Cross-language Influences in Bilingual Processing and Second Language Acquisition 9027212910, 9789027212917

Table of contents :
Cross-language Influences in Bilingual Processing and Second Language Acquisition
Editorial page
Title page
Copyright page
Table of contents
Chapter 1. Cross-language influences in bilingual processing and second language acquisitionAn introduction to the volume
1.Bilingualism and SLA: What unites them and what divides them, and how we reach across the aisle.
2.Preview of the volume
2.1Phonology
2.2Lexicon
2.3Morphosyntax
3.Conclusion
References
Part I. Phonology
Chapter 2. Cross-language influences in the perception and production of L2 phonetics and phonology in young bilinguals
Introduction
1.Models of cross-language influence in young bilinguals
2.Speech perception and production in young bilinguals
2.1Cross-language influence
2.2Studies of cross-language influence in speech perception
2.2.1Language discrimination and the rhythm typology
2.2.2Speech discrimination: Contrasts that exist in one language
2.2.3Speech discrimination: Contrasts that exist in both languages
2.2.4Word learning: Contrasts that exist in both languages
2.2.5Word learning: Contrasts that exist in one language
2.2.6Conclusion: Cross-language influence in speech perception
2.3Studies of cross-language influence in speech production
2.3.1Methodological limitations
2.3.2Voice Onset Time (VOT)
2.3.3Rhythm
2.3.4Syllable structure
2.3.5Segmental acquisition
2.3.6Conclusion: Cross-language influence in speech production
3.Themes related to cross-language influence in young bilinguals
3.1Language experience: Quantitative aspects
3.2Language experience: Qualitative aspects
3.3The developing lexicon
3.4Simultaneous versus early sequential bilingualism
Conclusion
References
Chapter 3. Cross-language influences in the processing of L2 prosody
1.Introduction
2.What is prosody?
3.Models for analysing L2 processing of prosody
4.Prosody and word segmentation
5.Prosody and syntactic processing
6.Prosody and information structure
7.Prosody and pragmatic and global features
8.Future research
References
Chapter 4. Cross-language influences in the acquisition of L2 and L3 phonology
1.Introduction
2.Cross-language influences in L2 and L3 phonological acquisition
2.1The acquisition of L2 phonology
2.2The acquisition of L3 phonology
3.Theoretical frameworks
3.1The Speech Learning Model (SLM) and the Revised Speech Learning Model (SLM-r)
3.2The Second Language Linguistic Perception model (L2LP)
3.3The Perceptual Assimilation Model (PAM) and the Perceptual Assimilation Model of Second Language Learning (PAM-L2)
4.Future directions in L2 and L3 speech learning research
4.1The relationship between L2 and L3 speech perception and production
4.2Acquisition of phonological processes vs. phonological contrasts in L2 and L3 speech
4.3Static and dynamic interactions in L2 and L3 speech
5.Concluding remarks
References
Part II. Lexicon
Chapter 5. Cross-language influences in L2 visual word processingA localist connectionist modelling perspective
1.Introduction
2.Overview of bilingual localist connectionist models of visual word recognition
2.1BIA model
2.2BIA-d
2.3BIA+
2.4SOPHIA
2.5Multilink and Multilink+
2.6CE-IAM
2.7Similarities and differences of bilingual localist connectionist computational models
3.Input coding
4.Lateral inhibition
5.L1 and L2 word frequency
6.Language membership
7.Task and decision processes
8.Limitations of localist connectionist models
9.Future directions
References
Chapter 6. Cross-language influences in L2 pre-lexical and lexical processing and acquisition
1.Introduction
2.Cross-language influences in L2 sublexical processing
2.1Consequences of script differences
2.2Diacritical marks
2.3Different scripts and shared letters
2.4Bigrams and letter transitional probabilities
2.5Grapheme to phoneme correspondences
3.Cross-language influences in L2 lexical processing
3.1Language membership
3.2Cross-language influences in bilingual auditory word processing
4.Cross-language influences in L2 lexical acquisition
4.1Cross-language influences in bilingual L2 word learning
5.Conclusions
References
Chapter 7. Cross-language influences in L2 semantic and conceptual representation and processing
1.Introduction
2.Traditional views of words and meaning
3.Sources of semantic non-equivalence between languages
3.1Feature level
3.2Word level
3.3Domain and language level
4.New views about word meanings within a language
4.1Embodied cognition provides experiential information
4.2Word co-occurrences provide lexical-semantic information
4.3Multimodal accounts
4.4The hub-and-spoke model
4.5The words-as-cues perspective
5.Challenges for crosslinguistic multimodal models
5.1How much information do computational lexical-semantic models provide about language equivalence?
5.2Lexical-semantic vectors based on bilingual input?
5.3How can embodiment effects be tested in L2?
5.4The role of proficiency?
5.5Other challenges
References
Chapter 8. Cross-language influences in the processing of L2 multi-word expressions
1.Introduction
2.Processing of congruent and L2-only MWEs
3.Processing of translated L1-only MWEs
3.1Research on idioms
3.2Research on collocations and binomials
3.3Reconciling research on idioms versus collocations and binomials
4.Mechanisms underpinning the congruency effect in L2 MWE processing
4.1L1 MWE activation account
4.2L2 MWE experience account
5.Conclusions
References
Chapter 9. Cross-language influences in the acquisition of L2 multiword expressions
1.Introduction
2.L1 influence on the acquisition of MWEs
2.1Collocations
2.2Phrasal verbs
2.3Idioms
3.Modulating the L1’s influence on the acquisition of MWEs: Toward a more comprehensive understanding
4.Directions for future research
5.Conclusion
References
Part III. Morphosyntax
Chapter 10. Cross-language influences on morphological processing in bilinguals
1.Within-L1 and within-L2 morphological processing
2.Theories of cross-language transfer
3.Cross-language morphological processing
3.1The locus of cross-language morphological transfer in visual word recognition
3.2Masked morphological translation priming
3.3Semantically independent morphological translation priming
3.4Hidden morpheme repetition priming effects
4.Theoretical implications and future directions
4.1Mechanisms of cross-language morphological transfer
4.2Theoretical implications for models of cross-language transfer
4.3The status of stems and affixes in cross-language transfer
5.Conclusions
Funding
References
Chapter 11. Cross-language influences in L2 syntactic processing and production in late L2 learners
1.Introduction
1.1From L2-specific to shared (L1 + L2) syntactic representations
1.2Cross-linguistic influence from the start
2.Factors affecting cross-language syntactic influences
2.1Lexical effects
2.2Proficiency
2.3Word order
2.4Case marking
2.5Linguistic distance
3.Discussion
References
Chapter 12. Cross-linguistic influences in bilingual morphosyntactic acquisition
1.Introduction
2.Defining CLI
3.Linguistic conditions for CLI
3.1Interface phenomena and CLI
3.2Surface overlap and CLI
4.Nature of CLI: Representation vs. processing
4.1Representations and CLI
4.2Representational differences in the absence of transfer: Economy principles
4.3CLI as a by-product of co-activation and priming of the two languages
4.4Parsing strategies and CLI
5.Individual factors modulating CLI
5.1Age
5.2Dominance
5.3Input quantity and quality and CLI
5.4CLI and language co-activation as drivers for language change
6.Concluding remarks and future research
References
Index

Citation preview

Bilingual Processing and Acquisition

16

Cross-language Influences in Bilingual Processing and Second Language Acquisition

EDITED BY

Irina Elgort, Anna Siyanova-Chanturia and Marc Brysbaert

John Benjamins Publishing Company

Cross-language Influences in Bilingual Processing and Second Language Acquisition

Bilingual Processing and Acquisition (BPA) issn 2352-0531

Psycholinguistic and neurocognitive approaches to bilingualism/multilingualism and language acquisition continue to gain momentum and uncover valuable findings explaining how multiple languages are represented in and processed by the human mind. With these intensified scholarly efforts come thought-provoking inquiries, pioneering findings, and new research directions. The Bilingual Processing and Acquisition book series seeks to provide a unified home, unlike any other, for this enterprise by providing a single forum and home for the highestquality monographs and collective volumes related to language processing issues among multilinguals and learners of non-native languages. These volumes are authoritative works in their areas and should not only interest researchers and scholars investigating psycholinguistic and neurocognitive approaches to bilingualism/multilingualism and language acquisition but also appeal to professional practitioners and advanced undergraduate and graduate students. For an overview of all books published in this series, please see benjamins.com/catalog/bpa

Executive Editor John W. Schwieter

Wilfrid Laurier University, McMaster University

Associate Editor Aline Ferreira

University of California, Santa Barbara

Editorial Advisory Board Jeanette Altarriba

University at Albany, State University of New York

Panos Athanasopoulos Lancaster University

Laura Bosch

Universitat de Barcelona

Marc Brysbaert

Ghent University

Kees de Bot

University of Groningen

Yanping Dong

Zhejiang University

Mira Goral

Arturo E. Hernandez University of Houston

Ludmila Isurin

University of Illinois at UrbanaChampaign

Janet G. van Hell

University of Illinois at Chicago

Walter J.B. van Heuven

University of Groningen

Iring Koch

University of York

Li Wei

Concordia University

Gerrit Jan Kootstra

University of Edinburgh

Ohio State University Pennsylvania State University University of Nottingham RWTH Aachen University UCL IOE

Lehman College, The City University of New York

Radboud University Nijmegen & Windesheim University of Applied Sciences

Roberto R. Heredia

Gary Libben

Texas A&M International University

Silvina Montrul

Kara Morgan-Short Greg Poarch

Leah Roberts

Norman Segalowitz Antonella Sorace

Brock University

Volume 16 Cross-language Influences in Bilingual Processing and Second Language Acquisition Edited by Irina Elgort, Anna Siyanova-Chanturia and Marc Brysbaert

Cross-language Influences in Bilingual Processing and Second Language Acquisition Edited by

Irina Elgort Te Herenga Waka – Victoria University of Wellington

Anna Siyanova-Chanturia Te Herenga Waka – Victoria University of Wellington/ Ocean University of China

Marc Brysbaert Ghent University

John Benjamins Publishing Company Amsterdam / Philadelphia

8

TM

The paper used in this publication meets the minimum requirements of the American National Standard for Information Sciences – Permanence of Paper for Printed Library Materials, ansi z39.48-1984.

doi 10.1075/bpa.16 Cataloging-in-Publication Data available from Library of Congress: lccn 2022049488 (print) / 2022049489 (e-book) isbn 978 90 272 1291 7 (Hb) isbn 978 90 272 5470 2 (e-book)

© 2023 – John Benjamins B.V. No part of this book may be reproduced in any form, by print, photoprint, microfilm, or any other means, without written permission from the publisher. John Benjamins Publishing Company · https://benjamins.com

Table of contents chapter 1. Introduction Irina Elgort, Anna Siyanova-Chanturia and Marc Brysbaert

1

part i. Phonology chapter 2. Cross-language influences in the perception and production of L2 phonetics and phonology in young bilinguals Margaret M. Kehoe

18

chapter 3. Cross-language influences in the processing of L2 prosody Sasha Calhoun, Paul Warren and Mengzhu Yan

47

chapter 4. Cross-language influences in the acquisition of L2 and L3 phonology Mark Amengual

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part ii. Lexicon chapter 5. Cross-language influences in L2 visual word processing: A localist connectionist modelling perspective Walter J. B. van Heuven and Ton Dijkstra chapter 6. Cross-language influences in L2 pre-lexical and lexical processing and acquisition Anna E. Piasecki and Ton Dijkstra chapter 7. Cross-language influences in L2 semantic and conceptual representation and processing Simon De Deyne, Marc Brysbaert and Irina Elgort

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126

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Cross-language Influences in Bilingual Processing and Second Language Acquisition

chapter 8. Cross-language influences in the processing of L2 multiword expressions Lingli Du, Anna Siyanova-Chanturia and Irina Elgort

187

chapter 9. Cross-language influences in the acquisition of L2 multiword expressions Brent Wolter

211

part iii. Morphosyntax chapter 10. Cross-language influences on morphological processing in bilinguals Hasibe Kahraman and Elisabeth Beyersmann

230

chapter 11. Cross-language influences in L2 syntactic processing and production Merel Muylle, Rob Hartsuiker and Sarah Bernolet

262

chapter 12. Cross-linguistic influences in bilingual morphosyntactic acquisition Vicky Chondrogianni

294

Index

317

chapter 1

Cross-language influences in bilingual processing and second language acquisition An introduction to the volume Irina Elgort,1 Anna Siyanova-Chanturia1,2 & Marc Brysbaert3 1

Te Herenga Waka – Victoria University of Wellington | 2 Ocean University of China | 3 Ghent University

In this chapter, we situate cross-language influences as an interdisciplinary research topic, outline the structure of the volume, and highlight key points from the state-of-the-art review chapters. The chapters critically evaluate the present state of research into cross-language influences across three core domains of linguistic knowledge (phonology, lexicon, and morphosyntax) and identify promising directions for future interdisciplinary research. Keywords: bilingualism, second language acquisition, cross-language influences, phonology, lexicon, morphosyntax

1.

Bilingualism and SLA: What unites them and what divides them, and how we reach across the aisle.

A great majority of people around the world speak two or more languages. Therefore, it is of great theoretical and practical interest to understand how multiple languages influence each other, during language learning, processing, and use. These cross-language influences1 may vary for different components, in terms of their developmental trajectories, learning rates, levels of attainment, and type of processes involved (early/late; automatic/controlled). The present volume offers state-of-the-art reviews of cross-language influences in the acquisition, processing, and use of phonology, lexicon, and morphosyntax by bilinguals, including

1. “Cross-language influences” (CLIs) also referred to as “crosslinguistic influences” and “language/knowledge transfer” (see Odlin, 2003, on problematising the term) is a term that describes how knowing one language affects the learning and use of additional language by an individual. https://doi.org/10.1075/bpa.16.01elg © 2023 John Benjamins Publishing Company

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Irina Elgort, Anna Siyanova-Chanturia & Marc Brysbaert

suggestions for future research. The interdisciplinary nature of this volume bridges the gap between bilingualism and second language acquisition (SLA) research, making it relevant to both fields of study. People who know more than one language are commonly referred to as “bilinguals”. How bilinguals came to know their languages and how they operate in these languages is of interest to diverse groups of researchers who study brain, memory, learning, language, behaviour, communication, philosophy, literature, and other domains. In this volume, we address the issue of bilingualism from two perspectives: language processing and use (comprehension and production), and language learning and acquisition. These two areas have been traditionally studied in the fields respectively delimited as bilingualism and second language acquisition (SLA). Bilingualism research is usually about existing states of language knowledge and use by different bilingual populations (e.g., early versus late bilinguals; simultaneous versus sequential bilinguals). In contrast, SLA research is mostly about how additional languages (L2), other than the first language (L1), are acquired (i.e., the research focuses on the attributes of change in the states of knowledge and use brought about by learning, and what affects this change). Michael Long identified key goals of SLA research agenda as understanding “how changes in [the] internal mental representation are achieved, why they sometimes appear to cease …, and which learner, linguistic, and social factors (and, where relevant, which instructional practices) affect and effect the process.” (Long, 1997, p. 319). Despite overlaps in the interests of bilingualism and SLA research (Alonso Alonso, 2016; Austin et al., 2019; de Bot, 2010; Gass & Selinker, 1992; Jarvis & Odlin, 2000; McManus, 2021; Selinker, 1972), the self-identified foci of both fields have limited cross-fertilisation. The divergence between bilingualism and SLA research can be seen in their preferred terminology (e.g., “bilinguals” versus “L2 learners” or “non-native speakers”; “language dominance”, such as “L1 dominant, L2 dominant”, versus “target language”, i.e., the language being learned), publication venues (e.g., Bilingualism: Language and Cognition versus Studies in Second Language Acquisition), research instruments (e.g., language background questionnaires versus target language proficiency tests), and key theories and models (e.g., bilingual processing models, including BIA-type models described in Chapter 5, syntax-based and constraint-based models, see Harrington, 2010, bilingual production models, see de Bot, 1992, versus SLA theories and models, such as, input-based and outputbased L2 acquisition theories, including Krashen, 1985, Ellis & Larsen-Freeman, 2009, MacWhinney, 2013, and theories emphasising the role of attention and noticing in L2 learning, e.g., Schmidt, 1990, 2001).

Chapter 1. Cross-language influences in bilingual processing and second language acquisition

To check how the terms, “bilingualism” and “SLA”, have been used by researchers in articles in five key SLA and bilingualism journals, we conducted a search in the Linguistics and Language Behavior Abstracts (LLBA) database. We included two predominantly SLA-oriented journals – Studies in Second Language Acquisition (SSLA) and Language Learning (LL); two bilingualism journals – Bilingualism: Language and Cognition (BLC) and International Journal of Bilingualism (IJB); and a journal that cannot be strictly aligned with one of the two fields – Applied Psycholinguistics (AP). We covered the period of 2000–2021 and used the following search terms: (1) (“bilingual” OR “bilingualism” OR “trilingual” OR “multilingual”) and (2) ((“acquisition” AND “second language”) OR (“acquisition” AND “L2”) OR (“acquisition” AND “L3”)). Not surprisingly, Figure 1 shows that terms related to SLA are more commonly used than terms related to bilingualism in articles published in SSLA and LL (across the whole article, as well as in the abstracts, as keywords, and in the titles), and bilingualism terms are more likely to be used in BLC and IJB. AP was indeed more balanced, with a small preference for bilingualism over SLA related terms. Importantly, both sets of terms have been used in all five journals, confirming our premise that the two fields have a significant overlap. Figure 2 shows the percentage of articles in the same journals, for the same time period, that contained the search terms (“cross-linguistic” OR “crosslinguistic” OR “cross-language” OR “CLI”) anywhere in the article and in the abstract. Interestingly, references to cross-language influences were proportionally more common in AP and SSLA (across the whole text) but were used more in the abstracts of the two journal that focus primarily on bilingualism, BLC and IJB. In this volume, we try to bridge the gap and strive to achieve a balance through state-of-the-art reviews of research into bilingual acquisition, processing, and use across three core domains of linguistic knowledge (phonology, lexicon, and morphosyntax) and identify promising directions for future interdisciplinary research into these topics. From a psycholinguistic perspective, bilingualism and SLA research offer complementary approaches to the study of common phenomena and, therefore, the chapters traverse issues of language acquisition, language learning, language representation, language comprehension, and language production, bringing together theories, models, and empirical evidence from bilingualism and SLA to create epistemic anthologies that can offer new insights and initiate new directions in bilingualism and SLA research.

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Figure 1. Percentage of articles containing the search terms related “bilingualism” and “SLA Note. “SLA” represents the following search terms ((“acquisition” AND “second language”) OR (“acquisition” AND “L2”) OR (“acquisition” AND “L3”)); “bilingual” represents the following search terms (“bilingual” OR “bilingualism” OR “trilingual” OR “multilingual”).

2.

Preview of the volume

This volume seeks to provide a comprehensive picture of research into crosslanguage influences in acquisition, processing, and production. The reviews con-

Chapter 1. Cross-language influences in bilingual processing and second language acquisition

Figure 2. Percentage of articles containing the search terms related to “cross-language” Note. “CLI” represents the following search terms (“cross-linguistic” OR “crosslinguistic” OR “cross-language” OR “CLI”).

sider how theoretical and computational models explain and predict cross-language influences, and what experimental evidence supports or challenges them. The three sections of the volume are organized as collections of review chapters that discuss and evaluate key insights into cross-language influences in the areas of phonology, lexicon, and morphosyntax and suggest directions for future research. The phonology section (Chapter 2–4) covers phonetics, phonology, and prosody. The lexicon section (Chapter 5–9) spans single words and multiword expressions, and reviews cross-language influences in pre-lexical, lexical, semantic, and conceptual knowledge components. Finally, the morphosyntax section (Chapter 10–12) includes chapters on morphology and syntax. Moving towards multilingualism, Chapter 4 reviews the acquisition of third language (L3) phonology. Although most of the chapters focus on cross-language influences in late L2 learners, the acquisition of phonetics and phonology (Chapter 2) and of morphosyntax (Chapter 12) is also considered in young bilinguals, as these aspects of language are known to be particularly sensitive to the age of acquisition effect (Austin et al., 2019; Birdsong, 2006). In research with unbalanced bilinguals, the influence of L1 on the additional language(s) is a commonly studied topic, but some chapters also consider bi-directional cross-language influences, including how the knowledge of L2 may affect L1 representations, comprehension, and production and whether bilingual representations and processes are shared or merely connected (e.g., Chapters 4, 7, 8, and 11).

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2.1 Phonology In the first chapter of the Phonology section (Chapter 2), Margaret Kehoe considers research into bilingual perception and production and how it develops in children exposed to two (or more) languages from their early childhood. In perception, her review suggests that bilingual acquisition of phonetic and phonological aspects appears to be guided by the rhythm typology and underpinned by the same perceptual mechanisms in monolingual and bilingual children. She finds little consistency in the outcomes of studies investigating the extent to which linguistic proximity influences speech perception in young bilinguals. When differences are observed between monolinguals and bilinguals, they may be explained in terms of general cognitive constraints, such as task demands and developmental levels, as predicted by the Processing Rich Information from Multidimensional Interactive Representations (Werker & Curtin, 2005) model adapted for bilingual acquisition (Curtin et al., 2011). Similarly, in production, where comparative frequency or complexity in L1 and L2 are assumed to influence acquisition, studies have not found consistent patterns, making it difficult to generalize cross-language influence (e.g., Kehoe, 2015). Furthermore, in speech production studies, it is not clear whether delayed production in a bilingual reflects cross-linguistic influence or reduced language experience, low vocabulary levels, or the influence of nonnative input. The review suggests that better control of these moderator variables is needed in future studies with young bilinguals. Kehoe’s chapter identifies a number of limitations in research into crosslanguage influences in bilingual acquisition of phonetics and phonology by young children, including an insufficient attention to sociolinguistic factors (such as community perception of phonological features), small sample sizes (particularly in production studies), and the view of monolingual speech as the normative ideal against which bilingual speech is compared. The author recommends conducting larger-scale studies, which may show whether cross-language differences in bilingual acquisition at a young age result from adaptive processes, specific to each combination of languages and the context of L2 acquisition. She also makes an intriguing suggestion that SLA studies of cross-language influences in young bilinguals could offer new insights into the nature of phonetic and phonological systems of the respective languages. The topic of bilingual acquisition and use of prosody is addressed by Sasha Calhoun, Paul Warren, and Mengzhu Yan in Chapter 3. The authors explore the connections between prosodic cues and L2 acquisition. They propose that general L2 acquisition and processing principles (such as differential L1-L2 cue-weighting, markedness, and shallow processing of the L2) can be applied to prosody. The authors use the L2 Intonational Learning Theory framework (Mennen, 2015)

Chapter 1. Cross-language influences in bilingual processing and second language acquisition

to explain and predict difficulties in L2 processing based on types of crosslinguistic prosodic differences in metrical structure (stress and phrasing) and tonal inventory (Arvaniti & Fletcher, 2020; Ladd, 2008). Calhoun et al. review cross-language influences in four aspects of prosody: word segmentation, syntactic processing, information structure, and pragmatic and global features; in doing so they refer to four dimensions along which L1 and L2 may differ: systemic (based on phonological elements’ inventories or distributional properties), realisational (i.e., how these elements are implemented), semantic (or functional), and the frequency of use. One finding of their review is a rather limited range of languages covered in the bilingual prosody research on cross-language influences, with a clear predominance of speakers and learners of European (particularly, Germanic) languages and a small number of well-researched Asian languages (e.g., Mandarin Chinese, Japanese, and Korean). This presents an opportunity for future bilingual prosody research into cross-language influences with a wider range of languages and their combinations. Calhoun et al. also point out the dominance of studies into bilingual production with relatively less evidence for cross-language influences in the perception of prosody. The chapter calls for the broadening of the present research scope, for instance, accounting for such individual factors as social contexts, motivations, and communicative goals of individual bilingual speakers. Importantly, the authors argue that the identified paucity of research into the relationship between trajectories of L2 prosody acquisition and two key individual variables – L2 proficiency and age of L2 acquisition – offers a fruitful direction for studies by researchers in SLA and bilingualism. In Chapter 4, Mark Amengual extends the review of cross-language influences from the acquisition of L2 to that of L3 phonology. He highlights the presence of bidirectional influences, known as forward, reverse, and lateral transfer in studies with L3 learners, who have already acquired two sound systems. The author identifies two key loci of cross-language influences, that is, language-specific phonetic contrasts and phonological processes in each language, and discusses the role played by such factors as perceived similarity/distance between the languages and learners’ orientation towards L3 based on their L2 learning experiences. The review shows that research into the acquisition of L3 phonology has matured into its own field, building upon but also going beyond theoretical frameworks and empirical goals of SLA. Importantly, third language acquisition studies hold new affordances for fine-tuning models of complex interactions between co-existing sound systems. In SLA research, theoretical frameworks tend to explain the success in L2 speech sounds by objective or perceived similarities between the L1 and target language sounds. After reviewing three key types of models that predict cross-language influences in L2 speech production, perception, and acquisition,

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Amengual shows how one of these models (the Perceptual Assimilation Model of Second Language Learning, PAM-L2) has been adapted to account for L3 phonological acquisition (following Wrembel et al., 2019). As the work on new models predicting L3 speech acquisition continues, more empirical evidence on L3 speech production and perception is needed to test the hypotheses put forward by the emerging theoretical frameworks and models of L3 speech acquisition. Notwithstanding recent advances in L3 phonological acquisition, Chapter 4 shows that, similar to L2 prosody acquisition, cross-language influences research into L3 speech perception lags behind that of production. Amengual, therefore, calls for building a more comprehensive picture of the role different acoustic cues play, across different language combinations, in the perception of segmental and suprasegmental patterns in L3 speech, to develop robust models of L3 phonological acquisition. He also underscored the value of examining the links between speech perception and production. Another proposed extension of existing L3 research is in the direction of language-specific phonological processing and the differences in the acquisition of phonemic contrasts and phonological processes in an L2 and L3. Aligned with the other two chapters in the Phonology section, Amengual also underscores a need to consider the contribution of situational language context in developing theoretical frameworks of cross-language influences in L2 and L3 speech learning.

2.2 Lexicon In the first chapter of the Lexicon section (Chapter 5), Walter van Heuven and Ton Dijkstra review structural and processing characteristics of several prominent bilingual localist connectionist computational models, including the Bilingual Interactive Activation (BIA) model; the Bilingual Interactive Activation+ (BIA+) model; the Semantic, Orthographic, and Phonological Interactive Activation (SOPHIA) model; and the Multilink+ model. The primary contribution of these models is that they can accurately simulate bilingual word recognition, including cross-linguistic influences, and are useful in generating and testing bilingual lexical processing hypotheses. The original BIA model, built upon McClelland and Rumelhart’s (1981) Interactive Activation (IA) model, accounts for how bilinguals process words “at this moment in time”. Its extension, the BIA-d model, includes a developmental component that can explain how the mental lexicon of a late L2 learner evolves and develops over time. However, as the authors note, the BIA and BIA-d models have a limited scope, because they rely purely on orthographic representations and fail to fully account for how word decisions are made in the context of different experimental tasks. The more powerful BIA+ model, however, considers orthographic and phonological sublexical and lexical representa-

Chapter 1. Cross-language influences in bilingual processing and second language acquisition

tions, semantic nodes, and language nodes. Various theoretical extensions of the BIA+ model have been proposed in the literature, such as the models proposed by van Kesteren et al. (2012) and Casaponsa et al. (2020). The models reviewed in the chapter work with several languages and scripts, including Dutch, English, French, Spanish, Italian, and Japanese kana. In addition, the Chinese-English Interactive Activation (CE-IAM) model (Wen & van Heuven, 2018) is discussed, one of few bilingual models that incorporates an alphabetic and non-alphabetic language. The chapter attests to the importance of these models in predicting bilingual processing behaviours. However, the models covered also have several limitations. For example, some models, such as BIA, only incorporate orthographic representations; the lexicons used in these models are limited in size and/or have simplified semantic representations. The models are further limited in that they concern adult visual word processing, largely disregarding other paradigms, age groups, and for most part failing to incorporate learning mechanisms, focusing solely on processing. Most of these models work only on alphabetic (European) languages (but see CE-IAM, Wen & van Heuven, 2018), with other writing systems (e.g., Japanese, Arabic) currently not accounted for. These and other limitations point to fruitful directions for future research in bilingual connectionist computational models. Chapter 6 in the Lexicon section by Anna Piasecki and Ton Dijkstra focuses on cross-language influences in L2 pre-lexical and lexical processing and acquisition, focusing on the evidence from printed and auditory word recognition. The chapter discusses effects of sublexical properties on word recognition in bilinguals, such as, script differences, diacritical marks (e.g., English vs. French), capitalisation (e.g., nouns in German vs. English), language-sensitive bigrams, and grapheme-to-phoneme correspondence. Empirical evidence suggests that bilinguals are highly sensitive to language-specific sublexical information, and that L2 proficiency modulates this sensitivity, with sublexical cues, such as capitalisation in German versus English, exerting a stronger impact on lower proficiency bilinguals compared to their higher proficiency counterparts. Evidence presented further shows that alphabetic representations are shared between different languages and are co-activated in both languages during language processing. Piasecki and Dijkstra explore the question of the extent to which phonological representations of different languages are activated during bilingual reading. They provide evidence showing a coactivation of grapheme-to-phoneme mappings in different languages. Piasecki and Dijkstra review effects of lexical properties on bilingual word recognition, such as orthographic neighbourhood, morphological family size, cognate status, and interlingual homographs. Again, evidence strongly suggests

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co-activation of lexical information in both of the bilingual’s languages. Similar cross-linguistic effects have also been found for cognates (words with similar meaning/origin in two languages, e.g., English fork and Dutch vork), and interlingual homographs (e.g., English list and Dutch list meaning ‘clever trick’). Finally, the chapter considers evidence from bilingual word learning, with L2 learners’ proficiency seen as developing over a period of time rather than being viewed as ‘frozen’ in time. This line of research indicates that the lexical network in second language learners is not unlike that of proficient bilingual speakers, that is, language non-selective in nature, resulting in cross-language effects, although future research is needed to corroborate this finding. Further research is also needed to confirm the findings into lexical processing in bilingual children who have been found to show cross-language influence effects not unlike those reported in adults. Chapter 7 by Simon De Deyne, Marc Brysbaert and Irina Elgort focuses on cross-language influences in L2 semantic and conceptual representation and processing. The chapter challenges the assumptions of cross-linguistic semantic and conceptual equivalence, adopted in earlier models of bilingual word representations, advocating new approaches to researching bilingual semantic representations and covering sources of semantic non-equivalence between languages. De Deyne et al. categorise sources of semantic non-equivalence into feature level, word level, and domain/language level and argue that they create translation, learning, and processing difficulties in bilinguals and should be accounted for in the models of bilingual processing. The authors further consider how traditional accounts of word meaning in bilinguals have evolved to include newer, bolder perspectives, such as embodied cognition and experiential information (i.e., some words are learned as a result of human interactions with the physical environment) and the role of immediate context (i.e., words are not learned or used in isolation, rather they exist in predictable contexts), among others. Lastly, the chapter discusses the challenges that crosslinguistic models face and directions in which this line of research can be taken in future. For example, how can embodiment effects be tested in L2? What is the role of proficiency? How can computational lexical-semantic models contribute to our (better) understanding of language equivalence? While Chapters 5–7 deal with (single) word processing, Chapters 8 and 9 focus on multi-word information. The last decade has seen an unprecedented amount of interest in the acquisition, processing, and use of sequences above the word level, to which Chapters 8 and 9 are a testament. Chapter 8 by Lingli Du, Anna Siyanova-Chanturia, and Irina Elgort discusses cross-language influences in the processing of multi-word expressions (MWEs) in an L2. Two lines of research are probed: L2 speakers’ processing of congruent MWEs (i.e., phrases that exist

Chapter 1. Cross-language influences in bilingual processing and second language acquisition

both in the speaker’s L1 and L2) versus L2-only MWEs (i.e., those that exist in L2 only); and L2 speakers’ processing of L1-only MWEs translated into L2 versus control phrases. Different types of MWEs are considered in this review: idioms, collocations, and binomials. The authors show that L1-L2 congruent MWEs are likely to exhibit a processing advantage over L2-only MWEs, irrespective of MWE type. Conversely, evidence is mixed with regard to translated L1-only MWEs, with facilitation reported for idioms, but not for collocations or binomials. The authors, note, however, that cross-language congruency effects have been investigated only in a handful of studies, and, hence, the evidence is still rather limited. In the chapter, Du et al. consider possible reasons behind the mixed findings for idioms versus other types of MWEs. It is argued that figurative (idioms) and literal language (collocation and binomials) may be processed differently. For example, although the form of translated L1-only idioms is unfamiliar to L2 speakers when they are presented in the L2, the idiom’s figurative meaning is known from the L1. Idioms can thus show facilitation over literal controls due to the conceptuallevel activation. In the case of collocations and binomials, which are literal MWEs, any processing advantage is likely to be the result of phrase frequency effects. The authors propose that speakers are sensitive to language-specific distributional properties of lexical items, which is why a processing advantage in one language may not automatically translate into a processing advantage in another language. Thus, unlike idioms, literal MWEs that exist only in one’s L1 may not show speeded processing in their L2. Finally, Du et al. put forward two accounts – the L1 MWE activation account and the L2 MWE experience account, in an attempt to explain the mechanisms behind the congruency effect. Which of these two accounts is more accurate in explaining and predicting the congruency effect in the processing of different types of L2 MWEs remains a matter for future research to adjudicate. While Chapter 8 focuses on the issues in bilingual MWE processing, Chapter 9 by Brent Wolter takes a holistic look at cross-linguistic influences in the acquisition of MWEs. The chapter first discusses the different historic approaches to defining MWEs (e.g., form-based, meaning-based, storage-based) and how they have each contributed to our understanding of MWEs as a complex linguistic phenomenon. Wolter argues that the form-based and meaning-based approaches have been useful in identifying properties of MWEs that can make the learning of MWEs more or less difficult. The bulk of the chapter focuses on the ways in which L1 influences the acquisition of MWEs in an L2, considering individual types of MWEs. Studies with collocations and phrasal verbs have, in particular, been useful in showing L1 influence on MWE acquisition. Collocations are extremely common in language, and are often arbitrary (e.g., one takes a shower in English,

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receives a shower in Russian, and makes a shower in Italian), while the acquisition of phrasal verbs, which are a feature of Germanic languages, may be heavily dependent on whether or not the learner’s L1 belongs to the Germanic language family. In many cases, the L1 has been found to play a facilitative role, providing a positive source of information for L2 learners. In other instances, however, L1 influence can be adverse, which is often the case in the acquisition of L2 collocation. In other cases, Wolter argues, the L1 may well be a potentially useful source of information, but learners may not be able to make correct judgments about the transferability of L1 information due to assumptions about the perceived uniqueness of L1 forms. Wolter notes that gauging the influence of the L1 on the acquisition of L2 MWEs may be challenging. This is because the acquisition of L2 MWEs is influenced by other factors, including frequency, L2 proficiency, learner perceptions of the transferability of L1 MWEs to L2 contexts, and word-level semantic knowledge. Future research should examine how these (and possibly other) factors interact with L1 influences, as well as with each other, and how these processes affect MWE learning in an L2.

2.3 Morphosyntax The morphosyntax section includes three chapters that review research on crosslanguage influences in the domains of morphology and syntax. In Chapter 10, Hasibe Kahraman and Elisabeth Beyersmann consider how morphological transfer is handled by the bilingual reading system, focusing on the processing of affixed and compound words. The authors point out that current computational models of bilingual word processing (such as the Multilink model, see Chapter 5) are underspecified to deal with the processing of morphologically complex words and propose how these models can be extended to account for cross-language influences in morphological processing. After reviewing evidence from a range of priming studies, the authors conclude that L1 – L2 morphological transfer (independent of lexical and semantic transfer) can occur automatically at the early stages of word processing. Some of the questions posed by the authors about the mechanisms underpinning cross-language morphological transfer (e.g., the relative timing of morphological decomposition) could not be answered fully based on the reviewed evidence, and Kahraman and Beyersmann put forward suggestions for future experimental designs to probe these mechanisms. Similarly, whether cross-language transfer mechanisms differ for stems and affixes is a new fertile area of bilingual morphological research. In line with the chapters in the Phonology section of the volume, the authors call for larger-scale bilingual studies in order to clarify how age of L2 acquisition and L2 proficiency modulate

Chapter 1. Cross-language influences in bilingual processing and second language acquisition

cross-language influences in inflectional and derivational morphological processing (such as decomposition). In Chapter 11, Merel Muylle, Rob Hartsuiker, and Sarah Bernolet review research into cross-language influences in the acquisition of L2 syntactic processing and production in late L2 learners. An L1-L2 connection in the acquisition of syntax has been clearly established in the bilingual production literature and, therefore, researchers are mainly interested in establishing at which stage of SLA these influences are strongest, and whether or not they persist as L2 proficiency improves. Muylle et al. review two theoretical approaches to modelling bilingual syntactic representations, representing two contrasting views of the developmental trajectory of cross-language influences: one approach models L1 transfer as a feature of early (but not later) stages of the development of L2 syntax (e.g., de Bot, 1992; Montero-Melis & Jaeger, 2020); the other approach predicts an increase in the integration between L1 and L2 syntactic representations and processing with increasing L2 proficiency, with fully shared syntactic representations assumed across languages for identical or similar structures (e.g., Hartsuiker & Bernolet, 2017). Similar to the findings reported in the phonology and morphology chapters, less is known about cross-language influences in the comprehension (compared with the production) of L2 syntactic structures. Therefore, the authors call for more studies in bilingual comprehension of syntactic structures, pointing out that such studies would allow the inclusion of lower proficiency L2 speakers, providing a way to testing cross-language influences in bilingual syntactic processing with a wider range of proficiencies. Conversely, Muylle et al. acknowledge that, in L2 syntactic processing, it may be more difficult to distinguish between crosslanguage influences and the effects of within-L2 proficiency (such as a delay in processing), and less intrenched syntactic knowledge or different learning rates and mechanisms in L2 compared with L1 syntactic processing. Chapter 12 on cross-linguistic influence in bilingual morphosyntactic acquisition by Vicky Chondrogianni concludes the section on morphosyntax. The chapter reviews cross-language influences in simultaneous and sequential bilingual children’s acquisition of morphology and syntax. The chapter raises a number of questions that have not yet been fully answered or understood. For example, does cross-language influence affect grammatical representations, and if so, how? Does cross-language influence lead to both qualitative and quantitative differences between monolingual and bilingual children? Does cross-language influence concern only early bilingual development, or is it also observable in older children? How does cross-language influence manifest itself in heritage bilingual speakers? Chondrogianni shows that the linguistic conditions traditionally viewed as contributors to cross-language influences are not always met and may not be necessary. For example, while cross-language influence has been

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reported for interface phenomena (e.g., interface between morphosyntax and another module, such as discourse), it has also been observed in the case of noninterface, purely morphosyntactic phenomena (e.g., word order). Chondrogianni asks whether cross-language influences are mostly a representational phenomenon, reflecting qualitative differences between monolinguals and bilinguals, or a by-product of bilingual processing where both languages of a bilingual are co-activated; that is, whether cross-language influences reflect qualitative or quantitative differences. Both accounts are carefully considered in view of the available empirical evidence. The chapter further focuses on individual factors that may drive cross-language influences, such as age, dominance, and quality and quantity of input. The review suggests that cross-language influences are not merely developmental, pertinent to young children whose language is still developing; rather, cross-language influences may be as prominent in older children as in younger ones. Chondrogianni calls for further research to better understand the nature of cross-language influences and how representations and/or processing may be affected by the presence of multiple languages, as well as the role of age, context of language use, and quality and quantity of exposure in bilingual morphosyntactic acquisition.

3.

Conclusion

One of the main contributions of the present volume is an in-depth consideration of the current state of research into cross-language influences in second language learning, and bilingual and multilingual comprehension and production, across three linguistic domains. This comprehensive interdisciplinary review shows that cross-language influences continue to be a vibrant research direction that is yet to reach its full maturity. Limitations and grey areas identified in the state-of-theart review chapters provide an excellent set of problems for future researchers in bilingualism and second language learning and acquisition. The contributors challenge future researchers to use larger samples and more robust data collection approaches to test and finetune our understanding of cross-language influences and transfer not only as a cognitive and behavioural but also a social and environmental phenomenon. The locus, architecture, and mechanisms of crosslanguage influences, as well as developmental trajectories, can be better understood and modelled by extending empirical evidence to combinations of more typologically diverse languages, a wider range of L2/L3 proficiencies, and achieving a better balance between perception and production studies across the linguistic domains.

Chapter 1. Cross-language influences in bilingual processing and second language acquisition

References Alonso Alonso, R. (Ed.) (2016). Crosslinguistic influence in second language acquisition. Multilingual Matters. https://doi.org/10.21832/9781783094837 Arvaniti, A., & Fletcher, J. (2020). The autosegmental-metrical theory of intonational phonology. In C. Gussenhoven & A. Chen (Eds.), The Oxford handbook of language prosody (pp. 78–95). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780198832232.013.4

Austin, L., Hernandez, A., & Schwieter, J. (2019). Proficiency predictors in sequential bilinguals: The proficiency puzzle (Elements in Second Language Acquisition). Cambridge University Press. https://doi.org/10.1017/9781108641395 Birdsong, D. (2006). Age and second language acquisition and processing: A selective overview. Language Learning, 56, 9–49. https://doi.org/10.1111/j.1467-9922.2006.00353.x Casaponsa, A., Thierry, G., & Duñabeitia, J. A. (2020). The role of orthotactics in language switching: An ERP investigation using masked language priming. Brain Sciences, 10(1), 22. https://doi.org/10.3390/brainsci10010022 Curtin, S., Byers-Heinlein, K., & Werker, J. (2011). Bilingual beginnings as a lens for theory development: PRIMIR in focus. Journal of Phonetics, 39, 492–504. https://doi.org/10.1016/j.wocn.2010.12.002

de Bot, K. (1992). A bilingual processing model: Levelt’s ‘Speaking’ Model adapted. Applied Linguistics, 13, 1–24. de Bot, K. (2010). Cognitive processing in bilinguals: From static to dynamic models. In The Oxford Handbook of Applied Linguistics (pp. 335–348). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780195384253.013.0023

Ellis, N. C., & Larsen-Freeman, D. (2009). Constructing a second language: Analyses and computational simulations of the emergence of linguistic constructions from usage. Language Learning, 59, 90–125. https://doi.org/10.1111/j.1467-9922.2009.00537.x Gass, S. M., & Selinker, L. (Eds.). (1992). Language transfer in language learning: Revised edition. John Benjamins. https://doi.org/10.1075/lald.5 Harrington, M. (2010). Computational models of second language sentence processing. In The Oxford handbook of applied linguistics (pp. 189–203). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780195384253.013.0013

Hartsuiker, R. J., & Bernolet, S. (2017). The development of shared syntax in second language learning. Bilingualism: Language and Cognition, 20, 219–234. https://doi.org/10.1017/S1366728915000164

Jarvis, S., & Odlin, T. (2000). Morphological type, spatial reference, and language fransfer. Studies in Second Language Acquisition, 22(4), 535–555. https://doi.org/10.1017/S0272263100004034

Kehoe, M. (2015). Cross-linguistic interaction: A retrospective and prospective view. In E. Babatsouli & D. Ingram (Eds.), Proceedings of the International Symposium on Monolingual and Bilingual Speech 2015 (pp. 141–167). Retrieved on 26 July 2022 from http://ismbs.eu/publications Krashen, S. (1985). The input hypothesis: Issues and implications. Longman. Ladd, D. R. (2008). Intonational phonology (2nd ed.). Cambridge University Press. https://doi.org/10.1017/CBO9780511808814

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Long, M. H. (1997). Construct validity in SLA research: A response to Firth and Wagner. The Modern Language Journal, 81, 318–323. https://doi.org/10.1111/j.1540-4781.1997.tb05487.x MacWhinney, B. (2013). The logic of the unified model. In S. Gass & A. Mackey (Eds.), The Routledge handbook of second language acquisition (pp. 211–227). Routledge. McClelland, J. L., & Rumelhart, D. E. (1981). An interactive activation model of context effects in letter perception: I. An account of basic findings. Psychological Review, 88(5), 375–407. https://doi.org/10.1037/0033-295X.88.5.375

McManus, K. (2021). Crosslinguistic influence and second language learning. Routledge. https://doi.org/10.4324/9780429341663

Montero-Melis, G., & Jaeger, T. F. (2020). Changing expectations mediate adaptation in L2 production. Bilingualism: Language and Cognition, 23(3), 602–617. https://doi.org/10.1017/S1366728919000506

Odlin. (2003). Cross-linguistic influence. In C. J. Doughty & M. H. Long (Eds.), The handbook of second language acquisition (pp. 436–486). Blackwell. https://doi.org/10.1002/9780470756492.ch15

Schmidt, R. (1990). The role of consciousness in second language learning. Applied Linguistics, 11, 129–158. https://doi.org/10.1093/applin/11.2.129 Schmidt, R. (2001). Attention. In P. Robinson (Ed.), Cognition and second language instruction (pp. 3–32). Cambridge University Press. https://doi.org/10.1017/CBO9781139524780.003 Selinker, L. (1972). Interlanguage. International Review of Applied Linguistics, 10(1–4), 209–241. https://doi.org/10.1515/iral.1972.10.1-4.209

van Heuven, W. J. B. (2021). Interactive activation models in JavaScript (jIAM). Retrieved on 26 July 2022 from https://waltervanheuven.net/jiam/ Wen, Y., & van Heuven, W. J. B. (2018). Limitations of translation activation in masked priming: Behavioural evidence from Chinese-English bilinguals and computational modelling. Journal of Memory and Language, 101, 84–96. https://doi.org/10.1016/j.jml.2018.03.004

Werker, J., & Curtin, S. (2005). PRIMIR: A developmental framework of infant speech processing. Language Learning and Development, 1, 197–234. https://doi.org/10.1080/15475441.2005.9684216

Wrembel, M., Marecka, M., & Kopecková, R. (2019). Extending perceptual assimilation model to L3 phonological acquisition. International Journal of Multilingualism, 16(4), 513–533. https://doi.org/10.1080/14790718.2019.1583233

part i

Phonology

chapter 2

Cross-language influences in the perception and production of L2 phonetics and phonology in young bilinguals Margaret M. Kehoe University of Geneva

This chapter provides an overview of findings on cross-language influence in the phonetic and phonological domain in children exposed to two (or more) languages in early childhood. The chapter first summarizes theoretical models which have been used to account for cross-linguistic interaction. It then examines findings on speech perception and production. It reviews the acquisition of phonetic and prosodic contrasts, differentiation of acoustic features such as Voice Onset Time and rhythm, and development of syllable structure and segments. Finally, it addresses important themes in early bilingualism such as quantitative and qualitative aspects of language input, the influence of the lexicon, and differences between simultaneous and early sequential bilinguals. Keywords: crosslinguistic interaction, early bilingualism, phonetic and phonological development, speech perception, speech production

Introduction Children growing up bilingually acquire the phonological systems of two languages, which include the phonetic features, the segmental inventory, the phonotactic structure, and the prosodic characteristics, all of which may differ on several typological dimensions. Most bilingual children manage this complex feat with ease. However, they may display differences in their acquisition patterns, in comparison to monolinguals, which reflect the influence of one language upon the other. We refer to this phenomenon as cross-linguistic interaction. Bilingual children are exposed to less input in each language and to input that is more variable than what monolingual children receive, which also influences their acquisition patterns (Unsworth, 2016). This chapter examines findings on the phonetic and phonological development of young bilinguals with a focus on cross-linguistic https://doi.org/10.1075/bpa.16.02keh © 2023 John Benjamins Publishing Company

Chapter 2. Cross-language Influences in Young Bilinguals

influence. By young bilinguals, I refer to simultaneous bilinguals (acquiring two languages from birth) or early sequential bilinguals (acquiring a second language after three years) aged less than five to six years. We refer to L1 as the first language spoken at home and L2 as the second, majority, language; however, in early bilingualism, the distinction between the two may be blurred. The field of early bilingualism has much in common with first and second language acquisition. Early bilingualism and first language acquisition are concerned with children who are in the process of developing their phonological representations, acoustic-perceptual, articulatory, and higher-level. An important theme in both fields is the developing lexicon and its relation to phonological acquisition. Early bilingualism and second language acquisition deal with language contact within an individual, and, as such, similar outcomes that arise from this contact may be expected. Patterns of cross-linguistic interaction in second language acquisition will thus be relevant when discussing young bilinguals as well. The chapter is organized into three sections. The first summarizes theoretical models which have been used to account for cross-linguistic interaction in young children. The second examines findings on speech perception and production in young bilinguals. The third discusses important themes related to early bilingualism.

1.

Models of cross-language influence in young bilinguals

Models that have been applied to early bilingualism include the Processing Rich Information from Multidimensional Interactive Representations (PRIMIR) (Curtin et al., 2011; Werker & Curtin, 2005), the Speech Learning Model (SLM), and the Speech Learning Model-revised (SLM-r) (Flege, 1995; Flege & Bohn, 2021), Paradis and Genesee’s (1996) framework of cross-linguistic interaction, and Vihman’s (2002, 2016) prosodic structure and template approach as applied to young bilinguals. The PRIMIR model is a theoretical framework for early speech perception and word learning (Werker & Curtin, 2005). It assumes that infants are exposed to rich information in the input which they organize along multidimensional interacting planes or spaces. The framework includes representational spaces for storing information; learning mechanisms for altering and building new representations; and dynamic filters for information processing. The representational spaces allow for the storage of all phonetic information in the signal, sound sequence exemplars that later become meaningful words, and higher-order regularities, akin to phonemes, which emerge from generalizations across the sound sequences and phonetic information. Processing is directed by dynamic filters

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such as perceptual biases, as well as the developmental level of the infant and the language-learning task. These filters are coupled with general learning mechanisms which are sensitive to statistical regularities in the input. In adapting the model to bilingual acquisition, Curtin et al. (2011) posit that bilingual children possess the same representational spaces, learning mechanisms, and dynamic filters as monolingual children. They also assume that the nature of the learning mechanism allows for language separation and that bilinguals may have different task demands than monolinguals, even in the same experimental situation. For example, bilinguals are faced with demands such as reduced input, a crowded phonetic space, and the presence of code-switching, all of which tax their limited resources when learning words (Fennell et al., 2007). As well as the above assumptions, they make two additions to the PRIMIR framework. First, they include a mechanism that aids bilinguals in comparing and contrasting information across an array of representations. This mechanism is able to identify categories and track information from multiple sources. For example, comparison across languages based on rhythm could aid bilinguals in discriminating and separating their languages. Keeping track of two sets of distributional statistics could allow bilinguals to establish separate phonetic categories in each language. Second, Curtin et al. (2011) assume that bilinguals will come to a language-learning task with different developmental levels compared to monolinguals since their knowledge and experience are divided between two languages. The developmental level of a bilingual is a product of the learning that has taken place in a particular language as well as general aspects of cognitive and linguistic development. The PRIMIR model, thus, predicts advanced development in some domains but delays relative to monolinguals in others. The Speech Learning Model (SLM) has motivated research in early bilingualism although, strictly speaking, it is a model which is intended for children acquiring a second language after the age of five to six years. Central tenets of the SLM are, nevertheless, implicit in speech perception and production research in early bilingualism, in particular, that the two linguistic systems share a common phonological space in which bidirectional interaction occurs. It also provides a taxonomy for classifying the relationship between L1 and L2 sounds. In the SLM, an L2 sound is new, identical, or similar to the L1. It is the similar sounds that create the most difficulty for second language learners. Their acquisition often leads to two processes: perceptual assimilation or dissimilation. The acquisition of a similar L2 sound may result in equivalence classification which prevents a new L2 category from being formed and the categories of the L1 and L2 are merged. Assimilation has been reported in the acquisition of Voice Onset Time (VOT), whereby second language learners produce stops in their L1 and L2 with similar VOT values (Flege, 1987). The acquisition of a similar L2 sound may lead to an

Chapter 2. Cross-language Influences in Young Bilinguals

opposite phenomenon in which the two categories move away from each other to avoid crowding the phonetic space. Dissimilation has also been reported in the acquisition of VOT (Mack, 1990). Recently, the SLM has been revised to provide a better understanding of how the phonetic system of the L2 learner reorganizes itself over the lifespan in response to phonetic input (Flege & Bohn, 2021). The SLM-r puts a stronger focus on the “input” than the SLM, acknowledging that monolinguals and bilinguals receive different input, which may lead to subtle native versus non-native differences even in experienced L2 learners. It also puts greater focus on individual differences, which may be related to the individual’s L1 cue weighting (the degree to which individuals exploit different featural cues) and their category precision. The “category precision” hypothesis of the SLM-r predicts that individuals having more precise L1 phonetic categories will be better able to distinguish differences between an L2 sound and the closest L1 sound than individuals having imprecise L1 categories (see Kartushina & Frauenfelder, 2014). Translating these findings to early bilingualism, young bilinguals, who display greater speech sound variability, may be the ones who show more difficulty establishing nativelike phonological categories in both of their languages, a prediction that remains to be tested (Kehoe, 2015). For further details on the SLM-r see Amengual, Chapter 4, this volume. Moving closer to young bilinguals, Paradis and Genesee wrote an influential article in 1996 which has since served as a framework for categorizing crosslinguistic interaction. In this article, they challenged the Unitary Language System hypothesis, which proposed that bilingual children begin language acquisition with one system. Instead, they argued that children have two language systems from the start but that these systems interact over the course of acquisition. They defined cross-linguistic interaction (or interdependence) as “the systemic influence of the grammar of one language on the grammar of the other language during acquisition, causing differences in a bilingual’s patterns and rates of development in comparison with a monolingual’s” (Paradis & Genesee, 1996, p. 3). They considered three potential manifestations of cross-linguistic interaction based on findings in syntactic acquisition: 1.

