Handbook of Psychology, Behavioral Neuroscience [3, 2 ed.] 0470890592, 9780470890592

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HANDBOOK OF PSYCHOLOGY

HANDBOOK OF PSYCHOLOGY VOLUME 3: BEHAVIORAL NEUROSCIENCE

Second Edition

Volume Editors

RANDY J. NELSON AND SHERI J. Y. MIZUMORI Editor-in-Chief

IRVING B. WEINER

John Wiley & Sons, Inc.

This book is printed on acid-free paper. Copyright © 2013 by John Wiley & Sons, Inc. All rights reserved. Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published simultaneously in Canada. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 646-8600, or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008. Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. This publication is designed to provide accurate and authoritative information in regard to the subject matter covered. It is sold with the understanding that the publisher is not engaged in rendering professional services. 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If this book refers to media such as a CD or DVD that is not included in the version you purchased, you may download this material at http://booksupport.wiley.com. For more information about Wiley products, visit www.wiley.com. Library of Congress Cataloging-in-Publication Data: Handbook of psychology / Irving B. Weiner, editor-in-chief. — 2nd ed. v. cm. Includes bibliographical references and index. ISBN 978-0-470-61904-9 (set) — ISBN 978-0-470-89059-2 (cloth : v.3) ISBN 978-1-118-28380-6 (e-bk.) ISBN 978-1-118-28202-1 (e-bk.) ISBN 978-1-118-28546-6 (e-bk.) 1. Psychology. I. Weiner, Irving B. BF121.H213 2013 150—dc23 2012005833 Printed in the United States of America 10 9 8 7 6 5 4 3 2 1

For our families . . .

Editorial Board

Volume 1 History of Psychology

Volume 5 Personality and Social Psychology

Donald K. Freedheim, PhD Case Western Reserve University Cleveland, Ohio

Howard Tennen, PhD University of Connecticut Health Center Farmington, Connecticut

Volume 2 Research Methods in Psychology

Jerry Suls, PhD University of Iowa Iowa City, Iowa

John A. Schinka, PhD University of South Florida Tampa, Florida

Volume 6 Developmental Psychology

Wayne F. Velicer, PhD University of Rhode Island Kingston, Rhode Island

Richard M. Lerner, PhD M. Ann Easterbrooks, PhD Jayanthi Mistry, PhD Tufts University Medford, Massachusetts

Volume 3 Behavioral Neuroscience

Volume 7 Educational Psychology

Randy J. Nelson, PhD Ohio State University Columbus, Ohio

William M. Reynolds, PhD Humboldt State University Arcata, California

Sheri J. Y. Mizumori, PhD University of Washington Seattle, Washington

Gloria E. Miller, PhD University of Denver Denver, Colorado

Volume 4 Experimental Psychology

Volume 8 Clinical Psychology

Alice F. Healy, PhD University of Colorado Boulder, Colorado

George Stricker, PhD Argosy University DC Arlington, Virginia

Robert W. Proctor, PhD Purdue University West Lafayette, Indiana

Thomas A. Widiger, PhD University of Kentucky Lexington, Kentucky

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Editorial Board

Volume 9 Health Psychology

Volume 11 Forensic Psychology

Arthur M. Nezu, PhD Christine Maguth Nezu, PhD Pamela A. Geller, PhD Drexel University Philadelphia, Pennsylvania

Randy K. Otto, PhD University of South Florida Tampa, Florida

Volume 10 Assessment Psychology

Volume 12 Industrial and Organizational Psychology

John R. Graham, PhD Kent State University Kent, Ohio

Neal W. Schmitt, PhD Michigan State University East Lansing, Michigan

Jack A. Naglieri, PhD University of Virginia Charlottesville, Virginia

Scott Highhouse, PhD Bowling Green State University Bowling Green, Ohio

Contents

Handbook of Psychology Preface Irving B. Weiner

xiii

Volume Preface xv Randy J. Nelson and Sheri J. Y. Mizumori Contributors

1

xxv

BEHAVIORAL GENETICS

1

Stephen C. Maxson

2

EVOLUTIONARY PSYCHOLOGY

26

Russil Durrant and Bruce J. Ellis

3

COMPARATIVE VISION

52

Gerald H. Jacobs

4

VISUAL PROCESSING IN THE PRIMATE BRAIN

81

Chris I. Baker

5

COMPARATIVE AUDITION 115 Cynthia F. Moss and Catherine E. Carr

6

AUDITORY PROCESSING IN PRIMATE BRAINS 157 Jon H. Kaas, Barbara M. J. O’Brien, and Troy A. Hackett

7

COMPARATIVE LOCOMOTOR SYSTEMS 176 Karim Fouad, David Bennett, Hanno Fischer, and Ansgar B¨uschges

ix

x

Contents

8

NEURAL MECHANISMS OF TACTILE PERCEPTION

206

Steven S. Hsiao and Manuel Gomez-Ramirez

9

THE BIOPSYCHOLOGY OF PAIN

240

Magali Millecamps, David A. Seminowicz, M. Catherine Bushnell, and Terence J. Coderre

10

TASTE AND OLFACTION

272

Patricia M. Di Lorenzo and Steven L. Youngentob

11

FOOD AND FLUID INTAKE

306

Timothy H. Moran and Randall R. Sakai

12

SEXUAL BEHAVIOR

331

Elaine M. Hull and Juan M. Dominguez

13

SLEEP AND BIOLOGICAL RHYTHMS

365

Stephanie G. Jones and Ruth M. Benca

14

MOTIVATIONAL SYSTEMS: REWARDS AND INCENTIVE VALUE Ryan T. LaLumiere and Peter W. Kalivas

15

EMOTION

422

Paul J. Whalen, M. Justin Kim, Maital Neta, and F. Caroline Davis

16

STRESS, COPING, AND IMMUNE FUNCTION

440

Angela Liegey Dougall, Minhnoi C. Wroble Biglan, Jeffrey N. Swanson, and Andrew Baum

17

ENVIRONMENTAL INFLUENCES ON DEVELOPMENT OF THE NERVOUS SYSTEM 461 Laura Petrosini, Debora Cutuli, and Paola De Bartolo

18

COMPARATIVE COGNITION

480

Edward A. Wasserman and Leyre Castro

19

BIOLOGICAL MODELS OF ASSOCIATIVE LEARNING Jeansok Kim, Richard F. Thompson, and Joseph E. Steinmetz

20

MEMORY SYSTEMS Howard Eichenbaum

551

509

395

Contents

21

SOCIAL RELATIONSHIPS, SOCIAL COGNITION, AND THE EVOLUTION OF MIND IN PRIMATES 574 Robert M. Seyfarth and Dorothy L. Cheney

22

THE NEURAL BASIS OF LANGUAGE FACULTIES

595

Chantel S. Prat

23

NEURALLY INSPIRED MODELS OF PSYCHOLOGICAL PROCESSES Eduardo Mercado III and Cynthia M. Henderson

24

NORMAL NEUROCOGNITIVE AGING Bonnie R. Fletcher and Peter R. Rapp

Author Index

665

Subject Index

725

643

620

xi

Handbook of Psychology Preface

Two unifying threads run through the science of behavior. The first is a common history rooted in conceptual and empirical approaches to understanding the nature of behavior. The specific histories of all specialty areas in psychology trace their origins to the formulations of the classical philosophers and the early experimentalists, and appreciation for the historical evolution of psychology in all of its variations transcends identifying oneself as a particular kind of psychologist. Accordingly, Volume 1 in the Handbook , again edited by Donald Freedheim, is devoted to the History of Psychology as it emerged in many areas of scientific study and applied technology. A second unifying thread in psychology is a commitment to the development and utilization of research methods suitable for collecting and analyzing behavioral data. With attention both to specific procedures and to their application in particular settings, Volume 2, again edited by John Schinka and Wayne Velicer, addresses Research Methods in Psychology. Volumes 3 through 7 of the Handbook present the substantive content of psychological knowledge in five areas of study. Volume 3, which addressed Biological Psychology in the first edition, has in light of developments in the field been retitled in the second edition to cover Behavioral Neuroscience. Randy Nelson continues as editor of this volume and is joined by Sheri Mizumori as a new coeditor. Volume 4 concerns Experimental Psychology and is again edited by Alice Healy and Robert Proctor. Volume 5 on Personality and Social Psychology has been reorganized by two new co-editors, Howard Tennen and Jerry Suls. Volume 6 on Developmental Psychology is again edited by Richard Lerner, Ann Easterbrooks, and Jayanthi Mistry. William Reynolds and Gloria Miller continue as co-editors of Volume 7 on Educational Psychology.

The first edition of the 12-volume Handbook of Psychology was published in 2003 to provide a comprehensive overview of the current status and anticipated future directions of basic and applied psychology and to serve as a reference source and textbook for the ensuing decade. With 10 years having elapsed, and psychological knowledge and applications continuing to expand, the time has come for this second edition to appear. In addition to wellreferenced updating of the first edition content, this second edition of the Handbook reflects the fresh perspectives of some new volume editors, chapter authors, and subject areas. However, the conceptualization and organization of the Handbook , as stated next, remain the same. Psychologists commonly regard their discipline as the science of behavior, and the pursuits of behavioral scientists range from the natural sciences to the social sciences and embrace a wide variety of objects of investigation. Some psychologists have more in common with biologists than with most other psychologists, and some have more in common with sociologists than with most of their psychological colleagues. Some psychologists are interested primarily in the behavior of animals, some in the behavior of people, and others in the behavior of organizations. These and other dimensions of difference among psychological scientists are matched by equal if not greater heterogeneity among psychological practitioners, who apply a vast array of methods in many different settings to achieve highly varied purposes. This 12-volume Handbook of Psychology captures the breadth and diversity of psychology and encompasses interests and concerns shared by psychologists in all branches of the field. To this end, leading national and international scholars and practitioners have collaborated to produce 301 authoritative and detailed chapters covering all fundamental facets of the discipline.

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Handbook of Psychology Preface

Volumes 8 through 12 address the application of psychological knowledge in five broad areas of professional practice. Thomas Widiger and George Stricker continue as co-editors of Volume 8 on Clinical Psychology. Volume 9 on Health Psychology is again co-edited by Arthur Nezu, Christine Nezu, and Pamela Geller. Continuing to co-edit Volume 10 on Assessment Psychology are John Graham and Jack Naglieri. Randy Otto joins the Editorial Board as the new editor of Volume 11 on Forensic Psychology. Also joining the Editorial Board are two new co-editors, Neal Schmitt and Scott Highhouse, who have reorganized Volume 12 on Industrial and Organizational Psychology. The Handbook of Psychology was prepared to educate and inform readers about the present state of psychological knowledge and about anticipated advances in behavioral science research and practice. To this end, the Handbook volumes address the needs and interests of three groups. First, for graduate students in behavioral science, the volumes provide advanced instruction in the basic concepts and methods that define the fields they cover, together with a review of current knowledge, core literature, and likely future directions. Second, in addition to serving as graduate textbooks, the volumes offer professional psychologists an opportunity to read and contemplate the views of distinguished colleagues concerning the central thrusts of research and the leading edges of practice

in their respective fields. Third, for psychologists seeking to become conversant with fields outside their own specialty and for persons outside of psychology seeking information about psychological matters, the Handbook volumes serve as a reference source for expanding their knowledge and directing them to additional sources in the literature. The preparation of this Handbook was made possible by the diligence and scholarly sophistication of 24 volume editors and co-editors who constituted the Editorial Board. As Editor-in-Chief, I want to thank each of these colleagues for the pleasure of their collaboration in this project. I compliment them for having recruited an outstanding cast of contributors to their volumes and then working closely with these authors to achieve chapters that will stand each in their own right as valuable contributions to the literature. Finally, I would like to thank Brittany White for her exemplary work as my administrator for our manuscript management system, and the editorial staff of John Wiley & Sons for encouraging and helping bring to fruition this second edition of the Handbook , particularly Patricia Rossi, Executive Editor, and Kara Borbely, Editorial Program Coordinator. Irving B. Weiner Tampa, Florida

Volume Preface

of psychology in general, behavioral neuroscience represents a distinctive fusion of biology and psychology in its theory and methods. For example, evolution as a fundamental tenet in the field of biology has long permeated the work of behavioral neuroscientists. The rapid growth in publications in the area of evolutionary psychology over the past two decades suggests a growing acceptance of the importance of evolutionary ideas in the behavioral sciences. In addition to this influence, the contribution of biology, rooted in evolutionary and ethological traditions, has sustained a broad base of comparative studies by behavioral neuroscientists, as reflected in the contents of this volume. Research in the field of psychology using different species serves a dual purpose. Many studies using nonhuman species are motivated by the utility of information that can be gained that is relevant to humans, using a range of preparations and techniques in research that are not otherwise possible. Of equal importance, comparative research provides insights into variation among biological organisms. Studies of a variety of species can show how different solutions have been achieved for both processing input from the environment and elaborating adaptive behavioral strategies. The organization and content of this volume focus squarely on the need to recognize these dual objectives in studies of biological and psychological processes. The question of how translation is made across species is ever more central to the undertaking of behavioral neuroscience. In the not-distant past, most psychologists viewed research using nonhuman animals as irrelevant to a broad range of psychological functions in humans, including affective and cognitive processes that were considered exclusive capacities of the human mind and social lives of humans in relationships. Today, animal models are increasingly recognized as possessing at least some elements of cognitive and affective processes

The topic of this volume represents a perspective that can be traced to the founding of psychology as a scientific discipline. Since the late 19th century, biological psychologists have used the methods of the natural sciences to study relationships between biological and psychological processes. Today, a natural science perspective and the investigation of biological processes have increasingly penetrated all areas of psychology. For instance, social and personality psychologists have become conversant with evolutionary concepts in their studies of traits, prejudice, and even physical attraction. Many cognitive psychologists have forsaken black boxes in favor of functional magnetic resonance imaging brain scans, and clinical psychologists, as participants in the mental health care of their clients, have become more familiar with the basis for the action of pharmacological therapeutics on the brain. Indeed, the reliance of neuroscience in psychology has provoked us to change the name of this volume from Biological Psychology to Behavioral Neuroscience. The scientific revolution in molecular biology and genetics will continue to fuel the biological psychology perspective. Indeed, it can be anticipated that some of the most significant scientific discoveries of the 21st century will come from understanding the biological basis of psychological functions. The contributors to this volume provide the reader with a highly accessible view of the contemporary field of behavioral neuroscience. The chapters span content areas from basic sensory systems to memory and language and include a perspective on different levels of scientific analysis from molecules to computational models of biological systems. We have assembled this material with a view toward engaging the field and our readership in an appreciation of the accomplishments and special role of behavioral neuroscience in our understanding of behavior. Notwithstanding the trend for a greater influence of biological studies in the field xv

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Volume Preface

that are potentially informative for understanding normal functions and disorders in humans. This progress has contributed to several research areas described in the ensuing chapters, many of which include insights that have come from using new gene targeting or imaging technology. Because human studies do not provide the opportunity for rigorous experimental control and manipulation of genetic, molecular, cellular, as well as brain and behavioral system processes, the use of genetically manipulated mice has become a powerful tool in research. At the same time, the limitations and pitfalls of wholesale acceptance of such animal models are clear to behavioral neuroscientists. In addition to the fact that mouse species have faced different evolutionary pressures and adapted to different ecological niches, the use of genetically altered systems presents new challenges because these novel mice are likely to express new constraints and influences beyond their target characters. The tradition of comparative studies of different animal species makes the role of biological psychology central to the effort to use these new and powerful approaches to advance scientific understanding. A related overriding theme in biological psychology is the significance of translating across levels of analysis. Biological descriptions of psychological processes are viewed by many, particularly outside the field, as a reductionist endeavor. As such, reductionism might represent merely a descent to a level of description in which psychological functions are translated into the physical and chemical lexicon of molecular events. It is increasingly evident that research directed across levels of analysis serves yet another purpose. In addition to determining biological substrates, such investigations can work in the other direction, to test between competing hypotheses and models of psychological functions. It is also the case that molecular biologists who study the brain are increasingly seeking contact with investigators who work at a systems level. More genes are expressed in the brain than in all other organs of the body combined. Gene expression is controlled by intricate information-processing networks within a neuron and is inextricably tied to the activity of neurons as elements in larger information-processing systems. Studies of psychological functions (e.g., the conditions that are sufficient to produce long-term memory or the environmental inputs that are necessary to elicit maternal behavior) will aid in understanding the functional significance of complex molecular systems at the cellular level. Scientific advances are rapidly shifting the biological psychology paradigm from one of reductionism to an appreciation that vertical integration across levels of analysis is essential to understand the properties of biological

organisms. The use of high-energy imaging technology has helped to bridge brain function with behavioral, cognitive, and affective questions. As mentioned, within the past 20 years a novel intellectual bridge has been formed between psychology and molecular biology. Molecular biologists have mapped large segments of the mouse genome as part of the ambitious Human Genome Project. As genes have been identified and sequenced, molecular biologists have begun the difficult task of identifying the functions of these genes. An increasingly common genetic engineering technique used to discover the function of genes is targeted disruption (knockout) of a single gene. By selectively disrupting the expression of a single gene, molecular biologists reason that the function of that targeted gene can be determined. In other cases, a specific gene is added (knockin). In many cases, the phenotypic description of knockout and knockin mice includes alterations in behavior. In Chapter 1 Stephen C. Maxson explores behavioral genetics, generally, and describes the implications of molecular genetics for psychology, specifically. He describes classic studies on the heritability of behavior (viz ., selective breeding) as well as twin and adoption studies. Maxson adroitly documents gene mapping and genome projects in relation to behavioral studies. After presenting an introduction to molecular and developmental genetics, he emphasizes the importance of population genetics in studies of the evolution of behavior. Finally, Maxson explores the ethical and legal manifestations of behavioral genetics in the context of academics and society as a whole. He uses new examples and provides a thoroughly updated version of his previous chapter. In Chapter 2 Russil Durrant and Bruce J. Ellis introduce some of the core ideas and assumptions in the field of evolutionary psychology. Although they focus on reproductive behaviors, Durrant and Ellis also illustrate how the ideas of evolutionary psychology can be employed in the development of specific, testable hypotheses, about human mind and behavior. Their ideas go far past the usual mating behaviors, and they even provide an adaptive scenario for self-esteem studies. Durrant and Ellis note that one of the most crucial tasks for evolutionary psychologists in the coming decades will be the identification and elucidation of psychological adaptations. Although most of the obvious and plausible psychological adaptations have already been cataloged, many more remain undiscovered or inadequately characterized. Because adaptations are the product of natural selection operating in ancestral environments, and because psychological traits such as jealousy, language, and self-esteem are not easily reconstructed

Volume Preface

from material evidence such as fossils and artifacts, direct evidence for behavioral adaptations may be difficult to obtain. One of the challenges for evolutionary psychology, according to Durrant and Ellis, is to develop increasingly more rigorous and systematic methods for inferring the evolutionary history of psychological characteristics, as well as to determine how best to characterize psychological adaptations. Using the comparative method has been particularly successful for understanding the sensory and perceptual machinery in animals. In Chapter 3 Gerald H. Jacobs describes the great success that he and others have had using the comparative approach to elucidate the mechanisms and processes underlying vision. Most studies of nonhuman vision are likely motivated to understand human vision. The remaining studies of vision in nonhuman animals are aimed at understanding comparative features of vision in their own right, often from an evolutionary perspective with the intent to discover common and different solutions for seeing. Jacobs considers both approaches in his review of comparative vision. After a description of the fundamental features of photic environments, he presents basic design features and discusses the evolution of eyes. Jacobs then focuses on photosensitivity as a model of the comparative approach. He details photopigments, ocular filtering, and the role of the nervous system in photosensitivity. Three important issues in comparative vision—detection of change, resolution of spatial structure, and use of chromatic cues—are also addressed. Finally, Jacobs includes a section on the difficulty of measuring animal vision, as well as his perspective of where this field is likely to evolve. Chris I. Baker reviews in Chapter 4 the latest knowledge about cellular to brain system mechanisms of the primate visual system. He draws on multiple levels of analysis to describe the parallel nature of visual processing from the retina to the thalamus, and how their input to the cortex begins a journey of complex analyses that result in, for example, stimulus recognition and a visual spatial understanding of the world around us. Information within the parallel pathways is integrated at all levels of processing. In this way, the culmination of visual information processing at all stages allows individuals to have unified percepts of their world. Cynthia F. Moss and Catherine E. Carr review some of the benefits and problems associated with a comparative approach to studies of hearing in Chapter 5. Comparative audition also has a primary goal of understanding human audition, but a larger proportion of this field is dedicated to understanding the relationship between the

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sensory system of the animal and its biologically relevant stimuli as compared to comparative vision. The ability to detect and process acoustic signals evolved many times throughout the animal kingdom, from insects and fish to birds and mammals (homoplasies). Even within some animal groups, there is evidence that hearing evolved independently several times. Ears appear not only on opposite sides of the head, but also on a variety of body parts. Out of this diversity, many fascinating, specific auditory adaptations have been discovered. A surprising number of general principles of organization and function have emerged from studies of diverse solutions to a common problem. Comparative studies of audition attempt to bring order to the variation and to deepen our understanding of sound processing and perception. Moss and Carr review many common measures of auditory function, anatomy, and physiology in selective species in order to emphasize general principles and noteworthy specializations. They cover much phylogenetic ground, reviewing insects, fishes, frogs, reptiles, birds, and mammals. The chapter begins with a brief introduction to acoustic stimuli, followed by a review of ears and auditory systems in a large sample of species, and concludes with a comparative presentation of auditory function in behavioral tasks. Behavioral studies of auditory systems reveal several common patterns across species. For example, hearing occurs over a restricted frequency range, often spanning several octaves. Absolute hearing sensitivity is best over a limited frequency band, typically of high biological importance to the animal, and this low-threshold region is commonly flanked by regions of reduced sensitivity at adjacent frequencies. Absolute frequency discrimination and frequency selectivity generally decrease with an increase in sound frequency. Some animals, however, display specializations in hearing sensitivity and frequency selectivity for biologically relevant sounds, with two regions of high sensitivity or frequency selectivity corresponding with information, for example, about mates and predators. One important goal of comparative audition is to trace adaptations in the auditory periphery and merge those adaptations with central adaptations and behavior. A detailed description of the neural mechanism of auditory processing in the primate brain is presented by Jon H. Kaas, Barbara M. J. O’Brien, and Troy A. Hackett in Chapter 6. Beginning with the transduction of sound pressure waves into neural signals of auditory receptor hair cells in the basilar membrane, an intricate sequence of processing occurs at multiple steps all the way up to the cortex. Early in the process, auditory signals are distinguished according to the location of the

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Volume Preface

sound source and the quality of the sound wave (e.g., frequency, amplitude, etc.). At the level of auditory cortex, sound-relevant neural signals are distributed to different cortical regions for the analysis of the meaning of the sound, the location of the sound, and sound processing relevant to voices and communication. Of great theoretical and clinical significance are findings that other sensory modalities may be processed in previously designated auditory regions of brain, and auditory information can reach other sensory cortical areas such as visual cortex. Such sensory integration has important implications for our understanding of language and speech. The history and state of the art of, as well as future studies in, comparative motor systems are presented in this updated Chapter 7 by Karim Fouad, David Bennett, Hanno Fischer, and Ansgar B¨uschges. The authors carefully construct an argument for a concept of central control of locomotion and the principles of pattern-generating networks for locomotion. In common with sensory systems to understand locomotor activity, the authors argue that a multilevel approach is needed and present data ranging from the molecular and cellular level (i.e., identification of the neurons involved, their intrinsic properties, the properties of their synaptic connections, and the role of specific transmitters and neuromodulators) to the system level (i.e., functional integration of these networks in complete motor programs). They emphasize that both invertebrate and vertebrate locomotor systems have been studied on multiple levels, ranging from the interactions between identifiable neurons in identified circuits to the analysis of gait. The review focuses on (a) the principles of cellular and synaptic construction of central pattern-generating networks for locomotion, (b) their location and coordination, (c) the role of sensory signals in generating a functional network output, (d) the main mechanisms underlying their ability to adapt through modifications, and (e) basic features in modulating the network function. Each human sensory system provides an internal neural representation of the world, transforming energy in the environment into the cellular coding machinery of vast networks of neurons. In studies of sensory information processing in nonhuman primates, particularly in the Old World monkeys, we encounter research that brings us close to understanding functions of the human brain. Tactile perception becomes a key modality in primates’ ability to identify and manipulate objects within arm’s reach. In Chapter 8 Steven S. Hsiao and Manuel Gomez-Romirez focus on tactile perception, a system that begins with the transduction of information by mechanoreceptor afferents that innervate our skin, muscles, tendons, and joints. The

authors review evidence that information from each of these receptor types serves a distinctive role in tactile perception. This chapter then discusses how such distinct tactile input becomes combined in cortex to provide multidimensional spatial form analysis, and an understanding of texture, vibration, and tactile motion. These and other mechanoreceptors share virtually identical properties in humans and in nonhuman primates. It appears that similar mechanisms exist for tactile and visual spatial feature analysis. However object identification mechanisms differ since cutaneous and proprioceptive inputs are needed for three-dimensional tactile-based object identification. Further, tactile perception is strongly modulated by attention and experience, and this effect is reflected in alterations in the temporal cohesiveness of neural firing. We all learned and accepted that there are five primary senses—that is, until we stubbed our toes and recalled our “sixth sense.” A critical sensory system that alerts us to real or potential tissue damage is pain. In Chapter 9 Magali Millecamps, David A. Seminowicz, Catherine Bushnell, and Terence Coderre explore the mechanisms underlying pain. They note that pain can be considered as two separate sensory entities: (1) physiological pain and (2) pathological pain. Physiological pain reflects a typical reaction of the somatosensory system to noxious stimulation. Physiological pain is adaptive. Rare individuals who cannot process physiological pain information frequently injure themselves and are unaware of internal damage that is normally signaled by pain. Predictably, such individuals often become disfigured and have a significantly shortened life span. Pathological pain reflects the development of abnormal sensitivity in the somatosensory system, usually precipitated by inflammatory injury or nerve damage. The most common features of pathological pain are pain in the absence of a noxious stimulus, increased duration of response to brief-stimulation stimuli, or perception of pain in response to normally nonpainful stimulation. The neurological abnormalities that account for pathological pain remain unspecified and may reside in any of the numerous sites along the neuronal pathways that both relay and modulate somatosensory inputs. Chapter 9 provides a comprehensive review of the current knowledge concerning the anatomical, physiological, and neurochemical substrates that underlie both physiological and pathological pain. Thus, Millecamps and colleagues have described in detail the pathways that underlie the transmission of inputs from the periphery to the central nervous system (CNS), the physiological properties of the neurons activated by painful stimuli, and the neurochemicals that mediate or modulate synaptic transmission

Volume Preface

in somatosensory pathways. The review is organized by neuroanatomy into separate sections: (a) the peripheral nervous system and (b) the CNS, which is further divided into (a) the spinal cord dorsal horn and (b) the brain. The authors made a special effort to identify critical advances in the field of pain research, especially the processes by which pathological pain develops following tissue or nerve injury, as well as how pain is modulated by various brain mechanisms. The multidimensional nature of pain processing in the brain emphasizes the multidimensional nature of pain, using anatomical connectivity, physiological function, and brain imaging techniques. Finally, the authors provide some insights into future pain sensitivity and expression research, with a focus on molecular biology and behavioral genetics. The ability to detect chemicals in the environment likely represents the most primitive sensory faculty and remains critical for survival and reproductive success in modern prokaryotes, protists, and animals. Chemicals in solution are detected by the taste sensory system; chemical sensation has a central role in the detection of what is edible and where it is found. It is well known, for example, that the flavor of food (i.e., the combination of its taste and smell) is a major determinant of ingestion. Humans are able to detect volatile chemicals in air with our olfactory sensory system. Individuals may use chemical senses to protect themselves from ingesting or inhaling toxins that can cause harm. The chemical senses, olfaction and taste, are reviewed in Chapter 10 by Patricia M. Di Lorenzo and Steven L. Youngentob. Until recently, the study of taste and olfaction has progressed at a relatively slow pace when compared to the study of the other sensory modalities such as vision or audition. This reflects, in part, the difficulty in defining the physical dimensions of chemosensory stimuli. We can use human devices to deliver exactly 0.5-m candles of 484 μm of light energy to the eye and then conduct appropriate psychophysics studies consistently across laboratories and across participants. Until recently, however, it has been impossible to present, for example, 4 units of rose smells to an experimental participant. In the absence of confidence that any given array of stimuli would span the limits of chemical sensibility, investigators have been slow to agree on schemes with which taste and olfactory stimuli are encoded by the nervous system. As Di Lorenzo and Youngentob reveal, technological advances, particularly in the realm of molecular neuroscience, are providing the tools for unraveling some of the longstanding mysteries of the chemical senses. Some of the surprising findings that have resulted from this increasingly molecular approach to chemosensation

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are the discovery of a fifth basic taste quality (i.e., umami) and the discovery that the differential activation of different subsets of sensory neurons, to various degrees, forms the basis for neural coding and further processing by higher centers in the olfactory pathway. For both olfaction and taste, the careful combination of molecular approaches with precise psychophysics promise to yield insights into the processing of chemical signals. Next, we move from input to output. To fuel the brain and locomotor activities, we need energy. Because most bacteria and all animals are heterotrophs, they must eat to obtain energy. What and how much we eat depends on many factors, including factors related to palatability or taste, learning, social and cultural influences, environmental factors, and physiological controls. The relative contribution of these many factors to the regulation of feeding varies across species and testing situations. In Chapter 11 Timothy H. Moran and Randall R. Sakai detail the behavioral neuroscience of food and fluid intake. They focus on three interacting systems important in the regulation of feeding: (1) signals related to metabolic state, especially to the degree adiposity; (2) affective signals related to taste and nutritional consequences that serve to reinforce aspects of ingestive behavior; and (3) signals that arise within an individual meal that produce satiety. Moran and Sakai also identify the important interactions among these systems that permit the overall regulation of energy balance. Individuals are motivated to maintain an optimal level of water, sodium, and other nutrients in the body. Claude Bernard, the 19th-century French physiologist, was the first to describe animals’ ability to maintain a relatively constant internal environment, or milieu int´erieur. Animals are watery creatures. By weight, mammals are approximately two-thirds water. The cells of animals require water for virtually all metabolic processes. Additionally, water serves as a solvent for sodium, chloride, and potassium ions, as well as sugars, amino acids, proteins, vitamins, and many other solutes, and is therefore essential for the smooth functioning of the nervous system and for other physiological processes. Because water participates in so many processes, and because it is continuously lost during perspiration, respiration, urination, and defecation, it must be replaced periodically. Unlike minerals or energy, very little extra water is stored in the body. When water use exceeds water intake, the body conserves water, mainly by reducing the amount of water excreted from the kidneys. Eventually, physiological conservation can no longer compensate for water use and incidental water loss, and the individual searches for water and drinks. Regulation of sodium intake and regulation of water intake are closely linked to

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one another. According to Moran and Sakai, the body relies primarily on osmotic and volumetric signals to inform the brain of body fluid status and to engage specific neurohormonal systems (e.g., the renin-angiotensin system) to restore fluid balance. As with food intake, signals that stimulate drinking, as well as those that terminate drinking, interact to ensure that the organism consumes adequate amounts of both water and electrolytes. The signals for satiety, and how satiety changes the taste and motivation for seeking food and water, remain to be specified. We continue with a review of motivated behavior in Chapter 12. Elaine M. Hull and Juan M. Dominguez review the recent progress made in understanding sexual differentiation, as well as the hormonal and neural mechanisms that drive and direct male and female sexual behavior. They begin their chapter by considering the adaptive function of sexual behavior by asking why sexual reproduction is by far the most common means of propagating multicellular species, even though asexual reproduction is theoretically much faster and easier. The prevailing hypothesis is that sexual behavior evolved to help elude pathogens that might become so precisely adaptive to a set of genetically identical clones that future generations of the host species would never rid themselves of the pathogens. By mixing up the genomic characters of their offspring, sexually reproducing creatures could prevent the pathogens—even with their faster generational time and hence faster evolution—from too much specialization. Pathogens that preyed on one specific genome would be extinct after the single generation of gene swapping that occurs with each sexual union. Thus, sexual reproduction has selected pathogens to be generalists among individuals, although sufficiently specific to be limited to a few host species. Hull and Dominguez next provide a description of the copulatory patterns that are common across mammalian species and summarize various laboratory tests of sexual behavior. After a thorough description of sexual behavior, the mechanisms underlying sexual behavior are presented. Because hormones are important for sex differentiation in all mammalian and avian species and because hormones also activate sexual behavior in adulthood, the chapter focuses on the endocrine mechanisms underlying sexual behavior and explores the mechanisms by which hormones modulate brain and behavior. The authors next describe the hormonal and neural control of female sexual behavior, followed by a similar treatment of the regulation of male sexual behavior. In each case, they first summarize the effects of pharmacological and endocrine treatments on sexual behavior. The pharmacological data

indicate which neurotransmitter systems are involved in the various components of sexual behavior (e.g., sexual motivation vs. performance). A variety of techniques has been used to determine where in the brain sexual behavior is mediated, including lesions and stimulation, local application of drugs and hormones, and measures of neural activity. Finally, Hull and Dominguez observe that the hormonal and neural mechanisms that control sexual behavior are similar to the mechanisms that regulate other social behaviors. The authors close with a series of questions and issues that remain largely unanswered. For example, they suggest that more neuroanatomical work is necessary to track the neural circuits underlying sexual behavior in both females and males. Neurotransmitter signatures of those neurons are important pieces of the puzzle, as well as neurotransmitter receptor interactions and intracellular signal transduction activation in response to various neurotransmitter and hormonal effects. What changes in gene transcription are induced by specific hormones? How do rapid membrane effects of steroids influence sexual behavior? What changes in gene transcription mediate the effects of previous sexual experience? They close with broader questions that include the interrelationships among sexual and other social behaviors, and how species-specific differences in behavior are related to their ecological niches. All of these issues are critical for a full understanding of sexual behavior. Life on Earth evolves in the presence of pronounced temporal fluctuations. The planet rotates daily on its axis. Light availability and temperature vary predictably throughout each day and across the seasons. The tides rise and subside in predictable ways. These fluctuations in environmental factors exert dramatic effects on living creatures. For example, daily biological adjustments occur in both plants and animals, which perform some processes only at night and others only during the day. Similarly, daily peaks in the metabolic activity of warm-blooded animals tend to coincide with the daily onset of their physical activity. Increased activity alone does not drive metabolic rates; rather, the general pattern of metabolic needs is anticipated by reference to an internal biological clock. The ability to anticipate the onset of the daily light and dark periods confers sufficient advantages that endogenous, self-sustained circadian clocks are virtually ubiquitous among extant organisms In Chapter 13 Stephanie G. Jones and Ruth M. Benca describe the importance of biological clocks and sleep on cognition and behavior. In addition to synchronizing biochemical, physiological, and behavioral activities to the external environment, biological clocks are important to multicellular organisms for

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synchronizing the internal environment. For instance, if a specific biochemical process is most efficiently conducted in the dark, then individuals that mobilize metabolic precursors, enzymes, and energy sources just prior to the onset of dark would presumably have a selective advantage over individuals that organized their internal processes at random times. Thus, there is a daily temporal pattern, or phase relationship, to which all biochemical, physiological, and behavioral processes are linked. Jones and Benca provide an overview of sleep, as well as the circadian system. Then, they discuss the regulation of sleep in the context of biological rhythms and show how sleepwake homeostasis interacts with alertness and cognitive function, mood, cardiovascular, metabolic, and endocrine regulation. Their chapter closes with a description of sleep disorders in the context of circadian dysregulation. Preceding chapters in the volume considered specific motivated behaviors, such as feeding and mating. In Chapter 14 Ryan T. LaLumiere and Peter W. Kalivas deal with neural circuitry in the brain that is relevant to many different goal-directed behaviors. Whether the goal is food or a sexual partner, common circuitry is now believed to be required for activating and guiding behavior to obtain desired outcomes. This brain system, referred to here as the motive circuit, involves a network of structures and their interconnections in the forebrain that control motor output systems. The authors present a scheme, based on much evidence, that the motive circuit comprises separate but interactive subsystems that integrate information from our external sensory world, internal drives, and decision mechanisms that drive behaviors. One of these subsystems provides control over goal-directed behavior under routine circumstances, where prior experience has established efficient direct control over response systems. The other subcortical-limbic circuit serves a complementary function to allow new learning about motivationally relevant stimuli. The motive circuit described in this chapter includes not only anatomically defined pathways but also definition of the neurochemical identity of neurons in the system. This information has proved vital because the motive circuit is an important target for drugs of interest for their psychological effects. Indeed, the field of psychopharmacology has converged to a remarkable degree on the brain regions described in this chapter. Substances of abuse, across many different classes of agents such as cocaine and heroin, depend on this neural system for their addictive properties. Consequently, the role of subsystems within the motive circuit in drug addiction is a topic of great current interest. Within the scheme described in the chapter, drug-seeking behavior, including

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the strong tendency to relapse into addiction, may reflect an inherent property of circuit function that controls routine responses or habits. Behavioral and neural plasticity underlying addiction is becoming an increasingly important topic of study in this area of behavioral neuroscience for providing an inroad to effective treatment for drug abuse. Emotion encompasses a wide range of experience and can be studied through many variables, ranging from verbal descriptions to the measurement of covert physiological responses, such as heart rate. In Chapter 15, Paul J. Whalen, M. Justin Kim, Maital Neta, and F. Carolyn Davis consider this topic with a focus on the nature of the contribution of the human amygdala to emotional responses. In particular these authors focus on how humans respond to emotional events such as threats and rewards, and how we learn to predict such situations in the future. Results from recent human neuroimaging studies are consistent with the vast literature on the role of nonhuman amygdala in emotional regulation, the relevant parts of which are reviewed here. This chapter also discusses how these data can be used to better understand the neural roots of emotion-related disorders such as that which occurs in pathological anxiety. Life is challenging. The pressure of survival and reproduction takes its toll on every individual living on the planet; eventually and inevitably the wear and tear of life leads to death. Mechanisms have evolved to delay death presumably because, all other things being equal, conspecific animals that live the longest tend to leave the most successful offspring. In the Darwinian game of life, individuals that leave the most successful offspring win. Although some of the variation in longevity reflects merely good fortune, a significant part of the variation in longevity among individuals of the same species reflects differences in the ability to cope with the demands of living. All living creatures are dynamic vessels of equilibria, or homeostasis. Any perturbation to homeostasis requires energy to restore the original steady-state. An individual’s total energy availability is partitioned among many competing needs, such as growth, cellular maintenance, thermogenesis, reproduction, and immune function. During environmental energy shortages, nonessential processes such as growth and reproduction are suppressed. If homoeostatic perturbations require more energy than is readily available after nonessential systems have been inhibited, then survival may be compromised. All living organisms currently exist because of evolved adaptations that allow individuals to cope with energetically demanding conditions. Surprisingly, the same neuroendocrine

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coping mechanisms are engaged in all of these cases, as well as in many other situations. The goal of Chapter 16, written by Angela Liegey Dougall, Minhnoi C. Wroble Biglan, Jeffrey N. Swanson, and Andrew Baum, is to present the effects of stress and coping on immune function. The authors themselves worked under stressful conditions. Our friend and colleague, Andy Baum, passed away during the preparation of this revised chapter. We are grateful to his coauthors and colleagues for updating this chapter. Because description should always precede formal analyses in science, it is important to agree on what is meant by stress. This first descriptive step has proved to be difficult in this field; however, it remains critical in order to make clear predictions about mechanisms. To evaluate the brain regions involved in mediating stress, there must be some consensus about what the components of the stress response are. The term stress has often been conflated to include the stressor, the stress response, and the physiological intermediates between the stressor and stress responses. The concept of stress was borrowed from an engineering-physics term that had a very specific meaning (i.e., the forces outside the system that act against a resisting system). The engineering-physics term for the intrinsic adjustment is strain. For example, gravity and wind apply stress to a bridge; the bending of the metal under the pavement in response to the stress is the strain. Had we retained both terms, we would not be in the current terminological predicament. It is probably too late to return to the original engineering-physics definition of these terms in biological psychology because despite the confusing array of indefinite uses of the term stress, an impressive scientific literature integrating endocrinology, immunology, psychology, and neuroscience has developed around the concept of stress. What, then, does it mean to say that an individual is under stress? For the purposes of this chapter, Dougall and colleagues use some of the prevailing homeostatic notions of stress to arrive at a flexible working definition. The authors next describe coping, which is a way to counteract the forces of stress. Next, Dougall and coauthors describe the psychological and behavioral responses to stress and emphasize the effects of stress on immune function. Although stress causes many health problems for individuals, not all the news is bleak. Dougall and colleagues review the various stress management interventions. In some areas researchers are making remarkable progress at identifying the genetic and molecular mechanisms of stress with little regard for the integrative systems to which these molecular mechanisms contribute. In other areas scientists are still struggling to parse out the interactive effects of behavioral or emotional

factors such as fear and anxiety on stress responsiveness. Obviously, a holistic approach is necessary to understand the brain stress system—perhaps more importantly than for other neural systems. Acute stress can actually bolster immune function, whereas chronic stress is always immunosuppressant. One important goal of future stress research, according to Dougall and coauthors, is to determine how and when acute stress becomes chronic and how to intervene to prevent this transition. Determinants of behavior have historically been discussed in terms of the influence of nature versus nurture. The more contemporary view is that behaviors are determined by interactions between one’s genetic makeup and one’s experiences. In Chapter 17 Laura Petrosini, Debora Cutuli, and Paola De Bartolo discuss these issues within the context of the emergence of experience-expectant and experience-dependent behaviors and cell functions through early development. Thus, this chapter begins with a review of the principles of early brain development, and this is followed by a summary of how experience alters gene-driven maturational processes. Examples of experience-expectant and experience-dependent processes are provided within the context of the development of a number of sensory systems (such as visual, auditory, and tactile systems) and the motor system, as well as the maturation of emotional regulatory systems. The notion that environmental enrichment can alter sensory, motor, affective, and cognitive functions is also reviewed. The chapter ends with the rather provocative suggestion that one may build up a “cognitive reserve” that can be used in older individuals to age gracefully. The comparative approach to understanding cognition is probably central within behavioral neuroscience and is thoughtfully reviewed in Chapter 18 by Edward A. Wasserman and Leyre Castro. Here the focus is on a deeply historical perspective, examining age-old questions, but with a modern point of view. To what extent are our mental functions similar to those of other animals? Are nonhuman animals intelligent? What do we know about the cognitive capacities of nonhuman animals? What forms and aspects of cognition have been studied? Wasserman and Castro examine how animals respond to the passage of time, how they remember the past, how they respond effectively in the present, and how they plan for the future. They also review the literature demonstrating that animals can master abstract and numerical concepts, and even display signs of analogical reasoning as well as many of the precursors to human symbolic language. The authors place this work in an adaptive functional context and consider the possibility that animals may monitor their current

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state of knowledge to control their behavior. Wasserman and Castro present a variety of preparations in which a neural systems analysis has shed light on the neural circuits and mechanisms of learning. Those preparations range from research in relatively simple organisms, such as invertebrates, to several forms of learning in mammals that have closely tied research in laboratory animals to an understanding of the neural bases of learning in humans. In Chapter 19, Jeansok Kim, Richard F. Thompson, and Joseph E. Steinmetz review the significant progress that has been made in terms of our understanding of the neurobiology of learning via targeted investigations of classical or Pavolvian networks of the brain. This chapter begins with a summary of a number of popular invertebrate conditioning models, one that describes our current understanding of the neural basis of conditioning phenomena such as sensitization and habituation. Following this is a brief discussion of spinal conditioning. These authors then turn their attention to providing an in-depth review of two well studied vertebrate models: fear conditioning and eyeblink conditioning. The reviews present both historical perspectives and contemporary analyses. The richness of the significant results from studies of both models can be attributed at least in part to the multilevel and multisystem approaches conducted by an impressive number of laboratories around the world. By knowing the specific pathways of the brain that process CS and US information, fundamentally new insights are drawn concerning the critical neurochemical cascades that are responsible for these special forms of learning. The convergent evidence that defines the properties of these models is sufficiently impressive that these models are now being used to study complex human behavioral disorders such as autism, and used as markers of lifespan changes in human learning and memory. The study of more cognitive forms of memory is now firmly grounded in the recognition that multiple memory systems exist. In Chapter 20 Howard Eichenbaum traces the historical antecedents of this understanding. As a record of experience, habits and skills develop with practice and are enduring forms of memory. Habits and skills control routine simple activities as well as the exquisitely refined performance of the virtuoso. Historically, memory in the form of habits and skills can be seen as the focus of behaviorism in which effects of experience were studied in terms of stimulus and response topographies. Such forms of procedural memory that are exhibited in performance have been distinguished from declarative memory. Declarative memory refers to deficits encountered in amnesic syndromes where habit and skill memory (among

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other procedural types of memory) are entirely preserved but patients have a profound inability either to recollect episodes of experience consciously from the past or to acquire new knowledge. The distinction between forms of declarative and procedural memory has become well established in studies of human memory. Eichenbaum shows how these distinctions are addressed in research on neurobiological systems. In particular, the chapter deals with the challenge of translating declarative memory into studies with laboratory animals. The neural circuitry critical for this form of memory is similarly organized in the human brain, and in the brains of other species including laboratory rodents. Neural structures in the medial temporal lobe, including the hippocampus, are linked to information-processing systems in the cortex. Chapter 20 deals with research that shows how the organization and function of this system allows for distinctive features of cognitive memory, involving representational networks that can be flexibly accessed and used in novel situations. These properties of memory can be tested across human and nonhuman subjects alike. The animal models, in particular, are an important setting for research on the neural mechanisms of memory, including the cellular machinery that alters and maintains changes in synaptic connections. A central problem in comparative biology and psychology is to determine the evolutionary mechanisms underlying similarity between species. As Robert M. Seyfarth and Dorothy L. Cheney emphasize in their chapter on social relationships, social cognition, and the evolution of mind in primates (Chapter 21), there has been a significant shift in how cognition has been studied during the past two decades. One path had gone molecular in the lab, whereas the second path has shifted from the laboratory to the field—from studies of animals’ knowledge of objects in a lab setting to research on what they know about each other in natural contexts. Primates and many other animals live in rich, complex societies where animals form highly differentiated relationships and selection has favored the formation of enduring, long-term bonds. As a result, the social environment is characterized by predictable patterns of interaction, statistical regularities that an individual must recognize if she is to predict other animals’ behavior. In nonhuman primates, social cognition has several striking properties. It involves the formation of concepts, and is computational; individuals recognize others based on discrete-value traits (rank, kinship) and classify individuals along multiple dimensions simultaneously. Social knowledge is rule-governed and open-ended. Vocalizations follow specific rules of delivery, and the classification of others by kinship and rank persists despite

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changes in the individuals involved. Finally, social knowledge involves the attribution of motives and implicit theories of causality. Individuals know when a vocalization is directed at them, and when calls from two individuals are heard together listeners assume that one has caused the other. As Seyfarth and Cheney point out, at present we do not know whether primate social knowledge is qualitatively different from that in other species: the comparative study of social cognition in animals remains a work in progress. For all its richness, nonhuman primate social cognition remains strikingly different from that found in humans. Interest in systems specialized for language in the human brain has a long history, dating from the earliest descriptions of aphasia by neurologists in the 19th century. In Chapter 22 Chantel S. Prat guides the reader through this field of study from its historical roots to the contemporary era, in which new tools and approaches are advancing knowledge in unprecedented ways. The chapter deals in detail with the kinds of inferences about the fundamental properties of language that have been gleaned from the patterns of language breakdown after brain damage. This area of cognitive neuropsychology has a long tradition in the field. The author then describes how recent studies of brain activation in normal subjects using functional neuroimaging technology have confirmed many functions assigned to specific brain regions and circuits based on cases of brain damage. She also considers the discrepancies that have emerged from comparison of these different approaches. This chapter further summarizes contemporary research at the level of representations and at the level of linguistic processes, especially in regards to individual differences and bilingualism. Finally, the chapter includes a discussion of another powerful approach in research in which computational modeling has become an important adjunct to empirical investigations in the biological study of language. A broad perspective on the use of computational models in behavioral neuroscience is the subject of Chapter 23 by Eduardo Mercado III and Cynthia M. Henderson. These authors start by explaining how quantitative modeling can be used to further our understanding of more complex relationships between brain and behavior. This is followed by a review of the basic principles of

computational neuroscience that can be used in artificial neural networks that are designed to better understand the neural circuitry underlying object recognition, perceptual and episodic learning, as well as age-related changes in cognitive function. These computational analyses take into consideration not only behavioral phenomenon but they also evaluate the known properties of single neuron physiology as information-processing units within the more extensive models. In this way, computational models have revealed the nature of fundamental algorithms that allow adaptive and experience dependent processing, such as supervised and unsupervised learning, error-correction learning, or self-organization. In these and other examples discussed in the chapter, computational modeling provides an important adjunct to the empirical base of research in the field. As illustrated in many chapters of this volume, our understanding of the behavioral relevance of different brain regions is based in large part on studies of the behavioral consequences of experimental or accidental brain damage. Further, many research programs have demonstrated a striking correlation between the natural progression of brain development and behavior. Less is known about the web of brain changes that occur in normal (i.e., non-disease-related) old age, and how age-related changes in memory and other cognitive abilities relate to the brain changes. In Chapter 24, Bonnie R. Fletcher and Peter R. Rapp provide a thoughtful review of the aging literature, describing the different research approaches and the current issues in the field. They conclude that age-related cognitive alterations occur in the absence of dramatic neural changes, and that successful cognitive aging can take place as adaptations to brain changes. Further, this chapter discusses the challenges that researchers face as they try to develop effective strategies to support successful aging. In closing this preface, we wish to express our gratitude to the contributing authors. This new edition of Volume 3 of the Handbook represents the field of behavioral neuroscience with its deep roots in the history of our discipline and its vital and exciting opportunities for new discovery throughout the 21st century. Randy J. Nelson Sheri J. Y. Mizumori

Contributors

Chris I. Baker, PhD Unit on Learning and Plasticity National Institutes of Mental Health Bethesda, Maryland

Dorothy L. Cheney, PhD Departments of Psychology and Biology University of Pennsylvania Philadelphia, Pennsylvania

Andrew Baum, PhD Department of Psychology University of Texas Arlington, Texas

Terence J. Coderre, PhD Anesthesia Research Unit McGill University Montreal, Quebec, Canada

Ruth M. Benca, MD, PhD Department of Psychiatry University of Wisconsin School of Medicine Madison, Wisconsin

Debora Cutuli, PhD Department of Psychology, “Sapienza” University of Rome I.R.C.C.S. Santa Lucia Foundation Rome, Italy F. Caroline Davis, PhD Department of Psychology & Neuroscience Duke University Durham, North Carolina

David Bennett, PhD Faculty of Rehabilitation Medicine University of Alberta Edmonton, Alberta, Canada Ansgar Buschges, PhD ¨ Zoological Institute University of Cologne Cologne, Germany

Paola De Bartolo Department of Psychology, “Sapienza” University of Rome I.R.C.C.S. Santa Lucia Foundation Rome, Italy

M. Catherine Bushnell, PhD Pain Mechanisms Laboratory Clinical Research Institute of Montreal Montreal, Quebec, Canada

Patricia M. Di Lorenzo, PhD State University of New York Psychology Department Binghamton, New York

Catherine E. Carr, PhD Department of Biology University of Maryland College Park, Maryland

Juan M. Dominguez, PhD Department of Psychology University of Texas Austin, Texas

Leyre Castro, PhD Department of Psychology University of Iowa Iowa City, Iowa

Angela Liegey Dougall, PhD Department of Psychology University of Texas Arlington, Texas xxv

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Contributors

Russil Durrant, PhD School of Social and Cultural Studies Victoria University of Wellington Wellington, New Zealand

Elaine M. Hull, PhD Department of Psychology Florida State University Tallahassee, Florida

Howard Eichenbaum, PhD Department of Psychology Boston University Boston, Massachusetts

Gerald H. Jacobs, PhD Department of Psychology University of California, Santa Barbara Santa Barbara, California

Bruce J. Ellis, PhD Department of Family Studies and Human Development University of Arizona Tucson, Arizona

Stephanie G. Jones, PhD Department of Psychiatry University of Wisconsin School of Medicine Madison, Wisconsin

Hanno Fischer, PhD School of Biology University of St. Andrews St. Andrews, Scotland Bonnie R. Fletcher, PhD Neurocognitive Aging Section National Institute on Aging Baltimore, Maryland Karim Fouad, PhD Faculty of Rehabilitation Medicine University of Alberta Edmonton, Alberta, Canada Manuel Gomez-Ramirez, PhD Krieger Mind/Brain Institute Department of Neuroscience Johns Hopkins University Baltimore, Maryland Troy A. Hackett, PhD Department of Hearing and Speech Sciences Department of Psychology Vanderbilt University Nashville, Tennessee Cynthia M. Henderson Department of Psychology Stanford University Stanford, California Steven S. Hsiao, PhD Krieger Mind/Brain Institute Department of Neuroscience Johns Hopkins University Baltimore, Maryland

Jon H. Kaas, PhD Department of Psychology Vanderbilt University Nashville, Tennessee Peter W. Kalivas, PhD Department of Neurosciences Medical University of South Carolina Charleston, South Carolina Jeansok Kim, PhD Department of Psychology University of Washington Seattle, Washington M. Justin Kim Department of Psychological & Brain Sciences Dartmouth College Hanover, New Hampshire and Department of Psychology, Korea Military Academy Seoul, South Korea Ryan T. LaLumiere, PhD Department of Psychology University of Iowa Iowa City, Iowa Stephen C. Maxson, PhD Department of Psychology University of Connecticut Storrs, Connecticut Eduardo Mercado III, PhD Department of Psychology University of Buffalo, SUNY Buffalo, New York

Contributors

Magali Millecamps, PhD Department of Dentistry McGill University Montreal, Quebec, Canada

Robert M. Seyfarth, PhD Departments of Psychology and Biology University of Pennsylvania Philadelphia, Pennsylvania

Timothy H. Moran, PhD Department of Psychiatry Johns Hopkins University Baltimore, Maryland

David A. Seminowicz, PhD Department of Dentistry McGill University Montreal, Quebec, Canada

Cynthia F. Moss, PhD Department of Psychology University of Maryland College Park, Maryland

Jeffrey N. Swanson, PhD Department of Psychology University of Texas Arlington, Texas

Maital Neta, PhD Department of Neurology Washington University School of Medicine St. Louis, Missouri

Joseph E. Steinmetz, PhD Department of Psychology Ohio State University Columbus, Ohio

Barbara M. J. O’Brien Department of Psychology Vanderbilt University Nashville, Tennessee

Richard F. Thompson, PhD Neuroscience Program University of Southern California Los Angeles, California

Laura Petrosini, PhD Department of Psychology, “Sapienza” University of Rome I.R.C.C.S. Santa Lucia Foundation Rome, Italy

Edward A. Wasserman, PhD Department of Psychology University of Iowa Iowa City, Iowa

Chantel S. Prat, PhD Department of Psychology University of Washington Seattle, Washington Peter R. Rapp, PhD Neurocognitive Aging Section National Institute on Aging Baltimore, Maryland Randall R. Sakai, PhD Department of Psychiatry University of Cincinnati Cincinnati, Ohio

Paul J. Whalen, PhD Department of Psychological & Brain Sciences Dartmouth College Hanover, New Hampshire Minhnoi C. Wroble Biglan, PhD Department of Psychology Pennsylvania State University Beaver, Pennsylvania Steven L. Youngentob, PhD Department of Psychiatry and Behavioral Sciences SUNY Upstate Medical University Syracuse, New York

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CHAPTER 1

Behavioral Genetics STEPHEN C. MAXSON

INTRODUCTION 1 SUBJECTS 1 GENOME PROJECTS 2 METHODS 2 GENETICS AND BEHAVIORAL TAXONOMY 3 GENETICS AND BIOLOGICAL MECHANISMS OF BEHAVIOR 7

GENES, ENVIRONMENT, AND BEHAVIORAL DEVELOPMENT 15 FUTURE DIRECTIONS 20 REFERENCES 20

INTRODUCTION

Thus, it is impossible to do so in this short review. Rather, the coverage in this chapter must be selective. The topics to be considered derive from the seminal paper of Ginsburg (1958), Genetics as a Tool in the Study of Behavior. In this paper, he cogently argued that in the context of evolution, genetics is a way of defining natural units of behavior, of analyzing the underlying biological mechanisms of behavior, and of studying the effects of environmental and experiential variables on behavior. He illustrated each of these with findings from his research programs on mouse seizures and aggression and from canid reproduction and sociality. This paper was published 5 years after those on Watson and Crick’s model of the structure of DNA and its implications for gene replication, mutation, and function. This was also many years before Sydney Brenner (1973) and Seymour Benzer (1971) made similar proposals for genetic studies of behavior respectively in Caenorhabditis elegans and in Drosophila melanogaster.

Behavioral genetics is a science with dual origins and goals. The study of behavioral genetics that originated in psychology is primarily concerned with the causes of individual variation. The Behavior Genetics Association and its journal, Behavior Genetics, have this as their focus. The emphasis is mainly on the genetics of human behavior and mind. Nonhuman animal, mainly rodent, studies of genes and behavior are of interest for their contribution to human behavior genetics. Behavioral genetics that originated in biology is primarily concerned with genetics as a tool to study behavior. The International Behavioral and Neural Genetics Society and its journal, Genes, Brain, and Behavior, have this as their focus. Here the genetics of behavior and mind of a wide range of animals as well as humans are of interest in themselves and in relation to each other. Regardless, evolution is an essential context for both subfields of behavioral genetics. Both subfields of behavioral genetics are wellestablished. The long history of behavior genetics and its many contributions to psychology and biology have been reviewed by Maxson (2007), Lohelin (2009), and Dewsbury (2009). The literature of both subfields of behavioral genetics is now so large that even multiauthor texts (e.g., Plomin, DeFries, McClearn, & McGuffin, 2008) or monographs with multiauthor articles (e.g., Jones & Mormede, 2007; Kim, 2009) do not cover the vast range of methods and findings across many species.

SUBJECTS The main animal subjects for behavior genetics are roundworms (C. elegans), fruit flies, zebrafish, mice, rats, canids, primates, and humans. This review will focus on mice, other rodents, primates, and humans. The interested reader may want to consult these reviews, articles, or books on the genetics of behaviors in C . elegans (Jansen & 1

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Behavioral Genetics

Segalat, 2007), fruit flies (Belay & Sokolowski, 2007; Bellen, Tong, & Isuda, 2010; Comas, Guillame, & Preat, 2007; Dickson, 2008; Vosshall, 2007), honeybees (Smith, Toth, Suarez, & Robinson, 2008), zebrafish (Norton & Bally-Cuif, 2010; Rinkwitz, Mourrain, & Becker, 2011), rats (Brush & Driscoll, 2002, Driscoll, Fernandez-Teruel, Corda, Giorgi, & Stelmer, 2009), canids (Scott & Fuller, 1965; Wayne & Ostrander, 2007; Parker, Shearin, & Ostrander, 2010), and primates (Lesch, 2003; Weiss & King, 2007). The interested readers may also want to consider a review of selective breeding and behavior studies mostly in fruit flies, mice, and rats (Greenspan, 2003), the chapter on other creatures in the text by Ehrman & Parsons (1981), and a review comparing genetic issues and findings for animal and human behaviors (Kendler & Greenspan, 2006).

frog (two species), chicken, zebra finch, duckbill platypus, opossum, mouse, rat, cat, dog, horse, sheep, cattle, pig, giant panda, marmoset, macaque monkey, chimpanzee, and orangutan (www.genomenewsnetwork.org/ resources/ sequenced_genomes/ genome_guide_p1.shtml). In progress are programs for some degree of DNA sequencing for 5,000 insect species (Robinson et al., 2011) and 10,000 vertebrate species (Hausler, O’Brien, & Ryder, 2009). The complete or partial DNA sequence of these species has facilitated or will facilitate the genetic analysis of behaviors in these species and a comparative genetics of behaviors across these species. A comparative analysis of genetics of behavior will eventually be firmly based on findings for the effects of homologous genes across species as considered by Robinson, Fernals, & Clayton (2008) for social behavior and by Maxson (2009) for aggression.

GENOME PROJECTS

METHODS

An individual’s nuclear genome consists of the DNA found in all the chromosomes in the nucleus of its cells. There is one molecule of DNA for each chromosome. The goal of a genome project is to determine the sequence of the nucleotide bases—adenine, cytosine, guanine, or thymine (A, C, G, or T)—of the nuclear genome of one or more individuals of the species. After the entire sequence is known for a species, it is possible to estimate the number of protein-coding genes in its genome. Also, the amino acid sequence in each protein can be deduced from the coding nucleotide triplets in the gene’s structural region. Other DNA sequences of a gene bind proteins known as transcription factors. These factors and sequences together are involved in controlling when and where a gene is transcribed as RNA (ribonucleic acid). A small fraction of the transcribed RNA is processed into a messenger RNA (mRNA), and the mRNA is then translated into the sequences of amino acids in its protein. Other transcribed RNA may regulate gene transcription or mRNA translation. There is also DNA in the mitochondria; this DNA codes amino-acid sequences of some of the proteins involved in energy metabolism. This DNA has been sequenced in many organisms. The DNA sequence of the human genome was initially published in 2001. To date, the DNA sequence of the following animal species by common names have also been partially or wholly published: hydra, round worms (two species), sea urchin, sea hare, fruit fly (two species), flour beetle, honeybee, wasp, aphid, mosquitoes, zebrafish, stickleback fish, green puffer fish, Japanese puffer fish,

There are essentially four approaches to finding and studying genes with effects on behavior. The first is based on linkage or association of naturally occurring genetic variants with behavior. The second is based on effects on behavior of induced genetic mutations. The third is based on effects on behavior of reducing or blocking the translation of a gene’s mRNA into its protein. The fourth is based on behavioral correlations with transcription into mRNA of one or more genes. Natural Genetic Variants and Behavior There are two approaches to finding and studying effects of naturally occurring genetic variants on behavior. These two approaches can be used with both animals and humans. The first maps quantitative trait loci (QTLs) with behavioral effects to regions of specific chromosomes. Within the QTL are one or more genes with effects on behavioral variation. This approach depends on wellspaced DNA markers across all the chromosomes, such as single nucleotide polymorphisms (SNPs). Once a replicable QTL is identified, the next step is to find the DNA variants of the gene or genes underlying the QTL. Some recent reviews on QTLs and behavior are: Cherny (2009), Molson (2007), MacKay, Stone, and Ayroles (2009), and Haworth and Plomin (2010). QTLs are considered further in the section on genetics and behavioral taxonomy. The second correlates DNA sequence variants in regulatory or coding or noncoding regions of a gene with

Genetics and Behavioral Taxonomy

behavior. This approach depends on knowing some, if not all, of the DNA sequence of the gene and identifying DNA sequence variants of the gene. Some recent reviews on this approach include: Caspi and Moffit (2006) for genotype by environment interactions, Epstein and Israel (2009) for human personality, and Rhee and Waldman (2009) for conduct and antisocial personality disorders. DNA variants of known genes are considered further in the section on Genetics and Behavioral Development. Gene Mutations and Behavior There are two approaches to induced mutations in single genes with large effects on behavior in animals but not humans. In the first, chemical mutagens are used to cause DNA changes at random across the genome. Often the mutations are in single base pairs. They may be in regulatory or coding or noncoding regions of the gene. Mutations of a gene’s coding region can cause the gene’s protein to be nonfunctional, to decrease its function or increase its function. Some reviews on this approach for mice are Goldowitz et al. (2004), Godinho and Nolan (2006), van Boxtel and Cuppen (2011), and, for rats, van Boxtel and Cuppen (2010). This approach has the potential to identify all the genetic variants with effects on a behavior of a species. In the second, the coding region of specific genes is targeted for a mutation that renders the gene’s protein inactive. These are sometimes referred to as knockout mutations. A gold standard for confirming the effect of a gene mutation on behavior is to replace the mutated gene with a functional copy of it and to assess whether or not this rescues the behavioral effects of the knockout mutation. These functional replacements are sometimes referred to as transgenes. A combination of a knockout mutant and temporal or tissue specific activation of its transgene can be used to identify when and where a gene has its initial effects. For mice, this knockout approach is reviewed by Crawley (2007). For rats, a knockout approach is reviewed by Jacob, Lazar, Dwinell, Moreno, and Geurts (2010). Also, knockout approaches useable with many other animals are reviewed by Remy, Tesson, Menoret, Usal, Scharenberg, and Anegon (2010). Knockout mutants are considered further in the section Genetics and Biological Mechanisms of Behavior. Translational Knockdowns and Behavior The effect of a gene’s protein on brain and behavior can also be assessed by attenuating or blocking the translation

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of its messenger RNA into its protein. There are two approaches for doing this. The first approach involves antisense RNA. DNA has two strands with complementary base pairing. One strand is transcribed as sense mRNA. This mRNA is translated into the amino-acid sequence of the gene’s protein. Transcripts from the other DNA strand are antisense mRNA. The base pair sequence of the antisense mRNA is complementary to the sense mRNA. If both DNA strands are transcribed, then the sense and antisense mRNA can hybridize into a double stranded DNA that cannot be translated. The sense strand of mRNA is usually the only transcript from a gene’s DNA. However, transgenes with transcription of antisense mRNA can be inserted into genomes or brains of some animals and behavioral effects assessed. An application of this approach is considered further in the section on genetics and biological mechanisms of behavior. The second approach for blocking translation is RNAi or interference RNA. RNAi are short sequences of RNA (about 22 bp). When combined with specific proteins, they can degrade a gene’s mRNA or attenuate or block a gene’s mRNA translation into its protein (Mattick, 2004; Sandy, Ventura, & Jacks, 2005). For mice, this approach is reviewed by Kuhn, Streif, and Wurst (2007) and Delic et al. (2008), and for rats, it is considered by Petit and Thiam (2010). Gene Expression Correlates With Behavior This approach correlates quantitative variation in mRNA transcription in brain or brain regions of one or many genes across variation in genotype or development or phenotype. mRNA levels are assessed postmortem. The level of more than one mRNA can be assessed with RNA microarrays (Johnson, Edwards, Shoemaker, & Schadt, 2005). RNA microarrays have been used to detect gene expression associated with psychopathologies in humans (Konradi, 2005), and gene expression differences between male and female brains of songbirds (Naurin, Hansson, Hasselquist, Kim, & Bensch, 2011). RNA microarrays and behavior are considered further in the section on genetics and biological mechanisms of behavior.

GENETICS AND BEHAVIORAL TAXONOMY A genetic variant can have effects on multiple traits. Such multiple effects of a genetic variant are known as pleiotropy. For example, there are pleitropic effects in homozygotes of the sickle-cell variant of the hemoglobin

4

Behavioral Genetics

beta gene on mental function, heart failure, rheumatism, abdominal pain, and enlarged spleen. Such pleiotropic effects of genes are the fundamental basis for using genetics to identify natural units of behavior. This is exemplified for four complex behaviors: mouse aggression, mouse emotionality, mouse cognition, and human psychopathology. Male Mouse Aggression Five aspects of mouse aggression taxonomy will be considered. The first is unique and common genetic effects on offense and defense types of aggression. The second is unique and common genetic effects on two aspects of offense. The third is genetic correlations for measures of offense. The fourth is the genetic relationship of coping strategies and aggression. The fifth concerns the distinction between adaptive aggression and maladaptive violence. Offense and Defense Offense and defense aggression differ in motor patterns and in attack target (Maxson, 2009). Two studies have assessed the effect of the same gene on offense and defense. Male mice with functional and nonfunctional monoamine oxidase A (MAOA) differ in measures of offense but not defense (Chen et al., 2007) whereas male mice with functional and nonfunctional alpha calcium/calmodulin kinase II (alpha CamK II) differ in measures of both offense and defense (Chen, Rainnie, Greene, & Tonegawa, 1994). Also, overexpression of alpha CamK II in mouse forebrain increased offense but had no effect on defense (Hasegawa et al., 2009). These findings suggest that there are both unique and common behavioral domains for offense and defense aggression.

offense of the subject is scored. These parameters were varied in two studies of the genetics of offense by Roubertoux et al. (1999, 2005). In a first study, there were 11 inbred strains of mice, and there were five combinations of life history and test arena. Four of these were: (1) nonisolated and neutral cage, (2) isolated 1 day and neutral cage, (3) isolated 13 days and neutral cage, and (4) isolated 13 days and home cage. The behavioral index was the percent of males attacking in a strain. The rank order correlations between strains for any condition were always positive but always less than one. Furthermore, a principal component analysis identified two factors. The first weighted heavily the first two conditions, and the second weighted heavily on the second two conditions. In the second study, QTLs were mapped in the F2 population descended from a cross of NZB and C57BL6 mice. There were two life history and test conditions. These were no isolation and a neutral cage arena versus isolation and home cage arena. There were four measures of offense. These were latency to tail rattle, tail rattle frequency, latency to attack, and attack frequency. Some but not all QTLs were the same for both life history and test conditions. Also, within a life history and test condition some but not all QTLs were the same for all measures of offense. For example, a QTL variant identified as the gene for steroid sulfatase had effects on latency to tail rattle, latency to attack, and frequency of attack, but not frequency of tail rattle for nonisolated, neutral cage, but not for the isolated, home cage test conditions. Elsewhere I have suggested on the basis of the findings in these studies that there may be distinct biology underlying offense that is dependent on life history and test arena and a common biology underlying offense that is independent of life history and test arena (Maxson, 2009; Maxson & Canastar, 2007).

Two Types of Offense Whether a genetic variant has an effect on offense in male mice depends on life history and test situation (Roubertoux, Le Roy, Mortaud, Perez-Diaz, & Tordjman, 1999; Roubertoux et al., 2005). Life history includes whether the subject male is housed alone or housed with a female prior to the aggression test (Maxson, 1992; Roubertoux et al., 1999). Being housed alone is often referred to as isolation. Test situation includes the type of test arena (Maxson, 1992; Roubertoux et al., 1999). Tests can occur in the subject’s home cage. This is known as a resident-intruder test. In this test, the offense behaviors of the resident are scored. Tests can occur in an arena that is not the subject’s home cage. This is known as a neutral cage test. In this test,

Measures of Offense In genetic studies of mouse offense, composite or single scores are often used. Composite scores often reflect the latency, frequency, or duration of fighting. Single scores include the latency, frequency, or duration of one of the motor patterns of offense. The use of either type of measure assumes that they will detect all genes with effects on offense. For this to be valid, all composite and single scores must be fully correlated. But they aren’t. For example, the number of chases and attacks are partially correlated across 11 inbred strains in a neutral cage test with no isolation (Roubertoux et al., 1999). Similar partial correlations among the 11 inbred strains were seen across

Genetics and Behavioral Taxonomy

several behaviors: number of tail rattles, number of chases, number of attacks, and latency to attack. Also, for both a neutral cage test with no isolation and a resident-intruder test with isolation, some QTLs influenced one or more but not all of the following measures of offense: number of tail rattles, latency to tail rattle, number of attacks, and latency to attack (Roubertoux et al., 2005). Other QTLs acted on all of them. Thus, from a genetic perspective, these measures of offense do not index a unitary trait of offense. Offense and Coping Strategies Two lines of mice were selectively bred from wild mice for short and long attack latencies in a resident-intruder test (van Oortmerssen & Bakker, 1981). The lines are respectively known as SAL and LAL. The SAL and LAL lines differ in a consistent way for active avoidance, defensive burying, nest building, routine formation, cue dependence, conditioned immobility, and flexibility (Koolhaas, de Boer, Buwalda, & van Reenen, 2007; Veenema & Neumann, 2007; Koolhaas et al., 1999). It has been suggested that these consistent behavioral strain differences reflect an underlying difference in coping strategies. Coping strategies are ways of responding to environmental challenges. In this context, the SAL mice would be proactive copers that are not guided by environmental stimuli and have rigid routines, whereas the LAL mice would be reactive copers that are guided by environmental stimuli and have flexible repertoires. With regard to aggression, SAL mice would develop routines to control territory, leading them to be more likely to attack an intruder, whereas LAL mice would not develop such rigid routines to control territory, leading them to be less likely to attack an intruder. This hypothesis is based on behavioral differences between two strains. Such strain association may be accidental rather than genetic. This is especially of concern in selected strains where there were only two selected strains and where limitations on colony size inevitably leads to some degree of inbreeding. Regrettably, it has never been determined whether or not these behaviors are correlated in F2 populations of SAL and LAL mice and whether or not the same QTLs affect these behaviors as determined from F2 populations of SAL and LAL mice. Yet there is partial support in mice and rats for the hypothesis that there is a genetic correlation between offense and coping strategies. As predicted by this hypothesis there is a negative correlation between attack latency and time spent burying a shock probe in an outbred population of wild derived rats (deBoer, Caramaschi, Natarajan, & Koolhaas, 2009). SAL mice show less defensive

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burying than LAL mice (Sluyter, Korte, & Van Oortmerssen, 1996). Also, as predicted by this hypothesis, mice selected for building large nests were more aggressive than mice selected for building small nests (Sluyter, Bult, Lynch, van Oortmerssen, & Koolhaas, 1995). SAL mice build larger nests than LAL mice. However, there is also evidence that offense and coping behavior are not perfectly correlated (Sluyter, Bult, Lynch, Meeter, & van Oortmerssen, 1997). Genes of the Y chromosome contribute to the attack latency and defensive burying but not to the difference in nest building between SAL and LAL mice (Sluyter et al., 1997; Sluyter, Korte, Van Baal, De Ruiter, & Van Oortmerssen, 1999). Adaptive Aggression and Maladaptive Violence Most aggression in animals is adaptive, and most genetic analyses in mice have been of adaptive aggression. In humans, some types of aggression have been labeled as maladaptive violence. This is usually considered as excessive aggression resulting in severe injuries or death to others. A recent attempt to distinguish adaptive aggression from maladaptive violence in animals was based on studies of three strains of mice selected for high levels of offense type aggression (Natarajan & Caramaschi, 2010; Natarajan, de Vries, Saaltink, de Boer, & Koolhaas, 2009). There are the SAI, TNA, and NC900 strains. Although the three strains were similar in high levels of offense against opponents, they differed qualitatively in dimensions of aggression. SAL and TA males were similar in structure and different in context. Structure refers to aggressive interactions with the opponent (presence or absence of ritualistic threat, pronounced aggressive escalation, postconflict appeasement, and sensitivity to the opponent’s submission cues). Context refers to effects of opponent by state (free-moving or anesthetized, sex, and home versus neutral territory) on aggressive behavior. With regard to structure, SAL mice were less likely to investigate opponents than TA or 900 mice. Also, TA and NC900 but not SAL mice displayed ritualistic and preescalatory behaviors, and attacks by TA and NC900, but not SAL mice, were inhibited by opponent’s submissive behavior. With regard to context, SAL but not TA or NC900 mice attacked anesthetized males and freely moving females. On this basis, it was suggested that the SAL but not TA or NC900 mice show maladaptive violence similar to that seen in some humans. The association of these structural and contextual aggressive traits has also been reported for WTG rats (de Boer et al., 2009; Natarajan & Caramaschi, 2010). This strengthens the possibility that these are correlated traits

6

Behavioral Genetics

for a dimension of adaptive aggression versus maladaptive violence. However, this hypothesis needs to be rigorously tested with factor analyses in an F2 of the SAL and TA or NC900 mice and QTL analyses for the SAL and TA or NC900 mice such as those used for offense in mice by Roubertoux et al. (2005), for emotionality in mice by Turri, Datta, DeFries, Henderson, and Flint (2001a) and for cognition in mice by Galsworthy et al. (2005). Mouse Emotionality In rodents, emotionality is often assessed by ambulation and defecation in an open field. An open field is a brightly lit, inescapable arena that initially is novel to the individual. Such novel situations are potentially threatening to rodents and decreases in ambulation and increases in defecation in the open field by rodents may be indicators of fear or anxiety. However, there has been much debate as to whether or not open field activity and ambulation index a unitary trait of fearful or anxious emotionality in mice and rats. One approach to this issue has been genetic analyses of strains of mice selected for open-field activity. Two high-activity lines (H1 and H2), two low-activity lines (L1 and L2), and two control lines (C1 and C2) were bred from an F3 population derived from crosses of the BALBc and C57BL6 inbred strains of mice (DeFries, Gervais, & Thomas, 1978). In the open field, the C57BL6 strain is much more active than the BALB/c strain. After 30 generations of selection, the high lines were 3 times more active than the low lines in the open field. There was also a correlated response to selection with the high lines having low defecation and low lines having high defecation in the open field. QTL mapping studies have been done in F2 crosses of the H1 and L1 lines and H2 and L2 lines not only for open field activity and defecation, but also behavior in the elevated plus maze, elevated square maze, light-dark box, and mirror chamber (Turri et al., 2001a, b). Each of these behaviors has been proposed to index a unitary trait of emotionality. In their first genetic analysis (Turri, Henderson, & Flint, 2001b), the QTLs on Chromosomes 1, 7, and X had opposite effects on open field activity and defecation in both the H1 by L1 and H2 by L2 crosses. There were also QTLS for open field activity on Chromosomes 4, 12, 15, and 18, and there were also a QTL on Chromosome 14 for open field defecation. For elevated plus maze, there were QTLs on Chromosomes 1, 15, and 18, and for lightdark box, there were QTLs on Chromosomes 1, 14, and 15. In the second genetic analysis (Turri et al., 2001a),

QTLs on Chromosomes 1, 4, 15, and 18 had effects on at least one measure in the open field, elevated plus maze, elevated square maze, light-dark box, and mirror chamber tests. There were also QTLs on some but not all tests on Chromosomes 7, 8, 11, 12, 14, and X. For defecation, there were QTLs on Chromosomes 1 and X for every test and QTLs on Chromosomes 8, 12, and 14 for at least one test. These findings support in part the hypothesis that emotionality as assessed by these tests indexes a unitary behavioral dimension. Rat and Mouse Learning and Cognition Some of the earliest behavior genetic analyses were focused on rat learning of mazes. For example, Tryon (1929) selectively bred two lines of rats that differed in errors in a simple maze. The line with few errors was known as the maze-bright rats, whereas the line with much error was known as the maze-dull rats. Later, rats were selectively bred for avoidance learning (Brush, 2003; Driscoll et al., 2009). Also, there have been many studies of different types of learning in inbred strains of mice (Bovet, 1977). There is some evidence that in rodents, performance on one learning task is correlated with performance on other learning tasks, especially for complex “cognitive” tasks. For example, learning in a T-maze, Morris water maze, a puzzle box, Hebb-Williams maze, object exploration, water plus-maze, and syringes was assessed in the CD-1 outbred stock of mice (Galsworthy et al., 2005). In this study, a common factor accounted for 36% of the variance in the test scores. In another study, CD-1 outbred mice were assessed for six learning tasks (Lashley II maze, Morris water maze, spatial plus maze, passive avoidance, odor discrimination, fear conditioning, as well as tests of sensory-motor function and fitness, exploration, emotionality, and stressreactivity (Matzel et al., 2006). Across tasks, the scores of individuals were correlated. A common factor also accounted for 32% of the variance across animals and tasks. This common factor was also involved in the variance across exploratory but not sensory motor behaviors, emotional responses, or stress-reactivity. It has been suggested that working memory capacity is the common factor correlating performance across these mouse learning tasks (Kolata et al., 2005). There needs to be QTL studies of mouse cognition similar to those on aggression and emotionality to further assess the common dimensions of mouse learning and memory.

Genetics and Biological Mechanisms of Behavior

Human Psychopathology Schizophrenia and manic depression are diagnostically distinct. Regardless, there is genetic evidence suggesting that they are etiologically related. First, there are the findings from family studies (Lichtenstein et al., 2009). On the one hand, relatives of probands with schizophrenia were at increased risk for manic depression. There were similar findings for both maternal and paternal half-siblings. On the other hand, relatives of probands with manic depression were at increased risk for schizophrenia. There were similar findings for both maternal and paternal halfsiblings. Second, some genetic variants affect both the risk for schizophrenia and manic depression (O’Donovan, Craddock, & Owen, 2009; Williams et al., 2011). These include the genes for the zinc finger-binding protein 804A, calcium channel voltage dependent L-type alpha 1C subunit, transcription factor 4, neurogranin, MHC antigens, ankyrin 3, node of Ranvier, and polybrom-1. Other genetic variants are specific to each of these psychopathologies. Similar findings have also been reported for genetic overlap of schizophrenia with neurodevelopmental disorders such as autism spectrum disorders, learning disabilities, and attention-deficit/hyperactivity disorder (Owen, O’Donovan, Thapar, & Craddock, 2011).

Summary Phenotypic and QTL correlations have been used to assess whether or not variation of conceptually related tests are due to one or more common factors. For aggression, emotionality, and cognition in mice and psychopathology in humans there is genetic evidence for both common and unique factors. There are two limitations to these approaches and to these findings. The first is that the identified common and unique factors are a function of the genetic variants included in a study. The second is that the phenotypic correlations and QTL correlates are due to pleiotropic effects of genes. These pleiotropic effects of genes may or may not cause correlated variation in phenotype by a common factor other than genotype.

