is optional in object relatives. We can say either that’s the bike that he stole, including the relative pronoun, or that’s the bike he stole, with the relative pronoun omitted. However, in relative clauses where the head is subject, the relative pronoun has to be included if the sentence is to be grammatical. That is, we are required to say He is the boy who stole your bike or He is the boy that stole your bike. A sentence like He is the boy stole your bike would be regarded as ungrammatical. However, Diessel (2004, p. 134) reports that omissions of relative pronouns in subject relatives are seen in TD children’s earliest attempts at these structures. And as we shall see in the following section, similar omissions are seen in data from children with SLI. The general finding for TD children has been that relative clauses where the head is subject are easier to deal with than those where the head is object (Kidd, 2011, p. 4). The responsibility for this imbalance is put down to the greater difficulty presented by the non-local dependency in

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(4), where to construe the sentence the child has to appreciate that the relationship between stole and that is between verb and direct object. But there is now evidence to challenge the comparative difficulty of object relatives, for TD children and those with SLI.

Children with Language Impairment Studies of complex sentences in children with language impairment vary in the focus of enquiry, the ages of children involved, and in the procedures used to explore the children’s competence. Accordingly, we need to be wary of generalizations about particular structures. Two studies explore the full range of complex constructions in school-age samples. In a cross-sectional study of 65 children with language impairment aged 6 to 11 years, Hesketh (2004) used sub-tests from a standardized procedure, Assessment of Comprehension and Expression 6–11 (Adams, Cooke, Crutchley, Hesketh, & Reeves, 2001), to elicit complex sentences. The main aim of the study was to compare the relative effectiveness of structured elicitation and a narrative task in drawing target structures out of children, but the study allows some general conclusions about the availability of complex sentences to these children. First, many of the children involved in the study, but not all, were using the constructions of interest. Approximate percentage values for children using the constructions were coordination, 74%; relative clause, 74%; subordination (verb clausal complement, adverbial with temporal or causal linkage), 64%; and conditional, 45%. Hesketh (2004, Figure 1, p. 170) gives values for both structured elicitation and narrative. The percentages quoted are for whichever value is the higher. Second, via the structured elicitation procedure, a developmental progression between 6 and 11 years was apparent. Finally, there is some indication in examples quoted in the study of the omission of obligatory grammatical markers. Marinellie (2004) examined language samples furnished by 10-year-old children with SLI for evidence of the use of complex structures. All four of the structures of interest are found in conversational samples from children with SLI, but the age-matched TD children with whom they are compared used complex sentences with greater frequency. In particular, they use more coordinate, adverbial, and relative clauses than the children with SLI. However, the two groups used similar types of adverbial clauses. Causal adverbials were the most common, followed by temporal adverbial clauses. The two groups did not differ on verb clausal complement structures. Also, more of the complex sentences from the TD children were what Marinellie called combined complex structures, a multiclausal utterance such as when the little boy woke up he saw that the frog was gone. In the TD group, 40% of the complex clauses were of the combined type, compared to 25% of those provided by the children with SLI. Despite the apparent availability of causal adverbial clause structures to children with language impairment as observed in Marinellie’s data, another study reported that children with SLI have “marked and extensive problems” using sentences with connectives because and so (Donaldson, Reid, & Murray, 2007). This conclusion does, however, relate to somewhat younger children, 5- to 7-year olds. The Donaldson et al. study also probed the children’s knowledge in depth, by requiring them to ask and answer questions, to complete and imitate causal sentences, and to produce causal sentences. Children with language impairment performed at a lower level than a group of age-matched TD children on all of these tasks. A useful complement to studies with multiple subjects is a longitudinal case study of MM, a boy with SLI, between the ages of 3;3 and 7;10 (Schuele & Dykes, 2005), in which a dozen samples of conversational speech were analyzed. The MLU range from the first to the last sample was 1.91 to 5.46. There is little evidence of the availability of complex constructions until sample 6 (age 4;8, MLU 3.12), when coordinating and subordinating conjunctions appear. Sample 8 (age 5;9, MLU 4.27) sees sentential verb complements, relative clauses, and conditionals. The percentage of complex sentence use increased from LS6 onwards, with 31% of utterances in sample 12 consisting

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of complex constructions. The data from MM confirm the persistence of grammatical marker omissions within these complex constructions as they develop. For example, the obligatory relative marker in subject relative clauses (e.g., that in I was scared of the boy that chased us) was omitted in every instance, over a long period. Studies of individual structures have concentrated on verb clausal complements and relative clauses, as seen in the following sections.

Verb Clausal Complement Owen and Leonard (2006) explored in some detail the ability of a group of children with SLI who were 6 years old on average to produce verb clausal complement structures. The study involved the elicitation of both nonfinite (e.g., Ernie told Elmo to pick up the box) and finite complements (e.g., Ernie told Elmo that Oscar picked up the box). The performance of the children with SLI was compared with TD children of their own age and a group of younger TD children, mean age 4;7, matched on vocabulary ability via the Expressive Vocabulary Test (Willams, 1997). It was apparent from this study that while children with SLI could produce both nonfinite and finite clauses following the verbs, they were less proficient than both of the comparison groups. In particular, they made more production errors than their age or vocabulary peers. They were also more likely to omit infinitive to in nonfinite complements (see also Barako Arndt & Schuele, 2012, who reported similar findings from children with SLI between 5 and 7 years), and to leave out verb arguments and tense marking in finite complement clauses. Eisenberg (2003, 2004) also found 5-year-old children with language impairment lacking in aspects of verb clausal complement structures when compared with language-matched peers.

Relative Clause Constructions Frizelle and Fletcher (2014a) used a sentence repetition (SR) procedure to investigate RC constructions in a group of children with SLI (mean age 6;10), a group of age-matched TD children, and a group of TD children 2 years younger on average. The items in the SR task compared presentational relatives and dual proposition relatives, and the relative clauses included involved a variety of syntactic roles for the head of the clause (subject, object, indirect object, oblique). The children with SLI were inferior to both age-matched peers and the younger children overall. They were better at presentational relatives than RC constructions, which involved two propositions. Overall, they performed better on subject role relatives than object role relatives. (This was also the case in another sentence repetition study involving relative clauses, this time on 15-yearold children with SLI, by Riches, Loucas, Baird, Charman, & Simonoff, 2010.) However, in TD children, the processing cost for object relative clauses can be mitigated (Kidd, Brandt, Lieven, & Tomasello, 2007). Object relatives with certain lexical features, which are more common in the speech that children hear, are as easy to process as subject relatives. RC constructions with inanimate objects in the matrix clause and a personal pronoun as the subject of the relative clause, as in (5), are common in the speech of adults to children and are easier to parse than other types of object relatives: (5) I can’t find the cover that I’m looking for. This was also the case for the children with LI in the study by Frizelle and Fletcher (2014b). And a study by Hestvik, Schwartz, and Tornyova (2010), addressing the comprehension of subject and object relatives via question probes, found no difference in performance between children with

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SLI, aged on average 9;7, and TD age controls. The children with SLI did, however, show processing differences to their age peers. There is also information on the provision of obligatory relativizers by children with SLI. Schuele and Tolbert (2001) found low rates of the obligatory relativizer in subject relatives produced by 5-, 6-, and 7-year-old children with SLI: 9%, 38%, and 49%, respectively. In three groups of TD children who were on average 2 years younger than the SLI group they were matched with, there were no instances of subject relativizer omission. Oetting and Newkirk (2008) also examined the provision of subject relativizers by 6-year-old children with SLI, compared to their age-matched TD peers and TD children who were 2 years younger. Because of possible dialectal differences from children speaking what they referred to as mainstream American English, Oetting and Newkirk selected children who spoke other dialects, namely African American English and Southern White English (see Chapter 14). Although there was some subject relativizer omission among the TD children from the two dialect backgrounds, provision did not fall below 80%, even in the younger group. For the SLI group, the average rate of provision was 59%. So the discrepancy between children with SLI and TD children that Schuele and Tolbert (2001) found is replicated even in dialect groups, which appear to permit some degree of subject relativizer omission. However, not all studies of RCs in children with SLI have found such high rates of subject relativizer omission. Hesketh (2004) found an overall omission rate of only 6% on an elicitation task, with British English participants, and Frizelle and Fletcher (2014b) noted an omission rate of close to 8% with children using Irish English. As children move out of the family and into school, control of the structures we have been discussing becomes even more important as they encounter more formal discourse modes and begin to learn to read and write. Scott (2004) reports on an analysis of clause connectivity in a database of narrative/expository and spoken/written discourse. She compared three groups of school-age children—one with language impairment, aged on average 11;5; an age-matched TD group; and a younger group of TD children, mean age 8;11. The children with LI performed significantly below the level of their age peers on coordinate clauses, finite complement clauses, relative clauses, and overall connectivity rate. The children with LI also showed more limited instances of what Scott referred to as depth of connectivity. This is the ability to chain different types of complex constructions into longer sequences, as an example from Scott (2004, p. 126) demonstrates: One day when Yanis was tending the goats in the mountain because the city that they live in was on a mountain, one of the goats ran away. Limitations on the ability of school-age children with LI to match their peers in controlling complex sentences, and in constructing connected clause “packages,” as Scott refers to them, takes us back to studies that track children with language impairment through later childhood and adolescence, and illustrate the consequences of their deficits on educational achievement and on life chances (Stothard, Snowling, Bishop, Chipchase, & Kaplan, 1998; Law, Rush, Schoon, & Parsons, 2009). The studies of complex constructions reviewed make it clear that restrictions on the grammatical ability of children with language impairment are not limited to verb forms, verb argument structure, or certain types of interrogatives. Clause connectivity, whether in the form of a two-clause construction or in a more intricate sequencing of structures, is also constrained in these children. A repertoire of primarily simple sentences may be adequate—just—for a schoolage child carrying on a conversation with a peer or familiar adult, but the extended linguistic demands that challenge children as they advance through the education system—to narrate, to explain, to justify, to argue, in both speech and writing—increasingly demand a more sophisticated grammatical control.

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Other Childhood Developmental Disabilities Other developmental disabilities that have attracted the attention of researchers in relation to syntactic impairment are Williams syndrome, Down syndrome, and autism (see Chapters 2 and 3). We will deal with these in turn and also note studies that have made comparisons between any of these groups. Williams syndrome (WS) is a rare, genetically based developmental disorder. Stromme, Bjornstad, and Ramstad (2002) give a prevalence estimate of 1 in 7,500 live births, based on data from Norway. Despite its rarity, the condition has played an important role in theoretical debate about the relationship between language and cognition, which is important for our understanding of normal language development and hence of language impairment. The significance lies in the apparent dissociation between language abilities in individuals with WS and their cognitive deficits (Mervis, 2003). Initial reports on small samples, in particular Bellugi, Sabo, and Vaid (1988), indicated intact language ability in the context of intellectual disability. Individuals with WS “were able to produce and comprehend complex grammatical constructions such as reversible passives, conditionals . . . yet are unable to conserve quantity or number” (Mervis, 2003, p. 2). Such observations appear to support the claims of those who argue for modularity in cognitive organization. Extensive research since the initial reports has, however, refined the initial view of a dislocation between linguistic and cognitive abilities. A number of later studies have indicated that individuals with WS have both receptive and expressive syntactic abilities not inconsistent with their mental age (MA). Mervis and Becerra (2007) reported on 110 individuals with WS between 5 and 18 years old. The average performance for this group on TROG-2 (a test of syntactic comprehension; Bishop, 2003) was two standard deviations below the mean, and the modal score was more than three standard deviations below the mean for their age. They also noted the particular difficulty these individuals experienced in understanding complex grammatical constructions. Expressive difficulties are also described by Mervis and Becerra (2007), who administered the formulated sentences subtest of the CELF–4 with 61 individuals with WS, aged 8–17 years. The average standard score fell within the range of a moderate language impairment and the modal score within the severe range. The authors highlight syntactic errors in relation to the use of a mandatory second clause (e.g., following the use of the conditional if) and difficulties using conjunctions, such as although. This syntactic profile is further reinforced by Stojanovik, Perkins, and Howard (2006), who reported on the formulated sentences subtest of the CELF–4, with five British children, between 7 and 12 years. Again the average standard score achieved fell within the range of a moderate to severe language impairment. Given that the cognitive abilities of most individuals with WS fall within the borderline to mild intellectual disability range (Bellugi, Lichtenberger, Jones, Lai, & St. George, 2000; Meyer-Lindenberg, Mervis, & Berman, 2006), this level of language/syntactic ability is in keeping with what we might expect. Down syndrome (DS) is also a genetically based disorder, resulting from a third copy of chromosome 21. Syntax has been identified as an area of deficit in individuals with DS. However, Abbeduto and Chapman (2005) claim that the deficit is more marked in production than comprehension, “until late adolescence, when losses in syntax comprehension are encountered” (Abbeduto & Chapman, 2005, p. 59). Children with DS evidence very slow expressive development. Throughout childhood, delays in expressive language, relative to receptive language and to nonverbal cognition, are identified, in vocabulary and in utterance length and complexity (Abbeduto & Chapman, 2005, Price, Roberts, Hennon, Berni, Anderson, et al., 2008). Syntactic competence has also been noted to be poorer than that predicted by their nonverbal cognitive ability. Price et al. (2008) analyzed conversational language samples from a group of 31 boys with DS, between 4;3 and 16 years, using Ipsyn (Scarborough, 1990; see section on “Language assessment using language samples”).

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They reported that the individuals with DS produced less complex noun phrases, verb phrases, sentence structures, questions, and negations than younger TD boys of a similar nonverbal age. While syntactic deficits persist into adolescence, syntactic progress does continue to be made. This is evidenced in a study by Elin Thordadottir, Chapman, and Wagner (2002). They compared a group of 24 DS adolescents, mean age 16.5, to a control group of TD children who were equivalent in terms of mean MLU (though much younger—average age 3;1 years). All of the individuals involved produced a 12-minute narrative sample, which was scrutinized for the use of complex sentences. Coordinated sentences and two types of subordinate sentences discussed earlier—clausal complements of verbs and relative clauses—appear in the samples from both DS and controls, and there are no significant differences between the groups for proportion of complex sentences or for diversity of complex sentence types. While acknowledging the absence of difference between the groups, Elin Thordordottir et al. (2002) drew attention to the high degree of variability in the DS group. Individual differences in developmental rate are also found in DS individuals. Chapman, Hesketh, and Kistler (2002) obtained a variety of measures from a group of 31 individuals with DS whose ages ranged between 5 and 20 years, at four time intervals over 6 years. They identified visual and auditory working memory, along with chronological age, as the best predictors of receptive syntactic ability. Comparisons have been made between individuals with DS and those with other developmental disabilities. Laws and Bishop (2003) compared adolescents with DS with children with SLI matched for nonverbal cognitive ability. They found that the linguistic profiles of the two groups were similar, with receptive language superior to expressive and grammar lagging vocabulary in both comprehension and production. In a study comparing adolescents with DS and WS, however, Ring and Clahsen (2005) did find differences in syntactic ability, in tasks involving the interpretation of passive structures, and of syntactic binding of reflexive and nonreflexive pronoun reference. The WS group performed no differently from controls matched for mental age on both passive structures and structures involving the interpretation of reflexive pronouns (assigning the correct referent in sentences such as Is Mowgli tickling himself?). The DS group were significantly poorer than the WS group on passives. They were as capable as the WS group and the controls in understanding nonreflexive pronouns (identifying the referent of her in Is Minnie the Minx tickling her?), but their success rate on reflexive pronouns (as in Is Minnie the Minx tickling herself?) was significantly lower than the WS and control groups. The issue of individual variability in linguistic ability, apparent in WS and DS, arises again in autism spectrum disorder (ASD). Communication impairment is one of the defining features of this condition (Baird, Cass, & Slonims, 2003), but the linguistic abilities of individuals with autism span a considerable range— from a delay in the development of expressive language to a total lack of expressive language, from problems with initiating or sustaining a conversation to use of stereotyped, repetitive and idiosyncratic language. (Gernsbacher, Geye, & Ellis Weismer, 2005, p. 73) While most attention has been focused on pragmatic problems in these children, Boucher (2012, p. 219) concludes, from a wide-ranging review, that a substantial subgroup of children with ASD have impairments of grammatical structure, in addition to their pragmatic and discourse difficulties. Studies investigating grammatical difficulties in language-impaired groups can be divided into those focusing on preschoolers and those that deal with school-aged children. Studies focusing on preschool groups of children collectively seem to indicate that up to the age of approximately 6 years, most children with ASD have significantly delayed language development, including grammatical

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deficiencies. A study by Eigsti, Benetto, and Dadlani (2007) carried out a detailed investigation of the syntactic abilities of children diagnosed with autism aged between 3 and 6 years, compared to children with nonspecific developmental delays (DD) matched on nonverbal IQ, gender, and chronological age, and younger TD children matched on nonverbal IQ and gender. Language samples were collected from all children and analyzed using the Inventory for Productive Syntax (IPSyn; Scarborough, 1990—see section on “Language assessment using language samples”). The children with ASD were significantly impaired on this measure, relative to the DD and TD groups. It seems that age is an important indicator of the grammatical profile we might expect to find in this group of children. Rapin, Dunn, Allen, Stevens, and Fein (2009) assessed language-impaired children with ASD at different time points (at preschool age and between 7;0 and 9;0 years). They found that on reassessment 73% of the children had mild to moderate impairments in syntactic comprehension and 11% had normal language. Kjlelgaard and Tager-Flusberg (2001) used a battery of standardized tests to explore phonological, lexical, and grammatical abilities. They found that while more than 90% of children were able to complete the phonological and vocabulary tests, only about 50% were able to complete the CELF-3 (Semel, Wiig, & Secord, 1995). The CELF explores receptive and expressive language skills in the areas of morphology, syntax, semantics, and working memory for language. Of the 44 children who were able to complete the CELF, 10 were designated as linguistically normal, with standard scores above 85; a further 13, with standard scores between 70 and 84, were labeled borderline; and the remainder (N = 21), with standard scores of 70 or less (i.e., two standard deviations or more below the mean) were considered impaired. A profile of poorer grammatical than lexical ability in the borderline and impaired individuals led Kjlelgaard and Tager-Flusberg (2001) to compare the performance of this subset of their sample to the phenotype of SLI (Tager-Flusberg & Cooper, 1999). The possible overlap between autism and SLI, and between autism and pragmatic language impairment (PLI), was discussed in some detail by Gernsbacher et al. (2005, p. 83ff; see also Botting & Conti-Ramsden, 2003). Condouris, Meyer, and Tager- Flusberg (2003) went on to analyze the grammatical structures produced in the spontaneous language of these same children; using the IPSyn, they noted no significant impairments relative to the age norms derived from standardized tests.

Assessment of Syntactic Impairment Standardized Tests Many of the tests referred to in Oetting and Hadley (Chapter 15) for the identification of language impairment have syntactic content and are viable for screening or initial identification purposes, to identify children who may be language-impaired and who may have syntactic problems. Standardized procedures do include some of the structures identified in previous sections as relevant to syntactic impairment. In standardized receptive assessments, the structures that tend to be included are relative clauses, adverbial clauses, and reversible passives, while expressive assessments tend to include a wider range of structures. Generic assessments designed to assess both comprehension and expression, which are widely used in North America, the UK, and elsewhere, include the Clinical Evaluation of Language Fundamentals 4 (CELF–4; Semel et al., 2003) and its preschool equivalent the CELF P-2 (Wiig, Secord, & Semel, 2004); the Assessment of Comprehension and Expression (ACE; Adams et al., 2001); the New Reynell Developmental Language Scales (NRDLS; Edwards, Letts, & Sinka, 2011); and the Preschool Language Scale (PLS–4; Zimmerman, Steiner, & Pond, 2002). Other assessments have a more specific focus, such as the widely used Test for Reception of Grammar–2 (TROG–2; Bishop, 2003), which as its name indicates, focuses on comprehension alone and requires the identification of pictures corresponding to spoken stimuli.

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The CELF–4 was normed on 2,650 individuals in the U.S. and a further 871 in the UK and is designed for use with children/adolescents between the ages of 5 and 16 years. Its preschool counterpart was normed on 800 children in the U.S. and 486 children in the UK and is used with children between 3 and 6;11 years. Both assessments focus on receptive and expressive aspects of language and require that different subtests be administered depending on the child’s age. Both receptive and expressive subtests include examples of coordinate constructions, relative clauses, subordinate structures (e.g., linked by although, after, because), and reversible passives, but a wider range of these constructions is represented in the expressive subtests. The ACE (Adams et al., 2001) was normed on 790 children from the UK and the Republic of Ireland and is designed for use with children between the ages of 6 and 11 years. The assessment is also divided into receptive and expressive components of language, with the expressive section using both elicitation and narrative techniques. The sentence comprehension section includes examples of relative clauses and reversible passives, while the expressive tasks include coordinate constructions, relative clauses, subordinate (adverbial) clauses, and nonfinite complement clauses. The NRDLS (Edwards et al., 2011) is normed on more than 1,200 children in the UK, aged between 2 and 7;5 years. The comprehension scale includes relative clauses and reversible passives; the expressive scale is designed to elicit relative clauses as well as reversible passives and interrogatives. The PLS–4 (Zimmerman et al., 2002) is normed on 2,400 children in the U.S. and a further 800 children in the UK. It is designed for use with children from birth to 6;5 years. The test is divided into two subscales—auditory comprehension and expressive communication. The comprehension section addresses coordination, nonfinite complement clauses, and reversible passives and the expressive section includes short story retell in which coordination, nonfinite complement clauses, and adverbial clauses (linked by when) are modeled. TROG–2 (Bishop, 2003) is normed on 750 children aged 4 to 14. It is organized in 20 blocks, each containing four items devoted to the same structure. Complex structures addressed include relative clauses and reversible passives. Finally, a language assessment that takes account of children in the U.S. who are speakers of dialects other than General American English is available to clinicians—the Diagnostic Evaluation of Language Variation (DELV; Seymour, Roeper, & de Villers, 2003). In seeking to identify features of children’s language that result from ethnic, regional, and cultural differences, and differentiate them from features that would imply language delay, this test is a rare exception among standardized assessment procedures. There are inherent limitations to the use of available tests for diagnostic purposes, relating primarily to their content validity. Time constraints preclude an adequate sampling of the full range of syntactic structures in the language, and those represented have few exemplars. Methodological difficulties mean that some central areas of syntax are also difficult to address. For example, it is not straightforward in comprehension procedures, which require an unequivocal behavioral response to pictures or the manipulation of toys, to construct stimuli to investigate the child’s understanding of the full range of modal verbs or sequences of auxiliaries. Interrogative structures tend not to be included in expressive language tests. For the exploration of the full range of structures in expressive syntactic impairment, language samples have been an important source of information. And for the in-depth investigation of particular syntactic structures, especially but not exclusively in comprehension, researchers have developed specific probes.

Language Sampling and Other Methods It is now hard to imagine the study of language development in children without conversational language samples. The detailed longitudinal samples described in Brown (1973) were the forerunners of other corpora from English-speaking children, both typical and atypical developers, and

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then from children learning languages other than English. Since Brown’s time, the development of large computer databases and of computational procedures for sample analysis procedures has greatly facilitated child language research in both typically and atypically developing children. Brown’s samples, and many of the corpora collected since, are available in the Child Language Data Exchange System (CHILDES) at the system’s website: www.psy.cmu.edu. The CHILDES system incorporates a standard transcriptional format (CHAT) and a series of programs (CLAN) for automatic analysis of language samples. Full details are available in MacWhinney (2013a, 2013b), which is downloadable from the CHILDES website. The CLAN programs include an extensive set of procedures for calculating MLU and for assigning parts of speech to a corpus, as well as a flexible coding facility whereby analysts can devise their own codes relevant to specific areas of interest and then compute frequencies. A recently added utility in CLAN, KIDEVAL, provides a selection of summary measures deemed suitable for clinical analysis of children’s language (Ratner & Brundage, 2013, p. 23ff.). An alternative computer-based procedure, Systematic Analysis of Language Transcripts (SALT), has a wide range of automatically derivable measures in English and Spanish (Miller & Iglesias, 2008; http://www.saltsoftware.com). The standard measures for English include MLU, lists and frequencies of bound morphemes, frequencies for elements of particular grammatical classes (e.g., question words, auxiliaries), and measures of fluency and rate. Measures derived from samples can be compared to those derived from a reference database of close to 400 children aged between 3 and 13 years. Profiling procedures were designed to extract from language samples information on syntax and morphology that would assist the clinician in assessment and remediation (e.g., Miller, 1981; Crystal et al., 1989; Leadholm & Miller, 1992). The procedure developed by Crystal et al. (1989), the Language Assessment, Intervention and Screening Procedure (LARSP), was an attempt to codify the major features of syntactic development in English from the onset of single words through to the development of complex subordinate structures, covering the age range from around 1;6 to 4;6 years. Applied to a language sample from a child in the clinic, a LARSP profile identifies areas of syntactic strength and weakness relative to the normal developmental course. The LARSP procedure has been computerized (Long, 2012). LARSP and other profiling procedures are essentially qualitative analyses. Indices designed to quantify developmental change via language samples, and so potentially identify syntactic differences between TD children and those with impairment, have also been developed. These include Developmental Sentence Scoring (DSS; Lee, 1974) and the Index of Productive Syntax (IPSyn; Scarborough, 1990). These systems give scores for the occurrence of specific structures and aggregate these scores into a total, which can then be compared to a norm. IPsyn works off a sample of 100 child utterances, which are examined for 56 specific language structures (e.g., auxiliaries, modals, specific types of subordinate clauses). The scores for individual structures (a range of 0–2 for each structure) are then added to provide an overall total. IPsyn shows good reliability and age discrimination for TD children between 2 and 4 years of age. IPsyn scores based on language samples have also been demonstrated to distinguish 6-year-old children with SLI from their TD peers (Hewitt, Hammer, Yont, & Tomblin, 2005).The automatic measurement of IPsyn has been addressed by Sagae, Lavie, and MacWhinney (2005) and Hassanali, Liu, Iglesias, Solorio, and Dollaghan (2014). Although language samples have an advantage in terms of their ecological validity, as evidence of the child’s ability to produce and respond to syntactic structures in real time and in naturalistic situations, they do have reliability limitations. Factors such as interviewer ability, topic choice, length of time involved, and the child’s own performance on the day can conspire to deliver a less than optimal profile of the child’s syntactic repertoire from a specific language sample. Transcriptional choices can also influence reliability, especially in relation to comparisons between samples

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from the same child or different children. An important issue here is segmentation—the demarcation of units upon which syntactic analysis is conducted. Unless a principled and consistent approach to utterance segmentation is adopted, particularly with data from school-age children who may be using more complex utterances, reliability can be compromised (for discussion, see Scott & Stokes, 1995). In exploring comprehension, or in investigating structures in a child’s expressive language which may rarely occur in language samples, researchers have had recourse to specific probes. There are many examples of ingenious procedures in the literature, including in papers cited in this chapter. Here, one example each for receptive and expressive language will suffice. For comprehension, a developmentally graduated series of procedures are described in detail in Miller and Paul (1995). The structures addressed range from the comprehension of two- and three-word instructions, through word order comprehension in reversible active sentences, to the comprehension of different question types and the making of inferences in discourse. For several of the procedures, alternative methods are provided. So, for comprehension of locative prepositions, three tasks are provided: (1) a search task, in which the child has to find an object; (2) a placement task, where the response involves placing an object in accordance with an instruction; and (3) a body placement task, where the child locates herself according to an instruction. The two techniques Novogrodsky and Friedmann (2006) provide for the elicitation of relative clauses can serve to exemplify expressive probes. Their first approach is purely verbal. The individual being tested is given a scenario and then asked a question: “There are two children. One gives a present, the other child receives a present. Which child would you rather be? Start with I would rather be . . . or The child . . .” The expected answer (the antecedent is the subject of relative clause) is either I would rather be the child that receives the present, or The child that receives the present. The alternative technique relies on the use of pictures. The individual being tested sees two scenes, one in which a child is washing a giraffe and the other in which a giraffe is washing a child. The picture is described, and a question is asked: “Here are two girls. In one picture the girl is washing the giraffe, and in the other picture the giraffe is washing the girl. Which girl is this (pointing to the picture of the giraffe washing the girl)? Start with This is the girl . . .” In this instance, the object relative (the antecedent is the object of the relative clause) is the desired response: This is the girl that the giraffe is washing.

Intervention In a systematic review of 25 studies (Law, Garrett, & Nye, 2003), the findings on the effectiveness of intervention (see also Chapter 23 by Finestack and Fey) on the expressive syntax of children with language impairment were mixed, with some studies showing positive outcomes and others not. Also, no significant differences in outcome were found between clinician-administered intervention and that supplied by trained parents, or between intervention supplied to individuals and that supplied to groups. Because of the terms of the systematic review, the studies analyzed were only those that could be considered as randomized controlled trials (RCTs). Although studies of intervention are limited compared to those that explore the nature and extent of syntactic impairment, a body of research is informative about potentially effective remediation methods, which include but go beyond RCTs. Ebbels (2014) provides a comprehensive review of intervention studies that focus on the improvement of grammatical abilities in children with language impairment 5 years of age and older. The studies included in the review are not limited to RCTs, and a range of syntactic areas were targeted. Ebbels points out that the majority of published studies concern expressive syntax, with relatively little published on intervention with children with receptive difficulties. Here we instance as exemplars studies addressed to two of the syntactic areas that have been identified as challenging for children with language impairment.

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Argument Structure Ebbels et al. (2007) carried out an RCT to investigate the effect of targeted intervention on the use of verb argument structure in children with SLI. They had noted that children with SLI between the ages of 11;0 and 16;1 were more likely to make errors with change-of-state verbs, like fill, than change-of-location verbs, like pour. The errors involved incorrect decisions as to which NP was to be placed in direct object position. For a change-of-location verb like pour, the item that changed location would be appropriately placed—she poured the water into the jug, but for change-of-state fill, Ebbels et al. quoted errors like the lady is filling the sweets into the jar. For these verbs, the item changes state via the action of the verb that is more appropriately located in direct object position. Ebbels et al. randomly assigned 27 children with receptive and expressive impairments to one of three groups. Two groups received intervention based on the assumption that issues with the verbs’ semantic representations were at the root of their difficulties. One approach—syntactic-semantic therapy—involved using a metalinguistic approach called shape coding to link the distinct verb meanings to the appropriate syntactic construction. The other approach—semantic therapy— concentrated on discussing and exemplifying the meanings of the set of verbs selected for therapy. The control therapy worked at improving the students’ ability to form inferences from text and drew no direct attention to argument structure. The interventions totaled 4.5 hours in all. Following the intervention, Ebbels et al. (2007) found that both the syntactic-semantic and the semantic interventions were effective at improving the use of appropriate verb argument structure in the participants. They also noted that improvements shown generalized beyond the targeted verbs and that progress was maintained 3 months post-intervention.

Complex Constructions In a study that combined assessment and intervention, Gummersall and Strong (1999) involved children with language impairment between 8 and 11 years in a narrative procedure that involved them first listening to a story illustrated by pictures. They are then taken through the story again and required to imitate each sentence after it is spoken. Then they retell the story in the absence of the pictures. The children with LI, in the retell phase, produced verb clausal complement and relative clauses and produced language that was more complex than a group of TD children who simply listened to the story and retold it, without any imitation of the component sentences of the narrative. It seems from the results of the study that drawing the children’s attention to the structures of interest via the imitation procedure had an effect on their performance. This could be seen as a version of syntactic priming, a well-recognized phenomenon, not just in children, whereby a syntactic structure recently heard will be more likely to be used (see Leonard, 2011). The effectiveness of syntactic priming was also addressed in an intervention study by Marinellie (2006). The children with language impairment were slightly younger (7;3–8;9) than those in the Gummersall and Strong study, and the eliciting stimuli were individual pictures, rather than a sequence illustrating a story. Children listened to either an adverbial clause (temporal or causal) or a relative clause, then saw the picture to which the stimulus sentence applied, then were required to formulate a sentence describing the picture. Probes to determine the effectiveness of the priming took place 2–7 days after the experimental procedure, when the children described the pictures again, this time spontaneously. The results indicated that priming for adverbial clauses was more successful than that for relative clauses. The notion of priming relies on the structure primed being available to the child at some level: it is assumed that the provision by the experimenter and repetition by the child of the adverbial clause scaffolds the production of a structure over which the child has a degree of control. The lack of a priming effect with relative clauses

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would indicate that they are less entrenched in the child’s system and hence more resistant to the effect of the priming procedure. As we have seen with the intervention study by Ebbels et al. (2007), if we are dealing with older school-age children, it is possible to draw children’s explicit attention to the structural features of target constructions. Hirschman (2000) undertook long-term intervention in the classroom with children with SLI between 9;3 and 10;5. The intervention took place twice a week (30 minutes for each session) over 9 months of the academic year, for a total of 55 sessions. The early sessions imparted a descriptive vocabulary of grammatical concepts (verb, subject, simple versus complex sentence etc.). With this information under their belts, children in the experimental groups (one for oral production, one for written) used their newly learned technical terms to discuss the construction of various Aesop’s fables and to practice combining simple sentences into complex constructions. The target constructions were adverbial sentences with a wide range of connectives, verb clausal complement sentences, and relative clauses. At post-test (which took place following the summer vacation, at least 3 months after the intervention had ceased), the children who had received the intervention showed significant increases in their provision of the target structures compared to controls. No breakdown is given, however, of relative success on different types of complex sentence.

Conclusion There is still much to learn about syntactic impairment in children with developmental disabilities, especially—but not exclusively—as regards receptive abilities. There are areas of expressive sentence syntax that are relatively unexplored, such as the structure of noun phrases, pre-verb modification, and complex sentences other than those involving relative clauses and the clausal complements of verbs. Even in the areas that have been explored in relative depth, it is not easy to see clearly a pattern of delay or deficit, given variation across studies in sample size, methodology, age of subjects, and results. And we know little about long-term growth trajectories for any area of syntax in any of the disabilities reviewed. This will be important not only for tracing the natural history of syntactic structure change in clinical populations but also for gaining insight into the performance variability inherent in these populations. As we have seen at various points in this chapter, and as Garman, James, and Stojanovik (2005, p. 161) emphasize, variability within groups of children with impairment is high, and its significance is not well understood. In a conclusion that still stands, they argued that future research that delivers both detailed case studies and longitudinal studies would have the potential to advance our knowledge of the constraints on syntactic learning in special populations.

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18 PRAGMATICS AND SOCIAL COMMUNICATION IN CHILD LANGUAGE DISORDERS Martin Fujiki and Bonnie Brinton

Although speech language pathologists, special educators, and others concerned with language difficulties in children have long been concerned with the social ramifications of impaired communication, the pragmatic aspects of language did not become a topic of general concern until the late 1970s (Prutting, 1982). The resulting innovations in language assessment and intervention were labeled the pragmatics revolution (Duchan, 1984). Over the past four decades, pragmatics has become an important consideration in the assessment and treatment of children with a variety of diagnoses, including autism spectrum disorders (ASD), language impairment (LI— synonymous here with specific language impairment, SLI), intellectual disability, social pragmatic communication disorder (SPCD), attention deficit/hyperactivity disorder, traumatic brain injury, and language-learning disability. The primary focus of this chapter is on children identified with LI, but we also include some discussion of individuals with other diagnoses (primarily children with ASD). Although pragmatics is an accepted area of clinical practice, the range of difficulties that children with LI may experience in social interaction has led to questions as to the nature of pragmatic impairment, the boundaries of these problems, and even what the term pragmatics actually means. In this chapter, we first consider definitional issues, arguing that a broad view of social communication, which subsumes pragmatics, provides the most viable clinical approach to the wide range of problems that individuals with language problems experience in social interactions. We then focus on general ideas of how to assess and treat these problems in children with LI.

Pragmatics From a traditional formalist view, pragmatics can be viewed as one of three major components of language, the other two being linguistic form and meaning. Within this framework, pragmatics is often broadly defined as the use of language in social contexts. As such, it has been considered to be a domain of linguistic behavior similar to phonology, semantics, or syntax. In accord with this viewpoint, attempts have been made to describe pragmatic behaviors and the rules for their application in much the same way that one might describe syntactic or semantic forms and rules (Craig, 1995). Some behaviors generally considered to fall within the domain of pragmatics include conveying communicative intent, managing conversations, adjusting contributions to conversation according to shared information, using grammatical forms within the context of shared meaning (e.g., knowledge of deictic forms), formulating various types of narratives, and understanding rules for linguistic politeness. 441

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Theorists often struggle with this general notion of pragmatics, however. As one considers behaviors that are traditionally viewed as pragmatic, it is difficult to find a unifying theme that links them together beyond the fact that they have something to do with communicative exchanges in social interaction. Additionally, general definitions like the one cited above make it difficult to separate pragmatics not only from other aspects of language but also from behaviors that are often considered to fall outside the domain of language altogether. Effective communication in social interaction involves behaviors that have deep social, cognitive, and cultural roots, such as aspects of Theory of Mind (ToM) and emotional intelligence. Few, if any, pragmatic theorists would include such behaviors within the realm of pragmatics (Ariel, 2010). Although it is important to have a clear definition of any behavior that one is studying, there are advantages to taking an encompassing view of the pragmatic problems experienced by children with impaired language. As clinicians have worked with children, the list of interactional behaviors addressed has grown longer, extending far beyond the boundaries of traditional pragmatics. These behaviors range from the joint attention skills of a toddler with ASD, to the production of appropriate conversational repairs by a child with LI, to the responsiveness in conversation of an adolescent with intellectual disability. As a whole, clinicians often refer collectively to these behaviors as social communication. In the following section, we discuss the domain of social communication using a framework developed by Adams (2005, 2008). In this approach, pragmatics is considered as one aspect of a larger collection of behaviors. We believe that this framework can provide the basis for a successful clinical approach.

Defining Social Communication Social communication is the ability to use “language in interpersonally appropriate ways to influence people and interpret events” (Olswang, Coggins, & Timler, 2001, p. 53). At first glance, this definition may seem little different than the general definition of pragmatics cited above. In fact, these terms have often been used as synonyms, implying that social communication and pragmatics are essentially the same thing. As Adams (2005) noted, however, social communication is a broader, more encompassing term that involves language content and structure, conversational ability, and aspects of social and emotional learning. Adams defined social communication as, “the synergistic emergence of social interaction, social cognition, pragmatics (verbal and nonverbal), and language processing (receptive and expressive)” (p. 182). Each of these four areas is briefly described in the following sections.

Social Interaction Adams (2005) uses the term social interaction to focus on the importance of early social exchanges between infants and caretakers in providing the foundation for later language abilities (as opposed to the more general use of the term, to refer to most interactions between people). Children in the earliest stages of development demonstrate intersubjectivity, or the basic sharing of experience with another person. This connection with others represents the earliest manifestation of ToM in that sharing with others requires at least a primitive recognition of the existence of other people. These early connections provide the basis for intentional communication with others, which in turn leads to the development of language (Westby, 2014). Social interaction, thus conceived, is most relevant for children who are in the beginning stages of language acquisition. Interventions that address social interactional behaviors such as joint attention are a central component of the treatment of young children with social communication difficulties (e.g., Kasari, Freeman, & Paparella, 2006). Social interaction, however, may continue to be an issue even in older individuals with disabilities.

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Language Processing Adams (2005, 2008) uses the term language processing to refer to the production and comprehension of the syntactic, morphological, phonological, and semantic aspects of language. Although social communication approaches focus heavily on interactional behaviors, communication involves the integration of all aspects of language. Even if a child’s problems appear to be primarily interactional, the structural aspects of language merit consideration in that structural and interactional problems may co-occur, often intertwining in complex ways. The frequent co-occurrence of interactional and structural problems is illustrated by the recent revision of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5; American Psychiatric Association, 2013). Within the category of neurodevelopmental disorders, there are sub-categories of language disorder and social (pragmatic) communication disorder (SPCD). Language disorder is characterized by deficits in the production and comprehension of language structure, including vocabulary words, morphological forms, and complex syntactic structures. SPCD is characterized by difficulties in social interaction, often in the face of relatively good structural skills. It is notable, however, that language disorder may be co-morbid with social communication disorder and autism spectrum disorder, both of which are characterized by interactional difficulties. At the same time, the most commonly associated feature of SPCD is language disorder, which may involve a history of, if not current, structural language problems.

Pragmatics In Adams’ (2005) framework, pragmatics refers to the ability to manipulate language form to communicate a speaker’s message appropriately in different contexts. This definition incorporates the traditional aspects of pragmatics mentioned previously, such as conveying communicative intentions and being relevant in conversation. Conversational abilities such as turn-taking and topic manipulation are also considered. Additionally, Adams also includes nonverbal aspects of communication such as the manipulation of paralinguistic features. Although the inclusion of these behaviors stretches the traditional boundaries of pragmatics, there is little question that intonation, stress, and other nonverbal aspects of language can convey important differences in meaning. Furthermore, when confronted with ambiguous interpretations in which the meaning conveyed by words and by intonation conflict, more sophisticated language users will generally select the meaning conveyed by intonation (Morton & Trehub, 2001).

Social Cognition Social cognition can be defined as “Those aspects of higher cognitive function which underlie smooth social interactions by understanding and processing interpersonal cues and planning appropriate responses” (Scourfield, Martin, Lewis, & McGuffin, 1999, p. 559). As such, social cognitive skills include behaviors such as ToM, inferencing, and aspects of emotional intelligence that are critical to successful social functioning (Beauchamp & Anderson, 2010). An individual’s ability to take another person’s perspective and adjust behavior accordingly is critical to successful communication. Aspects of this ability have been studied from a variety of perspectives and have been characterized under topical labels such as presupposition, social inferencing, and emotion understanding. The most commonly used term for these abilities, collectively, is ToM. Recent research has separated ToM into two broad categories. Cognitive ToM involves the ability to think about and reflect on the thoughts and beliefs of others (e.g., as studied by false belief tasks). Affective ToM is the ability to understand the emotional experiences of self and

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others, and as such, overlaps with the concept of emotion understanding. There is strong evidence that these aspects of ToM have different neurological underpinnings (see Westby, 2015, for detailed discussion). Although deficits related to ToM are most often associated with children with ASD, recent work has suggested that children with a variety of diagnostic labels have problems taking the perspectives of others, and these problems are likely to impact social communication. It is important to note that considerable interaction exists among these four aspects of social communication. For example, pragmatic behaviors depend on language processing. By the same token, producing utterances to communicate depends on taking the perspective of a listener. As such, it is important to consider the relationships and influences of these behaviors as one devises assessment and treatment strategies. Consistent with Perkin’s (2011) view of pragmatic impairment as emergent, we believe that social communication problems frequently arise from a variety of sources, often working (or failing to work) in concert with each other.

The Nature of Social Communication Impairment Considerable research has examined the nature of interactional problems in children with language difficulties. Most of this work has been category-specific, concentrating on describing the abilities of children with ASD, LI, intellectual disability, and other impairments (Adams, 2005). A good deal of this research has focused on individual pragmatic skills, often assessing performance in relation to other linguistic abilities. In the following sections we examine the categories of ASD, LI, SPCD, and PLI to illustrate the nature of the social communication difficulties associated with each and consider potential similarities and differences.

Autism Spectrum Disorder (ASD) Children with ASD may present with receptive and/or expressive LI in the traditional sense (see Chapter 3 by Gerenser and Lopez). The DSM-5 (2013), however, considers LI (impairment in language processing) per se as an associated deficit rather than a central component of the disorder. The fact that the DSM-5 combines communication and social functioning into a single domain reflects the clinical reality that communication and social behaviors are inextricably intertwined (Ornstein Davis & Carter, 2014). Deficits in social communication constitute a defining characteristic of ASD. These deficits may vary in manifestation and intensity, and they may reflect limitations in social interaction, social cognition, and/or pragmatics. Impairment in basic social interaction is often observed early in development. Difficulties may be evident in a lack of coordination of mutual gaze or eye contact for the purpose of making a social connection and sharing emotion (Hobson, 2014; Ornstein Davis & Carter, 2014). This difficulty undermines the development of the joint attention that is so important to the development of language and social interaction (Mundy & Burnette, 2005; Wetherby, 2006). Hale and Tager-Flusberg (2005) speculated that problems with joint attention may be a forerunner to the difficulties in discourse interaction and ToM that are so often associated with ASD. The nature and developmental role of ToM have received considerable attention in recent years. As indicated earlier, ToM can be conceptualized as having two components, cognitive ToM and affective ToM. Baron-Cohen et al. (2005) described empathizing (a term used to encompass ToM) as including “The ability to attribute mental states to self and others as a natural way to understand agents” as well as “an emotional reaction that is appropriate to the other person’s mental state” (p. 629). Although children with ASD are generally considered to have difficulty with cognitive ToM tasks, affective ToM (similar to emotion understanding) may be particularly problematic. Hobson (2014) summarized research suggesting that the lack of social engagement in children

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with ASD often extends to a limited ability to perceive emotion cues, to understand the emotions of others, and to express experienced emotion within interpersonal interactions. Hobson advised: We should pay special attention to the quality of emotional impairment in the interpersonal domain, for it would seem to be in the interpersonal domain—and specifically, in respect to sharing subjective states and coordinating attitudes with other people vis-à-vis the world, as in episodes of joint attention—that a critical abnormality is to be found. (p. 349) It is likely that poor understanding of emotion restricts the social communication of children with ASD by limiting the ability to establish a deep interpersonal connection and understanding with others. Problems that could be considered pragmatic are also a hallmark of individuals with ASD. Kim, Paul, Tager-Flusberg, and Lord (2014) noted that some of the pragmatic difficulties that children with ASD experience seem constant across development, but others may change with development and varying expectations and contexts. Children with ASD may show differences in the social uses of language, including the expression of intent. For example, the communicative acts that children with ASD produce may be limited in that these children may not use language to initiate interaction, share comments, or ask for information (Kim et al., 2014). Children with ASD may communicate to direct the behavior of others but not to gain attention or share feelings and experiences (Wetherby, Prizant, & Schuler, 2000). To illustrate, some children with ASD may use language or gesture to make requests without employing eye contact and gaze alternation, such as when a child takes a caregiver’s hand and extends it toward a desired object without looking at the caregiver (Travis & Sigman, 2001). Thus, the child may direct a caregiver without appearing to establish an interpersonal connection. Children with ASD also have difficulty managing conversational interactions in a way that is responsive to the needs of communicative partners. Summarizing research on the conversational patterns of individuals with ASD, Kim et al. (2014) noted “impaired skill in participating in communicative activities involving joint reference or shared topics” (p. 250). They also reported that the strategies that individuals with ASD use to manage conversations are often less sophisticated than their structural language abilities. These conversational difficulties may stem, at least in part, from the difficulties that individuals with ASD experience in appreciating the emotional states of others. To complicate matters, restricted interests and repetitive behaviors may exacerbate conversational difficulties. For example, a child with ASD who asks all attendees at a church service about the legality of their car registrations may not be perceived as a responsive conversational partner. Similarly, echolalic utterances, even those that convey communicative intent (Prizant & Duchan, 1981), are difficult for listeners to interpret and may derail conversational continuity. In summary, children with ASD may experience a wide variety of challenges in social communication. Differences may present in infancy as an inability to engage in emotion sharing, reciprocal exchanges, or basic joint attention. As individuals mature, problems in interpersonal interactions and relationships are likely to remain. Difficulties may be couched in terms of problems with basic social interaction, social cognition, or pragmatics. In fact, children with ASD demonstrate the extent to which these components of social communication are intertwined.

Language Impairment (LI) Children with LI (referred to as language disorder in the DSM-5) have syntactic, morphological, and semantic deficits that cannot be attributed to low cognitive abilities, poor motor skills, or

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impaired sensory systems. It is also sometimes suggested that these children have relatively intact interactional skills, except in cases in which their attempts to communicate are undermined by their impaired structural abilities. The nature of the social communication difficulties of these children is not as straightforward as this general characterization would suggest, however. A good deal of the early research examining the interactional skills of children with LI focused on their pragmatic skills. These abilities were generally studied by comparing their pragmatic behavior (e.g., responding to requests for conversational repair) to that of typically developing peers with a similar level of structural language development. When considered in this manner, the evidence for pragmatic problems was equivocal. In some comparisons, children with LI performed similarly to their language-age-matched peers (e.g., Fey & Leonard, 1984; Leonard, 1986). In studies revealing differences, children with LI generally demonstrated the same basic pragmatic functions as their typical peers, although they performed them in less appropriate or efficient ways (e.g., Brinton, Fujiki, & Sonnenberg, 1988; Conti-Ramsden & Friel-Patti, 1983). For example, Brinton et al. (1988) presented children with LI with stacked sequences of requests for conversational repair (Huh? What? and I didn’t understand that.). Comparisons were made to typical children of the same age and language level. Participants in all of the groups recognized the need to respond to these requests for clarification. However, children with LI did not employ the more sophisticated repair strategies used by even the language-age-matched children, who were on average about two and a half years younger. Some interpreted such findings to indicate that the pragmatic deficits observed in these children stemmed from limitations in structural skills rather than from a lack of pragmatic knowledge (see Craig, 1995, for discussion). There is evidence, however, that at least some children with LI have interactional problems that are not explained by their syntactic and semantic deficits. One source of evidence for this conclusion comes from detailed analyses of conversational behavior (Bishop, 2000). For example, Bishop, Chan, Adams, Hartley, and Weir (2000) examined the ability of young school-age children with SLI and their typical chronological-age- and language-age-matched peers to respond to conversational bids (e.g., requests for information) from an adult. The group with SLI was composed of two subgroups: children with traditional deficits of form and content and those with pragmatic difficulties. Both subgroups with SLI provided fewer adequate responses than did the control groups. Children with SLI in the pragmatic subgroup were more likely than children in either of the typical control groups to not respond at all to an adult bid. This finding is notable because nonverbal responses were considered as acceptable responses. Thus, the failure to respond was not directly linked to structural language limitations. A second example was provided by Brinton, Fujiki, and Powell (1997), who examined patterns of topic manipulation in children with LI. When presented with a verbal statement (e.g., “I walked to school this morning. I saw a dog. It almost bit me.”; p. 5), children in the group with LI produced a significantly higher percentage of off-topic utterances than did groups of peers matched for chronological age or for language level. In many instances, the children with LI talked more than their typically developing peers. In doing so, however, they produced more inappropriate utterances. A second indication of interactional difficulties in children with LI can be found in research examining aspects of their social cognitive abilities (see Bishop, 1997, for a review of earlier work). As noted previously, some recent studies have revealed deficits in areas outside of the traditional realm of language, including ToM and emotional intelligence. Although a comprehensive review is beyond the scope of this chapter, we focus briefly on an area that covers overlapping ground between these areas, affective ToM (emotion understanding). Children with LI perform more poorly than typical peers in several aspects of emotion recognition and emotion understanding. For example, these children have difficulty reading emotion

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expressed on faces (Spackman, Fujiki, Brinton, Nelson, & Allen, 2006) and identifying emotion conveyed by prosody (e.g., Fujiki, Spackman, Brinton, & Illig, 2008; Trauner, Ballantyne, Chase, & Tallal, 1993). Illustrative of this work, Boucher, Lewis, and Collis (2000) examined the ability of children with ASD to identify the emotion conveyed by a recorded voice and match it to a picture of a face expressing the emotion. Boucher et al. included groups of both typical children and children with SLI as controls. Surprisingly, the children with SLI not only performed more poorly than the typically developing children, but they also performed below the level of the children with ASD. Children with LI also have difficulty with more sophisticated emotion understanding tasks (Ford & Milosky, 2003, 2008; Spackman, Fujiki, & Brinton, 2006). Ford and Milosky (2003) asked kindergarten children with LI to infer what emotion would be experienced by a character, Twinky, given a short scenario (e.g., Twinky wanted a teddy bear for his/her birthday. S/he opened a present with a big, fluffy teddy bear. Twinky was (happy).”; p. 24). Scenarios examining the emotions of happy, sad, mad, and surprised were used. Children with LI not only performed more poorly than their typical peers, but they also made valence errors (confusing a negative emotion such as mad for a positive emotion such as happy). As another illustration, Brinton, Spackman, Fujiki, and Ricks (2007) asked children with LI and their typical peers to judge when a character in a short scenario should hide an emotional reaction. For example, in one scenario, the character, named Chris, received a disappointing gift from a favorite aunt. Typical children indicated that Chris should hide the emotion twice as often as did children with LI (e.g., that Chris should just say thanks rather than complaining to avoid hurting the aunt’s feelings). A final indication that children with LI have difficulties with multiple aspects of social communication comes from attempts to subcategorize these children by their deficits (Bishop & Rosenbloom, 1987; Conti-Ramsden, Crutchley, & Botting, 1997). These researchers have consistently identified a subgroup of children who have interactional difficulties. For example, Conti-Ramsden et al. (1997) studied a group of 242 children enrolled in language-based classrooms for children with SLI in England. Based on extensive testing, the children were placed in six subgroups, including a group of children with semantic and/or pragmatic problems. Considering results from two years of testing, Botting and Conti-Ramsden (1999) reported that about 23% of the children had pragmatic problems. What is notable about this figure is that children in this taxonomic system were carefully identified on the basis of the traditional diagnostic standards for deficits in linguistic form and content. In summary, strong evidence shows that children with a diagnosis of LI may experience problems in various areas within the domain of social communication. There is considerable variability, however, making the diagnostic process critical to effective treatment. Using a social communication framework, assessment goes beyond deficits in specific aspects of language to capture a more comprehensive view of the child’s abilities.

Social Pragmatic Communication Disorder Several case reports in the literature have described children whose interactional skills were much poorer than expected given their syntactic and semantic abilities (e.g., Blank, Gessner, & Esposito, 1979). Additionally, as noted above, several groups of researchers have described a subgroup of children within the broader category of developmental language disorders whose deficits were primarily pragmatic (Bishop & Rosenbloom, 1987; Conti-Ramsden et al., 1997; Rapin & Allen, 1983). These children were talkative but had problems with relevance (e.g., producing off topic utterances), leading to difficulties in general responsiveness. Problems with unusual word choices

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and word retrieval were also noted. At the same time, their structural language skills were relatively good. It should be noted that neither Bishop and Rosenbloom (1987) nor Conti Ramsden et al. (1997) included children with ASD in this category. Originally identified as semantic pragmatic disorder, Bishop (2000) proposed using the term PLI in recognition of the fact that the pragmatics and semantic problems of these children were not necessarily related. Additionally, pragmatic problems could occur in various combinations with structural language problems, and this variability was not indicative of a distinct category of impairment. There has been considerable debate in the literature as to whether children with PLI form a distinct group from high-level autism (Reisinger, Cornish, & Fombonne, 2011; Shields, Varley, Broks, & Simpson, 1996). Adding to the complexity is the relatively new category of SPCD, introduced in the recent revision of the DSM-5. In general terms, SPCD bears a fair amount of similarity to PLI. The diagnostic criteria for SPCD provided by the American Psychiatric Association (2013) specifies difficulty in each of four areas: (1) using communication to interact socially, appropriate to context; (2) adjusting language for different conversational partners and situations; (3) participating in different types of discourse, providing repair when needed, and using nonverbal cues effectively; and (4) comprehending jokes, idioms, metaphors, and other types of language that require an understanding of nonliteral or figurative forms. These deficits cannot be the result of other clinical conditions (e.g., ASD, intellectual disability) and cannot stem from deficits in language form and content. As noted previously, however, LI is commonly associated with this disorder (Bishop & Norbury, 2002, cited by the American Psychiatric Association, 2013). Norbury (2014) noted that there is a fair amount of confusion surrounding the identification of SPCD. Diagnosis is complicated by factors such as the limited availability of assessment instruments, the high co-morbidity with a variety of other seemingly unrelated categories of impairment (e.g., Attention-Deficit/Hyperactivity Disorder), and a lack of continuity with other related impairments (e.g., is SPCD essentially a milder form of ASD, or ASD without restricted or repetitive patterns of interests and behaviors, or a type of LI?). Norbury concluded that given current understanding and research, SPCD is best conceptualized as a symptom associated with a range of other categories of impairment rather than a category in and of itself.

Summary We have defined social communication as encompassing a range of abilities including social interaction, language processing, social cognition, and pragmatics. As such, a variety of individuals with differing diagnoses may experience difficulties with social communication. We have focused on three of these groups: individuals with ASD, LI, and SPCD. For children with ASD, deficits in social interaction, social cognition, and pragmatics constitute a central component of the impairment. For children with LI, the situation is more varied; however, many children with a diagnostic label of LI have difficulties in pragmatics and social cognition that cannot be attributed to their structural language problems. Similar to children with ASD, children with SPCD have a primary deficit in pragmatics and perhaps social cognition. These children lack some of the key features of ASD, however, such as repetitive behaviors and restricted interests. Although the newly defined category of SPCD includes difficulties with social cognition and pragmatics, there are some problematic issues associated with the actual clinical application of this diagnostic category. The fact remains that the borders of SPCD, LI, and to some extent, ASD, are fuzzy. It is likely that some children with high-functioning ASD, SPCD, and LI will be difficult to distinguish. Although diagnostic frameworks identify specific concerns with each label, perhaps the most important consideration clinically is the variability within each category and the recognition

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that deficits may involve components of social communication in varying combinations. This fact emphasizes the importance of a careful assessment and thoughtful considerations of treatment goals and procedures to provide the strongest support for an individual child.

Assessment of Social Communication An emphasis on social communication has an important impact on the way assessment is approached. Since communication in real-life contexts involves a variety of personal and contextual factors, assessment is designed to consider a range of behaviors and variables that are important to communication. These factors may extend beyond the traditional boundaries of language structure, vocabulary, or even pragmatics. Accordingly, we first discuss the importance of placing assessment within a social context. We then review some objectives in the assessment of social communication and suggest some procedures for achieving those objectives.

Considering Important Communicative Partners and Contexts Traditionally, language assessment focused primarily on describing the individual with a suspected deficit. More recent approaches, particularly for young children, have stressed working with the family as a whole (e.g., Paul & Roth, 2011). From this perspective, a basic consideration in assessing social communication is to identify important communicative partners and contexts. For young children, the most important partners will generally be family members, and the most important communicative contexts will involve interactions in the home. Other important contexts may include school and community settings. These contexts, and the new and varied interactional partners they bring, tend to increase in number as children develop. The involvement and influence of stakeholders (caregivers and family, friends, teachers, etc.) may vary at different stages in the child’s life. Nevertheless, the participation of these individuals in the assessment and intervention process will increase the likelihood of meaningful growth (Fujiki & Brinton, 2017). Social behavior is heavily influenced by cultural norms and expectations, and these standards will certainly influence communicative exchanges. For young children, parental beliefs about communicating with the child and the interactional strategies they use are important. For older children, beliefs and practices about literacy will be added to the mix. In order to view interactions and practices in the proper context, it is necessary to understand the cultural perspective of those interacting with the child. For example, in some Asian cultures, asking a teacher a question may be viewed as challenging the teacher’s authority. A child who has grown up in such a cultural context may be hesitant to ask questions, even when enrolled in a middle-class American classroom where questions are encouraged. Even in cases in which cultural diversity does not appear to be a factor, the individual perspectives of caretakers and other important stakeholders should be considered. Individuals may have unique views and perceptions that will be important to take into account in both the assessment and the intervention processes. For example, the mother of a child that we worked with insisted that the child say “please” before making a request. This complicated intervention because the child was only producing single words and was barely ready to begin combining words to form two-word utterances. This issue was resolved by using the word please to mark the request, as in, “Please juice” instead of “More juice.” Information regarding important conversational partners and interactional contexts may be gathered by interviewing stakeholders (caretakers, teachers, etc.) and the child directly, if appropriate. Ethnographic interviewing techniques provide a useful framework to gather this information. This methodology puts the person being interviewed on an equal footing with the professional

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and uses various types of open-ended questions to gain a deeper understanding of behavior. This strategy may also help to develop rapport (Westby, Burda, & Mehta, 2003). It may be useful to supplement the information gathered through interviews with a social network analysis (Blackstone & Berg, 2003). Originally developed for individuals with complex communication needs, Blackstone and Berg’s analysis can readily be adapted for use with children with LI. In this analysis, the child’s interactional partners are identified within different social circles. The innermost circle consists of family members. Moving outward, circles consist of friends, acquaintances, those paid to interact with the child (e.g., teachers), and unfamiliar people. This information can be supplemented by considering the child’s communicative strengths and challenges when communicating within each circle. An emphasis is placed on enhancing communication within the circles and also enabling the child to expand the circles. In summary, gathering information about communicative partners and contexts helps describe the child’s communicative needs within his or her social world. This perspective provides the basis for both the selection of assessment instruments and the interpretation of the resulting data.

Assessing Social Communication Behaviors The initial purpose of a social communication assessment is to identify individuals needing intervention. Once these individuals are identified, we focus on formulating specific intervention targets. Various strategies have been devised to address these issues. Although no single strategy is foolproof, several innovative methods are available that can provide valuable information about the child’s ability to communicate in social contexts. A few of these are discussed in the following sections to provide an illustration of some of the available procedures. Most of these strategies involve rating scales, observations, and specific behavioral probes. For more discussion, readers are referred to Fujiki and Brinton (2017) and Paul and Norbury (2012).

Identifying Individuals with Social Communication Problems Some children with language problems do quite well in social interactions. Most struggle, however, and will require careful assessment. It is useful to begin by obtaining a general idea of a child’s functioning in order to determine the focus of further assessment. Rating scales can be useful for this purpose. This method has several advantages in the assessment of social communication behaviors: (a) it uses the impressions of individuals (e.g., caregivers, teachers) who know the child well and have observed the child over a long period of time; (b) it can reveal infrequently occurring but influential behaviors that might not be observed in naturalistic observations; (c) it can provide information about individuals who cannot respond themselves; (d) it can provide an objective means of organizing observations; and (e) it is relatively inexpensive compared to observational methods (Merrell, 2003). Two useful examples are the Language Use Inventory for Young Children (LUI; O’Neill, 2009), normed for ages 18 to 47 months, and the Children’s Communication Checklist-2 (CCC-2; Bishop, 2003), normed for ages 4:0 to 16:11 years. Both are standardized and can provide information across a range of interactional behaviors that are important to social communication. Although highly useful, rating scales are not without concerns. In addition to various biases (e.g., a halo effect in which a child is rated positively because of a trait not related to the behavior in question), these instruments are limited by the fact that they reply on impressions of behavior, but not actual observations (Merrell, 2003). Thus, it is a good idea also to observe the child’s performance in important interactional contexts to support the information obtained from rating scales. Some instruments do this in a systematic manner by combining direct observations with the

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rating scale format. One example is the Communication and Symbolic Behavior Scale–Developmental Profile (CSBS-DP, Wetherby & Prizant, 2003). This instrument includes a 5–10-minute caregiver checklist, a 15–25-minute caregiver questionnaire, and a 30-minute sample of behavior in which the child is observed performing a series of actions with a caregiver. This measure focuses on early social communicative behaviors such as the use of gestures, the expression of emotion, and the production of sounds and words. The CSBS-DP is normed for children ages 6 to 24 months, but it can be given to older children who are in this developmental range (see Adams, Gaile, Lockton, & Freed, 2011, for an example in which the rating scale and observational formats are combined for use with older children).

Identifying Social Communication Objectives Once a general notion of the problem has been obtained, assessment can be narrowed to pinpoint and describe behaviors that appear to be problematic. In addition to weaknesses, it is also important to consider behaviors that are more robust, because these strengths may be useful when facilitating new behaviors. Behavioral observations are helpful to confirm the nature of a problem, and more extensive observations may also be used to identify specific objectives. Several analyses and procedures for observing interactions have been suggested (e.g., Adams, 2002; Brinton & Fujiki, 1989). Although generally considered to have a high degree of validity, observational methods may also be relatively time intensive. Several tools have been developed to make gathering and analyzing information from spontaneous interactions more feasible (e.g., Gibson, Hussain, Holsgrove, Adams, & Green, 2011; Rice, Sell, & Hadley, 1990). For example, Olswang, Coggins, and Svensson (2007) developed the Social Communication Coding System (SCCS) to focus on dimensions rather than discrete behaviors. A child’s classroom interactions are coded into dimensions of hostile/coercive, prosocial/engaged, assertive, passive/disengaged, adult seeking, and irrelevant behavior. Using a hand-held computer to record data, the examiner captures information regarding the occurrence, frequency, and duration of each dimension. Using these types of tools, the clinician may gain a sense of a child’s interactional abilities in a more time-efficient manner. Another strategy for examining aspects of social communication is to engage the child in an interaction and insert probes into the exchange to assess specific behaviors. For example, as noted previously, Brinton et al. (1997) introduced topics into a natural conversation and then analyzed the manner in which children maintained or developed these topics. Creaghead (1984) inserted a series of probes into a naturalistic interaction centered around making a peanut butter sandwich to examine the production of various speech acts (e.g., as the child enters the room, the examiner notes whether the child produces a greeting). Simmons, Paul, and Voklmar (2014) developed this format in the Yale in vivo Pragmatic Protocol (YiPP). The YiPP, developed for children with ASD, involves a series of probes inserted into a naturalistic interaction. These probes are designed to elicit specific pragmatic behaviors, including initiating conversation, requesting information, maintaining a topic, and providing appropriate background information. If a child does not respond to a probe, cues are administered to elicit the behavior. Scoring is based on the child’s response to the probes as well as the number and type of cues needed to elicit a response. An additional way of assessing aspects of social communication is to create a context for evaluation and reflection. This can be done in a variety of ways. One method that is widely used in assessment and intervention for individuals with ASD is video modeling (Bellini, Gardner, & Markoff, 2014). As an example, Brinton, Robinson, and Fujiki (2004) presented Larry, an adolescent with LI, with short video clips and then asked him to make judgments about the social interactions of the individuals in the clips. The clips portrayed individuals interacting in situations that had

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been difficult for Larry, such as greetings, hiding negative emotion, and accepting other’s opinions when they differed from his own. One of the scenarios, for instance, focused on hiding negative emotion when offered unfamiliar food by the hostess of a dinner party. Videos included portrayals of both appropriate and inappropriate behavior. Larry watched the clips and then reflected on the perspectives and emotions of the various individuals in the interactions. These methods were used with Larry for assessment and also for intervention activities. In utilizing various analysis systems and probes, one must maintain a delicate balance of holistic and narrow viewpoints. Because social communication is so complex, it is necessary to focus on those behaviors that initial overviews have suggested are most important. At the same time, it is wise to maintain a view that is wide enough to allow consideration of behaviors that may occur outside of the narrow focus. For example, in the midst of an observation structured to illustrate cooperative work in a triad, we once observed a child break down dramatically in response to a time limit imposed on the task. It was important to consider this behavior in addition to the various cooperative bids the child had demonstrated. As with all assessments, the value lies in the synthesis of the information obtained and the clinical decisions that follow.

Synthesizing the Data Because assessing social communication behavior demands so much time, thought, and effort, it is essential to maximize the clinically important information gleaned from the individual evaluations. As observations and analyses are conducted, it is helpful to consider the functional significance of behaviors. Given the context of the home, the classroom, and the community, what aspects of the child’s behavior enhance or limit social communication? It will be important to consider the consistency of the behaviors, the effectiveness of any compensatory behaviors, and the impact of the behaviors on peer relationships and learning opportunities. When social communication difficulties are judged to limit the child’s ability to participate in academic, social, and other life contexts, intervention is warranted.

Intervention Most intervention approaches espouse the idea that treatment should involve contexts that reflect real communicative interactions. Even within fairly structured behavioral programs, naturalistic tasks and contexts are often employed to engage and motivate children and to encourage generalization. A social communication approach, however, takes the notion a step farther. Such an approach is designed to address and integrate the knowledge, behaviors, skills, and dispositions that intertwine to support interaction and relationships within a child’s social world. A question that is not easily addressed, however, is whether it is possible to change the problematic social communication behaviors of children with LI and whether these changes can lead to improved perceptions of those who interact with the child, better interpersonal relationships, greater access to learning contexts, and enhanced learning. Research on the efficacy of social communication approaches has been limited, but emerging evidence is encouraging.

Efficacy of Social Communication Intervention There is a great deal of overlap between what is generally described as social and emotional learning and social communication. The evidence documenting the success of programs to enhance social and emotional learning in typically developing children is impressive. For example, Durlak, Weissberg, Dymicki, Taylor, and Schellinger (2011) reported the results of a meta-analysis of

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213 studies examining school programs focusing on social and emotional competence. Outcomes indicated that children and adolescents in these programs not only had significantly better social outcomes than controls, but they showed gains in academic performance as well. Unfortunately, a relatively small number of intervention studies focus on the interactional skills in children with LI (e.g., Gerber, Brice, Capone, Fujiki & Timler, 2012). Most of the available work focusing on children with diagnoses of either LI or PLI falls into three broad design categories of (1) randomized control trials, (2) single subject designs, and (3) case studies. This work is briefly summarized in the following sections, with selected references.

Randomized Controlled Trials Adams and colleagues (Adams et al., 2012; Adams, Lockton, Gaile, Gillian, & Freed, 2012) have reported the only randomized controlled trial to date involving children with PLI. These researchers presented a comprehensive intervention program to 57 children with social communication problems. Performance was compared to a group of 28 children who received their usual intervention. The social communication intervention addressed language processing, pragmatics, and social understanding and social interaction. Specific targets within these areas were selected on an individual basis as dictated by each child’s needs. Treatment targets included behaviors such as understanding the perspectives of others, social inferencing, narrative construction, and conversational abilities. Children were seen three times a week (1 hour per session). The intervention produced significant changes in conversational abilities as rated by examiners who were blind to group membership. Additionally, parent and teacher ratings improved significantly compared to those of controls.

Single Subject Designs A number of researchers have used single subject designs to study treatments targeting aspects of social communication in children with language difficulties. (e.g., Craig-Unkefer & Kaiser, 2002, Goldstein, Wickstrom, Hoyson, Jamieson, & Odom, 1988; Stanton Chapman, Denning, & Jamison Roorbach, 2012; Stanton Chapman, Roorbach Jamison, & Denning, 2008). An advantage of these designs is the ability to focus on the performance of individual children, which is important given the heterogeneity inherent in the larger population of children with LI. Much of the time, these studies have involved children with a range of problems, most commonly involving LI and various behavior problems. Intervention has generally employed methods involving peer interaction, such as script training (intervention built around a script for a common task such as going for fast food), socio-dramatic play, and story enactment. Intervention targets have focused on a range of social communication behaviors such as producing positive verbal initiations, responding to the social bids of others, and managing conversations (e.g., turn-taking). Although there has been variability in outcomes, the children in these studies have generally shown positive gains (Fujiki & Brinton, 2017).

Case Studies and Other Nonexperimental Designs A number of authors have reported case studies, multiple case studies, and other nonexperimental designs detailing the treatment of children displaying a range of social communication problems. Children in these studies have usually been identified with LI or PLI (e.g., Adams, Baxendale, Lloyd, & Aldred, 2005; Brinton et al., 2004; Fujiki, Brinton, McCleave, Anderson, & Chamberlain, 2013; Schuele, Rice, & Wilcox, 1995). Most of these studies have reported gains, illustrating the

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potential of social communication interventions. Case studies are usually considered as exploratory in nature, however, because they lack randomization. Additionally, these studies generally do not involve a control group or baseline segment with which to contrast performance.

Clinical Implications In order to be considered effective, a social communication intervention program should produce outcomes that reflect improved functioning in real social contexts. Even though much work remains to be done on these approaches, the research to date suggests that intervention can produce meaningful change. Because social communication requires the complex interaction of interactional, social cognitive, pragmatic, and linguistic abilities, implementing effective intervention is a complex proposition, and many factors must be taken into account. For example, it is important to consider what is known about the developmental trajectory of LI in order to predict possible associated social outcomes and to recognize practical clinical realities. Based on the available research on treatment efficacy, as well as our own clinical experience, we offer the following considerations that we feel are important when planning and implementing successful social communication intervention.

Look Ahead Although children with LI may have multiple clinical needs, it is important to look ahead to identify the abilities and behaviors that will contribute most significantly to their quality of life as they grow to adulthood. Research suggests that as they mature, children with LI are at risk not only for difficulties in language and academic achievement but also for a myriad of social and emotional difficulties, including withdrawal, peer rejection, limited friendship formation, victimization, and poor self-esteem (e.g., Conti-Ramsden & Botting, 2004; Fujiki, Spackman, Brinton, & Hall, 2004; Lindsay, Dockrell, Letchford, & Mackie, 2002; Wadman, Durkin, & Conti-Ramsden, 2008). It is important to determine the areas of clinical focus that will contribute most directly to children’s future abilities to learn in a variety of contexts, to form and maintain relationships, and to function independently within their communities. Looking ahead emphasizes the value of a social communication approach that integrates critical aspects of language, social, and emotional learning.

Make Intervention Accessible In order to bolster a child’s social communication, it is essential to ensure that both the language and the content of instruction are accessible to that child. As noted, many treatment programs have been successfully implemented to enhance social and emotional learning in typically developing children. Although these programs can provide a rich resource of tasks and ideas for enhancing social communication, both the language and the content of instruction may place undue demands on children with LI. To make new concepts accessible, children with LI may require particular support. For example, children with LI may benefit from a focus on acquiring the vocabulary to label emotions and the structures to express causes and effects of emotion. At the same time, children with LI may need an emphasis on aspects of social cognition such as understanding emotion and making social inferences (Brinton et al., 2007; Ford & Milosky, 2003, 2008). Instruction should be geared both to accommodate children’s challenges and to enhance their learning in these areas.