Transfer: the incorporation of a grammatical property into one language from the other; 2. Acceleration: the situation in which a certain property emerges in the grammar earlier than would be the norm in monolingual acquisition; 3. Delay: the situation in which the acquisition process is slowed down due to the burden of acquiring two languages.

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Since the publication of this article, slight changes have been made to Paradis and Genesee’s (1996) terminology. Some researchers characterize “delay” as the opposite of acceleration, namely, the situation in which a certain property emerges later than in monolingual acquisition (Tamburelli et al., 2015). Others recommend the term “deceleration” rather than “delay”, since the latter may have pejorative connotations (Fabiano-Smith & Goldstein, 2010). “Transfer” refers to the presence of a non-native sound or structure in one of the bilingual’s languages due to its presence in the other. It should not be confused with a more general employment of “transfer” which is used synonymously with crosslinguistic interaction. Finally, a different approach to the one articulated by Paradis and Genesee (1996) is advanced by Vihman (2016), who takes a critical stance toward the notion that bilingual children have two phonetic-phonological systems from the start, which interact. Given an emergent view of phonology, Vihman (2016) points out that the question of whether there are one or two systems need not be asked. In her approach, once children have produced a certain number of words, preferred whole-word patterns or templates that are based on early or over-learned motor routines are applied to challenging adult-word targets in both of the children’s languages. Templates are similar within and across languages, since they are constrained by limitations on speech planning and memory; however, they may differ according to the ambient language reflecting the influence of rhythmic or accentual patterning. Vihman (2016) proposes that, at the early stages, the input provided by the parents or the environment is supplemented by the child’s own input (i.e., the child hearing his own simpler productions) which may overlay the distinct linguistic patterns of the different language inputs. Only as children gain production experience are they able to match the ambient language input and derive two phonological systems. In sum, several theoretical approaches have been applied to early bilingual speech, which differ in the emphasis they give to phonetic and phonological learning at different stages of development. The SLM and SLM-r are theories of L2 phonetic learning which were not developed with early bilingualism in mind. The PRIMIR is designed for early bilinguals; however, its main focus is on speech perception and early word learning and not on speech production. Paradis and Genesee’s (1996) framework has been widely applied to describing patterns of cross-linguistic interaction in young bilinguals but it is a descriptive framework and not a fully-fledged model. It was also developed for syntactic and not phonological acquisition. Vihman’s (2016) approach is rooted in early phonetic and phonological development; however, it deals with the early and not later stages of phonological development. Thus, there is a need for a comprehensive model of phonetic and phonological development in young bilinguals which encompasses

Chapter 2. Cross-language Influences in Young Bilinguals

both speech perception and production and which includes both early and later stages of phonological acquisition.

2.

Speech perception and production in young bilinguals

2.1 Cross-language influence Whether cross-language influence takes place depends, to some extent, on how similar or different a bilingual’s two languages are. Thus, we consider how researchers working in early bilingualism have characterized linguistic proximity and, consequently, cross-language influence. Some researchers have grouped languages in terms of rhythm typology. Using acoustic measurement criteria, languages have been classified into three groups: stress- (e.g., English), syllable(e.g., Spanish), and mora-timed (e.g., Japanese) (Ramus et al., 1999). Studies have examined whether children can discriminate between languages within and across rhythm groups. For example, Byers-Heinlein et al. (2010) examined whether bilingual Tagalog-English newborn babies could discriminate between their two languages: Tagalog being a syllable-timed and English a stress-timed language (see Section 2.2.1). Speech perception researchers have also characterized phonological similarity in terms of whether a contrast is present in one or both of the bilingual’s languages. In the latter, the contrast could have the same acoustic realization or be different. For example, speech perception studies with bilingual Catalan-Spanish children have focused on differences in the vowel inventories of Catalan and Spanish (Bosch & Sebastián-Gallés, 2003b). Catalan contains the vowel contrast /e/ versus /ɛ/, but Spanish does not; Catalan and Spanish both contain the contrast /o/ and /u/, but it is realized acoustically differently in the two languages. These differences may influence speech perception findings (see Sections 2.2.2 & 2.2.3). In general, studies suggest that bilinguals follow the same time course as monolinguals when phonological categories are present in both of their languages and are realized in similar ways. When the contrast is present in only one language or is realized differently, bilinguals may experience difficulties (Havy et al., 2016; see, however, Section 2.2.6). Speech production researchers have also considered frequency and complexity differences between languages. “Frequency” refers to the high or low presence of a segment or phonological structure, as based on phoneme or syllable structure counts. Frequency differences are evident, for example, when comparing syllable structure in German versus Spanish. German has a higher proportion of closed syllables than Spanish (67% vs. 27%; Meinhold & Stock, 1980). Lleó et al. (2003) hypothesized that differences in the frequency of closed syllables in Ger-

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man and Spanish may influence bilingual German-Spanish children’s production of coda consonants (see Section 2.3.4). “Complexity” refers to typological markedness in either segmental or prosodic structure. A phonetic or phonological property that contains more elements (e.g., features) or structure is more complex than a phonetic or phonological property that contains fewer elements and less structure. Complexity differences can be observed when comparing clusters in Polish and English. Both Polish and English have clusters with small sonority differences (e.g., /sp/), but only Polish has clusters containing sonority plateaus (e.g., /pt/) in word-initial position. Tamburelli et al. (2015) posited that complexity differences between English and Polish clusters may influence bilingual English-Polish children’s productions of clusters (see Section 2.3.4). The basic premise underlying studies in cross-linguistic interaction is that a structure that has a higher frequency or complexity in the L1 compared to the L2 should facilitate acquisition in the L2, whereas a structure that has a lower frequency or complexity in the L1 should inhibit acquisition in the L2. The characterization of cross-language influence using terms such as Acceleration, Delay/Deceleration, Transfer, Assimilation, and Dissimilation stems from Paradis and Genesee’s (1996) and Flege’s (1995) approaches. The terms Assimilation and Dissimilation are suited to the acquisition of phonetic categories such as VOT and vowels since they capture the notion of “contrast”. In acquiring phonetic contrasts, some bilingual children maintain or enlarge the contrast between languages whereas other children reduce it. Flege’s (1995) terms refer to developed systems, however, and do not take into account the fact that differences between monolinguals and bilinguals may manifest as changes in the rate of acquisition, thus leading to terms such as Acceleration, Delay/Deceleration, Earlier or Later Acquisition. It is important to reiterate, nevertheless, that bilinguals have different task demands and developmental levels than monolingual children, as described in the PRIMIR model (Curtin et al., 2011). The different timing patterns between monolinguals and bilinguals can be viewed as compensatory processes employed by bilinguals when faced with different task demands (Fennell et al., 2007).

2.2 Studies of cross-language influence in speech perception This section focuses on findings in speech perception that inform us on crosslinguistic influence. They are reviewed according to the rhythm typology and whether a contrast is present in one or both of the bilingual’s languages. We separate speech discrimination studies from those which have examined children’s phonetic sensitivity in word-form learning.

Chapter 2. Cross-language Influences in Young Bilinguals

2.2.1 Language discrimination and the rhythm typology Rhythm type is an important linguistic parameter used to account for infants’ early discrimination patterns. Newborn and two-month-old monolingual infants can discriminate sentences drawn from different rhythm groups, for example, from stress- versus syllable-timed languages (Mehler et al., 1988). Byers-Heinlein et al. (2010) also showed that bilingual Tagalog-English neonates were able to discriminate between their two languages, which belong to two different rhythmic groups. Four-month-old monolingual infants are able to discriminate two languages from the same rhythm class as long as one of the languages is their native language (Bosch & Sebastián-Gallés, 1997). Bosch and Sebastián-Gallés (1997) found that four-month-old bilingual Spanish-Catalan infants could distinguish their native languages from either a rhythmically similar (e.g., Italian) or dissimilar (e.g., English) non-native language. In a later study, Bosch and SebastiánGallés (2001) found that bilingual Spanish-Catalan infants could successfully discriminate between their two languages, which belong to the same rhythmic group. Overall, the findings suggest that the same perceptual mechanisms guided by the rhythm typology support both monolingual and bilingual acquisition. 2.2.2 Speech discrimination: Contrasts that exist in one language A series of studies have tested the speech perception skills of bilingual SpanishCatalan children on contrasts that exist only in Catalan. Bosch and SebastiánGallés (2003b) tested bilinguals’ ability to perceive the vowel contrast /e/ versus /ɛ/. The Spanish /e/ is acoustically realized in between that of Catalan /e/-/ɛ/. At four months, monolingual Spanish, monolingual Catalan, and bilingual SpanishCatalan infants discriminated the contrast, but at eight months, only the monolingual Catalan infants did. At 12 months, the bilinguals could again discriminate the contrast. Bosch and Sebastián-Gallés (2003a) documented a similar Ushaped pattern when testing Spanish-Catalan children with the consonantal contrast /s/ vs. /z/, which exists only in Catalan. The bilinguals were not able to discriminate the contrast at 10–12 months whereas the monolingual Catalan infants were. The bilinguals were once again able to discriminate the contrast at 14–20 months. According to Bosch and Sebastián-Gallés (2003b), the U-shaped developmental pattern might arise from a crowded distribution in the phonetic space of the bilinguals. Over the years, researchers have queried whether other factors might explain Spanish-Catalan bilinguals’ vowel perception. Sundara and Scutellaro (2011) tested Spanish-English bilinguals on the same vowel contrast, /e/ and /ɛ/ being phonemic in English but not in Spanish, and found that the bilinguals were able to discriminate these vowels at eight months. Spanish and English belong to two

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different rhythmic groups which may aid the bilinguals in dealing with the overlapping phonetic distributions of the two languages. Using an eye movement paradigm, Albareda-Castellot et al. (2011) found that eight-month-old CatalanSpanish bilinguals displayed responses consistent with the discrimination of /e/ and /ɛ/ similar to the Catalan monolinguals. Thus, discrimination of the /e/-/ɛ/ contrast may depend on the population tested or on the experimental paradigm. Researchers have studied bilingual infants’ ability to perceive a prosodic contrast that exists in one of their languages only. Singh et al. (2018) examined English and Mandarin monolinguals and English-Mandarin bilinguals’ ability to discriminate tonal contrasts. Mandarin is a language in which tonal distinctions are lexically relevant whereas English is not. Mandarin monolinguals demonstrated sensitivity to salient tone contrasts at six months and to subtle and salient contrasts at nine months, whereas bilinguals did not demonstrate sensitivity to tonal contrasts at either age or even later at 11–12 months. This finding might suggest later acquisition of tonal contrasts in bilingual children; however, the findings are difficult to interpret since monolingual English infants were able to discriminate tonal contrasts at nine- and later at 11–12 months. The authors posit that the bilinguals may be at a disadvantage due to the lack of contextual support in this task, once again indicating that task demands may influence bilingual and monolingual children differently as predicted in the PRIMIR model (Curtain et al., 2011). 2.2.3 Speech discrimination: Contrasts that exist in both languages Apart from testing contrasts that exist in only one language, Sebastián-Gallés and Bosch (2009) tested bilingual Catalan-Spanish infants on two vowel contrasts that exist in both Spanish and Catalan. The first contrast /o/-/u/ is realized acoustically differently in the two languages. Sebastián-Gallés and Bosch (2009) documented the same U-shaped pattern that they did for the /e/-/ɛ/ contrast. The second contrast /e/-/u/ is a more acoustically distinct contrast. All infants, including bilingual 8-month-olds, could discriminate this contrast. Burns et al. (2007) used VOT to investigate bilingual French-English infants’ consonantal perception. French and English differ in where they set their boundaries along the voicing continuum to distinguish voiced and voiceless stops. A pre-voiced [ba] is a voiced /ba/ in French, and an aspirated [pʰa] is a voiceless /p/ in English; however, a short-lag [pa] could be a voiceless stop in French or a voiced stop in English. Burns et al. (2007) habituated six-to-eight-month-old infants to the ambiguous [pa] and then tested whether they could distinguish it from the unambiguous /b/ and /p/. They found that both English monolinguals and French-English bilinguals had similar discrimination abilities, being able to distinguish one boundary along the VOT continuum. They then tested infants

Chapter 2. Cross-language Influences in Young Bilinguals

at 10–12 and 14–20 months and found that the monolinguals and bilinguals had different discrimination abilities. The monolinguals could only perceive the VOT boundary specific to English whereas the bilinguals could perceive both the French and English boundaries, findings consistent with language-specific discrimination. Using a similar paradigm but with Event-Related Potentials (ERP), GarciaSierra et al. (2011) tested Spanish-English bilinguals’ neural responses to consonantal stimuli that differed along the VOT continuum. Spanish resembles French in its VOT contrast. Garcia et al. (2011) found that, at six-to-nine months, bilinguals did not show neural discrimination whereas at 10–12 months they did. In contrast, seven-month-old monolinguals displayed neural discriminatory responses for both the native and non-native contrasts (Rivera-Gaxiola et al., 2005). Garcia-Sierra et al. (2011) also observed that group differences in neural discrimination were related to the exposure children received in their two languages. These findings using ERP, thus, differed from the behavioural ones of Burns et al. (2007), perhaps reflecting the different methodology of the two studies. 2.2.4 Word learning: Contrasts that exist in both languages Another series of studies have tested bilinguals’ ability to learn object labels that differ by a single phonemic contrast. Studies are separated into those that have focused on distinctions present in one versus both languages. We start with the latter set since chronologically they were the first to be conducted. Fennell et al. (2007) employed the switch object-associative task to test bilinguals’ ability to learn minimally different [bɪ] versus [dɪ]. They tested a heterogeneous and then two homogeneous groups of bilinguals speaking English and French, and English and Chinese. The findings were similar across all bilinguals. The bilinguals used phonetic detail (i.e., perceptible properties that differentiate two meaningful speech sounds) to learn the associative link at 20 months but not at 17 months, which was three months later than monolinguals (Werker et al., 2002). Fennell et al. (2007) appealed to the resource limitation hypothesis (Fennell & Werker, 2003) to explain the different patterns of the bilinguals. Fennell et al. (2007) highlighted the challenging nature of the word-learning task for bilinguals. Bilingual children have more phonetic categories than monolinguals leading to a more crowded phonetic space; they need to associate each object with two phonetic labels and they need to determine which phonetic system is required in a given word-learning situation. Interestingly, Fennell et al. (2007) did not document any differences in wordform processing according to the bilingual group. The phonetic realization of /b/ and /d/ would have been different in English and French (due to differences in VOT and place of articulation) but less so in English and Chinese. Yet, both

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English-French and English-Chinese bilinguals were unable to learn the wordform association. Mattock et al. (2010) conducted a similar study with 17-monthold French-English bilinguals; however, they employed the contrast /b/ and /g/ which is perceptually more congruous for French and English listeners than /b/ and /d/. The stimuli were also produced by adult bilinguals rather than English monolinguals which was the case in Fennell et al. (2007). This time, the bilingual infants succeeded in forming a word-form association whereas the French and English monolinguals did not. In later experiments, Mattock et al. (2010) showed that when stimuli were restricted to native language productions, monolinguals were successful in the word-learning task. Closer to our theme of cross-linguistic influence, Havy et al. (2016) investigated whether the linguistic features of the L1 influenced French-speaking bilinguals’ phonetic sensitivity in a word-learning task. The task consisted of word-object pairings involving a place (pyf vs tyf ) and voicing contrast (koet vs. goet). They tested two groups of 16-month-old bilinguals: One group spoke Romance languages (e.g., Spanish & Italian), in which the place and voicing contrast were realized similarly to French, and one group spoke Germanic languages (e.g., German & English), in which the contrasts were realized differently. Results indicated that Romance bilinguals learned the words, whereas Germanic bilinguals performed at chance. Thus, performance varied as a function of the similarities and differences in the realization of contrasts in the two native languages. 2.2.5 Word learning: Contrasts that exist in one language Finally, we consider word-learning and recognition studies which have tested contrasts that exist in one of the bilingual’s languages. Ramon-Casas et al. (2009) investigated bilingual Catalan-Spanish toddlers’ (18–24 months) and preschoolers’ sensitivity to /e/-/ɛ/ contrast in a word recognition task with familiar words. Using an eye movement measure, the authors showed that Catalan monolinguals displayed responses consistent with their distinguishing the /e/-/ɛ/ contrast, whereas the Spanish monolinguals did not; the bilinguals exhibited a pattern between the two groups. Catalan-dominant were better at the task than Spanish-dominant bilinguals. Ramon-Casas et al. (2017) re-explored the /e/-/ɛ/ contrast in a minimal-pair word-learning task and found that both Catalan monolinguals and Catalan-Spanish bilinguals, aged 22-months, were able to use the phonetic contrast to phonologically encode words. According to the authors, the fact that bilingual toddlers had difficulty with this contrast in the earlier study may have been related to experimental input factors such as the use of cognates (e.g., train: [trɛn] in Catalan and [tren] in Spanish) and accented speech. Studies have also concentrated on bilingual infants’ sensitivity to tone in word-learning tasks. Singh et al. (2016) found that Mandarin-English bilinguals,

Chapter 2. Cross-language Influences in Young Bilinguals

aged 12–13 months, were able to integrate variation in lexical tone when learning new words in a Mandarin context but disregard it in an English context. In contrast, Mandarin monolinguals were only able to integrate lexical tone into newly learned words at a later time (i.e., 17–18 months). Burnham et al. (2018) also found that bilinguals were not disadvantaged in tonal sensitivity compared to monolinguals. They tested Mandarin monolinguals and Mandarin-English bilinguals, aged 17-months, on tonal contrasts present in both Mandarin (native) and Thai (non-native). Both monolinguals and bilinguals demonstrated sensitivity to certain Mandarin tonal contrasts and not others; however, the bilinguals were sensitive to Thai tonal contrasts as well, consistent with greater phonological flexibility for tonal boundaries than monolinguals. 2.2.6 Conclusion: Cross-language influence in speech perception Our review of speech perception studies indicates conflicting findings on whether bilinguals display similar speech perception patterns to monolinguals. Few studies have directly focused on cross-linguistic influence, an exception being Havy et al.’s (2016) study which revealed that the processing of consonantal contrasts is easier when both languages of the bilingual realize the contrast in the same way. When the contrast is present in both languages but realized in a different way, some studies have found similar (or better) performance in bilinguals compared to monolinguals (Burns et al., 2007; Mattock et al., 2010) whereas others have found mild delays (Garcia-Sierra et al., 2011; Fennell et al., 2007). When the contrast is present in only one language, as is the case of the vowel contrast /e/-/ɛ/, which is present in Catalan and English but not in Spanish, sensitivity to this contrast appears difficult in some cases but not in others, a finding which may relate to the rhythm typology (Sundara & Scutellaro, 2011) or to the task (Ramon-Casas et al., 2009). Findings are equivocal as to whether bilinguals acquiring one language with lexical tone have reduced sensitivity relative to monolinguals: bilinguals do less well in speech discrimination (Singh et al., 2018) but better in word-learning (Burnham et al., 2018; Singh et al., 2016), a finding which may relate to the importance of contextual support for bilinguals. Given the variability in outcomes, it is difficult to assess the extent to which linguistic proximity influences speech perception in young bilinguals. When differences are present between monolinguals and bilinguals, the PRIMIR model offers the possibility to explain these differences in terms of task demands and developmental levels.

2.3 Studies of cross-language influence in speech production A multitude of studies has compared the speech production skills of young monolingual and bilingual children. In this section, we focus on those studies that have

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tested phonetic and phonological properties in one or both of the bilingual’s languages, with the aim of determining whether there is cross-linguistic interaction (Almeida et al., 2012; Bunta & Ingram, 2007; Kehoe et al., 2004, 2011; Lleó et al., 2003). We consider cross-language influence in VOT, rhythm, syllable structure, and segmental acquisition. Before we discuss these findings, we draw the reader’s attention to some of the methodological limitations of these studies. 2.3.1 Methodological limitations An important limitation of research on bilingual speech production is the lack of studies with large numbers of children. Hambly et al. (2013), when conducting a review of bilingual speech production, note the high number of single or multiple case studies (29 out of 66 studies). Case studies are informative but they increase the risk that effects interpreted as cross-linguistic interaction are due to individual differences. In addition, over one-third of the studies (24 out of 66) in Hambly et al.’s (2013) review were based on Spanish-English bilinguals, indicating that much of what we know about bilingual speech pertains to a specific language combination. Another limitation of research on early bilingualism is the fact that data on monolingual controls is not extensive for many languages of the world. Given that the current definition of cross-linguistic interaction refers to differences between bilinguals and monolinguals, a decision as to whether crosslinguistic interaction takes place often depends on insufficient monolingual data. Additional limitations include the fact that many studies do not present detailed information on language input and language experience conditions. Many times, children’s lexical abilities, which are known to correlate with phonological abilities, are not taken into consideration (Core & Scarpelli, 2015; Kehoe, 2015). Few studies are experimental or contain longitudinal data. These limitations should be kept in mind when interpreting the following research findings. 2.3.2 Voice Onset Time (VOT) Many studies have reported cross-linguistic interaction in VOT in young bilinguals leading Kehoe et al. (2004) to observe that it was a domain particularly susceptible to interaction effects. Most attention has been given to bilinguals acquiring a majority language with a long versus short lag distinction, but, recently, studies have been conducted on bilinguals acquiring a majority language with a lead versus short lag distinction (Kehoe & Kannathasan, 2021; Stoehr et al., 2018). Johnson and Wilson (2002) found that two English-Japanese bilinguals growing up in Canada produced voiceless stops with long lag values in English but they also did so in Japanese whereas they should have produced them in the short lag region. This result suggests transfer of long lag voicing from English into Japanese.

Chapter 2. Cross-language Influences in Young Bilinguals

Similarly, Kehoe et al. (2004) documented transfer of long lag voicing from German into Spanish in one of four German-Spanish bilinguals, aged 2;3–2;6, growing up in Hamburg. Nils produced German voiceless stops with a mean VOT value of 70 ms, a value consistent with the expected long lag range; however, he produced Spanish voiceless stops with a mean value of 50 ms, a value greater than the short lag range. Monolingual children acquire long lag stops at around 2;0 to 2;6 in English and in German (Kehoe et al., 2004; Macken & Barton, 1980a). Fabiano-Smith and Bunta (2012) documented VOT values in the short lag region for English voiceless stops in eight 3-year-old Spanish-English bilinguals growing up in the United States, a finding consistent with the later acquisition of long lag voicing. Stoehr et al. (2018) also reported later acquisition of long lag voicing in the German of 29 bilingual German-Dutch children growing up in the Netherlands, whereby the majority language, Dutch, has a lead–short lag distinction. The bilinguals produced voiceless stops with shorter VOTs than monolingual German children. Later or incomplete acquisition of lead voicing has been reported in several studies of VOT acquisition, although this pattern is not easy to separate out from developmental effects since lead voicing is acquired late even in normal development (Macken & Barton, 1980b). However, Mayr and Siddika (2018) documented a virtual absence of pre-voicing in the productions of target voiced stops in Sylheti in 10 third-generation English-Sylheti children and reduced presence of them in 10 second-generation children, both groups aged 4 years, suggesting that bilingualism played a role. Transfer of lead voicing was reported by Stoehr et al. (2018) in bilingual German-Dutch children; they produced a high percentage of stops with lead voicing in German due to the influence of Dutch. The transfer and delay of long lag and lead voicing may lead to similar VOT systems in the bilingual’s two languages, resulting in assimilation, although findings suggest that children often maintain distinctions between languages even if they are not adult-like (Kehoe et al., 2004). 2.3.3 Rhythm Several authors have employed acoustic measurement procedures to determine whether bilingual children separate their two languages rhythmically. Studies with monolingual children suggest that differences in rhythm between the different language groups, characteristic of stress- or syllable-timing, are evident in the speech of three to four-year-olds (Grabe et al., 1999; Kehoe et al., 2011; Mok, 2011). Payne et al. (2012) have even documented cross-linguistic differences in rhythm indices in children as young as two years. English-speaking children obtained higher rhythm scores than Spanish- and Catalan-speaking children. In contrast, studies with bilingual children indicate a slower time-line of acquisition

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in rhythm. Kehoe et al. (2011) observed that six bilingual German-Spanish threeyear-olds displayed similar rhythmic patterns in both of their languages, producing greater vocalic variability in their Spanish, a syllable-timed language, and less vocalic variability in their German, a stress-timed language, consistent with assimilation of rhythmic patterns. Bunta and Ingram (2007) found that ten fouryear-old Spanish-English bilinguals distinguished the rhythmic patterns of their two languages, although vocalic variability in English, a stress-timed language, was lower than in the monolinguals. Schmidt and Post (2015) did not document differences in rhythmic patterns between Spanish and English in bilinguals (26 children across three age ranges) when the children were two years old; however, they found differences to be emerging at four years and to be clearly present at six years. Mok (2011, 2013) also found that five bilingual Cantonese-English children, aged 2;6, and six children, aged 3;0, did not make clear rhythmic differences between their languages; Cantonese being a syllable-timed language. The above patterns are consistent with the later acquisition of stress-timing, stress-timing being considered to be more marked than syllable-timing, or with an assimilation of rhythmic patterns. 2.3.4 Syllable structure Languages differ in the frequency and complexity of different aspects of syllable structure, and these differences have formed the basis for making predictions of cross-linguistic interaction (see Section 2.1 above). We review studies that have investigated the acquisition of codas and clusters. Lleó et al. (2003) posited that two types of interaction effects may be expected in German-Spanish bilinguals acquiring codas. There may be acceleration of codas in Spanish due to the high frequency of codas in German, or delay of codas in German due to the low frequency of them in Spanish. The findings confirmed the first of their hypotheses: five bilinguals, aged 1;1 to 2;4, produced a higher proportion of codas in Spanish than the Spanish monolinguals did. This was one of the first studies to document an acceleration effect in the speech of young bilinguals, which Lleó et al. (2003) ascribed to the high frequency of codas in German. Since this study, several others have reported acceleration of codas due to the higher frequency or complexity of codas in the other language of the bilingual (Keffala et al., 2018; Kehoe & Havy, 2019). The second prediction of Lleó et al. (2003), namely delay of codas, due to low frequency or complexity of codas in the other language, has also been reported (Gildersleeve-Neumann et al., 2008). Almeida et al. (2012) found later acquisition of word-medial codas in French due to the less frequent and more restricted presence of them in Portuguese in a bilingual child, aged 1;10 to 3;10.

Chapter 2. Cross-language Influences in Young Bilinguals

In the production of onset clusters, authors have reported acceleration due to increased segmental or structural complexity of clusters in the L1 (Keffala et al., 2018; Kehoe & Havy, 2019). For example, Tamburelli et al. (2015) found increased production of /s/ + obstruent clusters in the English of 15 Polish-English bilinguals in comparison to monolinguals. The more marked sonority patterns of clusters in Polish relative to English had a facilitative effect on cluster development in English. In the production of coda clusters, Mayr et al. (2015) observed faster acquisition of word-final clusters in the English of a group of 40 Welsh-English bilinguals, when compared to normative findings in English monolinguals. Coda clusters in both languages are complex. In sum, studies that have measured syllable structure acquisition in bilingual children have found support for crosslanguage influence, in the form of faster or slower rates of acquisition. 2.3.5 Segmental acquisition In the area of segmental acquisition, researchers have compared the consonant and vowel inventories of monolinguals and bilinguals and have documented the frequency of segmental transfer from one language to another. Studies employing acoustic analyses have reported on the acquisition of phonetic categories. Consonant and vowel inventories. Fabiano-Smith and Barlow (2010) classified the consonant inventories of eight bilingual Spanish-English children, aged three to four years, according to an implicational hierarchy of phonetic distinctions. In English, all monolinguals exhibited inventories at the highest complexity level whereas seven of the eight bilinguals did. In Spanish, all monolinguals exhibited inventories at the two highest levels whereas seven of the eight bilinguals did. Thus, there were no major differences between bilinguals and monolinguals. However, if examined closer, the bilinguals demonstrated difficulty with /r/ sounds. Six of the eight Spanish monolinguals produced both the tap and trill, the two /r/ sounds of Spanish, whereas none of the bilinguals did. Other authors have reported poorer performance by bilinguals on certain sound classes such as spirants and rhotics (Fabiano-Smith & Goldstein, 2010; Goldstein & Washington, 2001), which might suggest that complex (marked) sound classes are acquired later by bilinguals. This finding has not been universally reported, however. Kehoe (2018) observed earlier acquisition of Spanish /r/s by four GermanSpanish bilinguals, aged 2;0–3;6, which she attributed to the enhanced perceptual and articulatory capacities the children had developed from being able to produce the German /r/. Turning to vowels, studies, which have investigated bilinguals acquiring languages characterized by different-sized vowel inventories, have reported increased error rates in the language with the larger vowel system (Spanish vs. English: Schnitzer & Krasinski, 1994, 1996; Russian vs. English: Gildersleeve-

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Neumann & Wright, 2010). Kehoe and Girardier (2020) examined the influence of inventory size on French vowel accuracy in over 60 bilingual children, aged three to six years. The L1s of the bilingual children were separated into those which had small (e.g., Japanese, Spanish) and large vowel inventories (e.g., English, Swedish). The authors predicted that children who spoke languages with small inventories would be delayed in French vowel accuracy, in comparison to children who spoke languages with large inventories, a prediction which was marginally supported. Children who had small vowel inventories displayed a tendency to have lower French vowel accuracy. Segmental transfer. Keshavarz and Ingram (2002) observed transfer in the speech of a Farsi-English bilingual, aged 1;4, which included transfer of Farsi glottal stops to English words (e.g., happy [ʔæpi]) and English schwa, [ɪ], and [ɔ] to Farsi words (e.g., /bɑlɑ/ [bəlɑ] ‘up’; /mæŋɛ/ [mæŋɪ] ‘mine’; /pɑʃow/ [pɔʃow] ‘get up’). Fabiano and Goldstein (2005) also report several examples of transfer which included Spanish to English influence (e.g., /v/ ➔ [ß]) and English to Spanish influence (e.g., /o/ ➔ [ə], /r/ ➔ [ɹ]) in three bilingual Spanish-English children, aged 5 to 7 years. The general finding, however, is that segmental transfer is not frequent in young bilinguals. Goldstein and colleagues report percentages of below 1% in Spanish-English bilinguals (Goldstein & Washington, 2001; Goldstein et al., 2005). Acoustic studies of phonetic categories. Using Flege’s (1995) model as base, several authors have reported on phonetic category acquisition in young bilinguals. Barlow et al. (2013) conducted an acoustic study of /l/ in seven Spanish-English bilinguals, aged two to six years. English /l/ has a lower F2 and a darker quality than Spanish /l/. In addition, English has an allophonic rule that further darkens /l/ in postvocalic position. Findings indicated that the bilinguals had similar F2 values for prevocalic /l/ in both languages consistent with assimilation; however, they had F2 values for postvocalic /l/ consistent with separate phonetic categories possibly because Spanish and English postvocalic /l/s are sufficiently distinct. Thus, there was evidence of both assimilation and separation for the same sound. Yang and colleagues have examined the starting stage in L2 vowel acquisition in early sequential Mandarin-English bilinguals. In a case study of a Mandarin boy, aged 3;7, Yang, et al. (2015) observed that he employed his L1 vowel space as the initial base for building the L2 system, consistent with assimilation. That is, he clustered acoustically similar English and Mandarin vowels into larger groups in the vicinity of the L1 corners. At a later stage, he drastically reduced the English vowel space at the same time as expanding the Mandarin one so that he maximized the contrast between the two vowel systems, a pattern consistent with dissimilation. In a second study, Yang and Fox (2017) investigated L2 vowel acquisition in 15 Mandarin-English bilinguals, aged 5 to 6 years. The children were

Chapter 2. Cross-language Influences in Young Bilinguals

divided into two groups based on their proficiency in English. The Bi-high group had exposure to English for three years whereas the Bi-Low group, for less than six months. Yang and Fox (2017) documented bi-directional interaction between the two vowel systems. The L1 had a strong effect on the L2 in the Bi-low group, whereas the Bi-high group had L2 vowel measures which did not differ from English monolinguals. The Bi-low group had Mandarin vowel measures similar to monolinguals, whereas the Bi-high children produced some of their Mandarin vowels differently from monolinguals. The interaction patterns documented by Yang and Fox (2017) were assimilation, in which L2 values moved towards L1 values or vice versa. 2.3.6 Conclusion: Cross-language influence in speech production Our review of bilingual speech production across several phonetic and phonological domains provides support for different types of cross-linguistic interaction which can be roughly grouped into categories such as Acceleration, Delay/Deceleration, Transfer, Assimilation, and Dissimilation. Terms such as Delay/Deceleration should not be seen as negative processes but as compensatory strategies adopted by the bilingual child in response to his/her input conditions (e.g., increased or decreased frequency or complexity of phonetic and phonological structures in one language or the other). Often, these strategies result in similar production patterns across both languages which could be viewed as a pooling of phonetic and phonological resources rather than as a lack of differentiation. That a general delay is not supported is indicated by several studies which reveal patterns of both acceleration and delay at the same time depending upon the linguistic property under consideration (Almeida et al., 2012). The earlier studies based on single and multiple cases should be viewed with caution because of the high risk of individual variation; however, later studies based on larger groups of participants have tended to support earlier findings in providing evidence of crosslinguistic interaction (e.g., Keffala et al., 2018; Kehoe & Havy, 2019; Tamburelli et al., 2015). Nevertheless, an exhaustive review would show that in a given contact situation (e.g., high complexity clusters in one language and low complexity in the other), several different patterns may arise, making it difficult to generalize cross-language influence (Kehoe, 2015). Why such variability exists is still not well understood but it may reflect the methodological weaknesses highlighted above such as small participant numbers and failure to control language experience and lexical factors.

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3.

Themes related to cross-language influence in young bilinguals

Several factors may modulate the effects of cross-language influence. They include language experience, which has quantitative and qualitative components, the developing lexicon, and age of acquisition, which we address in terms of differences between simultaneous and sequential bilinguals.

3.1 Language experience: Quantitative aspects Language experience is often measured using parent-reported estimates of the frequency of language input and output, language proficiency tests, or parental rating scales (Goldstein et al., 2010; Ruiz-Felter et al., 2016). In the area of speech perception, studies report a modulating effect of language experience on perceptual performance (Garcia-Sierra et al., 2011; Ramon-Casas et al., 2009), although the findings are not universal (Höhle et al., 2020). Similar trends have been observed in speech production. Some studies show that the dominant language of a bilingual is associated with faster phonological acquisition (Law & So, 2006; Mayr et al., 2015). Law and So (2006), for example, observed that Cantonese dominant bilinguals have faster Cantonese phonological development than Putonghua dominant bilinguals and vice versa. However, when complexity and dominance compete, complexity may win out. In the case of Cantonese and Putonghua, bilingual children acquired the segmental aspects of Cantonese phonology faster than the more complex aspects of Putonghua, regardless of their dominance. Other studies have found only modest effects of language experience on phonological production (Almeida et al., 2012; Goldstein et al., 2005, 2010). Kehoe et al. (2021) hypothesize that phonology may be less susceptible to the influence of language experience than other language domains (e.g., vocabulary and morpho-syntax), due to a strong language-general component based on articulatory and shared phonological systems.

3.2 Language experience: Qualitative aspects Apart from quantitative aspects of the input, qualitative aspects such as whether the child is receiving non-native input or input from multiple speakers may influence phonetic and phonological acquisition. Mayr and Montanari (2015) compared the role of input setting on the VOT acquisition of two trilingual English-, Italian-, and Spanish-speaking children, aged six- and eight-years, growing up in the United States. The children’s input in English was from their native English father and from native speakers in the surroundings. The children’s English VOT values were native-like. The children’s input in Italian was from their native Italian-speaking mother but also from English-accented input in the Italian

Chapter 2. Cross-language Influences in Young Bilinguals

school the children were attending. The Italian VOT values were not nativelike. The children’s input in Spanish came from a single person, the Spanishspeaking nanny. Surprisingly, the children’s Spanish VOT values were similar to the adult model and were unaffected by cross-linguistic interaction. Thus, there were different patterns of VOT acquisition, depending upon the quality of the input. In another study on VOT acquisition, Stoehr et al. (2019) found that bilingual children’s VOT patterns correlated with their parents. They studied bilingual German-Dutch children, aged three-to-six years, growing up in the Netherlands. Individual variation in maternal input was associated with variation in the bilingual children’s speech. The bilinguals’ VOT values were affected by the mothers’ non-native speech in Dutch and the mothers’ attrited speech in German. More recently, Sim and Post (2022) reported a significant positive input-production relationship in coda stop release patterns in 14 English-dominant bilingual children growing up in Singapore. Mothers who released coda stops to a lesser degree had children who released them to a lesser degree and vice versa. Thus, it cannot be excluded that what has been interpreted before as cross-linguistic interaction may reflect the influence of parental input.

3.3 The developing lexicon Höhle et al. (2020) observe that delay in bilingual acquisition with respect to monolingual acquisition has been reported more in phonological processing at the lexical than at the phonetic contrast level. They relate this potential effect to the smaller vocabularies of bilinguals in each of their languages compared to monolinguals (Hoff et al., 2012). The slower vocabulary growth may hinder the use of phonetic information in word learning, a possibility that receives support in the PRIMIR model, whereby lexical and phonological development is closely entwined. According to PRIMIR, phonemes emerge when the infant has acquired sufficient word-object linkages to allow for phonetic regularities and stable phonetic categories. It takes longer for bilinguals to accrue sufficient word-object linkages in a single language due to their reduced input. Another perspective is provided by Vihman’s (2016) prosodic structure and template approach which puts a strong emphasis on the child’s own production patterns in influencing lexical development. In other words, children’s first words are individual rather than universal and are the result of their own production routines which are applied to their two languages. In an analysis of the first 100 words of five bilingual children, Vihman (2016) observed that all children drew on both of their languages in their first 10 interpretable words. Children adapted or assimilated target forms to their favoured motor routines in both of their languages leading to blurred phonological distinctions between the bilingual

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child’s languages. Differentiation of prosodic structures according to language was observed in only two of the five bilingual children. Beyond the first word stage, studies in bilingual phonological production report strong correlations between vocabulary and phonological ability (Keffala et al., 2020). Kehoe and Giradier (2020), when studying French-speaking bilinguals aged three-to-six years, found that vocabulary was the main factor accounting for phonological outcomes in statistical models which included age, phonological complexity of the L1, and dominance. Thus, lexical factors may explain many of the monolingual-bilingual differences documented in previous studies of bilingual phonological acquisition, emphasizing the need to take vocabulary size into consideration.

3.4 Simultaneous versus early sequential bilingualism Are there differences between bilinguals exposed to two languages from birth versus at three to five years? Studies on the speech sound development of children who are exposed to their second language after three years indicate that these children are not strongly disadvantaged in their speech production. Morrow et al. (2014) documented high consonant accuracy in sequential bilinguals in Canada who were not exposed to English until 3;3, some later than 5;0. At nine months following exposure to English, the average percent consonants correct was 88.8% which increased to 95% at approximately 33 months following exposure. In a similar vein, Ruiz-Felter et al. (2016) reported minimal differences in English phonological performance between simultaneous and sequential English-Spanish bilinguals, aged five-to-six years. Studies that have investigated the acquisition of VOT or vowel formants in early sequential bilinguals, nevertheless, document an early phase in which L2 values are non-target-like. McCarthy et al. (2014) measured the perception and production of the voicing contrast by four-year-old sequential Sylheti-English bilinguals. After seven months of English exposure, the bilinguals differed from English monolinguals by displaying less consistent categorization of one of the voicing contrasts and by producing shorter VOTs for target voiced stops. After 12 months of exposure, the bilinguals did not differ from monolinguals both in the perception and production of the voicing contrasts. Yang and Fox (2017) found non-target-like English vowel formants in seven Mandarin-English bilinguals, who had six-months exposure to English but target-like measures in eight bilinguals who had had three years of exposure, again suggesting that acquisition of L2 phonetics and phonology at this age is rapid. In contrast, studies comparing simultaneous and sequential bilinguals’ lexical processing tend to indicate subtle

Chapter 2. Cross-language Influences in Young Bilinguals

differences between groups particularly in their non-dominant languages (Persici et al., 2019; Poach & van Hell, 2012).

Conclusion This chapter has reviewed finding on cross-language influence in the speech perception and production of young bilinguals. This theme has not been a strong focus of speech perception research (see, however, Havy et al., 2016), and the heterogeneity of findings makes it difficult to assess the extent to which one language influences another. In contrast, cross-language influence has been the theme of many studies in speech production. Depending on the phonetic and phonological properties of the bilingual’s two languages, researchers have observed patterns of cross-linguistic interaction, which can be labeled according to the frameworks of Paradis and Genesee (1996) and Flege (1995). These patterns are assumed to reflect grammatical influence; however quantitative and qualitative aspects of language input and lexical effects may lead to monolingual-bilingual differences which resemble grammatical influence. For example, delayed speech production in a bilingual may reflect cross-linguistic influence but may also reflect reduced language experience, low vocabulary levels, or the influence of non-native input suggesting the need to control for these effects when conducting research with young bilinguals. We have focused on early bilingualism, and cross-linguistic interaction is often subtle and short-lived. Nevertheless, cross-linguistic interaction does not disappear at this age. Studies show that older simultaneous bilinguals may differ from monolinguals in certain aspects of phonetics and phonology (e., rhotics, VOT, foreign accent), particularly in their non-dominant language (Kupisch et al., 2014; Menke, 2018), emphasizing the importance of tracking cross-linguistic interaction from early childhood through to adulthood to provide information on its dynamic course. The end of this chapter is an opportune time to reflect on the current approach to cross-linguistic interaction in young bilinguals and reflect on whether we are heading in the right direction. First, we could ask whether it is appropriate to continue to make comparisons between bilingual and monolingual speech, with the assumption that monolingual speech is the normative ideal (see Bullock & Olson, 2017). Second, we could wonder whether the labels currently used to characterize cross-linguistic interaction are sufficient to capture all the phonetic or phonological changes that arise due to bilingual input. Several authors would suggest no (Kehoe, 2015; Lleó, 2015). Bullock and Olson (2017) also observe that simply categorizing cross-linguistic interaction in terms of

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labels overlooks the source of this interaction which may have its origin in sociolinguistic factors such as community perception of phonological features. Sociolinguistic factors are rarely taken into consideration in early bilingualism. Third, we could point out that, despite over 20 years of data collection on bilingual phonetic and phonological development, we have not gleaned sufficient numbers of generalizations to predict when and in what form cross-linguistic interaction will take place. Lleó and Cortés (2013) have attempted to model cross-linguistic interaction using a database of German-Spanish bilinguals, but we are far from being able to model cross-linguistic interaction with a larger and heterogeneous population of young bilinguals. These questions are just some that should be raised in future studies examining cross-language influence on bilingual phonetic and phonological development. Studies, which contain large numbers of bilingual children from diverse linguistic backgrounds, which control for language experience and proficiency, and which do not view cross-linguistic differences as a deviation from the monolingual norm but as the result of an adaptive process, are a step in the right direction. Such studies will provide valuable information on bilingual acquisition itself and on the nature of phonetic/phonological systems and their vulnerabilities.

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Mack, M. (1990). Phonetic transfer in a French-English bilingual child. In P. Nelde (Ed.), Language attitudes and language conflict (pp. 107–124). Dümmler. Macken, M. A., & Barton, D. (1980a). The acquisition of the voicing contrast in English: A study of voice onset time in world-initial stop consonants. Journal of Child Language, 7, 41–74. https://doi.org/10.1017/S0305000900007029 Macken, M. A., & Barton, D. (1980b). The acquisition of the voicing contrast in Spanish: A phonetic and phonological study of word-initial stop consonants. Journal of Child Language, 7, 433–458. https://doi.org/10.1017/S0305000900002774 Mattock, K., Polka, L., Rvachew, S., & Krehm, M. (2010). The first steps in word learning are easier when the shoes fit: Comparing monolingual and bilingual infants. Developmental Science, 13, 229–243. https://doi.org/10.1111/j.1467-7687.2009.00891.x Mayr, R., Howells, G., & Lewis, R. (2015). Asymmetries in phonological development: the case of word-final cluster acquisition in Welsh-English bilingual children. Journal of Child Language, 42, 146–179. https://doi.org/10.1017/S0305000913000603 Mayr, R., & Montanari, S. (2015). Cross-linguistic interaction in trilingual phonological development: The role of the input in the acquisition of the voicing contrast. Journal of Child Language, 42, 1006–1035. https://doi.org/10.1017/S0305000914000592 Mayr, R., & Siddika, A. (2018). Inter-generational transmission in a minority language setting: Stop consonant production by Bangladeshi heritage children and adults. International Journal of Bilingualism, 22, 255–284. https://doi.org/10.1177/1367006916672590 McCarthy, K., Mahon, M., Rosen, S., & Evans, B. (2014). Speech perception and production by sequential bilingual children: A longitudinal study of Voice Onset Time acquisition. Child Development, 85, 1965–1980. https://doi.org/10.1111/cdev.12275 Mehler, J., Jusczyk, P., Lambertz, G., Halsted, N., Bertoncini, J., & Amiel-Tison, C. (1988). A precursor of language acquisition in young infants. Cognition, 29, 143–178. https://doi.org/10.1016/0010-0277(88)90035-2

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Payne, E., Post, B. Astruc, A., Prieto, P., & Vanrell, M. (2012). Measuring child rhythm. Language and Speech, 55, 203–229. https://doi.org/10.1177/0023830911417687 Persici, V., Vihman, M. M., Burro, R., & Majorano, M. (2019). Lexical access and competition in bilingual children: The role of proficiency and the lexical similarity of the two languages. Journal of Experimental Child Psychology, 179, 103–125. https://doi.org/10.1016/j.jecp.2018.10.002

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Ramon-Casas, M., Fennell, C., & Bosch, L. (2017). Minimal-pair word learning by bilingual toddlers: The Catalan /e/-/ϵ/ contrast revisited. Bilingualism: Language and Cognition, 20, 649–656. https://doi.org/10.1017/S1366728916001115 Ramon-Casas, M., Swingley, D., Sebastián-Gallés, N., & Bosch, L. (2009). Vowel categorization during word recognition in bilingual toddlers. Cognitive Psychology, 59, 96–121. https://doi.org/10.1016/j.cogpsych.2009.02.002

Ramus, F., Nespor, M., & Mehler, J. (1999). Correlates of linguistic rhythm in the speech signal. Cognition, 73, 265–292. https://doi.org/10.1016/S0010-0277(99)00058-X Rivera-Gaxiola, M., Silva-Pereyra, J., & Kuhl, P. (2005). Brain potentials to native and nonnative speech contrasts in 7- and 11-month-old American infants. Developmental Science, 8, 162–172. https://doi.org/10.1111/j.1467-7687.2005.00403.x Ruiz-Felter, R., Cooperson, S., Bedore, L., & Peña, E. (2016). Influence of current input-output and age of first exposure on phonological acquisition in early bilingual Spanish-Englishspeaking kindergarteners, International Journal of Language and Communication Disorders, 51, 36–383. https://doi.org/10.1111/1460-6984.12214 Schmidt, E., & Post, B. (2015). Language interaction in the development of speech rhythm in simultaneous bilinguals. In E. Delais-Roussarie, M. Avanzi, & S. Herment (Eds.), Prosody and language in contact (pp. 271–291). Springer. https://doi.org/10.1007/978-3-662-45168-7_13 Schnitzer, M., & Krasinski, E. (1994). The development of segmental phonological production in a bilingual child. Journal of Child Language, 21, 585–622. https://doi.org/10.1017/S0305000900009478

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chapter 3

Cross-language influences in the processing of L2 prosody Sasha Calhoun,1 Paul Warren1 & Mengzhu Yan2 1

Te Herenga Waka – Victoria University of Wellington | 2 Huazhong University of Science and Technology

Prosodic cues play a key role in speech processing at multiple levels of linguistic structure. Despite this, research on the role of prosodic cues in second language (L2) speech processing, and on how prosody interacts with other linguistic systems, is comparatively underdeveloped. Meanwhile, our understanding of prosodic systems and their typological variation is substantially matured, making this an ideal time to grow such research. We briefly outline key models aiming to explain acquisition and use of prosody by L2 learners. We then explore cross-language influences in the processing of L2 prosody in four areas which have received the most attention to date: word segmentation, syntactic processing, information structure and pragmatics/global features. We end by highlighting key directions for future research. Keywords: prosody, L2 speech processing, L2 prosody learning models, L2 word segmentation, L2 syntactic processing, L2 information structure processing, L2 pragmatic comprehension

1.

Introduction

Prosodic cues play a key role in speech processing at multiple levels of linguistic structure. In first language (L1) processing, prosody is important for word segmentation and recognition, syntactic parsing and disambiguation, signalling information structure, as well as pragmatic, attitudinal and emotional signalling (Cole, 2015; Cutler, 2012; Cutler et al., 1997; Ladd, 2008). In second language (L2) acquisition, it has long been recognized that prosody training is key to phonological acquisition and fluency (e.g., Hahn, 2004; McNerney & Mendelsohn, 1992; Munro & Derwing, 1999). However, there is less research on prosodic cues in L2 speech processing, compared to segmental information (e.g., see Chapters 2

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and 4, this volume), or on how prosody interacts with other linguistic systems (e.g., see the relatively brief discussion in books such as Gass & Mackey, 2012; Jiang, 2018; Loewen & Sato, 2017). Meanwhile, linguistic research has deepened our understanding of prosodic systems and their typological variation (e.g., Gussenhoven & Chen, 2020; Jun, 2006b, 2014). Therefore, the time is ripe for examining cross-language influences in the processing of L2 prosody, taking into account the complexities of analysing the structure and functions of prosody. Given the aims of this volume, we focus on the use of prosodic cues in L2 speech processing. The goal of such research is to “understand the mental processes and mechanisms involved in L2 use and what such knowledge can tell us about L2 acquisition” (Jiang, 2018, p. 2), primarily using techniques from psycholinguistics. Studies of language perception are generally seen as the most direct evidence of processing, and we concentrate on these. Nonetheless, we report, where appropriate, on production studies: firstly, as production and perception are linked, albeit indirectly (e.g., see Baese-Berk, 2019); secondly, to understand what prosodic cues are available in the signal; and thirdly, where there are few perception studies available. Below, we introduce prosodic structure and key sites of typological variation. We then outline key models of L2 acquisition aimed at explaining the use of prosodic cues. Next, we explore cross-language influences on the processing of L2 prosody in four areas: word segmentation, syntactic processing, information structure and pragmatics/global features. We conclude with directions for future research.

2.

What is prosody?

Prosody is the organizational structure of speech, consisting of suprasegmental aspects such as stress, phrasing, rhythm, tone and intonation, but also their interaction with segmental properties (Gussenhoven & Chen, 2020; Ladd, 2008). We describe prosody using the widely adopted Autosegmental-Metrical (AM) framework (Arvaniti & Fletcher, 2020; Ladd, 2008). In AM, prosody is described on autonomous tiers for metrical structure (stress and phrasing) and tones, as shown in Figure 1 for English (the sound file for this utterance is available in the supplementary material). This shows metrical structure consisting of hierarchically organised phrasal units. While the exact inventory of these units is not settled, and differs between languages, these include the syllable (σ), prosodic word (ω), phonological phrase (φ) and intonation phrase (ɩ) (Arvaniti & Fletcher, 2020; Selkirk, 2011). These generally align with equivalent morphosyntactic units: morphological words, syntactic phrases and syntactic clauses respectively. However,

Chapter 3. Cross-language influences in the processing of L2 prosody

Figure 1. Example of AM analysis for the utterance I’m content with the content. Are you? (sound file is available in the supplementary material https://doi.org/10.1075/ bpa.16.03cal.audio.1). Tiers show tonal events, metrical structure including phrasing and stress (note stress grid marks are shown horizontally not vertically, as is more usual), syllables, segments (following the system for New Zealand English described in Bauer & Warren, 2004), and orthography. See text for further explanation.

they are not isomorphic, for example, in Figure 1 the function words with and the are grouped with the adjacent content words, and phrasing is subject to phonological constraints which differ by language (e.g., see Selkirk, 2011). The primary phonetic cues to these units are phrase-final lengthening (e.g., /tent/ in the unstressed syllable of content (N) in Figure 1), phrase-initial strengthening and/ or lengthening (e.g., stronger and longer articulation of /ɐː/ in are) and the distribution of connected speech processes (e.g., elision of the final /t/ in the phrasemedial content (V ) but not phrase-final content (N)) (Nespor & Vogel, 2007; Turk, 2011; White, 2014). Within each phrasal unit, the most prominent syllable forms the head, creating a cumulative prominence structure across the intonational phrase (shown by asterisks in the second tier of Figure 1) (Ladd, 2008). Prominence is signalled by the relative length and loudness of the syllables, segment realization (e.g., full vs. reduced vowels), and sometimes pitch movement (Ladd, 2008; Turk, 2011). The intonational tune consists of tonal events comprised of H(igh) and L(ow) tones associated with points in the metrical structure (Arvaniti & Fletcher, 2020; Warren & Calhoun, 2022). In English, these include: pitch accents (marked with *), for example, L+H*, shown by rising pitch, associ-

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ated with the stressed syllable in content (V ); as well as phrase accents (marked with -) and boundary tones (%) associated with the edges of phonological and intonational phrases respectively, for example, the falling L-L% and rising H-H% in Figure 1 (Warren & Calhoun, 2022). Research over the past 40 years has established key parameters of typological variation in prosodic structure (e.g., Gussenhoven & Chen, 2020; Jun, 2006b, 2014; Ladd, 2008). A few examples are given here that will be referred to further below. In English and many languages, lexical stress plays a key role in word identification and segmental realization, though stress position differs by language and may be fixed or variable (Gordon & Hulst, 2020). In other languages, tonal markers play a similar role, for example, in Korean accentual phrases (units in-between prosodic words and phonological phrases) are marked by patterns of L and H tones (akin to the H- tone on you in Figure 1) (Jun, 2006a). In English, tones are associated post-lexically, whereas in tone languages they are also associated with syllables lexically (Ladd, 2008). Languages with tones at the lexical or prosodic word level may or may not have lexical stress (Gordon & Hulst, 2020). There are also substantial differences in the expression of phrase-level prominence (Kügler & Calhoun, 2020). In languages like English with stress-based prominence, this is marked by pitch accents, although the inventory of pitch accents (and phrase/ boundary tones) differs by language. However, phrase-level prominence can also be marked by expansion of pitch range across phonological phrases, for example, in Mandarin, and/or by the placement and pitch scaling of phrase-edge tones, for example, in Korean, while some languages appear not to mark phrase-level prominence, for example, many Bantu languages and Malay (Kügler & Calhoun, 2020). There are correlations between types of word and phrase level prosodic systems, for example, languages with lexical tone are likely to use either pitch range expansion to mark phrase-level prominence, or show no phrase-level marking, but there are also exceptions to these patterns (see Jun, 2006b, 2014; Kügler & Calhoun, 2020).