GENETICS AND BIOLOGICAL MECHANISMS OF BEHAVIOR Pedigree of Causes There is a pedigree of causes tracing a gene’s effect on behavior from DNA sequence to its transcription into

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RNA to its translation into protein to its molecular and cellular function and to its neural function. Two approaches are key to tracing the pedigree of causes for a gene from its DNA to behavior. The first identifies where and when a gene’s DNA is transcribed into RNA and then processed into mRNA. This specifies the time(s) and place(s) of the initial steps in the pedigree of causes. The second determines whether or not more mutants and transgenics of a gene or translational knockdowns of a gene affect one or more behaviors. In these ways genetics can be used as a tool to investigate the biological mechanisms of behavior. Mouse Olfaction All mammals except the old-world primates, apes, and humans have two olfactory systems (Dulac & Wagner, 2006). The chemosensory neurons of these are in the main olfactory epithelium (MOE) and the vomeronasal organ (VNO). The chemosensory neurons of the MOE project to the main olfactory bulb (MOB) and of the VNO project to the accessory olfactory bulb (AOB). The neurons from the MOB project to many brain areas but primarily to cortical regions, whereas the neurons from the AOB project directly to the amygdala and other limbic areas. Both olfactory systems ultimately project to the same and to different hypothalamic areas. The MOE and VNO also have distinct genetics (DuLac & Wagner, 2006). In each, a chemosensory neuron expresses a single chemo-receptor protein. For the mouse, there are about 1,035 genes coding for the olfactory receptor proteins (OR) of the MOE neurons, and there are about 300 genes coding for the vomeronasal type 1 and type 2 receptor proteins (V1R and V2R) for the VNO neurons. V1R sensory neurons respond with high specificity to low molecular weight organic molecules, and V2R sensory neurons respond with high specificity to peptides. Coexpressed with the V2Rs are M1 and M10 major histocompatibility complex molecules and beta2microblobulin. Homozygous null mutant mice for beta2-microglobin have a defect in V2R receptor localization in VNO chemosensory neurons (Loconto et al., 2003). Male mice mutant for this gene were nonaggressive in a residentintruder test. The residents were isolated for 7 to 10 days before the aggression test. The intruder was a gonadectomized male with male urine swabbed on his back and anogential region. However, these mutant males can discriminate between males and females. The mutant and wild-types males mount females but do not mount males. Thus, it appears that the V2R receptors and MHC

8

Behavioral Genetics

molecules have some role in effects of pheromones on some behaviors. There were similar behavioral effects for male mice homozygous for a deletion of a 600-kilobase genome region that contains 12 of the V1R receptor genes (Del Punta et al., 2002). In a resident-intruder test, lactating females homozygous for the deletion were less aggressive toward an intruder male than were lactating females without this deletion. The mutant females had longer latency to attack, lower attack duration, fewer tail rattles, fewer attacks, and fewer fights than wild-type females. However, mutant and wild-type females did not differ in infanticide. In contrast to females, there were no differences between mutant and wild-type males in a resident-intruder test of aggression, but there were differences between mutant and wild-type males in mounting males and females. More wild-type than mutant males mounted males on the first of three tests and mounted females on the last three of five tests. Wild-type and mutant males had essentially the same adult concentrations of plasma testosterone, indicating that the mutant did not act via adult testosterone concentrations. Also, both mutants and wild types could find a hidden cookie, indicating that theV1R mutant had no effect on the function of the MOE. Thus, it appears that the V1R receptors have a role in effects of pheromones on some behaviors. The role of the VNO in effects of pheromones has also been further confirmed with a knockout mutation of Trp2 (Stowers, Holy, Meister, Dulac, & Koentges, 2002). TRP2 is a cation channel that is expressed exclusively in the chemosensory neurons of the VNO. Homozygous null mutant male mice are not aggressive in a resident-intruder test, but they mount intact male intruders, gonadectomized male intruders with male urine swabbed on back and anogenital areas, gonadectomized male intruders with no male urine swabbed on them, and female intruders in estrus. Wild-type and mutant males had essentially the same adult levels of plasma testosterone, indicating that the mutant did not act via adult levels of testosterone. Also, homozygous null mutant female mice are not aggressive in a resident-intruder test (Hasen & Gammie, 2009; Kimchi, Xu, & Dulac, 2007; Stowers et al., 2002). The female mice were lactating and the intruders were male. The null mutant females also displayed mounting, pelvic thrusts, anogenital sniffing, and complex ultrasonic vocalizations toward both male and female mice. However, homozygous null mutant mice could find a hidden cookie, indicating that the Trp2 mutant does not affect the function of the MOE. Behaviors of male and female mice with ablations of the VNO were essentially the same as

male and female mice homozygous for this null mutant (Kimchi et al., 2007). The findings described in this paragraph and the previous two paragraphs are consistent with a role of the VNO in behavioral responses to pheromonal chemosignals. It also appears that the MOE is also involved in behavioral responses to pheromonal chemosignals. CNGA2 (cyclic nucleotide-gated channel α2) is expressed exclusively in MOE neurons and is essential for odor-elicited responses in MOE neurons (Mandiyan, Coats, & Shah, 2005). In a resident-intruder test, homozygous null mutant males had lower frequency and duration of sniffing the opponent, chasing the opponent, and attacking the opponent than wild-type homozygotes. Also, homozygous null mutant males had lower frequency and duration of sniffing, mounting, and intromitting with an estrus female than did wild types. The behavioral deficits of the null mutant males resemble those of mice with olfactory bulbectomy. These findings are consistent with a role not only of the VNO but also the MOE in behavioral responses to pheromonal chemosignals. Dulac & Wagner (2006) have proposed an interesting model of how input from the MOE and VNO influence gender discrimination, mating, and aggression in males. In this model, VNO cues identify the sender as male or female and stimulate aggression. MOE cues stimulate mating and inhibit aggression. The VNO cues that stimulate aggression also inhibit the inhibitory effects of MOE cues on aggression. Also in the model, the VNO cues identifying a male stimulate mounting and intromitting and the VNO cues identifying a female inhibit mounting and intromitting. Rodent Social Recognition Chemosignals that act on MOE and/or VNO have a role in social recognition in rodents. Social recognition refers to the ability of animals to identify and recognize other members of the same species, and has a role in affiliation, aggression, mating, pair bonding, parenting, social learning, and social anxiety. Two approaches have been used to assess this ability in rodents. One of these is a habituation paradigm. First, there are a series of habituation tests with repeated presentation of the same individual to the subject. Across habituation trials, the time spent investigating the same individual decreases. At the end of the habituation tests, the subject is presented with a novel individual. If the time spent investigating the novel individual increases (dishabituation), this is taken as evidence that that subject is

Genetics and Biological Mechanisms of Behavior

familiar with the habituated individual and can tell it apart from others. The second approach is a social discrimination paradigm. The subject is presented with one individual or a pair of individuals on one or more trials. It is then presented with a pair of individuals, one familiar and one novel. If the subject investigates the novel individual more than the familiar one, this is taken as evidence that the subject recognizes the familiar individual. Gene Micronet A four-gene micronet has been proposed to be involved in performance on these tests of social recognition (Choleris, Clipperton-Allen, Phan, & Kavaliers, 2009; Choleris, Kavaliers, & Pfaff, 2004). The genes are those for the α estrogen receptor (ER-α) and oxytocin receptor (OTR) expressed in neurons of the medial amygdala, and those for the β estrogen receptor (ER-β) and oxytocin (OT) expressed in hypothalamic paraventricular nucleus (PVN). Neurons from the PVN project to the medial amygdala. Homozygous null knockout mutants of the ER-α, ER-β, OT, and OTR show habituation to repeated exposure to a conspecific, but do not show dishabituation to a novel conspecific (Choleris et al., 2003; Ferguson et al., 2000). On the basis of these and gene expression data, it was concluded that the estrogen/receptor complex is required for the synthesis of OT in the PVN and of the OTR in the medial amygdala, and that OT input from the PVN to OTR in the medial amygdala is required for social recognition as measured in habituation/dishabituation tests. A slightly different pattern is seen in social discrimination paradigms. Homozygous null knockout mutants of the ER-α gene and OT gene fail to show social discrimination, and of the ER-β gene show reduced social discrimination (Choleris et al., 2006). It was suggested from these data that the ER-α gene is necessary for social discrimination via its effect on oxytocin receptor synthesis in the medial amygdala and that the ER-β gene has modulatory roles in social discrimination by upregulating existing baseline levels of OT in the PVN. In addition, an OTR knockout expressed in forebrain after postnatal day 21 did not show a failure in social recognition in the habituation/dishabituation test (Lee, Cladwell, MacBeth, & Young, 2008). The OTR KO had normal levels of OTR in the olfactory bulb, olfactory nucleus, medial amygdala, and neocortex, and reduced levels of OTR in the lateral septum, ventral palladium, and hippocampus. These data are consistent with the previous studies showing that social recognition depends on OT acting on OTR in the medial amygdala. Two additional

9

experimental studies are also consistent with this neural basis for social recognition in these tests. In the first study, OT injected into the medial amygdala of OT KO mice before but not after the initial exposure to a stimulus mouse restored social recognition (Ferguson. Aldaq, Insel, & Young, 2001). In this test males are presented with an overiectomized female for 5 minutes. Thirty minutes later, wild-type males investigate the same female, doing so less on the second exposure. OT KO males investigate the same female on the second exposure just as much as on the first exposure. If OT KO males are injected with OT into the medial amygdala just before the first exposure, then they behave like wild types on the second exposure to the female. There was no effect on social recognition of OT KO mice when the OT was injected into the olfactory bulb before the initial exposure to the stimulus mouse. When an OTR antagonist was injected into the medial amygdala of wild-type mice before but not after the initial exposure to a stimulus mouse, they did not show social recognition, just as the OTKO mice did not show social recognition. In the second study, an shRNA of ER-α encoded in an AAV viral vector was injected into the medial amygdala (Spiteri et al., 2010). This attenuated translation of the ERα mRNA in the medial amygdala and thereby decreased the level of ER-α in the medial amygdala. Control mice were injected with just the AAV viral vector. This did not affect the level of ER-α in the medial amygdala. In a habituation/dishabituation test, the translations knockdown, but not the control mice, failed to show social recognition with juvenile stimulus mice. Injections of the shRNA of ER-α into the ventromedial nucleus of the hypothalamus educed translation of ER-α mRNA in this area but had no reduced effect on social recognition. Individual Chemosignals and Social Recognition It is likely that odors and pheromones are the stimuli for social recognition in rodents and many other mammalian groups, and that genetic variants are the basis of some if not all social recognition chemosignals. On the one hand, the VNO has direct input to the medial amygdala, and the MOE has indirect input to it. On the other hand, genetic variants of the major histocompatability complex (MHC) and of the Y chromosome can be discriminated in mouse urine (Monahan, Yamazaki, Beauchamp, & Maxson, 1993; Yamazaki, Beauchamp, Bard, Thomas, & Boyse, 1982). In mice, there are also the highly polygenic and highly polymorphic major urinary proteins that may be individual recognition chemosignals (Brennan & Kendrick, 2006).

10

Behavioral Genetics

In mice and humans, the MOE, but not VNO, mediates effects of the MHC chemosignals. Mice with lesions of the VNO can still discriminate MHC genetic variants (Wysocki, Yamazaki, Curran, Wysocki, & Beauchamp, 2004). Although humans do not have a functional VNO, they can still discriminate mouse MHC variants (Gilbert, Yamazaki, Beauchamp, & Thomas, 1986). In contrast, the VNO, but not the MOE, mediates the effects of MUP chemosignals (Chamero et al., 2007). Some studies suggest that MUP rather than MHC chemosignals are the basis of social recognition (Cheetham et al., 2007). Female mice prefer male mice that have countermarked another male’s scent mark. In the first scent preference test, a female was presented with mark and countermark of brothers from an outbred strain that either had the same or different MHC type. When males differed in MHC type and when the males were identical in MHC type, the females had the same preference for the countermark owner. In the second scent test, a female was presented with a mark and countermark from brothers that had the same or different MUP genotype. When the males differed in MUP genotype, the females preferred the countermarking male. It appears that in this test individuals are recognized by MUP genotype and not by MHC genotype. Whether or not this is the chemosensory basis of social recognition in habituation/dishabituation tests or social discrimination tests remains to be directly determined. However, there is indirect evidence that genotype may not be the basis of social recognition in the previously described studies involving the social discrimination test and knockouts. In a study by MacBeth and colleagues social discrimination was assessed in wild-type, OT knockout, and OTR knockout mice (MacBeth, Lee, Edds, & Young, 2009). The novel and familiar mice were either of the same inbred strain or different inbred strains and therefore respectively either genetically identical or genetically different. The inbred strains were BALB/c and C57BL/6. OT and OTR knockouts could not discriminate between novel and familiar mice of the same strain as previously reported, but they could discriminate between novel and familiar mice of different strains. Novel and familiar mice within an inbred strain are genetically identical and their social discrimination must be by environmentally based olfactory cues. Thus, it may be that the four-gene micronet previously described is essential for social recognition based on environmental but not genetic olfactory cues. In those studies, familiar and novel mice were of the same strain.

Mouse Offense Genetic Variants Over 80 genes have been shown to affect male offense in a resident-intruder test with the resident isolated or housed with a female, and there is at least one gene that affects offense on each mouse chromosome except for 13, 14, and 16 (Maxson, 2009). The primary biological effects of these genes are on urinary chemosignals, olfactory systems, hormonal systems (androgen receptor, aromatase, α and β estrogen receptors, corticotrophin releasing hormone receptor), neurotransmitter systems (acetylcholine, adenosine, argenine-vasopressin, cannabinoids, dopamine, enkephalin, GABA, glutamate, histamine, nitric oxide, norepinepherine, neuropeptide Y, oxytocin, serotonin, and substance P), second messenger systems, neurotrophins, neural development, and neural structures. Six of these genes act on the serotonin system (Cases et al., 1995; Hendriks et al., 2003; Holmes, Murphy, & Crawley, 2002; Kulikov, Osipova, Naumenko, & Popova, 2005; Saudou et al., 1994; Young et al., 2008; Zhuang et al., 1999). (1) PET-1 acts on the development of serotonergic neurons. Most serotonin neurons fail to develop in mice with the knockout for the PET-1 gene. These male mice show an increase in offense. (2) GTF2IRD1 is a transcription factor. Knockout mice for its gene have elevated levels of 5HIAA in frontal cortex, parietal cortex, and amygdala. These male mice show a decrease in offense. (3) TPH2 is the enzyme in serotonin cells that catalyzes the conversion of tryptophan to 5-hydroxytryptophan and it is the rate-limiting step in the synthesis of serotonin. TPH2 activity is higher in midbrain of mice homozygous for the 1473C allele than mice homozygous for the 1473G allele. Mice homozygous for the 1473C allele have more offensive attacks. (4) 5HT1AR and 5HT1BR are 2 of the 13 serotonin receptors. Male mice homozygous for a knockout of the 5HT1BR have increased offense, whereas male mice homozygous for a knockout 5HT1AR have decreased offense. (5) MAOA is a mitochondrial enzyme that degrades biogenic amines including serotonin, dopamine, and norepinepherine. It is found in the presynaptic terminals where it degrades the transmitter taken back up into the presynaptic neuron. Male mice with a knockout of MAOA have increased offense. (6) 5HTT is found in the presynaptic terminal of serotonergic neuron, where it acts to take up serotonin from the synaptic space back into the presynaptic serotonergic neuron. Mice homozygous and heterozygous for

Genetics and Biological Mechanisms of Behavior

a 5HTT knockout have reduced offense. The remainder of this section will focus on determining the pedigree of causes for the effect of the MAOA knockout on male offense. There are two null mutations of Maoa in mice. The first mutation occurred as the consequence of the insertion of a transgene in exon III of the Maoa gene in C3H/HeJ mice (Cases et al., 1995). This insertion results in a hybrid MAOA protein with no enzymatic activity. In a 10-minute resident intruder test, the latency to attack was shorter in the null mutant than in wild types for both resident males that had been isolated or that had been paired with a female. The second mutation occurred as a result of a single base pair change in the exon VIII of the Maoa gene in 129/SvEvTac mice (Scott, Bortolato, Chen, & Shih, 2008). This mutation truncates translation with the consequence that the MAOA protein had no enzymatic activity. In a 5-minute resident intruder test with 129 intruders, the Maoa mutant males had shorter latency to attack, more tail rattles, and more fight bouts than wild-type males. It is worth noting that two independent null mutations of the same gene had the same effects on offense. However, the latency to attack, attack frequency, and the percentage of mice attacking for the null mutant depends on the genotype of the intruder (Vishnivetskaya, Skrinskay, Seif, & Popova, 2007). A human transgene for MAOA was inserted into the genome of knockout null mutants (Chen et al., 2007). The transgenes were expressed postnatally, but not prenatally, and the transgenes were expressed in forebrain and not in hindbrain or cerebellum. Significant MAOA activity was detected in the frontal cortex, hippocampus, and striatum, but the MAOA activity was only 2 to 5% of that in wildtype mice. This postnatal expression of transgenic MAOA in the forebrain rescues the effect of MAOA knockout on offense. It may be concluded from this that the increase in aggression seen in the MAOA knockout is due to absence of MAOA postnatally in the forebrain. The absence of MAOA activity in biogenic amine neurons leads initially to an increase of the biogenic amine in the neurons presynaptic terminal and eventually in the synaptic space. This can be seen in whole brain and in regional increases in the biogenic amines. In the first study, the levels of dopamine, norepinepherine, and serotonin of knockout males were higher in whole brain at postnatal days 1 to 90 (Cases et al., 1995). In the second study, the levels of serotonin in knockout males were higher in frontal cortex, striatum, and hippocampus, whereas the

11

levels of norepinepherine in knockout males were higher in frontal cortex and striatum, but not hippocampus. Levels of dopamine in knockout males were higher in striatum, but not in frontal cortex or hippocampus (Chen et al., 2007). The insertion of the human transgene into the knockout reduced the level of the biogenic amine in the relevant forebrain. Thus postnatal elevation of one or more of the biogenic amines in the forebrain is the initial cause of the increase in offense in the null mutants and postnatal decrease of one or more of these biogenic amines in the forebrain is the cause of the decrease in offense of the rescue transgenics. Knockouts for the respective synaptic uptake transporters provide evidence for the role of one or more of their biogenic amines in the effects of the MAOA knockout on offense. A knockout of the serotonin transporter gene not only elevates brain levels of serotonin but also reduces offense (Holmes et al., 2002). In a resident-intruder test, the serotonin knockout mice had longer latency to attack on the second but not the first test, and fewer attacks on both the first and second tests. The residents were isolated, the intruders were DBA2 males, and the tests lasted for 15 minutes. Similarly, a null mutant of the serotonin transporter in rats elevates brain serotonin and decreases offense (Homberg et al., 2007). The residents had lower latency to attack over four tests. These tests were stopped after the intruder was attacked. A fifth test was allowed to go for 10 minutes after the first attack. On the fifth test, the null mutant had a lower percentage of time displaying offense behavior than the wild types. The residents were pair-housed with a female prior to the aggression test. Because both the MAOA knockout mouse and 5HTT knockouts in mice and rats had elevated brain levels of serotonin but differed in effect on offense, the elevated levels of brain serotonin in the MAOA null mutant cannot be the cause of its increased offense behaviors. Similarly, because both the MAOA and 5HTT knockout mutants in mice had disrupted barrel fields in somatosensory cortex but differed in offense, the disrupted barrel fields in the somatosensory cortex in MAOA null mutants cannot be the cause of its increased offense behaviors (Murphy et al., 2003). However, there is evidence that disrupted barrel fields of the sensory cortex in both MAOA and 5HTT null mutants is due to the elevated levels of brain serotonin. Barrel fields of the sensory cortex are restored in mice homozygous for double MAOA and 5HT1B null mutants and in mice homozygous for MAOA, 5HTT, and 5HT1B triple null mutants. Mice homozygous for the

12

Behavioral Genetics

double MAOA and 5HTT null mutants have disrupted barrel fields of the somatosensory cortex. A knockout of the norepinephrine transporter has elevated brain levels of norepinephrine and may have increased offense behavior (Haller et al., 2002). However, the knockout and wild-type mice were intruders rather than residents in resident-intruder tests. In this test, the intruder usually shows defense rather than offense behaviors. When attacked by the resident, the norepinephrine transporter knockout had more attacks than wild types on the first of 10 encounters. The two genotypes did not differ in defensive upright posture on any encounter, suggesting that the observed attacks were offense behaviors. Thus it is possible but not proven that in MAOA knockout mice, the increase in brain levels of norepinephrine is casual to the increased offense behaviors in this null mutant. Mice homozygous for the knockout of dopamine β-hydroxylase have a deficit in norepinephrine and have no offense behavior in a resident intruder test (Marino, BourdelatParks, Cameron Liles, & Weinshenker, 2005). Dopamine β-hydroxylase converts dopamine to norepinephrine. Knockout mice lacking the dopamine transporter had elevated brain levels of dopamine and increased offense behaviors in both resident-intruder and neutral cage tests (Rodriguez et al., 2004). Both threat postures and attacks were higher in the null mutant than in the wild types. Intruders were C3H/He males, and test duration was 5 minutes. However, although a knockout of COMT increases dopamine in the frontal cortex of mutant homozygotes, it does not increase offense behaviors in mice homozygous for the knockout (Gogos et al., 1998). The findings with these knockouts and the MAOA knockout imply that the increase in offense behavior of the MAOA knockout may be due to the increase in dopamine in the striatum but not frontal cortex. Furthermore, the MAOA knockout upregulates the A2A receptors in the basal ganglia, whereas the 5HTT knockout downregulates A2A receptors in the basal ganglia (Mossner et al., 2000). Thus, the MAOA and 5HTT knockouts not only have opposite effects on offense but also opposite effects on levels of A2A receptors in the basal ganglia. Since mice homozygous for a knockout of the A2A receptor gene have increased offense behaviors in a resident-intruder test (Ledent et al., 1997), it is uncertain as to whether or not the up regulation of the A2A receptors in the basal ganglia mediates the effect of the MAOA knockout on offense. The knockout A2A mice have shorter attack latencies and more tail rattles and attacks than wild-type mice. In this case, the residents were isolated, the intruders were CD1 males, and the test duration was 10 minutes.

This section has reviewed how the pedigree of causes for effects of a gene’s mutant on behavior can be traced by comparing and contrasting its effects with those for mutants of other genes. Gene Expression Gene expression profiles are another approach to identifying genes with effects on behavior and can be an initial step in tracing the pedigree of causes. Gene mutant methods identify individual genes with effects on a behavior. In contrast, gene expression profiles can find many genes with effects on a behavior. Here, two gene expression profile studies on maternal aggression are described. In the resident-intruder test, lactating female mice will attack male intruders (Gammie et al., 2007). On postpartum day 5, the duration of attacks is higher in lactating than in nonlactating females. In one study, gene expression in the hypothalamus was compared for lactating and virgin females (Gammie et al., 2005). A DNA microarray representing 1904 genes was used and mRNA was extracted from the hypothalamus, preoptic region, and nucleus accumbens of ICR mice. Gene transcription levels were significantly different for 92 genes. Among these, mRNA levels were higher in lactating than virgin females for neuropeptide Y, neuropetide Y receptor, proenkaphalin, and pololike kinase and were lower in lactating than virgin females in POMC and endothelial receptor type B. This study illustrates the detection of gene expression differences for behavioral state (i.e., lactating versus nonlactating females). In another study, gene expression in hypothalamus and preoptic area was compared for mice from a line selected for high maternal aggression (S) and a control line selected for neither high nor low maternal aggression (C) (Gammie et al., 2007). A DNA microarray representing over 40,000 genes was used and mRNA was extracted from the hypothalamus and preoptic area of females from the S and C lines. Gene transcription levels were significantly different for 200 genes. Among these, the S line had higher mRNA levels for neuronal nitric oxide synthase, K+ channel subunit Kcna1, corticotrophin-releasing factorbinding protein, GABA A receptor subunit 1A, adenosine A1 receptor, Fos, and Erg-1. Conversely, the S line had lower mRNA levels for neurotensin and neuropeptide Y receptor Y2. This study illustrates the detection of gene expression differences for populations differing in genotype. Both studies illustrate how gene expression studies of populations differing in either phenotype or genotype can detect a large number of candidate genes for a behavior.

Genetics and Biological Mechanisms of Behavior

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These candidate genes can then be assessed with studies of their knockouts on behavior.

specific transgenics or knockouts should be used to determine when and where a knockout acts.

Summary

For microarray gene expression studies, limitations are:

This section has demonstrated how gene expression, gene knockouts, transgenics, and translational knockdowns can be used to study the biological mechanisms of olfaction, social recognition, and aggression in mice. The approaches described in this section are also being applied to other behaviors, including: nocioception (Mogil, Yu, & Basbaum 2000), circadian rhythms (Bell-Pedersen et al., 2005), sleep (Cirelli, 2009), feeding (Bell, Walley, & Froguel, 2005), mating (Jazin & Cahil, 2010; Pfaff, Waters, Khan, Zhang, & Numan, 2011), emotionality (Gorden & Hen, 2004), learning and memory (Lee & Silva, 2009), and drug/alcohol effects (Hyman, Malenka, & Nestler, 2006). There are several methodological concerns for both the knockout and the gene expression approaches. For knockout studies, these are: • Differences between knockouts with knockout parents and wild types with wild-type parents may be due to maternal effects. To avoid maternal effects, the mother of the two genotypes must be the same, and the offspring should be the result of the mating of a heterozygous female to a heterozygous male. • Some knockout strain pairs are coisogenic, differing only in the normal and mutant alleles of a single gene, but others are only congenic, differing not only in the mutant and normal alleles of genes of interest, but also in alleles of genes linked to it. For congenic strains, any differences may be due to the genes linked to the knockout rather than due to the knockout itself, as discussed by Gerlai (1996). Rescue experiments with the transgenic genes are essential to differentiate between effects of the knockout and genes linked to it. • Often the knockout is made in one inbred strain, such as one of the 129 inbred strains, and then transferred to another strain. Sometimes the effect of a knockout seen in one strain background is not detected in another. For example, the effect of the knockout for the NOS-l (nitric oxide synthase-I) gene, which increases attacks, is lost after many generations of backcrossing to C57BL6 inbred strain of mouse (LeRoy et al., 2000). • For knockouts, the mutant gene is present from the time of conception. Thus, it is not possible to tell when or where in the mouse the gene was expressed with consequent behavioral effect. Tissue and temporal

• The microchip DNA arrays will not detect genes with low levels of mRNA. It is currently limited to detecting genes expressed at a relative abundance of 1/100,000 mRNAs. • There may be false positives with this technique. For this reason, findings on gene expression should be confirmed with other techniques for detecting mRNAs such Northern blots, RT-PCR (revere transcription polymerase chain reaction), or in situ hybridization. Genotype by Genotype Interactions The effect of a single gene variant on biology and behavior can often depend on the other genetic variants present in the individual. Two examples of this are described in this section. First, there is the interaction of the Y chromosome and autosomes with effects on aggression. These may be due to differential regulation by SRY of tyrosine hydroxylase or MAOA or β-endorphin gene expression. Second, there are interactions of variants of the serotonin transporter gene, of the DRD4 dopamine receptor gene, and of the COMT gene for human personality. Mouse Aggression There are two parts to the Y chromosome of all eutherian mammals, including mice. One part of the Y chromosome is male-specific. It is passed strictly from father to son. The other part pairs with and recombines with a homologous part of the X chromosome. It is not passed strictly from father to son. It was first shown that the male-specific part of the DBA1 and C57BL10 Y chromosomes differed in effects on several measures of offense and that the differential effect of this pair of Y chromosomes depends on one or more genes on the autosomes. Across several measures of offense, the DBA1 males are more aggressive than C57BL10 males and F1 males with the DBA1 Y chromosome are more aggressive than F1 males with the C57BL10 Y chromosome (Selmanoff, Maxson, & Ginsburg, 1976). Males were isolated from weaning until testing, and the test area was a neutral cage. These findings were confirmed with a tetrad of Y chromosome congenics. Y congenic strains are identical in mitochondria, maternal environments, autosomes, X chromosomes, and recombining parts of the Y chromosome, but differ in nonrecombining parts of the Y chromosome. On a DBA1 background,

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Behavioral Genetics

males with the DBA1 Y chromosome are more aggressive than those with the C57BL10 Y chromosome (Maxson, Didier-Erickson, & Ogawa, 1989), whereas on a C57BL10 background, males with the DBA1 Y chromosome and males with the C57BL10 Y chromosome are equally pacific (Maxson, Ginsburg, & Trattner, 1979). It has been proposed that this genotype-by-genotype interaction involves the Sry gene on the Y chromosome (Maxson, 1996). Sry codes for the SRY transcription factor. SRY is necessary for the primordial gonad to differentiate as a testis. Sry is also transcribed in mouse, rat, and human brains (Dewing et al., 2006; Lahr et al., 1995; Mayer, Mosler, Just, Pilgrim, & Reisert, 2000). In mouse brain, it is transcribed in cortex, medial mammilary bodies, other areas of the hypothalamus, and the midbrain (notably substantia nigra and ventral tegmentum). In cell cultures, SRY has been shown to regulate the expression of genes for tyrosine hydroxylase (Milsted et al., 2004) and MAOA (Wu, Chen, Li, Lau, & Shih, 2009). Also, in rats, Sry transcript is found in the cell bodies of dopaminergic neurons of the substantia nigra, and antisense DNA for Sry messenger RNA-reduced levels of tyrosine hydroxylase in the substantia nigra (Dewing et al., 2006). SRY may also regulate the transcription of the proopiomelanoocortin gene and thereby the levels of β-endorphin in the brains of mice (Botbol et al., 2011). If the SRY proteins of DBA1 and C57BL10 males differed in their binding to regulatory sites for tyrosine hydroxylase or MAOA or proopiomelanocortin genes, this may account for the interaction of Y chromosome and autosomes with regard to offense behaviors. The differential binding of SRY from these two Y chromosomes would be due to DNA sequence difference in the proteins coding part of the Sry genes of the D1 and B10 Y chromosomes and to DNA sequence differences in the regulatory part of tyrosine hydroxylase or MAOA or proopiomelanocortin genes of the D1 and B10 autosomes. Increased transcription of tyrosine hydroxylase and/or decreased transcription of MAOA leading to increased synaptic dopamine might be a cause of the increased offense behaviors in male mice with the DBA1 Y chromosome and autosomes. Regardless, six other Y chromosomal genes are expressed in mouse brain. These are Ddx3y, Ube1y, Kdm5d, Eif2s3y, Utf, and Usp9y (Xu et al., 2002). The temporal and spatial expression of these genes is now documented in the Allen Brain Atlas. One or more of these may vary between the DBA1 and C57BL10 mice and be involved in the interaction of Y chromosome and autosomes with regard to offense behaviors.

Mouse Emotionality The behavioral effect of the 5HTT null mutant was assessed in the C57BL6 and 129S6 genetic background (Holmes, Lit, Murphy, Gold, & Crawley, 2003). The mice were tested in the elevated plus maze and the light-dark box. On the C57BL6 but not the 129 background, the homozygous null mutants spent less time in the aversive light compartment of the light-dark box than the homozygous wild types. Similarly, on the C57BL6 but not the 129 background, the homozygous null mutants spent less time in the aversive open arms of the elevated plus maze than did the homozygous wild types. These findings are consistent with an effect on these behaviors of genotype-by-genotype interaction between the 5HTT null mutant and other genes in the two strains. Regardless, these genotype-by-genotype interactions were not due to effects on levels of serotonin or 5HT1A receptor binding in the null mutant. However, there are genotype-by-genotype interactions that can affect the level of serotonin in the 5HTT null mutant (Murphy, 2003). First, 5HTT and DAT double null mutants had one-third less brain serotonin than wild types. Second, mice homozygous for the 5HTT null mutant and heterozygous for brain-derived neurotrophic factor (BDNF) null mutant had less serotonin in brain stem, hypothalamus, striatum, and hippocampus than mice homozygous for 5HTT null mutant and homozygous for BDNF wild types. Personality Genotype-by-genotype interactions have also been reported for novelty-seeking scores on the Tridimensional Personality Questionnaire (Benjamin et al., 2000; Strobel et al., 2003). Three polymorphic genes were assessed. These were the genes for serotonin transporter (5HTT), catechol-o-methyl transferase (COMT), and dopamine receptor D4 (DRD4). These polymorphisms are for 5HTT in the promoter with either 14 (s) or 16 (l) copies of a 22bp sequence, for the DRD4 in exon VIII with either 7 (+) or less than 7 (−) copies of a 28bp sequence and for COMT in an SNP coding either for a valine or methionine. If the COMT genotype is homozygous for Val/Val individuals with the ll genotype for 5HTT and with – genotype for DRD4 have higher novelty-seeking scores than those with the ll genotype for 5HTT and with the + genotype for DRD4, whereas individuals with the sl or ss genotype for 5HTT and with the + genotype for DRD4 have higher novelty-seeking scores. The same pattern is seen for those with the Met/Met genotype. However, if the COMT

Genes, Environment, and Behavioral Development

genotype is Val/Met genotype, those with the ll genotype for 5HTT have lower novelty-seeking scores than those with the sl or ss genotypes, regardless of their DRD4 genotype. Summary Two types of genotype-by-genotype interactions have been described. On the one hand there are interactions involving gene regulation. These may be said to be at the genetic level. They are exemplified by possible explanations for the interaction of Y chromosome and autosomes with effects on offense. On the other hand, there are interactions of gene effects on one or more neural systems. The interactions may be said to occur at the neural phenotypic level. They are exemplified by interaction of two or possibly three neurotransmitter systems with regard to personality. Regardless, for both, the finding of the interaction and its analysis contribute to an understanding of the biological mechanisms for these behaviors.

GENES, ENVIRONMENT, AND BEHAVIORAL DEVELOPMENT Genotype by Environment Interactions Some effects of a gene on phenotype depend on specific environments, and some effects of environment on phenotype depend on specific genotypes. These are known as genotype by environment interactions. They can be of value in determining the effects of environments on behavioral development and expression. The earliest studies of genotype by environment interactions were, in fact, strain by environment interaction. These are considered for rat cognition and mouse aggression. More recent studies of genotype by environment interactions have focused on specific genes. These are considered here for MAOA and 5HTT. The distinction between risk and plasticity genotypes will also be considered in relation to genotype by environment interactions (Belsky et al., 2007). Rats Cognition Maze-dull and maze-bright rats were raised in restricted, normal, or enriched environments (Cooper & Zubek, 1958). Restricted environments were small gray rat cages. Normal environments were standard rat cages. Enriched environments were large cages with “toys.” In the normal environment, the maze-bright rats made fewer errors than

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the maze-dull rats. The enriched environment reduced the error scores of the maze-dulls to that of the maze-brights, but it had no effect on the error scores of the maze-brights. The restricted environment increased the error scores of the maze-brights to that of the maze-dulls but it had no effect on the error scores of the maze-dulls. This appears to be a strain by environment interaction. Alternatively, it may reflect floor and ceiling effects. That is to say, error scores cannot be lower than those of the maze-brights, and they cannot be higher then those of the maze dulls. The issue of floor and ceiling effects in reported strain by environment effects on behavior was critically evaluated by Henderson (1968). Mouse Aggression Sixty-nine years ago the first studies of aggression in inbred strains of mice were reported by Scott (1942) and Ginsburg and Allele (1942). Both assessed aggression in males of the C57BL10, C3H, and BALB/c inbred strains. Scott’s most pacific strain was Ginsburg and Allele’s most aggressive stain. Scott obtained the same strain rank order of these strains in both his laboratory and that of Ginsburg and Allele. Ginsburg and Allele obtained the same rank order of these strains in a first and second study. Many years later, an examination of the meticulously kept animal husbandry record revealed that Scott used a forceps to pick a mouse up by the tail to transfer it from cage to cage, and that Ginsburg transferred the mice from cage to cage in a small box. Later experimental studies showed that this experience affects the aggressive behavior of the C57BL10 males, but not that of the C3H and BALB/c males (Ginsburg, 1967). Human Personality and Psychopathology MAOA and Antisocial Behavior A polymorphism in the promoter of the human MAOA gene interacts with childhood maltreatment to affect antisocial behaviors (Caspi et al., 2002). A 30 base pair sequence in a promoter of the MAOA gene is repeated 2, 3, 3.5, 4, or 5 times. The common alleles have either 3 or 4 repeats. In-vitro studies of transfected cell lines have shown that there is more transcription of the 3.5and 4-repeat alleles than of the 2- and 3-repeat alleles and higher MAOA activity for the 3.5- and 4-repeat alleles than of the 2- and 3-repeat alleles. The alleles are frequently referred to respectively as high MAOA activity and low MAOA activity alleles. This was the genotypic variable.

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In the Dunedin Longitudinal Study, abuse was assessed from 3 to 11 years in 499 males. Abuse included rejecting mother, harsh discipline, changes in primary caregiver, physical abuse, and sexual abuse. Individuals were than sorted into no maltreatment, probable maltreatment, and severe maltreatment groups. This was the environmental variable. The dependent variables were conduct disorder assessed according to DSM-IV criteria, antisocial personality disorder assessed in a questionnaire by someone well-known to the individual, disposition to violence assessed by the Aggression scale of the MPQ, and police records in New Zealand and Australia of conviction for a violent crime. The four outcomes were correlated and a composite index of antisocial behavior was derived from the four dependent measures. In this study, there was no main effect of genotype on the individual antisocial behaviors and on the composite index of antisocial behavior. However, there was a main effect of childhood abuse on both the individual antisocial behaviors and the composite index as well as interaction of genotype and abuse for both the individual antisocial behaviors and the composite index. Individuals with both the low activity genotype and severe maltreatment/abuse were more at risk for antisocial behaviors than those with both the high activity allele and severe maltreatment and abuse. An additional intriguing finding of this study was that in a sample of girls with high MAOA activity, severe maltreatment did not increase the risk for adolescent conduct disorder. Since the report by Caspi and colleagues (Caspi et al., 2002), there have been at least 25 studies on the potential interaction of these MAOA genotypes, abuse, and antisocial behaviors (Gunter, Vaughn, & Philibert, 2010). Most, but certainly not all, studies have replicated the initial findings of Caspi and colleagues. However, a metaanalysis of five of these studies was consistent with an MAOA by maltreatment interaction for antisocial behavior (Kim-Cohen et al., 2006). The criteria for choosing the five studies in the metaanalysis were: (a) study published in a peer-reviewed journal, (b) included data on number of repeats of 30bp sequence in MAOA promoter, (c) included a measure of severe maltreatment known to have a main effect on the dependent variable, and (d) sampled from a nonclinical population. Such genotypes by environment interactions have the potential to identify biological mechanisms mediating the effects of environment on behavioral development. Here are some issues that need to be considered in attempting to identify the biological mechanisms mediating the effects of environment on

behavioral development from genotype by environment interaction studies. In vitro and in vivo transcription levels of the 3and 4-repeat allele of MAOA may not be the same. In fact, there was no difference in MAOA activity for the 3- and 4-repeat alleles in adult male cortex, basal ganglia, thalamus, or pons as measured by positron emission tomography with [11 C] clorgyline (Fowler et al., 2007). Similarly, there was no association of MAOA promoter polymorphism with MAOA activity in postmortem cortical tissue (Balciuniene, Emilsson, Oreland Pettersson, & Jazin, 2002). Also, allele-specific expression studies of postmortem frontal cortex, temporal cortex, occipital cortex, and cerebellum in mature females did not find any association of the MAOA promoter polymorphisms with MAOA transcription levels (Cirulli & Goldstein, 2007). There are three conclusions from these studies. First, the effect of the polymorphism in the MAOA promoter on its transcription depends on the cellular environment. Second, a cellular environment supporting effects of the MAOA promoter polymorphisms on MAOA transcription levels are found in some transfected cell lines but not in many regions of adult brain. Third, since there are behavioral effects of the promoter polymorphisms on MAOA, it is likely that differences in MAOA activity for the 3- and 4-repeat alleles occur in other regions of adult brain or occur in some brain regions in fetuses or children rather than in adults. In mice and in humans, null mutants of MAOA have effects on urinary or CSF levels of serotonin, dopamine, and norepinephrine metabolites (5HIAA, HVA, and MHPG, respectively). If adult males with the 3- and 4-repeat alleles of MAOA differed in one or more of these, it would indicate differential transcription and activity of MAOA in one or more regions of the adult nervous system. In two studies, there are higher CSF levels of HVA but not 5HIAA or MHPG in adult males with the 4-repeat allele than in those with the 3-repeat allele (Ducci et al., 2008; Zalsman et al., 2005). These findings are consistent with (a) higher MAOA transcription and activity for the 4repeat alleles than for the 3-repeat alleles in dopaminergic neurons of adult males, and (b) the effects on antisocial behavior of MAOA polymorphism by maltreatment interaction being mediated by dopaminergic rather than serotonergic or adrenergic interactions. This is similar to how the knockout of the MAOA genes affects offense behaviors in mice via dopamine neurotransmission. In humans, there are MRI and fMRI studies of normal (no maltreatment) individuals with 3-, 3.5-, 4-, and 5-repeat alleles of the MAOA gene (Buckholtz &

Genes, Environment, and Behavioral Development

Meyer-Lindenberg, 2008, Meyer-Lindenberg et al., 2006). The 3.5- and 4-repeat alleles had smaller cingulate cortex, amygdala, and hypothalamus than those with the 3-repeat allele. They also had greater activation in the amygdala to emotional stimuli, and a stronger functional connection between the prefrontal cortex and the amygdala. These may be developmental or functional consequences of the difference in dopaminergic metabolism in subjects with the 3 versus 3.5/4 repeats. There is also a promoter polymorphism in the MAOA gene of rhesus monkeys (Newman et al., 2005). There is an 18 bp sequence in this MAOA promoter with 5, 6, or 7 repeats. In human neuroblastoma cells, those with 5 and 6 repeats have more MAOA transcription than those with the 7 repeats. Monkeys were either mother- or peer-reared. For mother-reared monkeys, those with the 7-repeat allele showed more food competition aggression and social aggression than those with the 5-repeat allele. The reverse pattern was seen for peer-reared monkeys. In both humans and monkeys, there is a genotype by environment interaction with effects on behavior. In both, there are multiple alleles for a repeat sequence in the promoter with effects of this repeat on transcription in cell line. For both, it is possible that an increase in presynaptic dopamine in the low-activity variants is a critical step in the development of brain structure and function, putting an individual at risk for effects of adverse environments on behavior. In the monkeys, this behavior is clearly aggression. In humans, the trait is better characterized as antisocial behavior that may have an aggressive component. 5HTT, Stress, and Depression A polymorphism in the promoter of the human 5HTT gene interacts with both childhood maltreatment and with life stress events to affect depression (Caspi et al., 2003). A 20–23bp sequence in the promoter is repeated with 14 (s or short allele) or 16 (l or long allele) times. This is the genotypic difference. In this study, one of the environmental variables was maltreatment as described for the study by Caspi et al. (2002) with individuals classified again as having no maltreatment, probable maltreatment, and severe maltreatment. The other environmental variable was the number of stressful life events from 21 to 26. Stressful employment, financial, housing, health, and relationship events were included. The dependent variables were self-reported depression symptoms, informant reports of depression, probability of major depressive episode, and probability of suicidal

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ideation/attempts. There was no main effect of genotype on any of these. But there was a main effect of number of stressful life events and of childhood maltreatment on each of these. Also, there was a genotype by environment interaction for each of these. Individuals with ss or sl genotype and more than four stressful life events or severe maltreatment/abuse had higher scores for each of the measures of depression. Since the original report by Caspi et al. (2003) there have been more than 40 studies of these genotypes, stressors, and depression. Most but not all have replicated the original findings of Caspi et al. (2003). Also, it has recently been suggested that convergent evidence approaches support an interaction of 5HTT genotype and stress on depression (Caspi et al., 2003; Hariri, Holmes, Uher, & Moffitt, 2010; Wankerl, W¨ust, & Otte, 2010). The convergent evidence comes from the G by E replications in humans, effects of the 5HTT polymorphism on human brain, 5HTT polymorphism and stress in nonhuman primates, and 5HTT knockouts and stress in mice and rats. In lymphoblastoma cell lines, the s allele is associated with lower transcription of the 5HTT gene, lower 5HTT protein concentration, and lower serotonin uptake than is the l allele. This may mean that there is less transporter in the presynaptic terminal of serotonin neurons for the ss than the sl or ll genotypes, and that there is a higher level of serotonin in the synaptic space for ss than sl or ll genotypes. However, transcription of the 5HTT gene is the same for both s and l alleles in the postmortem pons of adult humans. The pons contains the serotonergic neurons of the dorsal and median raphe (Lim, Papp, Pinsonnealt, Sad´e, & Saffen, 2006). The dorsal and median raphe are the primary sites for synthesis of the serotonin transporter. Consequently, it may be that in adults there is no effect of the s versus l allele on the serotonin transporter or on reuptake of serotonin into the presynaptic neuron. But there may still be developmental effects on the brain of the 5HTT polymorphism (Gaspar, Cases, & Maroteaux, 2003). Healthy human carriers of the s allele have smaller gray matter volume of the anterior cingulate cortex and amygdala than ll homozygotes (Pezawas et al., 2005). There is also a promoter polymorphism in the 5HTT gene of rhesus monkeys (Bennett et al., 2002). There is a 21 bp sequence in this promoter that occurs in one or two copies. In cell lines, those with 42 bp (l allele) have more 5HTT transcription than those with the 21 bp (s allele). For peer-reared monkeys, those with the s/l genotype had higher ACTH levels in response to a stressor than those with the s/s genotype (Barr et al., 2004). For

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mother-reared monkeys, the ACTH response to a stressor was the same for s/l and l/l genotypes. In mice, the 5HTT genotype moderates the effect of maternal behavior on anxiety and depression related behaviors (Carola et al., 2008). F1 females had either C57BL6 or BALB/c mothers. Females with C57BL6 mothers lick their own progeny more from day 1 to day 13 postpartum than females with BALB/c mothers. There was no effect of this treatment on behaviors of wildtype mice in an open field test, elevated plus maze test, and tail suspension test. In contrast, there were effects of this treatment on behaviors in these tests of mice heterozygous for the null mutant of 5HTT. Heterozygotes that had been licked more by their mothers spent more time in the center of the open field, spent more time in the open arms of the elevated plus maze, and had longer latency to immobility in the tail suspension test than those that had been licked less. There was also less binding of GABA to GABAA receptors in the amygdala, decreased serotonin turnover in the hippocampus and increased brain-derived neurotrophic factor mRNA in the hippocampus of knockout heterozygotes whose mothers spent less time licking them. In humans, monkeys, and mice, there is a genotype by environment interaction with effect on behavior. In humans and monkeys, there are short and long alleles for sequences in the promoter of 5HTT with effects on transcription in cell lines and perhaps at some time in development. There is also a null mutant of 5HTT in mice. Wild-type and mice heterozygous for this null mutant differ in 5HTT activity. For all of these there are effects on brain structure and function that interact with stressful environments with effects on depression or depressionrelated behaviors. Risk Versus Plasticity Genotypes It has been proposed that the 5HTT, MAOA, and other genetic variants are plasticity rather than vulnerability genes (Belsky et al., 2009; Dick et al., 2011). Such genes increase vulnerability to negative effects of risky environments and increase the beneficial effects of supportive environments. Plasticity genes would have statistical crossover. In vulnerable environments, one allele of a gene would be associated with high values of a trait and the other allele with low values of the trait. In supportive environments, the effects of the alleles would be reversed. Two examples of this are: (1) interaction effects on depression of 5HTT genotypes and being caregiver or not of an Alzheimer’s patient (Belsky et al., 2009), and (2) interaction effects on adolescent externalizing behavior

of CHRMS (cholinergic muscariic 2 receptor) genotypes and parental monitoring (Dick et al., 2011). Summary It is obvious now that there are genotype by environment effects on behavior in animals and humans. These have two implications for the study of behavior. On the one hand, these have small effects on variance in a behavioral phenotype. On the other hand, they have the potential to identify the biological bases for environmental effects on behavior. There are at least two ways that such interactions could occur. In the first, the gene has a developmental effect on neural or biological systems and the environment has its effects on behavior via this neural or biological system. Different outcomes occur for behavior because the neural or biological phenotypes differ. This may be what occurs in the genotype and environment interactions described in this section for the 5HTT and MAOA polymorphisms. In the second, the environment acts on a gene’s transcription or translation. Here genotype by environment interactions occur because the genotypes differ based on whether or to what degree an environmental variable affects the gene’s transcription or translation.

Epigenetics Gene Regulation and Behavior (the Encoded and the Expressed Genomes) Individuals have both an encoded and an expressed genotype. The encoded genotype is the inherited DNA sequence. The expressed genotype is the RNA transcripts from the encoded genotype. Just as cells in the body have the same encoded genotype but different expressed genotypes, so can individuals have the same encoded but different expressed genotypes. Differences in expressed genotype are due to interactions of transcription factors with response elements of a gene that affect its transcription. There are two ways that the environment or experience can have transynaptic effects on the transcription of one or more genes. On the one hand, experience can transynaptically activate a transcription factor and thereby influence a gene’s transcription. Here there is no change to the ability of the response element to bind the transcription factor. On the other hand, experience can modify the response elements ability to bind the transcription factor. Here there is a change in the transcription factor’s accessibility to the response element. This involves either chemical changes to the DNA of the response element

Genes, Environment, and Behavioral Development

and/or to the histone proteins associated with the DNA. The changes in the DNA involve either methylation or demethylation of cytosines in the promoter and in the associated histones involving acetylation or deacetylation of lysine. These chemical changes are said to be epigenetic in that they are functional changes in the genome without changes in the DNA sequence. Rats, Maternal Behavior, Glucocorticoid Receptor, and Stress Some mother rats lick and groom their pups more than others. If this occurs during the first 7 days postpartum, the pups of high licking and grooming mothers have as adults lower levels of the mRNA for CRH (corticotropin releasing hormone) in neurons of the hypothalamus and higher levels of mRNA for glucocorticoid receptor (GR) in neurons of the hippocampus than do the pups of low licking and grooming mothers (Meaney & Szyf, 2005). This acts to dampen hypothalamic pituitary adrenal (HPA) axis response to stress. Subsequent studies focused on the mechanism for the effect of maternal licking and grooming of pups on adult levels of mRNA for GR in hippocampal neurons. It has been shown that tactile stimulation from the mother increases serotonin turnover in the hippocampus, and that this activated 5HT7 receptors on hippocampal neurons. This in turn induces the synthesis of the transcription factors, NGF1-A and CBP. These bind to the promoter of the GR gene. Subsequent demethylating the promoter DNA and acetylating of associated histones has long-term effects on the transcription of the GR genes in adults (Bagot & Meaney, 2010). Humans, Abuse, and Glucorticoid Receptor There is a similar effect in humans of early life experience on hippocampal GR mRNA and demethylation of the GR promoter (McGowan et al., 2009). In postmortem hippocampus from suicide victims, there was less GR mRNA and higher methylation of cytosines in the GR promoter of individuals with childhood abuse and neglect than in those with no childhood abuse or neglect. In vitro, this methylation of cytosines in the human GR promoter was associated with lowered binding of the NGF-1A transcription factor to the human GR promoter. Rats, Maternal Behavior, and Estrogen Receptor The daughters of high licking and grooming mothers show high licking and grooming of their own pups; conversely, daughters of low licking and grooming mothers show low licking and grooming of their pups (Champagne, 2008).

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Cross-fostering studies have demonstrated that this trait is transmitted to daughters via nongenetic mechanisms. The daughters of high licking and grooming mothers have more mRNA for ERα in the medial preoptic area of the hypothalamus than daughters of low licking and grooming mothers (Champangne et al., 2006). Also, levels of cytosine methylation across the promoter of the ERα gene was higher in the daughters of low licking and grooming mothers than of high licking and grooming mothers. Epigenetics and Learning DNA methylation in the adult forebrain may also have a role in learning and memory (Korzus, 2010). Dnmt1 and Dnmt3a are methyl transferases, and they are expressed in postmitotic neurons. There are effects on learning and memory in double conditional knockouts restricted to the postnatal forebrain (Feng et al., 2010). The double conditional knockout has impaired spatial learning and memory in the Morris water maze test and impaired memory consolidation in contextual fear conditioning tests. Summary Epigenetic regulation may have a role in effects of many kinds of experience on behavior in animals and humans. It may also account for behavioral differences between genetically identical individuals such as monozygous twins or mice from an inbred strain. There is also much speculation about the role of epigenetic regulation in the origin and treatment of psychiatric disorders (Bredy, & 2010; Tsanova, Renthal, Kumar, & Nestler, 2007). Further progress in this area will be made as the epigenome is mapped for mice, rats, other animals, and humans. The epigenome consists of the parts of the genome and associated histones that can be altered by experience. This mapping project is especially important as specific experiences may act on more than one gene. Maternal care in rats has recently been shown to affect methylation of DNA and acetylation of histones across a 7 million base pair segment of rat Chromosome 18 (McGowan et al., 2011). There is also speculation that epigenetic changes could be transmitted from parents to offspring (Crews, 2010; Jirtle & Skinner, 2007; Nadeau, 2009). In essence, this requires that somatic and gametic cells change in a parallel fashion. If this is ever validated, it would support a Lamarkian compliment to Mendelian inheritance and fundamentally affect our views on behavioral inheritance, development, and evolution.

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FUTURE DIRECTIONS The following extract is taken from the previous edition of this chapter (Maxson, 2003): The completion of the respective genome projects in nematodes, fruit flies, mice, and humans will make it possible to identify all the protein coding genes of these species as well as where and when the genes are transcribed, and the new protein initiative will eventually identify the structural conformation as well as metabolic or cellular function of each protein. This will greatly ease the task of identifying all the genes that can and do cause a behavior to vary in these four species, as well as that of tracing the pathways from gene to behavior. The great challenge will then be to understand how genes interact with each other, how they interact with the environment in the development and expression of behaviors, and how they relate to behavioral evolution. The study of the genetics of behaviors in animals can and should be for more than just the development of models relevant to human behaviors. The genetics of animal behaviors should also be researched in order to discover general principles relating genes to behavior across animal species and to have a comparative genetics of adaptive behaviors within related species. For this, there will need to be genome projects in other taxonomic groups; such work is already taking place on bees and other insects, many farm animals, domestic dogs, domestic cats, other rodents, and many primates; I believe that this process represents the future of behavior genetics.

As can be seen in the present review, much progress has been made in achieving the past goals for behavior genetics. Regardless, what was stated in 2003 remains a vision for the future of behavior genetics. As envisioned by Ginsburg (1958), a comparative approach and an evolutionary context is very much part of the vision for genetics as a tool in the study of behavior.

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CHAPTER 2

Evolutionary Psychology RUSSIL DURRANT AND BRUCE J. ELLIS

LEVELS OF EXPLANATION IN EVOLUTIONARY PSYCHOLOGY 27 THE METATHEORY LEVEL OF ANALYSIS 28 METATHEORETICAL ASSUMPTIONS THAT ARE CONSENSUALLY HELD BY EVOLUTIONARY SCIENTISTS 28 SPECIAL METATHEORETICAL ASSUMPTIONS OF EVOLUTIONARY PSYCHOLOGY 31

THE MIDDLE-LEVEL THEORY LEVEL OF ANALYSIS 34 THE HYPOTHESIS LEVEL OF ANALYSIS 38 THE PREDICTION LEVEL OF ANALYSIS 40 THE FUTURE OF EVOLUTIONARY PSYCHOLOGY 43 REFERENCES 48

Evolutionary psychology is the application of the principles and knowledge of evolutionary biology to psychological theory and research. Its central assumption is that the human brain is composed of a large number of specialized mechanisms that were shaped by natural selection over vast periods of time to solve the recurrent informationprocessing problems faced by our ancestors. These problems include such things as choosing which foods to eat, negotiating social hierarchies, dividing investment among offspring, and selecting mates. The field of evolutionary psychology focuses on identifying these information-processing problems, developing models of the brain-mind mechanisms that may have evolved to solve them, and testing these models in research (Buss, 1995, 2008; Tooby & Cosmides, 2005). The field of evolutionary psychology has emerged dramatically over the past 25 years, as indicated by the dramatic growth in the number of empirical and theoretical articles in the area (Table 2.1). These articles extend into all branches of psychology—from cognitive psychology (e.g., Todd, Hertwig, & Hoffrage, 2005) to developmental psychology (e.g., Ellis & Bjorklund, 2005), abnormal psychology (e.g., Nesse, 2005), social psychology (e.g., Kenrick, Maner, & Li, 2005), personality psychology (e.g., Nettle, 2006), motivation-emotion (e.g., De Waal, 2008), industrial-organizational psychology (e.g., Colarelli, 1998), and forensic psychology (Duntley & Shackelford, 2008).

In this chapter we provide an introduction to the field of evolutionary psychology. We describe the methodology that evolutionary psychologists use to explain human cognition and behavior. This description begins at the broadest level with a review of the basic, guiding assumptions that are employed by evolutionary psychologists. We then show how evolutionary psychologists apply these assumptions to develop more specific theoretical models that are tested in research. We use examples of sex and mating to demonstrate how evolutionary psychological theories are developed and tested.

TABLE 2.1 Growth of Publications in the Area of Evolutionary Psychology, as Indexed by the PsycINFO Database Years of Publication 1985–1989

Number of Total Publicationsa 32

Number of Journal Articles 6

1990–1994

92

31

1995–1999

225

110

2000–2004

690

394

2005–2009

1541

998

a Number

of articles, books, book chapters, book reviews, and dissertations in the PsycINFO database that include either the phrase evolutionary psychology or evolutionary psychological in the title, in the abstract, or as a keyword. All articles from the Journal of Evolutionary Psychology, which is a psychoanalytic journal, were excluded.

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Levels of Explanation in Evolutionary Psychology

LEVELS OF EXPLANATION IN EVOLUTIONARY PSYCHOLOGY Why do siblings fight with each other for parental attention? Why are men more likely than women to kill sexual rivals? Why are women most likely to have extramarital sex when they are ovulating? To address such questions, evolutionary psychologists employ multiple levels of explanation ranging from broad metatheoretical

assumptions, to more specific middle-level theories, to actual hypotheses and predictions that are tested in research (Buss, 1995; Ketelaar & Ellis, 2000). These levels of explanation are ordered in a hierarchy (see Figure 2.1) and constitute the methodology that evolutionary psychologists use to address questions about human nature. At the top of the hierarchy are the basic metatheoretical assumptions of modern evolutionary theory. This set of guiding assumptions, which together are referred to

Evolutionary Psychological Metatheory

Basic metatheoretical assumptions of modern evolutionary theory Special metatheoretical assumptions of evolutionary psychology

Middle-Level Theories

Attachment theory (Bowlby, 1969)

Good genes sexual selection theory

Parental investment theory (Trivers, 1972)

Hypotheses

Individuals who more fully display traits indicative of high genetic quality should be healthier and in better condition than should conspecifics who display these traits less fully

Males who display indicators of high genetic quality should have more sexual partners and more offspring

Around the time of ovulation women should express the strongest preference for males with good genes

Specific Predictions

More symmetrical individuals should have better mental and physical health, better immune system functioning, and lower parasite loads than should less symmetrical individuals

Figure 2.1

More symmetrical men should have more lifetime sexual partners and more extrapair sexual partners than should less symmetrical men

The hierarchical structure of evolutionary psychological explanations

Source: Adapted from Buss, 1995.

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Women’s preference for symmetrical men should be heightened around the time of ovulation when women are most fertile

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Evolutionary Psychology

as evolutionary metatheory, provide the foundation that evolutionary scientists use to build more specific theoretical models. We begin by describing (a) the primary set of metatheoretical assumptions that are consensually held by evolutionary scientists and (b) the special set of metatheoretical assumptions that distinguish evolutionary psychology. We use the term evolutionary psychological metatheory to refer inclusively to this primary and special set of assumptions together. As shown in Figure 2.1, at the next level down in the hierarchy, just below evolutionary psychological metatheory, are middle-level evolutionary theories. These theories elaborate the basic metatheoretical assumptions into a particular psychological domain such as mating or cooperation. In this chapter we consider two related middle-level evolutionary theories—parental investment theory and good genes sexual selection theory—each of which applies the assumptions of evolutionary psychological metatheory to the question of reproductive strategies. In different ways these middle-level theories attempt to explain differences between the sexes as well as variation within each sex in physical and psychological adaptations for mating and parenting. At the next level down are the actual hypotheses and predictions that are drawn from middle-level evolutionary theories (Figure 2.1). A hypothesis is a general statement about the state of the world that one would expect to observe if the theory from which it was generated were in fact true. Predictions are explicit, testable instantiations of hypotheses. We conclude this chapter with an evaluation of hypotheses and specific predictions about sexual behavior that have been derived from good genes sexual selection theory. Special attention is paid to comparison of human and nonhuman animal literatures.

THE METATHEORY LEVEL OF ANALYSIS Scientists typically rely on basic (although usually implicit) metatheoretical assumptions when they construct and evaluate theories. Evolutionary psychologists have often called on behavioral scientists to make explicit their basic assumptions about the origins and structure of the mind. Metatheoretical assumptions shape how scientists generate, develop, and test middle-level theories and their derivative hypotheses and predictions (Ketelaar & Ellis, 2000). These basic assumptions are often not directly tested after they have been empirically established. Instead they are used as a starting point for further theory and research. Newton’s laws of motion form the metatheory

for classical mechanics, the principles of gradualism and plate tectonics provide a metatheory for geology, and the principles of adaptation through natural selection provide a metatheory for biology. A metatheory operates like a map of a challenging conceptual terrain. It specifies both the landmarks and the boundaries of that terrain, suggesting which features are consistent and which are inconsistent with the core logic of the metatheory. In this way a metatheory provides a set of powerful methodological heuristics: “Some tell us what paths to avoid (negative heuristic), and others what paths to pursue (positive heuristic)” (Lakatos, 1970, p. 47). In the hands of a skilled researcher, a metatheory “provides a guide and prevents certain kinds of errors, raises suspicions of certain explanations or observations, suggests lines of research to be followed, and provides a sound criterion for recognizing significant observations on natural phenomena” (Lloyd, 1979, p. 18). The ultimate contribution of a metatheory is that it synthesizes middle-level theories, allowing the empirical results of a variety of different theory-driven research programs to be explicated within a broader metatheoretical framework. This facilitates a systematic cumulation of knowledge and progression toward a coherent big picture of the subject matter (Ketelaar & Ellis, 2000).

METATHEORETICAL ASSUMPTIONS THAT ARE CONSENSUALLY HELD BY EVOLUTIONARY SCIENTISTS The general principles of genetical evolution drawn from modern evolutionary theory, as outlined by W. D. Hamilton (1964) and instantiated in more contemporary socalled selfish gene theories of genetic evolution via natural and sexual selection, provide a set of core metatheoretical assumptions for evolutionary scientists. Inclusive fitness theory conceptualizes genes or individuals as the units of selection (see Dawkins, 1976; Hamilton, 1964; Williams, 1966). In contrast, “multilevel selection theory” is based on the premise that natural selection is a hierarchical process that can operate at many levels, including genes, individuals, groups within species, or even multispecies ecosystems. Thus, multilevel selection theory is conceptualized as an elaboration of inclusive fitness theory (adding the concept of group-level adaptation) rather than an alternative to it (Wilson & Sober, 1994; Wilson, Van Vugt, & O’Gorman, 2008). Although multilevel selection theory is now generally accepted by evolutionary psychologists (Gangestad & Simpson, 2007), and it may be important in

Metatheoretical Assumptions That Are Consensually Held by Evolutionary Scientists

understanding aspects of human evolution (Wilson, Van Vugt, & O’Gorman, 2008) it is a perspective that has yet to generate a significant body of empirical research. Thus, this review of basic metatheoretical assumptions only focuses on inclusive fitness theory. Natural Selection During his journey around the coastline of South America aboard the HMS Beagle, Charles Darwin was intrigued by the sheer diversity of animal and plant species found in the tropics, by the way that similar species were grouped together geographically, and by their apparent fit to local ecological conditions. Although the idea of biological evolution had been around for some time, what had been missing was an explanation of how evolution occurred—that is, what had been missing was an account of the mechanisms responsible for evolutionary change. Darwin’s mechanism, which he labeled natural selection, served to explain many of the puzzling facts about the biological world: Why were there so many species? Why are current species so apparently similar in many respects both to each other and to extinct species? Why do organisms have the specific characteristics that they do? The idea of natural selection is both elegant and simple, and can be neatly encapsulated as the result of the operation of three general principles: (1) phenotypic variation, (2) differential fitness, and (3) heritability. As is readily apparent when we look around the biological world, organisms of the same species vary in the characteristics that they possess; that is, they have slightly different phenotypes. Some of these differences found among members of a given species will result in differences in fitness —that is, some members of the species will be more likely to survive and reproduce than will others as a result of the specific characteristics that they possess. For evolution to occur, however, these individual differences must be heritable —that is, they must be reliably passed on from parents to their offspring through biological mechanisms of inheritance (e.g., via shared genes, the passage of epigenetic marks through the germline, the passage of maternal RNA molecules into the embryo). Over time, the characteristics of a population of organisms will change as heritable traits that enhance fitness will become more prevalent at the expense of less favorable variations. Several points are important to note. First, natural selection shapes not only the physical characteristics of organisms, but also their behavioral and cognitive traits. The shift to bipedalism in hominid evolution, for instance, was not simply a matter of changes in the anatomy of early

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hominids; it was also the result of changes in behavioral proclivities and in the complex neural programs dedicated to the balance and coordination required for upright walking. Second, although the idea of natural selection is sometimes encapsulated in the slogan the survival of the fittest, ultimately it is reproductive fitness that counts. It doesn’t matter how well an organism is able to survive. If it fails to pass on its heritable characteristics, then it is an evolutionary dead end, and the traits responsible for its enhanced survival abilities will not be represented in subsequent generations. Adaptation Natural selection is the primary process that is responsible for evolutionary change over times as more favorable variants are retained and less favorable ones are rejected (Darwin, 1859). Through this filtering process, natural selection produces small incremental modifications in existing phenotypes, leading to an accumulation of characteristics that are organized to enhance survival and reproductive success. These characteristics that are produced by natural selection are termed adaptations. Adaptations are inherited and reliably developing characteristics of species that have been selected for because of their causal role in enhancing the survival and reproductive success of the individuals that possess them (see Buss, Haselton, Shackelford, Bleske, & Wakefield, 1998; Sterelny & Griffiths, 1999; Williams, 1966). Adaptations have biological functions. The immune system functions to protect organisms from microbial invasion, the heart functions as a blood pump, and the cryptic coloring of many insects has the function of preventing their detection by predators. The core idea of evolutionary psychology is that many psychological characteristics are adaptations—just as many physical characteristics are—and that the principles of evolutionary biology that are used to explain our bodies are equally applicable to our minds. Thus, various evolutionary psychological research programs have investigated psychological mechanisms (for mate selection, fear of snakes, face recognition, natural language, sexual jealousy, and so on) as biological adaptations that were selected for because of the role they played in promoting reproductive success in ancestral environments. It is worth noting, however, that natural selection is not the only causal process responsible for evolutionary change (e.g., Gould & Lewontin, 1979). Traits may also become fixated in a population by the process of genetic drift, whereby neutral or even deleterious characteristics

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Evolutionary Psychology

become more prevalent due to chance factors. This may occur in small populations because the fittest individuals may turn out—due to random events—not to be the ones with the greatest reproductive success. It does not matter how fit you are if you drown in a flood before you get a chance to reproduce. Moreover, some traits may become fixated in a population not because they enhance reproductive success, but because they are genetically or developmentally yoked to adaptations that do. For example, the modified wrist bone of the panda (its “thumb”) seems to be an adaptation for manipulating bamboo, but the genes responsible for this adaptation also direct the enlarged growth of the corresponding bone in the panda’s foot, a feature that serves no function at all (Gould, 1980). There is much debate among evolutionary biologists and philosophers of biology regarding the relative importance of different evolutionary processes (see Sterelny & Griffiths, 1999). The details of these disputes, however, need not concern us here. What is important to note is that not all of the products of evolution will be biological adaptations with evolved functions. The evolutionary process also results in by-products of adaptations, as well as a residue of noise (Buss et al., 1998). Examples of by-products are legion. The sound that hearts make when they beat, the white color of bones, and the human chin are all nonfunctional by-products of natural selection. In addition, random variation in traits—as long as this variation is selectively neutral (neither enhancing nor reducing biological fitness)—can also be maintained as residual noise in organisms. Demarcating the different products of evolution is an especially important task for evolutionary psychologists. It has often been suggested that many of the important phenomena that psychologists study—for example, reading, writing, religion—are by-products of adaptations rather than adaptations themselves (e.g., Gould, 1991). Of course, even by-products can be furnished with evolutionary explanations in terms of the adaptations to which they are connected (Tooby & Cosmides, 1992). Thus, for example, the whiteness of bones is a by-product of the color of calcium salts, which give bones their hardness and rigidity; the chin is a by-product of two growth fields; and reading and writing are by-products (in part) of the evolved mechanisms underlying human language. The important question is how to distinguish adaptations from nonadaptations in the biological world. Because we cannot reverse time and observe natural selection shaping adaptations, we must make inferences about evolutionary history based on the nature of the traits we see today. A variety of methods can (and should) be

employed to identify adaptations (Andrews, Gangestad, & Matthews, 2002; Simpson & Campbell, 2005). Evolutionary psychologists, drawing on the work of George Williams (1966), typically emphasize the importance of special design features such as economy, efficiency, complexity, precision, specialization, reliability, and functionality for identifying adaptations (e.g., Buss et al., 1998; Tooby & Cosmides, 1990). One hallmark that a trait is the product of natural selection is that it demonstrates adaptive complexity —that is, the trait is composed of a number of interrelated parts or systems that operate in concert to generate effects that serve specific functions (Pinker, 1997). Many traits, however, may be difficult to identify as adaptations. Furthermore, there are often disputes about just what function some trait has evolved to serve, even if one can be reasonably sure that it is the product of natural selection. In adjudicating between alternative evolutionary hypotheses, one can follow the same sort of strategies that are employed when comparing alternative explanations in any domain in science—that is, one should favor the theory or hypothesis that best explains the evidence at hand (Andrews, Gangestad, & Matthews, 2002; Durrant & Haig, 2001) and that generates novel hypotheses that lead to new knowledge (Ketelaar & Ellis, 2000). Sexual Selection Not all adaptations can be conceptualized as adaptations for survival per se. As Darwin (1871) clearly recognized, many of the interesting features that plants and animals possess, such as the gaudy plumage and elaborate songs of many male birds, serve no obvious survival functions. In fact, if anything, such traits are likely to reduce survival prospects by attracting predators, impeding movement, and so on. Darwin’s explanation for such characteristics was that they were the product of a process that he labeled sexual selection. This kind of selection arises not from a struggle to survive, but rather from the competition that arises over mates and mating (Andersson, 1994; Andersson & Iwasa, 1996). If—for whatever reason—having elongated tail feathers or neon blue breast plumage enables one to attract more mates, then such traits will increase reproductive success. Moreover, to the extent that such traits are also heritable, they will be likely to spread in the population, even if they might diminish survival prospects. Although there is some debate about how best to conceptualize the relationship between natural and sexual selection, sexual selection is most commonly considered a component or special case of natural selection associated

Special Metatheoretical Assumptions of Evolutionary Psychology

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with mate choice and mating (Clutton-Brock, 2004). This reflects the fact that differential fitness concerns differences in both survival and reproduction. Whereas the general processes underlying natural and sexual selection are the same (variation, fitness, heritability), the products of natural and sexual selection can look quite different. The later parts of this chapter review sexual selection theory and some of the exciting research it has generated on human mating behavior. To summarize, we have introduced the ideas of natural and sexual selection and shown how these processes generate adaptations, by-products, and noise. We have also discussed ways in which adaptations can be distinguished from nonadaptations. It is now time to consider an important theoretical advance in evolutionary theorizing that occurred in the 1960s—inclusive fitness theory— that changed the way biologists (and psychologists) think about the nature of evolution and natural selection. Inclusive fitness theory is the modern instantiation of Darwin’s theory of adaptation through natural and sexual selection.

individuals carrying the genes. Thus, the genetic code for a trait that reduces personal reproductive success can be selected for if the trait, on average, leads to more copies of the genetic code in the population. A genetic code for altruism, therefore, can spread through kin selection if (a) it causes an organism to help close relatives to reproduce and (b) the cost to the organism’s own reproduction is offset by the reproductive benefit to those relatives (discounted by the probability that the relatives who receive the benefit have inherited the same genetic code from a common ancestor). For example, a squirrel who acts as a sentinel and emits loud alarm calls in the presence of a predator may reduce its own survival chances by directing the predator’s attention to itself; however, the genes that are implicated in the development of alarm-calling behavior can spread if they are present in the group of close relatives who are benefited by the alarm calling.

Inclusive Fitness Theory

In addition to employing inclusive fitness theory, evolutionary psychologists endorse a number of special metatheoretical assumptions concerning how to apply inclusive fitness theory to human psychological processes. In particular, evolutionary psychologists argue that we should primarily be concerned with how natural and sexual selection have shaped psychological mechanisms in our species; that a multiplicity of such mechanisms will exist in the human mind; and that they will have evolved to solve specific adaptive problems encountered in ancestral environments. Although these general points also apply to other species, they are perhaps especially pertinent in a human context and they have received much attention from evolutionary psychologists. We consider these special metatheoretical assumptions, in turn, in the following discussion.

Who are adaptations good for? Although the answer may seem obvious—that they are good for the organisms possessing the adaptations—this answer is only partially correct; it fails to account for the perplexing problem of altruism. As Darwin puzzled, how could behaviors evolve that conferred advantage to other organisms at the expense of the principle organism that performed the behaviors? Surely such acts of generosity would be eliminated by natural selection because they decreased rather than increased the individual’s chances of survival and reproduction. The solution to this thorny evolutionary problem was hinted at by J. B. S. Haldane, who, when he was asked if he would lay down his life for his brother, replied, “No, but I would for two brothers or eight cousins” (cited in Pinker, 1997, p. 400). Haldane’s quip reflects the fact that we each share (on average) 50% of our genes with our full siblings and 12.5% of our genes with our first cousins. Thus, from the gene’s-eye point of view, it is just as advantageous to help two of our siblings to survive and reproduce as it is to help ourselves. This insight was formalized by W. D. Hamilton (1964) and has come to be known variously as Hamilton’s rule , selfish-gene theory (popularized by Dawkins, 1976), kin-selection theory, or inclusive fitness theory. The core idea of inclusive fitness theory is that evolution works by increasing copies of genes, not copies of the

SPECIAL METATHEORETICAL ASSUMPTIONS OF EVOLUTIONARY PSYCHOLOGY

Psychological Mechanisms as the Main Unit of Analysis Psychological adaptations, which govern mental and behavioral processes, are referred to by evolutionary psychologists as psychological mechanisms. Evolutionary psychologists emphasize that genes do not cause behavior and cognition directly. Rather, the transcribed products of genes actively participate in biological pathways that comprise psychological mechanisms, which then interact with environmental factors to produce a range of behavioral and cognitive outputs. Most research in

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evolutionary psychology focuses on identifying evolved psychological mechanisms because it is at this level where invariances occur (i.e., species-typical design). Indeed, evolutionary psychologists assert that there is a core set of universal psychological mechanisms that comprise our shared human nature (Tooby & Cosmides, 1992, 2005). To demonstrate the universal nature of our psychological mechanisms, a common rhetorical device used by evolutionary psychologists (e.g., Ellis, 1992; Symons, 1987) is to imagine that a heretofore unknown tribal people are suddenly discovered. Evolutionary psychologists are willing to make an array of specific predictions—in advance—about the behavior and cognition of this newly discovered people. These predictions concern criteria that determine sexual attractiveness, circumstances that lead to sexual arousal, taste preferences for sugar and fat, use of cheater detection procedures in social exchange, nepotistic bias in parental investment and child abuse, stages and timing of language development, sex differences in violence, different behavioral strategies for people high and low in dominance hierarchies, perceptual adaptations for entraining, tracking, and predicting animate motion, and so on. The only way that the behavior and cognition of an unknown people can be known in advance is if we share with those people a universal set of specific psychological mechanisms (and are exposed to species-typical environments during development; Bjorklund, Ellis, & Rosenberg, 2007). Buss (2008) defines an evolved psychological mechanism as a set of structures inside our heads that (a) exist in the form they do because they recurrently solved specific problems of survival and reproduction over evolutionary history; (b) are designed to take only certain kinds of information from the world as input; (c) process that information according to a specific set of rules and procedures; (d) generate output in terms of information to other psychological mechanisms and physiological activity or manifest behavior that is directed at solving specific adaptive problems (as specified by the input that brought the psychological mechanism on-line). Evolutionary psychologists assume that humans possess a large number of specific psychological mechanisms that are directed at solving specific adaptive problems. This assumption is commonly referred to as the domain specificity or modularity of mind. Domain Specificity of Psychological Mechanisms Evolutionary psychologists posit that the mind comprises a large number of content-saturated (domain-specific)

psychological mechanisms (e.g., Buss, 1995; Cosmides & Tooby, 1994; Pinker, 1997). Although evolutionary psychologists assert that the mind is not composed primarily of content-free (domain-general ) psychological mechanisms, it is likely that different mechanisms differ in their levels of specificity and that there are some higher-level executive mechanisms that function to integrate information across more specific lower-level mechanisms. The rationale behind the domain-specificity argument is fairly straightforward: What counts as adaptive behavior differs markedly from domain to domain. The sort of adaptive problems posed by food choice, mate choice, incest avoidance, and social exchange require different kinds of solutions. As Don Symons (1992) has pointed out, there is no such thing as a general solution because there is no such thing as a general problem. The psychological mechanisms underlying disgust and food aversions, for example, are useful in solving problems of food choice but not those of mate choice. If we used the same decision rules in both domains, we would end up with some very strange mates and very strange meals indeed. A clear analogy can be drawn with the functional division of labor in human physiology. Different organs have evolved to fulfill those functions efficiently, reliably, and economically: The heart pumps blood, the liver detoxifies poisons, the kidneys excrete urine, and so on. A super, all-purpose, domain general internal organ—heart, liver, kidney, spleen, and pancreas rolled into one—faces the impossible task of serving multiple, incompatible functions. Analogously, a super, all-purpose, domain-general brain-mind mechanism faces the impossible task of efficiently and reliably solving the plethora of behavioral problems encountered by humans in ancestral environments. Thus, neither an all-purpose physiological organ nor an all-purpose brain-mind mechanism is likely to evolve. Evolutionary psychologists argue that the human brain-mind instead contains domain-specific information processing rules and biases. We should also expect—in addition to whatever taxonomy of specialized mechanisms that is proposed for the human mind—that there are some domain-general processes as well. The mechanisms involved in classical and operant conditioning may be good candidates for such domain-general processes. However, even these domain-general processes appear to operate in different ways, depending on the context in question. As illustrated in a series of classic studies by Garcia and colleagues (e.g., Garcia & Koelling, 1966), rats are more likely to develop some (adaptively relevant) associations than they are others, such as that between food and nausea but not between buzzers and nausea. Similar prepared learning

Special Metatheoretical Assumptions of Evolutionary Psychology

biases have been demonstrated in monkeys (Mineka, 1992) and also in humans (Seligman & Hagar, 1972). For example, humans are overwhelmingly more likely to associate anxiety and fear with evolutionarily relevant threats such as snakes, spiders, social exclusion, and heights than with more dangerous but evolutionarily novel threats such as cars, guns, and power lines (Marks & Nesse, 1994). In sum, although some doubt remains over the nature and number of domain-specific psychological mechanisms that humans (and other animals) possess (Barrett & Kurzban, 2006), the core idea of specialized adaptive processes instantiated in psychological mechanisms remains central to evolutionary psychology (Confer et al., 2010). An approach to the human mind that highlights the importance of evolved domain-specific mechanisms can advance our understanding of human cognition by offering a theoretically guided taxonomy of mental processes—one that promises to better carve the mind at its natural joints. The Environment of Evolutionary Adaptedness The concept of biological adaptation is necessarily an historical one. When we claim that the thick insulating coat of the polar bear is an adaptation, we are claiming that possession of that trait advanced reproductive success in ancestral environments. All claims about adaptation are claims about the past because natural selection is a gradual, cumulative process. The polar bear’s thick coat arose through natural selection because it served to ward off the bitter-cold arctic weather during the polar bear’s evolutionary history. However, traits that served adaptive functions and thus were selected for in past environments may not still be adaptive in present or future environments. In a globally warmed near-future, for example, the polar bear’s lustrous pelt may become a handicap that reduces the fitness of its owner due to stress from overheating. In sum, when environments change, the conditions that proved advantageous to the evolution of a given trait may no longer exist; yet the trait often remains in place for some time because evolutionary change occurs slowly. Such vestigial traits are eventually weeded out by natural selection (if they consistently detract from fitness). The environment in which a given trait evolved is termed its environment of evolutionary adaptedness (EEA). The EEA for our species is sometimes loosely characterized as the Pleistocene—the 2-million-year period that our ancestors spent as hunter-gatherers prior to the emergence of agriculture some 10,000 years ago.

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The emphasis on the Pleistocene is perhaps reasonable given that many of the evolved human characteristics of interest to psychologists, such as language, theory of mind, sophisticated tool use, and culture, probably arose during this period. However, a number of qualifications are in order. First, the Pleistocene itself captures a large span of time, in which many changes in habitat, climate, and species composition took place. Second, there were a number of different hominid species in existence during this time period, each inhabiting its own specific ecological niche. Third, many of the adaptations that humans possess have their origins in time periods that substantially predate the Pleistocene era. For example, the mechanisms underlying human attachment and sociality have a long evolutionary history as part of our more general primate and mammalian heritage (Foley, 1996). Finally, some evolution has also occurred in the past 10,000 years, as is reflected in population differences in disease susceptibility, skin color, and so forth (Cochran & Harpending, 2009; Irons, 1998). Most important is that different adaptations will have different EEAs. Some, like language, are firmly anchored in approximately the past 2 million years; others, such as infant attachment, reflect a much lengthier evolutionary history (Hrdy, 1999). It is important, therefore, that we distinguish between the EEA of a species and the EEA of an adaptation. Although these two may overlap, they need not necessarily do so (Crawford, 1998). Tooby and Cosmides (1990) summarize these points clearly when they state that “the ‘environment of evolutionary adaptedness’ (EEA) is not a place or a habitat, or even a time period. Rather, it is a statistical composite of the adaptationrelevant properties of the ancestral environments encountered by members of ancestral populations, weighted by their frequency and fitness-consequences” (pp. 386–387). Delineating the specific features of the EEA for any given adaptation, then, requires an understanding of the evolutionary history of that trait (e.g., is it shared by other species, or is it unique?) and a detailed reconstruction of the relevant environmental features that were instrumental in its construction (Foley, 1996). It is not uncommon to encounter the idea that changes wrought by “civilization” over the past 10,000 years have radically changed our adaptive landscape as a species. After all, back on the Pleistocene savanna there were no fast-food outlets, plastic surgery, antibiotics, dating advertisements, jet airliners, and the like. Given such manifest changes in our environment and ways of living, one would expect much of human behavior to prove odd and maladaptive as psychological mechanisms that evolved in ancestral conditions struggle with the many

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new contingencies of the modern world. An assumption of evolutionary psychology, therefore, is that mismatches between modern environments and the EEA often result in dysfunctional behavior (such as overconsumption of chocolate ice cream, television soap operas, video games, and pornography). Real-life examples of this phenomenon are easy to find. For instance, the dopamine-mediated reward mechanisms found in the mesolimbic system in the brain evolved to provide a pleasurable reward in the presence of adaptively relevant stimuli like food or sex. In contemporary environments, however, these same mechanisms are subverted by the use of psychoactive drugs, such as cocaine and amphetamines, that deliver huge dollops of pleasurable reward in the absence of the adaptively relevant stimuli—often to the users’ detriment (Durrant, Adamson, Todd, & Sellman, 2009; Nesse & Berridge, 1997). To summarize, in this section we have outlined three special metatheoretical assumptions that evolutionary psychologists use in applying inclusive fitness theory to human cognition and behavior. First, the appropriate unit of analysis is typically considered to be at the level of evolved psychological mechanisms, which underlie behavioral output. Second, evolutionary psychologists posit that these mechanisms are both large in number and constitute specialized information processing rules that were designed by natural selection to solve specific adaptive problems encountered during human evolutionary history. Finally, these mechanisms have evolved in ancestral conditions and are characterized by specific EEAs, which may or may not differ in important respects from contemporary environments.