Adjust for a Dynamic Target Behaviors that contribute to social communication can be dynamic targets in that the appropriate use of these behaviors depends on multiple factors that vary according to context. Targeting

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acquisition of a behavior or concept for purposes of social communication involves facilitating the linguistic, social, emotional, and world knowledge that underpin its use. For example, consider a seemingly straightforward behavior such as responding to product questions. How much information does one give to whom (“How much money does your mom make?”)? When is it acceptable, or even advisable, to avoid responding directly (“When will your parents be home?”)? How does one adjust the response, depending on one’s relationship with the conversational partner (“What do you want for your birthday?”)? What might be the emotional impact of one’s response on the questioner (“Who is your best friend?”)? Intervention should be designed to help children understand and predict the variable factors that determine the use and impact of social communication behaviors in a wide variety of contexts.

Facilitate Components Simultaneously As social communication is so complex, it is tempting to break the process down into manageable units with limited communicative utility or generalization. Although children with LI may have a number of identifiable deficits, there is danger in fragmenting behaviors in order to provide clinical targets (Damico, 1988). As Kamhi (1994) noted, however, “one cannot teach children to be better communicators without focusing on some specific aspect of language or communication” (p. 25). Therefore, we focus on larger, more communicative behaviors while we highlight and teach the specific elements that make those behaviors work. In this manner, components of social communication can be facilitated simultaneously within the same treatment activities. For example, consider a child who cannot enter into and interact with a group despite the fact that he wants to play with others. The larger treatment focus might be to join and stay with the play. As needed, component behaviors might be modeled or directly instructed as needed. These might include approaching the group, attending to the play, comprehending the talk, formulating relevant comments, responding to partners’ bids, employing appropriate words and structures, and remaining with the group. The child’s performance on several behaviors can be evaluated or facilitated within the same communicative activity. It is critical, of course, to support the child’s ability to integrate multiple behaviors and adjust performance based on contextual factors. Activities (such as journaling) that help children reflect on and evaluate the effects of their efforts form an important part of intervention.

Invest the Effort Facilitating social communication behaviors requires considerable time and effort. Social skill programs are designed for children with a range of learning disabilities, but results of treatment studies have not always been impressive (Bellini et al., 2014; Kavale & Mostert, 2004). For example, Kavale and Mostert performed a meta-analysis of 53 studies involving more than 2,000 students diagnosed with learning disabilities. They noted that a little over half of the students treated (58%) produced meaningful gains, with participants in the treatment group producing only a small gain (10%) over control subjects. The authors note that social skill programs are hindered by a variety of problems, including a lack of a clear rationale in the way that programs are constructed, problems in how social behaviors are measured, and conceptual difficulties as to what exactly is being taught. Currently it is not possible empirically to determine how intense a social communication program must be in order to be effective. In general, however, it is likely that more is better. As Norbury (2014) explains, Given the complexities of social communication and pragmatic language, it is also perhaps unrealistic to think that we can expect significant change in a relatively brief period

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of intervention. It is likely that these children will require on-going support as they get older and the complexity of social communication and language context increases in the expectation for more intimate social relationships, and for using language for learning and employment. (p. 212) Although it is encouraging that the outcomes of some relatively short-term interventions have been promising (e.g., Adams et al., 2012), it is likely that treatment will require a significant commitment of clinical resources over an extended period of time (Brinton et al., 2004).

Conclusion Research and practice in social communication has made a major impact on language intervention. Focusing on what it takes to communicate effectively in social contexts has greatly broadened the scope of clinical practice and has pushed intervention toward more natural communicative contexts and approaches. Effective social communication requires the complex interaction of several abilities, including interactional, structural language, pragmatic, and social cognitive skills. Recent work has suggested that it is possible to assess these behaviors and make meaningful changes through intervention. There is still considerable work to do, however, to develop more efficacious intervention strategies.

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19 READING AND WRITING IN CHILD LANGUAGE DISORDERS Pamela E. Hook and Charles W. Haynes

The National Institutes of Health have recognized that high illiteracy rates are a national health care crisis that needs immediate attention. Approximately 10–15% of school-age children have a learning disability, and, of these, around 70% display disabilities specific to the literacy skills of reading and writing. Given the magnitude of this challenge, there is a critical need for practitioners to understand and effectively diagnose and treat reading and related language-learning difficulties. This chapter provides an overview of the processing skills and deficits associated with reading and writing difficulties and outlines principles and methods for assessment and intervention.

Processes Involved in Reading and Writing A complex reciprocal relationship exists between spoken and written language. In fact, approximately two-thirds of children with oral language difficulties also have problems in the areas of reading and writing (Stackhouse & Wells, 1997; Tallal, 1988). Two separate, but highly interrelated components interact in the acquisition of reading and writing skills. The first is code-related and primarily affects word identification and spelling, whereas the second is content-related and influences primarily comprehension and written expression (Hoover & Gough, 1990). Even mild difficulties in word identification can reduce the speed of reading, draw attention away from the underlying meaning, and create the need to reread selections to grasp the meaning (Lyon, Shaywitz, & Shaywitz, 2003; Torgesen, Rashotte, & Alexander, 2001). Similarly, difficulties in spelling or handwriting (transcription) interfere with efficient and coherent written formulation (McCutchen, 1995). These basic relationships between word identification/spelling and comprehension/written expression are illustrated in Figure 19.1. Although all aspects of spoken language influence reading and writing, certain components are more directly related to word identification and spelling, whereas others are more directly related to reading comprehension and writing. As is illustrated in Figure 19.1, aspects of phonological processing, specifically the development of phonemic awareness, are more directly related to the acquisition of skills related to word identification and spelling, whereas morphology, syntax, semantics, discourse, and pragmatics are more directly related to reading comprehension and written expression. In addition to phonological processing, word identification involves orthographic processing (the abilities involved in identifying specific patterns of letters). To read a word, these two kinds of processing interact to create orthographic/phonological associations for accessing lexical

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Word Identification/Spelling Orthographic

Attention Executive Function Memory

Phonological

Orthographic/Phonological Association Automaticity

Oral Motor/ Visual Motor

Fluency

Comprehension/Written Expression Morphological Syntactic Semantic Discourse Pragmatic

Other Factors Affecting Reading/Writing: Intellectual Functioning Motivation/Emotional Factors Socioeconomic Level Cultural/Linguistic Differences Educational Opportunity Family Support/Home Environment Figure 19.1 Processes involved in reading and writing.

information in long-term memory storage (Adams, 1990). Additionally, automatic retrieval of the phonological representation that corresponds to a specific orthographic representation is crucial for fluent reading. Weaknesses in any of these systems will affect acquisition of efficient and effective reading and writing. On the left and right in Figure 19.1 are other intrinsic factors that affect reading and writing development: these include attention, executive function, and memory, as well as visual-motor and oral-motor skills. As indicated at the bottom of Figure 19.1, intellectual functioning as well as emotional and motivational factors also play important roles. Additionally, it is critical to examine extrinsic factors such as socioeconomic, cultural, and linguistic differences, the school environment, educational opportunities, family support, and the home environment. (For more in-depth discussions of these social contextual influences, see Chapter 12 by Peña, Bedore, & Baron; Chapter 13 by Leonard; and Chapter 14 by Newkirk-Turner & Green.) In addition to the acquisition of spoken language skills, the development of reading and writing requires metalinguistic abilities or the capacity to analyze linguistic rules and apply them in novel contexts. In fact, reading can be viewed as inherently metalinguistic in nature; it requires an awareness of the structure of the language across all areas: phonology, morphology, syntax, semantics, discourse, and pragmatics. This kind of awareness is not necessary for the acquisition of spoken language skills (Catts & Kamhi, 1999). The metalinguistic nature of reading acquisition can create confusion in diagnosis, when some children with reading problems display no overt deficits in their spontaneous oral language yet struggle with metalinguistic tasks such as breaking words apart at the level of the phoneme or applying morphological rules in decontextualized situations such as, Here is a wug. Now there is another one. There are two _____. (from Jean Berko’s Wug Test, 1958), understanding multiple meanings of words, or drawing inferences. Difficulty with developing an awareness of the rules that govern the structure of language has important implications for all aspects of reading, including word identification, fluent reading with appropriate phrasing, and higher-order thinking skills necessary for comprehension. For these reasons, most children who struggle with reading and writing need to be taught linguistic rules explicitly.

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Disorders of Reading and Writing There are various reasons why students may experience difficulties with reading and writing, but most revolve around issues related to the spoken language system. Reading problems primarily related to word identification are often associated with a diagnosis of dyslexia. In contrast, reading comprehension issues related to more broadly based language comprehension difficulties are often associated with diagnoses of specific language impairment (SLI) or language-learning disability (LLD). Consistent with this view, Bishop and Snowling (2004) argued that, although both categories of impairment (dyslexia and SLI/LLD) involve language-processing disorders that affect reading, they should remain diagnostically distinct. As illustrated in Figure 19.1, all of the components of language interact to affect reading; difficulties in specific areas of language will interfere with reading in different ways. The International Dyslexia Association defines dyslexia as: a specific learning disability that is neurobiological in origin. It is characterized by difficulties with accurate and/or fluent word recognition and by poor spelling and decoding abilities. These difficulties typically result from a deficit in the phonological component of language that is often unexpected in relation to other cognitive abilities and the provision of effective classroom instruction. (Lyon et al., 2003, p. 2) This definition highlights the importance of difficulties in automatic word recognition and stresses the importance of underlying phonological processing abilities. In fact, phonological processing, particularly phonemic awareness, is central to most word-identification difficulties, and a core phonological processing deficit has been posited as the most salient problem associated with wordidentification deficits (Brady, 1997; Share & Stanovich, 1995; Torgesen, 1997). For a diagnosis of dyslexia, problems with “reading comprehension and reduced reading experience that can impede growth of vocabulary and background knowledge” are considered secondary (Lyon et al., 2003, p. 2). For an in-depth discussion of the typology and definition of dyslexia, see Chapter 5 by Shaywitz & Shaywitz. Children with SLI may be identified on the basis of late-onset and delayed development of morphosyntactic, semantic, phonological, or pragmatic skills relative to other areas of development. They are generally identified between the ages of 3 and 5 during preschool (Tager-Flusberg & Cooper, 1999, p. 1276). Catts and Kamhi (1999) define students with LLD as possibly having deficits in “vocabulary, morphology, syntax, and /or text-level processing” (p. 65) and estimate that up to 50% of poor readers have language deficits that extend beyond phonological processing. The distinction between these diagnostic categories is unclear: SLI is often associated with preschool children, whereas LLD is more linked with school-age children. Regardless, children identified as SLI or LLD typically show significant difficulties with reading comprehension. These diagnostic categories can overlap: students with SLI/LLD may also share characteristics of dyslexia (i.e., phonological processing problems that interfere with word identification), and students with dyslexia can display weaknesses in language areas in addition to phonology (see Chapter 1 by Schwartz, Chapter 9 by Edwards & Munson, and Chapter 20 by Windsor). Other types of language-related reading difficulties are found in children who have limited intellectual potential or who have not had environmental opportunities to learn standard English. The first group, often categorized as garden variety poor readers, may have difficulties in the same areas of language as children with dyslexia or SLI/LLD, but they also have generally low cognitive abilities. Children with limited knowledge of standard English may speak a nonstandard dialect

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or speak English as an additional language (English language learners: ELL) or may come from a linguistically impoverished environment and have never been exposed to adequate language input. These additional, language-related factors may complicate diagnosis and intervention (see Chapter 12 by Peña, Bedore, & Baron and chapter 14 by Newkirk-Turner & Green).

Skills Linked to Processing Deficits Phonemic Awareness Acquisition of reading skills requires the development of phonemic awareness—the awareness of the sound structure of language at the single phoneme level. This skill is critical for learning how speech sounds map onto print and involves the ability to segment, blend, and manipulate those sounds. Phonemic awareness falls under the larger umbrella of phonological awareness, which also includes word, rhyme, and syllable awareness. The development of these phonological awareness skills follows a continuum that begins in the preschool years and is usually accomplished by age 8. It starts with a sense of rhyme and moves forward with an awareness of syllables in words, beginning and ending sounds, and then individual sounds within words. Ultimately, around third grade, children learn to manipulate sounds in word games such as Pig Latin (for discussion, see Mahfoudhi & Haynes, 2009). A robust relationship exists between early phonemic awareness and later reading success (Adams, 1990; Snow, Burns, & Griffin, 1998). Preschool and kindergarten phonemic awareness abilities are highly predictive of children’s word-identification performance at the end of first grade (Adams, Foorman, Lundberg, & Beeler, 1996; Ball & Blachman, 1991; Juel, 1991). In fact, the explicit awareness of the sound structure of language has been found to be the most accurate predictor of early reading achievement cited in the research literature. A recent meta-analytic review of children’s phonological skills and word reading skills supports the critical role of phonemic awareness as a predictor of individual differences in reading development (Melby-Lervag, Lyster, & Hulme, 2012). Although many children develop phonemic awareness naturally, roughly 25% of middleclass first graders and substantially more of those who come from less literacy-rich backgrounds need direct instructional support (Adams, 1990).

Orthographic Processing A second component of word recognition involves orthographic processing or the ability to recognize and retrieve underlying representations of letter sequences or patterns. Orthography is defined as the total writing system of a language and also refers to the spelling patterns that correspond to spoken words. Although controversy exists concerning the exact role of orthographic processing in reading disabilities, there is evidence that it is an important component, albeit secondary in importance to phonological processing (Badian, 1997, 2005; Share & Stanovich, 1995). Reading and spelling of words may be particularly difficult in English because the manner in which sounds map onto print is often ambiguous (in contrast with more transparent languages such as Spanish or Italian). In English, there are around 44 phonemes and only 26 letters, with sound/symbol correspondences for vowel sounds being particularly complex. Despite this complexity, up to 84% of English is regular for reading if students know enough about the structure of the orthographic system. Given the nature of English orthography, which includes many complex spelling patterns, the ability to form a strong orthographic representation, often referred to as a mental graphemic representation (MGR), is critical for accurate and automatic word identification and particularly for spelling (Apel, 2009).

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Rapid Serial Naming In addition to difficulties in phonemic awareness and orthographic processing, speed of processing plays a significant and independent role in the acquisition of word-identification skills (Wolf, 1997). Rate deficits are typically assessed through tasks involving rapid repeated naming of a small set of familiar words such as names for common objects, colors, numbers, or letters in response to visual stimuli. Difficulties in rapid naming have been found to directly affect automaticity both in earlier stages of reading, when students are memorizing sound/symbol associations, as well as at later stages, when fluent text-level reading is important for comprehension (Wolf & Bowers, 1999).

Phonological and Verbal Working Memory The term phonological memory span refers to the capacity to hold speech information temporarily (see Chapter 8 by Gillam et al.). Digit span tasks assess phonological memory span and are highly predictive of word-identification skills (Hulme, 1988). Verbal working memory is associated with phonological span and refers to the ability to manipulate speech information held in temporary store (Baddeley, 1986; Torgesen, 1996). Nonword-repetition tasks tap into phonological working memory and predict both word identification and syntactic comprehension (Caplan & Waters, 2013; Baird, Slonims, Simonoff, & Dworzynshki, 2011; Snowling, 2000).

Double or Triple Deficits Wolf and Bowers (1999) hypothesize that there are actually three subgroups of children with word-identification difficulties: (1) those with phonological processing deficits, (2) those with rapid automatic naming deficits, and (3) those with both phonological processing deficits and automatic naming deficits. Children in this last group (those with a “double deficit”) have the most serious difficulties in acquiring effective word-recognition skills (Manis & Freedman, 2001). For a review of current research on the double deficit hypothesis, see Vukovic and Siegel (2006). Badian (1997, 2005) has suggested a triple deficit hypothesis that incorporates orthographic processing deficits as an important third component for many children. The more severe the difficulties in each of these areas and the more areas that are affected, the more intensive the intervention needs to be.

Links among Phonemic Awareness, Phonics, and Orthographic Reading As illustrated in Figure 19.2, there is an interaction between the development of phonemic awareness and phonic word-attack strategies (Ehri, 1998; Share & Stanovich, 1995). With the development of a strong base in phonemic awareness (base of lower triangle), children are then able to acquire phonic word-attack strategies more easily (middle diamond); this in turn increases their phonemic awareness. Additionally, this strong base in phonemic awareness and related phonics skills is important to the development of automatic word identification through orthographic reading (upper triangle). Frith (1985) has developed a theory of reading/spelling development that highlights the changing importance of phonological and orthographic processing skills as children move through the steps involved in the acquisition of automatic word identification and spelling. Of course, all of these word-identification skills influence and are facilitated by the processes involved in language comprehension. Once students have achieved fluency, they are no longer leaning to read but are ready to shift to an emphasis on reading to learn, usually around the third or fourth grade (Chall, 1983). The types of text encountered shift from being primarily narrative to expository, and the language complexity of the written material begins to increase dramatically

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(including vocabulary level, sentence complexity, and text structure). The importance of background knowledge for comprehension also increases. By grades three and four, fluent reading is essential. As children move up through middle school, high school, and beyond, there is increasing emphasis on higher-order thinking skills, comparing and contrasting viewpoints, and constructing knowledge. (For a complete discussion of stage theory of reading acquisition, see Chall, 1983; Ehri, 1994.)

Automaticity and Fluency Some researchers and practitioners do not differentiate between automaticity and fluency; they define them as the fast and accurate identification of words either at the single word or text level. There is increasing consensus, however, that control of prosody, including correct intonation, phrasal reading, and rhythm, is an essential component of fluency (Bashir & Hook, 2009; Kuhn, Meisinger, & Schwanenflugel, 2010; Torgesen et al., 2001) and that prosody predicts unique variance in reading comprehension and reading fluency after controlling for word identification (Miller & Schwanenflugel, 2008; Schwanenflugel, Hamilton, Kuhn, Wisenbaker, & Stahl, 2004). For an in-depth review of the literature related to prosody, see Holliman, Wood, and Sheehy (2012). Thus, a broad, more inclusive definition of fluency will be used here with the assumption that developing automatic application of appropriate phrasing and prosodic features is important for comprehension and should be directly addressed, particularly with children who do not apply these prosodic features naturally. Automaticity is therefore defined as fast, accurate, and effortless word identification at the single-word level. Fluency, in contrast, involves not only automatic word identification but also the application of appropriate prosodic features (rhythm, intonation) and syntactic chunking at the phrase, sentence, and text levels. Thus, fluent reading incorporates elements from syntax, morphology, semantics, discourse, and pragmatics. These relationships are illustrated in Figure 19.1. Wood, Flowers, and Grigorenko (2001) emphasized that fluency also involves anticipation of what will come next in the text and that speeded practice alone is not sufficient for achieving fluent reading. Anticipation facilitates reaction time and is particularly important for comprehension.

Comprehension Skills Word Identification Skills Orthographic Reading (Automaticity--Fluency)

Phonics

Phonemic Awareness Pamela Hook, 2000

Figure 19.2

Development of orthographic reading skills.

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Processes in Reading Comprehension/Written Expression Reading comprehension is a complex cognitive linguistic process that involves multiple levels of processing, particularly in the higher grades. All components of language (phonology, morphology, syntax, semantics, discourse structure, and pragmatics) are involved, as well as attention, executive function, and memory skills (see Figure 19.1). Given these relationships, it is not surprising that oral language comprehension has been found to be the best predictor of reading comprehension in typically developing third-grade students (e.g., Spear-Swerling, 2006). Although the development of these components in spoken language is necessary for effective reading, it is not sufficient; one must also develop metalinguistic knowledge of the rules governing these systems. Fluent readers do not use context to help decode words, but struggling readers often rely more on the syntactic and semantic cues provided by context (Share & Stanovich, 1995). However, the ability to recognize the morphological structure of language is often weaker in children who struggle with reading (Carlisle, 2004), as is the syntactic complexity of their spoken and written language (Scott & Windsor, 2000). Thus, if these systems themselves or the metalinguistic abilities related to them are weak, the application of context strategies can be difficult and may need to be taught explicitly. The semantic system is, of course, heavily involved in reading and writing, both of which require active construction of meaning. In addition to vocabulary acquisition, an awareness of the rules governing meaning in different contexts and an understanding of the relationships between words become increasingly important as children progress through school. There is an increased need for higher-order cognitive linguistic skills such as categorization, getting the main idea, drawing inferences and conclusions, and making predictions. Pragmatics, or the rules for how language is used in a social context, plays a significant role in written language acquisition related to identifying the author’s intent as well as understanding different purposes for reading and writing reflected in different genres (see Chapter 18 by Fujiki & Brinton). It also includes aspects of language such as intonation and prosody. As noted above, fluent reading requires the application of appropriate phrasing and stress to text, which proves to be quite difficult for struggling or beginning readers. These prosodic features must be superimposed on the text by the reader (again, a metalinguistic activity), which is not the case in spoken language interactions. Clues to correct phrasing and intonation are often found in small visual signals such as commas, periods, and question marks, which tend to elude struggling readers.

Assessment Assessment should aid in the development of curricula and intervention strategies; it is important, therefore, to evaluate skills in critical areas related to listening, speaking, reading, and writing. An in-depth assessment should answer three basic questions: 1. What are the student’s underlying spoken language skills? 2. What are the student’s reading and writing skills, and how do they compare to the underlying spoken language skills? 3. What are the student’s underlying processing skills, and how are they affecting acquisition of listening, speaking, reading, and writing? All of this information needs to be combined with general developmental, cognitive, medical, behavioral, social, and cultural factors to create a complete profile of the individual student’s strengths and areas of need.

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Underlying Spoken Language Within the area of spoken language, it is vital to assess vocabulary knowledge because it is highly related to reading comprehension in upper grade school and beyond. If weaknesses in other components of the spoken system are suspected, it is useful to assess morphological and syntactic performance and to further examine the semantic system by assessing higher-order thinking skills (analogies, main idea, inferences, drawing conclusions, predicting, etc.). Some spoken language issues can be subtle and require examination of metalinguistic skills involved in comprehension of metaphors, jokes, riddles, or the application of syntax knowledge to a new situation.

Reading and Writing In the area of reading, it is important to consider the accuracy and the speed with which each task can be accomplished, as well as the effects of meaning and context on performance. Students who struggle primarily with word identification (e.g., individuals with dyslexia) frequently read more effectively when meaning is available, as in sentence or paragraph reading, and time is unlimited. In contrast, students with underlying comprehension difficulties, such as individuals diagnosed as SLI or LLD, may be overwhelmed by the complexity of the language and may not necessarily benefit from extended time. A comparison of timed and untimed reading performance on real words versus nonwords and on isolated words versus reading in context allows one to determine whether there are differences between accuracy and automaticity/fluency. Similarly, timed reading comprehension should be compared to untimed comprehension to determine if the difficulty in comprehension is primarily related to lack of automatic word identification or to a more fundamental language comprehension disorder. Because of their varying requirements, oral reading performance can also differ substantially from silent reading; some children understand better when they are allowed to read silently, whereas others, often younger or more severely reading impaired, benefit from reading orally. It is therefore important to consider the effects of different assessment modes on performance. In addition, for reading comprehension, some tests use paragraph-level input and questions given in a multiplechoice format, while others involve a cloze procedure (filling in a missing word from a sentence or paragraph). Still others ask for an open-ended response, such as retelling. These response formats place differing demands on word retrieval and language formulation. When evaluating writing, it is important to examine functions related to planning, translating, and reviewing (Hayes & Flower, 1980). This allows one to determine the extent to which problems in mechanics (spelling, handwriting, and punctuation) interfere with production and the extent to which language formulation and organization may be factors. Automatic mechanics are critical for adequate engagement of attention and executive function capabilities necessary for planning and editing (see McCutchen, 1995, for an excellent discussion of the factors involved in writing and how they relate to executive function as well as to automaticity and fluency). Obtaining a written language sample (two for the older student: one narrative and one expository) is essential and will demonstrate the student’s ability to perform the most complex of language tasks. These samples can be analyzed for phonological, orthographic, syntactic, semantic, and discourse structure skills.

Underlying Processing—the Why? In addition to obtaining basic knowledge of their spoken and written language skills, it is important to determine a struggling student’s strengths and weaknesses in phonological awareness, phonological working memory, rapid serial naming, and orthographic processing. These processing

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skills link directly to word identification as well as to automaticity, fluency, and comprehension. Tests of processing skills are typically administered individually and therefore take more time than group assessments. Commonly used tests of phonological processing measure aspects of phonological awareness skills such as rhyming, segmenting, blending, and manipulating sounds, often with real and pseudoword stimuli (e.g., The Phonological Awareness Test 2, Robertson & Salter, 2007; and the Comprehensive Test of Phonological Processing-2, Wagner, Torgesen, & Rashotte, 1999/2013). Elision tasks, which involve removing one or more phonemes from a word and determining what the new word will be (e.g., Say brought without the /r/. bought) are commonly used because of their high correlation with word-identification performance. For phonological memory, tasks of digit repetition (forward and backward) or nonword repetition are common. Standard batteries for rapid serial naming usually involve the timed repetition of six to eight randomly mixed numbers, letters, objects, or colors. Numbers and letters are the most highly related to word identification, but objects and colors can offer helpful information for younger, preliterate children and nonreaders. Although there are many tools for assessing phonological skills (awareness, memory, and rapid naming), fewer tests assess orthographic skills. Olson, Forsberg, Wise, and Rack (1994) have developed an experimental task that has been used to assess orthographic recognition; it involves choosing the correctly spelled word in a pair (e.g., take/taik; trowsers/trousers; aplause/applause). This task taps into orthographic memory and can be used for informal analysis but is not currently standardized. Another task (e.g., Test of Silent Word Reading Fluency; Mather, Hammill, Allen, & Roberts, 2004) assesses some aspects of orthographic processing by asking students to draw lines between words where spaces have been omitted (e.g., eachmuchthreezooapplefarfly). Divisions between words are often marked by orthographic patterns that are not present or are infrequent in English (e.g., hm [eachmuch] and hp [epochpreclude]). However, items increase in difficulty as the student moves through the test, and vocabulary knowledge becomes a significant factor for older children. If time constraints prevent individual testing, a group spelling test can serve as a window into phonological awareness and orthographic knowledge. Analysis of errors may indicate a phonological awareness problem and will suggest the degree to which a student has mastered the orthographic patterns of English. For example, a student who spells switch as wathch has difficulties with phonological awareness, often confusing the order of sounds, substituting sounds, and omitting sounds, whereas a student who spells throat as throte clearly has trouble with ambiguous words, indicating weaknesses in orthographic memory. Errors such as smug for smudge or traped for trapped indicate a weakness in orthographic memory as well as a lack of ability to apply orthographic rules and generalizations to rule-based words. Once spelling samples have been analyzed, further individual testing may be employed with students who exhibit difficulties.

Patterns of Performance A well-integrated assessment will link the patterns in the test data so that reading and writing abilities are considered in the context of the broader language system. For example, a problem in reading comprehension could be due to various factors, ranging from word-identification difficulties, to metalinguistic problems, to overt problems in one or more of the spoken language components. Figure 19.1 can be used to guide analyses of spoken-written language performance. For example, weak word identification combined with strong underlying listening skills may explain unexpectedly strong reading comprehension scores because the student uses strengths in aspects of language more related to comprehension to compensate. Alternatively, poor receptive and expressive spoken language in combination with strong word identification may explain poor reading comprehension. Reading comprehension problems could, of course, be related to a combination of

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these factors, as well as to deficits in executive function, attention difficulties, or to first-language differences in children acquiring English as a second or simultaneous language.

Intervention Over the last almost 50 years, there has been considerable debate over how reading should be taught. An important aspect of the discussion concerns whether or not there should be primarily a meaning emphasis (top-down) or a code emphasis (bottom-up) for teaching beginning or struggling readers. Reviews of the research on reading acquisition have consistently suggested that a code emphasis is more successful for teaching beginning reading skills (Adams, 1990; Chall, 1996; National Institute of Child Health and Human Development, 2000; Snow et al., 1998). This is not to imply, however, that meaning-based approaches are not important; in general, a relatively balanced approach is desirable, and the weighting of the balance should be determined by the children’s individual needs. The National Research Council (Snow et al., 1998) identified three “stumbling blocks” to becoming a skilled reader: (1) failure to grasp the alphabetic principle resulting in inaccurate word recognition, (2) failure to acquire comprehension skills and strategies, and (3) inadequate motivation (pp. 315–316). The Council concluded that instructional approaches that are more explicit have the strongest impact on the reading growth of children who are at risk for reading disabilities. More recently, the Common Core State Standards (CCSS) have been developed to serve as the scope and sequence for language arts and other subject-area instruction in the public schools (National Governors Association for Best Practices and the Council of Chief State School Officers, 2010). The CCSS support a focus on all aspects of oral language, reading, and writing and include detailed, rigorous standards in the following areas: Reading—Foundational Skills, Literature, Informational Text; Speaking and Listening; Language; and Writing. At present, 45 states have adopted the CCSS, and their departments of education increasingly required that intervention plans for reading and writing be indexed to anchor and grade-level standards in the Common Core (Academic Benchmarks, 2014). Other important issues in reading instruction for at-risk populations involve the timing and the intensity of intervention. There is strong converging evidence supporting the view that early intervention geared toward the prevention of reading failure is critical (Snow et al., 1998; Torgesen, 1997; Partanen & Siegel, 2014) and that the number of children in the elementary grades with reading difficulties significantly decreases with appropriate early intervention. Additionally, in a study of third- through fifth-grade children, Torgesen and colleagues (Torgesen, Wagner, & Rashotte, 1997) found that children who receive intensive appropriate intervention often catch up in word attack, text reading accuracy, and reading comprehension, but not in fluency. Preventative (early intervention) studies produced better outcomes for fluency than did remediation studies (National Early Literacy Panel, 2009; Torgesen et al., 2001). Because of the need for more explicit, direct instruction, struggling readers need a more intense kind of instruction, often in small groups over a longer period of time (Torgesen et al., 2001). Thus, when developing intervention programs, the intensity of instruction should differ, depending on the student’s responsiveness to different levels of intervention. Berninger and colleagues have proposed a three-tiered response to intervention (RTI) model, which conceptualizes intervention as layered over time and gauged to the child’s level of need (Berninger, Stage, Smith, & Hildebrand, 2001). Tier 1 instruction comprises research-based instruction for all children during the literacy block within the regular education classroom. According to the National Reading Panel (National Institute of Child Heath and Human Development, 2000), this instruction should include a core curriculum that systematically introduces phonemic awareness, phonics, fluency,

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vocabulary, and comprehension. Students who fail to progress adequately in Tier 1 move to Tier 2 instruction, which emphasizes small group supplemental instruction within the classroom. Students for whom Tier 2 instruction is insufficient are, in turn, referred for Tier 3 intervention. Teaching in Tier 3 is intensive and strategic and is provided by a professional who is qualified to deliver special instruction in reading. At each level, periodic benchmark testing is employed to determine whether or not a child should remain at his or her current level or move to a more or less intensive level of instruction (see also Vaughn, 2003). The RTI model also has diagnostic implications and has been incorporated into the Individuals with Disabilities Education Act (IDEA, 2004, 614b), as a means of identifying students with specific learning disability without the use of a discrepancy model (http:// idea.ed.gov/explore/search). (See Fuchs & Fuchs, 2006, for an in-depth discussion.) The most commonly used intensive Tier 3 types of instruction developed for struggling readers and writers are often referred to as multisensory structured language (MSL) approaches. These approaches share the following characteristics: (1) explicit presentation of concepts, (2) structured and sequential order of presentation, (3) multisensory stimulation (visual, auditory and tactile/ kinesthetic modalities), and (4) intensive review and practice. Many MSL approaches also incorporate mnemonics to aide recall of the arbitrary, nonmeaningful letter symbols as well as the structure of other aspects of language (e.g., narrative and expository structure). The focus of these programs is on developing an awareness of the rule structure of the various components of language: phonological, morphological, syntactic, semantic, discourse, and pragmatic (metalinguistic awareness). Instruction is highly scaffolded in the beginning with a gradual release of this external structure as the student takes on more responsibility until he or she can ultimately perform independently. Although these programs often contain a multisensory component, the relative value of this component is under-researched. For this reason the International Dyslexia Association has recently suggested the name ‘Structured Literacy’ for these types of approaches. However, after careful examination of the issues, Moats and Farrell (2005) concluded: Conceptions of memory organization, neural activation patterns in language processing, and the importance of metacognition are consistent with the efficacy of multisensory techniques. . . . Most likely, it is not simply the multimodal nature of such practice that explains its power but the mediating effect of various sensory and motor experiences on attention and recall. (p. 34) To examine the efficacy of phonemic awareness training and phonics instruction on word recognition, fluency, and reading comprehension for all children and for children with reading disabilities, the National Reading Panel conducted a large meta-analysis and computed effect sizes (.20 = small effect; .50 = moderate effect; .80 = large effect) based on the results of studies that met their criteria for being empirically sound (National Institute of Child Health and Human Development, 2000). Their word recognition findings were consistent with Ball and Blachman (1991) in that they found a stronger effect size for phonemic awareness with letters than for phonemes only (.67 vs. .38). The effect of systematic phonics for all children was larger in kindergarten and first grade than in grades 2–6 (.54 vs. .27). However, for children with reading disabilities, the effect size was larger than for typically developing children and continued from kindergarten through grade 6 (.74). Thus, phonics instruction is important for developing word-recognition skills in the early grades for all children and remains important for struggling readers throughout elementary school. Approaches to improving reading fluency have often involved repeated readings of the same material with corrective feedback, as well as increased raw amounts of reading through incentives for silent reading of more books. Guided repeated reading resulted in moderate effects on

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accuracy, speed, and comprehension (.55, .44, and .35, respectively). The effects on general reading abilities for average readers were moderate (.49) for kindergarten through fourth grade, and for children with reading disabilities the effect (.47) remained through grade 12. The implications of this analysis are that guided, repeated reading is proven to enhance reading accuracy, speed, and comprehension in reading-disabled children and in children overall. The National Reading Panel found no empirical support for improving reading fluency by simply increasing children’s amount of reading.

Word Identification and Spelling As noted above, English orthography is semi-alphabetic in nature, and thus emphasis must be placed both on developing phonic word-attack strategies and on establishing strong orthographic representations. Students must learn to decode unfamiliar words as well as recognize and recall words automatically in order to be effective readers and spellers; thus, specific techniques for developing these skills are essential.

Phonological Awareness and Phonic Word-Attack Strategies There are compelling reasons to teach phonological awareness skills to children acquiring reading skills. A number of researchers have investigated the efficacy of training phonological awareness on the acquisition of literacy skills in children (for recent meta-analyses, see National Early Literacy Panel, 2009; see also Bus & van IJzendoorn, 1999; Ehri et al., 2001). The ability to read and spell words that are regular depends on students’ development of phonemic awareness. In the 1970s, Elkonin (1973) devised what is now a classic and representative technique for developing phonological awareness that specifically addresses segmentation and blending skills by using tokens to represent sounds. The student segments the word into sounds (phonemes) while moving one token down to a segmented line or series of boxes for each sound in the word and then blends the sounds to form the word. The activity can be systematically adjusted to represent words of increasing phonemic complexity (VC, CVC, CCVC, CCVCC, and CCCVC) depending on an individual student’s abilities. It employs all the aspects of multisensory structured language techniques: explicit training of segmentation and blending, systematic and sequenced instruction, ample opportunity for practice, and multisensory delivery (involving motor movement as well as visual and auditory processing). Research indicates that intervention training methods that combine phonemic awareness with direct instruction in how sounds map onto letters (phonics) are more effective than phonemic awareness instruction alone at improving word-recognition skills (Ball & Blachman, 1991; Bus & van IJzendoorn, 1999; Lundberg, Frost, & Petersen, 1988; National Institute of Child Health and Human Development, 2000). These phoneme segmenting techniques are excellent for bridging between phonemic awareness and phonics instruction, as the student can complete the same activity using letters instead of tokens. The order in which sound-symbol correspondences should be taught varies from one program to another; regardless, a structured sequence is essential. Many programs teach high-frequency letters that are not visually or auditorily similar first in order to avoid unnecessary confusions (e.g., s, t, m will be easier than b, d, p in that the former are visually and auditorily distinct). A common technique employed by MSL programs is the use of tactile kinesthetic cues to aid in recall of sound-symbol correspondences. These programs often use techniques such as tracing on a rough surface or sky writing to help solidify the associations between the auditory, visual, and tactilekinesthetic modalities (see Gillingham & Stillman, 1997, for an in-depth discussion of this approach).

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Tactile-kinesthetic cues from the articulators also help students to create strong metalinguistic awareness of the phonological structure of spoken language—see LiPs (Lindamood & Lindamood, 1998), Lively Letters (Telian, 2001), and Reading by the Rules (Weiss-Kapp, 2005). For example, these programs organize sounds into pairs based on similarities in articulation and varying only in voicing (e.g., /p/-/b/, /d/-/t/, or /k/-/g/). Mnemonics such as a key word with a picture or stories help children to remember sound-symbol correspondences (Telian, 2001; Weiss-Kapp, 2005). In addition to learning sound-symbol correspondence, students need to recognize the orthographic patterns of letters within a syllable that govern pronunciation; to accomplish this end, many MSL programs teach six syllable types: closed (not), open (no), silent e (note), vowel combination (moat), r controlled (part), and consonant-le (noble) (see Moats, 2000, for an in-depth discussion of these syllable types). Knowledge of these syllable types allows students to first read one-syllable words and then words with two or more syllables. Most MSL programs have systematic instruction in the rules for syllable division.

Morphological Structure Application of phonic word-attack strategies is heavily dependent on awareness of the structure of language at the level of phonology and the syllable. However, for multisyllable words of more than two syllables, awareness of the morphological structure of language has proven to be helpful. This involves understanding prefixes, stems, and suffixes and the rules that govern their pronunciation within words. Although there are extremely large numbers of syllables, there are a finite number of prefixes, stems, and suffixes, which recur in many words (e.g., export, import, report; and repeat, reflect, retract). They also have meaning (ex means “out of ” and port means “to carry”); thus, the task of creating orthographic representations to aid decoding is simplified, and the study of morphology can also be used to enhance vocabulary knowledge. For a systematic sequence and scripted lesson plans for teaching morphological structure, see Henry (2010).

Spelling When dealing with words that are regular for spelling, reading and spelling can progress in parallel, but once irregular words are introduced, reading can progress more quickly than spelling. These words place much more strain on orthographic memory as well as lexical association because there are no rules governing their spelling. In order to know if the spelling is pain or pane, one must memorize the orthographic pattern in relationship to its meaning. Many English words follow orthographic rules that revolve around the vowel sounds, particularly short vowels. The spelling of words such as chatting, stitch, lodge, and smack involves the application of rules or generalizations related to single-syllable words with short vowels (i.e., these words contain an extra consonant after the single short vowel). Armed with the four most common spelling rules—the f-l-s-z rule (muff, tell), the doubling rule (running), the drop e rule (skating), the change y rule (candies)—and a handful of generalizations, students can learn to spell a large number of words that would otherwise elude them. Most of the MSL programs have systematic presentations of these orthographic rules.

Developing Automaticity and Fluency Most students who learn through a systematic approach to apply phonic word-attack and structural analysis strategies can become quite accurate in their reading. However, because of difficulties in forming strong orthographic representations, many have trouble developing automatic and fluent

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reading. They remain slow and labored in their word identification, which affects comprehension, particularly in timed situations. As a result, many older students and adults who have seemingly compensated for their reading difficulties often require extended time when taking tests. Approaches have been developed that use a variety of strategies to strengthen orthographic representations for automatic reading of words in isolation (see Hook & Jones, 2002). Extensive opportunity for repeated practice in pattern recognition is often necessary through the use of speed drills consisting of lists of six to eight isolated words that repeat randomly (e.g., rid, ride, hid, hide, kit, kite or from, of, come, some, one) (Fischer, 1999). Individual goals are established, and the number of words read correctly within a minute is recorded in successive sessions. Speed drills can also be used for multisyllable words that incorporate higher-level concepts of structural analysis related to prefix, stem, and suffix (Clark-Edmands, 2008).

Developing Fluency: Phrasing and Chunking Text Although speed and accuracy are the most important factors at the single-word level, the additional application of intonation and stress through syntactic chunking are considered critical for developing fluency at the text level. Although periodic timing of the reading of connected text is important for monitoring progress in developing fluency, equally important is the focus on applying prosodic features and chunking. Reading with natural prosody has been found to be most strongly facilitated by repeated readings of text printed with spaces between phrases and ends of lines at clause boundaries, thus providing visible support for sentence structure (LeVasseur, Macaruso, & Shankweiler, 2007). The incorporation of a multisensory component of scooping (drawing an arc) under predetermined syntactic chunks accompanied by an emphasis on prosody may benefit some students’ fluency. Repeated readings of the same text, accompanied by corrective feedback, is effective in increasing reading rate, with three or four readings of the text being the optimum number (Meyer & Felton, 1999; National Institute of Child Health and Human Development, 2000). However, research on repeated readings of paragraphs has indicated that generalization may be limited somewhat to the specific words being practiced (Rashotte & Torgesen, 1985). A recent meta-analysis indicated that repeated reading may not be an effective practice for students with or at risk for learning disabilities (Apichatabutra et al., 2009). As noted above, in addition to repeated readings and other sorts of speeded practice, being able to anticipate what will come next in the text enhances fluency; this, in turn, improves comprehension (Wood et al., 2001). The use of a cloze procedure where a word must be determined through the use of context, with or without first letter cues, can be helpful in developing a conscious use of syntactic and semantic cues. Activation of prior knowledge and reviewing what will be happening in the story can be instrumental in helping students predict text content. Other commonly used strategies such as reviewing the vocabulary and comprehension questions before reading the passage may also be helpful in this regard.

Comprehension and Written Expression Many traditional approaches to reading comprehension and writing involve practice rather than the acquisition of strategies. Practice can be helpful for some students, but most students with language disorders will need to be systematically taught strategies for comprehending and producing text. Intervention in reading comprehension and written expression requires that one determine in which areas the student struggles: morphology, syntax, semantics, discourse structure, or pragmatics.

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If understanding the rules that govern the morphological structure of language is an issue, then direct instruction is necessary. These difficulties can be apparent in oral reading where students tend to omit endings, or in writing when a child spells shopping as shopen versus shoping—neither is correct, but the second implies an awareness of morphological endings. Systematic teaching of prefixes, roots, and suffixes can substantially increase a student’s awareness of morphological rules, as can direct instruction in monitoring the correctness of written text. For students with weaknesses in syntax, explicit teaching of sentence structure will enhance comprehension as well as production. Solid understanding of sentence structure will also improve the student’s ability to develop syntactic chunking for fluency. Approaches that emphasize the core of a sentence (subject and predicate) and how that core can be systematically expanded with phrases answering such questions as when, where, how long, and why by the identification of signal words can improve a student’s awareness of syntactic structure at the phrase and sentence levels. Often work on writing at the sentence level will reinforce these same concepts. For example, Framing Your Thoughts (Enfield & Greene, 1997) combines explicit teaching of sentence structure through reading and writing by giving students extensive opportunities to analyze sentences in written text and to produce sentences in writing. Oral practice listening to and formulating sentences of increasing complexity can also aid students’ meta-syntactic awareness and support their proofreading skills (Haynes & Jennings, 2006/2011). Unfortunately, work at the sentence level is too often omitted from reading comprehension and writing programs. Semantics includes different levels of comprehension: literal, inferential, and applied. It incorporates vocabulary, categorical thinking, and higher-order thinking skills required for deeper comprehension of text-based material. As with all instruction for struggling readers, it is important that these skills be taught explicitly. For vocabulary, it is critical to choose appropriate words for instruction as well as expose the student to many personally meaningful activities to enhance acquisition. Isabel Beck has developed a three-tier model to guide educators in the choice of words. Tier 1 words are those from everyday life and typically do not need to be taught (e.g., baby, happy, talk), while Tier 3 words are low-frequency and domain-specific words (e.g., isotope, lathe, peninsula). She stresses the need to focus on Tier 2 words, which are high frequency for mature language learners and occur in many domains (e.g., coincidence, absurd, industrious). Bringing Words to Life (Beck, McKeowin, & Kucan, 2002) provides an in-depth discussion of this model as well as suggestions for how activities should be structured. Thematic approaches to choosing semantically associated vocabulary from content area themes can enhance word retrieval, writing, and language comprehension (Haynes & Jennings, 2006/2011; Poirier & Aubin, 1995). Systematic analysis of semantic features of key vocabulary is a complementary approach that has also been found to support comprehension and writing (Bos & Anders, 1992). Another important aid to comprehension is the use of visualizing strategies that involve students creating a mental image of what they are reading. For example, Visualizing and Verbalizing (Bell, 1991) incorporates a structured and sequential program that combines creating visual images with verbalization to enhance listening, speaking, reading, and writing skills. Through a questioning technique, students create a picture of what they are reading in their heads and then describe their visualization. This approach is designed to improve integration of information as well as memory for what was read, both of which are common problems for children who struggle with listening and reading comprehension. It systematically teaches children to identify facts, get the main idea, draw inferences and conclusions, predict, extend, evaluate, and summarize. Understanding discourse structures related to narrative and expository texts is also important for reading comprehension and written formulation, and along with persuasive text, are highlighted in the new CCSS for writing. Narratives are a useful starting point for oral and written expression because they have a familiar and predictable structure (main character, setting, kick-off

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event, attempts at resolution, and resolution) that can be taught through the use of pictures or manipulatives. The use of icons to represent each element of the structure and systematic practice retelling stories using these icons forms the basis for several approaches (see The Story-Grammar Marker, Moreau, 1994, and Story Form, Enfield & Greene, 1994). These types of programs also extend this structure into summarizing and creating narratives in written form. Students’ knowledge of narrative structure can be leveraged to support text elaboration and expository writing. For example, each element within a personal narrative can be expanded systematically to include the kind of factual information found in expository text (Why? How? Salient fact?). Also, sequential narratives, once learned to the level of automaticity, can provide a springboard for transitioning students to expository, process paragraphs in which steps of a procedure are described (Title: How to Build/Make a _______, First-, Then-, Next-, After that-, Finally-). Careful teaching of the structure of expository paragraphs and attention to the prerequisites for writing at the paragraph level can enhance students’ comprehension and written expression. Common expository paragraph types include, but are not limited to, descriptive, enumerative, sequential/process, and comparison/contrast. When introducing a given paragraph structure, it is helpful to teach first the key sentence type(s) at the core of that paragraph. For example, to write a comparison paragraph, students should first master sentences signaling comparison (Although-, While-, At the same time-, However- ). Similarly, descriptive paragraph writing can be enhanced by teaching adjective types (e.g., color, composition, inner feelings, age) and adjective stacking (Jennings & Haynes, 2002). In addition to language disorders, many students with reading difficulties have related problems with executive function, including planning, organization, attention, inhibition, and working memory (Denckla, 1996; see Chapter 8 by Gillam et al.). Techniques have been developed to aid students in their approach to reading tasks by providing them with strategies for before, during, and after reading. Focus on developing intentionality in the reader is a frequent component of these strategies. A common “before” and “after” reading activity is KWL, in which a student works with the teacher to (1) activate background knowledge: K = explore what they already know about the topic; (2) create a specific reason for reading: W = examine what they want to know; and (3) relate the information that they read with what they knew before and what they wanted to know: L = review what they have learned after they read. Self-questioning strategies such as reciprocal teaching (Brown & Palincsar, 1987) are good during-reading activities in that students are taught to ask themselves questions as they read to clarify information and direct their thinking. Developing Metacognitive Skills: Vocabulary and Comprehension developed by the Neuhaus Education Center (Carreker, 2004) incorporates multisensory activities as well as color coding to teach comprehension strategies. Even more emphasis must be placed on developing abilities related to executive function for writing. For an in-depth discussion of the processes involved in writing—long-term memory, planning, translating, and reviewing—see MacArthur, Graham, and Fitzgerald (2006) and McCutchen (1995). Singer and Bashir (2004) developed an instruction and intervention approach to teaching expository writing called EmPOWER™ that is specifically designed for students who exhibit language-learning disabilities and executive function disorders, as well as general education students who struggle with self-regulating their writing. EmPOWER™ represents a six-stage writing process: Evaluate, Make a Plan, Organize, Work, Evaluate, Rework. Specific strategies are taught explicitly to support the numerous cognitive and linguistic subprocesses of writing. Congruent with a focus on executive functioning, Harris and Graham (2009) have found robust support for self-regulated strategy development (SRSD) to aid students’ writing skills. This approach focuses on development of meta-cognitive strategies for self-regulation of planning, production, and revision of text. For example, a common SRSD strategy is the use of the mnemonic

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“TREE” to help students structure an opinion paragraph. Using the TREE strategy, “T” prompts students to state their topic (their opinion), “R” cues inclusion of three reasons, the first “E” prompts use of an example for each reason, while the second “E” cues an ending for the paragraph (Hoover, Kubina, & Mason, 2012).

Conclusions Given the direct relationships between spoken and written language, it is critical that professionals working with students who struggle to acquire these basic communication skills have sufficient knowledge in both areas. With prevention of problems in reading and writing through early identification and appropriate intervention as a goal, it is imperative that the students who are likely to struggle be identified early. Careful assessment of spoken and written language skills will enhance our ability to identify and address areas of need. The patterns of strengths and weaknesses in language skills and processing abilities of students who struggle with the acquisition of reading and writing vary enormously. These patterns, however, determine the kind of intervention that is necessary. The application of the RTI model and the implementation of evidence-based instruction in the regular classroom should reduce the number of children who need intensive intervention, thus allowing professionals to carefully tailor instruction to individual student needs.

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20 PROCESSING SPEED, ATTENTION, AND PERCEPTION IN CHILD LANGUAGE DISORDERS Jennifer Windsor

As researchers seek to better understand the nature of language disability in childhood, increasing emphasis has been placed on the underlying cognitive functions that accompany or cause child language disorders. This is true for language disorders that are secondary to known genetic syndromes and other conditions, as well as language disorders resulting from complex genetic interactions and/or occurring in the absence of frank neurological dysfunction. Child language disorders typically are part of a broader, often variable behavioral phenotype. This chapter focuses on the cognitive functions associated with the language abilities of children with language disorders and not the broader phenotypes. The first section summarizes cognitive constructs that have received substantial research attention and that may interact in fundamental ways in language. The second section highlights three cognitive constructs of interest: processing speed, attention, and perception. The third section overviews major methodological issues that influence current understanding of these constructs. Assessment and intervention research is considered in the final section.

Cognitive Constructs in Research on Child Language Disorders Understanding of cognitive function and language performance often is complicated by multiple concepts, definitions, methods, and theories. Current conceptions of child language disorders still are more prototypical than precise and reflect different research perspectives, the child language disorders of interest, and the specific anatomical or functional components of the brain thought to be involved. As examples, processing speed deficits recently have been studied in specific or primary language impairment (SLI), reading disability, attention deficit disorder, and autism (Beate, Matsushita, & Raskind, 2011; Cardy, Tannock, Johnson, & Johnson, 2010; Kenworthy, Yerys, Weinblatt, Abrams, & Wallace, 2013; Kofler et al., 2013; Park & Lombardino, 2013; Windsor, Kohnert, Loxtercamp, & Kan, 2008). Deficits in rapid temporal processing, auditory perception, attention, and executive function have been studied in many of the same groups (Basu, Krishnan, & Weber-Fox, 2010; Cardy, Flagg, Roberts, Brian, & Roberts, 2005; Cortese et al., 2012; Finneran, Frances, & Leonard, 2009; Fraser, Goswami, & Conti-Ramsden, 2010; Henry, Messer, & Nash, 2012; Stevens, Sanders, & Neville, 2006; Weber-Fox, Leonard, Wray, & Tomblin, 2010). The constructs of procedural memory; phonological, verbal, and visuospatial short-term memory; and working memory have been of interest in Down syndrome, Williams syndrome, autism, attention deficit disorder, SLI, and reading disability (Archibald & Gathercole, 2007; Carney et al., 2013; Lum,

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Conti-Ramsden, Page, & Ullman, 2012; Miller et al., 2013; Montgomery, Magimairaj, & Finney, 2010; Southwick et al., 2011; Wilcutt et al., 2013). The same constructs also have been applied to typical language development. Processing speed has received long-standing attention in research on cognitive changes and altered brain activity in healthy aging (Deary, Johnson, & Starr, 2010; Salthouse, 1996, 2011; Span, Ridderinkhof, & van der Molen, 2004). Attention in the form of executive control and interference or response inhibition has been of interest in typical bilingual and monolingual language development and adult performance (Kapa & Colombo, 2013; Morales, Gomez-Ariza, & Bajo, 2013). Speech perception has been of particular interest in research on infants (Galle & McMurray, 2014). How do these or other constructs inform theory and practice in child language disorders? The three constructs of processing speed, attention, and perception are proposed as having significant potential to move basic and applied research forward. The emphasis on these constructs is built on three observations in the literature. First, there appears to be an overarching qualitative similarity among child language disorders, at least for certain disorder subtypes at certain points in development (Bates, 2004). Also, different clinical subgroups often are co-morbid or show overlapping performance (Boada, Willcutt, & Pennington, 2012; Gooch, Hulme, Nash, & Snowling, 2014). Thus, we may gain deeper insights into language disorders by examining those cognitive constructs that have utility across the full range of variation in language performance rather than investigating constructs that are relevant to specific languages or clinical groups. Second, the cognitive constructs of processing speed, attention, and perception typically are defined in ways that align closely with spatial and temporal aspects of neural activity: neural processing speed, activation, and synchrony. Given the current emphasis on electrophysiological and neuroimaging indices as biomarkers of language performance, these cognitive constructs provide fertile ground for increasing understanding of child language disorders. Finally, we may better understand the nature and causes of child language disorders from this type of bottom-up approach of identifying disturbances in basic cognitive functions or lowerlevel constraints than we will from a top-down approach of examining language performance (Müllen, 2005; Joanisse, Chapter 11, this volume; Thomas & Karmiloff-Smith, 2003). Language performance is a highly complex and relatively later-occurring developmental phenomenon that is influenced heavily by exposure and experience as well as by any underlying disorder. Difficulties in relying on language-based performance measures alone to inform understanding of language disorders have become obvious, with cross-linguistic and bilingual research showing the impact of language experience. For instance, although there presumably are different reasons for the groups’ performances, there are commonalities in the language profiles of monolingual children with SLI and typically developing children learning a second language (Paradis, 2010).

Processing Speed, Attention, and Perception as Cognitive Constructs Processing speed, attention, and perception fit well as potential bottom-up explanatory mechanisms across a breadth of child language disorders. Processing speed and attention may well be key determinants of cognitive-linguistic development and breakdown across the life-span. Although working memory and the broader construct of executive function also have received a great deal of attention in the literature on child language disorders (see Chapter 8 by Gillam et al.), these multicomponent cognitive constructs appear to overlap substantially with attention and processing speed (described next). The third construct of interest, the automatic detection of change or preattentive perception, also is correlated with cognitive development. This construct is of increasing research interest in child language disorders. Brief definitions, neurological correlates, and studies of each construct as an index of cognitive-linguistic development and disorders are outlined as

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follows. There is an increasing literature on the cognitive abilities of typical bilingual children, especially from an executive function or control perspective (Barac, Bialystok, Castro, & Sanchez, 2014), and an emerging focus on bilingual children with language disorders (Peets & Bialystok, 2010). However, the historical focus of research has been on monolingual populations.

Processing Speed The speed of information processing is a lower-level cognitive process that is often considered a central functional basis of cognition. When cognitive function in a given time is of interest, processing speed is conceived of as processing efficiency or rate (Salthouse, 1996). Processing speed typically is measured by how fast individuals complete simple and choice response time (RT) tasks and by identification accuracy at different durations of stimulus exposure in inspection-time tasks. The construct of processing speed is assumed to reflect “neural processing speed” (Jensen, 1993), including excitatory and inhibitory synaptic transmission of cortical and subcortical neuronal circuitry. There may be an upper limit on the frequency of electrical oscillation in the brain that constrains neural speed (Grushin & Reggia, 2005; Rypma, Berger, Genova, Rebbechi, & D’Esposito, 2005). Neural speed also may be a function of specific cell and chemical receptor type. For example, mouse models have shown that cerebellar Purkinje and other cell plasticity is tied to fine motor coordination (Lamont & Weber, 2012). A loss of myelination and lower integrity of white matter axonal tracts, especially in frontal and cerebellar regions, have been thought to have a significant influence on slower processing speed in aging (Eckert, 2011; Penke et al., 2012). For children and adolescents, white matter maturation has been found to increase processing speed (Ferrer et al., 2013). Heritability and the influence of genetic variants on processing speed also have been examined (Beaujean, 2005; Luciano et al., 2010). Cultural factors that may interact with heritability also have been examined (Kail, McBride-Chang, Ferrer, Cho, & Shu, 2013). For at least one child language disorder, it has been proposed that a breakdown in processing speed may be tied intimately to underlying neural transmission. Welsh, Ahn, and Placantonakis (2005) suggested that there is a neural speed asynchrony in the inferior olive of the brainstem for individuals with autism. The asynchrony results in aberrant rhythmic output to the cerebellum, and thus leads to a deficit in using the rapid temporal cues underlying language. The main evidence for the primacy of processing speed in cognitive development is the longstanding finding that processing speed measures co-vary robustly with chronological age in samples of typical children as well as children with language disorders such as SLI and learning disabilities (Kail, 1991; Weiler, Forbes, Kirkwood, & Waber, 2003; Windsor, Milbrath, Carney, & Rakowski., 2001). Moreover, much of the association for children and adolescents between chronological age and individual differences in measures of other cognitive constructs, such as general intelligence, executive function and proactive interference, phonological short-term memory span, working memory capacity, and possibly inhibition of attention appear to be mediated by or directly cooccurring with the relation between age and processing speed (Bayliss, Jarrold, Baddeley, Gunn, & Leigh, 2005; Coyle, Pillow, Snyder, & Kochunov, 2011; Nettlebeck & Burns, 2010). Research on processing speed and language development has centered on the specific nature of speed constraints. It has been argued that if there is an overall upper limit on speed of neural transmission, then processing speed is best viewed as a finite cognitive resource. Also, if different neurons are associated with different transmission speeds, there may not be a single processing speed that drives complex language behavior. There is a long-standing debate about whether variation in language performance across development is best explained by general limitations on processing speed or limitations on only certain process-specific aspects of the cognitive architecture (Kail & Miller, 2006; Span et al., 2004).

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For child language disorders, this debate has been of particular interest in SLI. Using linear regression across multiple studies, several authors have demonstrated that the average group RT of children with SLI is slower by a constant proportion compared to typical chronological-age peers in many cognitive tasks (Kail, 1994; Miller, Kail, Leonard, & Tomblin, 2001; Windsor & Hwang, 1999). Using several statistical methods, Miller et al. (2006) showed there was equivalent slowing across language, motor, and cognitive domains for children with SLI. These findings suggest that, rather than the specific content or nature of individual tasks causing the slower RT, a general underlying factor slows all cognitive operations to the same extent. The most likely candidate as the general cognitive factor is a resource limit on speed of neural transmission. Conversely, using hierarchical linear modeling across multiple studies, Windsor et al. (2001) found that the RT of SLI groups relative to typical groups was not slower by a constant factor. Instead, the extent of slowing varied widely across different study comparisons. This particular result indicates that children with SLI may be more or less vulnerable to specific linguistic, higher-level nonlinguistic, and/or perceptual-motor processing demands. For example, as indicated by Montgomery (2005, 2006), children with SLI may have particular difficulty with speed of lexical access and integration rather than with speed of acoustic-phonetic processing. The exact role of general and process-specific constraints on processing speed in SLI remains an open question. It is plausible that both types of mechanisms play a role, with neither mechanism necessarily excluding the presence of the other (Windsor, 2002). What does seem clear is that the group results mask important heterogeneity within SLI samples. Although many children with SLI show a slower RT than typical peers, a substantial minority have an equivalent RT to typical peers (Miller et al., 2001; Miller et al., 2006; Windsor & Hwang, 1999). Substantial individual variability in processing speed also has been documented for other groups of children with learning and attention disorders (Calhoun & Mayes, 2005). Indeed, an important biological marker for clinical groups may be greater variability in processing speed within a given task for individual children (Kofler et al., 2013; Willcutt, Pennington, Olson, Chhabildas, & Hulslander, 2005). For example, in a meta-analysis of RT task performance in attention deficit disorder, Kofler et al. (2013) found that attention deficit disorder was associated with greater RT variability than in typical comparison groups. Moreover, the clinical and comparison groups had an equivalent mean RT after accounting for the increased individual variability.

Attention The other cognitive construct that has been proposed as a central basis of human cognition is attention. Attention often is viewed as an overarching network with three independent subset functions: alerting, orienting, and executive control. Respectively, these refer to achieving an alert state and vigilance, selecting information from the sensory input, and mediating conflicting responses or ignoring irrelevant stimuli (Petersen & Posner, 2012; Posner & Rothbart, 2007). Various concepts and terms are used in research on attention, such as self-regulation, vigilance, and effortful and strategic control, as well as selective, focused, directed, and divided attention and scope of attention. Of these, selective attention, the ability to focus on particular inputs while suppressing or filtering irrelevant information, has been of particular interest in studies of children (Stevens & Bavelier, 2012). The construct of attention also has been considered to include top-down (effortful) and bottom-up (automatic) systems in both the auditory and visual domains (Li, Gratton, Fabiana, & Knight, 2013; Pinto, van der Leij, Sligte, Lamme, & Scholte, 2013; Yoncheva, Zevin, Maurer, & McCandliss, 2010). The focus on attention in this chapter as a key cognitive construct draws on the same arguments raised for processing speed, that of being linked to development and co-varying or predicting performance on other cognitive constructs. To the extent that attention has been

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incorporated into working memory models, the recruitment and control of attention traditionally have been assumed to be a finite or limited-capacity resource. The biological correlates of attention have received substantial attention in the last decade as brain imaging techniques have become more available. Rather than neural speed, the magnitude of neural processing and activation in certain brain regions has been of interest in research on attention. The brainstem and right hemisphere are implicated in alertness and vigilance, the parietal cortex and some frontal areas are significant for orientation and prioritizing sensory information, and the anterior cingulate gyrus and supplemental motor area of the frontal lobe are significant in focal attention, divided attention, and executive control (Petersen & Posner, 2012). Activation of the lateral prefrontal cortex also has been found in executive control tasks and may represent a second, separate executive control network (Hampshire, Thompson, Duncan, & Owen, 2011; Kerns et al., 2004). Attention and attentional biases develop gradually in the first year of life, and development continues through childhood. Development of attention is nonlinear during maturation, perhaps corresponding to spurts in brain development (Klimkeit, Mattingley, Sheppard, Farrow, & Bradshaw, 2004). Children show rapid early growth in sustained attention (Richards, 2010; Richards, Reynolds, & Courage, 2010) with other periods of rapid growth in attention during the school years (Leclerq & Sieroff, 2013). As for processing speed, potential cultural differences in children’s attention during behavioral tasks have begun to be studied (Sobeh & Spijkers, 2013), extending from the literature on links between attention and caregiver behavior (Graziano, Calkins, & Keane, 2011). However, measures of attention do co-vary robustly with mental age for typically developing children and children with language disorders within certain age periods (Davis & Anderson, 2001). There appears to be a maturational lag or deficit in various aspects of attention across clinical groups. For example, children with autism may show difficulty disengaging attention (Landry & Bryson, 2004). Adolescents with Down syndrome may show difficulties in sustained attention and executive function (Lanfranchi, Jerman, Dal Pont, Alberti, & Vianello, 2010). Children with Fragile X have been found to show perseveration errors that lower their accuracy in selected attention tasks, but their RT appears to be the same as mental-age peers (Scerif, Cornish, Wilding, Driver, & Karmiloff-Smith, 2004). In an examination of attentional components across groups with Fragile X, Williams, and Down syndromes, Cornish, Scerif, and Karmiloff-Smith (2007) have argued that there are both commonalities and differences in attention across these clinical groups at different points in development. Ebert and Kohnert (2011) showed in a meta-analysis that children with SLI have lower accuracy relative to typical age peers in sustained visual and auditory attention tasks. For example, Finneran et al. (2009) found that young school-age children with SLI without concomitant attention deficit disorder were less accurate than typical peers in a continuous performance task in which they responded to a target stimulus and ignored a distractor stimulus. However, there was no group difference in task RT. Linking the constructs of attention and processing speed, one hypothesis is that an attentional deficit contributes to SLI through inefficient temporal engagement or processing (Dispaldro et al., 2013).

Perception Exposure to differentially salient stimuli lies at the core of the construct of perception. It has been argued that cognition inherently is perceptual and that perception rests on two components (van Dantzig, Pecher, Zeelenberg, & Barsalou, 2008). One component is an automatic, preattentive, or baseline neural state that is sensitive to change in stimulus type and frequency. The other is a conscious experience of, or selected attention to, perceived internal or external entities or events. The

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preattentive component is considered preprocessing of information necessary for later attentional processing. For example, automatically segmenting a visual field into possible objects/nonobjects is a preattentional prerequisite for a visual object search task (Treisman, Vieiria, & Hayes, 1992). The second component of conscious attention clearly links the constructs of perception and attention, as well as linking social context and experience with cognitive development. Perception develops very early and extends beyond infancy in its attentional component. Redundant, multimodal sensory input of an action or object may be most perceptually salient in early infancy. The redundancy of visual and acoustic or other sensory information may allow for abstraction of amodal temporal, spatial, and intensity information that facilitates learning to a greater extent than modality-specific input (Bahrick, Lickliter, & Flom, 2004). Cross-modal perception develops in infancy for typically developing children but appears to be delayed for infants with developmental disabilities (Ermolaeva, 2001). Electrophysiology and magnetoencephalography have provided new insights into child and adult preattentive perception and suggest that the ability to detect auditory and visual stimulus change is present early in life and matures throughout development (Bhatt, Bertin, & Gilbert, 1999; Clery et al., 2012; Marie & Trainor, 2013). Preattentive perception has been of particular interest as a biomarker of schizophrenia and other psychiatric disorders (Nagai et al., 2013). Preattentive processing also has been found to correlate with response inhibition for young children (Liu, Xiao, & Shi, 2013) and emotional processing for children with conduct disorder (Hung, Ahveninen, & Cheng, 2013). There is subcortical as well as cortical involvement in auditory perception and segregation, with brainstem and intraparietal sulcus activation of particular interest in adult preattentive auditory processing (Bidelman & Krishnan, 2011; Teki, Chait, Kumar, von Kriegstein, & Griffiths, 2011). While event-related potentials (ERPs) have been of most interest as a measure of preattentive perception, Musacchia et al. (2013) have suggested that event-related neural oscillations (EROs), low-frequency cyclical shifts in electrical activation, may be a useful correlate of infant perception of rapid auditory changes. Several studies have investigated preattentive language processing using ERP components, especially focusing on the early mismatch negativity component (MMN) that occurs very shortly after an auditory stimulus, as well as later mismatch components, such as the late discriminative negativity (LDN). The very quick auditory MMN response is assumed to indicate automatic detection of an unmatched stimulus in a stream of like auditory stimuli (Näätänen, Paavilainen, Rinne, & Alho, 2007). MMN responses are influenced by maturation. The temporal window in which separate acoustic events are processed as a single unit—that is, eliciting one or more MMN responses—is longer in children than adults (Wang, Datta, & Sussman, 2005). It also remains unclear whether the component of the MMN response that can be attributed to the matched or standard stimulus stream is modulated by attention (Sussman, 2007). A weaker MMN response, in terms of amplitude or latency, is not specific to any one clinical group but does appear to be a useful marker of risk for auditory deficits across a wide range of disorders during early and later life, including schizophrenia, aphasia, autism, reading disability, and cognitive decline with aging (Näätänen et al., 2011; Näätänen, Sussman, Salisbury, & Shafer, 2014). Several studies have examined whether the MMN response is weaker or absent in SLI (Bishop & Hardiman, 2010; Rinker et al., 2007; Shafer, Morr, Datta, Kurtzberg, & Schwartz, 2005; Uwer, Albrecht, & von Suchodoletz, 2002). For example, Shafer et al. (2005) found that unlike age peers, only a small number of individual school-age children with SLI showed the MMN effect during either passive or attention-demanding exposure to brief vowels. The authors interpreted their overall negative findings as indicating that automaticity may play a role in poor speech perception for some children with SLI. By comparison, Bishop, Hardiman, and Barry (2010) found that school-age children with SLI differed from typical children in their LDN but not MMN responses

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in a frequency discrimination task. This finding was interpreted as implicating a low-level perceptual impairment in SLI that occurs after the initial detection of a frequency change. Reduced MMN responses to changes in vowel duration have been shown for school-age children with a reading disability (Chobert, Francois, Habib, & Beeson, 2012) and reduced MMN responses to changes in a tone sequence have been found for children with autism (Dunn, Gomes, & Gravel, 2008). However, school-age children with attention deficit disorder have been shown to be similar to typically developing children in MMN responses to an alternating tone pattern (Huttunen, Halonen, Kaartinent, & Lyytinen, 2007).

Processing Speed, Attention, and Perception as Integrated Constructs Overlaps among the Constructs of Working Memory, Attention, and Processing Speed Although it is difficult to isolate reliably, the unconscious or automatic detection of the perceptual properties of stimuli is separable from perception that occurs during tasks in which conscious attention is engaged. However, the constructs of attention, processing speed, and the particular construct of working memory (i.e., short-term memory and a finite attentional component) are much more difficult to differentiate clearly. Certain types of conscious attention overlap with or have been considered to be an integral part of working memory (Cowan et al., 2005; Gruber & Goschke, 2004). This is especially apparent in examinations of child language disorders within Baddeley’s (2003) model of working memory. This model includes phonological and visuospatial memory components as well as a resource-limited central executive that functions as an overall attentional controller. Processing speed also appears to play an integral role in working memory and has been suggested as a key determinant of both short-term memory span and working memory capacity during childhood (Ferguson & Bowey, 2005; Fry & Hale, 2000; Nettlebeck & Burns, 2010). Working memory may not be an independent cognitive construct that has explanatory value beyond a collection of more fundamental constructs (Johnson, Im-Bolter, & Pascual-Leone, 2003; MacDonald & Christiansen, 2002; Pascual-Leone, 2000). This is not to say that examination of working memory does not yield insights into brain function and behavior (Logie, 2011) nor that working memory cannot be statistically separated from other constructs such as processing speed. For example, Leonard et al. (2007) and Montgomery and Windsor (2007) have shown that processing speed and working memory are related but statistically separable concepts in SLI. Moreover, working memory may mediate the influence of processing speed in this population (Poll et al., 2013). Using structural equation modeling with task data from typical adults, Ecker, Lewandowsky, Oberauer, and Chee (2010) found that memory updating processes, substituting outdated information with new information, is a component of working memory that provides unique variance beyond general working memory capacity. More recently, Ecker, Lewandowsky, and Oberauer (2014) have identified that the removal of outdated information alone, rather than also encoding new information, is the unique working memory process. Similarly, Shipstead, Lindsey, Marshall, and Engle (2014) have re-conceptualized working memory to include primary memory updating, secondary memory retrieval, and attention control rather than conceiving working memory as a unitary cognitive construct. This contemporary research provides interesting new directions. However, because much of the available literature incorporates attention and other components within working memory, working memory is not considered here as useful as processing speed and attention as a core cognitive construct in conceptualizing child language disorders from a bottom-up perspective.

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Overlaps between the Constructs of Attention and Processing Speed If working memory performance may be explained in part by the combined effects of attention and processing speed, then attention and processing speed may be different aspects of a constrained neural resource. Depending on the task measure, the constructs of processing speed and attentional control may be difficult to disentangle (Fox, Roring, & Mitchum, 2009). For example, while Stroop tasks have been used to measure selective attention in children with language disabilities (e.g., Gligorović & Buha-Durović, 2014), processing speed has been found to explain some proportion of Stroop performance in adult clinical groups (Ben-David, Nguyen, & Van Lieshout, 2011; Ben-David, Tewari, Shakuf, & Van Lieshout, 2014). Arguably, neural processing speed and attentional control or activation may be inter-related at the neuronal level, but a theoretical model of this remains to be explicated. These two constructs typically are framed from distinct research perspectives and literatures, and different measures have directed researchers to different conceptual constructs. Neuroimaging techniques, such as functional magnetic resonance imaging, focus on the extent of activation in certain brain regions, as in the study of attention. Electrophysiological techniques, such as ERP, focus on brain timing, as in the study of processing speed and perception. Gazzaley, Cooney, McEvoy, Knight, and D’Esposito (2005) suggested that both the speed and magnitude of neural activity are modulated by top-down directed attention. Using functional imaging and ERP, these authors showed that neural speed and magnitude in the visual association cortex of the occipital lobe could be enhanced or suppressed by active attentional processing.