3.

Models for analysing L2 processing of prosody

Prosodic cues play an important role in L1 speech production and perception at multiple levels of linguistic structure (see Section 1). We should therefore expect prosody to be important in L2 speech processing. Despite this, there is a relatively small body of research in this area (although it is growing), and that research is not always well integrated with the rest of the field: research on phonological processing has tended to concentrate on the segmental level (Best & Tyler, 2007; Flege, 1995; van Leussen & Escudero, 2015), and research on acquisition at other

Chapter 3. Cross-language influences in the processing of L2 prosody

levels often has not considered the use of prosodic cues (Gass & Mackey, 2012; Jiang, 2018; Loewen & Sato, 2017). Nonetheless, we expect the same principles to apply to the use of prosodic cues as to other aspects of L2 acquisition and processing, for example, L1 transfer and differential L1-L2 cue-weighting, markedness and shallow processing of the L2, all linked to proficiency (Clahsen & Felser, 2006; MacWhinney, 2012; Yavaş, 2011). However, the application of these principles can be less evident than for other linguistic features, as the form-function relationship can be more complex (see Cole, 2015). For example, the pitch rise on /ten/ in content (verb) in Figure 1 is simultaneously one cue to the phonological prominence of the word (head of its phrase) and to the L+H* pitch accent, as well as a direct cue to phonetic prominence (in all cases in concert with other phonetic cues). These signal respectively the lexical stress (and hence parsing of the utterance, as content is a verb), that content is focal in the discourse model, and the attitudinal stance of the speaker (e.g., disagreement). Form-function mappings vary by language, and hence make it challenging for L2 researchers to assess issues such as L1 transfer. However, our knowledge of prosodic typology is more comprehensive than ever, paving the way for sophisticated analyses based on sound understanding of the prosodic systems of the L1 and L2 (e.g., Goad & White, 2019; Mennen, 2015; Tremblay et al., 2016, 2018); see further later sections. One useful theoretical model is Mennen’s (2015) L2 Intonational Learning Theory (LILt), which predicts difficulties in L2 production based on types of cross-linguistic intonational differences within the AM framework, drawing from Ladd (2008) (see also Albin, 2015 for a more articulated typology). The LILt model was developed for intonation, but we think it can extend to prosody as a whole. The model aimed to explain difficulties in production, however, we believe it extends straightforwardly to processing in general as it deals with the underlying prosodic structures assumed to be involved, which should be relevant for production and perception; although for a given learner these may not be acquired at the same time (e.g., see Baese-Berk, 2019). The model recognizes four dimensions along which L1 and L2 intonation can be compared. The first is the systemic dimension, or the inventory and distribution of categorical phonological elements. For example, Xu (2009) (cited in Albin, 2015, p. 33) found Mandarin English as a Foreign Language (EFL) learners marked 80% of utterance-medial boundaries with a pause, rather than the more nativelike L- or H- phrase accents, which Mandarin lacks (Peng et al., 2005). The second is the realisational dimension, or the phonetic implementation of categorical elements. For example, Mennen (2004) showed that although both Dutch and Greek have pre-nuclear L+H* accents (cf. Figure 1), the peaks are much earlier in Dutch. This difference affects both L2 Greek from Dutch learners (early peaks) and L2 Dutch from Greek learners (late peaks). Thirdly, the semantic dimension,

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or the functionality of the categorical elements. For example, in producing Mandarin yes/no questions ending with the particle 吗/嗎 ma, English learners usually do not use the pitch range expansion typical of native speakers (Peng et al., 2005), since pitch range does not have this function in English, but may infelicitously produce a H% rise (cf. Figure 1) on the particle ma (Pytlyk, 2008; Viger, 2007). Finally, the frequency dimension relates to the frequency of use of different elements. For example, in Egyptian Arabic typically every content word is pitch accented, while in English this is more variable. Hellmuth (2010) found high proficiency L1 Egyptian Arabic learners of English produced accents on a much higher proportion of content words, as a result of this transfer. Classifying L2 use of prosody often involves multiple dimensions, as Mennen acknowledges. L1 transfer on the systemic, realisational or frequency dimensions may result in functional difficulties. For example, Japanese open questions are usually produced with a pitch accent on the interrogative pronoun, and lowered pitch afterward (Maeda, 2006) (cited in Albin, 2015, p. 43). Maeda (2006) found Japanese learners transfer this pattern to English, where, however, it signals contrastive focus, for example, WHY did you come to Japan?. The realisational difference has unintended pragmatic effects. Nonetheless, the LILt framework encourages the analyst to be explicit about which aspects of the prosodic systems of the L1 and L2 are at play, and their functions in both systems. With these comparisons in place, we can effectively apply L2 acquisition and processing theories to prosody. For example, the Prosodic Transfer Hypothesis (Goad & White, 2019) predicts differences in prosodic word structure between the L1 and L2 (i.e., the systemic dimension) will constrain production of L2 functional morphology. This is L1 interference (see Yavaş, 2011), but involving prosodic structure. (As with LILt, though the model deals with production, the analysis of systemic L1-L2 differences should be relevant for production and perception, albeit that a given learner may not show exactly equivalent difficulties in both). The Prosodic-Learning Interference Hypothesis (Tremblay et al., 2016, 2018, 2020; see further Section 4) predicts that learners with similar phonological systems in their L1 and L2 but phonetic differences (i.e., the realisational dimension in LILt) will find it harder to correctly process word segmentation cues than learners with phonologically distinct word segmentation cues (i.e., the systemic dimension). This is akin to the relative difficulty of a Single Category or Category Goodness assimilation, over a Uncategorised assimilation, in the L2 Perceptual Assimilation Model (PAM-L2) (Best & Tyler, 2007) (see also So & Best, 2014). Similarly, Rasier and Hiligsmann (2007) and Zerbian (2015) show how markedness can be applied to L2 processing of sentence prosody (see Section 6).

Chapter 3. Cross-language influences in the processing of L2 prosody

4.

Prosody and word segmentation

A key issue in speech perception is how the continuous stream of sound is segmented into words. Unlike infants learning their L1(s) (see Chapter 2, this volume), second language learners typically have expectations based around an existing vocabulary and strategies for segmentation in their L1. Since prosodic cues to word boundaries have language-specific characteristics, it is not surprising that they present a potential barrier to the successful identification and learning of words in a second language. Sanders et al. (2002) compared the use of lexical, syntactic and prosodic (stress) cues to segmentation by Japanese and Spanish learners of English. They found that late learners were like native speakers in their use of prior lexical knowledge in segmentation, but that their use of prosodic patterns for segmentation depended on the prosodic characteristics of their L1 and how they differed from those of the L2. At least some of the differences between languages involve what LILt would characterise as realisational differences, such as the selection of different subsets of universal parameters involving pitch, loudness and duration. For instance, languages with word-initial stress tend to mark stress through pitch and loudness on initial syllables, and those with word-final stress use duration and loudness on the final syllable (e.g., Hayes, 1995). Such cue combinations influence perceptual grouping via the identification of group onsets and offsets at various levels of the prosodic hierarchy. Endress and Hauser (2010) argue that their participants, with English as their L1 (with predominantly wordinitial stress), were able to select from this universal set of cues to parse syllables into words in typologically diverse languages previously unknown to them: Turkish (word-final stress), or Hungarian (word-initial stress). Similarly, in an artificial language learning task, Kim et al. (2012) found that while pitch and duration appear to be general cues cross-linguistically for various prosodic unit boundaries (and therefore for words at the edges of those units), the implementation of these cues is language-specific. For Korean listeners final F0 rises cued a boundary, but in an initial training phase, Dutch listeners also needed final lengthening. However, in a second training phase, the same Dutch listeners showed sensitivity to the pitch cue without the durational cue. Tremblay et al. (2018) also investigated pitch rises, specifically the use of French word-final rises in non-native word segmentation. Unlike French, both English and Dutch have lexical stress, typically on the initial syllable and often marked by pitch accents. English and Dutch differ, however, in that English also marks stress through the contrast between full and reduced vowels. In a visual world task, listeners heard temporarily ambiguous strings, for example, chat lépreux ‘leprous cat’ which contains the word chalet ‘cottage’, while looking at a visual display including both possible words, for example, chat and chalet. Listeners’ eye movements indicated

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Dutch learners made greater use of pitch cues than English learners to identify word boundaries in French (e.g., of chat), even though this meant re-purposing the pitch cues to indicate word ends in French rather than word beginnings as expected for Dutch. Tremblay and colleagues have synthesised their findings into the ProsodicLearning Interference Hypothesis (Tremblay et al., 2016, 2018, 2020; see Section 3). French and Korean have phonologically similar intonation patterns at the boundaries of accentual phrases (AP, final H tones followed by initial L tones) but differences in the phonetic alignment of these patterns are significant for word segmentation (initial L is anchored to the beginning of AP in Korean, but final H to the end of AP in French). English differs from both these languages, with H tones aligned with the first syllable of polysyllabic words. Tremblay and colleagues showed that when French and Korean native speakers learn one another’s language, they find L2 word segmentation more difficult than English learners of either language, in the way predicted by the hypothesis. English learners showed similar patterns to native speakers, but with delays reflecting the phonological differences between English and the L2s. Cutler and colleagues have proposed the Metrical Segmentation Strategy (e.g., Cutler & Norris, 1988), which highlights the importance of metrical or rhythmic properties for segmentation (see also Chapter 2, this volume). Languages differ in their rhythmic properties, so this strategy is proposed as a universal principle with language-specific parameters. For instance, Cutler et al. (1992) compared segmentation in two languages, French and English, which differ in their rhythmic properties (for discussion of cross-linguistic rhythmic structures see, e.g., Dauer, 1983; White & Malisz, 2020). Cutler et al. found that monolinguals used a segmentation strategy based on the rhythmic properties of their language, while bilinguals applied the strategy of their dominant language, even when processing their other language. Nevertheless, Cutler et al. also found that over time bilinguals become attuned to the phonological properties of the L2 and are able to abandon their L1 segmentation strategies if they detect they are not appropriate to the L2. Thus Hanulíková et al. (2011) found that Slovak native speakers could suppress their native language expectation of fixed word-initial stress when listening to German as an L2 (see White, 2018, for a review of other recent studies). Studies have also shown that learning context and experience are important factors in segmentation. For example, Tremblay et al. (2020) found that immersed L2 learners were less troubled by subtle phonetic realisational distinctions between Korean and French. Similarly, using an artificial language learning paradigm with final F0 rises as the segmentation cue (a systemic difference between how French and English mark word boundaries), Namjoshi et al. (2012) found

Chapter 3. Cross-language influences in the processing of L2 prosody

that recency of exposure to French was a better predictor of segmentation than whether the participants’ L1 was French or English. Gilbert et al. (2016) found that the segmentation strategies of English-dominant English-French bilinguals varied with proficiency, with more proficient listeners more likely to adapt a French-like strategy than an English-like one. Both of these studies suggest that systemic differences between languages can be overcome with exposure. Morrill’s (2016) word identification results suggest that exposure to a prosodically similar language has a positive effect on segmentation during early SLA, and reflects differences between systemic and frequency dimensions. With respect to word stress patterns, English is categorically different (on the systemic dimension of LILt) from Japanese in having lexical stress rather than lexical pitch accents, but differs from Finnish on the frequency dimension of LILt in that word-initial stress is frequent in English but obligatory in Finnish. English native speakers with no prior knowledge of either language were exposed to utterances in one of these languages and were subsequently better able to identify words in Finnish than in Japanese, suggesting that a difference on LILt’s frequency dimension has less of an impact than a difference on the systemic dimension.

5.

Prosody and syntactic processing

It is now well-established that prosodic information plays a valuable role in native-language sentence processing (for a review see Pratt, 2018), and there has been increasing research into the use of prosody in L2 syntactic processing. This includes the use of implicit prosody (Fodor, 1998), that is, the prosody we ‘hear’ in our heads while reading. Dekydspotter et al. (2006) identified that non-targetlike prosody in learners influenced processing and potentially led to non-targetlike outcomes, including in reading studies. Similarly, while Pratt and Fernandez (2016) found a processing advantage for native English speakers when text was visually presented in chunks that coincided with how it would be prosodically structured, no such advantage was found for Spanish learners of English. There are spin-offs of prosodic training for reading. Alazard et al. (2012) argued that oral training involving L2 prosody can help develop prosody-based L2 processing strategies for reading. They also found a strong correlation between L2 reading fluency and L2 prosodic skills. Several studies of spoken L2 sentence processing have considered the relative roles of syntactic and prosodic cues. Harley et al. (1995) found that, in contrast to L1 English speakers, Cantonese ESL students in Canadian schools attended more to prosody than to syntax when the cues clashed in ambiguous sentences. Ma (2007) adapted Harley et al.’s materials for middle and high school L1 Korean par-

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ticipants, and similarly found that learners were more likely to attend to prosodic than syntactic cues in their L2. The conflicting cues involved cross-splicing words such as watch from utterances such as The teacher’s WATCH has stopped and The teachers watch BASEBALL on TV (capitals show accented words), so that the grammatical structure distinguishing watch as noun or verb is potentially in conflict with accent placement. In apparent contrast to such findings, a number of studies have pointed to relative difficulties in processing L2 prosodic cues. Thus Nakamura et al. (2020) used the visual world paradigm to explore integration of prosodic information in prepositional phrase (PP) attachment ambiguities (Put the cake on the plate in the basket) and concluded that Japanese learners of English do not show nativelike integration of prosodic information (in this case a contrastive accent on plate, indicating where the cake should be put) and other information. They suggest that “processing of prosodic information might be highly demanding for L2 learners” (p 12). In a similar study, Nakamura et al. (2019) suggested that a processing delay in the use of prosodic information by L2 listeners compared with native speakers could be due to the increased cognitive demand of integrating different information sources. Contemori et al. (2020) found that while heritage Spanish speakers who learned English as an L2 in childhood were able to use informative prosodic cues to resolve English PP attachment ambiguities, they too showed a processing cost in the integration of different information. The cues tested involved prosodic phrasing with experimental manipulation of pause durations at phrase boundaries in utterances such as Put | the frog on the napkin | into the box, where | marks the relevant boundaries. Another study of PP ambiguity (Zhang et al., 2019) indicated that the use of prosody by Mandarin learners of English depended on their awareness of the ambiguity. More detailed studies of this type are needed to address whether processing advantages and disadvantages are related to the nature of the prosodic differences between languages and whether these involve the systemic, realisational, semantic or frequency dimensions of LILt. Working with highly-proficient German learners of English, Nickels et al. (2013) found native-like processing of speech prosody in the resolution of closure ambiguities. They tested utterances such as When a bear is approaching the people | the dogs come running vs. When a bear is approaching | the people come running. Critical conditions involved cross-splicing of such utterances (at the word the) so that in addition to the original stimuli there were stimuli that had either two such boundaries or none. Their data, from event-related brain potentials (ERP), provided the first demonstration in L2 of the Closure Positive Shift (CPS, a positivegoing ERP component associated with the detection of prosodic boundaries). In a subsequent study with the same ambiguity type, Nickels and Steinhauer (2018) found that participants’ L1 (Chinese vs. German) and proficiency influ-

Chapter 3. Cross-language influences in the processing of L2 prosody

enced the integration of prosodic and syntactic cues. The ERP measures showed that increasing proficiency resulted in a stronger CPS. However, the ERP activation patterns differed for the two L1 groups, leading the authors to conjecture that like native English speakers, the German L1 group – but not the Chinese L1 group – make stronger predictions of upcoming syntactic boundaries based on the prosody in the early part of the utterance. Both of these studies indicate that the interpretation of prosodic marking of structure can differ in the L1 and L2, but they do not explore how the L2 prosodic marking might overlap with or differ from that in the L1. Hwang and Schafer (2006) compared closure ambiguity resolution by native speakers and Korean learners of English, using a forced-choice continuation task in which participants had to choose between two sentence completion fragments. The authors predicted that since two levels of prosodic boundaries are found in both English and Korean (i.e., they are systemically similar in this respect), Korean learners of English should be sensitive to the relative strengths of the prosodic boundaries at the points of potential and actual syntactic closure (i.e., intonational phrase vs intermediate phrase boundaries, compared with word boundaries). While they found support for this prediction, they also found that Korean learners showed especial sensitivity to the distinction between intonational and intermediate phrase boundaries. They do not discuss whether this is due to an L2 processing effect or to potential realisational differences in the boundary hierarchies of Korean and English. A more complex result was found by Ip and Cutler (2018) who ran a speeded forced choice task involving ambiguities such as Grandma gave her dog meat to eat, in Mandarin and English, with Mandarin learners of English as well as with native speakers. Participants had to choose between two interpretation sentences, one compatible with the meaning ‘Grandma gave meat to her dog’ (the Early Juncture reading) and one with the meaning ‘Grandma gave dog meat to her’ (the Late Juncture reading). Experiments with native speakers showed that L1 Mandarin listeners have faster decision times for Early Juncture readings, and L1 English listeners have faster decision times for Late Juncture readings, reflecting different L1 processing strategies. Although the two languages have similar structural ambiguities, and both use durational marking of structural boundaries, there is greater reliance on pause length by Mandarin native speakers and on pre-pausal segment lengthening by English native speakers (a realisational difference, in LILt terms). Ip and Cutler found that response patterns for their Mandarin learners of English did not show clear transfer of processing strategies from the L1 to L2, nor were their responses comparable with those of native English listeners. What is more, they found that transfer of L1 processing preferences to the processing of L2 was more likely the longer the learners had been exposed to English. The authors

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conjecture that with increasing exposure, the learners were better able to detect the relevant durational cue (i.e., become sensitive to the realisational difference), but continued to interpret it using their L1 processing strategy, resulting in more reliable L1 transfer. Roncaglia-Denissen et al. (2014) suggest that the acquisition of prosodic features relevant to sentence processing might have to happen at an early developmental stage. They found that Turkish learners of German exposed to the language from about 1 year of age showed similar reliance on trochaic stress patterns in syntactic parsing to German monolinguals, and these both differed from late learners, who relied more on iambic patterns. It is unclear whether this is best regarded as a systemic or realisational difference in the structure of stress feet. Early familiarity with prosodic properties of the target language can provide bootstrapping for the acquisition of grammatical features such as word order, as demonstrated by Campfield and Murphy (2017), who found that exposure to English prosody through English nursery rhymes helped young Polish native speakers (a language with free word order) acquire the strict word order of English (as revealed through grammaticality judgement tasks). There is likely to be individual variation, however, since Stepanov et al. (2020) found that nineyear old Slovenians’ ability to distinguish between segmentally identical but prosodically distinct sentences in an unknown language (French) depended on their working memory capacity. The prosodic contrasts involved a constellation of the prosodic cues found in natural speech, including many of those discussed in Section 2 in connection with Figure 1. Finally, there is some evidence that L1-L2 prosodic transfer (Goad & White, 2019) affects syntactic processing. Prévost et al. (2016) found that Turkish learners of English, in whose L1 an indefinite article is stressed when it occurs before an adjective, showed ERP effects consistent with a prosodic anomaly when listening to English with an unstressed article in this position (as in Kristin fought a wild bear).

6.

Prosody and information structure

Researchers have long been interested in how prosody conveys meaning related to information structure (IS), that is, how information is organized in an utterance in relation to a discourse (Cole, 2015; Cutler et al., 1997; Yan & Calhoun, 2020). The role of prosody in IS processing in L2 has been a recent subject of interest (Ge et al., 2020; Lee & Fraundorf, 2017; Perdomo & Kaan, 2019; Takahashi et al., 2018; Yan et al., 2022b). Focus, a key notion of IS, has received the most attention, with L2 research addressing cross-linguistic influences on the production and percep-

Chapter 3. Cross-language influences in the processing of L2 prosody

tion of focus and given vs. new information, especially L1 transfer effects in the use of relevant prosodic cues (Foltz, 2020; Lomotey, 2013; Nguyễn et al., 2008; Rasier & Hiligsmann, 2007; Smith et al., 2019; van Maastricht et al., 2016; Yan et al., 2022). Across many languages, the focused constituent is the most phonologically prominent in the utterance; however, languages differ in the phonological and phonetic expression of prominence (see Section 2 and Kügler & Calhoun, 2020). Even among languages that use stress-based pitch accenting to mark focus, there are differences in how focus-marking interacts with structural constraints on pitch accent placement. The resulting difficulties for learners are predicted by Zerbian’s (2015) Markedness Scale of Sentence Prosody, building on Rasier & Hiligsmann (2007). According to the Markedness Differential Hypothesis (Eckman, 1977), if a phenomenon A in some language implies B but not vice versa, then A is more ‘marked’. Zerbian (2015) applies this to sentence prosody by hypothesising that structural constraints on nuclear accent placement (e.g., that it should fall on the right-most constituent in an intonation phrase) are ‘unmarked’, while other operations such as shifting the nuclear accent for information structural or pragmatic reasons (e.g., marking contrastive focus early in the sentence) are ‘marked’. Languages differ in the extent to which structural constraints are dominant (e.g., in Zerbian’s analysis only structural constraints operate in Spanish and Italian, while shifting is also allowed in Germanic, with French in between). Zerbian’s model predicts that, for native speakers of a language that has ‘unmarked’ use of prosody, the acquisition of ‘marked’ use would be much more difficult than the other way around. This prediction was confirmed in Rasier and Hiligsmann (2007) which tested two languages: Dutch with a more ‘marked’ prosody and French less ‘marked’. They found that fewer than 50% of L1-like Dutch accent patterns were produced by the French learners of Dutch, compared to 74% of L1-like French accent patterns by the Dutch learners of French. This theory can also be applied to the L2 processing of prosody in the auditory domain (see further below). Languages that differ in the phonological type of prominence marking might nonetheless show similar functional use of prominence to mark focus, for example, pitch accents in English and pitch range expansion in Mandarin (see Section 2 and Kügler & Calhoun, 2020). In production, studies have shown that such learners can successfully use prosodic prominence to signal focus in their L2, although L1 transfer means their detailed phonetic realization will differ from that of native speakers (e.g., Kao et al., 2016; Takahashi et al., 2018). L1 English listeners tend to interpret words marked with prosodic prominence as being focused or new information and those that are unaccented/deaccented as conveying given or accessible information (Welby, 2003). If information

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status and accents are mismatched, for example, if focal information is deaccented, then L1 processing is slowed. L2 studies in this area have shown L1 transfer effects. Takahashi et al. (2018) investigated how Mandarin learners of English processed appropriate or inappropriate contrastive focus prosody (e.g., Now click on the SCARLET mittens following either Click on the purple mittens or Click on the scarlet necklace). L2 learners were able to click on the correct item more quickly when they heard sentences with appropriate prosody, suggesting that they used prosody to process discourse information in a native-like way. However, Ortega-Llebaria and Colantoni (2014) investigated the use of prosodic prominence in the perception of focus in English with Mandarin and Spanish learners (see also Yan et al, 2022b). It was shown that the performance of Mandarin speakers was better than that of Spanish speakers. This finding can be explained by Zerbian’s (2015) Markedness Scale of Sentence Prosody that the acquisition of ‘marked’ use of prosody in English would be difficult for Spanish speakers whose L1 has only ‘unmarked’ use of prosody. In LILt this can be analysed as a difference on the semantic dimension. The markedness explanation is further supported by Ge et al.’s (2020) study which investigated how Dutch and Cantonese learners of English interpret prosodic cues to focus. They showed that while Dutch learners made native-like use of such cues, they played little role in the perception of focus by Cantonese listeners. It is argued that this is because English and Dutch have similar focus marking, but Cantonese is different, with focus marked syntactically by changing the position of focus particles. In languages which use prosodic focus marking, when focused words are marked with appropriate prosody (e.g., nuclear accented in English), they receive more attention. This results in faster processing in a range of tasks (Akker & Cutler, 2003; Calhoun et al., 2022; Cutler et al., 1997; Kember et al., 2019; Yan et al., 2022a). Compared with native listening, however, the processing of focused words even by highly proficient learners is less efficient. This is true even when L1 and L2 use similar prosodic cues to focus (e.g., English and Dutch). For instance, Akker and Cutler (2003) compared recognition times for highly proficient Dutch learners of English to phonemes embedded in focused words, in words where the preceding intonation contour predicts that they will be in focus, or in non-focus words. While both Dutch learners and English native speakers show a general effect of focal accent (e.g., responding more quickly to the phoneme /k/ in corner when that word was in focus, as in The man on the CORNER was wearing the blue hat), the additional effects of the focus expectations cued by a preceding question (e.g., Which man was wearing the blue hat?) differed in L1 and L2 listeners. The authors suggest that non-native listening makes less efficient use of prosody to process semantic information, even when the prosody-semantics mapping is sim-

Chapter 3. Cross-language influences in the processing of L2 prosody

ilar across the two languages. This could be because of realisational differences in phonetic cues to prominence in Dutch and English. In many languages, prosody, especially contrastive prosodic prominence, facilitates not only the processing of focused words but also the processing of other discourse referents such as focus alternatives (nurse as an alternative to patient in The PATIENT wore a mask). There has recently been increased interest in the role of prosody in processing focus alternatives in both L1 and L2 (Braun & Tagliapietra, 2011; Calhoun et al., 2022; Fraundorf et al., 2010; Lee & Fraundorf, 2017; Yan & Calhoun, 2019; Yan et al. 2022a). Generally, as learners’ proficiency increases, their L2 performance becomes more native-like, but it is difficult for them to achieve native-like ability. Lee and Fraundorf (2017) investigated how prosodic cues enhanced memory for alternatives in Korean learners of English at different proficiency levels. Korean differs from English in that focus is marked by prosodic phrasing rather than by pitch accenting, which suggests that pitch accenting may not be an effective focus cue for Korean learners of English. Compared to mid- and low-level learners, they found greater effects of prosodic cues for high proficiency learners, although the latter still did not use such cues in a native-like way. Braun and Tagliapietra (2011) showed that while native Dutch listeners generated alternatives only for contrastive accents, high proficiency German learners of Dutch generated alternatives for both contrastive and non-contrastive accents in Dutch. This is because non-contrastive accents in Dutch have prosodic similarity to German contrastive accents, that is, a negative prosodic transfer of intonational meaning from the L1, consistent with LILt. This further suggests that integrating semantic information is difficult, as even high proficiency learners cannot attain native-like performance. Further studies suggest that L2 learners face difficulties using prosody to predict upcoming discourse referents, especially when the form and function of prosody differ in the L1 and L2. Using a visual world paradigm, Perdomo and Kaan (2019) found that native English listeners used prosodic marking (contrastive prosodic prominence, i.e., L+H*) to anticipate the upcoming set of discourse referents, reflected in more anticipatory eye movements toward Benjamin’s cake upon hearing we ate Angela’s cake but saved BENjamin’s … rather than we ate Angela’s cake but saved Benjamin’s…. By comparison, Mandarin learners of English showed less clear anticipatory processing, which was not influenced by L2 proficiency.

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7.

Prosody and pragmatic and global features

Intonation is important for signalling pragmatic information including speech acts, attitude, emotion and politeness (see Cole, 2015; Ladd, 2008; Prieto, 2015). This area is understudied in L2 processing. The most common topic is distinguishing polar questions from statements using intonation (e.g., Gabriel & Kireva, 2014; Kainada & Lengeris, 2015; Liang & van Heuven, 2007; Son, 2018; Trouvain & Braun, 2020; Verdugo & Trillo, 2005; Yang & Chan, 2010). Across languages, questions are stereotypically marked by a final rise (e.g., L* H-H% in Figure 1) (e.g., see Bartels, 1999; Frota & Prieto, 2015), just H% (in languages with no pitch accents), and/or pitch range expansion (Cruttenden, 1997, pp. 151–160). However, at least for European languages, there is variability among L1 speakers, with no one-to-one mapping between tune and question type, reflecting, e.g., epistemic stance and politeness (Bartels, 1999; Braun et al., 2019; Frota & Prieto, 2015). Production studies show these patterns are difficult to acquire, reflecting L1 transfer on the LILt semantic and systemic dimensions (e.g., Gabriel & Kireva, 2014; Kainada & Lengeris, 2015; McGory, 1997). Most of the few L2 perception studies have explored perception of question versus statement intonation in tone languages. Liang and van Heuven (2007) looked at intonation and tone perception in Mandarin Chinese by learners with L1 tone (Nantong and Changsha dialects) and non-tone (Uygur) languages. The L1 tone learners performed equivalently to the native controls. The non-tonal L1 learners, however, were more sensitive to both pitch rise and level as question markers, though they were worse at lexical tone identification. Yang and Chan (2010) found English learners of Chinese of all proficiencies made more errors in statements with utterance-final syllable Tone 2 (rise), and in questions with final Tone 4 (high-fall). Native speakers also found the latter context difficult (see also Braun & Johnson, 2011; Luo, 2017). These results show difficulties on the LILt semantic dimension for both L1 and L2 speakers, where a pitch feature (final rise) has multiple functions in the L1 and L2. The few studies on other pragmatic meanings suggest this is very challenging for L2 learners (see Trouvain & Braun, 2020). Again, most studies look at production. Consistent with Sorace’s (2011) Interface Hypothesis, these show learners may acquire the most common tune for an utterance type, but often not pragmatic variability, whether or not their L1 makes much use of intonation for this (e.g., Pickering, 2004; Ramírez Verdugo, 2005; Son, 2018; Vargas & Delais-Roussarie, 2012; Verdugo & Trillo, 2005). To our knowledge, the only perception studies are by Mok et al. (2016) and Puga et al. (2017), looking at the perception of sentence types by advanced Cantonese and German learners of English respectively. Both found the listeners performed similarly to native controls in identifying statements and common question types, but worse at tag questions and sarcasm (the

Chapter 3. Cross-language influences in the processing of L2 prosody

latter only included in Puga et al.). Interestingly, both sets of learners could successfully recognise most sentence types, despite differences on the LILt systemic and realisation dimensions from their L1 being greater for Cantonese than German. However, differences on the semantic dimension caused difficulties, given the lack of equivalent range of functions for intonation in their L1s. Studies have also looked at emotion recognition given prosodic cues by L2 listeners. These are beyond the scope of this paper, but see Bhatara et al. (2016). Finally, studies have looked at global prosodic features, that is, use and frequency regardless of function. One such feature is speech rate: L2 speech is, on average, slower than L1, and speech rate influences perceptions of accentedness and comprehensibility (e.g., Munro & Derwing, 1998). L2 learners tend to perceive speech in the L2 as faster than their L1, the ‘foreign language (FL) effect’ (Bosker & Reinisch, 2017; Cutler, 2012; Hirozane, 2012; Pfitzinger & Tamashima, 2006). Pfitzinger and Tamashima (2006) found both German and Japanese listeners consistently overestimated the speech rate in the other language compared to their L1. Bosker and Reinisch (2017) showed this effect influences judgments of phonemic vowel length for L2 listeners, as perceived vowel length is affected by perceived speech rate. The strength of the effect seems to be closely related to listener proficiency and the familiarity/complexity of the speech (Bosker & Reinisch, 2017; Hirozane, 2012), that is, processing difficulty in the L2 is experienced as faster speech. Rhythmic and/or phonotactic differences between the L1 and L2 seem to play only a minor role (Bosker & Reinisch, 2017; Hirozane, 2012; Pfitzinger & Tamashima, 2006). Nonetheless, studies have found rhythmic differences in production between the L1 and L2 consistent with L1 interference (e.g., Gabriel & Kireva, 2014; Li & Post, 2014; White & Mattys, 2007). As reviewed in Section 4, these differences affect word segmentation in the L2 and likely have other processing effects. Languages differ in their global prosodic characteristics, but the processing implications are underexplored. For instance, languages differ in the levels and types of phrase marking, which affects L2 production (Herment et al., 2014; Jun & Oh, 2000; Vargas & Delais-Roussarie, 2012). These differences (on the LILt frequency and realisational dimensions) are not often considered in processing studies, despite the importance of phrasing in syntactic processing (see Section 2). Production studies show cross-linguistic differences in the type and realisation of pitch accents, which transfer to the L2 (Mennen, 2015; Trouvain & Braun, 2020; Ulbrich, 2013). These are relevant to word segmentation (see Section 4), but likely have other processing effects, for instance, where the function of a rising versus falling accent, differs between the L1 and L2.

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8.

Future research

The reviews above show growing awareness of the role of prosody in second language processing research. An increasing number of these studies ground their analysis in informed comparisons of typological differences between the prosodic systems of the learners’ L1 and L2, and the complexity of possible form-function mappings involving prosodic features; based on prosodic research. We affirm that such an approach is likely to be the most effective to properly place prosody within current frameworks of L2 acquisition and processing. The bulk of research on prosody is currently limited to native speakers or learners of European languages, particularly Germanic, or ‘popular’ languages such as Mandarin, Japanese and Korean. The range of languages studied needs to be much broader to deepen our cross-linguistic understanding of L2 processing and how this relates to prosodic typology at different levels of linguistic structure. Relatedly, while we have witnessed an increase in interest in how prosodic cues interact with other linguistic systems in L1 processing, there remain many unanswered questions about this in L2 processing. In each of the areas surveyed above, we have identified topics in need of research. Research on the use of prosody in L2 syntactic processing has focused on a relatively small number of syntactic contrasts for which good L1 evidence is available. The scope of such studies should expand to include more syntactic types, as well as a typologically richer range of languages. Similarly, for information structure processing, the bulk of research into the role of prosody in L2 processing lies in focus. More investigations are needed into other aspects of information structure, such as the relationship between focus, givenness and topicality, and focus scope. Further, the majority of these studies compared languages which use prominence to mark focus, it would be fruitful to compare with languages that do not (see Kügler & Calhoun, 2020). In pragmatics, research is needed which takes on board a broader framework of pragmatics, including factors such as speaker commitment and epistemic stance beyond question type, in line with L1 studies (e.g., Bartels, 1999; Prieto, 2015). There is also a need for more perception studies. Across all these areas, relatively little is known about how increasing proficiency affects the processing of different prosodic features, and there are unresolved differences in the published results. Likewise, few studies have investigated how age of acquisition affects L2 prosody (Huang & Jun, 2011), and L1 attrition of prosodic features (de Leeuw et al., 2012). We currently lack a clear picture about what aspects of prosody are acquired earlier and later in the L2, and what kinds of form-function differences are more or less problematic for lower and higher

Chapter 3. Cross-language influences in the processing of L2 prosody

proficiency learners. More studies are needed that investigate changes in learners’ reliance on prosodic vs. other information as they become more proficient.

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Alazard, C., Astésano, C., & Billières, M. (2012). From prosodic skills to L2 reading proficiency: An experimental approach to L2 phonetics teaching methods. In M. G. Busà & A. Stella (Eds.), Methodological perspectives on second language prosody (pp. 55–59). Coop. Libraria Editrice Università di Padova. Albin, A. L. (2015). Typologizing native language influence on intonation in a second language: Three transfer phenomena in Japanese EFL Llarners (Doctoral dissertation). Indiana University. https://doi.org/10.5967/K8JW8BSC Arvaniti, A., & Fletcher, J. (2020). The autosegmental-metrical theory of intonational phonology. In C. Gussenhoven & A. Chen (Eds.), The Oxford handbook of language prosody. Oxford University Press. https://doi.org/10.1093/oxfordhb/9780198832232.013.4 Baese-Berk, M. M. (2019). Interactions between speech perception and production during learning of novel phonemic categories. Attention, Perception, & Psychophysics, 81(4), 981–1005. https://doi.org/10.3758/s13414-019-01725-4 Bartels, C. (1999). The intonation of English statements and questions: A compositional interpretation. Routledge. Bauer, L., & Warren, P. (2004). New Zealand English: Phonology. In B. Kortmann, E. W. Schneider, K. Burridge, R. Mesthrie, & C. Upton (Eds.), A handbook of varieties of English: A multimedia reference tool (Vol. 1, pp. 580‒602). Mouton de Gruyter. https:// www.degruyter.com/document/doi/10.1515/9783110197181/html Best, C. T., & Tyler, M. (2007). Nonnative and second-language speech perception. In M. J. Munro & O. -S. Bohn (Eds.), Second language speech learning: The role of language experience in speech perception and production (pp. 13–34). John Benjamins. https://doi.org/10.1075/lllt.17.07bes

Bhatara, A., Laukka, P., Boll-Avetisyan, N., Granjon, L., Elfenbein, H. A., & Bänziger, T. (2016). Second language ability and emotional prosody perception. PLoS ONE, 11(6), e0156855. https://doi.org/10.1371/journal.pone.0156855

Bosker, H. R., & Reinisch, E. (2017). Foreign languages sound fast: Evidence from implicit rate normalization. Frontiers in Psychology, 8, 1063. https://doi.org/10.3389/fpsyg.2017.01063 Braun, B., Dehé, N., Neitsch, J., Wochner, D., & Zahner, K. (2019). The prosody of rhetorical and information-seeking questions in German. Language and Speech, 62(4), 779–807. https://doi.org/10.1177/0023830918816351

Braun, B., & Johnson, E. K. (2011). Question or tone 2? How language experience and linguistic function guide pitch processing. Journal of Phonetics, 39(4), 585–594. https://doi.org/10.1016/j.wocn.2011.06.002

Braun, B., & Tagliapietra, L. (2011). On-line interpretation of intonational meaning in L2. Language and Cognitive Processes, 26(2), 224–235. https://doi.org/10.1080/01690965.2010.486209

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Zhang, Y., Ding, H., & Zhang, H. (2019). Do cognitive constraints drive second-language listeners’ attention to prosodic information in speech? In S. Calhoun, P. Escudero, M. Tabain, & P. Warren (Eds.), Proceedings of the 19th International Congress of Phonetic Sciences, Melbourne, Australia 2019 (pp. 2238–2242). Australasian Speech Science and Technology Association.

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Cross-language influences in the acquisition of L2 and L3 phonology Mark Amengual

University of California, Santa Cruz

This chapter synthesises some of the main findings from research investigating cross-language influence (CLI) in Second Language (L2) and Third Language (L3) speech learning. In addition to an overview of the findings that have gathered growing consensus in the fields of L2 and L3 phonology, and how these results have been tested and explained in various theories of L2 speech, this chapter points to methodological and theoretical considerations discussed in three areas that hold promise for future research in L2 and L3 speech: (i) the relationship between L2/L3 speech perception and L2/L3 speech production, (ii) the acquisition of phonological processes vs. phonological contrasts, and (iii) the distinction between static and dynamic phonetic interactions in L2 and L3 speech. Keywords: bilingual speech, trilingual speech, L2/L3 acquisition, phonology, speech perception, speech production, phonological processes, phonological contrasts, static phonetic interactions, dynamic phonetic interactions

1.

Introduction

One of the central questions in second language (L2) and third language (L3) phonological acquisition research has been to determine to what extent the L2 and L3 learner’s phonological inventories are interconnected, and as a result influence each other but also in what ways they remain independent, in comparison to the single phonological system of monolingual speakers. The fact that the sound systems of bilingual and multilingual speakers are interconnected makes them prone to cross-language influence (CLI). A considerably larger amount of research has investigated the interaction between the two sound systems of L2 learners in comparison to the phonetic and phonological abilities of L3 learners. This growing body of research on L2 and L3 speech has examined CLI in the pro-

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Chapter 4. Cross-language influences in the acquisition of L2 and L3 phonology

duction, perception and processing of sounds in each language of the multilingual speaker, focusing on how L2 and L3 learners categorize speech sounds, their sensitivity to phonetic variation, and their production of language-specific phonological categories and processes. The remainder of this chapter will define cross-language phonological influence in L2 and L3 acquisition, present an overview of extant findings in the fields of L2 and L3 phonological acquisition, outline some of the most widely tested theoretical frameworks on L2 (and L3 speech learning), and provide a discussion of three topics that remain areas of active research: (a) links between speech perception and speech production, (b) the acquisition of phonological processes vs. phonological contrasts, and (c) static and dynamic phonetic interactions in L2 and L3 speech.

2.

Cross-language influences in L2 and L3 phonological acquisition

The term cross-language or cross-linguistic influence (CLI), first introduced by Sharwood-Smith (1983) and often used interchangeably with the terms “crosslinguistic transfer”, “interlingual influence” or “linguistic interference” is defined as the influence that knowledge of one language has on an individual’s learning or use of another language (James, 2012). CLI has been a central topic in research and theory of Second Language Acquisition (SLA) and Third Language Acquisition (TLA). Early studies on CLI include the pioneering work by Weinreich (1953), which offers one of the first examinations of CLI in the phonetic, grammatical, and lexical systems of bilinguals. Even though this chapter focuses exclusively on CLI in phonology, an extensive body of literature demonstrates that CLI involves various aspects of language, including phonology, orthography, morphology, discourse, syntax, and pragmatics (see other chapters in this volume). Research on L2 and L3 phonological acquisition has shown that CLI involves either positive or negative transfer at the segmental and suprasegmental level and has been found in both production and perception from the first language (L1) to the L2 (i.e., forward transfer) from the L2 to the L1 (i.e., reverse transfer), and even from an L2 to an L3 (i.e., lateral transfer), as described in Jarvis and Pavlenko (2008). The CLI that results from the phonological/phonetic interactions between the L1 and the L2 and L3 has been of special concern in the study of L2 speech, L3 speech, SLA, and TLA. Phonological CLI in L2 and L3 speech has implications with regard to the acquisition of language-specific phonetic contrasts and phonological processes in each language, providing an opportunity to understand how bilinguals and trilinguals categorize speech sounds, how they produce and hear

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L1, L2 and L3 sounds, how sensitive they are to phonetic variation across their languages, the nature of their phonological representations, and how these sounds are stored in the bilingual/trilingual mental lexicon. The remainder of this section will provide an overview of some of the most robust and consistent trends that have emerged in the fields of L2 and L3 phonological acquisition.

2.1 The acquisition of L2 phonology For an L2 learner, mastering the sounds of a non-native language is a challenging task, and when speaking in the L2, there is typically a detectable foreign accent, even after many years of experience in the language (Flege et al., 1995; Piske et al., 2001). In addition to speaking with a “foreign” or L1-influenced accent, language users also “hear with an accent” (Jenkins et al., 1995). This has been shown to be the case for native English-speaking adults, who experience difficulty discriminating the Hindi dental versus retroflex initial stop contrast (Polka, 1991), and Japanese speakers who have difficulties categorizing and discriminating the English /r/-/l/ contrast (Goto, 1971; Miyawaki et al., 1975; Sheldon & Strange, 1982). These studies have presented evidence of problems in perceiving L2 language-specific contrasts that are not phonemic in the L1, because speakers/listeners of various native languages rely on different acoustic cues when discriminating sounds in their L2 than in their L1. For instance, L1 Spanish listeners have been found to use vowel duration to distinguish English /i/ and /ɪ/, whereas L1 English listeners primarily use spectral cues (Bohn, 1995; Escudero & Boersma, 2004). Such findings indicate that the specific cues learners rely on in perceiving sounds in another language are affected by phonological structures and processes that may differ in each of the languages of the L2 learner. Previous L2 phonological acquisition studies have examined variables (such as age of acquisition, language proficiency, order of languages in early acquisition, language dominance, and amount of L2 input received) that modulate the influence of the L1 on the production, perception, and processing of language-specific phonological contrasts in the L2 (Amengual, 2019; Amengual & Chamorro, 2015; DeKeyser, 2000; Flege et al., 2003; Flege et al., 1995; Piske et al., 2001; Tsui et al., 2019). In addition to CLI from the L1 to the L2, recent work has also provided robust evidence of L2-to-L1 effects, also known as phonetic drift (see Chang, 2019a, for a detailed description). A sizable body of work has produced evidence of maturational effects, such that late learners face more difficulties in producing native-like targets in the L2 and typically exhibit phonological transfer from their L1 (DeKeyser, 2000; Flege, 1991; Lenneberg, 1967; Scovel, 1988). In other words, “the age of exposure to the L2 (i.e., the early vs. late distinction) is an important determinant of the extent to

Chapter 4. Cross-language influences in the acquisition of L2 and L3 phonology

which bilinguals can maintain a functional separation, or independence between their two languages” (Piske et al., 2002, p. 50). Early bilinguals are considered to be individuals who have acquired two languages early in childhood, and while there is debate concerning the cut-off age, bilinguals are typically classified as ‘early’ if they are exposed to the L2 between four and six years of age. Researchers have found that brain organization in bilinguals who acquire their L2 before 5 years old is different from that of bilinguals who acquire their L2 after that age (WeberFox & Neville, 1996). On the other hand, the late bilingual is often described as an individual who becomes a bilingual as an adolescent or adult (generally after the age of 12) (Birdsong, 2006). These late L2 learners are typically less sensitive to language-specific phonetic properties and have more difficulties perceiving and producing language-specific phonetic contrasts in comparison to early learners (Long, 1990). In addition to CLI occurring in the speech of late L2 learners, several studies have shown that early bilinguals also tend to transfer the phonetic features of the sound categories of their dominant language to their non-dominant language even with early and extensive exposure to native input (Amengual, 2016a, 2016b; Simonet, 2011; Kehoe, Chapter 2, this volume). Focusing on the acoustic realisation of language-specific phonological alternations, such as Spanish spirantization, Amengual (2019) compared two groups of Spanish-English bilinguals who have either learned both languages from birth (simultaneous bilinguals) or during early childhood (sequential bilinguals), with a group of L1 English-L2 Spanish speakers. In other words, this study investigated if simultaneous and sequential bilinguals differed in their production of allophonic variants in their heritage language as a result of differences in the type of early bilingualism. The results suggest that for early bilinguals, who have been exposed to both the minority (i.e., heritage) language and the majority language early in life, the effects of those structures that were acquired during the formative first years of life are extremely persistent and they are carried into adulthood. As a result, differences in the quantity of input and exposure during these early developmental stages have consequences for the production of language-specific acoustic targets. These findings complement those in Sebastián-Gallés et al. (2005), suggesting that even the slightest variation in the amount of language exposure during the earliest stages of language development can affect an individual’s perceptual abilities into adulthood. A bilingual speaker will use the L1 or the L2 depending on the linguistic environment, the interlocutor, and the communicative purpose or linguistic intent. Furthermore, a bilingual’s usage patterns and degree of CLI are likely to be associated with language fluency, proficiency, topic/register, and dominance. Even though bilinguals may have a high level of proficiency in both languages, the

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perfectly balanced bilingual probably does not exist: bilinguals usually have a dominant, or stronger language (Cutler et al., 1989; Flege et al., 2002). Language dominance covers many dimensions of language use and experience, such as proficiency, fluency, ease of processing, frequency of use, or cultural identification, and has been shown to be an important predictor of the L1-L2 interaction patterns in the speech production and perception of bilinguals (Amengual, 2016a, 2016b, 2016c; Bosch et al., 2000; Bullock et al., 2006; Sebastián-Gallés & Soto-Faraco, 1999; Simonet, 2011). According to the Language Mode framework (Grosjean, 1998, 2001), because of the communicative context one of the bilingual’s languages may be activated (monolingual mode), or both languages may be activated (bilingual mode). Recent studies have provided evidence of language mode effects on bilingual phonetic behaviour (Amengual, 2018; Antoniou et al., 2011; Simonet, 2014; Simonet & Amengual, 2020). For instance, Amengual (2018) investigated the interaction of language mode and language dominance on the acoustic realisation of Spanish and English laterals by early and late Spanish-English bilinguals in three different sessions (monolingual English, monolingual Spanish, and bilingual English/ Spanish). The results indicated that the lateral production of these bilinguals differed as a function of the experimental session or the speech production setting in which the words were uttered: when in bilingual mode, all four groups of bilinguals produced a less target-like lateral in their non-dominant language. Findings like these make clear that, in order to better understand the sound systems of L2 learners, we need to continue considering other variables in addition to age of acquisition, language proficiency, language dominance, or language use. Because the phonetic behaviour of L2 learners is affected by the increased activation of both languages depending on the communicative context it is important that studies on L2 speech distinguish between two different types of phonological CLI, static and dynamic, which are developed in more detail in Section 4.3.

2.2 The acquisition of L3 phonology Even though there is a wide diversity of L3 learning scenarios, sequential L3 learners are different from L2 learners in that they have already acquired two sound systems; thus, they have already become bilingual, and they have gained linguistic knowledge and language-learning experience on which they can rely when learning another language (Cenoz, 2003). There are, however, many points that L2 and L3 learners have in common. L2 and L3 learners must both acquire at least one new sound system and a set of phonological processes in order to produce and perceive sounds that may not exist in their native language(s). The last few decades have seen a significant increase in the experimental and theoretical work on L3 phonological acquisition. As a result, the field of L3 phonology is

Chapter 4. Cross-language influences in the acquisition of L2 and L3 phonology

beginning to move away from being an underexplored or “understudied domain” (Cabrelli Amaro, 2012; Cabrelli Amaro & Wrembel, 2016). The acquisition of phonology from a third language acquisition (TLA) perspective has matured into a separate but related discipline from the acquisition of phonology in second language acquisition (SLA), that addresses both the complexity of multilingual language acquisition as well as the theoretical demands of phonological acquisition. Research on cross-linguistic influence in L3 phonological acquisition widens the possibility to investigate a much more dynamic and complex interaction between co-existing sound systems than L2 phonological acquisition. Although the number of studies on L3 phonological acquisition still falls short in comparison to studies on the acquisition of L2 speech (Cabrelli Amaro & Iverson, 2018), an increasing number of studies have focused on the source and the factors that condition cross-language influence into the L3. These studies have examined the phonetic behaviour of sequential bilinguals and heritage speakers learning an L3 (Amengual 2021, Llama & López-Morelos, 2016, Wrembel, 2011, 2014), and compared these L3 learners with other monolingual (Llama & Cardoso, 2018), bilingual (Domene Moreno, 2021; Llisterri & Poch-Olivé, 1987), and trilingual groups (Wrembel, 2014). As in L2 phonology research, these L3 studies have either used foreign accentedness ratings or have provided phonetic analyses of a specific acoustic measurement (e.g., Voice Onset Time or vowel formants) as a proxy for CLI. This work has shown that L3 speakers produce L1-accented speech (Llama & Cardoso, 2018; Ringbom, 1987) and L2-accented speech (Wrembel, 2010; Llama, Cardoso, & Collins, 2010) in their L3, and that their speech displays evidence of a combined influence of the L1 and the L2 on L3 pronunciation (Blank & Zimmer, 2009; Wrembel, 2014, 2015), but that it also shows influence of the L3 on the L1 and L2 (Cabrelli Amaro, 2017). Williams and Hammarberg (1998) identify four important factors that predict which language, the speaker’s L1 or L2, influences the acquisition of L3 speech: language psychotypology, L2 status factor or foreign language effect, proficiency, and the recency of language use. As a predictor of CLI, psychotypology refers to the perceived similarity or subjective judgment about the perceived distance between languages. In this case, CLI between languages is predicted to be more likely if the learner perceives that they share some characteristics. The results in Llama et al. (2010) contradict this prediction. In their study, a group of L1 EnglishL2 French bilinguals and L1 French-L2 English bilinguals learning L3 Spanish showed that their production of voiceless stops in Spanish showed a strong influence from the L2 for both groups, even if their L1 was typologically closer to their L3, as was the case for the L1 French-L2 English bilinguals. These results, however, provide support for the L2 status as a strong predictor of the source of CLI in the production of L3. Hammarberg (2001, p. 37) defines the L2 status factor as

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“a desire to suppress L1 as being ‘non-foreign’ and to rely rather on an orientation towards a prior L2 as a strategy to approach the L3”. Proficiency has also been shown to be a significant variable in L3 phonological acquisition. More specifically, a number of studies suggest that a substantial proportion of L2-to-L3 transfer can be attributed to relatively low L3 proficiency (Dewaele, 2001; Hammarberg, 2001; Llama et al., 2010). CLI from the L2 to the L3 tends to decrease as L3 proficiency increases (Llama & Cardoso, 2018). With regards to recency, Hammarberg (2001, p. 23) states that “an L2 is activated more easily if the speaker has used it recently”. The assumption is that recent language use facilitates the degree of influence on the target language (e.g., CLI) due to a previous activation of components of the linguistic system in the multilingual individual’s mind. Most of the work to date on L3 speech has investigated speech production (Amengual et al., 2019; Gut, 2010; Missaglia, 2010; Llama & Cardoso, 2018; Sypianska, 2016) as opposed to speech perception. To provide a few recent examples, Kopecková et al. (2019) investigated the production of the /v/-/w/ contrast in L2 English and L3 Polish by child and adult groups of L1 German, L2 English learners of L3 Polish. The results indicated that the adult group showed a higher accuracy rate than the children in the target-like production of the L2 English /v/-/w/, however the children outperformed the adults in the production of L3 Polish /v/-/w/ during the first ten weeks of learning. The acoustic analyses showed that while both groups consistently used the second formant (F2) to distinguish the two sounds, only the children additionally used voicing/friction (as measured as harmonics-to-noise ratio) to distinguish this contrast in L3 Polish. With the analysis of Voice Onset Time (VOT) as a proxy for the degree of CLI, Llama and López-Morelos (2016) analysed the production of the Spanish, English, and French voiceless stops /p, t, k/ by heritage Spanish speakers with L2 English learning L3 French. The results of their production study showed that these trilingual speakers produced native-like values in their Spanish and English, and they transferred VOT values from English, their dominant language, in the production of their L3 French. Finally, in a more recent study, Amengual (2021) examined the acoustic realisation of the English, Japanese, and Spanish /k/ produced by two groups of English-Japanese bilinguals and a L1 Spanish-L2 EnglishL3 Japanese trilingual group. The experiment explored the effects of language mode and cognate status on the production of /k/ in each language of these L2 and L3 Japanese speakers. The results indicated that even though participants produced language-specific VOT patterns for each language, there was evidence of phonetic convergence as a result of language mode and cognate status. That is, the L1 Spanish-L2 English-L3 Japanese speakers showed more evidence of CLI when pronouncing cognates, such as Japanese katarogu (カタログ), a cognate with catalog (English) and catálogo (Spanish), than when producing a non-cognate, such

Chapter 4. Cross-language influences in the acquisition of L2 and L3 phonology

as kagami (かがみ), which translates as ‘mirror’ in English and ‘espejo’ in Spanish. Furthermore, all groups transferred phonetic characteristics (i.e., VOT) of the /k/ produced in the target language from the non-target language(s) when articulating these voiceless stops in bilingual and trilingual mode. While research on L3 speech perception to date lags behind production studies, there are several studies that have offered insight into the phonological system of L3 speakers (Cabrelli Amaro, 2017; Domene Moreno, 2021; Kopečková, 2015, Onishi, 2016; Wrembel et al., 2019; Wrembel et al., 2020). For instance, Wrembel et al. (2020) contribute to the few studies that have examined cross-linguistic effects in the speech perception of multilingual learners by exploring the development of speech perception in a group of young L1 Polish speakers learning L2 English and L3 German. These multilingual individuals performed a forcedchoice goodness task in their L2 and L3 to test their perception of rhotics (e.g., /r/ when pronounced at the end of a word or before a consonant, as in hard) and final obstruent (de)voicing (e.g., such as plosives /k/ and /g/). Data on their response accuracy and reaction times indicate that cross-linguistic influence in perceptual development is feature-dependent with relative stability evidenced for L2 rhotics, reverse trends for L3 rhotics, and no significant development for L2/L3 (de)voicing. These results also show that the source of CLI differed across the speakers’ languages. As cross-linguistic perceptual comparisons from a multilingual perspective are very scarce, the field of L3 acquisition will greatly benefit from future studies that investigate the various acoustic cues involved in the perception of segmental and suprasegmental patterns with different language combinations to ultimately contribute to a comprehensive model of L3 speech learning.