THE MIDDLE-LEVEL THEORY LEVEL OF ANALYSIS The metatheoretical assumptions employed by evolutionary psychologists are surrounded by a protective belt, so to speak, of auxiliary theories, hypotheses, and predictions (see Buss, 1995; Ketelaar & Ellis, 2000). A primary function of the protective belt is to provide an empirically verifiable means of linking metatheoretical assumptions to observable data. In essence, the protective belt serves as the problem-solving machinery of the metatheoretical research program because it is used to provide indirect evidence in support of the metatheory’s basic assumptions (Lakatos, 1970). The protective belt does more, however, than just protect the metatheoretical assumptions: It uses these assumptions to extend our knowledge

of particular domains. For example, a group of physicists who adopt a Newtonian metatheory may construct several competing middle-level theories concerning a particular physical system, but none of these theories would violate Newton’s laws of mechanics. Each physicist designs his or her middle-level theory to be consistent with the basic assumptions of the metatheory, even if the middlelevel theories are inconsistent with each other. Competing middle-level theories attempt to achieve the best operationalization of the core logic of the metatheory as it applies to a particular domain. The competing wave and particle theories of light (generated from quantum physics metatheory) are excellent contemporary exemplars of this process. After a core set of metatheoretical assumptions become established among a community of scientists, the day-today workings of these scientists are generally characterized by the use of—not the testing of—these assumptions. Metatheoretical assumptions are used to construct plausible alternative middle-level theories. After empirical evidence has been gathered, one of the alternatives may emerge as the best available explanation of phenomena in that domain. It is this process of constructing and evaluating middle-level theories that characterizes the typical activities of scientists attempting to use a metatheory to integrate, unify, and connect their varying lines of research (Ketelaar & Ellis, 2000). Middle-level evolutionary theories are specific theoretical models that provide a link between the broad metatheoretical assumptions used by evolutionary psychologists and the specific hypotheses and predictions that are tested in research. Middle-level evolutionary theories are consistent with and guided by evolutionary metatheory but in most cases cannot be directly deduced from it (Buss, 1995). Middle-level theories elaborate the basic assumptions of the metatheory into a particular psychological domain. For example, parental investment theory (Trivers, 1972) applies evolutionary metatheory to the question of why, when, for what traits, and to what degree selection favors differences between the sexes in reproductive strategies. Conversely, attachment theory (Bowlby, 1969; Simpson & Belsky, 2008), life history theory (e.g., Chisholm, 1999), and good genes sexual selection theory (e.g., Gangestad & Simpson, 2000) each in different ways applies evolutionary metatheory to the question of why, when, for what traits, and to what degree selection favors differences within each sex in reproductive strategies. In this section we review parental investment theory and good genes sexual selection theory as exemplars of middle-level evolutionary theories.

The Middle-Level Theory Level of Analysis

Parental Investment Theory Imagine that a man and a woman each had sexual intercourse with 100 different partners over the course of a year. The man could potentially sire 100 children, whereas the woman could potentially give birth to one or two. This huge discrepancy in the number of offspring that men and women can potentially produce reflects fundamental differences between the sexes in the costs of reproduction. Sperm, the sex cells that men produce, are small, cheap, and plentiful. Millions of sperm are produced in each ejaculate, and one act of sexual intercourse (in principle) is the minimum reproductive effort needed by a man to sire a child. By contrast, eggs, the sex cells that women produce, are large, expensive, and limited in number. Most critical is that one act of sexual intercourse plus 9 months gestation, potentially dangerous childbirth, and (in traditional societies) years of nursing and carrying a child are the minimum amount of reproductive effort required by a woman to successfully reproduce. These differences in what Trivers (1972) has termed parental investment have wide-ranging ramifications for the evolution of sex differences in body, mind, and behavior. Moreover, these differences hold true not only for humans but also for all mammalian species. Trivers (1972) defined parental investment as “any investment by the parent in an individual offspring’s chance of surviving (and hence reproductive success) at the cost of the parent’s ability to invest in other offspring” (p. 139). Usually, but not always, the sex with the greater parental investment is the female. These differences in investment are manifest in various ways, from basic asymmetries in the size of male and female sex cells (a phenomenon known as anisogamy) through to differences in the propensity to rear offspring. For most viviparous species (who bear live offspring), females also shoulder the burden of gestation—and in mammals, lactation and suckling. In terms of parental investment, the sex that invests the most becomes a limiting resource for the other, less investing sex (Trivers, 1972). Members of the sex that invests less, therefore, should compete among themselves for breeding access to the other, more investing sex. Because males of many species contribute little more than sperm to subsequent offspring, their reproductive success is primarily constrained by the number of fertile females that they can inseminate. Females, by contrast, are constrained by the number of eggs that they can produce and (in species with parental care) the number of viable offspring that can be raised. Selection favors males in these species who compete successfully with other males

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or who have qualities preferred by females that increase their mating opportunities. Conversely, selection favors females who choose mates who have good genes and (in paternally investing species) are likely to provide external resources such as food or protection to the female and her offspring (Trivers, 1972). Parental investment theory, in combination with the metatheoretical assumptions of natural and sexual selection, generates an array of hypotheses and specific predictions about sex differences in mating and parental behavior. According to parental investment theory, the sex that invests more in offspring should be more careful and discriminating in mate selection, should be less willing to engage in opportune mating, and should be less inclined to seek multiple sexual partners. By contrast, the sex investing less in offspring should be less choosy about whom they mate with, compete more strongly among themselves for mating opportunities (i.e., take more risks and be more aggressive in pursuing sexual contacts), and be more inclined to seek multiple mating opportunities. The magnitude of these sex differences should depend on the magnitude of differences between males and females in parental investment during a species’ evolutionary history. In species in which males only contribute their sperm to offspring, males should be much more aggressive than should females in pursuing sexual contacts with multiple partners, and females should be much choosier than should males in accepting or rejecting mating opportunities. In contrast, in species such as humans in which both males and females typically make high levels of investment in offspring, sex differences in mating competition and behavior should be more muted. Nonetheless, the sex differences predicted by parental investment theory are well documented in humans as well as in many other animals. In humans, for example, men are more likely than are women to pursue casual mating opportunities and multiple sex partners, men tend to have less rigid standards than women do for selecting mates, and men tend to engage in more extreme intrasexual competition than women do (Daly & Wilson, 1988; Ellis & Symons, 1990; Puts, 2010; Schmitt, 2005). Among mammalian species, human males are unusual insofar as they contribute nonnegligible amounts of investment to offspring. Geary (2000), in a review of the evolution and proximate expression of human paternal investment, has proposed that (a) over human evolutionary history fathers’ investment in families tended to improve but was not essential to the survival and reproductive success of children and (b) selection consequently favored a mixed paternal strategy, with different men varying in the

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extent to which they allocated resources to care and provisioning of children. Under these conditions, selection should favor psychological mechanisms in females that are especially attuned to variation in potential for paternal investment. This hypothesis has been supported by much experimental and cross-cultural data showing that when they select mates, women tend to place relatively strong emphasis on indicators of a man’s willingness and ability to provide parental investment (e.g., Buss, 1989; Ellis, 1992; Shackelford, Schmitt, & Buss, 2005). These studies have typically investigated such indicators as high status, resource accruing potential, and dispositions toward commitment and cooperation. The other side of the coin is that men who invest substantially in offspring at the expense of future mating opportunities should also be choosy about selecting mates. Men who provide high-quality parental investment (i.e., who provide valuable economic and nutritional resources; who offer physical protection; who engage in direct parenting activities such as teaching, nurturing, and providing social support and opportunities) are themselves a scarce resource for which women compete. Consequently, high-investing men should be as careful and discriminating as women are about entering long-term reproductive relationships. Along these lines, Kenrick, Sadalla, Groth, and Trost (1990) investigated men’s and women’s minimum standards for selecting both short-term and longterm mates. Consistent with many other studies (e.g., Buss & Schmitt, 1993; Symons & Ellis, 1989; Woodward & Richards, 2005), men were found to have minimum standards lower than those of women for short-term sexual relationships (e.g., one-night stands); however, men elevated their standards to levels comparable to those of women when choosing long-term mates (Kenrick et al., 1990). Parental investment theory is one of the most important middle-level theories that guides research into many aspects of human and animal behavior. Both the nature and the magnitude of sex differences in mating and parental behaviors can be explained by considering differences between the sexes in parental investment over a species’ evolutionary history. A host of general hypotheses and specific predictions have been derived from considering the dynamics of parental investment and sexual selection, and much empirical evidence in both humans and other animals has been garnered in support of these hypotheses and predictions. Parental investment theory is one of the real triumphs of evolutionary biology and psychology and gives support to a host of important metatheoretical assumptions.

Good Genes Sexual Selection Theory In order to characterize the evolution of reproductive strategies, one must consider parental investment theory in conjunction with other middle-level theories of sexual selection. In this section we provide a detailed overview of good genes sexual selection theory, as well as briefly summarize the three other main theories of sexual selection (via direct phenotypic benefits, runaway processes, and sensory bias). The male long-tailed widowbird, as its name suggests, has an extraordinarily elongated tail. Although the body of this East African bird is comparable in size to that of a sparrow, the male’s tail feathers stretch to a length of up to 1.5 meters during the mating season. These lengthy tail feathers do little to enhance the male widowbird’s survival prospects: They do not aid in flight, foraging, or defense from predators. Indeed, having to haul around such a tail is likely to reduce survival prospects through increased metabolic expenditure, attraction of predators, and the like. The question that has to be asked of the male widowbird’s tail is how it could possibly have evolved. The short answer is that female widow birds prefer males with such exaggerated traits—that is, the male widowbird’s extraordinary tail has evolved by the process of sexual selection. That such a female preference for long tails exists was confirmed in an ingenious manipulation experiment carried out by Malte Andersson (1982). In this study, some males had their tail feathers experimentally reduced while others had their tails enhanced. The number of nests in the territories of the males with the supernormal tails significantly exceeded the number of nests in the territories of those males whose tails had been shortened. Clearly female widowbirds preferred to mate with males who possess the superlong tails. To explain why the female widowbird’s preference for long tails has evolved, we need to consider the various mechanisms and theories of sexual selection. The two main mechanisms of sexual selection that have been identified are mate choice (usually, but not always, by females) and contests (usually, but not always, between males). The male widowbird’s elongated tail is an example of a trait that has apparently evolved via female choice. The 2.5-m tusk of the male narwhal, by contrast, is a trait that appears to have evolved in the context of male-male competition. Other, less studied mechanisms of sexual selection include scrambles for mates, sexual coercion, endurance rivalry, and sperm competition (Andersson, 1994; Andersson & Iwasa, 1996; Clutton-Brock, 2004). In his exhaustive review of sexual selection in

The Middle-Level Theory Level of Analysis

over 180 species, Andersson (1994) documents evidence of female choice in 167 studies, male choice in 30 studies, male competition in 58 studies, and other mechanisms in 15 studies. Four main theories about how sexual selection operates have been advanced: via good genes, direct phenotypic benefits, runaway processes, and sensory bias. We will focus on the first two here as they are most relevant to understanding the evolutionary psychology of human mating. The core idea of good genes sexual selection is that the outcome of mate choice and intrasexual competition will be determined by traits that indicate high genetic viability (Andersson, 1994; Williams, 1966). Males (and, to a lesser extent, females) of many bird species, for example, possess a bewildering variety of ornaments in the form of wattles, plumes, tufts, combs, inflatable pouches, elongated tail feathers, and the like. Moreover, many male birds are often splendidly attired in a dazzling array of colors: iridescent blues, greens, reds, and yellows. Keeping such elaborate visual ornamentation in good condition is no easy task. It requires time, effort, and—critically—good health to maintain. Females who consistently choose the brightest, most ornamented males are likely to be choosing mates who are in the best condition, which reflects the males’ underlying genetic quality. Even if females receive nothing more than sperm from their mates, they are likely to have healthier, more viable, and more attractive offspring if they mate with the best quality males. According to Hamilton and Zuk (1982), bright plumage and elaborate secondary sexual characteristics, such as the male peacock’s resplendent tail, are accurate indicators of the relative parasite loads of different males. A heavy parasite load signals a less viable immune system and is reflected in the condition of such traits as long tail feathers and bright plumage. What counts as genetic “quality” may also depend in part on genetic compatibility: Females may prefer to mate with males who are relatively dissimilar to them genetically speaking so as to increase the genetic heterozygozity of offspring. This, in turn, confers greater resistance to pathogens (Charlton, 2008; Roberts & Little, 2008). Many secondary sexual characteristics therefore act as indicators of genetic quality. Moreover, according to the handicap principle developed by Amotz Zahavi (1975; Zahavi & Zahavi, 1997), such traits must be costly to produce if they are to act as reliable indicators of genetic worth. If a trait is not expensive to produce, then it cannot serve as the basis for good genes sexual selection because it will not accurately reflect the condition of its owner. However, if the trait relies on substantial investment of metabolic resources to develop—as does the male

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widowbird’s tail—then only those individuals in the best condition will be able to produce the largest or brightest ornament. In this case, expression of the trait will accurately reflect underlying condition. Genes, of course, are not the only resources that are transferred from one mate to another in sexually reproducing species. Although the male long-tailed widowbird contributes nothing but his sperm to future offspring, in many species parental investment by both sexes can be substantial. It benefits each sex, therefore, to attend to the various resources that mates contribute to subsequent offspring; thus, one of the driving forces behind sexual selection is the direct phenotypic benefits that can be obtained from mates and mating. These benefits encompass many levels and types of investment—from the small nuptial gifts offered by many male insect species to the long-term care and provisioning of offspring. Homo sapiens is a species commonly characterized by long-term pair-bonding and biparental care of offspring. Therefore, in addition to traits that indicate the presence of good genes, both males and females should be attentive to characteristics that signal the ability and willingness of potential mates to devote time and external resources to future offspring. As has been demonstrated in many studies, individuals rate kindness and warmth as the most important desired attributes in long-term mates. A partner with the personality traits of kindness, honesty, and warmth is someone who is both more likely to remain in a long-term relationship and who will invest time and resources in future offspring. Women (more so than men) also rate the presence of status and resource-accruing potential as important attributes in potential mates (Buss, 1989; Ellis, 1992; Shackelford, Schmitt, & Buss, 2005), suggesting that males with the ability to contribute external resources to future offspring are favored. It is important to note that some characteristics may be indicative of both good genes and the ability to offer direct phenotypic benefits; thus, these two different theories of sexual selection are not necessarily incompatible. For example, a male bird with bright, glossy plumage may be preferred as a mate not only because of his high genetic quality, but also because he is less likely to transmit parasites to prospective sexual partners. However, compatibility between good genes and direct benefits is often not apparent, and it is expected that the relative importance of these two mate selection criteria will vary on a species-by-species basis. We also expect variation to occur within species in the relative weighting of good genes versus direct phenotypic benefits in mate selection (Gangestad & Simpson, 2000; Gross, 1996). For example,

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Gangestad and Simpson (2000) have argued that human females make trade-offs between males with traits indicating good genes and males with traits signaling high likelihood of paternal investment. Some women at some times pursue a relatively unrestricted strategy of engaging in short-term sexual relationships with partners who may be high in genetic quality, whereas other women may adopt a more restricted strategy of selecting longterm partners who are likely to offer substantial paternal investment. Good genes sexual selection is another important middle-level theory that has proven valuable in generating a number of interesting and testable hypotheses about both human and nonhuman animal behavior. As we have discussed, good genes sexual selection theory is one of a number of alternative (although often compatible) middlelevel theories of sexual selection. Making predictions that distinguish between these different middle-level applications of sexual selection metatheory can sometimes be difficult. However, as reviewed in the next section, good genes sexual selection theory (often in conjunction with parental investment theory) enables us to derive a number of general hypotheses and specific predictions that can be empirically tested.

THE HYPOTHESIS LEVEL OF ANALYSIS At the next level down in the hierarchy of explanation are the actual hypotheses drawn from middle-level evolutionary theories (see Figure 2.1). As noted earlier, a hypothesis is a general statement about the state of the world that one would expect to observe if the theory from which it was generated were in fact true. An array of hypotheses can often be derived from a single middlelevel theory. These hypotheses can be considered to vary along a continuum of confidence (Ellis & Symons, 1990). At the top of the continuum are so-called firm hypotheses (such as the relation between relative parental investment and intrasexual competition for mating opportunities) that are clear and unambiguous derivations from an established middle-level evolutionary theory. As one moves down the continuum, however, firm hypotheses give way to more typical formulations—hypotheses that are inferred from a middle-level theory but not directly derived from it. This distinction can be illustrated by considering the issue of paternity uncertainty. The supposition that in species characterized by both internal female fertilization and substantial male parental investment, selection will favor the evolution of male mechanisms for reducing the probability

of expending that investment on unrelated young is a firm hypothesis that can be directly derived from the theory. What form these mechanisms will take, however, cannot be directly derived from the theory because natural and sexual selection underdetermine specific evolutionary paths. Selection could favor the evolution of sexual jealousy, or it could favor the evolution of sperm plugs to block the cervix of female sexual partners following copulation. Given the universal occurrence of jealousy in humans (Daly et al., 1982), evolutionary psychologists have hypothesized that men’s jealousy should be centrally triggered by cues to sexual infidelity, whereas women’s jealousy should be centrally triggered by cues to loss of commitment and investment. This hypothesis is reasonably inferred from the theory but cannot be directly deduced from it. We refer to this type of hypothesis as an expectation. This hypothesis was originally proposed by Daly et al. (1982) and has since received considerable empirical support (Buss, Larsen, Westen, & Semmelroth, 1992; Kuhle, Smedley, & Schmitt, 2009; Sch¨utzwohl, 2005). As one moves farther down the continuum of confidence into the area where inferences from middle-level theories are drawn farther from their core, expectations grade insensibly into interesting questions or hunches. At this level, different interpretations of the theory can and do generate different hypotheses. In the following section, we review hypotheses derived from good genes sexual selection theory. We number these hypotheses and note whether (in our opinion) they are firm hypotheses, expectations, or hunches. Good Genes Sexual Selection Theory: Hypotheses The principles of good genes sexual selection theory in combination with parental investment theory have been used to generate a number of interesting hypotheses in a variety of species, including humans. In the following discussion we use the term females to refer to the sex that invests more in offspring and males to refer to the sex that invests less in offspring. We recognize, of course, that these sex roles are sometimes reversed. For a given trait to be a reliable indicator of genetic value, it must be costly to produce. According to the handicap principle (Zahavi & Zahavi, 1997), traits that indicate good genes can be maintained only by individuals who are the fittest in the population, as indicated by their ability to maintain steady growth rates, resist parasites, compete successfully in intrasexual contests, and so forth. Consequently, good genes indicators that are preferred by members of the opposite sex should require substantial

The Hypothesis Level of Analysis

metabolic resources to develop and maintain. It follows, therefore, that individuals who more fully display traits indicative of high genetic quality should be healthier and in better condition than should conspecifics who display these traits less fully (H1; firm hypothesis). An implication of this hypothesis is that individuals with elaborate secondary sexual characteristics should have lower levels of parasitic infection. Further, traits indicative of good genes can only be developed to their fullest potential in individuals with robust immune systems. Expression of traits indicative of good genes, therefore, should be positively related to effective immune system functioning. Evidence that sexually selected traits can increase reproductive success while reducing survival prospects (i.e., handicap traits) has accumulated in a number of species, including the European barn swallow. The male barn swallow is adorned with elongated tail feathers. Males with longer tail feathers are preferred by females and sire more offspring (Moller, 1994). However, males with such long tails are less efficient at foraging and are more likely to suffer predation by birds of prey, reducing their likelihood of surviving (Moller et al., 1998; Munoz, Aparicio, & Bonal, 2008). Thus, female preference for males with elongated tail feathers appears to reflect good genes sexual selection in action. A meta-analysis of studies assessing parasite load, immune function, and the expression of secondary sexual characteristics in a diverse array of species has found that the fullest expression of sexually selected traits is positively related to immune system functioning and negatively related to parasite load (Moller et al., 1999)—that is, the brightest, largest, most ornamented individuals are also the ones with the smaller number of parasites and the most robust immune systems. An important factor influencing the intensity of good genes sexual selection is variance in reproductive success. Two principles are relevant here. First, there tends to be greater variance in male than in female reproductive success; this is because males are more able to distribute their sex cells across multiple partners. Indeed, the ability of males to inseminate a large number of females often results in a sexual lottery in which some males win big while others lose out entirely. For example, in one study of elephant seals, a total of only eight males were found to be responsible for inseminating 348 females (Le Boeuf & Reiter, 1988). Second, because of this disparity, sexual selection tends to act more strongly on males than on females in shaping intrasexual competitive abilities and producing specialized fitness signals for attracting the opposite sex (Puts, 2010; Trivers, 1972).

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A core premise of good genes sexual selection is that certain traits have evolved because they are reliable indicators of genetic quality—that is, these traits reliably signal viability and good condition that can be passed on to offspring through genetic inheritance. All else being equal, individuals that possess such traits should be preferred as mates (H2; firm hypothesis), be more successful in intrasexual contests (H3; firm hypothesis), or both. Parental investment theory further suggests that males will be more likely than females to possess and display indicators of genetic quality (H4; expectation), whereas females will be more likely than males to select mates on the basis of these indicators (H5; expectation). In total, then, males that possess and display indicators of genetic quality should have more sexual partners and more offspring (H6; firm hypothesis). For example, among mandrills, a primate that inhabits the rainforests of West Africa, males who possess the brightest red and blue pigmentation on the face, rump, and genitals (which presumably are indicators of good genes) are more often preferred as mates by females. Further, DNA analysis has shown that they are also more likely than their less chromatically exuberant counterparts to sire offspring (Dixson, Bossi, & Wickings, 1993). In species in which females engage in nonreproductive, situation-dependent sexual activity (rather than strictly cyclical sexual activity), females’ preferences for males who display indicators of high genetic quality should vary as a function of their phase of the reproductive cycle. Around the time of ovulation, when females are most fertile, they should express the strongest preference for males with good genes. At other times in the reproductive cycle, when females are not ovulating, this preference should be more muted (H7; expectation). Humans are the clearest example of a primate that engages in sexual activity throughout the reproductive cycle. Other primates tend to be more seasonal and cyclical in their breeding activities than humans are, although not exclusively so (see Gangestad & Thornhill, 2008). Good genes sexual selection theory has been used to generate hypotheses about mating effort, parental effort, and trade-offs between them. There are essentially three strategies that individuals can use to increase their reproductive success: (1) Increase the fitness of their offspring by mating with individuals of high genetic quality, (2) increase the fitness of their offspring by enhancing parental investment (by one or both parents), or (3) increase the number of offspring produced. No one strategy is inherently better than any other, and the pursuit of one strategy usually involves trade-offs with the others (see Gangestad & Simpson, 2000). For example,

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Evolutionary Psychology

individuals who produce a greater number of offspring (3) tend to have lower fitness of offspring. Consistent with (1), females can increase their reproductive success by preferentially investing in offspring that are sired by males of high genetic quality. Thus, among females there should be a positive correlation between levels of parental investment in offspring and the genetic quality of the offspring’s father (H8; expectation). Peahens, for example, have been found to lay more eggs for peacocks with larger trains and more elaborate tails (Petrie & Williams, 1993). In species characterized by long-term pair-bonding and biparental care of offspring, but in which individuals sometimes engage in short-term and extra-pair mating, there should be a negative correlation between the genetic quality of males and levels of parental investment by males in offspring (H9; expectation). There are two bases for this hypothesis. First, males who possess reliable indicators of high genetic quality can afford to put less direct effort into offspring; this is because they make more valuable genetic contributions to offspring, and thus their female partners may be willing to tolerate less parental investment—devaluing (2)—in return for their good genes—enhancing (1). Second, diverting effort away from parental investment toward extra-pair matings should yield greater payoffs for males of high genetic quality (because they are more popular on the mating market). Thus, males with good genes can be expected to devote proportionally more reproductive effort to mating (3) and less to parenting (2). A corollary of this hypothesis is that males who possess reliable indicators of good genes will engage in more short-term and extra-pair mating (H10; expectation) and be more preferred by females as short-term and extra-pair mates (H11; expectation). Hypotheses 9–11 have been supported in an extensive series of studies on the European barn swallow. The barn swallow is a small, migratory, insect-eating bird, characterized by pair-bonds that last the length of the breeding season and biparental care of offspring. Male and female birds are similar in many respects except that males have much longer tails than do females, which suggests that tail length is a sexually selected characteristic (Moller, 1994). Males with longer tail feathers not only tend to spend less time incubating and feeding offspring (Moller, 1994), but also are more preferred by females as primary mates, engage in more extra-pair mating, and sire more extra-pair offspring than do males with shorter tails (Kleven, Jacobsen, Izadnegahdar, Robertson, & Lifjeld, 2006; Moller & Tegelstrom, 1997). These data suggest that (a) females are willing to trade off parental investment for good genes

in their primary pairbonds and (b) females pursue extrapair copulations with males who possess indicators of good genes. We find it interesting that the probability of females’ pursuing extra-pair copulations decreases as a function of the length of the tail feathers of their primary mate (Moller, 1994), suggesting females who are already receiving high-quality genetic benefits have less motivation for extra-pair mating. In sum, hypotheses derived from good genes sexual selection theory can explain the origins of a wide variety of physical and behavioral traits across a diversity of animal species, from humans to scorpion flies. The specific ways in which these hypotheses are played out, however, depends on the nature of the species being studied. Humans and barn swallows, for example, both engage in medium- to long-term pair-bonding, both have greater female parental investment, and both are characterized by relatively frequent extra-pair mating. We would expect, therefore, that females in both species will preferentially seek extra-pair sex partners who possess indicators of good genes. However, specific markers of good genes vary across species. Human males do not possess elongated tail feathers, bright spots on their rump, or bright red faces. Thus, although the general hypotheses derived from good genes sexual selection theory have wide applicability, the detailed predictions derived from these hypotheses depend on the species under consideration. In the next section we describe specific predictions as they apply to human mating.

THE PREDICTION LEVEL OF ANALYSIS Because hypotheses are often too general to be tested directly, it is at the next level of explanation—the level of specific predictions—where the battles between competing theoretical models are often played out. Predictions correspond to specific statements about the state of the world that one would expect to observe if the hypothesis were in fact true. They represent explicit, testable instantiations of hypotheses. One might argue that predictions form the substance of any theory, for here is where most of the action takes place as specific predictions are either supported or refuted. The performance of evolution-based predictions provides the basis for evaluating the more general hypotheses from which they are drawn. For example, a number of specific predictions have been derived from the evolutionary hypothesis that men (more than women) will be intensely concerned about the sexual fidelity of reproductive-aged partners. Some

The Prediction Level of Analysis

of these predictions include (a) sexual infidelity by wives will be a more frequent cause of divorce than will sexual infidelity by husbands (Betzig, 1989); (b) the use or threat of violence by husbands to achieve sexual exclusivity and control of wives will vary as a function of wives’ reproductive value, which peaks in the late teens and declines monotonically thereafter (Shackelford, Buss, & Weekes-Shackelford, 2003; Wilson & Daly, 2010); and (c) men who perceive that their partner is at a greater risk of engaging in extra-pair copulation (e.g., their partner is young and attractive or they have spent more time apart) will put more effort into mate retention strategies (Starratt, Shackelford, Goetz, & McKibbin, 2007). Each of these three predictions has been supported in empirical research, although the weight of evidence in favor of the first two predictions is currently greater than that for the third prediction. Ultimately, the value of the more general hypothesis and theoretical model is judged by the cumulative weight of the evidence (Ketelaar & Ellis, 2000). Good Genes Sexual Selection Theory: Predictions A number of specific, testable predictions can be derived from the hypotheses generated by good genes sexual selection theory. Although predictions can be made about the characteristics of a wide array of animal species, we focus in this section on a discussion of predictions pertaining specifically to humans. We consider the hypotheses outlined in the preceding section (“The Hypothesis Level of Analysis”) and derive predictions relating specifically to human health and reproductive behavior. For each prediction we also review studies, where relevant, that have been carried out to test these specific predictions. Before we examine these predictions in detail, it is worth considering just what traits in humans—like elongated tail feathers in male barn swallows—might be reliable indicators of good genes. One important marker of genetic quality that has emerged in research on a diverse array of species is a phenomenon known as fluctuating asymmetry (Moller & Swaddle, 1997). Fluctuating asymmetry refers to small random deviations from perfect bilateral symmetry in different parts of the body. Higher levels of fluctuating asymmetry (i.e., more asymmetry) are believed to reflect developmental instability. This developmental imprecision can arise because of a range of factors, such as food deficiency, parasites, inbreeding, and exposure to toxic chemicals. Biologists have hypothesized that individuals with good genes are better able to buffer themselves against these genetic and environmental insults

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and thus tend to be more symmetrical. Because fluctuating asymmetry has a heritable component, mate preference for symmetrical, developmentally stable individuals can be expected to result in more viable offspring (see Moller & Swaddle, 1997). The specific predictions reviewed in this section focus on the relations between fluctuating asymmetry and both health and reproductive behavior. 1. More symmetrical individuals should have better mental and physical health, better immune system functioning, and lower parasite loads than should less symmetrical individuals (from H1). Although these predictions have only been tested in a relatively small number of studies using human participants, initial results have been largely supportive. A number of studies have now found that levels of symmetry in both men and women are positively correlated with psychometric intelligence (Bates, 2007; Furlow, Armijo-Prewitt, Gangestad, & Thornhill, 1997; see Banks, Batchelor, & McDaniel, 2010, for a recent meta-analysis of the literature), and negatively correlate with measures of psychological, emotional, and physiological distress (Shackelford & Larsen, 1997). In addition, more symmetrical men have been found to have greater ejaculate size and better sperm quality (Manning, Scutt, & Lewis-Jones, 1998) and lower resting metabolic rates (Manning, Koukourakis, & Brodie, 1997) than have less symmetrical men. Perceived health has also been shown to be positively correlated with symmetry and averageness of male faces (Rhodes et al., 2001). Finally, in a study of men in rural Belize, the occurrence of life-threatening illnesses was found to be significantly higher in men who were less symmetrical (Waynforth, 1998), and the available evidence, although somewhat inconsistent, supports the general prediction that symmetrical men experience better health (Milne, Belsky, Poulton, Thomson, Caspi, & Kieser, 2003; Thornhill & Gangestad, 2006). Taken together, these findings suggest that more symmetrical individuals, as predicted, tend to be healthier and in better physical and psychological condition than do their less symmetrical counterparts. The remaining hypotheses (H2–H11) focus on the relations between markers of genetic fitness and reproductive behavior. Because of sex differences in parental investment, these hypotheses primarily concern female preferences for males who possess indicators of good genes and individual differences in male mating behavior as a function of genetic quality. An array of specific predictions has been derived from Hypotheses 2–11. As reviewed in the following discussion, empirical tests of these predictions

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have generated new lines of research that have substantially advanced our understanding of behavior in sexual and romantic relationships. 2. More symmetrical men should have more lifetime sexual partners (from H2–H6) and more extra-pair sexual partners (from H9) than should less symmetrical men. These predictions have been tested in an initial series of studies on American undergraduates (reviewed in Gangestad & Thornhill, 2004). Symmetry was assessed by totaling right-left differences in seven bilateral traits (e.g., ankle girth, wrist girth). Consistent with the predictions, men who were more symmetrical were found to have more lifetime sexual partners (even after controlling for age and physical attractiveness) and more extra-pair sexual encounters during ongoing relationships (even after controlling for relationship length, partners’ extra-pair sex, and both partners’ physical attractiveness). In contrast, no consistent relation was found between women’s symmetry and number of lifetime sexual partners or extra-pair sexual relationships. In ancestral environments, before the advent of reliable contraceptive methods, number of sexual partners can be expected to have been positively related to number of offspring. The finding that more symmetrical men in rural Belize both had more sexual partners and fathered more children lends support to this suggestion (Waynforth, 1998). 3. More symmetrical men should be more successful in intrasexual contests than should less symmetrical men (from H2, H4). This prediction has been tested both indirectly (by looking at the traits associated with fluctuating asymmetry) and directly (by examining behavior in experimental studies on mate competition). Men who are more symmetrical have been found to display higher levels of traits that are associated with success in intrasexual competition. Specifically, more symmetrical men tend to be bigger, to be more muscular and vigorous, to initiate more fights with other men, and to be more socially dominant than do less symmetrical men (reviewed in Gangestad & Thornhill, 2004). Consistent with these correlational data, Simpson, Gangestad, Christensen, and Leck (1999) found that more symmetrical men competed more aggressively with other men for a lunch date with an attractive woman in a laboratory experiment. Each male participant was interviewed by the woman and then at the end of the interview was asked by the woman why she should choose him for the lunch date rather than the competitor (who was ostensibly in the next room). Compared with men who were less symmetrical, more symmetrical men tended to engage in competition with the rival, such as by directly comparing themselves with and belittling him. In total, the

correlational and experimental data reviewed here suggest that more symmetrical men tend to display more costly traits, such as large size and social and physical dominance, which facilitate success in direct intrasexual contests. 4. More symmetrical men should be preferred by women as short-term and extra-pair sexual partners (from H10). Cousins, Gangestad, Simpson, and Christensen (1998) had women view videotapes of men being interviewed by an attractive woman (as described previously). The female participants then rated the male interviewee’s attractiveness both as a potential long-term mate and as a short-term mate. A short-term mate was defined as either as a one-time sex partner or an extra-pair sex partner. Women also completed a questionnaire that assessed their general willingness to have sex without commitment and emotional closeness. Women who reported more willingness to have sex without intimacy and commitment were categorized as being inclined toward short-term mating, whereas women who reported less willingness were categorized as being disinclined toward short-term mating. Among women who were inclined toward short-term mating, there was a significant positive correlation between the male interviewee’s symmetry and the women’s ratings of how attractive he was as a short-term mate (but not as a long-term mate). In contrast, among women who were disinclined toward short-term mating, male symmetry was uncorrelated with women’s ratings of how attractive he was as either a short-term or a long-term mate. These data suggest that men who are more symmetrical are preferred as short-term mates specifically by women who are most inclined to engage in short-term mating. Moreover, Gangestad and Thornhill (1997) found that male symmetry predicted the number of times that men were chosen by women as extra-pair mates. Taken together, these data support the prediction that more symmetrical men should be more preferred by women as short-term and extra-pair sexual partners. 5. Women’s preferences for symmetrical men should be heightened around the time of ovulation when women are most fertile (from H7). This prediction has been supported in provocative new research on women’s preference for the scent of symmetrical men as a function of variation in the menstrual cycle. This research employed what has been called a stinky T-shirt design, in which women sniffed shirts that had been slept in by different men and rated them on the pleasantness, sexiness, and intensity of their odors. The men who slept in these shirts were also measured on fluctuating asymmetry. The extraordinary finding was that the shirts worn by more

The Future of Evolutionary Psychology

symmetrical men were rated as smelling better than the shirts worn by less symmetrical men, but only by women who were likely to be in the fertile stage of their menstrual cycle (especially days 6–14). This finding was originally reported by Gangestad and Thornhill (1998) and has since been replicated in their own lab in the United States (Thornhill & Gangestad, 1999; Thornhill, Gangestad, Miller, Scheyd, McCollough, & Franklin, 2003) and in an independent lab in Germany (Rikowski & Grammar, 1999). These data suggest that the smell of men who are more symmetrical is preferred by women specifically when the women are most likely to conceive. 6. Women’s preferences for men with masculine facial characteristics should be heightened around the time of ovulation when women are most fertile (from H7). Research in the United Kingdom, United States, and Japan has examined variation in women’s preferences for male faces as a function of women’s stage in the menstrual cycle (Gangestad, Thornill, & Garver-Apgar, 2010; Penton-Voak et al., 1999; Penton-Voak & Perrett, 2000). Consistent with good genes sexual selection theory, more masculine-looking faces were preferred by women around the time of ovulation (when risk of conception is highest), especially in the context of short-term mating. In contrast, more feminine male faces, which may indicate dispositions toward increased paternal investment, were slightly preferred by women during other phases of the menstrual cycle (when risk of conception is lower). Additional research has found that various other masculine characteristics such as muscular bodies, low voice pitch, physical attractiveness, and dominant behavioral displays are also preferred at the time that women are ovulating (Gangestad, Garver-Apgar, Simpson, & Cousines, 2007; Gangestad, Thornhill, & Garver-Apgar, 2010; Little, Jones, & Burris, 2007; Puts, 2005). These data provide further evidence that men who display indicators of good genes are most preferred by women when the women are most likely to get pregnant. In conclusion, specific predictions drawn from hypotheses generated by good genes sexual selection theory have been tested across a range of studies. An accumulating body of evidence now supports the supposition that a collection of male traits (reflected in levels of fluctuating asymmetry) have been selected for because of their role in advertising genetic quality to prospective mates. Good genes sexual selection theory has proven valuable in guiding research in a number of ways and has led to the detection of new phenomena. It is difficult to imagine, for example, how other approaches to human mating could

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have predicted (let alone explained) the finding that men’s symmetry is positively related to judgments of odor attractiveness by women who are most likely to be in the fertile stage of their menstrual cycle. Of course, there is much more to the dynamics of sexual and romantic relationships than can be explained by good genes sexual selection theory. This middle-level evolutionary theory has proved valuable, however, in both explaining and predicting a host of interesting phenomena relating to behavior in sexual and romantic relationships—not only in humans, but also in a wide range of animal species.

THE FUTURE OF EVOLUTIONARY PSYCHOLOGY Evolutionary explanations have had a long—at times acrimonious—history in the behavioral sciences. Darwin’s revolutionary theory of adaptation through natural selection, which explained the origins of human mental and behavioral characteristics in terms of evolution, transformed a long-standing worldview. Before Darwin, the prevailing belief was that “man” was created in God’s divine image and held a special place at the center of the cosmos. Ever since Darwin, however, Homo sapiens have been viewed as firmly anchored in the natural world, as one species among millions in the great tree of life. Evolutionary psychology, as we have introduced it in this chapter, can be viewed historically as part of a long tradition of attempts to explain human psychological characteristics in evolutionary terms (Plotkin, 2004). The Impact of Evolutionary Psychology Perhaps one of the most interesting questions regarding the future of evolutionary psychology concerns its scope of influence in the behavioral sciences. There is no question that evolutionary psychology has a broad range of applications. Indeed, evolutionary theory has been used to generate explanations of social behavior in all species, even those that are as yet undiscovered. Although the present chapter has focused primarily on reproductive strategies, evolutionary psychological theory and research extends into all major branches of psychology (e.g., Buss, 2005). Will the endeavors of evolutionary psychologists thus serve to unify the currently fragmented discipline of psychology under the umbrella of a single metatheory? Does evolutionary psychology, as some suggest (e.g., Buss, 1995; Tooby & Cosmides, 1992), offer a radical new paradigm for psychological science?

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Evolutionary Psychology

To address this question, it is important to explicate the difference between explanatory frameworks that are and are not explicitly informed by evolutionary theory. Evolutionary models emphasize ultimate causation over proximate causation. Ultimate causes concern what the behavior or characteristic was designed by natural selection to do (i.e., they explain why traits such as language or sexual jealousy exist in terms of the functions they served in ancestral environments), whereas proximate causes concern how traits work in terms of social, developmental, cognitive, or neural processes operating in one’s lifetime. Consider the phenomenon of morning sickness in pregnant women. An ultimate explanation for morning sickness is that it is an adaptation that has evolved because it helps to protect the pregnant woman and the developing fetus from the ingestion of toxic substances (Flaxman & Sherman, 2000; Profet, 1992). Proximate explanations of morning sickness focus on current physiological and psychological processes involved in food aversions during pregnancy. Proximate explanations address such questions as What are the conditions under which morning sickness occurs? What neural circuits are involved? and What are the chemical changes that underpin increased olfactory sensitivity during the first trimester of pregnancy? Neither type of explanation is inherently better than the other, nor does one preclude the other. Rather, ultimate and proximate explanations are complementary and mutually enriching. Ultimate and proximate explanations, however, are not independent: They inform and influence each other. Discerning the evolved function of a psychological mechanism, for example, should aid in discovering how the mechanism works—that is, understanding evolved function can generate hypotheses about proximate mechanisms and causation. We suggest, then, that although evolutionary psychology may not offer a revolutionary new paradigm for psychological science, there are at least three ways in which evolutionary psychological theory and research influences the larger field of psychology. 1. Evolutionary psychology opens new lines of inquiry in psychology. The use of evolutionary psychological models sometimes generates novel hypotheses and lines of research that had not—and in many cases could not—be derived from other theoretical models. One example of this point is the research on fluctuating asymmetry and reproductive behavior that was reviewed in this chapter. Another example is theory and research on father involvement and timing of daughters’ reproductive development. Draper and Harpending (1982) have proposed a middle-level evolutionary theory of the role

of father presence and involvement in the development of female reproductive strategies. This theory posits that individuals have evolved to be sensitive to specific features of their early childhood environments, and that exposure to different early environments biases individuals toward acquisition of different reproductive strategies. Specifically, Draper and Harpending proposed that an important function of early experience is to induce in girls an understanding of the quality of male-female relationships and male parental investment that they are likely to encounter later in life. According to the theory, this understanding has the effect of canalizing a developmental track that has predictable outcomes for girl’s reproductive behavior at maturity. Girls whose early family experiences are characterized by father absence (where women rear their children without consistent help from a man who is father to the children) perceive that male parental investment is not crucial to reproduction; these girls are hypothesized to develop in a manner that accelerates onset of sexual activity and reproduction, reduces reticence in forming sexual relationships, and orients the individual toward relatively unstable pair-bonds (Draper & Harpending, 1982). Belsky, Steinberg, and Draper (1991; see also Surbey, 1990) added to this theory the hypothesis that girls from paternally deprived homes should also experience earlier pubertal maturation. From an evolutionary perspective, early pubertal maturation, precocious sexuality, and unstable pair-bonds are integrated components of an accelerated reproductive strategy. During human evolution, this accelerated strategy may have promoted female reproductive success in ecological contexts in which male parental investment was not crucial to reproduction. Although variation in the timing of pubertal maturation in girls is a socially relevant topic (i.e., early-maturing girls experience relatively high rates of breast cancer, teenage pregnancy, depression, and alcohol consumption; e.g., Caspi & Moffitt, 1991; Graber, Lewinsohn, Seeley, & Brooks-Gunn, 1997; Udry & Cliquet, 1982), there was almost no research on the psychosocial antecedents of this variation prior to publication of the evolutionary model. This gulf occurred because no other theory of socialization and child development provided a framework for studying timing of puberty. Indeed, researchers operating outside of the evolutionary umbrella had never thought to look at the relation between fathers’ role in the family and daughters’ maturational tempo. With the introduction of the evolutionary model of pubertal timing (see especially Belsky et al., 1991), this topic developed into a fruitful new area of research. Most studies suggest that

The Future of Evolutionary Psychology

girls reared in father-absent homes reach menarche several months earlier than do their peers reared in father-present homes (Moffitt, Caspi, Belsky, & Silva, 1992; Surbey, 1990; Wierson, Long, & Forehand, 1993). Tither and Ellis (2008) were able to demonstrate this effect in a genetically controlled sibling study. Moreover, some of these studies have found that the longer the period of father absence, the earlier the onset of daughters’ menstruation (Moffitt et al., 1992; Surbey, 1990). However, not all studies (see Campbell & Udry, 1995) have found an accelerating effect for years of father absence on menarcheal age. Ellis and Garber (2000) found that years of stepfather presence, rather than years of biological father absence, best accounted for girls’ pubertal timing (suggesting a possible pheromonal effect). Finally, Ellis et al. (1999) present longitudinal data showing that father-effects on daughters’ pubertal timing involve more than just father-absent effects: Within father-present families, girls who had more distant relationships with their fathers during the first 5 years of life experienced earlier pubertal development in adolescence. Consistent with the original theorizing of Draper and Harpending (1982), the quality of fathers’ investment in the family emerged as the most important feature of the proximal family environment in relation to daughters’ reproductive development (Ellis et al., 1999; see also Ellis & Essex, 2007). 2. Evolutionary psychology enriches existing bodies of knowledge in psychology. The use of an evolutionary psychological perspective may enrich existing bodies of theory and data in psychology. Evolutionary psychological metatheory, together with middle-level evolutionary theories, provide a powerful set of methodological heuristics that can provide guidance on what paths to follow (e.g., suggesting new hypotheses and providing criteria for recognizing significant observations) and what paths to avoid (e.g., raising suspicion of certain explanations or observations). Consider, for example, theory and research on sexual jealousy in humans. Psychologists working outside of an explicitly evolutionary framework have contributed to our understanding of jealousy in numerous ways. A large body of empirical research has documented an array of cultural, developmental, and personality correlates of jealousy; detailed models of the causes of jealousy have been constructed; and the clinical management of pathological jealousy has been investigated (see Salovey, 1991; White & Mullen, 1989). Psychologists working inside an evolutionary psychological framework have also addressed the topic of jealousy, and this research has enriched the extant literature on jealousy in at least three ways.

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First, the use of an evolutionary perspective has generated fruitful new lines of research on the topic (see Buss, 2000). For example, evolutionary psychologists have hypothesized that levels of jealousy experienced by men (but not women) and amounts of time and energy expended on mate retention by men (but not women) will be negatively correlated with partner’s age, regardless of one’s own age. This gender-specific, age-specific hypothesis is based on the supposition that men with young, reproductive-aged partners are most at risk of being cuckolded and thus investing in offspring who are not their own. Consistent with this hypothesis, Flinn (1988) found that the amount of mate guarding engaged in by men in a Caribbean village decreased significantly when partners were pregnant or postmenopausal. Furthermore, Buss and Shackelford (1997) found that the amount of mate retention behavior engaged in by men (but not by women) was inversely related to the female partner’s age, even after controlling for the male partner’s age. Recent research has also found that men engage in more mate guarding around the time that their partner is ovulating, consistent with the idea that women are more likely to engage in extra-pair copulations during this period and hence increased vigilance on the part of their male partners would function to reduce their possibility of being cuckolded (Haselton & Gangestad, 2006). Second, evolutionary psychological approaches have been instrumental in correcting certain errors regarding the nature of jealousy. For example, the contention that jealousy is entirely a socially constructed emotion— essentially determined by cultural factors such as social roles and political institutions (e.g., Bhugra, 1993; Hupka, 1991)—has been questioned by evolutionary psychologists. Evolutionary psychologists conceptualize sexual jealousy as a biological adaptation designed by sexual selection to reduce paternity uncertainty and the threat of relationship loss (e.g., Daly et al., 1982). Sexual jealousy should be a universal emotion that is experienced in all cultures when a valued sexual relationship is threatened by a rival. Although some writers have claimed that sexual jealousy does not exist in some cultural groups such as Samoans and the Inuit, not to mention the swinging couples of the 1970s, subsequent analyses have shown that jealousy truly is a cross-cultural universal (Buss, 2000; Daly et al., 1982) and a major motive for homicide throughout the world (Daly & Wilson, 1988; Wilson & Daly, 1996, 2010). Third, an evolutionary perspective may prove valuable in integrating various middle-level theories of sexual and romantic jealousy. An extensive psychological literature

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has documented that feelings of jealousy are related to such factors as relationship quality, rival characteristics, partner similarity, gender, and attachment style (see White & Mullen, 1989). Various social and cognitive models, such as appraisal theory (White & Mullen, 1989) and self-evaluation maintenance theory (DeSteno & Salovey, 1996), have been suggested to account for these relations. An evolutionary psychological approach to jealousy may help integrate such models by providing overarching explanations for why certain patterns of appraisal occur in the specific contexts they do, and why jealousy is modified by such factors as relative mate value and the characteristics of rivals (Buss, 2000). 3. Evolutionary psychology radically changes certain domains of psychological inquiry. In some domains, evolutionary psychology has offered more substantive changes to the kinds of explanations employed by nonevolutionary psychologists. For example, the metatheoretical assumptions of sexual selection theory, as instantiated in parental investment theory and good genes sexual selection theory, have radically changed theory and research on mate selection and intrasexual competition. Before the systematic application of evolutionary theory to human mate selection, most work in the area emphasized proximity (the tendency to date and marry people with whom one has regular social contact) and matching (the tendency to date and marry people whose value on the mating market is similar to one’s own) as causal agents in mate selection (e.g., Myers, 1993). The proximity effect was explained as a function of the frequency of social interaction together with the principle that familiarity breeds fondness. The matching effect was conceptualized as an outcome of basic principles of social exchange. Although proximity and matching are relevant to mate selection, the social models that were used to explain these phenomena have largely been supplanted by current evolutionary models of mating preferences and behavior. General principles of social exchange, familiarity, and interaction frequency simply proved inadequate to explain the facts about human mating. These principles could not account for universal differences between men and women in mate selection criteria (e.g., Buss, 1989), for systematic variation within each sex in orientation toward long-term versus short-term mating (e.g., Gangestad & Simpson, 2000), or for lawful variation across species in mating preferences and behavior. It is just these types of questions that are addressed by parental investment theory and good genes sexual selection theory. Although some attempts have been made to integrate evolutionary

and social exchange perspectives (e.g., Ellis, Simpson, & Campbell, 2002; Fletcher, 2002; Kenrick, Groth, Trost, & Sadalla, 1993), the bottom line is that evolutionary psychological models have dramatically changed the nature of research on mating preferences and behavior (as reviewed in this chapter). Future Directions In this chapter we have introduced some of the core ideas and assumptions that comprise the field of evolutionary psychology. We have also illustrated how these ideas can be employed in the development of specific, testable hypotheses about human mind and behavior. The rapid growth in publications in the area of evolutionary psychology over the past two decades suggests a growing acceptance of the importance of evolutionary ideas in the behavioral sciences. What can we expect, however, from evolutionary psychology over the course of the 21st century? What are the crucial issues that need to be addressed by evolutionary psychologists, and how are evolutionary psychological ideas likely to influence the various subdisciplines of psychology? Perhaps the most crucial task for evolutionary psychologists in the coming decades will be the identification and elucidation of psychological adaptations. As Buss (2008) notes, evolutionary psychologists have catalogued most of the obvious and plausible psychological adaptations (especially those relating to human mating), but many more remain undiscovered or inadequately characterized. The concept of biological adaptation, as George Williams (1966) has noted, is an onerous one and should be deployed only if the appropriate sorts of evidence to make such a claim are available. Because adaptations are the product of natural selection operating in ancestral environments, and because psychological traits such as jealousy, language, and self-esteem are not easily reconstructed from fossils and artifacts, direct evidence for biological adaptations may be difficult to come by (Lewontin, 1998; Richardson, 1996). One of the challenges for evolutionary psychology, therefore, will be to develop increasingly more rigorous and systematic methods for inferring the evolutionary history of psychological characteristics (see Andrews et al., 2002; Durrant & Haig, 2001; Simpson & Campbell, 2005). How best to characterize psychological adaptations also remains an important issue for evolutionary psychology. As we have seen, evolutionary psychologists assume that the human mind comprises a large number of domainspecific psychological mechanisms that have evolved to solve specific adaptive problems in our evolutionary past.

The Future of Evolutionary Psychology

However, many important questions remain regarding the relative specificity of such mechanisms, the way that they might develop over time in response to different environmental contexts, and how these mechanisms operate in terms of proximate cognitive and neurobiological processes (Barrett & Kurzban, 2006). Consider, for example, the theory that self-esteem acts as an interpersonal monitor—or sociometer—that tracks the membership status of individuals in social groups (Leary & Baumeister, 2000; Leary, Tambor, Terdal, & Downs, 1995). Leary and colleagues approached this well-studied psychological phenomenon by asking the important question: What is the (evolutionary) function of self-esteem? Their answer is that people do not strive for self-esteem as some kind of end-point or ultimate goal. Rather, self-esteem reflects one’s level of relative social inclusion or acceptance in social groups. Selfesteem, therefore, functions to motivate individuals to pursue courses of action that can restore or improve their acceptance by relevant others. In short, the self-esteem system is characterized as a psychological adaptation that has evolved to solve the recurrent adaptive problem of social exclusion and the fitness costs that such rejection would have entailed in ancestral environments. However, many important questions remain regarding the nature of the self-esteem system, even if it can be plausibly considered a psychological adaptation. For example, Kirkpatrick and Ellis (2001, 2006; Kavanagh, Robins, & Ellis, 2010) have suggested that one should expect selfesteem to be carved in to multiple domains to reflect the different types of interpersonal relationships that were important during human evolutionary history. Thus, they argue that there will be a number of different sociometers that gauge relative social inclusion in such domains as mating relationships, family relationships, and instrumental coalitions. Just how many different sociometers humans possess, however, remains an open question. Furthermore, we are only beginning to understand how the mechanisms underlying self-esteem develop over time in response to different environmental contexts and how they operate at a proximate cognitive and physiological level. One of the important challenges for evolutionary psychology, therefore, lies in fleshing out the details of putative psychological adaptations such as self-esteem. Another important issue concerns how evolutionary psychology incorporates culture into its explanatory framework. A useful distinction is drawn by evolutionary psychologists between “evoked” and “transmitted” culture (Tooby & Cosmides, 1992). Evoked culture refers to the idea that a significant amount of cultural variation arises

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from the interaction of universal evolved psychological mechanisms with local physical and social environments. For instance, the importance of physical attractiveness in a prospective mate correlates with ecological differences in the prevalence of pathogens illustrating how evolved mate preferences are sensitive to local cues indicating which characteristics of mates are most important in local environments (Gangestad, Haselton, & Buss, 2006). A good deal of cultural variation can also be viewed as instances of transmitted culture: that is, patterns of beliefs, attitudes, norms, and practices that are replicated within social groups through social learning and persist over time. Specific religious beliefs, food taboos, and the best way to construct houses are all instances of transmitted culture. Moreover, such beliefs, attitudes, and practices can be modified over time through a process of cultural evolution (Mesoudi, Whiten, & Laland, 2006; Richerson & Boyd, 2005). The capacity for transmitted culture, itself, reflects the evolution of adaptations for social learning and the form that cultural evolution takes is influenced in important ways by other evolved psychological mechanisms. However, patterns of transmitted culture can, in turn, influence the selection of psychological and physiological adaptations. For instance, the adoption of agriculture and the practice of dairy farming (examples of transmitted culture) has selected for alleles that allow individuals to digest lactose into adulthood—a characteristic unique among mammals and more prevalent in human populations with a history of dairy farming (Cochran & Harpending, 2009; Richerson & Boyd, 2005). How best to conceptualize and study these examples of gene-culture coevolution remains an important challenge for evolutionary psychology. In conclusion, Homo sapiens, like all other species, are the product of a history of evolution. Our opposable thumb, bipedal stance, and color visual system are all testimony to the gradual process of natural selection operating over vast spans of time. Just as the anatomical and physiological features of our bodies are explicable in evolutionary terms, so too are the complex array of psychological processes that make up the human brain-mind. The rapidly growing field of evolutionary psychology—from its broad metatheoretical assumptions to the specific predictions that are tested in research—offers a coherent and progressive paradigm aimed at uncovering the origins and functions of human mental and behavioral characteristics. In this chapter we have offered an introduction to some of the key ideas, issues, and methods that guide applications of evolutionary theory to human cognition and behavior. Although evolutionary psychology still meets resistance

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on some fronts, we believe that its value and potential for investigating questions of human nature is great.

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Kleven, O., Jacobsen, F., Izadnegahdar, R., Robertson, R. J., & Lifjeld, J. T. (2006). Male tail streamer length predicts fertilization success in the North American barn swallow (Hirundo rustica erythrogaster). Behavioral Ecology and Sociobiology, 59, 412–418. Kuhle, B. X., Smedley, K. D., & Schmitt, D. P. (2009). Sex differences in the motivation and mitigation of jealousy-induced interrogations. Personality and Individual Differences, 46, 499–502. Lakatos, I. (1970). Falsificationism and the methodology of scientific research programmes. In I. Lakatos & A. Musgrave (Eds.), Criticism and the growth of knowledge (pp. 91–196). Cambridge, UK: Cambridge University Press. Leary, M. R., & Baumeister, R. F. (2000). The nature and function of self-esteem: Sociometer theory. In M. P. Zanna (Ed.), Advances in experimental social psychology (Vol. 32, pp. 1–62). San Diego, CA: Academic Press. Leary, M. R., Tambor, E. S., Terdal, S. K., & Downs, D. L. (1995). Self-esteem as an interpersonal monitor: The sociometer hypothesis. Journal of Personality and Social Psychology, 68, 518–530. Le Boeuf, B. J., & Reiter, J. (1988). Lifetime reproductive success in northern elephant seals. In T. H. Clutton-Brock (Ed.), Reproductive success (pp. 344–362). Chicago, IL: University of Chicago Press. Lewontin, R. C. (1998). The evolution of cognition. In D. Scarborough & S. Sternberg (Eds.), An invitation to cognitive science: Methods, models, and conceptual issues (pp. 107–132). Cambridge, MA: MIT Press. Little, A. C., Jones, B. C., & Burris, R. P. (2007). Preferences for masculinity in male bodies change across the menstrual cycle. Hormones and Behavior, 51, 633–639. Lloyd, J. E. (1979). Mating behavior and natural selection. The Florida Entomologist, 62, 17–34. Manning, J. T., Koukourakis, K., & Brodie, D. A. (1997). Fluctuating asymmetry, metabolic rate and sexual selection in human males. Evolution and Human Behavior, 18, 15–21. Manning, J. T., Scutt, D., & Lewis-Jones, D. I. (1998). Developmental stability, ejaculate size, and sperm quality in men. Evolution and Human Behavior, 19, 273–282. Marks, I. M., & Nesse, R. M. (1994). Fear and fitness: An evolutionary analysis of anxiety disorders. Ethology and Sociobiology, 15, 247–261. Mesoudi, A., Whiten, A., & Laland, K. N. (2006). Towards a unified science of cultural evolution. Behavioral and Brain Sciences, 29, 329–383. Milne, B. J., Belsky, J., Poulton, R., Thomson, W. M., Caspi, A., & Kieser, J. (2003). Fluctuating asymmetry and physical health among young adults. Evolution and Human Behavior, 24, 53–63. Mineka, S. (1992). Evolutionary memories, emotional processing, and the emotional disorders. The Psychology of Learning and Motivation, 28, 161–206. Moffitt, T. E., Caspi, A., Belsky, J., & Silva, P. A. (1992). Childhood experience and onset of menarche: A test of a sociobiological model. Child Development, 63, 47–58. Moller, A. P. (1994). Sexual selection and the barn swallow . Oxford, UK: Oxford University Press. Moller, A. P., Barbosa, A., Cuervo, J. J., de Lope, F., Merino, S., & Saino, N. (1998). Sexual selection and tail streamers in the barn swallow. Proceedings of the Royal Society of London, 265B, 409–414. Moller, A. P., Gangestad, S. W., & Thornhill, R. (1999). Nonlinearity and the importance of fluctuating asymmetry as a predictor of fitness. Oikos, 86, 366–368. Moller, A. P., & Swaddle, J. P. (1997). Asymmetry, developmental stability, and evolution. Oxford, UK: Oxford University Press. Moller, A. P., & Tegelstrom, H. (1997). Extra-pair paternity and tail ornamentation in the barn swallow Hirundo rustica. Behavioral Ecology and Sociobiology, 41, 353–360.

Moller, A. P., & Thornhill, R. (1998). Male parental care, differential parental investment by females and sexual selection. Animal Behavior, 55, 1507–1515. Munoz, A., Aparicio, J. M., & Bonal, R. (2008). Male barn swallows use different resource allocation rules to produce ornamental tail feathers. Behavioral Ecology, 19, 404–409. Myers, D. G. (1993). Social psychology (4th ed.). New York, NY: McGraw-Hill. Nesse, R. M. (2005). Evolutionary psychology and mental health. In D. M. Buss (Ed.), The handbook of evolutionary psychology (pp. 903–927). Hoboken, NJ: Wiley. Nesse, R. M., & Berridge, K. C. (1997). Psychoactive drug use in evolutionary perspective. Science, 278, 63–66. Nettle, D. (2006). The evolution of personality variation in humans and other animals. American Psychologist, 61, 622–631. Penton-Voak, I. S., & Perrett, D. I. (2000). Female preference for male faces changes cyclically: Further evidence. Evolution and Human Behavior, 21, 39–48. Penton-Voak, I. S., Perrett, D., Castles, D., Burt, M., Koyabashi, T., & Murray, L. K. (1999). Female preferences for male faces changes cyclically. Nature, 399, 741–742. Petrie, M., & Williams, A. (1993). Peahens lay more eggs for peacocks with larger trains. Proceedings of the Royal Society of London, 251B, 127–131. Pinker, S. (1997). How the mind works. London, UK: Penguin. Plotkin, H. (2004). Evolutionary thought in psychology: A brief history. Oxford, UK: Blackwell. Profet, M. (1992). Pregnancy sickness as adaptation: A deterrent to maternal ingestion of teratogens. In J. H. Barkow, L. Cosmides, & J. Tooby (Eds.), The adapted mind : Evolutionary psychology and the generation of culture (pp. 327–365). New York, NY: Oxford University Press. Puts, D. A. (2005). Menstrual cycle and mating context affect women’s preference for male voice pitch. Evolution and Human Behavior, 27, 247–258. Puts, D. A. (2010). Beauty and the beast: Mechanisms of sexual selection in humans. Evolution and Human Behavior, 31, 157–175. Rhodes, G., Zebrowitz, L. A., Clark, A., Kalick, M., Hightower, A., & McKay, R. (2001). Do facial averageness and symmetry signal health? Evolution and Human Behavior, 22, 31–47. Richardson, R. C. (1996). The prospects for an evolutionary psychology: Human language and human reasoning. Minds and Machines, 6, 541–557. Richerson, P. J., & Boyd, R. (2005). Not by genes alone: How culture transformed human evolution. Chicago, IL: University of Chicago Press. Richters, J. E. (1997). The Hubble hypothesis and the developmentalist’s dilemma. Development and Psychopathology, 9, 193–229. Rikowski, A., & Grammar, K. (1999). Human body odour, symmetry and attractiveness. Proceedings of the Royal Society of London, 266B, 869–874. Roberts, S. C., & Little, A. C. (2008). Good genes, complementary genes and human mate preferences. Genetica, 132, 309–321. Salovey, P. (Ed.). (1991). The psychology of jealousy and envy. New York, NY: Guilford Press. Schmitt, D. P. (2005). Sociosexuality from Argentina to Zimbabwe: A 48-nation study of sex, culture, and strategies of human mating. Behavioral and Brain Sciences, 28, 247–311. Sch¨utzwohl, A. (2005). Sex differences in jealousy: The processing of cues to infidelity. Evolution and Human Behavior, 26, 288–299. Seligman, M., & Hagar, J. (1972). Biological boundaries of learning. New York, NY: Appleton-Century-Crofts. Shackelford, T. K., Buss, D. M., & Weekes-Shackelford, V. A. (2003). Wife killings committed in the context of a lovers’ triangle. Basic and Applied Psychology, 25, 137–143.

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CHAPTER 3

Comparative Vision GERALD H. JACOBS

ENVIRONMENTS AND EYES 52 THREE ISSUES IN COMPARATIVE VISION 66

CONCLUSION 75 REFERENCES 75

Evolution has endowed animals with an impressive array of sensory capacities to support the critical choices of life, but for many species vision provides an unparalleled source of information allowing access to sustenance, safe havens, and mates. Quite simply, vision is important for most species and paramount for many. Among the latter group are the members of our own order—the primates—and no doubt because of this there has long been a vigorous interest in studying vision. The fruits of this labor are represented in many thousands of published studies detailing virtually every aspect of vision. A significant fraction of this work involves studies of nonhuman subjects, and this research, though often motivated by an interest in simply using results from other animals to infer and understand various aspects of human vision, has done much to reveal the details of visual processing across very disparate species. In addition, many investigators have also pursued comparative studies of vision as ends in themselves, typically working toward the twin goals of understanding how visual capacities of various species are matched to environmental possibilities and how such arrangements may have evolved. In this chapter I appropriate results drawn from both of these approaches in order to summarize some comparative features of vision. The intent is to reveal examples of common solutions achieved by evolutionary experiments in seeing and to note cases where visual diversity allows solutions to particular environmental opportunities. Let us start by considering some basic features of photic environments and eyes.

visual systems, the principal shaping tool is the photic environment. Fundamental Features of Photic Environments Animals have evolved a range of photoreceptive devices that allow the harvest of light to be employed toward multiple ends—for instance, in biological timing and navigation, as well as in seeing. In natural environments sunlight is the principle source of photic energy. Solar radiation is heavily filtered as it passes into earth’s atmosphere such that the radiation spectrum of light reaching the planet surface encompasses only a relatively narrow range of wavelengths, from approximately 300 to 1,100 nanometers. As a result of some additional limitations that are inherent in biological light detectors, this range becomes even further truncated. In particular, longer wavelength lights contain insufficient energies to trigger effectively a change in photopigment molecules while the eyes of many species contain spectral filters of one sort or another that greatly attenuate short-wavelength lights and accordingly make them unavailable for further visual processing. The result is that vision in animals is effectively limited to a span of wavelengths that lay somewhere in the range from approximately 300 to 700 nanometers, and most species do not see well over this entire range. Within this interval, variations in natural conditions can produce virtually infinite variations both in the overall amount of light (the total radiance) and in the relative distribution of light at different wavelengths (the radiance spectrum). These possibilities are illustrated in Figure 3.1, which shows one set of radiance spectra measured for direct sunlight, for clouds, and for blue sky. Note, for example, that direct sunlight and blue sky differ not only in their relative distributions of spectral energy but also in peak radiance, in

ENVIRONMENTS AND EYES Sensory systems evolve within the constraints and opportunities provided by the environment. In the case of 52

Environments and Eyes

Figure 3.1 Radiance spectra for three natural sources of ambient light. The units of measurement for radiance used here are μmol/meter2 /second/steradian/nanometer. (Data from Endler, 1993.)

this case by more than five orders of magnitude. Examinations of spectra like these suggest that it would be useful to design visual systems that both allow for an analysis of variations in the spectral distribution of light and operate efficiently over an expanded range of overall radiance. The variations in the spectral distribution and overall radiance of ambient light are enhanced even more as a result of fluctuations in light at different times of the daily cycle and as a result of the physical properties of matter intervening in the light path. For example, sunlight filtered through a layer of foliage is preferentially absorbed by plant pigments and consequently presents a

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very different radiance spectrum from that of the same sunlight viewed in an open field. Filtering processes like these yield a wide range of potential photic habitats and serve to provide important constraints on the nature of vision best suited to exploit local conditions. Probably nowhere is that fact more clearly evident than in aquatic habitats. Longwavelength radiation is preferentially absorbed by water, the result being that as one descends from the surface of a body of water, the total radiance decreases, and the spectrum of available lights also shifts toward the shorter wavelengths. The filtering is such that in ocean waters at depths of 200 meters or so the total radiance has been greatly decreased and the remaining light is more nearly monochromatic with its energy centered near 480 nanometers (Warrant, 2004). Systematic measurements have been made on the nature of available light in some important aquatic and terrestrial habitats (Endler, 1993; Jerlov, 1976; Johnsen et al., 2006; Webster & Mollon, 1997). Of course, vision results mostly not from directly viewing light sources, but rather from observing objects in the environment. For visual purposes objects can be characterized by the efficiency with which they reflect light as a function of wavelength (the reflectance spectrum). As illustrated in Figure 3.2, the light reaching the eye of a viewer is, effectively, the product of the spectrum of the illuminant and the reflectance spectrum of the object. Accordingly, visual stimuli may be dramatically altered by changes in the quality of the illuminant, as for instance at different times of the day, or by changes in the reflectance properties of the object. These alterations can be employed as a means for changing the nature of signals exchanged between animals or between an animal and a food source. A common example is that of fruiting plants. As fruits mature and change from unripe to ripe, their reflectance properties are often dramatically shifted (Figure 3.3). For people and other species with the appropriate visual capacities (including various species of

Figure 3.2 The spectral distribution of light reaching the eye of a viewer. The spectral distribution of light reaching the eye of a viewer (right panel) is the product of the radiance spectrum of the source (left panel) and the reflectance spectrum of the viewed object (middle panel). Scaling on the ordinate axis is arbitrary. (After Sumner & Mollon, 2000a.)

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Eyes: Basic Design Features and Evolution

Figure 3.3 Reflectance spectra of unripe (solid line) and ripe (broken line) specimens of Sapotaceae, a fruit that forms part of the diet for a number of different nonhuman primates. As viewed by a human eye, the unripe fruit appears greenish, then turning to yellow as it ripens. (After Sumner & Mollon, 2000b.)

insects, nonhuman primates, fishes, and birds), this shift yields a large color change, and that information can allow a viewer to evaluate quickly and easily the potential palatability and nutritional quality of fruit, perhaps thus guiding a decision as to whether it is worthy of the effort of harvesting. In addition to overall radiance and spectral variation there is another feature of light that can potentially provide useful information to an animal. Light radiated from the sun is unpolarized; that is, the electric vector (e-vector) of such light vibrates in all directions in the plane perpendicular to the direction of propagation. However, as light passes through the atmosphere, it becomes polarized so that the e-vector is oriented in a particular direction at each point in the sky relative to the position of the sun. It has long been appreciated that some species can analyze e-vector information to orient the animal relative to these polarization patterns. For example, honeybees are known to analyze patterns of polarized skylight to provide directional maps that can be used to chart the location of food sources (Rossel, 1993). Polarization sensitivity is believed to exist to varying degrees in many insects and birds, as well as in some fishes (e.g., Cronin, Porter, Bok, Wolf, & Robinson, 2010; Dacke, Byrne, Scholtz, & Warrant, 2004; Frings, 2009; Hawryshyn, 2010; Labhart & Meyer, 1999; Wehner & Labhart, 2006). The biological mechanisms present in the visual systems of these animals that allow for an analysis of the plane of polarization of light are absent from the eyes of mammals; thus, mammals are unable to exploit this potential source of visual information.

Although primitive devices that can detect light abound in simple organisms in the form of eyespots and eyecups, structures that are effectively not much more than localized accumulations of light-sensitive pigments on the surface of the body, the eyes with which we are most familiar are prominent organs that serve to collect light and focus it onto photosensitive cells. The consensus is that such eyes first appeared in great numbers during the Cambrian period, a time that saw an explosive divergence of metazoan phyla (Fernald, 2008). Examination of fossils from the middle Cambrian period (about 515 million years ago, or MYA) reveals irregularities on the body surface that have the form of a series of closely aligned parallel ridges or grooves, perhaps permitting them to serve as diffraction gratings. If so, these structures could have made the animals appear iridescent in the ambient light of their watery habitats. It has been proposed that this structural feature may have enabled for the first time the exchange of informative visual signals between individuals (Parker, 1998). Whether this adaptation was a trigger for a rapid evolution of eyes remains a matter for discussion, but in any case a number of phyla, including mollusks, arthropods, and chordates, emerged from the Cambrian period with functional eyes (Land & Nilsson, 2002). Contemporary species provide examples of a variety of different eye designs. In essence, an eye images the outside world on a two-dimensional sheet of photoreceptor cells. Photoreceptors form immediate neural networks with other cells to make up the retina, and that structure constitutes the first stage of the visual nervous system. Two basically different kinds of optical systems have emerged to handle the task of image formation. In one, the retina is concave in shape; in the other, it is convex. If the retina is concave, a single optical element can be used to form an image across the retinal surface. This is the sort of arrangement found in the camera-type eyes characteristic of vertebrates. If the retina is convex, however, individual photoreceptive elements will be sensitive only to a narrow beam of incoming light, and this limitation is accommodated by the use of small lenses that are replicated again and again across the surface of the eye. The latter is characteristic of the compound eyes of arthropods (Goldsmith, 1990). Within these two design constraints are a number of qualitatively different imaging strategies. Several of these are illustrated schematically in Figure 3.4 (Land, 1991). The top half of Figure 3.4 shows alternative versions of retinas that maintain a basic concave shape. In each case the photosensitive portion of the eye appears as shaded,

Environments and Eyes

Figure 3.4 Principal mechanisms for image formation in the eyes of contemporary animals. The photosensitive portions of the eyes are indicated by the shading. Light rays are drawn to illustrate the image formation scheme. Further explanation is given in the text. (After Land, 1991.)

and light rays have been drawn to illustrate the image formation scheme. A possible precursor to the more elaborate versions of a concave retina is a simple pit (Figure 3.4, panel a), the bottom surface of which is pigmented so that shadowing of light allows the animal to gain an indication of the directionality of a light source. Two successors to this plan are illustrated in the middle. They each employ large refracting elements to form a retinal image. In one (panel c) this is done with a spherical lens (as is typical of the eyes of fishes and others); in the other (panel d), principal refraction of light is accomplished by the cornea. The latter arrangement is the one found in human eyes, as indeed it is in all terrestrial vertebrates. The eye schematized in panel g ingenuously accommodates the concave retina by reflecting light back from a concave surface (a mirror) onto the photoreceptor cells. Mirrored eyes like this are rare; the eyes of scallops provide a well-studied contemporary example (Land, 1965). The bottom half of Figure 3.4 illustrates various optical arrangements that have evolved in conjunction with a convex retina. The scheme shown in panel b, in which the individual elements are tube-like structures with a photopigment accumulation at the base of the tube, is again a possible primitive precursor for more sophisticated convex retina designs. In the middle are the two common optical arrangements found in compound eyes. One (panel e) uses so-called apposition optics, in which each of the lenses forms an image on photosensitive

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pigment in individual structures called rhabdoms. In the other version of the compound eye (panel f), the image is formed by the superposition of rays coming through many of the optical elements. Most insects have one or the other of these arrangements, and a usual generalization is that superposition eyes are found in diurnal insects whereas apposition eyes are found mostly in nocturnal insects (Stavenga, 2006). Finally, a reflecting-mirror version of the superposition eye (panel h) is found in decapod crustacea (shrimps, prawns, crayfish, and lobsters). All in all, animal eyes have managed to exploit an impressive array of image formation schemes—in the words of one of the foremost students of the matter, “It does seem that nearly every method of producing an image that exists has been tried somewhere in the animal kingdom” (Land, 1991, p. 133). The main business of the visual system is to detect and analyze the spatial structure of the environment. Consequently, the details and relative merits of various types of eyes as devices for resolving images have been the subject of intense scrutiny (for extensive reviews of this topic, see Hughes, 1977; Land, 1999a; Land & Nilsson, 2002; Stavenga, 2006). The principles governing the potential resolution of eyes are well understood. For instance, in single-lens eyes image resolution is conditioned by the focal length of the lens and the physical separation of the individual receptors. This is because a longer focal length yields higher magnification of the image whereas a denser packing of the receptors increases the spatial sampling of the image. A limitation on increasing the packing density of receptors is that as they are pushed closer together, they must necessarily become smaller. This is a problem because smaller receptors would enhance spatial sampling; but because they are smaller, they also become less efficient devices for trapping light. The consequence is the inevitable trade-off between these two features. The trade is such that, in general, the portions of vertebrate receptors responsible for absorbing light (the outer segments) have diameters of not less than about 1 μm and a receptorreceptor spacing of not less than about 2 μm (Goldsmith, 1990). Examinations of single-lens eyes suggest that the packing density of receptors has been optimized so as to take the fullest advantage of the optical quality of the image provided by the anterior portion of the eye. Compound eyes have a design problem not inherent in single-lens eyes. In compound eyes, the small sizes of the optical elements suffer a loss of resolution from diffraction of the focused light. This loss of resolution could be counteracted by an increase in the size of individual lenses and in the number of individual elements. Such increases

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in size are, however, difficult to accommodate in smallbodied animals. Just how difficult can be appreciated by an early calculation suggesting that if a compound eye was designed to yield the same spatial resolution as that produced by the single-lens human eye, it would have to be about 1 meter in diameter (Kirschfeld, 1976)! This is obviously impractical, but one means that has evolved to increase spatial resolution in compound eyes without having to increase overall eye size is to adapt small regions of compound eyes so that they can provide for localized regions of higher acuity. For example, preferential increases in the sizes of some individual facets allow some flying insects to have a zone of increased spatial acuity that is directed toward the horizon (Land, 1999a). Regional specializations designed to support zones of higher acuity are common to many types of eyes whether they are of the compound or single-lens type (Collin, 1999). For example, in vertebrate eyes the density of retinal ganglion cells (the output cells from the retina) can provide

a rough guide as to the way in which visual acuity varies across the visual field, with ganglion cell density highest in those regions of most acute vision. Figure 3.5 shows a map of the retinal distribution of ganglion cells in a large carnivore, the spotted hyena (Crocuta crocuta). The hyena has a pronounced horizontal streak that stretches across the retina and is made up of densely packed ganglion cells. Visual streaks of this sort appear in the eyes of a number of mammals, often in animals that live in open terrain and that may therefore be thought to benefit from having heightened visual acuity along a plane parallel to the surface of the ground where most objects of interest will appear (Hughes, 1977). Cell distributions like those of Figure 3.5 can be used to generate quantitative estimates of the spatial resolution capacity of the animal. This can prove useful for predicting visual acuity and thus avoiding having to ask the animal to serve as a subject in a behavioral test of acuity, an invitation one might issue only with some reluctance as in the case of subjects like the spotted hyena.

Figure 3.5 Ganglion-cell isodensity map for the retina of the spotted hyena (Crocuta crocuta). The tracing is a retinal flat map where each of the contours encloses the ganglion cell densities indicated in the box above. Note the presence of a pronounced “retinal streak” with the maximal ganglion cell density found along the horizontal meridian in the temporal portion of the retina. Estimates of visual acuity can be derived from ganglion cell density information such as that illustrated here. (Calderone, Reese, & Jacobs, 2003.)

Environments and Eyes

Another example of regional eye specialization is a little closer to home. Most primate retinas have a fovea, a central region that contains a heightened packing of cones, the daylight photoreceptors. The foveal region is also free of blood vessels, and these structural adaptations, in conjunction with the superior optical quality that derives for light rays that pass through the center of the lens as well as a robust representation of this region in the nervous system, yields a central visual field in which visual acuity is unusually high. Studies of structural features in the eyes of the kinds described here for fly, hyena, and human have been used to infer the presence of specialized visual capacities in many other animals (e.g., Ahnelt & Kolb, 2000; Hughes, 1977; Pettigrew, Bhagwandin, Haagensen, & Manger, 2010; Pettigrew & Manger, 2008). Photosensitivity The sensitivity of an animal to light is jointly determined by the operation of multiple mechanisms located in the visual system. Three such mechanisms that have major influences are the photopigments housed in the photoreceptors, the presence of a variety of different ocular filters, and the active processing of visual information that goes on in the neural networks of the visual system. Photopigments The translation of energy from light into nerve signals is initiated by the activation of photopigments.

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Photopigments are intrinsic membrane proteins called opsins that are covalently bound to retinoid chromophores. Light causes an isomerization of the chromophore, and that shape change initiates a biochemical cascade culminating in photoreceptor activation (Nickle & Robinson, 2007). Photopigments can be characterized according to the efficiency with which they absorb light (the spectral absorption curve), and it has been known for well more than a century that the absorption properties of photopigments contain important implications for understanding vision (Jacobs, 1998). The essential point is that only light that is absorbed by photopigments contributes to sight. An example of the intimate linkage that can exist between photopigment absorption characteristics and vision is given in Figure 3.6, which shows results obtained from the Syrian golden hamster, a small rodent that is somewhat unusual in having only a single type of photopigment active under daylight (photopic) conditions (Williams & Jacobs, 2008). The solid line is the absorption spectrum of the hamster cone photopigment. Like all photopigments, that of the hamster has broad spectral absorption, but the efficiency with which it absorbs light varies greatly as a function of wavelength with peak sensitivity of about 500 nm. This species has also been the subject of direct tests of vision and the triangles in Figure 3.6 plot those results, indicating the sensitivity of this species to lights of different wavelengths. It is apparent that the spectral absorption properties of the hamster cone photopigment almost perfectly predict the spectral

Figure 3.6 Comparison of photopigment and behavioral sensitivity. The continuous line is the absorption spectrum of cone of the single type of cone pigment found in the eye of the Syrian golden hamster (Mesocricetus auratus); the curve has a peak value of 505 nm. The triangles show average threshold sensitivity obtained from two hamsters in a behavioral test. The two sets of data are shifted on the vertical axis so that they best superimpose. (After Jacobs, 1998.)