Measuring Processing Speed, Attention, and Perception in Child Language Disorders It is not yet fully understood how slowed or asynchronous neural processing speed, attention, or perception relate directly to the complex and experience-dependent language and associated nonlinguistic breakdowns seen in child language disorders. Nonetheless, given the apparent stability and predictive ability of processing speed and attention as indices of cognitive development, they may be excellent candidates to reveal the underlying nature of language development and disorders. As direct measures of brain structure and function improve, there is increasing attention to preattentive perception. However, methodological issues influence current understanding of all three constructs. Although neurophysiological and neuroimaging studies of child language disorders are becoming more common, the logic of choosing particular variables and interpretation of study results still are in their relative infancy. Most information currently comes from wellestablished behavioral measures. While behavioral measures are very important in defining the phenotypes associated with child language disorders, these measures too can be difficult to interpret because task performance represents the sum of multiple information processing components. RT is a common behavioral measure of processing speed, and it is sometimes used to make inferences about attention and preattentive perception. There is consistent evidence that monolingual English-speaking children with SLI and children with mild developmental delay show a slower RT than typical age peers across many perceptual-motor, nonlinguistic, and language tasks (Miller et al., 2001; Windsor et al., 2001). Good readers show a faster RT than poor readers across the same range of tasks (Horowitz-Kraus & Breznitz, 2011). There are fewer RT data on children with autism and language disorders secondary to conditions such as Down syndrome and Fragile X, perhaps because RT is more difficult to measure reliably in these populations. Inspection-time tasks usually measure speed of processing in a way that minimizes the confounding influence of motor response time and may be more suitable than RT tasks for young children (Williams, Turley, Nettelbeck, & Burns, 2009). There are several inspection-time studies of children with autism, with the finding that there

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is preserved processing speed in this clinical group compared to typical chronological-age peers matched for IQ when task motor demands are minimized (Barbeau, Soulières, Dawson, Zeffiro, & Mottron, 2013; Kenworthy et al., 2013; Wallace, Anderson, & Happe, 2009). Although these results provide insights about language performance, what they convey about underlying cognitive function should be considered carefully. One problem is that broad inferences about cognitive constructs often are made on the basis of a single RT or inspection-time task containing a relatively small or unrepresentative number of stimulus items that fall within a single cognitive domain. Performance on any single behavioral measure could reflect several underlying constructs, not only the construct of targeted interest. Moreover, when single RT measures from timed tasks are correlated, any significant correlation may suggest nothing more than a shared general component related to rapid execution versus any deeper relation (Savage & Frederickson, 2005). A second problem is that RT and other behavioral tasks vary considerably in the demands they place on motor, perceptual, and elementary and higher-level cognitive processing. RT sometimes has been considered synonymous with processing speed; these two constructs need to be disambiguated more clearly in the study of child language disorders. Tasks that carry a minimal cognitive load (e.g., tone identification, very easy visual matching or search tasks) should reflect neural processing speed better than either purely perceptual-motor tasks or higher-level cognitive tasks. However, separating the role of perceptual-motor speed from other aspects of cognitive function in RT tasks remains challenging (Feldmann, Kelly, & Diehl, 2004). Factors other than processing speed that contribute to RT task performance have been examined in research on development and aging. Cepeda, Blackwell, and Munakata (2013) identified executive control contributions to processing speed tasks such as simple RT, choice RT, and letter comparison tasks for children and adults. Similarly, Hartley (2013) showed that RT task type, whether the task was spatial, digital, or verbal, introduced unique variance in explaining the relationship between adults’ processing speed and chronological age above the contribution of a general speed component. While inspection-time tasks can minimize the influence of motor speed, even these tasks and other simple processing speed tasks may implicate some level of motor output and executive control, including decision-making, response selection, and interference control (Kenworthy et al., 2013). Finally, RT and other processing speed measures of performance on any complex language or nonlinguistic task also will be influenced by experiential variables such as native language, acquired knowledge, world experience, and so on, in addition to underlying cognitive function (Birren & Fisher, 1995; Cepeda et al., 2013; Kohnert, Windsor, & Ebert, 2009). ERPs do provide better temporal correspondence to underlying brain function than RT and also enable exploration of qualitative differences in waveforms among clinical groups (Aydelott, Kutas, & Federmeier, 2005). However, current electrophysiological and neuroimaging studies of child language disorders also have some methodological and interpretive limitations. In a review of ERP studies of attention deficit disorder, Johnstone, Barry, and Clarke (2013) pointed to heterogeneous and small sample sizes and infrequent study replications. Language task-specific effects on ERP also have been shown for children (Pattamadilok, Perre, & Johannes, 2011). In a review of MMN studies of children with SLI, reading disabilities, and related language-learning disabilities, Bishop (2007) found similar methodological issues and also found that the ERP and behavioral findings did not always correspond. Bishop and Hardiman (2010) also have shown that the conventional MMN method of taking a single average amplitude measure may well be an unreliable clinical measure.

Assessment and Intervention The overall weight of evidence suggests that the observable language deficits in child language disorders are part of a broader cognitive profile(s) that include deficits in processing speed and

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attention, and likely in preattentive perception. These deficits may be most visible in the area of language, but they are not necessarily restricted to the language domain. That these cognitive constructs are implicated in explanations of child language disorders does not necessarily mean that assessment and intervention based on processing speed, attention, and perception will be more effective than traditional approaches focusing directly on children’s language performance. However, because a focus on these underlying capacities reduces the role of language-specific knowledge, assessments and interventions emphasizing processing speed, attention, and perception may be profitable to explore.

Assessment The preceding sections speak to the lower average performance of groups of children with language disorders in many experimental tasks of processing speed, attention, and preattentive perception and the predictive relationships among some tasks, neural correlates, and language ability. However, research on the diagnostic utility of these tasks during development remains relatively sparse. One reason for this may be that children are vulnerable to below-average language performance relative to age peers as a result of deficits in underlying cognitive functions and/or as a result of varying experiences with spoken or written language. Separating the contributions of these factors to language performance is difficult. There are inherent difficulties in identifying a single cognitive marker of complex behavioral disorders, where these disorders may reflect multiple neurodevelopmental, biological, and experiential factors (Belger, Yucel, & Donkers, 2012). From a clinical viewpoint, however, there is an increasing need to separate experiential factors from underlying cognitive contributions to language performance because of the increasing linguistic and cultural diversity of the United States and other countries. Practitioners need ways to reduce the differential effects of experience across multiple groups of speakers to reach a valid differential diagnosis about children’s language disorders. This is especially the case when the language disorder is not secondary to another known condition but is a primary language disorder such as SLI and reading disability that often is defined by exclusion. Psycholinguistic tasks that reduce acquired language knowledge, such as nonword repetition, have been shown to have some utility in this regard for monolingual children (Estes, Evans, Else-Quest, 2007; Melby-Lervag & Lervag, 2012). However, study methods and diagnostic accuracy remain limited for a range of psycholinguistic and language measures for bilingual children (Dollaghan & Horner, 2011). The diagnostic utility of nonlinguistic cognitive tasks remains plausible but tentative for children with language disorders (Kohnert et al., 2009). Drawing from research on cognitive aging, the processing speed measures of inspection-time and choice reaction-time tasks may have particular value. These measures are correlated with more complex cognitive tasks independent of chronological age, are a stable measure of individual differences across the life-span, and are less susceptible to task-specific effects (Deary et al., 2010). Inspection-time also is robustly correlated with intelligence for typically developing children and adults (Grudnik & Kranzler, 2001).

Intervention Interventions for children with language disorders that emphasize underlying cognitive function have begun to receive significant attention (Stevens & Bavelier, 2012). Intervention studies are critical to advance our understanding of the nature of child language disorders as well as to help individual children. Robust evidence for a direct relation between cognitive function and language abilities would be to demonstrate through systematic intervention that increases in processing speed, attention, or perception lead to improved language performance and changes in the brain.

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However, Bishop (2013) has cautioned that in seeking to enhance or change language behavior, we should not necessarily give greater credence to neurobiological studies than to high-quality behavioral studies. In a review of six studies, Bishop showed that current language intervention studies claiming changes on neurophysiological and imaging measures have several limitations, including, for example, the absence of an equivalent control group. Many cognitive interventions to date are of fairly short duration and holistic, with multiple aspects of processing speed, attention, and perception possibly involved as treatment components. Overall, intervention effects appear to be small if they are apparent. Ebert and Kohnert (2009) and Ebert, Rentmeester-Disher, and Kohnert (2012) showed small changes in processing speed (i.e., choice visual detection RT) among other expressive language changes in exploratory single-case studies of monolingual and bilingual children with SLI. Chenault, Thomson, Abbott, and Berninger (2006) compared the effects of receiving attention and executive function intervention or reading fluency intervention prior to providing composition instruction for children with a reading disability. There was no difference in the two interventions on children’s composition skills; however, children who received the attention intervention did show stronger writing performance subsequent to the composition instruction. Stevens, Fanning, Coch, Sanders, and Neville (2008) examined whether an intensive computerized language intervention, Fast ForWord, enhanced selective auditory attention in addition to receptive and expressive language skills for children with and without SLI. Fast ForWord has auditory processing and attentional components. The group of children with SLI showed enhanced receptive language and a greater ERP response in a language listening task after intervention, and the group of typical children showed a similar trend. However, Gillam et al. (2008) reported on a randomized controlled trial of the effectiveness of Fast ForWord intervention for children with SLI. There was no difference in language outcomes for the computerized intervention compared to other interventions of equivalent intensity. Similarly, Shipstead, Hicks, and Engle (2012) found no consistent evidence that a computerized working memory intervention, CogMed, enhanced the long-term attention or reasoning skills of children with attention deficit disorder and other developmental conditions that could not be explained by practice intensity or similarity of the training and test materials. Finally, a recent meta-analysis of cognitive training for children with attention deficit disorder showed no significant effect of attention or executive function training (Rapport, Orban, Koffler, & Friedman, 2013). It remains possible that a focus on the fundamental cognitive functions underlying child language disorders may provide new paradigms for assessment and intervention. However, these studies suggest there are several challenges to translate research findings on processing speed, attention, and perception into educational settings. If cognitive interventions are found to be effective, it may be that they will have the most impact as one component of an overall intervention program (Gathercole, 2014). At the same time, studies of children with language disorders consistently identify relative deficits in processing speed, attention, and perception. Thus, with the use of robust methods, research on these cognitive constructs to gain further insight into the nature of child language disorders seems promising.

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PART V

Research Methods in Child Language Disorders

21 LANGUAGE PRODUCTION APPROACHES TO CHILD LANGUAGE DISORDERS Liat Seiger-Gardner and Diana Almodovar

Introduction Language production limitations in children with language deficits are manifested in all areas of language: form (phonology, syntax, and morphology), content (semantics), and use (pragmatics). In the last two decades, the focus of research has been the underlying mechanisms involved in language production that may be inadequate or inefficient in children with expressive language impairments and may contribute to their limited expressive language abilities. This chapter will review the off-line and on-line methodologies, techniques, and procedures used in clinical and basic research to investigate the various aspects of child expressive language impairments and the underlying mechanisms that are postulated to subserve expressive language skills. It will also address the use of on-line procedures in the investigations of child language production, their constraints, as well as their potential application to research on child language impairment. Research exploring the underlying mechanisms involved in language production in child language impairment is determined by the methodologies used in the investigations. To date, direct observations of processes involved in language production are sparse and are limited to the application of electrophysiological and functional magnetic resonance imaging (fMRI) measurements. The most common research methods used involve indirect observational measures that inherently affect the investigation. Various aspects of these measures interact with the process being investigated, influencing their natural occurrence and making it difficult to make inferences about the investigated process itself. Methods that are used to examine the expressive language abilities in children with language impairments are either off-line or on-line. Off-line methods, such as picture naming tasks, sentence completion tasks, and discourse-narrative tasks, measure effects at the endpoints of production and sometimes use these end products to infer something about the underlying processes of production. However, off-line measures provide little to no information regarding the underlying processes that lead to specific responses. Because language production is affected by memory, attentional demands, and other executive functions (see Chapter 8 by Gillam et al. and Chapter 20 by Windsor) the end products may reflect a confounded interaction among these demands and not merely the language domain investigated. Whereas off-line methods probe the linguistic system more globally, on-line methods examine the linguistic system more locally, tapping into the elements of the process as they unfold over time and allow for a better control and evaluation of

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the effects of various executive functions on the process examined. These methods provide more fine-grained information about the ongoing process at different points in time.

Off-line Measures of Language Production Picture Naming (Confrontation Naming) Naming is a complex process that involves the identification of an object, the activation of the object’s name in the mental lexicon, and the generation of a response. It is a fundamental ability that children use very early in development to communicate (Johnson, Paivio, & Clark, 1996). They become more proficient in naming pictures and objects as they develop, evidenced by an increase in the speed of naming (Kail, 1991, 1992; Kail & Hall, 1994) and a decline in the number of errors produced while in the process of naming (Jaeger, 1992, 2005; Stemberger, 1989; Wijnen, 1992). Picture naming is the most commonly used task in research as well as in clinics to assess the semantic, phonological, and articulatory abilities of children with language impairment; it has become an integral part of every speech and language evaluation. Naming tasks have been frequently used with children with language impairment, as many of these children tend to demonstrate difficulties in vocabulary development and word naming. Naming tasks are often employed to examine whether the underlying cause of these deficits lies in the representations of words or in the retrieval of the semantic or phonological features of words from the lexicon. A picture-naming task was used by Leonard, Nippold, Kail, and Hale (1983) to examine the effect of word frequency (i.e., low- versus high-frequency words) on the ability of children with language impairment to name pictures. Sixty-four pictured objects of high and low frequency were shown to the children, who were asked to name the pictures as quickly as possible. Children with language impairment were slower in naming pictures compared to their age-matched controls; however, they showed a similar pattern of response to that of their age-matched peers, with high-frequency words being named more rapidly than low-frequency words. It suggested a similar lexical organization with respect to frequency for children with and without language impairment. In a similar study (Swan & Goswami, 1997), a picture-naming task was administered to children with dyslexia to examine the effects of word length (defined by the number of syllables) and word frequency (i.e., low versus high) on their naming. Forty pictures, half with short and half with long names, were presented to the children. Children with dyslexia, overall, named fewer pictures than their age-matched and reading age-matched controls. Their naming deficit was most evident in naming pictures with long names and low frequency. Their naming errors were frequently phonologically similar to the target words. The authors concluded that the picture-naming deficits in children with dyslexia are due in part to difficulties in specifying and retrieving the phonological counterparts of words. This picture-naming task has been used in children with autism spectrum disorder (ASD; Löfkvist, Almkvist, Lyxell, & Tallberg, 2014), children with cochlear implants (Wechsler-Kashi, Schwartz, & Cleary, 2014), and in children with language impairments to investigate specifically the verb lexicon (Kambanaros, 2014; Sheng & McGregor, 2010) or pragmatic effects on the semantic lexicon (Ketelaars, Hermans, Cuperus, Jansonius, & Verhoeven, 2011). Variations of the simple naming task, where pictured objects are presented to children, who are asked to name them, can be used to target specific information. For example, in a study by McGregor and Waxman (1998), children with word-finding difficulties, also called naming deficits, were asked to name pictured objects at multiple levels of noun hierarchy (i.e., superordinate, coordinate, and subordinate). Contrast questions were used in conjunction with the pictures in order to elicit the targeted level of noun hierarchy; thus, for a picture of a rose, the child was asked

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if it was an animal in order to elicit the superordinate level name of plants. The child was asked if it was a tree to elicit the coordinate level name of flower. Finally, the child was asked if it was a dandelion, in order to elicit the subordinate level name of rose. This revealed insufficient depth and breadth of the semantic lexicon in children with word-finding difficulties. Follow-up questions in conjunction with the picture-naming task were used in a study examining the effects of different lexical factors (i.e., word frequency, age of acquisition, lexical neighborhood, and stress pattern) on the ability of children with language impairment to name pictures (Newman & German, 2002). Participants were asked to name verb and noun depicted pictures following questions that were aimed to elicit the targeted word, such as What is she doing? or These are all . . . . High-frequency words from low-density neighborhoods that were acquired early in life and contained the typical stress pattern of the English language were easier to retrieve. While the effect of age-of-acquisition decreased with maturation for typically developing children, it continued to play a major role in the ability of children with word-finding difficulty to retrieve words. Another variant of the simple naming task is the Tip-of-the-Tongue (TOT) paradigm (Faust, Dimitrovsky, & Davidi, 1997; Faust, Dimitrovsky, & Shacht, 2003; Faust & Sharfstein-Friedman, 2003). In this paradigm, a simple confrontation-naming task is administered to participants who are asked to name the pictures as quickly as they can. If a participant fails to name a picture, the examiner inquires whether the participant felt that he knew the name of the picture but couldn’t retrieve it at that moment (i.e., a TOT response). The examiner then asks the participant to provide information about the object (semantics) or about the name of the object (phonology). If the participant responds with a don’t know response, the examiner goes to the next trial. This paradigm permits the assessment of the partial semantic and phonological lexical knowledge that children with word-finding difficulties have access to even in the absence of full access to target words. The application of this paradigm in the investigation of naming difficulties in children with language impairment (Faust, Dimitrovsky, & Davidi, 1997) and children and adolescents with dyslexia (Faust, Dimitrovsky, & Shacht, 2003; Faust & Sharfstein-Friedman, 2003; Suárez-Coalla, Collazo, & González-Nosti, 2013) attributed the naming deficits to problems in access and retrieval of phonological information during naming. Children with language impairment and dyslexia provided less valid phonological information and more instances of invalid phonological information about words while in the TOT state. Another task that has been widely used and has been considered the gold standard of naming tests in clinical practice and in research is the rapid automatic naming (RAN) task (Catts, Gillispie, Leonard, Kail, & Miller, 2002; Compton, Olson, DeFries, & Pennington, 2002; Katz, Curtiss, & Tallal, 1992; Krasowicz-Kupis, Borkowska, & Pietras, 2009; Manis, Doi, & Bhadha, 2000; Manis, Seidenberg, & Doi, 1999; Misra, Katzir, Wolf, & Poldrack, 2004; Schatschneider, Carlson, Francis, Forrman, & Fletcher, 2002; Waber, Wolff, Forbes, & Weiler, 2000). The traditional RAN task requires rapid naming of a visual array of 50 stimuli, consisting of five high-frequency stimuli that are presented 10 times in random order in five rows (Denckla & Rudel, 1976). Scores are based on the amount of time required to correctly name all 50 stimuli. The types of stimuli that can be presented in the RAN task are letters, pictured objects, colors, and numbers. The most common ones are the RAN letters and RAN pictures. The RAN letters task was found to correlate with reading ability and, thus, to successfully predict performance on reading measures (Misra, Katzir, Wolf, & Poldrack, 2004). It was a strong predictor of first and second graders’ performance on three tasks (i.e., orthographic choice, word-likeness judgment, and exception word pronunciation) in which orthographic information is key (Manis, Seidenberg, & Doi, 1999). The RAN pictures, however, were a reliable predictor of reading ability only up to kindergarten years but not thereafter (Misra, Katzir, Wolf, & Poldrack, 2004). In a more recent study examining the subcomponents of the RAN (i.e., pause and articulation time, and intra-individual variability), color- and letter-naming pause times and number-naming articulation time were significant predictors of reading fluency,

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whereas number and letter pause variability were predictors of reading comprehension (Li, Cutting, Ryan, Zilioli, Denckla, & Mahone, 2009). A variation of the RAN is the rapid alternating stimulus (RAS) task. This includes either two sets of letters and numbers or three sets of letters, numbers, and colors. The first includes 10 high-frequency stimuli (five letters and five numbers) and the latter is composed of 15 high-frequency stimuli (five letters, five numbers, and five colors) that are randomly repeated in an array of five rows for a total of 50 stimuli. Wolf (1986) found that performance on the RAS distinguished the poor readers from their typically developing peers. She determined that early RAS performance is a strong predictor of later reading abilities, particularly at the single-word reading level. Conversations, narrations, and discourse, unlike confrontation naming, provide contextual cues that facilitate word retrieval. Children with language impairment manifest word-finding difficulties, not only in single-word naming tasks but also in discourse (German & Simon, 1991), suggesting that they may not be able to utilize contextual primes to aid word retrieval during naming. The influence of language context primes on picture naming in children with word-finding difficulties was examined by asking children to name 40 pictured objects once under the primed condition and once under an unprimed condition (McGregor & Windsor, 1996; Chapter 16 by McGregor). The primed condition included semantic primes embedded in a carrier phrase (e.g., This man likes to go walking) that primed simple (e.g., cane) or compound nouns (i.e., walking stick), and served as a partial lexical prime only to the compound nouns. Although the children with word-finding difficulties used the primes to ease retrieval, evidenced by decreased error rate and increased use of compound nouns, they did not benefit as much from the primes as their typically developing peers. The primes did not fully compensate for the word-finding difficulties these children exhibited, as evidenced by the constant gap between the mean error rates of the two groups under the primed condition, with the children with language impairment producing significantly more errors. Picture-naming tasks have also been used in intervention studies as an assessment tool to measure the progress in naming abilities of children with word-finding difficulties (Bragard, Schelstraete, Snyers, & James, 2012; McGregor, 1994; McGregor & Leonard, 1989; Wright, 1993) and children with phonological disorders (Saben & Ingham, 1991) pre- and post-treatment. The naming task was also administered to children during the treatment phase in order to assess treatment success. Picture naming can also be employed in on-line methods, which will be reviewed later in the chapter.

Repeated Word Association Task The word association task has been used widely to measure semantic knowledge (De Deyne & Storms, 2008) in three variations: discrete word association task (Nelson, McEvoy, & Schreiber, 2004), free word association task (De Deyne & Storms, 2008), and repeated word association task (De Groot, 1989). Free word association requires the participant to generate as many words as possible in response to the target word, whereas the discrete association task requires the subject to generate only one word. In the repeated word association task, the participant is presented with the same target word multiple times and is asked to generate a different word each time the target is presented. The assumption is that repeated probing can yield information about the number and strength of links between semantically related words in one’s lexicon (Sheng & McGregor, 2010; Sheng, McGregor, & Marian, 2006). The repeated word association task was used in a study investigating whether children with specific language impairment show deficits in lexical-semantic organization. The children generated three associates for each of the 48 stimuli used in the study (Sheng & McGregor, 2010). They were invited to play a word game and were asked to say the first

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word that came to mind when hearing the target stimulus. To help children understand the game, the examiner prompted the children with the words mom and birthday and provided examples of semantic associations to these prompts. The children’s productions were analyzed based on the relationship they shared with the target stimuli (e.g., categorical, thematic, descriptive, syntactic, no responses, repetition, etc.). Children with SLI produced more errors and fewer semantic responses, suggesting deficits in lexical-semantic organization.

Verbal Fluency Tasks Verbal fluency tasks are commonly used in research in children (Weschler-Kashi, Schwartz, & Cleary, 2014), aging adults, and adults with aphasia, Alzheimer’s disease, schizophrenia, and other clinical populations (Bozikas, Kosmidis, & Karavatos, 2005; Diaz, Sailor, Cheung, & Kuslansky, 2004; Rogers, Ivanoiu, Patterson, & Hodges, 2006; Troyer, Moscovitch, & Winocur, 1997). Verbal fluency tasks include letter or sound fluency and semantic fluency. Letter fluency requires the participant to retrieve as many words as possible beginning with certain letters (e.g., F, A, and S) under a time constraint (e.g., 60 seconds). This task (Sauzeon, Lestage, Raboutet, N’Kaoua, & Claverie, 2004) has been modified for younger, preliterate children as a phonological fluency task requiring the production of words beginning with certain sounds (e.g., /f/, /s/, /a/). Semantic fluency tasks require the participant to retrieve as many words as possible from a specific semantic category (e.g., animals, fruits, supermarket) under a time constrain of usually 60 seconds. Verbal fluency reveals the phonological and semantic organization of the lexicon, requiring categorical knowledge (Diaz, Sailor, Cheung, & Kuslansky, 2004; Sauzeon, Lestage, Raboutet, N’Kaoua, & Claverie, 2004) and a degree of phonological awareness (Diaz, Sailor, Cheung, & Kuslansky, 2004; Sauzeon, Lestage, Raboutet, N’Kaoua, & Claverie, 2004; Weckerly, Walfeck, & Reilly, 2001). It also taps retrieval processes such as switching and clustering that aid in the retrieval of more lexical items. The switching strategy involves the retrieval of different phonological and semantic subcategories and reflects the ability to shift from one to the other; the clustering strategy involves the retrieval of lexical items within the same phonological or semantic category and reflects the category size. Adequate verbal fluency requires both intact semantic memory storage and an efficient retrieval mechanism (Troyer, Moscovitch, & Winocur, 1997). To date, only a small number of developmental studies have been reported (Kave, 2006; Koren, Kofman, & Berger, 2005; Matute, Rosselli, Ardila, & Morales, 2004; Riva, Nichelli, & Devoti, 2000; Sauzeon, Lestage, Raboutet, N’Kaoua, & Claverie, 2004), and even fewer have included clinical populations such as children with attention deficit hyperactivity disorder (ADHD; Hurks, Hendriksen, Vles, “Kalff, Feron, Kroes, van Zeben, Steyaert, & Jolles, 2004), children with specific language impairment (SLI; Weckerly, Wulfeck, & Reilly, 2001), and children with cochlear implants (CI; Wechsler-Kashi, Schwartz, & Cleary, 2014).

Nonword and Sentence Repetition The nonword repetition task (NWR) has been recommended as an unbiased, processing-dependent measure of language performance in children (Campbell, Dollaghan, Needleman, & Janosky, 1997). It is frequently used as a measure of phonological short-term memory in children with and without language impairments (Gathercole & Baddeley, 1990, 1995; see Chapter 8 by Gillam et al.). The nonword repetition task involves the perception and temporary storage of sequences of sounds, the construction of the sequences’ phonological representations, and their production. The difficulties children with language impairment exhibit in repeating nonsense words and recalling lists of real words suggest imprecise phonological representations, limited phonological storage

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capacity, or rapid decay of phonological traces in phonological working memory (Gathercole & Baddeley, 1990; Montgomery, 1995). Dollaghan and Campbell (1998) designed a nonword repetition task that consisted of 16 nonwords, four at each syllable length (i.e., one, two, three, and four syllables). All nonwords began and ended with consonants, and they contained no consonant clusters. Twenty children with language impairment performed less accurately than 20 age-matched controls, particularly for the three- and four-syllable level nonwords. The authors concluded that this is an unbiased tool that accurately distinguishes children with and without language impairment. These findings were confirmed in a longitudinal, epidemiological investigation of SLI (Weismer, Tomblin, Zhang, Buckwalter, Chynoweth, & Jones, 2000). Preschoolers with SLI exhibited similar deficits on the Children’s Test of Nonword Repetition (CNRep) developed by Gathercole, Willis, Baddeley, and Emslie (1994). The CNRep includes 40 target items, ranging from two to five syllables. The CNRep scores were found to be highly sensitive in discriminating between children with language impairment and children with typical language development (Gray, 2003). The nonword repetition task was used in another study to examine working memory capacity and its relation to language comprehension in children with SLI (Marton & Schwartz, 2003). Twenty-four nonwords (free of lexicality and phoneme predictability) were constructed such that they varied in syllable length (two, three, and four syllables). The poor performance of children with SLI on nonword repetition across tasks such as sentence memory span, especially as syllable length increased, suggests limitations in simultaneous processing rather than a deficit in phonological encoding and decoding (see Chapter 9 by Edwards & Munson). In contrast, nonword repetition was considered a reliable measure of phonological working memory in children with Down syndrome (Laws, 1998); performance on the CNRep (Gathercole, Willis, Baddeley, & Emslie, 1994) was highly correlated in these children with language-based memory measures (i.e., auditory digit span, word span, sentence repetition, and fluency) and language measures (i.e., receptive vocabulary, language comprehension, and reading). It was a reliable predictor of later language development in children with cochlear implants; it can be used early on to identify children who are at high risk for poor outcomes (Casserly & Pisoni, 2013). One of the challenges in using the NWR task as well as other production tasks is low compliance among young children. In a recent study by Polišenská and Kapalkováa (2014), a novel delivery of the NWR task used recorded material to improve compliance rates among typically developing children between 2 and 6 years of age. The novel NWR task included 26 recorded items that vary in syllable length and phonological complexity. The participating children were presented with a story about a boy and a girl fixing a necklace for their mom. The participating children were asked to help the boy and the girl in making the necklace by repeating the magic words (NWR stimuli), which consequently added beads to the necklace. The beads on the screen showed the child how many nonwords had already been repeated as well as how many more items needed to be repeated in order to finish the necklace. The task was animated and administered via computer. The overall noncompliance rate was 1.79% for children ages 2–6 years and 14.3% for the 2-year-olds, which was reported by the authors to be lower than previous studies. In addition to employing the NWR task to explore working memory, it has been used to explore phonological representations in children with SLI. Marshall, Harris, and van der Lely (2003) utilized their own version of the nonword repetition task to demonstrate that children with Grammatic-Specific Language Impairment (G-SLI) have difficulty repeating words that contain consonant clusters. By varying syllabic complexity in nonwords, they postulated that the deficits that these children exhibit in NWR tasks is not necessarily a reflection of a limited phonological storage capacity or rapid decay rate, but rather that deficits exist in their phonological representations. In addition, the NWR task was utilized to reveal qualitative differences in the phonology of

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children with SLI and dyslexia in comparison with typically developing children (Marshall & van der Lely, 2009). Edwards, Beckman, and Munson (2004) also used the NWR paradigm to examine how phonological processing of nonwords is generalized to their lexicon. That is, children were more fluent and accurate in sound productions in nonwords, when the nonword contained phonemes that were of a high-frequency sequence as those found in real words. Sentence repetition, also referred to as sentence imitation or sentence recall, is commonly part of many clinical standardized tests, such as the Clinical Evaluation of Language Fundamentals (CELF; Semel, Wiig, & Secord, 2003) and the Test of Language Development (TOLD; Newcomer & Hammill, 1997). Sentence repetition tasks entail the processing and production of phonological information but also involve syntactic and semantic information. A degree of semantic knowledge is needed in order for the child to be able to process, comprehend, and repeat the sentence that was heard (Conti-Ramsden, Botting, & Faragher, 2001). The performance of children with language impairment may vary on nonword and sentence tasks depending on the child’s deficits. Children exhibiting specific difficulties in encoding, processing, or holding of phonological information in short-term memory may have more difficulty performing on the nonword repetition task than on the sentence repetition task. The contextual information provided in sentences may serve as scaffolding for easier retrieval. However, children exhibiting morphosyntactic deficits may have no difficulties performing on the nonword repetition task but reveal a tremendous amount of difficulty repeating sentences that increase in syntactic complexity. Sentence repetition performance was also found to be a clinical marker for children with specific language impairment (SLI). In two different studies, Conti-Ramsden, Botting, and Faragher (2001) found sentence repetition performance to be the best indicator (very high sensitivity and specificity) of SLI compared to other linguistic tasks, such as nonword repetition and tense marking (Conti-Ramsden, Botting, & Faragher, 2001). The sentence repetition task is also thought to be sensitive in discriminating children with SLI from children with other language disorders, such as autism spectrum disorders (ASD; Botting & Conti-Ramsden, 2003), and it is sensitive in identifying Cantonese-speaking children with SLI (Stokes, Wong, Fletcher, & Leonard, 2006) and Czech SLI (Smolík & Vávru, 2014). Poor performance on the sentence repetition task in bilingual children with language impairment was suggested to be associated with weak nonverbal working memory (Ebert, 2014). Sentence imitation/repetition and nonword repetition tasks are potential diagnostic tools identify primary language impairment (PLI) and language development in children acquiring more than one language difference (Thordardottir & Brandeker, 2013). The NWR task revealed significant differences between the children with PLI and the bilingual children, with the latter achieving high NWR scores that were unaffected by syllable length. Hence, the NWR is advantageous as a clinical tool.

Sentence and Story Completion Tasks Obligatory context probes, also called cloze procedures—such as the Illinois Test of Psycholinguistic Abilities (ITPA) Morphological Closure (Hammill, Mather, & Roberts, 2001)—are commonly used to examine the use of morphological markers in children with language impairment (Bedore & Leonard, 2001; Bortolini & Caselli, 1997; Grela & Leonard, 2000; Leonard, Deevy, Miller, Rauf, Charest, & Kurtz, 2003; Leonard, Dromi, Adam, & Zadunaisky-Ehrlich, 2000; Leonard & Eyer, 1997; Marchman, Wulfeck, & Weismer, 1999; Rice, Wexler, & Hershberger, 1998; Rice, Wexler, Marquis, & Hershberger, 2000). Sentence completion requires a child to complete a sentence using a target morpheme. For example, “Here is one bird and here are two (birds)” could be used to target the noun plural, and “This is the girl’s dress and this is the ____ (boy’s hat)” could be used to target the possessive /s/ morpheme. The obligatory context can also be a question, such as

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“The girl got up in the morning, looked in the mirror, and then what did she do? She combed her hair”—for regular past tense (Leonard & Eyer, 1997). Another type of obligatory context is a story-completion task. A story-completion task was used to examine the ability of children with language impairment to produce sentences of varying complexity that contained intransitive, transitive, and ditransitive verbs (Grela & Leonard, 2000). They examined the influence of argument structure complexity on the omission of auxiliary be verbs. The children listened to short stories that were animated by the examiner using figures and objects, each targeting a specific type of verb. The child’s task was to complete the story by describing the final action performed by the figures using the verb that the examiner provided. A similar task was used in another study examining the use of tense and finiteness in children with language impairment (Leonard, Dromi, Adam, & Zadunaisky-Ehrlich, 2000). During the story, the children were asked to complete the examiner’s sentences with the appropriate verbs, altering the tense or finiteness of the verb in the preceding sentence. The use of story-completion tasks in these studies helped highlight the grammatical deficits demonstrated by children with SLI, cross-linguistically.

Structural Priming Structural priming is the tendency to use the same syntactic configuration or structure as that of a previously heard sentence (Bock & Griffin, 2000; Chang, Dell, Bock, & Griffin, 2000. For example, with a picture intended to target the auxiliary are, the examiner produces the sentence “The boys are washing the car” as a prime and then presents another picture and asks, “What’s going on now?” Tasks involving structural priming have been used in several studies to examine morphosyntax in children with language impairment (Bedore & Leonard, 2001; Bortolini & Caselli, 1997; Leonard & Dispaldro, 2013; Leonard, Miller, Deevy, Rauf, Gerber, & Charest, 2002; Leonard, Miller, Grela, Holland, Gerber, & Petucci, 2000; Rice, Wexler, Marquis, & Hershberger, 2000). Structural priming appears to increase the efficiency of sentence formulation in children (Leonard et al., 2002; Leonard et al., 2000), as well as in adults (Bock & Griffin, 2000). Some studies have shown that children with SLI were able to use structural priming to formulate sentences with the auxiliary forms is and are that are typically omitted in their spontaneous language production (Leonard, Dromi, Adam, & Zadunaisky-Ehrlich, 2000; Leonard et al., 2002). These results have clinical implications for language intervention with this population as a method in which to facilitate correct production of grammatical structures in children with SLI (Leonard, 2011).

Spontaneous Language Samples and Elicited Productions Spontaneous language sampling is a commonly used tool in clinical practice to assess a child’s strengths and weaknesses in all language areas: syntax and morphosyntax (e.g., calculating MLU, examining the length and complexity of utterances), phonology (e.g., phonetic inventory, syllable structure complexity, and phonological processes), semantics (e.g., vocabulary size), and pragmatics (e.g., cohesive ties, story grammar). It is particularly advantageous in child’s assessment in lieu of the limitations of standardized language tests and their unavailability in certain languages. It is also used to assess the effects of intervention programs on the language abilities of children with language impairment by comparing the language samples pre- and post-intervention (Windfuhr, Faragher, & Conti-Ramsden, 2002). Spontaneous language samples are frequently used to document the phonological profiles of young children with and without language impairment (Johnston, Miller, Curtiss, & Tallal, 1993; Leonard, 1982; Paul & Jennings, 1992; Rescorla & Bernstein-Ratner, 1996; Roberts, Rescorla, Giroux, & Stevens, 1998; Schwartz, Leonard, Folger, & Wilcox, 1980; Shriberg & Kwiatkowski, 1994;

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Whitehurst, Smith, Fischel, Arnold, & Lonigan, 1991). Lexical diversity, measured by the number of different words, the number of total words produced, and the type-token-ratio (TTR—ratio of number of different words to number of total words) can also be assessed in the spontaneous language samples of children (Owen & Leonard, 2002; Watkins & Kelly, 1995) as well as story grammar (Roth & Spekman, 1986). Spontaneous language samples can also serve as the basis for assessing morphosyntactic abilities (Bedore & Leonard, 1998; Beverly & Williams, 2004; Hewitt, Hammer, Yont, & Tomblin, 2005; Leonard & Eyer, 1997; Marinellie, 2004; Oetting & Horohov, 1997; Rescorla, Dahlsgaard, & Roberts, 2000; Rice & Oetting, 1993; Rice & Wexler, 1996; Rice, Wexler, & Hershberger, 1998). For example, spontaneous language samples were used to assess the concurrent validity and temporal stability of the MLU index in children ages 3 to 10, with and without SLI (Rice, Redmond, & Hoffman, 2006). A variant of language sampling is a sample of conversational language (Johnston, Miller, Curtiss, & Tallal, 1993). For example, samples consisting of answers to a series of questions about life experiences (e.g., daily routines, birthdays) were elicited from children (2;6 to 7;8) with and without SLI. This variant was adopted in this study purposely to examine the effects of adults’ questions on the children’s MLUs. Adults’ questions increased the frequency of ellipsis and, thus, reduced MLU, particularly for children with SLI. Southwood and Russell (2004) compared the effectiveness of three methods of language sample elicitation: conversation, free play, and story generation. The language samples elicited from typically developing 5-year-old boys using these three methods were compared with regard to the number of utterances, variety of syntactic structures, MLU, number of syntactic errors, and proportion of complex syntactic structures. Results suggested that language samples elicited during free play and conversation had more utterances than those elicited during story generation. Longer utterances, however, were more prevalent in the language samples elicited during story generation, and complex sentences were more likely to be produced during conversations and story generation as opposed to free play. Language samples have been used to examine the reliability and validity of using MLU as an index of language development in children (Chabon, Kent-Udolf, & Egolf, 1982; Rice, Redmond, & Hoffman, 2006), to highlight the association between reading ability and oral language (Miller, Heilmann, & Nockerts, 2006). While many researchers elicit their own language samples from their participants, some have relied on the use of language sample database programs such as the Child Language Data Exchange System (CHILDES; MacWhinney, 2014) and the use of the language analysis program, Computerized Language Analysis (CLAN), which is compatible with the transcriptions used in CLAN (MacWhinney, 2000). The CHILDES database contains hundreds of language samples collected by researchers. More recently, the use of transcription analysis programs often used in research for analysis of spontaneous speech samples, such as Systematic Analysis of Language Transcripts (SALT; Miller & Iglesias, 2006), have been recommended for use clinically. Price, Hendricks, and Cook (2010) stated that the use of such programs will benefit clinicians as it provides a method of quantifying the performance of various areas of a child’s language, within a more naturalistic context.

Elicited Narratives and Story Retelling A widely used clinical and research technique for evaluating discourse is narrative analysis. In narratives, all language components come together to form a cohesive, well-formulated, meaningful story. Thus, the analysis of narratives provides information about grammatical skills and the ability of children to formulate sentences (Gillam & Johnston, 1992; Liles & Duffy, 1995; Paul & Smith, 1993; Scott & Windsor, 2000; Thordardottir & Weismer, 2002), to use cohesive devices relating meanings across sentences (Gillam & Johnston, 1992; Hesketh, 2004; Liles, 1985a, 1985b; Liles & Duffy, 1995; Paul & Smith, 1993; Purcell & Liles, 1992; Ripich & Griffith, 1988; van der Lely, 1997),

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and to organize the story content in a meaningful way (Gillam & Johnston, 1992; Liles, 1987; Liles & Duffy, 1995; Merritt & Liles, 1989; Paul & Smith, 1993; Ripich & Griffith, 1988; Scott & Windsor, 2000). Similar to the language sample procedure, it provides information about the degree of overall language productivity (Liles, 1985a, 1985b; Merritt & Liles, 1987, 1989). Procedures used to elicit narratives from children are story generation, in which children are instructed to compose stories from sequencing cards or wordless books, and story retelling, in which children listen to stories narrated by the experimenter and are asked to retell the stories back to the experimenter. Both procedures require the children to produce stories that will be composed of well-formed, well-organized, coherent sentences. However, they differ in that the story generation procedure requires children to self-conceptualize the story plot, whereas the story retelling procedure requires children to understand the plot narrated by the experimenter (Merritt & Liles, 1989). Performance of children with and without reading disabilities on story retelling, story generation, and personal narratives was compared to explore the effects of the elicitation context on oral performance (Westerveld & Gillon, 2010). Oral performance was measured by verbal productivity, semantic diversity, and morphosyntactic complexity. Results revealed overall better performance of typical readers on measures of morphosyntax and semantic diversity, regardless of context, compared to their peers with reading disability. Both groups performed better, producing more language, in the story retelling context. The most significant differences in group performance were observed in the story retelling condition, with the typical readers performing significantly better compared to their peers with reading disability on measures of verbal productivity, number of different words, and percentage of complex sentences. The results highlighted the potential in using oral narrative samples to distinguish between good and poor readers and the efficacy in using story retelling to identify strengths and weaknesses in children’s oral narrative performance.

On-line Measures of Language Production The advantage of on-line measures is that although an off-line measure can identify a breakdown in language production or processing, an on-line measure provides finer details that better describe the nature or locus of the breakdown. Shapiro, Swinney, and Borsky (1998) described the challenges a clinician might face in developing an intervention program for a client with word-finding difficulties. Multiple factors may affect or cause word-finding difficulties. Knowing which factor underlies the deficit can facilitate the delivery of the right treatment. For example, a breakdown at the semantic level of processing versus a breakdown at the phonological level of processing might merit a different treatment approach, focusing on different areas of language. While an off-line task can identify a word-finding deficit, an on-line task that examines the timing and nature of different processing levels (i.e., phonology or semantics) of lexical access during naming may be more advantageous for developing a focused remedial program.

Picture-Word Interference Paradigm One commonly used task is the Picture-Word Interference (PWI) paradigm. PWI been widely used to evaluate lexical access during language production in adults (Alario, Segui, & Ferrand, 2000; Cutting & Ferreira, 1999; Hashimoto & Thompson, 2010; Jescheniak & Schriefers, 1998; La Heij, Dirkx, & Kramer, 1990; Levelt, Schriefers, Vorberg, Meyer, Pechmann, & Havinga, 1991; Peterson & Savoy, 1998; Schriefers, Meyer, & Levelt, 1990). It provides information about the timing at which specific lexical information (i.e., semantic, phonological, or morphosyntactic) becomes available during word production. PWI has also been used with children

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to children to investigate typical (Brooks & MacWhinney, 2000; Jerger, Martin, & Damian, 2002) and atypical lexical access for production (Brooks, Seiger-Gardner, & Sailor, 2012; Seiger-Gardner & Brooks, 2008; Seiger-Gardner & Schwartz, 2008) and to children with hearing impairments (Jerger, Lai, & Marchman, 2002a, 2002b). In PWI, participants are presented with pictures of common objects and are instructed to name them as quickly as they can. Interfering stimuli (words or pictures) are presented either auditorily or visually at different points in time relative to the presentation of the pictures, also called stimulus onset asynchrony (SOA); thus, the interfering stimuli can precede the onset of the pictures, appear simultaneously with the pictures, or follow the onset of the pictures (see Figure 21.1). Participants are instructed to ignore the interfering stimuli and to concentrate on naming the pictures as quickly as possible. Depending on the focus of the investigation, the interfering stimuli vary in their relationship to the target pictures. For example, the interfering stimuli may be auditorily presented words related in meaning or in phonological form to the target pictures. To measure the effects of the interfering stimuli on the naming process, response latencies under the related interfering stimulus conditions (i.e., semantic and phonological) are compared to the response latencies under the unrelated condition. In adults (Schriefers, Meyer, & Levelt, 1990) and in typically developing children (Brooks & MacWhinney, 2000; Jerger, Martin, & Damian, 2002; Seiger-Gardner & Schwartz, 2008), semantically related information, such as words that belong to the same superordinate category (i.e., coordinates, plane–bike), presented early in the word production process, delay the availability of semantic information of the target word, therefore inhibiting word production. The semantically related words are competitors to the target items, slowing their quick retrieval. Phonologically related information, such as words that share onset consonant(s) (e.g., cat–car), presented later, speed up the availability of phonological information of the target word, facilitating word production. The phonological features shared by the targets and the distractor words become activated as soon as the words are presented, contributing to a quicker retrieval of the target items (Jescheniak & Schriefers, 1998; Schriefers, Meyer, & Levelt, 1990). These effects suggest that semantic information becomes available for retrieval during word production prior to the availability of phonological information. They are the core assumptions of most of the current models of lexical access that view lexical access as a two-stage process (Dell & O’Seaghdha, 1992; Levelt, 1992).

Figure 21.1 Schematic of the cross-modal PWI paradigm in the study (Seiger & Schwartz, 2005).

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Theories of lexical access differ in their assumptions regarding the temporal interplay between the two levels of processing: cascading of activation versus discrete non-overlapping stages and forward versus backward activation. The Discrete Two-Stage model assumes a modular system with two serially ordered, non-overlapping, and independent stages that operate on different inputs (Levelt et al., 1991; Schriefers, Meyer, & Levelt, 1990). Thus, only semantic information is processed during semantic processing and only phonological information is processed during phonological encoding. In contrast, the interactive models, the spreading activation model (Dell, 1986) and the cascaded processing model (Cutting & Ferreira, 1999; Jescheniak & Schriefers, 1998; Peterson & Savoy, 1998), view the system of lexical access in a more continuous way. They view the production system as “globally modular but locally interactive” (Dell & O’Seaghdha, 1991, p. 604); activation is predominantly semantic during semantic processing and predominantly phonological during phonological encoding. However, some activation of phonological properties takes place during semantic processing and some activation of semantic properties takes place during phonological encoding; activation cascades between the two levels. Studies in adults using the PWI paradigm indicated early activation of the phonological properties of words while semantic information had been processed (Cutting & Ferreira, 1999; Griffin & Bock, 1998; Jescheniak & Schriefers, 1997, 1998; Peterson & Savoy, 1998). Distractors that were phonologically related (e.g., soda and count) to target pictures and their synonyms (e.g., sofa and couch, respectively) revealed phonological facilitation effects for both distractors (e.g., soda and count) early on in the process of lexical access (Jescheniak & Schriefers, 1997, 1998; Peterson & Savoy, 1998). Thus, the phonological forms of both target items (e.g., sofa and couch) were activated early on in the process, while the semantic properties of the lexical items were being specified. Similar results were revealed for children (Jescheniak, Hahne, Hoffman, & Wagner, 2006). The two interactive models diverge in their assumptions regarding the flow of activation. The spreading activation model assumes bi-directional flow of activation; activation can flow forward, from the semantic level to the phonological level and backward (Dell, 1988). In contrast, the cascaded processing model assumes unidirectional activation; thus, activation can flow only forward. The PWI paradigm has been applied to typically developing children, examining the development progression of lexicalization (Brooks & MacWhinney, 2000; Jerger, Martin, & Damian, 2002; Jescheniak, Hahne, Hoffman, & Wagner, 2006; Seiger-Gardner & Schwartz, 2008; Seiger, Brooks, Sailor, & Bruening, 2005). Jerger, Martin, and Damian (2002) investigated the stages of picture naming in children ages 5–7 years of age and in teenagers. Semantic and phonologically related auditory distractors were used at three SOAs (i.e., -150, 0, and +150) to infer about the time course of lexical access in children. The results from the children supported a nondiscrete time course of lexical access, revealing early activation of both phonological and semantic information. Information processing in children appeared to be more interactive than in adults, revealing periods of time where semantic and phonological information was processed simultaneously (Jerger, Martin, & Damian, 2002). Hanauer and Brooks (2005) found cross-modal semantic inhibition effect in children ages 3–7 when semantically related interfering words were paired with pictures that were presented 500 milliseconds after the offset of the interfering word. This early semantic inhibition was attributed to the children’s inefficient suppression mechanism. Phonological processing during language production has been further examined in children ages 4;11 to 11;9 (Brooks & MacWhinney, 2000). All children named pictures faster in the presence of phonological distractor words that shared the onset consonant(s) with the target pictures. When the phonologically related distractors rhymed with the target pictures, only the youngest group revealed faster response times. It suggested that children’s lexicons undergo a developmental shift from a rhyme-based structure to an onset-based structure as in adults. However, this lexical restructuring may not be a discrete process that ends during the school-aged years, but rather, it

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may continue to shift through development (Metsala & Walley, 1998), and holistic processing that relies on a rhyme-based structure may be evidenced in adults depending on task demands (Storkel, 2002) and the fragility of the phonological representation of a lexical item (Almodovar, 2014). Semantic-phonological mediated priming was used by Jescheniak and colleagues (Jescheniak, Hahne, Hoffman, & Wagner, 2006) to examine the cascade of activation during lexical access in children. Semantic-phonological mediated priming is referred to the priming effects revealed in the presence of distractors that are phonologically related to a semantic category coordinate of the target picture. Its presence suggests the activation of the phonological properties of lexical competitors. Distractors in this study were either phonologically related (cap) to the target picture or were phonologically related in a mediated fashion (e.g., doll) to a semantic coordinate (e.g., dog) of the target picture (e.g., cat). Phonological facilitation effects were found in the late stimulus asynchronies from the phonologically related distractors (cap-cat), but an interference effect was found from the mediated semantic-phonologically related distractors (doll-cat). The PWI paradigm was used for the first time with children with language impairment in a study investigating the locus of breakdown in the lexical systems of children with language impairment that cause word-finding difficulties (Seiger-Gardner & Schwartz, 2008). Word-finding difficulties are the most frequently observed lexical limitation in school-age children with language impairment (Faust, Dimitrovsky, & Davidi, 1997; German, 1979, 1984, 1987; Leonard, Nippold, Kail, & Hale, 1983; McGregor & Leonard, 1989; Nippold, 1992; Wiig & Becker-Caplan, 1984). Semantic or phonological deficits have been postulated to underlie word-finding difficulties. Despite a focus on finding the locus of breakdown in the lexical system of these children, most studies have used off-line techniques (e.g., analysis of naming errors), and even the addition of reaction time measures did not provide information about the subprocesses involved in the process of word finding. The PWI paradigm in Seiger-Gardner and Schwartz (2008) permitted an examination of the mechanisms underlying word-finding difficulties in children with language impairments. In this study, school-aged children ages 8;0 to 10;0 with and without SLI were asked to name black-andwhite line drawings quickly and accurately while ignoring auditorily presented distractor words. The distractor words were either semantically (coordinates: lion-pig) or phonologically (share the onset consonant/s: lion-lips) related to the target pictures, or were unrelated in neither form nor meaning to the target pictures (lion-boat). The presentation of the distractor words was manipulated in time relative to the presentation of the pictures such that they appeared 150 milliseconds before the pictures, with the pictures, or 150, 300, or 500 milliseconds after the pictures (see Figure 21.1). Children with SLI exhibited lingering semantic interference effects at all temporal points, as indicated by longer response latencies in the presence of semantically related distractor words as compared with the effects of unrelated distractor words. These effects were not seen in their typically developing peers. Phonological priming effects (i.e., shorter response latencies in the presence of phonologically related words) were apparent in children with SLI, suggesting that phonologically related words were used as primes to facilitate lexical access during word production, similar to typically developing children and normal adults. The use of the PWI paradigm provided a temporal picture of lexical access in children with SLI, tracking, moment-by-moment, the processes involved in lexicalization. It enabled the researchers to ascribe the locus of lexical breakdown that causes word-finding difficulties to the lexical-semantic level. The semantic-level deficits were further supported by Brooks, Seiger-Gardner, and Sailor (2012), in which they found that children with SLI demonstrated weaker facilitation of associates (e.g., a carrot paired with a rabbit) than children with typical language development. In adults and children with typical language development, words that are associates of one another show strong facilitative effects in production.

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Despite these findings, the deficits in lexical access for production associated with SLI may have a more complex explanation. Seiger-Gardner and Brooks (2008) examined phonological encoding using onset and rhyme-related primes through PWI in 7–11-year-old children with and without SLI. The purpose of this study was to investigate whether school-age children with SLI process words incrementally, rather than holistically, similar to their age-matched, typically developing peers. Surprisingly, the children with SLI demonstrated no effects of rhymerelated primes but demonstrated facilitation of the onset-related primes, similar to their typically developing peers. Although adults and older children demonstrate no phonological priming effect in the early stimulus asynchrony in the onset condition (Brooks & MacWhinney, 2000; Cutting & Ferreira, 1999; Jescheniak & Schriefers, 1998, 2001), children with SLI demonstrated a phonological inhibition effect in the early stimulus asynchrony (-150 SA), with facilitation noted in the later SA (+150). Phonological priming is evident as early as the 0 SA (synchronous distractor) in adults and typically developing, older children, while children with SLI only begin to exhibit this facilitation at the +150 SA. The phonological inhibition effect remained activated significantly longer than in the children with typical language development. This early inhibition effect and late priming effect was noted in Brooks and MacWhinney (2000) with their 5–7-yearold group. Seiger and Brooks (2008) and Seiger-Gardner and Schwartz (2008) have posited that children with SLI may have an inadequate suppression mechanism and a slower decay rate than their typically developing peers. Two other studies that utilized the cross-modal paradigm investigated the lexical abilities of children with and without hearing loss (Jerger, Lai, & Marchman, 2002a, 2002b). In the first study, semantically related (e.g., hotdog–pizza) and unrelated (e.g., dress–pizza) auditory distractors were presented at different points in time relative to the presentation of the pictures. Interference effects in the presence of semantically related distractors were observed in both groups of participants, suggesting similar lexical-semantic representations in children with and without hearing loss. A delay in the time course of semantic interference effect in some children with hearing loss suggested a rather different semantic processing, attributable to the early childhood hearing loss. In the second study, phonologically related and unrelated nonsense-syllable distractors were compared to the unrelated condition (i.e., vowel nucleus verbal baseline). The nonsense-syllable distractors were of two types: matching in place of articulation and voicing to the target picture (e.g., /ti/–teeth) or incongruent, conflicting in two features of voice and place (e.g., /bi/–teeth) with the target picture or one feature of voice (e.g., /di/–teeth) or place (e.g., / pi/–teeth) with the target picture. For children with and without hearing loss, naming latencies in the presence of the congruent distractors were shorter relative to the unrelated condition but longer in the presence of the incongruent distractors. As a group, the children with hearing loss seemed to have similar finegrained phonological representations as children with normal hearing. However, when phoneme discrimination skills were assessed, the children with hearing loss and poor auditory perceptual skills exhibited no effects in the presence of the incongruent distractors compared to their typically developing peers. Thus, the authors concluded that children with hearing loss and poor auditory perceptual skills may have more holistic and less-specified phonological representations compared to their typically developing peers and children with hearing loss with good auditory perceptual skills. This on-line task permitted a direct examination of auditory perceptual abilities in the phonological processing of phonological information for production. The PWI paradigm has also been modified to examine sentence processing in children with SLI. Hestvik, Schwartz, and Tornyova (2010) examined gap-filling in relative clauses in children with and without SLI. The children were presented with sentences in which target animal names

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were embedded as object nouns within relative clauses. In the “gap condition,” picture probes were presented at the temporal position of the gap. In the “pre-gap” control condition, the target picture was presented at the offset of a propositional phrase modifier that followed the relative clause. Children were then asked to listen to a sentence and name the picture that appeared on a computer screen as fast as they could. Children with SLI presented with different performance on this task when compared to children with typical language development (TLD). The children with SLI did not demonstrate priming when the picture was presented in the gap position. However, their performance on a comprehension probe demonstrated similar performance to children with TLD, indicating that the problems they have occur on a language processing level but are not related to grammatical knowledge. The PWI task is potentially a powerful tool that can be used to investigate the levels of processing in lexical access for production. Among its advantages are: (1) it allows a momentby-moment examination of lexical production; (2) it is sensitive to automatic processing, as the participant does not consciously analyze the distractors; and (3) it is finely tuned to the integration of cross-modal input and the interactions among processing levels. The PWI paradigm also allows the experimenter to vary systematically the distractor timing stimulus relative to the picture presentation. This reveals the time-course of information availability. It has the potential to provide more in-depth information about the mechanisms underlying the lexical production deficits in children with language impairment. Focused remedial programs can then be developed to address the specific deficits (Martin, Fink, & Laine, 2004; Shapiro, Swinney, & Borsky, 1998).

Electrophysiological Measures Many of the linguistic processes involved in language production are temporally short-lived and, thus, difficult to examine given the current methods. Event-related potentials (ERPs) are ideal for the examination of these temporally transient processes because of their millisecond-to-millisecond temporal resolution. This method allows an ongoing recording of information throughout the entire time course of the linguistic processes being investigated. Research employing electrophysiological techniques (see Chapter 24 by Shafer, Zane, and Maxfield) used scalp-recorded brain potentials to record the temporal course of information processing. Electrophysiological studies in adults have provided more specific information about the time course of lexical access during word production, particularly about the relative timing of semantic processing and phonological encoding during word production (Laganaro, Python, & Toepel, 2013; Laganaro, Valente, & Perret, 2012; Schmitt, Münte, & Kutas, 2000; van Turennout, Hagoort, & Brown, 1997, 1998). The go/no-go paradigm was used to distinguish the influence of semantic information from that of phonological information on response preparation. Participants were asked to classify a picture along semantic (animate/inanimate) and phonological (word final or initial phoneme) dimensions. Depending on the outcome of the semantic and phonological classifications, a left-hand response, a right-hand response, or no response was given. Two ERP components were used in the investigations, the Lateralized Readiness Potential (LRP) and N200. N200 is elicited when a potential response is withheld or inhibited. In a go/no-go paradigm, the N200 is elicited when the participant does not give a response. The presence of the N200 implies that the information, which determines whether a response should be provided, must have been processed. Thus, the N200 provides an estimate of the timing at which specific information has been encoded. In the case where the semantic classification determines the response side and the phonological classification determines the go/no-go decision, the N200 provides information about the availability

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of phonological information; when the semantic and phonological decisions are reversed such that the phonological decision determines the response side and the semantic decision determines the go/no-go execution, the N200 provides information about the availability of semantic information. A comparison between the onset and peak latencies of the N200 under the two conditions provides information about the type of information (semantic or phonological) that becomes available first. The LRP is a slow negative-going potential that starts to develop prior to the execution of a voluntary movement and reaches its maximum just after the movement onset. The LRP permits the investigation of motor-related brain activities before an overt response is executed. More importantly, it reveals motor activation, or response preparation, even in the absence of an overt response (Schmitt, Münte, & Kutas, 2000). It is important to note that the LRP does not indicate when information is available, but rather when it is used to prepare or influences a response (Turennout, Hagoort, & Brown, 1997). In Schmitt, Münte, and Kutas (2000), participants were shown pictures and needed to decide whether the picture was of an object or an animal (semantic information) and whether the picture’s name starts with a vowel or a consonant (phonological information). In the case where the semantic classification determines the response side and the phonological classification determines the go/no-go decision, an LRP effect is expected to initially develop on both go and no-go trials at about the same latency. After some time, response preparation on no-go trials is expected to decrease because of the completion of the phonological decision, and the LRP is expected to return to the baseline. In the case where the semantic and phonological classifications are reversed such that semantic information determines the go/ no-go and the phonological classification determines the response side, it is expected that the go/no-go decision can be made before information about the response hand becomes available. Therefore, the presence of an LRP is expected only for go trials. These outcomes are obviously expected only under the assumption that semantic information becomes available before phonological information. This assumption was confirmed by the availability of semantic information approximately 90 milliseconds prior to phonological information for word production in the measurements of both ERP components (N200 and the LRP) (Schmitt, Münte, & Kutas, 2000; Turennout, Hagoort, & Brown, 1997). One of the challenges in using electrophysiological measures in the investigation of language production operations is the presence of the muscle or movement artifacts during the electroencephalogram (EEG) recordings. The muscle artifact occurs due to jaw movement during speech, which can mask the smaller amplitude waveforms that reflect specific linguistic processes. Thus, in studies on language production that apply electrophysiological measures, the participants are asked to produce the verbal response after a post-stimulus delay to avoid the muscle artifact (Greenham & Stelmack, 2001; Greenham, Stelmack, & van der Vlugt, 2003; Jescheniak, Schriefers, Garrett, & Friederici, 2002). The measurement of production before the actual production occurs raises some concerns regarding the extent to which the ERPs reflect production operations per se and not some planning processes. In an ERP study investigating the time course of lexical access during word production in adults (van Turennout, Hagoort, & Brown, 1998), subjects named pictures aloud only on filler trials that were embedded within the experiment. On the target trials, they named the pictures aloud only after a button press response (i.e., after a delay following the offset of the picture stimulus). The authors argued that because the participants were instructed to name the pictures aloud on all trials, regardless of whether it was after a button press response or not, their subjects were in the mode of language production, and the ERPs reflected production operations rather than planning processes. A similar approach was employed in a study that adapted the behavioral PWI paradigm to examine the activation of semantic and phonological information during speech production using

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ERPs (Jescheniak, Schriefers, Garrett, & Friederici, 2002). While participants prepared the production of a depicted object’s name, they heard an auditory distractor word that was either related in form or in meaning to the target, or unrelated. Participants were asked to overtly name the pictures only after receiving a visual cue to avoid the movement artifact. The effects of semantic and phonological distractors compared to unrelated distractors on the N400 component were examined. As expected, the N400 was attenuated when the target words were paired with the related primes (phonological and semantic) compared to when they were paired with the unrelated primes. Laganaro and Perret (2011) sought to address the methodological issue of whether the processes involved in those tasks described above are comparable to the processes involved in overt speech production. They compared the integration of stimulus-aligned and response-aligned ERPs in immediate overt picture naming and delayed production. Results showed similar sequence and duration of topographic maps in the immediate and delayed production until around 350 milliseconds after picture onset, revealing similar encoding processes until the beginning of phonological encoding. Modulations linked to word Age of Acquisition (AoA) were only observed in the immediate production. The use of ERPs to examine language production in children with and without language impairments has been quite limited. In a study examining the role of attention in naming of pictures and printed words by children and adults (Greenham & Stelmack, 2001), the morphology of the ERP waveforms that children develop while selectively attending to and naming words or pictures was described. A negative waveform that develops at about 450 milliseconds (N450) over anterior scalp regions seemed to be sensitive to variations in semantic processing. An earlier negative waveform at about 280 milliseconds (N280) that develops over anterior sites, when the task involves superimposed arrays of pictures and words, appeared to index an early stage of attention processing. In a study of developmental learning disabilities (Greenham, Stelmack, & van der Vlugt, 2003), children with reading and spelling difficulties were expected to exhibit a normal N450 when naming pictures but a reduced N450 when naming words. Children were tested under three conditions: attend-word condition, attend-picture condition, and a control condition. In the attendword condition, they were presented with picture-word pairs and were instructed to attend to and to name the words and ignore the pictures and, in the attend-picture condition, they were instructed to attend to and name the picture and ignore the word. In the control condition, children were presented with the individual words and pictures and were instructed to name them. The 180 word-picture pairs consisted of 60 congruent stimuli (a picture of a doll and the word doll), 60 semantically associated stimuli (a picture of a nest and the word bird), and 60 incongruent stimuli (a picture of a dog and the word potato). As expected, children with reading and spelling difficulties exhibited a smaller N450 than their typically developing peers under the control condition when naming individually presented words but not when naming individually presented pictures. It suggests that children with learning disabilities exhibit specific difficulties in processing linguistic information. Although there were no differences in the amplitude of the N450 for pictures, these children exhibited behavioral word-finding deficits. The authors concluded that the word-finding deficit might influence and be reflected in a later stage of processing, during the generation or production of the naming response, rather than at the stage reflected by the N450. There was a similar response pattern for the attending conditions, with a reduced N450 for words and not for pictures, suggesting that the word-finding difficulties in these children are due to a linguistic rather than an attentional deficit. More recently, language production research in adults extended the investigation beyond the processing that takes place during production operations, moving toward localization of these

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operations in the brain using fMRI. Neuroimaging studies can provide information about the neuroanatomical organization of the lexical system that may refine the theoretical models of language production (De Zubicaray, Wilson, McMahon, & Muthiah, 2001). For example, Kemeny, Xu, Park, Hosey, Wettig, and Braun (2006) introduced a latency component into a conventional naming paradigm, delaying the production response, in order to dissociate the processes involved in lexical selection and articulation. There was activation in the left hemisphere perisylvian areas during early lexical access and activation of the motor and auditory areas during overt articulation. Another fMRI study examined the neural responses of adults associated with the semantic interference (SI) effect in a PWI task (De Zubicaray, Wilson, McMahon, & Muthiah, 2001). Overt vocalization of picture names occurred in the absence of scanner noise, allowing reaction time data to be collected. Activation of both the left mid-section of the middle temporal gyrus (MTG) and the left posterior superior temporal gyrus (STG) suggested that the SI effect occurs both in the conceptual and phonological levels of lexical access. This result supports the interactive models of lexical access that postulate bi-directional flow of activation between the different levels of processing. In a similar investigation, the neural correlates of grammatical gender selection in German were investigated during overt picture naming using fMRI techniques (Heim, Opitz, & Friederici, 2002). Subjects named black-and-white line drawings of real objects. Compared to simply naming a picture, the production of the definite determiner of the picture name, which required the participant to access the gender marker for that name, resulted in marked activation of a single region in the superior portion of Broca’s area. Many of the off-line techniques described in the earlier section of this chapter, such as verbal fluency (Holland, Plante, Weber, Strawsburg, Schmithorst, & Ball, 2001; Marsolais, Pernbarg, Benali, & Joanette, 2014) and picture naming (De Guilbert, Maumet, Ferré, Jannin, Biraben, Allaire, Barillot, & Le Rumeur, 2010; Grande, Heim, Meffert, Huber, & Amunts, 2011), have been utilized in the last decade with fMRI investigations of language production and brain mapping both in children and adults. As mentioned earlier in the chapter, the picture-naming task is widely used. It was considered an ideal task in examining modulation of brain function in both children and adults when the language production task becomes increasingly demanding (Krishnan, Leech, Mercure, Lloyd-Fox, & Dick, 2015). Participants were 37 children ages 7–13 and 19 young adults. A picture-naming task with three levels of difficulty (silly, easy, hard) was used in the fMRI investigation. Results showed that while neural organization for picture naming was in many ways preserved from school age to early adulthood, both in overall patterns of activation and in response to levels of naming difficulty, there were also remarkable differences in the ways in which children’s and adults’ brains responded to increased naming demands. Adults typically showed complexityrelated increases in activation (hard > easy > silly), while children showed complexity-related decreases (silly > easy > hard) in the same brain regions, inferior and prefrontal cortex. Developmental differences also emerged in regions typically associated with self-monitoring during speech production (posterior superior temporal gyrus and parietal operculum) and higher-level visual object processing (lateral posterior temporal and occipital regions). As can be expected, adults showed considerably more activation in all conditions compared with children, who tended if anything to deactivate relative to the resting baseline. Another study that used the picture-naming task in children using fMRI examined differences between low- and high-frequency words in children with dyslexia and their typically developing peers (Grande, Heim, Meffert, Huber, & Amunts, 2011). De Guilbert et al. (2010) studied 18 children using novel tasks involving covert naming: word generation or fluency task, naming to a description, and picture naming (targeting phonological processing) of three pictures presented successively sharing similar phonological composition. This study highlighted the importance of using various production tasks that tap different processes in mapping brain regions that are activated during language production.

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Age-related changes in language production abilities manifested in functional connectivity among cortical areas were examined in high-performing young and older adults using fMRI techniques (Marsolais, Pernbarg, Benali, & Joanette, 2014). Participants were asked to perform on two production tasks: (1) Automated Fluency Task—participants were asked to continuously name out loud all the months of the year, a task proven to identify brain activity related to automatic speech production; and (2) Verbal Fluency Tasks—participants were asked to produce as many words as possible within 90 seconds in four different semantic categories (animals, clothing, vegetables, and sports) and four different orthographic categories (starting with the letters P, M, V, L). Results showed that aging affects the functional integration of the cortical networks involved, without disrupting lexical speech production abilities in high-performing older adults. However, local interactions within the verbal fluency network were found to be modulated by age and task demands. The time course of brain activation during word production has become an area of increased focus in cognitive neuroscience. Magnetoencephalography (MEG) technique has also been used to examine lexical production. The notion that semantic and phonological processes are activated sequentially in production is well established in the psycholinguistic literature. Miozzo, Pulvermüller, and Hauk (2015) used a multiple linear regression approach to MEG analysis of picture naming in order to investigate early effects of variables specifically related to visual, semantic, and phonological processing. Results revealed brain activation in multiple regions associated with different psycholinguistic variables: (1) activation in the occipital cortex at about 100 milliseconds after picture onset, associated with the visual image complexity; (2) activation in the left fronto-temporal regions at about 150 milliseconds, associated with semantic processing; and (3) activation in the left middle temporal gyrus at approximately 150 milliseconds, associated with phonological processing. The results highlighted the advantages of the multiple linear regression analysis in detecting early effects of multiple psycholinguistic variables in picture naming. In addition, results suggested that access to phonological information co-occur with semantic processing around 150 milliseconds after picture onset. In another study, a simple naming task was used to examine the dissociation between noun and verb representations (Soros, Cornelissen, Laine, & Salmelin, 2003). The naming performance of 10 healthy adults was compared to that of an anomic patient who exhibited superior verb naming (compared to nouns). The task involved the presentation of a prompt word that indicated to the subject whether to name the action or the object in a line drawing. Marked differences between action and object naming were apparent in the left hemisphere of the anomic patient but not in that of the healthy adults. The recovery process from a disruption in the normal language network (i.e., aphasia) may lead to a reorganization of brain functions and, thus, to dissociation between the neural correlates associated with verbs and nouns production. While most research in language production focused on single-word production, Pylkkänen, Bemis, and Blanco (2014) investigated the spatio-temporal dynamics of the processes that combine individual words into larger units of language (i.e., simple adjective-noun phrases, ‘red tree’) during production. The authors argued that since conceptual and grammatical encoding for such productions is completed before articulation begins, measuring brain activity following the production prompt but preceding speech onset would allow them to obtain a spatio-temporal map of combinatorial mechanisms, such as the encoding of conceptual semantic relationships and structural syntactic relationships between elements, uncontaminated by movement artifacts. Combinatorial processing in the ventro-medial prefrontal cortex (vmPFC) and left anterior temporal lobe (LATL), two regions previously associated with comprehension of similar phrase-structure. With MEG, effects occurred at 250 milliseconds for the LATL and at 400 milliseconds for the vmPFC, while in production the vmPFC effect no longer followed the LATL effect but rather had a similar or an even earlier onset, suggesting that a late process in

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comprehension is an early process in production. The authors regarded the results as a “neural bridge” between psycholinguistic models of comprehension and production that postulate similar mechanisms operating in reversed order (Pylkkänen, Bemis, & Blanco, 2014).

Conclusion The use of on-line behavioral techniques (e.g., picture word interference paradigm), electrophysiological measures, and imaging techniques (fMRI and MEG) to investigate language production, although very promising, has to address several methodological challenges inherent both in the techniques themselves and the behavior examined (i.e., language production), as well as in the population investigated (i.e., young typically and atypically developing children). These measures can provide fine-grained, in-depth information about the processes involved in language production operations. This information can lead to a better understanding of atypical language production and processing and to the development of more accurate assessment tools and advanced remedial programs that can better serve children with language impairments.

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Developmental aspects of verbal fluency and confrontation naming in children. Brain and Language, 71, 267–284. Roberts, J., Rescorla, L., Giroux, J., & Stevens, L. (1998). Phonological skills of children with specific expressive language impairment (SLI-E): Outcome at age 3. Journal of Speech, Language, and Hearing Research, 41, 374–387. Rogers, T. T., Ivanoiu, A., Patterson, K., & Hodges, J. R. (2006). Semantic memory in Alzheimer’s disease and the frontotemporal dementias: A longitudinal study of 236 patients. Neuropsychology, 20, 319–335. Roth, F. P., & Spekman, N. J. (1986). Narrative discourse: Spontaneously generated stories of learningdisabled and normally achieving students. Journal of Speech and Hearing Disorders, 51, 8–23. Saben, C. B., & Ingham, J. C. (1991). The effects of minimal pairs treatment on the speech-sound production of two children with phonologic disorders. Journal of Speech and Hearing Disorders, 34, 1023–1040. Sauzeon, H., Lestage, P., Raboutet, C., N’Kaoua, B., & Claverie, B. (2004). Verbal fluency output in children aged 7–16 as a function of the production criterion: Qualitative analysis of clustering, switching processes, and semantic network exploitation. Brain and Language, 89, 192–202. Schatschneider, C., Carlson, C. D., Francis, D. J., Foorman, B. R., & Fletcher, J. M. (2002). Relationship of rapid automatized naming and phonological awareness in early reading development: Implications for the double-deficit hypothesis. Journal of Learning Disabilities, 35, 245–256. Schmitt, M. B., Münte, F. T., & Kutas, M. (2000). Electrophysiological estimates of the time course of semantic and phonological encoding during implicit picture naming. Psychophysiology, 37, 473–484. Schriefers, H., Meyer, A. S., & Levelt, W. J. M. (1990). Exploring the time course of lexical access in language production: Picture-word interference studies. Journal of Memory and Language, 29, 86–102. Schwartz, R. G., Leonard, L. B., Folger, M. K., & Wilcox, M. J. (1980). Early phonological behavior in normal-speaking and language disordered children: Evidence for a synergistic view of linguistic disorders. Journal of Speech and Hearing Disorders, 45, 357–377. Scott, C. M., & Windsor, J. (2000). General language performance measures in spoken and written narrative and expository discourse of school-age children with language learning disabilities. Journal of Speech, Language, and Hearing Research, 43, 324–339. Seiger, L., Brooks, P., Sailor, K., & Bruening, P. (2005). Semantic Processing during Language Production in Typically Developing Children. Poster presented at the 10th International Congress for the Study of Child Language, Berlin, Germany. Seiger-Gardner, L., & Brooks, P. (2008). Effects of onset- and rhyme-related distractors on phonological processing in children with specific language impairment. Journal of Speech, Language, and Hearing Research, 51, 1263–1281. 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22 LANGUAGE COMPREHENSION APPROACHES TO CHILD LANGUAGE DISORDERS Patricia Deevy

Introduction Historically, children’s language comprehension has been studied less extensively than language production. Whereas production offers explicit data for analysis, comprehension can only be measured indirectly, making it a more challenging object of study. As described in this chapter, we measure comprehension by observing behavior in response to language; this response can be as overt as acting out the meaning of a sentence with toys or as subtle as shifting eye gaze to a named object. In the study of adults, we assume that the methods we use allow us to isolate language comprehension to a large extent; in children, it is unclear that this assumption is warranted. In drawing conclusions about “comprehension” in children, as measured behaviorally, we must consider the nature of their developing cognitive and linguistic systems and how these contribute to observed performance. Increasingly, researchers are trying new methods that circumvent some of the difficulties of testing comprehension in young children and thereby provide better insight into children’s knowledge and receptive processing of language. Studies of language comprehension are also less common than studies of language production in children with language disorders. A distinction has traditionally been made between children who have both expressive and receptive delays and those who have only expressive delays. Careful testing, however, has shown that most children with specific language impairment (SLI) have at least some comprehension difficulties and that children differ only in the degree to which both receptive and expressive language is affected rather than falling into strict subtypes (Bishop, 1997; Leonard, 2009). Many of the methods discussed here have been used or are starting to be used to study language in atypical populations, particularly children with SLI. Before introducing specific methods, we review relevant concepts and outline some general considerations in testing comprehension in children with language disorders.