3.

Theoretical frameworks

Several theoretical models of L2 phonological/phonetic acquisition have been developed in the past twenty-five years. Even though phonetic correspondences are conceptualized differently in these frameworks, they all share the hypothesis that the success in the acquisition of non-native speech sounds can be predicted from similarities between the native and non-native target sounds. The discussion in this section focuses primarily on three main theoretical frameworks, which are among the most influential and widely tested theories of non-native and L2 speech production and perception. These theoretical models are the Speech Learning Model (SLM; Flege, 1995), the Revised Speech Learning Model (SLMr; Flege & Bohn, 2021), the Second Language Perception Model (L2LP, Escudero, 2005), the Perceptual Assimilation Model (PAM; Best, 1995), and the Perceptual Assimilation Model of Second Language Learning (PAM-L2; Best & Tyler, 2007).

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Two of them (the SLM and PAM) have their extensions and revised versions. According to these frameworks, the learnability of L2 segments depends on the perceived phonetic similarity between target sounds in the L2 and the most similar segments in the L1 phonetic inventory. The following section introduces some of the main tenets of the models outlined above and hypothesises how the PAM/PAM-L2 models can be applied to L3 phonological acquisition (Wrembel et al., 2019).

3.1 The Speech Learning Model (SLM) and the Revised Speech Learning Model (SLM-r) The Speech Learning Model (SLM; Flege, 1995) is a theory that explains how the interaction between sounds in the L1 and L2 interact leading to ‘accented’ production of the L2. The SLM is a theory of both L2 speech perception and production. Because the SLM postulates that phonetic categories of the L1 and L2 coexist in a shared acoustic-phonetic space, this theoretical framework assumes that the development of a second language will affect the native perceptual system. Additionally, the ability to acquire speech remains intact and operative throughout the lifespan, though with some restrictions in ultimate attainment. Early learners, as opposed to adult L2 learners, can establish additional phonetic categories for similar L2 sounds, as the SLM’s interaction hypothesis predicts that the sounds of the L1 and L2 are less likely to interact in early bilinguals than in late bilinguals (Flege et al., 2003). The SLM claims that L2 sounds differ in their learnability depending on their phonetic proximity to the L1 sounds. In other words, it proposes that success in L2 production depends on the establishment of new phonetic categories for the L2 segments, and critically, this success is based on the perceived similarity, or dissimilarity between the L2 sound, and any existing L1 category. The SLM specifically posits three types of L2 sounds: identical, new, and similar. The identical sounds are the least difficult to learn because the acoustical properties of the L2 sounds are exactly the same as their closest L1 sound, but new and similar sounds are hypothesized to be the most difficult to learn because of the disparity of the L2 sound in relation to the L1 sound equivalent. According to this model, there are two possible perceptual scenarios based on how the L1 and L2 sounds are perceptually linked to one another: (1) Phonetic category assimilation: if the L2 phonetic category is sufficiently similar to an L1 phone, it results in equivalence classification. This category assimilation results in a merged L1 and L2 phonetic category, which will differ from that of a monolingual speaker of either language. This assimilation will result in a difficult discrimination and, as a result, an L1-influenced production of these merged sounds. The other scenario is (2) Pho-

Chapter 4. Cross-language influences in the acquisition of L2 and L3 phonology

netic category dissimilation: if the L2 phone is very different from any L1 phonetic category, then it will not be assimilated to any L1 phonetic category and will establish a new, unique phonetic category. Importantly, this phonetic category will be different from the categories already existing in the L1, and consequently, the phonetic categories in the L1 will reorganize to accommodate the new L2 categories. In this scenario, both the L1 and the new L2 phonetic category will shift away from one another in the phonetic space to maximize distinguishability. The SLM establishes that a “new phone scenario” and a “similar phone scenario” depends on whether the acoustic difference perceived by the learner between an L2 sound and its closest L1 sounds is large enough. A revised version of the SLM was developed as the revised Speech Learning Model (SLM-r; Flege & Bohn, 2021). The SLM-r expands upon the SLM by focusing on the learning of the L2 sound system across the lifespan in response to the phonetic input received during naturalistic L2 learning, but not necessarily as a function of the age of acquisition of the L2. The SLM-r hypothesizes that L2 segmental perception and production co-evolve at a similar rate and, as a result, speech perception does not place an upper limit on the accuracy with which L2 sounds are produced. For the SLM-r, input is crucial for the formation of language-specific L2 phonetic categories and composite L1-L2 phonetic categories, and the accumulation of detailed phonetic information with increasing exposure by L2 learners to statistically defined input distributions for L2 sounds will lead to the formation of new phonetic categories. Importantly, the SLM-r maintains that L2 phonetic category formation is a gradual process, not a onetime event, and is possible regardless of age of first exposure to an L2 and is crucial for phonetic organization and reorganization across the lifespan. More specifically, the SLM-r proposes that both new L2 phonetic categories and composite L1-L2 phonetic categories are gradually shaped by the input distributions that define them. As in the SLM, the formation or non-formation of a new phonetic category for an L2 sound depends primarily on (1) the degree to which a sound is phonetically dissimilar from the closest L1 sound, (2) the quantity and quality of L2 input obtained for the sound in meaningful conversations, and (3) the precision with which the closest L1 category is specified, that is, the degree of explicit knowledge about the L1 category when L2 learning begins. This notion of category precision replaces the SLM’s age hypothesis and predicts that individuals who have relatively precise L1 phonetic categories will be better able to discern phonetic differences between an L2 sound and the closest L1 sound than individuals that have relatively imprecise L1 categories. The SLM-r category precision hypothesis postulates that individual differences in L1 category precision can predict how much L2 input learners need to establish consistent L2 phonetic categories. However, this model also aims to

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explain how we can account for differences between individuals who seem to have received similar L2 input. In addition to how much and what kind of L2 input they receive, the SLM-r proposes that most individual differences in L2 speech perception and production can be explained by examining how individual learners specify L1 phonetic categories when they begin learning an L2, how they map L2 sounds onto L1 categories, and how dissimilar they perceive L2 sounds to be from their closest L1 sound. In addition to these phonetically based factors, the authors admit that it may also become necessary to evaluate the role of endogenous factors, such as auditory acuity, early-state (precategorical) auditory processing, and auditory working memory (Flege & Bohn, 2021: 58). In sum, the SLM-r focuses on how individual learners acquire L2 sounds and how it influences their production and perception of L1 sounds, instead of the SLM’s attention on between-group differences. This model proposes that L2 phonetic input is accessible and that L2 learners of all ages exploit the same mechanisms and processes used earlier for L1 speech learning that remain malleable to create new phonetic categories for L2 sounds.

3.2 The Second Language Linguistic Perception model (L2LP) The Second Language Linguistic Perception model (L2LP, Escudero 2005) is a theoretical and computational model founded on phonological theory, that accounts for learners in three L2 phonological developmental stages (initial, developmental, and end state), and that provides predictions across three speech abilities (perception, spoken word recognition, and production). A crucial difference between the L2LP model and other frameworks is that it accounts for individuals from the time they are not yet learners, to the time they start learning the L2 up to their final stage of L2 learning. According to this model, the first step is to establish the initial state, also known as the onset of learning, and the L2LP model advises to thoroughly describe the acoustic properties of the native and target language. The optimal perception hypothesis states that the perception of a given speech category is determined by the stochastic ranking of cue constraints that refer to the relevant auditory information. In other words, perceivers will prefer certain auditory cues that will allow them to differentiate speech sounds (e.g., formants, duration), and this preference for certain auditory cues, known as cueweighting, will differ across languages as well as dialects. The L2LP proposes that the initial state is a copy of the properties of the native language, known as the full copying hypothesis. More specifically, the learner will create a duplicate of their L1 perception grammar and assign that to their L2 grammar to begin their process of acquiring the speech sounds of the L2, and this duplicate, which becomes the L2 perception grammar, will gradually modify itself as it receives perceptual input

Chapter 4. Cross-language influences in the acquisition of L2 and L3 phonology

in the L2. Once the duplicate copy is assigned to their L2, the L2LP puts forward three possible learning scenarios: New, Similar, and Subset scenarios. Elvin and Escudero (2019, p. 8) explain that the New scenario occurs when two non-native or L2 sounds are acoustically similar to, and subsequently perceived and categorized as one single native sound. This scenario creates a difficult learning task for the learner as they will need to either create a new sound category or split their existing L1 category. The Similar scenario occurs when two non-native sounds are acoustically similar to, and perceived and categorised as two separate native categories. The learning task in this scenario is predicted to be much easier as learners will be able to replicate their existing L1 categories and adjust their perceptual boundaries as needed. Finally, the third scenario is the Subset scenario and it occurs when two non-native or L2 sounds are acoustically similar to and/or perceived and categorized as two or more native L1 categories. The degree of perceptual difficulty in this scenario depends on whether or not an acoustic and/or perceptual overlap occurs. If the two non-native or L2 sounds are acoustically similar or perceived as the same native categories, then an acoustic and/or perceptual overlap is said to have occurred, resulting in a neutralization of the L2 contrast (comparable to the New scenario). But if the two sounds are perceived as separate multiple categories then no acoustic and/or perceptual overlap has occurred and no difficulties are expected. In this case, learners may face the Subset problem. That is, when a certain contrast does not exist in the target language and the learner must stop perceiving differences that are not meaningful in the target language, a task that may pose considerable difficulty, especially when a perceptual overlap has occurred. After the developmental stage, individuals will have developed a separate L2 grammar, while their L1 grammar remains intact. According to the L2LP framework, if learners receive ideal L2 input, they should be able to attain optimal L2 perception and production.

3.3 The Perceptual Assimilation Model (PAM) and the Perceptual Assimilation Model of Second Language Learning (PAM-L2) The Perceptual Assimilation Model (PAM; Best, 1995) postulates that non-native speech sounds assimilate to the most similar native speech sounds. The PAM proposes that both the native phonology and the goodness of fit of the nonnative sounds play a crucial role in the way non-native phonetic contrasts are perceived. Put succinctly, the ease with which non-native contrasts are perceived varies depending on how they are perceptually assimilated by the native perceptual system. This theoretical model aims to account for how the L2 sounds are related to and assimilated to the L1 system and predicts levels of difficulty in learn-

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ing non-native contrasts, and has done so focusing on non-native speech perception by naïve listeners. The PAM predicts that a non-native phone may be perceptually assimilated to the native system in one of three ways: as a good or poor exemplar of a native phonological segment (Categorized), as unlike any single native phoneme because the non-native sound will fall somewhere in between native phonemes (Uncategorized), or as a non-linguistic speech sound that does not resemble any native phoneme (Non-assimilated). The assimilation pattern displayed by the listeners will depend on the degree of similarity and discrepancy perceived between the native and non-native sounds. If non-native contrasts are perceptually assimilated, they are classified in different ways with expectations about discrimination performance. In the case of Two Category (TC) assimilation, two non-native phones are perceived as acceptable exemplars of two different native phonemes. The prediction is that perception will be very good to excellent. Another possibility is Single Category (SC) assimilation, in which two non-native phones are judged as equally good or poor tokens of the same native phoneme. Poor discrimination is predicted. Finally, there is the Category Goodness (CG) difference, in which both of the contrastive non-native phones are judged as tokens of a single native phoneme but they differ in goodness of fit to that phoneme. In this case, discrimination is predicted to be intermediate. If one non-native sound in a contrast pair is assimilated to the native perceptual system as an uncategorized speech sound, a different set of predictions and assimilation types are proposed: In Uncategorized-Categorized (UC) assimilation one non-native phone is perceived as a native phoneme while the other is heard as an uncategorized speech sound. Very good discrimination is expected because it reflects a phonological distinction between an exemplar of a known phoneme and a non-categorized one. The second option is Uncategorized-Uncategorized (UU) assimilation, in which both members of the pair are perceived as Uncategorized, and will be discriminated poorly to moderately. The third option is Nonassimilable, when the non-native contrast is so deviant from the native categories that they are not perceived as speech sounds. The prediction is that discrimination will range from poor to excellent. The Perceptual Assimilation Model of Second Language Learning (PAM-L2; Best & Tyler, 2007) expands upon PAM by incorporating a role for the additional phonological knowledge that L2 learners, but not naïve monolinguals, have about the target language. As with PAM, PAM-L2 follows the direct realist approach to speech perception (Best, 1995) and assumes that articulatory gestures, that is, the actions (i.e., mouth movements of speech) necessary to enunciate language are the basic phonetic unit. It also has a focus on speech perception, specifically accounting for L2 perceptual patterns using the same typology in terms of percep-

Chapter 4. Cross-language influences in the acquisition of L2 and L3 phonology

tual assimilations to L1 sounds that first appeared in PAM. In other words, PAML2 uses PAM contrast assimilation types as a basis for predicting the likelihood of acquiring new L2 categories when a learner is actively acquiring the non-native language and posits that perceptual learning can occur at the phonological, phonetic, and gestural level. All the theoretical models described above address CLI in speech perception from an L1-to-L2 direction (PAM-L2, L2LP) or bidirectional CLI in both speech perception and production (SLM, SLM-r), but none of these frameworks provide predictions regarding L3 phonological acquisition. One notable exception is a recent adaptation of PAM and PAM-L2 to the investigation of L3 speech perception (Wrembel et al., 2019). This study extends the PAM/PAM-L2 model for L3 learners, suggesting that the perceptual contrast assimilation types can also apply to multilingual acquisition. They found that L1 German-L2 English-L3 Polish trilinguals assimilated L3 sounds both to the L1 and L2, with a preference for the L2. However, these multilingual speakers were found to be more likely to perceive subtle differences between highly similar speech sounds that would typically follow the single-category assimilation pattern and to develop separate L3 categories for them. They conclude that in terms of perceptual acquisition, the beginner L3 learners seem to behave similarly to advanced L2 learners. This study provides a necessary step towards paving the way for future research that can contribute to a comprehensive model of L3 speech learning. In addition to proposing a theoretical model with testable hypotheses, such as the ones currently available for L2 speech acquisition, a wide variety of studies on L3 speech production and perception are needed to test the hypotheses of recent and forthcoming models of L3 speech acquisition, such as the Phonological Permeability Hypothesis (Cabrelli Amaro, 2017; Cabrelli Amaro & Rothman, 2010) or the Natural Growth Theory of Acquisition (NGTA; Dziubalska-Kołaczyk & Wrembel, 2022).

4.

Future directions in L2 and L3 speech learning research

CLI has taken a central role in the study of L2 and L3 speech, producing a wealth of findings on the phonological interactions in L2 and L3 learners of various language backgrounds (for further reviews, see Kartushina et al., 2016; Chang, 2019b; Wrembel, 2015). In the final section of this chapter, I discuss three topics that hold promise for future research in L2 and L3 phonetic learning: (a) links between speech perception and speech production, (b) the acquisition of phonological processes vs. phonological contrasts, and (c) static and dynamic phonetic interactions in L2 and L3 speech.

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4.1 The relationship between L2 and L3 speech perception and production The extant literature on speech perception and production mainly points to four potential scenarios for the relationship between the two modalities, namely that speech perception precedes production, that speech production leads perception, that speech production and perception coevolve in parallel, or a dissociation between both modalities. When conceptualizing the link between both modalities, it is perhaps logical to envision that speech production and perception reflect the properties of a unitary articulatory event and that speakers produce the same acoustic differences that are distinctive in perceptual analysis. Following this same idea, Liberman and Mattingly (1985) espouse the view that production and perception are different sides of the same coin. Evidence for this relationship has been found in studies that have shown a correlation between the perception and production of L2 segmental contrasts (Flege, 1993; Flege et al., 1997, 1999). Even though there is an undeniable link between speech production and perception, they have mostly been investigated independently (Fowler & Galantucci, 2008). As a result, it is still unclear which role each of these skills plays in relation to the sound systems of L2 and L3 learners. The findings of L2 speech research present a mixed picture regarding the relationship between speech perception and production. That is, which skill is first mastered, and whether one conditions the other. Several L2 speech studies on children and adults have suggested that perception conditions production and that L2 learners can only produce L2 sounds accurately if they perceive them accurately (Edwards, 1974; Neufeld, 1988; Rochet, 1995). An increase in performance in production may necessarily be preceded by an increase in perception, as studies show that training listeners to perceive non-native speech sounds results in improved production (Bradlow et al., 1997; Flege et al., 1999; Lengeris & Hazan, 2010). However, other studies maintain that production can precede perception. For instance, experimental studies with Japanese late learners of English have reported that their production of English /r/ and /l/ was more accurate than their perception (Goto, 1971; Sheldon & Strange, 1982). Other studies have also shown that L2 production accuracy does not seem to depend on high L2 perceptual ability (Sheldon & Strange, 1982), or do not provide evidence of a clear relationship between the perception and production of L2 or L3 speech (Domene Moreno, 2021; Kartushina & Frauenfelder, 2014). We are far from having a full understanding of the phonetic production and perception of language-specific sounds and whether speech perception patterns can explain the acoustic realisation of those same sounds in multilingual speakers. The lack of a one-to-one relationship between perception and production improvements, however, seem to suggest that the representations underlying multilingual speech perception and production may be distinct (Huensch &

Chapter 4. Cross-language influences in the acquisition of L2 and L3 phonology

Tremblay, 2015). Along these lines, Domene Moreno (2021, p. 189) argues that “there is more to speech production than speech perception, and merely being able to discriminate does not guarantee accurate production. Instead, perception is only one of a multitude of aspects that affect the (successful) production of a speech sound”. Given the disparity of results in research that examines both the production and perception of phonological segments by L2 and L3 learners, this is certainly a productive area for future research. The investigation of the relationship between speech perception and production in multilingual speakers will undoubtedly provide results with important implications for theoretical models of L2 and L3 phonological acquisition.

4.2 Acquisition of phonological processes vs. phonological contrasts in L2 and L3 speech Most research on L2 speech has investigated the acquisition of phonemic contrasts and the organisation of the speech sound system, but phonological knowledge for L2 learners goes beyond the acquisition of two or three phonemic inventories. Relative to how much we know about the production and perception of language-specific phonemic contrasts in L2 speech, we still know very little about the implementation of language-specific phonological processes, such as alternations and positional neutralization processes (Braun et al., 2011; Dmitrieva et al., 2010; Flege & Bohn, 1989; Lee et al., 2006; Smith et al., 2009). The scarcity of research on phonological processes is especially apparent in the field of L3 speech. Some studies that have investigated the acquisition of phonological processes, include those examining whether English learners of Russian or German neutralize obstruent voicing in word-final position (Dmitrieva et al., 2010; Smith et al., 2009). Dmitrieva et al. (2010) found that English-speaking L2 learners of Russian produced a larger phonetic difference between word-final voiced and voiceless consonants than native Russian speakers, and less proficient learners produced a larger acoustic difference between the Russian word-final voiced and voiceless obstruents than those with higher proficiency. Smith et al. (2009) analysed the productions of a group of L1 German-L2 English speakers. It was found that these bilinguals produced larger phonetic differences between word-final voiced and voiceless obstruents when speaking English than when speaking German. Nevertheless, the acoustic distance between word-final voiced and voiceless obstruents produced by these L2 English speakers was much smaller than the one produced by a group of native English speakers. These results suggest that L2 learners transfer, to a certain extent, the phonological processes of their native language into their non-native one (Smith et al., 2009), and it also suggests that they are likely

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to transfer the phonology of their native language when learning phonological processes specific to their L2 (Dmitrieva et al., 2010). Another positional neutralization process includes the unstressed vowel reduction alternations of English as produced by non-native speakers whose first language is Spanish (Flege & Bohn, 1989) and Korean or Japanese (Lee et al., 2006). In comparison to native English speakers, these studies showed that L1 Spanish, L1 Korean, and L1 Japanese learners of English did not reduce their unstressed English vowels, that is, they produced less centralized and less shortened unstressed English vowels. This suggests that learning L2 unstressed vowel reduction processes might be challenging for speakers of languages without such processes, such as Spanish, Korean, or Japanese, and that phonological processes are prone to CLI. A recent study on early Spanish-Catalan bilinguals, however, finds the opposite effect. Amengual and Simonet (2020) compared the effects of language dominance on the acoustic realisation of the Catalan [a]~[ə] alternation (a phonological process induced by lexical stress) and the Catalan mid vowel contrasts /e/-/ɛ/ and /o/-/ɔ/ (two phonemic contrasts). The results showed that even though there were significant differences between Spanish-dominant and Catalan-dominant bilinguals in their production of the Catalan mid-vowels, these effects were not found with respect to vowel reduction. These findings suggest that unstressed vowel reduction ([a]~[ə]) may be relatively easier to acquire than phonemic contrasts with a low functional load (/e/-/ɛ/, /o/-/ɔ/), perhaps because its predictability and high frequency attract attention and/or relieve cognitive resources, which could be conducive to phonetic learning. The fields of L2 and L3 phonological acquisition will benefit from further investigation to provide insight into the differences in the acquisition of phonemic contrasts in comparison to phonological processes in an L2 and L3.

4.3 Static and dynamic interactions in L2 and L3 speech By now it is undisputed that the sound systems of bilingual and trilingual individuals interact at multiple levels, and that these interactions result in CLIs in speech perception and production. It is pertinent, however, to distinguish between two types of phonological interactions: static and dynamic. Static phonological interactions are the result of long-term traces of one language influencing the other. This is a static process and is the consequence of cross-linguistic interactions between long-term memory representations (Simonet, 2014). This type of phonological interaction has been taken as the de facto source of CLI, and an extensive body of research has provided evidence of this interaction at the phonetic level (Flege et al., 2003; Piske et al., 2002; Wrembel, 2014). In addition to these static phonetic interactions, there is another type of interaction that is transient and

Chapter 4. Cross-language influences in the acquisition of L2 and L3 phonology

occurs during short-term operations. This dynamic process is the result of interactions between representations that are activated to the same extent in workingmemory. If we assume that when bilingual speakers are using two languages the representations of both languages are activated, therefore creating competition between the two languages, we can expect a deviation of the target phonetic implementation towards the non-target language during online speech processing. Grosjean (2001) distinguishes between these two types of interactions as the source of transfer and interference, respectively. Dynamic phonetic interactions as a source of CLI have received increasing attention in recent years, with L2 speech studies investigating the phonetics of code-switching (Antoniou et al., 2011; Bullock et al., 2006; Grosjean & Miller, 1994; Olson, 2016), the effects of language mode as induced by the experimental setting on speech production (Amengual, 2018, 2021; Simonet, 2014; Simonet & Amengual, 2020), and cognate effects in the acoustic realisation of languagespecific phonological categories (Amengual, 2012, 2016a, 2021; Goldrick et al., 2014; Jacobs et al., 2016). More work is needed on both types of phonological interactions (static/permanent and dynamic/contextual) to understand how they both contribute to CLI, and importantly, we need to account for both static and dynamic phonological interactions in our theoretical frameworks of L2 and L3 speech. The current theoretical speech learning models do not address how speech perception and production is affected by common language contexts (e.g., codeswitching or using cognates) for multilingual speakers, who are very infrequently, or ever, in a monolingual mode. A theory of speech perception and production that explicitly considers the contribution of the situational language context will increase our current theoretical explanatory value and enhance its predictive power for research on CLI in L2 and L3 speech.

5.

Concluding remarks

This chapter contributes to bridging the gap between L2 and L3 phonological acquisition by providing an overview of the main findings and current trends in research on cross-language influence (CLI) in L2 and L3 speech learning. After introducing the concept of CLI in L2 and L3 phonology and outlining some of the extant findings in the field, this chapter has attempted to synthesise core insights and claims of some of the most influential and widely tested theories in the field of L2 and L3 speech learning. The three selected theoretical frameworks discussed here, two of them with their extensions and revised versions, are the Speech Learning Model (SLM; Flege, 1995), the Revised Speech Learning Model (SLM-r; Flege & Bohn, 2021), the Second Language Perception Model (L2LP,

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Escudero, 2005), the Perceptual Assimilation Model (PAM; Best, 1995), and the Perceptual Assimilation Model of Second Language Learning (PAM-L2; Best & Tyler, 2007). It should be noted, however, that other theoretical models, such as the Ontogeny Phylogeny Model (Major, 2001), the Automatic Selective Perception Model (Strange, 2011), the revised and expanded Native Language Magnet Theory (NLM-e, Kuhl et al., 2008), the Phonological Permeability Hypothesis (Cabrelli Amaro & Rothman, 2010), the Natural Growth Theory of Acquisition (NGTA; Dziubalska-Kołaczyk & Wrembel, 2022), and additional usage-based or exemplar model approaches (cf. Ambridge, 2020) also address aspects of L2 and L3 speech learning that contribute to the growth and diversity of approaches that investigate these two subdomains of foreign language acquisition. In addition to an overview of some of the findings that have gathered growing consensus in L2 and L3 phonology, this chapter has discussed several methodological and theoretical considerations for future directions in L2 and L3 speech learning research: the relationship between L2/L3 speech perception and L2/L3 speech production, the acquisition of phonological processes vs. phonological contrasts, and the distinction between static and dynamic phonetic interactions in L2 and L3 speech. In closing, although the focus of this chapter has exclusively been on the acquisition of L2 and L3 segmental phonology, it is important to note that a wealth of studies on L2 prosody has led to new methodological and theoretical developments focusing on suprasegmental features of the L2, such as rhythm and intonation (Mennen, 2015), and that these approaches are also being expanded into L3 prosody (Zhu et al., 2019; Domene Moreno & Kabak, forthcoming; Calhoun et al., Chapter 3, this volume). The steady increase in L2 speech learning research coupled with the more recent growth in the field of L3 speech acquisition promises to enrich our understanding of how the sound systems of bilingual and multilingual individuals interact while also guaranteeing relevant new findings for years to come.

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chapter 5

Cross-language influences in L2 visual word processing A localist connectionist modelling perspective Walter J. B. van Heuven & Ton Dijkstra

University of Nottingham | Radboud University Nijmegen

Over the last decades, a variety of verbal models have been proposed to account for empirical findings on bilingual word processing and second language (L2) acquisition. However, in these domains only a small number of computational models have seen the light, including localist connectionist models like the Bilingual Interactive Activation (BIA) model (Dijkstra & van Heuven, 1998; van Heuven et al., 1998), the Bilingual Interactive Activation + (BIA+) model (Dijkstra & van Heuven, 2002), and Multilink(+) (Dijkstra et al., 2019; 2022a). In this chapter, we review structural and processing characteristics of these models, such as input coding, lateral inhibition, word frequency, language nodes, and task demands. Finally, advantages and limitations of current computational models are discussed. Keywords: bilingual visual word recognition, cross-language effects, computational models, localist connectionist models

1.

Introduction

Our initial understanding of the psychological mechanisms underlying processing in a particular cognitive domain is captured in verbal descriptions. To make further progress, however, it is necessary to refine our theoretical reasonings and quantify them. That is done in computational models. This chapter focuses on localist connectionist models of bilingual word recognition. We will describe and contrast implemented models developed to account for observed cross-language influences between native (L1) and foreign (L2) word recognition. Because we focus on model architecture and internal mechanisms, our discussion of empirical findings will be limited. For more extensive reviews of studies see, for example,

https://doi.org/10.1075/bpa.16.05van © 2023 John Benjamins Publishing Company

Chapter 5. Cross-language influences in L2 visual word processing

Dijkstra & van Heuven (2018), van Heuven & Coderre (2015), and Chapter 6 by Piasecki & Dijkstra (this volume). In order to build computational models for simulating cross-language influences in L2 visual word recognition, precise assumptions must be made about the processes involved. We must specify, for example, how the visual word input is encoded, how the L1 and L2 lexical networks are organised to enable crosslanguage influence, when language membership information becomes available and whether this information can influence lexical activation, how L1 and L2 word frequency should be incorporated, and how decisions are made in word retrieval tasks. Although specification of all these aspects is difficult, it yields great rewards. A computational model allows us to precisely mimic how humans recognise words. Model performance (e.g., in time cycles to recognize a word) can profitably be compared to behavioural data from word recognition tasks (e.g., lexical decision times in ms). A large number of monolingual computational models of visual word recognition and reading have been developed over the last 40 years (for an overview, see Norris, 2013). However, only a small number of bilingual computational models have seen the light, among which are the Bilingual Interactive Activation (BIA) model (Dijkstra & van Heuven, 1998; van Heuven et al., 1998), the Bilingual Interactive Activation + (BIA+) model (Dijkstra & van Heuven, 2002), Multilink(+) (Dijkstra et al., 2019, 2022a), DevLex-II (Zhao & Li, 2013), and Yang, et al.’s (2013) model of reading acquisition in Chinese and English. Most of these models involve a neural network of interconnected localist word representations (in which lexical representations are equated one-on-one with nodes in the network). In this chapter, we will focus on this type of computational model. The general framework of the bilingual models that we will hang on to is presented in Figure 1. The next section presents an overview of key characteristics of bilingual localist connectionist models of printed word recognition. These key aspects are then discussed in more detail with respect to the various input coding schemes that they use, and the role they assign to lateral inhibition, language membership nodes, and the task/decision system. Finally, general limitations of the models are discussed and future research directions are pointed out.

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Figure 1. General framework of localist connectionist models of bilingual visual word recognition

2.

Overview of bilingual localist connectionist models of visual word recognition

2.1 BIA model McClelland and Rumelhart’s seminal connectionist model of letter and word perception, called the Interactive Activation (IA) model, was published in 1981. Grainger and Dijkstra (1992) considered how their monolingual framework could be extended to bilingual word recognition. This culminated in the implementation of the Bilingual Interactive Activation (BIA) model some years later (Dijkstra & van Heuven, 1998; Dijkstra et al., 1998; van Heuven et al., 1998). The implemented BIA model is a localist connectionist model with four layers of representations. The visual input layer contains nodes for letter features that are present or absent in the input item. Feature nodes are organised in sets (pools) for each letter position. They send positive activation (excitation) to letters that contain a particular visual feature (e.g., a horizontal or vertical segment) and negative activation (inhibition) to letters that do not contain the feature. There are 26 letter units for each letter position. Activated letter units (with an activation above zero) excite word units that contain the letter in the correct position and inhibit words that do not contain the letter there (e.g., the letter L at the first letter position will activate the word land and inhibit the word sand). Crucial to the

Chapter 5. Cross-language influences in L2 visual word processing

BIA model is that words also activate their language node (e.g., English and Dutch words activate the English and Dutch language nodes, respectively). Furthermore, all activated words inhibit all other words (this is called lateral inhibition) independent of the language they belong to. Importantly, each activated language node can inhibit all words of the other known language(s). However, because the activation of a language node depends on bottom-up activation, it cannot completely de-activate another language; the associated inhibition simply occurs too late in the recognition process. The functioning of the language nodes will be further discussed later in this chapter. Finally, just as in the original IA model, words that are activated at the word level in BIA send activation back to the letters that they contain (top-down feedback, e.g., the word land excites the letter L at the first position, A at the second position, N at the third position, and D at the fourth position). All nodes in the model have a resting-level activation (RLA). For features, letters, and language nodes these RLAs are set to zero. Word nodes, however, have RLAs that are determined by their language-dependent word frequency. The RLAs vary between 0 for the highest frequency words and −0.05 for the lowest frequency words.1 As a consequence, it takes longer before the latter type of words are activated above a threshold of 0. This is important, because only then do they start to exert inhibition on other words. For balanced bilinguals, the RLAs used are standardly based on native word frequency estimates for each language. For unbalanced bilinguals, RLAs in their second language can be adapted to reflect the lower subjective proficiency in this language (see Dijkstra & van Heuven, 1998). A word is assumed to be recognised by the BIA model when it reaches a word recognition threshold (as in the IA model). Most simulations reported in the literature have used a fixed word activation threshold of 0.70. The original BIA model was implemented on an Apple Macintosh computer in the programming language C. An implementation of the BIA model that runs in web browsers using JavaScript is available at .

2.2 BIA-d The BIA model describes how bilinguals process words at this moment in time. It does not incorporate any theoretical account of how the mental lexicon of bilinguals develops with learning. An extension of BIA to L2 learning and devel1. Resting-Level Activations (RLA) in the IA model (McClelland & Rumelhart, 1981) vary between −0.92 and 0. However, the RLA is multiplied by the fgain parameter (0.05) and therefore, the effective range is between −0.05 and 0.

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opment was proposed by Grainger et al. (2010). This developmental Bilingual Interactive Activation (BIA-d) model of late L2 learners assumes that early in L2 acquisition, links at the word level develop between translation equivalents (e.g., between horse in English and paard in Dutch). The early development of lexical links is in line with the assumption of the revised hierarchical model (RHM; Kroll & Stewart, 1994) that L2 word access to meaning initially takes place via the L1, using excitatory form links between translations. When L2 proficiency increases, direct links from L2 lexical representations to meaning develop and, with further increase in L2 proficiency, these lexical links become inhibitory as in the BIA model. Note that the BIA-d and BIA should be seen as complementary rather than contradictory. Simulations are possible with the BIA model to capture different stages of L2 proficiency. There are several ways to accomplish this, for example, by changing the size and content of the L2 lexicon, by changing L2 resting-level activations, and by manipulating the degree of top-down inhibition from L2 to L1 (see Dijkstra & van Heuven, 1998). For example, Dijkstra et al. (1998) showed that word priming data of beginning and proficient L2 participants (Bijeljac-Babic et al., 1997) could be simulated by BIA through modifying L2 resting-level activations. Simulations presented in Dijkstra and van Heuven (1998) and van Heuven et al. (1998) used a combination of adapted resting-level activations for L2 and top-down inhibition from the L1 language node to L2 words.

2.3 BIA+ The IA, BIA, and BIA-d models have only limited scope, because they consider only orthographic representations and provide no clear explanation of how word decisions are made and different tasks are performed. By extending the theoretical framework, the Bilingual Interactive Activation plus (BIA+) model (Dijkstra & van Heuven, 2002) tries to account for a wider range of findings on L2 word recognition. The BIA+ model proposes the existence of a word identification system with orthographic and phonological sublexical (e.g., letters and phonemes) and lexical representations, semantic nodes, and language nodes. This system sends information to a task/decision system that specifies the cognitive operations (task schemas) necessary to perform the task at hand (e.g., lexical decision, word naming). Based on the empirical literature at the time, the BIA+ model made the important assumption that the language nodes in the BIA+ model do not influence word activation. Thus, there is no top-down inhibition from language nodes to word nodes. The language nodes only function as an indicator of language membership (i.e., a language tag).

Chapter 5. Cross-language influences in L2 visual word processing

A fully implemented BIA+ model is not available. However, simplified versions of the model have been implemented and several extensions of BIA+ have been proposed in the literature. For example, van Heuven (2021) implemented a version of BIA with only orthographic representations and without a full task/ decision system. This simplified version of BIA+ is identical to BIA, except that language nodes do not inhibit word nodes (cf. also the Non-selective Access Model or NSAM in Dijkstra & van Heuven, 1998). Other theoretical extensions of the BIA/BIA+ framework have been proposed. For instance, van Kesteren et al. (2012) proposed an extension of the BIA+ model to account for markedness effects in bilingual visual word recognition. Orthographic markedness refers to letter combinations that are legal or common vs. illegal or uncommon in a language. For example, the bigram OE is legal in Dutch but illegal in English. The model assumes direct links from letter feature nodes and letter nodes to sublexical language nodes, which are also connected to lexical language nodes. This enables activation of language nodes by specific letter combinations in both words and nonwords. Another extension of BIA+ was proposed by Casaponsa et al. (2020) to account for modulations of markedness on translation priming. Their BIA+s model assumes that sublexical orthographic and phonological representations can activate orthotactic and phonotactic language nodes. Importantly, these orthotactic/phonotactic representations can inhibit lexical representations. Thus, this model, like the BIA model, enables more language-selective word processing. Still other extensions are available. Kerkhofs et al. (2006) formulated a model of bilingual semantic priming and interlingual homograph processing that was based on the mechanisms underlying BIA+. Miwa et al. (2014) presented a model of English word recognition (with Japanese katakana equivalents) for JapaneseEnglish bilinguals based on BIA+’s architecture.

2.4 SOPHIA The Semantic, Orthographic, and Phonological Interactive Activation (SOPHIA) model (van Heuven, 2000) is an implementation of BIA+ with lexical and sublexical orthographic and phonological representations, as well as semantic representations. The model applies response thresholds set at various levels to simulate different tasks (e.g., at the lexical-orthographic level). The sublexical representations included in the model are onset, nucleus, and coda letter and phoneme clusters. Orthographic input to the model results in activation of onset letter, nucleus, and coda representations (e.g., letter clusters: B, OO, and K for the word BOOK). The letter clusters are directly connected to corresponding phoneme clusters to enable grapheme to phoneme conversion, for example, the letter B is

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connected to the phoneme /b/, the letters OO are connected to the phoneme /u/, and the letter K is connected to the phoneme /k/. These sublexical connections between orthography and phonology enable SOPHIA to simulate pseudohomophone effects. Thus, when the pseudohomophone bloo is presented to the model, the word blue is activated (van Heuven, 2005). Semantics in the model are implemented using single semantic nodes that connect word translations as well as to associated nodes. There is spreading activation at the semantic level with connection weights that can be based on, for example, free association norms (e.g., Nelson et al., 2004).

2.5 Multilink and Multilink+ Dijkstra et al. (2019, 2022a) developed a model of monolingual and bilingual word retrieval that includes a network of whole-word representations that are orthographic, phonological, or semantic in nature (Multilink, available at https:// multilink.donders.ru.nl). In addition, language membership nodes are linked to orthographic and phonological representations. To avoid the complicated issue of item-specific grapheme-to-phoneme conversion (subject of SOPHIA), the model does not focus on sublexical units like graphemes and phonemes; instead, word representations in the models’ lexical network are directly activated on the basis of similarity to the input following the metric of normalized Levenshtein Distance (nLD). Levenshtein Distance is the minimal number of deletions, substitutions, and insertions needed to edit one expression into another. For instance, it takes one deletion to turn strand into stand and one insertion to turn eat into feat. By normalizing the Levenshtein Distance for word length, the activation and retrieval of word candidates of different lengths can be accounted for. Multilink incorporates word collections for an increasing number of languages and scripts; at present, Dutch, English, French, Spanish, Italian, and Japanese kana. The orthographic and phonological word representations in this integrated multilingual lexicon are characterized by their (subjective) log frequency in occurrences per million. The orthographic representations of translation equivalents map onto shared semantic representations, but activation can (be made to) spread between semantically related items with incomplete semantic overlap as well by introducing links between semantic representations. All types of word representations have an RLA that depends on their frequency of usage, just as in BIA and SOPHIA. The activation process in Multilink follows the standard localist-connectionist activation function used in the BIA and SOPHIA. Word activation starts at RLA and then increases over discrete time cycles depending on the LD similarity between the stored representation and the input word. A lexical item is assumed to be recognized when its activation surpasses a threshold (usually set at .72) in

Chapter 5. Cross-language influences in L2 visual word processing

a way that takes into account the task at hand. For instance, in lexical decision, a word reaching threshold activation is recognized when it belongs to the target language. To account for different task demands, Multilink incorporates a task/decision system that specifies conditions for word retrieval and identification. The most complex cognitive control issues arise in the case of the translation of false friends (see Dijkstra et al., 2022b). In order to correctly translate a false friend like room, input and output languages must be correctly specified and linked via semantics. Furthermore, lateral inhibition can be introduced to speed up lexical identification. In sum, Multilink specifies representational, process, and cognitive control aspects of word retrieval. Although working memory aspects are also assumed to play a role, these currently only play a role in the background of the model’s implementation. Multilink has been shown to provide good word retrieval simulations for empirical data (a) in a number of different languages; (b) by participants aged between 15 and 45; and (c) including different item types, such as cognates, false friends, neighbours, translation equivalents, and control words. Tasks that can be simulated are language-specific and generalized lexical decision, word naming, and word translation. Multilink has recently been extended into Multilink+ (Dijkstra et al., 2022a). The new model includes larger lexicons for more languages, allows for lateral inhibition, simulates new tasks (e.g., orthographic and semantic priming), and accounts for new stimulus materials (e.g., items that are both cognates and false friends). The models of L2 word recognition described so far focus on alphabetic languages. There are only a few bilingual models that involve non-alphabetic and alphabetic languages (Chinese and English) and, as far as we are aware, there are no computational bilingual models that involve non-alphabetic languages. Below we will describe one of these Chinese-English models that involves localist connectionist representations. Other models, such as Yang et al. (2013), focussing on reading acquisition in Chinese and English, and DevLex II (Zhao & Li, 2013), involving a self-organizing neural network, are beyond the scope of this chapter, as these models are not localist connectionist in nature.

2.6 CE-IAM Wen and van Heuven (2018) developed a Chinese-English Interactive Activation model (CE-IAM) of bilingual word recognition in Chinese-English bilinguals. The model intends to account for translation priming effects and cross-language

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repetition priming effects from Chinese to English. The model’s architecture is similar to that of BIA+, except that, due to the orthographic differences between Chinese and English, no lateral inhibition exists between the two languages. However, at the lexical word level, lateral inhibition does still occur within each language. The alphabetic component of the model is simplified and contains only orthographic lexical representations. The Chinese part of the model contains Chinese two-character words (e.g., 事业) that are connected to Chinese characters (morphemes) at the first (e.g., 事) and second (e.g., 业) character position. The model does not contain sublexical alphabetic (e.g., letters) and sub-character (e.g., radicals) representations and has no phonological representations. Chinese visual input to the model activates Chinese single characters (morphemes) at the two Chinese character positions and English visual input activates English whole word orthographic representations. Word translations in Chinese and English are linked through a single node at the semantic level. Crucial for this model is that the connections from words to semantics are bi-directional. This enables the model to simulate masked translation priming effects, as well as the partial-hidden repetition priming effects observed in a masked priming experiment with Chinese-English bilinguals by Wen and van Heuven (2018). Partialhidden repetition priming refers to the repetition of a Chinese character that is present in the Chinese translation of an English target word and the Chinese prime, for example, the Chinese prime 事业 (meaning career) and the English target fact (Chinese translation: 事实), share the Chinese character 事.

2.7 Similarities and differences of bilingual localist connectionist computational models Table 1 summarizes key characteristics of most of the models presented above. In the next sections, we highlight several important aspects of the models. Table 1. Overview of the features of four models of bilingual visual word recognition BIA 1

BIA+ 2

SOPHIA 3

Multilink(+) 4

Model implemented?

yes

partly

yes

yes

yes

Sublexical level

yes

yes

yes

no

no

Phonology

no

yes

yes

yes

no

Semantics

no

yes

yes

yes

yes

Language nodes

yes

yes

yes

yes

no

CE-

IAM 5

Chapter 5. Cross-language influences in L2 visual word processing

Table 1. (continued) BIA 1

BIA+ 2

SOPHIA 3

Response threshold

word level

task task dependent dependent

Input Level

letters

sublexical

Bottom-up inhibition: sublexical to word level

yes

Top-down excitation: word to sublexical level

Multilink(+) 4

CE-

IAM 5

task dependent

word level

ONC 6

word

word / character

not specified

yes

no

no

yes

not specified

yes

no

no

Lateral inhibition at word level

yes

yes

yes

no (Multilink), yes (Multilink+)

yes

Top-down inhibition: language nodes to words

yes

no

no

no

no

[0, −0.05]

not specified

[0, −0.05]

[0, −0.20]

[0, −0.05]

Word node RLA range

Notes 1. BIA model (Dijkstra & van Heuven, 1998; Dijkstra, van Heuven, & Grainger, 1998), 2. BIA+ model (Dijkstra & van Heuven, 2002), 3. SOPHIA (van Heuven, 2000), 4. Multilink (Dijkstra et al., 2019), Multilink+ (Dijkstra et al., 2022a), 5. CE-IAM (Yun & van Heuven, 2018), 6. ONC: Onset, Nucleus, Coda representations that consist of letter(s).

3.

Input coding

In order to present an input word to a model, it must be decomposed into modelrelevant input units. The models of bilingual word recognition described above use different input encoding systems (see Table 1). For example, the IA and BIA models assume position specific letter coding and they present input in terms of letter feature constellations. Rather than using input at the feature level, the letter nodes in IA type models could be used as input if other letter nodes are maximally inhibited at the same time (see jIAM for implementations of the IA/BIA(+) without letter features). The position specific letter coding scheme in BIA can account for similarity effects based on orthographic neighbours within and between-languages (e.g.,

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van Heuven et al., 1998) and between-language orthographic priming effects (e.g., Dijkstra et al., 1998). However, this input coding system cannot account for orthographic similarity effects for words with somewhat different letter orders, such as transposition neighbours (e.g., trial and trail, e.g., Andrews, 1996), masked orthographic similarity effects with letter strings that are shorter or longer (e.g., Adelman et al., 2014), and effects of non-identical cognates (e.g., tomaat in Dutch and tomato in English, e.g., Dijkstra et al., 2010b). The coding scheme used in SOPHIA with its onset, nucleus, and coda letter cluster representations is also limited in terms of accounting for orthographic neighbourhood effects as letter order is also fixed. However, it can account for effects of orthographically similar shorter or longer words on target word recognition. A more flexible input coding system for models of bilingual word recognition could involve, for example, open-bigrams (Grainger & van Heuven, 2003) or a spatial coding scheme (Davis, 2010). So far, no model of bilingual visual word recognition exists that uses these solutions. However, the authors are currently working on a bilingual model that uses open-bigrams. Multilink’s solution for a more flexible input coding system was to remove the letter layer completely and activate words in the orthographic lexicon directly from the input using the Levenshtein distance (see description above). This pragmatic solution works well for simulations of lexical effects, e.g., for non-identical cognates and for translation pairs of different length (see Dijkstra et al., 2019). It can also be applied to words with exchanged letters (e.g., calm and clam). However, the removal of the letter layer also has consequences for model performance, due to the resulting lack of resonance between letters and words in the orthographic lexicon. In addition to letter to word excitation, the IA and BIA models assume the presence of strong top-down feedback (excitation) from words to letters (default setting is 0.30) that boosts the activation of letters contained in activated words. In fact, the resonance between the word and letter layers is so strong that IA and BIA type models only need a single time step of input (cycle) rather than continuous input for a word to be recognised after several processing steps. Whether top-down excitation is really needed in a model of printed word recognition to account for context effects (e.g., as in the Reicher paradigm) has been questioned in the literature (e.g., Massaro, 1988). Furthermore, Jacobs and Grainger (1992) showed that a stochastic version of the IA model without any top-down excitation was able to simulate neighbourhood density and neighbourhood frequency effects. In fact, the IA model without top-down excitation did much better in some simulations than the standard IA model with top-down excitation. Thus, the lack of an input layer in Multilink and the resulting absence of resonance between letters and words might not be critical. Further simulation studies are needed to investigate the role of this resonance in accounting for effects observed in bilingual word recognition.

Chapter 5. Cross-language influences in L2 visual word processing

Obviously, sublexical level representations are required for a model to perform particular tasks, like syllable detection or phoneme monitoring. This requires an implemented mapping of letters to phonemes and the possibility of interactions between sublexical phonology and orthography. Another key difference between Multilink(+) and other interactive activation models, is that Multilink(+) does not have inhibitory links between layers. In other models, input representations (e.g., visual features, letters, or letter clusters) have inhibitory as well as excitatory links to higher levels (e.g., word level). These inhibitory links reduce the number of representations activated by the excitatory links. Thus, investigation of the need for such inhibitory between-layer connections in Multilink(+) must be put on the research agenda.

4.

Lateral inhibition

All models of word recognition assume that in early stages of processing, multiple lexical candidates are activated on the basis of the input. This implies that a reduction of the set is necessary in order to identify the input word. Most models of monolingual and bilingual word recognition assume that target selection is facilitated by a competition of lexical candidates during the recognition process (e.g., monolingual domain: IA; MROM: Grainger & Jacobs, 1996; DRC: Coltheart et al., 2001; Spatial Coding Model: Davis, 2010; bilingual domain: BIA, BIA+, SOPHIA, CE-IAM). The lexical competition mechanism is implemented in all these models through lateral inhibition at the word level. Lateral inhibition has strong consequences for bilingual word recognition, because by definition cognates and false friends have identical or very similar orthographic representations in two languages; these therefore strongly compete with other when they are presented as model input. In fact, false friends and identical cognates do not even reach the standard word recognition threshold in BIA. However, the language node can be set to inhibit words in the non-target language, allowing target language cognates and false friends to reach the recognition threshold (see Dijkstra & van Heuven, 1998). In other words, language nodes implement stronger between- than within-language lateral inhibition. Crucially, the implementation of this stronger between-language lexical inhibition through language nodes is not fixed but depends on language node activation. Furthermore, it is a process that only becomes effective late during word recognition, after the language nodes have collected activation from bottom-up sources. Note that BIA only incorporates orthographic representations, while resonance between orthographic, phonological, and semantic representations could resolve lexical competition effects so that false friends and cognates are recog-

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nised using the standard recognition threshold. Evidence for this view comes from simulations with SOPHIA. Simulations with phonological and orthographic representations in SOPHIA revealed that false friends could be recognised due to resonance between orthographic and phonological representations (van Heuven, 2005). The existence and nature of lateral inhibition is still under debate. Evidence in the literature in favor of lateral inhibition is mixed. For example, the fragile effects of orthographic neighbours on target word recognition have been found to vary from facilitatory to inhibitory and are dependent on stimulus characteristics, language, task, and stimulus context (for review see, for example, Andrews, 1997; Ferrand, 2001). Between-language orthographic neighbourhood effects have also been found to be mixed (Dirix et al., 2017; Mulder et al., 2018; van Heuven et al., 1998). In terms of orthographic priming studies, only a few have found significant inhibitory priming effects of orthographically related word primes on target word recognition (Bijeljac-Babic et al., 1997; Boddaert et al., 2021; Dijkstra et al., 2010a). In contrast, orthographic priming effects with nonwords are mostly facilitatory (see Adelman, 2014), except when the prime and target share neighbours (e.g., van Heuven et al., 2001). These effects of shared neighbours are consistent with the notion of lexical competition. Lateral inhibition is useful as a mechanism because it reduces the number of active words and thus acts as a filter, speeding up input word recognition. When a target string is presented to a model, words that are orthographically related become active (e.g., between 1–20 words depending on input coding in the model and the amount of excitation from a matching representation). Lateral inhibition enables the lexical candidate that receives most excitation from the input to suppress other lexical candidates. In contrast, lexical competition effects in bilingual visual word recognition might often be due to response competition rather than lateral inhibition. For example, in an L2 lexical decision task including between-language competitors, a problem of response competition and selection might occur because active L2 words map on a ‘yes’ response, whereas active L1 words map on a ‘no’ response. The effect would especially occur with false friends and cognates because these words have (near-)identical orthographic representations and are co-activated. Evidence for response competition in these circumstances has been found in brain imaging studies (van Heuven et al., 2008; Hsieh et al., 2017, 2021). However, it is harder to apply this notion of response competition to within-language competition effects in, for example, a language specific lexical decision task. The only computational model of bilingual word recognition that does not implement lateral inhibition at the lexical level is Multilink. However, its successor Multilink+ does allow for lateral inhibition, as it improved simulation results (Dijkstra et al., 2022a).