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sensitivity of the behaving animal. Various assumptions and corrections are usually required in order to compare photopigment absorption characteristics and different measurements of vision legitimately, but as the example of Figure 3.6 suggests, photopigment measurements per se may be used to derive strong inferences about the nature of vision. It is partially because of that fact that enormous effort has been directed toward measuring and understanding photopigments in a wide range of different animals. The spectral absorption properties of photopigments depend on both of the essential components of photopigments. The width of the absorption curve is determined by the structure of the chromophore. In this case nature has been economical because only four different types of chromophores are used to construct all animal photopigments. These pigment chromophores are Vitamin A–based retinaldehydes: (a) retinal (A1), the chromophore widely used in the photopigments of many vertebrates and invertebrates; (b) 3,4-dehydroretinal (A2) used by most freshwater fishes, amphibians, and reptiles; (c) 3-hydroxyretinal (A3), found in the photopigments of certain insects such as flies, moths, and butterflies; and (d) 4-hydroxyretinal (A4), a chromophore apparently used exclusively by a bioluminescent squid (Schichida & Matsuyama, 2009). Some animals are able to utilize two different chromophores to make photopigments, and this can (and often does) yield mixtures of photopigments in the retina based on these different chromophores. In addition, some switch from one chromophore to another at different stages in their life cycle (e.g., during metamorphosis in some anurans and during the migration between freshwater and marine environments for some fishes.) Changing the chromophore can alter spectral sensitivity, sometimes drastically, and thus chromophore substitutions can significantly impact an animal’s vision. The spectral positioning of the absorption curve of the photopigment depends on the structure of the opsin (Carroll & Jacobs, 2008; Yokoyama, 2008). As was noted earlier, opsins are membrane-spanning proteins. Figure 3.7 is a schematic of the opsin for the primate middle and long wavelength cone photopigments. It is composed of seven alpha-helices that weave back and forth through the membrane. The chromophore is attached to the opsin at the site indicated. All photopigments share this general configuration, but there is individual variation in the total number of amino acids in the polypeptide chain (from about 350 to 400 in different types of photopigment) and in their sequence. These sequence variations determine the spectral absorption properties of the photopigment;

thus, because there are many alternate versions of the opsin molecule, there is a correspondingly large number of different photopigments. A particularly interesting example of the subtleties and complexities of the linkages between opsin genes and photopigments occurs in the cichlid fishes that are native to the Rift Lakes of East Africa. These fish comprise several hundred different species that are notable for having been very rapidly diversified, a capacity that allows them to successfully exploit a range of different habitats. Part of the reason for the success of this group seems to reflect the ability of these species to use a novel mechanism for easily modifying their retinal photopigments, and thus ultimately altering their visual sensitivities. That comes about because, although there are five different types of cone opsin genes in these fish, there is considerable variation in the pattern of gene expression. The upshot is that although the retinas of these fish each mainly express only three different types of cone pigment, differential degrees of expression of the five types of opsin genes allows for a tremendous diversity of the photopigment complement across the various species. In turn this allows closely related species, even those living within a single lake, to have radically different photopigment arrangements and, thus, experience greatly variant visual worlds (Carleton et al., 2010; Parry et al., 2005). Over the years measurements have been made of many dozens of different animal photopigments (for summaries, see Bowmaker, 2008; Briscoe & Chittka, 2001; Yokoyama, 2008). Some generalizations can be drawn from this effort. First, most species have more than one type of photopigment, and in those cases visual behavior is controlled by some combination of signals originating in different pigment types. It is fairly typical to find three, four, or five different kinds of photopigments in a retina, but there is variation from that number in both directions. For instance, the retinas of many deep-dwelling fishes make do with only a single type of photopigment (Bowmaker, 1995), whereas, at the other extreme, the current record for pigment production is held by the mantis shrimps, members of a group of crustaceans that are able to make adaptive use of at least 11 different photopigments (Cronin et al., 2010). Second, although photopigments of different species vary significantly in their spectral positioning along the wavelength axis, the shape of the absorption curves changes in a lawful manner as a function of the location of their peak sensitivity (λmax ). This means that mathematical expressions can be derived to produce templates to account economically for the absorption spectra of any photopigment. A variety of different templates have been proposed

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Figure 3.7 Two dimensional model of the human L and M cone opsins. Each circle represents a single amino acid. Dimorphic variations at a small number of the amino acid locations account for variations in the spectral absorption properties of the cone pigment. The location where the retinal chromophore is attached is indicated by the filled circle. (Modified from Carroll & Jacobs, 2008.)

to accomplish this task (Baylor, Nunn, & Schnapf, 1987; Carroll, McMahon, Neitz, & Neitz, 2000; Govardovskii, Fyhrquist, Reuter, Kuzmin, & Donner, 2000; Lamb, 1995; Palacios & Goldsmith, 1993; Stavenga, Smits, & Hoenders, 1993). Finally, pigments from different animals are frequently positioned at the same spectral locations. For example, in all cercopithecine primates (Old World monkeys, apes, and humans) two of the classes of cone photopigment have spectral locations that are virtually the

same for all of the species in this group. This reflects the fact that structural variations in opsins specifying particular pigment positions are often conservatively maintained during the evolution of an animal line and thus phylogeny often provides good predictions of photopigment complement. The absorption spectra for the daylight pigments of three species (goldfish, honeybee, and macaque monkey) appear in Figure 3.8. They exemplify the kind of variations

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Figure 3.8 Absorption spectra for the cone photopigments found in three different vertebrate species. The chromophore for the photopigments of the honeybee and macaque monkey is retinal; the chromophore used in the construction of goldfish photopigments is 3,4-dehydroretinal.

in photopigments found among animals. These species have three or four different types of such pigment. In addition, goldfish and monkey have a population of rods that support vision in low light, and this adds another pigment type to their retinal mix. The pigments from two of these, the honeybee and the macaque monkey, are based on retinal chromophores; goldfish pigments use 3,4dehydroretinal. Note that the latter allows one of the fish pigments to be shifted much further into the long wavelengths than can be achieved for pigments built with retinal chromophores and that the spectral absorption bandwidth is greater for these 3,4-dehydroretinal pigments. Both goldfish and honeybee have photopigments that absorb maximally in the ultraviolet (UV) part of the spectrum. Some mammals (mostly rodents, apparently) have been found

to have UV pigments, but primates are not among them (Jacobs, 2010). The results of variations in photopigment complements of these kinds are to provide spectral windows to the world that can vary greatly for different animal species. They also set the stage for significant variations in the color vision of different animals. Opsins are produced by actions of single genes, and the past 30 years have witnessed significant progress in studying and understanding these genes. Opsin genes belong to a large family of cell surface receptor genes (which include a significant number linked to hormone and neurotransmitter receptors as well as other sensory receptors) and are believed to derive from a single ancestor in this family. Many different opsin gene sequences have been derived: As of this writing, nearly 1,000 primary sequences for opsins have been deposited in GenBank , a database maintained by the National Center for Biotechnology Information (NCBI) (www.ncbi.nlm.nih.gov/pubmed). This accumulation of opsin gene sequences for diverse species in turn permits sequence comparisons from which ideas about the evolution of opsins can be derived. One caution in this enterprise is that the timing of evolutionary events derived from sequence comparisons depends on assumptions about the rate of molecular evolution (the so-called “molecular clock”), and that issue has generated considerable controversy. In particular, there is evidence that the mutation rates commonly used to calibrate such clocks differ for different groups of animals and thus that the assumption of a single clock rate appropriate for all cases is possibly unrealistic (Li, 1995). A consequence is that dates given for various events in photopigment evolution are provisional. The eyespots of green algae contain opsins that bear significant sequence similarity to opsins of both invertebrate and vertebrate animals, suggesting that motile microorganisms like them might have been the first to develop photopigments (Deininger, Fuhrmann, & Hegemann, 2000). For historical context, the bacteria that employ pigments for photosynthesis can be traced back at least 3 billion years (Des Marais, 2000). Photoreceptors are believed to have appeared prior to the divergence of protostomes and deuterostomes (some 600 MYA). In vertebrates, a single cone photopigment appeared first. Subsequently, this progenitor pigment gene duplicated, and the two diverged in structure, yielding two types of cone pigment having respective maximum sensitivity somewhere in the short and in the long wavelengths. Although the timings of these early events are necessarily tentative, from comparisons of opsin gene structures in conemporary jawless and jawed vertebrates it seems likely that four classes

Environments and Eyes

of cone opsin genes had appeared by at least about 540 MYA (Collin, 2010; Collin, Davies, Hart, & Hunt, 2009). This timing implies that multiple photopigments would have been available for exploitation during the explosive expansion of eyes of the Cambrian period. It is noteworthy that, contrary to what was originally believed, rod opsins apparently evolved from cone opsin genes as a result of gene duplication implying that cones were actually the primordial photoreceptors. A simplified phylogenetic tree summarizing the inferred relationships among a sample of vertebrate photopigments is shown in Figure 3.9. Note that whereas all contemporary vertebrates have rod pigments with only slightly variant spectral absorption properties, cone pigments have evolved to have maximum sensitivity over a much greater portion of the visible spectrum. Other photopigment phylogenies based on similar comparisons of opsin sequences appear elsewhere (Briscoe & Chittka, 2001; Collin et al., 2009; Hunt, Carvalho, Cowing, & Davies, 2009; Schichida & Matsuyama, 2009; Yokoyama, 2000). Examination of the vertebrate opsin phylogeny of Figure 3.9 reveals the important fact that photopigments have been both gained and lost during evolution. Thus,

representatives of all four families of cone photopigments (identified as SWS1, SWS2, RH2, LWS) appear in modern animals from several groups (e.g., birds, fishes), but mammals have maintained pigments from only two of these families. How did this occur? The most common interpretation is that early mammals were nocturnal and given that lifestyle it may well have been adaptive to increase the representation of rods in the retina in order to maximize sensitivity to the low light levels available during peak activity periods. Very likely this change was at the expense of losing some retinal organizations that support daylight (cone-based) vision. Reflecting this loss, most contemporary mammals have only two types of cone pigment (Jacobs, 1993, 2010). As we shall see, some primates have added pigments and provide a notable exception to this rule. At the same time, other mammals have moved in the opposite direction having lost a photopigment and thus a class of photoreceptors as a direct result of mutational changes in short-wavelength sensitive (S) cone opsin genes. Animals that have suffered this fate include some rodents and nocturnal primates, as well as many marine mammals and some bats (Levenson & Dizon, 2003; Levenson et al., 2006; Jacobs, Neitz, & Neitz, 1996;

human M human L chicken L chameleon L newt L goldfish L medaka L

LWS

Rh2

Rh1 SWS2

SWS1

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(490 nm–620 nm)

chicken M chameleon M goldfish M1 medaka M various rod opsins

(470 nm–510 nm)

(480 nm–510 nm)

chicken S chameleon S newt S goldfish S medaka S2 human S chicken S chameleon UV newt UV goldfish UV medaka S1

(440 nm–460 nm)

(360 nm–430 nm)

Figure 3.9 Opsin gene family tree. Shown here are a few representative species that draw their photopigments from the five families. The range of peak sensitivities characteristic of the pigments from each of the gene families are indicated to the right of the brackets. (The tree construction is derived from Hisatomi & Tokunaga, 2002.)

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Peichl, 2005; Williams & Jacobs, 2008; Zhao et al., 2009). It is not known why all these species have lost this particular cone pigment. Even so, the very presence of these nonfunctional genes makes clear that some ancestors of these animals must have had functional versions of these genes and the pigments they produce, and consequently these mutated genes stand as signposts that can guide us to a better understanding of the evolution of photosensitivity and vision. In any case, one major lesson learned from photopigment phylogenies is that the evolution of photopigments is not a one-way street. Ocular Filtering Light has to be absorbed by photopigments to contribute to vision, but light must reach the photopigments in order to be absorbed. The retinas of most animals contain photopigments that have the potential to absorb significant amounts of light well below 400 nanometers (see Figure 3.7), but that potential often goes unrealized because of intraocular filtering. These filters take many forms and can occur in many anatomical structures in both vertebrate and invertebrate eyes (for reviews, see Douglas & Marshall, 1999; Hart, 2001; Jacobs & Rowe, 2004). Thus, the corneas or lenses of many species contain pigments that preferentially absorb short-wavelength radiation. An example is given in Figure 3.10, which shows transmission curves measured for lenses taken from the eyes of a number of

different mammals. Lenses of this sort are nearly transparent to longer wavelength lights, but they begin to attenuate very steeply at some short-wavelength location, becoming progressively more optically dense and effectively opaque at still shorter wavelengths. The spectral location of the transmission cutoff varies among the species shown in Figure 3.10 such that, for instance, a light of 350 nanometers will be very heavily attenuated by the lens of the squirrel eye but hardly attenuated at all by the hamster lens. In some cases the photoreceptors themselves contain screening pigments. These pigments are called oil droplets, and they are positioned directly in the light path to the photopigment. Many birds and reptiles, as well as some amphibians and fishes, have oil droplets, and frequently there are several different varieties of oil droplets in a single retina (Douglas & Marshall, 1999). Like lens pigments, oil droplets serve to attenuate short-wavelength lights, but unlike lens pigments the region of the attenuation may extend well out into the visible spectrum. As a consequence, a person viewing a fresh piece of retina sees these oil droplets as having a deeply colored appearance (red, orange, yellow, and so on). These pigments can serve to change the absorption efficiency of the photopigment lying behind the droplet. Figure 3.11 illustrates this effect by comparing the absorption properties of the four cone pigments of the pigeon retina both with and without their

Figure 3.10 Transmission properties for the lenses of three rodent species. Note that all three transmit light longer than about 500 nanometers with high efficiency, but they vary greatly in the spectral location where each begins to attenuate a significant amount of short-wavelength light. (Data from Douglas & Marshall, 1999.)

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Drawing Inferences About Animal Vision From Photopigment Measurements

Figure 3.11 Spectral sensitivities of the four types of cone found in the pigeon. The two sets of curves show sensitivity without (dashed lines) and with (continuous lines) filtering by the oil droplets that are found in each of the four cone types. (Modified from Vorobyev et al., 1998b.)

oil droplet filtering. Note the very significant changes in absorption that can be produced by oil droplet filtering. There are a wide variety of other intraocular filters found in various animals. Considered as a group, these filters obviously greatly condition the extent to which environmental light contributes to sight. Intraocular filters serve to reduce the influence that light can have on the visual system, and at first glance this might seem maladaptive. From his studies of the eyes of many different animals, Gordon Walls (1942), one of the great comparative vision specialists of the past century, famously suggested that “everything in the vertebrate eye means something.” Following Walls’s dictum, many investigators have felt impelled to try to understand what positive role intraocular filters might play. A variety of possibilities has been suggested. Among the more popular are these: (a) Intraocular filters might serve to protect the retina from high-energy (and potentially injurious) light characteristic of the ultraviolet portion of the spectrum, and this may be particularly important for animals behaviorally active at high light levels; (b) intraocular filters could enhance the quality of the image formed on the photoreceptors by reducing the effects of chromatic aberrations, which are especially troublesome for shortwavelength lights; and (c) for oil droplets particularly, intraocular filters can effectively narrow the spectral sensitivities of photopigments and hence may serve to increase the number of spectral channels (Vorobyev, 2003). There is little evidence to indicate whether any or all of these ideas are correct, but the presence of intraocular filters in so many different species makes it quite certain that they must play a variety of adaptive roles.

In recent years it has become possible to characterize objectively the photopigments of a species by using procedures such as spectrophotometry and electrophysiology, as well as by applying various molecular genetic approaches. These techniques have proven invaluable in identifying the number of types of photopigments in a given retina and in predicting their spectral absorption properties. Paradoxically, it is usually a much more difficult and timeconsuming task to make direct measurements of vision in most animals in a way that permits an understanding of how these pigments are used to allow an animal to see (Jacobs, 1981, 1993; Kelber & Osorio, 2010; Osorio & Vorobyev, 2008). A consequence is that there is now a near-universal tendency to go directly from measurements of photopigments to conclusions about how an animal sees. Although there are plenty of logical and compelling linkages between pigments and vision (as Figure 3.6 reveals), there is also need for caution in trying to establish these links. One difficulty in making the jump from pigment specification to predictions about vision is that the techniques used for measurement typically do not give information about the prevalence of the receptors containing these photopigments or of their distributions within the retina. Obviously, a small number of receptors containing a particular photopigment that happen to be tucked away in some corner of the retina will have a very different potential impact than if this same photopigment is present in large numbers of receptors liberally spread across the retinal surface. Even when there is some information about photopigment prevalence and distribution, interpretational problems may persist, as they do in the following examples. In recent years it has become possible to use immunostaining of opsins to identify and chart the spatial distribution of photopigments contained within a retina (Peichl, 2005; Szel, Lukats, Fekete, Szepessy, & Rohlich, 2000), and when this technique was applied to a marsupial, the South American opossum (Didelphis marsupialis aurita), it was discovered that the animal’s retina contains two classes of cones, one with maximum sensitivity in the short wavelengths and the other in the middle-to-long wavelengths (Ahnelt, Hokoc, & Rohlich, 1995). The presence of two classes of cone pigments would ordinarily be interpreted to suggest the possibility of dichromatic color vision. However, opossum cones were also found to be scarce, never reaching a density greater than about 3,000/mm2 (by comparison, rod densities in this same retina may reach 400,000/mm2 ), and of these cones there are only a

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handful of short-wavelength sensitive cones (never more than 300/mm2 ). An obvious concern is whether there are sufficient cone photoreceptors to capture the light needed to generate neural signals that can lead to color vision. Perhaps even more to the point would be to ask whether a devoutly nocturnal species like the opossum would often be active at light levels high enough to ensure such inputs. A second example of the difficulty in arguing from pigment information to visual capacity comes from study of the coelacanth (Latimeria chalumnae), a fish that lives at depths of 200 meters or so in the Indian Ocean and that has attracted much attention over the years because it is considered a living fossil, having been little altered over the course of the past 400 million years. A few years ago investigators succeeded in isolating two opsin genes from the coelacanth, producing photopigment from these genes in an artificial expression system and then measuring the absorption characteristics of these pigments (Yokoyama, Zhang, Radlwimmer, & Blow, 1999). The coelacanth has two photopigments with closely spaced absorption spectra (peaks of 478 nm and 485 nm). These pigments are apparently housed in cone and rod receptors, respectively. What inferences can be drawn about vision in the coelacanth from these observations? By analogy to cases in mammals where feeble color vision may be derived from neural comparisons of rod and cone signals, the authors suggested that the pigments may give the coelacanth some color vision. But how likely is that? For one thing, the coelacanth has only a very small number of cone photoreceptors (Locket, 1980), so neural signals generated from one of the sets of receptors will be minimal at best. A similar reservation comes from the fact that the spectra for two pigments are very close together. In vertebrates, the signals used for color vision reflect a neural computation of the differences in spectral absorption between photopigments having different absorption spectra. Those differences will be very small for pigments whose spectra are so greatly overlapped, and an unavoidable consequence is that a considerable amount of light will be required to generate reliable difference signals (De Valois & Jacobs, 1984). Would the restricted amount of light available at the ocean depths that mark the home of the coelacanth be sufficient to generate such neural difference signals? The point to be drawn from these examples is not that measurements of photopigments are unimportant for understanding animal vision. To the contrary, they are quite essential. What should be appreciated, however, is that frequently it is not straightforward to go from photopigment information to an understanding of how and

what an animal sees. Care is always required in this step, and whenever possible it is useful to know something about visual behavior in the species under consideration. The Role of the Nervous System This section has dealt with a consideration of eyes and what can be learned about comparative vision by examining their structures and functions. Of course, for most animals the processing of information that leads to vision does not end with the eye, but rather continues through the networks of a visual nervous system that lie beyond the eye. These visual systems can be compact and simple or extensive and highly elaborate, comprised of anywhere from dozens to billions of nerve cells. The variations in structure and organization of visual systems across phyla are so profound as to make impractical any compact summary. Indeed, unlike the optical portions of eyes, where the components and their principals of operation are well understood, much of the detail of the function of visual systems remains still poorly understood, thus making comparison difficult. Certainly there are many organizational features common to the visual nervous systems of different animals. For example, most visual systems have point-to-point topographic mappings between the visual field and various target structures in the visual system (Kaas, 1997). Thus, topographic maps of this sort are found in mammalian visual cortex, in fish optic tecta, and in the optic lobes of insects. Such organization allows neural economy in the sense that information about contour boundaries in the visual world can be processed using only short-range neural interactions. Although perhaps less universal across phyla, there is also frequently some sort of parallel processing in the visual system that allows for a structural segregation of functional information. In mammalian visual systems, for instance, separate cortical regions are at least partially specialized for the analysis of different aspects of the visual scene (Casagrande & Xu, 2004). In insects an analogous segregation can be seen in the utilization of subregions of the nervous system for analysis of movement information (Strausfeld, Douglass, Campbell, & Higgins, 2006). Finally, species having restricted nervous systems generally accomplish much of the filtering of environmental information using peripheral mechanisms, whereas relatively less preprocessing is done for species with more expansive nervous systems. The ultimate expansion is in the primate central visual system, where a large fraction of the neocortex is dedicated to the analysis of visual information. This extra processing capacity allows for richness in visual behavior that seems largely absent from animals

Environments and Eyes

with smaller and more hardwired visual systems. Indeed, it has been argued that the large expansion of primate visual cortex principally reflects a need for an increase in the processing of complex social-cognitive signals that can be inferred uniquely from examination of the visual world (Barton, 1998). Measuring Animal Vision Two general strategies have been used in the study of animal vision. One probes animal vision to explore capacities that seem important based on our understanding of human vision or those that would be useful to establish in order to understand better some biological feature of the visual system. The stimuli in such applications usually isolate some dimension of the visual input (e.g., movement, contour orientation), and visual behavior is most often assessed by measuring thresholds using discriminationlearning paradigms of the sort developed over the years by animal behaviorists (Blake, 1998; Jacobs, 1981; Kelber & Osorio, 2010). An illustration of results from a study of this kind is shown in Figure 3.12. The goal of this particular experiment was to establish the temporal sensitivity of a small diurnal rodent, the California ground squirrel (Spermophilus beecheyi ). The stimuli were sinusoidally flickering lights that were varied in frequency

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(cycles/second) and in luminance contrast. Through an operant conditioning procedure, ground squirrels were trained to detect the presence of such stimuli. The solid line in Figure 3.12 shows the sensitivity of ground squirrels to stimulus contrast as a function of the frequency of flicker; the dashed line shows discrimination results obtained from a human subject who was equivalently tested. The experiment indicates that although people are superior at detecting flickering lights of low to moderate frequency, for very fast flicker ground squirrels become superior and, indeed, people are quite blind to rapidly flickering lights that can be seen by ground squirrels. As for most experiments of this kind, these results were interpreted in light of details of the biology of the visual system and, to a lesser extent, as a step toward a better understanding of the normal visual behavior of this species (Jacobs, Blakeslee, McCourt, & Tootell, 1980). A second general strategy for measuring animal vision relies on the use of natural behaviors. These are most frequently behaviors that are reliably elicited by some set of stimulus conditions and that therefore do not require that the animal be trained to perform an arbitrary response. Two examples illustrate this approach. The first involves an analysis of feeding behavior in a teleost fish, the black bream (Acanthopagrus butcheri ; Shand, Chin, Harman, & Collin, 2000). In fish like this there are developmental

Figure 3.12 Temporal contrast sensitivity functions. Temporal contrast sensitivity functions obtained from behavioral measurements made on two California ground squirrels (Spermophilus beecheyi ), indicated by the symbols and solid line, and an equivalently tested human observer (triangles and dashed line). All three subjects were required to discriminate the presence of a sinusoidally flickering light varying in its frequency and luminance contrast. (After Jacobs et al., 1980.)

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changes in the position of the portion of the retina that has the highest ganglion-cell density (the area centralis) so that as the fish grows, the area centralis migrates from the central retina to a more dorsal location. As noted earlier, high ganglion-cell density is a regional retinal adaptation associated with the presence of heightened visual acuity. The question was whether this developmental change is paralleled by changes in visual behavior. To provide an answer, fish were offered food on the surface, at mid depth, and on the bottom of an aquarium. It was discovered that as the fish grew, the preferred feeding location changes in accord with the position of its area centralis; that is, fish exploit that portion of the field that can be scanned with the highest visual acuity. A second example involves the predatory behavior of an insect, the praying mantis (Sphodromantis viridis). These insects sit in wait and skillfully dispatch passing flies by flicking out a leg and striking the prey in flight. The praying mantis has large forward-looking eyes that allow considerable binocular overlap between the two eyes. Rossel (1986) conducted a series of clever experiments involving the mantis’s ability to strike flies accurately. By positioning prisms in front of the eye he was able to demonstrate that accuracy in striking behavior depends on the ability of the mantis’s visual system to compare the angular extent of the target at the two eyes. Both of these examples illustrate the use of natural behaviors to understand better visual problems faced by particular species. Each also yields strong inferences about the relationships between the visual system and behavior.

THREE ISSUES IN COMPARATIVE VISION A significant amount of research done on human subjects involves measurement of the limits of vision (e.g., the minimal amount of light required for detection, the highest temporal modulations that can be seen, the smallest wavelength change that can be registered, etc.). This approach can have a number of goals—for instance, to provide insights into visual mechanisms or to serve as a prerequisite for the development of practical applications. Studies of vision in other species are also frequently designed to assess the limits of vision, and these often have the goal of understanding the biology of vision as well. Studies of nonhuman species, however, also serve to focus attention sharply on the utility of vision. If two insect species differ significantly in their abilities to detect moving targets or if two rodent species have very different absolute thresholds, it is quite natural to seek an explanation of that fact in

differences in the visual worlds of the respective species. Those investigators who study animals having more stereotypic visual behaviors, and correspondingly more simply organized visual systems, have been particularly avid in championing this latter kind of approach. Over the past two decades in particular the influence of this way of studying vision (usually subsumed under the title ecology of vision) has been steadily expanding to encompass a wider range of species, up to and including the primates. Here I cite some examples drawn from the comparative vision literature that are intended both to illustrate variations in animal vision and, where possible, to indicate how these variations relate to the visual demands placed on that animal. For convenience, these illustrations are divided according to three general problems that visual systems must solve in order for animals to succeed. Detecting Change From a rodent searching the sky for a flying predator to a driver scanning a crosswalk for the presence of an errant pedestrian, success in seeing requires that novel events in the visual world be quickly detected and accurately appreciated. It is hardly surprising then that biological machinery appropriate for detection of stimulus change is an integral feature of visual systems. The importance of visual change (i.e., space-time alterations in the distribution of light) was dramatically underlined by early observations on human vision showing that when there is no change in the pattern of light falling on the photoreceptor array (a condition achieved by stabilizing the image of an object formed on the retinal surface), the visual scene simply fades from view after a few seconds to be replaced by a formless percept (Riggs, Ratliff, Cornsweet, & Cornsweet, 1953). In that sense, and at least for people, change and its detection are not just important—they are absolute prerequisites for sight. Good examples of the ability of animal visual systems to detect change come from studies examining how animals use movement to control behavior. Consider the visual problems encountered by an insect in flight where image motion across the retina is a combination of inputs from stationary backgrounds that are initiated by self-movement and from other moving objects in the field of view. Both of these generate complex input patterns that alter rapidly in configuration, have very high angular velocities, and can change direction unpredictably. Some of these flight behaviors have been well studied. Among these, observations have been made on how houseflies pursue one another (Land & Collett, 1974; Wagner, 1986). The aerial

Three Issues in Comparative Vision

pursuits of flies are quite spectacular, characterized by quick turns made at high angular velocities often separated by periods of little or no turning. A clear implication is that the fly visual system must be capable of responding to very rapid change. Particularly intriguing is the observation that there are characteristic differences in flight behavior between the two sexes. Although both male and female houseflies pursue targets, males are pursuit specialists. This is because males avidly pursue females in flight, frequently intercepting and then mating with them. The differences in pursuit behavior of male and female houseflies are uniquely paralleled by anatomical differences in the eye. In male houseflies, a region located in the frontodorsal portion of the eye contains enlarged ommatidia, each of which has a correspondingly larger facet lens (Land & Eckert, 1985). As was noted earlier, enlargement of the lens is an evolutionary strategy that allows a local increase in the acuity of compound eyes. There are other differences in the structure of these ommatidia in eyes of males and females, and there are also wiring differences in the visual nervous systems unique to the two sexes. The result is that the visual systems of houseflies show a significant sexual dimorphism that appears to correlate with the differences in aerial pursuit behavior. The portion of the eye in the male housefly that is adapted for initiating visual pursuit behavior has been fittingly dubbed the “love spot,” and direct recordings made from photoreceptors in this region show clear differences between males and females (Burton & Laughlin, 2003; Hornstein, O’Carroll, Anderson, & Laughlin, 2000). Figure 3.13 illustrates some of these differences. The left panel shows an index of temporal resolution of the photoreceptors of male and female houseflies. The curves effectively compare how efficiently receptors code information about lights presented at different temporal frequencies. In general, fly photoreceptors respond very well to high-frequency temporal change (e.g., they detect changes in visual stimuli at temporal frequencies

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of better than 100 Hz, a rate above what the human eye can discern), but male photoreceptors are clearly superior in this regard, showing about a 60% improvement above that achieved by the receptors from female eyes. The right panel of Figure 3.13 shows measurements made of the spatial resolution of these same fly photoreceptors. Again, the specialized photoreceptors of the male flies are significantly better than the corresponding female receptors at resolving spatial change. These clear differences in photoreceptor organization give males superior spatial and temporal resolution, and this in turn better enables them to locate and pursue small targets, such as females in flight. Remarkably, calculations show that the specialized photoreceptors of the males are capable of detecting another fly at twice the distance that can be achieved by the photoreceptors found in the eyes of female flies (Burton & Laughlin, 2003). Specialized visual adaptations always incur some cost, and the case of the housefly provides an example. Specifically, the faster responses of the male fly photoreceptors, involving as they do shorter time constants and higher membrane conductances, will require greater metabolic energy. There is likewise the burden of increasing the size of the facet lens to support higher acuity. It has been suggested that the better vision these adaptations give male flies are balanced by the energetic demands that are uniquely placed on female flies in reproduction, including activities such as egg production and laying (Hornstein et al., 2000). Animals employ a variety of means to shift their direction of gaze—direct eye movements of various kinds, movements of the head, or indeed movements of the whole body. It has been argued that the goal of these movements is not to scan the surroundings continuously, as one might imagine, but rather to try to keep an image relatively fixed on the retina by employing rapid adjustments of eye position followed by a period of smooth

Figure 3.13 Temporal (left panel) and spatial (right panel) resolution properties. Temporal (left panel) and spatial (right panel) resolution properties of the photoreceptors of male and female houseflies. (After Hornstein et al., 2000.)

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tracking before further adjustments are initiated. In this view animals obtain a sampling of a series of more or less stationary images, as for instance many animals do during the fixations that are separated by rapid saccadic eye movements. In an excellent review Land emphasized the commonality of this strategy across many different species and pointed out several reasons why it is advantageous to sample more or less stationary images than to allow continuous image movement across the eye (Land, 1999b). For one thing, fast movement of an image across the receptors leads to blurring. The occurrence of motion blurring depends both on the response speed of the eye and its spatial resolution. Eyes with higher response speeds and lower spatial resolution can tolerate movement at higher speeds without losing resolution. For example, at movement speeds of only 3 deg/s much spatial detail disappears from human vision, whereas insects, having lower spatial resolution and receptors with faster response speeds, show little loss of resolution for objects moving with velocities of up to 100 deg/s (Land, 1999b). Second, measurements show that it is easier to detect moving objects if the retinal image of the background is held stationary, as can be achieved through the use of compensatory eye movements. There are other, more complicated, reasons for why it may be important to achieve a relatively stable fixation of an object of interest. What is remarkable is that even though the nature of their visual systems may differ drastically, most animals employ some combination of mechanisms to reach this same goal (Land, 1999b).

Resolving Spatial Structure A number of features determine whether an animal will see an object. Among these, the size of the target and the degree to which the target contrasts with its surroundings are centrally important. Following the trend set by studies of human vision, early research on other animals concentrated on the utilization of object size as a cue to detection. This was mostly accomplished by assessing visual acuity through measurement of the smallest target that could be seen or, more usually, the minimal size differences that could be detected (e.g., the smallest separation between two parallel lines). In more recent years the contrast dimension began to receive equal scrutiny. With the widespread application of linear systems analysis to vision and the visual system, a typical contemporary experiment involves the determination of detection threshold for sinusoidal grating patterns that are jointly variant in size (spatial frequency, specified in cycles per degree of visual angle and thus directly translatable into retinal dimensions) and contrast (the difference in luminance or chromaticity between the peak and trough of the sinusoid). The results are plotted as spatial contrast sensitivity functions, a depiction quite analogous to the temporal contrast sensitivity functions of the sort presented earlier in Figure 3.12. Figure 3.14 shows spatial contrast sensitivity functions obtained from five types of mammals. These include diurnal (man) and nocturnal (owl monkey) primates,

Figure 3.14 Spatial contrast sensitivity functions obtained from five species of mammal in behavioral tests. The continuous lines plot of each species shows the sensitivity to contrast in sinusoidal luminance grating patterns determined over a wide range of spatial frequencies. (After Petry et al., 1984.)

Three Issues in Comparative Vision

a nocturnal carnivore (cat), and diurnal (ground squirrel) and nocturnal (rat) rodents. In each case, the animal was asked to detect the presence of a stationary sinusoidal luminance grating; the curve plots the reciprocal of threshold contrast required to see each grating. These functions have an inverted U-shape, such that some intermediate-size grating is detectable at lowest contrast and then visibility declines, precipitously for higher spatial frequencies and, usually, more gradually for lower spatial frequencies. It has been argued that when properly scaled, contrast sensitivity functions from all species have a common shape (Uhlrich, Essock, & Lehmkule, 1981). There is obviously a large variation in the range of spatial frequencies these species can detect, such that if viewed from the same distance, a high-frequency grating plainly visible to primate subjects is completely invisible to the rodents. Given the shapes of these functions, the inverse conclusion also holds; that is, a low spatial frequency target seen by a cat may go undetected by a human observer. The spatial frequency value obtained from extrapolation of the upper limb of the curve down to maximum contrast (indicated by the dashed line for the human observer) defines the high-frequency cutoff. It approximates the single value obtained from standard acuity tests like the familiar eye charts in which all the stimuli are presented at maximum contrast. Contrast sensitivity functions are heavily influenced by the details of the test situation. In particular, (a) for technical reasons it is hard to arrange an adequate test at the very low spatial frequencies; (b) the shape and height of the function can depend on the average light level of the target; and (c) because all retinas are to some extent heterogeneous, the details of how the subject views the targets are important. Therefore, detailed comparisons of functions of the sort shown in Figure 3.14 require caution. One useful feature often employed in comparative experiments of these types is to include a reference standard—for instance, results from human observers tested in the same situation as the animal of interest. In any case, there is a clear relationship between the ranges of spatial frequencies to which an animal is sensitive and its visual world—many objects of interest to a primate will appear at a distance so that much of the object will consist of higher spatial frequencies whereas for rodents similar objects will be viewed at close range and thus have their energies concentrated at lower spatial frequencies. Table 3.1 provides estimates of spatial acuity obtained from a number of common (and not so common) vertebrates. Tabled there are single values that express (in cycles/deg) the limit of resolution in each species. These were obtained either by extracting the cutoff frequency of

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the spatial contrast sensitivity function or from a direct measure of visual acuity. Most were derived from behavioral tests, although a few were inferred from electrophysiological measurements. Additionally, as noted above, estimates of visual acuity have often been obtained from calculations made on measurements of the spatial density of retinal ganglion cells (Collin & Pettigrew, 1989). Compilations of such anatomically based estimates of visual acuity for a number of other vertebrate species appear in (Collin & Pettigrew, 1989; Harmening, Nikolay, Orlowski, & Wagner, 2009; Kirk & Kay, 2004). Even though the comparability of these results is subject to the reservations noted earlier, it is obvious that there is a huge (∼8 octaves) variation in the spatial acuity of these various animals. The wedge-tailed eagle (Aquila audax ) represents the upper end of the acuity distribution of Table 3.1. Like other raptors, this bird has phenomenal visual acuity, in this case about two and one-half times better than that of an equivalently tested human (Reymond, 1985). Raptor retinas like that of the eagle are bifoveate, having a deep central fovea pointing about 45 degrees away from the head axis and a shallower temporal fovea that points within 15 degrees of the head axis. Distant objects are principally viewed with the deep foveae; near objects are principally viewed with shallow foveae (Tucker, 2000). A number of adaptations in the eye of this bird support high spatial acuity. First, cone photoreceptors are very densely packed together in the deep fovea. Second, the axial length of the eagle eye is long (about 35 mm vs. 24 mm in the human eye), allowing for a large retinal image. Finally, with the fully constricted pupil produced by high ambient light levels, the eagle eye shows only minimal evidence of optical aberrations (Reymond, 1985). These features combine to allow eagles to specialize in resolving high-frequency targets, such as would be presented by a small prey viewed from great heights. An interesting limitation placed on eagle visual acuity is its extreme dependence on light level. As acuity targets are dimmed, there is a precipitous decline in visual acuity so that eagle acuity actually becomes poorer than that of human observers at low light levels. Among the terrestrial species of Table 3.1 with relatively poor spatial vision is the opossum, a marsupial whose visual acuity barely exceeds 1 cycle/deg. The differences between eagle and opossum spatial vision and their visual worlds are instructive. As high-flying predators that alter their position rapidly and have to detect small targets, eagles require excellent spatial and temporal resolution. The visual adaptations that yield these properties (minimal spatial and temporal summation) require

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TABLE 3.1

Spatial Acuity Measured in Some Representative Vertebrate Species

Class

Species

Mammalia

Human (Homo sapiens) Macaque monkey (Macaca fascicularis) Squirrel monkey (Saimiri sciureus) Owl monkey (Aotus trivirgatus) Bushbaby (Otolemur crassicaudatus) Lemur (Eulemur macaco) Domestic cat (Felis catus) Lynx (Lynx europea) Dog (Canis lupus familiaris) Horse (Equus caballus) Cattle (Bos primigenius) Camel (Camelus bactrius) Sea Lion (Zalophus californicus) Meerkat (Suricata suricatta) Tree Squirrel (Sciureus griseus) Ground Squirrel (Spermophilus beecheyi ) Rat (Rattus norvegicus) House Mouse (Mus musculus) Tree Shrew (Tupaia belangeri ) Opossum (Didelphis marsupalis) Bat (Fruit Eating) (Artibeus jamaicensis) Wallaby (Macropis eugenii ) Fat-tailed Dunnart (Sminthopsis crassicaudata) Honey Possum (Tarsipes rostratus) Freshwater Turtle (Psuedemys scripta elegans) Loggerhead Sea Turtle (Caretta caretta) Water Snake (Nerodia spideon pleuralis) Frog (Rana pipiens) Barn Owl (Tyto alba) Tawny Owl (Strix aluco) Domestic Chicken (Gallus gallus) Wedge-tailed Eagle (Aquila audax ) Falcon (Falco berigora) American Kestrel (Falco sparverius) Starling (Sturnus vulgaris) Japanese Quail (Coturnix coturnix ) Woodpecker (Melanerpis carolinus) Goldfish (Carassius auratus) Bluegill Sunfish (Lepomis macrocuris) African cichlids (several species)

Reptilia

Amphibia Aves

Actinopterygii

Cutoff Frequency (cycles/deg.)

Method

Reference

50 50 17–35 10 4.3 3.8–5.1 6 5–6 11.6 23.3 2.6

Beh. Beh. Beh. Beh. Beh. Beh. Beh. Elec. Elec. Beh. Beh.

De Valois, Morgan, and Snodderly, 1974 De Valois et al., 1974 Merigan, 1976 Jacobs, 1977 Langston, Casagrande, and Fox, 1986 Veilleux and Kirk, 2009 Blake, Cool, and Crawford, 1974 Maffei, Fiorentini, & Bisti, 1990 Odom, Bromberg, and Dawson, 1983 Timney and Keil, 1992 Rehkamper, Perrey, Werner, Opfermann-Rungeler, and Gorlach, 2000

10 5.4–5.7 6.3 3.9 4 1.2 0.5 1.2–2.4 1.3 0.1–0.4 2.7 5.2 0.6 4.4–9.9 5.6 4.25 2.8 2.6–4.1 8–10 6.5 140 52–73 30.1 7.6 6.4 2.3 1.5–2.5 3–4 0.6–2.6

Beh. Beh. Beh. Beh. Beh. Beh. Beh. Beh. Elec. Beh. Beh. Beh. Beh. Elect. Elect. Elect. Beh. Beh. Beh. Beh. Beh. Beh. Elect. Elect. Elect. Elect. Beh. Beh. Beh.

Harman et al., 2001 Schusterman and Balliet, 1971 Moran, Timney, Sorensen, and Desrochers, 1983 Jacobs, Birch, and Blakeslee, 1982 Jacobs et al., 1980 Birch and Jacobs, 1979 Prusky, West, and Douglas, 2000 Petry, Fox, and Casagrande, 1984 Silveira et al., 1982 Suthers, 1966 Hemmi and Mark, 1998 Arrese et al., 1999 Arrese, Archer, and Beazley, 2002 Northmore and Granda, 1991 Bartol, Musick, and Ochs, 2001 Baker, Gawne, Loop, and Pullman, 2007 Aho, 1997 Harmening et al., 2009 Martin and Gordon, 1974 Jarvis, Abeyesinghe, McMahon, and Wathes, 2009 Reymond, 1985 Reymond, 1987 Ghim and Hodos, 2006 Ghim and Hodos, 2006 Ghim and Hodos, 2006 Ghim and Hodos, 2006 Northmore and Dvorak, 1979 Northmore, Oh, and Celenza, 2007 Dobberfuhl, Ullmann, and Shumway, 2005

Note: Beh: behavioral; Elect: electrophysiological

bright visual environments. As noted, one cost to the eagle for good spatial vision is much diminished capacity at low light levels. As a slow-moving omnivorous creature with a foreshortened visual world, the opossum has almost the opposite problem. Its nocturnal environment offers only a small amount of light. To harvest photons efficiently, the papillary aperture is opened wide, a prominent tapetum in the eye increases the likelihood

of absorbing all available light, and the retinal wiring is arranged to support increased spatial summation of neural signals. The result is diminished spatial acuity but the ability to see under very low light levels. Interestingly, unlike the eagle, spatial acuity in the opossum changes little over a considerable range of luminance differences (Silveira, Picanco, & Oswaldo-Cruz, 1982). Whether nocturnal species like the opossum require increased spatial or

Three Issues in Comparative Vision

temporal summation or some specific combination of the two is a complex question, the answer to which appears to depend on the details of their behavior (Warrant, 1999). Here we have considered the spatial resolution abilities of various vertebrates. As indicated previously, compound eyes present an entirely different set of restrictions and opportunities. Visual acuity in compound eyes has been conceived classically as determined principally by the angular spacing of the ommatidia (Horridge, 2005), and on average this limits visual acuity to about 0.5 cycle/deg. Measurements of acuity of a number of insects reveal that some exceed this value through regional specializations of the compound eye of the sort described earlier. On the other hand, many nocturnal insects also have much poorer visual acuity than this value (Warrant, 2006). In a number of cases direct correspondences can be seen between insect spatial vision and their visual behaviors (Land, 1997). Exploiting Chromatic Cues At the beginning of this chapter I noted that there is virtually infinite variation in the spectral energy distributions from light sources and objects and that light thus offers an immense amount of potential information to a viewer. How much of this potential is realized? Photopigments effectively serve as counters that initiate a signal whose magnitude is proportional to the number of captured photons. Because of that fact individual photopigments cannot code for variations in wavelength. The photopigmentbased signal is transformed by operations that go on within the visual system and, among other ends, these signals may be translated into a visual sensation that correspondingly varies in magnitude. If the eye has only a single type of photopigment, that resulting sensation must also lie along a single dimension. In the case of human vision such a visual sensation is one that spans the dimensions of brightness or lightness. For human viewers tested at any single state of light adaptation, there are, at maximum, no more than a few hundred discriminable steps along this (achromatic) dimension. Presuming our earliest seeing ancestors also may have been restricted to information derived from activation from only a single photopigment, it is a reasonable guess that they would have been similarly limited, irrespective of the ends to which this information may have been used. An early step in the evolution of eyes was the addition of a second type of photopigment (Figure 3.8). This provides potential benefits because it expands significantly the size of the spectral window through which photons can be captured and thus could yield a considerable visual advantage given

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the broad spectral patterns of most naturally occurring stimuli. If the outputs from receptors containing the two types of pigments are simply added together, the number of potentially discriminable steps will not increase. On the other hand, if signals originating from the two photopigments are compared in the nervous system in a fashion that computes the relative effectiveness of any light on the two pigments, a new dimension of experience can emerge—color. The mechanism supporting this new (chromatic) dimension is the presence in the nervous system of what are called spectrally opponent neurons. These cells effectively subtract the (log transformed) inputs from afferents carrying signals that originated in the two types of pigments. Such information allows one to distinguish reliably between variations in the wavelength and radiance content of a stimulus. So, for example, color vision allows one to discriminate between stimuli having their peak radiances in the long and short wavelengths respectively (like the sun and blue sky of Figure 3.1) regardless of the absolute radiance levels of the two. The effect of this arrangement is to add a second, orthogonal, dimension to the animals’ discrimination space, and because this chromatic dimension adds information at each of the span-of-brightness steps, the net result is significant expansion in the number of radiance-wavelength combinations that can be effectively discriminated one from another. The presence of spectrally opponent cells in the visual systems of virtually all contemporary animals attests to the great advantage that accrues from both adding a second pigment and then using such mechanisms to gain a dimension of color vision. An alternative argument offered for the early evolution of spectrally opponent mechanisms is that they are relatively insensitive to fast flickering lights and thus that they may have served to remove the perception of flicker resulting from wave action that is inherent in shallow water environments. Such flicker, it is argued, would have made it difficult to detect the presence of potential predators (Maximov, 2000). In the course of evolution, opsin gene changes added pigments to the original two with the result that, for instance, many contemporary vertebrates have three or four separate types of cone pigments. Through additional spectral opponency, the presence of a third or fourth type of pigment can potentially be used to provide added dimensions of color vision. This allows for much finer discriminations among stimuli that vary in spectral content, and the net result is that the number of differences that can be discerned among stimuli that vary in wavelength and radiance climbs very significantly. Humans

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have three cone photopigments and two dimensions of chromatic experience (characterized according to their appearance as “red/green” and “yellow/blue”) in addition to an achromatic dimension (“black/white”). Depending on the assumptions on which they are based, quite variable estimates have been offered as to what the presence of these two dimensions of color vision add to the total number of potentially discriminable stimuli—various suggestions running from several thousand to several million. Whatever the actual number may be, there is little doubt that acquiring a color vision capacity will significantly increase the number of items an animal may be able to discriminate. The relationship between number of photopigments and color vision dimensionality just described was firmly established through studies of normal and defective human color vision. Most people have three types of cone pigments and so-called trichromatic color vision. Those individuals reduced to two types of cone pigments through opsin gene changes have a single chromatic dimension (dichromatic color vision). Pigment complements are now known for many species, and a basic question is how well these relationships between pigments and color vision hold for other animals. Studies sufficient to establish the dimensionality of color vision have been reported for quite a number of nonhuman species (for reviews, see Bowmaker, 1995; Jacobs, 1993, 2010; Hart & Hunt, 2007; Kelber, 2006; Kelber, Vorobyev, & Osorio, 2003; Neumeyer, 1998). The general outcome is that the number of photopigment types can indeed predict the dimensionality of color vision. Thus, for example, of the species whose pigments are represented in Figure 3.8, the honeybee and macaque monkey have three photopigments and are trichromatic, whereas the goldfish with four pigments has acquired an added dimension of color vision and is tetrachromatic. Although results from pigment measurements and tests of color vision often line up well, there are instances where human color vision does not provide a very adequate model for color vision in other species. One example of this comes from behavioral studies of insects showing that although the utilization of signals from different photopigment types are processed through opponent mechanisms, they may be compulsively linked to specific aspects of behavior; for example, feeding responses and egg laying in butterflies are triggered by the activity of different combinations of photopigment signals (Menzel & Backhaus, 1991). Color vision in these species simply does not have the generality across stimulus conditions of the kind characteristic of human color vision. In fact, although the issue goes beyond the scope of this chapter,

there has been considerable recent discussion as to just how the term “color vision” should be considered to apply across the broad range of nonhuman species (Kelber & Osorio, 2010; Skorupski & Chittka, 2011) Although knowledge of the number of photopigments may provide insight into the dimensionality of an animals’ color vision, that fact by itself does not predict how acute the resulting color vision will be. That property will depend on the number of cones containing different pigments types, on their spectral properties and spatial distributions in the retina, and on the nature of chromaticopponent circuits formed from their outputs. For example, domestic cats and human deuteranopes both have two types of cone pigments with spectral properties that are not greatly different for the two species, and both formally have dichromatic color vision. However, with many more cones and much more robust spectral opponency, the human dichromat has much keener color vision than the cat. The point is that the quality of the resulting color vision, and likely the centrality of its role in vision, can be established only through appropriate behavioral examinations. The human model gives us great familiarity with trichromatic color vision, but at least among vertebrates it seems possible that tetrachromatic color vision may be at least as widespread as trichromatic color vision. Many teleost fish have four types of cone pigments, as do many birds, and they could all be tetrachromats (Bowmaker, 1995; Hart & Hunt, 2007; Vorobyev, Osorio, Bennett, Marshall, & Cuthill, 1998b). The question of why some animals have dichromatic color while others are trichromats or even tetrachromats remains unanswered. One suggested answer is based on the nature of photopigment spectra and the spectral band over which animals see (Kevan & Backhaus, 1998; Osorio & Vorobyev, 2008). The spectral span covered by the photopigments of tetrachromats is larger than that of trichromats (compare goldfish photopigments to those of the other species shown in Figure 3.7). The generation of color signals by spectral opponency requires that the spectral sensitivities of the pigments being compared overlap. Because the bandwidths of pigment spectra are fixed, in order to assure sufficient overlap to yield usable color signals one necessarily requires additional pigments to cover a broader spectral window. Some calculations have suggested that for optimal color discrimination one pigment is required for about every 100 nanometers of spectral range (Kevan & Backhaus, 1998). This argument may seem a bit circular in the sense that it does not answer the question of why one needs an expanded spectral range to begin with. The

Three Issues in Comparative Vision

answer to that question presumably lies in the details of how individual species use their color vision. If adding more cone types and more dimensions in color spaces greatly expands the range of discriminations that can be made, why have all animals not become, say, pentachromats? This, too, undoubtedly reflects the nature of discriminations that are important for survival in any particular animal line. More generally, however, there are inevitable costs associated with adding cone types to support a new color vision capacity. For one thing, adding a new class of cone reduces the number of cones containing the previous pigment types and hence reduces the signal to noise ratio of each of the cone types. This could make the color vision less efficient (Vorobyev & Osorio, 1998). For another, in many visual systems the neural circuits required to yield spectral opponency are quite specific, so acquiring new color capacity may require elaborate nervous system changes as well as pigment addition. And, finally, there is the fact that adding new nerve cells and neural circuits is metabolically expensive, thus serving to limit the expansion of processing machinery unless it significantly enhances the fitness of the animal (Laughlin, 2001; Niven & Laughlin, 2008). Opsin genes were apparently lost during the early history of mammals (Figure 3.9), and as a result the baseline condition for contemporary mammals is the presence of two different types of cone photopigment in the retina. This allows many mammals to have dichromatic color vision, although even that capacity is often somewhat feeble because most animals of this group do not have retinas with high cone densities (Jacobs, 2010). Primates provide a striking exception to this picture: Their retinas typically contain lots of cones, and many of them have excellent trichromatic color vision (Jacobs, 2008). Because good color vision must somehow have reemerged among the primates, the story of color vision in this group provides a good example of the utility of exploiting chromatic cues. Some 90 species of catarrhine primates (Old World monkeys, apes, and people) share in common their color vision capacities (Jacobs & Deegan II, 1999). It appears now that all of these animals have keen trichromatic color vision based on the presence of three classes of cone containing pigments absorbing maximally in the short (S), middle (M), and long (L) wavelengths (all virtually identical to those illustrated for the macaque monkey in Figure 3.8) and an associated visual system that supports spectrally opponent comparisons of signals from these three. The genes specifying the three opsins are located, respectively, on Chromosome 7 (S opsin gene) and at neighboring locations on the X chromosome (M and L

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opsin genes), the latter two being almost structurally identical (Nathans, Thomas, & Hogness, 1986). New World (platyrrhine) monkeys present a very different picture. Most of these species have polymorphic color vision with individual animals having any of several versions of trichromatic or dichromatic color vision so that there may be as many as six discretely different forms of color vision within a species (Jacobs, 2007). The polymorphism reflects variations in the array of cone pigments found in individual animals that in turn arise from opsin gene variations. Like the catarrhines, the S-opsin gene in these monkeys is on Chromosome 7, but unlike in the Old World primates platyrrhine monkeys have only a single X-chromosome opsin gene. There is M/L gene polymorphism at that locus,

Figure 3.15 Schematic representation of the photopigment basis for the polymorphic color vision of most platyrrhine (New World) monkeys. At the top are the spectral absorption functions of four classes of cone photopigments characteristic. Shown below are the six possible combinations of these photopigments. Each combination is found in some individual animals, and each yields a different type of color vision.

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accounting for the individual variations in color vision. As indicated in Figure 3.15, all these monkeys share in common the S pigment, but individuals have either any one of the three M/L pigments or any pair of the three, leading to six different color vision phenotypes. An important feature of the polymorphism is that because males have only a single X chromosome, they will inevitably have only a single M/L pigment and, thus, dichromatic color vision. With the benefit of two X chromosomes, females may become heterozygous at the pigment gene locus, and if they do, they have two different M/L pigments and trichromatic color vision (Jacobs, 1984). There is even further variation in color vision of the New World monkeys. Earlier I noted that some mammals have lost functional S-cone pigments through opsin gene mutation. One such animal is the nocturnal owl monkey (Aotus) that, by having only a single M/L cone pigment and no S pigment, ends up with no color vision at all; its vision is monochromatic (Jacobs, Deegan II, Neitz, Crognale, & Neitz, 1993). A second exception to this polymorphic theme in the platyrrhine monkeys occurs in howler monkeys (Alouatta). Instead of being polymorphic, these monkeys have a gene/photopigment/color vision arrangement that is effectively the same as that of the Old World monkeys (i.e., they are universally trichromatic) (Jacobs, Neitz, Deegan, & Neitz, 1996). Finally, Figure 3.15 illustrates a polymorphism that is based on three M/L opsin genes, but the fact is that some species of New World monkey appear to have only two such genes while at least one species seems to have more than three (Jacobs, 2008). The third large group of primates is the strepsirrhines, a collection of animals comprised of some 125 species native to Africa and Southeast Asia. These primates are more primitive than both the catarrhine and the platyrrhine groups; for example, the retinas of strepsirrhine species lack a foveal specialization and contain lowered densities of cone photoreceptors, and their eyes often feature a reflective tapetum. It is only recently that we have begun to get some indications of the distribution of color vision in these animals and the picture is still quite incomplete. Principally from examinations of opsin genes it appears that many of these species will be restricted to having monochromatic or dichromatic color vision. However, a few species from this group display M/L cone opsin gene polymorphisms and, based on the experience with the polymorphic platyrrhines, this arrangement predicts that such species will likely include individuals with dichromatic color vision (all the males and a subset of the females) along with a number of trichromatic females (Jacobs, Deegan II, Tan, & Li, 2002; Kawamura & Kubotera, 2004;

Tan & Li, 1999; Tan, Yoder, Yamashita, & Li, 2005; Veilleux & Bolnick, 2009). Information about the predicted dimensionality of color vision among the different primates along with comparisons of the primate opsin gene sequences has provided plausible scenarios for the evolution of primate color vision. The predominant idea is that, emerging from a nocturnal past, our early primate ancestors had two cone pigments (derived from an autosomal S-opsin gene and from a single LWS opsin gene located on the X-chromosome) similar to that seen in most other mammals. About 40 million years ago in the line that led to contemporary catarrhines, the X-chromosome opsin gene duplicated, and the newly produced gene then diverged in structure so that the pigment this new gene specified differed in spectral absorption from the product of the original gene. This allowed for separate M and L pigments and set the stage for the emergence of routine trichromatic color vision. The fact that the details of color vision are so similar among all of the catarrhine primates is evidence that these gene changes must have occurred at a time early in catarrhine evolution. It further seems likely that the lines to modern platyrrhines and catarrhines diverged prior to the point of gene duplication (Arnason, Gullberg, & Janke, 1998) and an implication of this conclusion is that the opsin gene polymorphism that allows some individuals to have trichromatic color vision was separately invented by platyrrhine monkeys. The polymorphism that seems to exist in a few of the strepsirrhines must also have emerged quite independently. Tests of dichromatic and trichromatic humans show that there are enormous visual advantages in being trichromatic, and the same conclusion is supported by the fact that trichromacy has been conservatively maintained in all of the Old World monkeys and apes. Given that, one may wonder why the New World monkeys haven’t moved beyond the polymorphic arrangement that allows no more than about one-third of all individuals to have a trichromatic capacity. One possibility is that it may be nothing more than a matter of bad timing. The gene duplication that was required to convert the catarrhines from dichromatic to trichromatic is a low probability event. It occurred early in catarrhine history, well before the great burst of speciation in that line, and thus all succeeding animals were able to profit from the change. X-chromosome opsin gene duplication has also happened at least once in platyrrhines (in the howler monkeys, as mentioned earlier). Unfortunately, this duplication occurred subsequent (perhaps only about 12 MYA; Schrago, 2007) to much of the divergence that has led to modern-day platyrrhines, and thus no other New World monkeys have been able to

References

take advantage of the color vision arrangement that the howler monkeys invented. Studies such as those of primate color vision provide excellent examples of evolutionary changes in an important feature of animal vision. It is clear that color vision can increase dramatically an animal’s capability to discriminate differences in its visual world, and it is useful to consider briefly the ends to which this enhanced capacity is directed. There is much contemporary discussion of the practical utility of color vision, and of how a particular color capacity fits into local need. Thus, for example, the relationships between floral coloring and insect color vision (de Ibarra & Vorobyev, 2009), between bird plumage characteristics and avian color vision (Vorobyev, Osorio, Bennett, Marshall, & Cuthill, 1998a), and between fish color vision and fish coloration (Marshall, 2000) have all been examined. For the primates, a long-time hypothesis is that many primates are frugivorous and so seek colored fruits—targets that are often embedded in a sea of foliage. They also have to determine whether that fruit is properly ripe and thus ready for harvesting (Figure 3.2). Important cues for these tasks are the differences in the spectral reflectance properties of the fruit and their surroundings, and, as the argument goes, an ability to detect such differences could be materially aided by a trichromatic color capacity (Mollon, 1991; Osorio, Smith, Vorobyev, & Buchanan-Smith, 2004) and so it may be that this is the relationship that fostered the evolution of primate trichromatic color vision. A number of recent studies that examined direct measurements made of the spectral properties of target fruits do find that primate trichromacy is well suited for these fruitharvesting tasks (Osorio & Vorobyev, 1996; Parraga, Troscianko, & Tolhurst, 2002; Regan et al., 2001; Sumner & Mollon, 2000b). One difficulty with this line of argument is that most trichromatic primates are not dedicated frugivores, and some even seem to eschew fruits almost completely. For example, the howler monkey, the only routinely trichromatic platyrrhine, is almost exclusively a foliovore (Asensio, Cristobal-Azkarate, Dias, Vea, & Rodriguez-Luna, 2007). To account for such cases, and to cover the fact that most primates in fact have a quite varied diet, it has been suggested that trichromatic color vision is also particularly well suited for the detection and evaluation of leaves that make up a principal part of the diet of many species (Lucas, Darvell, Lee, Yuen, & Choong, 1998) and there is evidence for that possibility as well (Dominy & Lucas, 2001; Sumner & Mollon, 2000a). It seems clear that to better appreciate the details of the evolution of primate color vision we will have to

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understand how primates actually employ color vision in the pursuit of their daily goals. In pursuing this end platyrrhine monkeys provide unique opportunities since we know that the M/L photopigments of these polymorphic species have been under selection for a considerable period of time (Surridge, Osorio, & Mundy, 2003) and that means that there should be individual differences in behavior among these monkeys that correlate with their differences in color vision. Laboratory-based experiments have detected some differences in foraging efficiency for animals having different types of color vision (Caine & Mundy, 2000; Smith, Buchanan-Smith, Surridge, Osorio, & Mundy, 2003). Unfortunately, however, studies of a number of different platyrrhine species conducted in the wild have to this point proved mostly unsuccessful in detecting individual variations in behavior that can be reliably traced to corresponding individual variations in color vision (e.g., Dominy, Garber, Bicca-Marques, & AzevedoLopes, 2003; Melin, Fedigan, Hiramatsu, & Kawamura, 2008; Vogel, Neitz, & Dominy, 2007). Despite the difficulties to date, it seems clear that studies of this sort will eventually prove key to understanding how color vision may link to primate visual success.

CONCLUSION Although our discussion has focused on the importance and centrality of vision to animal success it is, nevertheless, clearly possible to survive and prosper without any vision at all, as for instance some species of cave fish and burrowing rodents are able to do. But such cases are clear exceptions, and the vast majority of animals has evolved and carefully maintained the biological machinery required to extract meaning from light. Comparative studies of vision and the visual system of the sort covered in this review have proven invaluable in revealing the impressive range of mechanisms that can be used to support sight. Understanding these adaptations provides a key to our appreciation of the natural world and that understanding is utterly indispensable in illuminating our own vision. REFERENCES Ahnelt, P. K., & Kolb, H. (2000). The mammalian photoreceptor mosaicadaptive design. Progress in Retinal and Eye Research, 19, 711–770. Ahnelt, P. K., Hokoc, J. N., & Rohlich, P. (1995). Photoreceptors in a primitive mammal, the South American opossum, Didelphis marsupalis aurita: Characterization with anti-opsin immunolabeling. Visual Neuroscience, 12, 793–804.

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CHAPTER 4

Visual Processing in the Primate Brain CHRIS I. BAKER

VISUAL PROCESSING BASICS 81 THE RETINA 83 LATERAL GENICULATE NUCLEUS (LGN) VISUAL AREAS IN PRIMATE CEREBRAL CORTEX 91 VISUAL TOPOGRAPHIC MAPS 92 V1—PRIMARY VISUAL CORTEX 93 V2 95

MAJOR CORTICAL VISUAL PROCESSING PATHWAYS 97 VENTRAL PATHWAY 97 DORSAL PATHWAY 101 LATERAL INTRAPARIETAL AREA (LIP) 103 CONCLUDING REMARKS 105 REFERENCES 105

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Vision is the most widely studied and dominant sensory system in human and nonhuman primates. Of the total surface area of the cerebral cortex roughly 50% in macaque monkey, and 20 to 30% in human, is largely or exclusively involved in visual processing (Van Essen, 2004; Van Essen & Drury, 1997). Intensive study of vision in nonhuman primates, and particularly the macaque, has produced a detailed anatomical and functional description of processing at many levels of the neural visual pathway. In the past 20 years, the advent and development of functional brain imaging, and in particular functional Magnetic Resonance Imaging (fMRI), has enabled the detailed study of cortical, and in some cases subcortical, visual processing in humans. This chapter will synthesize findings from both human and nonhuman primates toward an understanding of the visual processing stream.

evolved biological system, the goal of vision is not to produce a veridical description of the external world but a description that facilitates adaptive behavior. Those aspects of the input that contain information critical for behavior will be emphasized and those aspects that carry little information will be discarded. Vision begins at the eye with light passing through the cornea (refracts light), pupil (controls how much light enters the eye), and the lens (adjustably focuses light) onto the retina at the back of the eye. At the retina, photoreceptors convert the photons to electrochemical signals that are relayed along the optic nerve. The primary visual pathway from the optic nerves to the cerebral cortex passes through the dorsal Lateral Geniculate Nucleus (LGN) of the thalamus to the primary visual cortex (V1 or striate cortex) in the occipital lobes at the posterior of the brain. In thinking about the challenges of visual processing, it is important to remember that primates receive visual input through two constantly moving eyes and process that information in two cerebral hemispheres. Each eye receives input from a limited area of visual space termed the visual field of that eye (Figure 4.1). Due to the horizontal separation of the eyes, their visual fields are spatially shifted and do not overlap completely. To produce a unified percept of the external environment, the brain must be able to align the images in the two eyes (Parker, 2007) and maintain and register information across eye movements (Melcher & Colby, 2008).

VISUAL PROCESSING BASICS Vision is the process of extracting information about the external world from the light reflected or emitted by objects and surfaces. How light is reflected off an object or surface is determined by many factors including orientation, texture, movement, and absorbance. Thus, the reflected light carries information about those objects or surfaces and interpreting its pattern can aid an organism in interacting effectively with its environment. As an

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Visual Processing in the Primate Brain Left Visual Field Right Visual Field

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Figure 4.1 Visual fields and initial visual pathway. The overall visual field comprises a central binocular zone (double lines) and peripheral monocular zones (single lines). Size in vision is measured in terms of visual angle, the angle subtended on the retina. At a distance of 57 cm from the eyes, a stimulus 1 cm in size subtends 1 degree of visual angle. A useful approximation is that the width of the thumbnail at arms length is about 1.5 degrees of visual angle (O’Shea, 1991). The visual field of each eye is approximately 160 degrees and the overall visual field is approximately 200 degrees, with the binocular zone subtending approximately 120 degrees. Light from the point of fixation (thin dotted gray lines) falls on the fovea of each eye. In the binocular zone, light from the left visual field (thin black dotted lines) falls on the left nasal retina and the right temporal retina (and vice versa for light from the right visual field). The ganglion cell axons leave the retina via the optic disk. Signals from the left visual field are in dark gray, signals from the right visual field in light gray. Signals from the left eye are in solid lines, signals from the right eye in dotted lines. At the optic chiasm, the nerve fibers from the nasal retina of each eye crossover, so that each optic tract carries a complete representation of one half of the visual field to the contralateral LGN.

Within the binocular zone of the visual field, light reflected from the same point in space will hit the nasal retina in one eye and the temporal retina in the other eye (see Figure 4.1). These signals come together at the optic chiasm, where the optic nerves from the nasal retina of each eye crossover and join with the nerve fibers from the temporal retina of the other eye. Therefore, the optic tracts leaving the chiasm organize visual information by hemifield, with each tract conveying information about the contralateral visual field. This organization by visual field is maintained in the LGN and into V1. However, signals from the two eyes still remain segregated within each optic tract. Thus, each hemisphere receives visual input primarily from the contralateral visual field, segregated by

eye, and later processing is required to produce a unified percept of visual space. Parallel Processing in Vision The visual signal arriving at the retina contains many different types of information including color, motion, and shape. Any extracted information may be used in many different ways and for many different behaviors. For example, reaching for your coffee mug requires precise information about the distance and orientation of the mug’s handle, whereas recognizing your own mug likely depends on color, shape and size information. A general principle appears to be the evolution of specialized systems to

The Retina

extract different types of commonly used visual information efficiently. In the primate visual system (Nassi & Callaway, 2009), and indeed many other sensory processing systems (K. O. Johnson & Hsiao, 1992; Kaas & Hackett, 2000), this leads to parallel processing in which independent, specialized cells and circuits extract specific types of information simultaneously from the same position in visual space. Such parallel processing is evidenced at multiple levels of visual processing, from the retina (Wassle, 2004) to high-level visual cortex (Ungerleider & Mishkin, 1982). For example, in the retina different populations of ganglion cells with different functional properties each tile the whole retina, providing multiple complete representations of the visual field and creating a series of parallel pathways to the next level of processing. Similarly, in the LGN, there are at least three different pathways (parvocellular, magnocellular, and koniocellular—see below) that appear to capture different aspects of the visual input such as motion and color. Finally, in the cortex, visual processing beyond V1 segregates into distinct dorsal and ventral pathways that primarily process spatial and nonspatial (visual quality) information, respectively. While parallel processing is efficient, it creates a challenge for later processing stages—integrating and consolidating the different streams of information to ultimately produce a unified and coherent percept. This chapter focuses on what the structure, function, and connectivity at different levels of the visual processing pathway reveal about the types of computation being performed and highlights the divergence and convergence of multiple parallel processing pathways.

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Light

Ganglion Cell Layer Inner Plexiform Layer Inner Nuclear Layer Outer Plexiform Layer

Outer Nuclear Layer Inner Segment Photoreceptors Outer Segment Retinal Pigment Epithelium Choroid (a) Light

P H B A G Optic nerve (b)

THE RETINA The retina is a sheet of neural tissue, 0.3–0.4 mm thick, that receives focused light from the lens of the eye. There are three primary dimensions defining light falling on the retina: position (two-dimensional location on the sheet), time, and wavelength (visible human range ∼400–700 nm). Traditionally, the retina has been characterized as some sort of simple spatiotemporal filter prior to cortical processing, but recent results challenge this simplistic view (Gollisch & Meister, 2010). There are at least 50 distinct types of retinal cell, grouped into five classes: photoreceptors, horizontal cells, bipolar cells, amacrine cells, and retinal ganglion cells (Masland, 2001a). These cells are arranged in a highly organized laminar structure (Figure 4.2). Although retinal cell types and structure are largely conserved across

Figure 4.2 Structure of the retina. (a) Radial section through retina of Cynomolgus monkey (modified from Peters et al., 2007). Note that light has to pass through many different layers of the retina to reach the photoreceptors. At the fovea, the layers above the photoreceptors are displaced, forming a “foveal pit,” allowing light to fall more directly on the densely packed cone photoreceptors in this region. (b) Schematic representation of the connections between different retinal cells. The solid and open circles represent excitatory and inhibitory chemical synapses, respectively. Resistor symbols indicate electrical coupling (gap junctions) between cells (from Gollisch & Meister, 2010). Abbreviations: B, bipolar cell; H, horizontal cell; A, amacrine cell; G, ganglion cell; P, photoreceptor.

mammals (Wassle, 2004), the primate fovea, which enables high acuity vision, is unique (Gollisch & Meister, 2010). The following sections will briefly describe the properties of the different classes of retinal cell and the computations to which they contribute in the primate.

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Photoreceptors Specialization of visual processing begins with the photoreceptors of the retina. There are two different types, rods and cones, facilitating coverage of the full range of environmental light intensities. Rods are responsible for high-sensitivity, low-acuity vision (dim light: scotopic vision), responding to even single photons of visible light (Baylor, Lamb, & Yau, 1979). In the rod pathway, sensitivity is enhanced at the cost of spatial resolution by, for example, output from many individual rods converging onto a single rod bipolar cell (see below). In contrast,

cones form the basis of color vision (Solomon & Lennie, 2007) and are evolutionarily older than rods (Okano, Kojima, Fukada, Shichida, & Yoshizawa, 1992). Relative to rods, cones have a much lower sensitivity, but higher acuity since there is much less spatial summation across individual cones, especially near the fovea (Curcio & Allen, 1990), and are responsible for daylight vision. The spatial density of photoreceptors sets a fundamental limit on the spatial information available to higher levels of processing. The average human retina contains many more rods (∼90 million) than cones (∼4.5 million) but the distribution of the two photoreceptors

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Figure 4.3 Photoreceptor distribution. (A) Density of rods (dotted line) and cones (solid line) across the horizontal meridian of the retina. Cones are predominantly located in the fovea with a peak density of ∼200,000 cones per mm2 falling steeply with increasing eccentricity. Further, the distribution of cones is radially asymmetric around the fovea with greater density in nasal than temporal peripheral retina (Curcio, Sloan, Packer, Hendrickson, & Kalina, 1987; O. Packer et al., 1989). In contrast, the center of the fovea (∼0.35 mm diameter) is rod free with rod density increasing with eccentricity, peaking around the eccentricity of the optic disk. Interestingly, there is substantial between-individual variability in the distribution of photoreceptors (Curcio et al., 1987; O. Packer et al., 1989). (B) Optical sections of the cone mosaic from a single individual at different eccentricities in nasal retina. At the center of the fovea, there are only tightly packed cones present. With increasing eccentricity, the number of rods increases and the diameters of the cone inner segments also increase. Away from the center of the fovea, the larger profiles in the images are cones and the smaller profiles rods. Bar = 10 μm. (Replotted and modified from Curcio et al., 1990.)

The Retina

differs significantly across the retina (Curcio, Sloan, Kalina, & Hendrickson, 1990). The peak density of cones is found in the fovea and falls off rapidly with eccentricity (Figure 4.3). In contrast, there is a rod free area within the fovea (∼1.25 degrees diameter) with the density of rods increasing with eccentricity and peaking around the optic disk (where the optic nerve leaves the eye). In addition, for cones, but not rods, the inner segment, which serves as the light-catching aperture of the photoreceptor, increases with eccentricity (Packer, Hendrickson, & Curcio, 1989). Thus, while the maximum spatial resolution of the cone system decreases rapidly with eccentricity, its sensitivity increases. While all photoreceptors respond to light throughout much of the visible spectrum, their peak sensitivity varies. Rods have peak sensitivity at a wavelength of ∼500 nm. Further specialization occurs within cones, with different types of cone having different peak sensitivity. In some

M

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primates, including humans, there are three different types of cone (Baylor, Nunn, & Schnapf, 1987) giving rise to “trichromatic” vision. These cones have peak sensitivity to short (S, ∼430 nm), medium (M, ∼530 nm) or long (L, ∼560 nm) wavelengths (Figure 4.4). S cones constitute only 5% of all cones, form a semiregular array, are absent from the center of the fovea, and have peak density at 1 degree (DeMonasterio, Schein, & McCrane, 1981; Martin & Grunert, 1999). However, there is a highly variable ratio and somewhat random distribution of L to M cones that differs across the retina (Deeb, Diller, Williams, & Dacey, 2000; Hofer, Carroll, Neitz, Neitz, & Williams, 2005). Analysis of color requires the comparison of signals from different types of cones and consistent with psychophysical observations, there are red-green (L versus M) and blue-yellow (S versus L + M) opponent signals in the cone pathways.

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500 600 Wavelength (nm) (a)

(b)

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Figure 4.4 Sensitivity and distribution of cone photoreceptors. (a) S- (blue-line), M- (green-line), and L- (red-line) cones respond to light throughout much of the visible spectrum but show different peak sensitivity. For comparison the dotted lines show rods and the recently discovered photosensitive ganglion cell. (b) Spatial distribution of photorecpetors in the human retina, 0.8 degrees from fovea. The left panel shows the arrangement of photoreceptors. The right panel shows the cones (S, M, L) colored according to spectral sensitivity. The S-cones are relatively sparse but form a semiregular array. The L- and M-cones are distributed randomly with frequent clumping. (Modified from Solomon & Lennie, 2007.)

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Horizontal Cells There are two main types of horizontal cell in mammals (Wassle & Boycott, 1991), providing feedback to photoreceptors and bipolar cells. H1 cells contact both rods and cones, whereas H2 cells contact cones only (Masland, 2001b; Wassle et al., 2000). Since horizontal cells pool light from a larger area than photoreceptors, they effectively produce center-surround antagonism by subtracting a broad response from a local signal (Verweij, Hornstein, & Schnapf, 2003; Wassle, 2004), ideal for detecting local changes in input. Importantly, horizontal cell feedback creates the first stage of red-green (Crook, Manookin, Packer, & Dacey, 2011) and blue-yellow (O. S. Packer, Verweij, Li, Schnapf, & Dacey, 2010) opponency in the retina.

other amacrine cells, providing the major synaptic input to ganglion cells (Jacoby, Stafford, Kouyama, & Marshak, 1996). Although the different types of amacrine cells have different connections and neurotransmitters, suggesting distinct functions, their precise roles are poorly understood, except in a few cases. For example, AI amacrine cells have wide-spreading axonlike processes that cover long retinal distances (Stafford & Dacey, 1997) and are likely involved in computations enabling detection of object motion (Olveczky, Baccus, & Meister, 2003). Similarly, starburst amacrine cells (so called because of their radiating dendrites), found in many mammalian species, provide a feedforward excitation onto ganglion cells that is selective for motion-direction (Euler, Detwiler, & Denk, 2002; Hausselt, Euler, Detwiler, & Denk, 2007). Ganglion Cells

Bipolar Cells There are 11 types of bipolar cell in primates (Boycott & Wassle, 1991; Chan, Martin, Clunas, & Grunert, 2001; Joo, Peterson, Haun, & Dacey, 2011), 10 linked to cones and only 1 to rods. Rod bipolar cells contact between 6 (at fovea) and 40 (in periphery) rods (Wassle, 2004) and connect to ganglion cells only indirectly via amacrine cells that synapse onto cone bipolar terminals. This type of organization may reflect the late evolution of rods (Masland, 2001a). Each cone connects to several different types of bipolar cell. Thus, even at this first synapse in the retina, the cone signals diverge into multiple parallel pathways. Cone bipolar cells are stratified within the inner plexiform layer of the retina confining their synapses to cells at the same level and segregating their connections with different types of retinal ganglion cell. The output of cones is separated into ON and OFF types, excited by light onset or offset, respectively. Cone bipolar cells fall into two main categories with distinct morphology: midget and diffuse. While diffuse bipolar cells contact between 5 and 10 cones, midget bipolar cells can exclusively connect with single cones and synapse on to a dedicated class of midget retinal ganglion cells (Masland, 2001b), enabling the greatest possible spatial resolution.

Amacrine Cells There are at least 30 distinct types of amacrine cells in mammals (MacNeil, Heussy, Dacheux, Raviola, & Masland, 1999). Amacrine cells are inhibitory interneurons that connect with bipolar cells, ganglion cells, and

At least 17 distinct ganglion cell types have been identified, distinguished by the size and branching patterns of their dendritic trees, of which at least 13 project to the LGN (Dacey, 2004; Dacey, Peterson, Robinson, & Gamlin, 2003). The responses of a ganglion cell are often described in terms of its receptive field, the region of visual space over which stimuli elicit a response. Although often represented as Gaussians, ganglion cell receptive fields are irregular in shape but interlock to tightly map visual space (Gauthier, Field, Sher, Greschner et al., 2009). Further, the receptive fields of each ganglion cell type tile the whole retina (Field & Chichilnisky, 2007; Field et al., 2007), so a single spot of light at one point of the retina can stimulate multiple ganglion cell types conveying information in parallel to the brain (Wassle, 2004) (Figure 4.5). While differences in morphology may indicate functional differences this is not necessarily the case. For example, some ganglion cells show nearly identical receptive field overlap despite differences in dendritic overlap and this may reflect compensation within the circuitry for morphological differences (Gauthier, Field, Sher, Shlens et al., 2009). Ganglion cell receptive fields typically have a central region with a concentric surround and may be ON center/OFF surround or OFF center/ON surround (Kuffler, 1953). The dendrites of ON center and OFF center ganglion cells stratify in either the inner or outer part of the inner plexiform layer, respectively, with their center response driven by corresponding ON or OFF bipolar cells. Additionally, differences in stratification within each part of the inner plexiform layer further separate different types of ganglion cells (Dacey, 2004) (Figure 4.6).

The Retina

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Figure 4.5 Retinal ganglion cell receptive fields are irregularly shaped, tightly interlock, and tile the whole retina. Contour lines representing simultaneously recorded receptive fields from four different types of ganglion cell in macaque retina. (A) ON parasol cells. (B) OFF parasol cells. (C) ON midget cells. (D) OFF midget cells. Gaps in the mosaic reflect undersampling of cells rather than gaps in the retinal representation. (Modified from Gauthier, Field, Sher, Greschner et al., 2009.)

0%

inner nuclear layer inner plexiform layer ganglion cell layer

100% midget

parasol

sparse

giant sparse monostratified

broad thorny

narrow thorny

small

large

bistratified

Figure 4.6 Types of retinal ganglion cell. Schematic representations of 13 different types of monostratified and bistratified ganglion cells. The width of the horizontal bars indicates the extent of the dendritic arbors and the vertical height of the bars indicates their location within the inner plexiform layer. (Modified from Dacey, Peterson, Robinson, & Gamlin, 2003.)

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Only three types of retinal ganglion cell have been thoroughly characterized: midget, parasol, and bistratified. Midget and parasol cells together comprise over 65% of ganglion cells, and the bistratified cells around 35% (Dacey, 2004). These different types of ganglion cell are thought to be the origin of the parvocellular, magnocellular, and koniocellular pathways, respectively, that are anatomically segregated through the LGN and into V1. For this reason, the midget ganglion cells are often referred to as P-cells and the parasol ganglion cells as M-cells. Midget Cells The primary functional role of midget ganglion cells has been the subject of some controversy (Dacey, 2004). On the one hand, the midget ganglion cells appear well-suited to support high acuity vision with small receptive fields (Calkins & Sterling, 1999). Close to the fovea, each midget ganglion cell receives its input from a single midget bipolar cell, which in turn connects to a single cone. At the peak of the cone distribution, there are two midget ganglion cells for every cone (Ahmad, Klug, Herr, Sterling, & Schein, 2003). On the other hand, by reflecting the input to a single cone, the midget ganglion cells might be critical for comparison of signals from different cones and thus for color vision (Reid & Shapley, 2002). Consistent with this hypothesis, they do exhibit opponency of L- and Mcone signals, with stimulation of one type of cone causing excitation and the other inhibition. Alternatively, it might be that midget cells play a critical role in both high acuity and color vision. In peripheral retina, where ganglion cells pool signals from many cones, the nature and purity of the L- and M- cone inputs to the center and surround of midget cell receptive fields has been the subject of much debate. For example, L-M opponency could be produced by (a) pure L- or M-cone center signals with random sampling in the surround (e.g., Martin, Lee, White, Solomon, & Ruttiger, 2001), or (b) relatively pure signals in both center and surround (e.g., Buzas, Blessing, Szmajda, & Martin, 2006). Alternatively, it has been suggested that there is random cone sampling (Diller et al., 2004). Simultaneous recording of hundreds of retinal ganglion cells and subsequent mapping of all the cone inputs revealed dominant or exclusive L- or M-cone input in the receptive field center of midget cells with strong opponency (Field et al., 2010), demonstrating selective sampling that is not merely a reflection of the local density of L- and M-cones (Figure 4.7). Parasol Cells Compared to midget cells, parasol cells have much larger receptive fields, higher contrast, and higher temporal

frequency sensitivity, suggesting a role in motion processing. They receive input from multiple cone bipolar cells, and are stratified near the middle of the inner plexiform layer (Dacey, 2004). Parasol cells pool across both Land M-cones and, hence, respond strongly to achromatic stimuli. Small Bistratified Cells Small bistratified ganglion cells have receptive fields similar in size to parasol cells. They exhibit a strong ON response from the S cones, conveyed by selective blue cone bipolar cells, with an inhibitory input from L- and M-cones (Field et al., 2007). S-cone input is also observed in OFF midget cells, but very rarely in ON midget cells or any parasol cells (Field et al., 2010). Other Ganglion Cells Other types of ganglion cells that have been identified include large bistratified cells (another S-ON chromatic pathway), sparse monostratified cells (an S-OFF chromatic pathway), and smooth monostratified cells (Dacey, 2004). Many of these other types of ganglion cell are only found in small numbers. Importantly, however, this may simply be a reflection of the spatial sampling density (many have large dendritic fields) and not be indicative of overall significance in visual processing (Field & Chichilnisky, 2007). Indeed, the less common ganglion cell types in primates collectively provide more capacity than the entire cat retina (Wassle, 2004). Two recently discovered types of retinal ganglion cell are of particular interest. First, there are photosensitive ganglion cells that express melanopsin (Berson, 2003; Dacey et al., 2005). These cells exhibit a much slower light response than rods or cones and are important for control of circadian rhythms and the pupillary light reflex (Gooley, Lu, Fischer, & Saper, 2003). Second, upsilon ganglion cells (identified based on functional rather than morphological properties) have large receptive fields and exhibit highly nonlinear spatial summation (Petrusca et al., 2007). These cells may be the equivalent of the Y-cells reported in other mammalian species that are thought to signal texture motion with no directional selectivity (Gollisch & Meister, 2010). Optic Nerve The axons from all retinal ganglion cells stream towards the optic disk in the nasal retina of each eye and form the optic nerve. There are no photoreceptors at the optic disk, producing a “blind spot” in the visual field of each eye. However, the blind spot for each eye receives some

Lateral Geniculate Nucleus (LGN) ON parasol

OFF parasol

A

B

ON midget

OFF midget

C

D

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Figure 4.7 Functional connectivity in the retina. Simultaneous recording of hundreds of retinal ganglion cells and identification of the photoreceptors contributing to the receptive field center of each cell produces a detailed picture of the connectivity in the retina. Each panel shows a different type of ganglion cell (ON and OFF midget and parasol cells). Red, green, and blue circles are the L-, M-, and S-cones. Cones giving input to at least one ganglion cell are circled in white. White lines indicate cones contributing to the receptive field center with the thickness of each line proportional to weight. Note that S-cones largely contribute only to OFF midget cells. Scale bar = 50 μm. (From Field et al., 2010.)

input from the other eye and we are not normally aware of them. There are at least 15 distinct targets in the brainstem for projections in the optic nerve (Kaas & Huerta, 1988; Rodieck & Watanabe, 1993). The vast majority of ganglion cells (∼90%) project to the LGN (Perry, Oehler, & Cowey, 1984). There are six other major targets including the superior colliculus (involved in the control of eye movements), suprachiasmatic nucleus (involved in the regulation of circadian rhythms), the inferior pulvinar (high-order relay conveying signals between cortical areas), and the pretectum (involved in adjusting pupil size). In summary, the retina receives light input, which is converted to electrochemical signals and passed through highly specific and parallel circuits culminating in responses in at least 17 distinct ganglion cell populations that project out of the retina. Rather than being a simple spatiotemporal filter, the circuits in the retina are capable of computations for extracting information such as texture motion and motion-direction. The following section will follow these signals to the LGN, which provides the major input to the cerebral cortex.