Competence and Performance When measuring language comprehension in adult native speakers, it is safe to assume that they share a great deal of implicit knowledge of the words or sentences we are testing. This shared abstract knowledge has been called linguistic competence and is organized at many levels (including the phonological, lexical, syntactic, semantic, and discourse levels). How this knowledge should be

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described and how it develops in children are controversial open questions for theoretical linguists and psycholinguists; however, it is clear that the endpoint of language acquisition includes a good deal of stable, predictable knowledge. Linguistic performance or processing is the use of this knowledge to produce and comprehend language. Although competence in adults may be relatively stable, performance can be affected by transitory states (e.g., fatigue), inherent cognitive constraints (e.g., limitations in short-term memory), and experience (e.g., word frequency). These two aspects of language capacity cannot be considered entirely independent since competence can be accessed only through our performance systems (i.e., by examining patterns of linguistic judgments or through psycholinguistic tests). Still, the dichotomy can be useful when planning a comprehension experiment in the following ways. First, by investigating the linguistic description of the structure under investigation, the researcher will have a better appreciation of its nuances and how it is related with other aspects of language knowledge. This may lead to more refined hypotheses or predictions as well as improved stimuli. Second, detailed assumptions about abstract representation can provide a clearer basis from which to make predictions about processing. This does not necessarily mean that the assumptions will prove correct in the long run, but they can improve design and allow for clearer interpretation of results. To give an example, a researcher might assume that object relative clauses (1.b) are more structurally complex than subject relative clauses (1.a) and thus more difficult to process. Alternatively, with more detailed assumptions, the two could be considered equally complex from the point of view of structural representation (e.g., in both cases, a Noun phrase is co-indexed with a relative marker, the relative marker is related by movement to its empty (e) argument position). This specific representational assumption narrows the range of hypotheses about where the difficulty lies and suggests alternatives. For example, rather than differences in the complexity of the two structural representations, it has been suggested that there are differences in the processing demands associated with each. The object relative clause representation may place greater demands on working memory due to the greater distance between the Noun phrase and the point of its interpretation (e). (1) a. That’s the boyi whoi ei saw the girl. b. That’s the girli whoi the boy saw ei. Many of the methods described in this chapter were developed to ask questions about the nature of the adult processing system, assuming a common, stable knowledge state. For children, it is more difficult to claim that grammatical knowledge can be held constant while testing processing mechanisms. Children’s true ability can be obscured in comprehension tasks by immature memory, attention, and behavior. Many of the methods reviewed in this chapter reflect attempts to circumvent these limitations as much as possible in order to get a clear picture of children’s language knowledge. Methods for testing the processing of stable aspects of children’s grammatical knowledge are just beginning to provide hints at how children’s performance systems are similar to and different from those of adults. Overall then, testing children’s comprehension requires careful consideration of assumptions about how to describe the target knowledge, how to determine the baseline comprehension ability of a child, and what language processing or cognitive mechanisms may be deployed in interpreting the structures of interest.

Off-line and On-line Comprehension Tasks The methods reviewed in this chapter are categorized as either off-line or on-line. On-line methods measure the intermediate stages of receptive processing as it happens in real time. On-line testing

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of adults’ receptive processing has shown that comprehension is rapid, automatic, and incremental. Interpretation begins as soon as linguistic information is available. For example, as the initial sound of a word is processed, possible candidate words with the same initial consonant are accessed in the lexicon; as more information is processed, this set is incrementally narrowed down. Off-line methods measure the output of all stages of receptive processing. Typically, the subject does not respond until after the stimulus is presented in its entirety, and this response is not timed. As such, off-line measures do not provide insight into the many intermediate steps in comprehension that led up to a response. The difference between final and intermediate stages of comprehension can be seen in the processing of temporarily ambiguous sentences, such as The police shot the man with a knife. On-line studies have demonstrated processing difficulty at a knife. Setting aside the reasons for this difficulty, it is clear that when first encountered, the immediate preference is to treat with as introducing an instrument of shoot, not as a modifier of man; on the instrument reading, a knife is unexpected. However, the correct interpretation is available upon reflection, as the final product of processing the sentence. In terms of their use with children, off-line tasks can be used to establish the child’s ability to interpret a given structure. As will be seen, they are more often used to explore the conditions or limits on a child’s comprehension accuracy. On-line tasks are most commonly used to test the processing of aspects of language for which children have some degree of knowledge. When this is not clear (e.g., when testing young typical or atypical children), on-line studies often include an off-line task to establish whether or how consistently the child can interpret the target structure.

General Methodological Considerations Before reviewing individual tasks, we consider some aspects of research design that apply generally to studies testing comprehension in children with atypical language. In determining whether a particular method can address a research question effectively, it is necessary to consider the specific aspect of linguistic knowledge or processing to be targeted, the group’s capacity to perform a given task, and the researcher’s resources. The following methodological considerations will help to inform these decisions.

Comparison Groups A comparison group provides a point of reference in studies of children with atypical language development. Although a group matched for chronological age may be adequate, it is often desirable to include a group that matches on some aspect(s) of comprehension ability, as determined by a standardized test. The reasoning is as follows: language-impaired children are likely to perform more poorly than same-age peers on most language measures, so the comparison may not reveal anything interesting about the status of a given ability relative to other comprehension abilities (Leonard, 2014). Depending on the choice of matching criterion, this comprehension-matched group may be one to two years younger than the impaired group, so it is important to determine whether this younger group will also be able to complete the task successfully. The choice of matching criterion can reflect an approach that is more or less conservative, depending on the degree of overlap between the types of structures and abilities targeted by the standardized test used for matching and those targeted by the experiment. If there is significant overlap, it is possible that no differences between groups will be found because the children were so closely matched on the ability tested in the experiment. For example, when testing children’s sentence comprehension, one could match using raw scores from a test of receptive syntax or from

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a test of receptive vocabulary. Obviously, the former is more conservative and the latter is less so, in the sense just described. A compromise would be to use a receptive test that probes a range of comprehension skills, including, but not limited to, the one to be studied. Besides providing an appropriate measure of comprehension, this test must be standardized for the entire age range of the children participating in the study. The Peabody Picture Vocabulary Test–Fourth Edition (PPVT–4) (Dunn & Dunn, 2007) is an excellent choice for matching on receptive vocabulary; it is carefully normed on a very wide age range (2 to 90 years old). The Clinical Evaluation of Language Fundamentals (CELF) includes subtests of sentence structure, direction following, and word concepts that provide an overall receptive language score. The Preschool version (CELF P–2) (Semel, Wiig, & Secord, 2004) is normed for children aged 3–6; the CELF–5 (Semel, Wiig, & Secord, 2013) is normed for children aged 5–21. The Test for Reception of Grammar–2 (TROG–2; Bishop, 2003) targets grammatical morphology and sentence structure and is standardized for ages 4–16. Although it was standardized in the United Kingdom, there do not appear to be any dialectsensitive items in the test, and it has been used by both British and American researchers.

Additional Design Considerations All methods of testing comprehension are intended to provide insight into the knowledge and processes that underlie children’s language comprehension. However, factors other than language also play a role in performance. All tasks make demands on the child’s nonlinguistic abilities, and children vary in these abilities. When testing a clinical population, known or suspected nonlinguistic weaknesses could affect performance. For example, children with SLI may have weaknesses in auditory perception, motor ability, memory, or attention (see Chapter 1 by Schwartz). Although the group may be defined as having SLI by a common profile of diagnostic test scores, these weaknesses may create more heterogeneity in performance than would be found in a young typically developing group. On-line studies that measure reaction time are especially sensitive to these differences. For this reason, within-subject designs are strongly suggested (McKee, 1996). Another caution is to include an adequate number of trials per condition, keeping in mind that more trials than anticipated may be lost in the impaired or the young language-matched group due to immature attention or behavior. Studies for which reaction time (RT) is the dependent variable should include some sort of response baseline. For example, if a button press is used to measure RT, all children could complete a task in which they repeatedly press a button in response to a tone; this provides a baseline measure of motor response to an auditory signal, independent of language processing (Donders, 1969). Each child’s mean baseline RT can be included as a covariate or correction factor in the analysis of RTs, eliminating it as a confound. Working memory and attention measures administered at the time of the experiment may also be informative in interpreting the results. Researchers can also use innovative statistical analyses to better manage some of these issues. Baayen, Davidson, and Bates (2008) argue for the use of mixed-effect models because they allow for simultaneous consideration of factors related to participants and items (e.g., covariates such as IQ or frequency). Mixed models are also more flexible than standard analysis of variance (ANOVA) models in handling missing data. For more insightful analyses of RT data, Balota, Yap, Cortese, and Watson (2008) suggest examination of differences in RT distribution, rather than simply differences in mean RT. Furthermore, the use of logistic regression is argued to be more appropriate than ANOVA for use with categorical data, such as proportion correct for question accuracy (Jaeger, 2008). Mixed-effect modeling with logistic regression again offers the advantages of better data retention as well as the ability to account for random effects of subjects and items in the same analysis.

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Stimuli Many studies of children’s comprehension use visual stimuli as the context for the interpretation of the target linguistic stimulus. Visual stimuli include two-dimensional illustrations, animations, photographs, or videos. The degree to which children understand a given linguistic stimulus can be measured in their response to this visual stimulus, whether that response is pointing, looking, or a button press. Since the comparison will be between the response to the visual target and to one or more distractors, it is critical that they differ from each other minimally, so that one is not chosen on the basis of visual interest alone. When using pictures as the visual stimuli, it may be necessary to use a professional illustrator to achieve consistency and quality. Cautions about balancing the content of target and distractor also apply to videos, as detailed by Hirsh-Pasek and Golinkoff (1996). Depending on the type of linguistic stimuli being tested, one could also use a commercially available set of pictures, such as Snodgrass and Vanderwart (1980). This set includes 260 pictures of common nouns that were standardized for adults for a variety of properties, including name agreement, familiarity, and visual complexity. They have also been normed for 5- to 7-year-old children for name agreement, familiarity, and complexity (Cycowicz, Friedman, & Rothstein, 1997). It should be noted, however, that the addition of color to this set of drawings has been shown to significantly improve naming accuracy while reducing response time in adults (Rossion & Pourtois, 2004). This revised set and numerous other normed stimulus sets are available on-line (http://www.cogsci.nl/). In constructing the linguistic stimuli for sentence-comprehension tasks, it is important to control for the real-world plausibility of the sentences in each condition since young children may base their response on plausibility instead of linguistic structure. One should also take into consideration the frequency of the words used. Word frequency provides an indirect index of word familiarity and, consequently, of the ease of lexical access in auditory comprehension. If comprehension of a particular sentence structure is of interest, controlling for the familiarity of individual words across items can help ensure consistency in the difficulty of the sentences and allow better isolation of syntax as the variable of interest. If receptive processing of individual words is the focus of the study, then, clearly, frequency must be controlled, as it is the main determinant of speed of lexical access. Word frequencies for young school-aged children can be obtained through the corpus of Moe, Hopkins, and Rush (1982). The latter corpus was combined with another unpublished child corpus to create an on-line calculator that can be searched for word frequencies, along with other word characteristics such as neighborhood density and phonotactic probability (Storkel & Hoover, 2010). For spoken word frequencies for older school-aged children, see Carroll, Davies, and Richman (1971). Another way to ensure familiarity to young children is to draw vocabulary from an early expressive vocabulary checklist such as Rescorla (1989) or the MacArthur–Bates Communicative Development Inventory (CDI; Fenson et al., 1993). The linguistic stimuli in comprehension tasks should be recorded for presentation, rather than presented live-voice. This guarantees that presentation across children and across clinical groups does not vary in speed or intonation in a way that would differentially affect comprehension. Obviously, even young children are comfortable with viewing pictures on a computer screen and responding to linguistic stimuli presented over the computer’s speakers. A final consideration in most comprehension tasks is the control of context. If context is not provided or controlled, an otherwise well-formed sentence might seem unnatural or infelicitous. Furthermore, without adequate context, a child may be confused or make assumptions that could affect his or her responses in ways not anticipated by the researcher. Although children have some ability to use context in interpreting sentences, they may not be as proficient as adults at accommodation in the absence of context (Crain & Thornton, 1998).

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General Procedures As with any experimental task with children, careful piloting with the intended age group and population is very important. Pilot performance can reveal whether the task is too difficult, too long, does not hold children’s attention, or has any number of unforeseen problems. This is particularly important for on-line tasks that have not often been used with children. Along the same lines, the child should be given adequate opportunity for practice before the target items are presented. Given the smaller number of children included in studies of clinical populations, it is important that data are not lost because the child did not understand the task. Finally, the experimenter should be aware of any possibility for unconsciously cueing the child about an answer. The experimenter should continue to look down or at the child’s face, not at the visual stimuli so as to not unconsciously cue the child.

Off-line Methods The methods reviewed in the following sections have been designed primarily to explore sentencelevel syntactic and semantic knowledge in children who are learning their first language(s). Where appropriate, examples will be given showing how the technique could be used to test comprehension of individual words or discourses.

Picture Selection The picture selection task is the most widely used type of comprehension test. In this task, the child is simply presented with two or more pictures or scenes and is asked to choose the one that illustrates the meaning of a word or sentence. The nontarget options in the array (called distractors or foils) are chosen to provide alternatives to the correct answer that could plausibly tempt the child; they can also provide information about the nature of the child’s grammar when he or she answers incorrectly. This task is attractive as a test of comprehension in children because it is simple to administer; it can be done relatively quickly and does not require special facilities other than a quiet room. Also, because it does not require a complex response from the child, such as formulating a verbal response or acting out the meaning of the sentence, it can be used with very young children and with a variety of clinical groups. This wide applicability makes it useful in comparing performance across ages and groups (Blockberger & Johnston, 2003; Gerken & McIntosh, 1993; van der Lely, 1996). This selection task has also been referred to as sentence-picture matching or sentence-toscene matching and can be implemented with a variety of stimulus types, including scenes acted out with toys and/or animated scenes. The picture selection task has been used to test children’s comprehension at many levels of linguistic structure. To test for comprehension of phonological distinctions, Velleman (1988) had preschoolers respond to minimal pairs (fox/box) by pointing to the corresponding picture. To test the word-learning abilities of children with SLI and typical language development, Oetting, Rice, and Swank (1995) showed children videos with a narration using the novel words; they used a picture selection task in a follow-up test of comprehension. Deevy and Leonard (2004) investigated the role of processing limitations in the comprehension of wh-questions, varying syntactic complexity and length. Children indicated comprehension of questions (e.g., Who is washing the [happy brown] dog?) by pointing to the correct character in a picture that shows a monkey washing a dog, who in turn washes a rabbit. Picture selection has also been used to test children’s comprehension of passive sentences, relative clauses, pronouns, and reflexives (for a review, see O’Grady, 1997). The picture selection task relies on the ability to depict the meaning of the targeted linguistic structure and on the choice of foils. At least one foil must differ from the test sentence only by

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the grammatical feature tested. For example, for a sentence like The turtle was covered by the frog, the focus of testing could be the child’s comprehension of passive word order or past tense. Each requires a different critical distractor. Testing word order would require a distractor in which the turtle covers the frog; testing tense would require a distractor showing an uncovered turtle about to be covered by a frog. Although it is possible to use one distractor in a picture selection task, two or three is ideal. Assuming that each distractor represents an equally plausible alternative, the level of chance performance is reduced from 50% with one distractor to 25% with three distractors. The drawbacks to this type of task are related to the constraints imposed by using static, twodimensional images. For example, suppose we wanted to ask whether children know the meaning of tense marking on the auxiliary in sentences like she is dancing and she was dancing. There is no direct way to depict the contrast in static pictures. However, Wagner (2001) cleverly tested this concept with a sentence-to-scene matching task in which an event was acted out with toys at different points along a path. This concrete illustration of temporal order could be used by children to indicate their understanding of past, present, and future by pointing to the correct location. In other cases, the event may be too abstract or a construction too complex to profitably test with sets of drawings. In addition, young children may have difficulty interpreting drawings if they are not yet familiar with conventions such as shading to indicate depth or lines to indicate motion (Cocking & McHale, 1981). Although the task demands associated with picture selection are low, performance can still be affected by nonlinguistic factors. Shorr and Dale (1984) showed that children differ in their approach to this task: some respond slowly and only after studying the pictures and others are more impulsive. Obviously, impulsive children may do more poorly when tested with this task, even though they may have the same grammatical knowledge. Recent studies (Leonard, Deevy, Fey, & Bredin-Oja, 2013; Robertson & Joanisse, 2010) have shown that performance on a picture selection task does not necessarily reflect linguistic knowledge alone, especially in younger children or children with language disorders. Leonard et al. tested comprehension of simple transitive sentences for which all groups (preschool-aged SLI, language-, and age-matched TD) had high accuracy. Cognitive load was manipulated through the addition of adjectives and changes to the foils, which increased demands on the child’s ability to retain the details of the sentence, while scanning the pictures to find the target. This added load affected the younger children and children with SLI but not the age controls. Differences in attention, processing speed, and/or inhibitory control could have affected performance. Robertson and Joanisse imposed demands on verbal working memory by increasing the delay between the auditory presentation of the test sentence and the presentation of the picture stimuli. The delay affected accuracy more in school-aged children with SLI and younger controls than in typically developing age matches. Given this evidence, and assuming that the child’s grammatical knowledge, not processing ability, is of interest, it is important to consider the load imposed by the number and type of foils used. Depending on the complexity of the linguistic stimulus and pictures and on the child’s age, four pictures may be too many to reasonably expect the child to carefully consider while remembering the sentence. Also, in presenting the stimuli, the pictures should be presented before the target word or sentence. A procedure in which the sentence is presented before the pictures are seen may place an undue burden on verbal working memory, placing children with language disorders at a disadvantage that is unrelated to linguistic knowledge.

Act-Out In the act-out task, the child demonstrates his or her understanding of a sentence by acting it out with toys. As an off-line method for testing comprehension, the act-out task has a more limited

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applicability than picture selection. It has been used to test sentence-level comprehension of structures in which the referent of a noun phrase or pronoun is of particular interest (for references, see Goodluck, 1996). It has also been used to evaluate children’s knowledge of relative clauses (Hamburger & Crain, 1982; Tavakolian, 1981) and the role of syntactic knowledge in learning verb meanings (Akhtar & Tomasello, 1997; Naigles, Gleitman, & Gleitman, 1993). The act-out task can be illustrated with an example from Tavakolian (1981). She tested preschoolers’ comprehension of a variety of relative clause types including the following: The dog pushed the sheep that jumped over the pig. For each test sentence, children were given three toys (e.g., dog, sheep, and pig) with which to act out their understanding. Typical prompts in act-out tasks include Do what I say; Show me ____; or [puppet name] wants you to ____. In this study, rather than acting out the correct meaning of the relative clause sentence, many children made the dog push the sheep and then jump over the pig. This was consistent with Tavakolian’s claim that young children treat the second verb phrase in this sentence as conjoined with the first rather than modifying the sheep (i.e., The dog pushed the sheep and jumped over the pig). This task is of interest because it is not a forced-choice method; thus, it may reveal that the child has assigned an interpretation not anticipated by the experimenter. The act-out task is inexpensive to assemble and is appealing to children. Although it has been used with children under 3 years old to test for comprehension of fairly simple linguistic stimuli (Akhtar & Tomasello, 1997; Naigles et al., 1993), it is not an ideal task for children this young because they may not be as consistently willing or able to comply with instructions to act out what is heard. Even for older children, the task can be cognitively demanding in that it requires them to assign a syntactic representation to the sentence they heard, extract its meaning, and then plan and carry out a response based on this analysis. Thus, an incorrect response may be the result of the child’s mental recoding of the sentence into a simpler form rather than inadequate grammatical knowledge. Even when the child does respond in an adult fashion, we cannot rule out the possibility that his or her grammar allows some nonadult reading(s) as well; this is because the task only reveals the reading a child chooses to act out. Crain and Thornton (1998) point out that the conclusions that can be drawn about a child’s linguistic competence on the basis of a behavioral test of comprehension are always limited in this way. Response biases can also muddy the waters in interpreting results in the act-out task. For example, the bird-in-the-hand strategy may have been responsible for the response to the relative clause sentence observed by Tavakolian. Children had a tendency to act out the second clause (that jumped over the pig) with the dog as subject because it was already in hand, having just been used in performing the action of the main verb. Goodluck also describes how a bias can develop when the response to particular items tested in a study influences other items in the study. These difficulties should be kept in mind when planning an act-out task. Although the task apparently has not been used with language-disordered groups, there is no obvious reason why it could not. As always, when a method is being used with a new population, it would be best to test structures for which there is some history of interesting and interpretable results from children with typical language development at a similar language level.

Truth-Value Judgments The truth-value judgment task has been used primarily to probe knowledge of sentence-level syntactic and semantic constraints in children with typical language development. In brief, the child watches as an experimenter describes a scenario and acts it out with toys. The child must then decide whether a target sentence is a true or false statement about the scene just witnessed. Crucially, the test sentence can only be judged to be true or false on the basis of specific linguistic

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knowledge; the child must be able to construct a representation of the sentence on the basis of this knowledge and compare its meaning to the facts of the scenario in order to compute a truth value. Although this process sounds complex, it reflects the kind of interpretation in context that a language user does routinely. The task is less demanding than a grammaticality judgment task, which requires the child to think metalinguistically about the sentence, considering its form rather than, and possibly independent of, its meaning (e.g., detecting the error in Whenever she’s in New York, Sally visit the museums). Crain and Thornton (1998) described how this task could be used to test children’s interpretation of pronouns. An example of a target sentence is given in (2) (elements that share the same index are interpreted as referring to the same individual). A grammatical constraint prohibits interpreting He as referring to the Troll (2a); it can only refer to some other character (2b). (2) a. Hei said that the Trolli is the best jumper. b. Hej said that the Trolli is the best jumper. In order to test whether children know this constraint, a scenario is set up in which both of these readings are raised as possibilities, but the grammatical one turns out to be untrue. For example, the scenario for (2) involves a jumping competition with a judge and three competitors (Cookie Monster, Grover, and the Troll). The judge considers the Troll’s performance, admiring his ability, but ultimately deems Grover the best jumper. The Troll states that the decision is unfair, given his superior jumping. Next, the observing puppet attempts to describe the situation, stating the test sentence: “I know one thing that happened. He said that the Troll is the best jumper.” The child must decide whether or not this statement is correct. The sentence is true on the reading (2a) in which he refers to the Troll, but not true on reading (2b) in which he refers to another character. Thus, if children know the grammatical constraint, they will reject the puppet’s statement. The context presented with these sentences is a very important aspect of the method. By explicitly providing options for interpreting the sentence, it can be determined whether the child accepts a reading that was conceptually possible but excluded by the grammatical constraint. The fact that the child must show knowledge of the constraint by rejecting the puppet’s sentence makes it a more conservative test of children’s knowledge, in part because subjects are biased to accept a statement as true. As detailed in Crain and Thornton (1998), there are several advantages to using this task. First, by acting out the scenario with toys, it is possible to depict events that pictures could not show. They also believe that toys are more interesting to children than pictures and thus hold their attention better. The toys also provide an immediate physical record of the participants and events, reducing demands on memory. Task demands are generally reduced because the child must simply understand the scenario and the target sentence and choose a binary response. Ideally, this task can provide an opportunity to isolate and observe the child’s linguistic knowledge, relatively unhindered by immature nonlinguistic abilities. This task has been used to test children with language impairment. Schulz and Wittek (2003) used it to test German-speaking children with SLI (aged 4;3–6;3) for comprehension of aspectual properties of verbs. Katsos, Roqueta, Estevan, and Cummins (2011) used it to explore knowledge of quantification in children with SLI. The fact that the task is designed to minimize nonlinguistic demands makes it ideal for use with this population; in fact, it has been used with typically developing children as young as 2 years old (Crain & McKee, 1985). Unlike a grammaticality judgment, a truth-value judgment does not rely on metalinguistic skills, which may be weak in children with SLI; this judgment is strictly based on interpretation. However, processing deficits could still obscure grammatical knowledge in this task if the scenarios are too long or complex.

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One last comment about this task addresses the nature of the true/false judgment response. Most researchers have the child indicate his or her judgment by rewarding or punishing the puppet who states the target sentence. If the puppet’s statement is good or right, then the child feeds the puppet a cookie; if it is silly or wrong, then the puppet is fed a rag. This makes the task appealing and makes both types of response more likely. Still, when children have only two possible responses, they may have a bias towards one. In some cases, children may be more likely to judge sentences as acceptable, even when they are ungrammatical. Training on the task using simple examples may prevent this or at least alert the experimenter when a child does not consistently base his or her responses on the test sentence.

Intermodal Preferential Looking The development of the intermodal preferential looking paradigm (IPLP) represented a major step forward in testing comprehension in infants and toddlers. In this task, eye gaze is measured as the child views two video monitors (showing either objects or events) and listens to a word or sentence that describes one video but not the other. The total time spent looking at the screen that matches the audio compared to the screen that does not match provides the measure of comprehension. It is considered an off-line measure for our purposes because cumulative looking time indicates the child’s overall preference; thus, it does not provide information about intermediate stages of receptive processing. However, further innovations in this method have provided ways to assess moment-by-moment comprehension (see following discussion). The main advantage of using eye gaze is that it does not require the child to respond overtly; it requires only attention to the stimuli. Also, using video stimuli allows for presentation of events that could not be clearly depicted in static pictures. Golinkoff, Hirsh-Pasek, Cauley, and Gordon (1987) first used the IPLP to test 14- and 18-montholds for comprehension of familiar nouns and word combinations, respectively. They reasoned that if toddlers looked longer at an image of a shoe while hearing shoe than at an image of a boat, then the basis of this preference must be their understanding of the word (assuming that other factors were controlled). Because this study established that comprehension could be measured reliably with the IPLP, others have used it to test hypotheses about word learning in 1- to 2-yearolds (Hollich et al., 2000), comprehension of constituent structure in 14-month-olds (HirshPasek & Golinkoff, 1996), verb argument structure in 2-year-olds (Hirsh-Pasek & Golinkoff, 1996; Naigles, 1990), pronouns and reflexives in 3-year-olds (Hirsh-Pasek, Golinkoff, Hermon, & Kaufman, 1995), and abstract knowledge of word order in 21- and 25-month-olds (Gertner, Fisher, & Eisengart, 2006). A typical IPLP experiment is set up as follows (see Figure 22.1). The child sits in front of two adjacent monitors on a caregiver’s lap. A speaker located between the two screens plays the linguistic stimuli. A light above the speaker is used to attract the child’s attention to the midline. The child’s looks to the two screens and to midline are recorded by a video camera below the speaker. The pattern and timing of the child’s eye gaze are later coded by an experimenter who is blind to the linguistic condition. To guard against cueing, the caregiver cannot see or hear the stimuli. During the experiment, the child is first familiarized to the scenes, viewing them one at a time. Next, the two videos are presented simultaneously with a neutral linguistic stimulus (“What do you see?”). In the analysis phase, the experimenter can use the measurements from this trial to establish whether or not the child had a bias to look at one video more than the other in the absence of linguistic information to guide him or her. During the experimental trials, the child hears the (recorded) linguistic stimulus and then is shown both videos. Typically, for each structure, four to six trials are completed with each child, each testing a different token.

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Figure 22.1 Illustration of the intermodal preferential looking paradigm (IPLP) procedure.

There are several considerations in using this technique. Obviously, more resources must be invested in setting up a laboratory, creating stimuli, and analyzing results than is the case for the previously discussed methods. A limitation of this method is that it can only be used in group designs with infants and toddlers; it has not yet been shown to be statistically reliable in the study of individual performance. Bates (1993) argued that due to the limited number of trials that can be completed with these young children, and given binomial probabilities, performance must be perfect to reach significance (in this case, longer looking times to the correct video on six out of six trials). Although effects are significant at the group level, individual children do not perform with the necessary degree of consistency on the task; children’s bias to look at the target is at 66% on average (four out of six trials). In part because of the potential of this technique to help identify early symptoms of language disorders, there is great interest in improving reliability at the individual level. Current efforts in this direction include increasing the number of trials that can be completed by testing a child in multiple sessions, finding ways to design stimuli that will hold children’s attention across more trials, and finding measures that are more informative than total looking time (e.g., Schafer & Plunkett, 1998, have used longest look to target in an IPLP study of novel word learning). Another strategy that holds promise was tested by Killing and Bishop (2008) in a group of 20- to 24-month-olds. Testing vocabulary comprehension, they found that individual-level reliability could be improved by providing feedback at the end of each trial (by making the image “wobble” back and forth for 500 ms). Children who received feedback demonstrated significantly increased looking to the target once it was named. In addition, correlations between performance and parent-reported vocabulary knowledge were stronger in the feedback group.

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Because the IPLP is designed to minimize task demands for subjects, it could be useful in testing impaired populations. At this point, it is known to be reliable and informative for groups of children 3 years old and younger. For syndromes that can be identified early, it should be possible to use this method to test receptive language abilities in this age range. In fact, IPLP has been used in a study of children with autism spectrum disorder (ASD), aged 2 to 3 years old (Swenson, Kelley, Fein, & Naigles, 2007). These children showed similarities to language matches in comprehension of subject-verb-object word order, although neither group yet produce such utterances. This suggests that, although delayed, the process of language acquisition in the ASD group had similarities to that of the typical group. This method could also have utility in testing comprehension in late talkers. Because not all late talkers go on to show language impairment, this technique might reveal how early comprehension ability contributes to outcome. However, again, to use the IPLP prospectively in this way would require data that are reliable at the individual level. The IPLP has also been used to examine the development of word-learning skills in young children with cochlear implants (Houston, Stewart, Moberly, Hollich, & Miyamoto, 2012; Houston, Ying, Pisoni, & Kirk, 2001) and in children with otitis media with effusion (OME) (Petinou, Schwartz, Gravel, & Raphael, 2001). As this method does not require the ability to follow verbal directions, it is ideal for assessing very early stages of language development in children with a history of hearing loss. For example, Houston et al. (2001) were able to assess the pre-word-learning skills of children implanted earlier (7 to 15 months old) and later (16 to 25 months old). Petinou et al. examined the effects of OME in 26- to 28-month-olds on perception of /s/ and /z/ in phonological and morphophonological contexts. Numerous methodological innovations have extended the scope of the original IPLP. In a move away from measuring implicit comprehension, Noble, Rowland, and Pine (2011) kept many of the elements of IPLP but replaced the looking measure with a pointing response, dubbing this the forced choice pointing paradigm (FCPP). While FCPP is essentially a picture selection task, it evolved within a line of research carried out using IPLP that aimed to determine the degree to which toddlers understand syntax. While acknowledging that the FCPP may represent a return to higher task demands, Noble et al. used it successfully with children aged 2, 3, and 4 years old and cited several advantages. Notably, because the response is more direct, the data are less noisy; the pointing task is easier to administer and score; and it allows testing of a particular structure over a wider age range. Another adaptation of the IPLP, which qualifies as an on-line measure, is the Looking While Listening paradigm (LWL), developed by Fernald and colleagues (Fernald, Zangl, Portillo, & Marchman, 2008). The technical setup is the same as IPLP: while viewing two visual stimuli and listening to an auditory stimulus (word or sentence), the child’s face is video-recorded for later coding of eye gaze. However, the experimental design and analysis of LWL make it possible to time-lock shifts in looking to particular aspects of the verbal stimuli, providing a measurement of reaction time. Fernald, Pinto, Swingley, Weinberg, and McRoberts (1998) examined 15-, 18-, and 24-month-olds’ processing of individual words by analyzing the timing of their shift in eye gaze to a target picture relative to the presentation of the auditory stimulus. In addition to reaction time, it is possible to compare total looking time to the target (i.e., accuracy) in different regions of the sentence such that it can be determined how early, on average, a child responded to information provided in the sentence. For example, Mani and Huettig (2012) had 20-month-olds view an image of two objects (e.g., a cake and a bird) while hearing a sentence with a semantically constraining verb (e.g., The boy eats the big cake.) or a semantically neutral verb (e.g., The boy sees the big cake.). Analyzing frame-by-frame looking in the region of the verb, before the child had heard the object noun, they found significantly more looking to the target in the constraining condition, showing that children used verb semantic information to anticipate its object noun.

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Researchers have started to use LWL with clinical populations, establishing a new method for testing on-line language processing in these groups. Fernald and Marchman (2012) tested 18-month-olds who were classified as typically developing or late talkers. They found that speed and accuracy of processing familiar nouns at 18 months predicted rate of vocabulary growth over the next year. This important paper shows that the measures can provide predictive information at the individual level. Venker, Eernisse, Saffran, and Ellis Weismer (2013) also looked at individual differences in lexical processing, but within a group of 3- to 6-year-old children with ASD. While on average the children showed comprehension of the words tested, there was variability. Looking at individual performance, relationships were found between on-line accuracy and scores on standardized language tests concurrently and retrospectively. Finally, Deevy, Leonard, and Marchman (2013) used LWL to compare preschool children with SLI to typically developing age-matched children in their sensitivity to verb agreement information. Pairs of drawings (e.g., one duck, three flowers) were accompanied by sentences that either did or did not contain an early-appearing number cue to the target (e.g., Where are the nice little flowers? or Look at the nice little flowers.). Typically developing children looked significantly more to the target in the copula region. Children with SLI did not show above-chance looking to the target until the region of the noun. While LWL has been used successfully with clinical populations and older preschool children (3 to 6 years old), it has some practical limitations. The experimental questions that can be asked in this method are limited by the relatively coarse-grained measurement of eye gaze. Eye movements are coded frame-by-frame from video of the child’s face. Using this record, it is possible to tell when a child’s gaze is leaving one picture and when it has arrived on the other because the two are well separated, but it is not possible to reliably distinguish looks to multiple landing sites over a smaller area, as one can do with eye tracking (see the Eye Tracking section). While testing knowledge of a single word is straightforward (the child’s options are the target and any alternative referent), testing sentences is more difficult. To use this method, there must be a specific point in the sentence at which the structure or cue of interest can distinguish between two alternative pictures. The child’s sensitivity to this will be measured by the speed at which he or she orients visually to the target picture. For example, comprehension of grammatically marked gender or number is well-suited to this paradigm.

On-line Methods Lexical Priming Priming is a phenomenon wherein there is “an improvement in performance on a cognitive task, relative to an appropriate baseline, as a function of context or prior experience” (McNamara & Holbrook, 2003, p. 453). In the case of lexical priming, the task of accessing a word in memory is faster when a semantically or phonologically related word has recently been accessed compared to when an unrelated word has been accessed. As an example, a subject may be presented with a series of written words and asked to respond to targets either by saying the word (naming) or by making a lexical decision (Is it a word of English?). Adults respond faster to a target word like nurse when it has been preceded by doctor (semantic prime) or purse (phonological prime) than when it has been preceded by table (unrelated). Lexical priming effects are commonly described within a spreading activation model. In this model, words or concepts are represented by nodes that are organized according to phonological or semantic relatedness. When a word is accessed, the activation level of its node is raised; this activation then spreads to neighboring (related) words. When one of these related words is subsequently accessed, as in the priming task, its retrieval is facilitated by the residual activation. Although lexical priming does not test comprehension of

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word meaning, it allows the experimenter to probe receptive processing of known words (i.e., retrieval) and to make inferences about the nature of stored representations of words and how these are organized. As discussed next, lexical priming can also be used as a tool to explore aspects of sentence comprehension. Priming tasks have been adapted for use with children in a couple of ways. Studies concerned with language processing in younger children use pictures instead of written words as the visual stimuli. Primes and targets may both be presented auditorily (intramodal priming), or the prime word is presented auditorily and a picture is used to elicit the target (cross-modal priming). While older children, like adults, can make a metalinguistic decision about the status of a word (i.e., the lexical decision task), this is difficult for younger children. Instead, researchers have used categorical decisions about some property of the picture stimulus (e.g., Is it alive or not alive?). Lexical priming has been used by researchers to explore the nature of the lexicon in children with language disorders. Nation and Snowling (1999) provide an example of how priming can be used to examine the semantic organization of the lexicon. They tested children who show poor comprehension both in reading and in listening. Prime and target words were presented auditorily; the children (mean age 10.7 years) performed a lexical decision task. In a variation on the normal procedure, children made a lexical decision to all words—primes and targets—rather than just targets. Although this may have reduced the magnitude of the priming effect, it was meant to ensure that children attended to all of the prime words. They manipulated the type of semantic relation between prime–target pairs (category-related vs. functionally related) as well as whether the pairs were associated or not. For example, brother–sister and cow–goat both share a superordinate category, but only the first pair is also associated. Similarly, beach–sand and circus–lion are functionally related, but only the first pair is semantically associated. Priming effects were found for both groups in all conditions with one exception: the poor comprehenders did not show priming in the nonassociated category-related pairs (cow–goat). Functionally related pairs and highly associated category coordinates co-occur in the real world, but the organization of semantic memory around category relations is known to be later developing, requiring the abstraction of semantic properties. This study suggests that poor comprehenders are delayed relative to age matches in their development of the more abstract semantic network. McGregor and Windsor (1996) used a naming task with cross-modal priming in a study of preschoolers with word-finding deficits. Rather than measuring latency to name, they examined accuracy and error patterns. They found that the disordered group benefited from primes, but they benefited less than the typical age-matched group did. Based on models of the adult lexicon, they considered as explanations the possibility that the links among lexical entries were deficient or that the lexical entries were deficient in terms of the richness of semantic detail that was stored. Velez and Schwartz (2010) used priming in a study of lexical organization in school-aged children with SLI as compared to typically developing children. Prime-target pairs were presented in spoken word lists; foils were included in the lists, making the prime-target relationships undetectable. Children were asked to make a categorical decision (“Is it an animal?”) to each word, responding as quickly as possible. Targets were preceded in the list by semantically related primes, phonologically related (rhyme) primes, identical (repetition) primes, or no prime (neutral condition). In order to test the role of processing speed in the effects of spreading activation, the authors also manipulated the timing between prime and target using inter-stimulus intervals (ISIs) of 500 and 1000 milliseconds. All children in the study showed effects of priming in the repetition condition at both ISIs. Only the typically developing children showed semantic and phonological priming, and then only at the 1000-millisecond ISI. Thus, children with SLI did show priming in the course of lexical access when there is complete semantic and phonological overlap (repetition

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condition) between prime and target. However, the spreading activation through semantic and phonological representations of lexical entries does not appear to be robust enough to create priming effects, at least within the time allowed.

Cross-Modal Picture Priming Lexical priming can be done in the context of a single preceding word, as in the studies discussed thus far, or in the context of a sentence. In the first study to use lexical priming during sentence processing, Swinney (1979) explored how and when the meanings of an ambiguous word are accessed during on-line comprehension. This method uses cross-modal stimuli—the sentence is presented auditorily and, in studies with children, the target for priming is presented as a picture. Following from adult studies, this paradigm has been used with children to study the on-line comprehension of long-distance referential dependencies in sentences, including the relationship between a pronoun or reflexive and its antecedent or between an empty argument position and the noun phrase to which it is related. In a study of adults’ processing of pronouns, for example, the subject would listen to a sentence like that in (3). Of interest is how the adult interprets the word him. (3) The skieri said that the doctor for the team would blame himi *SKIER* for the recent injury. antecedent nonantecedent pronoun word prime If syntactic knowledge is used on-line to interpret the pronoun him, then the listener should implicitly refer back to skier upon hearing the pronoun, but not to doctor since doctor is not a syntactically permissible antecedent (although it was more recently heard). In the experiment, the written word skier or doctor would appear on-screen as a visual target for lexical decision immediately after the listener has heard the word him. The finding that skier is primed at this point, but doctor is not, is argued to result from the listener’s automatic activation of the antecedent at the pronoun as part of on-line interpretation. This syntactic activation of the antecedent causes priming and thus a faster response time for lexical decision for the target skier. In addition, while priming for skier occurs immediately after the pronoun, priming does not occur when skier is probed at an earlier point in the sentence. This effect has been found for adults for many types of syntactically mediated referential dependencies. In similar studies of children’s sentence comprehension, this paradigm has been adapted as crossmodal picture priming (CMPP) by replacing written word targets with picture targets. In CMPP studies, children listen to sentences for comprehension (i.e., they are required to answer comprehension questions periodically). At a probe point in the sentence, a picture target is presented on the screen for which the child must make a categorical decision (e.g., Is it or is it not alive? Is it edible or not edible?), pressing one of the two response buttons as fast as possible. Again, this secondary decision task does not depend on reading skills or the ability to make a metalinguistic judgment; consequently, it has proven successful in experiments with typically developing children as young as 4 years old (Love, 2007; McKee, Nicol, & McDaniel, 1993; Roberts, Marinis, Felser, & Clahsen, 2007; Swinney & Prather, 1989). In the McKee et al. study, processing of pronouns and reflexives was investigated in children between ages 4;1 and 6;4. This study sought to determine if children, like adults, use their knowledge of grammar on-line to access potential antecedents in the sentence. If children do access antecedents on-line, they should show priming effects to the target picture (leopard) immediately following himself in (4).

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(4) The alligator knows that the leopard with green eyes is patting himself *leopard* on the head with a soft pillow. antecedent

reflexive picture prime

Like adult controls in the study, the children responded faster to the picture prime leopard in the reflexive condition than in a pronoun or control condition—for example, where himself in (4) is replaced with him or the nurse, for which leopard could not be an antecedent. This shows that they did apply their grammatical knowledge immediately to interpret the reflexive. Studies have used CMPP in studies of school-aged children with language impairment to examine interpretation of empty argument positions in wh-questions and relative clauses. Marinis and van der Lely (2007) investigated the processing of wh-questions in 10- to 17-year-olds with a severe and persistent form of SLI. In this study, children heard a statement followed by a whquestion (5). A picture prime could appear at one of three positions during the wh-question. If children, like adults, use syntactic structure to interpret the question, priming for the referent of who (i.e., the rabbit) is expected only at point [3] (at the object of the preposition), because this is the syntactic location at which who can be interpreted. Typically developing age and comprehension matches, like adults in other studies, showed activation for the prime rabbit at point [3]. Children with SLI showed priming only at point [1], suggesting that they use lexical-thematic rather than syntactic information to interpret these questions. (5) Balloo gives a long carrot to the rabbiti. Whoi did Balloo give the long carrot to ei at the farm? ↑ ↑ ↑ [1] [2] [3] Although it has been used with typically developing children as young as 4 years old, this task is complex, requiring children to do two tasks at the same time: comprehend the sentence and make a timed judgment to the picture prime. For this reason, McKee et al. found highly variable reaction times. Preschoolers with language impairment probably could not do this task, due to the linguistic and nonlinguistic demands. McKee et al.’s study tried to offset the task’s challenges by using extensive piloting, extensive training of the child subjects, careful selection of stimuli, and a relatively large number of trials. In a recent study of relative clause comprehension in school-aged children with SLI, Hestvik, Schwartz, and Tornyova (2010) used CMMP to examine how children with SLI process the relationship between the head of a relative clause and the empty position where it is interpreted. As in the case of CMMP studies of wh-questions, the assumption is that when the listener encounters the empty argument position, there is an immediate attempt to establish its reference. Here, the head of the relative clause (the zebra) (6) is the only possible referent allowed by syntax. If children, like adults, use syntax on-line to interpret this structure, then priming effects for the referent are expected at the gap site but not at an earlier site. (6) The zebrai that the hippo on the hill had kissed ei *zebra* on the nose ran away. The authors took steps to offset the kinds of problems associated with this method. For example, in order to reduce cognitive demands, they had the children name the target picture rather than have them make a categorical decision about it. Another advantage of the naming task was

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that it might provide a more direct measure of lexical priming and lead to larger effects, an important advantage given the potential for small effects and large variability. Care was taken to include an adequate number of trials, given the possible loss of trials due to incorrect naming, incorrect response to the comprehension questions, inherently problematic items, and technical difficulties. Although the effects were not as robust as hoped, the typically developing children did show significantly faster naming times at the gap than at an earlier control position; children with SLI showed the reverse pattern. Still, the two groups did not differ in the pattern and level of accuracy on the comprehension questions, suggesting that children with SLI resolve the interpretation of the gap but do so more slowly than typically developing children. This study illustrates how methods initially used to study adult sentence processing continue to be adapted for more effective use in clinical populations. It also shows how on-line and off-line results can be compared in order to shed light on sources of comprehension difficulty.

Word Monitoring Tyler and Marslen-Wilson (1981) was the first study that used the word-monitoring task to test on-line comprehension processes in children. More recently, it has been used by Montgomery and colleagues to study language processing in children with SLI (Montgomery, 2000; Montgomery, Scudder, & Moore, 1990; Stark & Montgomery, 1995). As in the CMPP task, children are required to listen to a sentence for comprehension and perform a secondary task. Before they hear the sentence, they are given a target word and instructed that when they hear it in the sentence, they must respond as quickly as possible by pressing a button. The speed of response to the target reflects the ease with which it could be processed; if it follows some sort of violation (e.g., phonological, morphological), then response time is longer. The degree to which response is slowed relative to control conditions indicates whether the violation was noticed and to what extent the correct information was “necessary for the listener to develop the appropriate representation” of the unfolding sentence (Tyler, 1992, p. 6). A study by Montgomery and Leonard (1998) illustrates the use of this method. They compared sensitivity to grammatical morphemes of low phonetic substance (third singular -s, past tense -ed) and high phonetic substance (progressive -ing) in a group of children with SLI (mean age 8;6), a group of typically developing children matching in age, and younger comprehension matches (mean age 6;8). The children monitored sentences for familiar nouns; the position of the target noun was systematically varied so as to prevent subjects from predicting when it would appear. Preceding the target nouns—italicized in (7)—were verbs that either included or incorrectly omitted the grammatical inflection of interest. The typically developing groups showed sensitivity to all morphemes by responding more slowly after incorrect than after correct forms. The children with SLI showed this pattern only for -ing, indicating a lack of sensitivity to the morphemes of low phonetic substance. (7) a. Everyday he races home and eats cookies before dinner. b. She always gets up early and eat_ breakfast before she watches cartoons. The cautions mentioned above for the CMPP task also apply to the word-monitoring task. In all cases, this task has been used with school-aged children with language impairment. The youngest typical children were 5 years old. More variable reaction times and an inability to consistently perform the secondary task make this a challenging method to use with preschool languageimpaired children. In addition, the task is considered an indirect measure of receptive processing, for which the underlying source(s) of the effects may be hard to determine. Trueswell, Sekerina,

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Hill, and Logrip (1999) used the findings in Tyler and Marslen-Wilson’s (1981) study to illustrate this point. In that study, children showed increasingly longer RTs to a probe word in three kinds of sentences: a well-formed sentence, a syntactically sound but semantically anomalous one, and one that was both syntactically and semantically anomalous. Tyler and Marslen-Wilson interpreted this as supporting the view that children, like adults, use linguistic and nonlinguistic information to build a representation of meaning on-line; when these sources of information are not available, processing is disrupted. Trueswell et al. (1999) pointed out that rather than reflecting disruption of presumed syntactic and semantic processes, the effect could reflect children’s (and adults’) sensitivity to distributional information in language; as words become less predictable, RT to the probe word is slowed.

Self-Paced Listening Self-paced listening has been used to study sentence comprehension in school-aged children with typical language development (Booth, MacWhinney, & Harasaki, 2000; Felser, Marinis, & Clahsen, 2003) and in children with SLI (Marinis & Saddy, 2013). In a self-paced listening task, sentences are partitioned into segments containing a whole phrase or an individual word. Subjects listen to these sentences through headphones and press a computer key to advance the audio segment by segment. The computer records the time spent listening to each segment. The sentence stimuli are designed so that a critical region can be compared across sentence conditions; relative slowing in a region in a given condition is interpreted as processing difficulty. Comprehension questions are presented after all experimental sentences for two reasons. Having to answer questions about the sentences helps ensure that research subjects are processing their meaning. Also, it allows the researcher to remove from the analysis trials on which the subject did not accurately understand the sentence. Both Booth et al. (2000) and Felser et al. (2003) were interested in testing children’s processing of relative clauses, a later-developing complex structure. Felser et al. tested sentences like that in (8), asking whether 6- to 7½-year-old children preferred to interpret the relative clause as a modifier of nurse or pupil. The researchers manipulated verb agreement (who was. . .; who were. . .) in order to determine whether children preferred to associate the relative clause with one of the nouns as compared to the other. For example, if children automatically associated the relative clause to the first noun (in this case, a singular noun, nurse), the who were continuation should lead to disruption and relatively slower RTs in the segment containing the agreeing verb compared to the who was continuation. In addition to analysis of listening times to critical regions, the authors assessed accuracy of answers to comprehension questions (e.g., who was feeling tired?) and the relationship between memory span and interpretation preferences. (8) The doctor recognized the nurse of the pupils who was feeling very tired. In this, as in other on-line comprehension methods, trials are not included in the analysis of RT if the comprehension question is not answered correctly. Given complex sentences like those tested here, this can mean exclusion of many trials. Furthermore, when accuracy is low or close to chance, it is unlikely that the “correct” responses indicate comprehension. This is an issue that these authors raise and one that should be kept in mind in attempting to use it with languageimpaired children. Marinis and Saddy (2013) used self-paced listening to test comprehension of passive sentences in children with SLI. This method allowed them to test predictions of theories of SLI by pinpointing where in the sentence and under what conditions children with SLI slow down relative to

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comparison groups of typically developing children. The patterns of listening time, along with an off-line task testing comprehension of the passive, were used together to rule out a grammatical deficit explanation of the difficulties of children with SLI. Instead, the results were interpreted as supporting an explanation in terms of working memory.

Monitoring Eye Movements There has been increased use of eye tracking in recent years to study language processing in children with typical language development and children with language disorders. Newer headmounted and remote systems can track children’s eye movements in response to language in real time, as they view and interact with pictures, objects, and/or an interlocutor. Use of this visual world paradigm with adults has shown that eye movements are tightly time-locked to speech input; adults look immediately to objects as they are mentioned (Tanenhaus, Spivey-Knowlton, Eberhard, & Sedivy, 1995). The first published study of children’s comprehension using eye tracking in the visual world paradigm was conducted by Trueswell et al. (1999). While these authors used a head-mounted eye-tracker (Figure 22.2), many recent studies have used a remote eye-tracking system. The two systems share the same basic technology: one or both eyes is illuminated with a beam of infrared light; the cornea’s reflection of this light is recorded by an infrared-sensitive camera. The orientation of the eye can be derived from the position of the reflection relative to the position of the pupil. In the head-mounted system, this camera is on a visor worn by the child; in the remote system, it is below the computer screen that the child is viewing. A computer analyzing the ongoing

Figure 22.2 Illustration of the head-mounted eye-tracker and an example scene from the study by Trueswell et al. (1999).

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eye movement information plots the eye position and superimposes it onto the image of the scene. The two systems differ in the source of the scene image. For the head-mounted system, the scene is recorded in real time by a second camera worn by the child; for the remote system, the scene is presented on a computer screen and is thus fixed. In both systems, the video record of the child’s eye movements across the scene, along with the spoken stimuli, provide the raw data for analysis. Analysis is done on the proportion of fixations to particular objects in the scene as a function of the timing of the spoken stimulus. Trueswell and colleagues were interested in typically developing children’s sentence processing generally and, in particular, in their ability to use context on-line to interpret ambiguous sentences. They constructed four scenes with toys including (a) a frog sitting on a napkin, (b) an empty napkin, (c) an empty box, and (d) either a horse (neutral context) or another frog (informative context) (see Figure 22.2). The children (aged 4;8–5;10) heard instructions such as, Put the frog on the napkin in the box. This sentence was temporarily ambiguous; the prepositional phrase (PP) on the napkin could refer to a destination for the frog or the current location of the frog. The presence of a second frog in context affects interpretation; it introduces the possibility that the PP could serve to distinguish between the two by specifying location (i.e., the one on the napkin, not the other one). Eye movements during the words on the napkin show the listener’s immediate interpretation of the phrase. In the neutral context (no second frog), both children and adults were more likely to look to the empty napkin, indicating an immediate preference for the destination reading of the PP. However, when there was a second frog in the context, adults looked at the frogs, not the napkin. Here they interpreted the PP as a location indicating which frog. Children, however, still looked at the empty napkin indicating that they held fast to the destination meaning. Children’s eye movements to the objects in the scene showed that they did not consider context in interpreting the PP; in contrast, adults’ eye movements showed that they immediately took context into account in their interpretation. In this study, eye movements provided valuable information about the type and the timing of alternative interpretations considered. Since the initial study by Trueswell et al., researchers have used eye tracking to explore aspects of linguistic competence that could not be tested using other methods, including the comprehension of pronouns and reflexives (Bergmann, Paulus, & Fikkert, 2012; Sekerina, Stromswold, & Hestvik, 2004), focus markers (Hohle, Berger, Müller, Schmitz, & Weissenborn, 2009), and the interpretation of contrastive pitch (Ito, Bibyk, Wagner, & Speer, 2014). Other studies have examined children’s developing processing abilities, including spoken word recognition (Sekerina & Brooks, 2007), the ability to recover from a garden path (Choi & Trueswell, 2010; Kidd, Stewart, & Serratrice, 2011), use of prosodic cues to resolve syntactic or pragmatic ambiguity (Zhou, Su, Crain, Gao, & Zhan, 2012), as well as use of verbal (Sandgren, Andersson, van de Weijer, Hansson, & Sahlén, 2012) and nonverbal (Nappa, Wessel, McEldoon, Gleitman, & Trueswell, 2009) cues to track meaning in context. These studies have sometimes found children’s competence to be greater than previously suspected, but they have also found limitations in processing ability related to still-maturing cognitive systems. These themes are also apparent in the growing literature on the use of eye tracking with clinical populations. McMurray, Samelson, Lee, and Tomblin (2010) used the visual world paradigm to study spoken word recognition in four groups of adolescents categorized by their combined language (normal or impaired) and cognitive (normal or impaired) abilities. Viewing sets of four images on a computer screen, participants heard a word and mouse-clicked on the target image (e.g., candle). Two of the competitors were related to the target: sharing its onset (e.g., candy) or its offset (e.g., handle). The remaining competitor was unrelated (e.g., button). Because eye tracking provided a record of the fixation on these options over time, it allowed for a detailed and revealing analysis of lexical access processes in these children. While the four groups were similar in the timing and nature of their

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initial looking pattern, the two language-impaired groups diverged in the latter part of the trial. Specifically, they looked less to the target and more to the two related competitors than the other two groups, suggesting similarities in speed of lexical access but differences in rate of lexical decay. Other eye-tracking studies of clinical populations have found a similar general result: no group differences in the initial response to the target but differences in looking in the latter part of the trial and/or increased looking to nontarget images. This is illustrated in three sentence comprehension studies—two tested children with language impairment and one tested children with ASD. All three used the eye-tracking paradigm of Altmann and Kamide (1999), which was designed to explore whether listeners use semantic information to anticipate or predict possible objects of the verb. In this paradigm, participants view a set of objects (e.g., a cake, a boy, a toy car, a train set) and hear one of two kinds of sentences (9). (9). a. The boy will eat the cake. b. The boy will move the cake. For (9.a), the verb’s semantics constrain the choice of possible objects to cake, while for (9.b), all are potential objects of the verb. Accordingly, Altmann and Kamide found eye movements at the verb were significantly greater to the target (cake) in the eat condition than in the move condition. Thus, for adults, skilled comprehension appears to include the ability to use knowledge of verb semantics together with context to anticipate or predict the object of the verb. In the first reported study of this kind, Nation, Marshall, and Altmann (2003) tested auditory language processing abilities of children (10 to 11 years old) whose reading comprehension was poor (and who also had significantly weaker spoken language abilities). Like the typically developing control group, these children showed anticipatory looks to the target in the constraining (eat) but not in the neutral (move) condition. These authors also reported that the impaired group made a greater number of eye movements than the control group, and their fixations were of shorter duration. Nation et al. pointed to two possible sources of the difference: the impaired group’s (independently measured) weaknesses in verbal memory and inhibition. Weaker memory skills could have necessitated more frequent eye movements to refresh memory traces while inability to consistently suppress irrelevant information could have led to more eye movements to the distractor pictures. More recently, a study of Spanish-speaking children with SLI (Andreu, Sanz-Torrent, & Trueswell, 2013) used Altmann and Kamide’s paradigm, comparing 5- to 8-year-old Spanish-speaking children with SLI to MLU-matched and age-matched controls. All groups reliably showed anticipatory looks to the target, even when the target was an atypical instance of the object (e.g., eat broccoli) rather than a typical one (e.g., eat cake). While the pattern and timing of looking was not different for the two groups, the overall probability of looking to the target was lower for the children with SLI than for age matches; in addition, they never reached the same level of asymptote performance. Performance later in the trial is not analyzed quantitatively, so it is not clear whether the higher probability of looks to distractors observed later in the trial was related to lower probability of looks to the target. However, as the authors point out, this pattern is reminiscent of the McMurray et al. study, showing again results in which children with SLI are similar to controls in their early response but different in later processing. Brock, Norbury, Einay, and Nation (2008) used a variation on Altmann and Kamide’s paradigm to test the ability of adolescents with autism to integrate phonological and semantic information in their on-line sentence processing. Integration of information from different sources to create a coherent whole is thought to be problematic for children with ASD. In this design, an image of a phonological competitor was introduced to the set in (10) (e.g., hamster, hammer, medal, medicine).

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Adults are more likely to look at the phonological competitor than the other competitors, but this can be reduced if given constraining verb information (10.a). An even more stringent test of the ability to integrate semantic and phonological information is one in which the target is not present in the array (target-absent condition: coffee, hammer, medal, medicine). In this case, use of verb semantic information could result in fewer looks to the competitor, as it would be a semantically impossible object of the verb. (10) a. Joe stroked the hamster. b. Sam chose the hamster. The study used a control group of same-age peers that included both children with typical language development and children with language impairment, to allow examination of the role of language ability in performance. Brock et al. found no differences in response between children with autism and the control group, with both groups looking to the target early in the presence of constraining verb information. In the target-absent condition, again, constraining information played a role for both groups. However, a difference was found in this condition related to language ability. Language scores were related to proportion of fixations to the competitor such that children with lower scores, regardless of autism status, spent more time looking at the phonological competitor. Given the consistent finding that children with language disorders show differences in their looking patterns that are not related to the language manipulation, studies like that of Kelly, Walker, and Norbury (2013) take on particular importance. These investigators noted that while the use of eye tracking to study cognitive and social abilities of children with ASD continues to increase, evidence about the integrity of basic oculomotor control in this population is mixed. Obviously this can be a problem for interpreting between-group differences in eye movements in response to, for example, processing facial expressions or a social scene. Suspecting that the mixed results in previous studies of ASD might be in part due to the heterogeneity of the groups studied, Kelly et al. tested four groups: (1) children with ASD with normal language (ALN) or (2) with LI (ALI); (3) children with LI only; and (4) typically developing children. Eye movements were tracked as children performed looking tasks that required either reflexive looking to a target (prosaccade task), more volitionally controlled looking away from the target (anti-saccade task), or looking and maintaining fixation to a target presented among distractors (search distractor task). No group differences were found in the prosaccade task, indicating intact reflexive looking for all groups. On the anti-saccade task, the two language-impaired groups (ALI and LI) showed a higher rate of incorrect looks to the target. On the target distractor task, speed and accuracy of looks to the target were not different between the two groups, but the LI children were less able to maintain fixation in the presence of distractors. In sum, tasks requiring volitional control, in the form of suppressing reflexive looks to nontargets, were characteristic of the two groups exhibiting LI, and not the group with ASD only. These interesting results leave many open questions about what causal connection, if any, exists between language and oculomotor control in these tasks (although the authors suggest a possible role for verbal mediation in executive control). The important point for the use of eye tracking as a method to study language comprehension is the need to establish more clearly the source of differences in looking behavior in our clinical populations. As demonstrated by these studies, one of the great advantages of eye tracking is that it can tap into immediate and unconscious responses to language input without introducing confounds related to task demands, memory, and attention. In this way, it can provide a clearer picture of language processing abilities than had previously been found with other methods. In addition, as

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Farris-Trimble and McMurray (2013) demonstrate for typical adults, eye tracking has the potential to provide a set of fine-grained measures of response to language that are reliable at both the individual and group level. Although more needs to be known about the relationship between eye movements and coordination of attention and memory in children, this method clearly holds promise as a method for studying language processing in language-impaired populations.

Neuroimaging Methods In addition to the behavioral methods discussed in the preceding sections, there is increasing interest in the use of electrophysiological (EEG and MEG) and hemodynamic (fMRI and PET) methods to study language organization and processing in children in typical and atypical populations. These methods are discussed in depth in Chapter 24 of this volume. Of the two electrophysiological measures, EEG (electroencephalography) has proven to be a better match for use with children than MEG (magnetoencephalography), primarily for technical reasons (see Phillips, 2005, for discussion). The focus here will thus be on the use of EEG to study receptive processing of words and sentences in children. Electroencephalography (EEG) tracks changes in voltage associated with neural activity. When the EEG is averaged over many related trials, a waveform emerges that is time-locked to stimulus presentation; this measure is called the event-related potential (ERP). ERP provides tentative information about localization and lateralization of language function, however, the temporal resolution of the ERP is excellent, providing timing information in milliseconds. As described in the introduction to this chapter, comprehension is fast, automatic, incremental, and involves a multileveled analysis of the input. The ERP waveform varies in response to stimuli in polarity, latency, amplitude, and distribution over the scalp, providing a rich and sensitive measure that is highly suitable to investigating the processes that underlie language comprehension. Because ERPs can be recorded during passive listening, the brain’s automatic response to language can be observed without the demands of an explicit task, avoiding some of the problems of behavioral methods and making it attractive for testing children and impaired populations. Still, many ERP studies of language comprehension in school-aged children and adults require some sort of behavioral response to the stimuli, typically a judgment or an answer to a comprehension question. As with other on-line measures, the behavioral task can engage the child and allow filtering of trials. Behavioral tasks are not always used as they may induce movement artifacts in children (e.g., pressing a button) and may influence the neural response (Hahne & Friederici, 2002). Research using ERPs with adults has established signature waveforms that are associated with specific aspects of language processing. They are observed by comparing the response to sentences containing a well-formedness violation to the response to sentences without that violation. Generally speaking, semantic violations elicit the N400 while syntactic violations elicit the eLAN (early left anterior negativity) and/or the P600. These ERP components have provided the starting point for investigations of language processing in children. The expectation is that these same responses (or ones that are recognizably related) will also be found in children. In what follows, we review the use of ERP in studies of semantic processing (N400) and syntactic processing (LAN/P600) in typically developing children and children with language disorders. The N400, an index of semantic processing, is a negative voltage shift that reaches its peak amplitude at about 400 milliseconds post-stimulus. It was first observed in response to a semantically incongruous continuation of a sentence (e.g., He spread the warm bread with socks), but its amplitude and distribution can be modulated by priming and degree of incongruity (see Coch, Maron, Wolf, & Holcomb, 2002, for a review). Thus, “its amplitude reflects the effort for integrating a potentially meaningful stimulus into the current semantic context” (Friedrich & Friederici, 2006,

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p. 1). Studies with typically developing children have demonstrated an adult-like N400 response to semantic anomaly in sentences in children ranging in age from 5 to 15 years old. While these studies have found differences in the latency compared to adults (Hahne, Eckstein, & Frederici, 2004) or both amplitude and latency (Holcomb, Coffey, & Neville, 1992), these differences decreased with age. Atchley et al. (2006) also found that the N400 in the children they tested (aged 8–13) had increased amplitude and latency relative to adults in the study; however, they also found differences in distribution. ERPs have provided a very sensitive measure of infants’ and toddlers’ knowledge and processing of words. Several studies have used lexical priming to explore very early word learning and comprehension and to relate neural response to behaviorally measured language ability. To illustrate, Friedrich and Friederici (2005) used a cross-modal priming paradigm to elicit the N400 in 14-month-olds. They presented pictures of known words along with spoken words that were either congruent (matching) or incongruent (mismatching). The difference in congruence resulted in a difference in N400 amplitude, indicating that integration of the word with semantic (visual) context was more difficult in the incongruent condition. While 12-month-olds did not show a difference in N400 response in this study, a more recent study did find the effect, but only in the 12-month-olds categorized as having “high early word production,” not those with low vocabulary. Other findings using this paradigm have revealed organization within the developing lexicon (Rämä, Sirri, & Serres, 2013) and evidence of a relationship between vocabulary level and novel word-learning ability (von Koss et al., 2008). Studies of children with SLI and with ASD have used the cross-modal priming paradigm to examine semantic integration ability. Cummings and Čeponiene (2010) tested 7- to 15-year-olds with SLI and McCleery et al. (2010) tested 4- to 7-year-olds with ASD. In these studies, a picture (e.g., ball) was followed by either a congruent or incongruent word (e.g., ball, car) or a congruent or incongruent environmental sound (e.g., a ball bouncing, a car engine starting). Both studies found that incongruence in the nonverbal condition elicited the expected N400 in both typical and atypical groups. However, differences between groups were seen in the verbal condition, suggesting that problems of semantic integration in these groups are specific to language processing. Neville, Coffey, Holcomb, and Tallal (1993) were the first to use ERPs to study receptive processing in children with language disorders. Comparing 8- to 10-year-old typical and language-disordered children, they measured ERPs to open and closed class words and to semantically anomalous sentence-final words. Whereas typical children and adults show a larger N400 over the left hemisphere for closed class words, a subset of the SLI children who had particularly poor grammatical skills showed a symmetrical response. Also, the N400 to semantic anomaly was larger and significantly later in the SLI group. Because the children were also poor readers, the increased difficulty in lexical integration, which was indexed by the larger N400, may be partially due to reading ability. More recent studies that have used the N400 to examine semantic processing in the context of sentence level integration of words have had diverging results. Fonteneau and van der Lely (2008) found that a group of 10- to 21-year-olds with Grammatical SLI (G-SLI) and typically developing age controls showed a similar pattern of N400 response. However, in other studies the SLI group did not respond like the control group. For example, Sabisch, Hahne, Glass, von Suchodoletz, and Friederici (2006) had 10-year-old children with SLI listen to sentences in which the verb’s object was or was not semantically anomalous. While the typically developing group showed the expected difference, the children with SLI did not differ in the N400 response to the two conditions. The authors note that a higher amplitude N400 to both the anomalous and the non-anomalous condition was responsible for the lack of difference and that this may reflect a general difficulty in processing verb information, including semantic restrictions on possible objects (see also Popescu, Fey, Lewine, Finestack, & Popescu, 2009 for a similar finding).

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Turning to syntactic processing, two ERP components have been observed in response to syntactic anomaly in adults. The early left anterior negativity (eLAN) is lateralized to the left and peaks relatively early (100–500 milliseconds after violation). The eLAN has been elicited by phrase structure violations and morphosyntactic violations. Related to this is a left anterior negativity seen during the processing of syntactic long-distance dependencies (e.g., wh-questions). Accordingly, these anterior negativities have been interpreted as indexing the initial stages of syntactic structure-building and the working memory load associated with holding an unresolved syntactic dependency. The P600 is a positive voltage shift that reaches its peak latency at around 600 milliseconds post-stimulus. The P600 has primarily been demonstrated in response to anomaly, including ungrammaticality (e.g., The spoiled child are throwing the toy on the ground.) and unexpected continuations (e.g., The stockbroker persuaded to sell the stock called his lawyer is less expected than persuaded him to sell. . .). Hence, it is believed to index later-stage syntactic processing (re-analysis) or syntactic integration. The precise nature of each of these components remains controversial and is the subject of ongoing research; however, there is a great deal of interest in the potential of these measures to reveal new insights into child language disorders. Studies have sought to determine how typically developing children compare to adults in these measures of syntactic processing and to explore developmental change in the parameters. Researchers testing 7- to 8-year-olds (Friederici & Hahne, 2001) and 6- to 13-year-olds (Hahne et al., 2004) recorded ERPs while the children made grammaticality judgments to sentences. While adults’ response to anomaly was an early left anterior negativity (eLAN) followed by a P600, only the oldest children (13-year-olds) showed this biphasic pattern. The eLAN was not found in 6-year-olds and was not identifiably adult-like in 7- to 10-year-olds. The P600 peaked later and lasted longer in the youngest children; latency decreased with age. In the study of Atchley et al. (2006), 8- to 13-year-olds showed essentially the same response to ungrammaticality as the adults in that study: a P600 with the same latency, amplitude, and distribution. Studies have also investigated the eLAN/ P600 pattern in children in the 2- to 4-year-old range (Oberecker, Friedrich, & Friederici, 2005; Oberecker & Friederici, 2006; Silva-Pereyra, Klarman, Jo-Fu, & Kuhl, 2005, b). Published studies have begun to appear that report on the neural correlates of syntactic processing in children with SLI. Several of these studies are briefly summarized to highlight the kinds of questions that have been addressed with this method. In general, they seek to characterize processing differences between typically developing children and children with SLI and to address specific predictions of theories about SLI. For example, Fontenau and van der Lely (2008) sought support for the existence of a subgroup of children with SLI whose disorder affects syntactic but not semantic aspects of language (G-SLI). Recall that they found no differences between the G-SLI group and age controls in response to semantic anomaly (N400); however, they did find that controls showed an early left anterior negativity to a syntactic violation, but the children with G-SLI did not. The pattern of results is interpreted as evidence for this SLI subgroup. Purdy, Leonard, Weber-Fox, and Kaganovich (2014) used ERPs to test predictions of a theory about tense and agreement deficits in preschool children with SLI. According to this proposal, these deficits in young children are tied to their failure to grasp syntactic constraints on finiteness. If true, older (school-aged) children with a history of these deficits (H-SLI) should show insensitivity to agreement errors like boy talks, in the embedded clause in (11.b); here, finiteness constraints are entailed by the larger syntactic context. Typically developing children were expected to show sensitivity to both errors, since they recognize both local constraints on finiteness marking (11.a) as well as the long-distance constraint imposed in (11.b). (11) a. Every night they talk/*talks on the phone. b. He makes the quiet boy talk/*talks a little louder.

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As predicted, these authors found both groups to be sensitive to the error in (11.a). For sentences of the type in (11.b), the typically developing group showed a robust P600, while the P600 of the H-SLI group was delayed, reduced in amplitude, and shorter in duration compared to that of the typically developing group. In this study, as in others, the detailed information about timing and type of response that is afforded by ERP measures made it possible to detect subtle differences in processing that remain, even as related symptoms have resolved. The relationship between language deficits and nonlinguistic processing deficits is one of the most important in current research on SLI. ERPs are an ideal methodology to investigate these questions because both language processing and nonlinguistic aspects of cognitive processing are associated with distinct waveforms. Weber-Fox, Leonard, Hampton Wray, and Tomblin (2010) investigated two areas of weakness in children with SLI for which a causal relationship has been proposed: rapid auditory processing of nonlinguistic stimuli and morphosyntactic processing. Children with SLI showed differences from controls in both types of task. While some correlations between auditory processing and morphosyntactic processing were significant, the authors concluded that auditory processing could account for only a small amount of variance in the morphosyntactic processing of only some of the participants. Epstein, Hestvik, Shafer, and Schwartz (2013) also used ERPs to investigate whether an aspect of language processing in children with SLI (comprehension of object wh-questions) could be directly related to a more general cognitive capacity (working memory). The LAN has been associated with working memory load during sentence processing in adults. The authors thus tested the processing of wh-questions in children with SLI, using the LAN as an index of the working memory load incurred during the processing of this long-distance dependency. Adults showed the expected (although non-significant) LAN to object vs. subject questions. Unexpectedly, both the SLI and typically developing groups showed a sustained anterior positivity to object vs. subject questions, but this difference was significant for the typically developing group only. This difference is interpreted as suggesting that children with SLI are less efficient at maintaining syntactic information in working memory in the object condition. ERPs will no doubt contribute greatly to the understanding of receptive processing in language-disordered children, but certain obstacles to this research must be overcome, as detailed in Phillips (2005). Among these is the need for continued basic research with unimpaired children in order to reliably establish ERP profiles for typically developing children. A second is the need for detailed models of the temporal characteristics of processing in children with language disorders from which testable hypotheses can be derived; without these, it is difficult to interpret the amount of detailed data generated by the method. A third is the need for better signal-to-noise ratios. In order to get reliable measurements, many trials must be completed. The time required to complete the experiment may be prohibitive for very young or disordered populations, especially when presenting sentence-length stimuli.