Chapter 5. Cross-language influences in L2 visual word processing

5.

L1 and L2 word frequency

The most important predictor of response times in word recognition tasks is word frequency (Brysbaert et al., 2018). It accounts for 30 to 40% of the variance in latencies (Brysbaert et al., 2016). Therefore, it is crucial to consider how word frequency is implemented in models of visual word recognition. Word frequency differences are implemented in most models in terms of resting-level activation (RLA) differences between word nodes. A high frequency word has an RLA of (close to) zero so that the word becomes active with just a little bit of input activation, whereas low frequency words have more negative RLAs. In BIA, as in the IA model, RLAs are set between −0.05 and 0. Multilink(+) uses transformed frequencies (reciprocal-of-root function) to map the word frequency distribution more precisely onto the shape of the reaction time distribution and then scales the RLAs to the domain between −0.20 and 0 (Dijkstra et al., 2019). This range of RLAs is similar to what has been used in the monolingual Spatial Coding Model (Davis, 2010). What are the consequences of using different RLA ranges for the performance of localist connectionist models of visual word recognition? To investigate this, we conducted simulations with the IA model focussing on 996 four-letter words that occur in the original word list of the IA model and the British Lexicon Project (BLP; Keuleers et al., 2012) with accuracies above 80 percent in the BLP. The lexicon used for the simulations contained 4665 four-letter words from SUBTLEXUK with Zipf values larger than 2 (van Heuven et al., 2014). With standard IA parameters, resting-level calculations, and a word recognition threshold of 0.70, the Pearson correlation between the response times of the IA model (in cycles) and the reaction times in BLP was .109 (p < .001). Increasing the range of the resting-level activations in the model to between −0.20 and 0 (as in Multilink) resulted in a correlation of .373. When resting-level activations in the IA model were calculated in the same way as Multilink, the correlation between response cycles and BLP reaction times increased further to .454. Thus, the frequency to resting-level activation equations used by Multilink(+) further significantly improve the amount of explained variance. Unlike Multilink, the IA model has lateral inhibition and top-down excitation from words to letters. Dijkstra et al. (2019) showed that simulations with Multilink revealed an even higher correlation (.553) between response cycles of 892 words in Multilink and the reaction time in the English Lexicon Project (Balota et al., 2007). The correlation between response cycles of the IA model and BLP reaction times might increase further with a higher level of letter to word inhibition because increasing the amount of bottom-up inhibition may especially be important for lexicons of more realistic sizes. Most computational models have so far

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worked with relatively small lexicons. For example, the lexicon of the original IA model contains only 1179 words, whereas the BIA model has an English lexicon of 1323 English words and a Dutch lexicon of 983 words. The bilingual lexicon used for recent simulations with Multilink contains at best 892 translation pairs (1784 words). Target words will have a larger number of orthographically similar words when lexicon size significantly increases. For example, when the word CARE is presented to the IA model with a lexicon of 1179 words, in total 27 words become active. However, when the IA model has a lexicon of 4665 words, as in the earlier simulations, the number of words that become active increases to 42 words. Because of resonance between the word and letter layers, and lateral inhibition at the word level, the activation curve of the word CARE oscillates, and the word is recognised very slowly. Increasing the letter to word inhibition reduces the number of activated words and resolves this oscillation issue. Simulations with the IA model described earlier (with increased RLA range [−0.20 to 0] and RLA calculations identical to Multilink) revealed that increasing the letter to word inhibition from −0.04 to −0.10 resulted in a correlation that increased from .454 to .555 between the response cycle times of the 996 words and the reaction times of these words in the BLP. This accounts for 31% of item-level response time variance. When simulating word recognition by balanced bilinguals, word frequency estimates for monolinguals can be used for each language. However, when simulating performance of an unbalanced bilingual, the word frequency estimates in L2 need to be adapted. Dijkstra and van Heuven (1998) conducted simulations for unbalanced bilinguals by reducing the RLA range of L2 words from between −0.05 and 0 to between −0.05 and −0.015.2 This adapted RLA range was also used in simulations presented in van Heuven et al. (1998). Multilink uses a slightly different approach. All L2 word frequencies are divided by 4 and RLAs are calculated across both lexicons and scaled between −0.20 and 0. Interestingly, frequency effects have been found to be larger in L2 than L1 (e.g., Brysbaert et al., 2017; Duyck et al., 2008; Van Wijnendaele & Byrsbaert, 2002; Lemhöfer et al., 2008). Diependaele et al. (2013) argued that this effect is due to lexical entrenchment that depends on language exposure rather than qualitative differences between L1 and L2. Simulations with the bimodal Interactive Activation Model (IAM) (Diependaele et al., 2010) showed that increasing the RLA range or reducing lateral inhibition at the word level produced a larger L2 frequency effect. Simulations with the IA model presented earlier showed that increasing the RLA range improved overall correlations between responses in 2. In Dijkstra & van Heuven (1998) and van Heuven et al. (1998) the maximal resting-level activation (0.00) was reduced to −0.30, which effectively is −0.015 (−0.30 * 0.05, see Footnote 1).

Chapter 5. Cross-language influences in L2 visual word processing

the model and reaction times. Furthermore, correlations between response times obtained with the IA model (using the same set of 4-letter words as above) and L1 and L2 response time data taken from Mandera et al. (2019) and Brysbaert et al. (2021) revealed that a larger RLA range increases the correlations for both L1 and L2. If the RLA range would be reduced in L1, higher correlations would be expected, however, correlations became lower. Thus, it seems unlikely that a RLA range increase captures lexical entrenchment as argued by Diependaele et al. (2013). Furthermore, reducing lateral inhibition did not lead to increased frequency effects in simulations that we conducted with the IA model. Differences between the computational model used by Diependaele et al. (2013) and the IA model might explain the different simulation findings when manipulating the amount of lateral inhibition. Therefore, more research is needed to investigate how lexical entrenchment can be simulated.

6.

Language membership

Language membership is an aspect of a word that is important for some tasks, such as language decision (e.g., deciding if bloke is an English or Dutch word) and word translation (e.g., naming aloud the Dutch translation of English bike). Whether language membership is part of a word’s semantic features or more like a (morpho)syntactic function of an item (like noun, singular, or masculine) is still an open question. It is clear, however, that language membership is an important characteristic of a word. In models of bilingual word production, language membership has been operatized as a language tag associated with a lemma (e.g., Green, 1998). BIA was the first model of bilingual visual word recognition that included language membership information. A language node in this model links a word to the language it belongs to. When a word form in a language becomes active, its associated language node will become active as well. All localist models of bilingual word recognition we discussed assume that words activate their associated language node. However, key differences between the models are whether there is top-down inhibition or excitation from language nodes to word representations in the same or other language, and whether sublexical representations can also activate language nodes. Language nodes in BIA can inhibit words in the other language. This inhibition can be asymmetrical, with stronger inhibition from the L1 language node to L2 words than from the L2 language node to L1 words. This top-down inhibition of non-target language words acts as a filter to reduce influence of orthographically related non-target language words, in addition to lateral inhibition. Simulations revealed that BIA with asym-

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metric top-down inhibition was able to produce a pattern of results quite similar to that observed in experiments (van Heuven et al., 1998; Dijkstra et al., 1998). The BIA+ model also includes language nodes, but these cannot influence the activation levels of words in the lexicon. Thus, there is no top-down inhibition from language nodes to words in this model. The same assumption is made in SOPHIA. Multilink(+) leaves open the possibility of bi-directional connections between language nodes and words, but simulation work needs to be done to test to what extent these connections are necessary. Variants of the BIA+ model proposed by van Kesteren et al. (2012) and Casaponsa et al. (2020) assume that language nodes can also be activated by sublexical representations. These links between sublexical representations and language nodes would enable the model to account for markedness effects found in behavioural experiments (Casaponsa et al., 2020; Commissaire et al., 2019; van Kesteren et al., 2012).

7.

Task and decision processes

In this section, we briefly consider how the models discussed in this chapter handle word recognition decisions in diverse tasks used to study cross-language interactions in bilingual word recognition. All models assume that a written word is recognised when its node at the lexical level reaches a word activation threshold. This threshold is generally set close to the asymptotic value of node activations (e.g., 0.70 or 0.72). Such a threshold has been used to account for word recognition in lexical decision (e.g., English lexical decision, general lexical decision) and word identification tasks (e.g., progressive demasking) in, for example, BIA, BIA+, SOPHIA, and Multilink(+) (Dijkstra & van Heuven, 1998; Dijkstra et al., 1998; Dijkstra et al., 2019, 2022a). One of the challenges for bilingual models is to account for the range of behavioural effects observed with false friends and cognates (for a review see, Dijkstra & van Heuven, 2018) because these words have identical or similar representations at orthographic and/or phonological levels (cf. the cognates film-film and tomaat-tomato in Dutch and English). As pointed out earlier, in BIA identical false friends and cognates do not reach the default word identification threshold due to lateral inhibition effects at the word level. To enable BIA to recognise these special word types requires adaptation of its decision criteria (e.g., lowering the threshold based on activity at the lexical level). However, cognate effects cannot be simulated in BIA anyway, because the model lacks semantic representations. Models that use slot-based input coding systems (BIA, SOPHIA) have only a limited ability to account for behavioural effects of non-identical cognates.

Chapter 5. Cross-language influences in L2 visual word processing

This is different in the case of Multilink(+), which implements word-type specific representations and combines these with the use of Levenshtein distance and frequency-determined RLAs. Non-identical cognates, but also identical cognates, false friends, and translation equivalents have their own specific representation that is accompanied by item-dependent co-activation of similar word forms and/or converging semantics (see Dijkstra et al., submitted). In addition, the application of the Levenshtein distance metric makes Multilink(+) sensitive more generally to lexical characteristics like neighbourhood density and word length. Combining this index with frequency-determined RLAs leads to good simulations of empirical data, not only for ‘special’ item-types, but for languagespecific words as well (Dijkstra et al., 2019). Priming and masked priming can be simulated in bilingual localist models by presenting a prime to the model before replacing it with the target. For example, to simulate a masked priming paradigm, the prime is generally presented for two time steps (cycles) before it is replaced by the target word. This paradigm can then be combined with a word activation threshold at the lexical level to simulate, for example, performance in a masked priming lexical decision task. This implemented (masked) priming scheme can also be used by computational models for simulating cross-language orthographic priming (Dijkstra et al., 1998), as well as phonological and semantic priming, as in, for example, Multilink(+) (Dijkstra et al., 2019, 2022a). Models that contain semantic representations could also be used to simulate translation priming. For instance, in English lexical decision, the target word bike could be preceded briefly by the masked Dutch translation prime fiets. However, such simulations remain to be done. In such simulations, the word activation threshold would be set at the target word lexical level. Crucial for translation is the presence of a cross-linguistic link between lexical representations in L1 and L2. CE-IAM has Chinese and English orthographic representations that are linked through a single semantic node. Multilink(+), unlike the RHM (Kroll & Stewart, 1994), also assumes orthographic and phonological representations in L1 and L2 that are only connected cross-linguistically through a shared semantic node. So far, Multilink has only been applied to simulate word translation production. For instance, upon presentation of the Dutch word fiets, the participant utters the English word bike. To simulate this task, a printed word of one language is presented and its phonological translation in the other language is produced when its activation threshold is surpassed. Multilink is able to accurately simulate the effects of cognates and non-cognates on forward translation reported by Christoffels et al. (2006) and in a replication study (see Dijkstra et al., 2019).

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8.

Limitations of localist connectionist models

The computational models discussed in this chapter have many limitations. For example, some models only incorporate orthographic representations (BIA and CE-IAM), have slot-based input coding (BIA, SOPHIA), or discern no sublexical representations (Multilink, Multilink+, and CE-IAM). The lexicons used in the models are often limited in size and when models contain semantic representations at all, these are mostly simplified to single nodes that link representations in L1 and L2. A further limitation is that the models discussed in this chapter focus on visual word processing in adults and are therefore limited in terms of simulating L2 acquisition. However, as discussed in the section about BIA-d, there are ways to adapt the processing focused models discussed in this chapter to simulate different levels of L2 proficiency. However, despite these limitations, the models are already remarkably powerful in explaining effects of cross-language influence on L2 visual word recognition. Because computational models offer frameworks that anchor our thinking, they have heuristic value and may guide future research and theory development.

9.

Future directions

Computational models of bilingual word recognition are currently lacking in terms of all necessary components that a language processing system should incorporate. First, all models have impoverished representations. For instance, although the models in this chapter simulate word recognition in bilinguals for different languages (e.g., Dutch-English, French-English, Chinese-English), most models are limited to alphabetic languages. It is crucial that we start building bilingual models that can handle a range of different writing systems (e.g., Korean, Hindi, Arabic). Doing this will help us to understand universal aspects of input coding, connections between orthography and phonology, and interactions between non-alphabetic languages in printed word recognition. In addition, the nature of lexical-semantic representations should be considered in much more detail. Second, current models are lacking in a specification of processing in different task situations. For instance, how decisions in particular tasks are implemented in bilingual models is still rather simplistic. We should consider the implementation of more sophisticated decision criteria for nonwords (e.g., Dufau et al., 2012) and words in the target language (e.g., Grainger & Jacobs, 1996) and the non-target language. An important ‘parsimony assumption’ that is embodied in the models we have discussed is that the word recognition process itself (in terms of activa-

Chapter 5. Cross-language influences in L2 visual word processing

tion) is not directly affected by the task at hand. However, the opposing possibility that the word activation process itself depends on task demands has scarcely been investigated. Finally, in order for presently available models to be applicable to (L2) word learning, the incorporation of learning mechanisms must seriously be considered. This may also require a clarification of the role of Working Memory and other sorts of memory (e.g., episodic memory) in the models. In all, we hope to have shown there is a cornucopia of opportunities for further integrating empirical studies on L2 word recognition and computational models by careful research.

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chapter 6

Cross-language influences in L2 pre-lexical and lexical processing and acquisition Anna E. Piasecki & Ton Dijkstra

University of the West of England | Radboud University Nijmegen

We review how second language (L2) printed and spoken word recognition is affected by first language (L1) characteristics. First, sublexical word properties in bilingual word recognition are considered, in particular diacritical marks and Capital Letters in a script, script-specific letters, language-sensitive bigrams, and grapheme to phoneme correspondences (GPCs). Next, we focus on cross-language effects for words varying in orthographic neighbourhoods and morphological family size, cognates, and interlingual homographs. For both sublexical and lexical aspects, we examine if language membership information might be used to facilitate processing. Finally, we describe how cross-language similarities and differences play out during second language acquisition. A summary of sublexical and lexical cross-linguistic effects in L2 processing and acquisition concludes the chapter. Keywords: cross-language, L2, sublexical, script, Second Language Acquisition

1.

Introduction

The language in which this chapter is written, English, is not the first language (L1) for many of its readers. Some readers may have Dutch or German as their first language, which use the same alphabet as English. The first language of other readers may be Russian or Chinese, in which case they had to learn a partially or completely different type of script as part of the cognitive equipment required to understand this text. Irrespective of whether it was in their L1 or not, readers probably processed the preceding paragraph quite fast and efficiently. Although the average adult reader knows at least 42,000 words in their first language (L1; Brysbaert et al., 2016), they are nevertheless able to read about 230–260 words per minute,

https://doi.org/10.1075/bpa.16.06pia © 2023 John Benjamins Publishing Company

Chapter 6. Cross-language influences in L2 pre-lexical and lexical processing and acquisition

depending on the length of the words in the text (Brysbaert, 2019; see also https:// psyarxiv.com/js49r/, and Rayner & Clifton, 2009). Depending on the proficiency level in their second language (L2), the estimate for the L2 reading rate of many bilinguals is only about 10–20% lower than that of L1 readers. For instance, in an eye-tracking study involving Dutch-English bilinguals, Cop et al. (2016) showed that the average total reading time for a word in a novel by Agatha Christie was about 261 ms in the L2 (English) original and about 232 ms in the L1 (Dutch) translation. In light of such fast reading times for L2, one starts to wonder to what extent reading and learning to read in a weaker second language are affected by the stronger first language. On the one hand, one might propose that what L2 has in common with L1 might facilitate L2 learning and reading. For instance, when L2 and L1 share their alphabet, or when L2 words share form and/or meaning with L1 words (as cognates or translations do), knowing the L1 might facilitate processing. On the other hand, learning a new language may increase the size of the mental lexicon by tens of thousands of words, all of which might experience competition from their old L1 and new L2 ‘neighbours’. Furthermore, to properly capture the different characteristics of the new language, for instance, in terms of its script (e.g., differences in letters, bigram distributions, and grapheme to phoneme conversion rules), it might be necessary to organize L2 in a substantially different way from L1. In three sections, we will review available evidence on cross-language effects between the L1 and other languages of a reader at sublexical / prelexical and lexical levels during processing and learning. All cross-language effects concern first and other language representations underlying bilingual word recognition. Different models propose the presence and processing of different types of representation (see van Heuven & Dijkstra, this volume). The proposed units may be directly derived from the input signal (e.g., graphemes corresponding to letters, or phonemes corresponding to speech sounds; as in BIA+ and TRACE) or may be of a more abstract nature (e.g., open-bigrams or rhyme units). Although we will provide theoretical links to models where they are relevant, our review will follow a data-oriented, bottom-up approach to avoid any theoretical bias. First, we consider cross-language influences during prelexical stages of processing and for sublexical units. This includes a discussion of units like diacritical marks, letters, bigrams, and grapheme to phoneme correspondences (GPCs) that are script-specific. Second, we discuss cross-language influences in L2 lexical processing. Given the recent increase of scientific interest paid to the auditory modality, we consider both bilingual printed and spoken word recognition. Here we describe effects of cross-linguistic similarity (neighbourhood and cohort membership), morphological family size, and special item types (cognates, interlingual

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homographs) in adult bilinguals. Next, we consider the role of language membership with respect to cross-linguistic effects at the level of sublexical and lexical representations. Third, we investigate cross-linguistic effects of L1 on L2 during L2 lexical acquisition. We end the paper with a summary of its most important conclusions.

2.

Cross-language influences in L2 sublexical processing

When readers encounter scribbles on paper, they do not know whether these are words at all. Even when the scribbles consist of letters, they might constitute nonsensical letter strings that form unpronounceable nonwords or pronounceable pseudowords. And when they do indeed represent words, they could still be words in multiple languages, because it is not uncommon that the language of a sentence changes midway (especially during speaking) or because ‘borrowed’ terminology is used (cf. lockdown or black-out in Dutch). These observations imply that that, in its very first stages, word recognition must be signal-driven and bottom-up. In line with this, most researchers now argue that the bilinguals’ orthographic mental lexicon is organized in a languageindependent way, and that their reading entails an early co-activation of overlapping words in both languages (i.e., language non-specific access, see van Heuven et al., 1998). Of course, this view is not applicable in cases where languages use different scripts and it only applies to words that are sufficiently similar to the presented input (the input cork might activate both work and vork, but not battle or tafel in Dutch-English bilinguals). The bottom-up processing assumption also fits well with the assumption of an alphabetic representation that is shared by the languages in question. Assuming a shared alphabet on the one hand, but separate word form lexicons and language-specific lexical activation on the other, would require complex theoretical justification (which was lacking in early bilingual processing models presuming separate storage of languages, cf. Kroll & Stewart, 1994). According to most (symbolic) views of bilingual word recognition, letters and whole words are the units that are represented in the mental lexicon of the reader, irrespective of whether these are monolingual or bilingual. This is understandable, because these units are physically there time and again. There is a general consensus that in early stages “individual letters are the key elements for orthographic processing and that it is the mechanism used to code for the positions of these letters that critically determines the nature of the orthographic code” (Grainger, 2008, pp. 22–23). What is still under debate is the precise letter position coding scheme used at the sublexical level, and how the scheme is learned (also

Chapter 6. Cross-language influences in L2 pre-lexical and lexical processing and acquisition

see van Heuven & Dijkstra, this volume). With respect to bilinguals, it is unknown whether the letter position coding scheme is different for their two languages and how it is sensitive to the orthographic depth of their L1 and L2 (cf. Meade, 2020). Conceivably, in a sensitive learning system not only ‘black-and-white’ representations, like letters and words, might emerge, but also an array of ‘grey’ units (cf. Seidenberg & McClelland, 1989). For instance, sensitivity to statistical regularities in language input might result in the emergence of consonants and vowels as letter units with different distributional characteristics, and of bigrams or trigrams (e.g., Seidenberg & McClelland, 1989), syllable-like (ONC) units with vowel-centers (possibly affected by phonology) (e.g., Taft, 1979), phonologically defined syllables (e.g., Conrad et al., 2007), and morphemes (e.g., Taft & Forster, 1976). Due to the different statistical properties of vocabularies across languages, some intermediate-level units would be more important in L1 than in L2 and vice versa (as an extreme case, consider the lexical representation of tone, absent in English but present in Chinese).

2.1 Consequences of script differences In many computer models, input detectors are assumed to be largely case, font, and size insensitive (cf. van Heuven & Dijkstra, this volume), and a lot of word recognition research has been focused on later processing stages. However, research employing more recent sensitive research techniques (e.g., electroencephalography or EEG, event-related potentials or ERP, and functional magnetic resonance imaging or fMRI), suggests that the first processing stages are not invariant to the size and font (or shape) of the input, but rather that script deviations are resolved early on (e.g., Chauncey et al., 2008). It is largely unknown to what extent bilingual readers are sensitive to script differences. Scripts differ in many respects, for instance, in their use of diacritical marks (e.g., French vs English), Capitals as indicators of nouns (e.g., German vs English), number of letters that overlap (e.g., alphabets may differ in only a few letters or more substantially; compare English to Norwegian, Russian, or Greek), the prominence of particular letter combinations (e.g., bigrams), and the grapheme to phoneme conversion (GPC) rules they apply. Such script differences might affect lexical competition and the speed of item recognition in a target language, and also result in a faster or slower differentiation of the two languages. In addition, they might lead to a faster or slower identification of the language of a word, which in itself might facilitate processing. Few studies have so far investigated the consequences of these more subtle script differences for bilingual processing. They are summarized in the following sections.

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2.2 Diacritical marks One immediately obvious difference between printed languages such as English and French concerns the relative prominence of diacritical marks. Relative to English, French abounds with various accents that have consequences for pronunciation (e.g., é, è) or mark a deleted consonant that is no longer pronounced (e.g., ô ‘covers up’ an s in hôpital, meaning ‘hospital’). It has been shown by Mathey and Zagar (2000) that such diacritical marks are relevant for monolingual processing, because their presence or absence affects the similarity of a word to other words (i.e., its neighbourhood characteristics). It is therefore likely that accent will also play a discriminatory role in bilingual processing, but we are unaware of any research with respect to this issue. Another case of diacritical marking concerns the use of upper case letters for noun onsets in German. In German, nouns are indicated by onset capitalization, irrespective of word position in the sentence: Die Frau arbeitet an der Uni (‘The woman works at the university’). Studies investigated whether onset capitals can act as language-specific cues constraining lexical interaction between the bilingual’s languages (e.g., Hose-hose, the first being a German word meaning ‘trousers’ in English). Bilingual speakers were indeed found to be sensitive to language-specific sublexical information (Piasecki, 2012; Piasecki & Warren, 2008). In addition, there was a stronger impact of sublexical cues in lower L2 proficiency bilinguals. In a later study, German-English and English-German bilinguals with varying proficiency in their L2 completed word naming tasks in English and in German (Piasecki & Dijkstra, 2022). The critical stimuli were non-identical cognates that were presented in different formats, e.g., with an onset capital letter (indicating German), with all-small letters (indicating English), with all-capital letters (neutral condition), or with a capital letter inserted in the middle of the word (control condition). Cognates are translation equivalents with word-form overlap, like paper (English) and Papier (German). This unpublished study confirms the previous findings of a proficiency-driven use of sublexical cues. It suggests that this dependency is stronger for languagespecific lexical items (non-cognates) and that effects can occur in both L1 and L2.

2.3 Different scripts and shared letters Scripts can differ considerably in that they may use (partially or fully) different letter sets. For instance, English and Russian use largely different Latin and Cyrillic alphabets, but some letters occur in both with a similar or different mapping onto phonology. For instance, in Russian the letters A and P are used, respectively, to represent a and r sounds. There is evidence that letters that are shared between quite different scripts are still co-activated in both languages during processing.

Chapter 6. Cross-language influences in L2 pre-lexical and lexical processing and acquisition

Iakovleva et al. (2015) had Russian-English bilinguals perform an English lexical decision task involving cognates and non-cognates differing in the degree of English-Russian overlap of their constituent letters. They observed an English cognate facilitation effect that was modulated by cross-language overlap in orthography and whether this overlap was ambiguous or transparent relative to its phonology. An example of an ambiguous item is the English word guru, which, due to differences in writing systems, might be read as /digi/ by a Russian monolingual. Transparent is an English word like koala, which only mismatches with Russian in the grapheme l. An English control word like filter is composed mostly of letters that do not exist in the Russian alphabet. These results suggest that alphabetic representations are shared by different languages, which may have important consequences for bilingual processing. First, it implies that it is the summed frequency of alphabetic units in L1 and L2 that determines initial letter input activation in both languages. As a consequence, L2 processing may not necessarily be at a disadvantage in the first stage of perceptual processing of a word.

2.4 Bigrams and letter transitional probabilities So far, we have argued that the specific ‘make-up’ of a language in terms of diacritical marks, capital letters, or script-specific letters, may affect which word candidates of different languages are activated. Another language-sensitive cue lies in the orthographic regularities and letter transitional probabilities of a language, for instance, in the composition of letter pairs (bigrams) and letter triplets (trigrams) of words (Chetail, 2015). As an example, the letter combination wh is typical for English, so there is a higher chance that an encountered word is English if this bigram is detected in the input. (However, the word whiskey is also acceptable as a word in Dutch, so dismissing the input word in this case would not be wise.) It is currently unclear to what extent sublexical “cues” to language membership are limited to consecutive letters or to more flexible structures (e.g., open bigrams). Several studies have used the prominence or exclusive presence of some bigrams in a particular language to investigate how indicative sublexical information is used in bilingual word recognition. Vaid and Frenck-Mestre (2002) presented French-English bilinguals with words that contained or lacked bigrams specific to their L1 (French; e.g., voeu vs. loup) or L2 (English; e.g., snow vs. drop). Participants had to decide whether an item belonged to L1 or L2 by pressing one of two response keys. Faster processing took place for words that contained a language-specific bigram (i.e., orthographically marked words), particularly in L2 (English). The researchers interpreted this pattern of results as support for a perceptual search strategy (Vaid & Frenck-

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Mestre, 2002). They argued that orthographically marked words allowed participants to employ bottom-up cues by means of a redundancy check in their L2. That is, a letter string that stood out as an L2 word (e.g., snow) was accepted as such, arguably even prior to its actual lexical identification. In contrast, responses to orthographically marked words in L1 (e.g., voeu) were considerably slower than those to orthographically marked L2 words, suggesting that for these L1 items an actual lexical identification took place. In addition, these L1 words were recognized as fast as words without language-specific bigrams in either language (i.e., orthographically unmarked words; loup and drop). This was interpreted as further evidence that the items required full identification. Finally, among the unmarked words, the authors detected a tendency towards the ‘classic’ languagedominance effect, with items in the dominant language being recognized faster than items in the less dominant language. Because this study’s research design included stimulus materials distinct to the bilinguals’ L1 as well as L2, and participants made a language decision rather than a lexical decision, lexical activation in both languages was important to performance, which may also have induced certain strategies (e.g., a perceptual versus a lexical search strategy). Thus, limiting activation in the experimental trials to one of the languages was not possible. This complicates an evaluation of the extent to which language-specific cues can restrict activation to one language.

2.5 Grapheme to phoneme correspondences Reading is not just a matter of activating and identifying the orthographic aspects of printed words. Associated sublexical and lexical phonological representations become active during reading as well (e.g., Coltheart et al., 2001). This raises the question to what extent phonological representations of different languages are activated during bilingual reading, and to what extent these interact and feedback activation to their orthography (Meade, 2020). Brysbaert et al. (2002) proposed that, when a bilingual’s languages share their alphabets, a proportion of the grapheme (letter) to phoneme (sound) mappings involved might also be shared, which could result in a comparable speed of activation of sublexical phonological codes. Brysbaert et al. mention the analogous mappings of the letters b, c and d to the corresponding phonemes in Dutch and French. In all, when grapheme to phoneme conversion correspondences overlap for L1 and L2, this may make it easier to learn the L2. Mappings that are unique to the L2 might, however, be acquired at a different rate, depending on L2 input. Furthermore, Brysbaert et al. point out that sometimes L1 and L2 grapheme to phoneme correspondences are incongruent. Two examples are the letter u, which may be mapped differently in English (as a /schwa/ in duck) and in French (as

Chapter 6. Cross-language influences in L2 pre-lexical and lexical processing and acquisition

/u:/ in dur, meaning ‘hard’); and, a grapheme like ee, which is pronounced /i:/ in English (as in feet) but /e:/ in Dutch (as in keet, meaning ‘shed’). In this discussion, combined L1-L2 letter frequency and separate L1 and L2 word frequencies are deemed to be important determinants of processing times in bilingual word processing. In two studies, Jared and colleagues (Jared & Kroll, 2001; Jared & Szucs, 2002) demonstrated a coactivation of grapheme to phoneme mappings in both French and English when English-French and French-English bilinguals named words (e.g., interlingual homographs like pain or word body neighbours like English bait and French fait, sharing the orthographic rime -ait) in their dominant or less dominant language, depending on the order of presented target languages, the participants’ language fluency, and the recency of language usage. In line with Brysbaert et al. (2002), incongruent mappings in the interlingual homographs led to longer latencies and more errors (cf. Haigh & Jared, 2007).

3.

Cross-language influences in L2 lexical processing

We have presented evidence that at the sublexical level (often assumed to involve units that are prelexical as well), shared alphabetic representations of letters and bigrams result in co-activation of lexical candidates in the bilingual’s languages. For instance, if the input word film activates shared English/Dutch letter representations, both the English and Dutch word forms of film will be activated. In fact, one might propose that – just as we proposed for letters – the English and Dutch orthographic word forms are actually one and the same. However, from a theoretical perspective, this would be undesirable. For instance, the plural of the word troll in English is trolls, whereas the plural of troll in Dutch is trollen. As another example, in Dutch the determiner of auto is de (masculine / feminine), whereas in German the determiner of auto is das (neuter). Such distinctions would justify a separation of word forms, or at least of lemmas or morphemes. There is also empirical evidence on interlingual homographs and cognates suggesting that language-specific word form representations are required. An example of a non-cognate interlingual French-English homograph is the item coin, which means corner in French. It has been shown that the frequency of a homograph in each language has a separate role to play in processing (Dijkstra et al., 1998). Similarly, it is the frequency of the cognate in the L2 that is the first determinant of the reaction time in an L2 lexical decision task, rather than the item frequency in L1 or the summed L1+L2 frequency (see Peeters et al., 2013, for evidence on French-English bilinguals performing an English lexical decision experiment).

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Assuming that word form representations are differentiated with respect to language (even when they are orthographically identical), pairs of items that are highly similar in the two languages should be co-activated when one of them is presented (cf. Peeters et al., 2013). This could lead to facilitated or inhibited processing relative to a control word that exists in only one language. This reasoning has been applied to a number of different item types: cross-linguistic neighbours (van Heuven et al., 1998; Mulder et al., 2018), words with differently sized morphological families (Dijkstra et al., 2005), cognates (Dijkstra et al., submitted), and interlingual homographs (Lemhöfer & Dijkstra, 2004). Here we have space for only a limited review of studies involving these special word types (also see Dijkstra & van Heuven, 2018). In line with the signal-driven assumption formulated above, a printed input word leads to the coactivation of a whole set of possibilities. For instance, the input cork may initially co-activate other similar items in the mental lexicon, like corn and work. Such items are called neighbours. Research indicates that not only neighbours of the input word’s language are activated, but also of other languages. For instance, English cork may co-activate the word vork (a cognate with fork) in Dutch as well (see RT data in van Heuven et al., 1998, and Dirix et al., 2017; see EEG data in Midgley et al., 2009, and Grossi et al., 2012). Van Heuven et al. (1998) found clear contributions of both English and Dutch neighbours to English lexical decision times in Dutch-English bilinguals. However, in a recent study by Mulder et al. (2018) English word characteristics, like neighbourhood size, bigram frequency, and word frequency in English, dominated the responses in lexical decision experiments for a new generation of the same participant group. Although L1 (Dutch) neighbours did exert some effect on responses (especially for nonwords), even when a stronger neighbourhood manipulation (involving ‘hermits’, words without any neighbours) was applied, the effect of Dutch on English neighbourhood size was limited. The different result patterns in the two studies were accounted for in terms of L2 (English) proficiency differences: today’s Dutch university students are much more proficient in English than their ‘mates’ were 25 years ago. For a study examining the relationship between neighbourhood and markedness characteristics, we refer to Commissaire et al. (2019). In addition to word form neighbour, reading a word also leads to the activation of many other words that are morphologically (rather than lexically) related. For instance, the word heart has morphological family members like heartless and heartache. Some words are relatively productive with respect to the number of their morphologically family members (e.g., house), while some others only have a very limited family (e.g., horizon). Mulder et al. (2014) showed that in DutchEnglish bilinguals making English lexical decisions, increasing an item’s morphological family size in both L1 Dutch and L2 English facilitated the processing of

Chapter 6. Cross-language influences in L2 pre-lexical and lexical processing and acquisition

both cognates and English control words. Similar results were obtained for interlingual homographs in a study by Dijkstra et al. (2005). As mentioned above, cross-linguistic effects have been consistently reported for special items like cognates and interlingual homographs. Cognates have often been reported to be facilitated in L2 processing relative to unrelated control items (for an overview see Voga & Grainger, 2007). The size of the facilitation effect depends on the degree of between-language overlap of the two cognate members in terms of orthography, phonology, and semantics (Comesaña et al., 2015), with possibly a disproportionally large effect for orthographically identical cognates (Dijkstra et al., 2010; see below for further discussion). Other factors of importance are relative frequency in the two languages and the task at hand (Peeters et al., 2013), and stimulus list composition (Comesaña et al., 2015; Poort & Rodd, 2017; Vanlangendonck et al., 2019). In a recent Dutch-to-English masked priming study with Dutch-English bilinguals, Dijkstra et al. (2022) compared cross-linguistic orthographic and semantic priming effects for neighbour cognates (boek – book), translation equivalents (kooi – cage), orthographically related neighbours (neus – news), and unrelated words (huid – coat). Using prime durations of 50 ms and 83 ms, significant facilitation effects were found for cognate and translation pairs, but no orthographic neighbour effects. According to the authors, the observed cognate facilitation effect could in large part be ascribed to orthographic-semantic resonance. They suggest that lexical competition effects (e.g., in terms of lateral inhibition) might be sensitive to stimulus list composition. The result pattern was replicated by simulations with a computational model, Multilink+. In contrast to cognates, interlingual homographs only share form aspects across language, but not meaning. An example is the English word list and its Dutch counterpart (with the Dutch list meaning ‘clever trick’). Interlingual homographs have been reported to be processed more slowly than matched controls, especially in stimulus lists that mix items of different languages and less so in pure (only target language) lists (e.g., Lemhöfer & Dijkstra, 2004). Cross-language inhibition effects for interlingual homographs further depend on the relative frequency of the homograph readings and task demands (Dijkstra et al., 1998; cf. Durlik et al., 2016). A recent study (Goertz et al., 2022) indicates that many Dutch-English interlingual homographs are problematic in the sense that they may also be considered as cognates or because they have cognates as their neighbours. Examples are the English items beer and bear, with Dutch translations bier and beer. Both beer and bier, and bear and beer, can be considered to be neighbour cognates, but beer and beer are also interlingual homographs. Goertz et al. show that lexical decision

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times to items that are both neighbour cognates and interlingual homographs are affected by both homograph interference effects and cognate facilitation effects.

3.1 Language membership An issue that has become increasingly important over the years is whether the word recognition process of bilingual language users might benefit from cues that signal that an input word belongs to one language rather than another. Such ‘language membership’ information might facilitate the word recognition process by reducing or eliminating activation of non-target language competitors of the input word. Language membership could be derived from both sublexical and lexical information sources. For instance, the presence of language-specific cues like accents and capital letters could speed up bottom-up word recognition processes by allowing a faster reduction of the lexical competitor set. Diacritical marks often signal the language membership of an item, the use of which would immediately have its effects in a task like language decision (‘Is this an English or a French word?’), which requires the distinction of languages. In addition, language membership information could be used in a top-down way by reducing interference by lexical competitors from a non-target language (Dijkstra & van Heuven, 1998) or even result in selective access to the target language (Dijkstra, 2005, p. 187). Alternatively, language membership might function just as a label indicating to which language a word belongs. Bilingual word retrieval models like BIA, BIA+, and Multilink all assume the presence of language membership nodes in their lexical networks (see van Heuven & Dijkstra, this volume). If language membership can operate as an effective cue for word recognition, effects of diacritical marks should become evident in lexical decision tasks. There is some evidence with respect to this issue for Norwegian and English in the context of script differences. Van Kesteren et al. (2012) argued that if early bigram information is directly linked to language membership nodes, this could act as an early activator of the language of the word. Next, such language membership information might be used to discard any activated neighbours from the nontarget language. To investigate both aspects, Van Kesteren et al. considered the language combination of Norwegian and English. One Norwegian written norm is Bokmål, which uses a few language-specific letters (like å and ø) and bigrams (sj), while lacking some English letters (like w, as in hawk) and bigrams (like ea as in veal). In their study, Norwegian-English bilinguals performed different tasks: Norwegian-English language decision, mixed English lexical decision, and mixed Norwegian lexical decision. The words from the nontarget language in the mixed lexical decision experiment required a ‘no’ response. The decisions of the bilin-

Chapter 6. Cross-language influences in L2 pre-lexical and lexical processing and acquisition

guals were found to be facilitated not only by the presence of language-specific letters, but also of language-specific bigrams. Comparing their three experiments, the authors concluded that lexical decisions were affected by language membership information that was directly derived from sublexical stimulus characteristics (e.g., bigrams). At the same time, the use of sublexical information was strategically dependent on the task at hand. For instance, language-specific letters facilitated responding in language decision. However, it did not facilitate response times in lexical decision when the incorporated nonwords included the same letters. Note that the word-nonword decision could not be facilitated in this situation by strategic use of language-specific letter information. Participants were apparently picking up the sublexical information, but did not act upon it when it was not useful in the task context. Further evidence by Casaponsa et al. (2014) indicates that the use of orthotactic information across languages depends not only on task demands, but also on the participants’ L2 proficiency. At present, available models of bilingual word recognition (see van Heuven & Dijkstra, this volume) regard language membership as a lexical property only. However, the sublexical evidence collected in these and Piasecki’s (2012) studies indicates that they need to be adapted in this respect. A model like BIA+ (Dijkstra & van Heuven, 2002) acknowledges that language membership is activated on the basis of both lexical and sublexical information, although it does not specify how the latter information source is used (see van Kesteren et al., 2012, for some ideas). Additionally, in BIA+, it is assumed on the basis of the prevalent literature, that language membership information does not usually play a significant facilitatory role in bilingual word processing, except in language decision tasks. Accordingly, in BIA no feedback link from language membership nodes to lexical nodes is indicated. Such feedback would either be not useful or too slow to affect word recognition. However, some researchers do not agree with this conclusion. Hoversten et al. (2015) suggested that a quick identification of the language of the input might guide attention to the language at hand. Strategic global language suppression might then facilitate processing for that language. In their EEG study, SpanishEnglish bilinguals performed both a Language Go Task (LGT) and a Semantic Go Task (SGT). In both tasks, the response required a decision based on both animacy and language membership. In LGT, the decision was language-dependent (‘go’ for English words, ‘no-go’ for Spanish) and the response hand assignment was animacy-based (left hand for living items, right hand for non-living). This allocation was reversed in SGT. Here, participants made a go response to animate words and a no-go response to non-animate words, using their left hand for English words and their right hand for Spanish words. Participants were found to respond significantly faster in LGT than in SGT (1000 ms vs 1028 ms). Interestingly, ERP latencies for go and no-go trials indicated that language membership

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became available more quickly than semantic information (at about 300 ms poststimulus in LGT vs 400 ms in SGT). In addition, a significant N400 frequency effect was found between go and no-go responses for LGT, but not for SGT. The N400 marker, a negative-going deflection in the ERPs peaking between 350–500 ms after event onset, has been proposed to reflect lexical-semantic integration efforts. The data indicate that (a) language membership became available before semantic information, and (b) the depth of word processing differed between the two tasks. On the basis of the results, Hoversten et al. conclude (2015, p. 2114) that “language membership information seems to have arrived early enough to filter ongoing word processing, but some degree of semantic processing was still observed in the nontarget language.” In this interpretation of the results, language membership information becomes available so rapidly that this information can be used to selectively modulate the degree of processing in each language. However, we would like to point out that this conclusion is only valid when the decision processes in LGT and SGT would make comparable use of semantic information. This does not appear likely, because semantic information is not indicative of language membership. In other words, the observed difference in depth of processing might be a consequence of the task demands at hand, rather than indicate that semantic processing is modulated by language membership information (also see De Deyne et al., this volume). If stimulus list composition affects decision processes in an analogous way, this can explain differences in results between experiments that involve the same task but different stimulus materials (refuting, for instance, the conclusion by Comesaña et al., 2015, that topdown language membership effects exist).

3.2 Cross-language influences in bilingual auditory word processing Most research on cross-language effects has been concerned with bilingual word reading. Pioneering research on bilingual auditory word processing was done by Marian and colleagues (e.g., Blumenfeld & Marian, 2005; Marian et al., 2003; Spivey & Marian, 1999). For instance, using an eye-tracking paradigm, Spivey and Marian (1999) showed Russian-English bilinguals a panel of four objects, including a marker (flomaster in Russian) and a stamp (marka in Russian), and instructed them in English to Put the stamp below the cross or in Russian Poloji marku nije krestika with the same meaning. Both the Russian and the English instructions resulted in a longer looking-time of participants at between-language distractors than at control items with a name that did not begin with the same onset phonemes as the Russian or English target item. The interference effect of between-language items was later found to be present even on trials incorporating within-language distractors (Marian et al., 2003).

Chapter 6. Cross-language influences in L2 pre-lexical and lexical processing and acquisition

In line with these conclusions, Schulpen et al. (2003) found that lexical decisions made by Dutch-English bilinguals in two cross-modal priming experiments were slower for interlingual homophones (e.g., English /leaf/) than for onelanguage control words (e.g., Dutch /lief/) when primed with their corresponding English or Dutch counterparts. These findings indicate the presence of competition between the near-homophonic items. Later research (e.g., Ju & Luce, 2004; Lagrou et al., 2013) further supported and refined these findings. Ju and Luce (2004) showed that the observed auditory cross-language effects in the eye fixations of Spanish-English bilinguals were sensitive to similarity in voice onset times, while Lagrou et al. (2013) found for auditory lexical decision that both the semantic sentence constraint and the firstlanguage accent of the speaker modulated the interlingual homophone effect, although it did not eliminate it. Also using an eye-tracking paradigm, Blumenfeld and Marian (2005) showed that both members of a cognate pair (e.g., English /hen/ and German /Henne/) with high phonological overlap were co-activated by English-German and German-English bilinguals. L2 proficiency and duration of phonological overlap were found to affect the results. Recent research has considered several aspects that are ‘special’ to spoken cognate processing. For instance, Muntendam et al. (2022) investigated how word stress in Turkish-Dutch early bilinguals affects bilingual cognate processing in auditory lexical decision in Turkish (non-dominant L1) and Dutch (dominant L2). Cross-linguistic inhibition effects arose in Turkish, while some facilitation effects occurred for Dutch. Cognate effects were found to depend on stress position, showing that, in spite of Dutch dominance, cognate processing in these Turkish-Dutch early bilinguals was especially sensitive to Turkish stress position. Mulder et al. (2015) found that effects of cognate status are sensitive to the degree of reduction during spoken word processing, underlining that there is an important bottom-up component to auditory cognate facilitation. In an auditory English (L2) lexical decision task, proficient Dutch-English bilinguals and English monolinguals responded to Dutch-English cognates and control words of three syllables that were presented as full forms or reduced with their poststress schwa removed (e.g., /sumri/ instead of /summary/). Reduction negatively affected the performance of both participant groups to the same extent, while a word’s cognate status further influenced how listeners processed reduced or full forms. A cognate accuracy advantage was observed only in full forms, and it was somewhat less for reduced cognates than for reduced controls. There was also a larger negative reduction effect for cognates relative to controls. Mulder et al. (2021) further examined reduction effects in a complex study combining auditory lexical decision with EEG. The results of Mulder et al. (2015)

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were essentially replicated in a post-stress RT analysis. Dutch-English bilinguals responded more accurately in English (L2) to cognates than to controls, but only when cognates were presented as full forms. The reduced cognate form apparently did not benefit from co-activation of first and other language representations. Furthermore, bottom-up form representations were shown to play a major role for both pre-stress and post-stress reduced words. Bringing together evidence from reading and listening, Frances et al. (2021) compared the effects of L1 on L2 when phonological and orthographic similarity of items was varied across modalities. High proficiency Spanish-English bilinguals performed English (L2) visual and auditory lexical decision tasks. When items were highly similar in one modality and not in the other, faster RTs arose (cf. Dijkstra et al., 1999). However, similarity across modalities had a negative effect. In line with Dijkstra et al. (2010), the authors found support for identical cognates as a special category (also see Miwa & Baaijen, 2021). The authors concluded that co-activation of languages takes place in both modalities simultaneously.

4.

Cross-language influences in L2 lexical acquisition

We have so far reviewed the L1 and L2 word recognition performance of more or less proficient bilinguals ‘frozen’ at a particular moment in time. Second language acquisition research, however, considers how L2 learners develop into more proficient bilinguals ‘fluidly’, over a period of time. Thus, the two research domains can be considered as complementary. In an approach like the distributed connectionist paradigm, every encounter with a word has a small effect on the word’s representation and entrenchment in the mental lexicon. As a consequence, the established L1 might be considerably more stable and less sensitive to change than the new L2. Nevertheless, for both L1 and L2, learning effects accrue and significantly change the lexicon over time. The performance focused upon in localist connectionist models could then be considered as the end point of the learning process for a participant with a certain degree of L2 proficiency. This theoretical view is worked out in the BIA-d model (Grainger et al., 2010), where L2 development is arranged in terms of a Hebbian learning rule that gradually brings the bilingual lexicon in line with available L1 and L2 aspects (see van Heuven & Dijkstra, this volume). To consider how L2 word acquisition is affected by the omnipresent L1 representations, we must first decide how L2 word representations are formed at all. Storkel and colleagues (e.g., Storkel & Lee, 2011, pp. 193–195) discern three processes that underlie L2 lexical acquisition: triggering, configuration, and engagement. During the triggering process, it is decided if an encountered item

Chapter 6. Cross-language influences in L2 pre-lexical and lexical processing and acquisition

corresponds to an existing or a yet unknown representation. In the latter case, a new representation must be allocated to the input item in long term memory (i.e., the mental lexicon). Next, the input’s phonology (sound sequence) or orthography (letter string) and its meaning (semantic referent) are stored as part of this representation during a configuration phase. Initially, this configuration may be underspecified or even partially incorrect. Later encounters with the item will strengthen its memory trace and update it, resulting in faster activation and selection. Furthermore, the new item must be integrated (entrenched) within the already available lexicon. This engagement process may occur (also outside of training) during memory consolidation (which turns a fragile episodic memory trace into a more stable representation), for instance, during sleep. According to the empirical literature on proficient bilinguals (reviewed above), the mental lexicon is integrated over languages and modalities. Word processing is language nonspecific and visual and auditory representations interact. In contrast, many studies on L2 learners, especially children, assume that children begin their bilingual language development with relatively separate representations for each language that do not show much interaction. More recently, the theoretical view in this domain appears to shift in the direction of more integrated views. Considering the issue from a general perspective, the presence of an L1 could both help and hinder learners of a new language (cf. Elgort & Piasecki, 2014). Similarity in scripts between L1 and L2 should help their word learning, as would the lexical overlap between the languages due to etymology and borrowings (e.g., presence of cognates and less obvious similarities). Indeed, in an extended study, Schepens et al. (2016) demonstrated that larger lexical and morphological distances between L1 and L3, and L2 and L3, are associated with lower L3 learnability. In this study, the L3 was Dutch and the learners came from all over the world. The L1 orthographic representations and GPCs that L2 learners already have at their disposal could provide them with a starting set of ‘guidelines’ or ‘cues’, especially for new languages with shallow or L1-like orthography. For instance, learning the Italian word abastanza as an L2 word might profit from the availability of orthographic representations and simple grapheme to phoneme correspondences in their L1 English (cf. Meade 2020). Furthermore, the learner of an L2 often already has a concept available that is close enough to the meaning of the new word to use it for initial meaning representation. It seems likely that learning would also be facilitated in this case (Meade & Dijkstra, 2017). Using the characteristics and rules of the L1 also provides a good scaffold for developing new representations because early in the L2 learning process, a lot of information is available for the L1, whereas the new information about the L2 is still sparse. However, the more L2 proficient the learner becomes, the big-

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ger the L2 vocabulary and the stronger the necessity of differentiating L2 from L1 in sublexical and lexical orthographic and phonological representations and semantics. After all, the ultimate goal is to develop L2-like representations for (non-embarrassing) communicative purposes (cf. Bordag et al., 2021). When L2 learners are acquiring the new language (e.g., in an immersion context) primarily from spoken input, the initial orthographic representations might be too general or a little ‘off ’ in their specification, possibly even more so if the new language also has a different script (cf. Cook et al., 2016). A related hypothesis is that acquiring the precise pronunciation of cognates is delayed (cf. Amengual, 2012, 2018). Finetuning of the item’s non-L1 meaning might occur with more L2 experience. According to these considerations, no special mechanism is required to account for L2 learning and there is no special ‘protection’ for the newly learned and still weak L2 (see Dijkstra et al., 2012, for BIA simulations arriving at the same conclusion). Word learning processes are not separated for L1 and L2. The available data are in line with an account of L2 word learning in which the same processes underlie acquisition of L1 and L2 (cf. Meade & Dijkstra, 2017; Ellis, 2006). During development, the weak L2 is neither ‘protected’ from L1 influences nor fully ‘parasitic’ on it. Because the L1 was learned earlier, L1 and L2 differ in their sensitivity to language characteristics. For instance, on the basis of their L1, Japanese speakers have learned that the phonetic difference between /r/ and /l/ is non-informative, and so it is hard to pick up on that ‘cue’ when learning English as their L2 (cf. Ellis, 2006). As Bartolotti and Marian (2017, p. 5) state, “prior language experience shapes later language learning”. According to this view, the ‘wordlikeness’ of items to be learned, relative to already available vocabulary, should be an important determinant of learning. Wordlikeness could be assessed in terms of neighbourhood properties (cf. Vitevitch, 2012) and orthotactic probability distributions of L1 and L2 words. In addition, new words could be cognates, interlingual homographs, or homophones. In the beginning, L2 learners might be affected by these properties in L1, both in orthography and phonology (Meade, 2020). For efficient L2 word processing to occur, they should become sensitive to relevant L2 properties. We will now discuss the neighbourhood and orthotactic probability aspects of wordlikeness. In a study considering cross-language effects for phonological neighbours, Magnuson et al. (2003) taught L1 English speakers to associate geometric shapes with novel pseudowords (e.g., sheed). The pseudowords were formed by changing the final consonants of existing L1 words (e.g., sheep) with few or many phonological neighbours. When the speakers were tested in a visual world paradigm, no reliable effects of L1 neighbourhood density showed up in their pseudoword learning or in their target fixations. In other words, L1 phonological neighbour-

Chapter 6. Cross-language influences in L2 pre-lexical and lexical processing and acquisition

hood structure had little effect on the integration of novel words. The authors concluded that their artificial lexicon, conforming to native phonotactics, might be “considered functionally isolated from a participant’s native lexicon” (2003, p. 223). These results stand in contrast to those of another novel neighbour study by Meade et al. (2018). Speakers with English as their L1 performed a language decision task in which their RTs and EEG were measured twice, before and after they acquired 80 L2 (pseudo)words with few or many English neighbours. During the intermediate session, they learned to associate these novel (pseudo)words with pictures of familiar objects. On three consecutive days, their performance was tested in a two-alternative forced-choice (2AFC) task and a typing task. The recognition of the novel words in the 2AFC task turned out not to be affected by the number of L1 neighbours, but RTs and N400 amplitude in the language decision task were, both before and after learning. In addition, L2 words with many L1 neighbours elicited slower RTs and larger-amplitude N400s than those with few L1 neighbours, suggesting co-activation of L1 neighbours. After learning, smaller N400s and faster RTs were observed for L2 words with many L1 neighbours. Thus, the L1 neighbourhood effect was smaller at posttest than at pretest. However, when the novel L2 words had few L1 neighbours, their performance was relatively stable across sessions. The combined findings suggest that, once learned, the novel words were able to modulate the influence of their L1 neighbours (e.g., by lateral inhibition). Resolving the theoretical conflict between this study and the eye-tracking study reported by Magnuson and colleagues requires further empirical work. Bartolotti and Marian (2017, 2019) considered how learning a new L3 vocabulary was affected by wordlikeness to the already known L1 and L2 of the participants. In their 2017 study, they taught English-German bilinguals novel words from an artificial language for which word likeness relative to English and German varied orthogonally in terms of neighbourhood size and orthotactic probability. Novel items such as nist and baft had a high English and high German wordlikeness, while gofp and kowm had a low wordlikeness in both languages. Items like sumb and gonk had high-English low-German wordlikeness, in contrast to gach and kenf with low-English and high-German wordlikeness. Wordlikeness led to improved word production accuracy, but it did not matter whether the wordlikeness applied to one or both languages. Said differently, there was no cumulative effect of orthotactic properties shared by German and English, although participants were sensitive to the letter distributions in the novel language. The authors conclude that language learners indeed make use of word anchors in their known language(s) to facilitate learning in a new language (in accordance with a ‘scaffolding’ approach), but the absence of a cumulative effect

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of overlap with several already known languages led them to reject an ‘accumulation’ variant of this approach. Ring et al. (2022) set out to test if the role of orthotactic probability and neighbourhood density found in laboratory studies is also reflected in more naturalistic learning situations. Analyzing the Duolingo corpus of vocabulary learning (Blanco, 2020), they found that for English L1 speakers learning Spanish nouns, the wordlikeness of L2 Spanish items to the new language vocabulary in terms of orthotactic probability and neighbourhood density was as important in explaining learners’ errors as the wordlikeness relative to the L1 English items.