LATERAL GENICULATE NUCLEUS (LGN) Located in the posterior part of the thalamus, the LGN is often viewed as a simple relay as signals pass from retina to cortex. However, the LGN also receives extensive input from nonretinal sources (including brainstem and feedback from cortex), that in combination exceed the retinal input, and it appears to be actively involved in the regulation of visual input to cortex (Briggs & Usrey, 2011; Casagrande, Sary, Royal, & Ruiz, 2005; Sherman & Guillery, 2002). Despite the small size of the LGN and the limited spatial resolution of fMRI, recent studies have started to reveal its functional organization in humans (Kastner, Schneider, & Wunderlich, 2006). The LGN is largely a six-layered structure, reduced to four layers in regions responding to eccentric locations in the visual field beyond the optic disk (Kaas, Guillery, & Allman, 1972; Malpeli & Baker, 1975) (Figure 4.8). Each layer receives input from one eye, with layers 1, 3, and 5 (numbered from ventral to dorsal) receiving input from nasal retina of the contralteral eye, and layers 2, 4, and 6 receiving input from temporal retina of the ipsilateral

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temporal retina of ipsilateral eye

parvocellular koniocellular magnocellular

nasal retina of contralateral eye

Figure 4.8 Organization of LGN. Top row shows the LGN in four different species of primate. In all species, the LGN has six layers with the magnocellular, parvocellular, and koniocellular neurons occupying distinct layers. Bottom row shows a schematic of the organization of the LGN and its inputs. (Modified from Briggs & Usrey, 2011.)

eye. In human and macaque, layers 1 and 2 contain magnocellular neurons, which receive input primarily from the parasol ganglion cells, while layers 3-6 contain parvocellular neurons, which receive input primarily from the midget ganglion cells. Intercalated between these six layers are koniocellular neurons (Hendry & Reid, 2000; Hendry & Yoshioka, 1994), which receive input primarily from the bistratified ganglion cells. Each koniocellular layer is innervated by the same retina that innervates the immediately overlying M or P layer (Hendry & Reid, 2000). Parvocellular neurons are the most numerous in the LGN, but there are roughly equal numbers of magnoand koniocellular neurons (Blasco, Avendano, & Cavada, 1999). Given this layered structure, the standard model of visual processing in the primate has been of three LGNdefined “pathways” from retina to cortex (magnocellular, parvocellular, and koniocellular). However, this is likely to be an oversimplification. First, the magnocellular layers are composed of more than one cell type, possibly reflecting input from nonparasol as well as parasol cells (Kaplan & Shapley, 1982). Second, there are multiple types of cells in koniocellular layers with different spatial, temporal, and contrast characteristics, likely reflecting inputs from multiple types of ganglion cells (Hendry & Reid, 2000; Xu et al., 2001). In addition to the organization by eye, the LGN is also retinotopically organized, with an ordered map of contralateral visual space in each layer. In macaques, the representation of the horizontal meridian divides the LGN into a superior and medial half corresponding to the lower visual field and an inferior and lateral half corresponding to the upper visual field. Eccentricity is represented serially along the posterior-anterior dimension

with the fovea represented at the posterior pole. A similar organization is found in the human LGN (W. Chen, Zhu, Thulborn, & Ugurbil, 1999; Schneider, Richter, & Kastner, 2004). The receptive fields of LGN neurons are very similar to those observed in retinal ganglion cells showing centersurround antagonism (ON center, OFF surround and vice versa) and increasing in size with retinal eccentricity (Xu, Bonds, & Casagrande, 2002; Xu et al., 2001). In addition, LGN neurons exhibit modulation by stimuli that extend beyond the classical receptive field, often referred to as the suppressive surround since any stimuli (on or off) reduce the neuronal response (Solomon, White, & Martin, 2002). Surround suppression is stronger for magnocellular than parvocellular and koniocellular neurons (Solomon et al., 2002) and is produced by feedforward mechanisms from the retina, rather than feedback from the cortex (Alitto & Usrey, 2008). This type of suppressive effect controls the gain of LGN neurons. The functional properties of magnocellular and parvocellular neurons are also similar to those observed for parasol and midget ganglion cells, respectively (e.g., Alitto, Moore, Rathbun, & Usrey, 2011; Derrington & Lennie, 1984; Kaplan & Shapley, 1982, 1986). Magnocellular neurons respond better to low contrast stimuli and have sensitivity to higher temporal frequencies than parvocellular neurons. Most parvocellular neurons have color-opponent (red-green) center-surround receptive fields and at any given eccentricity have higher spatial resolution than magnocellular neurons (Shapley, Kaplan, & Soodak, 1981). The functional properties of koniocellular neurons have been less well studied (in part due to the difficulty of isolating these neurons, especially in macaque) and are very diverse (e.g., Irvin, Norton, Sesma, & Casagrande, 1986; White, Solomon, & Martin, 2001). However the majority of cells carrying S-cone signals are located in the koniocellular layers (Roy et al., 2009). This means that in the LGN there is an anatomical separation between neurons carrying red-green (parvocellular layers) and those carrying blue-yellow (koniocellular layers) opponent signals. Nonretinal Inputs to LGN The LGN receives only 30–40% of its input from the retina (Wilson & Forestner, 1995). Neurons originating in the cortex comprise the largest source of synaptic input (Erisir, Van Horn, Bickford, & Sherman, 1997; Erisir, Van Horn, & Sherman, 1997). In addition, there are inputs from the visual sector of the thalamic reticular nucleus, the superior colliculus (primarily to the koniocellular layers) and

Visual Areas in Primate Cerebral Cortex

other brainstem structures (Casagrande et al., 2005). The feedback from V1 appears to maintain three separate pathways with three groups of corticogeniculate neurons whose properties resemble those of the neurons in the parvocellular, magnocellular, and koniocellular layers (Briggs & Usrey, 2009) with projections into distinct layers in the LGN (Fitzpatrick, Usrey, Schofield, & Einstein, 1994; Ichida & Casagrande, 2002). Thus, feedback from the cortex can provide a stream-specific modulation of LGN processing. This feedback from the cortex multiplicatively increases the response of both magnocellular and parvocellular LGN neurons (Przybyszewski, Gaska, Foote, & Pollen, 2000) and attention can modulate responses in LGN in both monkey (McAlonan, Cavanaugh, & Wurtz, 2008) and human (O’Connor, Fukui, Pinsk, & Kastner, 2002). Further, activity in human LGN correlates strongly with the perceived stimulus in binocular rivalry, an experimental paradigm in which stimuli presented separately to each eye compete for representation and subjects perceive only one stimulus at a time (Wunderlich, Schneider, & Kastner, 2005). These properties are consistent with a role for the LGN beyond a simple relay, and suggest involvement in controlling attentional response gain and visual awareness (Kastner et al., 2006). In summary, the LGN receives input from the different retinal ganglion cell populations in the retina as well as descending inputs from V1. The major output of the LGN is to layer 4 of primary visual cortex (V1), and the LGN may be involved in actively controlling the input to cortex rather then simply relaying information. The following sections will briefly discuss the overall organization of visual areas in the cortex before discussing V1 in detail.

VISUAL AREAS IN PRIMATE CEREBRAL CORTEX The cerebral cortex is typically parcellated into a number of distinct areas based on measured differences in (a) architechtonics (e.g., differences in laminar structure), (b) connectivity, (c) visual topography (maps of visual space), and (d) functional properties (Felleman & Van Essen, 1991). However, different studies have emphasized different combinations of these criteria and many different parcellation schemes have been proposed (Van Essen, 2004). Consensus has only been achieved for a limited number of areas, including V1, V2, V4, and the middle temporal area (MT). Further, for many areas it is hard to identify the homologues in different primate species (Rosa & Tweedale, 2005). Overall, there is evidence for

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at least 40 anatomically and/or functionally distinct subdivisions of visually responsive cortex in the macaque (Van Essen, 2004). In general, there are three different types of long-range connection within and between these cortical areas supporting visual processing: feedforward, feedback, and horizontal (or intrinsic) connections (for detailed review, see Bullier, 2004). Feedforward connections transfer information away from the thalamic input (e.g., LGN → V1 → V2) (Shipp, 2007). In contrast, feedback connections link cortical areas in the opposite direction. Finally, horizontal connections link neurons within a cortical area and facilitate the local processing of information in conjunction with short-range vertical connections between cortical layers. Most of cortex, including sensory cortex, comprises six layers and the different types of connections within and between cortical areas are often layer-specific (Figure 4.9). Feedforward connections, including those from the LGN, project to layer 4 (Rockland & Pandya, 1979), which contains many small densely packed cells and is often referred to as the “granular layer.” These connections typically originate in layers 2/3 of lower cortical areas. In contrast, feedback connections often originate in the infragranular layers (5/6) and typically project to layers 1 and 5/6 of the lower area. Finally, horizontal connections are reciprocal intralaminar projections (Rockland & Lund, 1983) and exhibit patchy termination patterns that may correspond with underlying functional properties (Malach, Amir, Harel, & Grinvald, 1993; Yoshioka, Blasdel, Levitt, & Lund, 1996). Thus, the cortex contains many visual areas with elaborate connectivity within and between them facilitating the processing of visual information. The next section focuses on one property often used to define a visual area, the presence of discrete visual topography.

V1

V2

V3.....

V(n)

1 4 6

Feedforward LGN

Feedback

Figure 4.9 Laminar characteristics of connections between cortical areas. Schematic characterization of the main pathways for feedforward and feedback processing within visual cortex. (Adapted from Shipp, 2007.)

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VISUAL TOPOGRAPHIC MAPS

Wedge (polar angle)

(a)

(b)

CC S

PO

S Ca

A common feature of visual cortex is retinotopic organization, similar to that observed in LGN. For example in V1, neighboring neurons respond to stimuli presented at adjacent locations in the visual field and the arrangement of responses constitutes an ordered map of visual space. There are a multitude of such retinotopic maps throughout visual cortex, and the presence of a complete map of visual space is one of the criteria used to define a distinct cortical area. While these maps were first identified based on human patients with lesions to visual cortex (Horton & Hoyt, 1991) and invasive studies in nonhuman primates (e.g., Hubel & Wiesel, 1974; Tootell, Silverman, Switkes, & De Valois, 1982), the advent of fMRI has enabled the simultaneous and noninvasive elucidation of multiple visual field maps in both human (Arcaro, McMains, Singer, & Kastner, 2009; Wandell, Dumoulin, & Brewer, 2007) and monkey (Arcaro, Pinsk, Li, & Kastner, 2011; Brewer, Press, Logothetis, & Wandell, 2002). While there are many similarities between human and monkey, more field maps have been identified in human and there may be differences in the mapping even in areas as early as V4 (Winawer, Horiguchi, Sayres, Amano, & Wandell, 2010). Visual field maps can be identified with fMRI by systematically varying the location of a stimulus in the visual field (Figure 4.10). They are defined with respect to a fixation point, and thus, the fovea. There are two principal dimensions to retinotopic maps: distance from the fovea (eccentricity) and angular distance from the horizontal and vertical meridians (polar angle). Commonly, the maps can be measured by varying either the eccentricity of a ring stimulus or the polar angle of a wedge stimulus in the visual field and identifying the optimal eccentricity and angle of a stimulus from fovea for each location in the cortex (DeYoe et al., 1996; Engel et al., 1994; M. I. Sereno et al., 1995). Using these methods, field maps have been identified in the occipital lobe (corresponding to V1, V2, and V3) (Brewer et al., 2002; Dougherty et al., 2003; M. I. Sereno et al., 1995), in dorsal regions extending into parietal cortex (Arcaro et al., 2011; Swisher, Halko, Merabet, McMains, & Somers, 2007) and in lateral and ventral regions extending into the temporal lobe (Arcaro et al., 2009; Brewer, Liu, Wade, & Wandell, 2005; Larsson & Heeger, 2006). Further, systematic manipulations of the location of attention or eye movements in visual space have also identified maps in parietal cortex (Schluppeck, Glimcher, & Heeger, 2005; M. I. Sereno,

Ring (eccentricity)

(c) 16 deg

(d)

(e)

Figure 4.10 Identifying visual field maps. Retinotopy can be measured by varying the eccentricity of a ring stimulus (a) and the angle of a wedge stimulus (b) and determining the optimal eccentricity and angle for every voxel in the brain (c). The expanded view of the medial occipital cortex shows the maps obtained for eccentricity (d) and polar angle (e) (see the color legends in each panel). The solid lines show the borders between V1 and V2, which correspond to the representation of the vertical meridian, in the lower visual field (upper border) and the upper visual field (lower border). The dotted line marks the fundus of the calcarine sulcus. Note that polar angle varies perpendicular to the border, whereas eccentricity varies with anterior-posterior position along the calcarine sulcus. CC, corpus callosum; CaS, calcarine sulcus; POS, parietaloccipital sulcus. (Modified from Wandell et al., 2007.)

Pitzalis, & Martinez, 2001; Silver, Ress, & Heeger, 2005). A common feature of visual field maps, especially those in occipital areas, is a disproportionately large representation of the foveal region. This can be expressed in terms of a linear Cortical Magnification Factor (CMF)—the mm

V1—Primary Visual Cortex

of cortex per degree of visual angle—which shows a rapid decrease with increasing eccentricity (M. I. Sereno et al., 1995). Relative to owl monkeys and macaques, the change in CMF is much steeper in humans, suggesting a greater emphasis on foveal vision. This foveal bias partially reflects the greater density of cones in the fovea compared with the periphery, further exaggerated by later stages of processing in the retina and LGN, which oversample foveal signals (Connolly & Van Essen, 1984). While a retinotopic reference frame is dominant in many visual areas, particularly in posterior parts of the brain, there is evidence for head- (Duhamel, Bremmer, BenHamed, & Graf, 1997; M. I. Sereno & Huang, 2006), body- (Makin, Holmes, & Zohary, 2007; Snyder, Grieve, Brotchie, & Andersen, 1998), and world-centered reference frames (Chafee, Averbeck, & Crowe, 2007; Snyder et al., 1998) in parietal cortex. How retinotopic input is transformed into these alternate reference frames remains unclear. In summary, retinotopic or visual field maps are a common feature of visual cortex. Having described general features of visual cortex, the next sections will discuss the first two major visual cortical areas (V1 and V2) before turning to the major pathways of visual processing in cortex.

V1—PRIMARY VISUAL CORTEX V1 is also known as striate cortex because of the prominent stripe of white matter (stria Gennari ) running through layer 4. It is the largest single area in the cerebral cortex of the macaque (Felleman & Van Essen, 1991), occupying around 13% of the total cortical surface area (Sincich, Adams, & Horton, 2003). In humans, the fractional area of cortex occupied by V1 is only around 2% (Van Essen, 2004). However, the intrinsic shape of V1 in human and monkey is very similar (Hinds et al., 2008). V1 and V2 have a mirror-symmetric retinotopic organization with the vertical meridian represented along their common border (J. M. Allman & Kaas, 1974; M. I. Sereno et al., 1995). The upper field in V1 is represented ventrally and the lower visual field dorsally. In human, most of V1 is contained within the banks of the calcarine sulcus (Hinds et al., 2008; Rademacher, Caviness, Steinmetz, & Galaburda, 1993; Stensaas, Eddington, & Dobelle, 1974) (Figure 4.10). Staining for cytochrome oxidase (CO), a mitochondrial enzyme indictaing the level of metabolic activity, reveals a distinctive pattern in V1 (Horton, 1984). Specifically, the

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CO density in each layer mirrors the strength of the input from the LGN with highest density in layers 2/3, 4C, 4A, and 6. Further, there is a regular pattern of dark patches (“blobs”) interspersed with lighter areas (interpatches or interblobs) prominent in layers 2/3. The three streams defined by the three major cell types in the LGN are maintained into V1 (Sincich & Horton, 2005a). The magnocellular layers of LGN project to layer 4Cα in V1, the parvocellular layers to layer 4Cβ, and the koniocellular layers to the CO rich blobs in layers 2 and 3, layer 1, and layer 4A (Hendry & Yoshioka, 1994). The magnocellular and parvocellular layers of LGN also provide input to layer 6. While early reports suggested that the different streams remained segregated even within V1, more recently it has become clear that there are extensive interactions between the streams beyond the input layer in V1 (Sincich & Horton, 2005a). For example, the blobs and interblobs in layer 2/3 receive input from the magnocellular and parvocellular recipient layers of V1 (Lachica, Beck, & Casagrande, 1992; Yabuta & Callaway, 1998) (Figure 4.11). However, some compartmentalization may still be maintained by specialized connectivity of cell types within laminae (for review, see Nassi & Callaway, 2009). Visual input from the two eyes—segregated into separate layers in the LGN—remains segregated in layer 4C of V1 as alternating bands across the entire thickness of the cortex. These “ocular dominance columns” can be visualized in human with fMRI (Cheng, Waggoner, & Tanaka, 2001; Yacoub, Shmuel, Logothetis, & Ugurbil, 2007). The first intermixing of the inputs from the two eyes in visual processing occurs in the layers above and below layer 4 with neurons that respond better to binocular than monocular stimulation. These binocularly driven neurons are often sensitive to retinal disparity, the small geometric differences between the images in each eye (Cumming & Parker, 1999, 2000). Such sensitivity is necessary for stereopsis, the sense of depth (Ponce & Born, 2008). Neurons in V1 also show selectivity for color, direction, and orientation. While neurons in the input layers of V1 have similar center-surround receptive fields to those observed in LGN, in other layers, receptive fields become elongated, responding strongly to oriented bars. “Simple cells” have receptive fields consisting of distinct excitatory and inhibitory subregions, whereas “complex cells” have subregions that are intermixed (Hubel & Wiesel, 1968). This difference between simple and complex cells may reflect underlying differences in spike threshold (Priebe, Mechler, Carandini, & Ferster, 2004).

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A

1st intracortical synapse

2nd intracortical synapse

1 2 3 4A 4B 4Cα 4Cβ 5 6

Pyramidal neuron Stellate neuron

konio parvo magno

magno + parvo parvo

B

V1 2 3 4A 4B

V2 Thin Stripe Pale Stripe

Pulvinar

Thick Stripe

Figure 4.11 Connections of V1. (A) Intracortical connections of V1. After the initial input to V1 from the LGN, there is substantial mixing of the signals from the magnocellular, parvocellular, and koniocellular layers with common projections into layers 2/3 and an increasing emphasis on horizontal projections. (B) Projections from V1 to V2. There are two major pathways: (1) CO blobs (patches) → thin stripe, and (2) interblobs (interpatches) → pale and thick stripes. The pulvinar projections to V2 are complementary to those from V1 and may account for the different CO staining of the thick and pale stripes. In addition to projections from layers 2/3, 4A, and 4B, there are some projections from layers 5 and 6 (not shown). (Modified from Sincich & Horton, 2005a.)

A simple feedforward model of V1 simple cells proposed that each simple cell gets its input from an array of LGN center-surround receptive fields arranged along a straight line in visual space (Hubel & Wiesel, 1962). While this model has held up well, it is debated whether feedback in the form of lateral inhibition is also needed to explain the response properties of V1 neurons and in particular the sharpness of tuning (Priebe & Ferster, 2008; Shapley, Hawken, & Xing, 2007). There is a columnar organization for orientation preference in V1. Columns run perpendicular to the cortical surface and neurons within a given column share the same

orientation selectivity and have receptive fields in the same part of the visual field. Along the cortical surface all orientations are represented. Further, at points where neurons with different orientations meet, a characteristic pinwheel pattern is formed (Obermayer & Blasdel, 1993) with a center that tends to occur near the center of an ocular dominance patch. Although orientation columns are much smaller then the size of standard voxels in fMRI experiments, it has been demonstrated that orientation can be decoded from the response across human V1 (Haynes & Rees, 2005; Kamitani & Tong, 2005). While this could in principle

V2

reflect local biases in the orientation column sampling of individual voxels, suggesting that fMRI can be sensitive to subvoxel information, it more likely reflects a large-scale orientation map in human cortex (Freeman, Brouwer, Heeger, & Merriam, 2011; Sasaki et al., 2006). Many V1 neurons are color-selective and some of these show no orientation selectivity (E. N. Johnson, Hawken, & Shapley, 2001). However, the organization of color selectivity in V1 has been the source of controversy over two issues (for review, see Sincich & Horton, 2005a). First, it has been debated whether color and orientation selectivity are segregated in V1 with some groups finding evidence in favor of segregation (e.g., Livingstone & Hubel, 1984) and others not (e.g., Leventhal, Thompson, Liu, Zhou, & Ault, 1995). Second, it has been debated whether color selectivity aligns specifically with blobs, with some groups finding close correspondence (Tootell, Silverman, Hamilton, De Valois, & Switkes, 1988) and others not (Landisman & Ts’o, 2002a, 2002b). While these debates may not be fully resolved, a recent study using intrinsic optical imaging reported both good alignment between color blobs and CO blobs and also low orientation selectivity within color blobs (Lu & Roe, 2008). In summary, V1 receives input from the LGN in three streams (magnocellular, parvocellular, and magnocellular) segregated by eye. Within V1, the signals from the two eyes come together, the response properties of neurons become more complex than those observed at earlier levels of visual processing and there is some mixing of signals from the three streams. The major output of V1 is to V2, the focus of the next section. V2 V2 is smaller then V1, but still occupies around 10% of the total cortical surface area in macaques (Sincich et al., 2003). CO staining in V2 reveals a pattern of stripes perpendicular to the V1 border (Horton, 1984; Tootell, Silverman, De Valois, & Jacobs, 1983) consisting of thick and thin dark stripes interleaved by pale thin stripes. Neurons in the different V2 stripes differ in their physiological properties (e.g., G. Chen, Lu, & Roe, 2008; DeYoe & Van Essen, 1985; Hubel & Livingstone, 1987; Lu & Roe, 2008; Shipp & Zeki, 2002a) and there appear to be separate visual field maps for each stripe type (Roe & Ts’o, 1995; Shipp & Zeki, 2002b). Early studies suggested that three functional streams were maintained from V1 to V2 with (1) the blobs in layer 2/3 in V1 projecting to the thin stripes, (2) the inter-blobs projecting to the pale stripes, and (3) layer 4B

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projecting to the thick stripes. These three streams were proposed to contribute to color, form, and motion/depth processing, respectively (M. Livingstone & Hubel, 1988). However, more recent studies have revealed greater intermixing between the projections from V1 (Sincich & Horton, 2002a) with just two major streams (Figure 4.11). First, the blobs in layer 2/3 project to the thin stripes (Sincich & Horton, 2005b) but there are also some projections from cells in other layers (Sincich, Jocson, & Horton, 2007). Second, the interblobs in layer 2/3, as well as cells in layers 4A, 4B, and 5/6, project to both the thick stripes and the pale stripes (Sincich & Horton, 2002a; Sincich, Jocson, & Horton, 2010). The different CO staining for thick and pale stripes despite their similar input may reflect different contributions from the pulvinar (Sincich & Horton, 2002b). There is also extensive feedback from V2 to V1, but organization of feedback projections with respect to the blobs and interblobs is unclear (Shmuel et al., 2005; Stettler, Das, Bennett, & Gilbert, 2002). Like V1, V2 contains orientation- (Hubel & Wiesel, 1970; Levitt, Kiper, & Movshon, 1994; S. M. Zeki, 1978) and direction-selective neurons (Burkhalter & Van Essen, 1986), although the receptive fields are larger in V2 (Gattass, Gross, & Sandell, 1981; Smith, Singh, Williams, & Greenlee, 2001). Many neurons are selective for stimulus color (Burkhalter & Van Essen, 1986; Levitt et al., 1994) and there are a greater proportion of color-oriented neurons compared with V1 (A.W. Roe & Ts’o, 1997). There appears to be some segregation of neurons with selective responses to color, size, and motion between the different stripes in V2, consistent with some continued separation of parvocellular and magnocellular pathways, although this segregation is not absolute and all neuron types are found in all stripes (Levitt et al., 1994). However, direction-selective neurons are most prominent in thick stripes with direction maps reported in thick and pale stripes (Lu, Chen, Tanigawa, & Roe, 2010), thin stripes appear to contain a hue map (Lim, Wang, Xiao, Hu, & Felleman, 2009; Xiao, Wang, & Felleman, 2003), and orientation selectivity is strongest in thick and pale stripes (Sincich & Horton, 2005a). V2 is thought to play a critical role in binocular depth perception (for reviews, see Cumming & DeAngelis, 2001; Parker, 2007). Most V2 neurons are binocularly driven and like V1 many are selective for retinal disparity (Hubel & Livingstone, 1987). However, tuning for disparity is very different in V1 and V2. When assessing depth of objects, human observers rely on relative disparity more than absolute disparity (e.g., Westheimer,

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1979) and neurons in V2 show consistent selectivity for relative disparity (Thomas, Cumming, & Parker, 2002). In contrast, V1 neurons are selective for absolute disparity only (Cumming & Parker, 1999). While simple orientation selectivity is common in V2, many neurons respond better to more complex visual stimuli such as angles, curves, intersecting lines, and complex gratings (e.g., polar and concentric) (Hegde & Van Essen, 2000; Ito & Komatsu, 2004; Mahon & De Valois, 2001). Further, while around 70% of V2 neurons show consistent orientation selectivity across locations within the receptive field, many others exhibit subregions within the receptive

fields selective for different orientations (Figure 4.12), suggesting coding of combinations of orientations (Anzai, Peng, & Van Essen, 2007). V2 neurons are also responsive to illusory (von der Heydt, Peterhans, & Baumgartner, 1984) and texture-defined (von der Heydt & Peterhans, 1989) contours and to border ownership (which side of a contour is figure and which is ground) (Zhou, Friedman, & von der Heydt, 2000). Analysis of V2 neuronal response during viewing of natural images (Willmore, Prenger, & Gallant, 2010) suggests that V2 contains two distinct subpopulations: one with properties similar to those observed in V1, and one with more complex properties, exhibiting 2 V2

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Figure 4.12 Orientation subregions within V2 receptive fields. Responses (filled blue curves) of macaque neurons relative to mean firing rates (red circles) plotted in polar coordinates as a function of stimulus orientation. The gray circles show the locations tested and the radius of the circle corresponds to the maximum firing rate observed (numbers on lower right corner of each map). Solid and dashed black lines highlight subregions tuned to different orientations. (a) V1 neuron with uniform tuning. V1 neurons showed minimal variation in tuning. (b) V2 neuron with uniform tuning. (c–f) V2 neurons showing nonuniform tuning with different orientation preferences in different locations (c–e) or bimodal tuning (f). (Modified from Anzai et al., 2007.)

Ventral Pathway

strong suppressive tuning. Whether any of these functional differences correlate with the anatomical structure of V2 remains to be determined. In summary, V2 receives strong input from V1. Mixing of the parvo-, magno-, and koniocellular pathways in V1 and in the projections from V1 and V2 makes it hard to assess the distinct contributions of these pathways. However, the presence of some functional clustering by, for example, color and direction-selectivity, suggests that there may still be some separation between the signals originating from the different LGN layers that may further influence the next stages of processing.

MAJOR CORTICAL VISUAL PROCESSING PATHWAYS A key framework that has guided visual neuroscience is the division of cortical visual processing into distinct ventral and dorsal pathways (Mishkin, Ungerleider, & Macko, 1983; Ungerleider & Mishkin, 1982). Both pathways originate in V1, with the ventral pathway coursing through occipitotemporal cortex into the temporal lobe and the dorsal pathway coursing through occipitoparietal cortex into the posterior parietal cortex. Lesions of the ventral pathway in monkey produced selective deficits in object discrimination, leading to its characterization as a “What” pathway. Conversely, lesions of the dorsal pathway produced selective deficits in visuospatial tasks, leading to its characterization as a “Where” pathway. Further, the dorsal and ventral pathways were found to extend into dorsolateral prefrontal cortex and ventrolateral prefrontal cortex, respectively (Macko et al., 1982). Subsequently, a patient (D.F.) (Milner et al., 1991) with a large bilateral lesion of the occipitotemporal cortex and mostly spared occipitoparietal cortex (James, Culham, Humphrey, Milner, & Goodale, 2003), was found to have impaired perception of objects (agnosia) but intact ability to reach for objects, including shaping her grasping hand to reflect the size, shape, and orientation of an object (Milner et al., 1991). Further, while D.F. could not adjust the orientation of her hand to match the orientation of a distant slot, she could orient her hand appropriately when posting a card through the slot (Goodale, Milner, Jakobson, & Carey, 1991). These findings combined with the dense interconnections between the posterior parietal and premotor areas in frontal regions (Gentilucci & Rizzolatti, 1990) led to the proposal that the dorsal stream was more appropriately characterized as a “How” than as a “Where”

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pathway (Milner & Goodale, 2006). More recently, it has been proposed that neither “Where” nor “How” are sufficient to adequately capture the diversity of visuospatial functions supported by this pathway and that instead the dorsal pathway should be viewed as a neural nexus of visuospatial processing giving rise to at least three distinct pathways: parieto-prefrontal, parieto-premotor, and parieto-medial temporal, which primarily support spatial working memory, visually guided action, and spatial navigation, respectively (Kravitz, Saleem, Baker, & Mishkin, 2011). In monkey, the segregation into ventral and dorsal pathways becomes most pronounced after initial processing in V1, V2, and V3. The ventral pathway emerges from these areas to include V4 and the inferior temporal (IT) cortex. The dorsal pathway projects from V1, V2, and V3 to MT and other areas in the parietal cortex. There are both direct projections as well as indirect projections via V6 in the anterior wall of the parietooccipital sulcus (Fattori, Pitzalis, & Galletti, 2009; Galletti et al., 2001). Parietal and superior temporal areas within the dorsal pathway, including MT and the Lateral Intraparietal (LIP) area, are heavily interconnected with each other. In the following sections, some of the major characteristics and functional properties of regions within both the ventral and dorsal pathways will be discussed.

VENTRAL PATHWAY There are two striking functional properties that vary with the progression along the ventral pathway: size of receptive fields and complexity of stimuli required to elicit a response (Kobatake & Tanaka, 1994; Rousselet, Thorpe, & Fabre-Thorpe, 2004; Rust & Dicarlo, 2010). At the anterior end of the ventral pathway, neurons respond to complex stimuli within receptive fields much larger than those found in V1 and V2. These properties are consistent with a processing stream that is involved in object recognition, producing specific representations of objects or object features that are abstracted away from the spatial structure of the input at the retina (DiCarlo & Cox, 2007).

Position Tolerance One of the biggest challenges faced by the visual system is to enable rapid and accurate object processing despite vast

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differences in the retinal projection of an object produced by changes in, for example, viewing angle, size, illumination or position in the visual field (DiCarlo & Cox, 2007; Edelman, 1999; Ullman, 1997). Such “tolerance” or “invariance” is often considered one of the key characteristics of object recognition (DiCarlo & Cox, 2007). Changes in position (translations) are among the simplest of these transformations, because only the retinal position of the projection of an object is affected, and not the projection itself. The increase in receptive field size along the ventral pathway has long been thought to reflect one way in which tolerance for changes in position of an object was achieved. In V1, receptive fields are typically small (∼1 degree of visual angle) becoming larger in V2 (Gattass et al., 1981) and V4 ( 20 degrees) (Desimone & Gross, 1979; Gross, Rocha-Miranda, & Bender, 1972; Richmond, Wurtz, & Sato, 1983) and a largely preserved stimulus preference within receptive fields (Ito, Tamura, Fujita, & Tanaka, 1995; Lueschow, Miller, & Desimone, 1994; Schwartz, Desimone, Albright, & Gross, 1983). Such properties are consistent with a visual representation that is largely divorced from the retinotopic nature of the input. However, more recent studies have reported the presence of small receptive fields even in anterior IT (DiCarlo & Maunsell, 2003; Logothetis, Pauls, & Poggio, 1995) and a systematic study reported a range of sizes from 2.8 to 26 degrees with a mean size of 10 degrees, and large variability in response within receptive fields. These findings are consistent with the report of retinotopic maps in human visual cortex anterior to V4 (Arcaro et al., 2009; Brewer et al., 2005; Larsson & Heeger, 2006). Even in anterior regions where there is currently no evidence for retinotopic maps, there is substantial position information available (Schwarzlose, Swisher, Dang, & Kanwisher, 2008) and the representation of complex visual stimuli may still be dependent on position in the visual field (Cichy, Chen, & Haynes, 2011; Kravitz, Kriegeskorte, & Baker, 2010). This conclusion is supported by behavioral studies suggesting that complete position invariance is never achieved (Afraz, Pashkam, & Cavanagh, 2010; Kravitz et al., 2010; Kravitz, Vinson, & Baker, 2008). Thus, while tolerance for changes in position increases along the ventral pathway (Rust & Dicarlo, 2010), visual object representations are never completely abstracted away from the spatial nature of the input on the retina.

Form Selectivity in Single Neurons In monkeys, the increase in the complexity of the functional properties observed from V1 to V2 continues into V4, although some properties are similar between these areas (Hegde & Van Essen, 2007). Like V2, many neurons in V4 are selective for complex gratings (Gallant, Connor, Rakshit, Lewis, & Van Essen, 1996), direction of motion (Cheng, Hasegawa, Saleem, & Tanaka, 1994) and disparity (Hinkle & Connor, 2001), with stronger effects of relative disparity than V2 (Umeda, Tanabe, & Fujita, 2007). Selectivity for color is highly prevalent in V4 and it was initially characterized as a color-processing area (S. Zeki, 1983; S. M. Zeki, 1973). Recent work suggests there may be some segregation of color and orientation sensitivity within V4 (Conway, Moeller, & Tsao, 2007; Tanigawa, Lu, & Roe, 2010). In comparing responses along the ventral pathway, much work has focused on selectivity for stimulus form. In V4, parametric variation of simple two-dimensional contours, varying in curvature and orientation, revealed selectivity for angles and curves oriented in a particular direction with a bias for convex over concave features (Pasupathy & Connor, 1999) and acute curvature (Carlson, Rasquinha, Zhang, & Connor, 2011) (Figure 4.13). Such selectivity is also observed when the contours form part of a complex shape boundary, with neurons responding, for example, whenever a shape contains a sharp convex contour pointing to the right, with little impact of other parts of the shape (Pasupathy & Connor, 2001). Given these discrete responses to parts of a shape, it is possible to reconstruct a presented shape from the population response in V4. More anteriorly, one of the most striking early observations of IT cortex was the presence of selectivity for complex shapes, real world objects, and even faces and body parts (Bruce, Desimone, & Gross, 1981; Desimone, Albright, Gross, & Bruce, 1984; Gross et al., 1972; Perrett, Rolls, & Caan, 1982) (Figure 4.14). To determine the critical features necessary to elicit responses an early approach was to use stimulus reduction. Starting with a complex object, features were incrementally removed to determine the minimum features necessary to elicit a strong response. While simple features are often sufficient in V4 and posterior IT, more complex features are required in anterior IT cortex (Kobatake & Tanaka, 1994; Tanaka, Saito, Fukada, & Moriya, 1991; Tsunoda, Yamane, Nishizaki, & Tanifuji, 2001). In many cases the critical features correspond more to object parts than to whole objects.

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Figure 4.13 Position-specific tuning for boundaries in V4. Example V4 neuron in a macaque tuned for acute convex curvature near the top of the shape. Each circle corresponds to a single stimulus presented and the gray level surrounding each stimulus indicates the strength of response (see scale bar on right). (Modified from Pasupathy & Connor, 2002.)

Parametric variations of the dimensions of novel geometric stimuli in IT cortex have revealed selectivity for a variety of shape dimensions (Brincat & Connor, 2004; De Baene, Premereur, & Vogels, 2007; Kayaert, Biederman, Op de Beeck, & Vogels, 2005). In particular, neurons respond selectively to the presence of multiple two- or three-dimensional features with some neurons showing independent selectivity for separate features, as in V4, and others selectivity for specific multipart configurations (Brincat & Connor, 2004; Yamane, Carlson, Bowman, Wang, & Connor, 2008), which may increase with training (Baker, Behrmann, & Olson, 2002). This selectivity for complex configurations appears to evolve over the time course of the neural responses (Brincat & Connor, 2006). As in V1, columnar organization has been reported in IT cortex (Fujita, Tanaka, Ito, & Cheng, 1992; Tanaka, 1996; Tsunoda et al., 2001) with each column representing particular types of object features. However, the organization may not be strictly columnar, but reflect

the presence of some discrete clusters with neurons in each cluster sharing a limited degree of selectivity (Sato, Uchida, & Tanifuji, 2009). Overall, single-unit recording studies in monkeys have highlighted an increase in stimulus selectivity of single neurons along the ventral visual pathway, with selectivity for complex features and even faces. In single neuron studies, it is difficult to determine the large-scale organization of stimulus selectivity in the cortex. fMRI, however, is ideal for investigating large-scale structure and one of the main characteristics revealed has been the presence of large regions with consistent patterns of selectivity.

Category Selectivity in fMRI By contrasting responses to different categories of stimuli, human fMRI has revealed the presence of a limited (Downing, Chan, Peelen, Dodds, & Kanwisher, 2006) number of regions selective for particular categories, including faces

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Figure 4.14 Example stimulus selectivity in inferior temporal cortex of the macaque. Response of four single inferior temporal neurons to baton stimuli composed of separate upper and lower parts. Responses are shown over 2,000 ms, aligned to stimulus onset (vertical line). Rasters show the firing of the neurons on individual trials and the histograms show the average firing rate across trials. (a) Neuron exhibiting no obvious selectivity with a strong visual response to all batons. (b) Neuron showing a strong response in the presence of the oval upper part, independent of the lower part. (c) Neuron with response modulated by both upper and lower parts. (d) Neuron responding only to a specific combination of upper and lower parts. Neurons showing this type of selectivity were more common for batons the monkey had been trained to discriminate. (Modified from from Baker, Behrmann, & Olson, 2002.)

(Kanwisher, McDermott, & Chun, 1997; Puce, Allison, Asgari, Gore, & McCarthy, 1996), objects (Kourtzi & Kanwisher, 2000; Malach et al., 1995), body parts (Downing, Jiang, Shuman, & Kanwisher, 2001; Peelen & Downing, 2007), scenes (Epstein & Kanwisher, 1998), and letter strings (Baker et al., 2007; Cohen & Dehaene, 2004; Puce et al., 1996) (Figure 4.15). Similar fMRI studies in monkeys have also revealed corresponding categoryselective regions (Bell, Hadj-Bouziane, Frihauf, Tootell, & Ungerleider, 2009; Pinsk et al., 2009; Pinsk, DeSimone, Moore, Gross, & Kastner, 2005; Tsao, Freiwald, Knutsen, Mandeville, & Tootell, 2003; Tsao, Freiwald, Tootell, & Livingstone, 2006). For faces, six face-selective patches have been identified in the temporal lobe of the macaque (Moeller, Freiwald, & Tsao, 2008) (Figure 4.15). Critically, fMRI-guided single-unit recording studies within some of these patches have revealed that the vast majority of individual neurons are face-selective (Freiwald & Tsao, 2010; Tsao et al., 2006) although the information about faces appears to vary across patches (Freiwald & Tsao, 2010). Further, electrical stimulation within an individual

face patch tends to elicit activation in the other patches (Moeller et al., 2008) suggesting that the patches may constitute a face-processing circuit (for discussion, see Baker, 2008). While these category-selective regions demonstrate clustering of neurons by selectivity, it is also clear that information is somewhat distributed throughout parts of the ventral stream with category-selective regions containing information about both preferred and nonpreferred categories (Haxby et al., 2001). Support for the significance of category-selective regions comes from studies of individuals with brain damage to regions of the ventral pathway. Specific impairments have been reported for faces (e.g., Barton & Cherkasova, 2003), bodies (e.g., Moro et al., 2008), objects (e.g., Behrmann, Winocur, & Moscovitch, 1992), scenes (Aguirre & D’Esposito, 1999), and words (e.g., Mycroft, Behrmann, & Kay, 2009). Further, disrupting processing within category-selective regions using transcranial magnetic stimulation can produce specific deficits in the processing of those categories of visual stimuli (Pitcher, Charles, Devlin, Walsh, & Duchaine, 2009).

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Given these findings, does category define the organizational structure of the ventral pathway? As described earlier, full retinotopic maps have been described in posterior parts of the ventral stream (Arcaro et al., 2009; Brewer et al., 2005; Larsson & Heeger, 2006) as well as eccentricity biases along the ventral temporal cortex (Hasson, Levy, Behrmann, Hendler, & Malach, 2002; Levy, Hasson, Avidan, Hendler, & Malach, 2001), and it has been suggested that there might be a topography of simple shape features across IT cortex (Op de Beeck, Deutsch, Vanduffel, Kanwisher, & DiCarlo, 2008). One possibility is that the ventral pathway contains several overlapping maps for different stimulus dimensions and that category selectivity

reflects the intersection of these maps producing complex and nonlinear response patterns across the cortex (Op de Beeck, Haushofer, & Kanwisher, 2008). Future work on the ventral pathway will need to address how category selectivity emerges from experience, whether it is composed of overlapping maps, and how this representational structure contributes to adaptive behavior. DORSAL PATHWAY While the ventral pathway is characterized by its selectivity for stimulus form, the prominent feature of the dorsal stream is selectivity for the spatial position of stimuli

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and, in particular, the direction of visual motion. Further, regions within the dorsal pathway seem to play a particular role in the planning and execution of limb (for reviews, see Culham & Valyear, 2006; Milner & Goodale, 2006) and eye (e.g., Konen & Kastner, 2008) movements and posterior parietal cortex has been implicated in many aspects of attention (e.g., Medina et al., 2009; Shomstein & Behrmann, 2006) and other high-level cognitive functions (Culham & Kanwisher, 2001; J. Gottlieb & Snyder, 2010). The following sections will focus on two highly studied regions within the dorsal pathway, Middle Temporal Area (MT), and the Lateral Intraparietal area (LIP). Middle Temporal Area (MT) MT, sometimes referred to as V5 (S. Zeki, 2004), is a functionally and anatomically distinct area on the posterior bank of the superior temporal sulcus in monkey (Van Essen, Maunsell, & Bixby, 1981). Anatomically, it is characterized by direct inputs from V1 and heavy myelination. Functionally, it contains a representation of the contralateral visual field with a high concentration of direction-selective neurons (S. M. Zeki, 1974). In humans, MT can be localized with fMRI, based on its responsiveness to visual motion, to the posterior/dorsal limb of the inferior temporal sulcus (Dumoulin et al., 2000; Huk, Dougherty, & Heeger, 2002). However, it is difficult to separate human MT from adjacent regions based on functional properties alone and it is often referred to as hMT+.

The input to MT is typically regarded as being primarily magnocellular with direct and indirect projections from layer 4B in V1 (Figure 4.16), which receives most of its input from the magnocellular layers of the LGN via layer 4Cα of V1 (Fitzpatrick, Lund, & Blasdel, 1985; Yabuta, Sawatari, & Callaway, 2001). The V1 neurons that give rise to this MT projection are highly selective for motion direction (Movshon & Newsome, 1996). MT also receives indirect V1 input from V2 and V3. Importantly, the V1 neurons projecting directly to MT are distinct from those neurons projecting to V2 from the same layer (Sincich & Horton, 2003). There are also at least three nonmagnocellular inputs into MT. First, there is a direct LGN input from koniocellular neurons (Sincich, Park, Wohlgemuth, & Horton, 2004; Stepniewska, Qi, & Kaas, 1999). Second, there is a disynaptic input from parvocellular cells in the LGN via V1, which may involve the Meynert cells of layer 6 (Nassi, Lyon, & Callaway, 2006). Finally, there is a trisynaptic input from parvocellular cells via layer 4Cβ likely through the thick stripes of V2 (Nassi & Callaway, 2006). Neurons in MT show selectivity for the direction, speed, and binocular disparity of moving visual stimuli with high-contrast sensitivity (for review, see Born & Bradley, 2005). There is some columnar organization by direction (Albright, Desimone, & Gross, 1984) and additional clustering according to disparity selectivity (DeAngelis & Newsome, 1999). The receptive fields of MT neurons are much larger than those in V1 and have strong suppressive surrounds, with similar direction and V2

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Lateral Intraparietal Area (LIP)

disparity selectivity in the center and surround (J. Allman, Miezin, & McGuinness, 1985). This makes the neurons particularly sensitive to local changes in direction and depth, useful for figure-ground segregation. While surround suppression weakens sensitivity, the suppression is reduced when stimulus contrast is decreased (Pack, Hunter, & Born, 2005), suggesting dynamic modulation of sensitivity. However, reducing stimulus strength by reducing the coherence of motion stimuli has the opposite effect, increasing surround-suppression, suggesting that modulation of surround suppression is not a general response to noisy stimuli (Hunter & Born, 2011). Importantly, the responses of MT cells can account for perceptual decisions about motion (Britten, Shadlen, Newsome, & Movshon, 1992; Newsome, Britten, & Movshon, 1989) and depth (Uka & DeAngelis, 2003, 2004). In some cases (Newsome et al., 1989; Uka & DeAngelis, 2003), but not all (J. Liu & Newsome, 2005), single neurons are found to be as sensitive as the observers. Further, judgments about motion-direction can be biased by electrical stimulation of MT (Salzman, Murasugi, Britten, & Newsome, 1992). These findings suggest a close link between the responses of MT neurons and the perception of motion. Given the direct and indirect projects from V1, what is the role of these different projections? Reversible inactivation of V2 and V3 with cooling disrupts selectivity for binocular disparity more than selectivity for motion direction (Ponce, Lomber, & Born, 2008). This finding is consistent with the different disparity tuning in direction-selective V1 neurons compared with that in V2 and MT (Cumming & DeAngelis, 2001). In addition, cooling V2 and V3 shifts speed-tuning in MT toward slower speeds and slightly decreases surround suppression (Ponce, Hunter, Pack, Lomber, & Born, 2011). There was no evidence for patchy inputs of the projections from V2 and V3, suggesting that the indirect projections from V1 do not correspond directly with the functional organization observed. In summary, MT receives the bulk of its input from V1, the first stage of cortical visual processing, and primarily encodes the motion information that contributes directly to motion perception. This motion information feeds forward into the rest of the dorsal stream contributing to visually guided action and spatial perception. LATERAL INTRAPARIETAL AREA (LIP) LIP, in the lateral wall of the monkey intraparietal sulcus, receives strong input from MT and is interconnected

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with the frontal eye field (FEF) and the superior colliculus (Blatt, Andersen, & Stoner, 1990). However, the putative human homologue of LIP is in the posterior medial, not lateral, intraparietal sulcus (Grefkes & Fink, 2005; Koyama et al., 2004). LIP contains a representation of the contralateral visual field, with receptive fields that are larger than MT and often centered around the fovea (BenHamed, Duhamel, Bremmer, & Graf, 2001). Numerous single-unit and functional imaging studies have implicated LIP and the intraparieal sulcus in the control of attention and eye movements, but the precise function of LIP is unclear. The response properties observed are often complex and cannot be covered in detail here, but some of the major findings from monkeys are summarized below. One theory about LIP is that it serves as a priority (or salience) map, reflecting the behavioral priority of stimuli in a spatial map, enabling the effective allocation of spatial attention (Bisley & Goldberg, 2010). Support for this view comes from data showing that responses in LIP appear to reflect the salience of stimuli within the receptive field (Balan & Gottlieb, 2006; Buschman & Miller, 2007; J. P. Gottlieb, Kusunoki, & Goldberg, 1998) and are modulated by task demands (e.g., Kusunoki, Gottlieb, & Goldberg, 2000; Mirpour, Arcizet, Ong, & Bisley, 2009). Activity in LIP also reflects perceptual decisions (Gold & Shadlen, 2007). In tasks in which monkeys must evaluate a noisy stimulus (e.g., motion), activity in LIP gradually increases in those neurons representing the location of the chosen target, with the speed of increase correlating with the strength of the perceptual signal (Shadlen & Newsome, 2001) and evidence accumulation. Further, the time at which the neural activity reaches a critical level (“decision bound”) reflects reaction time (Roitman & Shadlen, 2002) and may terminate the decision process (Kiani, Hanks, & Shadlen, 2008). The same neurons that accumulate evidence also appear to represent the confidence in the decision (Kiani & Shadlen, 2009). LIP also plays a role in maintaining a stable representation of the visual world despite the constant eye movements we make. LIP neurons encode remembered locations (Gnadt & Andersen, 1988) and the spatial representation of those locations is dynamic, shifting to the corresponding retinal location around the time of a saccade (Duhamel, Colby, & Goldberg, 1992). Effectively, the retinal coordinates of stimuli are updated to anticipate the upcoming eye movement. This stimulus “remapping” (Hall & Colby, 2011) has also been observed in the human parietal cortex (Merriam, Genovese, & Colby, 2003) and in other monkey and human extrastriate areas

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ry go te

Ca 1 30°

ry go te

Ca 2

Category boundary (a)

75

Fixation

Sample

Delay

50

25

Test 40 Firing rate (Hz)

Firing rate (Hz)

(b)

30 20 10 0 120 240 Motion direction (deg)

0

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(c) 100 75 50 25 0

65 Firing rate (Hz)

Firing rate (Hz)

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55 45 35 0 120 240 Motion direction (deg)

Figure 4.17 Category representations in LIP. (a) Monkeys were trained to group 12 possible motion directions into two categories (marked as red and blue) in a delayed-match-to-sample task. The black dotted line corresponds to the category boundary. (b) and (c) Responses to the 12 directions in two sample LIP neurons. The red and blue lines correspond to the two categories and pale lines correspond to the directions closest to the category boundary. The vertical dotted lines correspond to stimulus onset, stimulus offset and test-stimulus onset. The graphs on the right show the average responses to the 12 directions in either the delay period (a) or delay and test period (b). (Modified from Freedman & Assad, 2006.)

(Merriam, Genovese, & Colby, 2007; Nakamura & Colby, 2002). Neurons in LIP also contain nonspatial information about stimulus attributes such as shape (A. B. Sereno & Maunsell, 1998), color (Toth & Assad, 2002) and numerosity (Roitman, Brannon, & Platt, 2007). In monkeys trained to categorize motion direction, responses in LIP, but not MT, reflected category information (Freedman &

Assad, 2006) even when the stimuli were presented away from the cells receptive field (Freedman & Assad, 2009) (Figure 4.17). Finally, given the varied functional properties that have been reported, it is worth noting that LIP in monkeys actually contains a dorsal and a ventral subdivision (Blatt et al., 1990) with different connectivity (Lewis & Van Essen, 2000; Medalla & Barbas, 2006) and some

References

distinct functional properties (Y. Liu, Yttri, & Snyder, 2010). In summary, LIP exhibits complex response properties that differ substantially from those observed in MT, one of its major inputs. One of the challenges in characterizing the functional properties of LIP is to try and reconcile the diverse properties observed in these different, but welldefined and extensively studied tasks (e.g., Freedman & Assad, 2011).

CONCLUDING REMARKS This chapter has surveyed visual processing in the primate from when light first enters the eye to the ends of the ventral and dorsal cortical visual processing pathways. A common theme highlighted throughout the different levels of processing, from the retina through to the cortex, is parallel processing in specialized cells or circuits. However, the segregation between different pathways is rarely complete and it is important not to oversimplify the complexity of visual processing. Through these diverging and converging pathways, the brain ultimately converts the patterns of light falling on the retina into representations of the visual world that are capable of supporting adaptive behaviors such as reaching and recognition of food, mates, and predators. Three major aspects of visual processing were not covered in detail in this review. First, there is strong evidence that feedback plays a critical role in complex visual processing and awareness. For example, damage to the posterior parietal cortex can lead to hemispatial neglect, in which patients can neither attend to nor be aware of stimuli in one visual field. This deficit occurs despite the lack of damage to early visual areas or the ventral stream, speaking to the critical role of feedback and attention play in defining the contents of visual awareness. Further, the fact that feedback and attentional effects are found as early in the visual processing stream as the LGN, speaks to likely importance of these mechanisms in optimizing and modulating almost all levels of visual processing. Second, from the retina through the cortex, processing is modified by experience and learning over many different timescales. Many neuronal properties, even in V1, such as orientation selectivity, directional selectivity, and ocular dominance, depend on visual experience early in life (first few months after birth) (Chiu & Weliky, 2004). Short-term exposure to a particular stimulus can produce perceptual after-effects, which are reflected in changes in cell responses for example, MT (Kohn & Movshon,

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2004) and retina (Hosoya, Baccus, & Meister, 2005). Finally, long-term training with novel objects can shape the response properties in the IT cortex (Op de Beeck & Baker, 2010). Finally, this chapter only considered the major visual pathways from the retina through LGN to the cortex. However, many other pathways contribute to visual processing, including, for example, several that pass through other nuclei of the thalamus (Wurtz, Joiner, & Berman, 2011), conveying internally generated information from other brain regions to areas of the visual cortex. For many years, the macaque monkey has been the primary model of primate visual processing. As highlighted in this chapter, the advent of human fMRI has added a wealth of new information about visual processing in the human brain. However, one of the major areas of ignorance in human brain is connectivity. Developing techniques such as Diffusion Tensor Imaging (DTI) and the analysis of common slow fluctuations between brain regions (functional connectivity) promise to shed further light on the visual processing pathways in human.

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CHAPTER 5

Comparative Audition CYNTHIA F. MOSS AND CATHERINE E. CARR

INTRODUCTION 115 OVERVIEW OF ACOUSTIC STIMULI AND THEIR TRANSMISSION THROUGH THE ENVIRONMENT 116 AUDITORY FUNCTION AND BEHAVIOR 116

AUDITORY PERIPHERY 133 CENTRAL AUDITORY PATHWAYS 136 SUMMARY AND FUTURE DIRECTIONS 146 REFERENCES 148

INTRODUCTION

1997; Grothe, Carr, Casseday, Fritzsch, & K¨oppl, 2005). Their ancestors, the earliest land-dwelling vertebrates, were probably sensitive to bone conduction and sound waves traveling through the ground, much as are modern lungfish (Christensen-Dalsgaard, Brandt, Wilson, Wahlberg, & Madsen, 2010). Lungfish are close relatives of the tetrapods, and their ear has good low-frequency vibration sensitivity, like recent amphibians, but poor sensitivity to airborne sound. There is evidence that hearing evolved independently several times, even within some animal groups. Ears appear not only on opposite sides of the head, but also on a variety of body parts. Out of this diversity, one finds fascinating specializations, but also a surprising number of general principles of organization and function. Comparative studies of hearing attempt to bring order to these findings and deepen our understanding of sound processing and perception. Research on comparative hearing includes a vast number of behavioral measures of auditory function, as well as elaborate neuroanatomical and neurophysiological studies of the auditory structures and signal processing. To review all common measures of auditory function, anatomy, and physiology, in all species studied to date, is far beyond the scope of this chapter. Instead, we review selected data from representative species, which allow us to highlight general principles and noteworthy specializations. We begin with a brief introduction to acoustic stimuli, followed by comparative presentation of auditory function in behavioral tasks, and finally a review of ears and auditory systems in a large sample of species. Due to the breadth

The world is filled with acoustic vibrations, sounds used by animals for communication, predator evasion, and in the case of humans, also for artistic expression through poetry, theater, and music. Hearing can complement vision and other senses by enabling the transfer of useful information from one animal to the next. In some instances, acoustic signals offer distinct advantages over visual, tactile, and chemical signals. Sound can be effectively transmitted in complete darkness, quickly and over long distances. These advantages may explain why hearing is ubiquitous in the animal world, in air and underwater. The ability to detect and process acoustic signals evolved many times throughout the animal kingdom, from insects and fish to birds and mammals. Hearing is evolutionarily ancient; vertebrate fossils possess labyrinths, and primitive vertebrates like lampreys have inner ears (Popper & Fay, 1999). Tympanic ears capable of receiving airborne sound evolved independently among the ancestors of modern frogs, turtles, lizards, birds, and mammals (Christensen-Dalsgaard & Carr, 2008; Clack, C. F. Moss is supported by NIH R01 MH056366, R01 EB004750MH56366, and NSF IOS-1010193. C. E. Carr is supported by NIH R01 DC00436. Both C.F.M. and C.E.C. are supported by NIH P30 DC0466 to the University of Maryland Center for the Evolutionary Biology of Hearing. We are grateful for the editorial assistance of Janna Barcelo and Wei Xian. Wei Xian, along with Amy Kryjak, prepared many of the figures for this chapter. 115

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of this topic, we have omitted most biophysical observations. For the reader who wishes to follow up on any or all topics covered here in more detail, we recommend the Springer Handbook in Auditory Research volumes edited by Fay, Popper, and colleagues. OVERVIEW OF ACOUSTIC STIMULI AND THEIR TRANSMISSION THROUGH THE ENVIRONMENT Many features of hearing organs have evolved to respond to the nature of sound waves. These are fluctuations in pressure propagating away from the source with a certain velocity. Therefore, devices sensitive to pressure, either pressure receivers or pressure gradient receivers, may detect sound. Since movement of particles in a medium is directional, receivers sensitive to this component of sound are inherently directional. Both types of detectors have evolved in the animal kingdom. Sound behaves in a complicated manner close to a sound source (the near field) because sources are rarely the ideal small pulsating sphere. Further away in the far field (about 1 wavelength) sound behaves more simply, especially if there are no reflections. Sound waves can be characterized by their intensity or sound pressure level, frequency, and wavelength, all of which impact the detection, discrimination, and localization of acoustic signals. Sound transmission is influenced by the characteristics of the acoustic signal and the environment (e.g., Wiley & Richards, 1978). Attenuation and degradation of acoustic signals occur over distance: Spherical spreading losses, combined with environmental factors (temperature, humidity, landscape and background noise) determine the features of sound waves at the listener’s ear. Distantdependent changes to acoustic signals, together with psychophysical measures of auditory function, can be used to estimate the communication range of a given species (e.g., Kuczynski, V´elez, Schwartz, & Bee, 2010; Lohr, Wright, & Dooling, 2003; see Figure 5.1). For a detailed discussion of acoustics and their constraints on hearing in different environments, we refer the reader to comprehensive books by the following authors: Beranek (1988) and Pierce (1989). AUDITORY FUNCTION AND BEHAVIOR Absolute Auditory Thresholds A fundamental behavioral measure of hearing sensitivity is the audiogram, a plot of detection thresholds for pure

tones across the audible spectrum, which provides an estimate of the frequency range and limits of an animal’s hearing. These parameters are influenced by the features of the peripheral auditory system (Wever, 1949); and in terrestrial vertebrates, the size and impedance matching characteristics of the middle ear system (Dallos, 1973; Geisler & Hubbard, 1975; Guinan & Peake, 1967; Møller, 1983; Nedzelnitsky, 1980; Rosowski, 1994; ChristensenDalsgaard, 2010), the length and stiffness of the basilar membrane or basilar papilla (B´ek´esy, 1960; Manley, 1972; Echteler, Fay, & Popper, 1994), the size of the helicotrema (a small opening at the cochlear apex; Dallos, 1970), the density of hair cells (Burda & Voldrich, 1980; Ehret & Frankenreiter, 1977), and the density of hair-cell innervation (Guild, Crowe, Bunch, & Polvogt, 1931) along the basilar membrane/papilla. In other animals, features of the auditory periphery also play a role in defining the limits and range of hearing in birds (Gleich, Fischer, K¨oppl & Manley, 2004), fish (Popper & Fay, 1999), anurans (Capranica & Moffat, 1983; Lewis, Baird, Leverenz, & Koyama, 1982), and insects (Yager, 1999). For most vertebrates, the audiogram is a smooth Ushaped function; thresholds are high at the lower and upper frequency boundaries compared to intermediate frequencies where thresholds are lowest (e.g., Masterton, Heffner, & Ravizza, 1969). Mammals differ greatly in the octave range over which they can hear, from as little as 3.5 octaves in the mouse and horseshoe bat to over 8 octaves in the dolphin, raccoon, cat, and kangaroo rat. The smaller octave range of hearing in the mouse and bat nonetheless covers a large frequency bandwidth, as these animals hear ultrasound, in which a single octave (frequency doubling) spans a minimum of 40 kHz. Humans show greatest sensitivity between 1 and 4 kHz and hear over a range of about 7 octaves (Sivian & White, 1933). See Figure 5.2. Some animals show enhanced sensitivity nested within their range of hearing. For instance, the audiogram of the echolocating horseshoe bat is highly irregular in shape (Long & Schnitzler, 1975). Between 10 and 40 kHz, a plot of threshold change with frequency resembles the standard U-shaped function of most vertebrates, but the range of this animal’s hearing extends far above 40 kHz. Threshold declines gradually at higher frequencies between 40 and 70 kHz before rising rapidly at approximately 81 kHz. The audiogram then shows a very sharp peak in sensitivity at about 83 kHz; the auditory threshold at neighboring lower and upper bounding frequencies (81 and 90 kHz) is elevated by about 30 dB. This bat emits echolocation signals adjusted to return at 83 kHz and has evolved a highly specialized auditory system to

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Figure 5.1 (A) Theoretical maximum communication distances based on detection and discrimination thresholds for budgerigar and zebra finch calls in broadband flat noise. Curves illustrate distances based on detection thresholds and discrimination thresholds, and assume excess attenuation of 5 dB/100 m and a source intensity of 95 dB SPL measured using the maximum fast (125-ms RMS) setting on a sound level meter. The vertical dashed line represents a distance of 40 m. Maximum transmission distance for a songbird, calculated for given background noise levels at different values of excess attenuation (dB/100m). Background noise level is given as a spectrum level. (From Lohr, Wright, & Dooling, 2003.) (B) Transmission of synthesized advertisement calls of Cope’s gray treefrog (36 pulses over 800 msec), recorded over distances of 1, 2, 4, 8, and 16 meters. Peaks of waveforms normalized to 60% of maximum amplitude and plotted in a dimensionless scale from −1 to +1. (From Kuczynski, V´elez, Schwartz, & Bee, 2010.)

detect this biologically important sound frequency. The basilar membrane of the horseshoe bat shows considerable expansion of its frequency map in the region that responds to frequencies around 83 kHz, and this magnification is preserved in the tonotopic organization of the ascending auditory pathway. Thus, the unusual shape of this animal’s audiogram reflects an adaptation to facilitate the reception of species-specific acoustic signals (Neuweiler, Bruns, & Schuller, 1980). Adaptations in the auditory periphery also support specializations for low-frequency hearing. Examples are the kangaroo rat, mole rat, and the Mongolian gerbil, small mammals that have evolved enlarged external ears and middle ear cavities that serve to collect and amplify low-frequency sounds (Ryan, 1976; Heffner & Masterton,

1980; Ravicz, Rosowski, & Voigt, 1992). In fact, these organs take up roughly two-thirds of the cross-section of the Mongolian gerbil’s head. These animals rely on low-frequency hearing to receive warning signals from conspecifics that must carry over long distances (Ravicz et al., 1992). Elephants also hear very low frequencies (65 dB SPL at 16 Hz; Heffner & Heffner, 1982), which is presumably important for long-distance communication through infrasound (Payne, Langbauer, & Thomas, 1986). In vertebrate animals whose hearing sensitivity spans a narrow frequency range, a communication receiver may appear to dominate the auditory system. The frequency range of maximum sensitivity in birds is about 1–5 kHz, with absolute hearing sensitivity approaching 0 dB SPL (Dooling, 1980; Dooling, Lohr, & Dent, 2000). There

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Figure 5.2 Comparative audiograms of selected birds, anurans, mammals, and fish. Data on budgerigar (Dooling & Saunder, 1975), barn owl (Konishi, 1973), starling (Dooling, Okanoya, Downing, & Hulse, 1986), song sparrow (Okanoya & Dooling, 1987), bullfrog and treefrog (Megela-Simmons, Moss, & Daniel, 1985), dolphin (Johnson, 1967), mouse and kangaroo rat (Heffner & Masterton, 1980), cat (Heffner & Heffner, 1985), elephant (Heffner & Heffner, 1982), human (Sivian & White, 1933), horseshoe bat (Long & Schnitzler, 1975), goldfish (Fay, 1969), blind cave fish (Popper, 1970), Atlantic salmon (Hawkins & Johnstone, 1978), and shad fish (Mann, Lu, Hastings, & Popper, 1998). Sound level for detection of pure tones (Hz). The y-axis scale shows dB SPL, except for fish, in which sound pressure is plotted re 1 dyne/cm2 .

appears to be a general correspondence between a bird’s peak auditory sensitivity and the average power spectrum of its species-specific song (e.g., canary, budgerigar, field sparrow, red-winged blackbird; Konishi, 1970; Dooling, Mulligan, & Miller, 1971; Dooling & Saunders, 1975; Heinz, Sinnott, & Sachs, 1977), suggesting the relative importance of a communication receiver in the avian auditory system. Nocturnal predators (hawks and owls) generally have lower thresholds than songbirds and nonsongbirds, and they use acoustic signals, in part, to detect and localize prey. Hearing sensitivity in birds falls off dramatically at 8–12 kHz, depending on the species. Behavioral measures of hearing in anurans (frogs and toads) also suggest that a communication receiver dominates the auditory system of these animals, but most data come from experiments that have relied on behavioral responses that the animals normally make in the context of

vocal communication. One such technique, evoked calling, exploits the observation that male frogs will vocalize in response to recordings of natural or synthetic conspecific mating calls, while recordings of other species’ calls fail to elicit vocalizations. In the bullfrog, the sound pressure level of a species-specific call must be approximately 60 dB SPL to evoke calling (Megela, 1984). Another technique commonly used to measure hearing in frogs is selective phonotaxis, which exploits the observation that a gravid female will approach a speaker that broadcasts either natural or synthetic conspecific mating calls in preference to one that broadcasts other acoustic stimuli. The female green treefrog exhibits selective phonotaxis to pure tone stimuli at frequencies corresponding to the two major spectral peaks of the mating call, 900 and 3000 Hz (Gerhardt, 1974). The minimum sound pressure level that elicits selective phonotaxis from the female green treefrog

is approximately 55 dB SPL for a 900 Hz pure tone and 90 dB SPL for a 3000 Hz pure tone (Gerhardt, 1976). With a synthetic mating call (900 and 3000 Hz tones presented together), the phonotaxis threshold is 48 dB SPL (Gerhardt, 1981). Using a neutral psychophysical technique that does not require behavior in the context of acoustic communication, Megela-Simmons, Moss, & Daniel (1985) measured hearing sensitivity in the bullfrog and green treefrog at frequencies within and outside those used by these animals for species-specific communication. The bullfrog’s audiogram, like many other vertebrates, is a U-shape function, ranging between about 300 and 3000 Hz, with highest sensitivity between 600 and 1000 Hz, where this species’ mating call contains peak spectral energy. By contrast, the green treefrog’s audiogram is a W-shape function, with highest hearing sensitivity at 900 and 3000 Hz, frequencies where spectral energy in the species-specific mating call is greatest. The differences between the audiograms of the bullfrog and the green treefrog can be attributed to a larger separation of frequency tuning of the two hearing organs in the frog’s auditory periphery. In both species, the amphibian papillae respond to frequencies up to about 1200 Hz, but the basilar papilla of the green treefrog resonates to approximately 3000 Hz, higher than that of the bullfrog’s basilar papilla, which resonates to approximately 1800 Hz (Lewis et al., 1982). The frequency range of hearing is generally largest in mammals, followed by birds, frogs, fish, and insects (for example, see goldfish audiogram plotted in Figure 5.2). However, there are some noteworthy exceptions to this trend. One example is the American shad, a fish species that shares its habitat with the echolocating dolphin. The shad can hear sounds over a frequency range from 100 Hz to an astonishing 180 kHz. While this fish’s threshold is higher in the ultrasonic range than in the audible range, this species can detect 100 kHz signals at about 140 dB re 1 Pa (Mann, Lu, Hastings, & Popper, 1998; Wilson, Montie, Mann, & Mann (2009). Although a variety of fish species are subject to predation by dolphins, the shad has apparently evolved ultrasonic hearing to detect the sonar signals of its predator. The importance of audition for the evasion of predators is well illustrated by insects that have evolved hearing for the evasion of echolocating bats. The hearing range and sensitivity in insects are often inferred from responses of auditory neurons, and many hear ultrasonic frequencies, which are produced by echolocating bats as they hunt insect prey (see Figure 5.3). Examples of insects that hear ultrasound include the praying mantis (a single ear located

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on the midline of the ventral thorax; Yager & Hoy, 1986), green lacewings, (ears on the wings; Miller, 1970, 1984), noctuid moths (ears on the dorsal thorax; Roeder & Treat, 1957), hawkmoths (ear built into mouthparts; Roeder, Treat, & Vande Berg, 1970), Hedyloidea butterflies (ears at the base of the forewings; Yack & Fullard, 1999), crickets (prothoracic tibia; Oldfield, Kleindienst, & Huber, 1986; Moiseff, Pollack, & Hoy, 1978), and tiger beetles (ears on the abdomen; Spangler, 1988; Yager & Spangler, 1995; Yager, Cook, Pearson, & Spangler, 2000). Generally, insect auditory thresholds in the ultrasonic range are high, at or above 50 dB SPL, and the frequency range of hearing is typically one to two octaves (Yager, 1999). Examples also exist for insect sound detection in the human audio range, and often (but not exclusively), lowfrequency hearing supports species-specific acoustic communication. Crickets and bushcrickets have ears on the proximal tibiae of the prothoracic legs, and the lowfrequency range of a large set of auditory receptors corresponds with the spectral content of their species-specific communication calls, generally between 2 and 6 kHz (Michelsen, 1992; Pollack, 1998; Imaizumi & Pollack, 1999).

Masked Auditory Thresholds When an acoustic signal coincides with interfering background noise, its detection may be partially or completely impaired. The process by which one sound interferes with the detection of another is called masking. Several stimulus parameters influence the extent to which masking

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occurs, including the relation between the temporal structure, amplitude, and frequency composition of the signal and the masker (e.g., Jeffress, 1970; Scharf, 1970). Predictably, the more similar the temporal and spectral characteristics of the masker are to those of the signal, the more effectively it interferes with the detection of the signal (e.g., Jesteadt, Bacon, & Lehman, 1982; Small, 1959; Vogten, 1974, 1978; Wegel & Lane, 1924). And when the sound-pressure level of the masker increases, so does the detection threshold of the signal (e.g., Egan & Hake, 1950; Greenwood, 1961a; Hawkins & Stevens, 1950; Moore, 1978; Vogten, 1978; Zwicker & Henning, 1984). If a masking stimulus is broadband white noise, only a portion of the noise band actually contributes to the masking of a pure tone stimulus. This was originally demonstrated by Fletcher (1940), who measured detection thresholds in humans for pure tones against white noise of varying bandwidths. In this experiment, noise bands were geometrically centered at the frequency of a test tone. The spectrum level of the noise (i.e., the power of the noise in a 1 Hz band) remained constant, but as the bandwidth varied, so did its total power. Since the total power of white noise is proportional to its bandwidth, it is perhaps not surprising that the threshold for detecting the pure tone increased as the noise band widened. The interesting observation was, however, that the detection threshold for the pure tone increased as the noise band increased only up to a critical value, beyond which the threshold remained constant. Fletcher termed this value the critical band—the frequency region about a pure tone that is effective in masking that tone. This effect is illustrated in Figure 5.4A. Figure 5.4 presents a schematic representation of the stimulus conditions in a critical band experiment. The solid bar in each graph (a–e) represents a pure tone of a fixed frequency, and the shaded area represents white noise, centered at the frequency of the tone. The spectrum level of the noise in each graph is the same; however, the bandwidth increases from a to e. Accordingly, the total power of the noise also increases from a to e. The height of each bar indicates the level of the pure tone at threshold, when measured against the noise. From a to d, the height of the bar increases, indicating that a higher amplitude tone is required for detection, as the noise band widens. However in e, the height of the bar is the same as that in d, even though the bandwidth of the noise has again increased. Below (Figure 5.4B), the amplitude of the pure tone at threshold is plotted for each of the five noise bandwidths. This figure summarizes the data presented above, showing that threshold increases up to bandwidth

d and thereafter, remains constant. The breakpoint in the function at bandwidth d represents the critical band. The importance of the results of critical band experiments rests on the implication that the ear sums the noise power or energy over a limited frequency region. A large critical band indicates that the noise must be summed over a wide frequency band in order to mask the signal and therefore indicates relatively poor frequency resolution of the auditory system. By contrast, a small critical band indicates relatively high-frequency resolution. Fletcher (1940) included in the concept of the critical band a hypothesis proposing that the power of the noise integrated over the critical band equals the power of the pure tone signal at threshold. This implies that a critical band can be determined indirectly by measuring the detection threshold for a pure tone against broadband masking noise, rather than directly, by measuring the threshold against a variety of noise bandwidths. If one knows the level of the tone at threshold and the spectrum level of the noise, the ratio of the two provides the necessary information to determine the critical bandwidth based on Fletcher’s assumptions. The level of the tone and the spectrum level of the noise are expressed in logarithmic units (decibels, or dB); and therefore, the ratio of the two is simply: dB tone – dB noise spectrum level. Given this ratio, one can then calculate the frequency band over which the noise must be integrated to equal the power of the pure tone. Figure 5.4C illustrates this analysis. In Figure 5.4C, the solid line represents a pure tone, and the boxed-in area (both open and shaded portions) represents broadband white noise. The height of the bar denotes the amplitude of the pure tone at threshold (50 dB SPL), when measured against the background noise (spectrum level 30 dB SPL/Hz), and the difference between the two is 20 dB. This ratio of 20 dB, in linear units, equals a ratio of 100 (10 log10 100 = 20 dB). That is, the power of the pure tone is 100 times greater than the power in one cycle of noise; and therefore, 100 cycles of the noise must be added together to equal the power of the tone. The shaded portion of the noise represents the 100 Hz frequency region about the pure tone that contributes to the masking. If Fletcher’s assumptions were correct, this value (100 Hz) should equal the critical band, as measured directly; in accordance with this logic, the ratio of the pure tone at threshold to the spectrum level of the broadband noise has been termed the critical ratio (Zwicker, Flottorp, & Stevens, 1957). Fletcher’s assumptions have been tested, and it is now well established that critical bands (measured directly) are in fact approximately 2.5 times larger than estimates

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Figure 5.4 (A) Stimulus conditions for a critical band experiment. Each solid line represents a pure tone, and each shaded region represents white noise whose frequency is centered on the tone. The height of the solid line indicates the sound pressure level of the tone at threshold, when measured against a particular noise band. The height of each noise band represents the spectrum level, and this remains constant in a–e. The width of the noise band, however, increases from a to e. (B) Detection thresholds for the pure tone are plotted as a function of the noise bandwidths (a–e). Note that threshold rises from a to d. The critical band (illustrated here at bandwidth d) is the frequency band about a pure tone that is effective in masking. Beyond the critical band, masked threshold remains constant. (C) Schematic representation of Fletcher’s hypothesis, which states that the power of the noise integrated over the critical band equals the power of the pure tone signal at threshold. The solid bar represents a pure tone, and the box (both open and shaded portions) represents broadband noise. The height of the bar represents the sound pressure level of the tone at threshold, when measured against the noise. The difference between the sound pressure level of the tone at threshold (50 dB) and the spectrum level of the noise (i.e., the power in a 1 Hz band: 30 dB/Hz) yields a ratio of 20 dB. This means that the level of the tone is 100 times greater than the spectrum level of the noise (10 log10 100 = 20). It then follows that 100 cycles of the noise must be summed to equal the power of the pure tone. The shaded region of the noise denotes this 100 Hz band.

made from critical ratios (Zwicker et al., 1957; Saunders, Denny, & Bock, 1978). This outcome indicates that Fletcher’s assumptions were not entirely correct; however, the two measures do follow almost parallel patterns of change with signal frequency. Figure 5.5 illustrates this relation, summarizing data collected from several vertebrate species, including humans. The critical ratios have

been transformed to estimates of critical bands, following Fletcher’s assumption that the power of the pure tone at threshold equals the power integrated over the critical band of noise. For most species tested, both critical bands and critical ratios increase systematically as a function of signal frequency, and that the proportionality between the critical band and the critical ratio exists across a wide

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range of frequencies. In fact, had Fletcher assumed that the critical band contained 2.5 times the power of the masked tone at threshold (rather than equal power), the two functions would overlap for human listeners at frequencies above 300 Hz. There are other empirically determined parallels between critical bands and critical ratios. Results of both critical band and critical ratio experiments show that the threshold for detecting a pure tone signal varies with the spectrum level of the masking noise. As the power of the noise increases, there is a proportionate increase in detection threshold (e.g., Hawkins & Stevens, 1950; Zwicker et al., 1957). Moreover, experimental findings also indicate that estimates of both the critical band and the critical ratio are invariant with the level of the masking stimulus, except at high noise spectrum levels (exceeding 60–70 dB; Greenwood, 1961a; Hawkins & Stevens, 1950). Prior to Fletcher’s study of the critical band, research on the peripheral auditory system revealed the existence of a frequency map along the cochlear partition (Guild et al., 1931; Steinberg, 1937). High frequencies are coded at the base of the basilar membrane, and lower frequencies are coded progressively toward the apex. This place coding arises from changes in the stiffness of the basilar

membrane from base to apex. At the base, where the membrane is stiffest, high frequencies produce maximal displacement, and towards the apex, where relative stiffness decreases, lower frequencies produce maximal displacement (von B´ek´esy, 1960). Fletcher approached his work on the critical band with the assumption that this measure would permit psychophysical estimates of the frequency coordinates of the basilar membrane. Indeed, he found that the function relating stimulus frequency to position along the basilar membrane paralleled the function relating stimulus frequency to the width of the critical band. Both the range of frequencies encoded by a fixed distance along the basilar membrane and the size of the critical band increase as an exponential function of sound frequency (Fletcher, 1940; Greenwood, 1961b). This observation led to the hypothesis that a critical band represents a constant distance along the basilar membrane over which the neural response is integrated (Fletcher, 1940; Zwicker et al., 1957). Following the early psychophysical studies of critical ratios and critical bands in humans, auditory masking research began on other vertebrate species. These experiments have permitted a comparative approach to the study of frequency selectivity in the auditory system.

Auditory Function and Behavior

Remarkably, in a variety of vertebrates—for example, cat (Watson, 1963; Costalupes, 1983; Pickles, 1975), mouse (Ehret, 1975), chinchilla (Miller, 1964), rat (Gourevitch, 1965), measures of critical bands and critical ratios show similar frequency dependent trends, and this pattern resembles that observed in humans, that is, increasing systematically with signal frequency (3 dB/octave). This general pattern has led to the suggestion that frequency selectivity in the auditory systems of vertebrates depends upon a common mechanism, the mechanical response of the cochlea (Greenwood, 1961b). Direct measures of frequency selectivity in single VIIIth nerve fibers differ from those obtained psychophysically, indicating that critical ratios and critical bands are not simple correlates of the tuning curves of primary fibers (Pickles & Comis, 1976). This finding does not rule out the possibility that neural integration along the cochlear partition lays the foundation for frequency selectivity, although it does suggest that other processes, such as the distribution and temporal pattern of neural discharge in the central auditory system, may be involved in frequency discrimination. Although critical bands and critical ratios increase systematically with signal frequency in most vertebrates, there are noteworthy exceptions. The parakeet shows a U-shaped function; critical ratios are lowest at an intermediate frequency of this animal’s hearing range, and this frequency region corresponds to the dominant frequency components of its vocalizations. Also in this frequency region, the parakeet’s absolute detection thresholds are lowest (Dooling & Saunders, 1975). A second example can be found in the echolocating horseshoe bat, which shows a sharp decline in critical ratio (i.e., a marked increase in frequency resolution) at 83 kHz, relative to neighboring frequencies (Long, 1977). This specialization for frequency resolution at 83 kHz parallels that observed for absolute sensitivity described above (Neuweiler et al., 1980). In the parakeet and the horseshoe bat, the spectral regions of greatest frequency selectivity and absolute sensitivity coincide; however, it is important to emphasize that these two measures of auditory function are not typically related. The shapes of the audiogram and the critical ratio function differ markedly in most animals; at frequencies where absolute sensitivity is relatively high, frequency selectivity is not also necessarily high. Nonetheless, measures of hearing in the parakeet and horseshoe bat suggest that auditory specializations (possibly, for example, the mechanical response of the cochlea, hair-cell density and innervation patterns, tonotopic representation in the central

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auditory system, etc.) do occur to facilitate discrimination of biologically significant signals from noise. The shape of the green treefrog’s critical ratio function departs from that of most vertebrates. This animal shows a W-shaped critical ratio function, with lowest critical ratios at 900 and 3000 Hz, corresponding to the dominant spectral peaks of its mating call. The smallest critical ratios obtained in the green treefrog are approximately 22 dB, indicating good resolving power of this animal’s ear at biologically salient frequencies, 900 and 3000 Hz. These data compare closely with estimates from other vertebrates at 900 and 3000 Hz, and suggest that the ear of the anuran, despite its distinct morphology, can filter frequency as well as that of other vertebrates, including those that possess basilar membranes (Moss & Simmons, 1986). The mechanical response of the tonotopically organized cochlea can adequately account for measures of frequency selectivity among most vertebrates; and this implies that frequency selectivity is mediated by the spatial distribution of neural activity in the auditory system. However, data obtained from fish (e.g., goldfish, Fay, 1970, 1974; cod, Hawkins & Chapman, 1975) present a challenge to this commonly accepted notion. There is no biophysical evidence that the auditory receptor organ of the fish, the sacculus (also lacking a basilar membrane), operates on a place principal of frequency coding like the cochlea (Fay & Popper, 1983). Yet fish exhibit the same pattern of frequency dependent changes in critical ratios as do other vertebrates whose peripheral auditory systems show place coding of frequency. Instead, frequency selectivity in fish has been explained in terms of temporal coding of neural discharge (Fay, 1978a, 1978b; Fay & Popper, 1983). That is, the temporal pattern of neural discharge in primary auditory fibers, regardless of their innervation sites along the sacculus, may carry the code for frequency selectivity. At present, differences and similarities in the mechanisms of frequency selectivity between fish and other vertebrates are not well understood. Frequency Difference Limens The discrimination of sounds on the basis of signal frequency is a common acoustic problem solved by species throughout the animal kingdom. In laboratory studies of frequency discrimination, reference and comparison tones are typically presented in sequence, and the listener is required to report when there is a change in frequency (see Figure 5.6). The data are plotted as the change in frequency required for discrimination, as a function of the

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Figure 5.6 Frequency discrimination performance (F/F), plotted for a number of vertebrate species, as a function of sound frequency. (a) Mammals: data from bottlenose dolphin (Thompson & Herman, 1975), human (Weir, Jesteadt, & Green, 1976), cat (Elliott, Stein, & Harrison, 1960), elephant (Heffner & Heffner, 1982), guinea pig (Heffner, Heffner, & Masterton, 1971), and mouse (Ehret, 1975). (b) Birds: data from pigeon (Quine & Kreithen, 1981 [1-20 Hz]; Price, Dalton & Smith, 1967 [500-4000 Hz]), barn owl (Quine & Konishi, 1974), and budgerigar (Dooling & Saunders, 1975). (c) Fish: data from sea bream (Dijkgraaf, 1952) and goldfish (Fay, 1970).