Conclusion This chapter describes the methods available for testing general comprehension of words and sentences in children and for testing the nature of real-time receptive processing of known words and structures. The list encompasses basic tried-and-true techniques that are common in research and in clinical practice (e.g., picture selection and act-out) as well as newer and more sophisticated techniques (e.g., eye tracking and neuroimaging). Taken together, results obtained using a range of different methods can provide complementary or converging evidence for a particular model of language development and disorders, or they may reveal discrepancies that point toward important new lines of questioning for researchers. Clinicians, too, can benefit from familiarity with

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these methods. Understanding the strengths and weaknesses of each method allows one to better understand and assess the significance of reported findings. In addition, it has been argued (Bishop, 1997; Leonard, 2009) that children diagnosed with “expressive language disorder” typically also have limitations in receptive language, although these may not be detected by standardized language tests. To the degree that difficulty in receptive processing of language contributes to general language delay, more refined methods of testing comprehension are critical for understanding and treating language disorders in clinical populations. Testing comprehension in children with atypical language can present challenges that may not arise when testing typically developing children. For example, there is increased likelihood with a disordered population that nonlinguistic deficits will interfere with linguistic performance. While this increased heterogeneity can create problems for interpretation of findings, in particular in online behavioral studies of comprehension, it also presents an opportunity to explore the important individual differences that underlie language and cognitive development. In addition to motivating a great deal of interesting and innovative research, research on atypical language development will pay dividends in terms of our progress in understanding language development and cognitive development more generally.

Acknowledgments The writing of this chapter was supported by Research Grant R01 00–458 from the National Institute on Deafness and Other Communication Disorders. Thanks also to George Hollich, Laurence Leonard, and Chris-Weber Fox for helpful discussion and comments.

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23 TRANSLATIONAL AND IMPLEMENTATION RESEARCH IN CHILD LANGUAGE DISORDERS Lizbeth H. Finestack and Marc E. Fey

In the first edition of this handbook, Fey and Finestack (2009) presented a five-phase model of child language intervention research spanning from pre-trial through effectiveness studies. This model was an effort to illustrate to researchers of child language intervention the benefits of following a systematic path from hypothesis generation studies, which form the basis for efficacy questions, to tests of intervention effectiveness, which test such questions under typical, everyday conditions. The model was based heavily on frameworks used in other clinical professions (Piantadosi, 2013; Pring, 2004; Robey & Schultz, 1998) and shared features with the goals of research solicited by the Institute of Education Science (IES, 2014). It was designed to support and guide the development of programmatic approaches specific to child language intervention research, and we believe it has provided the desired support and guidance to many child language intervention researchers to date. At this point, however, although the model is still relevant, we believe there is a need to ground it in the perspective of translational and implementation science and to expand it accordingly. In this chapter, we begin by presenting the original intervention research and development model. It has been modified slightly from the original version, based on our experiences with it in carrying out our own reviews of the literature (e.g., Cleave, Becker, Curran, Owen Van Horne, & Fey, 2015). These small changes reflect our interest in ensuring that each phase is operationally defined and that researchers can use the model reliably. We then introduce some concepts relevant to translational and implementation research, which are needed to give the revised framework new footing in the broader world of implementation science. Next, we suggest how child language investigators may incorporate these relatively new areas of science into their research programs. We contend that programmatic efforts, such as those that follow the paths of our model, are essential to the development and evaluation of new treatments in children’s language disorders. Such efforts have not and will not ensure the population-wide dissemination and implementation of new treatments, however. Rather, broad, efficient adoption and use of evidence-based interventions require a system that explicitly integrates intervention research efforts with the motivations and methods of translational and implementation research.

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A Model of Child Language Intervention Research and Development Our framework for research-based development of child language interventions is illustrated in Figure 23.1, essentially as proposed by Fey and Finestack (2009). 1. Pre-trial studies: Pre-trial studies include basic research on mechanisms that can facilitate development of language among children with and without language impairment. The outcomes of pre-trial studies may be hypotheses about possible developmental mechanisms and even interventions, but their designs do not enable them to examine cause-andeffect relationships between a proposed clinical approach and an outcome. Thus, they are observational or correlational studies or qualitative case studies rather than experimental evaluations of clinical hypotheses. Pre-trial studies may serve as the basis for development of hypotheses about intervention procedures, intervention goals, treatment dosage, and/or provide information on the reliability and validity of dependent variables that could ultimately serve as endpoints for clinical trials. Child language studies in this phase may address questions such as: • What factors are associated with faster language development among children developing typically and those with language disorders? • What distinguishes children with typical language from children with language disorders? • What relationships exist between the form, content, and use of language throughout development? • How can we best measure language growth? 2. Feasibility studies: Feasibility studies are small-scale exploratory and preliminary investigations with the purpose of evaluating clinical viability of a relatively untested intervention component or package. To save time and limit costs, feasibility studies are small in scale and generally lack experimental control. Thus, pre-post designs with no control group are common. In this updated version of the five-phase model, we include as feasibility studies experimental tests of the efficacy of some intervention component that involves only children with typical development. Such studies are designed to test a hypothesis about some proposed language facilitation factor operational in typical development. Because typical children are much more accessible than children with language impairments, the costs of an early efficacy experiment with typical children often can be small. Such studies are viewed as feasibility studies, however, because their outcomes may not be generalizable to children with language impairment. Child language research studies in this phase may address questions such as: • Do the hypothesized intervention mechanisms appear to have the predicted effects? • What outcome measures (or endpoints) are most useful clinically and/or are most sensitive to the intervention? • How frequently and over how long a period is intervention likely to be necessary to achieve a measurable effect? • Do the activities lend themselves to the frequent administration of intervention procedures? • Do the children (and parents) enjoy and/or will they tolerate the approach and the intensity of treatment anticipated to be needed? • Do the children stay engaged in the activities planned?

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Figure 23.1 Fey and Finestack’s (2009) original five-phase model for child language intervention research.

3. Early efficacy studies: Efficacy studies evaluate hypothesized causal relationships between treatments and outcomes under carefully controlled conditions, such as using highly trained, closely monitored project staff, in carefully controlled teaching contexts. Thus, they place a premium on internal validity and replicability. The principle purpose of any efficacy study is to provide a relatively bias- and subjectivity-free demonstration of a causal relationship between an intervention and an outcome. Early efficacy studies include (quasi-)experimental research designed to determine if there is a cause-and-effect relationship between the treatment variable and the target outcome. Dependent measures in early efficacy studies are proximal to what is taught in the intervention, including contrived probes, in highly specified contexts that limit generalizability to very specific outcomes like those found in the intervention. Child language studies in this phase may address questions such as: • Compared to children who do not receive the target intervention, do children who receive the target intervention: ° imitate the target form more frequently? ° use the target form more frequently during intervention? ° produce more correct responses when elicited in assessment probes? 4. Later efficacy studies: Later efficacy studies are (quasi-)experimental clinical studies directly comparing the target intervention to no intervention or an alternative intervention to determine if there is a cause-and-effect relationship between the treatment variable and the target under more generalizable conditions. These studies may be larger and less tightly controlled than early efficacy studies, but the main distinction between early and later efficacy studies is that later efficacy investigations have more distal outcome measures that evaluate language performance beyond the specific goals and contexts in which the intervention takes place. Child language studies in this phase may address questions such as: • Compared to children who do not receive the target intervention, do children who receive the target intervention: ° more frequently produce the target form during spontaneous conversational language samples? ° generalize use of the target form for communicating with their parents? ° demonstrate greater improvement on standardized norm-referenced assessments? 5. Effectiveness studies: In contrast to efficacy studies, effectiveness studies evaluate the effects of treatments that have already been shown to be efficacious across broader, more typical populations and under broader, more typical clinical conditions. Like later efficacy studies, effectiveness studies tend to use distal outcome measures that sample language form,

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content, and use beyond the specific goals used in the intervention. This includes standard scores from omnibus language tests, as well as broad indices of grammatical development, such as mean length of utterance, Developmental Sentence Score (Lee, 1974), or the Index of Productive Syntax (Scarborough, 1990). Unlike later efficacy studies, the intervention agents of effectiveness experiments are typically the child’s own caregiver, clinician, or teacher working in the contexts in which they fulfill their typical responsibilities (e.g., the home, the classroom), rather than a project assistant. Thus, for studies of effectiveness, there is a premium on external validity and generalizability to real-world clinical applications. Effectiveness studies may be further characterized as comparative effectiveness research if they directly compare alternative treatments, rather than comparing a single treatment to a notreatment control. Comparative effectiveness studies help determine which prevention and treatment approaches are most appropriate for specific patients, subgroups, or populations (Vickrey, Hirtz, Waddy, Cheng, & Johnston, 2012). Effectiveness studies generally are randomized controlled trials that address questions such as: • Are effects similar to those found in later efficacy studies observed: ° using the intervention agent’s regular work context (e.g., home, clinic, classroom) rather than a project site? ° using limited project resources and oversight? ° with different and more heterogeneous (sub)populations? ° when other interventions addressing other basic goals (and, thus, more intermediate and specific goals) are added to address children’s more comprehensive needs? ° using different service delivery options (e.g., different intervention agents and contexts, treatment intensities, goal attack strategies)? with unplanned variations in the protocol (e.g., variable parent cooperation, numerous ° child or clinician absences)? ° using more functional outcome measures than those that often characterize efficacy studies?

Translational and Implementation Research According to the National Institutes of Health’s initiative, NIH Roadmap for Medical Research (http://nihroadmap.nih.gov/), the purpose of translational research is to increase the efficiency and speed of clinical research to improve health and prevent disease. Somewhat more broadly, Rubio et al. (2010) proposed that translational research be characterized as the “multidirectional integration of basic research, patient-oriented research, and population-based research, with the longterm aim of improving the health of the public” (p. 470). We prefer this latter characterization, because it stresses both the steps alluded to by the five phases in our model and the bi-directional nature of the model. It extends the model by emphasizing steps necessary to take successful interventions to the public, resulting in routine selection and administration of effective language interventions for children. We made the phases of our original model explicit because we anticipated that heightened awareness of and attention to these phases would encourage more and better research at each level, ultimately leading to a richer evidence base to guide clinicians in their selection of interventions for children with language impairments. Although this “If you build it, they will come” attitude seemed reasonable at the time, our own heightened awareness of the motivations and goals of translational and implementation research has led us to view it as rather naïve. The goal of implementation research is to identify and evaluate hypotheses regarding possible barriers to the adoption and use of evidence-based interventions as standard care. Furthermore, implementation research is designed to test strategies intended to overcome those obstacles so that evidence-based 564

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interventions can be incorporated into community health policies and practices. Implementation science broadly reflects the: scientific study of methods to promote the systematic uptake of research findings and other evidence-based practices into routine practice, and, hence, to improve the quality and effectiveness of health services. It includes the study of influences on healthcare professional and organizational behavior. (Eccles & Mittman, 2006, p. 1) Thus, implementation science goes beyond dissemination of research findings at the conclusion of a study through media such as publications, presentations, or press releases. Instead, it involves deliberate actions to examine and facilitate the complex task of changing clinical practice at levels as narrow as the individual clinician and as broad as national organizations. The remainder of the chapter focuses on how translational and implementation research may be integrated into our model of child language intervention research (Fey & Finestack, 2009).

Translational Research Our child language research and development model was an application based largely on research and development in drug testing clinical trials from the National Library of Medicine (Piantadosi, 1997) and in other areas of health research, such as oncology, disease prevention, surgical trials, and medical device development (Piantadosi, 1997; Robey, 2004; Robey & Schultz, 1998). However, more recently, medical researchers have proposed expanded models that incorporate both the transfer of knowledge and implementation science. The development of such models was motivated by observations of the existence of a significant gap between the establishment of evidence-based treatments and the use of such treatments in real-world clinical settings. This gap has been documented across health disciplines. For example, a recent review of health care practice indicated that, across 25 different health conditions, only approximately 50% of the time patients received the most recommended, evidence-based care, with significant variance across conditions (McGlynn et al., 2003). In the review, 10.5% of individuals who were alcohol dependent received recommended care, while 78.7% of patients with a senile cataract received recommended care. The expectation of experts in implementation science is that these gaps and their variability across health conditions are due to a combination of research-related factors and attitudes of health care providers with regard to the certainty with which they make clinical decisions. For example, Kamhi (2011) has argued that once clinicians become certain in their practices, they become resistant to change regardless of the existence of empirical evidence supporting an alternative practice. Thus, change within clinical communities is slow, even when clinicians are made aware of new evidence that should be seriously considered as the basis for significant modification of current practices. Although the extent of the gap between basic knowledge of child language development and appropriate intervention approaches and actual use of that knowledge is largely unknown, we would make four relevant assertions. First, such a gap exists, and it is formidable (cf. Kamhi, 2011, 2014). Second, we believe that over the past 10 years or so, investigators have made small, but significant, strides towards increasing high-quality research focused on improving treatments for children with language disorders (e.g., Cirrin & Gillam, 2008). Third, despite these gains in the availability of empirical information on language intervention, there has been relatively limited uptake of this available body of research. Therefore, results of much of this research have not been well disseminated or implemented beyond academic settings. Fourth, the gap between research supporting particular child language interventions and the use of these interventions in clinical practice can be attributed to (a) translation barriers, which limit 565

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Figure 23.2 Modified model for child language intervention research that includes translational blocks and implementation research.

our ability to translate available research findings to clinical practice, and (b) our failure to form hypotheses regarding what these barriers are and to evaluate strategies for limiting, if not eliminating, them. Researchers across many other disciplines have proposed numerous models that identify crucial junctions for knowledge transfer to occur and avoid significant blocks in intervention development (see Rajan, Sullivan, Bakker, & van Harten, 2012). Here we suggest that investigators and clinical instructors of child language interventions, as well as funding agencies, peer reviewers, and promotion committees, recognize three crucial junctions where knowledge transfer often fails. These junctions are denoted in Figure 23.2, with the first occurring after the pre-trial studies, but before feasibility and efficacy studies; the second occurring between efficacy studies and effectiveness studies; and the third critical junction following effectiveness studies, leading to studies of implementation. These three types of translational research, based on medical models (e.g., Thornicroft, Lempp, & Tansella, 2011; Westfall, Mold, & Fagnan, 2007), are described in the following sections.

T1 Blocks and T1 Research T1 translational research reflects clinical application of basic science. Traditionally, T1 research is considered to be purely translational research, in which scientific discoveries are moved from the bench to the patients’ bedside in the form of efficacy studies (Rabin, Brownson, Haire-Joshu, Kreuter, & Weaver, 2008). In our model, we suggest that this is the transition from pre-trial studies to feasibility and/or early efficacy studies. A translational block may occur at this juncture if principles of language development and learning with theoretical and empirical support are never evaluated in intervention contexts. Perhaps the most common reason for blocks at this juncture is the basic researcher’s lack of knowledge and experience working with clinical populations. The basic researcher may be unable to independently move basic findings to the development and evaluation of clinical hypotheses in feasibility or efficacy studies. In this case, basic researchers need to collaborate with clinical researchers. We can illustrate the process of moving from the evaluation of language principles in pre-trial studies to evaluation in efficacy studies with a series of studies carried out by Plante, Gómez, and their colleagues (Gómez, 2002; Grunow, Spaulding, Gómez, & Plante, 2006; Plante et al., 2014; Torkildsen, Dailey, Aguilar, Gómez, & Plante, 2013). Many studies have demonstrated that human adults and even infants are good learners of the statistical properties of artificial languages, and therefore, likely also at statistical probabilities found in natural languages. Until recently, these studies have had a limited impact on either intervention research or practice. For example, Gómez (2002) played to adults and infants three-element strings of nonsense words (e.g., pel, kicey, jic) in which there was a dependency between the first and third words. This relationship was not unlike 566

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that of auxiliary verbs (e.g., is, am, was, are) and -ing in the progressive verb construction (e.g., The boy is swimming). In these exposures, the middle element (the main verb in the progressive example) was varied so as to include as many as 24 different words or as few as two words repeated 12 times each. Both adults and infants exhibited evidence of greatest learning of the dependencies when the middle item of each three-element string had 24 exemplars. In a follow-up study to determine whether adults with a history of language impairment would exhibit a nontypical pattern of learning three-element nonsense words, Grunow et al. (2006) used a similar experimental design. They found that both the participants with and without a history of a language/learning disability performed significantly better when exposed to 24 unique middle word exemplars rather than 12 words repeated once for a total of 24 exposures. Importantly, however, unlike the typical group, the group with a history of language difficulties could not learn the dependencies with 12 instead of 24 unique exposures. Torkildsen et al. (2013) observed similar results with a different artificial grammar. With the T1 block in place, this line of research might never have moved on to evaluations of its relevance for learning among children with language impairment. This T1 block was averted, at least in part, however, when Plante et al. (2014) successfully conducted a translational study that moved this research program from observing performance on experimental probes to a demonstration of the impact of exemplar variability on natural language learning. In this tightly controlled early efficacy study, treatment was delivered to eighteen 4- to 5-year-old preschoolers at a university clinic, following strict protocols specifying activities and dosage. Goals were real English bound and free morphemes rather than nonce strings. The investigators randomly assigned participants to a condition in which clinicians included the target form in 24 recasts each containing a unique main verb, or a condition in which clinicians included the target form with 12 unique verbs recast two times each. Results from this study indicated that the children in the high-variability condition significantly outperformed the children in the low-variability condition. This finding needs to be replicated and tested on additional rule-based forms, such as semantic relations, basic clausal and phrasal patterns (e.g., subject + verb (+ object), (subject) + verb + object, article + noun), to determine how strong and how generalizeable the principle of variable exemplars is. If the results of such studies support the variability principle, they would support clinicians’ attempts to strive to use greater variability in the models they present as exemplars of language concepts. At the same time, they would demonstrate that the phenomenon of exemplar variability is not just a property that facilitates performance of adults and babies in an impressive auditory analysis epiphenomenon or trick. Instead, it appears to be a key property of early language learning. As Plante and colleagues were aware, feasibility and early efficacy studies can serve as compelling tests of theoretical principles and related hypotheses at the same time they fulfill a crucial step in the path of research and development of intervention practices (Camarata & Wertz, 2004). This bidirectional influence (schematized in Figure 23.2) should lead to increases in the formulation of clinical hypotheses based on basic research findings and increases in the number of rigorous tests of those hypotheses in efficacy trials. As noted above, T1 blocks can be overcome by basic and clinical researchers developing collaborative relationships that encourage participation of each researcher and the pooling of their resources in both basic and intervention studies. Similarly, many child language specialists, such as Plante in our example, have interest and expertise in both developmental and intervention research. These arrangements can have a substantial impact on shortening the time between basic research discoveries and tests of related intervention innovations.

T2 Blocks and T2 Research T2 translational research involves the translation of efficacy findings to clinical populations in clinical settings (i.e., effectiveness studies). These studies are best undertaken when there is strong 567

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supportive evidence from efficacy trials that the hypothesized active ingredients of the intervention are causally related to the desired outcomes in children’s language performance. T2 studies tend to have greater variability in delivery of the intervention protocol, because treatment sessions are conducted in more natural environments with interventionists (e.g., parents, paraprofessionals, clinicians) who are not directly affiliated with the research team performing the experimental intervention. Additionally, the clinical setting in which the intervention is delivered should be the place where the interventionists typically work with clients and complete other daily responsibilities. Although there is interest in measures of fidelity of the intervention administration, in T2 studies, the most crucial intervention outcomes are those that reflect treatment-related change in child performance. Translation blocks at this level may occur if researchers never seek to apply findings from tightly experimentally controlled intervention (i.e., early efficacy studies) to less experimentally controlled environments (i.e., some later efficacy and, especially, effectiveness studies). Perhaps the greatest block is the unavailability of the crucial efficacy evidence supporting the causal relationship between intervention and improved child language and communication performance. Beyond this, typical blocks involve the especially high costs in time and money; limited research experience and expertise with such large, multi-site trials; and limitations in the number of researchers who can invest large amounts of time in studies that yield small numbers of publications. Prototypical effectiveness studies (i.e., T2 studies) are exceedingly rare in speech-language pathology. Perhaps the best examples in child language intervention are those by Justice and colleagues. For example, Justice, Mashburn, Pence, and Wiggins (2008) evaluated the effects of a classroom curriculum in which the presumed active ingredients were language stimulation techniques (e.g., recasts, open-ended questions). Fourteen teachers of at-risk preschoolers were assigned at random to either the standard or the language-focused curriculum. After they received three days of training either on the language-focused curriculum or the comparison curriculum, the interventions were carried out over a full academic year. The primary differences between the curricula were the explicit focus on particular language constructions and use of eight language stimulation strategies in the language-focused curriculum only. The primary outcome measures were children’s use of language, including percentage of complex sentences, rate of noun use, number of different words, and sentence length. The experimental hypothesis of the Justice et al. (2008) study was that children participating in the language-focused curriculum would exhibit the most growth on the language sample measures by the end of the academic year. This hypothesis was not confirmed; neither curriculum group exhibited greater growth on the target language variables over the academic year. The study did reveal, however, that children who attended preschool classes most regularly made significantly greater language gains if they were in the language-focused group or received relatively frequent teacher use of language stimulation techniques. This moderated effect of attendance on the language outcomes illustrates the importance of collecting measures of the extent to which the intervention is properly implemented. We revisit this issue in the section on T3 research. A key form of T2 research that may be most feasible is community-based research. In contrast to traditional approaches in which investigators conduct studies for the community, communitybased research involves researchers working with community partners across stages of intervention development and evaluation to conduct research. Such research strives to establish 50/50 researcher–community partnerships in which the intervention study conducted is immediately mutually beneficial for both partners. For example, community-based research may begin with a school district contacting a researcher with published grammatical intervention studies to help the district develop and evaluate a group-based grammar intervention for first- and second-graders. The researcher and district may then work together to develop a group-based intervention that

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would be appropriate for targeted district elementary schools and to evaluate the developed intervention in an effectiveness study conducted within the district. This type of community-based research approach has several advantages. First, the researcher is addressing an immediate concern of the district (community). Second, the district is able to provide the researcher with a laboratory for examining treatment outcomes in everyday settings. Third, the district is able to provide students with immediate access to new treatments, with the ultimate goal to improve the quality of their education (Westfall et al., 2007).

T3 Blocks and T3 Research T3 research is most typically referred to as implementation research. As noted above, implementation research involves deliberate actions by researchers and stakeholders (e.g., parents, clinicians, classroom teachers, supervisors, policy makers) to examine possible barriers to the complex task of changing clinical practice at levels as narrow as the individual clinician and as broad as national organizations. For example, an implementation study may aim to determine if a particular intervention innovation meets a specific elementary school’s needs with respect to factors such as the demographic characteristics of the school’s students, the number of students on the school clinicians’ caseloads, the willingness of parents to participate in intervention activities, and the classroom space available for conducting the intervention. As another example, an implementation study may aim to determine the degree to which clinicians in a school district currently are using a particular intervention, such as telegraphic input when targeting multiword combinations in preschoolers’ expressive language (Bredin-Oja & Fey, 2014). Before committing to an implementation study, stakeholders should want to know: (1) How many clinicians/teachers/paraprofessionals working in the school districts’ preschool program currently use telegraphic models when addressing children in the preschool program? (2) What is their basis for using this practice? and (3) Have they tried using an alternative approach such as short but grammatical models and requests for imitation? T3 research is clearly differentiated from T2 research with respect to its primary aims and dependent measures. In fact, implementation research outcomes may be orthogonal to those of effectiveness trials to the extent that it is possible to have an effective intervention that is poorly implemented and an ineffective treatment that is successfully implemented. Whereas effectiveness research principally examines growth in child language performance and measures the association of this progress with the intervention, outcome measures of T3 are closely tied to the implementation process (Peters, Adam, Alonge, Agyepong, & Tran, 2013; Proctor et al., 2011) and may address issues and questions such as the following: • Acceptability: Is the intervention agreeable or satisfactory to stakeholders (e.g., parents, clinicians, classroom teachers, supervisors, policy makers)? • Adoption: Do clinicians understand the need to make the targeted clinical change? Are they comfortable with using the new intervention? Would they adopt the planned change in intervention if it were their own decision and not that of the school district’s representatives? • Appropriateness: How does the clinician perceive the fit, the relevance, or the compatibility of the intervention in relation to the particular clinical population and setting? • Feasibility: To what degree can the intervention be successfully employed within a particular clinical setting (e.g., consider the complications that might arise if the intervention is to be carried out in a hospital clinic or via computer rather than at school or home)? • Fidelity: To what degree can the intervention be used as directed according to the original intervention protocol? Is the training sufficient to ensure fidelity?

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• Implementation cost: How much does it cost to use the intervention? Are there ways to reduce costs to the schools/clinics/families? What are the effects of cost-cutting on the effectiveness of the intervention? • Coverage/Penetration: To what degree does the population of children eligible to benefit from the intervention actually receive the intervention? • Sustainability: To what extent is use of the intervention maintained after the intervention agent (e.g., parent or other caregiver) is trained in the methods of the intervention? T3 translational research blocks generally occur if investigators fail to disseminate research findings to the broader clinical community and underestimate the complexity of implementing research findings that have been supported by strong efficacy or effectiveness evidence. The goals of T3 research will be more readily achieved if, during earlier phases of the intervention research process (e.g., efficacy studies, effectiveness studies), investigators engage in communitybased research projects. Moreover, implementation frameworks (Rogers, 2003) suggest that a target intervention is more likely to be successfully implemented and used broadly depending on several characteristics of the innovation. This includes its perceived relative advantage over current approaches; its compatibility with values, experiences, and needs of clinicians; its relative ease of use; its trialability (i.e., the degree to which the target intervention may be tried for a period before committing to full use of the intervention); and the degree to which the outcomes of the target treatment are observable to the clinician, the client, and the client’s family. For example, clinicians are more likely to adopt and use a new intervention for targeting grammatical errors if the new intervention is consistent with their theoretical views regarding language development, there is evidence that the new intervention will lead to quicker child gains than their current modeling/ recast approach, and the new intervention appears to be easy to use. T3 research may be the area of child language research that currently requires the greatest attention, as very few studies of intervention implementation exist. However, the relative lack of research focused on implementation is not unique to child language intervention or speechlanguage pathology, more generally. Significant gaps in moving effective interventions to implementation exist across many areas of health care. An often cited health care translation statistic is that it takes an average of 17 years for research evidence to be incorporated into clinical practice (Balas & Boren, 2000). This includes a minimum of one year for research publication, at least six years for evidence to reach reviews and textbooks, and at least nine years before at least 50% of practitioners are using the evidence clinically (Balas & Boren, 2000). However, investigators must not be complacent with the status quo. To emphasize the importance of engaging in implementation research, Glasgow, Eckstein, and Elzarrad (2013) illustrated the diminishing impact of an intervention, even when 50% of stakeholders (e.g., school districts, clinicians, children) are compliant at each level of use. In Figure 23.3, we modify the Glasgow et al. illustration to reflect a probable situation in child language intervention. Thus, with optimistic estimates of 50% uptake at each level of potential impact, merely 3.2% of children with language impairment may receive long-term benefits from a new intervention even if the intervention has a strong evidence base.

Hybrid Studies As we noted above, there are very few implementation studies focused on child language intervention. Of the studies that do exist, most, if not all, may be best characterized as hybrid studies. In hybrid studies, co-primary outcome measures address child outcomes and implementation outcomes within a single efficacy or effectiveness trial. Researchers in other healthcare-related disciplines have advocated for the use of hybrid effectiveness-implementation research designs

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Figure 23.3

Diminishing impact of child language intervention research.

to increase the speed at which effective interventions are routinely implemented (Curran, Bauer, Mittman, Pyne, & Stetler, 2012), especially when researchers have established the effectiveness of the target intervention in a different setting, with an alternative population, or using a new service delivery method than the one currently used. Likewise, we recommend that speech-language pathology adopt hybrid effectiveness-implementation studies as the most common way to bring studies ready for effectiveness trials to the stage of implementation in clinical practice. There are a few examples of this type of research in child language intervention. Again, the best examples have been produced by Justice and colleagues (e.g., Biancone, Farquharson, Justice, Schmitt, & Logan, 2014; Justice et al., 2008; Justice, Skibbe, McGinty, Piasta, & Petrill, 2011). In these studies, the primary aim was to measure the performance of teachers, aides, and other associates in their accurate administration of an intervention or curriculum designed for students who are at risk for oral and written language disabilities. Each study, though, was a part of a successful effectiveness study in which child language outcomes were emphasized. Thus, technically, these are examples of hybrid T2-T3 research. In the study of Justice et al.’s (2008), which was previously discussed, the main child outcome was an interaction between days attended and the intervention; children used significantly more complex language if they received the new language-focused curriculum, but only if their attendance rate was high. Pence, Justice, and Wiggins (2008) presented implementation evidence taken from observations of teachers and the results of surveys throughout the effectiveness study that is crucial to the interpretation of the child results. For example, although the teachers trained in the language-focused curriculum increased their use of the language stimulation techniques after their training, the teachers’ rates of use were generally low, and differences among groups were not statistically significant. In addition, teachers who used the comparative or standard curriculum rated their own delivery of the curriculum more highly than did the experimental group teachers. They also indicated higher levels of comfort in their administration of the comparative curriculum than did the teachers who adopted the language-focused curriculum. If interventionists are not comfortable administering a new approach, it is unlikely that the approach can have its full potential impact on the child’s performance.

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Based on this study, implementation of the language-focused curriculum is likely to require some strategy or combination of strategies to ensure high levels of use of the language stimulation techniques. This might include more preliminary training, more refresher in-service activities, or different types of training, such as observation of speech-language pathologists using techniques in the classroom context. It might also be feasible to eliminate some of the less frequently used language stimulation techniques, so that the known active intervention components (e.g., expansions and recasts) can be readily learned and employed in classroom contexts confidently and consistently. It is important to recognize that not all evidence-based child language intervention practices must be supported by a comprehensive body of efficacy and effectiveness trials to be adopted as standard care. For example, Kamhi (2014) has identified 10 examples of knowledge-translation gaps reflecting differences between what is known about language learning and what clinicians do in their clinical practices. Using his knowledge of learning and language development, he demonstrates how reasonable, rational clinical principles relevant to diverse areas of practice can be based on this knowledge even when the essential clinical studies are not available. A good example is provided by Bredin-Oja and Fey (2014), who studied the use of telegraphic requests for imitation by clinicians working with young children with delayed grammatical development. Following is an example of a teaching episode with a telegraphic request for imitation and a copy of that episode with a grammatical request for imitation. Both episodes begin when the child puts a man figure in a truck and makes the truck move. (1) Child: Drive. Clinician: Yeah, say Man drive. Child: Man drive.

(2) Child: Drive. Clinician: Yeah, say, the man drives. Child: Man drives.

The clinical assumption that motivates this widely used practice and supports the use of telegraphic models is that by highlighting only content words, the clinician simplifies the child’s imitative task and increases the likelihood that the child can correctly imitate the target and more readily learn to produce agent-action constructions. However, others claim that by modifying prosodic aspects of the model, clinicians actually may increase the complexity of the task and limit both immediate grammatical comprehension and later acquisition of morphemes that are excluded from telegraphic targets. Bredin-Oja and Fey (2014) carried out a carefully controlled early efficacy trial involving five children who had multiword semantic relations as their intervention targets. During play, each child was given opportunities to respond to both telegraphic and grammatical requests to imitate, as in the example above. The results indicated that there were no differences in the children’s likelihood to imitate the semantic relation correctly. Three of the children, however, were significantly more likely to imitate a grammatical morpheme from the adult’s stimulus in the grammatical condition than they were in the telegraphic condition. This outcome, which certainly warrants replication, supports the assertion that use of telegraphic models has no positive effects on child learning and may even have a negative impact on development. Given what is currently known, there would seem to be little reason to wait for further evidence from later efficacy or effectiveness studies to employ grammatical rather than telegraphic models as standard care for children with developmental language impairments.

Challenges of Translational and Implementation Research Few investigators will argue that any one specific type of research is easy. However, most researchers agree that intervention research is inherently difficult because of its longitudinal nature. This is likely one of the reasons why for many years there was a dearth of child language intervention

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studies. When proposing a model to guide child language intervention research programs, Fey and Finestack (2009) suggested that the model would highlight the need for studies ranging in scale to address distinct clinical questions. This placed special value on programmatic intervention research programs and publishing findings at each research stage to continue to advance the research program. The model allowed for smaller-scale, shorter-term studies requiring less time and fewer resources to be recognized as important, thus enabling researchers to gradually build their research portfolio to include larger, more costly effectiveness studies. In terms of translational and implementation research, scientists have suggested that there are varying reasons why translational blocks may occur across research phases. Some reasons cited include lack of willing participants, regulatory burden, fragmented infrastructure, lack of qualified investigators, career disincentives, time constraints of practitioners, high costs, and lack of funding (Curran et al., 2012; Sung et al., 2003). Such challenges are not insurmountable, and in the past decade, mechanisms and infrastructure to support translational research have improved dramatically. Agencies have created specific funding mechanisms to support translational research. For example, the U.S. Department of Education’s Institute for Education Sciences (IES) funds research spanning five levels of research: Exploration, Development and Exploration, Efficacy and Replications, Effectiveness, and Measurement (Institute of Education Science, 2014). The IES requires researchers to specify procedures to promote research translation. For example, although some aspects of the first three levels of IES projects may be conducted in laboratory settings, IES stresses the need for research to take place in more natural settings, such as center-based pre-kindergarten programs, schools, or school districts. Moreover, the research plans submitted for review by IES must include dissemination plans for project findings that specify (1) the target audiences (e.g., researchers, federal or state policy makers, state and local school administrators, principals, teachers, parents, students), (2) the method of dissemination (e.g., publications, presentations, products), and (3) primary use of findings (e.g., for effectiveness studies, to support wider use of the target intervention in less ideal or different conditions). Such requirements aim to help eliminate gaps in research translation and expedite the time required to take research from bench to bedside. In short, IES funding mechanisms provide incentives for researchers to establish a programmatic approach to intervention research. Health sciences have also made great strides toward supporting translational and implementation research. At least 16 of the National Institutes of Health’s institutes support funding for dissemination and implementation research, including the National Institute for Deafness and Other Communication Disorders (NIDCD; Glasgow, Chambers, & Cynkin, 2013). Additionally, NIH’s National Center for Advancing Translational Sciences currently funds 62 Clinical and Translational Science Awards (CTSAs) across 31 states (http://www.ncats.nih.gov/research/cts/ctsa/ about/about.html). CTSAs provide academic homes for translational research and support the full range of translational research through the provision of research resources needed by local and national communities to improve the quality and efficiency of translational studies. CTSAs also support the training of clinical scientists to conduct translational research. Further evidence for the support of translational and implementation research, pertaining more specifically to child language intervention, is evident in the research priorities set in NIDCD’s 2012–2016 Strategic Plan. These research priority areas were defined in consultation with communication research scientists and the public. Priority Area 4 aims to “accelerate the translation of research discoveries into practice; increase access to health care; and enhance the delivery, quality, and effectiveness of care to improve personal and public health.” These aims are motivated by evidence that “scientifically validated prevention and treatment models will lead to better personal and public health only if they are translated effectively into routine practice” (http://www.nidcd.

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nih.gov/about/plans/2012–2016/Pages/2012–2016-Strategic-Plan-Full.aspx). Moreover, in the Voice, Speech and Language Area, NIDCD specifically aims to improve outcomes for human communication through the use of comparative effectiveness research and evidence-based medicine, engagement in community-based research, development of experts to hasten the translation of research advances to routine community practice, and determination of effective dissemination and implementation strategies that enhance the adoption of clinical discoveries into routine practice. In line with these priorities, NIDCD has funding opportunities specifically targeting Disorders of Human Communication: Effectiveness, Outcomes and Health Services. These opportunities utilize R01 (http://grants.nih.gov/grants/guide/pa-files/PA-13–102.html) and R21 (http://grants. nih.gov/grants/guide/pa-files/PA-13–103.html), among other funding mechanisms, to support clinical research including implementation research. Given the expense of intervention research that includes all research phases, including steps supporting translation and implementation of the target treatment, it may be necessary for child language researchers to develop specific research priorities. Research priority setting will require obtaining input from multiple professional societies, advocacy organizations, relevant institutes of NIH, and investigators. Ideally, research priority setting for child language interventions will involve the use of systematic and transparent state-of-the-art methods (Vickrey et al., 2012). In other health areas, such as neurology, guidelines have been established to help direct priority setting. When developing their 2010 Strategic Priorities and Principles (National Institute of Neurological Disorder and Stroke, 2010), the National Institute of Neurological Disorders and Stroke (NINDS) published Analysis and Recommendations for Translational Research and Development (NINDS Advisory Panel for Translational Research, 2008) to help drive research priority setting. These criteria include consideration of potential to reduce disease burden, appropriateness of the biological target, feasibility of modulating that target, availability of research tools and resources, and trajectory to clinical trials and commercial development. The NINDS also stressed the importance of working within research and patient communities to evaluate priorities and the implementation of translational research.

Conclusion Intervention researchers have made great strides towards increasing research focused on improving treatments for children with language disorders. This increase in intervention reflects greater focus across different phases of intervention development, including phases focused on the feasibility, efficacy, and effectiveness of the intervention. Implementation is not appropriate for all interventions. Researchers need to be careful to promote implementation for only interventions that have been shown to be efficacious or that have been recommended as alternatives to practices shown to be potentially harmful. Given advancements toward establishing efficacious and effective interventions, researchers must continue to push through potential translational blocks and, when appropriate, engage in implementation research to ensure that effective child language interventions are readily used by clinicians in meaningful contexts.

References Balas, E. A., & Boren, S. A. (2000). Managing clinical knowledge for health care improvement. In Yearbook of Medical Informatics. Stuttgart, Germany: Schattauer Verlagsgesellschaft mbH. Biancone, T. L., Farquharson, K., Justice, L. M., Schmitt, M. B., & Logan, J. A. R. (2014). Quality of language intervention provided to primary-grade students with language impairment. Journal of Communication Disorders, 49, 13–24.

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24 NEUROSCIENCE APPROACHES TO CHILD LANGUAGE DISORDERS Valerie L. Shafer, Emily R. Zane, and Nathan D. Maxfield

Introduction Researchers are increasingly relying on neurobiological evidence to expand our understanding of child language disorders. The aim of this chapter is to provide an overview of some of the more relevant neurobiological methods that are currently in use. Understanding the role of brain function in typical, as well as atypical, language development is important for addressing the causes of a disorder, because it provides a basis for observing where, in the physical system, known processing activities deviate from normal and how such deviations might ultimately contribute to atypical language development and behavior. Our understanding of brain structure and function derives from a number of different methods. To a great extent, our knowledge of how the brain works at a micro level (i.e., cellular level) derives from methods using autopsied brain tissue or other highly invasive techniques (e.g., cortically implanted electrodes). Similarly, for most of the 20th century, our knowledge of structure and function at the macro level (i.e., gross neural structure-language function relationships) derived from examination of clinical cases, such as people with aphasia, whose brains were also examined invasively, albeit postmortem. Today, a number of neuroscience methods allow for the examination of brain structure and function, with minimal risk, in living participants (in vivo). These latter methods, which also focus on the identification of macro systems underlying language performance, are likely to be the most used for the study of developmental language disorders, at least for the next decade. Even so, it is important to examine brain structure and function at the micro level and the methods used to uncover this knowledge, because a real and useful understanding of findings from macro-level imaging studies requires us to relate these results to function and structure at the micro level, as is illustrated in this chapter. In this chapter, we first describe some important basic principles of neurobiology at the level of the neuron. Knowledge of these principles is necessary to understand what neuroscience methods are measuring. This section also includes a brief discussion of the methods that have been used to derive this knowledge. The second part of the chapter describes the methods that have been used most frequently for studying higher-level brain function and that may be used, or in some cases have already been used, in the study of language development and disorders. In the final section, we discuss some factors that should be considered in interpreting data from these neurobiological methods.

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Basic Principles of Neurobiology In recent years, considerable advances have been made in our understanding of the neurobiology of speech and language in adults. The study of language-supporting neural structures suggests that the language system consists of many interconnected functional modules. Each module is composed of cells, cell assemblies, and cell assembly networks that contribute to language processing in specialized ways (see Bookheimer, 2002; Price, 2012). This view extends our prior understanding of language processing and organization, which was largely based on investigations of language breakdown (e.g., aphasia) that were interpreted as pointing to somewhat localized, languagespecific processing regions in the brain. Current research on typical language-brain function is focused on precisely identifying the locations and architecture of language-supporting modules, as well as the directionality and timing of information exchange along pathways connecting these modules. Understanding how these modules interact to contribute to language processing, and how their dysfunction might contribute to language disorder, requires knowledge of a number of basic principles of neuroscience. In this section, we first describe several of the methods that have been used to understand the function and structure at the micro level. This is followed by a description of micro-level structure and function.

Neuroscience Methods for Studying Microstructure Much of our knowledge of the function and structure of the brain at the cellular level comes from research on nonhuman animals. In this research the electrochemical properties and structural and biochemical organization (cytoarchitechtonics) of brain cells are examined using samples of brain tissue from dead specimens or invasive experiments, typically using living nonhuman animals (Kolb & Wishaw, 2009). The principal method for identifying structural organization of brain cells consists of using selective stains on dissected brain tissue, which allows researchers to identify types of structures, such as cell type (e.g., size, shape) and distribution of cells, along with their connectivity. The identification of Brodmann’s areas (e.g., primary auditory cortex is Brodmann’s Area 41) is the result of these investigations. Methods used to study the biochemical activity of brain cells include determining the function of a neurotransmitter (e.g., excitatory), demonstrating that stimulation leads to release of the neurotransmitter, identifying the biochemical structure of the neurotransmitter, and experimentally demonstrating its function. Methods for studying the electrical activity of brain cells include testing the electrical properties of brain structure after dissection (e.g., conductivity of dissected axons) or using electrodes implanted in the brain tissue of live animals to examine the physiology of clusters of brain cells. These methods have been instrumental in determining the basic principles of how the brain functions and is organized (see Kolb & Wishaw, 2009).

Neural Circuitry The brain stores and communicates information by transmitting electrochemical signals between neurons. The neuron has three major parts: a cell body, an axon, and numerous dendrites. The cell body is responsible for metabolic functions. The axon conducts information (or electrical activity) away from the cell body to other neurons, while dendrites gather information from other neurons. The neural impulse sent down an axon terminates at a synapse and causes chemical agents, called neurotransmitters, to be released in a cleft between the synapse and the membrane of another

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neuron (often on a dendrite). The type of transmitter determines whether the synapse is excitatory or inhibitory. Receptors on the postsynaptic membrane are sensitive to particular transmitters. These chemical messages can lead to changes in the electrical potential of the receiving neuron. Sufficient change in potential can lead to depolarization and excitation (firing) or hyperpolarization and inhibition of firing. A neural circuit is made up of axonal connections between thousands of neurons. Within a circuit, neurons fire synchronously to particular stimulus properties or processing demands (Hebb, 1949; see Vaughan & Kurtzberg, 1992). Intracranial recordings have revealed that neurons operate in clusters, or cell assemblies (Engel, Moll, & Fried, 2005). Groups of tens of thousands of neurons, spaced closely together in a radius equal to that of a pencil eraser, can become excited or inhibited all at once in response to stimulation (Calvin, 1975). Furthermore, different neural circuits, or cell assemblies, can be linked together to accomplish more complex processing tasks. Specific patterns of excitation and inhibition across cell assemblies have been traced to specific complex processing tasks.

Neural Circuitry for Learning and Memory Language processing, similar to other functions, requires the learning and storing of information in memory. The manner in which this learning occurs at a neurophysiological level has been called Hebbian learning. Hebbian learning is defined as the strengthening or weakening of synaptic connections between neurons through repeated stimulation of connections to create a circuit (e.g., Hebb, 1949; Vaughan & Kurtzberg, 1992). A large literature focuses on this memory/learning process, which is called long-term potentiation (LTP) (see Lisman, Grace, & Duzel, 2011, for a technical description and review). There is some biological determination of circuits, but maintenance of a circuit, even one that is prewired, often requires stimulation; further tuning and strengthening of the circuit can occur with stimulation. These changes can lead to the creation of novel neural circuits or to the linking of multiple circuits into higher-order circuits. Memory at higher levels of the nervous system is often the result of synaptic modifications. Brain circuits that function in memory for a stimulus or task also participate in the learning of this information. Memory for information may be seen as patterned electrochemical activity across neurons responding to some event or task, with the pattern of activation established during the Hebbian learning process described previously. Humans have evolved specialized neural subsystems, including the limbic structures, the hippocampus, and the basal ganglia, that support learning and memory (e.g., by acting as sophisticated association areas, or relay stations, that connect different parts of the brain). Attentional systems, including the cingulate gyrus and basal ganglia structures, also play a major role in language learning and processing (see Koch, 2004; van Boxtel, Tsuchiya, & Koch, 2010). These systems all contribute significantly to learning language and the processes involved in language comprehension and production (Squire & Kandel, 1999; Mayford, Siegelbaum, & Kandel, 2012).

Structure of Neocortex The cortex in primary sensory regions (e.g., visual, auditory) is dominated by a layer of granular neurons that receives sensory information from the periphery. The primary motor cortex is dominated by a layer of granular neurons that sends motor commands to the peripheral muscular system. In contrast, the association cortex is dominated by layers of neurons that both send and receive information to and from other cortical areas. Differences in brain tissues can be identified at the cortical level fairly easily, because neurons are organized in four to six layers, with motor

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output to the muscles (efferent) originating at Layer V, sensory inputs from the periphery (afferent) terminating at Layer IV, and axonal connections between neurons in different brain regions (i.e., cortico-cortical) generally originating and terminating in Layers II and III. Layer I (closest to the scalp), called the molecular layer, largely contains dendrites of neurons in deeper layers and axons synapsing on these dendrites, or axons with targets in other layers passing through this layer. Layer VI, called the fusiform layer, consists of a mixture of dendrites from cell bodies in Layers II (external granular layer) and IV (internal granular layer). Different types of neurons are often found in different layers. For example, pyramidal neurons, which are larger than other neuron types, send (efferent) projections to more distal sites and are more abundant in Layer V than in Layers II and III (Kandel, Schwartz, & Jessell, 2000; Kolb & Wishaw, 2009). It is noteworthy that allocortex, an evolutionarily older region of the brain including many of the limbic structures (e.g., cingulated gyrus, hippocampus, parahippocampal gyrus), is composed of just three layers of neurons, many of which communicate with prefrontal cortex via the thalamus. In summary, this section describes some basic principles of the nervous system, which are necessary for understanding neuroscience methods used to study developmental language disorders and to interpret results from these investigations.

In Vivo Brain Imaging The most exciting and potentially profitable neuroscience methods are those that are minimally invasive and allow the investigation of structure and function in healthy participants. The most commonly used methods are described in this section, along with examples of how they have been or could be applied to the study of developmental disorders. In addition, we describe some exciting new techniques that have been developed over the last decade and that show great promise for furthering our understanding of neurodevelopmental disorders.

Structural Imaging The volume of particular brain regions can be measured in vivo using brain imaging methods such as computerized tomography (CT) scans and magnetic resonance imaging (MRI). MRI is the currently favored method, because it provides high resolution. Specifically, MRI is sensitive to the density of hydrogen atoms in tissue. These atoms normally exhibit random orientation but line up roughly in parallel with respect to the force lines of a strong magnetic field. Radio waves are passed across the atoms, which then emit detectable signals that can be used to reconstruct their density. Unique radio frequency pulses are delivered to different loci in the brain. Thus, the precise location of the returning signal measured by the magnet’s coils is known via its frequency. The intensity of the signal is used to reconstruct the tissue density at the different loci (Papanicalaou, 1998; Mills & Tamnes, 2014). MRI can also be used to examine fiber tract pathways (Le Bihan, Mangin, Poupon, Clark, Pappata, Molko, & Chabriat, 2001; Mills & Tamnes, 2014). Diffusion tensor imaging (DTI) takes advantage of the internal fibrous structure of white matter—specifically, its anisotrophic nature (i.e., directional dependency). Water diffuses more rapidly in the direction aligned with the white fibers compared to the perpendicular direction. This basic principle is used to perform tractography, to trace the fibers in relevant fiber tracts, such as the arcuate fasciculus, and to examine white matter properties, called fractional anisotropy (FA; see Le Bihan et al., 2001; Mills & Tamnes, 2014, for more technical descriptions of the method). The MRI method has been used to study developmental disorders by examining structural differences between the brains of persons with and without disorders. Anatomical studies have

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suggested that adults with typical language skills show a hemispheric asymmetry for languageprocessing regions. For example, the planum temporale is larger in the left than the right hemisphere in most right-handed individuals (for review, see Kolb & Wishaw, 2009). The left planum temporale is believed to play a special role in speech and language processing (Scott & Wise, 2004). A number of investigations have suggested abnormalities in size or in asymmetry of languagerelated regions in children with developmental language disorders (Plante, 1991; Leonard, Lombardino, Walsh, Eckert, Mockler, Rowe, et al., 2002; De Fosse, Hodge, Makris, Kennedy, Caviness, & McGrath, 2004). For example, Gauger and colleagues found a significantly smaller pars triangularis in the left hemisphere for children with SLI (Gauger, Lombardino, & Leonard, 1997). While these studies’ findings are enlightening, gross anatomical measures do not provide information about how deviant anatomy relates to function. Also, as with any measure, arbitrary measurement decisions can lead to different results. For example, some studies have measured the entire volume of the planum temporale, whereas others have divided it into two portions. The DTI method has primarily been used to examine fiber tract connectivity and FA in children and adults with dyslexia (see Vandermosten, Boets, Wouters, & Ghesquière, 2012, for review), although a few studies focus on autism spectrum disorders (e.g., Verly, Verhoeven, Zink, Mantini, Oudenhove, Lagae, et al., 2013), language impairment (e.g., Kim, Kim, Park, Park, Kim, Lee, & Kim, 2006), and developmental stuttering (e.g., Cykowski, Fox, Ingham, Ingham, & Robin, 2010). These studies reveal that DTI can elucidate the relationship between a particular disorder and the connectivity of specific fiber tracts. For example, many studies of dyslexia indicate that reduced fractional anisotropy measures in left temporoparietal and frontal regions (in particular, the left arcuate fasciculus and corona radiata) are related to dyslexia and/or poorer reading (Vandermosten et al., 2012). In summary, methods for imaging brain structure in vivo have provided valuable information and should continue to be used. These methods, however, are more powerful when used in conjunction with neuroimaging studies of function.

In Vivo Methods for Identifying Function A number of other brain imaging methods are available for investigating how the brain’s neural circuitry functions in order to drive language performance. Functional brain imaging methods that are increasingly being used are electrophysiology (or electroencephalography, EEG), magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and functional near-infrared spectroscopy (fNIR). Each of these methods is described in the following sections, along with current or proposed ways of using it to investigate childhood language disorders. The description of these methods is followed by a general discussion of interpretation, analysis, and other issues.

Electrophysiology Electrophysiology, or EEG, is the brain imaging method most commonly used with pediatric populations, primarily because it is easy to use with children and is relatively inexpensive; for this reason, we present more detailed information on this method than we do the other four. EEG methods exploit the phenomenon that neuronal firing leads to changes in the electrical potential (specifically, postsynaptic excitatory and inhibitory potentials) of the extracellular solution. In many regions of neocortex, adjacent neurons are arranged in parallel, so that the axons of the neurons are mainly aligned on one end, while inputs into the dendrites are mainly aligned on the other end. This arrangement leads to the circuit behaving like a dipolar current source when the neurons

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in a neural circuit or cell assembly fire in synchrony. (For a neurophysics explanation, see Nunez & Srinivasan, 2006.) A dipole describes the property of an electric system with two opposite charges (negative versus positive) at the poles. Thus, on one end (e.g., negative end), ions of the opposite polarity will be attracted, and this perturbation will propagate to the scalp, while on the other end, the opposite pattern will be found. Following firing, the system returns to equilibrium (i.e., no net negative or positive charge). Note that this is the principle on which a battery functions. When the positive and negative poles of the battery are connected, current flows from the negative to positive end, until equilibrium is reached (at which point, the battery is dead). Of critical importance, these perturbations propagate (volume conduct) to the surface of the scalp, where they can be recorded and, ultimately, amplified and measured. Surface scalp recordings are made comparing the electrical potential at one electrode to another electrode (the reference). The electrodes make contact with the scalp via a saline solution (liquid or gel) to increase conductivity. EEG research in animal models using intracortical electrodes has been able to pinpoint sources of dipolar volume-conducted activity seen at the scalp to discrete cortical layers. For example, in response to visual stimuli, a scalp-recorded negativity peaking at 40 ms is found to reflect excitation in cortical Layer IV in the visual cortex of primates. These data demonstrate that, at least for sensory and cognitive processes shared by humans and animals, there is the possibility of explaining scalp-recorded EEG components in terms of their neural correlates (Vaughan & Kurtzberg, 1992; Steinschneider, Liégeois-Chauvel, & Brugge, 2011). Different methods of signal processing have been developed to isolate the electrical patterns related to a single event of interest. The most common method involves averaging portions of the EEG that are time-locked to a repeated event. The goal is to decrease the contribution from processes that are not time-locked to this event (e.g., background noise), while increasing the contribution from event-generated, time-locked fluctuations elicited from a neural source(s) that is too small to resolve in the unprocessed EEG. This method is called averaged evoked potentials (AEPs) or averaged event-related potentials (ERPs) (see Luck, 2014; Jackson & Bolger, 2014, for introductions to ERP methods). Figure 24.1 illustrates how noise approaches zero with averaging, leaving electrical potential fluctuations time-locked to the stimulus. With averaging, the response to the stimulus increases, whereas the non-time-locked responses or noise averages to near zero. EEG/ERP measures provide high-temporal resolution, making them suitable for addressing questions regarding the relative time-course and speed of cognitive processing. It should be noted, however, that the change in electrical activity found in some brain regions (e.g., sulci) cannot be measured at the scalp, because the orientation of neurons are such that the activity cancels out (Nunez & Srinivasan, 2006). ERPs are typically evaluated in terms of the latency and amplitude of positive- or negativegoing peaks relative to the timing of the event. The scalp location of the peak pattern is also generally important. In some cases, the peak pattern is specific to a particular stimulus or task type and will be called an ERP component and acquire a specialized name, such as the P600 component. The decision to use the label component, rather than peak, is related to a number of factors. The use of component generally implies that other researchers have observed the ERP pattern under similar stimulus and task conditions and that two or more experiments have been undertaken to attempt to understand the functional significance of the component. For example, the first negative peak elicited to an auditory stimulus at fronto-central sites (top of the head) is often labeled the N1 peak. The N1 peak is a descriptive term that does not imply its relationship to function. When it is referred to as the N1b component, the researcher is relating it to other studies that have examined how it is modulated by various stimulus and task factors. Thus, by using the label N1b component, the researcher is making the implicit claim that the observed N1 peak reflects the same processes as found in other studies using the same label. In some cases, an ERP component is isolated by

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Figure 24.1 The graphs illustrate how noise approaches zero with averaging, leaving potential fluctuations time-locked to the stimulus. The top left graph displays one epoch (trial) of the EEG versus 30 epochs, all time-locked to the onset of consonant-vowel-consonant (CVC) words. The top right, bottom left, and bottom right display 2, 12, and 24 trials, respectively, compared to 30 trials. The activity in the prestimulus baseline (–100 to 0 ms) diminishes as more trials are added; the series of peaks between 0 and 400 ms become more distinct, and less variability is seen with additional trials. Amplitude (in microvolts) is plotted on a finer scale for the bottom graphs.

subtracting two conditions (e.g., the Mismatch Negativity or MMN component). It is important to recognize that ERP components do not necessarily reflect one underlying neural source or process. In particular, ERP components that index cognitive processes are likely to reflect the summation of multiple underlying neural sources. ERP components fall into two main classes: exogenous (obligatory) components and endogenous (cognitive) components. The principal ERP components that have been used to study speech and language development primarily are endogenous potentials, although exogenous components can also be used to rule out lower-level sensory or motor deficits. Endogenous potentials/components are dependent on the nature of the processing task. In contrast, exogenous potentials are elicited or evoked by the physical characteristics of the stimulus. The frontocentrally recorded P1 (50–100 ms) and N1b (80–140 ms) auditory ERP components and the laterally recorded T-complex (Ta-Tb) auditory ERP components are considered exogenous indices, and each component’s peak amplitude, latency, and topography (pattern across scalp sites) is largely determined by the physical characteristics of a stimulus. Although these components, to a large extent, do not reflect

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higher-level language processing, they can be useful for assessing the presence of a sensory processing disorder. In addition, these exogenous components are affected by cognitive processes, such as language experience and attention, and, thus, should not be ignored (e.g., Shafer, Ponton, Datta, Morr, & Schwartz, 2007; Shafer, Schwartz, & Martin, 2011). Figure 24.2a shows that latency and amplitude of the P1 and N1 reflect the physical differences between stimuli, in this case a vowel versus a CVC syllable. Endogenous components used to study speech and language include the mismatch negativity (MMN) for examining phonetic and phonological discrimination, a centro-parietal negativity at around 400 ms post-stimulus onset (the N400) for examining lexical access and semantic/pragmatic integration, the (early) left anterior negativity (the [e]LAN) for examining morphosyntactic processing, and a late, parietal positivity peaking around 600 ms (the P600) for examining late syntactic processes related to reevaluating and/or repairing sentence structure (see Friederici, 2002; Kuperberg, 2007). Several studies have also used the N2 and lateralized readiness potential (LRP) to examine access of linguistic information in production (van Turennout, Hagoort, & Brown, 1998; Schmitt, Munte, & Kutas, 2000). Most of these endogenous components are elicited by some deviant form and are best observed by subtracting the ERP to the control from that of the deviant, as demonstrated for the N400 in Figure 24.2b. Each component also has a specific topography. For example, MMN shows negativity at frontocentral sites and positivity at inferior sites, as shown in Figure 24.2c, and eLAN shows left inferior anterior negativity and right posterior positivity, as seen in 24.2d. It is important to note that the elicitation of several of these responses is not exclusive to speech and language processing. The MMN is elicited to deviant detection for nonspeech auditory stimuli (review in Näätänen, Paavilainen, Rinne, & Alho, 2007). The eLAN and P600 responses have been shown to be sensitive to anomalies in musical syntax as well as linguistic syntax; that is, deviant patterns in music elicit both an eLAN-like response and a P600 response (Maidhof & Koelsch, 2011; review in Koelsch, 2011). Our understanding of the factors leading to the elicitation of language-related components, however, is not complete. There is some controversy about whether some of the responses indicate linguistic processing only or at all. For instance, authors have argued that the P600 response is merely a late member of a family of large, late positivities (generally called the P300, or P3b components) that are elicited by deviant or infrequent patterns of information (review in Federmeier, Kluender, & Kutas, 2003; but see Frisch, Kotz, von Cramon, & Friederici, 2003 for a counterargument). Also, the eLAN has recently come under scrutiny. Its very early latency suggests that it is a sensory response, or at least sensitive to sensory information, rather than an amodal response to syntactic anomaly. Indeed, in an MEG study, Dikker, Rabagliati, and Pylkkänen (2009) showed that an early negativity to syntactic violations was observed over the visual cortex when stimuli were presented visually (as written sentences), while studies using auditory sentences found an early left negativity (eLAN) over the left anterior cortex. They concluded that the eLAN is a marker of surprise based on the predicted visual or auditory representation of an upcoming word, rather than an early indicator of phrase-structure building. Steinhauer and Drury (2012) make an even stronger criticism, suggesting that the eLAN is merely a side effect of how the data are baseline corrected and that the eLAN reflects a very late effect from processing occurring in the segments prior to the epoch containing the early negativity. Despite these debates, because some ERP components to language processing, like the N400 and MMN, are uncontroversial, and because others, like the P600, have been well-tested and are known to be elicited by particular types of linguistic stimuli, ERP methods are extremely useful for studying language processing in children with typical development and those with language disorders. Electrophysiology is relatively tolerant of movement, which is difficult to control in

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b

a

c

d

Figure 24.2 ERPs typically recorded in speech and language studies show at the vertex (CZ) referenced to the nose. (a) P1, N1, and P2 latencies are determined primarily by the physical properties of the stimulus. The amplitude of these components is dependent on stimulus intensity and on interstimulus interval (ISI). The 250-ms stimulus was presented with a shorter ISI (350 ms) compared to the CVC words (1,500 ms) and thus shows greater amplitude attenuation. Child N1 and P2 peaks to the CVC words occur approximately 30 ms later than the adult peaks. (b) The subtraction of the second word of a pair of words differing in consonant onset (bad—gad) from the second word of a pair with no change (gad—gad) is a useful way to identify a component peak, in this case a phonological N400 at site PZ (see Shafer, Schwartz, & Kessler, 2003). (c) The time course of the topography of MMN is illustrated by graphing a left superior central site (C3) against left and right inferior mastoid sites (LM and RM) (see Shafer, Morr, Datta, Kurtzberg, & Schwartz, 2005). (d) The early left anterior negativity (LAN) shown in a contour maps for one time-point at all sites (see Hestvik, Maxfield, Schwartz, & Shafer, 2007).

young children. It is also noninvasive and, thus, is a low-risk method that can be used with typical populations. Figure 24.3 shows a child wearing a 65-electrode Geodesic net, after she has comfortably sat through a 30-minute speech perception study. Children as young as 2 years of age exhibit speech- and language-related ERP components; however, there are striking differences between child and adult data. In particular, child ERP components peak at later latencies (see Figure 24.2a), but there are also instances in which child ERP data show an additional component that does not appear to be directly related to an adult component, such as the positive mismatch response (MMR) found in infants (e.g., Shafer, Yu, & Datta, 2011). A large body of work has used ERPs to investigate developmental language disorders (see Chapter 7 by Epstein & Schwartz) and illustrate the value of these methods. For example, a number of

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Figure 24.3 A four-year-old child wearing a 65-electrode Geodesic net. Application of the net and checking contact impedances takes between 10 and 20 minutes. In this study the child then listens to speech sounds for 20 to 30 minutes while watching a movie with the sound muted.

studies show reduced or absent MMN to speech and nonspeech auditory contrasts in children with language impairments or dyslexia (e.g., see Bishop, 2007, for review). Several other studies find that children and adolescents with SLI show deviant or even absent ERP components to language, including the eLAN, LAN, N400, and P600, and have extrapolated from these results possible underlying contributors to language impairment, such as problems with auditory and prosodic processing (e.g., Sabisch, Hahne, Glass, von Suchodeletz, & Friederici, 2009; Weber-Fox, Leonard, Hampton Wray, & Tomblin, 2010). There is also interest in determining whether some ERP measure will be able to provide early identification of children who are at risk for developmental language disorders. There is evidence that certain ERP components are deviant in infants with familial risk for SLI and in those retroactively diagnosed with SLI (e.g., Weber, Hahne, Friedrich, & Friederici, 2005; Benasich, Choudhury, Friedman, Realpe-Bonilla, Chojnowska, & Gou, 2006; Choudhury & Benasich, 2011) or dyslexia (Leppanen, Pihko, Eklund, & Lyytinen, 1999; Guttorm, Leppänen, Tolvanen, & Lyytinen, 2003). Several studies also suggest that the T-complex peaks are reasonably sensitive measures of language impairment (LI) and could possible serve as early markers of risk for LI (e.g., Bishop et al., 2012; Shafer et al., 2011a). As of yet, it is unknown which ERP measure(s) from infants will be most predictive of language outcome. A few methodological issues must be considered when designing a study for children and for participants with developmental disorders. Excessive movement during testing can lead to considerable loss of data because noise from movement is large in amplitude relative to the small signal of interest. Children generally show more movement artifact than adults do. In addition, children cannot tolerate long sessions compared to adults. Thus, the proportion of usable data is lower for children than it is for adult data, particularly for children with developmental disorders. For

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example, in an infant study using an oddball design, 60% of the trials were retained after cleaning for artifact (Shafer, Yu, & Datta, 2011), whereas adult rates are typically above 80%. In sentenceprocessing experiments, the rate is generally lower because a longer epoch is used (and thus, more opportunity for artifact). For example, in one of our studies with adults and children ranging from 3 to 11 years of age, the average percentage of retained trials for adults was 68%, while it was only 49% for children (Zane, Shafer, Kresh, Schwartz, & Benasich, forthcoming). The study also included minimally verbal children with ASD, and the average proportion of retained trials for this population was even lower, at 33%. A number of analysis strategies can be used to retain trials. In particular, several methods have been developed to correct for eyeblinks, including the use of independent components analysis (ICA). In addition, for multi-electrode data sets, data from a bad electrode can be replaced by interpolating the activity from surrounding electrodes. Most software packages designed for EEG data include the tools to carry out these data-cleaning techniques. However, data-cleaning techniques cannot correct for all noise. Thus, it is important to include a sufficient number of trials and develop techniques to encourage less movement in difficult-to-test populations. The number of trials needed is dependent on the component amplitude. For example, the MMN can be as small as 0.5 μV, whereas the P600 can be as large as 5 μV. Thus, MMN requires 150 to 300 trials, whereas a P600 difference can be evaluated with as few as 15 trials.

Magnetoencephalography (MEG) MEG is less commonly used with pediatric populations because it requires the participant to remain stationary. However, it offers a unique picture of brain function, and thus the effort needed to study children using MEG is worthwhile. MEG complements the ERP method in that it can better index neural activity in cortical sulci than EEG, which is more sensitive to activity along gyral surfaces. MEG also provides good temporal resolution, similar to ERPs. Figure 24.4 shows a child who has successfully participated in an MEG study examining language processing.

Figure 24.4 The magnetometers are housed in the container surrounding the child’s head. (Image courtesy of Jeffrey David Lewine, PhD, Director, MEG Program, Hoglund Brain Imaging Center.)

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MEG is a measure of the magnetic fields at the scalp surface, which arise from electromagnetic source currents. The magnetic field is perpendicular to the source current, and its direction is such that if the thumb of the right hand points in the direction of the source current flow, the direction of the magnetic field is that of the curled fingers. These magnetic fields are not distorted by passing through tissue and bone to reach the head surface, which, in principle, allows for more accurate source localization than ERPs (see Nunez & Srinivasan, 2006, pp. 84–90). The magnetic fields measured in MEG are primarily source activity from dendritic currents, because activity at synaptic and axon terminals cancel out. MEG is poor at detecting deep sources and sources oriented perpendicularly to the scalp (i.e., on the surface of gyri), because the magnetic fields are too weak at the locations where they emerge from the head (Papanicalaou, 1998; Nunez & Srinivasan, 2006). The magnetic fields are recorded by instruments called magnetometers, which are arranged such that they surround the head. A magnetometer consists of a loop of wire placed parallel to the surface of the head. Magnetic field (or flux) lines pass through the loop and induce a current that is proportional to the density of the flux strength. The intensity of these currents is so small that the instruments need to be cooled to 4 degrees Kelvin to make them superconductive. This necessity leads to MEG experiments being considerably more expensive than those using EEG. The intensity of the recorded signal is proportional to the distance from the source and to the orientation of the flux lines. Lines that are closer to perpendicular induce greater current than do those that approach parallel. This knowledge makes it possible to infer the source of a signal, although the inference is never certain because researchers typically do not know how many sources contributed to the signal (as is discussed further under interpretation of neuroimaging data). Some of the common language-related MEG components directly correspond to ERP components in terms of the time windows in which they occur and the type of stimuli used to elicit them. For example, the P50m and M100 are obligatory responses to visual or auditory stimuli, indexing activation in the respective sensory cortical regions. Endogenous MEG components to language include the mismatch magnetic field (MMF) (corresponding to the ERP MMN), a left temporal component peaking at 300–450 ms post stimulus onset (M350) (corresponding to the N400), which indexes lexical access and semantic integration, an early (100–180 ms) activation in the anterior superior temporal gyrus, which indicates morphosyntactic processing, and a late activation (600–800 ms) (corresponding to P600) in temporal regions (Service, Helenius, Maury, Salmelin, 2007; review in Friederici, 2011). The same guidelines for designing speech and language stimuli and tasks for EEG studies apply for MEG studies. Several MEG measures have been found to predict later language skills (Cardy, Flagg, Roberts, & Roberts, 2008; Yoshimura, Kikuchi, Ueno, Shitamichi, Remijn, Hiraishi, et al., 2014). MEG has also been used in a few pediatric studies of older children with various disorders (e.g., Paul, Bott, Heim, Eulitz, & Elbert, 2006). For example, Lewine, Andrews, Chez, Patil, Devinsky, Smith, et al. (2005) used MEG to show that some children with regressive autism spectrum disorders have multifocal epileptiform activity, including the same perisylvian regions that underlie the language breakdown in Landau–Kleffner syndrome. They also found that MEG showed greater sensitivity to this deviant activity than did EEG (Lewine et al., 2005). This study illustrates the utility of MEG for examining childhood behavioral disorders, since the behavioral manifestation of seizures had not been observed for half of the children with autism.

Functional Magnetic Resonance Imaging (fMRI) Functional MRI takes advantage of the fact that oxygenated blood and deoxygenated blood have different magnetic properties. Increased metabolic activity of a brain region requires more glucose and oxygen to be sent there, and thus there is increased blood flow. The increased blood flow

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delivers greater amounts of oxygenated blood to the activated area of brain tissue. Even though the trigger for this increased blood flow is oxygen depletion, the measurable difference in metabolism is observed following this depletion as an increase in the amount of oxygenated compared to deoxygenated blood, leading to a more homogenous magnetic field and, thus, to greater signal intensity. This method, which involves measuring these magnetic fields with a magnet, can provide fairly precise localization, on the order of millimeters, but has relatively poor temporal resolution, given that the increase in blood flow occurs 5–8 seconds following the event (Papanicalaou, 1998; Bookheimer, 2000; Price, 2012). Using fMRI with children is challenging for several reasons. As with MEG, participants must remain still while in the magnet, or else the spatial resolution is compromised. Methods for correcting the data for movement have been developed and improve the possibility of acquiring good data from more challenging populations (e.g., Nöth, Volz, Hattingen, & Deichmann, 2014). The environment of the magnet can also be daunting, because it not only requires people to remain still with their heads inside an enclosed space for a long time, but also because the machine makes a loud noise while it is functioning. These requirements have led to researchers developing techniques to train children to hold still in the magnet and to acclimatize them to the magnet environment (Bookheimer, 2000; Seyffert & Castellanos, 2005). Even with these new techniques it will remain challenging to collect good fMRI data in children under the age of 6 years old, because young children find it difficult to remain still (while awake). Despite the difficulties in performing these studies with pediatric populations, there have been an increasing number of studies examining developmental language and communication disorders (e.g., Verly et al. 2013; see Chapter 7 by Epstein & Schwartz). For example, adolescents with SLI (mean age 13;10) were found to show less activation than controls in brain regions typically activated in attention, memory, and language processes using verbal working memory tasks (Weismer, Plante, Jones, & Tomblin, 2005). The designs of fMRI studies have become more sophisticated in recent years. The first studies typically presented a stimulus condition in a block and allowed listeners to passively listen (or look) at stimuli. These methods may result in a participant who is not fully engaging in the intended processing. Event-related designs, in some cases, are preferable because the conditions can be mixed, which will preclude developing strategies that might be different for conditions (Hugdahl, Gundersen, Brekke, Thomsen, Rimol, Ersland, & Niemi, 2004). In addition, introducing a task (e.g., button press to a target) ensures that the participant is engaging in processing the stimuli. Many of the design issues pointed out for EEG and MEG will hold for fMRI, particularly the necessity to keep the study short for pediatric populations. A particular concern in fMRI is controlling for task difficulty when comparing a disordered population to controls or in comparing different age groups (Bookheimer, 2000). Although this issue has been discussed openly in fMRI research, it should also apply to EEG and MEG.

Positron Emission Tomography (PET) PET works by detecting photons that are emitted during the process of decay of a radioactive substance with a relatively short half-life, such as Oxygen-15 (15O). When the radioactive substance decays, it emits its excess positive charge (positron). After traveling a short distance (generally less than 2 mm), the positron collides with an electron to produce two photons that travel in opposite directions with equal velocity. The detectors record the time and location of each photon, and an image is reconstructed by calculating the most probable origin of the collisions. The radioactive substances (radioisotopes) are made by bombarding stable atoms with protons. The extra positive charges are unstable and will be emitted over time, and each radioisotope has a characteristic halflife. This half-life—or the time a substance takes to lose half of its excess charge—determines the

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temporal resolution of the PET images, with short half-lives resulting in better resolution. The radioisotope is typically injected intravenously into the participant for rapid uptake by brain structures. FMRI, described previously, provides better spatial resolution than does PET, but it is limited to measuring oxygenation. In contrast, PET can be used to create images of how different regions use various substances in the brain, such as neurotransmitters and glucose, in addition to oxygen (Papanicalaou, 1998). While PET studies are generally not used with healthy pediatric populations because of their invasive implementation, studies of adult language processing and studies of neurological disorders can help inform our understanding of language processing in children and of underlying neural contributors to language disorder. For child populations, PET imaging has mainly been used only to study clinical cases that may require surgery, like epilepsy (Juhasz & Chugani, 2003). While studies like these do not directly examine language disorder, their results can inform our understanding of them. For example, since certain types of epilepsy (e.g., Landau-Kleffner) lead to language impairment, PET imaging of epileptic brains can reveal what role neural substances like neural transmitters play in these disorders, which may provide insight into the causes of corresponding language impairment. In addition, PET studies have helped define the language network underlying mature (adult) language processing (Price, 2012).