4.1 Cross-language influences in bilingual L2 word learning Koutamanis et al. (2021) conducted several cross-language priming studies to investigate the lexical network in bilingual children with respect to phonology and semantics. In one study, bilingual Greek-Dutch children and monolingual Dutch children performed a primed picture selection task combined with eye-tracking. Children saw two pictures while they listened to spoken prime and target words of the same or different languages. They were instructed to indicate the picture of the target word they heard. Semantically related prime-target combinations (like dress – skirt) led to within-language RT priming effects, depending on the children’s relative use of Dutch. An early phonological inhibition effect (i.e., a longer dwelling time for the target image in an item pair like Dutch rots – rok, in English meaning rock – skirt) was found here in the eye-gaze data. Both phonological and translation priming effects were found between languages. When the Greek prime roda (meaning ‘wheel’) was followed by the Dutch word rok (meaning ‘skirt’ in English), phonological facilitation was found in the RTs, but phonological inhibition in the eye-tracking data. When Greek fousta served as prime for Dutch target rok (both meaning ‘skirt’ in English), the RTs showed semantic facilitation relative to an unrelated condition. In sum, there was coactivation of both the phonological form and meaning representations of Greek and Dutch words processed by school-aged bilingual children, resulting in cross-language effects. Although this evidence suggests that lexical processing in these bilingual children proceeds similarly to that in adults, further research is needed here. Duñabeitia et al. (2015) tested Spanish-Basque bilingual children (8–15 y) in a translation recognition task. Younger children were significantly more affected by cognate status than older children, suggesting that the effect of cross-language similarity diminishes as a function of exposure and maturation.

Chapter 6. Cross-language influences in L2 pre-lexical and lexical processing and acquisition

To wrap up, it may be concluded that, when learning a new language, learners take the characteristics of their first language as a starting point, but quickly differentiate the new language in terms of its properties when learning proceeds. Empirical studies investigating cross-language effects in L2 acquisition thus indicate that the bilingual word recognition process becomes increasingly sensitive to the relevant bottom-up characteristics specific to the L2, in addition to those of the L1.

5.

Conclusions

In this chapter, we have considered the existence of cross-language effects in sublexical and lexical processing and acquisition of a second language (L2). Given space limitations, we did some cherry-picking of studies in these domains to illustrate general conclusions and broadly shared theoretical views. In all three domains, cross-language effects were logically expected and observed. With respect to sublexical units of representation, shared units appear to be coactivated, while script differences and mismatches between languages facilitate target word selection in a bottom-up way soon after the input stimulus is internally represented (reactively). Diacritical marks, capital letter information, language-specific letters and bigrams all contribute to facilitate target word recognition by differentiating words from different languages. With respect to lexical units of representation, cross-language effects are prominent in both visual and auditory modalities. Differential effects of neighbourhood density and orthotactic probability (or bigram information) in the L1 and L2 were attested. In the case of cognates and interlingual homographs, these arise naturally as a consequence of the definition (e.g., shared overlap in form and meaning for cognates). Evidence in favor of top-down effects of (sub)lexically activated language membership on L2 lexical processing is as yet inconclusive and further investigation is warranted. Distance between the old and new languages affects second language learning (Schepens et al., 2015). In early stages, overlap between old and new is important to establish a basis for the new language. Later, the new language’s lexical representations need to become fine-tuned and more specific. Nevertheless, research indicates that the lexical network in learners is, just like that in more L2 proficient language users, essentially language non-selective in nature.

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van Heuven, W. J. B., Dijkstra, T., & Grainger, J. (1998). Orthographic neighborhood effects in bilingual word recognition. Journal of Memory and Language, 39(3), 458–483. https://doi.org/10.1006/jmla.1998.2584

van Kesteren, R., Dijkstra, T., & de Smedt, K. (2012). Markedness effects in Norwegian– English bilinguals: Task-dependent use of language-specific letters and bigrams. The Quarterly Journal of Experimental Psychology, 65(11), 2129–2154. https://doi.org/10.1080/17470218.2012.679946

Vanlangendonck, F., Peeters, D., Rueschemeyer, S-A., & Dijkstra, T. (2019). Mixing the stimulus list in bilingual lexical decision turns cognate facilitation effects into mirrored inhibition effects. Bilingualism: Language and Cognition, 23(4), 836–844. https://doi.org/10.1017/S1366728919000531

Vitevitch, M. S. (2012). What do foreign neighbors say about the mental lexicon? Bilingualism: Language and Cognition, 15(1), 167–172. https://doi.org/10.1017/S1366728911000149 Voga, M., & Grainger, J. (2007). Cognate status and cross-script translation priming. Memory & Cognition, 35(5), 938–952. https://doi.org/10.3758/BF03193467

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chapter 7

Cross-language influences in L2 semantic and conceptual representation and processing Simon De Deyne, Marc Brysbaert & Irina Elgort

University of Melbourne | Ghent University | Te Herenga Waka – Victoria University of Wellington

Languages come with a unique set of words to label concepts so that sometimes a word in one language does not have a semantic equivalent in another language. This lack of equivalence is multi-faceted: words in two languages can be defined at different levels of abstraction, have different senses, or have conflicting affective connotations. What factors determine semantic equivalence across languages, and how are they incorporated in current theories of bilingual semantic representation? How do bilinguals navigate conflicting meanings or leverage semantic equivalence between two languages? To address these questions, this chapter will draw on recent proposals that combine multimodal experiential and linguistic representations to capture meaning. The multimodal view provides a framework to review distinct types of semantic equivalence at the feature, word, and language level. Finally, the implications of differentiating different types of semantic equivalence for bilingual studies are discussed. Keywords: cross-linguistic, semantic equivalence, bilingual meaning representations, distributional semantics, dual-coding

1.

Introduction

January 20, 2021 was the day of the inauguration of a new president of the United States of America. For many, the most memorable event was “The Hill we Climb”, a poem recited by Amanda Gorman to commemorate the beginning of a new era. In the aftermath of the elections, questions were raised whether someone lacking the same lived experience would be capable of understanding or translating this poem. Beyond the many layers of meanings communicated in a poem, understanding a word in a second language can be challenging as some meanhttps://doi.org/10.1075/bpa.16.07ded © 2023 John Benjamins Publishing Company

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ings that have a single lexical label in one language may not have such a label in another language. For example, the closest English translation for the Dutch word smeuïg, would be creamy or smooth, yet none of these translations captures its exact meaning. This question of conceptual translatability is relevant to how bilinguals process meaning in their multiple languages as, beyond a good grasp of the language, understanding the meaning in a second language (L2) may also require exposure to similar experiences. In recent years, there have been several advances in monolingual1 theories of meaning that capture meaning more broadly. A significant contribution was a shift towards computational models that quantify both experiential and linguistic information at scale. Another contribution is the increasing awareness of the systematic differences in meaning within a language group. These differences can be very general and widespread, reflecting differences in age, gender, or education. One example is a study where young and older participants named containers (e.g., cup, bowl) in their first language (Dutch or French). The researchers found systematic differences in the category labels used by these two age groups (White et al., 2018). This result indicates that differences in experiences (even when separated by a relatively short period) shape the conceptual structure of speakers within their native language. The idea that language shapes the way people think has reignited the so-called Whorfian question or linguistic relativity hypothesis. While some researchers suggest that the evidence supporting such a hypothesis is weak (e.g., Pinker, 1994), the important and transformative effects of language on thought have been emphasised in more recent work (e.g., Cassanto, 2016). For some proponents of this stronger view, words do not simply map onto concepts but are cues that help people construct meanings on par with perceptual, affective, pragmatic, and other inputs. This words-as-cues perspective assumes that both linguistic and non-linguistic experiences operate on and contribute to a common representational space, and language can directly affect semantic knowledge obtained from perception or action (Lupyan & Lewis, 2019). This chapter explores how a second language might fit in such a view.

2.

Traditional views of words and meaning

Earlier bilingual models of word representation and processing assumed that word forms are stored in language-specific memory, whereas conceptual memory is language-independent and shared between the languages (e.g., Abutalebi & 1. In this chapter, the term monolingual stands shorthand for studies where only one language is considered (implicitly or explicitly). The term is used for comparison with studies that focus on bilingual processing and representations.

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Green, 2007; de Groot & Poot, 1997; Jiang, 2000; Kroll & Stewart, 1994). Figure 1 illustrates this view for an English-Dutch bilingual and shows meaning encoded in a localist network (see also van Heuven & Dijkstra, Chapter 5, this volume, for computational localist models). The view commonly adopted by earlier linguistic and bilingual researchers distinguished between semantic and conceptual representations, with semantic closely tied to lexical, language-specific knowledge while the conceptual system associated with experiential and encyclopaedic knowledge. In psychology, the terms semantic and conceptual are often treated as interchangeable. Here we will use the term lexical-semantic to refer to word meaning acquired through language.

Figure 1. Traditional view of word meaning in bilinguals Note. Word forms are stored as nodes in language-specific, modality-specific lexicons and connect with language-independent and modality–independent (abstract) concept nodes. The concept nodes are part of a network, encoding the meaning.

The traditional view exemplified by models like the Distributed Feature Model (Kroll & de Groot, 1997; Van Hell & de Groot, 1998) makes two key assumptions. First, word forms are assumed to be language-dependent (separate lexicons for L1 and L2) and modality-dependent (separate lexicons for spoken and written language) because different forms are involved. Second, in contrast

Chapter 7. Cross-language influences in L2 semantic representations

to the language-specific lexicons, the conceptual nodes are assumed to be represented in an abstract and language-independent format (see also Du et al., Chapter 8, this volume, for conceptual representations and processing of L2 multi-word expressions). The meaning of a concept, like bicycle in Figure 1, consists of activation spreading to connected concepts or features (e.g., ride, helmet, wheels). A significant problem with models like the Distributed Feature Model is that they assume one-to-one mappings between language-specific word forms and shared concepts so that words in one language can be translated unequivocally into the other, retaining precisely the same meaning. However, finding semantically equivalent terms between languages shares many of the same challenges as identifying near-synonyms within a language. For a native speaker of English, near-synonyms, like forest and woods, might reflect differences that are not easy to express, referring to a complex mixture of size, wildness, and distance from an urban area (Hirst, 1995). Near synonyms highlight the vague, ill-defined boundaries between concepts within a language. The situation is even more complex when multiple languages are considered. In German, for example, the word Wald denotes a “rather small and more urban area of trees than a forest”, which means that it could be both translated as forest or woods, depending on the situation (Hirst, 1995). Even when translation candidates across languages have very high translation probabilities in both directions and are entirely substitutable in linguistic contexts, they may still have distinct conceptual representations (de Groot, 1993). For example, Dutch-English bilinguals varied in their semantic similarity judgments of verified translation equivalents (Tokowicz et al., 2002). To account for the finding that words in different languages are not always exact translations, Kroll and de Groot (1997) replaced the local word concept nodes in Figure 1 with a set of conceptual features. The English word bicycle no longer activated the sole concept but also the concepts to various degrees. Similarly, the Dutch word fiets would activate several concepts related to bicycles. The larger the overlap of activated concepts between words of both languages, the easier it is to translate one to the other. An example of the cultural and linguistic factors contributing to meaning alignment is shown in Figure 2. It shows a word association network for the concept freedom in four different languages, highlighting both shared and languagespecific aspects of meaning discussed throughout this chapter.

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Figure 2. Semantic networks for freedom in English, Mandarin, Dutch and Rioplatense Spanish (spoken in Argentina and Uruguay) taken from the small world project (De Deyne et al., 2019, https://smallworldofwords.org/project). Note. Word associations are encoded as a network with nodes indicate cues and edges indicate the strength of association response (e.g., the proportion of people that associate bird with freedom). Colours indicate highly clustered areas in the network, similar to meaning senses obtained by a network clustering algorithm. The networks show language-specific connotations (e.g., slavery in English, economic independence in Mandarin), metaphors (Spanish, Dutch: flying), and cross-cultural differences (e.g., Dutch focuses on individual freedom, whereas Spanish frames freedom in political terms).

3.

Sources of semantic non-equivalence between languages

An elaborate classification scheme to understand the equivalence of translation pairs was proposed by Šipka (2015). The scheme differentiates between full, partial, multiple and zero equivalence. Full equivalence between words in a source

Chapter 7. Cross-language influences in L2 semantic representations

and target language means a one-to-one mapping with the same meanings and senses.2 Partial equivalence means that a word in a target language can only be used for a subset of the word’s meanings/senses in the source language. Multiple equivalence is found when a word must be translated by different L2 words depending on the context. Zero equivalence means that a word in the target language does not have a corresponding word in the source language. These equivalence forms can be depicted within a model that builds upon earlier semantic feature models (e.g., Distributed Feature Model by Kroll & de Groot, 1997, or Sense Model by Finkbeiner et al., 2004), as shown in Figure 3.

Figure 3. Full, partial, multiple, and zero equivalence between L1-L2 translation candidates with multiple senses/meanings (S1, S2, S3). Note. Partial equivalence can be nested under multiple equivalence. The central conceptual circles in this figure represent bundles of features associated with words (rather than single concepts as in Figure 1). The colours refer to different types of features (lexical-semantic vs. experiential), which will be explained later.

The sections below will focus on partial and multiple equivalence, as these pose more challenges for bilinguals (and models). To address the equivalence question, we need to consider the primary sources of semantic/conceptual equivalence. This issue will be discussed for multiple interdependent scales covering equivalence in terms of shared or language-specific features, word mappings (e.g., one-to-many or many-to-many mappings), and systemic differences between domains and languages.

2. Meanings refer to homonyms or unrelated concepts, such as bank as a financial institute and bank as the border of a river. Senses refer to polysemes or related concepts, such as uniform as way of dressing and uniform as an adjective meaning ‘of a similar form’. This distinction between meanings and senses may be better described as located on the opposite ends of a continuum (cf. Rodd, 2020).

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3.1 Feature level A common source of differences in word meanings between languages is partial equivalence due to language-specific features. As an illustration, Figure 4 lists the features measured as word associations for the English words, embarrassment and shame and the Dutch translation schaamte. The partial equivalence of both embarrass and shame with schaamte shows the subtle meaning differences between separate synonymous words from the one language and their translations to a different language. This partial equivalence can reflect differences in structural (e.g., related to morphology, syntax, see Pavlenko, 2008), semantic, thematic (Troyer & McRae, 2021), and conceptual (Rosch et al., 1976) features. Conceptual features. Many theories represent word meaning through a set of conceptual features. Features are typically obtained in a feature elicitation task (elephant: , , …, e.g., Buchanan et al., 2019; De Deyne et al., 2008) or word association task3 (e.g., De Deyne et al., 2019; Nelson et al., 2004). This feature-based representation is central to prototype theory, which has been highly influential in cognitive psychology (Rosch, 2002) and linguistics (Geeraerts, 2008). How conceptual features are lexicalised and whether features in lexical form themselves have zero, partial, multiple, or full equivalence directly affects lexical-semantic representations. Distinctions in different languages can lead to different conceptual representations. In English, horses have legs, whereas horses have patas or animal legs in Spanish. Most animals have poten (animal legs) in Dutch, but horses have benen (human legs). In addition, this distinction is correlated with another property horses share with humans but not with other animals: the head of horses and humans are translated as hoofd, whereas other animals have an animal head (kop). Concept features for colours may also denote different properties that have become conventionalised. For example, red might be the colour for blood, but it could also refer to the colour of hair, skin, potatoes, or wine (Clark, 1991). In short, the idea of a set of shared features across languages becomes considerably more complex depending on whether these features refer to a modal-specific (perceptual) property or information encoded at the lexical-semantic level. The processing and learning implications of non-equivalence of such distinctions at the feature level have not received as much attention as other aspects of meaning variation, making it an interesting area of future work. 3. Feature elicitation can be seen as a special case of the word association task, with the former providing triples of a source concept, semantic relation, and target concept, whereas the semantic relation is omitted in word associations. A second difference is that word associations provide a wider range of semantic relations, making it more adaptable to concepts that aren’t nouns (e.g., verbs, adjectives).

Chapter 7. Cross-language influences in L2 semantic representations

Figure 4. Translation ambiguity through shared and language-specific associations for embarrass and shame (EN, blue and red labels) and the Dutch translation candidate schaamte (black labels) derived from the Small World of Words project (De Deyne et al., 2019). Note. This figure represents a tiny portion of a simplified bilingual semantic network constructed by translating the associations between Dutch and English. The shared associations between embarrass, shame, and schaamte in this figure are not independent but are highly organized as bundles or clusters of correlated associations/properties (e.g., red cheeks, red, blush, flush).

Thematic relations. Thematic relations refer to highly situational and contextdependent properties between different entities in a situation, often discussed as an interaction of syntactic and conceptual knowledge engaged in comprehension. Differences in thematic relations (syntactic arguments allowable for a given verb, such as agents, patients, objects) can be a significant factor that drives non-

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equivalence due to the recursive nature of the meaning for the different entities involved. Considering that relational concepts like agree, destruction, or obstacle rely on the meaning of other words as part of a thematic relation, the chances of high degrees of equivalence across languages is relatively low. For instance, the word ascoltare in Italian is transitive (i.e., can take a direct object), whereas its English translation to listen is not. This means that the Italian word can have an object (ascoltare una canzone), whereas this is impossible in English (to listen [a song]), creating translation and learning difficulties. In addition to syntactic factors, thematic relations are likely to pose a challenge for the bilingual learner as abstract concepts tend to be relational with more context-dependent meanings than concrete words (cf. Troyer & McRae, 2021). While quantitative evidence for the prevalence of crosslinguistic thematic non-equivalence is lacking, individual, language, and cultural differences in experience or events are likely to play a leading role in semantic non-equivalence. Mental imagery and figurative meaning. Differences in mental imagery are typical for artifacts that vary systematically across cultures. The conceptual features for the word table are different in English and Japanese, as tables in the West have longer legs than traditional tables used in a room with tatami (straw mats). Word association frequencies taken from the Small World of Word project (De Deyne et al. 2019) for the English table and Japanese テーブル (tēburu) confirm this is indeed the case. In English, chairs is the dominant associate but not in Japanese. Furthermore, legs is associated only with English tables. Mental imagery differences may be manifested by language-specific metaphorical extensions. For example, the word computer in Kaurna, a language spoken by indigenous Australians near Adelaide, is ukarntu, which derives its form from two words: mukamuka (brain) and karntu (lightning). Similarly, language-specific mental imagery may also represent an ideal exemplar of an abstract category. Continuing with an example from the Small World of Words project, the word statue in English strongly evokes the Statue of Liberty, whereas the same word in Chinese evokes a marble statue (like the Venus of Milo). This different imagery may lead to different inferences as language-specific properties are foregrounded (e.g., differences in material and size). Finally, mental imagery also plays an essential role in abstract concepts through figurative use in similes (free as a bird) or metaphors (time as moving forwards), which rely on imagery to establish a comparison or mapping between two kinds of concepts (Jackendoff, 2002). New metaphors are readily created as well using a process that combines experiential and linguistic knowledge, highlighting the potential for misunderstanding if any of these sources of information is language- or culturespecific.

Chapter 7. Cross-language influences in L2 semantic representations

Connotation and emotion. The seminal work by Osgood et al. (1957) established that much of the meaning of a word is expressed by affective connotation, captured by three major dimensions: valence (positive vs negative), arousal (high vs low) or dominance (being in control vs being controlled by). For example, the English word shame is associated with sad, fear, wrong and bad (Figure 4), which convey a negative valence to the word (Warriner et al., 2013). Studies with bilinguals suggest that words are perceived as less emotional in a foreign language (Imbault et al., 2021), regardless of whether the word is positive or negative. Furthermore, it is not uncommon to find words with incongruent affective connotations in two languages. For example, the word submissive to describe a female’s personality has a negative connotation in English but a positive one in Korean (Ma, 2009; Šipka, 2015). In Arabic, the word dog has an outspoken negative connotation, illustrating that differences in connotation are not limited to abstract words. These differences go beyond the accidental and whimsical and often reflect a system of cultural values. Emotional arousal, for example, is a dimension that is less universal and perceived differently across a wide range of concepts. Systematic differences between Western (individualistic) and EastAsian (collective) cultures, where adjusting and conforming to other people is easier to achieve through low-arousal emotions, might explain such differences (Lim, 2016).

3.2 Word level Semantic equivalence. Semantic equivalence at the word level can be studied within the various levels shown in Figure 3 (see Šipka, 2015, for a detailed treatment). In the zero-equivalence case, a word in a source language does not have a translation in the target language. The untranslatable Czech word lítost, which the Czech writer Milan Kundera described as “a state of torment created by the sudden sight of one’s own misery”, is a well-known example. Multiple equivalence refers to cases where a word in a source language must be translated in various ways in a target language depending on what the speaker wants to convey, such as the Japanese 羽/ hane, which can be translated as either feather or wing. Partial equivalence, which indicates differences in features, is often nested within multiple equivalence. For example, the English sense of wing as part of a building may not be present in the translation to another language. Multiple and partial equivalence combined leads to more translation errors than full or zero equivalence. For example, in a study on dictionary translations based on a sample of the 1000 most frequent words in Russian and their translations to Polish, Serbian, German and English, multiple equivalence was the most common case (54 to 69% of cases), partial equivalence accounting for between 6% and 9% of cases, and full equiva-

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lence between 17% and 32% of cases (Šipka, 2019). Zero equivalence was not found in this sample. Applying cross-linguistic semantic alignment to potential meaning conflicts in L2 learning and translation, there is no between-language conflict in fullequivalence and zero-equivalence, so errors typically reflect word-finding problems. However, in the case of multiple and partial equivalence, translations can be incorrect because they are acceptable, but only in specific contexts. Denotation and level of abstraction. In linguistics, the denotative meaning of a word is the referential, objective meaning, as found in dictionary definitions. However, various studies have shown that differences in category boundaries and prototypes between languages affect how things are named. For example, category members of things called a cup or a bowl will depend on the language (Ameel et al., 2009). While such differences can be accounted for in terms of language-specific features (partial equivalence), denotational differences between words in different languages will frequently result in zero or multiple equivalence. Focusing on the latter, denotation differences can reflect varying degrees in specialisation or levels of abstraction, as analogous to over- and under-extension in the development of concepts in children (e.g., calling all dogs Fido or only labelling a rose as a flower). Some evidence for this comes from comparisons of hierarchical taxonomic ontologies that encode hypernyms (superordinate relations) and hyponyms (subordinate relations like WordNet). In WordNet, apple has hyponyms such as eating apple > Granny Smith and hypernyms > pome > fruit. The depth of WordNet taxonomies (i.e., the number of hierarchical hyper- and hyponyms) varies between languages. Those with many words tend to have deeper taxonomies than languages with a smaller lexicon (Postma & Vossen, 2014). Differences in levels of abstraction and denotations have also been studied extensively in anthropology and psychology. Studies on colour terms, kinship terms, and folk taxonomies for biological kinds like birds or trees show that the distinctions people make strongly depend on the environment’s social, cultural, and ecological aspects (Atran, 1998). As Gairns and Redman (1986) argued, “words like sleet or double glazing are about as useful to Brazilians as mangos and cockroaches are to Scandinavians” (mentioned in Šipka, 2015, p 44). Polysemy. It is often unclear how many senses a word has, what counts as a sense, and how new senses emerge. Nevertheless, most lexicographers agree that polysemy is widespread across many languages. Multiple equivalence will be affected by the presence of shared and language-specific senses. Determining how polysemy affects equivalence is complicated since polysemy is entangled with other lexical factors. For instance, in general, frequent words have more senses

Chapter 7. Cross-language influences in L2 semantic representations

than infrequent words, so differences in word frequency between two languages are likely to covary with the number of senses. Polysemy not only refers to the number of senses but also to the relative dominance of the senses. Across all languages, the most common sense is used more frequently than all other senses combined. The frequency distribution of the number of senses roughly follows a Zipfian power-law (Kilgarriff, 2004), meaning that the most common sense will be used in about 66% to 88% of the cases (Lindén, 2005). As a result, differences in sense dominance between languages can easily lead to translation confusion. Evidence for the role of polysemy in bilingual word-processing comes from the Sense Model (Finkbeiner et al., 2004), which was introduced to account for asymmetric semantic priming effects in bilinguals. Typically, priming an L2 target word with an L1 word leads to larger effects than L2 words as primes in both semantic and translation priming. According to the Sense Model, the size of the priming effect depends on the proportion of word senses that are activated. Since non-dominant senses in L2 are not fully represented, especially when proficiency is low, many of the L1 senses will never be activated by an L2 prime, which explains weaker priming in the L2-L1 direction (Finkbeiner et al., 2004). Other studies have also shown that the number of senses in L2 predict L2-L1 translation recognition. In a study with Japanese and English bilinguals, Allen and Conklin (2013) found that the recognition speed of translation pairs covaried with the number of senses of a cue word presented in English (L2), independent from confounding factors such as its concreteness and similarity to its Japanese (L1) translation. Word frequency and semantic richness. As we have seen above, certain concepts are more important in some cultures than others. The cultural significance of concepts is reflected in the frequency with which words for these concepts are used. Words like the Dutch gezellig (convivial/fun) are considered cultural keywords because they have different statistical properties, including different frequencies, than words communicating similar concepts in other languages (Peeters, 2020). Related to differences in word frequency is the notion of semantic richness. Words with more features are assumed to have a processing advantage compared to words with few features in tasks such as lexical decision, pronunciation, and semantic categorisation (e.g., Yap et al., 2012). Semantic richness effects are operationalised in a variety of ways. To illustrate, consider the connections between words in Figure 2. Semantic richness could be quantified in terms of the number of neighbouring nodes or the internal clustering of these nodes, suggesting multiple senses. The measures most used in the literature are either closely aligned to lexical-semantic richness, such as the number of senses or semantic neighbourhood density (i.e., the number of similar words within a radius of tar-

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get word), or experiential information, such as the number of features, number of distinct first associates, imageability, and body-object interaction. These different measures tend to be only modestly correlated and account for unique variance in behavioural tasks, although the effect sizes are rather small (Pexman et al., 2008). A recent study shows that using the same instructions to classify senses for word pairs embedded in natural language or word associations results in vastly different measures that explain word processing independently (Rice et al., 2019). Finally, semantic richness can also be derived from corpora by calculating the similarity of the different contexts in which words occur. This approach goes beyond counting the number of senses, resulting in a continuous measure that explains additional variance beyond other measures of semantic richness (Hoffman et al., 2014). So far, few studies have investigated the role of semantic richness in bilinguals. One example is a study by Taler et al. (2016), where English monolinguals and English-French bilinguals, highly proficient in both languages, participated in a lexical decision task. Semantic richness was manipulated by contrasting words with a high and a low number of senses (NoS). Faster and more accurate responses were observed for words with many senses regardless of the native language. However, the NoS effect was more robust for monolinguals, suggesting that bilingual semantic representations might be more impoverished in both languages due to limited experiences in either language.

3.3 Domain and language level The presence of zero, partial, multiple, or full equivalence between words in various languages is far from arbitrary but strongly depends on the semantic domains and language proximity. The following section presents a selective overview of some factors that shed light on the role of both experience-related and languagerelated properties at the domain and language level. Domain differences. Certain classes of words or domains are assumed to have higher semantic equivalence than others. This finding has been widely demonstrated for concrete words in a series of studies by de Groot (1992), showing that words like father have a higher equivalence than more abstract words like idea. This concreteness advantage assumes that the shared perceptual referents result in a higher overlap in conceptual features. The meaning is assumed to be more context-dependent for abstract words, leading to fewer shared conceptual features and larger semantic differences across languages (Schwanenflugel, 1991). Concrete words also lead to shorter RTs in decontextualised first translation tasks where participants give the first translation that comes to mind. Concrete words are less affected by translation ambiguity than abstract words in

Chapter 7. Cross-language influences in L2 semantic representations

such a task (Tokowicz & Kroll, 2007). Other evidence comes from translation ambiguity studies, which indicate widespread ambiguity depending on part-ofspeech. In these studies, verbs were more ambiguous than nouns or adjectives (Prior et al., 2007). This is also consistent with several studies (e.g., Talmy, 1975; Gentner, 2006) highlighting substantial variation when it comes to motion verbs, where different languages incorporate the manner of motion (e.g., run out in English), or the motion path (e.g., sale corriendo, literally, exit running, Spanish). More generally, this suggests that the relational nature of both abstract words and verbs results in more context-dependent and thus ambiguous translations. Beyond general factors like concreteness or part-of-speech, cultural factors might strongly affect where differences are likely to be found. A recent study that used large scale alignments between over 1,000 word meanings in 41 languages found that the most aligned words were not necessarily the most concrete ones (Thompson et al., 2020). Instead, categories with words that have a rich internal structure (number, quantity, and kinship) had higher alignment than words referring to natural kinds, everyday actions, or artifacts. Moreover, in contrast to the idea that the meaning of concrete words is more consistent across languages and cultures (de Groot et al., 1994), words for emotions and values tended to show closer alignment than words related to agriculture and vegetation, or clothing and grooming. Potentially the last two domains cover broad perceptual distinctions that aren't completely conveyed by language (English is an exception, see below). Furthermore, the comparison across a broader range of languages instead of the more common comparison between Indo-European languages such as English and Dutch might also contribute to discovering differences in concrete categories (cf. de Groot, 1992). Language proximity and semantic equivalence. The overall language proximity also affects the prevalence of semantic equivalence. One factor that might affect semantic equivalence at the language level is the difference in lexicon size. While it is generally accepted that some languages have larger vocabularies than others, establishing the different number of words (and thus the potential for semantic equivalence) is controversial. Dictionaries in different languages may be compiled for different purposes and use different methods to define word entries. Even within languages, very different estimates are obtained depending on how words are defined (Brysbaert et al., 2016). Alternative approaches that estimate vocabulary size using measures of lexical diversity (the type-token ratio) or word entropy suggest large differences between languages, with languages such as ancient Hebrew being very diverse, Tahitian being having low lexical diversity, and English sitting somewhere in between (Bentz, 2018). Besides quantitative differences, there are also qualitative reasons to expect systematic semantic equivalence patterns between languages. Wray and Grace

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(2007) differentiate between esoteric or inward-looking languages such as Māori (Calude & Pagel, 2014) and exoteric, or outward-looking languages such as English, German, French or Spanish. According to Wray and Grace (2007), esoteric communication occurs within a closely-knit community where speakers know each other well. In these languages, common knowledge is shared, and cultural practices tend to be less semantically transparent. These languages are more idiomatic and formulaic, less compositional, more irregular, and harder to learn by outsiders. Exoteric communication occurs across multiple groups whose speakers do not know each other, nor share common knowledge or cultural practices as much. The languages tend to be more semantically transparent, less idiomatic, more compositional, rule-based, regular, easier to learn and understand by outsiders (see also Thurston, 1989). Language proximity also affects translation ambiguity in different pairs of languages (see Schwieter & Prior, 2020, for a recent overview). Perhaps unsurprising, the degree of translation ambiguity strongly depends on how related the source and target language were. In Dutch and English that are close relatives, only 25% of items had more than one translation in one or both translation directions. On the other hand, for English Spanish, Prior et al. (2007) found that roughly 55% of translations were ambiguous. More distant languages like Mandarin Chinese have higher ambiguity, with studies finding up to 65% (Tseng et al., 2014) or 71% (Wen & van Heuven, 2017) translation ambiguous cases. However, the findings of these studies should be taken as a rough approximation, as the methods and samples across studies are not fully comparable, and sometimes items were chosen that were assumed to have a single translation (Tseng et al., 2014). Furthermore, the likelihood of encountering multiple translations will depend on the sample size and the speakers’ proficiency. Despite these limitations, this comparison shows considerable agreement between closely related languages, but ambiguity increases rapidly among more distant languages.

4.

New views about word meanings within a language

The traditional view of lexical word forms interacting with abstract concepts (or conceptual features), depicted in Figure 1, has witnessed significant changes in the past few decades. We discuss these changes before we outline the implications for bilingual language processing.

Chapter 7. Cross-language influences in L2 semantic representations

4.1 Embodied cognition provides experiential information According to the embodied view, word meaning depends on the physical interactions between our body and the environment. People do not learn meaning based on words alone, and some of the words must be grounded in the physical environment. When a child learns the word car, this is likely done by pointing to cars in the environment. Vincent-Lamarre et al. (2016) estimate that some 1% of the words in a dictionary (about 1,000 words) cannot be defined based on other words and must be grounded in physical reality. Proponents of embodied cognition argue that experiential information is likely to contribute to the meaning of many more words than the core 1% (e.g., Barsalou, 2008). Although a word like elephant can be defined based on other words, our understanding of what an elephant is involves non-linguistic information. This information might include knowledge about what elephants look like, where they live, their behaviour, how they interact with humans and perhaps even recollections of movies or books where elephants were mentioned. This knowledge covers qualitatively distinct properties reflecting experiential information about elephants’ colour, shape, and motion. Understanding a word like elephant would involve activating some of the same brain regions that process the perceptual, somatosensory, and motor information we experience when we encounter an elephant. These representations are modality-specific (not abstract as in the traditional view) and arise from activation in modality-specific perceptual and motoric brain areas (Barsalou, 2008). Indeed, there is good evidence that reading the word kick co-activates the foot part of the motor cortex, whereas reading the word thunder is likely to co-activate the auditory cortex (Garcia et al., 2019). Early accounts of embodied cognition focused on sensory and motor modalities. More recent views also point to the importance of affective information (valence, arousal, emotional state) in understanding words, in particular abstract words, like joy or sadness. According to the Affective Embodiment Account (Kousta et al., 2011), emotions can be considered an additional modality and are as crucial to our understanding of abstract words as sensorimotor modalities are to the understanding of concrete words. Recent work explicitly contrasted the contribution of affective and visual information in understanding concrete and abstract words and found that the gains of including affective information seem to be at least as large as the gains of including visual features (De Deyne et al., 2021).

4.2 Word co-occurrences provide lexical-semantic information In parallel with the embodied cognition movement, computational linguists that rely on large text corpora discovered that the meaning of words could be grasped

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to a large extent by looking at the contexts in which they appear. Lexical-semantic models built from text corpora thus reflect Firth’s (1968, p. 11) idea that “you shall know the meaning of a word by the company it keeps”. Meaning in lexicalsemantic models is derived by counting the number of times words co-occur within a context. This context is a document in Latent Semantic Analysis (Landauer & Dumais, 1997), or a small number of words preceding or following the target word in the Hyper Analogue of Language model (Lund & Burgess, 1996). More recent word embedding models have improved count-based distributional models by employing a simple neural network that predicts a word in context. In word embedding models such as the popular word2vec model (Mikolov et al., 2013), the meaning of a word is represented by the connection weights of a hidden layer or embedding. This layer encodes meaning using a small set of nodes; the representations are learned incrementally, in an error-driven way, and resemble human learning. Word embedding models often result in better predictions of human behaviour than count-based models across a range of tasks, including human similarity ratings, analogies (Baroni et al., 2014), and semantic priming (Mandera et al., 2017). A further improvement of word embedding models is the fastText model (Bojanowski et al., 2016), which can account for out-of-vocabulary words (i.e., words not encountered in the corpus) by blending embeddings of various sublexical units (e.g., unhappy = un + happy). The ability to capture morphological knowledge is essential in languages with productive morphology, such as Chinese and Japanese, and facilitates the comparison of word embeddings among many languages (Thompson et al., 2020). Lexical-semantic models typically result in semantic vectors. These are strings of some 200–300 numbers describing word meanings. Some of these numbers coincide with traditional conceptual features (such as valence or arousal), but others are more difficult to describe (Hollis & Westbury, 2016). A practical advantage of semantic vectors is that words with similar vectors tend to have very similar meanings. Semantic vectors can also be used to estimate the valence and arousal of thousands of words based on human ratings for a few hundred words (Hollis et al., 2017), in the form of semi-supervised learning. For instance, words with a semantic vector similar to negative words are likely to be negative. The estimates calculated based on semantic vectors are almost as good as direct human valence ratings and somewhat lower for arousal ratings.

4.3 Multimodal accounts Experience-based and language-based semantic models were developed independently and were first considered competing models. Rapidly, however, researchers

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acknowledged that the approaches were largely complementary and augmented each other. Multimodal models combining linguistic information with experiential information outperform singular-mode models in predicting human performance across a range of semantic benchmarks (e.g., Andrews et al., 2009; Steyvers, 2010). In early work, modality-specific information was derived from human-generated property norms, such as a canary (e.g., Steyvers, 2010). More recent approaches use a training corpus of millions of images to feed into the semantic vectors (Deng et al., 2009). In addition, experiential models are no longer limited to the visual modality; multimodal models have started to explore other modalities, such as auditory features (Kiela & Clark, 2015) or olfactory features (Kiela et al., 2015). Experience-based and language-based information are not independent but provide converging and overlapping information. Relations between words in a language are not arbitrary because language has evolved to mirror the structure of the world (Clark, 2006). According to the Symbol Interdependency Hypothesis, words encode perceptual information in the physical environment so that a network of words will also capture sensory representations (Louwerse, 2018). For example, in the COCA corpus (Davies, 2010) elephant frequently co-occurs with trunk, seals, African, tusks, ears, which capture salient experiential characteristics of what elephants look like. At the same time, the word in COCA co-occurring most with the word elephant is room, due to the idiom, the elephant in the room. This shows how lexical-semantic information not only supports experiential information but also augments it. According to the words-as-cues view (Lupyan, 2016), language enriches experiential information by setting the conventionalised way objects, events, and relations are reified in a language and provides a “filter” through which concepts are both viewed and encoded (Gathercole & Moawad, 2010). In many situations, shallow, lexical-semantic processing of meaning is sufficient. Louwerse (2018), for instance, argues that lexical-semantic information is activated faster than experiential information, so we would mainly rely on this information in communication. Empirical evidence for linguistic features preceding perceptual ones comes from electrophysiological (EEG) studies (Louwerse & Hutchinson, 2012) and qualitative analyses of responses given in continued word association tasks (De Deyne & Storms, 2008). While this pattern might hold in general, words may differ in how important experience-based information is to understand them. Garcia et al. (2019) provided brain imaging evidence that experiential information may precede linguistic information in the processing of action words. Another example is the case of words that strongly describe modalities. Louwerse and Connell (2011) found that modality-specific experiential information is needed to discriminate between sensory adjectives for taste-related and

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smell-related words (e.g., delicious) or to fully understand touch-related words such as sharp and sight-related words such as specked.

4.4 The hub-and-spoke model A challenge for multimodal accounts is to explain how semantic information of different entities across different modalities can be integrated. An interesting idea was put forward in the hub-and-spoke model (Patterson & Lambon-Ralph, 2016), building on an idea initially proposed by McClelland (1981). According to this model, accessing the meaning of a concept involves activating distributed modality-specific information (in spokes) combined with the activation of intermediate transmodal nodes in a central hub connecting the various spokes. Verbal descriptors and linguistic features form one of the spokes. The perceptual modalities (sounds, olfaction, praxis, vision) and actions form further spokes, as do internal somatosensory states, particularly those representing affective states. Information in the spokes referring to the same entity is connected to a specific node in the central hub, which allows meaning to be represented in a modalityinvariant format by integrating multiple streams of information from the spokes. In contrast to earlier multimodal views, the proposal of a hub offers the benefit that concepts can now be represented in a more coherent way that accommodates more elaborate meaning representations, such as propositions and events. One of the implications of the hub-and-spoke model is that the distinction between experiential/conceptual and lexical-semantic representation is blurred as both systems contribute to the acquisition, representation, and retrieval of word meaning. This view is distinct from strictly embodied, or lexical-semantic distributional accounts as meaning is encoded redundantly across multiple modalities or spokes, including a linguistic spoke.

4.5 The words-as-cues perspective According to Lupyan (e.g., Lupyan & Lewis, 2019), words do not map onto pre-existing concepts but directly contribute to constructing a shared common representational space. One of the interesting aspects of this proposal is that in contrast to multimodal approaches where no strong theoretical positions are taken on how different modalities are combined, this view strongly emphasises the role language plays in shaping our concepts and semantic knowledge. It does so by emphasising that “much more of our semantic knowledge is derived from language than often assumed, with some domains potentially entirely derived from language” (Lupyan & Lewis, 2019, p. 1320). One way language shapes our

Chapter 7. Cross-language influences in L2 semantic representations

concepts and categories is by providing targets for learning that are shared among speakers of the same language. A further difference with the hub-and-spoke view is that according to the words-as-cues perspective, language provides a way of augmenting knowledge derived from direct motor or perceptual experience. In contrast to the embodied view, the word-as-cues view alters our perspective by shifting focus from the role of non-linguistic, modality-specific aspects of meaning towards the question about what experience with language broadly does with our mental states while learning and using language.

5.

Challenges for crosslinguistic multimodal models

Several theoretical bilingual models have considered multimodal representations. One of the earliest was the Modified Hierarchical Model (MHM, Pavlenko, 2009). The MHM explicitly distinguishes between conceptual and semantic representations and allows for language-specific and shared features. According to Pavlenko, the distinction between conceptual and semantic representations is needed to differentiate between different types of translation errors. Semantic transfer errors result in linking words in the second language to the wrong concept, for example, in the case of polysemy. An example is when a native speaker of French would confuse the English words language and tongue, translated from the French langue. Another type of conceptual transfer error might be due to a failure to recognise the denotation or structure of the category, for example, when naming a container a cup instead of a mug (Ameel et al., 2009). While the distinction between conceptual (experiential) and lexical-semantic representations can be helpful to classify errors, both experiential and lexical-semantic information likely contributes to misunderstanding. For example, colexification of the English words language and tongue is not merely accidental across multiple languages but has a conceptual basis (Xu et al., 2020). Moreover, integrative views, such as the hub-and-spoke model, suggest a strict distinction between experiential and lexical-semantic information might represent a highly idealised view of how we grasp the meaning of words. One of the challenges for multimodal models is determining how linguistic or experiential information affects understanding when both types of information redundantly encode similar properties, which requires sophisticated tasks to tease apart both types of information (see Section 5.3). Another challenge for traditional views like the MHM model is that it is a verbal model, which does not allow the detailed predictions of a running computational model. These could either consist of crosslinguistic models that quan-

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tify the semantic/conceptual equivalence across different languages/cultures, or bilingual models that include information about processing and are capable of developmental predictions. In the next section, we will provide some pointers of what would be needed to build such a model and will focus primarily on semantic equivalence between languages. While language shapes our concepts, as in the word-as-cues view discussed earlier, a computational implementation might show that experience-based information is shared more broadly than lexical-semantic information when trained on language and experiential input similar to that of a bilingual. For example, in many bilinguals, L2 words may be less grounded in experience if L2 is mainly limited to written communication or acquired without access to the L2 cultural environment. Representing lexical-semantic information in two or more languages is likely to be more challenging than doing so for one language. Below we discuss developments and research challenges in the search for a computational theory of bilingual representation that addresses some of the limitations of older theories.

5.1 How much information do computational lexical-semantic models provide about language equivalence? Computational models mapping lexical-semantic representations across languages are attractive because they allow us to quantify how similar words are across languages (cf. Figure 2). Crosslinguistic similarity is usually computed using a small set of verified seed translations to align words across languages. In turn, this measure also allows us to build a larger bilingual dictionary (see Ruder et al., 2019, for an overview). The major advantage of this automated approach is that the degree of semantic equivalence can be calculated for all kinds of concepts allowing us to characterise different types of equivalence and how these types interact with domains (e.g., concrete vs abstract domains, Thompson et al., 2020). Word embedding models can account for a wide range of psycholinguistic phenomena, but the results for crosslinguistic embeddings are not always consistent. One example is a study where L1 English, German, Italian, or Russian participants performed a similarity judgment task (Leviant & Reichart, 2015). After translating the items in each language, the crosslinguistic similarity was calculated between human ratings and based on word-embeddings derived from Wikipedia in the four languages. Surprisingly, although the correlations between human ratings were consistent with the intended language (e.g., English, ratings were the best predictor for English ratings), similarity ratings for English, Italian or Russian were better predicted with German embeddings. Notwithstanding some outstanding questions, these embeddings demonstrate how meanings between

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different languages and modalities can be derived and contribute to new computational theories of bilingualism.

5.2 Lexical-semantic vectors based on bilingual input? An exciting question is what semantic vectors will look like if they are based on input from two languages to match a bilingual person’s experiences. To our knowledge, no such models have been tried out yet. A peculiarity of bilingual input is that words are mostly different in the two languages and tend to be presented within the context of a single language, except for shared words and direct translations (Brysbaert et al., 2014). Based on what we know about connectionist models, this will likely result in semantic vectors with language-specific representations (Hernandez et al., 2005) as part of connectionist word processing models fitted with a special language node (see van Heuven & Dijkstra, Chapter 5, this volume). At the same time, early stages of L2 learning might result in co-activation and encoding of L1 features, which would lead to crosslinguistic lexical-semantic links and meaning similarities across languages. It is unclear to what extent these will be present within the current operationalisation of semantic vectors or what changes will need to be introduced to mimic human performance.

5.3 How can embodiment effects be tested in L2? Any model of bilingualism that distinguishes between lexical-semantic and embodied representations is only useful insofar as embodied representations can be measured independently from language. Moreover, embodiment effects should be a functional part of processing meaning and not just a by-product (e.g., Mahon & Caramazza, 2008). The question about L2 embodiment is not so much about conflicting meanings, but more about whether L2 words have direct access to the rich modality-specific representations built up in L1. Furthermore, under what circumstances are these modal-specific representations accessed? Some researchers have argued that embodied effects will be diminished when learning L2 after childhood or in specific contexts such as classrooms (Kühne & Gianelli, 2019). However, other work shows that embodied effects are present even among late learners and even after relatively short exposure to L2 (Kogan et al., 2020). Recent reviews have evaluated the role of embodiment in L2 across various paradigms. Behavioural tasks include reaction time measurement in Stroop tasks, sensibility judgements, and sentence-picture matching. One finding is that the contribution of modal-specific experiential vs distributional lexical-semantic information may be up or down-regulated depending on the task (Lupyan, 2016). This regulation can be achieved by changing task demands through training,

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increasing the depth of processing demands (e.g., choosing non-words that resemble real words in lexical decision tasks), the use of interference, or contrasting specific instructions. Psycholinguistic tasks can be designed to probe how conceptual or lexico-semantic information becomes activated over time. One example of such a task is the visual world paradigm in which eye movements to multiple visual objects are tracked while a participant listens to a sentence (Huettig et al., 2011; Spivey & Marian, 1999). In such a task, the sentence might direct the participant to attend a target object while ignoring distractor objects. While these studies have mostly looked at the question of automatic language activation, it can also be used to measure language-specific conceptual interference by tracking eye-movements that share a conceptual property. For example, think of a task where participants are asked to “Find the lemon” among a set of pictures that include a distractor such as taxi. In such a task, language-specific colour associations may affect the eye movements differently where the prototypical colour of a taxi is yellow or not. Another type of task that tracks the time course of semantic activation combines psycholinguistic experimentation with electrophysiological measurements (Kogan et al., 2020, Kühne & Gianelli, 2019). Most studies employing this technique have searched for embodiment effects of action-related words or emotional grounding in abstract words. For action-related words, the behavioural and electrophysiological evidence suggests that sensorimotor information is activated in L2. However, this activation tends to be weaker and is less distributed in the brain compared to the L1 equivalent (De Grauwe et al., 2014; see also Kogan et al., 2020). For emotion words, the evidence is mixed. Some have argued that the L2 lexicon might be disembodied for emotion terms (Pavlenko, 2012), other studies suggest higher emotional involvement during L1 processing (e.g., Harris et al., 2003; Dewaele, 2004), whereas others found that emotionality was similar among proficient bilinguals (Pavlenko, 2015). Overall, the affective language embodiment tends to be less reliable than studies investigating the link between action words and sensorimotor embodiment (Kogan et al., 2020). However, it is likely that this line of research will continue as weakened emotional responding in L2 has been implicated in other areas of bilingualism, such as decision making (Miozzo et al., 2020). A significant challenge in much of the previous work is that L1 equivalents might mediate embodiment effects. This idea is supported by studies that only found embodiment effects if the meaning of L1 words had sufficient overlap with L2 (Vukovic & Williams, 2014), although others have shown that embodiment effects in L2 might be as strong as in L1, regardless of the overlap in meaning (De Grauwe et al., 2014). Chronometric studies might shed some light on direct and automatic access to embodied representation in L2. The best evidence comes

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from studies with action-related verbs; when hearing action words spoken in L2, the motor system is activated as early as 200 ms after a words’ onset (Vukovic & Shtyrov, 2014). Another way to test embodied L2 representations involves neurostimulation techniques such as transcranial magnetic stimulation. This approach might offer a causal test to counter the view that embodied effects are a byproduct and show whether motor areas are accessed when processing meaning (e.g., Monaco et al., 2021). This will be important to refine proposals, such as the hub-and-spoke model and the words-as-cues view (Lupyan & Lewis, 2019), that emphasise different ways of integrating meaning across modalities and language.

5.4 The role of proficiency? Several models of bilingual word representation and processing are developmental, that is, they aim to account for the role of changing L2 proficiency in how L2 words are processed. Both Revised Hierarchical Model (RHM), the Modified Hierarchical Model (MHM) and Jiang’s (2000) three-stage model of L2 vocabulary acquisition assume that participants in the early stages of L2 acquisition rely on L2-to-L1 translation links (the lexical mediation route). Direct links with the conceptual system (language-independent in RHM, and shared L1-L2 categories in MHM) gradually emerge as bilinguals become more proficient in L2 (the conceptual mediation route). MHM further considers a conceptual restructuring process where more culture-specific aspects of meaning are acquired as one becomes proficient in L2. Empirical evidence comes from studies that focus on conceptual restructuring while trying to minimise the role of language. Examples include the case of shape versus material biases in long and short-stay JapaneseEnglish bilinguals (Cook et al., 2006), or studies that investigate how linguistic features can result in conceptual restructuring, for example, when words for concepts have different grammatical genders. To test this hypothesis, Boroditsky and colleagues, presented Spanish and German speakers with a task in English to write down three descriptive adjectives for words, like bridge. In contrast to English, these L1s use a grammatical gender system, in which bridge is feminine in German and masculine in Spanish. German speakers described the object more often as beautiful, elegant, fragile, etc., whereas Spanish speakers more often said bridges were big, dangerous, and strong (Boroditsky et al., 2003). The possibility of conceptual restructuring when exposed to language suggests that a developmental bilingual multimodal model would not only need to account for simultaneous knowledge of two different languages; it would also need to consider how language shapes our conceptual representations as proficiency increases. The multimodal view allows for even more complex patterns due to the overlap of language-specific lexical-semantic and experiential information. This

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way, lexical-semantic and experiential representations can develop at different rates depending on the language and cultural environment. For example, L2 lexical-semantic representations for concepts with prominent experiential components might be inadequate without L2 socialisation, both for concrete concepts like colours (e.g., blu in Italian and blue in English) and abstract concepts like emotions (e.g., envy vs. jealousy in English / Russian). In that case, a culturally immersed bilingual might have better experiential representations for L2 concepts. A particular challenge for the multimodal view will be to specify the developmental hypothesis both at the lexical-semantic and experiential level. In line with monolingual developmental studies, increased proficiency should be accompanied by a shift from context-specific/syntagmatic relations (e.g., doctor-hospital) to context-independent/paradigmatic relations (e.g., doctor-psychiatrist; Ervin, 1961). However, bilingual word associations also show evidence for such a pattern where more conceptual, context-independent, or paradigmatic responses are produced in more proficient speakers (Fitzpatrick & Thwaites, 2020). At the same time, many questions remain about how L1 lexical-semantic knowledge and preexisting conceptual knowledge affect the development of L2 lexical-semantic representations. Developmental changes are also seen in translation errors as a function of language proficiency. Figure 5 illustrates what factors might contribute to mistranslation. This figure shows the aggregated single forward (Dutch-English) and backward (English-Dutch) translation responses two groups of Dutch native speakers gave for the concept stupid (De Deyne & Verheyen, 2022). The figure shows a more intricate system of interlingual associations and potential ambiguity than what is schematically represented as L1-L2 associations in the RHM (Kroll & Stewart, 1994) and similar models. In Figure 5, various translations reflect form-related structural factors (e.g., boring, bored, boredom, and errors like boredness). Other translations reflect multiple equivalence (e.g., gek [NL], insane [EN], crazy, weird), and lack of close semantic alignment in cases such as dom (NL) and lame (EN). Several studies have shown that translation ambiguity is lower in highly proficient speakers than in less proficient speakers (Prior et al., 2007; Wen & van Heuven, 2017). This is likely to reflect a combination of retrieval difficulties, smaller L2 vocabularies, and different conceptual representations in less proficient bilinguals. In the latter case, multiple equivalence might reflect poorer distinctions between different senses in speakers with lower L2 proficiency. In a single translation study, Tseng et al. (2014) investigated form errors, meaning errors, or omission errors (not knowing the translation) in English-Chinese translations from speakers with varying proficiency levels. More than 31% were form errors, compared to 38% omissions

Chapter 7. Cross-language influences in L2 semantic representations

Figure 5. Translation ambiguity for Dutch and English translations of the word stupid by L1 Dutch speakers represented as a network with lexical translation links. Note. The translations in this graph were obtained from a single word forward and backward translation task by more than 40 Dutch university students. Despite the relative homogeneity of the group, the graph shows considerable translation ambiguity.

and 27% meaning errors. The results showed a small insignificant correlation between proficiency and form errors but a moderate correlation between proficiency ratings and the number of meaning errors and omissions. This was the case in translations where either mutual equivalence or partial equivalence was present. Increased proficiency resulted in fewer omissions but more meaning errors. While this does not shed light on what kinds of lexical-semantic or experiential features determine translation errors, it highlights the presence of systematic variability in the representation of L2 meaning and ways of discriminating types of meaning errors in future studies.