Auditory Function and Behavior

test frequency. Frequency difference thresholds (limen) measured in mammals, birds, and fishes show a common trend: F/F is approximately constant (Weber’s law holds), with thresholds tending to rise steadily with test frequency. In most animal groups tested (but see below), individual species tend to fall within the same range, from less than 1% to about 10%. A low-frequency specialist is the pigeon, and it is hypothesized that this animal uses infrasound for homing (Quine & Kreithen, 1981). The bottlenose dolphin shows well-developed frequency discrimination from 1000 Hz to 140 kHz (Thompson & Herman, 1975). A cross-species comparison of sound frequency discrimination illustrates that common patterns in the data can arise through different mechanisms. Frequency discrimination in insects arises from different auditory receptors that are tuned to different sound frequencies (Michelsen, 1966; Oldfield et al., 1986). Mechanical tuning of the basilar papilla may support frequency discrimination in birds, but other mechanisms may also operate (Gleich & Manley, 2000). In the case of frogs and toads, the tectorial membrane over the amphibian papilla appears to support a traveling wave (Hillery & Narins, 1984; Lewis et al., 1982), and its mechanical tuning may contribute to frequency discrimination (Fay & Simmons, 1999), but temporal processing or hair-cell tuning may also play a role. The fish ear lacks a hearing organ that could support a mechanical place principle of frequency analysis (B´ek´esy, 1960; Fay & Simmons, 1999), but nonetheless fish show a pattern of frequency discrimination that resembles mammals and most birds. Hair-cell micromechanics (Fay 1997), hair-cell tuning (Crawford & Fettiplace, 1980), and timedomain processing (Fay, 1978b) have been proposed as mechanisms for frequency discrimination in fish. Frequency discrimination in mammals is generally assumed to depend on the mechanical tuning of the basilar membrane (B´ek´esy, 1960), but the variety of mechanisms that presumably operate in non-mammalian species challenges us to look more closely at this problem in these animals as well. In anurans, no psychophysical studies have yet measured frequency discrimination across the audible spectrum, as have been conducted in mammals, birds, and fish. However, there are frequency discrimination data that warrant mention. Evoked calling and selective phonotaxis methods have been used to estimate frequency discrimination in several different anuran species, each species tested only over a narrow frequency range that was appropriate for the methods that required behavioral responses in the context of acoustic communication behavior. Most

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threshold estimates were between 9 and 33%, generally higher than those taken from other species (for example, Doherty & Gerhardt, 1984; Gerhardt, 1981; Narins & Caprainica, 1978; Ryan, 1983; Schwartz & Wells, 1984). The higher threshold estimates may reflect the methods employed or differences in the frequency resolving power of the anuran ear (Fay & Simmons, 1999). The psychophysical data on critical ratios measured in the green treefrog (Moss & Simmons, 1986), which fall within the range of birds, mammals, and fish, speak against the latter interpretation, but direct psychophysical studies of frequency discrimination in anurans would address the question directly. Positive and negative phonotaxis have been used to measure frequency discrimination in insects. For example, the cricket, Teleogryllus oceanicus, steers towards a 5 kHz model of a conspecific calling song broadcast through a loudspeaker and steers away from a 40 kHz model of a bat echolocation call. By systematically manipulating the stimulus frequency between that of the conspecific call and that of the echolocation signal, the cricket shows a shift in its phonotactic behavior, which is related to its frequency discrimination of these sound frequencies (Moiseff, Pollack, & Hoy, 1978). This is shown in Figure 5.7. Temporal Resolution Temporal processing of sound stimuli is an important aspect of hearing, which contributes to the perception of complex signals and the localization of sound sources (see below). There are many different approaches to study temporal processing in the auditory system, but not all have been widely applied to study different animal species. Since we emphasize comparative hearing in this chapter, we’ve selected for discussion two measures of temporal resolution that have been studied in several animal groups: temporal modulation transfer function and gap detection. Both measures require the subject to detect changes in the envelope of acoustic stimuli. Abrupt onset or offset of pure tones produces spectral smearing of the stimulus that could provide unintended cues to the subject, and therefore, experimenters generally study temporal resolution of the auditory system using noise stimuli. Gap Detection The shortest silent interval that an animal can detect in an acoustic signal is referred to as the gap detection threshold, and in mammals, this measure has been shown to depend on noise bandwidth (see Figure 5.8A). In both the human (Fitzgibbons, 1983) and chinchilla, gap detection

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thresholds are over 10 msec for narrowband noise, and systematically decrease with noise bandwidth to a minimum of about 3.5 msec for the human (Fitzgibbons, 1983) and 2.5 msec for the chinchilla (Salvi & Arehole, 1985). The minimum gap detection threshold in the rat is 3.4 msec (Ison, 1982) and in the parakeet 4.3 msec (Dooling & Searcy, 1981). Experimentally induced hearing loss above 1 kHz in the chinchilla can raise the gap detection threshold to 23 msec (Salvi & Arehole, 1985). The goldfish shows a gap detection threshold of 35 msec (Fay, 1985). Striking is the very small gap detection threshold of the starling, only 1.8 msec (Klump & Maier 1989). Temporal Modulation Transfer Function Detection of the sinusoidal amplitude modulation of broadband noise depends upon the rate and depth of stimulus modulation. Measurements of the minimum amplitude modulation depth required for modulation detection across a range of modulation rates can be used to estimate a temporal modulation transfer function (TMTF). Behavioral data taken from the human, chinchilla, and

parakeet all yield temporal modulation transfer functions with low-pass characteristics. The rate at which temporal modulation detection falls to half power (–3dB) is 50 Hz for human (Viemeister, 1979), 270 Hz for chinchilla (Salvi, Giraudi, Henderson, & Hamernik, 1982b) and 92 Hz for the parakeet (Dooling & Searcy, 1981). At higher rates, detection of temporal modulation requires increasing depths of amplitude modulation up to around 1000 Hz and thresholds remain high up to about 3000 Hz (Fay, 1992), after which the auditory system can no longer resolve the temporal modulation. By contrast, the TMTF of the goldfish does not resemble a low-pass filter, but rather, it remains relatively constant across modulation rates between 2.5 and 400 Hz (see Figure 5.8B). In mammals, data show that detection of temporal modulation of broadband noise depends on hearing bandwidth. High-frequency hearing loss in the chinchilla produces a rise in threshold of amplitude modulation detection across rates, and a drop in the half-power temporal modulation rate (Salvi et al., 1982b). These findings, together with those from gap detection studies, suggest an influence of

Auditory Function and Behavior

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frequency tuning in the auditory periphery on performance of temporal tasks. Comparative hearing loss data are not, however, entirely consistent with this notion. Both gap detection and TMTF may also reflect limitations of neural time processing in the central nervous system (Fay, 1992). Localization Sound source localization plays a central role in the lives of many animals, to find conspecifics, to find food, and to avoid predators. A large number of species use acoustic signals for social communication, and commonly such signals convey the message, “Here I am. Come find me.” For example, the advertisement calls of male frogs attract gravid females to their position along the pond (Capranica, 1976). Calls of birds serve a similar function. Thus, localization of the sender is an important function of acoustic communication in social animals. Some animals detect and localize prey from the acoustic signals they produce. The barn owl, for example, listens to rustling sounds generated by mice that move over the ground. The owl can track and capture the prey in complete

darkness (Payne, 1971) by localizing the sounds generated by its movements through the grass and leaves on the ground. Another example of an animal that uses sound to localize prey is the echolocating bat. In this case, the bat transmits ultrasonic acoustic signals that reflect off the prey, and the bat uses the features of the reflected echo to localize and capture small flying insects, and it can do so in the absence of vision (Griffin, 1958). The acoustic signals produced by predators can also serve as a warning to prey, and the localization of predatorgenerated signals can aid in the evasion of predators. For example, moths, crickets, and praying mantises can detect and localize the ultrasound of an echolocating bat, which can guide its flight away from the predator (Moiseff et al., 1978; Payne, Roeder, & Wallman, 1966; Yager & Hoy, 1986; 1989). Playing several crucial functions, it is not surprising that the capacity to localize sound sources occurs widely throughout the animal kingdom. In most animals, sound localization is enabled by the presence of two ears and a central auditory system that can compare the direction-dependent signals that each receives. The comparison of the signal arrival time (onset, amplitude peaks in the envelope and ongoing phase) and

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amplitude spectrum at the two ears provides the basis for sound=source localization in most vertebrates, referred to as interaural time difference and interaural intensity difference cues (see Figure 5.9). Directional hearing in some animals, however, depends upon directionality of hair cells of the auditory receptor organ (e.g., in fish, Fay & Popper, 1983; Fay & Simmons, 1999) or directionality of the external ear (e.g., in insects, Michelsen, 1998). The acoustic cues used by mammals for horizontal sound localization depend on the time-frequency structure of the signals. Ongoing phase differences between signals received at the two ears can be discriminated unambiguously only if the period of the signal is longer than the interaural distance. In humans, the distance between the two ears is roughly 17 cm, and the maximum interaural time delay is therefore about 0.5 msec (sound travels in air at a speed of approximately 344 m/sec). Humans can use the phase difference of a pure tone signal to localize sound if the frequency is below 1400 Hz (Mills, 1958). At higher frequencies, humans use interaural intensity differences for sound localization. In all land vertebrates, interaural intensity difference cues become available when wavelength of the sound is smaller than the dimensions of the animal’s head, so that sufficient sound shadowing occurs to produce amplitude differences of the signal at the two ears. Masterton et al. (1969), Heffner and Masterton (1980), and Heffner and Heffner (1992) report a negative correlation between interaural distance and highfrequency hearing, and suggest that high-frequency hearing evolved in animals with small heads to enable sound localization using interaural intensity cues. There are many different approaches to measuring the accuracy with which a listener can localize a sound source. The listener may indicate the direction of a sound source by pointing or aiming the head. Here, the accuracy of the listener’s motor behavior is included in the localization measure. Some tasks simply require the subject to lateralize a sound source relative to a reference point. In many psychophysical experiments, the subject is asked to indicate whether a sound source location changed over successive presentations. Localization resolution measured in this way is referred to as the minimum audible angle (MAA, Mills, 1958). The MAA depends on the sound stimulus, with pure tones generally yielding higher thresholds than broadband signals. In mammals, MAA can be very small, about 0.8 degrees in the human (Mills, 1958), 1.2 degrees in the elephant (Heffner & Heffner, 1982), and 0.9 degrees in the bottlenose dolphin (Renaud & Popper, 1975). The macaque monkey has an MAA of

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Figure 5.9 Schematic of acoustic cues used for binaural sound localization in the horizontal plane. (A) Schematic illustrates a sound source to the right of the listener’s midline, resulting in a signal level that is greater and arrives earlier at the listener’s right ear. (B) Illustration of interaural time differences for a signal that arrives at the right ear before the left ear. For this sine wave stimulus, only the first cycle provides a reliable interaural time difference cue, after which the temporal offset of the signal at the two ears becomes ambiguous. This ambiguity occurs for sine wave stimuli with wavelengths shorter than the listener’s interaural separation. (C) Illustration of level differences at the right and left ears. Because the signal is stronger at the right ear than the left ear, it exceeds auditory threshold earlier in time. This shows how interaural intensity differences can translate into interaural time differences. (Adapted from Yost and Hafter, 1987.)

Auditory Function and Behavior

4.4 degrees (Heffner & Masterton, 1978), similar to the opposum with an MAA of 4.6 degrees (Ravizza & Masterton, 1972). Data from the horse shows a surprisingly large MAA of 37 degrees (Heffner & Heffner, 1984). The pallid bat, an echolocating species that is also known to use passive listening for prey capture has an MAA of 2 degrees (Fuzessery, Buttenhoff, Andrews, & Kennedy, 1993), whereas the echolocating big brown bat has an MAA of 14 degrees, in a passive listening paradigm (Koay, Kearns, Heffner, & Heffner, 1998). Estimates of azimuthal localization accuracy in the actively echolocating big brown bat are considerably lower, 1–3 degrees (Masters, Moffat, & Simmons, 1985; Simmons, Kick, Lawrence, Hale, Bard, & Escudie, 1983). See Figure 5.10. Vertical localization in mammals depends largely on spectral cues, created by the direction dependent filtering of acoustic signals by the external ears, head, and torso (Yost & Gourevitch, 1987). The vertical position of a sound source influences the travel path of the sound through the pinna, which in turn shapes the signal spectrum (Batteau, 1967; Heffner, Heffner, & Koay, 1995; Heffner, Koay, & Heffner, 1996). Human listeners can discriminate the vertical position of a sound source with accuracy of about 3 degrees, but performance falls apart when pinna cues are disturbed (Batteau, 1967; Heffner et al., 1996). Vertical localization performance in mammals is typically poorer than horizontal localization, with thresholds of about 2 degrees in dolphins (Renaud & Popper, 1975), 3 degrees in humans (Wettschurek, 1973), 3 degrees in rhesus pig-tailed monkey (Brown, Schessler, Moody, & Stebbins, 1982), 3 degrees in bats (Lawrence & Simmons, 1982), 4 degrees in cat (Martin & Webster, 1987), to 13 degrees in opossum (Ravizza & Masterton, 1972), and 23 degrees in chinchilla (Heffner et al., 1995). Certainly free movement of the head and pinnae can aid in an animal’s localization of a sound source. The echolocating bat’s foraging success depends on accurate localization of prey in azimuth, elevation, and distance. The bat uses the same acoustic cues described above for sound source localization in azimuth and elevation. The bat determines target distance from the time delay between its sonar vocalizations and the returning echoes and uses the three-dimensional information about target location to guide the features of its sonar vocalizations and to position itself to grasp insect prey with its wing or tail membrane (Erwin, Wilson, & Moss, 2001). Psychophysical studies of echo delay difference discrimination report thresholds as low as 30 microseconds, corresponding to a range difference of 0.5 cm (reviewed in Moss & Schnitzler, 1995).

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Behavioral studies demonstrate that birds also use interaural time and intensity differences to localize a sound source in the horizontal plane; however, there is some debate over the mechanisms. Researchers have argued that the bird’s ears are too closely spaced to use interaural time differences from two independent pressure receivers, and the sound frequencies that they hear are too low for their small heads to generate sufficient sound shadowing to use interaural intensity differences. This problem can be solved if one assumes that the bird’s ears act as pressure difference receivers (K¨uhne & Lewis, 1985). A pressure difference receiver is distinct from a direct pressure receiver, in that the left and right ears are acoustically coupled through an interaural canal, allowing stimulation of the tympanic membrane from both directions, i.e., from the outside of the head and through the opposite ear via the interaural canal. The interaural intensity and time cues available to the animal are enhanced through a pressure-difference receiver, and substantial data support this hypothesized mechanism for sound localization in birds. However, owing largely to methodological difficulties in fully testing this hypothesis, some researchers continue to challenge the notion (Klump, 2000). The minimum resolvable angle (MRA) measures the absolute localization performance of an animal, as opposed to relative localization tasks that only require the subject to detect a change in sound source location (e.g., MAA). MRA has been studied in a number of bird species, and thresholds range from 1 to 3 degrees in the barn owl (Rice, 1982; Knudsen & Konishi, 1979), saw-whet owl (Frost, Baldwin, & Csizy, 1989), and marsh hawk (Rice, 1982) to over 100 degrees in the zebra finch (Park & Dooling, 1991) and the bobwhite quail (Gatehouse & Shelton, 1978). (See Figure 5.10.) It is not clear whether the very high thresholds reported for some species reflect poor localization ability or limitations in the psychophysical methods used to study localization performance. The great horned owl has a minimum resolvable angle of 7 degrees (Beitel, 1991), the red-tailed hawk 8–10 degrees, and the American kestrel 10–12 degrees (Rice, 1982). The budgerigar shows a minimum resolvable angle of 27 degrees (Park & Dooling, 1991), and the great tit 23 degrees (Klump, Windt, & Curio, 1986). It is not surprising that the smallest minimum resolvable angles have been measured in raptors. The barn owl, for example, is a nocturnal predator that depends largely on hearing to find prey, has developed exceptional sound localization abilities. It hears higher frequency sounds than most birds (see Figure 5.2), and it shows specializations for temporal processing in the central auditory

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Figure 5.10 Minimum audible angle plotted for a number of mammals (top). Minumum resolvable angle plotted for a number of bird species (bottom). (A) Mammal data: Elephant (Heffner & Heffner, 1982), horse and human (Heffner & Heffner, 1984), macaque monkey (Heffner & Masterton, 1978), dolphin (Renaud & Popper, 1975), harbor seal (Terhune, 1974), sea lion (Moore, 1975), opossum (Ravizza & Masterton, 1972), hedgehog (Chambers, 1971), cat (Casseday, & Neff, 1975, kangaroo rat (Heffner, & Masterton, 1980). (B) Bird data: Bobwhite quail (Gatehouse & Shelton, 1978), saw whet owl (Frost, Baldwin, & Csizy, 1989), canary, zebra finch & budgerigar (Park & Dooling, 1991), and marsh hawk, barn owl, American kestral, and red-tailed hawk (Rice, 1982).

Auditory Function and Behavior

system that presumably supports its horizontal sound localization (see Figure 5.10). The dominant cue used by the barn owl for vertical sound localization is interaural intensity cues, created by the asymmetrical positions of its right and left ear canals. In addition, the barn owl’s feather ruff enhances elevation-dependent changes in signal intensity (Moiseff, 1989). Vertical sound localization thresholds, like MRA in the horizontal plane, are lowest for broadband signals, as small as 2.2 degrees for a 1000 sec duration noise burst (Knudsen & Konishi, 1979). Minimum audible or resolvable angles have not been measured in frogs, but binaural hearing is required for sound localization in anurans (Feng, Gerhardt, & Capranica, 1976). Selective phonotaxis studies have been conducted to examine the female frog’s localization accuracy in approaching a speaker that broadcasts a species-specific advertisement call. Taking the mean error between the frog’s position and the position of the sound source, averaged across all jumps during phonotactic approaches, have yielded estimates of sound localization accuracy in several species: The dendrobatid frog, 23 degrees (Gerhardt & Rheinlaender, 1980); green tree frog, 15.1 degrees (Rheinlaender, Gerhardt, Yager, & Capranica, 1979); the painted reed frog, 22 degrees in 2-D, 43 degrees in 3-D (Passmore, Capranica, Telford, & Bishop, 1984); and the gray tree frog, 23 degrees in 3-D (Jørgenson & Gerhardt, 1991) Sound localization by the frog derives from a combined pressure/pressure difference system (Feng & Shofner, 1981; Michelsen, Jørgenson, Christensen-Dalsgaard, & Capranica, 1986, Michelsen, 1992). Fish can localize sound underwater, as they are sensitive to the acoustic particle motion that changes with the sound source direction. Cod can make angular discriminations of sound source location on the order of 20 degrees in the horizontal plane and 16 degrees in the vertical plane (Chapman & Johnstone, 1974), and sound localization depends on the integrity of both ears (Schuijf, 1975). It appears that vector coding within and across auditory receptor organs in the fish ear supports sound localization: While the underwater acoustics that impact sound localization in fish differ from those for sound localization in terrestrial animals, it is interesting to note that similar organizational principles appear to operate, namely binaural cues are used by fish for azimuthal localization and monaural cues for elevational localization (Fay & EddsWalton, 2000). In spite of their small size, studies of some insect species show that they are able to localize sound sources. Localization of acoustic sources is important to social

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communication and/or predator evasion in many insect species, which is achieved largely through pressure difference receivers and movement receivers (Autrum, 1940; Michelsen, 1998), although experimental evidence shows that acoustic cues are also available for some insect species to use pressure receivers (Payne, Roeder, & Wallman, 1966). Pressure difference receivers are more sensitive than movement receivers in acoustic far fields. As in other animals that apparently use pressure-difference receivers (e.g., frogs and birds, see earlier discussion), sound waves reach both surfaces of the tympanal membrane, and the directional cues are enhanced by the different paths of acoustic activation. Long, lightly articulated sensory hairs protruding from the body surface of an insect are inherently directional, and the activation pattern of these movement receivers can be used to determine sound source location, particularly at close range, where their sensitivity may be comparable to that of a pressure difference receiver (Michelsen, 1992, 1998). Some insects are capable not only of lateralizing the direction of a sounds source but also scaling the direction of a phonotaxic response according to the angular position of the sound source. For example, crickets, placed in an arena, adjust the angle of each turn towards a loudspeaker broadcasting a conspecific call (Bailey & Thomson, 1977; Latimer & Lewis, 1986; Pollack, 1998). It is noteworthy that behavioral studies of sound localization in crickets using a treadmill apparatus that can elicit and track phonotaxis behavior, while keeping the distance from the sound source fixed, have found that localization ability is still intact after removal of one ear or the tracheal connections between the ears. New data shows that crickets retain 1–2 dB of directionality after surgical removal of one ear, which appears to be adequate for localization in simplified laboratory tasks (Michelsen, 1998). Auditory Scene Analysis: From Psychoacoustics to Ethology Auditory scene analysis involves the organization of complex acoustic events to allow the listener to identify and track sound sources in the environment. Although the detection, discrimination, and localization of simple acoustic elements lay the building blocks for audition, it is clear that auditory systems across the phylogenetic scale must also organize dynamic acoustic information into a coherent whole. At the symphony, for example, individuals in the audience may be able to distinguish the sounds produced by separate instruments, or differentiate between music played from individual sections of the orchestra.

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At the same time, a listener may also track a melody that is carried by many different sections of the orchestra together. In effect, a listener groups and segregates sounds, according to similarity or differences in pitch, timbre, spatial location and temporal patterning, to perceptually organize the acoustic information from the auditory scene. In animal communication systems, auditory scene analysis allows an individual to segregate, track, and interpret the acoustic signals of conspecifics that may overlap other environmental signals in frequency and time. The same principle holds for identifying and tracking the signals produced by predators. Auditory scene analysis thus allows the listener to make sense of dynamic acoustic events in a complex auditory world (Bregman, 1990), which is essential to the lives of all hearing animals. Psychoacoustic studies of auditory scene analysis have explored the spectro-temporal parameters that give rise to the perceptual grouping and segregation of acoustic events, commonly referred to as auditory streaming. For example, when a listener is presented a sequence of slowly alternating low- and high-frequency tones over a repeating loop, she or he hears the tones in the sequence in which they were recorded on the tape loop (e.g., 300, 2000, 400, 2100, 500, 2200 Hz). However, when the tone presentation rate is increased (e.g., less than 100 ms between tone onsets), the listener hears streams of tones, containing the low-frequency tones (300, 400, 500 Hz) and the high-frequency tones (2000, 2100, 2200 Hz). In some cases, the listener perceives these tone streams in alternation and in other cases hears two simultaneous streams that differ in perceived amplitude (Bregman & Campbell, 1971). The phenomenon of auditory streaming in the above example appears to follow the Gestalt laws of proximity and similarity, namely the effect depends on the temporal separation and frequency separation of the stimuli (Bregman, 1990). Auditory streaming builds on both simultaneous and sequential analysis of sound elements in a complex acoustic pattern. A listener may use both simultaneous and sequential analysis of acoustic signals to separate and track sounds generated by conspecifics, heterospecifics, and environmental noise. Examples of natural speech and animal communication signals shown in Figure 5.11 illustrate the need for sequential and simultaneous integration to extract information from complex sounds (Bee & Micheyl, 2008). In the past decade, there has been an explosion of research on auditory scene analysis in humans and other animals. Behavioral experiments with European starlings

(Braaten & Hulse, 1993; Hulse, MacDougall-Shackleton, & Wisnewski, 1997; MacDougall-Shackleton, Hulse, Gentner, & White, 1998), goldfish (Fay, 1998, 2000), and frogs (Bee & Riemersma, 2008) have demonstrated that spectral and temporal features of acoustic patterns, as well as spatial location, influence the perceptual organization of sound in these animals. Using procedures ranging from conditioning to phonotaxis, these researchers provide empirical evidence that both fish and birds can distinguish complex acoustic patterns into auditory streams. The use of biologically relevant stimuli in the study of auditory scene analysis has not been widely applied, but this approach has guided ethologically inspired research. For example, Wisniewski & Hulse (1997) examined the European starling’s perception of conspecific song and found evidence for stream segregation of biologically relevant acoustics in this bird species. Auditory scene analysis is a multifaceted process that operates at many levels, engages different circuits for a variety of tasks, and involves feedback across levels. Often overlooked in studies of auditory scene analysis is the importance of a listener moving through the environment, and motion cues that contribute to the analysis of acoustic signals in a complex scene. This is an important direction for future researchers to explore. Neurophysiological studies have begun to explore the neural correlates of auditory scene analysis in a variety of species, ranging from invertebrates to primates. For example, Schul & Sheridan (2006) report that dendritic processes intrinsic to TN-1, a first-order auditory interneuron, can operate to compartmentalize acoustic signals with different temporal characteristics, which a katydid uses at the behavioral level to differentiate between conspecific calls and echolocating bat predators. In vertebrates, there is evidence that both brainstem (Pressnitzer, Sayles, Micheyl, & Winter, 2008) and forebrain cortical mechanisms contribute to auditory scene analysis processes (Bee & Klump, 2004, 2005; Bee, Micheyl, Oxenham, & Klump, 2010; De Sanctis, Ritter, Molholm, Kelly, & Foxe, 2008; Elhilali, Ma, Micheyl, Oxenham, & Shamma, 2009; Fishman, Arezzo, & Steinschneider, 2004; Fishman, Reser, Arezzo, & Steinschneider, 2001; Micheyl, Tian, Carlyon, & Rauschecker, 2005; Micheyl, Carlyon, Gutschalk, Melcher, Oxenham, Rauschecker, Tian, & Wilson, 2007; Micheyl, Hunter, & Oxenham, 2008, 2010; Shamma, Elhilali, & Micheyl, 2011; Shamma & Micheyl, 2010). These findings advance our basic knowledge of the structure and function of the auditory system, which is detailed in the remainder of this chapter.

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Figure 5.11 Auditory scene analysis of communication signals involves both simultaneous and sequential integration of acoustic signals (adapted from Bee & Micheyl, 2008, Fig. 1). Spectrograms (top traces) and oscillograms (bottom traces) of animal vocalizations. (A) Human speech (“Up to half of all North American bird species nest or feed in wetlands”) spoken by President George W. Bush during an Earth Day celebration at the Laudholm Farm in Wells, Maine, on April 22, 2004 (courtesy of “The George W. Bush Public Domain Audio Archive,” http://thebots.net/GWBushSampleArchive.htm). (B) “Phee” calls of the common marmoset, Callithrix jacchus (courtesy Rama Ratnam). (C) Advertisement call of the gray tree frog, Hyla chrysoscelis (recorded by Mark Bee). (D) Song motif from a European starling, Sturnus vulgaris (courtesy Lang Elliot). E: Portion of an advertisement call of the plains leopard frog, Rana blairi (recorded by Mark Bee). Note that in all cases, the vocalizations consist of sequences of sound elements (e.g., syllables and words [A], call notes [B, E], pulses [C], and song syllables [D]), many of which are comprised of simultaneous spectral components (e.g., harmonics), thus illustrating requirements for sequential and simultaneous auditory integration.

AUDITORY PERIPHERY Absolute auditory thresholds provide an estimate of the frequency range and limits of an animal’s hearing, and depend directly upon ear structure and sensory transduction. Although the basics of mechanoelectrical transduction are very similar among animals, there are many ways to achieve movement of hair-cell cilia bundles, because of the different physical constraints on the animals that detect sound in air or water. In water, soft tissues are acoustically transparent. Therefore, sound waves traveling through the body in water cause little direct displacement of hair-cell bundles. Fish and amphibians solve this problem through relative motion between the hair cells and a

denser overlying structure called an otolith (Lewis, Narins, Fay, & Popper, 1998; Popper & Fay, 1999). In air, tympanic membranes and middle ear bone(s) of terrestrial vertebrates compensate for the impedance mismatch between air and the water-filled inner ear cavities, while in those insects with auditory sensitivity, sound vibrates a tympanum, and transmits motion to the sensory cell. Tympanic ears capable of receiving airborne sound evolved independently among the ancestors of modern frogs, turtles, lizards, birds, and mammals (ChristensenDalsgaard & Carr, 2008; Clack, 1997; Grothe, Carr, Casseday, Fritzsch, & K¨oppl, 2005). Their ancestors, the earliest land-dwelling vertebrates, were probably sensitive to bone conduction and sound waves traveling through

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the ground, much as are modern lungfish (ChristensenDalsgaard, 2010). Lungfishes are close relatives of the tetrapods, and their ear has good low-frequency vibration sensitivity, like recent amphibians, but poor sensitivity to airborne sound. Despite the independent evolution of tympanic hearing among the different tetrapod groups, some notably similar principles have emerged, presumably due to similar selection pressures for localizing and identifying auditory targets. In this section, we will review the auditory function of ears in insects, bony fish, and land vertebrates in order to highlight general principles and noteworthy specializations.

Invertebrates

Marine Invertebrates Sound propagates well underwater, and some marine invertebrates like snapping shrimp may use sound for communication (for reviews, see Budelmann, 1992). Cephalopods have otocysts and behavioral studies suggest squid are sensitive to sound. Recent studies have measured auditory-evoked potentials to both near-field acoustic and acceleration stimuli, with sound-field pressure and particle motion components (Mooney & Hanlon, 2010). Auditory-evoked potentials were measured between 30 and 500 Hz, with the lowest thresholds between 100–200Hz. Thus squid detect sound similarly to most fish, with the statocyst acting as an accelerometer through which squid detect the particle motion component of a sound field.

Insects Insect ears appear often in evolution, and generally have three key features. One, the thinning of cuticle over the organ to form a tympanum that is moved by air pressure, two, formation of an air cavity of tracheal origin sometimes expanded into a chamber, and three, innervation of this complex by sensory cells (Yager, 1999). Sound vibrates the tympanum, and transmits motion to the sensory cell. Thus unlike vertebrate ears, where airborne vibrations are converted into vibrations in fluid by middle ear bones, no such conversion is required in insects. Most insect ears do not have many receptor cells, but tonotopic organization is associated with an increase in receptor cells. For example, crickets have relatively large ears, with 60–70 auditory receptor neurons divided into two groups. The proximal group is sensitive to lower frequencies, while the larger distal population is tuned over a frequency range from cricket song best frequency to ultrasound. Another strategy has been to develop different ears with different sensitivities: Mantis ears exhibit sensitivity to low frequencies in their mesothoracic ear and sensitivity to high (ultrasonic) frequencies in the metathoracic ear (Yager, 1999). Not only are insect ears sensitive to sound, but also studies of mosquitoes has revealed that auditory sensitivity is achieved by a process that is comparable to the active amplification observed in vertebrate ears (Gopfert & Robert, 2001). The active processes in the antennal hearing organs of mosquitoes and in the vertebrate cochlea both display a combination of mechanical characteristics that support the presence of active auditory mechanics. Both vertebrate and mosquito ears exhibit spontaneous otoacoustic emissions, revealing the presence of active amplification processes (Hudspeth, 2005).

Bony Fish In fish, relative motion between hair cells and an overlying otolith activates hair cells (Lewis & Narins, 1999; Popper & Fay, 1999). The otolith lags as the sound passes through the head, generating a shearing force. The ear detects this particle motion much as does an accelerometer. The auditory otolith organs of fishes are the saccule, lagena, and utricle. Each has sensory tissue containing hair cells and support cells overlain by an otolith. The organ most used in hearing varies, but is usually the saccule. In addition to simple particle motion detection, bony fish have achieved increased sensitivity through coupling to their swimbladder or other gas-filled “bubble” in the abdominal cavity or head. When sound pressure fluctuations occur, the bubble expands and contracts according to the amplitude of motion characteristic of the enclosed gas. The swimbladder thus becomes a monopole sound source, and causes relative motion between the otoliths and underlying hair cells. In this case, the sound pressure amplitude determines hair-cell stimulation. In most fishes, response of the ear to sound is determined simultaneously by the ear detecting particle motion in its “accelerometer” mode, and by the ear detecting sound pressure via the swimbladder response. In some species, the swimbladder is specifically linked to the ear via specialized mechanical pathways. The best-known such pathway is the Weberian ossicle system; a series of four bones connecting the anterior swimbladder wall to the ears. Fishes with such ossicles are considered to be “hearing specialists” in that their sensitivity and bandwidth of hearing is generally greater than in animals lacking such a system. The herrings and the mormyrids have gas bubbles near

Auditory Periphery

the ears in the head and are also considered to be hearing specialists (Popper & Fay, 1999). Frogs After the movement onto land, all the major features of the amniote ear appeared in parallel among the major tetrapod groups, including the amphibians. Despite this independent evolution, all amniote ears include a tympanum, middle ear, impedance matching ossicles inserting in the oval window, a tectorial body overlying the hair cells and specialized auditory endorgans (Lewis & Narins, 1999). The ossicles act as an impedance transformer because they transmit motion of the tympanic membrane to the fluid-filled inner ear. Differential shearing between the membranes and hair-cell cilia stimulates the hair cells. The sensory hair cells of the frog, Rana, have been used to identify the cellular basis of transduction, mechanosensitive channels located at the tips of the stereocilia (Hudspeth, 2005). Hair cells are depolarized by movement of the stereocilia, and release neurotransmitter onto their primary afferents. Frogs have very specialized peripheral auditory systems, with two endorgans, the amphibian and basilar papillas. This duplication may increase the frequency range because the basilar papilla is sensitive to lower frequencies than the amphibian papilla (Lewis & Narins, 1999).The amphibian papilla is functionally similar to the amniote papilla, although the lack of structural similarity suggests that these organs arose in parallel, with the common function reflecting a basic auditory role. In frogs, the middle ear air spaces open widely to the mouth cavity via large Eustachian tubes (Gridi-Papp, Feng, Shen, Yu, Rosowski, & Narins, 2008). This wide pathway of communication between the two ears and the mouth and lungs makes possible several potential pathways of sound both to the outer and inner surface of the tympanic membrane. The ears of some anurans operate both as pressure receivers and pressure gradient receivers in certain frequency ranges (ChristensenDalsgaard, 2005). Since pressure gradients are vector quantities, the ear operating in this mode is inherently directional (Lewis & Narins, 1999). Reptiles The evolution of basilar papilla of modern amniotes begins with the stem reptiles (Manley, 2000; Miller, 1980). The key new feature of this auditory organ is the basilar membrane, a thin partition in the fluid filled inner ear along

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which alternating pressures are transmitted (Wever, 1978). In turtles, the basilar papilla is a flat strip of membrane populated by approximately 1,000 hair cells (K¨oppl & Manley, 1992). Salient features in papilla evolution include lengthening and curvature of the sensory epithelia, features thought to both enhance sensitivity and extend the audible frequency range (Manley, Gleich, Dooling, Popper, & Fay, 2000). Elongation of the papilla is seen in all groups. Lizards typically have two types of hair cell, tectorial and free-standing. Tectorial hair cells resemble those found in birds and mammals. Auditory nerve fibers that innervate them encode low-center frequencies (100–800 Hz) and have sharp asymmetric tuning curves. Fibers from free-standing hair cells have high-center frequencies (900–4000 Hz), high spontaneous rates, and broad symmetric tuning curves. Free-standing hair cells are unique to lizards and sensitive to higher frequencies. Recent reports show hearing limits and vocalization energy of some lizards extended to frequencies far above those reported for any other lizard group, being 14 kHz and >20 kHz, respectively (Manley & Kraus, 2010). In lizards and some birds, like frogs and toads, sound can travel relatively unobstructed from one ear to the other (Christensen-Dalsgaard, 2010). This acoustical coupling allows the direct component of sound at the external surface of the eardrum to interact with the indirect component at the internal surface to reduce or enhance tympanic motion, and enhance the directionality of the ear (Calford & Piddington, 1988; Wever, 1978; for reviews, see Feng & Christensen-Dalsgaard, 2007; Klump, 2000; Michelsen, 1998). Acoustical coupling is greatest in lizards, which can have interaural transmission gains approaching 0 dB, and directionality differences up to 40 dB (Christensen-Dalsgaard & Manley, 2005). Thus, although nonmammalian tetrapods do not have moveable external ears, they still exhibit well-developed directionality. Birds The outer ear of birds includes an external canal, and a middle ear similar to those of the amphibians and reptiles in having a single major ossicle, the columella. The efficiency and frequency response of this system is not unlike that of mammals in the frequency range below about 5000 Hz. The inner ear of birds includes a papilla in addition to an associated lagena (Manley, 2010). A cross-section of the bird basilar membrane and papilla reveals rows of hair cells, with tall hair cells on the neural (innervated) side of the papilla, and short (motor)

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hair cells on the abneural side. There are not two types of hair cells, like in mammals, but the tall hair cells closest to the neural edge of the papilla provide the most afferent input to the auditory nerve dendrites, while short hair cells furthest from the neural edge receive purely efferent innervation. In general, the height of the hair-cell stereocilia varies smoothly from one end of the papilla to the other. Long stereocilia have been associated with low-frequency sensitivity, and short with high-frequency sensitivity. It is likely that a frequency analysis occurs along the basilar membrane of the bird ear in much the same way that it occurs among mammals (Fuchs, Nagai, & Evans, 1988). In nonmammalian tetrapods, hearing sensitivity is enhanced by an active process that both amplifies and tunes hair-cell movement (Hudspeth, 2005). The active processes result from the interaction of negative stiffness on the hair bundles with two motor processes, one due to myosin-based adaptation and the other to Ca2+-dependent closing of transduction channels. Mammals Mammals generally have moveable external ears, and three middle ear bones that work together as a lever system to amplify the force of sound vibrations. The inner end of the lever moves through a shorter distance but exerts a greater force than the outer end. In combination, the bones double or triple the force of the vibrations at the eardrum. The muscles of the middle ear also modify the amplification of this lever system, and can act to protect the ear from large vibrations. The stapes passes the vibrations to the oval window or opening in the bony case of the cochlea. The oval window is 15–20 times smaller than the eardrum, which produces some of the amplification needed to match impedances between sound waves in the air and in the cochlear fluid and set up the traveling wave in the inner ear. Mammalian sensory cells are typically organized on the basilar membrane into one row of inner hair cells (described as inner because they are closer to the central core of the cochlear) and three to five rows of outer hair cells (Dallos, 1996). Inner hair cells innervate type 1 primary afferents and are innervated by very few efferents. Outer hair cells sparsely innervate type 2 primary afferents and receive more efferent terminals. Type 1 afferents comprise 95% of the total afferents, and convey the frequency, intensity, and phase of signal to the auditory nerve. Sound frequency is encoded by place on the cochlea, intensity is encoded by the DC component of receptor potential, and

timing by the AC component. Such a system must act as a low-pass filter, which places limits on phase locking. Outer hair cells are hypothesized to act as a cochlear amplifier, boosting responses by a local electromechanical amplification process. If outer hair cells are destroyed, frequency tuning is greatly diminished (Brownell, Bader, Bertrand, & de Ribaupierre, 1985).

CENTRAL AUDITORY PATHWAYS Auditory information is encoded in the activity of both single neurons and arrays of neurons. Neural coding strategies in the auditory pathways include rate, temporal, ensemble, and the labeled line-place principle codes (Brugge, 1992; Figure 5.12). These codes assume the existence of a sensitive receiver, or set of neurons whose activity changes in response to the code. None of the codes appear capable of transmitting the entire array of spectral and temporal information (Brugge, 1992). Instead they appear to operate in various combinations depending on the acoustic environment. Coding strategies also appear to change at different levels of the central auditory pathway, for example when the phase-locked spikes of the temporal code are converted to the place code output of neurons sensitive to interaural time differences. There is no evidence that coding strategies differ among animals. Neurons selective for complex stimulus features have been found in every auditory system. These include the song-specific responses found in songbirds, pulse interval specific neurons in the mormyrid electric fish and frog midbrain, space specific neurons in the space mapped region of the inferior colliculus of the barn owl (Figure 5.13), and delay-tuned neurons in the bat auditory cortex. These examples show that many combinations of inputs and intrinsic properties can create such specificity. The basic anatomical organization of the central auditory system does not differ greatly among vertebrates. These connections are reviewed in chapters in The Mammalian Auditory Pathway: Neuroanatomy, edited by Webster, Popper, & Fay (1993), in Neurobiology of Hearing, edited by Altschuler et al. (1991), and in The Central Auditory System, edited by Ehret and Romand (1997). The primary auditory nuclei send a predominantly contralateral projection to the auditory midbrain, and in some vertebrates, to second-order (olivary) nuclei, and lemniscal nuclei. The auditory midbrain generally projects bilaterally to dorsal thalamus and then to hypothalamus and telencephalon. Major differences among central auditory structures seldom appear in evolution. Selective forces

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Figure 5.12 Brugge (1992) has reviewed four major codes used in the auditory system: rate, temporal, ensemble, and the labeled line-place principle. (A) Rate: In the auditory nerve, sound intensity is encoded by the number of action potentials, which increase linearly with sound intensity over some range before reaching a plateau. This plot shows a rate-intensity function for a cat cochlear afferent to a tone at best frequency. The y-axis plots discharge rate in spikes/sec (from Sachs & Abbas, 1974). (B) Temporal: Action potentials phase-lock to the waveform of the acoustic stimulus. Spikes occur most frequently at a particular phase of the tone, although not necessarily in every tonal cycle. Thus the discharge pattern of a cochlear nerve fiber can encode the phase of a tone with a frequency above 1000 Hz even though the average discharge rate is lower. Recording from a barn owl cochlear nucleus magnocellularis neuron plots the timing of action potentials with respect to stimulus phase in a period histogram. The best frequency of this neuron was 5.2 kHz (from Sullivan & Konishi, 1984). (C) Ensemble. The existence of an ensemble code may be inferred, or recorded with an array of microelectrodes. In the central nucleus of the inferior colliculus of the barn owl, an array of neurons is individually sensitive to interaural phase differences, and their responses show phase ambiguity. The ensemble encodes interaural time difference (from Wagner, Takahashi, & Konishi, 1987). (D) Place: Within both the nucleus laminaris and the medial superior olive, inputs from left and right ears encode the timing of the timing of the stimulus at the two ears. This temporal code is converted into a place code for interaural phase difference by circuit composed of delay lines and coincidence detectors (see text). Position or place within the nucleus confers sensitivity to particular interaural phase differences. (From Carr & Konishi, 1990.)

driving these changes have been ascribed to the development of new endorgans in the auditory periphery and/or the increased use of sound (Wilczynski, 1984).

Insects Insects hear to obtain information about their environment; for example, moths and mantises hear the echolocating sounds of bats, while crickets localize their mates (see Hoy, Popper, & Fay, 1998). The tasks of the insect auditory system are to filter important signals out of the environment noise including specific frequencies, patterns, and loudness and to determine location of sound source. Behavioral studies have shown that crickets show phonotaxis, or orientation towards sound (see Figure 5.7). Crickets are sensitive to a wide range of frequencies, with intraspecific signals being most important (Figure 5.3; Pollack, 1998). They recognize cricket song, particularly

pulse period. In the cricket CNS, there are neurons that encode the frequency, intensity, direction, and temporal patterns of song. These include multiple pairs of identified interneurons, including the intrasegmental neurons that respond to the temporal pattern of the song (Pollack, 2000).

Fishes Psychophysical studies have shown that fish hear in the same sense that other vertebrates hear (Figure 5.2; Fay, 1988; Popper, Fay, Platt, & Sand, 2003). This conclusion is based on behavioral studies of their sensitivity and discriminative acuity for sound. The best sensitivity is found in species that have structures that directly connect an air bubble (pressure receptor, such as the swimbladder) to the inner ear (e.g., goldfish, catfish, some squirrelfish, some croakers). These fish have hearing sensitivities of 50

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Figure 5.13 High-level neurons are sensitive or selective for complex stimulus features. A combination of auditory inputs, plus cellular features of these neurons, underlie this emergent sensitivity. (A) Selective neuron in the torus semicircularis of the mormyrid fish responds to the temporal features of complex sounds used in acoustic displays. This neuron was sensitive to particular inter-click intervals found in the grunt element of the courtship signal (from Crawford, 1997). (B) Selective neuron in the higher vocal center of the white crown sparrow responds to the bird’s own song. The song has three phrases: an introductory whistle, a buzz, and a trill. Multiunit neuronal activity was elicited by five repetitions of the bird’s own song. Arrows mark the end of each phrase (from Margoliash & Konishi, 1985). (C) Selective response of a space specific neuron in the external nucleus of the inferior colliculus of the barn owl, plotted as a function of interaural time difference. These neurons are also selective for particular interaural level differences (not shown). When stimulated with noise, this neuron responds to a characteristic delay of −60 μsec. (From Takahashi, 1989.)

to 60 dB re 1 μP (microPascal), between 300 and 1000 Hz. Fishes that do not have connections between an air bubble and the inner ear, i.e. the majority of fish species, have best hearing from about 200 to 500 Hz, with sensitivity in the range of 110 to 135 dB re 1 μPa. Fishes without swimbladders, such as flatfish and sharks and rays, do not detect the sound pressure component of sound, but instead, detect particle motion. The hearing range in these species is generally narrower than in fishes with an air

bubble. Fish ears are also inherently directional (Popper et al., 2003). In all vertebrates, the auditory nerve enters the brain and divides into ascending and descending branches. In bony fish, the ancestral pattern is for auditory and vestibular inputs to project to the anterior, magnocellular, descending and posterior nuclei of the ventral octaval column. For fish that are auditory specialists, new, more dorsal auditory areas arise from the ventral column (McCormick, Fay, & Popper, 1999). Auditory projections to the descending and anterior octaval nuclei have appeared independently many times in hearing specialists. Both the anterior and descending nuclei project to the auditory area of the central nucleus of the midbrain torus. This area is located medial to the region of the torus sensitive to lateral line stimuli. In hearing specialists, secondary octaval and lemniscal nuclei may be identified in the hindbrain. These secondary octaval nuclei receive input from the descending nucleus and project to the central nucleus of the torus. Many toral neurons phase-lock to the auditory stimulus, and some exhibit sharp frequency tuning, although the majority of toral units are more broadly tuned (Feng, Schellart, Fay, & Popper, 1999). Some fish use sound for communication and there are neurons in the central nucleus of the torus that are sensitive to the grunts, moans, and howls produced by vocalizing mormyrids (Crawford, 1997). The central nucleus of the torus has major ascending projections to the dorsal thalamic nuclei (central posterior, and sometimes anterior). It also projects to the ventromedial nucleus of the ventral thalamus, the posterior tuberculum, and the hypothalamus (McCormick et al., 1999). The central nucleus of the torus and hypothalamus are reciprocally interconnected, which may reflect the role of sound in reproductive and/or aggressive behavior in some fish. The telencephalon in bony fish is divided into dorsal and ventral areas, with the dorsal area proposed to be homologous to the pallium of other vertebrates, and the ventral area to the subpallium (Northcutt, 1995; Striedter, 1997). Two dorsal areas have been shown to respond to sound, but little is known about auditory processing rostral to the midbrain. Frogs (Anurans) Among amphibians, psychophysical hearing data exist only for frogs. Many frogs vocalize during mating and other social interactions, and they are able to detect, discriminate, and localize species-specific vocalizations. Behavioral studies have exploited the natural tendency of

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Figure 5.14 Simplified summary of possible acoustic circuits in ranid frogs, from Endepols, Walkowiak, and Luksch (2000). The ascending auditory connections of the torus semicircularis and lower auditory nuclei are shown in green, the torus in yellow, and projections from thalamus and ventral striatum (STv) in blue. Descending projections from the torus are shown in red. The midline dorsolateral nucleus (Ndl), also known as DLM, projects to both the torus and the superior olive (Os). The torus contains three subdivisions, laminar (Tl), principal (Tp), and magnocellular (Tm). The caudal region of the central thalamic nucleus (C) is concerned with auditory input, and projects to the ventral striatum (STv), anterior preoptic area, and ventral hypothalamus (VH).

frogs to orient to sounds broadcast in a more or less natural setting (Zelick, Mann, & Popper, 1999). In frogs, eighth nerve afferents project to the specialized dorsal medullary nucleus, and ventral and lateral to the vestibular column (Figure 5.14). The dorsal nucleus is tonotopically organized with high-frequency responses from the basilar papilla medial, and lower bestfrequency responses from the amphibian papilla mapped lateral (McCormick, 1999), and typical V-shape tuning curves (Feng et al., 1999). A major transformation in the signal representation takes place in the dorsal nucleus, with primarylike, onset, pauser, and chopper discharge pattern responses emerging (Feng & Schellart, 1999). The dorsal medullary nucleus projects both directly and indirectly to the auditory midbrain torus, with projections to the superior olive and superfical reticular nucleus

(Figure 5.14). The superior olive receives bilateral input from the dorsal nucleus, and neurons there respond to a wide range of amplitude modulated stimuli. The ventral zone of the torus receives most of the ascending inputs. It is tonotopically organized, its neurons are often selective to amplitude modulated stimuli, and more neurons respond to complex sounds than in the medulla (Feng et al., 1999). These include neurons that respond selectively when two or more sound elements are presented in a particular temporal order (Figure 5.13A). The precise relative timing of these elements is particularly important for neurons tuned to the specific interpulse intervals that characterize many frog calls (Edwards, Alder, & Rose, 2002). The torus projects to the central and posterior nuclei of the thalamus and to the striatum. Recordings from the posterior nucleus show sensitivity to the frequency combinations

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present in the frog advertisement calls, while many neurons in central thalamus are broadly tuned and sensitive to specific temporal features of the call (Feng et al., 1999). The central thalamus projects to the striatum, anterior preoptic area, and the ventral hypothalamus. These connections may mediate control of reproductive and social behavior in frogs. The anterior thalamic nucleus supplies ascending information to the medial pallium, although little is known about pallial auditory processing.

Reptiles The auditory system is organized in a common plan in both birds and reptiles, presumably due to the conserved nature of the auditory sense and their close phylogenetic relationship (Carr & Code, 2000; see Figure 5.15). The brainstem auditory nuclei of lizards and snakes, however, differ somewhat from other reptiles and birds (Gleich & Manley, 2000). This may be because snakes have lost the tympanum, middle ear, and high-frequency sensitivity

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(Young, 1998), while lizards have evolved unique hair cells sensitive to high-frequency sound (Manley, 2000). In both snakes and lizards, auditory nerve fibers from tectorial hair cells project to the first order nucleus magnocellularis and the lateral nucleus angularis. Neurons that contact free-standing hair cells project primarily to the nucleus angularis medialis (Szpir, Sento, & Ryugo, 1990). Nucleus magnocellularis projects to the nucleus laminaris, that in turn projects to the superior olive, to the lemniscal nuclei and to the central nucleus of the auditory midbrain (torus semicircularis, nucleus mesencephalicus lateralis dorsalis, inferior colliculus). The nucleus angularis projects to the superior olive, to the lemniscal nuclei and to the central nucleus of the auditory midbrain. Hindbrain auditory connections are generally bilateral, although contralateral projections predominate. The lemniscal nuclei project to midbrain, thalamic, and forebrain targets. The central nucleus of the auditory midbrain projects bilaterally to its dorsal thalamic target (nucleus medialis or reuniens in reptiles, nucleus ovoidalis in birds). The auditory thalamus projects to the auditory region of the forebrain (medial dorsal ventricular ridge in reptiles, Field L in birds). Field L projects to other forebrain nuclei that may be involved in the control of song and other vocalizations. Descending projections from the archistriatum to the intercollicular area (and directly to the hypoglossal nucleus in some) appear to mediate vocalization.

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Figure 5.15 Connections of the auditory system in birds and reptiles. Nucleus angularis projects to the contralateral auditory midbrain, with a smaller ipsilateral projection. Nucleus angularis also projects bilaterally to the superior olive and dorsal nucleus of the lateral lemniscus and to the contralateral ventral nucleus of the lateral lemniscus. Projections from nucleus laminaris were demonstrated to the ipsilateral superior olive, to the contralateral lemniscal nuclei and a small medial region in the auditory midbrain bilaterally with the contralateral projection being much denser than the ipsilateral one. Other nuclei having ascending connections with the midbrain include the contralateral superior olive, the ipsilateral dorsal nucleus of the lateral lemniscus, the contralateral ventral nucleus of the lateral lemniscus and the contralateral midbrain. The midbrain projects to the nucleus ovoidalis, which projects to Field L in the neostriatum (see Figure 5.16). (Modified from Kubke & Carr, 2005.)

Birds use sound for communication to a greater extent than other reptiles, and generally hear higher frequencies (Dooling, Lohr, & Dent, 2000; Klump, 2000). Most birds hear up to 5–6 kHz, while the barn owl has exceptional high-frequency hearing, with characteristic frequencies up to 9–10 kHz in the auditory nerve (Konishi, 1973). Some landbirds such as pigeons, chickens, and guinea fowl are also sensitive to infrasound, below 20 Hz (Carr, 1992). Infrasound signals may travel over great distances, and pigeons may use them for orientation. Cochlear Nuclei Encode Parallel Ascending Streams of Auditory Information The auditory nerve projects to nucleus magnocellularis and nucleus angularis in the pattern described for the bird and reptile morphotype (see above, Figure 5.15). In the owl, nucleus magnocellularis is the origin of a neural pathway that encodes timing information, while a parallel pathway for encoding sound level originates with nucleus angularis (Takahashi, 1989). Auditory responses

Central Auditory Pathways

include primary-like, onset, chopper, and complex type IV responses, very similar to response types found in the amphibian and mammalian cochlear nuclei. Recordings in the chicken cochlear nuclei have found a similar but less clear segregation of function. The similarities between the owl and the chicken suggest that the functional separation of time and level coding is a common feature of the avian auditory system. The auditory system uses phase-locked spikes to encode the timing of the stimulus (Figure 5.12B). In addition to precise temporal coding, behavioral acuity is also assumed to depend upon the activity of neural ensembles (Figure 5.12C). Phase-locking underlies accurate detection of temporal information, including ITDs (Klump, 2000) and gap detection (Dooling, Lohr, & Dent, 2000). Neural substrates for phase-locking include the specialized endbulb terminal in the nucleus magnocellularis, termed an endbulb of Held . This large synapse conveys the phase-locked discharge of the auditory nerve fibers to its postsynaptic targets in the nucleus magnocellularis (Trussell, 1997, 1999). AMPA-type glutamate receptors contribute to the rapid response of the postsynaptic cell by virtue of their rapid desensitization kinetics (Greig, Donevan, Mujtaba, Parks, & Rao, 2000). Detection of Interaural Time Difference in Nucleus Laminaris Nucleus magnocellularis projects to the nucleus laminaris (Carr & Konishi, 1990; Rubel & Parks, 1988). The projections from the nucleus magnocellularis to the nucleus laminaris resemble the Jeffress model for encoding interaural time differences (see Joris, Smith, & Yin, 1998). The Jeffress model has two elements: delay lines and coincidence detectors. A Jeffress circuit is an array of coincidence detectors, every element of which has a different relative delay between its ipsilateral and contralateral excitatory inputs. Thus, interaural time difference is encoded into the position (a place code) of the coincidence detector whose delay lines best cancels out the acoustic interaural time difference (for reviews, see Joris, Smith, & Yin, 1998; Konishi, 2003). Neurons of the nucleus laminaris phaselock to both monaural and binaural stimuli, but respond maximally when phase-locked spikes from each side arrive simultaneously, that is, when the difference in the conduction delays compensates for the interaural time difference (Carr & Konishi, 1990). The cochlear nuclei also receive descending GABAergic inputs from the superior olive that may function as gain control elements, or a negative feedback to protect nucleus laminaris neurons from losing their sensitivity to interaural time differences at high sound intensities (Pena, Viete, Albeck, & Konishi, 1996).

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Regarding birds and mammals, there is ongoing discussion about what algorithms are used for computation of ITDs and interaural level differences (Grothe, Pecka, & McAlpine, 2010; K¨oppl, 2009). It appears that birds use computed sensory maps or place codes, where the individual neurons that make up sensory maps respond maximally to different preferred values of ITD (Konishi, 2003; Wagner et al., 2007). In small mammals like the gerbil, however, the range of physiological ITDs often is not well represented by peak firing of low best-frequency neurons (see section on mammalian auditory systems). Instead, McAlpine, Jiang, & Palmer (2001) proposed that the azimuthal position of a sound source could be computed from the overall discharge rate within the broadly tuned ITD channel on one side of the brain. Thus, for a sound moving away from the midline, activity will increase in the contralateral hemisphere, toward the peak of the ITD functions, indicating that the sound source has shifted to a more lateral position (McAlpine, Jiang, & Palmer, 2001). Lemniscal Nuclei The lemniscal nuclei are ventral to the auditory midbrain. There are two identified lemniscal nuclei in reptiles, dorsal and ventral, and three in birds, dorsal, intermediate, and ventral. These names are the same as those of the lemniscal nuclei in mammals, but it is not known whether the nuclei are homologous. The dorsal nucleus (LLDp) mediates detection of interaural level differences in the barn owl (Carr & Code, 2000). Interaural level differences are produced by the shadowing effect of the head when a sound source originates from off the midline (Klump, 2000). Some owls experience larger than predicted differences because their external ears are also oriented asymmetrically in the vertical plane. Because of this asymmetry, interaural level differences vary more with the elevation of the sound source than with azimuth. This asymmetry allows owls to use interaural level differences to localize sounds in elevation, while they use interaural time differences to determine the azimuthal location of a sound. The level pathway begins with the cochlear nucleus angularis, which respond to changing sound level over about a 30dB range (for review, see Carr & Code, 2000). Each nucleus angularis projects to contralateral LLDp (Kr¨utzfeldt, Logerot, Kubke, & Wild, 2010; Wilde, Kr¨utzfeldt, & Kubke, 2010). The cells of LLDp are excited by stimulation of the contralateral ear and inhibited by stimulation of the ipsilateral ear. Mapping of interaural level differences begins in LLDp, with neurons organized according to their preferred interaural level difference.

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LLDp neurons do not encode elevation unambiguously, and may be described as sensitive to interaural level difference, but not selective because they are not immune to changes in sound level. The encoding of elevation improves in the auditory midbrain. Midbrain and Emergence of Relevant Stimulus Features The auditory midbrain receives ascending input and projects to the thalamus. It is surrounded rostrally and laterally by an intercollicular area that receives descending input from the forebrain archistriatum (Puelles, Robles, Mart´ınez-de-la-Torre, & Mart´ınez, 1994). The auditory midbrain mediates auditory processing, while the intercollicular area appears to mediate vocalization and other auditory-motor behaviors. The auditory midbrain is divided into an external nucleus and a central nucleus. Nucleus angularis, LLDp, and nucleus laminaris all project to regions of the central nucleus (Conlee & Parks, 1986; Kr¨utzfeldt, Logerot, Kubke, & Wild, 2010; Takahashi, 1989). Interaural time difference and interaural level difference signals are combined, and the combinations conveyed to the external nucleus contain a map of auditory space (for reviews, see Knudsen, 2002; Konishi, 2003). Studies of the owl auditory midbrain have shown that most neurons are binaural, excited by inputs from the contralateral ear and inhibited by the ipsilateral ear, although bilateral excitation and contralateral excitation are also present. Many neurons are sensitive to changes in interaural level and time difference. The tonotopic organization is consistent with the tonotopy observed in lizards and crocodiles, with low best-frequencies dorsal (Carr & Code, 2000). Space-specific responses in the barn owl appear to be created through the gradual emergence of relevant stimulus responses in the progression across the auditory midbrain. Information about both interaural time and level differences projects to the external nucleus, and each space-specific neuron receives inputs from a population of neurons tuned to different frequencies (Takahashi, 1989). The nonlinear interactions of these different frequency channels act to remove phase ambiguity in the response to interaural time differences. The representation of auditory space is ordered, with most of the external nucleus devoted to the contralateral hemifield (Knudsen, 1980). The external nucleus projects topographically to the optic tectum that contains maps of visual and auditory space in register. Activity in the tectum directs the rapid head movements made by the owl in response to auditory and visual stimuli (Knudsen, du Lac, & Esterly, 1987).

Thalamus and Forebrain The central nucleus projects to both the external nucleus and the nucleus ovoidalis of the thalamus. Nucleus ovoidalis in turn projects ipsilaterally to Field L. Nucleus ovoidalis has been homologized to the mammalian medial geniculate nucleus (Karten & Shimizu, 1989). Nucleus ovoidalis is tonotopically organized, with high best frequencies located dorsally, and low best frequencies ventrally (Perez, Shanbhag, & Pena, 2009; Proctor & Konishi, 1997). In the barn owl, all divisions of the central nucleus project to ovoidalis, and the physiological responses in ovoidalis reflect this diverse array of inputs. Most neurons had responses to interaural time difference and/or interaural level difference, at stimulus frequencies similar to those found in the midbrain. In contrast to the mapping found in the midbrain, however, no systematic representation of sound localization cues was found in ovoidalis (Perez, Shanbhag, & Pena, 2009; Proctor & Konishi, 1997). Nevertheless, sound localization and gaze control are mediated in parallel in the midbrain and forebrain of the barn owl (for review, see Carr & Code, 2000). Field L is the principal target of ascending input from ovoidalis. It is divided into three parallel layers, L1, L2, and L3, with L2 being the major thalamic input layer (Scheich, Bonke, Bonke, & Langer, 1979). L1 and L3 may function to extract different acoustic features from complex sounds (Woolley, Gill, Fremouw, & Theunissen, 2009). Woolley has classified auditory responses in midbrain and forebrain into functional groups, each of which plays a specific role in extracting distinct complex sound features, such as the cues for the fundamental acoustic percepts of pitch, timbre, and rhythm. Comparing spectrotemporal receptive fields in midbrain and forebrain neurons suggested that tuning properties are both inherited and emerge independently as information ascends the auditory processing stream (Scheich, Bonke, Bonke, & Langer, 1979; Woolley, Gill, Fremouw, & Theunissen, 2009). The general avian pattern is that Field L projects to the adjacent hyperpallium and to other pallial nuclei (Margoliash, 1997); members of the field developed new terminology that more accurately reflects our current understanding of the avian cerebrum and its homologies with mammals (Jarvis et al., 2005). Auditory pallial targets of Field L (direct and indirect) include dorsal pallium in the pigeon and HVC in songbirds. These pallial nuclei project to the auditory areas of the arcopallium (AIVM, RA), which project back down to the auditory thalamus and midbrain (Wild, Karten, & Frost, 1993).

Central Auditory Pathways

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and respiration (Brainard & Doupe, 2000; Konishi, 1985; Nottebohm, 1980). These pathways are characterized by song selective responses (Figure 5.13B). The posterior pathway is required throughout life for song production, while the anterior forebrain pathway is needed during song learning, but not for normal adult song production, and is made up of a projection from HVc to X to DLM to LMAN to RA. The posterior pathway is the presumed site where the motor program underlying the bird’s unique song is stored, while the anterior pathway contains neurons that respond to song stimuli, consistent with the idea that this pathway is a possible site of template storage and or song evaluation (Brenowitz, 1997; Margoliash, 1997). The anterior pathway projects to the posterior pathway, so is well positioned to provide a guiding influence on

Song System Comprises Two Forebrain Pathways Many animals make elaborate communication sounds, but few of them learn these sounds. The exceptions are humans, and the many thousands of songbird species, as well as parrots and hummingbirds, that acquire their vocal repertoire by learning (Bolhuis, Okanoya, & Scharff, 2010; Doupe & Kuhl, 1999). Both humans and songbirds learn their vocal motor behavior early in life, with a strong dependence on hearing song from both the adults they will imitate, and hearing themselves as they practice. The song system is composed of an anterior and a posterior pathway (Figure 5.16). The posterior forebrain, or motor pathway, is composed of a circuit from the HVC to the robust nucleus of the arcopallium (RA) and then to the motor nuclei that control the syrinx

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Figure 5.16 The classic song system. Brain areas are shown in yellow and connection arrows in black for the adult motor pathway and in purple for the basal-ganglia pathway. Auditory areas and areas implicated in song memorization are shown in red, connected by blue arrows. Also shown is a pathway parallel to the basal-ganglia pathway that is involved in song learning, with brain areas depicted in green and connection arrows in dashed green lines. Ad: dorsal arcopallium; Area X: Area X of the striatum; CLM: caudolateral mesopallium; CMM: caudomedial mesopallium; CSt: caudal striatum; DLM: medial part of the dorsolateral thalamic nucleus; (DL: dorsolateral, VM: ventromedial); dNCL: dorsal region of the caudolateral nidopallium; Field L and HVC used as proper names; LMAN: lateral magnocellular nucleus of the anterior nidopallium; MLd: dorsal part of the lateral mesencephalic nucleus; NCM: caudomedial nidopallium; Ov: nucleus ovoidalis; pHVC: para HVC; RA: robust nucleus of the arcopallium. (From Hahnloser, 2010.)

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the developing motor program. It is also homologous to cortical basal-ganglia circuits in other species (Bottjer & Johnson, 1997). Mammals Mammals hear high frequencies and use sound for communication. Most mammals hear over 20 kHz, while microchiropteran bats have evolved high-frequency hearing for use in sonar, with characteristic frequencies of 50–120 kHz. Some large mammals (elephants) are also sensitive to infrasound, which they use for communication (Payne, Langbauer, & Thomas, 1986). Auditory Nerve There are two types of auditory nerve afferents in mammals, type 1 and type 2. Type 1 afferents receive inputs from inner hair cells, and send myelinated axons into the brain, where they divide into two. The ascending branch goes to the anterior region of the ventral cochlear nucleus and the descending branch to the posterior region of the ventral cochlear nucleus and to the new dorsal cochlear nucleus. Type 2 afferents are assumed to be unique to mammals, are innervated by outer hair cells, and have thin, unmyelinated axons. They project to granule cell caps of the ventral cochlear nucleus and the dorsal cochlear nucleus, and are involved in the efferent feedback to the cochlea (Robles & Delano, 2007; Ryugo, 1993). Tonotopy is preserved in the projections of the auditory nerve. In mammals, the ventral part of each cochlear nucleus receives low-frequency (apical) input and dorsal areas receive high-frequency input. These tonotopic projections are not point-to-point because each point on the basilar membrane projects to an iso-frequency plane across the extent of the cochlear nucleus. Thus the cochlear place representation is expanded into a second dimension in the brain, unlike the visual and somatosensory systems, which are point-to-point. These tonotopic sheets are preserved all the way to the cortex, although it is not clear what features are processed in these isofrequency slabs. Divergent and convergent connections within isofrequency planes may be observed at all levels. The auditory nerve forms different types of terminals onto different cell types in the cochlear nucleus (Ryugo, 1993). Endbulbs of Held terminals are formed on bushy cells (see further on), while more varicose or boutonlike terminals form on other cell types in the cochlear nuclei. The auditory nerve appears to use glutamate as a transmitter, often with the postsynaptic cell expressing “fast” AMPA type glutamate receptors that mediate the precise temporal coding

that characterizes many auditory responses (Oertel, 1999; Trussell, 1999; Parks, 2000). Cochlear Nucleus Produce Ascending Parallel Projections There are four major cell types in the ventral cochlear nucleus (Rhode & Greenberg, 1992; Rouiller & Roman, 1997). First are bushy cells, which respond in a primary or auditory nerve-like fashion to the auditory stimulus. Second are octopus cells, which respond to onsets or stimulus transients, and third, there are two classes of multipolar neuron that respond principally with “chopper” firing patterns. Bushy cells receive endbulb inputs from the auditory nerve and exhibit accurate temporal coding. There are two forms of bushy cell: spherical and globular. Spherical cells dominate the anterior ventral cochlear nucleus, respond to lower best frequencies, and project to the medial superior olive, which is sensitive to ITD. Globular bushy cells by comparison sometimes chop or exhibit onset responses to the stimulus, respond to higher frequencies, and project to the lateral superior olive and the medial nucleus of the trapezoid body. These projections may mediate detection of interaural level differences. Octopus cells in the posterior ventral cochlear nucleus are multipolar, with thick dendrites that extend across the nerve root (Oertel, Bal, Gardner, Smith & Joris, 2000). This morphology enables them to integrate auditory nerve inputs across a range of frequencies. Octopus cells encode the time structure of stimuli with great precision and exhibit onset responses to tonal stimuli (Oertel et al., 2000). Onsets play an important role in theories of speech perception and segregation and grouping of sound sources (Bregman, 2008). Two classes of multipolar neuron respond to tones principally with “chopper” firing patterns (Cant & Benson, 2003). The dorsal cochlear nucleus appears for the first time in mammals, perhaps associated with the development of high-frequency hearing and motile external ears. It is composed of a cerebellar like circuit in the superficial layers, with projection cells below that receive auditory nerve inputs (Berrebi & Mugnaini, 1991; Young, 1998). Dorsal cochlear nucleus cells exhibit wide variety of response types, with one theory of function relating to echo suppression. The granule cells in the superficial layers receive ascending somatosensory input that may convey information about head and ear position. The deep portion of the dorsal cochlear nucleus contains fusiform and stellate cell types. Fusiform cells exhibit complex (Type IV) frequency tuning curves, with small areas of excitation at best-frequency and at sides. This response

Central Auditory Pathways

is well suited to detecting the notches in sound level created by the pinna that provide cues for locating sound in elevation (May, 2000). Binaural Interactions and Feedback to the Cochlea Originate in Peri-Olivary and Olivocochlear Nuclei The superior olivary complex consists of the lateral and medial superior olivary nuclei and a large number of smaller cell groups known as the periolivary nuclei, which are sources of both ascending and descending projections (Helfert & Aschoff, 1997). All receive input from the cochlear nuclei. Their functions include efferent control of the cochlea and encoding sound level (Warr, 1992). The medial nucleus of the trapezoid body projects to the lateral superior olive, ventral nucleus of the lateral lemniscus, medial superior olive, and other periolivary nuclei. Responses of medial nucleus of the trapezoid body cells were similar to their primary excitatory input, the globular bushy cell, which connects to the medial nucleus of the trapezoid body via an endbulb synapse. The medial nucleus of the trapezoid body cell output forms an important inhibitory input to a number of ipsilateral auditory brain stem nuclei, including both medial and lateral superior olives (Grothe et al., 2010). Medial nucleus of the trapezoid body neurons are characterized by voltagedependent potassium conductances that shape the transfer of auditory information across the bushy cell to trapezoid body synapse and allow high-frequency auditory information to be passed accurately across the synapse (Trussell, 1999). Two populations of olivary neurons project to the cochlea, lateral and medial (Robles & Delano, 2007; Warr, 1992). Thin olivocochlear fibers arise from the lateral olivocochlear group located ipsilaterally in the lateral superior olive. Thick olivocochlear fibers arise from the medial olivocochlear group located bilaterally in the periolivary nuclei. Although they project primarily to the cochlea, olivocochlear neurons also give off branches to a variety of nuclei in the brainstem, and to inferior colliculus, thus involving auditory and nonauditory nuclei in the olivocochlear reflex system. Olivocochlear neurons can be activated by sound, while activation of the medial olivocochlear bundle results in suppression of spontaneous and tone evoked activity in the auditory nerve. Olivary Nuclei and Interaural Interactions The olivary nuclei regulate the binaural convergence of acoustic information, and mediate spatial hearing. Neural computations of sound location take place at this first site of binaural convergence. The lateral superior olive

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encodes interaural level difference, while the medial superior olive encodes time differences. Thus an important transformation takes place here. Information conveyed by temporal and rate codes is transformed in the olivary nuclei into labeled line-place principle codes for location. The lateral superior olive principal cells receive excitatory inputs from ipsilateral globular bushy cells, and inhibitory glycinergic inputs onto their cell bodies and proximal dendrites, relayed from the contralateral ear via the medial nucleus of the trapezoid body. The trapezoid body input acts to reverse the sign of bushy cell input from excitatory to inhibitory to make an EI (Excited (E) by the ipsilateral ear and inhibited (I) by the contralateral ear) response. Traditionally, the lateral superior olive has been assigned the role of extracting high-frequency sound azimuthal angle information from interaural level difference (ILD). Some sensitivity to time differences has also been observed. Almost all lateral superior olive responses have monotonic rate-level functions, typically with sigmoidal ILD sensitivity functions. In general, as the strength of the contralateral input increases with increasing loudness in the contralateral ear, the maximum rate decreases. Thus lateral superior olive rate signals a range of interaural level differences (Batra, Kuwada, & Fitzpatrick, 1997). Sensitivity to ITDs originates in the medial superior olive, and recent studies have led to a reevaluation of the Jeffress model’s utility as a description of mammalian ITD coding (see “Detection of Interaural Time Difference in Nucleus Laminaris” section). The Jeffress model works well for birds, where delay lines create maps of ITD, even at low-frequency sounds. For mammals with small heads, like guinea pigs and gerbils, the data do not fit the Jeffress model. Instead, small-headed mammals are hypothesized to use the “slope” of the ITD curve, or the change in firing rate. They could then estimate ITD by comparing the output of left and right coincidence detectors (Grothe et al., 2010; Joris et al., 1998). Auditory Midbrain: Inferior Colliculus and the Emergence of Biologically Important Parameters The inferior colliculus is the midbrain target of ascending auditory information. It has two major divisions, the central nucleus and dorsal cortex, and both divisions are tonotopically organized. The inputs from brainstem auditory nuclei are either distributed across or superimposed upon maps to form what are believed to be locally segregated functional zones for processing of different aspects of the auditory stimulus (Ehret & Merzenich, 1988, Oliver & Huerta, 1992). The central nucleus receives both direct

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monaural input and indirect binaural input. Physiological studies show both binaural and monaural responses (Ehret & Merzenich, 1988). Thalamus Three major features characterize the auditory forebrain (De Ribaupierre & Romand, 1997; Winer, 1992). First, there is a primary, lemniscal pathway from the cochlear nuclei to primary auditory cortex (A1) with a systematic representation of tonotopy, binaural signals, and level. Second, a parallel nonprimary pathway arises in midbrain tegmentum, dorsal medial geniculate body, and nonprimary auditory cortex with broad tuning curves and nontonotopic representations predominate. Third, an even more broadly distributed set of connections/affiliations link auditory forebrain with cortical and subcortical components of the limbic forebrain and associated autonomic areas, and elements of motor system that organize behavioral responses to biologically significant sounds (Winer, 1992). The primary target of the inferior colliculus in dorsal thalamus is the medial geniculate. This nucleus has three subdivisions: medial, ventral, and dorsal. The ventral division receives major ascending input from the central nucleus of the inferior colliculus, and contains sharply tuned cells like those of the inferior colliculus. The ventral division is tonotopically organized, while the cells of the dorsal and medial divisions are fairly unresponsive to tones or noise, and respond with long latencies, consistent with major projection back from peri-rhinal cortex. The ventral division projects to layer 4 of auditory cortex (Figure 5.17), while the antecedents of cortical specialization can be attributed in part to the structural and functional characteristics of thalamocortical inputs (Schreiner & Winer, 2007; Storace, Higgins, & Read, 2011). The functional role of the dorsal and medial divisions is not clear, except to note that nonmonotonic (i.e., selective) responses are common there. In the mustache bat, both medial and dorsal divisions contain fine delaytuned neurons (Olsen & Suga, 1991; Suga, 1988). Recent studies on the bat’s auditory system indicate that the corticofugal system mediates a highly focused positive feedback to physiologically “matched” subcortical neurons, and widespread lateral inhibition to physiologically “unmatched” subcortical neurons, to adjust and improve information processing (Suga, Gao, Zhang, Ma, & Olsen, 2000). Suga has proposed that the processing of complex sounds by combination-sensitive neurons is heavily dependent on the corticofugal system.

Auditory Cortex The greatest difference between mammals and other vertebrates is the evolution of the cortex in place of the nuclear organization of the forebrain (Karten & Shimizu, 1989). Whether this new structure has facilitated the creation of new auditory areas or not, new areas are a feature of mammalian auditory specialists. Primitive mammals like tenrecs have few auditory areas (Krubitzer, Kuenzle, & Kaas, 1997), while there are at least seven tonotopic maps in cat and the mustached bat. In the cat these areas include A1, A2, the anterior auditory field, posterior, ventral, and ventral posterior areas as well as insular, Te, and other anterior ectosylvian fields with uncertain tonotopy (de Ribaupierre, 1997). A1 and A2 share physiological features of alternating bands of EE and EI neurons that are mapped orthogonal to the tonotopic axis. Examination of these patterns using new methods, such as in vivo two-photon Ca2+ imaging, still reveal tonotopy, but show that it is fractured on a fine scale, with groups of nearby neurons able to perform independent parallel computations (Bandyopadhyay, Shamma, & Kanold, 2010). Distinct subregions can also be observed using various measures of spectral integration, including pure-tone tuning curves, noise masking, and electrical cochlear stimulation. This modularity in the representation of spectral integration is expressed by intrinsic cortical connections. This organization has implications for our understanding of psychophysical spectral integration measures such as the critical band and general cortical coding strategies (Schreiner & Winer, 2007). In the mustached bat, Pteronotus, there are at least seven cortical areas, many of which are related to identification of echolocated prey. A1 systematically represents frequency with an enlarged representation of the Doppler shift compensation region (pulse frequency range), mapping not just frequency but amplitude. There are several maps of echo delay, for delays that represent near, midrange, and far targets. There is also a map of the contralateral azimuth and a second d¨oppler shift region. Suga used these data to construct an innovative early scheme for cortical signal processing (Suga, 1988).

SUMMARY AND FUTURE DIRECTIONS This chapter takes a comparative approach in its review of behavioral, anatomical, and neurophysiological studies of auditory systems. Selective pressures to encode the salient features of the auditory stream have produced a suite of convergent physiological and morphological

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Figure 5.17 Auditory cortical areas in two mammalian species. (A) Cat auditory cortex has at least 13 areas, of which 5 are tonotopic (black), 3 are nontonotopic (dark gray), and 5 are multimodal and/or limbic-related (light gray). A color gradient indicates the frequency map along the basilar membrane (depicted beneath the cochlea) and its replication in the primary auditory cortex (AI). Arrows indicate low/high-frequency gradients in the five tonotopic fields. (B) In the rhesus monkey, the superior temporal gyrus contains multiple tonotopic fields divided into core (R, AI, etc.), belt (AL, ML, etc.), and less well-defined parabelt regions along the superior temporal plane. From Schreiner and Winer (2007). Abbreviations for all figures: AAF, anterior auditory field; AES, anterior ectosylvian area; AI, primary auditory cortex; AII, second auditory field; AL, anterolateral belt; CL, caudolateral belt; CM, caudomedial auditory belt; DZ, dorsal auditory zone; ED, posterior ectosylvian gyrus, dorsal part; EI, posterior ectosylvian gyrus, intermediate part; EV, posterior ectosylvian gyrus, ventral part; In, insular cortex; pes, posterior ectosylvian sulcus; LS, lateral sulcus; P, posterior auditory field; R, rostral auditory field; RM, rostro-medial region; RT, rostrotemporal area; RTL, rostrotemporal cortex, lateral area; RTM, rostrotemporal medial auditory belt; STG, superior temporal gyrus; sss, suprasylvian sulcus; STS, superior temporal sulcus; Te, temporal cortex; Ve, ventral auditory area.

features, which contribute to auditory coding. All auditory systems, from insects to mammals, are organized along similar lines, with peripheral mechanisms responsive to acoustic vibrations, which serve to activate neurons in the ascending auditory pathway. Most auditory systems also contain efferent systems that can modulate activity in the periphery, and it is also noteworthy that both invertebrate and vertebrate auditory systems appear to use comparable neural codes to carry information about sound source spectrum, amplitude, and location in space. Behavioral studies of auditory systems reveal many common patterns across species. For example, hearing occurs over a restricted frequency range, often spanning

several octaves. Absolute hearing sensitivity is highest over a limited frequency band, typically of biological importance to the animal, and this low-threshold region is commonly flanked by regions of reduced sensitivity at neighboring frequencies. Absolute frequency discrimination generally decreases with an increase in sound frequency, as does frequency selectivity. Some animals, however, show specializations in hearing sensitivity and frequency selectivity for biologically relevant sounds, with two regions of high sensitivity and/or frequency selectivity. Often, but not always, the specializations for sound processing can be traced to adaptations in the auditory periphery.

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Comparative Audition

Future research directions should include neural recording from awake, behaving animals, in order to better understand how task and context may modulate auditory activity. The emergence of multielectrode array recordings and telemetry permit these advances. Similar neuroanatomical and imaging advances could mediate new insights into structure-function relationships in the auditory systems across species. In sum, this chapter reviews the basic organization of the auditory systems in a host of animal species. We detail the anatomical and physiological features of the auditory system and describe how these features support a broad range of acoustic behaviors. We present data from auditory generalists and specialists to illustrate both common principles and species-specific adaptations for acoustic communication, sound source localization, predator evasion, and echolocation. The topic of this review is so broad that we also attempt to provide some direction for individuals who wish to read more in-depth coverage of research in comparative studies of audition.

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CHAPTER 6

Auditory Processing in Primate Brains JON H. KAAS, BARBARA M. J. O’BRIEN, AND TROY A. HACKETT

INTRODUCTION 157 FROM SOUND TO A NEURAL CODE: THE FUNCTIONS OF THE EXTERNAL, MIDDLE, AND INNER EAR 157 AUDITORY STRUCTURES OF THE BRAINSTEM: EXTRACTING INFORMATION FROM THE AUDITORY AFFERENTS 160 THE SUPERIOR COLLICULUS 162 THE AUDITORY THALAMUS: THE DISTRIBUTION OF AUDITORY INFORMATION TO THE NEOCORTEX 163 CORTICAL AUDITORY AREAS AND NETWORKS IN PRIMATES 164 THE FIRST STAGE OF AUDITORY PROCESSING IN CORTEX: THE AUDITORY CORE 164 THE SECOND STAGE OF CORTICAL PROCESSING: THE AUDITORY BELT 166

HIGHER LEVELS OF AUDITORY AND MULTISENSORY CORTICAL PROCESSING: THE PARABELT AND BEYOND 168 THE ROLE OF FRONTAL CORTEX IN AUDITORY PROCESSING 169 THE MULTISENSORY REGION OF THE UPPER BANK OF THE SUPERIOR TEMPORAL SULCUS 170 AUDITORY FUNCTION OF INSULAR CORTEX 170 AUDITORY PROJECTIONS TO VISUAL CORTEX AND VISUAL ACTIVATION OF AUDITORY CORTEX 170 PATHWAYS FOR AUDITORY MEMORY 171 SPEECH AND LANGUAGE 171 REFERENCES 172

INTRODUCTION

a universal attraction to music (Hauser & McDermott, 2003). Our ability to locate sources of sounds, and identify and interpret them, depends on a complex neural system for processing sounds. Primates and other mammals share many features of this complex system, from the peripheral transduction of sounds into a neural code that is transformed at higher levels of cortical processing, but primates differ especially in how sounds are processed at cortical levels, and humans have cortical specializations for language and speech. This review focuses on auditory processing systems in primates, with an emphasis on what is known about the human audition system. We start with the ear and proceed to higher cortical areas.