Functional Near-Infrared Spectroscopy (NIRS) Functional near-infrared spectroscopy (NIRS) is a recent, noninvasive method that can be used to investigate localization of brain activity associated with speech and language processing. Similar to fMRI, NIRS measures changes in brain hemodynamic responses. Functional NIRS takes advantage of the fact that skin and bone tissue are transparent to light in the range of 700–1000 nm, but hemoglobin and deoxygenated-hemoglobin absorb light in this range, and differences in absorption of these allow for measurement of changes in oxygenation. More specifically, two wavelengths, one above and one below the wavelength where the absorption coefficient is identical (810 nmisosbestic point) are selected. Light emitter/detectors are placed ipsilaterally on the skull, and the back-scattered light is recorded (e.g., Shalinsky, Kovelman, Berens, & Petitto, 2009). The light source and sensor need to be placed a distance of 3 to 6 cm from each other at ipsilateral locations. Because of limitations related to hair, most studies of adults focus on frontal and temporal areas. For example, a study of aging and language used five sources and 14 sensors covering frontal and temporal scalp regions over each hemisphere (Amiri, Pouliot, Bonnery, Leclerc, Desjardins, Lesage, & Joanette, 2014). The sources and sensors were placed in standard electrode positions used in the 10–20 system developed for EEG. Thus, the sensor cap looked very similar to electrode caps (see Figure 2 in Amiri et al., 2014). For the source light to penetrate the scalp, however, it is necessary to make sure that the pathway is not blocked by hair. With EEG, the concern is creating a good, conductive contact with the scalp, by using a saline solution. Compared to fMRI and PET, NIRS is considerably less expensive and more feasible for use with very young children. It is portable and more tolerant to movement. NIRS also has better temporal resolution (100 Hz) compared to fMRI. However, NIRS is limited to measuring brain activity to a depth of 5 to 10 mm beneath the skull inner surface in adults. Lateral spatial resolution is limited by the separation of the light source and light detector, which is typically about 3 cm in adults. This resolution, however, is better than for EEG and MEG, because one can have certainty that the source signal is actually within the 3 cm region. Several recent studies demonstrate the utility of this method for studying noncompliant populations (e.g., Bortfeld, Wruck, & Boas, 2007; Bortfeld, Fava, & Boas, 2009; Minagawa-Kawai, Cristià, Vendelin, Cabrol, & Dupoux, 2011; Wagner, Fox, Tager-Flusberg, & Nelson, 2011). For example,

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Gomez and colleagues used NIRS to study newborn infants’ neural responses to different types of syllable structures (Gomez, Berent, Benavides-Varela, Bion, Cattarossi, Nespor, & Mehler, 2014). A few studies have begun to use NIRS to examine speech or language processing in clinical populations (e.g., Kuwabara, Kasai, Takizawa, Kawakubo, Yamasue, Rogers, et al., 2006; Gallagher, Thériault, Maclin, Low, Gratton, Fabiani, et al., 2007). As a first step, several studies compared results of NIRS to fMRI. Gallagher et al. (2007) showed that NIRS was able to reveal lateralization for language in epileptic and control children, comparable to fMRI. Sevy and colleagues showed similar NIRS and fMRI patterns in adults and then went on to demonstrate that NIRS revealed speech-evoked activity in 82% of normal-hearing children and 78% of children with cochlear implants (Sevy, Bortfeld, Huppert, Beauchamp, Tonini, & Oghalai, 2010).

Interpretation of Imaging Data In interpreting brain images, it is necessary to consider a number of factors. Images of brain structure and function constructed from these various methods represent different aspects of the actual processes and structures. The relationship between the representations and the reality of these functions and structures is dependent on what is being measured (e.g., electrical current, blood flow), the nature of the recording instruments, and the choice of techniques for constructing the images. These factors are discussed in the following sections.

Activation Functional images are typically described as representing brain activation. This activation, however, can be the result of neural signaling or changes in metabolism (Papanicalaou, 1998). EEG and MEG measure aspects of neural signaling, whereas fMRI and NIRS measure aspects of metabolism. PET reflects aspects of neural signaling when neurotransmitters are tagged and aspects of metabolism when oxygen or glucose is tagged. The spatial and temporal resolutions of neural activation are limited by what aspect of function is measured. For example, the spatial extent of activation of neural tissue is delimited more precisely by neurotransmitter release. In contrast, increases in oxygen consumption would lead to increases in oxygen in the activated neural tissue and in the neighboring blood vessels. The spatial extent of activation of neural tissue in this latter case can only be estimated from the metabolic activation, because the relationship between extent of neural activation and blood supply is not precisely known. In addition, the temporal interval of neural tissue activation is precisely delimited by changes in electrical potential but is less precise for changes in metabolism. These factors limit the spatial and temporal resolution of neural activation as measured by a particular method. Thus, improvements in the resolution of the instrumentation or analysis techniques do not necessarily lead to improvement in resolution of neural activation. The physiology of the system limits the resolution. For example, the blood vessels can become stimulated over an area of a few mm in diameter near the area of neural activation (Howseman & Bowtell, 1999). In addition, oxygenation changes can also be found in the venous system downstream of the neural activation (Howseman & Bowtell, 1999). Thus, intrinsic physiological properties of the system limit resolution beyond what can be attained via the instrumentation. An issue of interpretation specifically affecting fMRI and NIRS is that signal intensity is a relative measure rather than being a direct measure of blood flow, because a number of variables, other than blood flow, can affect the intensity. For this reason, interpretation of fMRI (or NIRS) data requires comparison of signal intensity across at least two conditions. Bookheimer (2000) pointed out that this factor leads to challenges in pediatric research. In particular, it is important

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to equate performance on control tasks between groups (as mentioned above). Otherwise, apparent differences in the experimental variable could, rather, be due to differences in performance on the control task. An example of this problem is the following: increases in blood flow are often related to increases in the rate or effort of task performance, and decreases in blood flow are typically interpreted as reflecting relative ease of performance. However, in a study where children and adults are given tasks with varying levels of difficulty, results might reveal that children show decreased blood flow in more difficult tasks. It would be strange to interpret these results as suggesting that children find the more difficult tasks easier than the adults do or easier than the less difficult tasks. A more likely interpretation is that children stop performing the tasks altogether when they become too difficult. This example shows the importance of using accompanying behavioral measures during fMRI, where an increase in error combined with decreased blood flow would suggest that the participant had struggled with the task so much that s/he had stopped attempting it. Similarly, it is possible that differences in task difficulty lead to the use of additional brain regions to perform the task. Thus, finding additional areas of activation in an impaired group versus a control group may indicate the recruitment of additional areas in performance of a difficult task rather than a deviant pattern of processing (Bookheimer, 2000). These potential confounds apply, to different degrees, to all studies comparing groups differing on some dimension.

Instrumentation The instruments also impose limitations on spatial and temporal resolution. Spatial resolution is limited by the number and size of detectors. For example, older PET instruments had fewer and larger detectors and thus poorer spatial resolution than do newer instruments. MRI instruments also vary in magnetic strength (1 Tesla is weaker than 3 Tesla), and more powerful magnets can provide better spatial resolution. As mentioned above, use of overlapping light source/sensor configurations can improve spatial resolution of NIRS. EEG and MEG have improved the spatial resolution dramatically in the past decade by increasing the number of electrode scalp sites/sensors, respectively; many current systems record from more than 60 sites/sensors. Different factors of instrumentation limit the spatial resolution of PET, MEG, and EEG. The spatial resolution is limited by the distance of the recording instruments from the brain source for EEG and PET because the signal becomes distorted with increasing distance from the source. Spatial resolution of PET is a function of the distance a positron travels before a collision, which releases positrons. Also, photons are increasingly likely to be deflected when the source is in deep brain structures, because they must travel through a greater extent of tissue. In EEG, signals become distorted due to the varying level of conductivity of different tissues and to the summation at the scalp of electrical activity from an unknown number of sources. MEG signals do not suffer from distortions in traveling through tissue, but the number of sources contributing to the signal is typically unknown (Nunez & Srinivasan, 2006). In contrast, identifying the location of source signals using MRI and fMRI is precise, because a unique code for position is provided in terms of signal frequency. Thus, in this latter case, spatial resolution of metabolic activation is limited only by the size of the pixel that can be measured by the instrument (Papanicalaou, 1998). NIRS has better spatial resolution than MEG or EEG but is more limited than fMRI. NIRS is limited related to depth (5–10 mm) and lateral extent. Usually, lateral resolution is around 3 cm for adults and 1.5–3 cm for infants, but resolution can be improved by a factor of two by using overlapping sensors (Boas & Franceschini, 2009). Functional near-infrared spectroscopy (fNIRS) is particularly useful for examining infants, because they have small heads and little hair (because hair can block the light), allowing imaging in posterior brain regions.

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The temporal resolution of the instruments is determined by the sampling rate and can be quite high (on the order of a millisecond) in the case of EEG and MEG, and tens of milliseconds for NIRS (Boas & Franceschini, 2009). The temporal resolution of PET is limited by the half-life of the chosen isotope. Specifically, to determine whether there is increased activation, the instrument needs a sufficient number of photon pairs. An isotope with a shorter half-life leads to a greater number of photons emitted per unit of time and, thus, to better temporal resolution than an isotope with a longer half-life. The temporal resolution of MRI/fMRI is poor (related to the time scale of changes in blood oxygenation, on the order of 8 seconds). Another factor of instrumentation that must be considered when studying child populations is the size of the equipment, because conventionally sized equipment, designed for adults, may not acquire accurate measurements from children. For PET scans and fMRI, the size of the system is not an issue, since these measurements do not require equipment to fit to the head. EEG and NIRS recordings require that electrodes and optodes (respectively) make direct contact with the scalp, so smaller caps must be purchased for use with children. For MEG, some authors argue that an entirely separate, smaller-sized MEG system should be used for children in order to ensure that sensors make appropriate contact with the head and, thus, get accurate measurements (Yoshimura, Kikuchi, Shitamichi, Ueno, Munesue, Ono, et al., 2013).

Analysis Each method presents unique challenges in developing analysis techniques that will provide the most valid and reliable representation of a particular function or structure. One challenge facing the construction of PET images is in developing methods for estimating sources, given that there is noise in the data from deflections of photons and from erroneously pairing two photons that do not derive from the same positron source. A second challenge, which is a concern for fMRI and NIRS as well, is determining what level of activation above the baseline noise should be considered to be significant. The choice to use a more restrictive significance level will lead to a smaller area showing significant activation. Thus, the wrong decision can lead to erroneous interpretation of the data. DTI presents a different challenge, in that tracing the fiber tracts is laborious because they require a researcher to map out the tracts; the more automatic methods of tractography are currently suboptimal (personal communication, Susanne Reiterer). Specifically, the automatic methods of tracing tracts do not show high agreement with manually-traced tracts. The major analysis challenge facing EEG and MEG is determining the number of sources or components contributing to the electromagnetic signals recorded at the surface. Sources are locations of the neural activation. Components reflect neural activation that covaries. Using a known set of source signals and their strength and direction, it is possible to predict the surface distribution of electrical potential or magnetic flux. However, any given surface distribution can be generated by multiple solutions (Mosher, Baillet, & Leahy 1999; Tadel, Baillet, Mosher, & Leahy, 2011). Thus, it is impossible to determine the location, direction, and strength of the sources of the scalp distribution without having some a priori information. Unfortunately, in research with humans, it is unlikely that we will have prior knowledge of all the sources contributing to a signal or their signal strength and direction. Even so, researchers have developed methods that can be used to make intelligent guesses about the number of sources, location of sources, direction of signals, temporal properties of components, and strength of components. These methods are only beginning to be widely used, since the advent of multichannel recordings. Techniques such as principal components analysis (PCA) and independent components analysis (ICA) have been used to identify combinations of electrodes (spatial mode) or time-points (temporal mode) at which similar EEG/

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ERP or MEG variance was recorded (e.g., Makeig, Jung, Bell, Ghahramani, & Sejnowski, 1997; Hyvarinen, Karhunen, & Oja, 2001; Spencer, Dien, & Donchin, 2001). Inverse modeling of brain sources has become increasingly popular over the past decades, particularly for MEG data (Mosher, Baillet, & Leahy 1999; Darvas, Pantazis, Kucukaltun-Yildirim, & Leahy, 2004; Tadel, Baillet, Mosher, & Leahy, 2011). The goal of this modeling is to identify the current sources that generated to electromagnetic signals detected at the scalp (e.g., Scherg, Vajsar, & Picton, 1989). Because the inverse solution is greatly underdetermined, it is necessary to constrain the possible solutions. Constraints can be imposed on the researcher; for example, the source is fixed in auditory cortex and only allowed to vary in strength and orientation. Alternatively, a general constraint can be imposed; for example, only sparse solutions are considered, meaning that the final solution can only include a small set of dipole sources. This latter “sparse” constraint is suggested for processes that are expected to have only a limited number of contributing sources. These methods require using a priori knowledge of where sources are likely to be, in the first case, and whether sources are likely to be few in number, in the second case. Figure 24.5 shows the results of a dipole analysis of the child P1 (or P100) component to a speech sound plotted on a standardized MRI. Methods that use information for all the sensors/sites, but that do not require a priori decisions about sources, include calculating Global Field Power (GFP) and spatial correlations. GFP is used to calculate the peak and the temporal extent of variance across scalp sites (Lehmann & Skrandies, 1980). Power can be calculated as the standard deviation or root mean square (RMS) across sites for each time point. This method is useful for determining timing of peak activation because the dipolar nature of many of the EEG sources result in large variance across sites that are distributed fairly evenly across the scalp. Comparison of ERP/EEG topographies using spatial correlations (Pearson’s r) (note that Global Dissimilarity is a transformation of Pearson’s r) is an objective method for determining whether topographies are significantly different between groups, conditions, or successive times (Tzovara, Murray, Michel, & De Lucia, 2012). Detailed descriptions of these different methods are beyond the scope of this chapter, but it is necessary to have a thorough understanding of them in order to evaluate their validity (for an introduction, see Luck, 2014; for advanced reading, Nunez & Srinivasan, 2006; Cohen, 2014).

Figure 24.5 Results of a dipole analysis of the child P100 component to a speech sound plotted on a standardized MRI (courtesy of Curtis Ponton).

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Convergence of Methods Each of the neuroscience methods described so far has different strengths and limitations. Thus, the most powerful approach to studying the neurobiological basis of developmental language disorders is to use all of these methods. Having results from several different methods point to a common finding can only strengthen confidence in that finding. In addition, data from multiple methods is likely to lead to the creation of markers of risk for a disorder that have greater reliability than those derived from one method. For example, Ingalhalikar and colleagues used MEG and DTI measures as a means to classify children as typically developing, ASD-plus-language-impairment, or ASD-minus-language-impairment (Ingalhalikar, Parker, Bloy, Roberts, & Verma, 2014). They determined that use of measures from more than one type of neuroimaging data resulted in improved classification of participants in each group. It will also be important to tie the results from neuroimaging studies to the micro level of brain structure and function. Several examples of why this can be informative follow. Evidence from structural cytoarchitectonics can help interpret findings from ERP investigations. For example, structural cytoarchitectonics has been used to examine maturational changes. Specifically, researchers have shown that primary cortical regions, which are dominated by Layer IV or Layer V, mature earlier than association cortex, which is dominated by Layers II and III (Huttenlocher & Dabholkar, 1997). The deficits found in developmental language disorders have not been investigated in terms of deviancy or differences at this cellular level. However, there are studies relating changes in neurophysiological measures to maturation of cortical layers, which illustrate the importance of this knowledge. Specifically, Tonnquist-Uhlen, Ponton, Eggermont, Kwong, and Don (2003) suggest that the predominance of a positive component (P1) and absent negative component (N1) around 100 ms in the child auditory evoked potential (AEP) reflects early maturation of Layer IV in the superior temporal plane of secondary auditory cortex (seen as P1) and immaturity of Layers II and III in primary and secondary cortex (resulting in absent N1). The Ta and Tb AEP components recorded at temporal sites (approximately 1 cm above the ear) reflect activation of cells in early-maturing Layer IV of secondary auditory cortex on the lateral surface (TonnquistUhlen et al., 2003). P1 and Ta/Tb index parallel streams of input from the thalamus to auditory cortex. We have found across four different experiments of children with SLI that a small subset of children with SLI have delayed P1 latencies (Shafer et al. 2007), but a larger proportion have reduced amplitude Ta components (Shafer, Schwartz, & Martin, 2011). Our knowledge of the cortical source of Ta and the structural design of this cortical region allows us to speculate that deviant processing is occurring in Layer IV on the lateral surface of secondary auditory cortex and that, possibly, maturation of Layer IV in auditory cortex is particularly delayed or deviant. Thus, relating these different research findings allows development of novel hypotheses and research directions. It will also be important to relate neuroimaging findings to the underlying biochemistry. As discussed above, we pointed out the deviant AEPs seen at temporal electrode sites in children with SLI might indicate deficits in Layer IV of the lateral secondary auditory cortex. To understand why these AEP components are abnormal, we ultimately need to go further and determine whether the deviance is due to structural deficits, such as a reduced number of neurons in Layer IV or a delay in myelination of axons, or is due to biochemical deficits, such as reduced levels of some neurotransmitter or abnormality of receptor cells for a particular neurotransmitter. Recent studies of persons with schizophrenia suggest a deficit in NMDA receptors may contribute to the disorder and that this deficit may account for attenuated MMN responses to auditory information (Näätänen, Sussman, Salibury, & Shafer, 2014). Studies with a similar focus on the neurotransmitters and neural function need to be undertaken for developmental disorders. For example, recent studies are examining treatment of ASD using neurotransmitters that target the synaptic and signaling

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abnormalities that are purported to underlie dysfunction (e.g., Canitano, 2014). Directly examining how such treatment programs affect neurophysiological indices from many perspectives will be important to further our understanding of the system. A few investigations of childhood cognitive disorders suggesting abnormalities related to biochemistry illustrate the importance of examining language disorders in these terms. Researchers have argued that the neurotransmitter serotonin plays a role in cortical abnormalities that underlie autism and epilepsy (Chugani, 2004). This type of knowledge provides the impetus for investigating possible reasons for abnormal amounts or uptake of neurotransmitters, such as inadequate diet, genetics, and so on. In another example, learning-disabled children seem to have significant differences in the levels of a number of different elements (e.g., manganese, lead; Kolb & Wishaw, 2009), and these elements can affect neurotransmitters or be the by-product of abnormal biochemistry.

Conclusions The past 20 years of research demonstrate that neurobiological methods provide valuable information regarding developmental language disorders. The value of this research is greatly increased if we have information from multiple methods, because each method measures different aspects of function and structure and provides different levels of spatial and temporal resolution. A continuing challenge for research in this area is in developing methods that can be used with young children and infants. Despite the difficulties in this endeavor, the information that will result from these studies makes this challenge worth meeting.

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600

AUTHOR INDEX

Aarons, G. 569 Abbeduto, L. 54–67, 91, 371, 372, 428 Abbott, R. 491 Abdulsabur, N. 71 Aboitz, F. 189, 190 Abrahams, B. S. 267 Abramoff, B. 248 Abrams, D. 489 Accardo, P. J. 86 Adam, G. 340, 508 Adam, T. 569 Adams, A. M. 213, 219, 228 Adams, C. 94, 171, 242, 308, 393, 425, 430, 431, 442, 444, 443, 446, 448, 451, 453 Adams, C. V. 6, 368 Adams, J. 201, 565 Adams, M. 462, 464, 470 Adamson, L. 88 Addis, L. 368 Adi-Japha, E. 366 Adlard, A. 368 Adlof, S. M. 23 Adolf, S. 245 Adolphs, R. 56 Agocs, M. M. 33 Aguilar, J. M. 566, 567 Agyepong, I. A. 569 Aharodnik, K. 21 Aharon, I. 92 Ahlgren, M. 396 Ahmed, M. M. 53 Ahn, E. 483 Ajmone, P. F. 72 Akhtar, N. 536 Alarcon, M. 267 Alario, F. X. 280, 510 Albers, R. J. 371

Alberti, A. 485 Albrecht, R. 29, 199, 201, 486 Albro, E. R. 312 Alcock, K. J. 193, 261, 281 Aldred, C. 453 Alegria, J. 279 Alexander, A. 461 Alford, S. 367 Alho, K. 201, 486, 584 Allaire, C. 518 Allal, L. 313 Allen, D. 430, 447 Allen, E. 469 Allen, E. G. 53 Allen, G. D. 9, 195, 240 Allen, J. 86, 447 Allen, M. 201 Allen, R. E. 398 Alloway, T. P. 10, 213, 225, 373 Alm, P. 366 Almazan, M. 282, 285, 373 Almgren, M. 298 Almkvist, O. 502 Almodovar, D. 173, 287, 501, 513 Almqvist, B. 122 Alonge, O. 569 Alpert, C. L. 404 Alred, C. 395 Alt, M. 21, 34, 308 Altmann, G. T. M. 549 Álvarez, E. 298, 304 Ambridge, B. 170, 177, 178, 275, 339 Ameel, E. 299 Amiri, M. 590 Amman, A. 63 Amorosa, H. 15, 225 Amunts, K. 188, 518

601

Author Index Anastasi, A. 121 Ancharski, A. 34 Anders, P. 475 Anderson, A. 135, 397 Anderson, B. 393 Anderson, D. 399 Anderson, J. R. 216 Anderson, J. S. 92 Anderson, K. E. 564 Anderson, M. 485, 489 Anderson, R. 310, 332, 405 Anderson, V. W. 443, 453 Andersson, R. 548 Andreu, L. 549 Andrews, R. 58, 588 Ang, Q. W. 267 Angell, A. L. 405 Angkustsiri, K. 71 Annaz, D. 373, 394 Ansari, D. 394 Anteunis, L. 110 Antonarakis, S. E. 53 Antshel, A. M. 71 Apel, K. 464 Apichatabutra, C. 474 Appel, A. 308, 309, 392, 402 Appelbaum, G. 205 Araki, T. 486 Aram, D. M. 26, 94, 257, 398 Arbataitis, K. 112, 113 Archbold, S. 118 Archibald, L. 246, 481 Archibald, L. M. D. 10, 229, 287, 373 Ardila, A. 505 Arfé, B. 310, 331 Ariel, M. 442 Arking, D. E. 267 Armon-Lotem, S. 307 Armstrong, C. 15 Arnold, D. S. 26, 257, 405, 509 Aronoff, M. 154 Asch, S. M. 565 Ash, A. C. 373, 374 Ashburner, J. 193, 261 Ashby, F. G. 216 Ashley, A. 86, 89 Ashwal, S. 86 Ashwin, C. 444 Aslin, R. N 177, 222 Atchley, R. 552, 553 Atkins, P. 276 Atkinson, C. M. 8 Auger, E. 27 Augustyn, A. 90, 91 Austin, J. L. 155 Auza, A. 310 Avrutin, S. 173

Aydelott, J. 489 Ayers, A. J. 34 Aylward, E. 138 Baayen, R. 532 Bachevalier, J. 219 Badcock, N. A. 194 Baddeley, A. 10, 54, 483, 487, 491 Baddeley, A. D. 213, 214, 217, 219, 221, 230, 259, 264, 274, 281, 287, 393, 394, 465, 505, 506 Badian, N. 464, 465 Bahnsen, A. 308, 309, 377, 392 Bahnsen, A. J. 91 Bahr, R. H. 304 Bahrick, L. 486 Bailet, L. 134 Bailey, A. 82 Bailey, A. J. 87 Bailey, D. 55 Baillet, S. 593 Bain, B. 313 Baird, G. 87, 88, 426, 429, 465 Bajo, T. 482 Bakeman, R. 88 Baker, P. S. 97 Baker, S. 474 Baker, W. J. 245 Bakker, S. 566 Balas, E. A. 570 Balason, D. V. 365, 377 Balbo, A. 53 Baldeweg, T. 193, 197, 486 Baldwin, D. A. 88, 98, 395 Ball, E. 464, 471, 472 Ball, W. S., Jr. 518 Balla, D. 58 Ballantyne, A. 201, 447 Balota, D. 532 Baltaxe, C. A. 90 Bamiou, D. E. 36 Bando, M. 72 Banks, J. 397 Bant, S. 112, 120, 121 Barac, R. 312, 483 Baraitser, M. 27 Barajas, J. 369 Barako Arndt, K. 422, 426 Baranek, G. T. 86, 90 Barbeau, E. 489 Barham, Z. 285 Barillot, C. 518 Barker, B. A. 116, 122 Barlow, J. A. 306 Baron Cohen, S. 86, 88, 95, 96, 98, 395, 444 Barreña, A. 298 Barrett, L. F. 14 Barrett, S. 238

602

Author Index Barrouillet, P. 224, 226 Barry, J. 486, 489 Barry, J. G. 194 Barry, R. 489 Barsalou, L. 485 Bartke, S. 308, 310, 373 Bartlett, C. 197, 258 Bartlett, C. W. 263, 265 Bartling, J. 201 Bartolucci, G. 93, 371 Bashir, A. 55, 62–3, 466 Bashir, A. S. 476 Bassett, A. 263 Bast, J. 398 Bastiaanse, R. 171 Bastin, M. 483 Basu, M. 242, 274, 481 Bateman, B. 130 Bates, D. 532 Bates, E. 68, 114, 245, 298, 301, 310, 312, 378, 394, 399, 403, 482, 533, 539 Bates, J. C. 91, 92 Battell, J. 6, 171, 422 Bauer, M. 571 Bauminger, N. 55 Bavelier, D. 371, 483, 490 Bavin, E. 88, 370 Bavin, L. E. 166 Baxendale, J. 453 Bayliss, D. 483 Bean, A. 395 Bean, A. J. 91, 92 Bean, L. H. 53 Bear, M. F. 54 Bearden, C. E. 71 Beate, P. 481 Beauchamp, M. H. 443 Beauchamp, M. S. 591 Beaujean, A. 483 Beaulieu, C. 138, 139 Beaumont, S. 72 Bebko, J. M. 121 Beccera, A. M. 52, 55, 67–70 Becerra, A. 428 Becerra, A. M. 394 Beck, I. L. 404, 475 Beck, S. J. 228 Beckel-Mitchener, A. 62 Becker, S. D. 561 Becker-Caplan, L. 513 Beckman, M. E. 153, 221, 239, 247, 507 Bedford, R. 91 Bedi, G. 9, 289 Bedore, L. 329, 331, 332, 337, 367, 368, 370, 378 Bedore, L. M. 22, 281, 297, 298, 300, 301, 302, 303, 304, 305, 306, 308, 309, 310, 311, 312, 313, 314, 315, 393, 507, 508, 509

Beeghly, M. 58 Beeler, T. 464 Beeson, M. 487 Beitchman, J. H. 26, 257 Bekkering, H. 96 Bel, A. 310 Bélanger, R. 308 Belardi, K. 86, 90 Belger, A. 490 Belichenko, P. 54 Bell, D. 594 Belletti, A. 168 Bellini, S. 451, 455 Belliveau, J. W. 96 Bellon-Harn, M. L. 380 Bellugi, U. 52, 55–6, 69, 205, 394, 428 Belser, R. C. 55, 63 Belton, E. 28, 193, 197, 261 Bemis, D. K. 519 Benali, H. 518, 519 Benasich, A. 486 Benasich, A. A. 26, 202, 241, 243, 257, 586 Benavides-Varela, S. 591 Ben-Bashat, D. 171 Ben-David, B. 488 Benetto, L. 430 Bennet, L. 112, 120, 121 Bennetto, L. 63 Benninger, K. L. 228 Benninger, W. B. 228 Ben-Shachar, M. 171 Benton, A. L. 94 Benton, G. 59, 62, 64 Berckelaer-Onnes, I. A. V. 95 Bereiter, C. 346 Berens, M. S. 590 Berent, G. P. 171 Berent, I. 591 Berg, J. M. 96 Berger, A. 505 Berger, F. 548 Berger, J. 196, 483 Bergland, E. 58 Berglund, C. 199, 202 Bergmann, C. 548 Berkley, R. 31 Berko, J. 281, 462 Berman, K. F. 428 Berman, S. 111 Bernard, B. S. 111 Bernhardt, B. 153 Berninger, V. 138, 267, 470, 491 Berns, A. J. 91 Bernstein, L. E. 290 Bernstein-Ratner, N. 508 Bernthal, J. E. 238 Berry, P. 58

603

Author Index Berry-Kravis, E. 54, 62 Bertelson, P. 279 Berthoud, I. 69, 373 Bertin, E. 486 Bertrand, J. 56, 68–70, 373 Berwick, R. C. 275 Besle, J. 486 Besner, D. 139, 289 Besson, M. 199 Best, C. A. 92 Best, T. 53 Beth, P. 63 Betjemann, R. 482 Betz, S. 552, 553 Betz, S. K. 368 Bever, T. G. 119 Beverly, B. L. 367, 509 Bhadha, B. 503 Bhakar, A. L. 54 Bharadwaj, S. 113 Bhat, M. 139 Bhatt, R. 486 Bialystok, E. 307, 312, 483 Biancone, T. L. 571 Biber, M. 221 Biber, M. E. 10 Bibyk, S. 548 Bidelman, G. 451, 455 Biedenstein, J. 120, 122 Biemiller, A. 404 Bigler, E. D. 92 Bihrle, A. 52 Biklen, D. 97, 98 Billington, J. 444 Billow, J. L. 25 Bion, R. A. 591 Biraben, A. 518 Biran, M. 171 Bird, A. 238 Birren, J. 489 Bishop, D. 57, 68, 219, 463, 486, 489, 539 Bishop, D. V. 119, 311, 586 Bishop, D. V. M. 3–4, 6, 8, 10, 15, 17–18, 20, 22, 25–7, 29, 93, 94, 189, 193, 194, 196, 197, 199, 201, 202, 204, 213, 217, 218, 219, 221, 242, 243, 246, 256, 259, 260, 267, 268, 275, 281, 290, 329, 367, 368, 372, 373, 374, 392, 393, 394, 398, 427, 428, 429, 431, 446, 447, 448, 489, 491, 529, 532, 539, 555 Bishop, M. V. D. 171 Bishop, S. J. 7–8, 17–18, 86 Bjornstad, P. G. 55, 428 Blachman, B. 464, 471, 472 Black, L. M. 90 Blackstone, S. W. 450 Blackwell, K. 489 Blagowidow, N. 72

Blake, R. 354 Blamey, P. J. 113 Blanco Elorrieta, E. 519 Bland, L. 351 Bland-Steward, L. 367, 351, 352, 355 Blank, M. 477 Blaxill, M. F. 82 Blennow, G. 199, 201, 202 Bless, D. 56 Bliss, L. S. 311 Block, N. 275 Blockberger, S. 534 Blom, E. 306, 310 Blossom, M. 367, 368 Bloy, L. 595 Boada, R. 482 Boas, D. 592, 593 Boas, D. A. 590 Bock, K. 508, 512 Bode, J. 213, 418 Boerger, K. M. 300, 303, 314, 315 Boets, B. 139, 581 Boggio, P. S. 92 Bogolepova, I. N. 188 Bohman, T. 303, 491 Bohman, T. M. 305, 306, 311, 312 Bohnenkamp, G. 112, 113 Boivin, I. 300 Bol, G. 330, 334 Boland, J. 170 Bolger, D. J. 582 Bolton, P. 82 Bondy, A. S. 97 Bonnery, C. 590 Bontempo, D. E. 379 Bookheimer, S. Y. 86, 89, 578, 589, 591, 592 Boons, T. 112, 120, 121 Booth, J. 546 Booth, R. D. 96 Booysen, L. 311 Borecki, I. 132 Borella, E. 397 Boren, S. A. 570 Borkowska, A. R. 503 Boroditski, L. 393 Borsky, S. 510 Bortfeld, H. 590, 591 Bortolini, U. 9, 310, 329, 331, 336, 337, 367, 368, 370, 507, 508 Bos, C. 475 Bosch, L. 300 Boser, K. 163 Bosman, A. M. 397 Bott, C. 588 Botting, N. 10, 308, 311, 367, 368, 371, 394, 402, 447, 454, 507 Botwinik-Rotem, I. 151, 159, 178

604

Author Index Boucher, J. 93, 429, 447 Boudreau, D. 59–60 Boult, J. 352 Bouras, C. 54 Bowers, P. 465 Bowey, J. 487 Bowler, D. M. 91, 92, 95 Boyd, B. A. 85 Bozikas, V. P. 505 Braden, J. P. 121 Braden, M. 55 Bradley, L. 140 Bradshaw, J. 485 Brady, N. 60, 62–3 Brady, S. 97, 289, 463 Bragard, A. 504 Brahmbhatt, K. 71 Brambati, S. 135 Brand, R. 538 Brandeis, D. 138 Brandeker, M. 507 Brandl, L. 56, 394 Brandt, S. 426 Braun, A. R. 518 Braver, T. S. 196 Brea, M. R. 305 Bredin-Oja, S. 24, 177, 223, 569 Breg, R. 62 Breg, W. 60, 62 Brekke, C. 197, 589 Brem, S. 138 Brenner, C. 111, 120, 122 Brewer, C. C. 72 Breznitz, Z. 488 Brian, J. 481 Brice, A. 453 Brier, J. 143 Briggs, R. 111 Briggs-Gowan, M. J. 19 Bright, P. 17 Brinton, B. 18, 248, 254, 446, 447, 449, 450, 451, 453, 454 Briscoe, J. 219, 329, 373 Brkanac, Z. 267 Brock, J. 221, 373, 394, 549, 550 Brodie, M. 299 Brogan, C. A. 86 Broks, P. 94, 448 Brokx, J. P. 112, 120, 121 Bronte, T. 485 Brookman, A. 246 Brooks, J. P. 511, 512, 514 Brooks, P. 60, 62, 312, 511, 512, 513, 514, 548 Brooks, P. J. 511, 512, 513 Brophy, P. J. 267 Brosseau-Lapré, F. 301, 314 Brown, A. L. 476

Brown, B. 379, 380 Brown, C. M. 184, 515, 516, 584 Brown, E. 88, 96, 538 Brown, G. D. A. 12 Brown, J. 142, 481 Brown, M. C. 516 Brown, P. M. 114, 115, 116, 117 Brown, R. 3, 174, 283, 366, 416, 417, 422, 431 Brown, W. T. 59–60, 62, 64–6, 372 Brownell, H. 170 Brownson, R. C. 566 Brownson, S. 405 Bruce, B. 308 Bruck, M. 132, 134, 137, 140, 142 Bruder, J. 201 Bruening, P. 512, 513 Bruer, J. T. 406 Brugge, J. F. 582 Brumbach, A. C. D. 23 Brundage, S. 432 Bruneau, N. 486 Bruner, J. 395 Bruno, L. 65 Bryant, B. 141 Bryant, C. 378 Bryant, P. 140 Bryk, A. S. 19 Bryson, S. 86, 485 Brzustowicz, L. M. 197, 258, 263 Bucher, K. 138 Buckingham, L. L. 268 Buckley, S. 372 Buckwalter, P. 98, 221, 238, 243, 259, 260, 306, 506 Budoff, M. 313 Buffington, D. M. 97 Buha-Durović, N. 488 Buitelaar, J. 92 Bukelis, I. 55 Bunce, B. 379, 380 Bunge, S. 483 Bunger, A. 569 Bunting, M. 221 Burchinal, M. 62 Burd, L. 94, 398 Burda, A. 450 Burdo, S. 117, 118 Burkholder, R. A. 121 Burlingame, E. 7 Burner, K. 552 Burnette, C. 96, 444 Burnette, C. P. 95 Burns, M. 141, 142 Burns, M. S. 33 Burns, N. 483, 487, 488 Burns, S. 312 Burton, R. 566, 567 Bus, A. G. 472

605

Author Index Busby, P. A. 121 Buschkuehl, M. 220, 229 Bushnell, I. W. R. 88 Butkovsky, L. 32, 380 Butter, F. 268 Butterworth, G. 394 Bybee, J. 283 Bybee, J. L. 306 Cabrol, D. 590 Cain, K. 398 Cairns, H. S. 359 Cairns, N. 53 Caldwell, M. D. 114 Caldwell-Tarr, A. 121 Calhoun, B. M. 3 Calhoun, S. 483 Calkins, S. 401 Callan, D. E. 86, 89 Caltagirone, C. 54 Calvin, W. H. 579 Camarata, M. 379, 380 Camarata, M. N. 32, 379, 380 Camarata, S. 308, 379, 380 Camarata, S. M. 32, 379, 380, 567 Camilleri, B. 313, 402 Camos, V. 224, 226 Campanelli, L. 12 Campbell, R. 28, 190, 192 Campbell, R. C. 94 Campbell, T. 213, 221 Campbell, T. F. 31, 111, 220, 221, 223, 393, 402, 505, 506 Canitano, R. 596 Cantor, R. M. 267 Cantrell, J. P. 367 Capirci, O. 69 Caplan, D. 11, 122, 465 Capone, N. C. 392, 453 Caprici, O. 372, 373 Caramazza, A. 171 Cardon, P. V. 15 Cardoso-Martins, C. 58 Cardy, J. 262, 481 Cardy, J. E. O. 588 Carey, A. 399 Carey-Sargeant, C. 396 Carlesimo, A. 54 Carlile, R. M. 213, 221 Carlisle, J. 467 Carlo, M. S. 301 Carlson, C. 71 Carlson, C. D. 503 Carlson, G. M. 170 Carlson, S. 486 Carney, D. 481 Carney, E. 483

Carney, E. J. 13 Carpenter, M. 97 Carpenter, N. 53 Carpenter, P. A. 14, 122, 227, 287 Carré, R. 398 Carrell, T. D. 201 Carretti, B. 397 Carroll, J. B. 533 Carron, J. D. 109 Carrow-Woolfolk, E. 56, 57, 61, 62, 65, 374 Carta, J. 406 Carter, A. 86 Carter, A. K. 115 Carter, A. S. 19, 444 Carter, C. 485 Carter, J. 138 Carver, L. 86 Cary, L. 279 Casby, M. 369, 381 Case, L. 93 Caselli, M. 70 Caselli, M. C. 9, 117, 118, 310, 329, 331, 337, 367, 368, 370, 372, 373, 507, 508 Casey, B. J. 186 Cass, H. 429 Casserly, E. D. 506 Castel, C. 280 Castellanos, F. 481 Castellanos, F. X. 589 Castles, A. 276, 278, 280 Castro, D. 298, 483 Cattarossi, L. 591 Catts, H. 140, 245, 462, 463 Catts, H. W. 23, 32, 204, 398, 503 Cauley, K. M. 538 Caulfield, M. 405 Cavallaro, M. C. 86 Caviness, V. S. 205 Caviness, V. S. Jr. 581 Cawthon, S. 56, 57, 60, 61, 63 Cazier, J. 265 Cepeda, N. 489 Čeponienė, R. 199, 201, 552 Cerella, J. 13 Chabernaud, C. 481 Chabildas, N. 134 Chabon, S. S. 509 Chabriat, H. J. 580 Chabris, C. F. 92 Chait, M. 486 Chakrabarti, B. 444 Chakrabarti, S. 86 Chakraborty, R. 246 Chall, J. 465, 466, 470 Chamberlain, J. P. 453 Chambers, D. A. 570, 573 Champlin, C. 241, 491

606

Author Index Champlin, C. A. 213, 241 Chan, A. S. 88 Chan, J. 25, 446, 448 Chandler, S. 87, 91 Chang, F. 508 Changeux, J. P. 186 Channell, R. 30 Chapman, K. 308 Chapman, R. 53, 54, 56, 57, 58, 59, 60, 62, 68, 239, 428, 429 Chapman, R. S. 116, 372, 394, 403 Charak, D. 95 Chard, D. 474 Charest, M. 333, 422, 507, 508 Charles-Luce, J. 244 Charlop, M. 97 Charlop-Christy, M. H. 97 Charman, T. 87, 91, 395, 426 Charnay, Y. 54 Chase, C. 447 Chawarska, K. 88, 90, 95 Chee, A. 487 Chein, J. M. 214 Chen, A. 90 Chen, S. P. 54 Chen, X. 138, 305 Chen, X. N. 53 Chenault, B. 491 Chenault, M. 110 Cheng, E. M. 564, 574 Cherkassky, V. L. 97 Cheung, D. 505 Cheung, J. 88 Cheung, M. 88 Cheung, R. 88 Chez, M. 588 Chhabildas, N. 483 Chi, J. G. 190 Chiappe, D. L. 289 Chiappe, P. 289 Chiat, S. 401, 404, 418 Chien, Y. C. 173 Childers, J. B. 34 Chiles, M. 56 Chin, S. B. 113, 114 Chipchase, B. B. 20, 398, 427 Chiras, J. 138 Cho, J. 483 Cho, M. 53 Cho, R. 485 Chobert, J. 487 Choi, Y. 548 Chojnowska, C. 202, 586 Chomsky, N. 151, 152, 160, 161, 164, 165, 168, 170, 172, 174, 175, 216, 275, 286, 333, 365, 416, 424 Chondrogianni, V. 307 Choudhury, N. 26, 202, 257, 486, 586

Chow, B. W. Y. 405 Chow, E. W. C. 263 Christiansen, M. 122, 487 Christiansen, M. H. 10, 249, 287 Christodoulou, J. 139 Chugani, D. D. 596 Chugani, H. T. 590 Chung, H. L. 538 Chynoweth, J. 243 Chynoweth, J. G. 221, 506 Cicchetti, D. 58 Cini, E. 88 Cirrin, F. M. 565 Clahsen, H. 163, 282, 285, 308, 310, 373, 429, 543, 546 Clark, C. A. 580 Clark, D. 134 Clark, G. M. 121 Clark, J. 92 Clark, J. M. 502 Clark, M. 194, 354, 358 Clarke, A. 489 Clarke, M. 392 Clarke, M. G. 19–20 Clarke, P. 284, 397, 398 Clarke, P. J. 404 Clark-Edmands, S. 474 Claverie, B. 505 Cleary, M. 121, 502, 505 Cleave, P. 367, 379 Cleave, P. L. 5, 32, 307, 367, 368, 369, 377, 393, 399, 561 Cleland, J. 90 Clement, C. J. 112 Clements-Stephens, A. 138 Clemmons, M. 20, 378, 392 Clery, H. 486 Cleveland, L. H. 351, 367 Clopper, C. 248 Coady, J. 222 Coady, J. A. 7–9, 240, 248, 308 Coch, D. 203, 491, 551 Cocking, R. 535 Coffey, S. 370, 552 Coffey, S. A. 29, 199, 201 Coggins, T. E. 442, 451 Cohen, D. 60, 62 Cohen, D. J. 90, 91, 95, 395 Cohen, H. 117 Cohen, I. 55, 63 Cohen, J. 485 Cohen, J. D. 196 Cohen, L. 138 Cohen, M. 28, 166, 190, 192 Cohen, M. X. 594 Cohen, M. J. 94 Cohen, W. 33

607

Author Index Colborn, D. K. 111 Cole, K. I. 405 Colé, P. 276 Colgan, S. E. 86 Collazo Alonso, A. 503 Colledge, E. 260 Colley, A. F. 72 Collins, B. A. 300 Collins, J. 71 Collis, G. M. 447 Colombo, J. 482 Colozzo, P. 311, 315 Colson, B. G. 111, 120 Coltheart, M. 274, 276, 278, 280 Compton, D. 143 Compton, D. L. 503 Conboy, B. T. 301 Concha, L. 138 Condouris, K. 430 Connell, P. 380, 381 Connelly, A. 193, 197, 261 Conners, F. A. 54, 56 Connolly, J. L. 109 Conrad, R. 121, 398 Constable, R. 134, 136, 138, 141, 143, 144 Conti-Ramdsen, G. 10, 12, 18–19, 21–2, 25–6, 217, 221, 308, 367, 368, 369, 371, 393, 394, 447, 448, 454, 481, 482, 507, 508 Conway, A. 487 Conway, A. R. A. 14 Conway, T. 142, 143 Cook, C. 509 Cook, E. H., Jr. 86 Cooke, R. 425 Cooney, J. 488 Cooper, J. 430, 463 Coplan, J. 114 Copp, A. 197, 217 Copp, A. J. 261 Corbett, B. A. 98 Corcoran, K. M. 244 Cordes, D. 138 Core, C. 298, 303 Corkum, V. 88 Cornelissen, K. 519 Cornish, K. 55, 63, 485 Cornish, K. M. 448 Corradi, N. 485 Correa, L. M. 166 Corriveau, K. 242 Corsello, C. M. 98 Cortell, R. 55 Cortese, M. 532 Cortese, S. 481 Cortez, C. 300 Costa, A. 53 Costa, D. 395

Courage, M. 485 Courchesne, E. 86, 201 Courrege, P. 186 Cowan, N. 10, 14, 213, 214, 221, 487 Cowan, R. 114, 115, 116, 117 Cox, A. 88 Coyle, C. 313, 315 Coyle, T. 483 Crago, M. 281, 306, 310, 331, 332 Crago, M. B. 22, 27 Craig, H. 59, 401 Craig, H. K. 25–7, 31, 355, 357, 441 Craig, I. W. 262 Craig-Unkefer, L. A. 453 Craik, F. I. M. 307 Crain, S. 17, 174, 533, 536, 537, 548 Crain-Thoreson, C. 405 Crais, E. R. 86 Creaghead, N. 451 Cristià, A. 590 Critchley, M. 130 Croley, K. 374, 375 Crossland, J. 140 Crossley, R. 97 Crowley, W. F. Jr. Genel, M. 573 Crowson, M. 88, 94, 98, 395 Crutchley, A. 425, 447 Crystal, D. 420, 432 Cummings, A. 199, 201, 552 Cummins, C. 537 Cummins, J. 315 Cunningham, A. E. 398 Cuperus, J. 502 Curran, G. M. 571 Curran, M. K. 561 Curtin, S. 247 Curtis, B. 276 Curtiss, S. 26, 257, 503, 508, 509 Cutler, A. 244 Cutting, J. C. 512, 514 Cutting, L. 138 Cutting, L. E. 504 Cycowicz, Y. 533 Cykowski, M. D. 581 Cynkin, L. 570, 573 D’Amico, S. 312 D’Esposito, M. 196, 483, 488 D’Odorico, L. 372 Dabholkar, A. S. 595 Dadlani, M. 430 Daemers, K. 113 Dahlsgaard, K. 509 Dailey, N. S. 566, 567 Dal Pont, E. 485 Dale, P. 68, 114, 378, 533, 535 Dale, P. S. 27, 260, 262, 297, 301, 378, 399, 403, 405

608

Author Index Dall’Ara, F. 72 Damarla, S. R. 97 Damian, M. F. 511, 512 Damico, J. S. 455 Danahy Ebert, K. 10 Danchin, A. 186 Daneman, M. 122 Danielsson, H. 481 Danna, M. 135 Dapretto, M. 86, 89 Daruna, J. H. 199 Darvas, F. 594 Das Gupta, R. 71 Das, J. P. Dasari, S. 85 Datta, H. 201, 280, 486, 584, 585 Daugherty, K. 282 Daumer, C. 53 Davelaar, E. J. 373 Davidi, S. 503, 513 Davidovitch, M. 87 Davidson, D. 532 Davidson, L. S. 111 Davies, B. 405 Davies, G. 483 Davies, K. E. 267 Davies, M. 56, 68, 69, 70, 373 Davies, P. 533 Davis, A. C. 116 Davis, B. L. 299, 300 Davis, E. 55 Davis, H. 485 Davis, J. 10, 23, 335 Davison, M. D. 303 Dawson, G. 85–6, 87, 88, 92, 94, 96, 97, 98 Dawson, J. 374, 375 Dawson, M. 489 Dawson, P. W. 121 Dax, M. 184 Day, L. S. 10 Deardorff, M. 72 Deary, I. 482, 483, 490 De Barbieri, Z. 265 DeBaryshe, B. D. 405 De Beukelaer, C. 113 DeCristofaro, A. 565 De Deyne, S. 504 Deevy, P. 6, 10–11, 16, 24, 31, 166, 177, 223, 310, 329, 331, 333–5, 339, 377, 380, 418, 422, 507, 508, 534, 541 De Fossé, L. 205, 581 DeFries, J. C. 132, 133, 482, 503 Degasperi, L. 310, 331 De Groot, A. M. B. 504 De Guibert, C. 518 Dehaene, S. 138 De Haan, M. 185

De Houwer, A. 297, 300, 306 Deichmann, R. 589 Dejaeme, S. 138 Dejerine, J. 135 de Jong, J. 418 Delattre, P. 338 Delcroix, J. 54 De Ley, L. E. 33 Dell, G. S. 508, 511, 512 De Lucia, M. 594 Demark, J. 55 Demonet, J. F. 398 Demuth, K. 22, 338 Denays, R. 204 Denckla, M. 138, 476, 503–4 Denning, C. B. 453 Dennis, M. 260 DePape, A. M. R. 90 Deprey, L. 71, 92, 163, 169 Derr, A. 312 Derwing, B. L. 245 DesBarres, K. 33 Desjardins, M. 590 Desroches, A. S. 276, 280, 284, 287, 289, 290 DeThorne, L. S. 365, 403 Dettman, S. 111 Deus, J. 194 Deutch, M. 346 Deutsch, G. 138–9 Devescovi, A. 312 de Villiers, J. 349, 393, 431 de Villiers, J. G. 164, 166, 171, 353, 354, 358, 359, 418 de Villiers, P. A. 164, 171, 353, 354, 358, 359 Devine, F. 86 Devinsky, O. 588 Devlin, J. 138 Devoti, M. 505 Devriendt, K. 71 De Zubicaray, G. I. 518 Dhooge, I. 121 Diaz, M. 505 Dick, F. 518 DiDonato Brumbach, A. 31, 34, 246 Diehl, V. 489 Dien, J. 594 Diessel, H. 223, 224, 424 DiFrancesca, S. 398 Dignan, P. 53 Dikker, S. 584 DiLalla, D. L. 87 DiLavore, P. C. 86, 395 Dillon, H. 111 Di Martino, A. 138, 481 Dimitrovsky, L. 140, 503, 513 Dinno, N. 88 Dirkx, J. 510

609

Author Index Dispaldro, M. 9, 15, 331, 485, 508 Disteche, C. 53 Doabler, C. 474 Dobrich, W. 20 Dobson, L. A. 56 Docherty, G. 239 Dockrell, J. 18, 20, 368, 392, 418, 454 Dodd, B. 313 Doherty, S. 52 Doi, L. 276, 503 Dolan, G. 54 Dolan, R. 135 Dolish, J. 59, 62, 64 Dollaghan, C. 213, 243, 247, 248, 432, 490, 505–6 Dollaghan, C. A. 10, 20, 111, 221, 307–9, 359, 365, 375, 377, 393, 402 Don, M. 199, 595 Donaldson, M. L. 308–9, 425 Donchin, E. 594 Donders, F. C. 13, 532 Donkers, F. 490 Donlan, C. 26, 221, 259, 393 Donnellan, M. 369 Donohue, B. 138 Dooling, E. C. 190 Döpke, S. 298–9 Doran, J. 397 Dorman, M. 113 Doron, E. 169 Dougherty, R. 139 Douglas, J. M. 368, 369, 372 Dove, G. O. 200 Dow, K. A. 244 Dowell, R. 111 Drai, D. 166, 171 Driver, J. 485 Dromi, E. 117–19, 340, 508 Dronkers, N. F. 27, 261 Drury, J. E. 176 Dubé, S. 332 DuBray, M. B. 92 Duchan, J. 441, 445 Duchan, J. F. 307 Ducrey, G. P. 313 Duff, D. 91, 308–9, 392, 404 Duffy, R. J. 311, 509 Dugmore, S. 86 Dumoulin, M. 71 Duncan, A. 86 Duncan, D. 133 Duncan, J. 485 Duncan, T. S. 309 Dunlop, G. 97 Dunn, C. 302, 309 Dunn, D. M. 119–20, 532 Dunn, L. M. 56, 67, 119–20, 120, 302, 309, 532 Dunn, M. 430, 487

Dunn, M. A. 91–2 Dunning, D. L. 228 Dupoux, E. 590 Duran, L. 312, 315 Durkin, K. 96, 454 Durlak, J. A. 452 Dutton, R. A. 205 Dworzynshki, K. 465 Dyar, D. 118 Dyck, M. J. 397 Dye, C. D. 366 Dyer, L. 223 Dykens, E. 60, 62, 55, 55, 58, 63, 72 Dykes, J. 370, 425 Dymicki, A. B. 452 Eadie, P. A. 88, 368, 369, 372 Ebbels, S. 6, 171, 418, 419, 433, 434, 435 Eberhard, K. 547 Ebert, K. 485, 489–91, 314, 507 Eccles, M. 565 Echols, L. 398 Ecker, U. K. H. 12, 487 Eckert, M. A. 204, 205, 483, 581 Eckman, F. R. 298 Eckstein, E. T. 570 Eckstein, K. 552, 553 Eden, G. 135 Edgin, J. 53 Edmonston, N. 68 Edwards, A. 405 Edwards, J. 13, 17, 20, 23, 221, 247, 239, 247, 247, 308, 392, 507 Edwards, S. 120, 430–1 Eernisse, E. 541 Eggermont, J. J. 199, 595 Egolf, D. B. 509 Ehri, L. 465–6, 472 Eicher, J. D. 265, 268 Eichorn, N. 12 Eigsti, I. M. 430 Eilers, R. E. 112–13 Einav, S. 549, 550 Eisenbeiss, S. 308 Eisenberg, L. S. 116 Eisenberg, S. 377, 378, 420, 426, 426 Eisenberg, S. L. 365, 377, 378 Eisengart, J. 538 Ekelman, B. L. 257 Eklund, K. M. 586 Eklund, M. 203 Elbert, T. 203, 588 Eley, T. 27, 260 Elfenbein, J. 240 Elias, R. 88 Eliez, S. 71, 205, 213 Elin Thordardottir, T. 418, 419, 429

610

Author Index Eliopulos, D. 204 Elkonin, D. B. 472 Ell, P. 395 Ell, S. W. 216 Ellefson, M. R. 202 Elleman, A. 143 Elliot, E. 398 Elliott, E. 487 Ellis Weismer, S. 10, 195, 217, 223, 243, 245, 245, 281, 316, 329, 367, 368, 418, 419, 430, 541 Ellis, D. 8 Elman, J. L. 176, 177 Elmasian, R. O. 201 Elsabbagh, M. 91 Else-Quest, N. 490 Elzarrad, M. K. 570 Emanuel, B. S. 71 Emslie, H. 219, 264, 506 Enard, W. 262 Enfield, M. 475–6 Eng, N. 331 Engel, A. K. 579 Engelbrecht, V. 196 Engle, R. 487, 491 Engle, R. W. 14, 213, 229 Englemann, S. 346 Epstein, B. 8, 15, 24, 27, 29, 199, 223, 554 Epstein, C. J. 53 Epstein, J. N. 405 Erickson, J. G. 307 Ericsson, K. A. 214 Eriksson, M. 58 Ermolaeva, V. 486 Ersland, L. 197, 589 Ertmer, D. J. 113, 114 Escera, C. 486 Escobar, M. 132 Escudero, P. 88 Esposito, A. 477, 564 Estes, K. 490 Estevan, R. A. C. 537 Estigarribia, B. 58, 372 Ethington, C. A. 309 Eulitz, C. 588 Eva, M. 7 Evans, D. 58 Evans, H. M. 94 Evans, J. 59, 368, 490 Evans, J. L. 11, 15, 19, 25, 121, 218, 244, 219, 220, 222, 224–6, 228, 244, 248, 240, 248, 308, 392, 393 Evans, P. D. 27 Everett, D. 424 Everhart, V. 402 Ewart, A. 56 Ewers, C. 405 Eyer, J. 329, 368, 370, 374–5

Eyer, J. A. 22, 281, 393, 507, 508, 509 Eyskens, B. 71 Fabiana, M. 483, 591 Fabiano-Smith, L. 298, 306 Facoetti, A. 485 Facon, B. 56, 393, 394 Facon-Bollengier, T. 56, 393, 394 Fagnan, L. 566, 569 Faherty, A. 87 Falcaro, M. 264, 368 Falco, F. L. 405 Fanning, J. 203, 491 Faragher, B. 308, 367, 368, 393, 507, 508 Farinella, K. 374 Farkas, L. 11 Farnetani, E. 338 Farquharson, K. 571 Farrell, M. 471 Farris-Trimble, A. 551 Farrow, M. 485 Farrugia, C. 397 Fasnacht, K. S. 304 Faust, M. 140, 503, 513 Fava, E. 590 Fazio, B. 56 Fazio, B. B. 394 Federico, J. E. 304 Federmeier, K. D. 199, 489, 584 Fein, D. 91, 92, 93, 395, 430, 540 Feinstein, C. 71 Feldman, C. 395 Feldman, E. 204 Feldman, H. M. 111, 402 Feldman, M. 55 Feldmann, G. 489 Felser, C. 543, 546 Feltmate, K. 309 Felton, R. 132, 474 Fennell, C. T. 244 Fenson, J. 19, 392 Fenson, L. 19, 68, 114, 378, 399, 403, 533 Ferguson, A. 487 Fernald, A. 540, 541 Fernández, M. 265 Fernández, M. C. 301, 314 Fernández, S. C. 301, 314 Feron, F. 139 Feron, F. J. 505 Ferrand, L. 510 Ferré, J. C. 518 Ferreira, V. S. 512, 514 Ferrer, E. 98, 131, 483, 483 Ferrier, L. 55, 62, 63 Ferrieux, S. 138 Fersko, T. M. 365, 377 Feuk, L. 262

611

Author Index Fey, M. 177, 233, 377, 399, 379, 380, 381, 552 Fey, M. E. 24–5, 29–33, 36, 203, 368, 369, 372, 380, 381, 398, 446, 561, 569 Fidler, D. J. 58, 73 Fidler, L. J. 30–1 Field, L. L. 268 Fiestas, C. 302, 308 Fiestas, C. E. 304, 305, 313, 315 Fiez, J. A. 214 Figueroa, R. 300 Fikkert, P. 548 Filipek, P. A. 86, 205 Filippello, P. 96 Fillmore, C. 170 Fine, S. E. 71 Finestack, L. 379, 380, 552 Finestack, L. H. 29, 31–2, 36, 56, 58, 203, 371, 372, 381, 561 Fink, N. E. 116 Fink, R. 515 Finn, E. 138 Finneran, D. 370, 481, 485 Finneran, D. A. 15, 368 Finney, M. 482 Finucane, B. M. 55, 63, 72 Fischel, J. E. 26, 257, 405, 509 Fischer, M. L. 91 Fischer, P. E. 474 Fischer, R. 404 Fishcel, J. E. 405 Fishco, V. 142 Fisher, C. 538 Fisher, L. 489 Fisher, S. E. 27, 197, 261, 267, 268 Fishman, R. 394 Fitch, R. H. 274, 281, 290 Fitch, W. 424 Fitzgerald, C. 377 Fitzgerald, J. 476 Flagg, E. 481, 588 Flax, J. F. 197, 258 Fletcher, J. 132, 134, 137, 140, 143 Fletcher, J. M. 503 Fletcher, P. 6, 20–1, 24, 120, 193, 281, 307, 314, 329, 333, 365, 379, 393, 403, 418, 419, 420, 422, 426, 427, 432, 507 Flipsen, P., Jr. 90 Flom, R. 486 Flower, L. 468, 478 Flowers, D. 135 Flowers, L. 466, 474 Flynn, S. 298 Focarelli, M. L. 72 Fodor, J. 274 Folger, M. K. 508 Fombonne, E. 82, 87, 307, 307, 448 Fonkalsrud, J. 374, 375

Fonteneau, E. 200, 370, 552 Foorman, B. 143, 464, 503 Foraker, S. 223 Forbes, P. 483 Forbes, P. W. 503 Ford, J. A. 447, 454 Forner, L. 238 Forsberg, H. 469 Fortnum, H. M. 111 Fortunato-Tavares, T. 25 Foulkes, P. 239, 248 Foulon, M. 204 Foundas, A. L. 28, 190, 195 Fourie, Y. 311 Fowler, A. 377 Fox, M. 488 Fox, N. 114 Fox, P. T. 581 Fox, R. A. 247 Fox, S. E. 590 Frackowiak, R. 135, 204, 261 Fralin, L. 20, 378, 392 Frances, A. 481, 485 Franceschini, M. A. 592, 593 Francis, A. L. 15 Francis, D. 132, 483, 487 Francis, D. J. 503 Francois, C. 487 Frangiskakis, J. 56 Frank, I. 302 Fraser, J. 481 Fraser, R. 397 Frauenfelder, U. 332 Frederickson, N. 489 Freed, J. 451, 453 Freedman, L. 465 Freeman, S. 88, 395, 442 Freeman, S. B. 53 Freese, P. R. 31 Freilinger, J. J. 238 Fremont, W. 71 Freudenthal, D. 177 Freund, L. 55 Fricke, J. S. 56 Fried, I. 579 Friederici, A. 244, 370, 551, 552, 553 Friederici, A. D. 29, 202, 204, 516, 517, 518, 584, 586, 588 Friedman, D. 533 Friedman, J. 193 Friedman, J. T. 202, 586 Friedman, L. 481, 483, 491 Friedman, N. P. 20 Friedman, R. M. 308 Friedmann, N. 6, 16, 18, 24, 33, 118, 119, 151, 160, 166, 167, 171, 178, 340, 433 Friedrich, M. 29, 202, 551, 552, 553, 586

612

Author Index Friel-Patti, S. 26, 33, 446, 491 Frijns, H. 112, 120 Frijns, J. H. 121 Frisch, S. 584 Fristoe, M. 113 Friston, K. 135, 193 Friston, K. J. 261 Frith, C. 135 Frith, U. 87, 89, 95, 96, 140, 204, 398, 465 Frizelle, P. 426, 427 Froehlich, A. L. 92 Frost, J. 472 Frost, L. A. 97 Fry, A. 487 Frye, D. 95 Fujiki, M. 18, 25–6, 248, 254, 447, 446, 449, 450, 451, 453, 454 Fulbright, R. 134, 135, 136, 141, 143, 144 Fullbright, R. K. 196 Fulton, L. A. 55 Fulton, R. S. 55 Fung, P. C. 405 Fusté-Herrmann, B. 304 Fyfe, J. 397 Gaab, N. 139 Gabard-Durnam, L. 85–6 Gabig, C. S. 395 Gabrieli, J. 139 Gadian, D. G. 28, 193, 197, 217, 261 Gagliardi, A. 178 Gaigg, S. B. 91 Gaile, J. 451, 453 Gaillard, A. W. 201 Gaillard, R. 138 Gaillard, V. 226 Galaburda, A. 55 Galaburda, A. M. 28, 189, 190, 205 Gallagher, A. 140, 204, 591 Galle, M. 482 Gallon, N. 23 Ganong, W. F. 7 Gantz, B. J. 116 Gao, L. 548 Garayzábal, E. 372 García, M. 300 Garcia-Marti, G. 28, 194 Garcia-Perez, R. M. 89 Gardiner, J. M. 91 Gardiner, K. 53 Gardiner, K. J. 53 Gardner, L. 451, 455 Garlock, V. 247 Garman, M. 420, 432, 435 Garnsey, S. M. 170 Garrett, M. F. 516, 517 Garrett, Z. 213, 433

Garrity, A. W. 351, 367 Gascoigne, M. 401 Gastgeb, H. Z. 92 Gathercole, S. 68, 70, 259, 481, 491 Gathercole, S. E. 10, 20, 213, 217, 219, 225, 228, 221, 230, 264, 274, 281, 287, 393, 505, 506, 506 Gathercole, V. C. M. 307 Gauger, L. 581 Gauger, L. M. 28, 194, 199 Gaulin, C. A. 220, 223 Gavens, N. 226 Gavin, W. 379 Gavin, W. J. 307, 365, 403, 420 Gavruseva, E. 298 Gayan, J. 268, 280, 379, 398 Gayley, C. 72 Gazzaley, A. 488 Geber, E. 370 Geers, A. E 111, 114, 116, 120, 121, 122, 171, 396 Geffner, D. 116 Geidd, J. N. 186 Gelardo, M. 94 Gelnic, C. 393 Genesee, F. 300, 303, 306, 310 Gennari, S. 338 Genova, H. 483 Gentner, D. 393 George, C. 60, 62 George, F. 280 George, R. 392 Gérard, C. L. 332 Gerber, E. 20, 367, 392, 508 Gerber, S. 453 Gerdes, M. 71 Geren, J. 118 Gerenser, J. E. 91, 92 Gerken, L. 393 Gerken, L. A. 153, 246, 534 German, D. J. 18, 20, 313, 403, 503 German, J. D. 504, 513 Gernsbacher, M. 430 Gerrelli, D. 261 Gerrits, E. 110 Gerstein, M. B. 269 Gertner, B. L. 406 Gertner, Y. 538 Gervasini, C. 72 Gerwehr, S. 53 Geschwind, D. H. 96, 267 Geschwind, N. 28, 189, 190 Gessner, M. 477 Geurts, B. 92 Gewillig, M. 71 Geye, H. 430 Ghahramani, D. 594 Ghesquiere, P. 139, 581

613

Author Index Ghosh, T. 82 Giard, M. 486 Gibbon, F. E. 90 Gibson, J. 94, 451 Gibson, T. A. 309 Gijsel, M. A. 397 Gilbert, J. 486 Gildersleeve-Neumann, C. E. 299, 300 Giles, N. 65 Gilger, J. 132 Gillam, R. 420, 491 Gillam, R. B. 7, 9–11, 15, 33, 213, 220, 221, 240, 241, 300, 301, 303, 305, 306, 309, 311, 312, 313, 314, 315, 501, 505, 510, 565 Gillberg, C. 86 Gilles, F. H. 190 Gillespie-Lynch, K. 88 Gillette, J. 393 Gilley, P. 113 Gilliam, T. C. 267 Gillian, E. 453 Gillingham, A. 472 Gillis, S. 113 Gillispie, M. 503 Gillon, G. T. 510 Girbau-Massana, D. 28, 194 Girolametto, L. 33 Giroux, J. 508 Giuliani, A. 117, 118 Givón, T. 423 Gladfelter, A. 377, 378 Glaser, Y. G. 12 Glasgow, R. E. 570, 573 Glass, E. 204, 370, 552, 586 Gleason, J. 114 Gleeson, J. G. 96 Gleitman, H. 393, 536 Gleitman, L. 393, 536, 548 Glick, L. 87 Gliga, T. 91 Gligorović, M. 488 Gluer, K. 87, 90, 92 Goad, H. 281 Gobet, F. 177 Godfrey, J. J. 276, 289 Goffman, L. 20, 23, 31, 34, 238, 246, 335, 377, 378 Goffman, L. A. 113, 114 Golaz, J. 54 Gold, D. 98 Goldberg, A. E. 16, 170, 176, 177 Goldberg, R. 71 Goldfield, S. 256 Golding-Kushner, K. J. 71 Goldknopf, E. J. 95 Goldman, R. 113 Goldsmith, J. 152 Goldstein, B. 301, 303, 309, 312, 314, 315

Goldstein, B. A. 298 Goldstein, H. 453 Goldstein, S. 373, 374 Golinkoff, R. 538 Golinkoff, R. M. 533, 538 Göllner, S. 310 Golse, B. 86 Gomes, H. 487 Gómez, D. M. 591 Gomez, R. 393, 566, 567 Gómez, R. L. 566 Gomez-Ariza, C. 482 Gomot, M. 486 Gonsky, R. 53 González-Nosti, M. 503 Gooch, D. 482 Goodlin-Jones, B. 71 Goodluck, H. 536 Goods, K. S. 88 Gopnik, M. 27, 216, 281 Gordon, B. 86, 159, 160 Gordon, L. 538 Gore, J. 135 Gorman, B. K. 368 Gosch, A. 56, 70 Goschke, T. 487 Goswami, U. 242, 276, 280, 481, 502 Gothelf, D. 71 Gottardo, A. 276 Gottesman, I. I. 202 Gottesman, J. 82 Gou, K. 202 Gou, Z. 586 Gough, P. B. 461 Gould, J. 86 Gould, T. D. 202 Govaerts, P. J. 113 Grados, M. 72 Graesser, A. C. 312 Graham, F. 27 Graham, S. 476 Grande, M. 518 Grant, C. 397 Grant, J. 68, 69, 70, 285, 373 Gratton, C. 483 Gratton, G. 591 Gravel, J. 111, 487, 540 Graves, M. 398 Graves, T. A. 55 Gray, B. B. 32 Gray, K. M. 93 Gray, M. 397 Gray, R. 55 Gray, S. 307, 308, 309, 401, 405, 506 Graziano, P. 401 Gree, L. 349 Green, C. 483

614

Author Index Green, J. 94, 451 Green, L. 351, 352, 355, 367 Greenberg, F. 72 Greenberg, J. 33 Greene, K. J. 297, 298, 300, 302, 303, 304, 313, 314, 315 Greene, V. 475, 476 Greenham, S. L. 516, 517 Greenough, W. 54 Greenspan, S. I. 97 Greenwald, C. 58 Greenwood, C. 406 Gregory, S. 114, 115 Grela, B. 329, 368, 369, 370, 418, 508 Grela, B. G. 22, 25, 281, 507, 508 Grice, H. P. 155 Grieco, E. 299 Grieco-Calub, T. M. 115 Griffin, P. 141, 142 Griffin, P. 464 Griffin, R. 444 Griffin, Z. M. 508, 512 Griffith, G. M. 72 Griffith, P. L. 509, 510 Griffiths, H. 91, 395 Griffiths, T. 486 Grigorenko, E. L. 94, 268, 398 Grigorenko, E. 466, 474 Grimm, H. 310, 330 Grimshaw, J. 159, 160 Grinstead, J. 369 Grizenko, N. 139 Grodzinsky, Y. 166, 171, 173 Gropman, A. L. 72 Grosjean, F. 313 Gross, A. L. 87 Gross-Glenn, K. 204 Groszer, M. 267 Grubar, J. 56 Grubar, J. C. 393, 394 Gruber, O. 487 Grudnik, J. 490 Gruen, J. R. 268 Grunow, H. 566 Grushin, A. 483 Grüter, T. 332 Gsödl, M. 285, 394 Gu, E. 71 Guasti, M. T. 171 Guberman, A. 396 Guenther, F. 239 Guilfoyle, E. 163 Guillory, B. 352 Gulesserian, T. 53 Gulsrud, A. 395 Gummersall, D. 434 Gundersen, H. 28, 197, 589

Gunn, D. 483 Gunn, P. 58 Gunnar, M. 55 Guo, L. 114, 377, 378 Guo, L. Y. 377, 378 Gupta, P. 259 Gurman, M. 120 Gusev, A. 269 Gustafson, D. 8 Gutierrez-Clellan, V. 331, 332 Gutiérrez-Clellen, V. F. 22, 301, 302, 304, 305, 306, 309, 310, 312, 313, 314, 316 Guttorm, T. K. 586 Gvion, A. 171 Gwaltney, M. 96 Habib, A. 114 Habib, M. 487 Hack, N. 159, 160 Hadar, U. 160 Haddad-Hanna, M. 118, 119 Hadley, P. 377, 380, 421 Hadley, P. A. 9, 21, 26, 359, 369, 377, 378, 379, 406, 421, 451 Hadzipasic, M. 138 Haegglund, K. 199, 202 Hafeman, L. 372 Hagen, E. 54 Hagerman, R. 55, 63, 65, 67, 371 Hagerman, R. J. 54, 66, 91 Haggard, M. 111 Hagoort, P. 92, 184, 515, 516, 584 Hahne, A. 204, 513, 551, 552, 553, 586 Hahne, C. 370 Hains, S. M. J. 88 Haire-Joshu, D. 566 Håkansson, G. 24 Hakuta, K. 166 Hale, C. 392 Hale, C. A. 20, 513 Hale, C. M. 444 Hale, S. 13, 487 Hall, A. 454 Hall, G. B. 90 Hall, J. 111 Hall, L. K. 502, 503, 513 Hall, S. 72 Hall, V. C. 221 Halle, M. 152, 154, 276 Halliday, A. M. 200 Halonen, A. 487 Ham, H. 204 Hamann, C. 332 Hambly, C. 307 Hamburger, H. 536 Hamilton, A. 466 Hamilton, A. C. 12

615

Author Index Hammer, C. S. 303, 315, 432, 509 Hammill, D. 469 Hammill, D. D. 374, 507 Hampshire, A. 485 Hampton, J. A. 92 Hampton Wray, A, 370, 554, 586 Hanauer, J. B. 512 Hancock, T. B. 32 Hand, L. 238 Haney, K. R. 257, 368 Hanley, S. 134 Hanlon, C. 174 Hanna, G. 142 Hansell, N. 483 Hansen, H. 552 Hanson, C. A. 228 Hanson, R. A. 14 Hansson, K. 24, 308, 310, 330, 334, 336, 548 Haosheng, Y. 308 Happe, F. 95, 96, 489 Harasaki, Y. 546 Harashima, C. 53 Harbers, H. M. 20, 392, 402 Hardiman, M. 29, 486, 489 Hardiman, M. J. 194 Hardmeier, R. 53 Hargrave, A. C. 405 Harlow, J. M. 184 Harm, M. W. 279, 280 Harms, L. 201 Harms, M. B. 93 Harpell, S. 313, 315 Harris, D. 112, 120, 121 Harris, G. J. 92 Harris, J. 23, 398, 506 Harris, K. 476 Harris, M. 6, 171, 286 Harris, S. 71 Hart, B. 32, 402, 406 Hartley, A. 489 Hartley, J. 25, 446, 448 Hartung, J. 378, 533 Hartung, J. P. 114, 399 Hasher, L. 14 Hashimoto, N. 510 Haskill, A. 379 Hassanali, K. N. 432 Hasson, N. 313 Hastings, R. P. 72 Hatt, N. 96 Hattingen, E. 589 Hatton, D. 55, 65 Haubus, C. 86 Hauk, O. 519 Hauser, M. 424 Havinga, J. 510 Hay, J. F. 7