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5.5 Other challenges As with any review covering a topic studied across multiple disciplines, this chapter comes with the usual caveats and omissions. It is impossible to do justice to the complexity of meaning, multilingual speakers’ diversity, learning history, and cultural competence. An example of a significant omission here is the consideration of languages that use logographic scripts like Chinese or Japanese (but see van Heuven & Dijkstra, Chapter 5, this volume for a discussion of a Chinese-English word processing model). Logographic languages add another source of semantic equivalence through a systematic mapping between form and meaning, which is mostly absent in alphabetic languages. A second aspect that deserves more attention is that words are hardly ever used in isolation, but are combined to form multi-word expressions (cf. Du et al., Chapter 8, and Wolter, Chapter 9, this volume), sentences, and texts. Textual units larger than the word and the role of context are crucial to integrate in any account of meaning (e.g., Elgort et al., 2018). Consequently, a theory of bilingual meaning should outline context-specific or context-independent meaning and potential task dependencies. For example, form similarity effects, that is, the inclination to produce cognate-like translations, is likely to be less strong when words are translated in context than in single word translation tasks (e.g., Prior et al., 2011). Conversely, it is also possible that context is more beneficial for resolving surface-level differences or structural non-equivalence (e.g., using the right part of speech) than addressing semantic differences (e.g., the experiential difference between the Italian blu and the English blue). Furthermore, there is a broad consensus that the early activation of word meanings or translations is automatic and capable of overriding context (e.g., Elston-Güttler & Williams, 2008). This non-selective access to specific senses or forms in different languages has been demonstrated widely (Brysbaert & Duyck, 2010) and is part of several localist connectionist network models discussed in Chapter 5 (van Heuven & Dijkstra, this volume). It suggests a third possible elaboration of existing models beyond dual and potential overlapping experiential and lexical-semantic representations. Such a model would include a more expressive representational system than one-to-one L1-L2 links by encoding this information as a rich network of crosslinguistic lexical links (such as those in Figure 5). For a while now, questions have been raised about what can be learned from psycholinguistic studies that use single word translation, similarity ratings, or word associations, which seem to trade in ecological validity for experimental control (Pavlenko, 2000). Moreover, some of the original findings reported here have relied on small-scale factorial experiments, which are often limited due to the difficulty of controlling several covariates (Balota et al., 2013). Nevertheless,

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various methods will likely remain essential to tackle the complexity of studying meaning in general, and bilingual meaning in particular. A promising direction is the use of large-scale approaches such as mega-studies that, so far, have had a substantial impact in monolingual studies but are less common in bilingual contexts (e.g., Balota et al., 2013; Brysbaert et al., 2021; De Deyne et al., 2019). New methods such as online testing also allow us to study a wider variety of bilinguals even though the tasks are still somewhat artificial. For instance, it would make it possible to collect translation norms in multiple languages at once, which would allow us to triangulate ambiguity at the side of the source or the target language by comparing translations from one language to multiple other languages (see, for instance, Degani et al., 2016; Tseng et al., 2014). The sheer access to speakers worldwide would allow us to probe the role of context systematically. Megastudies could also be used to collect experiential information about concepts using massive picture naming or word associations studies in various languages, which, combined with lexical-semantic models, could be used to construct a computational multimodal account of bilingual representations. Finally, megastudies enable us to conduct virtual experiments at multiple scales simultaneously, which seems essential if we wish to investigate the role of equivalence for a wide variety of features, words, categories, domains, and languages where the meaning of a word depends on the meaning of all other words. As such, there is a sense of optimism on how controlled lab-based studies that systematically probe a complex system complement meticulous case studies and fieldwork to bridge general observations with very detailed observations about specific words.

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chapter 8

Cross-language influences in the processing of L2 multi-word expressions Lingli Du,1, 2 Anna Siyanova-Chanturia1, 3 & Irina Elgort1 1

Te Herenga Waka – Victoria University of Wellington | 2 Henan University of Thechonology | 3 Ocean University of China

The present chapter provides a state-of-the-art review of research into cross-language influences in the processing of multi-word expressions (MWEs) in a second language (L2). Two lines of research are considered: first, how L2 speakers process congruent MWEs versus L2-only MWEs; second, how L2 speakers process L1-only MWEs translated into the L2 compared with control phrases. Studies have shown that congruent MWEs generally have a processing advantage over L2-only MWEs in L2 speakers. In contrast, evidence is mixed with regard to whether or not translated L1-only MWEs exhibit a processing advantage over matched controls in L2 speakers, with facilitation so far observed for idioms, but not for other types of MWEs. We consider possible reasons for these mixed findings. Keywords: Crosslinguistic influence, multi-word expressions (MWEs), activation (L1 activation), congruency effect, MWE processing, binomials, idioms, collocations

1.

Introduction

Cross-language overlap or congruency (i.e., similarity in form and meaning between the first language/L1 and the second language/L2) has been shown to benefit bilingual language processing; in particular, an overlap between the L1 and L2 facilitates L2 processing. A classic example of the congruency advantage is the cognate facilitation effect, wherein cognates (words that have a similar form and meaning across languages, such as film, taxi, and restaurant in French and English) are processed faster and more accurately than matched control words (de Groot et al., 2000, Experiment 2; Dijkstra et al., 1998; Libben & Titone, 2009). Importantly, recent research has suggested that the processing advantage afforded by congruency may also extend to units beyond the word level, to multi-word

https://doi.org/10.1075/bpa.16.08du © 2023 John Benjamins Publishing Company

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expressions (MWEs). MWEs are (semi-)fixed expressions which are made up of at least two words (e.g., idioms, collocations, binomials, etc.). A number of studies have shown that congruent MWEs (i.e., those that have an equivalent form in the L1) are likely to be processed faster and more accurately than incongruent L2-only MWEs that have no equivalent in the L1 (see Conklin & Carrol, 2018, for a review). The aim of the present chapter is to provide an up-to-date account of what is currently known about cross-language influences in the processing of MWEs in an L2. We will focus on empirical evidence for, and against, cross-language influences in L2 MWE processing and the mechanisms behind L1 influence on the processing of MWEs in an L2, and propose directions for future research. There are various types of MWEs, such as idioms (spill the beans), lexical bundles (in the middle of), binomials (bride and groom), collocations (spread news), and other phrasal elements (Siyanova-Chanturia & Van Lancker Sidtis, 2019). MWEs vary in size and complexity, compositionality, fixedness, and other properties. Nevertheless, what MWEs have in common is that (1) many of them are highly frequent in language, and (2) they are highly conventional, in that proficient language users are familiar with them (Carrol & Conklin, 2020; SiyanovaChanturia & Van Lancker Sidtis, 2019). A consequence of being frequent and/ or highly familiar is that the sequence becomes highly predictable (SiyanovaChanturia & Omidian, 2020).1 For example, on hearing or reading bride and …, a proficient language user is likely to complete the phrase with the most likely word groom. Due to their frequency, familiarity, and predictability, MWEs have been found to be processed faster than matched novel strings of language by L1 speakers (Arnon & Snider, 2010; Tremblay et al., 2011; Vilkaite, 2016) and L2 speakers (Hernández et al., 2016; Jiang & Nekrasova, 2007; Siyanova-Chanturia et al., 2011b).2 Specifically, using priming and eye-tracking paradigms, researchers have shown that the beginning of an MWE can prime its terminal component, in the L1 and L2, such that the recognition of the terminal word is faster when it is within an MWE than when it appears in a matched control phrase (Carrol & Conklin, 2014, 2017; Durrant & Doherty, 2010; Wolter & Gyllstad, 2011). While much of the research has centred on such factors as frequency, familiarity, and predictability in the processing of MWEs by L1 and L2 speakers (see Siyanova-Chanturia & Van Lancker Siditis, 2019, for a review), how cross1. Many idioms have low frequency but are still highly familiar to language users (e.g., Hallin & Van Lancker Sidtis, 2017). 2. In this review, we use the terms second language (L2) speakers and bilinguals interchangeably. Both early and late learners of an additional language are deemed bilinguals (e.g., see Siyanova-Chanturia et al., 2019, for a similar approach).

Chapter 8. Cross-language influences in the processing of L2 multi-word expressions

language congruency affects the processing of MWEs in L2 speakers has received limited attention, with only a handful of studies investigating this issue (Carrol & Conklin, 2014, 2017; Carrol et al., 2016; Du et al., 2021; Wolter & Gyllstad, 2011, 2013; Wolter & Yamashita, 2015, 2018; Yamashita & Jiang, 2010). These studies have mainly focused on how L2 speakers process congruent MWEs versus incongruent L2-only MWEs (i.e., L2 expressions that do not have a translation equivalent in the L1), or how they process translated L1-only MWEs (i.e., L1 expressions that do not have a translation equivalent in the L2 and are translated into the L2) versus matched control (novel) phrases. While most studies agree that congruent MWEs are processed faster and more accurately than incongruent L2-only MWEs (but see Cieślicka & Heredia, 2017, who observed that congruence led to slower processing), and that L1 influences L2 MWE processing, research evidence is mixed with regard to whether translated L1-only MWEs are processed faster than matched controls. In what follows below, we first review the studies looking at the processing of congruent versus incongruent L2-only MWEs (collocations, idioms, and binomials), then review research on the processing of translated L1-only MWEs versus matched controls, attempting to reconcile mixed findings on the processing of translated L1-only MWEs.

2.

Processing of congruent and L2-only MWEs

Emerging evidence indicates that congruency plays an important role in L2 MWE processing. L2 speakers can process congruent MWEs more rapidly and accurately than incongruent L2-only MWEs that are matched (Carrol et al., 2016; Du et al., 2021; Wolter & Gyllstad, 2011, 2013; Yamashita & Jiang, 2010). The relative processing advantage in speed and accuracy for congruent over incongruent L2-only MWEs has been interpreted as cross-language influences in the processing of MWEs in an L2. Yamashita and Jiang (2010) were one of the first to investigate the role of congruency in the online processing of MWEs in an L2. Using a phrase-acceptability judgment task (i.e., whether a test item is acceptable in English, YES/NO), this study investigated the processing of congruent verb-noun and adjective-noun collocations versus incongruent combinations that do not exist in the L1. Three groups of participants were recruited: English as a foreign language (EFL) Japanese learners, English as a second language (ESL) Japanese learners, and L1 speakers of English. Congruent and incongruent collocations were matched in terms of length, word frequency, and phrase frequency in English. They found that lower proficiency EFL learners made more errors with and responded more slowly to incongruent collocations (kill time, whose Japanese equivalent literally translates as ‘crush/break time’) than to congruent colloca-

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tions (kill animals). In contrast, although higher proficiency ESL users also made more errors on incongruent collocations than on congruent ones, they responded equally fast to the two types of collocations. The control group of L1 speakers, however, processed congruent and incongruent collocations with no difference in speed or accuracy. Based on these results, Yamashita and Jiang (2010) concluded that L1 influences L2 collocation processing in the early, but not late, stages of acquisition. More recent evidence suggests that the L1 continues to affect the processing of MWEs in an L2 even when L2 speakers are highly proficient (Du et al., 2021; Wolter & Gyllstad, 2011, 2013; Wolter & Yamashita, 2018). Using a primed visual lexical decision task, Wolter and Gyllstad (2011) investigated the processing of verb-noun collocations with advanced Swedish learners of English and L1 speakers of English. The participants were first shown the prime (the verb in a verbnoun collocation, e.g., give) and then asked to decide whether or not the target (the noun which collocates with the verb, e.g., answer) was a legal word in English. They observed that in L2 speakers, congruent collocations (e.g., give an answer) were processed significantly faster and more accurately than L2-only collocations (e.g., pay a visit, whose Swedish equivalent literally translates as ‘make a visit/do a visit’). The effect was found despite the fact that congruent and L2-only collocations were matched for collocational strength (as measured by t-scores) and phrase frequency in the L2. In contrast, L1 speakers showed no difference in terms of speed or accuracy for congruent and English-only collocations. Interestingly, L2 speakers demonstrated an even stronger priming effect for congruent collocations than L1 speakers, suggesting there was a doubling up of activation (i.e., simultaneous activation) for congruent collocations in both the L1 and L2 which contributed to the stronger facilitation in L2 speakers (Wolter & Gyllstad, 2011, p. 443). Note that what Yamashita and Jiang (2010) call ‘incongruent collocations’, Wolter and Gyllstad (2011) label ‘L2-only collocations’. Wolter and Gyllstad (2011) argue that corresponding L1 collocations are likely to be activated in the processing of collocations in an L2, even when L2 speakers are highly proficient and have established direct links between L2 collocations and the concepts, which appears to contradict Yamashita and Jiang (2010) who argue that L1 influences L2 collocation processing only in the early stages of acquisition. Similar results were reported in a follow-up study by Wolter and Gyllstad (2013). Using the same task as Yamashita and Jiang (2010) – a phrase-acceptability judgment task – Wolter and Gyllstad (2013) investigated how the L1 knowledge and frequency of input influence the processing of adjective-noun collocations in the L2 with advanced Swedish learners of English and L1 speakers of English. Three types of items were employed: congruent collocations (handsome man), incongruent L2-only collocations (identical twins), and non-collocational novel

Chapter 8. Cross-language influences in the processing of L2 multi-word expressions

items (angry use). Consistent with Wolter and Gyllstad (2011), Wolter and Gyllstad (2013) found that congruent collocations were recognized more quickly and accurately than incongruent collocations by the L2 speakers, but were recognized as quickly and accurately as incongruent collocations by the L1 speakers. Notably, the L2 speakers responded to the congruent collocations as fast as the L1 speakers, suggesting they were highly proficient. The L1 speakers responded faster than the L2 speakers only on the incongruent items. They further found that collocational frequency in the L2 (English), rather than that in the L1 (Swedish), affected variation in response times (RTs) between the congruent and incongruent collocations for the L2 speakers. This suggests that, in addition to congruency, frequency in an L2 (but not frequency in an L1) is an important factor for explaining RTs to L2 collocations. In addition, the effect of congruency on collocation processing in an L2 is reported in a recent study. Employing the same task and collocation type – phrase-acceptability judgment task and adjective-noun collocations – as Wolter and Gyllstad (2013), Wolter and Yamashita (2018) investigated how intermediate and advanced Japanese speakers of English, as well as L1 speakers of English, processed congruent (strong wind) and incongruent L2-only (busy road) collocations. Both groups of L2 speakers (intermediate and advanced) responded significantly faster to congruent than to incongruent collocations, with L1 speakers showing no processing differences for these two types of collocations. In line with Wolter and Gyllstad (2011, 2013), this study further indicates that congruency with the L1 influences collocation processing in the L2. However, in this study congruent collocations were significantly more frequent than English-only items, which may have contributed to their faster processing compared with English-only collocations. Yamashita (2018) argues that the above studies on collocations have a possible confounding effect of semantic transparency – the extent to which constituents contribute straightforwardly to the meaning of an MWE. Yamashita (2018) pointed out that there were more transparent and fewer opaque collocations in the congruent than incongruent conditions in the studies reviewed above (i.e., Wolter & Gyllstad, 2011, 2013; Wolter & Yamashita, 2018; Yamashita & Jiang, 2010), which could have affected RTs. Semantic transparency has been previously found to affect the processing of collocations. For example, using a visual semantic judgment task (i.e., Is the item meaningful and natural in English? YES/NO), Gyllstad and Wolter (2016) investigated how high-proficiency Swedish learners of English and L1 English-speaking controls processed (1) less transparent verb-noun collocations (draw a conclusion, run a risk) with the verb appearing in a specialized sense and the noun used literally, and (2) more transparent verb-noun free combinations (write a letter, kick a ball) with both constituent words used literally. The

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two types of combinations were matched for phrase frequency in English. Collocations and free combinations were congruent, in the sense that they could be translated word for word into L1 Swedish. Gyllstad and Wolter (2016) found that not only L2 learners, but also L1 speakers, showed slower response times (RTs) and higher error rates for collocations than free combinations, indicating that the lower level of transparency in collocations may have led to an increased processing cost. It is important to note that the finding was not replicated in a follow-up study by Gyllstad (2016), who found that only L2 speakers processed free combinations faster than collocations and idioms, while L1 speakers processed free combinations, collocations, and idioms in a similar way. In line with Tabossi et al. (2009), this study suggests that familiarity, rather than transparency, may be a more powerful predictor of processing time. Although the congruency effect might have been confounded with semantic transparency in studies with collocations (Wolter & Gyllstad, 2011, 2013; Wolter & Yamashita, 2018; Yamashita & Jiang, 2010), the influence of congruency on the processing of L2 MWEs has been reaffirmed in a recent study with a different type of MWEs − binomials (Du et al., 2021). Binomials are three-word phrases that are realised in English as A and B (e.g., noun and noun, verb and verb, adj. and adj.), where a specific word order is preferred, as in knife and fork versus fork and knife (Benor & Levy, 2006; Carrol & Conklin, 2020). Using a primed visual lexical decision task, Du et al. (2021) investigated how advanced Chinese-English bilinguals and English monolinguals processed congruent binomials (knife and fork) and English-only binomials (bread and butter), which were matched for phrase frequency. Further, the two content words within the binomial (knife and fork) and the matched control (spoon and fork) were equally strongly associated. Lexical decision latencies to the second content word (fork) in a binomial (knife and fork) were compared with response latencies to the same word in a matched control phrase (spoon and fork). L2 speakers showed a significant priming effect for congruent binomials, but no facilitation for English-only binomials; while L1 speakers showed comparable priming for congruent and English-only binomials. Consistent with previous studies on collocations (Wolter & Gyllstad, 2011, 2013; Wolter & Yamashita, 2018; Yamashita & Jiang, 2010), these findings suggest that L1 influence extends to the processing of binomials in an L2. A potential limitation of this study, however, is that L2 proficiency was not considered in the analysis; hence, it remains unclear how proficiency may have interacted with congruency in the processing of binomials in an L2. Similarly, the facilitative effect of congruency has also been observed in the processing of idioms in an L2. Using the eye movement paradigm, Carrol et al. (2016) investigated how high-proficiency Swedish learners of English and L1 speakers of English processed congruent (break the ice) and L2-only (kick the

Chapter 8. Cross-language influences in the processing of L2 multi-word expressions

bucket) idioms, compared to their corresponding matched literal controls (crack the ice, drop the bucket). Carrol et al. (2016) found that L2 speakers processed congruent idioms faster than literal controls, as suggested by early measures such as the likelihood of skipping for the final words, and late measures such as total reading time and regression path durations. However, for L2-only idioms, L2 speakers only showed facilitation for the meaning integration (via late measures), but no facilitation for the form (via early measures). In contrast, L1 speakers showed comparable advantage in both early and late measures when reading congruent and incongruent, English-only idioms compared to their matched controls. Further, research has shown that congruency facilitates L2 idiom processing even when idioms contain a code-switch. In a sentence-meaningfulness judgment task, Titone et al. (2015) asked English-French bilinguals to read idiomatic (She lived a lie.) or literal control sentences (She told a lie.) word by word and decide whether or not the sentence was meaningful. The idiom-final words were presented either in English (intact condition: He played with fire.) or in French (code-switched condition: He played with feu.). Critically, English idioms differed in the degree of their overlap with French, from not having a French equivalent idiomatic meaning, to having the same idiomatic meaning but no shared component words, to having the same idiomatic meaning and one or two shared constituent word(s), to having the identical idiomatic meaning and complete word-to-word overlap. The authors found that participants took more time to read idioms when the final word was presented in French (code-switched) than in English, whereas the presence of a code-switch caused significantly less disruption for literal control sentences, suggesting that idioms are understood through direct retrieval rather than a compositional analysis of figurative meanings. However, the disruptive effect of code-switch was found to decrease as cross-language overlap increased, whereby idiomatic sentences containing French final words (codeswitched condition) were responded to progressively faster and more accurately than literal control sentences with the increase in cross-language overlap. Interestingly, no effect of cross-language overlap was found for non-code-switched sentences containing English final words (intact condition). According to the authors, a code-switch may have explicitly cued the nontarget language (here, French) and, thus, the activation of the nontarget language might have facilitated idiom processing. In contrast, for non-code-switched sentences, high level of familiarity with these English idioms may have lessened the effect of crosslanguage overlap. Whereas the above studies suggest that congruency between L1 and L2 facilitates L2 MWE processing, other studies using idioms have demonstrated that congruency may in fact hinder L2 processing (Cieślicka & Heredia, 2017). Using

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eye-tracking, Cieślicka and Heredia (2017) looked at how Spanish-English bilinguals varying in English proficiency processed English ditropically ambiguous idioms, that is, idioms that can be interpreted both literally (hit the sack: punch the bag) and figuratively (hit the sack: go to sleep). The idioms were presented in sentences biasing their literal or figurative meaning, while in control phrases, the last word of the idiom was replaced with a matched control (hit the sack: hit the clerk). Half of the idioms were transparent (e.g., break the ice, where the figurative meaning can be deduced from analysing the idiom literally), and half were opaque (e.g., hit the sack, where the meaning cannot be inferred from idiom constituents). Out of these idioms, half had a direct translation and expressed the same meaning in Spanish (dubbed similar idioms), and the other half lacked any written or spoken form similarity but also expressed the same concept in Spanish (dubbed different idioms). Results showed that opaque idioms were more difficult to process than transparent ones, as suggested by reading measures (fixation count, first pass/gaze reading time and total reading time). Importantly, cross-language similarity interacted with idiom transparency, such that similar transparent idioms took longer to process than different transparent idioms (in the total reading time data for the whole phrase analysis). In addition, the final words in similar opaque idioms were characterised by longer reading times and more fixations than the final words in different opaque idioms. The authors concluded that, for similar idioms, the translation equivalent in the L1 is automatically activated in the course of L2 processing, which slows down the processing time; whereas for different idioms, no competing lexical items from L1 get activated and thus their processing in L2 is not affected. This finding is in contrast to those reported by Carrol et al. (2016) and Titone et al. (2015). (See van Heuven & Dijkstra, Chapter 5, and Piasecki & Dijkstra, Chapter 6, this volume for cross-language activation in bilingual lexical and pre-lexical processing of single words). Further, no effect of cross-language overlap, facilitative or disruptive, was reported in another study using idioms. Similar to Cieślicka and Heredia (2017), Beck and Weber (2016) looked at the processing of L2 idioms that have a wordfor-word equivalent and a matching concept in the L1 versus idioms that have a matching concept but no translation equivalent in the L1 in high proficiency German learners of English (Exp. 1) and L1 speakers of English (Exp. 2). Note that what Cieślicka and Heredia (2017) call ‘similar idioms’, Beck and Weber (2016) label ‘translatable or lexical level idioms’; what Cieślicka and Heredia (2017) call ‘different idioms’, Beck and Weber (2016) label ‘non-translatable or post-lexical level idioms’. In a cross-modal priming experiment, participants listened to English sentences that contained idioms and judged whether or not the string of letters on the screen that immediately followed the audio sentence was a real English word. Target words were related to the figurative meaning of the idiom (joke – to

Chapter 8. Cross-language influences in the processing of L2 multi-word expressions

pull someone’s leg), to the literal meaning of the final content word of the idiom (walk – to pull someone’s leg), or were unrelated to either of them (ship, milk). Results showed no effect of translatability, in that translatable idioms (lend [someone] an ear) and untranslatable idioms (kick the bucket) produced a comparable priming effect in L2 learners, for both literally- and figuratively-related targets. Contrary to Titone et al. (2015), higher degree of cross-language overlap did not lead to greater facilitation for translatable idioms (overlap in form and meaning) than for non-translatable idioms (overlap in meaning but not form). In addition, unlike Cieślicka and Heredia (2017), Beck and Weber (2016) found that the existence of word-for-word translation equivalents in the L1 did not hinder the processing of translatable idioms in the L2 compared to non-translatable idioms. Taken together, the congruency effect has been found to be robust across different types of MWEs, both literal compositional (collocations, binomials) and figurative non-compositional (idioms), and with different paradigms employed (RTs and eye movements). Whereas most of the research suggests that congruency facilitates MWE processing in an L2 (e.g., Carrol et al., 2016; Titone et al., 2015), several studies found that congruency may in fact slow down idiom processing in the L2 (Cieślicka & Heredia, 2017). In addition to the disputed nature of the cross-language effect (i.e., whether it is facilitative or disruptive), a conflicting finding is also reported with regard to whether or not the congruency effect increases with the increase in cross-language overlap (Yes: Titone et al., 2015; No: Beck & Weber, 2016).

3.

Processing of translated L1-only MWEs

While the studies reviewed above show a clear influence of L1 knowledge on the processing of MWEs in an L2, evidence is mixed with regard to whether translated L1-only MWEs can also show a processing advantage over matched controls. Research into this issue, however, can add to our understanding of what underpins the congruency effect in the processing of MWEs in an L2 (e.g., whether the L1 is activated in parallel with and facilitates the processing of MWEs in an L2). Studies with idioms have found a processing advantage for translated L1-only idioms over control phrases, suggesting direct L1 involvement in L2 processing, even when presented and processed entirely in the L2 (Carrol & Conklin, 2014, 2017; Carrol et al., 2016). In contrast, studies with collocations and binomials have not found a robust processing advantage for such items over control phrases (Du et al., 2021; Wolter & Yamashita, 2015, 2018). In what follows, these studies and the possible reasons for the conflicting findings are discussed in some detail.

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3.1 Research on idioms Carrol and Conklin (2014, 2017) and Carrol et al. (2016) were the first to investigate the processing of translated L1-only MWEs. Carrol and Conklin (2014, 2017) focused on the processing of Chinese-only idioms translated into English by intermediate proficiency Chinese speakers of English and L1 speakers of English. Carrol and Conklin (2014) investigated whether the initial words of Englishonly idioms (to spill the …) and translated Chinese-only idioms (draw a snake and add …) primed the final words (beans, feet, respectively) in a lexical decision task. L2 speakers responded faster to targets that formed idioms in their L1 (draw a snake and add feet) than to targets in corresponding matched control items (draw a snake and add hair). That is, they showed priming for translated L1-only idioms. However, they showed no advantage for English-only idioms (spill the beans) over matched novel language (spill the chips). In contrast, L1 speakers showed a different pattern of results. Targets that formed English idioms were reliably faster than controls; targets that formed Chinese idioms were not faster than controls. Both L1 and L2 speakers showed priming only for idioms taken from their respective L1s. The results suggest L1 influence extends to the processing of translated L1-only idioms. However, because the task was not performed under time pressure, and participants could view the primes for as long as they wanted, participants may have actively anticipated the completion of a conventional phrase. The processing advantage for translated L1-only idioms relative to control novel phrases was also reported in a follow-up study that used eye movements (Carrol & Conklin, 2017). This study compared reading times for idioms versus control novel phrases (draw a snake and add feet / hair) in Experiment 1, and figurative versus literal uses of idioms (add oil and vinegar – figurative meaning ‘to embellish a story’ vs. literal meaning ‘to add some dressing’) in Experiment 2. Target items were embedded in short sentence contexts biasing the figurative meaning (Exp. 1) or biasing either the figurative or literal meaning (Exp. 2). Consistent with earlier behavioural evidence (Carrol & Conklin, 2014), Chinese speakers of English, but not L1 speakers of English, showed significant facilitation for the final word of translated L1-only idioms compared to control phrases, evidenced in the analysis of first fixation durations and total reading time. In contrast, L1 speakers, but not L2 speakers, showed significant priming for English-only idioms relative to matched controls, as suggested by the likelihood of skipping and total reading time. However, in Experiment 2, L2 speakers read expressions used figuratively more slowly than those used literally, regardless of whether the idioms were English-only or Chinese-only, evidenced by total reading times. In contrast, L1 speakers showed no difference in reading times for literal or figurative uses of English-only idioms, whereas they read the figurative uses of translated Chinese-

Chapter 8. Cross-language influences in the processing of L2 multi-word expressions

only idioms more slowly than the literal uses. Carrol and Conklin (2017) took these results as evidence that the recognition of the form of a translated L1-only idiom (as suggested in Experiment 1) did not automatically lead to the access of the figurative meaning of the idiom (as suggested in Experiment 2). Notably, these results are consistent with Siyanova-Chanturia et al. (2011a) who found that figurative uses of idioms are processed more slowly than literal ones by L2 speakers; they are also in line with the Literal Salience Model proposed by Cieślicka (2006). According to this model, literal meanings of idiom constituents enjoy processing priority over their figurative interpretations for L2 speakers, in that computation of literal meanings is obligatory in understanding L2 idioms. This applies even to L2 idioms which are highly familiar to the L2 user and have been automatized and incorporated into the L2 lexical network (Cieślicka, 2006). According to Carrol and Conklin (2017), the processes underlying recognition of form, and access to the phrase-level figurative meaning of an idiom, may not be the same. Recognition of form may be affected by strong intra-lexical links among the individual words of an idiom, while access to phrasal meaning may be affected by familiarity and (language specific) frequency of encounters with a whole form structure and its associated figurative meaning (see De Deyne et al., Chapter 7, this volume for a discussion of different views on bilingual conceptual memory). In addition, the recognition of form may be realised via a lexical/translation route whereby L2 words automatically activate L1 equivalents (i.e., cross-language translation priming), which in turn triggers a known L1 idiom, facilitating the L2 processing. Access to meaning, however, may be realised via a conceptual route, whereby L2 words directly trigger their underlying concepts (e.g., draw, snake, add), the association of which in turn triggers the underlying idiom concept (ruin with unnecessary detail). These two routes are referred to as the lexicaltranslation mechanism and the conceptual priming mechanism, respectively. As Carrol and Conklin (2017) argue, for Chinese speakers of English, translated L1-only idioms had never been encountered in English and, thus, L2 representations of whole forms and their associated figurative meanings were likely weak. Therefore, idioms were more difficult to process when used figuratively than literally. However, intra-lexical links among the individual constituents of an idiom may be triggered by fast, automatic cross-language translation priming, whereby the initial words of an English expression (draw a snake and add …) automatically activate their translation equivalents in the L1 (here, Chinese), which in turn trigger a known idiomatic expression in the L1 (e.g., hua she tian zu /画蛇添足). The triggered L1 idiom facilitates form recognition, making the final word (zu / 足) available and in turn priming its translation equivalent in English (feet). Interestingly, translated L1-only idioms have shown a similar level of facilitation as congruent idioms relative to their corresponding matched controls. Carrol

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et al. (2016) found that L2 speakers showed a processing advantage for translated Swedish-only (play monkey) over their matched controls (taste monkey) in the word-level analysis, as suggested by the likelihood of skipping. Further, they observed that the processing advantage for translated Swedish-only idioms over controls extended to the phrase-level, as evidenced by shorter total reading times and fewer fixations. The results suggest that there was form activation as well as meaning activation in the processing of translated L1-only idioms in L2 speakers. The results, however, are inconsistent with Carrol and Conklin (2017), who only found evidence for form recognition of translated L1-only idioms, but no evidence for access to their figurative meanings. The results from Carrol et al. (2016) provide evidence against the argument proposed by Carrol and Conklin (2017) that access to meanings of idioms may be due to familiarity and (language specific) frequency of encounter of a whole form structure and its associated figurative meaning. Instead, these results suggest that the figurative meanings of translated L1-only idioms can also be activated with no prior L2 exposure. Importantly, in Carrol et al. (2016), the processing advantage for Swedish-only and congruent idioms was found to be comparable, suggesting that congruent idioms did not show more facilitation than translated L1-only idioms, despite (additional) L2 exposure. Note that L1 Swedish participants in this study had higher English proficiency than the L1 Chinese participants in Carrol and Conklin (2017). The authors thus concluded that it was L1 (rather than L2) knowledge that determined the ease of processing of idioms in L2 speakers. This conclusion, however, does not seem to align with the finding of Wolter and Gyllstad (2013) that collocational frequency in the L2, rather than that in the L1, affected the difference in RTs between the congruent and incongruent L2-only collocations for the L2 speakers. However, robust facilitation for translated L1-only idioms over literal control phrases reported above was not replicated in a recent study by Zhu and Minda (2021). In a cross-modal priming experiment, Chinese-English bilinguals and L1 English speakers listened to an idiom up until the last word (e.g., draw a snake and add) and then made lexical decisions to the visual word presented (e.g., idiom ending feet vs. matched control ending hair). Idioms were Englishonly or Chinese-only (translated into English). Consistent with previous studies, Chinese-English bilinguals responded faster to Chinese idioms than to control phrases. Surprisingly, L1 English speakers also showed faster responses to Chinese idioms than controls, where no response difference should have been observed. The authors attributed this unexpected result to the possibility that Chinese idioms might have been more literally plausible than control phrases, which led to facilitation for Chinese idioms in both groups of participants.

Chapter 8. Cross-language influences in the processing of L2 multi-word expressions

3.2 Research on collocations and binomials Unlike robust facilitation for translated L1-only idioms relative to control phrases, studies with collocations and binomials paint a different picture. For example, using a double lexical decision task that presented both words on the screen simultaneously with the first word appearing directly above the second word, Wolter and Yamashita (2015) investigated how the L1 influences collocation processing in the L2 with intermediate and advanced Japanese speakers of English, as well as L1 speakers of English. Three types of items were used: translated Japanese-only verb-noun and adjective-noun collocations (buy anger), Englishonly collocations (catch breath), and non-collocational baseline items (bad gift). The results showed that there was no significant difference for either RTs or error rates between translated Japanese-only items and non-collocational baseline items, suggesting that there was no activation of L1 collocations when processed entirely in the L2. Similar results were observed in their follow-up study which used a phrase acceptability task (Wolter & Yamashita, 2018). Both groups of Japanese speakers of English, intermediate and advanced, judged translated L1-only collocations and matched baseline items with a similar speed, indicating no activation of L1 collocations when processing entirely in the L2. Moreover, a recent study with binomial expressions provides further evidence militating against the processing advantage for translated L1-only MWEs (Du et al., 2021). This study found that, similar to English monolinguals, advanced Chinese-English bilinguals showed robust priming for congruent binomials, with faster RTs for the terminal word in the binomial than control items. However, neither bilinguals nor monolinguals showed a priming effect for translated Chineseonly binomials. These results suggest that although the L1 influences the processing of binomials in the L2, this influence may not extend to translated L1-only binomials when processed in the L2. This finding is consistent with Wolter and Yamashita (2015, 2018), but goes contrary to Carrol and Conklin (2014, 2017) and Carrol et al. (2016).

3.3 Reconciling research on idioms versus collocations and binomials The studies reviewed above suggest distinct processing patterns for translated L1-only idioms versus collocations and binomials. Below we consider a number of plausible explanations of these findings. One possibility is that figurative (e.g., idioms) and literal (e.g., many collocations and binomials) language is processed differently. Idioms are “strings of words whose figurative meaning does not necessarily derive from that of the constituent parts” (Cacciari, 2014, p. 267). That is, idioms have a conventional figurative phrase meaning which is different from

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literal meanings of their individual component words. Thus, the processing advantage observed for idioms may come from two processes: (1) recognition of specific word combinations presented in a particular order or configuration, that is, form activation, and (2) access to figurative meanings of idioms, that is, meaning activation (Carrol et al., 2016). The locus of form activation is lexical, while the locus of meaning activation is conceptual (Du et al., 2021). In the case of translated L1-only idioms, although the form may be unfamiliar to L2 speakers when presented in an L2, the figurative meaning of the whole phrase is known from the L1. Thus, translated L1-only idioms can show facilitation compared to literal controls when processed in the L2, due to the conceptual-level activation. For example, Beck and Weber (2006) found that idioms that overlap in form and meaning between L1 and L2 (i.e., translatable idioms) showed comparable facilitation compared to idioms that overlap in meaning but not form (i.e., nontranslatable idioms), relative to their matched control novel phrases. This suggests that facilitation for the translated L1-only idioms is likely to be driven by the conceptual overlap, rather than form overlap. In addition, it is possible that the phrase figurative meaning of an idiom is activated while the form is not. For example, Carrol et al. (2016) found that for L2-only idioms, L2 learners showed facilitation only for the meaning integration (via late measures), but no facilitation for the form (via early measures). In contrast, in literal MWEs, the processing advantage is likely to be due to the effect of phrase frequency and form activation (Du et al., 2021). The studies with collocations and binomials reviewed above all adopted a frequency-based approach to identifying the stimuli (Du et al., 2021; Wolter & Gyllstad, 2011, 2013; Wolter & Yamashita, 2018). For such MWEs, the processing advantage appears to be largely frequency-based (Arnon & Cohen Priva, 2014; Ellis, 2002; SiyanovaChanturia, 2015). That is, prior exposure to L2 word sequences is necessary for significant facilitation to be observed, compared to control novel phrases. Language users are sensitive to the language-specific distributional properties of MWEs, and a processing advantage in one language may not be automatically transferred into another language. Thus, lexical combinations that exist solely in bilinguals’ L1 should not automatically show speeded processing in the L2. For example, Wolter and Gyllstad (2013) found that it was L2 rather than L1 collocational frequency that accounted for the differences in RTs between the congruent and incongruent L2-only collocations for L2 speakers. Conflicting findings reported in bilingual studies with idioms compared with collocations and binomials may also be due to the use of different methods and tasks. In most studies with idioms, the test items were embedded in sentence contexts (Carrol & Conklin, 2017; Carrol et al., 2016), while the studies with collocations and binomials investigated the processing of these items out of con-

Chapter 8. Cross-language influences in the processing of L2 multi-word expressions

text. A biasing context greatly increases predictability in the processing of idioms (Cieślicka, 2013; Titone & Connine, 1999), which could have contributed to the facilitation for translated L1-only idioms over matched controls. (Note, however, that Carrol et al., 2016, found a processing advantage for translated L1-only idioms over control phrases for L2 speakers without using biasing contexts.) Critically, most of the studies that found facilitation for translated L1-only idioms employed eye-tracking, while studies with collocations and binomials employed reaction time measures such as phrase-acceptability judgment tasks and primed lexical decision tasks.

4.

Mechanisms underpinning the congruency effect in L2 MWE processing

The review above suggests that congruency plays an important role in L2 MWE processing, such that congruent MWEs are processed faster than incongruent L2-only MWEs and that translated L1-only idioms (but not collocations or binomials) are processed faster than literal control phrases. So, what mechanisms may underpin the congruency effect? The literature has so far provided two potential explanations. One is that L1 translation equivalents are automatically activated in parallel while processing in the L2 (e.g., Carrol & Conklin, 2014, 2017; Carrol et al., 2016; Wolter & Gyllstad, 2011). This activation may occur via lexical translation or conceptual connections (Conklin & Carrol, 2018). To verify this hypothesis, research has looked at the processing of L1-only MWEs (translated into the L2) relative to control novel L2 phrases. If bilinguals process translated L1-only MWEs faster than L2 controls, without ever experiencing that exact L2 word combinations previously, we could argue that this facilitation is due to the online activation of known L1 MWEs during L2 processing. This account is referred to as the L1 MWE activation account (Du et al., 2021; Pulido & Dussias, 2020; Yamashita, 2018; Zeng et al., 2020). If, however, bilinguals do not process translated L1-only MWEs faster than L2 controls, the congruency effect may be due to sources other than the online activation of known L1 MWEs. This points to the second possible explanation for the congruency effect, whereby congruent MWEs are acquired earlier than L2-only MWEs, as a result of cross-language transfer, and they are thus processed faster than L2-only MWEs due to the acquisition order advantage (e.g., Wolter & Gyllstad, 2013; Wolter & Yamashita, 2015, 2018). This account is referred to as the L2 MWE experience account (Du et al., 2021).

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4.1 L1 MWE activation account In the L1 MWE activation account, known L1 MWEs are assumed to be automatically activated during L2 processing, leading to a greater processing advantage for congruent MWEs when compared to incongruent L2-only ones (e.g., Carrol & Conklin, 2014; Carrol et al., 2016; Wolter & Gyllstad, 2011). According to this account, the initial words of an L2 MWE automatically activate their L1 translation equivalents via cross-language translation priming (Thierry & Wu, 2007; Wu & Thierry, 2010; Zhang et al., 2011), which, in turn, triggers the activation of a known L1 MWE via within-language lexical priming between the constituents of MWEs (e.g., idiom priming, collocational priming). This account is not unlike the lexical-translation mechanism proposed by Carrol and Conklin (2014). For example, when L2 speakers of English (whose L1 is Chinese) read a prime phrase of draw a snake and add …, the Chinese translations of 画 (hua: ‘draw’) 蛇 (she: ‘snake’) 添 (tian: ‘add’) might be automatically activated as each word is encountered. The activated Chinese words then trigger a known idiomatic sequence 画 蛇添足 / hua she tian zu via direct retrieval, making the final character 足/ zu available, which in turn primes its English translation feet (Carrol & Conklin, 2014, 2017). As Carrol and Conklin (2014) argue, MWEs may show cross-language priming in the same way as single words. The L1 MWE activation account is built on the literature on cross-language activation in bilingual processing and on within-language lexical priming between the constituents of MWEs. The literature on bilingual language processing has found that the two languages of a bilingual can be activated simultaneously, even when processing entirely in one language (for a review, see van Hell & Tanner, 2012). For example, using a within-L2 semantic relatedness task and event-related brain potentials, Thierry and Wu (2007) investigated how ChineseEnglish bilinguals and English monolinguals processed semantically related (e.g., post – mail) and unrelated (e.g., train – ham) English word pairs. Half of the word pairs shared a character when translated into Chinese. For example, train and ham are not related in meaning but their Chinese translations huo che (火车) and huo tui (火腿) share one Chinese character huo (火); while apple and table are neither related in meaning nor overlap in their Chinese translations (i.e., ping guo [苹果] – zuo zi [桌子]). They found that L2 speakers showed reduced N400 amplitudes for English word pairs that shared a character in their Chinese translations (such as train – ham), but not for those that did not (such as apple – table). This suggests that L1 translation equivalents are automatically activated in L2 processing, and they in turn activate associative links within the L1. MWE studies have found lexical priming between the constituents of an MWE, such that the beginning of an MWE can facilitate the recognition of its terminal word (Carrol

Chapter 8. Cross-language influences in the processing of L2 multi-word expressions

& Conklin, 2017; Du et al., 2021; Durrant & Doherty, 2010; Siyanova-Chanturia, Conklin, & Schmitt, 2011; Underwood et al., 2004; Wolter & Gyllstad, 2011). A similar account is the lemma activation model outlined by Jiang (2000). In this model, all lexical entries consist of a lexeme level (containing information about form, such as phonology, orthography, and morphology) and a lemma level (relating to information about meaning). Wolter and Yamashita (2015) argue that lemma-level information may also include information such as the collocational links and patterns of association. In the case of L2 learners, however, an L2 lexeme entry may link to an existing L1 lemma, coping the L1 lemma into the L2 entry, especially at the early stage of learning the L2. Encountering an L2 form may therefore activate L1 lexical networks, including possible collocations and commonly co-occurring words from the L1 (Yamashita & Jiang, 2010). This may explain why we see facilitation for congruent collocations over incongruent L2-only collocations, as known L1 collocations are likely to be activated. In addition, a related but different online processing model is proposed by Zeng et al. (2020). Different from the L1 MWE activation account or the lexical translation mechanism, this model assumes that L2 MWEs and their L1 counterparts are linked at the phrasal level in bilingual lexicon in the same way as single words are linked across languages, and that L1 MWEs are activated in L2 processing through this between-language phrasal link.

4.2 L2 MWE experience account In the L2 MWE experience account, congruent MWEs are assumed to be acquired before incongruent MWEs, because acquisition is more straightforward when there is correspondence between the L1 and L2 (due to positive crosslanguage transfer). Thus, congruent MWEs should be processed faster than incongruent MWEs due to the age/order-of-acquisition effect (e.g., Wolter & Gyllstad, 2013; Wolter & Yamashita, 2015, 2018). Rather than assuming that L1 MWE translation equivalents are activated simultaneously in L2 processing due to cross-language activation and that this activation leads to faster processing, the experience account holds that prior exposure to a word sequence in an L2 is essential for it to be processed faster than its control, whether or not it has an equivalent form in the L1. Having an equivalent form in the L1, however, does facilitate the acquisition of a (congruent) L2 MWE, in that, acquiring congruent MWEs may take less time and require less exposure to the L2 than acquiring incongruent L2-only MWEs. Earlier acquisition of congruent MWEs translates to more experience with the expression in the L2 and leads to greater processing advantage for congruent MWEs compared to incongruent L2-only MWEs. According to this account, when encountered in an L2 for the first time, translated

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L1-only MWEs will not be processed faster than matched L2 control phrases, because L2 speakers are not familiar with them. This account is in line with usageand exemplar-based acquisition and processing accounts which assume that frequency of encounters with and use of a lexical item (e.g., word, MWE) determines the strength of its mental representations and ease of processing (Bybee, 2006; Langacker, 2000; Wolter & Gyllstad, 2013). The L2 MWE experience account is based on the literature on crosslinguistic influences in MWE acquisition and, to some extent, on the age/order-ofacquisition effect in MWE processing. A number of studies have shown that L1 influences the acquisition of L2 MWEs (Nesselhauf, 2005; Römer et al., 2014; Sonbul et al., 2020; Yamashita & Jiang, 2010; see also Wolter, Chapter 9, this volume). When encountering a congruent MWE in an L2 that exists in the learners’ L1, an initial strong episodic memory trace is likely established, which can facilitate the gradual acquisition of procedural knowledge from input (Ullman, 2014). Thus, the multi-word sequence is acquired procedurally and may be processed faster and more automatically than L2-only MWEs (Du et al., 2021). There is also evidence showing that age/order-of-acquisition affects not only the processing of single words, but also the processing of MWEs (e.g., Arnon et al., 2017; also see Juhasz, 2018, for evience with compounds). Using a phrasal decision task on three-word lexical bundles, Arnon et al. (2017) investigated whether age-of-acquisition affected MWE processing (akin to what has been reported for single words). They found that L1 adults responded faster to earlyacquired phrases (a good girl) compared to late-acquired ones (a good dad), suggesting that there are parallels in processing words and MWEs. Similarly, using the eye movement paradigm, Juhasz (2018) investigated the role of age-ofacquisition in the processing of compound words in sentence context in L1 speakers of English. Juhasz (2018) found that the age-of-acquisition of the compound word (e.g., airport, bodyguard), rather than that of the individual morphemes, affected their processing, as shown by the gaze durations and total fixation durations. Although new evidence is being added on the origin of the congruency effect in bilingual processing of MWEs, it is still insufficient to unequivocally support one of the two accounts. Studies on idioms show priming for translated L1-only idioms over novel L2 phrase controls, suggesting that there might be automatic activation of known L1 MWEs (Carrol & Conklin, 2014, 2017; Carrol et al., 2016). Conversely, studies on collocations and binomials have not found facilitation in the processing of translated L1-only collocations (Wolter & Yamashita, 2015, 2018) or binomials (Du et al., 2021). Additionally, because facilitation in L2 processing for translated L1-only MWEs is found for idioms but not for collocations or binomials, the question

Chapter 8. Cross-language influences in the processing of L2 multi-word expressions

remains whether the distinct findings are related to the type of MWEs being processed, i.e., literal versus figurative expressions. It is possible that the facilitation for translated L1-only idioms is driven by the conceptual L1-L2 overlap, in which case, the figurative meaning of idioms is activated even if they have not been encountered in the L2 (Carrol et al., 2016, but see Carrol & Conklin, 2017). To test the hypothesis whether the facilitation for translated L1-only idioms is due to conceptual overlap, future research could investigate the processing of L1-only idioms which are paraphrased into an L2, rather than translated wordby-word (e.g., instead of the word-by-word translation draw a snake and add feet, researchers may use the phrase add feet to a snake). If the modified translated L1-only idioms, which retain their figurative meanings, show a processing advantage over control novel phrases, the conceptual-overlap priming explanation will be confirmed. A recent eye-tracking study by Kyriacou et al. (2021) showed that final words in modified versions of idioms (e.g., spill the [spicy, (red)] beans) were processed with the same speed as the same words in familiar, canonical idioms (e.g., spill the beans). The results suggest that modifying idioms may not impede their processing. In addition, there is evidence for form activation in the processing of translated L1-only idioms (Carrol & Conklin, 2017; Carrol et al., 2016). If this form activation occurs via the lexical-translation route for idioms, as suggested by Carrol and Conklin (2014), one may wonder why form activation does not occur in the processing of translated L1-only collocations or binomials in a similar way. In summary, the two accounts of the congruency effect discussed in this chapter are both theoretically and empirically motivated. The L1 MWE activation account is aligned with the non-selective lexical access account of bilingual processing (Dijkstra & van Heuven, 2002; van Heuven et al., 1998), supported by empirical evidence for masked translation priming, which has been observed even when the two languages do not share the same writing system (e.g., HebrewEnglish: Gollan et al., 1997; Chinese-English: Wang & Forster, 2010) and in purely monolingual contexts (Thierry & Wu, 2007; Zhang et al., 2011). The L2 MWE experience account, on the other hand, is aligned with usage- and exemplarbased acquisition and processing accounts (Bybee, 2006; Langacker, 2000; Wolter & Gyllstad, 2013), supported by empirical evidence for phrase frequency effects in language processing (e.g., Arnon & Snider, 2010; Siyanova-Chanturia et al., 2011b). We argue that this important line of enquiry should continue, as more evidence is needed to tip the balance towards one or the other account of the congruency effect in L2 MWE processing.

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5.

Conclusions

The studies reviewed in the present chapter have been instrumental in our understanding of the nature of cross-language influences in the processing of MWEs in an L2. On the one hand, converging evidence for a greater processing advantage for congruent MWEs than incongruent L2-only MWEs suggests a clear influence of L1 knowledge on the processing of MWEs in an L2. The congruency effect has been reported in studies with idioms, collocations, and binomials. On the other hand, it is still unclear what mechanisms underpin the congruency effect in the processing of L2 MWEs. While some evidence suggests known L1 MWEs are automatically activated in L2 processing (e.g., Carrol & Conklin, 2014, 2017; Carrol et al., 2016), other studies do not support this tenet (e.g., Du et al., 2021; Wolter & Yamashita, 2015, 2018), suggesting that the greater processing advantage of congruent MWEs may stem from their likely earlier acquisition (and, therefore, greater experience with their L2 form), compared with incongruent L2-only MWEs. The underlying mechanisms behind MWE congruency effect thus remain to be clarified in future research.