Hearing is an important sense in all mammals, both for obtaining information about the environment, and for communicating with other individuals. Mammals differ from their nonmammalian ancestors by having a longer, coiled cochlea of the inner ear, and tiny bones (ossicles) of the middle ear that transfer oscillations in air pressure into oscillations of fluid of the inner ear. These evolved changes in the middle and inner ear allowed mammals to increase their hearing range into high frequencies and evolve high frequency communication sounds that could not be heard by the predatory reptiles that overlapped early mammals (Allman, 1999). Hearing is especially important for most primate species as they live in habitats of dense vegetation that limit vision. Thus, communication sounds can be used to identify and locate others (Seyfarth & Cheney, 2010), and modify behaviors, and sounds can warn of the approach of an unseen predator. Humans, of course, use audition for language, which is basic to the formation of socially complex groups, and we have

FROM SOUND TO A NEURAL CODE: THE FUNCTIONS OF THE EXTERNAL, MIDDLE, AND INNER EAR The ear is a remarkable sensory organ that transforms the air pressure oscillations of sound into fluid pressure waves 157

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Bone Semicircular canals Maleus Incus

windows

ditory nerve Au

Eardrum Auditory canal

Stapes l ea Coch

Figure 6.1 The peripheral auditory system of humans. The external ear or auricle and ear canal conduct sound pressure waves to the eardrum. Sound pressure changes vibrate the eardrum and these vibrations are conducted mechanically by the three bones of the middle ear to the oval window of the cochlea. The inner ear, or cochlea, includes the sensory receptors and neurons that form the auditory nerve (see Figure 6.2).

that displace membranes and hair cells to activate hair cells and the auditory nerve ganglion cells. The peripheral auditory system is commonly divided into three compartments (Figure 6.1). The pinna of the external ear modifies and focuses sounds on the auditory canal so that air pressure oscillations reach the eardrum (tympanic membrane) at the middle ear junction. The external ear is shaped to reflect sounds from different locations differently, so that sounds from the front and side are emphasized, and slightly different from above and below, thereby providing information that can be used for locating the source of a sound. Humans have little or no ability to move their ears, so this way of modifying the intensities and relative frequencies of sounds is lost to us. However, we can compensate somewhat by moving our head. The tympanic membrane is found in all amniotes (reptiles, mammals, birds) where oscillations in air pressure vibrate the membrane, which transmits the vibrations via the small stapes bone (or in mammals, via the malleus, incus, and stapes) to vibrate the oval window of the fluid filled cochlea. The bones of the middle ear amplify the low pressure of airborne sound waves to the higher pressure needed to create

fluid-borne waves in the cochlea (for review, see Reichenbach & Hudspeth, 2010). The force of the footplate on the membrane of the oval window can be dampened for loud sounds by reflective contractions of small muscles attached to the bones of the middle ear. The mammalian cochlea is a long tube of three fluidfilled compartments that is coiled (except in monotremes) to allow its long length to fit within the surrounding bone (Figure 6.2). The cochlea and auditory nerve are similar across mammals, including humans and monkeys, as well as cats, rats, and guinea pigs, with few functional differences (Manley, 2000; Nadol, 1988). Therefore, it is possible to study these structures in nonprimates and generalize their functions to all mammals. The middle compartment contains the sensory cells of the cochlea, the inner hair cells, the outer hair cells with motor functions, and supporting cells resting on the flexible basilar membrane. When vibrations from sounds are transmitted to the cochlear fluids via the oval window, the basilar membrane and the hair cells move relative to the tectorial membrane that caps the hair cells, thereby bending the stereocilia of the hair cells, and opening channels that

From Sound to a Neural Code: The Functions of the External, Middle, and Inner Ear

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Scala Vestibuli Outer hair cells Tectorial membrane Inner hair cells

Scala Media

Spiral ganglion Auditory nerve

Organ of Corti

Basilar membrane

Bony wall of Cochlea Scala Tympani

Figure 6.2 A cross section through the coiled cochlea showing the three fluid-filled compartments, or scalae. The organ of corti within the scala media has the inner and outer receptor cells, and rests on the basilar membrane while being capped by the tectorial membrane.

allow potassium to enter the hair cells and change the membrane potential. The inner hair cells activate afferents of the ganglion cells that form the auditory nerve, while the outer hair cells act as both receptors and effectors as they convert the receptor potential into cell length changes thereby mechanically amplifying auditory signals. Movements of the basilar membrane start at the base of the cochlea and travel as a wave toward the apex. Highfrequency vibrations travel least, and maximally displace the basilar membrane near the base, while low-frequency vibrations travel further and displace the membrane near the apex. Thus, sounds of different frequencies maximally stimulate different populations of hair cells, while more intense sounds more effectively activate hair cells, and activate larger populations. The human cochlea has about 3,500 inner hair cells and 14,000 outer hair cells. Hair cell loss can follow overstimulation, and hair cell loss is permanent in all mammals. The auditory nerve provides the output of the cochlea. This output encodes the frequencies and intensities of sounds. One source of frequency information is based on phase locking (Versteegh, Meenderink, & van der Heijden, 2011). Because the movements of the basilar membrane

follow the frequency of sound waves, and hair cells are stimulated during the phase of the membrane movement that bends the hair bundles, action potentials are timelocked to the frequency of the stimulating sound. This time locking is most precise for the longer waves produced by low-frequency tones, where the temporal pattern of neuronal discharges (spikes) provides a temporal code for sound frequency. The other source of information about sound frequency is from the different places along the strip of inner hair cells that are maximally activated by sounds of different frequencies. Hair cells near the base of the cochlea are best activated by high frequency sounds, while those near the apex are best activated by lowfrequency sounds. Thus, the locations of the activated receptor provide a place code for sound frequency. Finally, sound intensities are coded by the discharge rates of auditory afferents. Low sound-pressure levels activate fewer afferents at lower discharge rates over more restricted portions of the cochlea. While most of the information sent to the brain comes from afferents subserving the 3,500 or so inner hair cells (large Type I afferents), a small number of thin, slowly conducting afferents from the outer hair cells also occupy the

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auditory nerve. The role of these thin Type II afferents is uncertain. Recordings from afferents in the auditory nerve indicate that they all respond to sounds in a similar way (at least for the 90–95% of Type I afferents). Afferents respond to a narrow range of sound frequencies, and they are most sensitive to a particular frequency. Thus, they respond at the lowest level of effective stimulation to a “best” or “characteristic” (CF) frequency, and at greater levels of sound respond to more and more frequencies within a narrow range. The receptive field of auditory nerve afferents includes the frequencies that activate the afferent, and the receptive field is usually represented as a tuning curve of response thresholds across the effective range of sound frequencies and intensities. The responses of neurons in auditory structures from the auditory nerve to primary auditory cortex are often represented by tuning curves that chart threshold response levels across frequencies on one axis and sound intensity on the other. Systematic arrangements of neurons by their characteristic or best frequency can be found at brainstem, thalamic, and cortical levels of auditory processing, and such arrangements are portrayed as tonotopic maps or representations.

AUDITORY STRUCTURES OF THE BRAINSTEM: EXTRACTING INFORMATION FROM THE AUDITORY AFFERENTS The afferents from the cochlea project to subdivisions of the cochlear nucleus, which in turn project contralaterally to the superior olivary complex, the nuclei of the lateral lemniscus, and the inferior colliculus, and ipsilaterally to the superior olivary complex (Figure 6.3). The more limited ipsilateral projections and subsequent connections between paired structures of the two sides of the brainstem allow inputs from the two ears to be compared, so that information about sound location can be extracted. The cochlear nuclear complex includes three main divisions and several types of neurons (Oertel, Ramazan, Gardner, Smith, & Joris, 2000). The afferents of the auditory nerve branch as they enter the nucleus, with the ascending branch innervating the anteroventral division of the cochlear nucleus (AVCN), and the posterior branch innervating the posteroventral (PVCN) and dorsal (DCN) divisions. Different populations of neurons in these three subnuclei interact to extract particular features of the encoded auditory inputs to send to other auditory

to auditory thalamus Commissure of ior Colliculus Inferr Inferior Colliculus

Nuclei of the lateral lemniscus

Inferior Colliculus

Commissure of Probst

DNLL

VNLL

D

DNLL

VNLL

L S O

M S O

M T B

Superior Olivary Complex

M T B

M S O

L S O

P A

Co

ch

le u s

Cochlear Nucleus

Dorsal division Posteroventral Anteroventral

Figure 6.3 Connections of auditory nuclei of the brainstem. While nerve fibers of each cochlea project to the ipsilateral cochlear nucleus, some of the subsequent projections are bilateral, and commisures interconnect the nuclei of the lateral lemniscus (dorsal, DNLL; ventral, VNLL) and the inferior colliculus. LSO, lateral superior olive; MSO, medial superior olive; MTB, medial nucleus of the trapezoid body.

Auditory Structures of the Brainstem: Extracting Information from the Auditory Afferents

structures. In the AVCN, two morphologically different types of neurons, globular and bushy spherical cells, preserve the features of the auditory nerve inputs so that the codes for sound frequency and intensity can be relayed to the superior olivary nuclei of both sides of the brainstem, as well as to the contralateral nuclei of the lateral lemniscus and inferior colliculus. Octopus cells in the PVCN receive inputs from a broad portion of the cochlea, and thus they are broadly selective to tone frequencies. They detect synchrony in the firing of groups of auditory nerve fibers. The PVCN projects contralaterally to the nuclei of the lateral lemniscus and inferior colliculus, providing precise information about sound onset. The fusiform and giant pyramidal neurons of the DCN exhibit several types of discharge patterns, and project directly to the inferior colliculus. While these DCN neurons do not encode temporal information by phase locking, they may provide information about the timing of sounds relative to other sensory events (Manis, 2008). For example, somatosensory inputs have been shown to modify spike timing in the DCN (Koehler, Pradhan, Manis, & Shore, 2011). Granule cells in the DCN receive vestibular and somatosensory inputs signaling head and ear positions and movements, and the strengths of these nonauditory inputs are modulated by use, providing plastic adjustments to DCN circuits, which provide outputs important in sound localization (Burger & Rubel, 2008; Oertel & Young, 2004). DCN projects contralaterally to the nuclei of the lateral lemniscus and the inferior colliculus. The superior olivary complex has nuclei that are especially important for encoding the locations of sound sources. The three main nuclei (or subnuclei) are the lateral superior olive (LSO), the medial superior olive (MSO), and the medial nucleus of the trapezoid body (MNTB). The MSO contains neurons that are sensitive to time differences in the activation of neural outputs of the two ears when one ear is closer to the sound source than is the other ear, and thereby provide an important source of information about the location of a sound source in the horizontal plane (Oliver, Beckius, Bishop, Loftus, & Batra, 2003; Yin & Chan, 1990). MSO inputs are from the spherical bushy cells of the ventral cochlear nucleus of each side of the brainstem. These inputs are arranged so that MSO neurons are activated by a shorter-to-longer pathway in one direction across the nucleus by ipsilateral inputs and in the opposite direction by the contralateral inputs (Beckius, Batra, & Oliver, 1999). For neuronal spikes generated in the cochlear neurons by low-frequency sounds, where the spikes are phase locked to the sound frequency cycle, spikes will arrive in different locations

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in the MSO according to the lengths of the ipsilateral and contralateral pathways. Because of axon conduction times, axon lengths translate to times of spike arrival. As MSO neurons respond to spikes arriving at the same time from the two sides, neurons in ventral MSO respond best to contralateral sound, neurons in the middle of MSO respond best to a sound directly in front, and neurons in dorsal MSO respond best to an ipsilateral sound. The MSO has a tonotopic organization with neurons that are selective for lower frequencies. The LSO generates information about sound location in another way. As the head forms a “sound shadow,” thus reducing the intensity of the sound reaching the ear farthest from the sound source, differences in sound intensity at the two ears is a second source of information about sound location in the horizontal plane (Tollin & Yin, 2002). Neurons in the LSO receive excitatory inputs from the bushy cells of the ipsilateral ventral cochlear nucleus, and inhibitory inputs from the ipsilateral medial nucleus of the trapezoid body (MNTB). The MNTB cells are activated by globular, bushy cells of the contralateral ventral cochlear nucleus. Thus, LSO cells are excited when the activation from the ipsilateral ear is greater than the activation from the contralateral ear. LSO has a tonotopic organization and it has predominately neurons with high characteristic frequencies. Together the MSO and LSO compare cochlear nuclear outputs to provide information about the horizontal location of sound sources, with MSO extracting information about low-frequency sounds based on differences in the arrival times of spikes and the LSO extracts information about high-frequency sounds by responding selectively when the sound is more intense in the ipsilateral ear. These nuclei project ipsilaterally and contralaterally to the inferior colliculus (Figure 6.3). The nuclei of the lateral lemniscus, nuclei embedded in the auditory fiber tract from the cochlear nuclei and superior olivary complex to the inferior colliculus, receive various ascending auditory input and project to the inferior colliculus (Bajo, Merchan, Lopez, & Rouiller, 1993; Hutson, Glendenning, & Masterton, 1991; Merchan, Saldana, & Plaza, 1994). Both the dorsal and ventral nuclei of the lateral lemniscus appear to contribute to binaural auditory processing for sound location, and the dorsal nucleus provides some inputs to the deeper layers of the superior colliculus, where neurons responsive to sounds and sound location are located (Stein, Jiang, & Stanford, 2004). The inferior colliculus (IC) is the major auditory structure of the midbrain. The IC contains at least three functionally distinct divisions (Morest & Oliver, 1984). The large, laminated central nucleus (ICc) makes up most of

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the IC. The central nucleus consists of sheets of cells coursing from the dorsomedial margin of the nucleus to the ventrolateral edge, with neurons in each sheet having the same characteristic (best) frequency and low frequencies represented in sheets dorsolateral to high frequencies (Bulkin & Groh, 2011; Fitzpatrick, 1975; Malmierca et al., 2008; Schreiner & Langner, 1997; Webster, Serviere, Crewther, & Crewther, 1984). Corticofugal inputs from neurons with similar characteristic frequencies in layer V in A1 modify the responses and receptive fields for the ICc neurons (Lim & Anderson, 2007). Many of the neurons with low characteristic frequencies are sensitive to time differences between sounds reaching the two ears, as they get inputs from MSO, while many of the neurons with high characteristic frequencies are sensitive to intensity differences like their LSO inputs. Sensitivities to time (latency) difference may be also created in part within the central nucleus as the dorsal nucleus of the lateral lemniscus sends inhibitory projections to the inferior colliculus (Irvine, 1986). While neurons in the central nucleus are sensitive to time and intensity differences in the two ears, and thereby have information about the locations of sounds relative to the head, the inferior colliculus does not have a systematic representation or map of sound location. The central nucleus of the inferior colliculus projects densely to frequency-matched laminae in the tonotopically organized ventral nucleus of the medial geniculate complex of the auditory thalamus. These thalamic projections are bilateral, with the ipsilateral projections much denser than the contralateral projections. The inferior colliculus gets inputs from auditory cortex that are sparse in the central nucleus, and dense in the dorsal cortex (Fitzpatrick & Imig, 1978; Luethke, Krubitzer, & Kaas, 1989). The central nucleus of the inferior colliculus is surrounded by two thin, less structured regions; the dorsal cortex and the external nucleus (Geniec & Morest, 1971; Oliver & Morest, 1984). This shell region projects to the dorsal and medial divisions of the medial geniculate complex of the auditory thalamus (Hu, 2003). Neurons in the shell region respond broadly to sound frequencies, especially low frequencies (Bulkin & Groh, 2011), and some neurons respond to both sound and touch (Aitkin, Kudo, & Irvine, 1988; Merzenich & Reed, 1974). Neurons in the external nucleus are tuned for auditory space, and they project to deep layers of the superior colliculus where there is a coregistration of representations of auditory and visual space (King & Hutchings, 1987; Knudsen, du Lac, & Esterly, 1987; Konishi, 2003). Neurons in the inferior colliculus also project to matching locations in the inferior

colliculus of the other side of the brainstem (Hutson et al., 1991). Both the central nucleus and the external nucleus of the inferior colliculus project to the nucleus of the brachium of the inferior colliculus (nBIC), which is another midbrain structure that extends to the base of the auditory thalamus. This nucleus contains a representation (map) of auditory space, and projects to the deep layers of the superior colliculus (Schnupp & King, 1997). Thus, the external nucleus of the inferior colliculus and the nucleus of the brachium of the inferior colliculus both send information about sound source location to the superior colliculus where it is presumably shaped into a map of auditory space that is coregistered with the visual map as a developmental process (Kaas & Hackett, 2000; Thornton & Withington, 1996). The connections from the superior colliculus to the nBIC may update the auditory space map in the nBIC during eye movements relative to head movements (Doubell, Baron, Skaliora, & King, 2000). Descending projections from the superior colliculus to motor and premotor neurons in the brainstem function to direct the head and eyes toward sounds and sights of interest. The inferior colliculus also receives visual signals, possibly from the superior colliculus, that influence auditory neurons and likely their roles in auditory perception (Porter, Metzger, & Groh, 2007). The inferior colliculus also receives some inputs from nuclei of the posterior and intralaminar thalamus (Winer, Chernock, Larue, & Cheung, 2002). The function of these inputs is unknown, but they likely modulate auditory response properties.

THE SUPERIOR COLLICULUS The deep layers of the superior colliculus receive auditory as well as visual and somatosensory inputs. The main function of the superior colliculus is to help control movements of the eyes and head, so that objects of interest are brought into central vision (Wurtz & Albano, 1980). The superior colliculus has a map of the contralateral visual field in the superficial layers that is based on both direct retinal inputs and on inputs from visual cortex, and a deeper motor map that mediates eye and head movements via projections to motor and premotor neurons in the brainstem (Gandhi & Sparks, 2004). Although there are somatosensory and auditory inputs to the deep layers of the superior colliculus from brainstem sources (Harting, Feig, & van Lieshout, 1997), the important inputs appear to be from multisensory areas of cortex (Stein et al., 2004). While neurons in the inferior colliculus, as well as the

The Auditory Thalamus: The Distribution of Auditory Information to the Neocortex

auditory thalamus and auditory cortex, have neurons that code for sound sources by responding better to sounds from one location compared to others, there is no evidence for a map of auditory space that is a place code for sound location in these structures. Yet, there is evidence for such a map in the deep layers of the superior colliculus that is roughly aligned with the visual map (King & Palmer, 1983; Middlebrooks & Knudsen, 1984; SterbingD’Angelo, 2007). This alignment of auditory and visual maps likely plays a large role in the orientation responses of the head and eyes toward a sound source.

THE AUDITORY THALAMUS: THE DISTRIBUTION OF AUDITORY INFORMATION TO THE NEOCORTEX The auditory thalamus includes the medial geniculate complex as the main source of auditory information to neocortex. The three traditional architectonic subdivisions of the medial geniculate complex are the ventral (MGv), the dorsal (MGd), and medial or magnocellular (MGm) nuclei (Jones, 2007). In primates, the dorsal nucleus is sometimes divided into anterodorsal and posterodorsal nuclei. The connections of the suprageniculate nucleus, limitans, posterior nucleus, and the medial pulvinar with auditory and multisensory areas of cortex indicate that these structures also have a role in auditory processing, but their significance is uncertain. The large MGv is composed of tightly packed neurons that are arranged in isofrequency laminae that reflect a tonotopic organization. MGv receives tonotopically organized projections most densely from the central nucleus of the ipsilateral inferior colliculus, but also from the contralateral inferior colliculus (Andersen, Roth, Aitkin, & Merzenich, 1980; Hackett, Neagu, & Kaas, 1999). The projections are to a series of thin, parallel layers that represent low to high frequencies in a ventrocaudal to dorsorostral progression in monkeys (Morel & Kaas, 1992). In addition, MGv receives projections from auditory cortex that modulate the auditory information flow (Sun et al., 2007). Neurons in MGv that project densely to layer 4 of the core (primary) areas of auditory cortex are generally driving inputs, while MGd and MGm projections to layers 2–3 are thought to be modulatory (Viaene, Petrof, & Sherman, 2011). Projections from the MGv are sparse to the adjoining secondary areas of the auditory belt (Hackett, Stepniewska, & Kaas, 1998b). The dorsal division of the MG complex, MGd receives auditory inputs from the shell (dorsal cortex) of the

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inferior colliculus, and possibly other brainstem nuclei (Calford & Aitkin, 1983; Andersen et al., 1980; Jones, 2007). Neurons in MGd are broadly tuned to frequency, and the nucleus does not appear to be tonotopically organized (Calford & Aitkin, 1983; Toros-Morel, de Ribaupierre, & Rouiller, 1981). Many neurons responded well to complex sounds rather than pure tones (Buchwald, Dickerson, Harrison, & Hinman, 1988). MGd projects mainly to the belt and parabelt auditory regions of cortex outside the primary core (de la Mothe, Blumell, Kajikawa, & Hackett, 2006b; Hackett et al., 1998b; Molinari et al., 1995; Morel, Garraghty, & Kaas, 1993). The medial or magnocellular nucleus, MGm, has neurons of a range of sizes, but is uniquely characterized by a population of the largest cells, the magnocellular neurons (Hackett et al., 1998b; Molinari et al., 1995). Inputs to MGm include those from the external nucleus and central nucleus of the inferior colliculus (Calford & Aitkin, 1983). Many neurons in MGm have short latency responses to tones, are sensitive to binaural stimuli, and have best frequencies (Calford & Aitkin, 1983). Others respond to a wide range of frequencies, and some respond to somatosensory stimuli (Bordi & LeDoux, 1994). MGm projects to primary areas of the auditory core cortex, the surrounding belt, and the parabelt region (Hackett et al., 1998b; Luethke et al., 1989; Morel & Kaas, 1992). The recording evidence suggests that there is little or no tonotopic organization in MGm (Anderson, Palmer, & Wallace, 2007). Several other thalamic nuclei have less distinct roles in auditory processing than the medial geniculate nucleus. The suprageniculate nucleus (Sg) just dorsomedial to the medial geniculate complex receives multisensory inputs, including those from neurons responding to vision, from the deep layers of the superior colliculus (Kaas & Huerta, 1988; Katoh & Benedek, 1995). The Sg projects broadly to cortex, including cortex of the auditory core, belt, and parabelt in primates (Hackett et al., 1998b). The Sg may have a role in multisensory processing that helps the orientation of the head and eyes toward sensory stimuli. The medial pulvinar is a large division of the pulvinar complex of primates (Kaas & Lyon, 2007). While some subcortical inputs come from the deep layers of the superior colliculus, thereby providing multisensory inputs, most of the inputs to the medial pulvinar come from various parts of cortex. The medial pulvinar projects broadly to the temporal, parietal, frontal, insular, and limbic cortices. Projections to the auditory parabelt in primates originate from medial and central parts of the medial pulvinar (Hackett et al., 1998b). There is little

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understanding of how the connections relate to auditory functions.

CORTICAL AUDITORY AREAS AND NETWORKS IN PRIMATES In this section we focus on research conducted using monkeys and humans because the organization of auditory cortex in mammals varies across taxonomic groups; and cortical arrangement in primates clearly differs in many ways from the cortical organization in other mammals, even at the earliest steps of cortical processing. For instance, all mammals appear to have a region of primary auditory cortex that receives inputs from MGv, but this primary region, termed here the auditory core, varies in organization and contains up to three or even four primarylike areas, of which only one has, by tradition, been called the primary area, A1 (Kaas, 2011). As these areas are similar in thalamic inputs, cortical architecture, and in neuron response properties, and all have tonotopic organization, it is not certain if the same area in monkeys and cats, for example, have been called A1. Outside of the auditory core, cortical processing appears to vary even more across taxonomic groups, so all conclusions that follow are based on studies on primates. Of course, we know from the unique language abilities of humans that auditory cortical networks are not the same even across primate species, as humans have neural specializations for language. Thus, for some aspects of cortical organization and processing, results obtained from primates and often humans are necessary. Fortunately, much can be learned about auditory cortex organization and function from noninvasive functional magnetic resonance imaging (fMRI) studies. The number of studies on auditory cortex in monkeys and humans has increased greatly in recent years, and progress has been rapid, but there is still much to do. Here, we consider the early levels of auditory processing in cortex, auditory core, the belt, and the parabelt, followed by even higher stages of auditory and multisensory processing.

THE FIRST STAGE OF AUDITORY PROCESSING IN CORTEX: THE AUDITORY CORE Our model of auditory cortex organization in primates includes a core of three primary or primarylike areas, the traditional primary area, A1; the more recently identified rostral auditory area, R; and the rostrotemporal area, RT.

These areas are illustrated here on the brain of a macaque monkey (Figure 6.4), where much of the research was done to determine the organization of the auditory core (Kosaki, Hashikawa, He, & Jones, 1997; Merzenich & Brugge, 1973; Petkov, Kayser, Augath, & Logothetis, 2006). Important results have also come from studies of several species of New World monkeys (Bendor & Wang, 2008; Imig, Ruggero, Kitzes, Javel, & Brugge, 1977; Luethke et al., 1989; Morel & Kaas, 1992; Petkov et al., 2006; Philibert et al., 2005). The most compelling evidence for these three core areas in monkeys comes from microelectrode mapping studies that demonstrate that each of the three core areas has a different pattern of tonotopic organization, so that tones are represented from high-to-low frequency in a caudorostral sequence in A1, mirror opposite rostrocaudal sequence in R, and in another mirror reversal caudorostral sequence in RT (Figure 6.4C). Note also that neurons best activated by any particular frequency are arranged in bands or rows orthogonal to the direction of the best frequency progression (the isofrequency bands). In humans, evidence from tonotopic patterns of organization have been more difficult to obtain, but more recent functional magnetic resonance imaging studies have provided strong evidence for at least two tonotopic maps in the auditory core (Formisano et al., 2003), which can be identified by histological criteria (Hackett, Preuss, & Kaas, 2001). The tonotopic organizations of the three core areas are based on inputs from the tonotopically organized ventral nucleus, MGv, of the medial geniculate complex. The three core areas have histological features of primary sensory fields. These histological features include a well-developed layer 4 that is densely packed with small neurons, the so-called koniocortex (Galaburda & Sanides, 1980; Pandya & Sanides, 1973). Core areas express more of other markers that are associated with a well-developed layer 4, such as cytochrome oxidase, acetylcholinesterase, parvalbumin, and vesicular glutamate transporter VGluT2 (Hackett & de la Mothe, 2009; Hackett, Stepniewska, & Kaas, 1998a; Jones, Dell’Anna, Molinari, Rausell, & Hashikawa, 1995; Kosaki et al., 1997; Morel et al., 1993). Thus, core areas can be histologically distinguished from the belt, but differences between core areas are slight. The existence of these core areas with similar inputs from the auditory thalamus indicates that auditory processing at the cortical level already includes three parallel thalamocortical streams or networks, unlike the visual system of primates where almost all of the thalamic relay of visual information in the lateral geniculate nucleus projects to only one primary area, V1. The probable advantage is

The First Stage of Auditory Processing in Cortex: The Auditory Core

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A CS

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Figure 6.4 Subdivisions of auditory cortex in macaque monkeys. (A) A lateral view of a macaque monkey brain showing the location of the auditory parabelt on the superior temporal gyrus. For reference, arrows point to the superior temporal sulcus, STS, the lateral sulcus, LS, the central sulcus, CS, and the arcuate sulcus, AS. (B) After much of anterior parietal cortex has been cut away, cortex of the lower bank of the lateral sulcus and the insula (INS) in the fundus of the sulcus can be seen. In macaques, the auditory core, consisting of auditory cortex (A1), the rostral auditory area (R1), and the rostrotemporal area (RT), is on the lower bank of the lateral sulcus, as are the medial areas of the auditory belt (hidden in a fold in Figure 4B). The lateral auditory belt extends slightly onto the superior temporal gyrus. (C) A schematic of the core, belt, and parabelt areas. The core areas are tonotopically organized, representing high-to-low tones in a caudorostral direction in A1, the opposite direction in R, and a caudorostral direction again in RT. The curved lines in A1 and R indicate the contours along which neurons respond to the same best frequency (contours of isofrequency). The tonotopic organization of some belt areas is also indicated from high (H) to low (L). See text for terms of belt areas, named by location relative to the core. The parabelt, a third level of cortical processing, has been divided into rostral (RPB) and caudal (CPB) segments.

that each stream can produce different analytical outcomes, and parallel streams provide different outcomes faster than a single serial stream. Differences between the three core areas can be seen anatomically in slight differences in

cortical architecture, and marked differences in cortical connections. Somewhat different functions of the three core areas are suggested by slight but possibly important differences in how neurons in each area respond to

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auditory stimuli. Thus, neurons in R respond at a longer latency than neurons in A1 and respond less strongly to pure tones by having a longer temporal integration time that allows more spectral integration (Recanzone, 2000; Woods, Lopez, Long, Rahman, & Recanzone, 2006). Such features suggest that R has a greater role in early stages of coding features of vocalizations and other biologically relevant sounds (Bendor & Wang, 2008). RT neurons have response characteristics that are more similar to those of R neurons (Bendor & Wang, 2008), but more evidence is needed. The cortical connections of R and RT also associate them with being more involved in the identification of sounds, and together R and RT relate more strongly to the so-called ventral “what” pathway or network (Kaas & Hackett, 1999; Romanski, Tian et al., 1999). In contrast, the more selective responses of neurons in A1 suggest that A1 is more involved in a network for localizing the sources of sounds, and as a core cortical area, more involved in the “where” pathway. Similar processing streams on “what” and “where” have been proposed in the visual and somatosensory cortices (Mishkin, 1979). All core areas receive inputs from the ventral nucleus of the medial geniculate complex, but there are also some inputs from MGm and MGd as well (de la Mothe et al., 2006b; Molinari et al., 1995; Morel et al., 1993). Thalamocortical axons largely terminate in layer 4, and these axon arbors may be elongated along the isofrequency contours. Reciprocal cortical connections are most dense between A1 and adjoining cortical fields, including core area R and belt areas MM, CM, CL, and ML (Figure 6.5). The only long cortical connections of A1 are with A1 and other auditory areas of the other cortical hemisphere (de la Mothe, Blumell, Kajikawa, & Hackett, 2006a; Hackett & Phillips, 2011). The cortical connections of R and RT are also largely local. R connects most densely with A1, R, AL, and RM (Figure 6.5), while RT connects with adjoining areas R, RTM, and RTL (de la Mothe et al., 2006a; Morel et al., 1993). These local connections of A1, R, and RT result in a partial segregation of outputs into more ventral pathways for R and RT and more dorsal pathways for A1, starting the ventral “what” and the dorsal “where” streams. Nevertheless, the connections between A1 and R, and R and RT provide an early location for the two pathways to interact. THE SECOND STAGE OF CORTICAL PROCESSING: THE AUDITORY BELT The core areas of auditory cortex interconnect almost exclusively with a narrow band of cortex surrounding the

core. Patterns of connections with core areas, response properties of neurons (Rauschecker & Tian, 2004; Tian & Rauschecker, 2004), and slight architectonic differences (de la Mothe et al., 2006a, 2006b; Hackett et al., 1998a) suggest that the belt contains as many as seven or eight auditory areas, named by location relative to the core (Figure 6.4). Overall, the belt differs from the core by expressing less parvalbumin, cytochrome oxidase, and acetylcholinesterase, and layer 4 is thinner and less densely packed with small neurons in Nissl preparations. Neurons are typically less responsive to pure tones than complex sounds, as they are more broadly tuned to frequency. Not all of the belt areas have been well studied, but some, especially CM, have been of focused interest. The caudomedial area, CM, is much more primarylike than any of the other belt areas. While CM has a distinctly less pronounced layer 4 and other architectonic core features than core areas, CM is more densely myelinated, and has more parvalbumin and cytochrome oxidase in layer 4 than in other belt areas. CM is clearly distinguished from the core by having dense thalamic inputs from anterior MGd rather than MGv (de la Mothe et al., 2006b). While A1 is densely interconnected with core area R and with CM, lesions of A1 abolish the auditory responses of neurons to tones in CM, but not R (Rauschecker, Tian, Pons, & Mishkin, 1997). Neurons in CM are more broadly tuned to frequency than those in A1 (Merzenich & Brugge, 1973; Rauschecker et al., 1997; Recanzone, 2000), but there is an overall tonotopic organization within CM that is a reversal or at least differs from that in A1. CM neurons respond with somewhat longer latencies than neurons in A1, and they are more selective for the location of the sound source. Somewhat surprisingly, most neurons in CM are responsive to somatosensory as well as auditory stimulation, with both touch and body movement (proprioception), activating neurons (Fu et al., 2003; Schroeder et al., 2001). The somatosensory inputs could come from a number of somatosensory or multisensory structures that have CM connections, but dense connections with the adjacent retroinsular cortex, known to respond to somatosensory stimuli, is a likely source (de la Mothe et al., 2006a). Other ipsilateral cortical connections of CM are with CL, ML, and RM of the belt, the caudal parabelt, and weakly with regions of posterior parietal cortex (Figure 6.6). The densest callosal connections are with CM and A1. Overall, the recording and connection results are consistent with the view that CM, together with CL and ML, are key secondorder cortical areas in the dorsal “where” stream of cortical processing.

The Second Stage of Cortical Processing: The Auditory Belt

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Figure 6.5 Connections of core auditory areas A1 (primary) and R (rostral area). Each panel is from temporal-parietal cortex of a small marmoset monkey. The auditory areas correspond to those illustrated for macaque monkeys (Figure 6.4). In marmosets, about half of the core extends from the lower bank of the lateral sulcus (LS) onto the superior temporal gyrus (STG). The left hemisphere is on the right, and the right hemisphere on the left panel. Anterior parietal cortex has been cut away to reveal cortex on the lower bank of the lateral sulcus, insula in the fundus of the sulcus arrows indicate connections. Part of the superior temporal sulcus has been opened. ITG, inferior temporal gyrus, Ri, retroinsular cortex, Pro, proisocortex. (Modified from de la Mothe et al., 2006a.)

While it has been difficult to determine if belt areas can be distinguished by having different gradients of tonotopic organization, narrow bands of noise that are frequency-centered have been used to more effectively activate neurons in belt that are not very responsive to pure tones. This approach has provided evidence that ML has a tonotopic representation from high-to-low frequencies in

a caudorostral sequence parallel to that in A1, while RL has a reversed representation from low-to-high frequency in a caudorostral sequence as in R (Rauschecker & Tian, 2004; Rauschecker, Tian, & Hauser, 1995). CM and CL may have tonotopic organizations that are mirror reversals of that in A1 (Kajikawa, de la Mothe, Blumell, & Hackett, 2005). Belt areas AL, RTL, RM, and RTM appear to be

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Figure 6.6 The cortical connections of belt areas CM and RM (conventions as in Figure 6.5). Source: Modified from de la Mothe et al. (2006a).

more involved in the ventral “what” stream of auditory processing. RTM, RM, R, RTL, and AL project mainly to the rostral parabelt cortex, RPB (de la Mothe et al., 2006a; Hackett et al., 1998a). HIGHER LEVELS OF AUDITORY AND MULTISENSORY CORTICAL PROCESSING: THE PARABELT AND BEYOND After the second stage of auditory processing in the auditory belt, large portions of the cerebral hemispheres

are involved in audition. In a remarkable experiment, Poremba et al. (2003) determined what parts of cortex were more metabolically active with auditory stimulation than without stimulation in macaque monkeys. They prevented auditory information from reaching one hemisphere by ablation of the inferior colliculus on one side, and cutting the commissures between the two hemispheres, and then comparing the differences in levels of auditory activity across cortex in the two hemispheres after 45 minutes of being exposed to a wide variety of sounds using a metabolic marker. Greater activation in

The Role of Frontal Cortex in Auditory Processing

the hearing rather than the deaf hemisphere was found in the entire superior temporal gyrus including the temporal pole, large regions of the inferior parietal gyrus, the caudal insula, part of the parahippocampal gyrus, and large parts of prefrontal cortex. Additional areas of cortex are likely influenced by auditory stimuli, but not at high enough levels to produce a clear metabolic difference (see below). Connections from auditory cortex to visual cortex implicate additional areas of visual cortex in auditory functions. Since the role of auditory activation of much of this cortex has not been explored, our understanding of higher order cortical processing of auditory information is just beginning. A third stage of auditory processing is the parabelt, a large region of the superior temporal gyrus in macaque monkeys (Figure 6.4) that was defined by connections with the belt areas of auditory cortex (Hackett et al., 1998a). Because the rostral half of the parabelt has mainly connections with the rostral belt areas, and the caudal half with caudal belt areas, the parabelt was divided into the rostral (RPB) and caudal (CPB) parabelt regions. The parabelt receives auditory inputs from MGd and MGm of the auditory thalamus, but not MGv (Hackett et al., 1998b). The functions of the parabelt are unknown, as there have been a few attempts to record from this region. The parabelt has extensive connections with other regions of cortex (Hackett, Stepniewska, & Kaas, 1999b; Romanski, Bates, & Goldman-Rakic, 1999). Some of

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these connections match those of belt areas, while others are more extensive (Figure 6.7). The rostral parabelt (RPB) targets rostral and (polar) parts of the superior temporal cortex, as well as orbital prefrontal cortex, are parts of a ventral “what” network involved in auditory sound recognition (Romanski, Tian, et al., 1999). The caudal parabelt (CPB) projects to dorsolateral and periarcuate prefrontal regions involved in multisensory spatial localization as part of a dorsal “where” network. However, the segregation of the two networks is not complete, and there are interconnections on multiple levels (Kaas & Hackett, 2000). The callosal connections of the parabelt are most dense between matching locations (Hackett et al., 1999a).

THE ROLE OF FRONTAL CORTEX IN AUDITORY PROCESSING Romanski et al. (1999a, 1999b; Romanski, Bates et al., 1999; Romanski, Tian et al., 1999) proposed that projections from the caudal (dorsal) parabelt to caudal prefrontal cortex are in regions that have roles in auditory spatial processing, as neurons responsive to auditory stimuli in the periarcuate region of frontal cortex of macaque monkeys are sensitive to the location of sound sources. The caudal portion of dorsolateral prefrontal cortex with auditory input is also important in visuospatial working memory. In contrast, projections from the rostral (ventral) auditory

CS 46d

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Figure 6.7 Ipsilateral connections of the auditory parabelt (arrows) on a lateral view of a macaque monkey brain. The connections include cortex of the upper bank of the superior temporal sulcus (STS), the rostral part of the superior temporal gyrus (STG), the multisensory temporoparietal region (Tpt), the dorsal prearcuate cortex (AS), cortex of the dorsal bank of the principal sulcus (8a, 46d), lateral prefrontal cortex (12vl), and orbital frontal cortex (10orb). See Hackett et al. (1999b) for details. LS, lateral sulcus, AS, arcuate sulcus, CS central sulcus.

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belt and parabelt to ventrolateral prefrontal cortex appear to project to regions more involved with the identification of sounds (Romanski & Averbeck, 2009; Romanski, Averbeck, & Diltz, 2005; Romanski & Goldman-Rakic, 2002; Sugihara, Diltz, Averbeck, & Romanski, 2006). Neurons in ventrolateral prefrontal cortex respond to complex sounds, including vocalizations. In macaque monkeys, most neurons are at least somewhat selective to specific macaque vocalizations, although responses appear to be based more on acoustic features of calls, than the meaning of the calls. Many neurons in the region have either a predominantly visual or auditory response that is enhanced or suppressed by the presence of an auditory or visual stimulus, suggesting the neurons integrate audiovisual communication stimuli from the face and calls. Neurons typically responded better when face expressions were matched with vocalizations. Multisensory inputs may come from projections from multisensory cortex of the superior temporal sulcus (Romanski, Bates et al., 1999). The auditory region of ventrolateral prefrontal cortex in macaques is similar in location to Broca’s area in the inferior frontal gyrus of humans, suggesting a possible role in motor control of vocalizations (Romanski & Averbeck, 2009).

AUDITORY FUNCTION OF INSULAR CORTEX

THE MULTISENSORY REGION OF THE UPPER BANK OF THE SUPERIOR TEMPORAL SULCUS

AUDITORY PROJECTIONS TO VISUAL CORTEX AND VISUAL ACTIVATION OF AUDITORY CORTEX

The cortex of the upper bank of the superior temporal sulcus of macaques has long been as a multisensory region responsive especially to auditory and visual stimulation (Cusick, 1997; Ghazanfar & Schroeder, 2006). Neurons in this region best activated by either auditory or visual stimulation are sometimes grouped or mixed with other neurons responding well to both auditory and visual stimuli (Dahl, Logothetis, & Kayser, 2009). Neurons in the so called “temporal polysensory area” of the superior temporal sulcus appear to be selective for faces, motion, and objects. Neurons responsive to monkey vocalizations are activated more strongly when the face movements during a vocalization are matched with that vocalization call (Ghazanfar, 2008). Thus, the polysensory region of temporal cortex appears to be another part of cortex that is highly involved in identifying communication signals found in movements of the face and mouth during vocalization. Auditory inputs to the polysensory region include those from the auditory parabelt (Hackett et al., 1998a). Somatosensory and visual inputs originate in parts of the posterior parietal cortex and higher-level visual areas (Cusick, 1997).

While the auditory core and the belt areas lie on the lower bank of the lateral sulcus, and somatosensory areas occupy the upper bank, the fundus of the sulcus, like the cap of a mushroom, broadens into a large flat surface in monkeys that is proportionately larger in humans. This insular region has been roughly divided from caudal to rostral into granular, dysgranular, and agranular architectonic regions presumably corresponding to a progression from sensory to motor functions. The insula appears to have subdivisions or areas that are devoted to emotional states, such as pain and disgust, empathy and social affiliation (Caruana, Jezzini, Sbriscia-Fioretti, Rizzolatti, & Gallese, 2011; Craig, 2009; Keysers, Kaas, & Gazzola, 2010). The caudal part of the insula next to the medial auditory belt areas receives auditory inputs from the medial belt, as well as sparser connections from the lateral belt and parabelt and adjoining cortex of the superior temporal cortex (de la Mothe et al., 2006a; Hackett, 2007). Neurons in the caudal insula of macaques respond to auditory stimuli, with a preference for macaque vocalizations (Remedios, Logothetis, & Kayser, 2009).

One of the surprising findings of recent years is that blind humans use the fine-grain processing capability of primary visual cortex to process somatosensory information during Braille reading (Fridman, Celnik, & Cohen, 2004). There is also evidence that visual cortex is activated in congenitally blind individuals in verbal tasks (Bedny, PascualLeone, Dodell-Feder, Fedorenko, & Saxe, 2011) and in spatial processing of sounds (Collignon et al., 2011). These unexpected uses of visual cortex for somatosensory and auditory tasks are thought to depend on the potentiation of plastic pathways between early auditory, somatosensory, and visual sensory areas that become more functional as a result of early blindness. Potential sources of auditory influences on visual cortex in macaque monkeys include projections of belt and parabelt areas to parts of primary visual areas, V1, representing peripheral vision (Falchier, Clavagnier, Barone, & Kennedy, 2002) with denser connections to the second visual area, V2 (Falchier et al., 2010; Rockland & Ojima, 2003). There are many possibilities for indirect, multistep pathways between auditory and visual fields. All these pathways are

Speech and Language

reciprocal, and neurons in core and belt auditory areas are modulated by visual stimuli (Kayser, Petkov, Augath, & Logothetis, 2007). Such visual modulation, often enhancement, of auditory responses in early stages of auditory cortical processing could reflect the focusing of attention on sources of sound, as well as a mechanism to improve the spatial localization of visual-auditory events. Additionally, the integration of face with vocal information starts to occur at even early stages of auditory cortical processing in the auditory belt and parabelt (Ghazanfar, Maier, Hoffman, & Logothetis, 2005).

PATHWAYS FOR AUDITORY MEMORY Humans have a type of long-term memory called episodic memory, that allows us to recall what happened in everyday events, and when and where. Something like this episodic memory appears to exist, at least for visual events, in monkeys, where it depends on a series of connections from higher-order memory and multisensory areas through entorhinal and perirhinal cortex to the hippocampus during encoding and back again for long-term storage (Munoz-Lopez, Mohedano-Moriano, & Insausti, 2010). One of the surprises from studies of episodiclike memory in macaques is that they typically fail to hold auditory stimuli in memory for the amount of time required to be considered long-term memory rather than working memory, which is mediated by prefrontal cortex (Fritz, Mishkin, & Saunders, 2005). Such results suggest that the human and monkey auditory memory systems may differ, and that the auditory pathways for long-term memory for monkeys may be less direct than the visual pathways (Munoz-Lopez et al., 2010).

SPEECH AND LANGUAGE Humans differ from other mammals in that we have both species-specific communication calls (crying and laughter) and language based on a learning of the arbitrary meanings of sounds. Speech processing in humans is thought to involve a hierarchy of cortical areas starting with the human homologues of core, belt, and parabelt areas (Hackett et al., 2001) which are sensitive to acoustic information, and more rostral caudal regions of the left superior temporal lobe where there is less sensitivity to specific acoustic features and more sensitivity to meaningful categories of sound, followed by an involvement of the left inferior frontal cortex for speech comprehension

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and production (Okada et al., 2010; Peelle, Johnsrude, & Davis, 2010; Rauschecker & Scott, 2009). Auditory areas retained from early anthropoid ancestors were likely modified for language functions, and new language areas may have been added. Besides the emphasis on processing in the left cerebral hemisphere, many other modifications of cortical auditory processing and cognition streams must have occurred as modern humans evolved. One of the anatomical changes appears to be in the axon pathway that connects temporal lobe auditory regions with frontal lobe auditory regions. In humans, a large arcuate fasciculus of the left cerebral hemisphere connects large regions of temporal and posterior parietal cortex, including Wernicke’s language area or region of temporal cortex, with ventrolateral frontal cortex, the location of Broca’s region for language expression (Brauer, Anwander, & Friederici, 2011; Frey, Campbell, Pike, & Petrides, 2008; Geschwind, 1970). The arcuate fasciculus occupies the relative position of the dorsal stream auditory pathway of monkeys. Rilling et al. (2008) compared this pathway in macaques, chimpanzees, and humans using noninvasive imaging methods. In monkeys, the dorsal auditory-frontal pathway originates from dorsal belt and parabelt regions, as well as adjoining parts of posterior parietal cortex, while this pathway also originates from additional portions of the temporal lobe in chimpanzees, and even more of the temporal lobe in the left hemisphere in humans, where it greatly exceeds and probably replaces in part the ventral pathway. However, there is also evidence that there are both dorsal and ventral pathways for language in the left cerebral hemisphere. Hickok and Poeppel (2007) proposed that early cortical stages of processing speech in the superior temporal gyrus of humans is further processed in two streams, a dorsal “how” stream involving inferior parietal and posterior frontal regions and a ventral “what” stream involving middle and ventral portions of the temporal lobe and ventrolateral prefrontal cortex. The dorsal auditory-motor pathway was for speech production while the ventral pathway was for determining the meaning of speech sounds. Using functional magnetic imaging and diffusion tensor imaging to evaluate the functional roles of temporal-frontal pathways Saur et al. (2008) found that the dorsal route via the arcuate fasciculus was largely involved in sensory-motor mapping for speech production, while a ventral pathway, as in macaque monkeys, was involved in the linguistic processing of sound into meaning. Humans also have a voice-sensitive region in the anterior superior temporal sulcus (Berlin, Zatorre, Lafaille, Ahad, & Pike, 2000) that monkeys do not appear to have.

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Instead, cortex sensitive to conspecific vocalizations in macaques is located just ventrorostral to the auditory belt on the lower bank of the lateral sulcus (Petkov et al., 2008). Such differences in cortical pathways and organization are consistent with the view that a language system evolved gradually in the ancestors of humans from the already elaborate cortical processing systems of early anthropoid ancestors that have been retained in other primates (Ghazanfar, 2008). Although the human brain has evolved in a way that makes language, and other abilities such as the appreciation of music possible, these abilities take a long time for the developing brain to acquire. Cortical areas that may have had other functions are modified by experience, and so become able to participate in the uniquely human functions. Since there are many different languages across cultures, learning must be, to a great extent, specific to a language and a type of music.

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CHAPTER 7

Comparative Locomotor Systems ¨ KARIM FOUAD, DAVID BENNETT, HANNO FISCHER, AND ANSGAR BUSCHGES

INTRODUCTION AND HISTORY 176 CONSTRUCTION PRINCIPLES OF PATTERN-GENERATING NETWORKS FOR LOCOMOTION 177 LOCATION OF PATTERN-GENERATING NETWORKS FOR LOCOMOTION 183 SENSORY SIGNALS CONTROLLING LOCOMOTOR ACTIVITY 184

MODULATION OF LOCOMOTOR ACTIVITY 189 PLASTICITY IN MOTOR SYSTEMS 194 CONCLUSIONS 196 REFERENCES 197

INTRODUCTION AND HISTORY

of gait. This review will focus (a) on the principles of cellular and synaptic construction of central patterngenerating networks for locomotion, (b) on their location and coordination, (c) on the role of sensory signals in generating a functional network output, (d) on basic features in modulating the network function, and (e) on the main mechanisms underlying their ability to adapt through modifications. Due to the limited space available for this introduction to this lively and fast-developing field in neurosciences, the authors will restrict citations mostly to recent in-depth reviews on individual aspects mentioned and will refer to original articles only as specifically needed.

In the animal kingdom, various kinds of locomotion— swimming, walking, flight, and crawling—have evolved. Understanding locomotor function is of vital scientific interest for two reasons: first, because locomotion serves as multipurpose behavior in various more complex behavioral programs and issues; second, because the motor system is one, if not the prime, output of the central nervous system. Knowledge of the neuronal mechanisms underlying locomotion has long attracted scientists as a field of study that leads to understanding nervous system function in general and for medical use and robotics. To understand locomotor systems requires a multilevel approach ranging from the cellular level (i.e., identification of the neurons involved, their intrinsic properties, the properties of their synaptic connections, the role of particular transmitters and neuromodulators) to the system level (i.e., functional integration of these networks in complete motor programs). Our current understanding of locomotor networks is the outcome of investigating and comparing various invertebrate and vertebrate locomotor systems in which rhythmic behaviors can be studied on multiple levels, ranging from the interactions between identifiable neurons in identified circuits to the analysis

Understanding “The Act of Progression”: Historical Aspects The problem of how locomotion is generated has been considered for more than 2,400 years, starting in the time of Aristotle, about 400 B.C. Between the second and third centuries A.D., the Aristotelian concept of a vital pneuma (transformed from the omnipresent ether by the lungs and transported by the bloodstream to the muscles) as the ultimate cause underlying locomotor ability was first modified by Galen, who discovered that nerves originating in the brain and spinal cord innervate the muscles. In the 17th century, Descartes and Borelli integrated Galen’s discoveries in a more mechanically based theory, suggesting that muscles contract by a corpuscular animal spirit, released

We would like to thank M. Cohen from the Institute for Advanced Study in Berlin, for careful editing of an initial draft of the manuscript. 176

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from the nervous system. In the mid 19th century (and based on the work in the 18th century by Schwammerdam, Galvani, and others), Matteucci, Helmholtz, and du BiosReymond discovered the electrical properties of axons and their implication for neuromuscular transmission (the modern term “synapse” was adopted much later by Sherrington in 1897). Benefiting from the general progress in detailed anatomical knowledge and with a new basic concept (Cajal’s neuron doctrine), late 19th-century physiology initiated a common understanding of the nervous system’s function and its role in the generation and control of behavior (for an in-depth review of the early history, see Bennett, 1999).

The Neural Basis of Locomotor Pattern Generation: A First Concept At the end of the 19th century, the discovery of proprioceptive pathways in the nervous system (e.g., by Bell, Golgi, & K¨uhne in the mid 19th century, early review: Sherrington, 1900a, 1900b), the description of numerous different reflex responses in the limbs of monkeys, dogs, and cats after skin or nerve stimulation (establishing what are called “reflex laws”; Pfl¨uger, 1853) and the apparent resemblance of these reflex responses, including “scratch” reflexes and “spinal stepping,” to parts of the limb movement cycle during real locomotion led to the idea that the antagonistic activation of effector organs during locomotion might be triggered by feedback from sense organs in the skin and the moving parts of the body. Coordinated limb movement during locomotion was thought to be the result of a chain arrangement of these reflex arcs. Remarkably, this concept already included the principle of a reciprocal innervation of antagonistic muscles (Sherrington, 1905a, 1905b) and the demonstration of postural reflexes (Sherrington, 1900b).

Toward a Concept of Central Control of Locomotion In the early 20th century, the putative role of the spinal cord in the basic generation of locomotion was established by experiments in mammals, mainly in the dog and the cat, that could still produce alternating leg movements after the brain was disconnected (Brown, 1911, 1912; Sherrington, 1905a, 1905b). In cats, Brown (1911) demonstrated that the spinal cord was capable of producing locomotor patterns after complete deafferentation of the moving limb. He concluded that alternating rhythmic movements derive from a central spinal process and proposed a simple

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half-center model (see below) as the basis of the alternating activity of flexors and extensors during walking: Each half-center is responsible for activating either flexors or extensors, and both half-centers are connected by reciprocal inhibition in order to silence one center while the other is active. Much later, when reciprocal inhibition was first shown on the interneuron level in the spinal cord (Jankowska et al., 1967a, 1967b), the suggested spinal network organization incorporated the basic features of Brown’s half-center model. The Concept of a Central Pattern Generation (CPG) The combined evidence from the first half of the 20th century suggested that the central nervous system does not necessarily require sensory feedback to generate rhythmic movement resembling repetitive behaviors such as locomotion. This conclusion emerged from experiments in a variety of invertebrate and vertebrate species, in which the ability to generate a patterned rhythmic activity was not abolished by (a) paralysis using neuromuscular blockers to prevent proprioceptive input evoked by movements, (b) deafferentation, or (c) the complete physical isolation of the nervous system from all sources of possible feedback (review, Grillner, 1985; see also Delcomyn 1980). The ensemble of neuronal elements necessary and sufficient for the production of locomotor patterns was defined as a Central Pattern Generator (CPG; Grillner & Zangger, 1975; Wilson, 1961). However, since the motor patterns observed after deafferentation are often imprecise and sometimes even lack important elements of motor output as compared to intact conditions (e.g., Grillner & Zangger, 1979; Pearson, 1985; Sillar et al., 1987), the validity of such a concept for completely central locomotor pattern generation was questioned (e.g., B¨assler, 1987, 1988; Pearson, 1985). At present it is clear that, in the majority of locomotor systems, sensory feedback, and central-pattern-generating networks interact to generate the functional locomotor program, whereby sense organs form integral elements of the pattern-generating mechanisms (e.g., review in B¨uschges & El Manira, 1998; Pearson, 1995; Prochazka & Mushahwar, 2001) with only a few exceptions (e.g., Arshavsky et al., 1993). CONSTRUCTION PRINCIPLES OF PATTERN-GENERATING NETWORKS FOR LOCOMOTION Most locomotor patterns have in common that they are based on rhythmic movements, that is, cyclic motor

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patterns. Each cycle can be generally divided into two phases, a power stroke and a return stroke. During the power stroke, locomotor organs exert force against the surrounding medium and move the organism relative to its environment; during the return stroke, the locomotor organs are moved back to their starting position for the next power stroke. Two examples: In walking, the power stroke of the locomotor cycle is the stance phase, when the limb is on the ground and generates force to propel the body relative to the ground, either forward or backward. The return stroke of the leg is the swing phase, during which the leg is moved back to its starting position. In general, antagonistic muscles of leg joints exhibit phases of alternating activity during the generation of stepping movements. In vertebrates like fish or agnaths, swimming is generated by a rostral-caudally or caudal-rostrally directed undulating contraction wave, depending on the direction of swimming. This contraction wave wanders along the trunk musculature. In every cycle, the myotomal musculature of both sides of each segment contracts in an alternating fashion. In this chapter we will review the main features of current knowledge of the construction of neural networks and Choice and organization of behavior Internal state Information Enviromental about cues

Command system

Orientation in space

On&Off speed of progression Decision to locomote

the mechanisms underlying the generation of locomotor patterns in vertebrates and in invertebrates. When considering the generation of locomotor programs, several different aspects and levels of nervous control are important (Figure 7.1). For a very detailed review of this, see Orlovsky et al. (1999), where the summary of knowledge that was presented is still valid concerning the operational level of functional network interactions. The highest level of control is represented in the decision to locomote. Activation of this system is mediated by external or internal cues, like sensory stimuli or motivation. The decision to locomote activates two different systems of descending control. One system has commandlike features and controls the starting and stopping of the locomotor program as well as the intensity, for example, speed, of locomotion. In vertebrates, the reticulospinal pathways, receiving signals from the mesencephalic locomotor region and the subthalamic locomotor region and sending their axons into the spinal cord, are elements of this system (e.g., Armstrong, 1988; Grillner, 2003; Jordan et al., 1992; Mori et al., 1992; Orlovsky et al., 1999). In invertebrates, groups of interneurons or individual descending interneurons have been identified that serve Locomotor organs

Controllers

Sensory feedback

Acitvity of controllers

1

1

2

2

3

3

N

N

Locomotion Posture equilibrium steering

A

A

B

B

C

C

Figure 7.1 Schematic of functional organization of a locomotor control system. This schematic summarized the components and functional organization of a generalized system for generation and control of locomotion (see text for details and explanation) adapted from Orlovsky et al., 1999. Please note that for multijointed locomotor organs, the controllers of the individual locomotor organs may be composed of several separate, but interacting modules responsible for motor pattern generation at the different joints (see inset to the bottom right of the figure).

Construction Principles of Pattern-Generating Networks for Locomotion

“command-like” functions in the initiation and maintenance of motor programs (e.g., Bowerman & Larimer, 1974a, 1974b; Brodfuehrer & Friesen, 1986a, 1986b, 1986c; Gamkrelidze et al., 1995; Kupfermann & Weiss, 1978). The second system is in charge of generating and controlling the animal’s posture and equilibrium during locomotion, as well as its direction of locomotion. In vertebrates, the cerebellum, brainstem, and spinal cord serve this system; in invertebrates, this system is distributed among various ganglia. The information from these two systems is fed into the neuronal networks of the locomotor system itself, the controllers, located downstream close to the locomotor organs in spinal segments (vertebrates) or ganglia (invertebrates). The construction and action of these controllers will be our main focus in the following chapters. The controllers (Figure 7.1) encompass the neuronal networks, including the central pattern generators that generate activity of the locomotor organs by driving specific sets of motoneurons. These motoneurons form the neuronal output stage and innervate the muscles moving the locomotor organs. Rhythmic motoneuron activity is generated by alternating excitatory and inhibitory synaptic impulses from the premotor neural networks, the controllers, to the motoneurons. The generation of functional locomotor programs often relies on feedback about the executed action from each level to the next-higher level. Information about the activity of the controllers is fed back to the command level. Sensory information reporting the actual movement generated by the locomotor organs is fed back to the controllers. Therefore, in many locomotor systems, sense organs have to be considered important elements enabling the systems to generate functional locomotor programs (see also Orlovsky et al., 1999). Finally, locomotor systems consisting of a multitude of locomotor organs need to generate coordinating mechanisms to adjust and time the sequence of movements between the individual locomotor organs (e.g., Borgmann, Hooper, & B¨uschges, 2009; Cruse, 1990). Marked differences appear to exist in the degree of coupling between the actions of individual controllers for locomotor systems, on the one hand, and a multitude of locomotor organs, on the other. For example, evidence suggests that the wing-control system in the locust flight system acts in general as one integrated common pattern generator for driving all four wings (Robertson & Pearson, 1983; Waldron, 1967). However, more recent evidence gathered from various vertebrate and invertebrate organisms suggests that each locomotor organ may indeed have its own controller (B¨uschges, 2005), that is, each segment of a lamprey for swimming (Cangiano & Grillner, 2005;

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Grillner et al., 1995), each leg of a vertebrate or invertebrate (B¨assler & B¨uschges, 1998; Orlosky et al., 1999) and each leg of a human (Gurfinkel et al., 1998) for walking, and each wing of an insect for flying (Ronacher et al., 1988). The construction of such controllers has been well studied for the generation of the swimming motor pattern in mollusks and lower vertebrates (Arshavsky et al., 1993; Grillner et al., 2000) and annelids (Brodfuehrer et al., 1995) and for walking pattern generation in crustaceans (Cattaert & LeRay, 2001), insects (B¨assler & B¨uschges, 1998), anurans (Cheng et al., 1998), and mammals (Goulding, 2009; Orlovsky et al., 1999). In humans, less evidence is presently available on the construction principles of the limb controllers themselves, but present data suggest that the main features in the organization of the walking control system of humans has similarities to those of both cats and arthropods (e.g., Yang & Gorassini, 2006; B¨uschges, 2005). The complexity of the controllers’ construction depends (a) on the complexity of the locomotor organs and (b) on the requirements of the locomotor behavior to be generated out. Thus, the complexity of the controllers increases with the segmentation of the locomotor organs, from unitary wings to multisegmented legs. For example, in Clione, a mollusk, the swimming motor activity is generated by elevation and depression of wing-like appendages (Arshavsky et al., 1998). In invertebrates and vertebrates, however, walking is generated by the movements of multijointed limbs, which requires the coordination of the activities of several individual leg joints (B¨assler, 1983; B¨uschges et al., 2008; Grillner, 1979; Orlovsky et al., 1999). Controllers that need minimal sensory feedback, like the one controlling locomotion in Clione, are constructed more simply than are controllers governing locomotor programs that depend on sensory feedback, like walking systems. Pattern generation in these latter locomotor systems relies heavily on sensory signals about movements of the joints and the limbs, signals about forces or strain exerted on each segment of the limb, and coordinating signals between adjacent limbs (e.g., B¨assler & B¨uschges, 1998; Pearson, 1995; Prochazka, 1996a). This also applies to the walking system of humans (Dietz, 1992; Gurfinkel et al., 1998; Sinkjaer et al., 2000; Yang & Gorassini, 2006). Construction Principles of Pattern-Generating Networks for Locomotion Central neuronal networks have been identified that are capable of generating ongoing rhythmic activity in motoneurons that contribute to the cyclic locomotor

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output generated for swimming, walking, and flying in vertebrates and invertebrates (see above). These networks can also be activated in very reduced preparations either by the application of drugs, by sensory stimulation, or by stimulating higher-order centers in the central nervous system (for a comparative review, see Orlovsky et al., 1999). Using these approaches, more or less complete patterns of a locomotor program can be generated, allowing their detailed investigation. Neuronal networks have been analyzed on several levels: First, the operational level , focusing within the systems level on mechanisms for the generation of functional motor programs (e.g., B¨assler & B¨uschges, 1998; Grillner, 1979; 2003); Second, the level of the neuronal networks themselves, analyzed by investigating the topologies of the neural network and the synaptic interactions among its elements (e.g., Arshavsky et al., 1993; B¨assler & B¨uschges, 1998; Grillner et al., 1995; Kiehn et al., 1997; Ritzmann & B¨uschges, 2007; Roberts, 2000); Third, a lot of attention has concentrated recently on the cellular and subcellular level and the cellular properties of individual neurons or neuron classes within the networks and their role in generating rhythmic locomotor activity (e.g., Dale, 1997; Grillner, 2003; Grillner et al., 2001). Finally, in the past two decades, simulation studies using artificial neural networks or computer models have increasingly helped to investigate the necessity and sufficiency of neuronal mechanisms and the construction of the neuronal networks underlying the generation of locomotor patterns as presently understood (e.g., reviews in Cruse et al., 1995; D¨urr et al., 2004; Ekeberg & Pearson, 2005; Grillner et al., 1995). Despite differences between phyla, species, and locomotor tasks, it has become clear by now that there are some specific common outlines of networks generating rhythmic locomotor activity. Two prominent basic neural network topologies have been identified: It is today’s common notion that in most of the well-studied locomotor systems two levels of network interaction can be differentiated that serve different functions for pattern generation for locomotion: One level of network action generates ongoing neural activity, for example, a basic rhythmicity and second subsequent level of network action, activated by synaptic drive from the previous level that then patterns the neural activity generated (Kiehn et al., 2005). Two prominent basic neural network topologies have been identified that are known to contribute to patterning neutral activity in order to generate basic locomotor output: (1) reciprocal inhibition and (2) forward excitation and reciprocal inhibition. Those will be exemplified in the following sections.

Reciprocal Inhibition This construction principle found in various locomotor systems is based on reciprocal inhibition between neurons or groups of neurons within the neuronal networks (Figure 7.2A). Each group of neurons is in charge of generating one phase of the locomotor activity. Through this mechanism, one group of neurons is active at any given time, once the activity of the network has been started. Transition between the activity of the two neurons or groups of neurons emerges through mechanisms that either generate fatigue in the activity of the currently “active” group of neurons and/or enable the silent, “inactive” group of neurons to escape inhibition. Such topology is called “half-center” construction, and, long before experimental verification was possible, Brown (1911) conceived it for the generation of alternating activity during stepping in the cat. Reciprocal inhibition has been identified as a building block underlying the generation of alternating motor activity in the neuronal networks for swimming in vertebrates: lampreys (Buchanan, 1982; Grillner, 1985) and tadpoles (Roberts et al., 1985; Soffe et al., 1984); and for swimming and other locomotor behaviors, like crawling, in invertebrates: mollusks (Arshavsky et al., 1985a, 1985b, 1985c, 1985d; Getting et al., 1980; Getting 1981; Katz et al., 1994) and annelids (Friesen et al., 1978, Friesen & Hocker, 2001; Stent et al., 1978). For example, in the lamprey, swimming network alternating activity of both sides of each segment is based on reciprocal inhibition between groups of crossed inhibitory neurons on each side of the spinal cord (Figure 7.2B; Buchanan, 1982; Grillner 1985).

Forward Excitation and Reciprocal Inhibition Another identified network interaction is forward excitation from one neuron to another neuron via a delay and reciprocal inhibition from the second neuron to the first neuron (Figure 7.3A). In the central pattern-generating network for locust flight, this element has been found to underlie alternating activation of wing elevator and depressor motoneurons, representing some kind of “switch-off mechanism” (Figure 7.3B; Robertson & Pearson, 1983, 1985). For example, activity of one neuron (type 301) increases and excites another neuron (type 501) through the action of an excitatory influence with a certain delay. At some point, neuron 501 is pushed past its spike threshold and activated. Its activity then in turn terminates the activity of 301 through the inhibitory synapse (Robertson & Pearson, 1985). Ongoing rhythmic activity in such a circuit relies on some mechanism that enables neuron

Construction Principles of Pattern-Generating Networks for Locomotion

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Figure 7.2 Reciprocal inhibition in central pattern generating networks. (a) Reciprocal inhibition as building block for pattern generation in neural networks for locomotion. Filled circles, inhibitory synapse. Please note that some tonic background excitation is needed for inducing oscillatory activity of this network. (b) Wiring diagram of the central pattern generator for swimming in the lamprey spinal cord, based on data from Grillner and coworkers (see Buchanan & Grillner, 1987; Grillner et al., 1995; Mentel et al., 2008). Each circle denotes an identified class of interneurons in the spinal cord. EIN, excitatory interneuron; CC-I, contra lateral crossing inhibitory interneuron. Open triangles: excitatory synapse; filled circle, inhibitory synapse; mMN, myotomal motoneuron. Connections from neurons of the right half of the segment are drawn with solid lines, connections from neurons of the right half of the segment are drawn with broken lines. The vertical stippled line denotes the midline of the spinal cord. The motor output of the pattern generator for swimming is exemplified at the bottom by an intracellular recording from a left myotomal motoneuron together with extra cellular recordings from both ventral roots of the segment, in which the motoneuron is located. Please note the strictly alternating activity between both sides of the segment. While rhythmic activity is generated on each side of the spinal cord by the network of interconnected EINs, patterning towards left-right alternation of this activity is mediated by means of reciprocal inhibition between sets of commissural interneuroans (CC-I) driven by the netowrks of EINs and the contralatetral network. Activity was initiated and maintained by super fusion of the cord with the glutamate agonist NMDA (150 μM).

301 to have pacemaker or burst-producing properties (see below). Finally, it is now known that other locomotor systems also combine different types of building blocks for the generation of rhythmic motor patterns, like the locomotor network of the nudibranch Tritonia, which includes both elements described above (Getting, 1981; Getting et al., 1980; Katz et al., 1994). Although presently no definite information on network topology is available, the finding that spinalized primates can produce locomotor patterns provides evidence that

central pattern generators for locomotor activity also exist in the spinal cord of higher mammals (Fedirchuck et al., 1998). Evidence is also growing for the existence of spinal central pattern generators controlling locomotion in humans (Calancie et al., 1994; Dimitrijevic et al., 1998). In general, locomotor networks are constructed redundantly, that is, they contain multitudes of small neuronal circuits, for example, five in case of the locust flight CPG (Grimm & Sauer, 1995). They thereby gain substantial robustness against synaptic noise or functional failure in individual neuronal elements.

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Figure 7.3 Forward excitation and reciprocal inhibition in central pattern generators. (A) Forward excitation and backward inhibition as building block for pattern generation in neural networks for locomotion. Open triangle: excitatory synapse; filled circle, inhibitory synapse; D, delay of unknown origin. Please note that some tonic background excitation is needed for inducing oscillatory activity of this network. (B) Wiring diagram of the central pattern generator for flight in the locust thoracic ganglia, based on data from Pearson and coworkers (see Pearson & Ramirez, 1997; Robertson & Pearson, 1985), utilizing the above building block. Each circle denotes an identified interneuron in the nervous system of the locust. The numbers denote certain types of identified interneurons. Open triangles: excitatory synapse; filled circle, inhibitory synapse; D, delay of unknown origin. El MN, wing elevator motoneuron; Dep MN, wing depressor motoneuron. The motor output of this pattern generator is exemplified at the bottom by a paired intracellular recording from hind wing elevator and depressor motoneurons during activity of the flight CPG. Please note that the membrane potential oscillations also carry action potentials (arrowheads). Due to the fact that the recordings were made from the soma these are very small in amplitude in invertebrates. Activity was initiated and maintained by a wind stimulus to the head of the locust.

The pattern of activity generated by locomotor networks need not be two-phased, as the motor output often suggests. Locomotor networks can be constructed and operate in a way that leads them to generate a rhythm

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Figure 7.4 Motoneuron activity in a locomotor network. Recording from a motoneuron in an invertebrate locomotor network exemplifying schematically the contribution of some of the known synaptic and intrinsic factors, that is, building blocks, for locomotor pattern generation. Activity in the neuron is initiated by depolarizing, excitatory synaptic inputs (synaptic excitation); burst activity in the neuron is then supported and maintained by bistable properties of the neuronal membrane (plateauing of the neuron); over time spike activity of the neuron decays due to spike frequency adaptation (SFA), a mechanism reducing excitability of the neuron; activity of the neuron can be terminated by inhibitory synaptic inputs (synaptic inhibition) and intrinsic burst termination properties (burst term. (KCa ), both inducing repolarization of the membrane potential below spike threshold. For detailed description, see text.

with more than the two phases that are obvious from the locomotor program. For example, the locust flight system generating a two-phase motor output for wing elevation and depression is driven by the output of a three-phase neuronal network (Robertson & Pearson, 1985). As the above description makes clear, synaptic interactions within neuronal networks are important prerequisites for generating the rhythmic motor activity underlying locomotion. In addition, intrinsic properties of neurons contribute to and cooperate with the network topology in the generation of rhythmic motor activity. Intrinsic properties of neurons are generated within neurons themselves and have been studied in great detail. Some of the most prominent ones are summarized in Figure 7.4 and will be briefly introduced in the following: • Plateau potentials: Besides the generation of action potentials, neurons can be capable of generating plateau potentials, which are spike-like quasi-stabile operating characteristics. A plateau potential is basically a prolonged, rather slow regenerative depolarization (Hille, 1991). It usually results from a voltage-dependent inward current mechanism: sufficient depolarization initiates an inward current flow, which causes further depolarization, leading to a self-sustained depolarized state of the neuron. The membrane potential remains in this depolarized state for some time. Since the membrane potential of the plateau is usually above spike threshold, the plateau phase is characterized by burst

Location of Pattern-Generating Networks for Locomotion

activity of the neuron. A sufficient hyperpolarizing synaptic input or a mechanism for burst termination (see below) can terminate the plateau by turning off the voltage-dependent inward current. In-depth reviews on the role of this property for pattern generation are found in Marder and Calabrese (1996), Pearson and Ramirez (1992), and Kiehn et al. (1997). • Burst termination properties: In addition to inhibitory synaptic inputs, intrinsic properties of neurons can contribute to terminating bursts of activity. One example is the calcium-dependent potassium channel (KCa ; Hille, 1991). During strong bursts of action potentials, calcium ions enter a neuron through cation channels underlying the depolarization and the burst of activity. Over time, this leads to an accumulation of Ca2+ ions in the neuron, which in turn activate KCa channels. The potassium outward current initiates a hyperpolarization of the neuron below its spike threshold and thereby terminates its depolarization and activity (e.g., Grillner & Wallen, 1985; in-depth review by Grillner et al., 1995). • Spike frequency adaptation (SFA): There are presently many examples of the activity of neurons adapting over time to a given depolarization in membrane potential. The mechanism behind this phenomenon is often the slow afterhyperpolarization (sAHP) that follows each action potential (Hille, 1991; Schwindt & Crill, 1984). The slow sAHP is generated by calcium-dependent potassium channels (KCa ). With the generation of action potentials, not only Na+ , but also Ca2+ ions enter a neuron. These Ca2+ ions activate a KCa , which initiates a slow afterhyperpolarization of the neuron following spike activity. sAHPs accumulate over time and can thereby reduce the excitability of a neuron and thus its activity (in-depth review in Grillner et al., 2000). • Intrinsic oscillations/Endogenous bursting: Neurons can be capable of steadily producing phases of alternating activity consisting of bursts and silence. This property is called endogenous bursting or intrinsic oscillation (Hille, 1991). The underlying ionic mechanisms are diverse here, too. For example, the active phase of a neuron can display similarities to plateau potentials. There are also automatic ionic mechanisms, that is, conductances in the neuron that terminate activity after some time by opening ion channels, for example, K+ channels. These allow an outward current to hyperpolarize the membrane potential below spike threshold. The next cycle of activity is then started either by rebound properties of the neuron or by a tonic background excitation (Grillner & Wall´en, 1985; Hochman et al., 1994; Sillar & Simmers, 1994a; Sigvard et al., 1985).

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Through the action of premotor neuronal networks, a basic rhythmic activity is generated that has to be modified for a functional locomotor pattern, depending on the complexity of the locomotor organs and the locomotor task executed. Getting (1989) coined the term “building block” for identified types of network connections, synaptic properties, and intrinsic neuronal properties in charge of generating rhythmic motor activity. The controllers of the locomotor organs can contain a multitude of pattern-generating networks. Where the locomotor organ is segmented, for example for walking, the number of pattern-generating networks can be increased as well (B¨uschges et al., 1995; Cheng et al., 1998; Edgerton et al., 1976; Puhl & Mesce, 2010; for summary see B¨uschges, 2005). For example, in the stick insect walking system, each of the three main leg joints of each leg is driven by an individual neural network capable of generating rhythmic motor activity (B¨uschges et al., 1995). The activity of the individual pattern generators can be coupled by sensory signals (e.g., Hess & B¨uschges, 1999; summary in B¨assler & B¨uschges, 1998; and see below). Similar results have recently been presented for the cervical spinal cord controlling the forelimb of a vertebrate, the mudpuppy (Cheng et al., 1998). In this investigation, evidence was presented that the motoneuron pools innervating the elbow joint, that is, the flexor and extensor, are driven by one central pattern-generating network for each of the two antagonistic muscle groups moving the tibia—the flexor and the extensor. These findings verified an “old” hypothesis, the “unit-burst generator concept” initially proposed by Edgerton et al. (1976). They suggested that there are unitary central pattern-generating networks present in the vertebrate spinal cord for each muscle group of the limb. The basic rhythmic activity of the pattern-generating networks is shaped for a functional locomotor output by sensory signals from the locomotor organs and synaptically transmitted to the output elements of the locomotor system, the motoneurons. There are only a few examples of locomotor systems in which motoneurons themselves are elements of the pattern-generating networks, for example, in crustaceans (Chrachri & Clarac, 1987), annelids (Poon et al., 1978), and a lower vertebrate, the tadpole (Perrins & Roberts, 1995a-c). LOCATION OF PATTERN-GENERATING NETWORKS FOR LOCOMOTION As stated above, rhythmic locomotor activity is generated within the controllers of the locomotor organs. These controllers are the neuronal networks located in the central

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nervous system, mostly in close apposition to locomotor organs, that is, in the segments from which the locomotor organs arise and from where they are innervated. Segmental organization of the organism or segmental structure of the locomotor organs has no prejudicative meaning for the localization of the pattern-generating networks in the nervous system. Let us consider the generation of locomotor patterns on the level of rhythmic activity that drives one locomotor organ, for example, the chain of myotomal segments in swimming in the lamprey and the tadpole, each wing in flying (or swimming), or each limb in terrestrial locomotion. With few exceptions, the controllers for locomotion are distributed across several segments of the central nervous system. The pattern-generating network for locust flight is a distributed neuronal network encompassing the three thoracic ganglia and some condensed abdominal neuromeres attached to the metathoracic ganglion (Robertson & Pearson, 1985). Distribution is also present in the walking pattern-generating networks of vertebrates, for example, for the forelimb in the cervical spinal cord (Cheng et al., 1998) and for the hind limb in the lumbar spinal cord (e.g., Cazalets et al., 1995; Kjaerulff & Kiehn, 1996). In the tadpole, the CPG for swimming that drives the chains of myotomal segments is distributed along the segments of the spinal cord (see above). In the lamprey however, the CPGs for swimming that drives the chains of myotomal segments are located in each segment along the spinal cord (Cangiano & Grillner, 2003, 2005). Similarly, the patterngenerating networks for crawling and swimming in the leech are distributed along the chain of segmental ganglia (comparative summary in Orlovsky et al., 1999). However, these locomotor systems are special in the sense that, for swimming, the motor pattern results from the coordinated action of the subsequent segments of the organism, that is, the locomotor organ is the organism “itself.” For all controllers generating swimming movements, it is known that the nervous system of each individual segment contains neural networks capable of generating a rhythmic motor output for the segment. This is most obvious in the CPG for swimming in Clione, which is generated by a network of interneurons, most of which are located in the pedal ganglia of the nonsegmented organism (Arshavsky et al., 1985a–e). Only in arthropod walking systems has a clear segmental organization been found, with the controller of each leg mainly restricted to the segmental ganglion of the locomotor organ, which has been studied in great detail for the stick insect (summary in B¨assler & B¨uschges, 1998). Regarding mammalian locomotion, important new findings were recently presented on the organization and

location of the pattern-generating networks for the individual limbs. Individual neuronal networks for the generation of rhythmic motor activity for both elbow flexor and elbow extensor motoneuron pools were identified in the mudpuppy forelimb (Cheng et al., 1998). The data presented support the “unit-burst generator” concept of locomotion (see earlier). Similarly, lesion experiments in the neonatal rat lumbar spinal cord have revealed that the CPGs controlling hind limb movements are distributed throughout the hind limb enlargement and most likely also in the lower thoracic cord (Cazalets et al., 1995; Kjaerulff & Kiehn, 1996). Together with additional evidence, this suggests that, also in mammals, the CPG for the hind limb is not a unitary entity, but is again composed out of several unit-burst generators controlling single muscles or joints. Between the lumbar segments, the capability to generate rhythmic activity declines from rostral to caudal (summary in Kiehn & Kjaerulff, 1998). In patients with spinal cord injuries, it was reported that the higher the level of the injury was, the more “normal” the locomotor pattern appeared (Dietz et al., 1999). This indicated that, also in humans, the CPGs for locomotion are not restricted to specific levels of the spinal cord.

SENSORY SIGNALS CONTROLLING LOCOMOTOR ACTIVITY In the majority of locomotor systems, sensory signals are utilized, on the one hand, to generate a functional locomotor pattern and, on the other hand, to stabilize the locomotor pattern, by adapting to biomechanical changes and responding to unexpected events. Third, sensory information plays a crucial role in controlling the posture and equilibrium of the locomotor system during the behavioral task (MacPherson et al., 1997; Orlovsky et al., 1999). Such information is gathered from multiple sensory systems and integrated in the networks controlling locomotion and related to the current position and condition of the body and the limbs (e.g., the phase of a movement). The dependence of motor control systems on proprioceptive signals has been well characterized in a statement by Prochazka (1996b): “You can only control what you sense.” Proprioceptors located in muscles and joints characterize the position of the limbs, and together with exteroreceptors they sense contact with the ground or obstacles and the load carried by the limb. Their general role is to establish the temporal order of the locomotor pattern and to reinforce ongoing activity (B¨uschges et al., 2008; B¨uschges & Gruhn, 2008; Duysens et al., 2000; Grillner,

Sensory Signals Controlling Locomotor Activity

1979; Pearson, 1995; Pearson & Ramirez, 1997; Prochazka 1996b). Other sensory information involved in the generation and control of locomotor behavior itself is provided by visual cues. Visual cues play a decisive role in controlling goal direction in locomotion, allowing the preadjustment of the locomotor activity and the interpretation of visual flow yielding information on walking speed and direction (review in Rossignol, 1996a). Furthermore, together with the vestibular apparatus or comparable gravity sensorsystems in invertebrates, visual input is involved in controlling the body’s orientation in space. This is especially important for animals locomoting in a 3D environment (i.e., flying or swimming; Orlovsky et al., 1992, Reichardt, 1969; Ullen et al., 1996). The following paragraphs briefly review the major sensory systems and their role in the control of locomotion. Visual Regulation of Locomotion Visual control of locomotion is very powerful. Apparently, visual input is used to direct locomotion, to avoid obstacles on the way to reach a target, and for orientation. Due to its complex nature, very little was known until recently about the visual control of locomotion; however, advances in computer technology allowing artificial simulation of the optical system (like virtual reality, Warren et al., 2001) provided deeper insight into the mechanisms of visuomotor control. As introduced by Gibson (1958), movements of the body in space generate a continuously changing pattern of motion on our retina, referred to as optic flow. This self-induced optical flow has to be distinguished in speed and direction from the optic flow induced by moving objects. Confusion about this distinction is typically experienced when sitting in one train observing another and being unable to identify which train is moving. Generally, optical flow is used to assess the velocity of the locomotion and the direction of self-movement. Consequently, information gained from visual flow is used to control multiple aspects of locomotion, including goal-directed spatial behavior, locomotor speed, and gross adaptation to environmental changes. The association of changes in optic flow with changes in movement is so strong that artificially induced or perturbed optic flow can modulate the velocity and direction of locomotion or even initiate locomotor behavior. This is an observation commonly found throughout the entire animal kingdom, for example in lobsters and crayfish. A front-to-rear optokinetic stimulation provided by horizontal stripes on an underlying treadmill

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can trigger forward locomotion with the velocity depending on the velocity of the stripes (Davies & Ayers, 1972). Insects flying tethered inside a striped drum will tend to turn in the direction in which the drum is rotated (Reichard, 1969), and expanding the size of a target during the time a gerbil is walking (giving the impression that the target is getting closer) causes the animal to decrease its velocity (Sun, Carey, & Goodale, 1992). Powerful effects of changes in visual flow have also been reported in humans. For example, during forward walking in a room in which the walls can be displaced, moving them forward (instead of backward as it would appear during forward locomotion) will create the impression of walking backward, despite contradictory proprioceptive signals from the limbs (Lee & Thompson, 1982). Toddlers who have just learned to walk will tip over if the walls are set in motion (Stoffregen et al., 1987). Comparable experimental approaches showed that, in humans as well, walking velocity is adjusted to visually perceived walking speed (Konczak, 1994; Prokop et al., 1997). Thus, visual input during locomotion in vertebrates and invertebrates is not only used to avoid obstacles, but also to guide locomotor direction and velocity. In the field of visual locomotor control, there is an ongoing discussion whether visual flow is the dominant optical influence on target-directed locomotion or whether the walking direction is simply determined by the current body orientation and the perceived direction of the target. In light of this dispute, it has recently been demonstrated that humans do not guide locomotion by relying either on visual flow alone or on egocentric direction alone. Both components are probably used in a complementary way, for example when the optical flow is reduced or distorted: on a grass lawn or at night, behavior tends to be governed by egocentric direction (Harris & Carre, 2001; Rushton et al., 198; Warren et al., 2001; Wood et al., 2000). Visual information is also essential to perform anticipatory foot placement in order to avoid obstacles. To avoid bumping into an object, it is crucial to calculate how much time is left for corrective action. The distance to the obstacle is only relevant in relation to the speed of selfmotion. Thus, visual information is used in a feedforward rather than an on-line control mode to regulate locomotion. Humans do not fixate on obstacles as they step over them, but perform a planning in the steps before (Hollands & Marple Horvat, 1996, 2001; Patla et al., 1996, 1997). This feedforward information is very important in the control of walking, and it is only possible to walk up to 8 seconds or 5 meters without visual feedback (Lee & Thompson, 1982).