Hayes, A. 55, 371, 486 Hayes, H. 120, 122, 396 Hayes, J. 468, 478 Hayiou-Thomas, M. E. 27, 260 Haynes, C. 20, 464, 475, 476 Haywood, H. C. 312 Hazan, V. 238 Healy, J. M. 94 Hebb, D. 579 Hedenius, M. 366 Heilman, K. M. 195 Heilmann, J. 509 Heim, S. 203, 518, 588 Heinz, J. M. 240 Heisler, L. 246 Helenius, P. 588 Hellemann, G. 395 Hemphill, L. 59, 60 Henderson, H. 96 Hendler, T 171 Hendricks, S. 506 Hendrickson, J. M. 404 Hendriksen, J. 139 Hendriksen, J. G. 505 Henning, S. C. 111, 120 Henrichsen, M. 401 Henry, C. 138 Henry, L. 481 Henry, L. A. 10 Henry, M. 473 Hensley, M. 569 Hepburn, S. 55 Hepburn, S. L. 58, 73, 371 Herman, P. A. 405 Herman, R. 340 Herman, S. 62, 373 Hermans, D. 397 Hermans, S. I. A. 502 Hermon, G. 538 Hermon, S. 69 Hernández, A. 301, 312 Hernández, M. 483 Hershberger, S. 306, 367, 368, 507–9 Hesketh, A. 425, 427, 509 Hesketh, L. 54, 56, 57, 58, 59, 429 Hesketh, L. J. 14, 372 Hesselink, J. 195 Hesselink, J. R. 28, 192, 193, 199 Hessl, D. 62 Hestvik, A. 18, 24, 166, 171, 173, 199, 223, 287, 426, 514, 544, 548, 554, 585 Hevers, W. 262 Hewitt, L. 432 Hewitt, L. E. 509 Hick, R. F. 308, 394 Hickok, G. 187 Hicks, J. 565

616

Author Index Hicks, K. 491 Higgins, A. M. 71 Hilbich, C. 53 Hildebrand, D. 470 Hildenbrand, H. L. 72 Hill, E. L. 217 Hill, N. 546, 547, 548 Hillier, L. W. 55 Hillyard, S. A. 199 Hinard, C. 71 Hipfner-Boucher, K. 305 Hiraishi, H. 588 Hiramatsu, K. 369 Hirokawa, K. 261 Hirsch, L. S. 197 Hirschman, M. 435 Hirsh-Pasek, K. 533, 538 Hirtz, D. 564, 574 Hismjatullina, A. 487 Hitch, G. J. 14 Hnath-Chisolm, T. 305 Ho, T.-H. 301, 305, 309, 312 Hoban, E. 164, 171 Hobson, J. A. 90 Hobson, P. 444 Hobson, R. P. 89, 90 Hodapp, R. 55, 58, 63, 72 Hodge, S. M. 205, 581 Hodges, J. R. 505 Hodgkinson, K. A. 263 Hoeffner, J. J. 282 Hoff, E. 298, 303 Hoffman, L. 329, 368, 491, 509 Hoffman, L. M. 240 Hoffman, S. 513 Hogan, T. 245 Hogan, T. P. 23, 35, 373, 374 Höhle, B. 307, 548 Holahan, J. 131, 132, 134, 136, 137, 138, 139, 140 Holbrook, J. 541 Holcomb, P. 370, 551–2 Holcomb, P. J. 29, 199, 201 Holden, J. 55 Holland, A. 508 Holland, A. L. 370 Holland, S. K. 518 Hollich, G. 116, 538, 540 Holliman, A. 466 Hollis, W. 20, 392, 402 Holmes, J. 228 Holmes-Brown, M. 397 Holowka, S. 301, 314 Holsgrove, S. 451 Holt, J. 377–8, 421 Holt, J. A. 398 Holtsman, G. 87 Honer, W. G. 263

Hood, J. 26, 257 Hook, P. 466, 474 Hooper, C. 398 Hooper, S. R. 139 Hoover, B. 112, 113 Hoover, B. M. 396 Hoover, J. 336, 533 Hoover, J. R. 381 Hoover, W. 461 Hopkins, C. 533 Horner, E. 490 Horner, E. A. 307, 359 Hornstein, N. 174 Horohov, J. E. 281, 367, 368, 369, 370, 509 Horowitz, S. J. 94 Horowitz-Kraus, T. 488 Horwitz, B. 138 Horwitz, S. J. 398 Hosey, L. A. 518 Hoshino, Y. 87 Houston, D. 540 Houston, D. M. 115–16 Hovmand, P. 569 Howard, D. 308 Howard, S. 205, 428 Howlin, P. 56, 68, 69, 70, 72, 371, 373, 395 Hoyson, H. 453 Hsu, H. J. 218, 219 Huang, Y. 196, 204 Hubbard, A. L. 86, 89 Hubbs, S. 116 Huber, K. M. 54 Huber, W. 518 Huddleston, R. 421 Hudgins, C. 113 Hudry, K. 91, 395 Hudson, A. 98 Huelsmann, K. 122 Huerta, M. 86 Huettig, F. 540 Huffman, L. 55 Hugdahl, K. 29, 197, 589 Hughes, A. 120 Hughes, D. 379 Hughes, D. L. 32 Hughes, E. 307 Hughes, E. K. 396 Hulk, A. 299 Hulme, C. 56, 394, 404, 464, 465, 482 Hulme, C. A. 397 Hulshoff, P. H. E. 138 Hulslander, J. 134, 483 Humphreys, B. 85, 190 Humphreys, P. 28, 190 Hunt Berg, M. 450 Hunter, L. 111 Huntley Bahr, R. 305

617

Author Index Huppert, T. J. 591 Hurks, P. P. 505 Hurst, J. A. 27, 197, 261 Hus, V. 86 Hussain, J. 451 Hutman, T. 88 Huttenlocher, P. R. 595 Hutton, U. 14 Huttunen, T. 487 Hutzler, F. 135 Hwang, M. 13, 483 Hynd, G. W. 204 Hyvarinen, A. 594 Iglesias, A. 30, 301, 308, 309, 312, 313, 314, 370, 403, 432, 509 Illig, T. 447 Iluz-Cohen, P. 300, 305, 311 Im-Bolter, N. 15, 487 Ingalhalikar, M. 595 Ingham, J. C. 504, 581 Ingham, R. 21, 418–19 Ingham, R. J. 581 Inglis, A. 26, 257 Ingram, D. 152, 367 Ingram, T. T. S. 257 Irwin, J. R. 19 Isaac, T. 85 Ito, K. 548 Itoh, T. 72 Ivanoiu, A. 505 Iyer, S. N. 112 Jackendoff, R. 161 Jackson, A. F. 582 Jackson, J. 359 Jackson, T. 194 Jacobson, P. 331, 369 Jacobson, P. F. 19, 22, 306, 309–10, 314 Jaeger, J. J. 502 Jaeger, T. F. 532 Jaeggi, S. M. 220, 228–9 Jaffery, G. 395 Jakubowicz, C. 332 Jallad, B. 204 James, C. 17 James, D. 435 James, D. G. H. 311, 504 Jamieson, B. 453 Jamison Roorbach, K. 453 Jäncke, L. 204 Jannin, P. 518 Janosky, J. 10, 213, 505 Janosky, J. E. 111, 402 Jansonius, J. K. 502 Jara, L. 264, 265 Jared, D. 278

Jarmulowicz, L. 309 Jarrold, C. 54, 221, 394, 483 Jarrold, W. 96 Jeffries, R. 19, 392 Jennings, P. 508 Jennings, T. 475–6 Jennische, M. 366 Jensen, A. 483 Jensen, J. K. 121 Jerger, S. 511, 512, 514 Jerman, O. 485 Jernigan, T. 52, 193 Jernigan, T. L. 28, 192, 193, 199 Jescheniak, D. J. 510, 511, 512, 514 Jescheniak, J. D. 513, 516–17 Jessell, T. 580 Jeste, S. 96 Jezzard, P. 135 Jinde, S. 486 Joanette, Y. 518, 519, 590 Joanisse, M. 223, 246, 535 Joanisse, M. F. 245, 276, 280, 282–4, 287, 288, 289, 290 Jobert, A. 138 Jodoin-Krauzyk, J. 396 Jo-Fu, L. 553 Johannes, C. 489 Johansson, I. 58 John, A. E. 56, 67, 68, 394 Johns, C. L. 12, 24 Johnson, A. 481 Johnson, B. 381 Johnson, B. W. 365 Johnson, C. 481 Johnson, C. J. 502 Johnson, E. 94 Johnson, J. 15, 487 Johnson, M. 369 Johnson, M. H. 185, 186, 187, 198, 205 Johnson, M. J. 396 Johnson, S. P. 88 Johnson, V. 393 Johnson, V. E. 418 Johnson, W. 482, 490 Johnston, J. 55, 56, 62, 63, 393, 418, 534 Johnston, J. R. 21, 213, 217, 311, 365, 379, 393, 394, 501, 505, 510, 508, 509 Johnston, R. B. 246 Johnston, S. C. 564, 574 Johnston, S. S. 373 Johnstone, S. 489 Joliffe, T. 96 Jolles, J. 139, 505 Jones, C. 313 Jones, M. 21, 29, 195, 221, 243, 393, 447, 448, 506, 589 Jones, S. 474

618

Author Index Jones, W. 56, 205, 428 Jongman, A. 552, 553 Jongmans, M. 227 Jongmans, M. J. 228 Jonides, J. 196, 220, 229 Jonsson, R. 71 Joober, R. 139 Joseph, K. 368, 369 Joseph, K. L. 306, 308 Joseph, R. M. 395 Joyner, D. 313 Juel, C. 464 Juhasz, C. 590 Jung, T.-P. 594 Jusczyk, A. 244 Jusczyk, A. M. 244 Jusczyk, P. 238, 244 Jusczyk, P. W. 244 Just, M. A. 14, 97, 122, 227, 287 Justice, L. 420 Justice, L. M. 405, 568, 571 Jutagir, D. 138 Kaartinent, J. 487 Kadaravek, J. 405, 420 Kaderavek, J. N. 213, 221, 311 Kaganovich, N. 8, 200, 553 Kahn, I. 171 Kail, R. 195, 241, 274, 281, 392, 483, 487, 488, 502, 503, 513 Kail, R. V. 13, 20, 503 Kaiser, A. P. 32–3, 404, 453 Kaiser, C. L. 113 Kalb, L. 90 Kalb, L. G. 87 Kalff, A. C. 505 Kambanaros, M. 502 Kamhi, A. 143, 462, 463 Kamhi, A. G. 204, 455, 572 Kamide, Y. 549 Kamio, Y. 92 Kan, P. 481 Kan, P. F. 312, 315, 393 Kana, R. K. 97 Kandel, E. 579, 580 Kandel, E. R. 579 Kane, M. J. 14, 225 Kaneko, M. 87 Kanner, L. 89, 94 Kapa, L. 482 Kapalková, S. 372, 506 Kapantzoglou, M. 313, 402 Kaplan, B. J. 268 Kaplan, C. A. 20, 398, 427 Kaplan, D. 268 Karadottir, S. 56, 57, 60, 61, 62, 63 Karaminis, T. N. 286

Karasinski, C. 368 Karavatos, A. 505 Karchemskiy, A. 205 Karhunen, J. 594 Karmiloff-Smith, A. 16, 68, 69, 70, 187, 191, 283, 284, 285, 286, 373, 394, 482, 485 Karzon, R. K. 110 Kas, B. 332, 333 Kasai, K. 486, 591 Kasari, C. 55, 58, 58, 88, 395, 442 Kass, R. 368 Kates, W. 71 Katsnelson, V. 11 Katsos, N. 537 Katz, L. 134 Katz, W. F. 503 Katzir, T. 503 Katzman, R. 87 Kau, A. 55 Kaufman, D. 538 Kaufman, S. B. 121 Kaufmann, W. 55, 138 Kaufmann, W. E. 28, 190 Kavale, K. A. 455 Kave, G. 505 Kawakubo, Y. 591 Kay, P. 170 Kayne, R. 165 Kay-Raining Bird, E. 54, 56, 58, 59, 62, 307, 309, 372, 394 Keane, S. 485 Keating, P. 245, 280 Keenan, J. 63, 482 Keesey, J. 565 Keith, R. W. 14 Kellenbach, M. L. 199 Keller, T. A. 14, 96, 97 Kellet, K. 97 Kelley, E. 91, 92, 540 Kello, C. T. 290 Kelly, A. S. 36 Kelly, C. 138, 481 Kelly, D. 550 Kelly, D. J. 20, 392, 402, 509 Kelly, J. F. 117 Kelly, R. 489 Kemeny, S. 518 Kenan, N. 160 Kennedy, D. N. 205, 581 Kent, R. 238 Kent-Udolf, L. 509 Kenworthy, L. 93, 489 Kerbeshian, J. 94, 398 Kerns, J. 485 Kerr, E. A. 565 Kesin, E. 62 Kessler, J. W. 94

619

Author Index Kessler, K. L. 200, 585 Kester, E. S. 299, 300, 315 Ketelaars, M. P. 502 Ketterlin-Geller, L. 474 Keulers, E. 139 Key, A. P. F. 200 Keysor, C. 55 Khong, P. 139 Kidd, E. 122, 166, 177, 178, 275, 424, 426, 548 Kiernan, B. 380, 405 Kiese-Himmel, C. 397 Kikuchi, M. 588, 593 Killackey, J. 159, 160 Killen, D. H. 122 Killing, S. 539 Kim, D. I. 581 Kim, H. H. 581 Kim, J. 66, 138, 581 Kim, J. S. 372 Kim, K. 96 Kim, S. H. 445 Kim, Y. W. 581 King, G. 418, 419 King, W. M. 241, 249, 289 Kinnear, M. 552 Kintsch, W. 214 Kiparsky, P. 153 Kirby, R. S. 82 Kirchner, D. M. 93 Kirihara, K. 486 Kirk, K. 540 Kirk, K. I. 115 Kirk, S. 130 Kirkwood, M. 483 Kistler, D. 54, 57, 59, 429 Kjelgaard, M. M. 91, 93, 99, 204, 371, 395, 430 Klarman, L. 553 Klaver, P. 138 Klee, T. 20, 307, 365, 402, 403, 379, 420 Klein, B. 69, 70 Klein-Tasman, B. 56, 69, 394 Klein-Tasman, F. 52, 55, 68, 69, 70 Klima, E. 69 Klimkeit, E. 485 Klin, A. 88, 90, 91, 93, 94, 95, 96, 398 Kline, A. D. 72 Kline-Burgess, A. 53 Klingberg, T. 139, 228 Klinger, L. G. 92 Klinger, M. R. 56 Klintsova, A. 54 Klop, D. 311 Kluender, K. R. 7, 240 Kluender, R. 584 Klugkist, I. 228 Klusek, J. 372 Knight, R. 483, 488

Knoors, H. 397 Knowlton, B. J. 216 Knox, C. M. 276 Knox, E. 22 Knussen, C. 86 Koch, C. 579 Koch, D. B. 201 Kochunov, P. 483 Koegel, R. L. 97, 98 Koelsch, S. 584 Koffler, M. 491 Kofler, M. 481, 483 Kofman, O. 505 Kohnert, K. 10, 241, 314, 481, 485, 489, 490, 491 Kohnert, K. J. 301, 306, 309, 310, 312, 315 Kolb, B. 578 Kolomeyer, E. 481, 483 Komaroff, E. 303, 315 Kong, Y. 268 Konstantantareas, M. M. 97 Koopman, H. 162 Koopmans-van Beinum, F. J. 112 Koren, R. 505 Korenberg, J. 55 Korenberg, J. R. 53 Kori, S. 338 Kosmidis, M. H. 505 Kotz, S. A. 584 Kovelman, I. 139, 590 Kover, S. 62, 63, 66, 371 Kover, S. T. 57, 59, 60, 61, 62, 64, 65, 66, 91, 372 Koyama, M. 138 Krafnick, A. 135 Krajewski, G. 177 Kramer, P. 510 Krantz, L. R. 367 Krantz, P. J. 97 Kranzler, J. 490 Krapp, G. P. 346 Krasowicz-Kupis, G. 503 Kraus, N. 199, 201 Krause, M. 373 Kreegipuu, K. 486 Kreiter, J. 313 Kreuter, M. W. 566 Kreuzer, J. A. 199 Krishnan, A. 242, 274, 481, 486 Krishnan, S. 518 Kroes, M. 505 Krok, W. 22 Kronbichler, M. 135 Kronenberger, W. G. 111, 120, 228 Krueger, D. D. 54 Kruth, K. 311 Kucan, L. 404, 475 Kucukaltun-Yildirim, E. 594 Kuhl, P. 553

620

Author Index Kuhl, P. K. 241, 243 Kuhn, M. 466 Kujala, T. 90, 486 Kumar, S. 486 Kumashiro, H. 87 Kumin, L. 58, 394 Kuniholm, E. 193 Kunkel, F. 113 Kuno, S. 175 Kuperberg, G. R. 584 Kupietz, S. 139 Kurs-Lasky, M. 111, 402 Kurtz, B. A. 221, 247 Kurtz, R. 333, 422, 507 Kurtzberg, D. 199, 200, 201, 486, 579, 585 Kushch, A. 204 Kuslansky, G. 505 Kutas, M. 199, 489, 515, 584 Kuwabara, H. 591 Kwasny, K. 552, 553 Kwiatkowski, J. 152, 508 Kwong, B. 199, 595 La Heij, W. 510 Labban, J. D. 86, 90 Labov, W. 346, 352, 356 Lacadie, C. 138 Lacert, P. 276 Ladd, D. R. 153 Ladurner, G. 135 Lagae, L. 581, 589 Laganaro, M. 515, 517 Lahey, M. 13, 17, 20, 23, 376, 392 Lahey, M. E. 308 Lahti, J. 483 Lai, C. S. L. 27, 197, 261, 262 Lai, L. 511, 514 Lai, Z. 56, 205, 428 Laine, M. 515, 519 Laing, E. 285, 394 Lainhart, J. E. 92 Lakoff, G. 176 Lakusta, L. 285 Lam, K. 305 Lambrecht, L. 86 Lamme, V. 483 Lamont, G. 483 Landa, R. J. 87 Landry, R. 485 Landry, S. H. 89, 90 Lane, A. B. 28, 190 Lanfranchi, S. 485 Langacker, Ronald, W. 170 Langdon, R. 278 Lange, E. 12 Lange, N. T. 92 Lanham, D. 138

Lannon, C. 111 Lanter, E. 86 Lanzi, G. 135 Larcombe, M. 86 Lasky, K. 97 Lasnik, H. 175 Lavie, A. 432 Law, J. 213, 256, 401, 427, 433 Law, J. K. 87 Law, P. A. 87 Laws, G. 372, 394, 429, 506 Lawson, J. 444 Layman, D. 55 Layton, T. L. 97 Lázaro, M. 372 Le Bihan, D. 138, 580 Le Couteur, A. 87 le Normand, M. 22 Le Normand, M. T. 117 Le Rumeur, E. 518 Le, L. 97 Le, T. 53 Leadbitter, K. 395 Leadholm, B. 403, 432 Leahy, R. M. 593, 594 Lebeaux, D. 163 Lebel, C. 139 LeBlanc, L. A. 97 Leckman, F. 60, 62 Leclerc, P. O. 590 Leclerq, V. 485 LeCouter, A. 82, 86 Leddy, M. 58 Lederberg, A. R. 115, 402 Lederer, A. 393 Lee, A. 89, 90 Lee, J. 88 Lee, K. 392 Lee, L. 564 Lee, L. L. 432 Lee, L. S. 564 Lee, S. H. 548, 549 Lee, S. K. 581 Leech, R. 518 Leekam, S. R. 396 Leekman, S. R. 86 Leevers, H. J. 26 Lefly, D. 141, 142 Legate, J. 339 Legius, E. 71 Lehmann, D. 594 Leigh, E. 483 Leigh, J. 111 Leinbach, J. 282 Lempp, H. 566 Lenneberg, E. H. 245 Lennon, P. A. 262

621

Author Index Lento, C. L. 117 Leonard, C. M. 28, 189, 190, 194, 199, 241, 249, 289, 581 Leonard, J. 20, 377, 378 Leonard, J. S. 314 Leonard, L. 58, 200, 223, 329, 330, 331, 332, 333, 334, 335, 336, 337, 339, 340, 367, 369, 370, 392, 422, 481, 483, 485, 487, 488, 545, 554 Leonard, L. B. 28, 117, 154, 164, 166, 177, 189, 195, 240, 241, 243, 246, 281, 301, 302, 308, 309, 310, 314, 315, 359, 367, 368, 369, 370, 377, 378, 379, 392, 393, 403, 422, 426, 434, 446, 502, 503, 504, 507, 508, 509, 513, 517, 531, 535, 541, 545, 553, 555, 586 Lepistö, T. 90 Leppanen, P. 586 Leppänen, P. H. 586 Leppänen, P. H. T. 26, 201, 203 Leppäsaari, T. 29 Lervag, A. 490 Lesage, F. 590 Leslie, A. M. 95 Lestage, P. 505 Letchford, B. 454 Letts, C. 120, 430, 431 Leung, W. W. M. 88 LeVasseur, V. 474 Levelt, W. J. 134 Levelt, W. J. M. 510, 511 Leventhal, B. L. 86 Leversha, M. A. 72 Levi, S. 248 Levin, B. 167, 418 Levin, R. 299 Levine, B. A. 159, 160 Levitas, A. 55 Levitsky, W. 28, 189, 190 Levitt, H. 117, 118 Levy, H. 33, 171 Levy, H. P. 72 Levy, Y. 69, 373 Lewandowsky, S. 12, 487 Lewedeg, V. 301 Lewine, J. 552 Lewine, J. D. 203, 588 Lewis, B. A. 26, 257 Lewis, D. 112, 113 Lewis, D. E. 396 Lewis, G. 443 Lewis, K. 379 Lewis, P. 65, 66, 371, 372 Lewis, R. 223 Lewis, V. 447 Lewis, W. 177 Lewy, A. L. 94 Leyfer, O. T. 56 Li, J. J. 504

Li, L. 483 Liang, A. 205 Libby, S. J. 86 Liberman, I. 134 Liceras, J. 310 Lichtenberger, L. 55, 428 Lickliter, R. 486 Lidz, C. S. 308, 313 Lidz, J. 178 Lieberman, D. 139 Lieberman, P. 193 Liegeois, F. 27–8, 193, 197, 261, 262 Liégeois-Chauvel, C. 582 Lieu, J. E. 110 Lieven, E. 275, 339, 369, 421, 426 Lightfoot, D. 174 Liles, B. Z. 25, 311, 509, 510 Lincoln, A. 56 Lincoln, A. J. 201 Lindamood, P. 142, 143, 473 Lindgren, M. 199, 201, 202, 552 Lindsay, G. 454 Lindsey, D. 487 Lindstrom, M. J. 19 Linebarger, M. C. 171 Lisowski, M. 267 Litchfield Thane, N. 68 Litovsky, R. Y. 115 Liu, F. C. 261 Liu, H.-M. 241, 243 Liu, J. 267 Liu, T. 486 Liu, Y. 432 Lloyd, J. 453 Lloyd, M. 91 Lloyd-Fox, S. 518 Lobitz, K. F. 306, 309, 310 Lockton, E. 94, 451, 453 Loeb, D. 419, 491 Loeb, D. F. 21–2, 25, 32–3, 369, 393 Loeb, J. W. 365 Loene, A. E. 302, 306 Löfkvist, U. 502 Löfqvist, A. 122 LoFranco, L. 304 Logan, J. A. R. 571 Logie, R. 487 Logrip, L. 546, 547, 548 Lohmander, A. 71 Lombardino, L. 481 Lombardino, L. J. 28, 194, 199, 204, 241, 249, 289, 581 Long, S. 379, 380, 432 Long, S. H. 30, 32 Longhi, E. 394 Lonigan, C. J. 257, 405, 509 Lookadoo, R. 54

622

Author Index López, B. 396 Lopez, K. 7, 18, 25 Lopez, L. M. 303, 315 López-Sala, A. 194 Lord, C. 86, 87, 88, 395, 395, 445 Lorsbach, T. C. 15 Lorys, A. R. 204 Losh, M. 58, 372 Lott, I. T. 53, 54 Loucas, T. 87, 426 Lovaas, O. I. 89, 97, 98 Love, T 14, 18, 170, 543 Loveall, S. J. 54 Loveland, K. A. 89, 90, 94 Low, K. 591 Lowell, S. 380 Lowenstein, J. H. 121 Loxtercamp, A. 481 Lu, M. M. 261 Lubec, G. 53 Lubs, H. 204 Lucas, E. 32 Luce, P. 244 Luciano, M. 265, 483 Luck, S. J. 582 Luetje, M. 135 Lugo-Neris, M. J. 305, 311, 312 Luk, G. 307 Lukács, Á. 332, 333, 373 Lum, J. 370, 481, 482 Lum, J. A. G. 12, 217, 218, 393 Luna, B. 186 Lundberg, I. 464, 472 Lundgre, C. 365, 377 Lust, B. 163 Lustig, C. 14 Luxon, L. M. 36 Luyster, R. J. 202 Lynch, J. 114 Lyon, G. 130, 134, 461, 463 Lyster, S. 464 Lyster, S. A. 394 Lyster, S. H. 56 Lyxell, B. 502 Lyytinen, H. 201, 203, 487, 586 Maas, J. 55 MacArthur, C. 476 Macaruso, P. 474 MacAuley, M. 397 MacDermot, K. D. 262 MacDonald, A. 485 MacDonald, M. 487 MacDonald, M. C. 10, 122, 249, 287 Mace, A. L. 109 Macias, D. 8 Macken, C. 313

Mackie, C. 368, 454 Mackintosh, N. J. 121 MacLean, M. 140 Maclin, E. 591 MacWhinney, B. 30, 176, 282, 298, 306, 403, 432, 509, 511, 512, 514, 546 Madell, J. R. 121 Madison, L. 60, 62 Maestro, S. 86 Maggart, Z. 311 Magimairaj, B. 482 Magimairaj, B. M. 121 Magliano, J. P. 312 Maguire, M. J. 200 Mahecha, N. R. 305 Mahfoudhi, A. 464 Mahone, E. M. 504 Maidhof, C. 584 Maillart, C. 331, 332 Mainela-Arnold, E. 7, 217, 218, 224, 240, 244, 248, 308, 393 Mair, A. 135 Maitinsky, S. 139 Majerus, S. 213, 221 Makeig, S. 594 Makris, N. 205, 581 Makuch, R. 132 Malamut, B. 219 Malek, M. 313, 315 Malesa, E. 86 Malkin, C. 56 Maller, S. J. 121 Malofeeva, L. I. 188 Malt, B. C. 299 Malvern, D. 20, 403 Mandel, D. 238 Manganari, E. 289 Mangin, J. F. 580 Mani, N. 540 Maniega, S. 483 Manis, F. 465 Manis, F. R. 245, 276, 280, 503 Mann, M. 268 Manolitsi, M. 311 Mansfield, T. C. 25 Mantini, D. 92, 581, 589 Mantysalo, S. 201 Marakovitz, S. 171, 173 Maranion, M. 60, 62 Marantz, A. 154 Marantz, P. R. 564 Maravilla, K. 138 Marchine, K. 134, 137, 140 Marchione, K. 131, 132, 136, 139 Marchman, V. 244, 245, 282, 329, 367, 540, 541 Marchman, V. A. 245, 281, 297, 301, 399, 507, 511, 514

623

Author Index Marcus, G. F. 276, 283 Marcus, L. M. 86 Marian, V. 300, 302, 504 Marie, C. 486 Marin, O. 171 Marinellie, S. 425, 434 Marinellie, S. A. 509 Marinis, T. 6, 18, 24, 307, 543, 544, 546 Marino, F. 96 Marinova-Todd, S. H. 307 Markides, A. 114 Markoff, K. 451, 455 Marler, J. 221, 225, 240, 241, 243 Marler, J. A. 213, 240, 241 Maron, L. 551 Marquez, A. 310 Marquis, J. 213, 329, 368, 418, 508 Marrinan, E. 71 Marshall, C. 340, 370, 549 Marshall, C. R. 22–4, 274, 281, 56, 507 Marshall, D. H. 111 Marshall, R. 487 Marslen-Wilson, W. 545, 546 Marsolais, Y. 518, 519 Marti-Bonmati, L. 28, 194 Martin, A. 93 Martin, A. E. 12 Martin, B. 584, 595 Martin, G. 61, 65, 372 Martin, G. E. 58, 372 Martin, K. 113 Martin, N. 443, 515 Martin, N. G. 257 Martin, R. C. 12, 511, 512 Martin, S. C. 72 Martínez-Sussmann, C. 297, 301 Martins, R. 53 Marton, K. 10–12, 15, 24, 248, 393, 506 Marulis, L. M. 404 Masapollo, M. 338 Mashburn, A. 568 Mason, K. 340 Mason, S. M. 200 Mather, N. 141, 142, 469, 507 Mathes, P. 134 Mathy, P. 90 Matsushita, M. 267, 481 Matthews, N. J. 91 Matthews, P. 135 Mattingley, J. 485 Mattox, S. 487 Mattys, S. 244 Matus, I. 55 Matute, E. 505 Maumet, C. 518 Maurer, U. 138, 483 Maury, S. 588

Maxfield, N. 585 May, C. P. 14 Maybery, M. 96 Mayer-Crittenden, C. 308 Mayes, S. 483 Mayford, M. 579 Mayne, A. M. 399 Mazzocco, M. 55 McAfee, M. C. 117 McArthur, G. 242, 243 McArthur, G. M. 8, 29, 199, 201, 202, 242, 290 McBogg, P. 55 McBride-Chang, C. 276, 405, 483 McBurnett, K. 139 McCabe, A. 311 McCabe, P. C. 26 McCandliss, B. 483 McCann, R. S. 289 McCarthy, D. 70 McCarthy, J. J. 153 McCauley, R. 375, 376 McCauley, R. J. 97, 98 McClannahan, L. E. 97 McCleave, C. P. 453 McCleery, J. 552 McClelland, J. L. 274, 278, 279, 282, 289 McComish, C. 86 McConachie, H. 395 McCormack, P. F. 311 McCullough, E. 338 McCutchen, D. 468, 476, 479 McDaniel, D. 359 McDaniel, D. 532, 543, 544 McDermott, S. 85 McDonald, D. 246 McDonald, J. L. 351, 352, 355, 356, 357, 365, 367 McDonald, S. 246 McDonald-McGinn, D. M. 71 McDuffie, A. 59, 60, 62, 65, 66, 67, 91, 371, 372 McEldoon, K. 548 McElree, B. 12, 223, 227 McEvoy, C. L. 504 McEvoy, K. 488 McEvoy, R. E. 89 McFadden, T. U. 15 McGarr, N. 113 McGarr, N. S. 114 McGee, T. J. 199, 201 Mc-Ginn, D. M. 71 McGinty, A. S. 571 McGlynn, E. A. 565 McGrath, L. 205, 581 McGregor, K. 153, 246, 329, 331, 336, 337, 369, 542 McGregor, K. K. 9, 18–20, 22–3, 91, 92, 195, 240, 300, 302, 308, 309, 313, 367, 368, 370, 392, 393, 395, 396, 397, 402, 404, 502, 504, 513 McGrew, K. S. 141, 142

624

Author Index McGuffin, P. 443 McHale, S. 535 McIntosh, B. 538 McIntyre, N. 96 McKay, C. M. 121 McKean, C. 256 McKee, C. 359, 532, 537, 543, 544 McKee, G. 403 McKee, L. 26, 248 McKeown, M. G. 404, 475 McKinnon, E. E. 121 McLaughlin, B. 301 McMahon, K. L. 518 McMurray, B. 21, 240, 248, 482, 548, 549, 551 McNamara, T. 541 McNealy, K. 86, 89 McRoberts, G. 540 McSweeny, J. L. 90, 246 Meaburn, E. 27, 133, 262 Medina, A. 312 Medland, S. 483 Meffert, E. 518 Mehler, J. 591 Mehta, Z. 450 Meir, I. 182 Meisel, J. M. 297, 298 Meisinger, E. 466 Melara, R. 15 Melby-Lervag, M. 56, 394, 464, 490 Meldrum, D. 87 Mellits, E. D. 246 Mellon, J. A. 113 Mellor, D. H. 200 Meltzoff, A. N. 88, 96 Membrino, I. 420 Mencl, W. 134, 135, 136, 141, 143, 144, 196 Mendell, N. 139 Mendez-Pérez, A. 303 Menendez, M. 54 Menn, L. 340 Mennen, I. 307 Mensah, F. 256 Menyuk, P. 91 Mercure, E. 518 Merrill, E. 54, 56 Merrill, E. C. 54 Merritt, D. D. 311, 510 Mervis, C. 55, 56, 68, 69, 70, 428 Mervis, C. B. 52, 55, 56, 58, 67, 68, 69, 70, 373, 394, 428 Meryash, D. 55, 62, 63 Merzenich, M. M. 9, 33, 240, 241, 249, 289 Mesibov, G. 65, 90 Mesite, L. 93 Messer, D. 392, 481 Messer, D. J. 10, 18, 20 Metsala, J. 247, 513

Metzger, M. 112 Meyer, A. S. 134, 510 Meyer, E. 430 Meyer, J. A. 95 Meyer, M. 474 Meyer-Lindenberg, A. 428 Meyers, C. 34, 566, 567 Michel, C. M. 594 Michels, S. A. 91 Miezin, F. 138 Mikulajova, M. 313 Milani, D. 72 Milbrath, R. 483 Milbrath, R. L. 13 Mildenberger, K. 15, 225 Milech, D. 93 Miles, S. 59, 60 Milham, M. 481 Millay, K. 276 Miller, A. 143, 482 Miller, C. 333, 367, 370, 377, 392, 483, 487, 488 Miller, C. A. 13, 200, 241, 368, 370, 422, 503, 507, 508 Miller, J. 56, 58, 58, 59, 311, 370, 393, 402, 432, 433, 466, 509 Miller, J. F. 21, 116, 394, 403, 508, 509 Miller, J. S. 92 Miller, L. L. 268 Miller, S. 258, 274, 281, 290 Miller, S. L. 9, 289 Mills, D. 55, 205 Mills, K. L. 580 Milosky, L. M. 447, 454 Minagawa-Kawai, Y. 590 Minamino, M. 72 Minati, L. 92 Minor-Corriveau, M. 308 Minow, F. 15, 225 Minshew, N. J. 92, 96, 97 Miolo, G. 56, 57, 58, 68 Miozzo, M. 519 Miranda, A. E. 311 Mirenda, P. 307, 395 Mirrett, P. 62 Mirsky, A. F. 15 Mirza, G. 265 Mishkin, M. 193, 197, 217, 219, 261 Misra, M. 503 Mitchum, A. 488 Mittman, B. 565, 571 Miyake, A. 14 Miyamoto, R. 540 Miyamoto, R. T. 114, 116 Moats, L. 471, 473 Moberly, A. 116, 540 Moberly, A. C. 121 Mockler, J. L. 204, 581

625

Author Index Modglin, A. 202 Mody, M. 7, 96, 289 Modyanova, N. 371, 373 Moe, A. 533 Moeller, M. P. 112, 113, 121, 396 Moen, I. 552 Moeschler, J. 60, 62 Mogford, K. 114, 115 Mold, J. 566, 569 Molesworth, C. J. 92 Molfese, D. L. 202 Molko, N. 580 Moll, C. K. E. 579 Moltz, C. 393 Monaco, A. 265 Monaco, A. P. 27, 197, 261, 264, 267, 268 Montanelli, D. S. 118, 119, 171 Montgomery, J. 482, 483, 487, 545 Montgomery, J. W. 9–11, 14, 24, 121, 213, 219, 220, 221, 222, 226, 228, 309, 368, 370, 393, 396, 506 Monti, F. 72 Moog, J. S. 120, 121, 122, 171, 396 Moon, J. 53 Moore, C. 88, 545 Moore, D. 111 Moore, M. 369 Moore, M. S. 54 Morais, J. 279 Moraleda, E. 372 Morales, G. 505 Morales, J. 482 Moran, R. 85 Moreau, M. 476 Morehead, D. 367 Morey, C. 487 Morgan, A. 256 Morgan, A. T. 217 Morgan, B. 96 Morgan, G. 340 Morgan, G. P. 310, 316 Morgan, J. 244 Morgan, W. 130 Moriarty, J. 395 Morr, M. 486 Morr, M. L. 199, 200, 201, 584, 585 Morris, A. P. 268 Morris, C. 55, 56, 69, 70, 373 Morris, R. 134 Morris, S. 381 Morrisey, E. E. 261 Morrow, B. 71 Morton, J. 204 Morton, J. B. 443 Morton, M. 98 Moscovitch, M. 505 Mosher, J. C. 593 Moss, E. 71

Moss, J. 72 Mosse, E. 394 Mostert, M. 98 Mostert, M. P. 455 Mottron, L. 489 Moyle, M. J. 19, 368 Muir, D. W. 88 Mukunda, K. V. 221 Mulder, G. 199 Mullen, E. M. 68 Müllen, R. 482 Müller, A. 548 Müller, N. 299 Müller-Myhsok, B. 201 Mullin, J. T. 88 Multhaup, G. 53 Munakata, Y. 489 Mundy, P. 58, 58, 88, 94, 95, 96, 98, 444 Munesue, T. 593 Munro, N. 392 Munson, B. 13, 221, 239, 246, 247, 507 Munson, C. 240 Munson, J. 87 Münte, F. T. 515 Munte, T. F. 584 Murata, S. 87 Muratori, F. 86 Murphy, M. 55, 56, 57, 60, 61, 63, 63, 65 Murphy, M. M. 55, 56, 62, 63, 64, 372 Murray, C. 425, 483 Murray, J. C. 27, 262 Murray, M. M. 594 Musacchia, G. 486 Musiek, F. E. 36 Musio, A. 72 Muthiah, S. 518 Myklebust, H. R. 131 N’Kaoua, B. 505 Näätänen, R. 90, 201, 486, 584, 595 Nadel, L. 53 Naess, K. A. 394 Næss, K. B. 56 Nagai, T. 486 Nagarajan, S. S. 9, 289 Nagel, N. 159 Naglieri, J. A. 312 Nagy, V. 304 Nagy, W. E. 405 Nagy, Z. 138 Naigles, L. 91, 93, 536, 538, 540 Naigles, L. R. 92, 395 Nakamura, K. 137, 138 Nakato, R. 72 Nakayama, M. 174 Namazi, M. 331 Nance, W. E. 109

626

Author Index Nandym, R. 138 Napoliello, E. 135 Nappa, R. 548 Narter, D. B. 202 Nash, G. 10, 481 Nash, H. 405, 482 Nash, L. 332 Nash, M. 308, 309 Nathan, L. 248 Nathani, S. 113 Nation, K. 91, 94, 242, 284, 395, 397, 398, 542, 549, 550 Naves, R. 302 Naylor, C. 132 Neal, A. R. 88 Needleman, H. 10, 213, 505 Needleman, R. 398 Neils, J. 26, 257 Nelson, C. 96 Nelson, C. A. 85, 203, 590 Nelson, D. 447 Nelson, D. L. 504 Nelson, K. 379, 380 Nelson, K. E. 32 Nelson, L. 72 Nelson, P. B. 239 Nemeth, S. 122 Nespor, M. 591 Nett, K. 312, 315 Nettelbeck, T. 488 Nettelbladt, U. 24, 201, 202, 330, 334, 336 Nettlebeck, T. 483, 487 Netzloff, M. 240 Neuhoff, N. 201 Neuman, S. B. 404 Neville, H. 203, 205, 370, 481, 491, 552 Neville, H. J. 29, 199, 201, 406 Newbury, D. F. 27, 265, 266, 267, 268, 368 Newcomer, P. L. 374, 507 Newkirk, B. 427 Newkirk, B. L. 357, 367, 370 Newman, R. 244 Newman, R. L. 276 Newman, R. M. 308 Newman, R. S. 18, 20, 313, 503 Newman, R. M. 392 Newport, E. L. 177 Ng, S. 287, 289, 290 Nguyen, L. 488 Nichelli, F. 505 Nicholas, J. G. 116, 120, 121, 122 Nicholson, J. 256 Nicod, J. 267 Nicol, J. 170, 532, 543, 544 Nicol, T. G. 199, 201 Nicoladis, E. 299, 300, 303, 306 Nicolae, D. 269

Niemi, J. 29, 589 Nieto-Castañon, A. 300 Nighingale, N. 88 Nikolopoulos, T. P. 118 Niogi, S. N. 138 Niparko, J. K. 116 Nippold, M. 392 Nippold, M. A. 20, 25, 30, 513 Nishimura, C. 27, 262 Nittrouer, S. 121 Noble, C. 540 Nockerts, A. 509 Noens, I. L. 95 Noll, D. C. 196 Noll, K. R. 310, 330 Noonan, M. 163 Norbury, C. 219, 549, 550 Norbury, C. F. 15, 22, 25, 35, 86, 91, 93, 94, 311, 329, 349, 368, 395, 396, 373, 404, 448, 450, 455 Norell, S. 27 North, T. 26, 221, 259, 393 Norton, E. 139 Notari-Syverson, A. 405 Noterdaeme, M. 15, 225 Nöth, U. 589 Nott, P. 114, 115, 116, 117 Novey, E. S. 204 Novogrodsky, R. 6, 18, 24, 166, 171, 340, 433 Nowaczyk, M. J. M. 262 Nudel, R. 266 Numbers, F. 113 Nunes, S. 472 Nunez, P. L. 582 Nye, C. 213, 433 Nystrom, L. E. 196 O’Brien, A. 62 O’Brien, E. K. 27, 262 O’Brien, E. K. 27 O’Brien, M. 238, 306, 372 O’Connor, B. 331 O’Connor, E. 300 O’Connor, T. 82 O’Dell, M. C. 97 O’Donoghue, G. M. 118 O’Grady, W. 534 O’Hanlon, L. 20, 378, 392 O’Hara, M. 393, 418 O’Hare, A. 90 O’Hearn, K. 186 O’Riordan, M. 96 O’Seaghdha, G. P. 508, 511, 512 Oakes, A. 57, 61 Oakhill, J. 397, 398 Oberauer, K. 12, 487 Oberecker, R. 553 Odegard, T. N. 138

627

Author Index Odom, S. 85, 453 Oetting, J. 329, 418, 534 Oetting, J. B. 9, 20–2, 213, 217, 242, 281, 351, 352, 355, 356, 357, 365, 367, 368, 369, 370, 393, 418, 427, 509 Oga, T. 137, 138 Oghalai, J. S. 591 Ogiela, D. A. 369, 381 Ogilvie, T. 566, 567 Ohashi, J. K. 307 Ohayon, S. 332 Ohlwein, S. 397 Oja, E. 594 Okada, T. 137, 138 Oleson, J. 308, 309, 392 Oliva, P. 96 Oliver, B. 27, 260 Oliver, C. 72 Oliver, P. L. 267 Oller, D. K. 86, 90, 112, 113, 301, 309, 314 Olson, R. 133, 134, 469, 482, 483 Olson, R. K. 280, 503 Olswang, L. 313 Olswang, L. B. 442, 451 Ono, Y. 593 Oostra, B. 54 Opitz, B. 518 Orban, S. 481, 483, 491 Ordóñez, C. L. 301 Orgill, G. 86 Ormel, E. A. 397 Ornstein Davis, N. 444 Ors, M, 199, 201, 202 Ortiz, H. 194 Ortiz-Mantilla, S. 486 Osberger, M. J. 113 Oskarsdottir, S. 71 Osterling, J. 86, 87, 88, 96, 98 Osterweil, E. K. 54 Otten, L. J. 199 Oudenhove, L. V. 581, 589 Ouellet, C. 117 Owen Van Horne, A. 561 Owen, A. 335, 485 Owen, A. J. 20, 24, 91, 359, 366, 367, 370, 403, 426, 509 Ozonoff, S. 95, 96 Pääbo, S. 262 Paal, N. 121 Paavilainen, P. 201, 486, 584 Pacton, S. 216 Padmanabhan, A. 186 Pae, S. 213, 418 Page, D. 217, 481, 482 Pagin, P. 87, 90, 92 Paivio, A. 502

Palacio-Espasa, F. 86 Palfai, T. 95 Palincsar, A. S. 476 Pallie, W. 190 Palomino, H. 264, 265 Palti, D. 160, 171 Panagiotaki, G. 394 Pang, E. W. 199 Pankau, R. 56, 70 Pantazis, D, 593, 594 Papademetris, X. 138 Papagno, C. 219, 259 Papanicalaou, A. C. 580, 588 Paparella, T. 88, 395, 442 Pappata, S. 580 Paracchini, S. 268, 368 Paradis, J. 22, 299, 303, 306, 309, 310, 311, 331, 332, 482 Paradise, J. L. 111, 402 Parigger, E. 373 Parisse, C. 331, 332 Park, C. I. 581 Park, C. J. 93 Park, E. S. 581 Park, G. H. 518 Park, H. 481 Parker, W. A. 595 Parr, J. R. 87 Parra, M. 298, 303 Parsons, C. L. 368, 369, 372 Parsons, S. 401, 427 Partanen, M. 470 Pasco, G. 91 Pascual-Leone, J. 15, 487 Pasquini, E. 242 Passingham, R. 193, 281 Passingham, R. E. 193, 261 Pate, D. C. 171 Paterson, S. 68, 70, 394 Patil, A. A. 588 Patrolia, M. 338 Pattamadilok, C. 489 Patten, E. 86, 90 Patterson, J. L. 301, 314 Patterson, J. V. 200 Patterson, K. 279, 289, 505 Paul, D. 449 Paul, I. 588 Paul, J. J. 91 Paul, R. 60, 62, 87, 88, 90, 91, 349, 367, 402, 433, 445, 450, 451, 508, 509 Paulesu, E. 204 Pauls, D. L. 398 Paulson, L. A. 138 Paulus, M. 548 Pavetto, M. 62 Pavetto, M. M. 63, 64

628

Author Index Pawlowska, M. 31, 370, 379, 380 Pearce, W. M. 311 Pearson, B. Z. 301, 314, 359 Pearson, N. 141 Pease-Alvarez, L. 300 Pecher, D. 485 Pech-Georgel, C. 280 Pechmann, T. 510 Peeraer, L. 112, 120, 121 Peeters, R. 92 Peets, K. 483 Pei, F. 86 Pelegrina, S. 397 Peña, E. D. 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 308, 309, 311, 312, 313, 314, 315, 316 Pence, K. L. 568, 571 Penke, L. 483 Penke, M. 373 Pennell, C. E. 267 Penniman, L. 71 Pennington, B. 53, 63, 130, 132, 133, 134, 141, 142, 482, 483 Pennington, B. F. 94, 95, 98, 280, 503 Pepin, K. H. 55 Peppé, S. J. 90 Perales, S. 310 Perani, D. 135 Pérez-Leroux, A. T. 310 Perfetti, C. A. 135 Perkins, M. 205, 428 Perkins, M. R. 444 Perlmutter, D. M. 167 Pernbarg, V. 518, 519 Perona, K. 374, 375 Perovic, A. 371, 373 Perozzi, J. 31 Perozzi, J. A. 316 Perre, L. 489 Perret, C. 515, 517 Perrig, W. J. 220 Perruchet, P. 216 Perry, C. 276, 278, 280 Persson, C. 71 Persson, J. 366 Peter, B. 267 Peters, A. M. 315 Peters, D. H. 569 Peters, J. 393, 418 Petersen, A. 276 Petersen, J. M. 307 Petersen, O. 472 Petersen, S. 138, 483, 485 Peterson, B. 112, 113 Peterson, R. 130 Peterson, R. L. 280 Peterson, R. R. 510 Pethick, S. 114, 378, 399, 533

Pethick, S. J. 68, 399, 403 Petinou, K. 540 Petitto, L. A. 301, 314, 590 Petrill, S. 482, 571 Petryshen, T. L. 268 Pettinato, M. 238 Petty, J. 72 Pettygrove, S. 82 Petucci, M. 370, 508 Pezzini, G. 69 Pham, C. T. 306, 309, 310, 314 Pham, G. 10 Philips, B. 112, 120 Phillips, B. A. 56 Phillips, C. 54, 551, 554 Phillips, W. 82 Philofsky, A. 55, 58, 73, 371 Piantadosi, S. 561, 565 Piasta, S. B. 571 Picardi, N. 59, 60 Piccirillo, J. F. 110 Pickett, E. R. 193 Pickles, A. 86, 87 Picton, T. W. 594 Piepsz, A. 204 Pierce, A. 163, 169, 171 Pierce, S. 93 Pierce, S. J. 371 Piercy, M. 7, 200, 213, 221, 240, 287, 240, 241 Pierpont, E. I. 12, 27, 34, 64, 66, 194, 216, 217, 230, 282 Pierrehumbert, J. 153 Pietras, I. 503 Pihko, E. 203, 586 Pijnacker, J. 92 Pillow, D. 483 Pimperton, H. 397 Piñango, M. 166, 171 Pine, J. 339, 368, 369, 540 Pine, J. M. 22, 177, 306 Pinker, S. 154, 216, 281, 282, 283 Pinto, J. 540 Pinto, Y. 483 Pinto-Martin, J. 71 Pisoni, D. 238, 540 Pisoni, D. B. 111, 115, 120, 121, 122, 506 Pitcairn, D. L. 111 Pitcairn, D. N. 402 Pittman, A. 112, 113 Pizzioli, F. 332 Placantonakis, D. 483 Place, S. 303 Plaisted, K. C. 96 Plante, E. 21, 28–9, 30, 34, 190, 192, 194, 195, 197, 199, 204, 226, 308, 374, 375, 379, 380, 393, 401, 518, 566, 567, 581, 589 Platt, T. 85

629

Author Index Platzack, C. 163 Plaut, D. C. 279, 289, 290 Pléh, C. 332, 373 Plewes, C. 138 Plomin, R. 27, 260, 262 Plunkett, K. 282, 539 Poelmans, H. 139 Poeppel, D. 163 Poirier, M. 475 Poizner, H. 69 Poldrack, R. A. 503 Polišenská, K. 372, 506 Polite, E. 329, 335 Poljac, E. 96 Poll, G. 487 Poll, G. H. 368 Pollak, S. D. 224, 225 Pollock, J.-Y. 168 Pond, R. 120, 314, 430, 431 Ponton, C. 486, 584 Ponton, C. W. 199, 595 Popescu, E. 552 Popescu, E. A. 203 Popescu, M. 29, 203, 552 Portillo, A. L. 540 Posner, M. 483, 485 Postal, P. M. 167 Poulakis, Z. 396 Poulin-Dubois, D. 302 Pouliot, P. 590 Poulson, C. L. 97 Poupon, C. 580 Pourtois, G. 533 Powell, J. M. 25, 451 Power, D. J. 171 Powers, N. R. 268 Prat, C. 205 Prather, P. 18, 170, 547 Preis, S. 196, 204 Prelock, P. 97, 98 Prezbindowski, A. K. 115, 402 Price, C. 58, 135, 138 Price, C. J. 193, 261, 578, 589, 590 Price, J. 56, 61, 65 Price, J. R. 372, 428 Price, L. H. 506 Price, T. S. 27, 260 Prince, A. 153 Pring, T. 561 Prior, M. 88 Prizant, B. M. 87, 89, 94, 97, 445, 451 Proal, E. 481 Proctor -Williams, K. 32, 379 Proctor, E. 569 Protopapas, A. 193 Provencal, S. 95, 96 Prutting, C. A. 31, 93, 441

Puffenberger, S. S. 228 Pugh, K. 135, 136, 144, 196 Pujol, J. 194 Pullman, M. Y. 12, 34 Pullum, G. K. 176, 421 Pulvermüller, F. 519 Puranik, C. S. 241, 249, 289 Purcell, S. L. 311, 509 Purdy, J. D. 200, 370, 553 Purdy, S. C. 36 Putman, C. 112, 113 Pye, C. 21, 393, 419 Pylkkänen, L. 519, 584 Pyne, J. M. 571 Python, G. 515 Qiu, D. 138, 139 Quigley, S. P. 118, 119, 171 Quill, K. 91 Quinn, R. 308, 313 Quittner, A. L. 116 Rabagliati, H. 584 Rabin, B. A. 566 Rabin, M. 204 Raboutet, C. 505 Rack, J. 469 Racsmány, C. 373 Radford, A. 161, 163 Raghavan, R. 569 Raiker, J. 481, 483 Räikkönen, K. 483 Rajan, A. 566 Rakowski, S. 483 Rakowski, S. E. 13 Rämä, P. 552 Ramando, N. 93 Ramkalawan, T. W. 116 Ramstad, K. 55, 428 Ramus, F. 289 Rapcsak, S. 192 Raphael, L. 540 Rapin, I. 87, 430, 447 Rappaport-Hovav, M. 167 Rappazzo, C. 302, 314 Rapport, M. 481, 483, 491 Räsänen, S. 339 Rashiti, L. 370 Rashotte, C. 141, 142, 143, 461, 469, 470 Rashotte, C. A. 276, 280, 474 Rasinski, T. 143 Raskind, W. 267, 481 Rastle, K. 278 Ratner, N. B. 244, 403, 432 Rau, A. E. 199 Raudenbush, S. W. 19 Rauf, L. 333, 422, 507, 508

630

Author Index Rauman, M. C. 97 Ravizza, S. M. 214 Realpe-Bonilla, B. 486 Realpe-Bonilla, T. 197, 202, 258, 586 Rebbechi, D. 483 Reber, A. S. 216 Reber, P. J. 216 Records, N. L. 31, 238, 306 Redanz, N. 244 Redcay, E. 86 Reddig, C. 121 Redick, T. S. 229 Redmond, S. 329, 393, 419 Redmond, S. M. 10, 21–2, 31, 35, 367, 368, 370, 373, 374, 509 Rees, L. 96 Reeves, D. 425 Reggia, J. 483 Reid, J. 425 Reilly, J. 25, 56, 378, 392, 505 Reilly, J. S. 114, 399, 533 Reilly, R. 392 Reilly, R. M. 308 Reilly, S. 3, 88, 256 Reimer, J. F. 15 Reimers-Kipping, S. 262 Reinhart, T. 167, 173 Reinikainen, K. 201 Reisinger, L. M. 448 Reiss, A. 55 Reiss, A. L. 62, 205 Reitsma, P. 398 Remijn, G. B. 588 Remschmidt, H. 201 Rentmeester-Disher, J. 491 Rescorla, L. 19–20, 377, 399, 508, 509, 533 Resendiz, M. 313, 315 Resendiz, M. D. 300, 303, 314, 315 Restrepo, A. 380 Restrepo, M. A. 22, 309, 310, 311, 313, 314, 316, 331, 332, 402 Retallack, R. 53 Reuland, E. 173 Reynolds, G. 485 Reynolds, M. 139 Reznick, J. 378 Reznick, J. S. 68, 114, 399, 403 Reznick, S. 533 Ribeiro, T. C. 92 Rice, C. 121 Rice, M. 69, 204, 329, 330, 333, 336, 368, 369, 370, 371, 377, 393, 418, 451, 534, 552, 553 Rice, M. L. 4–5, 20–2, 26, 31, 35, 213, 217, 242, 257, 281, 306, 310, 367, 368, 369, 370, 371, 376, 378, 379, 380, 393, 398, 406, 418, 421, 453, 507, 508, 509 Richards, B. 20, 403

Richards, C. 72 Richards, J. 485 Richards, T. 138 Richardson, A. J. 368 Richardson, E. 139 Richardson, L. 393, 419 Richardson, L. Z. 21 Riches, N. G. 426 Richman, B. 533 Richman, W. 329 Richman, W. A. 368 Richmond, E. 56, 57, 60, 61, 63, 65 Richmond, E. K. 64 Rickards, F. W. 396 Rickford, G. 350 Ricks, J. 254, 447 Ridderinkhof, K. 482 Rigamonti, C. 72 Rigaut, C. 332 Rilea, S. 54 Rimland, B. 55 Rimoin, D. 573 Rimol, L. M. 197, 589 Rimrodt, S. L. 138, 139 Rinaldi, J. 88, 96 Rinaldi, P. 117, 118 Rinehart, N. 92 Ring, H. 395 Ring, M. 429 Ring, S. M. 268 Rinker, T. 486 Rinne, T. 486, 584 Ripich, D. N. 509, 510 Risi, S. 86 Risley, T. R. 32, 402 Rispoli, M. 369, 377, 380, 421 Riva, D. 505 Rivard, M.-E. 302 Rivera-Gaxiola, M. 553 Rizzi, L. 163, 332 Roark, S. D. 109 Roberts, F. 329 Roberts, J. 58, 61, 65, 111, 204, 371, 508, 509 Roberts, J. E. 56, 62, 110, 372 Roberts, L. 543 Roberts, R. 469, 481, 507 Roberts, S. S. 336 Roberts, T. 481 Roberts, T. P. 588, 595 Roberts, W. 262, 588 Robertson, C. 469 Robertson, E. 223, 535 Robertson, E. K. 276, 280, 284, 287, 289–90 Robe-Torres, K. 218, 244, 393 Robey, R. R. 561, 565 Robillard, M. 308 Robin, D. A. 581

631

Author Index Robins, D. 92 Robinson, B. 52, 55, 56, 68, 69, 70, 373 Robinson, B. F. 373, 394 Robinson, B. W. 373 Robinson, C. 82 Robinson, L. A. 248, 451, 453 Robinson, S. 370 Rockette, H. E. 111 Rockstroh, B. 203 Rodriguez, B. L. 303, 315 Roelofs, A. 134 Roeper, T. 349, 353, 354, 358, 359, 431 Roesler, C. 486 Rogalsky, C. 187 Rogers, C. 247 Rogers, E. M. 570 Rogers, J. G. 72 Rogers, M. A. 591 Rogers, S. 55, 58, 65, 86 Rogers, S. J. 87, 97, 98, 371 Rogers, T. T. 505 Rohrbeck, K. 248 Roizen, N. 53, 71 Romeo, R. 238 Ronnberg, J. 481 Roorbach Jamison, K. 453 Roqueta, C. A. 537 Roring, R. 488 Rose, D. F. 398 Rose, E. 142, 143 Rosen, G. D. 189, 190 Rosen, I. 199, 201, 202 Rosen, S. 7, 242, 243, 289, 289, 368 Rosenbloom, L. 447, 448 Rosenfeld, R. 111 Rosenfeld, R. M. 110 Rosenthal, M. Z. 96 Rosenzweig, S. 248 Rosin, M. 56 Ross, R. 26, 257 Rosselli, M. 505 Rossion, B. 533 Rosti, R. O. 96 Rota-Donahue, C. 36 Roth, F. P. 114, 449, 509 Rothbart, M. 483 Rothenberg, A. 301 Rothstein, M. 533 Roux, S. 486 Rowan, L. E. 308 Rowe, L. 581 Rowe, L. A. 204 Rowe, M. 52, 55, 68, 69, 70 Rowe, M. L. 394 Rowland, C. 369, 421, 540 Rowland, C. F. 177, 178, 275, 421 Rowley, K. 340

Roy, D. 138 Royle, N. 483 Ruben, E. 88, 95 Rubio, D. M. 564 Rudel, R. G. 503 Ruder, C. C. 117 Ruffin, C. V. 111, 120 Ruffino, M. 135, 485 Rugg, M. D. 199 Ruigendijk, E. 173 Ruiz-Felter, R. 313, 315 Rumble, B. 53 Rumelhart, D. 274, 282 Rumiche, R. 303 Rump, K. M. 92 Rumsey, J. 138 Rundblad, G. 373, 394 Rush, R. 427 Rush, T. 533 Ruskin, E. 58 Russell, A. F. 509 Russo, S. 72 Rutherford, M. 90 Rutter, M. 87, 88, 96 Rutter, M. L. 82, 86 Rutter, T. 372 Ryals, B. 240 Ryan, M. 504 Rydell, P. J. 89, 94, 97 Rykhlevskaia, E. 139 Rypma, B. 14, 196, 483 Sabbadini, L. 69, 314, 329, 331, 337, 367, 368, 370 Sabel, T. 313, 315 Saben, C. B. 504 Sabisch, B. 204, 370, 552, 586 Sabo, D. L. 111 Sabo, H. 428 Sadato, N. 137, 138 Saddy, D. 546 Sadek, A. A. 96 Saffran, E. M. 171 Saffran, J. 244, 541 Saffran, J. R. 115, 171, 177, 218, 244, 275, 393 Safir, M. P. 87 Sagae, K. 432 Sahlén, B. 122, 201, 202, 548 Sahni, S. 275 Sai, F. 88 Sailor, K. 505, 511, 512, 513 Saint-Aubin, J. 475 Saito, M. 175 Salahi, A. 54 Salber, P. 573 Salisbury, D. 486, 595 Salmelin, R. 519, 588 Salmond, C. H. 28, 193, 197

632

Author Index Salter, W. 469 Salthouse, T. 482, 483 Sam, A. 85 Samar, V. J. 117 Samelson, V. M. 548, 549 Sams, M. 201 Sanchez, M. 483 Sanchez, M. C. 316 Sánchez-Naranjo, J. 310 Sander, E. 113 Sanders, L. 29, 203, 481, 491 Sandgren, O. 548 Sandman, C. A. 200 Sandy, L. 573 Sans, A. 194 Santelmann, L. 163 Sanz-Torrent, M. 549 Sarant, J. 112, 120, 121 Sarver, D. 481, 483 Sattler, J. 54 Saults, J. 221, 487 Sauzeon, H. 505 Savage, R. 489 Savoy, P. 510 Scarborough, H. 20, 69, 132, 140, 377, 428, 430, 432, 564 Scarpino, S. E. 303, 315 Scerif, G. 485 Scerri, T. S. 268, 368 Schacter, D. L. 219 Schaeffer, J. 330, 334 Schafer, G. 539 Scharfstein-Friedman, S. 140 Schatschneider, C. 503 Schauwers, K. 113 Scheinost, D. 138 Schelletter, C. 418, 419 Schellinger, K. B. 452 Schelstraete, M.-A. 332, 504 Scherg, M. 594 Scheuer, J. 12 Schickman, K. 98 Schiff-Myers, N. 313 Schildroth, A. 121 Schipper, R. 53 Schittler, P. 204 Schlaggar, B. 138 Schmithorst, V. J. 518 Schmitt, B. M. 584 Schmitt, C. 369, 381 Schmitt, J. E. 205 Schmitt, M. B. 515, 571 Schmitz, B. 395 Schmitz, M. 548 Schneider, P. 309 Schnell, R. D. 311 Schoedel, C. 72

Schoen, E. 90 Schoenbaum, E. E. 564 Scholte, H. 483 Scholtz, B. 176 Schonfeld, I. S. 19 Schoon, I. 427 Schopler, E. 87 Schorr, E. 114 Schrank, F. 141, 142 Schreiber, T. A. 504 Schreibman, L. 97, 98, 552 Schriefers, H. 510, 511, 512, 514, 516, 517 Schroeder, S. 55 Schteingart, D. E. 564 Schubert, A. 98 Schuele, C. 370, 422, 426, 427 Schuele, C. M. 24, 370, 425, 453 Schuler, A. L. 87, 445 Schulte-Körne, G. 201 Schultz, E. 82 Schultz, M. C. 561, 565 Schulz, E. 138 Schulz, P. 537 Schumaker, J. 8 Schuster, B. 472 Schütze, C. 333, 369 Schütze, C. T. 22, 369 Schwanenflugel, P. J. 466 Schwartz, G. 139 Schwartz, J. 580 Schwartz, M. 305 Schwartz, R. 223, 248, 331, 369, 426, 486, 511, 512, 513, 540, 542, 544, 554 Schwartz, R. G. 3–51, 121, 166, 171, 177, 194, 199, 200, 201, 223, 287, 309, 310, 314, 393, 502, 505, 506, 508, 514, 584, 585, 595 Schwartz, R. S. 116 Schwartz, S. 54, 56, 58, 59, 62 Schwartz, S. E. 372 Scott, C. 427, 433, 467 Scott, C. M. 368, 509 Scott, S. 581 Scourfield, J. 443 Scudder, R. 545 Searle, J. R. 155 Sebastián-Galles, N. 300 Secord, W. 264, 397, 430, 532 Secord, W. A. 120, 122, 314, 374, 507 Seddoh, A. 370 Sedey, A. 58 Sedey, A. L. 120, 121, 399 Sedivy, J. 547 Seergobin, K. 289 Seery, A. 202 Seery, A. M. 203 Segui, J. 510 Seibert, L. 19, 392

633

Author Index Seibert, M. 352 Seidenberg, M. S. 245, 276, 278, 279, 280, 282, 288, 503 Seidl, R. 53 Seiger, L. 512, 513 Seiger-Gardner, L. 20, 23, 31, 173, 287, 511, 512, 513, 514 Sejnowski, T. 594 Sekerina, I. 545, 546, 547, 548 Selicorni, A. 72 Selinger, C. 224, 225 Selkirk, E. 153 Sell, M. A. 451 Semel, E. 120, 122, 264, 314, 374, 430, 507, 532 Semrud-Clikeman, M. 204 Sénéchal, M. 405 Senft, R. 398 Senman, L. 262 Señor, M. 303 Sereno, J. 552, 553 Serlin, R. C. 55 Serniclaes, W. 276, 398 Serratrice, L. 308, 548 Serres, J. 552 Serry, T. A. 113 Service, E. 219, 588 Seung, H.-K. 54, 58, 59, 62, 239, 372 Sevy, A. B. 591 Seyffert, M. 589 Seymor, P. H. 94 Seymour, H. 367, 431 Seymour, H. N. 31, 349, 351, 352, 355 Shacht, T. 140, 503 Shafer, D. 114 Shafer, V. 223, 486, 486, 554 Shafer, V. L. 8, 15, 18, 24, 27, 29, 31, 36, 199, 200, 201, 584, 585, 595 Shah, P. 229 Shakuf, V. 488 Shalinsky, M. H. 590 Shanahan, T. 472 Shankey, J. 89 Shankweiler, D. 134, 397, 474 Shapiro, L. P. 159, 160, 166, 167, 171, 510 Share, D. 463, 464, 465, 467 Sharfstein-Freidman, S. 503 Sharma, A. 113, 199 Sharma, M. 36 Shaul, Y. 305 Shaywitz, B. 131, 135, 136, 139, 461, 463 Shaywitz, B. A. 132, 134, 138, 141, 143, 144, 196 Shaywitz, S. 131, 135, 136, 139, 142, 461, 463 Shaywitz, S. E. 132, 134, 137, 138, 140, 143, 144, 196 Sheehy, K. 466 Sheldon, A. 166 Shen, X. 138

Sheng, L. 300, 302, 308, 313, 502, 504 Sheppard, D. 485 Sherbondy, A. 139 Sherman, G. F. 189, 190 Sherman, S. L. 53 Sherwood, L. M. 573 Shetreet, E. 160 Shi, J. 486 Shields, J. 94, 448 Shimpi, P. M. 17 Shin, S. 114 Shipstead, Z. 229, 487, 491 Shitamichi, K. 588, 593 Shlonsky, U. 169 Shneider, A. 132, 134, 137, 140 Shochet, I. M. 397 Shorr, D. 535 Short, H. 365, 377 Short, K. 59, 62, 64 Shprintzen, R. J. 71 Shriberg, L. D. 23, 90, 152, 246, 508 Shu, H. 483 Shu, W. 261 Shulman, C. 395, 396 Siarey, R. 53 Sideris, J. 58 Siegel, L. 134, 465, 470 Siegel, L. S. 276, 289 Siegelbaum, S. A. 579 Siegmüller, J. 373 Sieroff, E. 485 Sigman, M. 58, 58, 91, 445 Silberberg, M. C. 94, 397, 398 Silberberg, N. 94, 397, 398 Silberg, J. 82 Silliman, E. R. 304, 305 Silmere, H. 569 Siloni, T. 167 Silva-Pereyra, J. 553 Silverman, S. 403 Silverman, W. 55, 63 Simkin, Z. 22 Simmons, E. 451 Simmons, J. Q. 90 Simms, G. 53 Simon, E. 403, 504 Simon, T. J. 63, 71 Simon-Cereijido, G. 22, 302, 305, 306, 309, 310, 314, 316 Simonoff, E. 82, 87, 426, 465 Simonsen, A. 308 Simonsen, H. 552 Simpson, A. 94, 448 Simpson, N. H. 27 Sindberg, H. 56, 57, 68 Singer, B. D. 476 Sininger, Y. S. 10

634

Author Index Sinka, I. 21, 419, 430, 431 Siok, W. 139 Sirotkin, H. 71 Sirri, L. 552 Sistrunk, W. 357 Sitter, S. 15, 225 Ska, B. 315 Skibbe, L. E. 571 Skinner, M. 55, 65, 121 Skrandies, W. 594 Skudlarski, P. 134, 135, 136, 141, 143, 144, 196 Skuse, D. H. 86 Slater, W. 398 Sligte, I. 483 Sloan, R. B. 114 Slobin, D. 283 Sloman, S. A. 299 Slonims, V. 395, 429, 465 Smit, A. B. 238 Smit, D. 311 Smith, A. C. M. 72 Smith, B. 238 Smith, B. J. 91 Smith, C. G. 111 Smith, C. R. 114 Smith, D. 470 Smith, E. 238, 306 Smith, E. E. 196 Smith, J. 313 Smith, L. 393, 552 Smith, M. 26, 257, 405, 509, 588 Smith, M. E. 316 Smith, M. R. 72 Smith, N. L. 171 Smith, R. L. 509 Smith, S. 132, 133, 135 Smith, S. D. 398 Smith, T. 89, 98 Smith, V. 395 Smith-Locke, K. M. 367 Smolensky, P. 153, 274 Smolík, F. 507 Snodgrass, J. G. 533 Snook, L. 138 Snow, Burns, M. S. 464 Snow, C. 141, 142 Snow, C. E 301, 470 Snow, D. 380 Snowling, M. 140, 398, 405, 463, 465, 482, 542 Snowling, M. J. 20, 35, 204, 284, 397, 398, 404, 427 Snyder, A. 483 Snyers, P. 504 Soares, M. 370 Sobeh, J. 485 Soderpalm, E. 71 Solomon, J. 173 Solorio, T. 432

Solt, S. 177 Song, J. Y. 338 Sonnenberg, E. A. 25, 446 Soriano-Mas, C. 194 Soros, P. 519 Soulières, I. 489 South, M. 95, 96 Southwick, J. 482 Southwood, F. 509 Souto, S. 332 Souto, S. M. 377, 378, 380 Sowell, E. 28, 192, 193, 199 Spackman, M. P. 254, 447, 454 Span, M. 482, 483 Sparrow, S. 58 Spaulding, T. 374, 375 Spaulding, T. J. 226, 566 Spear-Swerling, L. 467 Speer, S. 548 Speirs, S. 92 Spekman, N. J. 509 Spencer, C. 248 Spencer, K. M. 594 Spencer, L. J. 114, 116, 117, 122 Spencer, P. E. 115, 402 Spijkers, W. 485 Spinath, F. M. 260 Spivey-Knowlton, M. 547 Sponseller, P. 72 Sportiche, D. 162 Sprenger-Charolles, L. 276, 398 Spruytenburg, H. 405 Squire, L. 579 Squire, L. R. 216 Squires, K. E. 305, 311, 312 Srinivasan, R. 582 St Pierre, L. 396 St. George, M. 428 Stackhouse, J. 461 Stading, G. 70 Staffen, W. 135 Stage, S. 470 Stager, C. L. 244 Stahl, S. 466 Stainton, M. 122 Stallings, L. M. 117 Stallone, K. 71 Stampe, D. 152 Stanberry, L. 138 Stanovich, K. 134, 398, 463, 464, 465, 467 Stanovich, K. E. 276, 280, 398 Stanton-Chapman, T. L. 26, 453 Stark, C. 216 Stark, R. 213, 221, 545 Stark, R. E. 240, 246, 290 Starke, M. 332 Starr, J. 482, 490

635

Author Index Stavrakaki, S. 24, 171, 340 Steacy, L. 143 Stedron, J. 53 Steele, J. 483 Steele, S. 92 Steffenson, A. 299 Stein, J. F. 368 Stein, N. L. 312 Steiner, V. 314, 430, 431 Steiner, V. G. 120 Steinhauer, K. 176 Steinman, S. 116 Steinmetz, H. 196, 204 Steinschneider, M. 582 Stella, G. 135 Stelmachowicz, P. 112, 113 Stelmachowicz, P. G. 109, 397 Stelmack, R. M. 516, 517 Stemberger, J. 153 Stemberger, J. P. 502 Stenger, V. 485 Stephens, E. 394 Sterling, A. M. 56, 56, 58, 371 Stern, D. 86 Sternberg, R. J. 121 Sterponi, L. 89 Stetler, C. 571 Stevens, C. 29, 203, 371, 481, 483, 490, 491 Stevens, L. 508 Stevens, M. 430 Stevens, T. 394 Stewart, A. 548 Stewart, J. 116, 177, 540 Steyaert, J. 505 Stillman, B. 472 Stock, P. S. 138 Stockman, I. J. 347, 351, 352, 355, 359 Stoel-Gammon, C. 113 Stojanovik, V. 205, 428, 435 Stoke, C. 33 Stokes, P. 20 Stokes, S. 314, 329, 333, 422 Stokes, S. F. 6, 307, 365, 403, 507 Stokes, S. L. 433 Stollwerck, L. 5–6, 173, 257, 287 Stoltzfus, E. R. 14 Stone, W. L. 86 Storkel, H. 336, 533 Storkel, H. L. 381, 513 Storms, G. 299, 504 Stothard, S. E. 20, 398, 427 Stout, C. 374, 375 Straus, E. 205 Strauss, M. S. 92 Strawsburg, R. H. 518 Street, C. K. 368 Streiner, D. 93, 371