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Pulido, M. F., & Dussias, P. E. (2020). Desirable difficulties while learning collocations in a second language: Conditions that induce L1 interference improve learning. Bilingualism: Language and Cognition, 23(3), 652–667. https://doi.org/10.1017/S1366728919000622 Römer, U., O’Donnell, M. B., & Ellis, N. C. (2014). Second language learner knowledge of verb–argument constructions: Effects of language transfer and typology. The Modern Language Journal, 98(4), 952–975. https://doi.org/10.1111/modl.12149 Siyanova-Chanturia, A. (2015). On the ‘holistic’ nature of formulaic language. Corpus Linguistics and Linguistic Theory, 11(2), 285–301. https://doi.org/10.1515/cllt-2014-0016 Siyanova-Chanturia, A., Canal, P., & Heredia, R. R. (2019). Event-related potentials in monolingual and bilingual non-literal language processing. In J. W. Schwieter & M. Paradis (Eds.), The handbook of the neuroscience of multilingualism (pp. 508–529). John Wiley & Sons. https://doi.org/10.1002/9781119387725.ch25 Siyanova-Chanturia, A., Conklin, K., & Schmitt, N. (2011a). Adding more fuel to the fire: An eye-tracking study of idiom processing by native and non-native speakers. Second Language Research, 27(2), 251–272. https://doi.org/10.1177/0267658310382068

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Siyanova-Chanturia, A., Conklin, K., & van Heuven, W. J. B. (2011b). Seeing a phrase “time and again” matters: The role of phrasal frequency in the processing of multiword sequences. Journal of Experimental Psychology: Learning, Memory, and Cognition, 37(3), 776–784. https://doi.org/10.1037/a0022531

Siyanova-Chanturia, A., & Omidian, T. (2020). Key issues in researching multi-word items. In S. Webb (Ed.), The Routledge handbook of vocabulary studies (pp. 529–545). Routledge. Siyanova-Chanturia, A., & Van Lancker Sidtis, D. (2019). What online processing tells us about formulaic language. In A. Siyanova-Chanturia & A. Pellicer-Sánchez (Eds.), Understanding formulaic language: A second language acquisition perspective (pp. 38–61). Routledge. Sonbul, S., El-Dakhs, D. A. S., & Al-Otaibi, H. (2020). Productive versus receptive L2 knowledge of polysemous phrasal verbs: A comparison of determining factors. System, 95, 102361. https://doi.org/10.1016/j.system.2020.102361 Tabossi, P., Fanari, R., & Wolf, K. (2009). Why are idioms recognized fast? Memory & Cognition, 37(4), 529–540. https://doi.org/10.3758/MC.37.4.529 Thierry, G., & Wu, Y. J. (2007). Brain potentials reveal unconscious translation during foreignlanguage comprehension. Proceedings of the National Academy of Sciences, 104(30), 12530–12535. https://doi.org/10.1073/pnas.0609927104 Titone, D., Columbus, G., Whitford, V., Mercier, J., & Libben, M. (2015). Contrasting bilingual and monolingual idiom processing. In R. R. Heredia & A. B. Cieślicka (Eds.), Bilingual figurative language processing (pp. 171–207). Cambridge University Press. https://doi.org/10.1017/CBO9781139342100.011

Titone, D., & Connine, C. M. (1999). On the compositional and noncompositional nature of idiomatic expressions. Journal of Pragmatics, 31(12), 1655–1674. https://doi.org/10.1016/S0378-2166(99)00008-9

Tremblay, A., Derwing, B., Libben, G., & Westbury, C. (2011). Processing advantages of lexical bundles: Evidence from self-paced reading and sentence recall tasks. Language Learning, 61(2), 569–613. https://doi.org/10.1111/j.1467-9922.2010.00622.x Ullman, M. T. (2014). The declarative/procedural model: A neurobiologically motivated theory of first and second language. In B. VanPatten & J. Williams (Eds.), Theories in second language acquisition: An introduction (pp. 135–160). Routledge. Underwood, G., Schmitt, N., & Galpin, A. (2004). The eyes have it. In N. Schmitt (Ed.), Formulaic sequences: Acquisition, processing, and use (pp. 153–172). John Benjamins. https://doi.org/10.1075/lllt.9.09und

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Wolter, B., & Gyllstad, H. (2013). Frequency of input and L2 collocational processing. Studies in Second Language Acquisition, 35(3), 451–482. https://doi.org/10.1017/s0272263113000107 Wolter, B., & Yamashita, J. (2015). Processing collocations in a second language: A case of first language activation? Applied Psycholinguistics, 36(5), 1193–1221. https://doi.org/10.1017/s0142716414000113

Wolter, B., & Yamashita, J. (2018). Word frequency, collocational frequency, L1 congruency, and proficiency in L2 collocational processing. Studies in Second Language Acquisition, 40(2), 395–416. https://doi.org/10.1017/s0272263117000237 Wu, Y. J., & Thierry, G. (2010). Chinese-English bilinguals reading English hear Chinese. Journal of Neuroscience, 30(22), 7646–7651. https://doi.org/10.1523/JNEUROSCI.1602-10.2010 Yamashita, J. (2018). Possibility of semantic involvement in the L1-L2 congruency effect in the processing of L2 collocations. Journal of Second Language Studies, 1(1), 60–78. https://doi.org/10.1075/jsls.17024.yam

Yamashita, J., & Jiang, N. (2010). L1 Influence on the acquisition of L2 collocations: Japanese ESL users and EFL learners acquiring English collocations. TESOL Quarterly, 44(4), 647–668. https://doi.org/10.5054/tq.2010.235998 Zeng, T., Branigan, H. P., & Pickering, M. J. (2020). Do bilinguals represent between-language relationships beyond the word level in their lexicon? Journal of Neurolinguistics, 55, Article 100892. https://doi.org/10.1016/j.jneuroling.2020.100892 Zhang, T., van Heuven, W. J. B., & Conklin, K. (2011). Fast automatic translation and morphological decomposition in Chinese-English bilinguals. Psychological Science, 22(10), 1237–1242. https://doi.org/10.1177/0956797611421492 Zhu, T., & Minda, J. P. (2021). An investigation of idiom processing advantage using translated familiar idioms. Canadian Journal of Experimental Psychology/Revue Canadienne de Psychologie Expérimentale, 75(2), 162–168. https://doi.org/10.1037/cep0000245

chapter 9

Cross-language influences in the acquisition of L2 multiword expressions Brent Wolter

Idaho State University | Ocean University of China

As with all aspects of L2 acquisition, the L1 has a marked influence on the acquisition of multiword expressions (MWEs). However, providing a unified framework to explain the L1’s influence on the acquisition of MWEs is challenging, particularly because MWEs vary in a number of ways. In this chapter, I begin by discussing various types of MWEs and how these can affect acquisition. I then move on to review research to date documenting the L1’s influence on L2 MWE acquisition before considering additional factors that have been shown to alter the effects of the L1. I conclude by calling for a holistic account of the acquisition of L2 MWEs that incorporates the influence of the L1 and other additional factors. Keywords: Multiword expression acquisition, multiword expression processing, cross-linguistic influences, transfer strategies, psycholinguistics

1.

Introduction

From a linguistic perspective, multiword expressions (MWEs) can be described as lexicalized multiword constructions or, in other words, multiword units that function together to represent a single unit of meaning. As has been discussed at length elsewhere (see, e.g., Wray, 2002, p. 9), there is a dizzying range of terms and descriptions for the various types of MWEs, so I will not repeat these here. Instead, I will focus on some general qualities of MWEs and how these can vary from MWE to MWE, as these differences can affect the L1’s influence on L2 acquisition. Nation (2013, p. 485) provides a useful starting point by noting that researchers have used three main criteria when attempting to identify MWEs: form-based, meaning-based, and storage-based. Form-based approaches rely on frequency of co-occurrence in large corpora as the defining criterion. Though a variety of measures exist, the key notion behind each of these is whether or not https://doi.org/10.1075/bpa.16.09wol © 2023 John Benjamins Publishing Company

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the words comprising a potential MWE co-occur with significantly greater than expected frequency given the relative frequency of the component words in the corpus as a whole (see, e.g., Cheng et al., 2006). Meaning-based approaches, associated with research in the area of phraseology, are concerned with compositionality, or the extent to which the words in the MWE retain their core sense versus taking on a more figurative sense. In this respect, researchers using a meaningbased approach are interested in MWEs whose meaning cannot easily be ascertained based on the core semantic value of the individual words alone. To illustrate the distinction between these two approaches, it is useful to look at two examples using the verb read. Under a form-based framework, the expressions read a book and read [someone’s] mind would both be classified as MWEs since they both occur at a significantly higher than chance frequency given the relative frequency of the individual words. Under a meaning-based approach, however, only the latter of these two would be classified as a MWE since read is used in a nonliteral sense only in this expression. In more precise terms, the form-based approach would classify both expressions as collocations, while the meaning-based approach would only consider read [someone’s] mind as a collocation and read a book as a free combination (see Howarth, 1998). Because of this fundamental difference in approaches, form-based approaches tend to capture a wider range of MWEs than meaning-based approaches since the former include expressions that are not necessarily figurative, but occur with greater than chance frequency in large corpora. Regardless of which of these two approaches one subscribes to, however, neither can serve as a basis for a theory of MWE acquisition since they are concerned first and foremost with external, descriptive qualities of MWEs. In contrast, the storage-based approach is centrally concerned with how MWEs are understood on a psychological level, and as such it is the only one of these three approaches that can serve as a basis for acquisition. Though there is some variation in perspectives, the consensus in research literature tends to endorse the view that MWE acquisition occurs once a particular MWE becomes stored, processed, and accessed as a single unit of meaning on a psychological level (see, e.g., Schmitt, 2004; Wray, 2002, 2008). Wray refers to these as morpheme equivalent units, and although understanding what occurs at a psychological level always comes with a number of practical and theoretical challenges, this view of MWE acquisition is consistent with general assumptions in SLA research that acquisition occurs once a language structure can be accessed automatically with little conscious thought or attention. Still, the form-based and meaning-based approaches warrant closer scrutiny here as they are extremely useful in helping us to describe properties of MWEs that can make them more or less amenable to acquisition. One long-held view

Chapter 9. Cross-language influences in the acquisition of L2 multiword expressions

is that MWEs vary along two main continua: a structural fixedness continuum and an idiomaticity/compositionality continuum (e.g., Wray, 2002). To illustrate this, it is useful to start with binomials as one type of MWE. Binomials can be described as pairings of words that occur in a fixed order in a certain language. In English, for example, it is conventional to talk about a bride and groom or fish and chips rather than a groom and bride or chips and fish. There is no syntactic explanation for this, nor is it related to semantics. In this way, the ordering of the component words of these expressions is dictated by structural restrictions alone. Numerous similar examples exist in English, such as washer and dryer, or pros and cons. It would be incorrect, however, to suggest that all binomials can be defined in terms of structure alone, and therefore, they present minimal challenges to learners. In some cases (e.g., bride and groom) this may be true, but in other cases, binomials can take on idiomatic meanings as well, which are less straightforward. A good example of this is the binomial black and white. Though this expression can be used in a literal sense (e.g., a black and white picture), it is commonly used figuratively as well to describe a situation, decision, etc. that has no clear right or wrong answer or choice. Other examples include binomials such as free and clear, do or die, life or death, etc., all of which are commonly used in a figurative sense. Finally, other binomials are largely, if not entirely, figurative, such as part and parcel or high and dry. Thus, while binomials tend to be fixed structurally, they can vary greatly in respect to compositionality. Idioms, on the other hand, tend to be defined in terms of their non-literal compositionality, but they often vary in respect to their structural rigidity. Some idioms, such as raining cats and dogs are immutable and resistant to change (e.g., *pouring cats and dogs, *snowing cats and dogs, *raining dogs and cats). Other idioms, such as beating a dead horse allow some variation, particularly in the verb slot (e.g., flogging/beating a dead horse). Still others allow for a wide range of syntactic variation, such as she blew my idea out of the water, my idea was blown out of the water, etc. As is the case with binomials, however, it would be a mistake to assume that there is no variation in the literalness/figurativeness of idioms. Although idioms, by definition, violate the principle of compositionality, there is a great deal of variation in the extent to which they represent a wholly versus only a partially figurative use. To help clarify this, Howarth (1998) makes a distinction between what he terms figurative idioms and pure idioms. Figurative idioms are idiomatic expressions that have both a literal and a figurative meaning, while pure idioms have no obvious literal meaning. Howarth (1998, p. 28) provides contrasting examples such as under the microscope (figurative idiom) versus under the weather (pure idiom), and blow your own trumpet (figurative idiom) versus blow the gaff (pure idiom). Because of their clearer connection to literal expressions,

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figurative idioms are generally less opaque than pure idioms, and thus likely more amenable to acquisition. The connection between the literal meaning of under the microscope and the figurative meaning (meaning under close scrutiny) is fairly uncomplicated in contrast to under the weather which, although quite common, has no obvious corresponding literal interpretation for present day speakers of English. Not surprisingly, other types of MWEs also demonstrate variation in respect to structural fixedness and figurative variability. In addition, some MWEs involve additional challenges that add layers of complexity to acquisition. Collocations provide one good example of this. Like the other MWEs reviewed so far, collocations can vary considerably in terms of idiomaticity, but they are fairly straightforward structurally since they tend to follow the standard rules of syntax. What can make collocations challenging is recognizing which patterns are preferred or dispreferred by native speakers and other proficient language users. Phrasal verbs also present a number of unique challenges to learners due to their highly polysemous nature. White (2012), for example, noted that there are no fewer than 21 different definitions for the phrasal verb go on in The American Heritage Dictionary of Phrasal Verbs. Compounding this issue is the fact that the various meanings of many phrasal verbs are unrelated, and even when they are related, the relationship will still often be ambiguous. To illustrate this, White (2012, p. 420) notes that some usages of go on are linked to the meaning to start [something], providing the examples he just went on Vicodin and she’s going on 40. Nonetheless, he follows this observation by asserting that “the relationship between beginning to take medication and turning a particular age is not obvious.”

2.

L1 influence on the acquisition of MWEs

As is evident in the discussion of MWEs in the previous section, the main quality of MWEs that allows us to group them together into a single linguistic category is the simple fact that they consist of more than one word linked to a single meaning. Outside of this, there is great variation not only between different types of MWEs, but also within them. These variations, in turn, make it difficult to propose a single crosslinguistic explanation for how they are acquired. This may be one reason why researchers have tended to avoid empirical studies focused on MWEs as a whole; instead, most studies have tended to explore particular types of MWEs. For this reason, the review of research presented in this section will be structured around different types of MWEs as defined by the researchers in the research literature. However, it is important to remember that structural and compositional

Chapter 9. Cross-language influences in the acquisition of L2 multiword expressions

variation can be present in any type of MWE, and, because of this, researchers will often use different classifications for the same MWE (see, e.g., Wolter, 2019). With these considerations in mind, I will now turn my attention to a fuller consideration of crosslinguistic influences in the acquisition of L2 MWEs. As noted by Jarvis and Pavlenko, (2010), there is comparatively little research into the crosslinguistic influences of one’s L3, L4, etc. on their L2, so this discussion will focus on the crosslinguistic influences of the L1 on the acquisition and processing of L2 MWEs.

2.1 Collocations A number of studies investigating the L1’s influence on L2 collocation acquisition have relied heavily on learner corpora as their source of data. Nesselhauf (2003), for example, investigated the collocations produced by advanced L1 German learners of L2 English, and found that nearly half of the erroneous collocations could be traced back to German. A similar finding was reported by Zhou (2016), who explored the collocations produced with the English verb have by L1 Chinese learners, and Wanner et al. (2013), who investigated the L2 collocations produced by L1 Spanish speakers. Altenberg and Granger (2001) also conducted a thorough analysis of collocations incorporating the English verb make used by L1 speakers of French and Swedish and found evidence of L1 transfer. Psycholinguistic studies looking at the activation and processing of L2 collocations have also indicated that the L1 has a lasting effect on these behaviours as well (see Du et al., Chapter 8, this volume). In a series of studies, Wolter and colleagues (Wolter & Gyllstad, 2011, 2013; Wolter & Yamashita, 2018) found that congruent English collocations (i.e., those with an acceptable word-for-word translation in the learners’ L1) were recognized faster and more accurately than incongruent collocations (i.e., those with an infelicitous word-for-word equivalent in the L1). Furthermore, this pattern was observed across learners with different L1s and regardless of proficiency level. Nonetheless, gauging acquisition (as defined by the storage-based definition) of L2 collocations, and the influence of the L1, presents a considerable challenge, particularly for those collocations that are low in compositionality (what phraseologists describe as “free combinations”; see Howarth, 1998). This is because learners have at their disposal both their knowledge of previously acquired collocations and their knowledge of syntax combined with word-level semantics. If, for example, a learner produces a collocation like read a book or read a newspaper, it is impossible to know how they arrived at these utterances. Though there is some evidence learners might recognize these as regularly-occurring patterns in their input (see Durrant & Schmitt, 2010), it is also possible that they simply generated

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the utterances using nothing more than common sense and a rudimentary understanding of English syntax and semantics. For collocations with a higher degree of compositionality like read one’s mind, however, there seems to be a greater likelihood the learner has acquired the collocation as a whole and committed it to memory as such, especially since the word read takes on a much more metaphorical and less literal meaning. The question that arises here is what is the influence of the L1 in situations such as this? At first glance, we might expect the effect of the L1 to be straightforward: learners (at least initially) adopt a transfer strategy and the success of this strategy depends on whether or not the transferred collocation is congruent in the L2. This, in turn, means that learners whose L1 and L2 are closely related (e.g., from the same language family) are statistically more likely to generate acceptable collocations in the L2 than those whose L1 is more distantly related to their L2, and there is little doubt that L1-L2 language distance does have an impact on a learner’s tendency to generate acceptable L2 collocations. Nonetheless, this explanation oversimplifies the learner’s cognitive role and their assumptions about what is and is not likely transferable from the L1 to the L2. Briefly stated, there are situations in which learners will choose to reject an L1-L2 transfer strategy, even when this strategy would have produced an acceptable L2 collocation. This was demonstrated some time ago in a seminal study by Kellerman (1979). Kellerman provided participants with a list of sentences in their L1 (Dutch) and asked them to make judgments about their transferability to English. All sentences included the Dutch word breken (break in English), and the items constituted acceptable uses of break in English. However, the literalness of breken varied from sentence to sentence, with some sentences incorporating a rather literal use (e.g., he broke his leg) and others a more metaphorical use (e.g., some workers have broken the strike). Despite the fact that all uses of breken in these sentences rendered directly transferable into English, the participants showed a reluctance to assume transferability of sentences incorporating a more metaphorical use of breken. In this way, L1-based assumptions were overridden by their psychotypological assumptions, resulting in a rejection of what would have otherwise been a successful transfer strategy.

2.2 Phrasal verbs Although phrasal verbs have often been classified as an element largely present in casual spoken language, corpus research (using English-based corpora) has indicated that they are prominent in written English as well (e.g., Liu, 2011). One aspect of phrasal verbs that differentiates them from other MWEs, such as collocations, is the fact that structures are mostly found in Germanic languages

Chapter 9. Cross-language influences in the acquisition of L2 multiword expressions

(Siyanova & Schmitt, 2007). Not surprisingly, this lends them more readily to acquisition via positive transfer for speakers whose L1 and L2 both have Germanic origins. And even though L2 English learners whose L1 is not Germanic will likely know and recognize phrasal verbs in English, full acquisition will likely prove more challenging for them. Indirect evidence for this can be found in avoidance studies for learners of L2 English whose L1 is not Germanic. One such early study was conducted by Dagut and Laufer (1985), who examined preference for and use of phrasal verbs amongst university-aged, L1 Hebrew speakers of L2 English. Predictably, they found that their participants tended to avoid the use of English phrasal verbs, particularly in productive activities like L1 to L2 translation tasks and memorization and recall tasks. However, structural absentness from Hebrew was not the only factor involved. The results also showed that the participants were much more likely to use literal over figurative phrasal verbs, indicating that idiomaticity also played a role. Another study investigating the L1’s influence on phrasal verb usage was conducted by Sjöholm (1995). Sjöholm investigated the L2 English phrasal verb use of L1 speakers of Swedish (a Germanic language) and Finnish (a Finno-Ugrian language). Sjöholm found, in line with expectations, that the Finnish speakers tended to make more phrasal verb errors than the L1 Swedish speakers, despite similar years of education in English and similar amounts of English exposure. Once again, however, Sjöholm found that idiomaticity also affected accuracy, with both groups of participants tending to make fewer errors on literal phrasal verbs (e.g., go back, take out) than figurative phrasal verbs (e.g., put off, brush up). As with collocations, however, L1 similarities are insufficient in explaining learners’ tendency to use or to avoid phrasal verbs. Hulstijn and Marchena (1989), for example, investigated the use of phrasal verbs by intermediate and advanced L1 speakers of Dutch (a Germanic language that also uses phrasal verbs) using three tasks assessing preference for phrasal verbs versus single-word equivalents: a multiple choice test, a memorization task, and a translation task. As was the case in Kellerman’s studies cited above, Hulstijn and Marchena (1989, p. 241) found that L1 Dutch learners of L2 English tended to avoid idiomatic phrasal verbs that they “perceive[d] as too Dutch-like”, even when these were acceptable in English (e.g., break out, go off). However, this tendency was moderated in part by proficiency, with advanced learners demonstrating more frequent preference for, and more accurate use of, English phrasal verbs than intermediate learners, the latter of which tended to choose single word alternatives to phrasal verbs, a strategy Hulstijn and Marchena described as a “play-it-safe” approach (p. 250). In contrast to this, Laufer and Eliasson (1993) investigated phrasal verb preference and avoidance in L2 learners of English whose L1 was Swedish. Using

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both a multiple choice task and a translation task, the researchers found little evidence of avoidance on the part of their participants, regardless of whether or not the phrasal verbs in question were more figurative in meaning or whether or not there was a closely equivalent phrasal verb in Swedish. This suggests a different approach to English phrasal verbs than was seen in Hulstijn and Marchena’s (1989) similar study. However, proficiency also needs to be taken into consideration in comparing results across these studies, since the Swedish participants’ proficiency was likely higher than the Dutch participants used in Hulstijn and Marchena's (1989) study. The Swedish participants were estimated to be comparable to the Cambridge First Level Certificate of Proficiency (p. 40), while Hulstijn and Marchena defined “advanced” as being a first-year university student of English. More recent studies have investigated the use of phrasal verbs by learners coming from non-Germanic L1 language backgrounds. Using multiple choice, translation, and recall tasks similar to those used in the previous studies by Dagut and Laufer (1985) and Hulstijn and Marchena (1989), Liao and Fukuya (2004) investigated the use of phrasal verbs amongst a group of L1 speakers of Chinese, which has no phrasal verb equivalent structures. They found that phrasal verb use was conditioned largely by frequency and proficiency, with advanced speakers demonstrating a much higher preference for phrasal verbs than intermediate learners. Based on this finding, they concluded that phrasal verb avoidance was more closely related to interlanguage development than L1 difference/similarity. Nonetheless, this perspective has been partially challenged by Cervantes and Gablasova’s (2019) corpus-based research on the use of L2 English phrasal verbs amongst four non-Germanic L1 language groups: Chinese, Italian, Russian, and Spanish. Although Cervantes and Gablasova (2017) observed significant increases in phrasal verb use with increases in proficiency level, they also found significant differences across language groups, with Chinese speakers using phrasal verbs with the highest frequency, followed by speakers of Russian, Italian, and Spanish respectively. It must be stated, however, that the results of post-hoc comparisons indicated the significant difference was solely between the L1 Chinese speakers and the L1 Spanish speakers and the effect size was very small. Furthermore, Cervantes and Gablasova (2017) offer little in the way of L1-based explanation for their findings, opting instead to attribute the difference to “external reasons” such as L2 exposure (p. 41). Nonetheless, even though these findings clearly need further validation and exploration, they do provide some empirical evidence that L1 background might make a difference, even amongst speakers of different nonGermanic language L1 speakers. As we can see, then, the situation with phrasal verbs is not unlike the situation with collocations. Acquisition of both structures is clearly influenced by the L1,

Chapter 9. Cross-language influences in the acquisition of L2 multiword expressions

but there appear to be a number of other factors at play as well, including proficiency level, L1 background, and learner assumptions about transferability of L1 forms.

2.3 Idioms In order to understand how L2 idioms are acquired, it is first of all useful to take a closer look at theories of idiom compositionality and processing emerging from L1 research. A number of theories of idiom acquisition and processing have been proposed, but most fall into a limited number of classifications. On the one hand, there are the so-called non-compositional explanations, which assume that idioms are stored and processed much like single words. Under this view, when a person encounters an idiomatic expression, they quickly recognize it as idiomatic and match it to its meaning; it is not necessary to break the expression down into its component parts, so they make no attempt to do so. Compositional accounts, on the other hand, assume that language users will often process idioms by breaking them down into their component parts en route to extracting the holistic figurative meaning. More recently, researchers have favored a hybrid account, which suggests that L1 speakers simultaneously process idioms both non-compositionally and compositionally in real time, settling on the interpretation that makes the most sense given the contextual information available to them. With L2 speakers, however, the situation may be different. A good deal of research suggests that L2 speakers process idioms primarily in a compositional manner, even in cases where they ultimately settle on a non-literal interpretation. Siyanova-Chanturia et al. (2011), for example, used an eye-tracking design to assess advanced learners’ processing of literal and non-literal interpretations of figurative idioms that were well-known by their English L2-speaking participants (all of whom were enrolled at a British university). When compared to nonidiomatic control phrases, the authors found a processing advantage for contexts that promoted a literal interpretation, but not for contexts that promoted a figurative interpretation. Similar findings were reported in another reaction time study using advanced speakers of L1 Polish (Cieślicka, 2006). More recent research, however, has challenged these findings (see Du et al., Chapter 8, this volume). Also using eye-tracking, Carrol et al. (2016) explored the reading behaviour of advanced L2 English speakers (L1 Swedish) when encountering four types of expressions: congruent idioms (those found in both English and Swedish), L2-only idioms (those found in English but not Swedish), L1-only idioms (those found in Swedish but not English), and non-idiomatic control phrases used as a baseline measure. The results showed processing advantages for

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all three types of idioms when compared to the control measures. Of particular interest was the processing advantage for Swedish-only idioms, as this finding suggests the influence of the L1 even when processing idioms that were infelicitous in English. Once again, however, we cannot rule out the possible effects of proficiency when compared to the earlier studies, even though direct comparisons between the different L1 groups are not possible. On-line studies such as these are clearly useful in helping us gain a better understanding of how L2 learners process L2 idioms in real time which, in turn, provides some insights into the extent of acquisition for L2 idioms when compared to L1 speakers. However, they are less useful in helping us gain a better understanding of the early stages of L2 idiom acquisition and the L1’s influence in mapping non-literal meanings to idiomatic expressions. Cieślicka (2015) suggests that the best way to understand the L1’s influence on the early stages of idiom acquisition is by turning to existing models of L2 acquisition such as MacWhinney’s (1992, 2005) Competition Model or Hall’s (2002) parasitic model. These models assume that during the initial stages of learning, L2 forms are mapped to L1 meanings. When this is combined with an L2 learner’s tendency to approach idiomatic expressions compositionally, assuming the learner has identified a particular expression as idiomatic, it leads to a situation in which the learner attempts to understand the L2 idiom by breaking it down into its component parts and referring to knowledge gained through the L1. Naturally, the learner’s level of success in correctly interpreting the meaning of the idiom in this situation will depend to a large extent on L1-L2 similarity. Cieślicka (2015) provides the example of an L1 Polish learner encountering the English idiom playing with fire (meaning to engage in something potentially dangerous). She notes that Polish has a different but similar idiomatic expression (toying with fire) with the same meaning, so correct interpretation should pose little difficulty. In cases like this, it is not only form-meaning mappings that assist the learners’ interpretation, but also the shared underlying conceptual metaphor linking fire with danger.1 As famously pointed out by Lakoff and Johnson (2003), idioms are an extension of metaphorical thinking, and as fire poses potential danger to all living things, it is likely that most (if not all) languages include at least some idiomatic expressions that compare fire with dangerous situations. Naturally, however, languages do not always share conceptual metaphors, which leads to a situation in which there is complex interplay between the L1 and the L2’s degree of linguistic and conceptual similarity.

1. In this chapter, I am using the convention of writing words in all upper case letters to indicate representation of a concept rather than a word.

Chapter 9. Cross-language influences in the acquisition of L2 multiword expressions

This interplay was investigated in a study by Charteris-Black (2002; see also Türker, 2019), who looked at the acquisition of L2 English idioms by L1 Malay learners. Charteris-Black compared receptive and productive knowledge of six types of idioms: (1) same (L1-L2) forms and same (L1-L2) conceptual bases, (2) similar forms and same conceptual bases, (3) same forms but different conceptual bases, (4) different forms but same conceptual bases, (5) transparent idioms with different forms and different conceptual bases, and (6) opaque idioms with different forms and different conceptual bases. The results showed that the learners performed best on idioms with the same forms and same conceptual bases, followed by those with different forms but same conceptual bases. Performance was almost identical for idioms with similar forms and same conceptual bases and transparent idioms with different forms and bases. Next was the opaque idioms with different forms and different bases, followed by idioms with the same forms but different conceptual bases. These findings generally support Cieślicka’s (2015) supposition regarding the potential for positive transfer in the acquisition of L2 idioms, but they also indicate that the L1 can also have a negative effect when there is form alignment without conceptual alignment. This is because, as stated by CharterisBlack (2002, p. 123), “literal translation from the linguistic form may tempt learners into activating a first language conceptual basis that is of little assistance correctly interpreting the L2 figurate sense.”

3.

Modulating the L1’s influence on the acquisition of MWEs: Toward a more comprehensive understanding

In reviewing the findings of the empirical studies on different types of MWEs, it quickly becomes evident that the L1 plays a role, but this role is less than straightforward. In some cases, the L1 has a facilitative effect, providing a positive source of information for learners. In other cases, however, the L1’s influence can be debilitating, especially when there is alignment in L1/L2 forms but a difference in the associated meanings mapped to these forms. In still other cases, the L1 could be a positive source of information, but learners make well-reasoned, though ultimately incorrect, judgments about the lack of transferability of L1 information due to assumptions regarding the perceived uniqueness of particular L1 forms. In this section, I will look beyond the role of the L1 to consider other factors that can also have an influence on the acquisition of L2 MWEs. In considering these factors, however, it is important to keep in mind that they typically do not operate in isolation from L1 influences. In many cases, the L1 influences merge with other factors creating a cumulative effect on acquisition.

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Foremost among these additional factors are MWE frequency and learner proficiency. The general consensus in L2 acquisition research is that L2 learners, like L1 speakers, are highly sensitive to frequency in their linguistic input (see, e.g., Ellis, 2002). Recently, research in this area has started to explore the effects of frequency in L2 MWE acquisition. A number of reaction time studies investigating L2 collocations have found a significant effects for collocational frequency, with higher frequency collocations being recognized more quickly and accurately than lower frequency collocations (e.g., Gyllstad & Wolter, 2016; Öksüz et al., 2021; Wolter & Gyllstad, 2013; Wolter & Yamashita, 2018). Using an acceptability judgment task, Wolter and Gyllstad (2013), for example, found that learners attended to collocational frequency in a similar fashion to L1 speakers, particularly with gains in proficiency. As noted above, however, the influence of collocational frequency appeared to merge with the influence of the L1 creating a cumulative effect. Wolter and Gyllstad (2013) compared reaction times and error rates for congruent and incongruent collocations selected from eight frequency bands. The results showed that higher frequency incongruent collocations were recognized more quickly than lower frequency congruent collocations. However, when comparing congruent and incongruent collocations within each of the eight frequency bands, the congruent collocations were on average always recognized more quickly than the incongruent collocations. Again, these findings suggest cumulative effects of frequency and L1 similarity. Another factor that warrants more careful consideration is L2 proficiency. As might be expected, the L1’s influence appears to weaken as proficiency improves, with L2 speakers behaving more and more like L1 speakers of the target language. This was seen in the previously discussed research into phrasal verb acquisition, but has also been observed in collocational and idiom research. In collocational research, both Öksüz et al. (2021) and Wolter and Yamashita (2018) found that L2 speakers tended to switch their attention from greater reliance of word-level frequency to greater reliance on collocation-level frequency with improvements in proficiency, resulting in a pattern that closely resembled L1 speakers. Similarly, Carrol et al. (2016) found that highly advanced L2 speakers processed L2 idioms, both congruent and incongruent, in a holistic manner that suggested similarity in their acquired knowledge. One issue regarding proficiency that it is in need of greater exploration, however, is if there is a threshold level of proficiency that is required before the L1’s influence starts to weaken. Lee (2021), for instance, found no significant proficiency-related differences in recognition of incongruent (English-only) versus congruent collocations amongst L1 speakers of Korean and Mandarin on an untimed acceptability judgment task, but it is unclear if the proficiency level of these participants was on par with those in the other studies, particularly, Wolter and Gyllstad (2013) and Öksüz et al. (2021).

Chapter 9. Cross-language influences in the acquisition of L2 multiword expressions

Clearly, then, proficiency can have an influence on the efficient processing and effective use of L2 MWEs. Still, it is important to note that there seem to be some aspects of the L1 that continue to affect L2 speakers’ acquisition of MWEs regardless of proficiency level, such as the influence the L1 has on congruent MWEs which has been demonstrated repeatedly in the studies surveyed above in learners at various levels of proficiency. On the other hand, there also seems to be a reluctance to assume the transferability of certain L1 forms in line with Kellerman’s (1979) findings regarding psychotypology. One study that demonstrates this is Lee (2021), who used a phrase acceptability and a phrase recognition task to assess judgments of L1 speakers of Korean and Mandarin regarding word combinations that could be potential English collocations. The items consisted of collocations that were: (1) acceptable in all three languages, (2) acceptable only in English, (3) acceptable only in Korean, and (4) acceptable only in Mandarin. Lee found that although there was some variation between the two groups of L1 speakers, there was not a significant difference based on whether or not the incongruent collocations were Korean-based or Mandarin-based. From these findings, Lee (2021, p. 202) concluded that “learners are not influenced by having corresponding L1 collocations when they encounter unfamiliar L2 word combinations” (see also Wolter & Yamashita, 2015).

4.

Directions for future research

Although our understanding of the L1’s influence on the acquisition of L2 MWEs has advanced considerably, there are still many aspects that need further explication and exploration. A first step in refining our understanding of MWE acquisition is developing a more fine-tuned understanding of what L2 MWE acquisition is and what it looks like. As has been pointed out in numerous studies (e.g., Biskup, 1992; Laufer & Waldman, 2011; Nesselhauf, 2003), receptive understanding of L2 MWEs is not always a reliable indicator, particularly for MWEs that are low in idiomaticity. This is because correct receptive understanding does not necessarily indicate that learners will be able to use the same MWEs productively when a situation calls for it (see Wray, 2002). From a research perspective, therefore, it would be useful if researchers could agree upon a basic threshold for acquisition. One possibility is to move away from receptive-based definitions and adopt a production-based definition that requires a learner to be able to accurately generate a particular MWE in appropriate contexts. Once such a standard has been established, it will be easier to begin unpacking some of the existing issues related to L2 MWE acquisition. In rest of this concluding section, I will discuss what I feel are some of the most important

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issues, starting with gaining a better understanding of how the different factors affecting acquisition integrate to create a more complete picture. As noted above, many studies have observed the effects of the L1 congruency, frequency, compositionality, and learner assumptions in the acquisition of L2 MWEs, but as of yet there have not been, to the best of my knowledge, any attempts to provide a unified and cohesive account of precisely how these factors interact. A good starting point would be trying to determine with greater precision what factors learners take into account when deciding whether or not to adopt a transfer strategy. Research has shown that learners are reluctant to transfer L1 structures they view as language-specific (Kellerman, 1979), but how do they make these determinations? Although we cannot be certain until more research is done, we can at least apply what we know about idiomaticity to infer that learners will tend to reject a transfer strategy for highly-idiomatic expressions, such as figurative and pure idioms, collocations with a highly idiomatic use of one of the constituent words (e.g. kick the habit), or highly idiomatic phrasal verbs, unless, naturally, they already know these expressions are acceptable in the L2. Of course, the real challenge, both for learners and for researchers, is accounting for the wide range of MWEs that fall between the extremes of highly idiomatic and highly compositional, many of which are classified as collocations. This will require, perhaps ironically, a fuller understanding of the effects of word-level knowledge on the acquisition of MWEs. Although descriptions of what it means to “know” a word often list factors like meaning and collocation as separate types of knowledge (e.g., Nation, 2013), it is almost certain that meaning and collocation are co-dependent and closely interconnected. Regardless of whether one takes the view that collocations dictate word-level meaning (e.g., Firth, 1957), or that collocations are an extension of word-level meaning (e.g., Jarvis & Pavlenko, 2010), the overall conclusion is the same: to fully understand the influence of the L1 on L2 collocations, we also need to take into consideration the L1’s influence on the acquisition of meaning for single words in the L2. There is an abundance of studies and theoretical papers suggesting that learners map L2 word forms to L1-based meanings (see, e.g., Chapter 7, this volume; Jiang, 2000; Pavlenko, 2009). Furthermore, these studies report that this can represent the final state of knowledge for many words in the L2 lexicon. When there is a high degree of similarity in L1 and L2-based meanings, it is likely that these words will share a number of collocations that should come fairly intuitively to L2 learners. When there is only partial overlap, however, the learners may not recognize the misalignment in meaning between the L1 and L2-based meanings, making it more challenging to acquire divergent collocations in the L2. This mismatch is probably particularly pronounced for L2 collocations that involve a figurative use of one of their constituent words. Kousta et al. (2011) assert that core senses

Chapter 9. Cross-language influences in the acquisition of L2 multiword expressions

are more universal and shared across languages than figurative senses, which are more language-specific. This is almost certainly why learners do not struggle with expressions like read a book or carry an umbrella. The words comprising these expressions are used in their core senses and felicitous word-for-word translations of these expressions are likely to be found across a wide range of languages. When it comes to expressions like read [someone’s] mind or carry a disease/grudge, etc., however, the difficulty in acquisition is likely to be related to the extent to which the L1 and L2 share the more figurative extensions for the node words read and carry. In the case of carry, for example, if a L2 speaker’s L1 word for carry is used exclusively for physical things, it seems likely they will have a harder time acquiring expressions like carry a disease than a learner whose L1 word incorporates such a metaphorical extension. Again, this underscores the importance of taking word-based meanings into account when attempting to understand L1 influences on the acquisition of certain L2 MWEs.

5.

Conclusion

In this chapter, I have attempted to take a holistic look at cross-linguistic influences in the acquisition of L2 MWEs. I began by focusing on the different historic approaches to defining MWEs and how these distinct approaches have each contributed something unique to our understanding. After this, I analysed different types of MWEs with an eye on establishing that discrete categories are misleading and that most MWEs vary along two continua: a structural fixedness continuum and a transparency/idiomaticity continuum. I then reviewed a number of empirical studies testing the assumption that the L1 has an influence on the acquisition of L2 MWEs. The body of evidence to date suggests that the L1 exerts a strong influence on the acquisition of L2 MWEs; however, the L1’s influence appears to tell only part of the story. A number of other factors also come into play, such as frequency, learner proficiency, learner assumptions regarding the transferability of L1 MWEs, and word-level semantic knowledge. In order to gain a fuller understanding of how the L1 influences the acquisition of L2 MWEs, therefore, it will be useful to consider how these factors interact with L1 influences, and with each other, to affect the trajectory of acquisition for L2 MWEs. It is my hope that future researchers will continue to explore the acquisition of MWEs in such a holistic manner so that these factors can be incorporated into our understanding.

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Biskup, D. (1992). L1 influence on learners’ renderings of English collocations: A Polish/German empirical study. In P. J. L. Arnaud & H. Béjoint (Eds.), Vocabulary and applied linguistics (pp. 85–93). Palgrave Macmillan. https://doi.org/10.1007/978-1-349-12396-4_8

Carrol, G., Conklin, K., & Gyllstad, H. (2016). Found in translation: The influence of the L1 on the reading of idioms in a L2. Studies in Second Language Acquisition, 38(3), 403–443. https://doi.org/10.1017/S0272263115000492

Cervantes, I. M., & Gablasova, D. (2019). In V. Brezina & L. Flowerdew (Eds.), Learner corpus research: New perspectives and applications (pp. 28–46). Bloomsbury Academic. Charteris-Black, J. (2002). Second language figurative proficiency: A comparative study of Malay and English. Applied Linguistics, 23(1), 104–133. https://doi.org/10.1093/applin/23.1.104 Cheng, W., Greaves, C., & Warren, M. (2006). From n-gram to skipgram to concgram. International Journal of Corpus Linguistics, 11(4), 411–433. https://doi.org/10.1075/ijcl.11.4.04che

Cieślicka, A. (2006). Literal salience in on-line processing of idiomatic expressions by second language learners. Second Language Research, 22(2), 115–144. https://doi.org/10.1191/0267658306sr263oa

Dagut, M., & Laufer, B. (1985). Avoidance of phrasal verbs – A case for contrastive analysis. Studies in Second Language Acquisition, 7(1), 73–79. https://doi.org/10.1017/S0272263100005167

Ellis, N. C. (2002). Frequency effects in language processing: A review with implications for theories of implicit and explicit language acquisition. Studies in Second Language Acquisition, 24(2), 143–188. https://doi.org/10.1017/S0272263102002024 Firth, J. R. (1957). Studies in linguistic analysis. Blackwell. Gyllstad, H., & Wolter, B. (2016). Collocational processing in light of the phraseological continuum model: Does semantic transparency matter? Language Learning, 66(2), 296–323. https://doi.org/10.1111/lang.12143 Hall, C. (2002). The automatic cognate form assumption: Evidence for the parasitic model of vocabulary development. IRAL – International Review of Applied Linguistics in Language Teaching, 40(2), 69–87. https://doi.org/10.1515/iral.2002.008 Howarth, P. (1998). Phraseology and second language proficiency. Applied Linguistics, 19(1), 24–44. https://doi.org/10.1093/applin/19.1.24 Hulstijn, J. H., & Marchena, E. (1989). Avoidance: Grammatical or semantic causes? Studies in Second Language Acquisition, 11(3), 241–255. https://doi.org/10.1017/S0272263100008123 Jarvis, S., & Pavlenko, A. (2010). Crosslinguistic influence in language and cognition. Routledge. Jiang, N. (2000). Lexical representation and development in a second language. Applied Linguistics, 21(1), 47–77. https://doi.org/10.1093/applin/21.1.47 Kellerman, E. (1979). Transfer and non-transfer: Where we are now. Studies in Second Language Acquisition, 2(1), 37–57. https://doi.org/10.1017/S0272263100000942

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Kousta, S. -T., Vigliocco, G., Vinson, D. P., Andrews, M., & Del Campo, E. (2011). The representation of abstract words: Why emotion matters. Journal of Experimental Psychology: General, 140(1), 14–34. https://doi.org/10.1037/a0021446 Lakoff, G., & Johnson, M. (2003). Metaphors we live by. The University of Chicago Press. https://doi.org/10.7208/chicago/9780226470993.001.0001

Laufer, B., & Eliasson, S. (1993). What causes avoidance in L2 learning: L1-L2 difference, L1-L2 similarity, or L2 complexity? Studies in Second Language Acquisition, 15(1), 35–48. https://doi.org/10.1017/S0272263100011657

Laufer, B., & Waldman, T. (2011). Verb-noun collocations in second language writing: A corpus analysis of learners’ English. Language Learning, 61(2), 647–672. https://doi.org/10.1111/j.1467-9922.2010.00621.x

Lee, S. (2021). L1 transfer, proficiency, and the recognition of L2 verb-noun collocations: A perspective from three languages. International Review of Applied Linguistics in Language Teaching, 59(2), 181–208. https://doi.org/10.1515/iral-2018-0220 Liao, Y., & Fukuya, Y. J. (2004). Avoidance of phrasal verbs: The case of Chinese learners of English. Language Learning, 54(2), 193–226. https://doi.org/10.1111/j.1467-9922.2004.00254.x Liu, D. (2011). The most frequently used English phrasal verbs in American and British English: A multicorpus examination. TESOL Quarterly, 45(4), 661–688. https://doi.org/10.5054/tq.2011.247707

MacWhinney, B. (1992). In R. J. Harris (Ed.), Cognitive processing in bilinguals (pp. 371–390). North-Holland. https://doi.org/10.1016/S0166-4115(08)61506-X MacWhinney, B. (2005). Extending the competition model. International Journal of Bilingualism, 9(1), 69–84. https://doi.org/10.1177/13670069050090010501 Nation, I. S. P. (2013). Learning vocabulary in another language. Cambridge University Press. https://doi.org/10.1017/CBO9781139524759

Nesselhauf, N. (2003). The use of collocations by advanced learners of English and some implications for teaching. Applied Linguistics, 24(2), 223–242. https://doi.org/10.1093/applin/24.2.223

Öksüz, D., Brezina, V., & Rebuschat, P. (2021). Collocational processing in L1 and L2: The effects of word frequency, collocational frequency, and association. Language Learning, 71(1), 55–98. https://doi.org/10.1111/lang.12427 Pavlenko, A. (2009). Conceptual representation in the bilingual lexicon and second language vocabulary vearning. In The Bilingual Mental Lexicon (pp. 125–160). Multilingual Matters. https://doi.org/10.21832/9781847691262-008 Schmitt, N. (Ed.). (2004). Formulaic sequences: Acquisition, processing, and use. John Benjamins. https://doi.org/10.1075/lllt.9 Siyanova, A., & Schmitt, N. (2007). Native and nonnative use of multi-word vs. One-word verbs. IRAL – International Review of Applied Linguistics in Language Teaching, 45(2), 119–139. https://doi.org/10.1515/IRAL.2007.005 Sjöholm, K. (1995). The influence of crosslinguistic, semantic, and input factors on the acquisition of English phrasal verbs: A comparison between Finnish and Swedish learners at an intermediate and advanced level. Åbo Akademi University Press. Türker, E. (2019). Idiom acquisition by second language learners: The influence of crosslinguistic similarity and context. The Language Learning Journal, 47(2), 133–144. https://doi.org/10.1080/09571736.2016.1221441

White, B. J. (2012). A conceptual approach to the instruction of phrasal verbs. The Modern Language Journal, 96(3), 419–438. https://doi.org/10.1111/j.1540-4781.2012.01365.x

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Wolter, B., & Gyllstad, H. (2011). Collocational links in the L2 mental lexicon and the influence of L1 intralexical knowledge. Applied Linguistics, 32(4), 430–449. https://doi.org/10.1093/applin/amr011

Wolter, B., & Gyllstad, H. (2013). Frequency of input and L2 collocational processing: A comparison of congruent and incongruent collocations. Studies in Second Language Acquisition, 35(3), 451–482. https://doi.org/10.1017/S0272263113000107 Wolter, B., & Yamashita, J. (2015). Processing collocations in a second language: A case of first language activation? Applied Psycholinguistics, 36(5), 1193–1221. https://doi.org/10.1017/S0142716414000113

Wolter, B., & Yamashita, J. (2018). Word frequency, collocational frequency, L1 congruency, and proficiency in L2 collocational processing: What accounts for L2 learner performance? Studies in Second Language Acquisition, 40(2), 395–416. https://doi.org/10.1017/S0272263117000237

Wray, A. (2002). Formulaic language and the lexicon. Cambridge University Press. https://doi.org/10.1017/CBO9780511519772

Wray, A. (2008). Formulaic language: Pushing the boundaries. Oxford University Press. Zhou, Xiujuan. (2016). A corpus-based study on high frequency verb collocations in the case of “HAVE.” International Forum of Teaching and Studies, 12(1), 42–50.

part iii

Morphosyntax

chapter 10

Cross-language influences on morphological processing in bilinguals Hasibe Kahraman & Elisabeth Beyersmann Macquarie University

Several decades of reading research in bilinguals have revealed evidence for cross-language influences on the visual recognition of simple words (e.g., farm). However, comparatively little is known about cross-language transfer mechanisms involved when reading morphologically complex words (e.g., farmer or farmhouse). In this chapter, we provide a review of studies examining the processing of affixed and compound words in bilinguals, with a particular focus on studies directly targeting cross-language transfer. The key findings support the idea that bilinguals rapidly and simultaneously activate the morphological features in both of their languages during the early, automatic stages of visual word recognition. Implications for theoretical models of morphological processing in bilinguals and future directions are discussed. Keywords: visual word recognition, morphological processing, crosslanguage influences, bilinguals

A substantial amount of research on first language (L1) processing has shown that visual word recognition processes are guided by the morphological characteristics of the experimental stimuli. A morphologically complex word (e.g., packing, packed, unpack, backpack, etc.) is assumed to be quickly segmented into its morphemic constituents (e.g., pack + ing; pack + ed; un + pack; back + pack) during the early stages of lexical processing (for reviews, see Amenta & Crepaldi, 2012; Marslen-Wilson, 2007; Rastle & Davis, 2008; Stevens & Plaut, 2022). This kind of morphological analysis in L1 speakers has been reported for both affixed words (Beyersmann, Ziegler et al., 2016; Grainger et al., 1991; Longtin et al., 2003; Rastle et al., 2004) and compound words (Beyersmann et al., 2019; Duñabeitia et al., 2009; Fiorentino et al., 2016; Fiorentino & Fund-Reznicek, 2009). Compound words represent a concatenation of at least two stem morphemes (e.g., back [stem] + pack [stem]), whereas affixed words consist of the combination of at

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Chapter 10. Cross-language influences on morphological processing in bilinguals

least one stem and one affix (e.g., un [prefix] + pack [stem]; pack [stem] + ing [suffix]). What is clearly evident from several decades of reading research is that L1 speakers are experts at rapidly and automatically identifying morphological structures from print. Yet, turning to second language (L2) processing, the mechanisms of morphological processing are less understood. The vast majority of work examining morphological processing in bilinguals comes from studies comparing morphological processing within-L1 to morphological processing within-L2 (for earlier reviews, see Clahsen et al., 2010; Clahsen & Felser, 2006). In contrast, cross-language morphological processing research, investigating morphological transfer effects between the L1 and L2, is more sparse and comparatively more recent. As such, within-language research provides a broader empirical context across a variety of different languages, word types, and proficiency groups. Therefore, although the focus of the current chapter is on cross-language morphological transfer effects in bilinguals, the manuscript begins with a summary of what is known from withinlanguage morphological processing to set the scene and provide a foundation on which cross-language investigations are built. Crucially, as the below literature review will show, the mechanisms of morphological processing within the L1 and L2 appear to be broadly comparable (see Table 1), although, as also discussed below, this depends on a number of factors such as individual differences in language proficiency (e.g., Diependaele et al., 2011; Feldman et al., 2010; Viviani & Crepaldi, 2022). The comparability of L1 and L2 morphological processing points to the idea that bilinguals are able to segment morphologically complex letter strings into morphemic subunits, even when reading in their L2. The evidence from within-language research thus provides an important basis for the examination of cross-language morphological transfer effects. Given that morphological segmentation appears to be a core principle in second language processing, it raises the question of how morphological transfer is handled by the bilingual reading system. Therefore, based on these critical findings from within-language research, we need to ask whether or not there is any interplay between the morphemic parsing systems across languages. Hence, the aim of the current chapter is to address this question by going beyond what is known from within language morphological processing.

1.

Within-L1 and within-L2 morphological processing

In this first section of the chapter, we provide a broad empirical overview of studies comparing morphological processing within the L1 and L2 (see Table 1). Some report substantial differences in morphological processing between the L1 and L2,

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showing that effects of morphological structure are weaker in the L2 than the L1, possibly due to a larger dependence on storage than on combinatorial processing in L2 learners (e.g., Clahsen et al., 2010; Neubauer & Clahsen, 2009; Silva & Clahsen, 2008). However, the majority of studies (see Table 1) seem to reveal comparable effects of morphological processing across the L1 and L2 (e.g., Coughlin & Tremblay, 2015; Dal Maso & Giraudo, 2014; De Grauwe et al., 2014; Diependaele et al., 2011; Feldman et al., 2010; Foote, 2017; Kahraman, 2022), suggesting that the underlying morphological processing mechanisms are equally automatic and combinatorial. Of course, a closer look at methodological differences between studies reveals that the comparability of morphological processing in the L1 and L2 depends on a variety of different factors (e.g., Jacob et al., 2018; Kırkıcı & Clahsen, 2013), including individual differences in bilingual proficiency (Coughlin & Tremblay, 2015; Feldman et al., 2010), target word frequency (Dal Maso & Giraudo, 2014; Lehtonen & Laine, 2003), as well as productivity and transparency (Gor & Jackson, 2013; Portin et al., 2007). Moreover, morphological processing has been found to be modulated by individual differences in vocabulary, reading, and spelling proficiency in both L1 speakers (e.g., Andrews & Lo, 2012; Beyersmann, Cavalli et al., 2016; Beyersmann et al., 2015; Medeiros & Duñabeitia, 2016) and L2 speakers (Kahraman & Kırkıcı, 2021; Viviani & Crepaldi, 2022). Probably one of the most striking points that has surfaced from these past years of morphology research in bilinguals is the robust divide in empirical findings between studies examining inflectional morphology compared to those investigating derivational morphology. Investigations of inflectional morphology show that bilingual speakers tend to decompose L1 inflected words, whereas L2 inflected words are more likely to be processed holistically (for a review, see Clahsen et al., 2010). In contrast, investigations of derivational morphology suggest that derived words are rapidly decomposed into morphemic subunits in both the L1 and L2 (e.g., Ciaccio & Clahsen, 2020; Dal Maso & Giraudo, 2014; Diependaele et al., 2011; Jacob et al., 2018; Kahraman, 2022; Kırkıcı & Clahsen, 2013; J. Li et al., 2017; Li & Taft, 2020; Silva & Clahsen, 2008; Viviani & Crepaldi, 2022), lending support for the sublexical “morpho-orthographic” segmentation hypothesis by which decomposition takes place whenever a letter string has a morphologically complex surface structure (e.g., Rastle et al., 2004).

Chapter 10. Cross-language influences on morphological processing in bilinguals

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Masked priming studies on derived and compound words comparing visual processing in monolinguals (ML) and bilinguals (BL). PD= Prime duration (in ms). Study

ML

1

Status

AoA2

3

Transparent ML4 BL 6

Eng. (n

Derivation

n Chi. (n Ger. (n Chi. (n

Ger. (n Eng. (n

late

Opaque

PD Form

ML vs BL5 ML BL ML vs BL ML BL ML vs BL ML=BL ML=BL

N/A N/A

N/A N/A

ML=BL ML=BL Derivation

n

N/A N/A

Derivation

n

N/A N/A ML=BL

ML