Stromme, P. 55, 428 Stromswold, K. 197, 259, 548 Strong, C. 434 Stuart, E. A. 87 Stubbe Kester, E. 312, 315, 316 Studdert-Kennedy, M. 7, 289 Stuebing, K. 132, 134, 137, 140 Sturgeon, X. 53 Su, Y. 548 Suárez-Coalla, P. 503 Suárez-Orozco, C. 300 Sudhalter, V. 55, 60, 62, 63, 377 Sullivan, P. M. 121 Sullivan, R. 566 Sulzby, E. 213, 221, 311 Summerfield, A. Q. 111 Summers, C. L. 300, 303, 314, 315 Sun, Z. 53 Sunaert, S. 92, 139 Sundarrajan, M. 114 Sung, N. S. 573 Sunshine, J. 138 Suro, R. 299 Sussman, E. 486, 595 Sussman, H. M. 7, 289 Sussman, J. E. 7, 240, 249, 287, 289 Sutton, A. 307 Sutton, S. K. 95 Svangstu, J. 552 Svenkerud, V. 244 Svensson, L. 451 Svirsky, M. A. 114, 117 Swainson, B. 92 Swan, D. 502 Swank, K. 534 Swank, L. 393, 418 Swank, L. K. 243 Sweeney, J. A. 97 Sweet, M. 316 Swenson, L. 540 Swettenham, J. 88, 96 Swift, E. 56 Swillen, A. 71 Swingley, D. 540 Swinney, D. 13, 18, 167, 170, 510, 543 Swisher, L. 192, 380, 381 Sylvestre, V. 315 Sylvia, L. 36 Synder, W. 369 Syrdal-Lasky, A. K. 276 Szagun, G. 116, 117, 118 Szatmari, P. 307 Szenkovits, G. 289 Szterman, R. 118, 119, 166, 171 Tada, M. 486 Tadel, F. 593

636

Author Index Taeschner, T. 299 Taffe, J. R. 93 Tager-Flusberg, H. 59, 60, 85–6, 88, 90, 91, 92, 93, 95, 99, 166, 202, 203, 204, 371, 372, 377, 395, 396, 430, 444, 445, 463, 590 Tajudeen, B. 114 Takahashi, H. 261 Takahashi, K. 261 Takayama, Y. 137, 138 Takizawa, R. 591 Talbott, M. R. 202 Taliancich-Klinger, C. 302, 308, 313 Tallal, D. 370 Tallal, P. 193, 195, 197, 199, 200, 201, 204, 213, 221, 240, 241, 243, 246, 257, 258, 274, 281, 287, 289, 290, 311, 393, 447, 461, 503, 508, 509, 552 Tallal, P. A. 7, 9, 21, 26, 28–9, 33 Tallberg, I. M. 502 Tamnes, C. K. 580 Tan, L. 139 Tan, Y. 12 Tanaka Welty, Y. 340 Tanenhaus, M. 547 Tanenhaus, M. K. 170 Tang, F. 368 Tannock, R. 481 Tansella, M. 566 Taranto, G. 167 Tarr, E. 121 Tattersall, P. 374, 375 Tavakolian, S. 536 Taylor, A. 63 Taylor, C. L. 379 Taylor, M. J. 199 Taylor, R. D. 452 Team, A. S. 265 Tees, R. 276, 289 Tek, S. 93, 395, 486 Telian, N. 473 Temple, C. 373 Temple, K. 395 Termine, C. 135 Terrell, B. Y. 34 Terry, A. 284 Ter-Stiepanian, M. 139 Teshima, I. 262 Tewari, A. 488 Thaiss, L. 95 Thal, D. 68, 114, 378, 394, 399, 403, 533 Thal, D. J. 116, 19–20, 301, 378, 392 Theakston, A. 339, 369, 421 Theakston, A. L. 177, 178, 275, 421 Thériault, M. 591 Thériault-Whalen, C. 302, 309 Thibodeau, L. M. 33, 289, 491 Thomas, E. 307

Thomas, E. M. 307 Thomas, K. M. 186 Thomas, L. E. 56 Thomas, M. 191, 373, 394, 482 Thomas, M. S. C. 16, 27, 283, 285, 286 Thompson, C. K. 171, 510 Thompson, H. L. 373, 374 Thompson, L. A. 26, 35 Thompson, M. S. 313, 316 Thompson, M. T. 402 Thompson, P. 205 Thompson, R. 485 Thompson, T. 71 Thomsen, T. 197, 589 Thomson, J. 311, 491 Thordardottir, E. T. 25, 298, 301, 303, 316, 331, 372, 507, 509 Thorn, A. S. C. 394 Thorndike, R. 54 Thornicroft, G. 566 Thornton, R. 17, 533, 536, 537 Thurman, A. J. 66, 67 Tierney, A. L. 85–6 Tierney, E. 55 Timler, G. R. 442, 453 Tirosh, E. 87 Tisdale, J. 259 Tobey, E. A. 113, 114, 116 Toepel, U. 515 Tolbert, L. 370, 379, 427 Tolvanen, A. 586 Tomas, E. 22 Tomasello, M. 16, 34, 170, 176, 275, 312, 339, 424, 426, 536 Tomblin, B. 217, 218, 256 Tomblin, J. 221, 243 Tomblin, J. B. 4, 13, 21, 23, 25–7, 29–32, 116, 117, 122, 195, 238, 240, 241, 246, 257, 258, 259, 260, 262, 306, 329, 368, 370, 372, 393, 398, 432, 481, 483, 487, 488, 506, 509, 548, 549, 554, 586, 589 Tondeur, M. 204 Tonge, B. 92 Tonini, R. E. 591 Tonnquist-Uhlén, I. 201, 595 Tonucci, F. 70, 372, 373 Topol, D. 396 Toppelberg, C. O. 300 Torgesen, J. 134, 141, 142, 143, 461, 463, 465, 466, 469, 470 Torgesen, J. K. 276, 280, 474 Torkildsen, J. v. K. 566, 567 Tornyova, L. 29, 166, 171, 223, 426, 514, 544 Townsend, J. 201, 552 Towse, J. N. 14 Trabasso, T. 312 Tracy, R. 299

637

Author Index Trainor, L. 486 Trainor, L. J. 90 Tran, N. 569 Trask, R. L. 416 Trauner, D. 52, 193, 195 Trauner, D. A. 447 Travis, L. L. 445 Traxler, C. B. 398 Trehub, S. E. 443 Treiman, R. 396 Treisman, A. 486 Tremblay, A. 338, 366 Triantafyllou, C. 139 Troia, G. A. 33 Tropper, B. 18, 29 Troyb, E. 92 Troyer, A. K. 505 Trubetzkoy, Nikolai 152 Trudeau, N. 302, 307, 315 Truelove, E. 404 Trueswell, J. 545, 546, 547, 548, 549 Trueswell, J. C. 548 Tsao, F.-M. 241, 243 Tsuchiya, N. 579 Tucker, R. 396 Tugade, M. M. 14 Tuholski, S. W. 14 Tulving, E. 216, 219 Tunali, B. 94 Turennout, M. 515, 516 Turk, J. 55, 63 Tur-Kaspa, H. 117, 118, 119 Turley, C. 488 Twilley, L. 289 Tye, L. D. 54 Tye-Murray, N. 110, 117 Tyler, A. 379, 380 Tyler, L. 545, 546 Tzovara, A. 594 Tzuriel, D. 313 Udwin, O. 56, 68, 69, 70, 373 Ueno, S. 588, 593 Uhry, J. 134, 141 Ukrainetz, T. A. 313, 315, 420 Ullman, M. 368, 370, 481, 482 Ullman, M. T. 12, 27, 34, 122, 194, 216, 217, 230, 281, 282, 284 Umbel, V. 301, 314 Ungerer, J. A. 91 Unsworth, S. 303 Urban, T. 92 Urban, Z. 240 Urbina, S. 121 Urv, T. K. 62 Uwer, R. 29, 199, 201, 486 Uylings, H. B. M. 188

Vaid, J. 428 Vaillancourt, T. 307 Vajsar, J. 594 Valasek, C. A. 92 Valdes, G. 300 Valdez-Menchaca, M. C. 257, 405 Valente, A. 515 Valian, V. 177 Vallar, G. 219 Vallet, P. 54 Valleta, J. 54 van Balkom, H. 307 Van Boxtel, J. J. A. 579 van Daal, J. 307 van Dantzig, S. 485 van de Vijver, R. 307 van de Weijer, J. 548 van den Dikkenberg-Pot, I. 112 Van den Heuvel, M. 138 van der Leij, A. 483 van der Lely, H. 370, 393, 418, 506, 509, 534, 544, 552, 553 van der Lely, H. K. 5–6, 17–18, 22–4, 171, 173, 200, 217, 257, 274, 281, 282, 284, 287, 289, 308, 368, 370, 422, 506, 507 Van der Linden, M. 213 van der Mark, S. 138 Van der Merwe, H. 311 van der Molen, M. 227, 482 van der Molen, M. J. 228 van der Molen, M. W. 228 van der Vlugt, H. 516, 517 Van Dongen, R. 311 Van Dyke, J. 223 van Harten, W. H. 566 Van Herwegen, J. 373, 394 van IJzendoorn, M. H. 472 van Lambalgen, M. 92 Van Lieshout, P. 488 van Luit, J. E. H. 227, 228 Van Matre, A. J. 202 Van Petten, C. 199 Van Riper, C. 32 van Santen, J. P. 90 Van Stone, E. 71 van Turennout, M. 516, 584 van Wieringen, A. 112, 120, 121 van Zeben, T. M. 505 van Zonneveld, R. 171 Vance, R. 28, 30, 191, 192, 196, 226, 374, 375, 401, 566, 567 Vandenberg, B. 134 Vander Zanden, J. W. 88 Vandergift, N. 61, 65 Vandermosten, M. 139, 581 Vanderwart, M. 533 Vargha-Khadem, F. 27–8, 193, 197, 217, 261, 281

638

Author Index Varley, R. 94, 448 Varuzza, C. 117, 118 Vasič, N. 173 Vasishth, S. 223 Vasquez, J. 299 Vaughan, A. E. 95 Vaughan, H. 579 Vaughn, S. 471 Vaux, K. K. 96 Vávru, P. 507 Ve’ rane, B. 213 Velez, M. 21, 542 Velleman, S. 534 Vendelin, I. 590 Veness, C. 88 Venker, C. 541 Vergauwe, E. 224, 226 Vergnaud, J.-R. 165 Verhoeven, J. 92, 581, 589 Verhoeven, L. 307, 397, 502 Veríssimo, J. 366 Verly, M. 92, 581, 589 Verma, R. 595 Vermeer, A. 301 Vermeulen, A. 112, 120, 121 Vernes, S. C. 267 Vernon-Feagans, L. 111 Verstraeten, F. 204 Vetter, D. 56 Vianello, R. 485 Vicari, S. 54, 69, 70, 372, 373 Vickrey, B. G. 564, 574 Victorino, K. R. 15 Viding, E. 260 Vieiria, A. 486 Viele, K. 368 Viescas, R. 393 Vihman, M. M. 113 Villanueva, P. 264, 265 Visser, M. 311 Vizziello, P. 72 Vles, J. S. 505 Vogel, A. 138 Vogel-Farley, V. 85–6, 203 Vohr, B. 396 Voklmar, F. 451 Volden, J. 395 Volkmar, F. 398 Volkmar, F. R. 86, 88, 90, 91, 93, 94, 95 Volle, E. 138 Volterra, V. 69, 299, 314, 372, 373 Volz, S. 589 von Cramon, D. Y. 584 von Koss, T. 552 von Kriegstein, K. 486 von Suchodeletz, W. 29, 199, 201, 204, 370, 486, 552, 586

Vorberg, D. 510 Voullaire, L. E. 72 Vu, T. 267 Vukovic, C. 465 Vyma, G. 289 Waber, D. 483 Waber, D. P. 503 Waddy, S. 564, 574 Wadman, R. 454 Wagner, J. B. 590 Wagner, L. 248, 372, 429, 535, 548 Wagner, R. 141, 142, 143, 469, 470 Wagner, R. K. 276, 280 Wagner, V. 513 Wagner-McPherson, C. 55 Wake, M. 256, 396 Walden, P. R. 306 Walden, T. 86 Waldman DeLuca, Z. 171 Wales, R. 89 Walker, D. 406 Walker, E. A. 396, 397 Walker, R. 550 Wallace, G. 489 Wallace, G. L. 93 Wallace, I. 111 Wallace, K. L. 109 Walley, A. 247, 513 Walley, A. C. 247 Walsh, C. 313, 315 Walsh, K. 204, 377, 581 Walter, J. 368 Walters, J. 300, 305, 307, 311 Waltzman, S. 114 Wandell, B. 139 Wang, L. W. 54 Wang, N.-Y. 116 Wang, P. P. 71, 394 Wang, W. 486 Wang, X. 9, 289 Warnke, A. 201 Warren, S. 380 Warren, S. F. 35, 371 Warren, S. T. 54 Washington, J. 401 Washington, J. A. 26, 31, 355, 357 Wassenberg, R. 139 Watanabe, J. 340 Watanabe, M. 87 Waterhouse, L. 93 Waters, G. 11, 122, 465 Watkins, K. 281 Watkins, K. E. 21, 27–8, 193, 194, 197, 261 Watkins, R. 379, 380, 393 Watkins, R. V. 20, 392, 402, 403, 509 Watling, R. 98

639

Author Index Watrin, E. 72 Watson, B. U. 121 Watson, J. 532 Watson, L. R. 86, 90 Watson, M. 60, 62 Waxman, S. R. 396, 502 Weaver, N. L. 566 Webb, S. S. 97 Weber Byars, A. 518 Weber, A. 66, 372 Weber, C. 29, 202, 586 Weber, J. 483 Weber-Fox, C. 200, 242, 274, 370, 481, 553, 554, 586 Wechsler-Kashi, D. 121, 502, 505 Weckerly, J. 505 Wehner, E. 55, 65 Weiler, M. 483 Weiler, M. D. 503 Weinberg, A. 540 Weinblatt, R. 489 Weir, F. 25, 446, 448 Weismer, S. 487 Weismer, S. E. 10–11, 13–15, 19, 23–5, 28–9, 221, 506, 507, 509, 589 Weissberg, R. P. 452 Weissenborn, J. 163, 307, 548 Weiss-Kapp, S. 473 Weissman, A. 71 Weissman, M. 56, 57, 60, 61, 62, 63 Weitzman, E. 33 Weller, G. 71 Wells, B. 248, 461 Welsh, J. 32, 380, 483 Welsh, M. C. 94 Wendelken, C. 483 Wener, S. E. 229 Werker, J. 276, 289 Werker, J. F. 244, 247 Werner, E. 85–6, 87, 88 Wertz, R. T. 567 Wessel, A. 548 Wessels, J. 244 West, R. 398 Westby, C. 311, 442, 444, 450 Westerveld, M. F. 510 Westfall, J. M. 566, 569 Wetherby, A. M. 87, 97, 444, 445, 451 Wetherell, D. 25 Wettig, C. M. 518 Wexler, K. 5, 22, 26, 31, 163, 173, 257, 281, 306, 329, 330, 333, 334, 335, 367, 368, 369, 371, 373, 376, 378, 507, 508, 509 Whan, M. Q. 109 Wheelwright, S. 444 Whitaker, K. 483 White, E. J. 176

White, T. 398 Whitehouse, A. J. O. 267 Whitehouse, D. 398 Whitehurst, G. J. 26, 257, 402, 405, 509 Whitman, J. 163 Whitney, S. D. 33 Wickström, B. A. 298 Wickstrom, S. 453 Wieder, S. 97 Wiederholt, J. 141 Wietecha, L. 139 Wigal, S. B. 139 Wiggins, A. K. 571 Wiggins, A. 568 Wigglesworth, G. 114, 115, 116, 117 Wiig, E. 430, 532 Wiig, E. H. 120, 122, 264, 314, 374, 397, 430, 507, 513 Wijers, A. A. 199 Wijnen, F. 502 Wijsman, E. 267 Wilbur, R. B. 117, 118, 119, 171 Wilcox, K. A. 453 Wilcox, M. J. 508 Wilcutt, E. 482 Wilding, J. 485 Willams, K. T. 426 Willcutt, E. 134, 482, 483 Willemsen, R. 54 Williams, C. 398 Williams, C. C. 367, 509 Williams, D. 139 Williams, D. L. 97 Williams, D. M. 95 Williams, K. 68 Williams, S. 397, 488 Willis, C. 10 Willis, C. S. 219, 264, 506 Willows, D. 472 Willstedt-Svensson, U. 122 Wilson, D. P 177 Wilson, G. 392 Wilson, R. 177 Wilson, R. K. 55 Wilson, S. 15 Wilson, S. J. 518 Wimmer, H. 135 Winchester, L. 267, 368 Windfuhr, K. L. 393, 508 Windsor, J. 13–1, 31, 221, 241, 247, 306, 308, 309, 310, 312, 368, 393, 467, 481, 483, 487, 489, 490, 504, 509, 542 Wing, L. 86 Wingate, M. 82 Wingfield, A. 170 Winocur, G. 505 Winsberg, B. 139

640

Author Index Wisbeck, J. 55 Wise, B. 469 Wise, R. 581 Wisenbaker, J. 466 Wishaw, I. Q. 578 Witelson, S. F. 190 Wittek, A. 537 Wiznitzer, M. 85 Wodka, E. L. 90 Wolf, M. 465, 503, 504, 551 Wolff, P. H. 55, 62, 63, 503 Wolf-Schein, E. 55, 63 Wolters, P. L. 72 Wong, A. M. 6, 20, 307, 329, 333, 365, 403, 422, 507 Wood, C. 466 Wood, F. 132, 466, 474 Wood, H. 85 Wood, M. 311 Wood, S. 112, 113 Woodcock, R. 141, 142 Woodin, M. F. 71 Woodruff-Borden, J. 56 Wouters, J. 112, 120, 139 Wouters, J. 581 Wray, A. 481 Wright, B. 241, 398 Wright, B. A. 241, 249, 289 Wright, M. J. 257 Wright, S. H. 504 Wruck, E. 590 Wu, C. 54 Wu, H. 359 Wu, S. 482 Wuisman-Frerker, M. 139 Wulfeck, B. 195, 201, 245, 281, 329, 367, 505, 507 Wydell, T. 137, 138 Wynn-Dancy, M. 240 Xiao, T. 486 Xu, J. 518 Xuyang, Z. 27 Yaghmai, F. 28, 190, 192 Yaghoub-Zadeh, Z. 472 Yamasue, H. 591 Yang, C. 177, 339 Yang, H. 261 Yang, L.-X. 12 Yang, Y. 54 Yap, M. 532 Yeargin-Allsopp, M. 82 Yeatman, J. 139 Yelland, G. 92 Yelland, G. W. 93 Yerys, B. 489

Yim, D. 312, 315 Ying, E. 116, 117, 540 Ying, E. A. 115 Yirmiya, N. 86 Yoder, P. 86 Yoder, P. J. 380, 404, 405 Yokoyama, F. 87 Yoncheva, Y. 483 Yont, C. 432 Yont, K. M. 509 Yoon, J. 12 Yoshimura, Y. 588, 593 Yoshinaga-Itano, C. 399 Young, G. A. 122 Young, N. M. 113 Younger, B. 246 Yu, C. 393 Yu, Y. H. 585 Yucel, G. 490 Yule, W. 70 Yuzda, F. Y. 82 Zackai, E. H. 71 Zacks, R. T. 14 Zadunaisky-Ehrlich, S. 340, 508 Zaidman-Zait, A. 395 Zampini, L. 372 Zangl, R. 540 Zdorenko, T. 303 Zecker, S. 241 Zecker, S. G. 201 Zeeland, S. V. 86, 89 Zeelenberg, R. 485 Zeesman, S. 262 Zeffiro, T. 489 Zehr, K. 398 Zeisel, S. A. 110 Zeki, S. 135 Zelaznik, H. N. 246 Zelazo, P. D. 95 Zelazo, P. R. 87 Zesiger, P. 332 Zevin, J. 483 Zhan, K. 54 Zhan, L. 548 Zhang, L. 261 Zhang, X. 30, 116, 217, 218, 221, 238, 243, 306, 393, 398, 483, 506 Zhang, X. Y. 262 Zhou, K. 139 Zhou, P. 548 Ziegler, J. 278 Ziegler, J. C. 276, 280 Zilioli, M. 504 Zilles, K. 188 Zimmerman, B. 143 Zimmerman, I. 314, 430, 431

641

Author Index Zimmerman, I. L. 120 Zink, I. 92, 581, 589 Zlatic-Guinta, R. 301, 302, 313 Zola, S. M. 216 Zorzi, M. 276 Zubrik, S. R. 379

Zuckerman, S. 171 Zukowski, A. 69, 373 Zukowski, A. 69 Zumach, A. 110 Zurif, E. 159, 160, 166, 170, 171 Zwaigenbaum, L. 86

642

SUBJECT INDEX

22q11.1 deletion syndrome 71 22q11.2DS 73 ABCC13 gene 265 acoustic-phonetic processing 484 acoustic variability, reduction 239 act-out 535–6 adjuncts 158 adverbial constructions 423 African American English (AAE): AAE-speaking children, language (development) 347–8; assessment 348; categorization, criteria 355–6; characterizations 349–54; child language impairment, relationship 345; contrastive/noncontrastive features 355–9; frequency/occurrence rate 356–8; deficit hypothesis (deviant language/ intervention) 346; disorders 349; disorders, characterizations 349–54; dual components approach 352–3; features-based approach 350–1; general American English (GAE), differences (comparative paradigm) 346–7; grammar 355–9; impairment, locus 355–9; language disorders 349; research, phases 346–8; speaking 31; study, disorders/approaches 348–54; system context, usage patterns 358–9 African American Vernacular English (AAVE) 350 age-appropriate words/phrases, subsample 401 age-matched controls 218, 368, 373 age-matched TD 535; children 422 agrammatic aphasia 18 agreement (AGRS) 5, 333 agreement inflection 331 agreement morphemes 377 alternation, types 419 American Sign Language (ASL) 112, 399 American Speech-Language-Hearing Association (ASHA) 347–8; AIT efficacy literature review 98

analysis of variance (ANOVA) models 532 Applied Behavior Analysis (ABA) 97 argument structure 417–19; alternation, types 419; syntax 434 articulation (ASD) 90–1 articulation difficulties 193 articulatory knowledge 245–7 articulatory muscles, usage 134–5 articulatory variability, studies 246 artificial grammar learning 216 ASD. see autism spectrum disorders association primes 92 ATP2C2 (candidate gene) 266–7 attention 224–7, 481, 484–5; biases, development 485; constructs, overlaps 488; control 487; executive functions, relationship 14–16; integrated constructs 487–8; measurement 488–9; model 214–16; overlaps 487; sustaining 225; usage 482–7 attentional mechanisms 139 attention-deficit hyperactivity disorder (ADHD) 35–6, 139, 228–9, 373–4 atypical language behavior, atypical brain structure (correlation) 194–5 auditory brainstem response (ABR) 109 Auditory Continuous Performance Test 14–15 auditory ERPs 200–1 auditory evoked potential (AEP) 595 auditory integration therapy (AIT) 98 auditory perception problems 242 auditory processing: deficits, identification 202–3; ERP studies 201 auditory processing disorders (APDs) 36 auditory stimuli, early neural processing 202 autism: autism-specific issues 89–90; comorbid FXS, relationship 65; core deficits, theories 95–7; language, usage 65–7; linguistic abilities, range

643

Subject Index 429; morphosyntactic profiles 371–2; primes 92; subgroups 87; verbal children, speech 90 Autism Diagnostic Interview-Revised (ADI-R) 86; score derivation 66 Autism Diagnostic Observation Schedule (ADOS) 66, 71 Autism Diagnostic Observation Schedule-Generic 86 autism spectrum disorders (ASD) 19, 82, 429, 441; articulation 90–1; communication skills 87–95; diagnostic criteria, overview 83–4; DSM-5 criteria 84; DSM-5 criteria, overview 84; early regression 86–7; hyperlexia 94–5; intervention 97–8; joint attention, language acquisition (relationship) 88–9; language acquisition 87–8; language acquisition, joint attention (relationship) 88–9; language skills 87–95; morphology 92–3; nonverbal IQ 87–8; phonology 90–1; pragmatics 93–4; prosody 90–1; semantic deficits 394–6; semantics 91–2; severity levels 85; social communication impairment 444–5; speech 87–95; symptoms 65; syntax 92–3 autistic-like behaviors 55 Automated Fluency Task 519 automaticity 466; absence 142; development 473–4 automatic reading, orthographic representations 474 auxiliaries: development 421–2; emergence, average age 421 Auxiliary (AUX) 335 auxiliary verbs/articles/clitics, problems 332 averaged evoked potentials (AEPs) 582 Avon Longitudinal Study of Parents and their Children (ALSPAC) 265–6 babbling 112–13 backward-masking condition 241 basal ganglion 188 basic-level cognitive processes 243 behavior, syndrome-specific profiles 53–6 beta-amyloid precursor protein (APP) gene 54 bilateral hearing loss 110 bilingual children: code-switching patterns 302; Down syndrome, studies 309; intervention, language study 316; language assessment 313–15; language development 300–4; language impairment 312; morphology 303–4; narrative performance, research (increase) 311; semantic performance 308; semantics 301–2; syntax 303–4 bilingual children, language impairment 306–12; intervention 315–17; morphology/syntax 309–11; narrative performance 311–12; semantic performance 307–9 Bilingual English Spanish Assessment (BESA) 301–2, 312, 314 Bilingual English Spanish Assessment-Middle Elementary 309 bilingualism 297; social/linguistic influences 299–300; theoretical perspectives 297–9

Binding Theory 172 bite-block, holding 246–7 Bookshare 144 Botwinik-Rotem, Irena 151 bound pronouns, interpretation 288 brain: abnormal development 188; activation 160; asymmetry, commonness 191–2; circuits 579; development, studies 198; function 197; in vivo methods for identifying function 581–91; imaging 580–1; language areas 186–8; left hemisphere, primary language-related areas 187; normal development 185–8; scientific mapping 184 brain structure 197, 577; pre-specification 185 Brinton, Bonnie 441 broad-band noise 241 Broca’s area 92, 187, 196–8 Brodmann’s area (BA) 187, 578 canonical babbling 113 Cantonese, SLI grammatical profile 333 capacity limitations 215 CASPR2 protein 267 category primes 92 caudate nucleus 188, 192 causative alternation 419 cause-and-effect relationship 563 CDI: Words and Sentences (CDI:WS) 378–9 CdLS. see Cornelia de Lange syndrome cerebral dysplasia 190 CFTR gene 255 CGG expansion 54–5 CGG repeats 54 chain formation 171 Channell, Marie Moore 52 Checklist for Autism in Toddlers (CHAT) 86 childhood developmental disabilities: morphosyntactic profiles 373–4; syntax, relationship 428–30 childhood language: neuroimaging studies 191–8; neurophysiological studies 198–205 Child Language Data Exchange System (CHILDES) 432, 509 child language development, knowledge 565; studies 198 child language disorders: activation 591–2; analysis 593–4; assessment 489, 490; assessment, implications 312–17; assumptions, simplification 289; attention 481; attention, measurement 488–9; connectionism, predecessors 275–6; connectionist modeling 274–5; cross-linguistic studies 328; electrophysiology 581–7; eventrelated potentials (ERPs), recordation 585; functional magnetic resonance imaging (fMRI) 588–9; functional near-infrared spectroscopy (NIRS) 590–1; function, in vivo methods 581–91; goals 403–4; grammatical deficits, accounts/

644

Subject Index cross-linguistic findings 333–8; grammatical morphology 281–4; imaging data, interpretation 591–4; input, contributions 339–40; instrumentation 592–3; intervention 489, 490–1; intervention 403 –6; intervention, implications 312–17; magnetoencephalography (MEG) 587–8; methods, convergence 595–6; microstructure (study), neuroscience methods (usage) 578; model-based accounts, challenges 289–90; model-based approaches 274; morphosyntax 365; narrative discourse 304–6; neocortex, structure 579–80; neural circuitry 578–9; neurobiology 184, 204–5; neuroscience approaches 577; noise, potential fluctuations 583; P100 component, dipole analysis (results) 594; perception 481; perception, measurement 488–9; perception/production 238; phonological deficit theory, concerns 289–90; populations, past tense deficits 284–6; positron emission tomography (PET) 589–90; processing speed 481; processing speed, measurement 488–9; reading 461; reading disorders 276–81; research, cognitive constructs 481–2; semantics 392; 65-electrode Geodesic net, usage 586; structural imaging 580–1; syntactic deficits 286–9; syntax 416, 425–7; techniques 404–6; theory/practice, impact 482; in vivo brain imaging 580–1; working memory 213; writing 461 child language impairment: African American English (AAE), relationship 345; pragmatics 441; social communication 441 child language interventions: coverage/penetration 570; development (translation/implementation research) 561; early efficacy studies 563; effectiveness studies 563–5; feasibility 569; feasibility studies 562; fidelity 569; hybrid studies 570–2; implementation cost 570; implementation process, issues/questions 569–70; implementation research 564–72; implementation research, challenges 572–4; later efficacy studies 563; pre-trial studies 562; research/development model 562–4; research, five-phase model 563; research, impact (reduction) 571; research, translational blocks/implementation research (modified model) 566; sustainability 570; T1 blocks/research 566–7; T2 blocks/research 567–9; T3 blocks/research 569–70; translational research 564–72; translational research, challenges 572–4 child-oriented approaches 32 children: AAE-speaking children, language (development) 347–8; attention 225–7; auxiliaries, development 421–2; bilingualism 297; bilingualism, theoretical perspectives 297–9; communication ability 450; developing children, sentence combining 422–5; intellectual disability, genetic origin 52; interactional difficulties 446–7;

language exposure 304; lexical deficits 19–21; long-term memory research 217–18; modals, development 421–2; morphosyntactic deficits 21–2; morphosyntactic deficits, identification 377; obligatory arguments 418; phonological deficits 23–4; pragmatics 25–6; semantic deficits 19–21, 393; SLI 17–19; syntactic deficits 24–5 children, language impairment 297, 425–7 Children’s Communication Checklist (CCC) 18–19 Children’s Communication Checklist-2 (CCC-2) 450 Children’s Test of Nonword Repetition (CNRep) 264, 506 chronological age (CA) controls 225, 247 chunking 474 clausal complement: verb clausal complement 426; verb, combination 423 Clinical and Translational Science Awards (CTSAs) 573 Clinical Evaluation of Language Fundamentals (CLEF) 507, 532; Word Definition subtest 31 Clinical Evaluation of Language Fundamentals, 4th Edition (CLEF-2) 430 Clinical Evaluation of Language Fundamentals: 4th Edition (CELF-4) 120, 122, 428, 430–1; Preschool-2 English and Spanish Editions (CELF-P2) 314, 532; Revised (CELF-R) 225, 264; (CELF-IV), word structure subtest 229 cloze procedure 468 CMIP (candidate gene) 266–7 CNTNAP2 (candidate gene) 261, 267 cochlear implants (CIs) 111–14, 228, 396, 502 code-switching patterns 302 CogMed, usage 228–9 cognition: hearing loss, relationship 121–2; syndrome-specific profiles 53–6 cognitive components 583–4 cognitive constructs 481–2; perception 482–7 cognitive functions 195–6 cognitive processing 14 Common core State Standards (CCSS) 470, 475–6 Communication and Symbolic Behavior ScaleDevelopmental Profile (CSBS-DP) 451 communication methods (hearing loss) 112 communication skills (ASD) 87–95 Communicative Development Inventory (CDI) 19–20 communicative gesture use 66–7 communicative partners/contexts, consideration 449–50 comorbid FXS, autism (relationship) 65 co-morbid FXS, language (usage) 65–7 comparative effectiveness research 564 compensatory neural systems 137 Competing Language Processing Task (CLPT) 220, 223–4, 226 complementizer layer (CP) 162; formation 163 completion tasks 507–8

645

Subject Index complex sentences 417 comprehension 143, 529; levels 475; written expression, relationship 474–7 Comprehensive Test of Phonological Processing-2 469 Comprehensive Test of Phonological Processing, Second Edition (CTOPP-2) 141 computational explanations 5–6 computational grammatical complexity (CGC) hypothesis 6 Computerized Language Analysis (CLAN) 30; software 403, 432, 509 computerized tomography (CT) 580 confrontation naming 502–4 connected clause packages, construction 427 Connecticut Longitudinal Study data 131 connectionism: dialogue 282; predecessors 275–6 connectionist approach 278–80 connectionist framework 274–5 connectionist modeling 274–5 connectionist models: dual-route models, differences 280; pronoun deficits 287–9 connectionist theory 282 consonant-vowel-consonant (CVC) words, onset 583 consonant-vowel (CV) sequences, appearance 112–13 consonant-vowel (CV) syllables 201–2 Construction Grammar 170 constructs, overlap 488 context-dependent words 92 continued L1/L2 exposure 305 Continuous Performance Test, auditory version 15 contrastive features 355–9 control probands, family members (language impairment rate) 257 conversational language, sample 509 convolutions 187 coordinate structures 423 co-primary outcome measures 570–1 co-reference, conveyance 165 core phonological processing deficit 463 Cornelia de Lange syndrome (CdLS) 72 cortex, structural asymmetries 190 cortical differentiation 185–6 criterion-referenced tools 376 cross-generation linkage 264 cross-linguistic: findings 333–8; variation 175 cross-modal interference 13 cross-modal paradigm, usage 514 cross-modal picture priming (CMPP) 287, 543–5 cross-modal priming 542 cross-modal PWI paradigm, schematic 511 cultural diversity 449 cytoarchitectonics 578 dative alternation 419 DCDC2/KIA0319 (candidate gene) 268 declarative memory 12, 218–19; studies 217 declarative memory system 216

Declarative-Procedural (DP) language model 216 declarative sentences (simple type) 417 decoding skills, acquisition 140–1 decontextualized situations, morphological rules (application) 462 deficit hypothesis (deviant language/intervention) 346 definite articles, omission 336 deixis 89–90 deoxyribonucleic acid (DNA), information transcription (schematic) 255 determiner+adjective+noun (DAdjN) structures, child usage 420 Determiner (D) feature 333 determiner+noun (DN) structures, child usage 420 Determiner Phrase (DP) 333 developing children, sentence combining 422–5 Developing Metacognitive Skills:Vocabulary and Comprehension 476 developmental coordination disorder 373–4 developmental delay 4; semantic deficits 393–4 developmental dyslexia 242, 276, 279, 280; simulation 280 developmental language disorders, semantic deficits 406 developmental motor-skill milestones, tests 111 Developmental Sentence Score 564 Developmental Sentence Scoring (DSS) 378, 432 deviant language/intervention 346 Diagnostic and Statistical Manual of Mental Disorders (DSM-5) 18–19, 82, 84, 443–4; ASD criteria 84 diagnostic criteria, overview 83–4 Diagnostic Evaluation of Language Variation (DELV) 31, 431 Diagnostic Evaluation of Language Variation-Norm Referenced test (DELV-NR) 349 Diagnostic Interview for Social and Communication Disorders 86 diffusion tensor imaging (DTI) 138–9, 580–1, 595 discourse-narrative tasks 501 discourse structures, understanding 475–6 Discrete Two-Stage model 512 distractor words, usage 513 Distributed Morphology 154 ditransitive verbs 157 dizygotic twins (DZ) 196, 258–9 DNA heritable information, storage 254–5 domain-general deficits 5 domain-specific deficits 5, 274 domain-specific language processing modules 274 domain-specific syntax deficits 287 dorsal inferior frontal gyrus 197 dorsolateral prefrontal cortex (DLPFC) 139 double deficits 465 downstream effects 134 Down syndrome (DS) 52–4, 239–40, 394; language ability, syndrome-specific profiles 56; language comprehension 56–8; language production 58–60; monolingual peers 3007;

646

Subject Index morphosyntactic profiles 372; phrases 429; profile 56–60 Dual components approach 352–353 dual-language environments 300 dual mechanism, dialogue 282 dual n-back tasks, performance 220–1 dual-route cascaded (DRC) model 277, 280 dual-route models 276–8; connectionist models, difference 280 Dutch, SLI grammatical profile 329–30 dynamic assessment 402; focus 313 dyslexia 35, 130; accommodations 143–4; assessment 141–2; attentional mechanisms 139; clinical manifestations 139–42; cognitive influences 133–5; comprehension 143; connectivity 138–9; decoding 143; definition 130–2; diagnosis 140–1; dual-route models 276–8; epidemiology 132; etiology 132–3; extended time, neural basis 137; fluency 143; functional connectivity studies 138; International Dyslexia Association definition 463; management 142–4; neural signature 135, 136; neurobiological studies 135–9; phonological deficit theory 276; reading 132; reading systems 135–6; students, high-stakes testing 137; theories 133–5; visual word form area (VWFA) 136–8 early efficacy studies 563 early left anterior negativity (eLAN) 553, 584 early MLU 116–17 early neural processing 202 early sequential bilingual children, lexicalization 302 Early Start Denver Model (ESDM) 97 early vocabulary, hearing loss (relationship) 114–15 early vocal development 112–13 echolalia 89 effectiveness studies 563–4, 568 efficacy study: evaluation 566; purpose 563 electrical brain responses, ERP measurement 8 electroencephalogram (EEG) recordings 516 electroencephalography (EEG) 198, 581; spatial resolution 592 electrophysical measures 370, 515–20 electrophysiology 27, 581–7 elicited narratives 509–10 embedded inversion constructions 354 embedded-processes model 2014 emergentist perspective 176–8 endogenous components 583–4 English: mazes, percentages 304; SLI grammatical profile 328–9 English language learners (ELL) 464 errors of omission 368 European Concerted Research Action 307 Evaluate, Make a Plan, Organize, Work, Evaluate, Rework (EmPOWER) 476 event-related neural oscillation (ERO) 486

event-related potentials (ERPs) 8, 27, 184, 198, 488; brain responses 200; components 370–1, 582; discriminative response 8; electrophysiology 27–8; methodology 199–200; predictive use 203; recordation 224; research 551; responses 203; usage 29, 202–31, 516–17; waveforms 200 evidence-based child language interventions: development, translation/implementation research 561; practices, support 572 exclusionary criteria, usage 245 executive functions (EFs): attention, relationship 14–16; theory 95 exogenous components 583–4 exons, DNA sequence (transcription) 255 expository paragraphs, structure 476 expressive language, impairment (phenotypic pattern) 60 Expressive Vocabulary Test (EVT-2) 68–9 extended optional infinitive (EOI) 5 Extended Unique Checking Constraint (EUCC) 333–5 extended unique checking constraint (EUCC) 5 eye movements, monitoring 547–51 eye tracking: advantages 550–1; studies 549 face-to-face affective exchanges 88 facilitated communication (FC) 97–8 family aggregation 197, 257–66; twin design 258–9 family members, language impairment rate 257 Fast Forword intervention, effectiveness (RCT) 491 Fast ForWord 7, 33 fast mapping 20–1 Features-based approach 350–1 finite morphemes 367 finite verb morphology, children difficulties 368 first-language fluency, configurations 299 first words: appearance 58; hearing loss, relationship 114–15 fissures 187 floortime 97 fluency 143; development 473–4; impact 466; verbal fluency tasks 505 FMRP protein 54 forced-choice formats 401–2 forced choice pointing paradigm (FCPP) 540 FOXP2 abnormalities, cases (isolation) 262 FOXP2 gene 198; language impairment, relationship 261–3 fractional anistropy (FA) 138–9, 580–1 fragile X syndrome (FXS) 52, 54–5; behavioral phenotype 63; co-morbid FXS, language (usage) 65–7; FXS results from the mutation of a single gene (FMR1) 54; language ability, syndromespecific profiles 60; language comprehension 60–61; language, gender difference 63–4; language production 62–3; morphosyntactic profiles 371–2

647

Subject Index free recall tasks 91 free-standing morphemes 153 French, SLI grammatical profile 330–2 Frog, Where Are You? (picture book) 60 frontal lobes 187 functionality, continuation 404 functional magnetic resonance imaging (fMRI) 27, 92, 135, 191, 588–91; measurements 501; studies 186; usage 195 functional near-infrared spectroscopy (NIRS) 590–1 functional specialization, development 186–7 Ganong effect 7–8 gap condition 515 garden variety poor readers 463–4 General All Purpose (GAP) verbs 418 general American English (GAE) 348, 351, 357–8; African American English, differences (comparative paradigm) 346–7; child speakers 345; patterns 352 generalizations 33, 425; argument structure generalizations 177; morphological generalizations 245 genetic influence: models 254–66; nonmolecular evidence 256–7 genetic research 96–7 genetics 254; candidate genes 266–8; SLI, relationship 26–7; syndromes, language impairments (association) 71–2 genetic syndromes 53 genome-wide association studies (GWAS) 133, 263, 265–6; method, usage 266 genome-wide linkage studies 263–5 genome-wide scans 263 Gerenser, Joanne 82 German: SLI grammatical profile 329–30; SLI literature 339–40 Gillam, Ronald B./Sandra 213 Global Field Power (GFP) 594 Goldman-Fristoe Test of Articulation, 2nd Edition (GFTA-2) 114 grammatical concepts, expression 298 grammatical deficits 333–5; accounts/crosslinguistic findings 333–8; phonology/prosody contributions 335–8 grammatical morphology 281–4 grammatical profile. see specific language impairment grammatical SLI (GSLI) (G-SLI) 17–18, 200, 422, 552 grammatical words, number (limitation) 157 grammaticity 312 grapheme-to-phoneme correspondence (GPC) rules 277 gray matter volume (GMV) 28–9; measurement 135 Gray Oral Reading Test-Fifth Edition (GORT-5) 141 gray-white matter gyral volume, examination 194

gyral morphology; examination 194; gyral morphology/volume head-mounted eye-tracker, illustration 547 head movement 164 hearing aids (HAs) 111–14 hearing impairment 4, 6, 109, 164, 166, 171, 239; semantic deficits 396–7 hearing loss (HL) 109; amplification 111–12; babbling 112–13; cognition 121–2; communication methods 112; degree 110–11; early vocabulary 114–16; early vocal development 112–13; first words 114–15; identification, age (level) 109–10; language, global measures 119–21; language, relationship 121–2; lexical development 115–16; morphology 116, 117–18; morphosyntax 116–19; phonetics/ phonology 113–14; presence/absence 514; syndrome-related conditions, etiology/issues 111; syntax 116, 118–19 Hebbian learning 579 Heschl’s gyrus 188, 204 hierarchical linear modeling 57, 59, 484 hierarchical sentence structure 160–4 higher-level phonological knowledge 247–8; deficits 247 higher verbal IQ 90 high-frequency words 518 high-resolution volumetric MRI scans, usage 195 homophony 339 Hook, Pamela E. 461 Hungarian, SLI grammatical profile 332–3 hyperlexia (ASD) 94–5 hyperplexia, prevalence 94–5 Illinois Test of Psycholinguistic Abilities (ITPA) 507 imaging data, interpretation 591–4 implementation research 561 implicit learning 216 Individuals with Disabilities Education Act (IDEA) 471 infant speech perception 243–4 infinitives 338–9 inflectional morphology 93 inflection phrase (IP) 162; formation 163 input: contributions 339–40; effects 177 Institute for Education Sciences (IES) 573 integrated constructs 487–8 integrative account 138 intellectual disability 52–73, 82, 89, 256, 428, 441, 442, 444, 448; etiologies 62; genetic origin 52; genetic syndromes (association) 71; inherited form 54; range 56, 82 Intelligence quotient (IQ); measures 121; reading (uncoupling) 131 interactional problems, structural problems (co-occurrence) 443

648

Subject Index intermodal preferential looking 538–41 intermodal preferential looking paradigm (IPLP) 538–41; procedure, illustration 539 interrogatives, types 427 intervention: approaches typology 34; outcomes (tracking), event-related potentials (possibility) 203; packages 379–80; programs, development 470–1 intransitive verbs, types 167 introns, DNA sequence (transcription) 255 Inventory for Production Syntax (IPSyn) 430; scores 69, 432 irregular morphosyntax, usage 373 Italian, SLI grammatical profile 330–2 item analysis, categorical distinction 375t I-to-C movement 169 Joanisse and Seidenberg model 288 Joanisse/Seidenberg model (sentence comprehension) 288; past tense model 277 joint attention, language acquisition (relationship) 88–9 Kanji script, reading 137 KIDEVAL 432 knowledge: articulatory knowledge 245–7; base, advances 380; higher-level phonological knowledge 247–8; perceptual knowledge 240–5 laminar structure 190 Landau-Kleffner syndrome 588 language: AAE-speaking children, language (development) 347–8; ability, syndromespecific profiles 56; areas (brain) 186–8; assessment 313–15; autism spectrum disorders 87–95; behavior, scientific mapping 184; breakdown 588; capacity 530; childhood language, neuroimaging studies 191–8; childhood language, neurophysiological studies 198–205; competence 529–30; construction 177; Declarative-Procedural (DP) model 216; deficits 19–26; delay, history 140; deviant language/intervention 346; gender difference (FXS) 63–4; genetic influence, models 254–66; global measures 119–21; hearing loss, relationship 121–2; internal phenomenon 299; knowledge 151–2, 227; levels, depressed level 397; matching, usage 370; morphological deficits, patterns 22; performance 529–30; pragmatic constraints 306; sampling 376–7; sampling (syntactic impairment) 431–3; skills, ongoing development 64; skills, speech perception (relationship) 243–5; specific language impairment (SLI), impact/issues 338–40; spoken language development 112–15; spontaneous language samples 508–9; syndrome-related differences 73

language acquisition 87–8; autism-specific issues 89–90; joint attention, relationship 88–9; nativist view 173–6 Language Assessment, Remediation and Screening Procedure (LARSP) 420, 432 language comprehension: act-out 535–6; approaches 529; comparison groups 531–2; cross-modal picture priming (CMPP) 543–5; design considerations 532; Down syndrome, relationship 56–8; eye movements, monitoring 547–51; fragile X syndrome, relationship 60–1; head-mounted eye-tracker, illustration 547; intermodal preferential looking 538–41; lexical priming 541–3; methodological considerations 531–4; neuroimaging methods 551–4; off-line comprehension tasks 530–1; off-line methods 534–41; on-line comprehension tasks 530–1; on-line methods 541–54; picture selection 534–5; procedures 534; self-paced listening 546–7; stimuli 533; truth-value judgments 536–8; Williams syndrome, relationship 67–8; word monitoring 545–6 language development: bilingual children 300–4; emergentist views 176–8; factors 109–12; mixed profiles 301; outcome 191 language disorders 159–60, 446; neurobiology 184, 204–5; see also child language disorders language-impaired children, delay 287 language-impaired monolinguals 310 language impairment (LI) 3–4, 71, 188–9, 328, 417–26; bilingual children 306–12; bilingual children, intervention 315–17; bilingual children, narrative performance 311–12; bilingual children, semantic performance 307–9; children 297; FOXP2 gene, relationship 261–3; genetic influences, nonmolecular evidence 256–7; genetic syndromes, association 71–2; genome-wide association studies 265–6; genome-wide linkage studies 263–5; history 567; molecular genetic studies 260–1; phenotype 256; postmortem studies 189–91; proband 259; rate 257; sensitive measures 586; social communication impairment 445–7; twin studies 259–60; see also specific language impairment (SLI) language-learning disability (LLD) 463–4, 468 language-matched controls 22, 367–70, 418 language-nonverbal IQ discrepancies 260 language processing 443; elicited productions 508–9; neural activation patterns 471 language production 64, 501; abilities, age-related changes 519; Down syndrome, relationship 58–60; electrophysical measures 515–20; elicited productions 508–9; fragile X syndrome. relationship 62–3; off-line measures 502–10; on-line measures 510–20; picture naming (confrontation naming) 502–4; picture-word

649

Subject Index interference (PWI) paradigm 510–5; research 517–18; structural priming 508; Williams syndrome, relationship 68–70 language-related MEG components 588 language samples 359; morphosyntactic deficits 376–8; semantics 402–3 language-specific mechanisms, impairments 286 Language Use Inventory for Young Children (LUI) 450 late discriminative negativity (LDN) 486 Lateralized Readiness Potential (LRP) 515–16 learning: disabilities 202; impairments 53–4; neural circuitry 579 Learning Ally 144 left anterior temporal lobe (LATL) 519 left hemisphere, primary language-related areas 187 left-right perisylvian areas, atypical patterns 192 lexical access, theories (differences) 512 lexical categories 161 lexical deficits 19–21, 514 lexical development (hearing loss) 115–16 lexical diversity 509 lexical entries, example 157 lexical knowledge 217 lexical organization, study 542–3 lexical priming 13, 541–3 lexicon, syntactic-semantic information 156–60 LI see language impairment linguistics 151; competence 529–30; constructs 170; correlates, search 191–5, 199–200; cross-linguistic findings 333–8; hierarchical sentence structure 160–4; impairments, profile 73; knowledge 5–6; language acquisition, nativist view 173–6; language development 176–8; language disorders 159–60; morphology 153–4; movement 164–73; non-linguistic correlates, search 200–2; phonology 152–3; pragmatics 155–6; predicate argument structure 159–60; psychological reality 159–60; semantics 154–5; structures, number 176–7; subcategorization frames 157–9; syntax 156; thematic structure 157–9 literacy deficits 204 locatives, alternation 419 logarithm of odds (LOD), usage 263–4 long-distance dependencies 24 long-term memory (LTM) 213–14, 216–19; components 221–2; deficits 230; hypotheses 216–17; information/mental models 225; procedural memory strategies 227; research 217–18; system 214 long-term potentiation (LTP) 579 Looking While Listening (LWL) paradigm 540–1 MacArthur-Bates Communicative Development Inventory (MBCDI) 399 MacArthur Communicative Development Inventory (CDI) 533; Words and Sentences version 68

macrostructure, study 311–12 magnetic resonance imaging (MRI) 138; advances 191; neuroradiological measures 197; study 192; usage 27–8, 184 magnetoencephalography (MEG) 519, 551, 587–8; spatial resolution 592; usage 595; housing of magnetometers 587 MA referencing 30 Martin Luther King, Jr. Elementary School Children v. Ann Arbor School District Board 347 matching criteria, choice 531–2 matrix clause type 424 Matthew effect 398 maturational lag/deficit 485 McCarthy Scales of Children’s Abilities 70 McGurk effect 9 mean length of utterance (MLU) 20–2, 30, 58–9, 116, 328; early MLU 116–17; measures 303; range 116–17; TD-MLU 22, 329–31, 336–7 memory 9–12; long-term memory 216–19; model 214–16; neural circuitry 579; organization, conceptions 471; training programs 228; working memory 219–24; working memory, model 215 mental age (MA) 428 mental age-matched peers 96 mental grapheme representation (MGR) 464 metacognition, importance 471 microstructure (study), neuroscience methods (usage) 578 minimal competency core (MCC) 352–3 mismatch magnetic field (MMF) 588 mismatch negativity (MMN) 201, 225, 486–7, 583–7; responses 202, 595–6 mismatch response (MMR) 585 mixed-effect modeling 532 mixed knowledge 315–16 MLU. see mean length of utterance modalities 416 modals, development 421–2 model-based accounts, challenges 289–90 Modified Token Test 31 monosyllables, nonfinal position 337 monozygotic twins (MZ) 196, 258–9 Montague Grammar 154 morphemes, morphosyntactic framework 366; syllabic morphemes 333 morphology: autism spectrum disorders (ASD) 92–3; bilingual children 303–4; bilingual children, language impairment 309–11; grammatical morphology 281–4; hearing loss 116, 117–18; linguistics, relationship 153–4; morphological acquisition 117; morphological deficits 22; morphological languages 153–4; morphological structure 473; theories, evolution 154; usage 93 morphosyntactic deficits 21–2; assessment 374–9; criterion-referenced tools 376–7; intervention

650

Subject Index 379–81; item analysis, categorical distinction 375t; language samples 376–8; norm-referenced tests 374–5; parent report tools 378–9; population basis 366–74; specific language impairment 366–71 morphosyntactic profiles: autism 371–2; childhood developmental disabilities 373–4; Down syndrome 372; fragile X syndrome 371–2; specific language disorders 366–71; Williams syndrome 373 morphosyntactic structures 369–70 morphosyntax 365; facilitation 380–1; irregular morphosyntax, usage 373; studies 117–18 mosaicism 53 Mullen Scales of Early Learning (MSEL) 68 multilingual society, language impairment 307 multisensory stimulation 471 multisensory structured language (MSL) approaches 471, 473 multisyllable nonwords, SLI performance 222 Munson, Benjamin 238 N1b component 582 N1-P2 auditory brain responses 8 N1-P2-N2 region 201 N200 515–16 N400 584–5; responses 203, 551–2 N450 517 naming tasks 502 narratives: construction 453; discourse 304–6; elicited narratives 509–10 National Institute for Deafness and Other Communication Disorders (NIDCD) 573–4 National Institute of Neurological Disorders and Stroke (NINDS) 574 National Institutes for Health (NIH), NIH Roadmap for Medical Research 564 National Library of Medicine, clinical trials 565 National Research Council 142 National Research Panel 142 nativists: argument 174; assumptions 177; language acquisition perspective 173–6 n-back: span task 230; training, usage 229 NDST4 265 neocortex, structure 579–80 neural activation 591 neural activity 186 neural circuit 579 neural circuitry 578–9; usage 579 neural connectivity, dynamic structuring 254 neural processing speed 483 neural profiles 204 neural signature (dyslexia) 136 neural speed 483 neurobiological studies (dyslexia) 135–9

neurobiology 27–30, 184; characterization 28; examination 205; language disorders 184; primer 185 neurobiology, principles 578–80 neurodevelopment, genetic/environmental effects 185–6 neuroimaging methods 551–5 neuropathological anomalies 190 Newkirk-Turner, Brandi L. 345 New Reynell Developmental Language Scales (NRDLS) 430–1 NMDA receptors 595–6 noise: broad-band noise 241; potential fluctuations 583 nominal phrase (NP) 159 non-contrastive features 355–9 non-contrastive properties 358 nonfinite morphemes 367 nonfinite subject-verb sequences 339 nonfinite verbs, usage 330 nonlinguistic cognitive tasks, diagnostic utility 490 non-linguistic correlates, search 195–6, 200–2 non-meaningful syllables 7–8 non-past contexts 354 nonsense words, three-element strings 566–7 nonspecific language impaired (NLI) 256, 260 nonspeech auditory processing 241–3 nonspeech stimuli 241 nonverbal communication skills, deficits 94 non-verbal intelligence 188–9 nonverbal intelligence tests 111 nonverbal IQ 87–8, 90; language, discrepancy 256; language outcome variance 66; matched controls 225; matching 61–2 nonverbal visual cognition 54 nonword repetition (NWR) 10, 11, 505–7; impact 27; task 221 normal hearing (NH) children 111–17 norm-referenced tests 374–5; interpretation 374–5 no-rule condition 92 noun phrase (NP) 418–20, 530; form 160–1; structured NPs, occurrence 420 NP1 reactivation 223 number of different words (NDW) 301, 312, 402–3; limitation 403 obligatory components 583–4 obligatory context 508 off-line comprehension tasks 530–1 off-line measures (language production) 502–10 off-line methods (language comprehension) 401, 534–41 omission errors 310, 368–9 on-line comprehension tasks 530–1 on-line measures (language production) 510–20 on-line methods (language comprehension) 501, 541–54

651

Subject Index on-line tasks, control 92 Optimality theory (OT) 153 Oral and Written Language Scales (OWLS) 62 orthographic forms, phonological outputs 279 orthographic processing 464 orthographic reading: phonemic awareness/phonics, links 465–6; skills, development 466 orthographic rules 473 otitis media with effusion (OME) 110–11, 540 otoacoustic emissions (OAEs) 109 P600 554, 587; responses 584 parallel distributed processing (PDP) framework 274–5 parent report: semantics 399; tools, morphosyntactic deficits 378–9 Passive verbs 167–8 past tense: deficits 284–6; Joanisse model 277 PDD-NOS 82, 86 Peabody Picture Vocabulary Test (PPVT) 302; vocabulary 120 Peabody Picture Vocabulary Test (PPVT-4) 67, 68 Peabody Picture Vocabulary Test, Revised (PPVT-R) 309 Peabody Picture Vocabulary Test-Revised (PPVT)Third Edition, score 56–7 Pediatric Imaging Neurocognition and Genetics (PING) Consortium 265 pedigree analysis 197–8 PEER training/storybooks 405 Peña, Elizabeth D. 297 perception 481; child language disorders 238; integrated constructs 487–8; measurement 488–9; speech perception 240–1; speech perception, language skills (relationship) 243–5; usage 482–7 perceptual knowledge 240–5 perceptual processing 14 performance IQ (PIQ) 264 perservative language, relationship 64 personal pronouns, usage 89–90 phonemes 247; co-occurrence, relative probability 222; identification 286 phonemic awareness 142, 464; phonics/orthographic reading, links 465–6 phonetics (hearing loss) 113–14 phonics 134, 143; phonemic awareness/orthographic reading, links 465–6; word-attack strategies 472–3 phonological acquisition 239 phonological awareness 23, 472–3; development 464 Phonological Awareness Test 2, The 469 phonological codes, access/retrieval 140 phonological deficits 23–4; theory, concerns 289–90 phonological disorders, short-term/long-term normalization 23 phonological impairments 246 phonological information, importance 284 phonological knowledge (higher-level) 239, 247–8

phonological memory (PM) 259–60, 465; deficits 219 phonological processing 512–13; deficit 281; difficulties 134; weaknesses 134 phonological short-term memory resource 187 phonology: accounts 338; autism spectrum disorders (ASD) 90–1; contributions 335–8; hearing loss 113–14; impact 336; layer units, noisier activation values 279; linguistics 152–3 phonotactic information, usage 244 phrasal movement 164 phrases, hierarchical structure 161 phrasing 474 physical impairments 373–4 Picture Exchange Communication System (PECS) 97 picture naming (confrontation naming) 502–4 picture-naming tasks 504 picture selection 534–5; drawbacks 535 Picture-Word Interference (PWI) 21, 512; gap condition 515; paradigm 510–5; cross-modal PWI paradigm, schematic 511 Pig Latin 464 plana temporale (PT) 190–1; measurement 191–2 poor comprehenders 397 populations: past tense deficits 284–6; semantic deficits 392–8 positron emission tomography (PET) 191, 581, 589–90; investigation 193; spatial resolution 592 pragmatic impairments 18–19 Pragmatic Language Impairment (PLI) 35, 94, 447–8; levels 19 pragmatics 25–6, 441–2, 467; autism spectrum disorders (ASD) 93–4; child language impairment, relationship 441; deficits 93; linguistics, relationship 155–6 predicate argument structure 159–60 Preferential Looking Paradigm (PLP), usage 116 pre-gap control condition 515 prelinguistic communicative gestures 58 premotor area 197 prepositional phrase (PP) 159, 160, 417 Preschool Language Scale (PLS) 120 Preschool Language Scale (PLS-4) 430–1 Preschool Language Scale-5 Spanish Edition (PLS-5) 314 primary auditory cortex 188 primary language disorder (PLD) 203 primary memory updating 487 priming: cross-modal picture priming 287; effect 92; identity primes 92; lexical priming 13; phological priming 21; production priming 17, 34; semantic priming 21, 199; tasks 542 Principles and Parameters (P&P) approach 175 probes (semantics) 401–2 procedural deficit hypothesis 194 Procedural Deficit Hypothesis (PDH) 216–18

652

Subject Index process-based explanations 6–16 processing deficits: assessment 467–470; skills, linkage 464–6 processing speed 13–14, 481, 483–4; constructs, overlaps 488; evidence 483; integrated constructs 487–8; measurement 488–9; neural processing speed 483; overlaps 487; process-specific constraints 484; usage 482–7 productive language, characteristics 59 proficiency 176 profiling procedures 432 Projection Principle 160 pronoun: analysis 157; antecedents, relationship 6; case marking 22; deficits, connectionist deficits 287–9; distribution 173; production 117; relative pronoun, optionality 424 prosody 35, 55; absence 140; accounts 338; autism spectrum disorder (ASD) 90–1; contributions 335–8; impact 336; usage 156 proto-declarative pointing 88–9 prototype abstraction 216 psychological reality 159–60 pure tone average (PTA): hearing losses/labels 110; threshold 110 randomized controlled trials (RCTs) 433–4, 491 rapid automatic naming (RAN) task 503–4 rapid serial naming 465 reaction time (RT) 217–18, 488, 532; analysis 546; comparison 21; differences 15, 218; measurement 92, 489; times, deceleration 6–7, 15, 217; prediction 36; usage 13 reading: accuracy 468; accuracy, improvement 141; assessment 467–70; attentional mechanisms 139; automaticity, absence 142; automaticity/ fluency 466; child language disorders 461; comprehension, processes 467; disability 130; double/triple deficits 465; dual-route cascaded (DRC) model 280; dual-route models 276–8, 277; fluency, improvement 471–2; in-depth assessment 467; intelligence quotient, uncoupling 131; intervention 470–7; models 277; orthographic processing 464; orthographic reading skills, development 466; performance, patterns 469–70; phonemic awareness 464; phonemic awareness, phonics/orthographic reading (links) 465–6; phonological memory 465; processes 461–2, 462; processing deficits, skills (linkage) 464–6; rapid serial naming 465; underlying processing 468–9; verbal working memory 465 Reading by the Rules 473 reading disorder 265, 276–81, 463–4 reading impairment 190–1; connectionist approach 278–80; focus 398; semantic deficits 397–8 reading systems: development 136–8; dyslexia 130, 135–6

referential NPs: distribution 173; R-expressions 172 region of interest (ROI) 192 relative clause (RC) 165, 426–7; constructions 423–7; dimensions 424; elicitation 433; head, syntactic role 424; position, impact 424 relative operator 165 relative pronoun, optionality 424 repeated word association task 504–5 Representational Deficit for Dependent Relations (RDDR) proposal 5–6 response biases 536 response to intervention (RIT) model 470–1 Rice-Wexler Test of Early Grammatical Impairment (TEGI) 376, 378 rote memorization 70 running span task 230 school-age children, language impairment 241 Scientific Learning Corporation (SLC) 33 secondary memory retrieval 487 second language (L2) fluency, configurations 299 second language (L2) vocabulary, knowledge 315 Seidenberg and McClelland model 277 self-paced listening 546–7 self-regulated strategy development (SRSD) 476–7 semantic anomalies 553; N400 response 205 semantic coordinate 513 semantic deficits 19–21; autism spectrum disorders (ASD) 394–6; developmental delay 393–4; hearing impairment 396–7; reading impairment 397–8; remediation 405; source 393; specific language impairment (SLI) 392–3 semantic fluency 505 semantic information, impact 511 semantic interference (SI) effect 518 semantic-level deficits, support 513 semantic-phonological mediated priming, usage 513 semantics: assessment 398–403; autism spectrum disorders (ASD) 91–2; bilingual children 301–2; child language disorders 392; comprehension levels 475; development, standardized tests (usage) 400–1; focus 398; language samples 402–3; linguistics, relationship 154–5; mapping 283; parent report 399; performance 307–9; probes 401–2; processing, index 551–2; standardized tests 399–401; system, involvement 467 sensorineural HL 110 sensory integration 33; proposal 34 sensory processing disorders 34 sentence repetition (SR) 426, 505–7; procedure, usage 426 sentences: adverbial constructions 423; combination 422–7; completion tasks 501, 507–8; comprehension, Joanisse/Seidenberg model 288; coordinate structures 423; developing children 422–5; processing 13; real-world plausibility 533; relative clause (RC) constructions 423–5;

653

Subject Index SVO interpretation 119; types 417; verb/clausal complement combination 423 Separate Development Hypothesis 297–8 severity levels (ASD) 85 short-term intervention, differential responses 308 Short-term memory: skill 121 short-term memory 213 simple declarative sentences 417 simple sentences: language impairment 417–19; modalities 416, 417, 422; noun phrases 419–20; verb pre-modification 419, 420–2; verbs/ argument structure 417–19 single nucleotide polymorphism (SNP) 268 skilled reader, stumbling blocks 470 SM89 model 278–80 Smith-Magenis syndrome 72 social behavior, influence 449 social bids, frequency 395 social cognition 443–4 social communication: assessment 449–56; behaviors, assessment 450–2; child language impairment, relationship 441; communicative partners/contexts, consideration 449–50; components, facilitation 455; data, synthesis 452; defining 442–4; language processing 443; objectives, identification 451–2; pragmatics 443; problems, identification 450–1; social cognition 443–4; social interaction 442 Social Communication Coding System (SCCS) 451 Social (Pragmatic) Communication Disorder (SPCD) 35, 86, 94, 443; diagnostic criteria 448 Social-Communication, Emotional Regulation and Transactional Support (SCERTS) model 97 social communication impairment: autism spectrum disorder (ASD) 444–5; language impairment 445–7; nature 444–9; pragmatic language impairment (PLI) 447–8 social communication intervention 452–6; accessibility 454; case studies 453–4; clinical implications 454–6; dynamic target adjustment 454–5; efficacy 452–4; effort, investment 455–6; nonexperimental designs 453–4; randomized controlled trials, usage 453; single subject designs 453 social cues 91 social-indexical knowledge 239 social inferencing 453 social interaction 26, 442 social smiling 66–7 sound fluency 505 Sound Pattern of English model 152–3 Southern African American English (SAAE) 356 Southern White English 367 Spanish: mazes, percentages 304; prosodic characteristics 338; SLI grammatical profile 330–2 spatial correlations 594

speaker-listeners, linguistic performance 152 specific language impairment (SLI) 3; accounts 6–7; assessment 30–1; atypical language behavior, atypical brain structure (correlation) 194–5; auditory perception problems 242; auxiliary, correct use (percentage) 421; average age 421; brain asymmetry, commonness 191–2; children, attention 225–7; children, long-term memory research 217–18; children, subgroups 17–19; computational explanations 5–6; Consortium 264–5; constraints, checking 333–5; disorders 34–6; emergenist perspective 16–17; etiology 196–8; familial aggregation 197; genetic contributions 196; genetics, relationship 26–7; grammatical deficits 333–5; grammatical deficits, phonology/prosody contributions 335–8; grammatical profiles 328–38; gyral morphology/ volume, commonness 194; heritability 260; 553–4; history of SLI (H-SLI) 8–9, hypotheses 216–17; impact/issues 338–40; intervention 31–4; intervention outcomes (tracking), eventrelated potentials (possibility) 203; language impairment (SLI/LI) phenotype 256; language performance, statistical models 10; linguistic knowledge 5–6; morphosyntactic deficits 366–71; morphosyntactic profiles 366–71; morphosyntactic skills 370; morphosyntactic studies 367; neuroanatomy 191–8; neurobiology 189; neurobiology, relationship 27–30; nonlinguistic correlates, search 195–6, 200–2; pedigree analysis 197–8; phenotypes 204; prediction, event-related potentials (usage) 202–3; present/past forms, omission 421–2; semantic deficits 392–3; subcortical structures, commonness 193–4; syntactic deficits 163–4; theories 5; twin studies 196 specific reading disability (SRD) 8 speech: acts, characterization 155; autism spectrum disorders (ASD) 87–95; perception 7–9, 240–1; perception, language skills (relationship) 243–5 speech-language development, support 97 spelling 473; word identification, relationship 472–3 Split-Infl Hypothesis 168 spoken language 468; acquisition, expectations 119–20; development 112–15; skills, acquisition 462 spontaneous language samples 508–9 SRT task, administration 217 standardized tests: semantics 399–401; syntactic impairment 430–1 Stanford-Binet Intelligence Test (SB-IV), Bea Memory subtest 54 statistical learning 216 stimulus onset asynchrony (SOA) 511 story: completion tasks 507–8; retelling 228, 395, 509–10 story-completion task 508

654

Subject Index story retelling 228, 395, 509–10 strong-strong sequences 246 Stroop test 229 structural imaging 580–1 structural priming 508 structured NPs, occurrence 420 Structured Photographic Expressive Language Tests (SPELT) 374–5 subcategorization frames 157–9 subcortical structures: commonness 193–4; examination 193 subject relative clauses/questions, creation 166 subject-transitive verb-object (SVO) 417; preferred version 419; sentence interpretation 119; sentences 61, 227; SVO-like structures 222 sulci 187 superior temporal gyrus (STG) 197, 518 surface dyslexia 280 Swedish: SLI grammatical profile 329–30; SLI literature 339–40 syllabic morphemes 333 Sylvian fissure 188 syndrome-related conditions, hearing loss etiology/ issues 111 syndrome-specific profiles 56 syntactic complexity 11 syntactic deficits 24–5, 171, 286–9 syntactic developments 118 syntactic impairment: assessment 430–3; language sampling 431–3; standardized tests 430–1 syntactic priming, effectiveness 434–5 syntactic relationships, learning/processing 287, 289 syntactic-semantic information 156–60 syntactic structures, establishment 32 syntax: abstract theories 118–19; argument structure 434; autism spectrum disorders (ASD) 92–3; bilingual children 303–4; bilingual children, language impairment 309–11; childhood development disabilities, relationship 428–30; child language disorders 416; children, language impairment 425–7; constructions, complexity 434–5; deficits, domain specificity 287; generative description 156; hearing loss 116, 118–19; intervention 433–5; linguistics, relationship 156; production 92–3 Systematic Analysis of Language Transcripts (SALT) 30, 432, 509 T1 blocks/research 566–7 T2 blocks/research 567–9 T3 blocks/research 569–70 Tallal Auditory Repetition Test, performance 243 temporal lobes 187, 188 tense (TNS) 5, 333 tense/agreement verb inflections, usage 332–3 tense-marking forms, production 310 tense morphemes 377

tense phrase (TP) 168 Test for Auditory Comprehension of LanguageRevised (TACL-R) 57, 61; measurement 62; subtests 63–4 Test for Auditory Comprehension of LanguageThird Edition (TACL-3) 56–7, 65 Test for Reception of Grammar (TROG-2) 61, 68, 374, 428, 430, 532; performance 70Test of Auditory Comprehension of Language-4 374 Test of Language Development (TOLD) 507 Test of Language Development-Primary 4 374 Test of Relational Concepts (TRC) 68 Test of Variables of Attention (TOVA) 225 Test of Word Reading Efficiency, 2nd edition (TOWRE-2) 141–2 text, phrasing/chunking 474 thematic structure 157–9 theory-based descriptions 154 theory of mind (TOM): categories 443–4; model 95, 442 Theta Criterion 160 three-element string 566–7 Tier Two words, targeting 404 Tip-of-the-Tongue (TOT) paradigm 503 tone pairs, response (atypical lateralization) 202 total communication (TC) 112 trainer-oriented approaches 323 translation blocks 568 translation research 561, 566 TREE strategy 477 triple deficits 465 truth-value judgments 536–8 Twins Early Development Study (TEDS) 260 two-alternative forced-choice paradigm 240–1 type-token ratio (TTR) 509; calculation, repetition 20 typical language development (TLD) 29, 200 typically developing (TD) children 427 typically developing (TD) controls 418 Unaccusativity Hypothesis 167 under-identification 375 unilateral hearing loss 110 United States, bilingualism (social/linguistic influences) 299–300 Universal Grammar (UG) 175–6 ventro-medial prefrontal cortex (vmPFC) 519 verbal children, speech 90 Verbal Fluency Tasks 505, 519 verbal IQ age-matched peers 92 verbal working memory 465 verb phrase (VP) 161, 417; formation 162 verbs: arguments 158; clausal complement 426; clausal complement, combination 423; inflections, agglutinating characteristic 332; paradigm 421; phonological/semantic forms,

655

Subject Index encoding 277; pre-modification 419, 420–2; semantics 549; structure 417–19; theta-grid 159 verb-second languages 330 Vineland Adaptive Behavior Scales-Interview Edition 58 visual detection RT, choice 491 visual-spatial procedural memory task 218 visual word form area (VWFA) 136–8 visual world paradigm, usage 548–9 vocabulary 143; development 394; knowledge, cross-language associations 301; size, function 245; skills 20 Vocabulary, Oral Language and Academic Readiness (VOLAR) program 316 vocalizations 90 VOCD program 403 VP-internal Subject Hypothesis 162 weak central coherence (WCC) theory 96 Wechsler Intelligence Scale for Children–Fifth Edition (WISC-V) 142 Wernicke’s area 160, 188, 197 wh-dependency 199–200 “which” questions 118 wh-movement 165 Williams syndrome (WS) 52, 55–6, 394, 428; cognitive profile 56; language ability, syndromespecific profiles 67; language comprehension 67–8; language production 68–70; morphosyntactic profiles 373 Woodcock-Johnson IV (WJ-IV) 141 words: association tasks, repetition 504–5; automatic reading, orthographic representations 474; categories 156–7; consonant-vowel-consonant (CVC) words, onset 583; differences 331;

exposure, increase 404; frequencies 533; identification, spelling (relationship) 472–3; learning 244; learning skills, development (examination) 540; mapping skill, extension 115–16; monitoring 545–6; morphological structure 473; nonword repetition 505–7; phonic word-attack strategies 472–3; phonological awareness 472–3; slow word learning 396–7; spelling 473; word-finding difficulties 21; wordlearning tasks 308; word-recognition skills 472 working memory (WM) 121–2, 214, 219–24; CogMed, usage 228–9; constructs, overlap 487; deficits 230; examination 10–11; impairment 229; model 215; n-back training, usage 229; study 220; task 219–20; treatment, implications 228–9 writing: accuracy 468; assessment 467–70; automaticity/fluency 466; child language disorders 461; disorders 463–4; double/ triple deficits 465; in-depth assessment 467; intervention 470–7; orthographic processing 464; performance, patterns 469–70; phonemic awareness 464; phonemic awareness, phonics/ orthographic reading (links) 465–6; phonological memory 465; processes 461–2, 462; processing deficits, skills (linkage) 464–6; rapid serial naming 465; underlying processing 468–9; verbal working memory 465; written expression, processes 467 written expression: comprehension, processes 467, relationship 474–7 Yale in vivo Pragmatic Protocol (YiPP) 451 ZNF385D 265

656