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Compared to invertebrate systems, knowledge about the mechanisms of visual locomotor control in vertebrates is rather incomplete. The approach of recording cortical cells during visually guided locomotion in cats demonstrated increased firing in pyramidal cells when modification of the step cycle was required to clear an obstacle (Drew et al., 1996a). It has been suggested that the increased discharge is used to modify the step cycle, since it has been shown that inactivation of these cortical areas results in the inability to clear visible obstacles. A comparable feedforward mechanism to anticipate collision was recently described in locusts (Hatsopuolus et al., 1995). The lobula giant motion detectors (LGMD) in the locust optic lobe (Strausfeld & Naessl, 1981) are neurons that receive inputs from afferents that are sensitive to local motion over the entire visual hemifield (Rowell et al., 1977) and that respond most strongly to objects approaching the eye (Rind & Simmons, 1992). LGMD synapse directly to a large neuron (descending contralateral motion detector, or DCMD), which is involved in the generation and control of flight and jump maneuvers (Pearson et al., 1980; Robertson & Pearson, 1983). In the visual system of the fly, a number of processing stages have been identified and several interneurons with sensitivity to various directions of motion have been found (Borst & Egelhaaf, 1989), making the visual system of flies the best-described model in visual processing (reviewed in Krapp, 2000). Proprioceptive Regulation of Locomotion The question of how proprioceptive signals regulate locomotor activity has been an intensive field of research including studies in man, cats, and various arthropods (reviewed in B¨uschges & El Manira, 1998; B¨uschges & Gruhn, 2008; Duysens et al., 2000; Pearson, 1995; Pearson & Ramirez, 1992; Prochazka, 1996b). These studies made it clear that there are common principles in the proprioceptive control of locomotion throughout the entire animal kingdom, indicating their general importance in the generation of functionally relevant locomotor movements (B¨uschges, 2005; Pearson, 1993). A prominent example providing evidence that local proprioceptive reflex pathways are strongly involved in the control of stepping is the finding that spinalized cats walking with their hind limbs on a treadmill are able to adapt the speed of stepping to the speed of the treadmill belt (Brown, 1911). The only explanation for this ability is that sensory feedback from proprioceptors in the limbs is involved in controlling the step cycle. Generally, proprioceptive feedback serves two separate functions in the control of locomotion: (1) the

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Figure 7.5 A summarization of the mechanisms involved in the control of locomotor output by central and proprioceptive signals. When the CPG is active, phase- or state depending priming of mono- and polysynaptic sensory-motor paths via interneurons (INs) between sensory neurons (SN) and motoneurons (MN) takes place. Arrow 1 symbolizes the state- or phase dependent alterations in the processing in the processing of sensory information. Arrow 2 represents the influence of premotor interneurons in sensory-motor pathways on the CPG. Arrow 3 represents the presynaptic inhibition of afferent terminals in the CNS. Arrow 4 represents the pathway from sensory neurons, which can affect the timing of the locomotor output. (Modified from B¨uschges & El Manira, 1998.)

control of phase transition, and (2) the regulation of the magnitude of muscle activity. A common principle in sensory control of locomotion is the task- or phase-dependency of sensory feedback. Transmission in proprioceptive reflex pathways, for example, strongly depends on the motor task and the phase of the movement (reviewed in B¨uschges & El Manira, 1998; Pearson, 1995). For example, reflex pathways can generate opposite motor outputs of varying strength or gain, depending on the actual behavior or the phase of the movement (e.g., standing compared with walking; Figure 7.5). This phenomenon has been termed reflex reversal and is found in vertebrates (Pearson, 1993, 1995; Prochazka, 1996b) as well as in invertebrates (B¨uschges et al., 2008; Clarac et al., 2000). This flexibility of reflex pathways ensures that the motor output is adjusted properly to the actual behavioral task, depending on the behavioral and the biomechanical state of the locomotor apparatus. The Proprioceptive Control of Phase Transition Sherrington (1910) already introduced the concept that somatosensory afferents from the limbs are involved in the regulation of the step cycle during walking in vertebrates. One mechanism regulating the duration of the stance phase in vertebrates is hip extension, since preventing

Sensory Signals Controlling Locomotor Activity

hip extension in a limb prevents the onset of the swing phase and thus of stepping in cats and rats (e.g., Fouad & Pearson, 1997a; Grillner & Rossignol, 1978; for summary see Pearson et al., 2006). The afferents responsible for signaling the hip angle and subsequently for the initiation of the swing phase are probably muscle spindles in hip flexor muscles (Hiebert et al., 1996). Another signal in the control of the step cycle in vertebrates arises from Golgi tendon organs and muscle spindles from extensor muscles (Conway et al., 1987; Whelan et al., 1995a). Both sensors are active during stance, with the Golgi tendon organs providing a gauge of the load carried by the leg (reviewed in Dietz & Duysens, 2000). The excitatory activity during walking is opposite to its inhibiting action during standing (reflex-reversal). The functional consequence is that the swing phase is not initiated until the load is taken off of the limb (otherwise balance would be lost), as occurs at the end of the stance phase when the weight of the animal is borne by the other limbs (for review, see Ekeberg & Pearson, 2005; Pearson et al., 2006). The fact that, at the end of the stance phase, signals both about joint/limb displacement and about load on the limb are involved in the initiation of the swing phase is a general rule in vertebrate and invertebrate walking systems. It has been commonly found in stick insects, cockroaches, lobsters, cats, and humans (Figure 7.6; Anderson & Grillner, 1983; B¨assler & B¨uschges, 1998; Clarac, 1982; Pang & Yang, 2000; Pearson 1993). In the stick insect, for example, two types of proprioceptors have been found to influence the timing of the onset of the swing phase. These sensors are (1) the campaniform sensillae, which measure load on a limb or strain on the cuticle, in a manner analogous to the Golgi tendon organs in vertebrates, and (2) the femoral chordotonal organ, which, by being stretch-sensitive in a manner analogous to muscle spindles in vertebrates, signals the movement and position of the femur-tibia joint (B¨assler, & B¨uschges, 1998; B¨uschges & Gruhn, 2008). Prochazka (1996b) has formulated a general rule for the transition from the stance phase to the swing phase during stepping in vertebrates: IF extensor force low AND hip extended THEN initiate swing. However, not only in walking systems phase are transitions controlled by proprioceptive signals (reviewed in Pearson, 1993, Pearson & Ramirez, 1997). In the flight system of insects, especially well studied in locusts,

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Figure 7.6 Sensory feedback in the control of phase transition during locomotion. This figure shows the common principle of sensory feedback controlling the initiation of the swing phase in invertebrates (stick insect, cockroach and crayfish) and vertebrates (cat and infant). When the limb is unloaded, and thus no force is detected in the limb, and the limb is extended, the swing phase will be initiated. (Modified from Prochazka, 1996b, and Pang & Yang, 2000.)

sensory information about movements of the wings is also utilized for phase-transition in motor activity. Two wing sensory systems, that is, the wing tegulae, a hinge mechanoreceptor, and the stretch receptors control the initiation and duration of elevator activity during flight motor activity (Ausborn et al., 2007; Wolf & Pearson, 1988). Similarly, in vertebrate swimming, for example, in the lamprey spinal locomotor network, sensory signals that report bending of the spinal cord contribute to the alternation of motor activity between the myotomal motoneuron pools of both sides of the spinal cord (Grillner, 2003). The functional significance of regulating phase transition by means of afferent pathways might be to limit a

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movement, such as the amount of leg extension (Whelan et al., 1995a) or the amplitude of wing depression during flight (Wolf & Pearson, 1988), to a range compatible with effective function. A second advantage of afferent phase control is to ensure that a certain phase of the movement is not initiated until a defined biomechanical state has been reached. This allows the transition without destabilizing the system.

CPG

5 E

F

Ext

Flex

Stance

Swing

4

3 2 1

The Regulation of the Magnitude of Muscle Activity The second principle of locomotor control found in vertebrates and invertebrates is the control of motor activity via afferent feedback (B¨uschges, 2005; Duysens et al., 2000; Pearson, 1993). A generalization emerging from studies in various walking systems is that afferent feedback from leg proprioceptors contributes to the generation of stance phase activity (see Figure 7.6). For example, in invertebrates such as the stick insect, the chordotonal organ in the femur of the front leg signals flexion of the femur-tibia joint during the stance phase. In walking animals, these sensory signals reinforce the activity in flexor motoneurons (B¨assler, 1986) as a result of the action of a parallel and distributed neuronal network driving the leg motoneurons (B¨uschges et al., 2000). Also, in the walking system of the crayfish, sensory signals are utilized to reinforce motor activity during stance (El Manira et al., 1991; Sillar et al., 1986). In vertebrates, sensory signals underlying the reinforcement of motor activity arise both from Golgi tendon organs and primary muscle spindle afferents (Guertin et al., 1995; McCrea et al., 1995; Whelan et al., 1995a). At least three excitatory reflex pathways transmit proprioceptive information from extensor muscles to motoneurons or the CPG (Figure 7.7): (1) the well-known monosynaptic pathway from muscle spindles to motoneurons, (2) a disynaptic pathway from spindles and Golgi tendon organs that is opened during the stance phase, and (3) a polysynaptic pathway. The latter pathway includes the extensor half-center in a way that also controls the timing of the stepping pattern (Pearson & Ramirez, 1997). The neural mechanisms that contribute to the modulation of sensory pathways in the control of locomotion have been investigated in great detail in the past decade. There are two key factors currently known: (1) in many locomotor systems the actual motor output is the result of the action of a distributed neural network that can modulate that magnitude of motor activity generated by differentially weighting, or opening and closing of individual parallel, sometimes opposing, interneuronal pathways between sense organs and motoneurons (B¨assler & B¨uschges, 1998;

Group la/ll Flexors

Group lb Group la Extensors

Excitatory connection

Inhibitory connection

Figure 7.7 Sensory feedback during walking in cats. This figure is a summary of the reflex circuits regulating the timing and magnitude of extensor activity during walking in the cat. 1 symbolizes the excitatory monosynaptic and 2 the disynaptic inhibitory pathway. Pathways 3 to 5 are opened only during locomotion. Transmission in the dissynaptic pathway 3 occurs during extension and reinforces ongoing extensor activity. One function of excitatory pathway 4 and 5 is to regulate the duration of extensor activity. (Modified from Pearson, 1995.)

B¨uschges et al., 2000; Pearson, 1995; 2000; Figure 7.7). Phase dependency of this mechanism in the locomotor cycle is generated by the action of the central pattern generators. (2) Phasic modulation of efficacy of the individual pathways from sense organs onto motoneurons are the target of pre- and postsynaptic modulatory mechanisms at the intercalated synapses, for example, presynaptic inhibition (Clarac et al., 1992; Nusbaum et al., 1997; Rossignol, 1996b). In humans, as well, the load carried by the extensor muscles increases the magnitude of their activity (Dietz & Duysens 2000; Stephens & Yang, 1999; for summary, see Yang & Gorassini, 2006). In cats, removing feedback from these afferents reduces extensor activity by more than 50% and, in man, the contribution to extensor muscle activity has been estimated to be about 30% (Yang et al., 1991). The general integration of proprioceptive feedback in locomotor systems throughout the animal kingdom strongly indicates the functional necessity of such a feature. The benefits are appropriate and effective control of motor rhythm, integrating biomechanical changes and external perturbations in the system.

Modulation of Locomotor Activity

189

The Role of Exteroceptive Input

MODULATION OF LOCOMOTOR ACTIVITY

Compared to proprioceptive control, much less knowledge has been gathered on the role of exteroceptive inputs (e.g., cutaneous afferents in vertebrates) in the control of locomotion. Exteroceptors in the skin have a strong influence on the central pattern generator for walking (Forssberg, 1979) and on brainstem areas controlling locomotion (Drew et al., 1996b). One important function is to respond to unpredicted perturbations from obstacles on the ground. To be functionally meaningful, these reflexes are strongly modulated during the gait cycle, as has been demonstrated in cats (Abraham et al., 1985; Anderson et al., 1978; Duysens et al., 1980; Forssberg, 1979) and humans (Duysen et al., 1990; Yang & Stein, 1990). A mechanical stimulus applied to the dorsal part of a paw in cats or to a cutaneous nerve in humans during the onset and middle of the swing phase produces a strong, short latency excitement of flexor motoneurons and inhibition of extensor motoneurons to increase elevation of the limb and clear the obstacle. Forssberg (1979) introduced this reflex pattern as the “stumbling corrective response.” In contrast, the same stimulus applied during the end of the swing phase and during the stance phase produces the opposite response, since the limb cannot be lifted at this phase in the step cycle, because otherwise the animal or person would fall. However, the stimulus evokes increased flexor activity in the subsequent step. This state-dependent modulation has been found to be mediated by the convergence of primary afferents and the output from the central pattern generator to premotor interneurons (Degtyarenko et al., 1996). Exteroceptive signals, like cutaneous stimuli, are also known to trigger swimming and/or turning in animals. A well-defined system has been described for swimming behavior in tadpoles (Roberts et al., 2000). A brief stimulus to the skin, for example, of the head, can trigger sustained swimming or struggling sequences, depending on stimulus intensity and duration (Soffe, 1991). This response is mediated via a single skin sensory pathway directly accessing the CPGs in the spinal cord, the RohonBeard sensory neurons. In conclusion, in many locomotor systems, sensory input plays a prominent role in shaping the motor output of the controllers toward functional locomotor behavior. The major tasks of sensory signals are (a) to control the direction of locomotion, (b) to control posture and equilibrium, (c) to control phase transitions in the step cycle, (d) to control the magnitude of muscle activity, (e) to avoid obstacles, and finally, (f) to respond to perturbations.

In order to initiate, maintain, adapt, or terminate locomotor activity to meet the requirements of the current environmental conditions, neuronal networks for motor control have to be flexible. Whereas the fast, task-dependent, cycle-by-cycle adaptation of locomotor activity is achieved by sensory feedback, which, in a broad sense, can be viewed as being an integral part of the pattern-generating networks, neuromodulatory inputs can reshape the motor output by affecting intrinsic network properties of motor circuits and thus provide the general flexibility observed in motor behavior. The term neuromodulation has been in common usage for more than three decades and was originally defined as the changes in cellular or synaptic properties of a neuron, rather than the direct fast activation of the neuron, mediated by a substance (neuromodulators like serotonin, 5-HT) released from another neuron (Kaczmarek & Levitan, 1987; Kupfermann, 1979). Generally, these changes are caused by intracellular Gprotein coupled second messenger pathways activated by receptors to the neuromodulators (e.g., Gs-coupled 5-HT7 receptors). In contrast, fast neurotransmission is generally mediated by ionotropic receptors (like AMPA or NMDA glutamate receptors). However, the line between neurotransmission and neuromodulation is often blurry, with many uses of the term neuromodulation in scientific literature (for an overview of the large variety of phenomena nowadays referred to as neuromodulation see Katz, 1998). This chapter introduces the main mechanism of generating locomotor flexibility, the modulation of locomotor network function by neural active substances (neuromodulators). Sources of Neuromodulators In vertebrates and invertebrates, neuromodulators are mostly released from cell groups consisting of relatively few neurons located in the CNS, but outside the specific locomotor circuits and not participating in the basic locomotor rhythm generation (referred to as extrinsic neuromodulation; Jordan et al., 2008; Katz, 1998; Schmidt & Jordan, 2000). Most of the modulators of vertebrate locomotor networks are synthesized in distinct cell clusters in the brainstem. In many vertebrate systems, the axonal projections of the relatively few cells, however, can supply very large areas of the brain and spinal cord, via extensive axonal branching (Kuypers & Huisman, 1982; van Mier et al., 1986). Classically, the sources of neuromodulator biogenic amines are from the following nuclei: serotonin (5-HT) from nucleus raphe,

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Comparative Locomotor Systems

noradrenaline (norepinephrine) from locus coeruleus and area A5, or dopamine from A11 cell group of hypothalamus (Jordan et al., 2008). However, these classic nuclei are not all near the locations of the brainstem that are important for initiating locomotion (e.g., mesencephalic locomotor region not near A11), and so their role is still uncertain. Other regions, like the parapyramidal region (PPR) of the medulla seem to be important in locomotion, providing a primary source of 5-HT for locomotion (Jordan et al., 2008). Invertebrate neuromodulators, such as serotonin or the neuropeptide proctolin, are normally released from neurosecretory cells either clustered in cerebral ganglia regions or from single, paired, or small groups of cells spread over the whole ventral nerve cord (Beltz & Kravitz, 1987; Keshishian & O’Shea, 1985; Stevenson & Sporhase-Eichmann, 1995). This not only enables the coordinated release of neuromodulators to TABLE 7.1

Neuromodulators Contributing to the Initiation and Modulation of Locomotor Network Activity

Type Vertebrate Systems Dopamine

Serotonin (5-HT)

Noradrenaline Noradrenergic Agonists Substance P1 Invertebrate Systems Dopamine Octopamine

Proctolin Serotonin (5-HT)

Pilocarpine 2

1 Often

defined targets within a particular CPG, but also underlies the simultaneous control of locomotor networks consisting of multiple CPGs (e.g., networks driving leg joints or spinal networks for swimming). Interestingly, some of the amines thought to be exclusively involved with invertebrate locomotion, now turn out to be able to evoke locomotion in vertebrates. In particular, trace amines like octopamine and tyramine are present in the mammalian spinal cord, produced by intrinsic cells, and are capable of increasing locomotor-like activity (Gozal et al., 2006), like in invertebrates (Mulloney, Acevedo, & Bradbury, 1987; Skorupski, 1996; Sombati & Hoyle, 1984). For vertebrates and invertebrates, classic neuromodulators associated with locomotor systems and their controllers, that is, the spinal cord in vertebrates and the segmental ganglia in invertebrates, are summarized in Table 7.1.

Motor System

Function

Species

Selected References

Spinal Spinal Spinal Spinal Spinal Spinal Spinal Spinal Spinal Spinal Spinal Spinal Spinal Spinal Spinal

CPGs CPGs CPGs networks networks network CPGs CPGs CPGs CPGs CPGs network CPGs CPGs networks

Initiation Initiation Initiation Modulation Modulation Modulation Initiation/modulation Initiation Initiation Modulation Modulation Modulation Initiation/modulation Modulation Modulation

Rabbit Cat Neonatal rat Lamprey Lamprey Tadpole Neonatal rat Mouse Rabbit Mudpuppy Neonatal rat Tadpole Cat Neonatal rat Lamprey

Viala and Buser (1969) Forssberg and Grillner (1973) Kiehn and Kjaerulff (1996) Kemnitz (1997) Harris-Warrick and Cohen (1985) Sillar et al. (1992) Kiehn and Kjaerulff (1996) Jiang et al. (1999) Viala and Buser (1969) Jovanovic et al. (1996) Squalli-Houssaini and Cazalets (2000) McDearmid et al. 1997 Barbeau and Rossignol (1991) Barthe and Clarac (1997) Parker and Grillner (1999)

Escape motor system Swimmeret networks Swimmeret networks Swim network Flight motor system Leg locomotor networks Escape motor system Swimmeret network Escape motor system Swim network Swim network Leg locomotor networks Swimmeret network Leg locomotor networks Leg locomotor networks Swimmeret networks

Modulation Initiation Initiation/modulation Modulation Initiation Modulation Modulation Initiation/modulation Modulation Modulation Initiation Modulation Modulation Initiation Initiation Initiation

Cockroach Lobster Crayfish Medicinal leech Locust Locust Cockroach Crayfish Cockroach Medicinal leech Mollusc Tritonia Locust Crayfish Locust Stick insect Crayfish

Goldstein and Camhi (1991) Barthe et al. (1989) Mulloney et al. (1987) Mesce et al. (2001) Ramirez and Pearson (1991a,b) Sombati and Hoyle (1984) Goldstein and Camhi (1991) Mulloney et al. (1987) Goldstein and Camhi (1991) Mangan et al. (1994) Katz et al. (1994) Parker (1995) Barthe et al. (1993) Ryckebusch and Laurent (1993) B¨uschges et al. (1995) Braun and Mulloney (1993)

co-localized with 5-HT or close to serotonergic neurons. cholinergic agonist, used as a tool to activate rhythmic motor networks in invertebrates to study locomotor pattern generation.

2 Muscarinic

Modulation of Locomotor Activity

Effects of Neuromodulators on the Output of Locomotor Networks In principle, neuromodulators alter the expression of motor patterns by affecting the controllers of a locomotor system that drives the effector organs, such as the muscles in the body wall or within limbs or wings. The best-recognized function of neuromodulators in vertebrate and invertebrate motor control is the alteration of the ongoing motor activity level. Such alterations in the intensity of locomotor activity are important, for instance, in adjusting the instantaneous speed of locomotion, which involves acceleration as well as deceleration/termination, or in changing locomotor intensity. This can occur by changes in properties of interneurons or even the motoneurons that directly drive the muscles (Rekling et al., 2000; Schmidt & Jordan, 2000). Furthermore, in a variety of vertebrate and invertebrate species, neuromodulators are able to initiate locomotor activity (see Table 7.1).

Initiation of Locomotor Activity In the simplest examples, initiation of long-lasting periods of locomotor activity often requires no more than a short external stimulus that triggers long-lasting selfsustained network activity (e.g., escape swimming in tadpoles [Roberts, 1990], or locomotion with tail stimulation in rats [Strauss & Lev-Tov, 2003]). However, locomotor activity can also be initiated by administering neuromodulators. Intravenous injection of dopamine (L-DOPA) elicits locomotor activity in spinalized cats on a treadmill (Forssberg & Grillner, 1973; Jankovska et al., 1967a, 1967b) as well as in rabbits (Viala & Buser, 1969) and decerebrated adult rats (Iles & NicolopoulosStournaras, 1996). Intrathecal application of noradrenaline (Kiehn et al., 1992) or intravenous injection of adrenoreceptor agonists to acutely (Forssberg & Grillner, 1973) or chronically spinalized cats (e.g., Barbeau & Rossignol, 1991) also evokes locomotor activity (reviewed in Rossignol et al., 1998). Sublesional transplantation of noradrenergic embryonic neurons from the locus coeruleus into the spinal cord (i.e., close to the locomotor networks) is also able to trigger automatic locomotion in spinalized cats (Yakovleff et al., 1989). In addition, noradrenaline or dopamine (or associated agonists), when applied to in vitro spinal cords of vertebrates, activate or at least modulate the locomotor networks (Gabbay & LevTov 2004; Gordon & Whelan 2006; Kiehn et al., 1999; Kiehn & Kjaerulff, 1996; Madriaga et al., 2004; SqualliHoussaini & Cazalets, 2000).

191

Serotonin (5-HT) is a neuromodulator that can initiate locomotion in some vertebrates, but not in others. In the rabbit (Viala & Buser, 1969) and the neonatal rat (Cazalets et al., 1992; Kiehn & Kjaerullf, 1996), 5-HT induces alternating rhythmic activity in muscle antagonists. Furthermore, sublesional transplantation of serotonergic brain stem cells into the spinal cord of chronically spinalized rats was shown to activate spinal locomotor networks and to improve locomotion (Feraboli-Lohnherr et al., 1997). However, 5-HT cannot initiate locomotion in the cat (Barbeau & Rossignol, 1991), the lamprey (Harris-Warrick & Cohen, 1985), or the tadpole (Sillar et al., 1992), though it can generally modulate ongoing locomotion. These species differences might depend on the balance of 5-HT receptor types activated, with for example 5-HT2 and 5-HT7 receptors playing a role in activating locomotion in rats (Jordan et al., 2008), whereas other receptor types might inhibit motor activity (Hammar et al., 2007). Finally, in some vertebrates, acetylcholine can cause a strong activation of spinal pattern generators (Cowley & Schmidt, 1994; Panchin et al., 1991). In invertebrates, injection or bath application of neuromodulators to isolated nervous systems elicits locomotion or locomotorlike patterns (summary in Table 6.1). However, we do not at present fully understand what mechanisms underlie such an initiation (e.g., Pearson, 1993). Evidence from invertebrate systems suggests that neuromodulators might either directly alter cellular properties of specific network neurons (Kleinhaus & Angstadt, 1995; Ramirez & Pearson, 1991a, 1991b), resulting in locomotor activity onset, or activate modulatory pathways external to the locomotor network, successively initiating pattern generation (Katz & Frost, 1995). A similar understanding of vertebrate cellular and neuromodulatory organization is now emerging (Butt et al., 2002; Dougherty & Kiehn 2010; Hagglund et al., 2010; Hinckley et al., 2005a, 2005b; Miles et al., 2007; Wilson et al., 2010).

Modulation of Ongoing Locomotor Activity In general, a neuromodulator can alter ongoing locomotor activity by affecting three major parameters of the locomotor pattern: (1) it can change the cycle period of the locomotor pattern, (2) it can change muscle force within each activity cycle by altering the duration and intensity of motoneuronal activity bursts, and (3) it can change the coordination of the activity cycles, not only between different neuron pools driving particular muscles, e.g., within one limb during walking, but also the longitudinal

192

Comparative Locomotor Systems

coordination of body segments, e.g., during swimming in aquatic animals. In vertebrates, noradrenergic pathways particularly affect the duration of the movement cycle. For example, administration of noradrenergic agonists increases spinalized cats’ step-cycle length during walking (reviewed in Ribotta et al., 1998). Noradrenaline and its agonists consistently lengthen cycle periods during swimming in amphibian tadpoles (Fischer et al., 2001; McDearmid et al., 1997). However, an increased duration of the movement cycles is also mediated by 5-HT and dopamine (Grillner et al., 1995; Kiehn & Kjaerullf, 1996). In a wide range of vertebrates, 5-HT increases the duration and intensity of the activity bursts within each movement cycle—not only in swimming animals, such as the lamprey (Harris-Warrick & Cohen, 1985) and amphibian tadpoles (Sillar et al., 1992), but also in the locomotor systems of walking animals, such as rabbits (Viala & Buser, 1969), rats (Kiehn & Kjaerulff, 1996; SqalliHoussaini et al., 1993), and cats (Barbeau & Rossignol, 1991). Finally, modulators such as noradrenaline, 5-HT, and dopamine not only influence the longitudinal coordination of the locomotor pattern between successive body segments in swimming animals (Fischer et al., 2001; Grillner et al., 1995), but also shift the activation of particular muscles within a step cycle and thus may alter the complete movement pattern of the limb (Kiehn & Kjaerullf, 1996). Receptors Classes That Modulate Locomotion In many systems, the effects of neuromodulators are mediated via numerous pharmacologically distinct receptor subclasses (for 5-HT, see e.g., Wedderburn & Sillar, 1994; Wikstr¨om, Hill, Hellgren, & Grillner, 1995), enabling a multimodal control of the motor pattern. In vertebrates, direct pharmacological activation of, for example, the adrenoreceptors, which are defined as putative target receptors for catecholamines such as noradrenaline (alpha1 and alpha2 receptors e.g., Hirst & Nield, 1980), can modulate motor output (Barbeau & Rossignol, 1991; Forssberg & Grillner, 1973; Kiehn et al., 1992), with each subclass affecting particular facets of the motor pattern (Fischer et al., 2001; Sqalli-Houssaini & Cazalets, 2000). Generally, Gq-protein coupled alpha1 receptors tend to facilitate locomotion, speeding up the rhythm and increasing the burst amplitude in rats and mice (Gabbay & Lev-Tov 2004; Gordon & Whelan 2006), whereas the role of Gi coupled alpha2 receptors is more complex. For example, while the alpha2 agonist clonidine facilitates locomotion in the

chronic spinal cat (Chau et al., 1998), it inhibits locomotion in the neonatal mouse (Gordon & Whelan 2006) and generally does not seem to facilitate locomotion in normal rats and mice (Lapointe & Guertin, 2008). This may point to an alpha2 receptor related difference in species and/or a difference that emerges with chronic spinal cord injury (Chau et al., 1998; Rank et al., 2011). Likewise, numerous 5-HT receptors mediate the locomotor actions of 5-HT. For example, 5-HT7 receptors in the upper lumbar region are important for control of step cycle duration (speeding steps) and left-right coordination of steps, in mice and rats (Jordan et al., 2008; Liu & Jordan 2005), suggesting that these receptors modulate the central pattern generator. These receptors are activated by the PPR regions of the brainstem (Jordan et al., 2008; Liu & Jordan, 2005). 5-HT1A receptor activation appears to also play a role in initiation in mice (Landry et al., 2006). Likewise, 5-HT2A and 5-HT2C receptors are important for locomotion, though they tend to modulate the amplitude of the steps, and the weight support, rather than the step cycle duration, suggesting that their action may be on motoneurons or premotor cells, rather than the rhythm generator (Fouad et al., 2010; Jordan et al., 2008; Kao et al., 2006; Liu & Jordan 2005). Constitutive Receptor Activity Remarkably, some 5-HT receptors, including 5-HT2B and 5-HT2C receptors, exhibit spontaneous activity that does not depend on the presence of 5-HT itself (constitutive receptor activity; Murray et al., 2010; Murray et al., 2011). This constitutive activity plays an especially important role for locomotor and general motoneuron function after spinal cord injuries that remove most or all of the 5-HT innervation of the spinal cord in rats. Drugs that block this constitutive 5-HT2 receptor activity leave these rats unable to produce sufficient hindlimb weight support to locomote, whereas prior to the block the rats are able to locomote, despite a severe spinal cord injury (Fouad et al., 2010; Murray et al., 2010). Constitutive activity in alpha1 adrenergic receptors also occurs and helps support motoneuron function after spinal cord injury (Rank et al., 2011). Neuromodulators Affect Cellular Properties and the Synaptic Efficacy of Network Neurons Most of the classic neuromodulators exert their effects by changing intrinsic properties of one or a few network neurons and/or of one or more particular synaptic

Modulation of Locomotor Activity

connections, which affects the overall network output, resulting in a more or less extensive alteration of the motor pattern (see above). For many motor systems, more than one neuromodulatory substance is known (e.g., Grillner et al., 1998), each of which has distinct effects on the network output (Kiehn & Kjaerulff, 1996; Sillar et al., 1998) and has to be coordinated for proper pattern modulation. However, in the majority of motor systems, we are just beginning to understand how such multiple and sometimes even contradictory modulatory inputs are processed and integrated and thus enable a functional pattern modulation. The following sections summarize the most common elementary effects of neuromodulators acting extrinsically on particular cellular and synaptic properties of neurons within locomotor networks, and further details can be found elsewhere (Butt et al., 2002; Dougherty & Kiehn, 2010; Hagglund et al., 2010; Harvey et al., 2006; Hinckley

193

et al., 2005a, 2005b; Kiehn & Katz, 1999; Kjaerulff & Kiehn, 2001; Miles et al., 2007; Murray et al., 2010; Rank et al., 2011; Wilson et al., 2010). Alteration of Intrinsic Cellular Properties of Network Neurons Features of neuronal activity such as action potential formation, spike rate, and activity threshold of neurons may vary widely between the different classes of neurons within a network. The shape of a particular type of neuron’s characteristic activity pattern in response to synaptic drive depends on intrinsic biophysical membrane parameters, that is, on its set of steady and transient voltage-dependent ionic conductances within the membrane. These intrinsic cellular properties determine: (a) membrane resting potential (i.e., the state of excitability of a neuron), (b) burst

TABLE 7.2 Effects of Common Neuromodulators on Intrinsic Cellular Properties of Neurons in Locomotor Networks Neuromodulator

Cellular Properties Affected

Motor System

Selected References

Serotonin (5-HT)

Membrane resting potential K and Ih currents

Rat Rat Embryonic chick Tadpole Molluscs Locust Lamprey Locust Mollusc Cat Rat Rat Lamprey Turtle Tadpole Rat Rat

Hochman and Schmidt (1998) Kjaerulff and Kiehn (2001) Hayashi et al. (1997) Sillar and Simmers (1994b) Straub and Benjamin (2001) Parker (1995) Wallen et al. (1989) Parker (1995) Satterlie et al. (2000) Hounsgaard et al. (1988) plateau Hochman et al. (1994) Hochman et al. (1994) Sigvardt et al. (1985) Hounsgaard and Kiehn (1989) Sillar and Simmers (1994a, 1994b) Murray et al. (2010) Harvey et al. 2006

Rat Frog Rat

Sqalli-Houssaini and Cazalets (2000) Wohlberg et al. (1987) Rank et al. (2011)

Spike after-hyperpolarization (AHP) Spike narrowing during burst Intrinsic oscillatory properties and formation from persistent calcium currents

Sodium spike and persistent sodium currents Noradrenaline

Membrane resting potential Persistent calcium and sodium currents and sodium spike threshold.

Dopamine

Membrane resting potential Spike after-hyperpolarization (AHP) Intrinsic oscillatory properties and plateau formation

Mollusc Lamprey Cat

Lotshaw and Levitan (1988) Kemnitz (1997) Schomburg and Steffens (1996) Conway et al. (1988)

Octopamine

Membrane resting potential Spike after-hyperpolarization (AHP) spike ratio

Crayfish Locust Locust Locust

Plateau formation

Locust

Skorupski (1996) Sombati and Hoyle (1984) Parker (1995) Matheson (1997) Br¨aunig and Eder (1998) Ramirez and Pearson (1991a,b)

Spike ratio Intrinsic oscillatory properties

Crayfish Crayfish

Barthe et al. (1993) Murchison et al. (1993)

Proctoline

194

Comparative Locomotor Systems

activity, including spike frequency adaptation (SFA), (i.e., codetermining the activity period of a neuron), (c) mechanisms underlying persistent inward calcium and sodium currents (i.e., the ability of plateau potential generation to maintain a prolonged period of activity), and (d) the mechanisms enabling a post-inhibitory rebound (PIR) of a neuron (helping to escape a phase of strong inhibition), all of which may contribute to the basic shaping of the network output. Most of the classic neuromodulators alter such intrinsic cellular properties by affecting one or more transient ionic conductances. An overview is given in Table 7.2. Alteration of Synaptic Transmission Between Network Neurons Besides their effects on intrinsic cellular properties, neuromodulators can affect synaptic transmission either by targeting presynaptic neurons (i.e., resulting in an altered amount of transmitter release, e.g., Shupliakov et al., 1995) or by changing the responses of the post-synaptic cell to a transmitter (e.g., by alteration particular membrane properties, Parker, 1995). In spinal locomotor networks, biogenic amines such as 5-HT and noradrenaline can control locomotor intensity by increasing or decreasing the amount of the inhibitory transmitter released from neurons responsible for the reciprocal coupling between antagonistic motoneuron pools. Strengthening (or weakening) an inhibitory phase between two consecutive movement cycles causes a delayed (or earlier) onset of activity in the succeeding cycle and thus modulates the cycle duration during ongoing locomotor activity (e.g., Sillar et al., 1998). During locomotor activity, the properties of the synaptic transmission between neurons in a locomotor network can also depend on the connection’s activity history (i.e., on the previous cycles of movement in the same episode of locomotion, so-called activity-dependent synaptic plasticity, e.g., Parker & Grillner, 2000). Neuromodulators can affect these activity-dependent properties of a synaptic connection, enabling synaptic metaplasticity (reviewed in e.g., Parker, 2001), which adds a further degree of functional flexibility to the network output. In some cases, neuromodulators can even reverse the sign of a particular synaptic connection (Johnston et al., 1993).

PLASTICITY IN MOTOR SYSTEMS For many years, the central nervous system in adult mammals has been seen as a hard-wired and rigid structure.

The same was believed about the nervous system of invertebrates, whose relatively short life spans were thought to make adaptive processes in the nervous system unnecessary. This view has changed and today it is accepted that the CNS in vertebrates and invertebrates is capable of major reorganizations in response to injury or loss of parts of the nervous system under experimental or pathological conditions (Fouad & Tse, 2008; Kolb & Teskey 2010; Meinertzhagen, 2001). Theoretically, reorganization can occur on multiple levels: in preexisting neural circuits by changing synaptic strength (referred to as synaptic plasticity), by anatomical reorganization through the sprouting of uninjured axonal branches and dendrites (referred to as anatomical plasticity), or by changes in neuronal properties. Because plasticity after injuries to the CNS is frequently associated with functional recovery, ongoing research in adult vertebrates focuses on understanding the mechanism of plasticity, since this could lead to new treatments for patients suffering from stroke or traumatic injuries of the brain or spinal cord. This chapter will introduce examples of plastic rearrangements in locomotor systems of the nervous system with the focus on vertebrates and discuss possible mechanisms of spontaneous recovery after injuries to the nervous system. This topic has been extensively reviewed by Rossignol et al. (2011), Edgerton et al. (2004), Raineteau & Schwab (2001), and Pearson (2000).

Injury-Induced Plasticity of Central Pattern-Generating Networks Regardless of its location, injuries to the nervous system are followed by a phase of plasticity that is frequently related to the moderate functional recovery found especially after mild or moderately severe injuries. Prominent examples of plasticity and functional recovery are found in the locomotor system: For example, following spinal cord injury, leg muscle activity in patients returns during assisted locomotion a few months after injury (Dietz et al., 2009), and in rodent models with incomplete spinal cord injury, significant return of hindlimb locomotor movements is found within weeks after injury (Ballermann & Fouad, 2006; Basso et al., 1995). A prominent demonstration linking plasticity to locomotor recovery comes from a finding in cats with complete thoracic spinal cord transection. Following such an injury, which is completely disconnecting spinal locomotor centers from the brain, cats can be trained to walk on a treadmill with their hind limbs (Barbeau & Rossignol, 1994; De Leon et al., 1998;

Plasticity in Motor Systems

Lovely et al., 1990; reviewed in Rossignol, 2000). A beautiful example that spinal networks are able to “learn” and to retain information comes from another study of the Rossignol laboratory (Barri`ere, Leblond, Provencher, & Rossignol, 2008). The researchers trained cats to recover locomotion following a hemisection of the thoracic spinal cord, which was then followed by a complete transection of the cord. Surprisingly, these animals quickly recovered locomotor function in comparison to animals that received a complete lesion only, which normally need weeks of intensive training to reach a comparable locomotor performance. In humans, too, treadmill training has proven its validity in the rehabilitation phase of patients with spinal cord injuries (Behrman & Harkema, 2000; Dietz et al., 1994; Wernig & Muller, 1992). Regular training with partial weight support by suspending patients in harness over a treadmill increased the return of rhythmic muscle activation, and improved weight support capability. However, it has been debated whether weight-supported treadmill training in patients is actually superior to weightsupported over ground locomotor training (Dobkin et al., 2006). A mechanism probably involved in training-induced functional recovery is the enhanced excitability of spinal pattern-generating networks after spinal cord injury (De Leon et al., 1999a; Tillakaratne et al., 2002). Results supporting this conclusion come from numerous sources. First, administration of strychnine, a glycinergic receptor antagonist, to chronic spinal cats trained for 12 weeks to stand improves stepping for about 45 minutes but has no effect in animals trained to step (De Leon et al., 1999b). Because no weight-bearing steps are produced in stand-trained spinal animals without strychnine, these observations indicate that step training may reduce glycinergic inhibition of locomotor pattern generating network. In addition, locomotor training can reverse the depression of the spinal circuits by reducing the number of glycine receptors and the level of GAD67 expression (Edgerton et al., 2001; Tillakaratne et al., 2002). Plasticity in Afferent Pathways Controlling Locomotion The finding that treadmill training in spinalized cats enhances locomotor recovery already indicated that adaptive changes in pattern-generating networks are driven by sensory signals from the stepping limbs. A good example that afferent pathways are modifiable is the conditioning of the well-known H-reflex in a learning task in

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rats, monkeys, and humans (Segal & Wolf, 1994; Wolpaw, 1997), which was reported to improve locomotion in spinal cord injured rats and humans (Chen et al., 2006; Chen et al., 2010). Also an example of instrumental learning within spinal circuits in rats with spinal transection has been reported by Grau and colleagues (Gomez-Pinilla et al., 2007; Grau et al., 2006). It is known that, after injuries to the central nervous system (especially the spinal cord), reflexes are exaggerated (Burke et al., 1970; Hochmann & McCrea, 1994; Nelson & Mendell, 1979). One factor in the increased reflex gain is the sprouting of sensory afferents and a simultaneous increase in their effectiveness. Another example of injury-induced reflex plasticity is the finding that afferent pathways that are involved in the initiation of the swing phase and in reinforcing extensor activity are enhanced by partial denervation of extensor muscles (Gritsenko et al., 2001; Pearson et al., 1999; Whelan et al., 1995b). Increases in proprioceptive reflex strength occur within a week and are paralleled by the recovery of stepping. The location of the adaptive changes is probably in the lumbar spinal cord, since the amplitudes of group I (rising from Golgi tendon organs and muscle spindles) field potentials from a spared synergistic muscle are increased in the intermediate nucleus of lumbar segments (Fouad & Pearson, 1997b). The finding that a comparable increase in the effectiveness of group I input from this muscle can also be found in chronically spinalized cats also indicates that reflex adaptations are occurring at the level of the spinal cord (Bouyer et al., 2001). In the light of the adaptive capabilities of the spinal cord, Pearson (2001) reviewed the role of plasticity in reflex function and in the recovery of locomotion after injuries to the CNS. The fact that plasticity can occur on several levels, for example, that spinal reflex pathways are able to “learn,” has been also demonstrated by Lou and Bloedel (1988), who showed that decerebrated walking ferrets were able to change the trajectory of the swing phase when an obstacle was interjected into the step cycle during the swing phase. In insects as well, the removal of sensory organs involved in the regulation of locomotion or even amputation of a limb can be compensated. For example, in the locust flight system, complete or partial removal of the tegulae (mechanoreceptors at the wing base) leads to compensatory anatomical and synaptic rearrangements resulting in functional recovery of flight motor behavior within 2 weeks following the lesion (B¨uschges et al., 1992a, 1992b; Fischer & Ebert, 1999; Gee & Robertson, 1996; Wolf & B¨uschges 1997). Interestingly, it is reported that

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this recovery occurs spontaneously and does not depend on training or activity. The plasticity of locomotor systems can also be expressed on an immediate short-term scale. An example of such injury-induced plasticity is the recovery of walking after leg amputation in cockroaches. The walking pattern adapts to the loss of the limb by switching interleg locomotor coordination from hexapods to that of tetrapods (Hughes, 1957; Wilson, 1966). Injury-Induced Plasticity of Descending Tracts Due to limited self-repair after traumatic injuries to the CNS in higher vertebrates, it is believed that functional recovery, too, is rather limited. Recent studies, however, linked injury-induced sprouting of lesioned (Weidner, Ner, Salimi, & Tuszynski, 2001) and spared fibers above the level of the injury to improved motor recovery (Bareyre et al., 2004; Fouad et al., 2001). It has been demonstrated that these new sprouts from injured fibers can connect onto interneurons that bridge the injury site to neurons below the injury (Bareyre et al., 2004; Vavrek et al., 2006). Thus, this plasticity could be viewed as a naturally occurring repair mechanism. The functional relevance of such rerouting of descending control on locomotor recovery has been demonstrated by Courtine et al., (2008). In adult mice they performed a high thoracic hemisection of the spinal cord, followed by a recovery phase. A few weeks later the animals received a second hemisection of the lower thoracic spinal cord on the other side (staggered), therefore disconnecting locomotor networks in the lumbar spinal cord from all direct descending control. Nevertheless, the animals recovered a surprising degree of locomotor abilities including weight-supported stepping. This recovery was proposed to be based on spinal interneurons bridging the gap between both hemisections and allowing descending signals to indirectly reach the lumbar spinal cord. Axonal sprouting associated with locomotor recovery has also been observed in uninjured fibers that were spared by a spinal lesion. For example, rats substantially recover locomotor function within weeks following a unilateral hemisection of the spinal cord. In parallel to this recovery we found significant sprouting of reticulospinal tract fibers of the spared side, projecting into the gray matter to intermediate lamina, even crossing the midline (Ballermann & Fouad, 2006). Although no direct link between this “compensatory sprouting” and the locomotor recovery has been established, the role of the reticulospinal tract in triggering locomotor function and the time course of recovery point to an import role of compensatory sprouting.

Injury-Induced Plasticity in Neuronal Properties Over the past years it became clear that neurons also can change their properties due to injury and rehabilitation. This has been well documented in the case of motoneurons below the injury that not only lost their glutamatergic input from the brain, but also neuromodulatory drive from the brainstem, rendering them unresponsive. Over the weeks following SCI, these cells recover their excitability and two mechanisms involved in this recovery of neuronal excitability have recently been discovered. On one hand the potassium-chloride cotransporter KCC2 is downregulated after SCI in motoneuron membranes, thereby depolarizing the Cl– equilibrium potential and reducing the strength of postsynaptic inhibition (Boulenguez et al., 2010). Another change in motoneuron properties that contributes to locomotor recovery following a staggered lesion is the developing independence of serotonin, by constitutively (spontaneously) active serotonergic receptors (5-HT2C; Fouad et al., 2010; Murray et al., 2010). Not surprising is the finding that together with the lack of descending inhibition, both mechanisms can also contribute to excessive excitability of the injured spinal cord and to muscle spasms. Training and the subsequent neuronal activity likely influences these changes by mechanisms that may include BDNF-activating Trk receptors that can upregulate KCC2 expression and thus restore endogenous inhibition (Boulenguez et al., 2010). Opposite to the recovery of motoneuron excitability, a progressive decline (exhaustion) in interneurons that control locomotion CPG has been reported in individuals with complete SCI (Dietz & Muller 2004). The same individuals did not have a decline in reflex function or muscle spasms, thus suggesting changes in interneurons rather than in motoneurons.

CONCLUSIONS Research in the field of locomotor control has greatly benefited from studies in lower vertebrates and invertebrates, like lamprey, tadpole for swimming, and crayfish and stick insect for walking pattern generation. These accessible animal models helped to unravel basic principles for neuronal networks controlling locomotion. However, a major outcome of the past research in various lower and higher animal species is that many of the mechanisms described also contribute to locomotor pattern generation in higher vertebrates, including humans, that are often less accessible, experimentally indicating that common principles

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CHAPTER 8

Neural Mechanisms of Tactile Perception STEVEN S. HSIAO AND MANUEL GOMEZ-RAMIREZ

OVERVIEW 206 PERIPHERAL NEURAL MECHANISMS OF TACTILE PERCEPTION 206 CENTRAL MECHANISMS OF FORM AND TEXTURE PERCEPTION 212

MECHANISMS OF ATTENTION IN TOUCH SUMMARY 233 REFERENCES 233

OVERVIEW

and whether it is moving across the skin (motion), (b) the global features of objects (size, shape, and weight), and (c) the perceptions of texture and shape that we indirectly sense when exploring objects with tools. In this chapter, we concentrate on the neural mechanisms underlying tactile perception from the hand. We give a detailed description of the mechanoreceptive afferents that innervate the skin, muscles, tendons, and joints, briefly review the ascending and cortical pathways, and review the neural mechanisms of form, texture, motion, and shape processing. We show that similar neural mechanisms are used in touch and vision for processing local surface features and that the neural mechanisms for processing global features are unique to the somatosensory system. Where possible, we link the underlying neural responses with human psychophysical studies. We end the chapter with a discussion of the neural mechanisms underlying tactile selective attention.

Research over the past 30 years has converged to produce an emerging picture of the neural mechanisms underlying tactile perception from the hand. The studies show that embedded in the skin, joints, and muscles are a diverse set of afferents that show specificity for different environmental energies and are responsible for different aspects of touch perception. Encoded within the firing patterns of these afferents are parallel sets of modality specific neural representations that are sent to the central nervous system, where they are integrated and transformed into the central representations that underlie behavior. Specificity is the result of (a) the type of receptor channel located at the afferent ending (TRP or mechanoreceptive channels), (b) the location and organization of where the ending(s) are located, and (c) on whether the afferent has a specialized structure enveloping the receptor ending. Most of the afferent fibers end bare nerve endings and specificity is based on whether the receptor channel is responsive to temperature (warmth or cold), nociceptive inputs (sharp or dull pain), pleasant sensations, or itch. The rest of the afferents are mechanoreceptors that provide feedback about body forces and body position (proprioception), and information necessary for tactile perception. Tactile perception includes the perception of (a) the local surface features of objects (texture and form)

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PERIPHERAL NEURAL MECHANISMS OF TACTILE PERCEPTION In the next two sections we provide a review of the sensory modalities conveyed by the large diameter A-beta afferents that are divided into the cutaneous mechanoreceptors that innervate the skin and then review the proprioceptive afferents that innervate the joints, tendons and muscles. We show later in the chapter that each of these afferents play a specific functional role in perception.

Supported by grants from the National Institutes of Health (R01 NS34086 and NS18787). We thank Natalie Trzcinski for her helpful comments on the manuscript. 206

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The skin can be segregated into two major components depending on what types of receptors innervate it: (1) the glabrous skin, which is found on the palms, soles of the feet, lips, and the skin around the genitals; and (2) the rest of the body, which is considered to be hairy skin. Glabrous and hairy skins are innervated by different complements of cutaneous mechanoreceptors and as such the neural mechanisms underlying tactile perception from these regions are different. Besides containing the afferent fibers that are observed in glabrous skin (see below), hairy skin contains A-β and A-δ afferents fibers that end in lanceolate endings that wrap around the base of the hairs and unmyelinated low-threshold mechanoreceptive afferent fibers (C-LTMR) that are not found in glabrous skin. These C-LTMR fibers have been linked to cortical areas related to affective sensations that are perceived as being pleasant (Bjornsdotter, Loken, Olausson, Vallbo, & Wessberg, 2009; Olausson et al., 2002). The neural mechanisms underlying sensory perception from hairy skin is not well understood and as such in the rest of this chapter we concentrate on the inputs from glabrous skin of the hand, which has been studied extensively in both humans and nonhuman primates. It is well established that cutaneous perception from the hand is based on signals from four mechanoreceptive afferent types: slowly adapting type 1 (SA1), rapidly adapting (RA), Pacinian (PC), and slowly adapting type 2 (SA2) afferents. Two of the four, the RA and PC afferents, are classed as rapidly adapting because they respond to the transient period when a probe is either indented into or retracted from the skin; they do not respond to sustained indentation of the skin. The other two, the SA1 and SA2 afferents, are classed as slowly adapting because they respond to sustained skin deformation with a sustained discharge that declines slowly. All of these afferent types are highly sensitive to skin movement. The neural response properties of these cutaneous afferents have been studied extensively in both human and nonhuman primates and, except for the SA2 afferents, which have rarely been observed in nonhuman primates, there are no interspecies differences. When we use our hands to explore the environment, all of the peripheral afferent fibers are vigorously active but the responses differ greatly depending on the specificity of the afferent to the stimulus. SA1 afferent fibers branch repeatedly before they lose their myelin and terminate in the basal layer of the epidermis. There, they are enveloped by specialized endings called the Merkelcell-neurite-complex (MCNC) that develop from epidermal

cells. Although there are synapse-like junctions between the MCNC complex and the axon terminals, action potentials arise as the result of mechanosensitive ion channels in the bare nerve endings (Diamond, Mills, & Mearow, 1988; Ogawa, 1996). The MCNC are located at the base of the intermediate ridges (also called the primary epidermal ridges) where the epidermis interfaces with the dermis of the skin (Figure 8.1). At the top of the primary epidermal ridges lie the papillary ridges, which are the ridges that form the fingerprints and are the sites where the sweat ducts emerge from the skin. It is estimated that each SA1 afferent receives inputs from about 5–10 MCNC that innervate multiple ridges. SA1 afferents innervate the skin of the finger pad densely and have small circular receptive fields (RF, ∼2–3

Finger print epidermis

Epidermis

Primary epidermal ridge

Dermis

Merkel Meissner

Meissner

Merkel (MCNC)

PC (a) 10

10 Proximnal Distal (mm)

Cutaneous Mechanoreception

207

RA

SA1

0

0

Left Right (mm) 0

10

0

10 20 30 Fire Rate (Hz)

0

Left Right (mm) 0

10

4 8 12 16 Fire Rate (Hz)

(b)

Figure 8.1 Cutaneous mechanoreceptors. (a) Anatomical location of the Merkel Cell Neurite complex (MCNC) and Meissner corpuscles in the skin. The Meissner corpuscles lie in the pockets between the limiting and intermediate ridges, while the MCNC are located at the base of the intermediate ridges. Right picture shows a cartoon illustration of these mechanoreceptors and a Pacinian corpuscle (PC) and the innervation by their respective sensory afferents (SA-1, RA and PC). (Adapted with permission from Rice & Albrecht 2008.) (b) Receptive field density of a typical SA1 and RA afferents. (Adapted with permission from Hsiao, 2009.)

1.0

10

SA-1

50 0 Bar Width = 0.5mm

RA

0 Bar Width = 0.5mm

0.5

0

−4

−2

0

Y-Distance (mm) (a)

2

4

PC

20 10 5mm 0 Bar Width = 0.5mm (b)

50 50 50 50 50 50

100 80 60 40 20 0

chance 0.0

0.5

1.0

1.5

2.0

Percent correct (Letter identification)

1.5

100

Percent correct (Gap and grating tasks)

694 m−1 521 m−1 340 m−1 256 m−1 172 m−1 80.6 m−1 0 m−1

Fire Rate (Hz)

Normalized Responses

X=0

FR (Hz)

Neural Mechanisms of Tactile Perception

Fire Rate (Hz)

208

2.5

Element width (mm) (c)

Figure 8.2 Spatial sensitivity of SA1, RA, and PC afferents and human psychophysical performance. (a) Curvature response of the SA1 afferents. The graph displays the responses of an SA1 to different spheres of varying curvatures. Data are shown for seven curved surfaces with radii ranging from 1.4 mm (curvature = 694 m−1 ) to a flat surface (curvature = 0 m−1 ). The responses are displayed as a function of proximal-distal distance from the center of indentation. (Adapted with permission from Goodwin et al. 1995.) (b) Responses of monkey SA1, RA, and PC afferents to a grating pressed into the skin. The bars are 0.5 mm wide with the grooves of 0.5, 0.5, 0.75, 1.0, 1.5, 2.0, 3.0, and 5.0 mm wide. (Adapted from Phillips and Johnson, 1981a.) (c) Human performance in gap detection (open circles), grating orientation discrimination (filled squares), and letter recognition task. The x -axis represents the element width for each task, which was gap size for the gap detection task, bar width for the grating orientation discrimination task, and the average bar and gap width within letters for the letter recognition task. Threshold is defined as the element size producing performance midway between chance (50% correct for the gap and grating tasks, and 1/26 for the letter recognition) and perfect performance. (Adapted with permission from Johnson and Phillips 1981.)

mm in diameter, Figure 8.1B) that have a central “hot” spot and a steep decline in sensitivity as the stimulus moves towards the edge of the RF. This gradient in sensitivity to stimulus height is responsible for these afferents being highly sensitive to stimulus curvature (Figure 8.2A). There are about 100 SA1 afferent/cm2 at the fingertips in both humans and monkeys (Darian-Smith & Kenins, 1980; Johansson & Vallbo, 1976; Johansson & Vallbo, 1979b). A characteristic response property of the SA1 afferents is surround suppression (Vega-Bermudez & Johnson, 1999). Surround suppression is mechanical in origin and is based on receptive endings showing sensitivity to a specific component of tissue strain (e.g., strain energy density or a closely related component of strain (Phillips & Johnson, 1981; Srinivasan & Dandekar, 1996; Sripati, Bensmaia, & Johnson, 2006), which presumably activates mechanoreceptive channels located in the afferent endings. As a consequence, SA1 afferents fire vigorously to points, edges, and curves where the stresses are high and are inhibited to stimuli presented to tissue surrounding the edge where the stresses are minimal (illustrated in Figure 8.8). Further, the SA1 afferents are weakly responsive to uniform skin indentation where the local stresses are low (e.g., the horizontally oriented features of the letters in Figure 8.3). As a consequence of these properties, SA1 afferents transmit a

high-resolution isomorphic two-dimensional spatial neural image of stimuli contacting the finger pad (Figure 8.3). RA afferent fibers also branch repeatedly as they near the epidermis. Each RA afferent ends in ∼30–80 Meissner’s corpuscles (Johnson, Yoshioka, & Vega-Bermudez, 2000), and are tethered in the dermal pockets between the intermediate ridges and limiting ridges (Guinard, Usson, Guillermet, & Saxod, 2000; Munger & Ide, 1988) (the tethers cannot be seen in Figure 8.1A but they bridge the

SA-1

RA

PC

Figure 8.3 Responses of peripheral afferents to embossed letters of the alphabet. Responses of SA1, RA, and PC afferents to scanned letters on the finger pad. The figure shows that SA1 afferents transmit a high-resolution isomorphic neural image of stimuli contacting the finger pad. RA afferents also display strong spatial fidelity, but this response is significantly blurred compared to those of the SA1. As a consequence of their largesize spatial RFs, PC afferents convey a poor spatial image of scanned stimuli.

Peripheral Neural Mechanisms of Tactile Perception

corpuscle with the surrounding tissue). Meissner’s corpuscles consist of an encapsulated structure with individual stacked laminar disks (Figure 8.1A) and are innervated by an A-β afferent and a C-afferent, which suggests that they may play a dual role in mechanoreception and nociception (Par´e, Elde, Mazurkiewicz, Smith, & Rice, 2001). The mechanisms that give these afferents their rapidly adapting properties are not known. Meissner corpuscles lie in the dermis as close as possible to the surface of the epidermis (Figure 8.1A), which may explain their high sensitivity to mechanical stimuli (Quilliam, 1978). RA afferents innervate the skin of the finger pad more densely than the SA1 afferents (150 RA afferents/cm2 at the fingertip), which suggests that they may provide a more detailed neural image of the stimulus (Darian-Smith & Kenins, 1980; Johansson & Vallbo, 1976; Johansson & Vallbo, 1976). However, as shown in Figure 8.1B, the RA afferents have larger RF than the SA1 afferents (∼3–5 mm diameter) as well as a more uniform, flat RF profile (Figure 8.1B). Thus these afferents, unlike the SA1 afferents, are unable to resolve spatial details of stimuli below about 3 mm (Figure 8.2B). Continuum mechanics studies (Phillips & Johnson, 1981; Sripati et al., 2006) show that these afferents are maximally sensitive to the horizontal tensile strain in the skin that is not enhanced at edges. Consequently, RA afferents do not exhibit surround suppression (Figure 8.8). As a result of these properties, these afferents also provide an isomorphic representation of two-dimensional stimuli (Figure 8.3) that exhibit less spatial resolution to scanned and statically indented stimuli compared to the SA1 afferents (Figure 8.3). The prominent feature of RA afferents is their high sensitivity to minute skin motion combined with a poor sensitivity to stimulus height. RAs respond to indentations that range from ∼4–400 μm and saturate to stimuli of higher intensity; the comparable SA1 range is about 15–1500 μm or more (Blake, Johnson, & Hsiao, 1997; Johansson & Vallbo, 1979a; Mountcastle, Talbot, & Kornhuber, 1966; Vega-Bermudez & Johnson, 1999). Thus, the RA and SA1 afferents convey complementary isomorphic neural images to the cortex. The RA image is a high-fidelity motion signal with poor spatial acuity and limited dynamic range. SA1 image is a spatially crisp neural image with high spatial resolution and high dynamic range for stimuli that vary in intensity. Because of their high sensitivity to motion, the RA afferents are ideally suited for signaling tactile motion to the cortex and spinal cord. They play an important role in the detection of microscopic movements when an object is slipping across the skin. Detecting slip is

209

important for fine grip control (reviewed in Johnson et al., 2000). Mountcastle and Carli (1972) reported that the frequency threshold detection curves are nearly identical for humans and nonhuman primates. They also showed that low frequencies (below about 100 Hz) are accounted for by the responses of RA afferents, whereas high frequencies above ∼100 Hz are accounted for by the responses of PC afferents (Figure 8.4). As shown in Figure 8.4, the SA1 afferents play no role in vibratory perception. These were the first studies that compared human psychophysics with monkey neurophysiology and demonstrated that if the behavior in the two species is similar, then the neural mechanisms of human perception can be studied directly by comparing human psychophysics with animal neurophysiology. Each PC afferent terminates in a single Pacinian corpuscle, which occurs in the dermis at a significant distance below the MCNC (Figure 8.1A). The history, structure, and electrophysiological properties of this receptor are reviewed by Bell, Bolanowski, and Holmes (1994). The most prominent feature of the PC response is its extreme sensitivity to minute vibratory stimuli (Figure 8.4). These afferents respond to vibratory amplitudes as low as 3 nm that are applied directly to the corpuscle (Bolanowski &

Figure 8.4 Human and peripheral afferent thresholds to vibratory stimuli. Threshold response (I0 ) of the SA1, RA, and PC afferents to vibrations and human vibratory threshold perception (dashed black lines). RA afferents account for the low frequencies (below about 100 Hz) PCs for the high-frequency range. (Adapted with permission from Mountcastle et al., 1972, and Freeman & Johnson, 1982.)

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Neural Mechanisms of Tactile Perception

Zwislocki, 1984) and 10 nm applied to the skin (Brisben, Hsiao, & Johnson, 1999). The corpuscles comprise multiple layers of fluid-filled sacs (Figure 8.1A), and these sacs act as a cascade of high-pass filters that shield the unmyelinated ending from the large, low frequency deformations that accompany most manual tasks (Hubbard, 1958; Loewenstein & Skalak, 1966). In addition, recent studies show that there are GABAergic synapses at the neurite ending and that glutamate is released from the lamina during static indentation (Pawson, Pack, & Bolanowski, 2007). This finding suggests that the response properties of these afferents are not entirely attributed to the mechanical interactions between the neurite ending and the surrounding tissue. The role that these GABAergic synapses play in PC function is not understood. Because of their extreme sensitivity to indentation and because they are located deep in the dermis, PCs have large RFs with boundaries that are difficult to define. Some PCs have RFs that encompass an entire hand or even an entire arm; other PCs have RFs restricted to a single phalanx. There are about 2,500 Pacinian corpuscles in the human hand and they are about twice as numerous in the fingers as in the palm (about 350 per finger and 800 in the palm; reviewed in Brisben et al., 1999). PC afferents transmits little, if any, information about the spatial properties of a stimulus (Figures 8.2B and 8.3). Instead they respond well to high-frequency vibrations that are transmitted through objects, tools, or probes held in the hand (reviewed in Johnson et al., 2000). The fourth type of cutaneous afferent is the SA2 afferents. These afferents have large and poorly defined RFs (Johansson & Vallbo, 1980) that are extremely sensitive to skin stretch in a particular direction (Edin, 1992) and also adapt slowly to sustained skin stretch. In addition, these afferent have very regular firing patterns with narrow interspike interval distributions (Chambers, Andres, von During, & Iggo, 1972; Edin, 1992), suggesting that their mechanism of spike generation is different from the SA1 afferents. The SA2 afferents are distinguished by their exquisite sensitivity to forces orthogonal to the skin (Macefield, Hager-Ross, & Johansson, 1996) and their TABLE 8.1

Proprioception The neural mechanisms underlying proprioception are generally not well understood. The word proprioception is derived from the Latin word proprius, which refers to “one’s own” senses, and in the somatosensory field, it

Summary of Cutaneous Afferents That Innervate the Glabrous Skin

Afferent Type

Receptor

SA1

Merkel-Cell Neurite complex Meissner Pacinian Ruffini (?)

RA PC SA2

poor sensitivity to indentation normal to the skin. It was originally thought that these afferents end in a specialized ending called a Ruffini complex (Iggo & Andres, 1982), which was thought to be an oriented, encapsulated structure with endings that were tied into the dermis of the skin. However recent studies have been unable to find conclusive evidence of Ruffini complexes in the human skin (Pare, Behets, & Cornu, 2003). Thus far, SA2 afferents have been rarely observed in nonhuman primates, indicating that there may be a species difference. SA2 afferents have been reported in the hairy skin of the rodent (Maricich et al., 2009). In those studies the researchers report the existence of a slowly adapting afferent that has uniform interspike interval distributions suggesting that they may be analogous to the SA2 afferents found in humans (Wellnitz, Lesniak, Gerling, & Lumpkin, 2010). Furthermore, they find that while disruption of the MCNC eliminates the SA1 response it has no effect on the SA2 response, suggesting that the end organ for this afferent is not derived from the Merkel cells and is based on some still to be discovered specialized receptor structure. The SA2 afferents respond poorly to scanned patterns and indented curved surfaces, illustrating that they play no role in local form perception (Goodwin, Macefield, & Bisley, 1997; Phillips, Johansson, & Johnson, 1990). However, evidence suggests that they play a role in global shape perception by signaling hand conformation. Because of their sensitivity to skin stretch, SA2s may also play a role in signaling lateral forces incurred on the skin when pulling objects and could also play a role in signaling the direction that objects move across the skin (Olausson, Wessberg, & Kakuda, 2000). A summary of the response properties of the peripheral cutaneous afferents is shown in Table 8.1.

Adaptation to RF Size Steady Deformation

Spatial Resolution

Temporal Sensitivity(Hz)

Slow

Small

0.5 mm

0–100

Rapid Rapid Slow

Small Large Large

3–5 mm 2 cm 1 cm

2–100 10–1000 0–20

Function Form and texture perception Motion perception, grip control Transmitted vibration, tool use Lateral force, hand conformation, motion direction

Peripheral Neural Mechanisms of Tactile Perception

is broadly defined as perception of muscle force, body position, joint angle, and body movements. In addition to the cutaneous afferents described earlier, which contribute toward signaling joint angle, four additional afferent fiber types have been shown to provide proprioceptive information: (1) golgi tendons 1b, (2) joint afferents, (3) muscle spindles Ia, and (4) muscle spindles II. Golgi tendon organs (GTO), which are the end organs of 1b afferents, are located in the tendons of muscles. These receptors lie in series with the muscles, and while they are poorly suited for signaling information about muscle length, they are well suited for signaling information about muscle force. GTOs fire vigorously to isometric contractions of the muscles (Dimitriou & Edin, 2008). They have been closely tied to tendon reflexes. Although GTO provide information about muscle force, they are not solely responsible for the perception of muscle force, which also relies on an efferent copy of the amount of effort needed to lift objects. This explains why objects are perceived to feel heavier when you hold them for extended periods of time. Joint afferents are located in the joint capsules and end in either bare nerve endings or in paciniform structures, which are encapsulated endings. Because they are located in the joints, these afferents were originally thought to be responsible for the sense of joint angle. However, it was shown that joint afferents only respond at the extremes of extension and flexion movements (Burgess & Clark, 1969). Supporting this claim, Clark and his colleagues (1979) showed that anesthetizing the joint capsule and surrounding skin had no effect on the ability of humans to judge joint angle. The current notion is that joint afferents function as limit detectors and provide signals to limit the movements of joints. There are two kinds of muscle spindles, called the type Ia and II afferents. As suggested by their name, they are imbedded in specialized spindle-like structures located in the intrafusal muscle fibers, and are part of the gamma feedback control system used to control muscle movement. Goodwin and his colleagues (1972) showed that vibratory stimuli applied to tendons of the biceps or triceps, which activates the spindle afferents, produced significant misjudgments in the joint angle at the level of the elbow (∼40◦ error judgments). These results suggest that muscle spindles play a significant role in representing joint angle. Another example that illustrates the involvement of these afferents in joint angle perception is the “Pinocchio illusion.” In this illusion, subjects place a finger on their nose and perceive their nose growing as the spindle in the triceps’ muscle is activated. Yet, while several studies

211

provide evidence that muscle spindles may play a role in the perception of joint angle, there are two lines of evidence that argue against this hypothesis. One comes from studies that take advantage of a curious phenomenon that happens when the second and fourth digits of the hand are extended and the third digit is flexed. When the hand is placed in this conformation, the tendons of the muscles to the distal finger pad become entrapped, which disconnects the muscle spindle input to the distal finger pad. Taylor and McCloskey (1990) showed that under these conditions joint angle is preserved in spite of the loss of muscle spindle input, which implies that another afferent must play a role in signaling joint angle. The second line of evidence comes from neurophysiological studies showing that during dynamic movements, muscle spindle afferents provide a poor representation of muscle length and velocity (Dimitriou & Edin, 2008). Studies since the mid 1990s have shifted the focus to the SA2 afferents as playing a critical role in signaling joint angle. Edin (1991) showed that changes in hand configuration caused systematic changes in the patterns of skin stretch across the back of the hand and hypothesized that the nervous system decodes this pattern to encode hand conformation. This hypothesis is supported by a study by Collins (2000), where he showed that skin stretch near a finger produces the illusion of finger movement. Edin (2004) showed that the static and dynamic strain sensitivity of the peripheral SA2 afferents, and another class of fibers that they classified as being SA3 afferents, best matches human perception of joint angle. Thus, evidence emerging from several studies point to the cutaneous SA2 afferents with perhaps some input from the muscle spindle afferents as providing the input needed to signal joint angle. Summary of the Peripheral Afferent Inputs Tactile processing from the hand is initially encoded into several distinct afferent systems with each system conveying a separate isomorphic spatial and temporal pattern of activity to the cortex. The SA1 afferents provide a spatially detailed and crisp representation of the cutaneous inputs, the RAs provide a temporally detailed but spatially blurred representation, and the PC afferents provide a representation that shows high temporal modulation with no spatial structure. Both cutaneous and deep (i.e., proprioceptive) receptors located in the muscles, tendons, and joints provide a detailed representation of hand conformation. In the next section we review the ascending and central pathways in the somatosensory system and

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Neural Mechanisms of Tactile Perception

discuss how shape, texture, and motion is represented in the cortex.

fibers ascend in the ipsilateral dorsal columns to synapse on neurons in the dorsal column nuclei (DCN), where the proprioceptive inputs remain segregated from the cutaneous inputs. Second-order neurons in the DCN cross the midline to form the medial-leminiscal tract that projects to the ventroposterior lateral nucleus (VPL) and ventroposterior inferior nucleus (VPI) of the thalamus (Craig & Dostrovsky, 1999; Jones, 1990; Jones & Friedman, 1982; Perl, 1998; Poggio & Mountcastle, 1960). VPL is organized into a central core region that receives primarily cutaneous inputs with small RF surrounded by an inner shell of neurons that have larger cutaneous RF. These cutaneous regions are surrounded by an outer shell region composed of neurons that have deep or proprioceptive-like inputs (Jones & Friedman, 1982). VPL then projects to primary somatosensory cortex (SI), while VPI, which contains neurons with PC-like responses, projects to secondary somatosensory cortex (SII) (Friedman, Murray, O’Neill, & Mishkin, 1986; Jones & Burton, 1976; Jones & Friedman, 1982).

CENTRAL MECHANISMS OF FORM AND TEXTURE PERCEPTION Ascending and Central Pathways Related to Form and Texture Perception A principle of organization within the somatosensory system is that neurons responsible for the different aspects of sensory perception are separated into separate anatomical pathways that are modality specific. The division begins at the level of the peripheral nerves and continues as axons leaving the dorsal root ganglion send their projections into the spinal cord. The primary pathway for processing cutaneous and proprioceptive mechanoreceptive input is called the dorsal-column medial-lemniscal pathway, which carries the large and medium diameter myelinated fibers (A-α and A-β afferents) to the cortex (Figure 8.5A). These Cerebrum

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Figure 8.5 Central somatosensory pathways. (a) Dorsal-column medial lemniscal (DCML) pathway. The DCML pathway conveys cutaneous and proprioceptive inputs to cortex. Note the decussation at the level of the lower medulla, at the cuneate nucleus, through the internal arcuate fibers. Inputs from the medial lemniscus reach the VPL, which in turn relay information to primary somatosensory cortex (Adapted from Purves et al. 2008). (b) Box diagram of the somatosensory system. Arrows represent feed forward projections. SI and SII cortex have particular subdivisions, which are primarily based on the types of inputs they process. Feedback connections are not shown. (c) SII cortex is located in the upper bank of the lateral sulcus in the parietal and temporal operculum. The lower graph shows parts of the body-map representation of SII cortex in each of the subdivisions (a = anterior, c = central, and p = posterior fields). M = mouth, A = auditory, and HL = hind-limb. (Adapted with permission from Fitzgerald et al., 2004.)

Central Mechanisms of Form and Texture Perception

SI is composed of four areas (3a, 3b, 1, and 2) and all four areas receive parallel inputs from VPL with neurons in area 3a and 2 receiving heavy projections from the shell region and neurons in 3b and 1 receiving inputs from the central and surrounding core of VPL (Figure 8.5B). Areas 3a and 3b can be considered to be the first somatosensory cortical areas since they only receive inputs from VPL. Areas 1 and 2 are considered as being further along the processing pathway since these areas receive inputs from the thalamus as well as areas 3a and 3b. Area 1 also sends its outputs to area 2 (for a review see Felleman & Van Essen, 1991). Based on the pattern of input/output connections, it is thought that areas 3b and 1 are important for processing cutaneous input, area 3a is important for processing proprioceptive input and area 2 is important for integrating proprioceptive and cutaneous input, and thus neurons in area 2 are thought to play a role in representing the global features of objects (Hsiao, 2008). There are two main projections from SI. One is toward area 5, which then projects to area 7b. The other is toward SII, which lies in the parietal operculum in the upper bank of the lateral sulcus (UBLS) (Figure 8.5B, C). Ettlinger and his colleagues demonstrated that monkeys with ablations of SII were unable to perform almost all tactile tasks that required touch (Garcha & Ettlinger, 1978; Ridley & Ettlinger, 1976). In addition, they found that animals that had SII cortex ablated were impaired in their ability to learn new tasks and were unable to do tasks that required intermanual transfer of information. Murray and Mishkin (1984) showed that when SII is ablated, monkeys were unable to discriminate texture (hard versus soft and rough versus smooth), form (square versus diamond and convex versus concave shapes), object orientation (horizontal versus vertical) and were significantly impaired in size discrimination (small versus large objects) as well. These deficits were not observed in animals with area 5 ablations. Area 5 has long been associated with reaching and grasping objects (Mountcastle, Lynch, Georgopoulos, Sakata, & Acuna, 1975; Sakata, Takaoka, Kawarasaki, & Shibutani, 1973). Numerous studies have shown that the neurons in area 5 might be important for translating somatic inputs into a general reference frame for guiding and making prehension movements, and is closely tied to the motor system (di Pellegrino, 2001; Gardner et al., 2007; Iwamura & Tanaka, 1996; Lacquaniti, Guigon, Bianchi, Ferraina, & Caminiti, 1995; Mountcastle, 2005). Besides receiving inputs from SI cortex, SII also receives inputs from areas Ri and 7b (Figure 8.5B). Neurophysiological mapping studies (Fitzgerald, Lane, Thakur, & Hsiao, 2004; Robinson & Burton, 1980;

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Whitsel, Petrucelli, & Werner, 1969), anatomical tracer studies in macaque monkeys (Burton, Fabri, & Alloway, 1995; Diwbrow, Litinas, Recanzone, Padberg, & Krubitzer, 2003; Friedman et al., 1986; Krubitzer, Clarey, Tweedale, Elston, & Calford, 1995) and neural imaging studies in humans (Disbrow, Roberts, & Krubitzer, 2000) show that SII cortex extends approximately 10 mm across the upper bank of the lateral sulcus. In Figure 8.5C we have depicted SII as being composed of three separate areas SIIa, SIIc, and SIIp (Fitzgerald et al., 2004), however the number of areas and organization of SII is still highly debated. Disbrow (2003) proposes that the UBLS is composed of four areas (PR, PV, SII, and 7b) with PV and SII receiving inputs from the cutaneous areas of SI (areas 3b and 1) and sending their projections to the parietal reach region (PR) and 7b. Hsiao and his colleagues showed that in the non-human primate, neurons in the anterior (i.e. SIIa) and posterior (i.e. SIIp) fields of SII respond to both cutaneous and proprioceptive input, while neurons in the central field, (i.e. SIIc) respond mainly to cutaneous inputs (Fitzgerald et al., 2004). Based on cytoarchitectonic and imaging studies in humans, Eickhoff and colleagues (2006) and Burton and his colleagues (Burton, Sinclair, & McLaren, 2008) identified four distinct areas in the UBLS of humans, which were denoted by Eickhoff as the Opercular Parietal regions 1–4 (OP1–OP4). How the different fields from these separate studies register between each other, and across species, is not understood. There is a debate whether SI and SII process somatosensory input serially or in parallel. Evidence from anatomical tracer studies (Friedman & Murray, 1986) and studies where areas of cortex are selectively deactivated by cooling suggest that SI and SII process information in parallel (Murray et al., 1992; Zhang et al., 1996). In contrast, lesion (Pons, Garraghty, Friedman, & Mishkin, 1987) and neurophysiological studies (Burton & Sinclair, 1990; Fitzgerald et al., 2004; Hsiao, O’Shaughnessy, & Johnson, 1993) suggest otherwise. In the cooling studies, Rowe and his colleagues were unable to completely abolish SII responses by cooling SI cortex, thus indicating that SII cortex also receives inputs from other somatosensory areas, such as VPL, and not just from SI (Zhang et al., 1996). In contrast, Pons et al. (1987) showed that when SI is ablated, neurons in SII become silent; whereas when SII is ablated, neurons in SI remain active (Pons, Wall, Garraghty, Cusick, & Kaas, 1987). Other studies showed that selective lesions in the subregions of SI result in modality selective deficits in the response properties of neurons in SII (Garraghty, Pons, & Kaas, 1990; Pons, Garraghty, & Mishkin, 1992). Indeed, as we show later in the chapter, evidence from

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single cells studies supports the hypothesis that SII lies at a higher processing stage than SI. Briefly, we note that neurons in SI tend to have small simple receptive fields confined to one or a few digits on a single hand (Iwamura, Tanaka, Sakamoto, & Hikosaka, 1983; Sur, Wall, & Kaas, 1984). In contrast, neurons in SII tend to have larger and more complex receptive fields that span multiple digits on one or both hands (reviewed further on). Another piece of evidence supporting the serial hypothesis is that almost all of the neurons in SII are strongly affected by the animal’s focus of attention and are more closely related to decision processes (Chow, Romo, & Brody, 2009; Hsiao et al., 1993; Jiang, Tremblay, & Chapman, 1997) suggesting that this area is farther away from encoding the sensory inputs. Where tactile information is processed after SII cortex is not well understood. It is clearly known that many areas beyond SII have somatosensory responses, including sensory areas in auditory (Kayser, Petkov, Augath, & Logothetis, 2005), and visual cortex (such as V1, V4, and MT) (Hagen et al., 2002a; Maunsell, Sclar, Nealey, & DePriest, 1991; Merabet et al., 2004; Merabet et al., 2007). In addition, somatosensory responses have been observed in, prefrontal (Hagen, Zald, Thornton, & Pardo, 2002; Ku et al., 2007), motor (Gardner, Ro, Babu, & Ghosh, 2006), posterior parietal and limbic areas including the insula (Schneider, Friedman, & Mishkin, 1993). More studies are needed to understand the cortical hierarchy in processing and determine the correlation between human imaging studies that implicate in particular multiple parts of the intraparietal sulcus (IPS), lateral occipital complex (LOC), or other extrastriate areas as being important for tactile shape processing (James et al., 2002; Kitada et al., 2006; Lucan, Foxe, Gomez-Ramirez, Sathian, & Molholm, 2010; Peltier et al., 2006). Form and Texture Perception In the following sections we discuss the neural mechanisms underlying local cutaneous form and 3D shape perception, texture, and motion. In each case we will link psychophysical studies with humans with neurophysiological studies and describe how information is initially encoded in the peripheral afferent discharge and how it is transformed and represented in the responses of cortical neurons. Form Perception Two-dimensional form perception is constant over a wide range of stimulus conditions. The ability to discriminate object or surface features and the capacity for pattern

recognition at the fingertip are the same whether the object is contacted by active touch or is applied to the passive hand. Form perception is only marginally affected by moving the object relative to the skin at scanning speeds of up to 40 mm/s; it is unaffected by contact forces at least within the range of 0.2–1 N; and it is unaffected by the relief height of spatial features over a wide range of heights (Johnson & Lamb, 1981; Loomis, 1981, 1985; Phillips, Johnson, & Browne, 1983; Vega-Bermudez, Johnson, & Hsiao, 1991). The evidence discussed below shows that the SA1 system is responsible for encoding and relaying signals about fine form. Figure 8.2C shows the results of three psychophysical studies of human’s ability to discriminate stimuli with the distal pad of the index finger. In all three studies, the element width that resulted in performance midway between chance and perfect discrimination was between 0.9 and 1.0 mm, which is close to the theoretical limit set by the innervation density of SA1 and RA primary afferents at the fingertip. Acuity declines progressively from the index to the fifth finger (Vega-Bermudez & Johnson, 2001) and it declines progressively with age (Sathian, Zangaladze, Green, Vitek, & DeLong, 1997; Stevens & Choo, 1996; Wohlert, 1996). The decline with age is thought to arise from the loss of receptors with age and a subsequent decline in innervation density. Spatial acuity at the lip and tongue is significantly better than at the fingertip (Essick, Chen, & Kelly, 1999; Sathian & Zangaladze, 1996; Van Boven & Johnson, 1994). Tactile spatial acuity is nearly identical across humans and monkeys (Hsiao et al., 1993). Women have higher acuity than men, and shorter people tend to possess finer spatial acuity than tall people because they have higher innervation densities (Peters, Hackeman, & Goldreich, 2009). The differences in height provide a better explanation for differences in tactile spatial acuity than differences between genders. The ability to discriminate gratings and letters with element widths around 1 mm means that at least one of the afferent systems must sustain a neural image with 1 mm resolution or better. That requires an innervation density of at least one afferent per mm2 , and it further requires that individual afferents resolve the spatial details with a resolution that accounts for human discrimination performance. The only afferent that comes close to explaining human performance is the SA1 afferent type (Figure 8.2B). In contrast, RAs are unable to distinguish between grooves finer than 3 mm wide from a flat surface (Figure 8.2B). Further evidence comes from studies using an Optacon, which is a sensory substitution device for the blind (Bliss, 1969). The Optacon has 6 × 24 sets of

Central Mechanisms of Form and Texture Perception

piezoelectric pins that activate RA and PCs well but do not activate SA1 afferents. Kops and Gardner (1996) showed that the threshold acuity when an Optacon is used to stimulate the finger is about 5 mm, which is 5 times greater than the threshold that are found when subjects are tested with embossed patterns that activate the SA1 afferents (Kops & Gardner, 1996). The acuity threshold found with the Optacon is close to the threshold spatial acuity found for the RA afferents (Figure 8.2B), showing that only the RA afferents can account for spatial pattern recognition performance with the Optacon (Gardner & Palmer, 1989; Palmer & Gardner, 1990). Vega-Bermudez et al. (1991) showed that there is no detectable difference in the ability of humans to perform letter identification tasks during active and passive scanning. The accuracies in letter recognition ranged from 15% (for the letter N) to 98% (for the letter I) with subjects showing characteristic patterns of confusions with more than 50% of the confusions confined to 7% of all possible confusion pairs (22 out of 325 possible confusion pairs). The patterns of confusions are similar to what is observed in humans performing a visual letter discrimination task suggesting that the mechanisms of form processing in the somatosensory and visual systems are similar (Apkarian-Steilau & Loomis, 1975; Hsiao, 1998; Phillips et al., 1983). Human letter recognition performance improves steadily with repeated testing (Vega-Bermudez et al., 1991). One possible explanation for the steady improvement is that subjects may learn the idiosyncrasies of the neural representations (perceptual learning) or alternatively there may be plastic changes in the underlying circuitry with repeated exposures to the stimuli resulting in more robust neural representations (Blake, Heiser, Caywood, & Merzenich, 2006). Peripheral Processing of Spatial Form Studies of curvature perception also implicate the SA1 system in form processing (Goodwin, Browning, & Wheat, 1995; Goodwin, John, & Marceglia, 1991). These studies show that estimates of curvature are unaffected by changes in contact area and force. Similarly, it has been shown that estimates of force are unaffected by changes in curvature. This latter finding is particularly surprising considering that SA1 firing rates are strongly affected by curvature and that SA1 mean firing rates provide a neural code for the perception of force (Goodwin et al., 1995; Srinivasan & LaMotte, 1987). Moreover, psychophysical observations suggest that the spatial profile of the LaMotte, 1993 neural activity in one or more of the afferent populations is used

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for the perception of curvature (LaMotte & Srinivasan, 1993). Indeed, only the SA1 population response provides a veridical representation of curvature and accounts for the psychophysical observations (Dodson, Goodwin, Browning, & Gehring, 1998; Goodwin et al., 1995) (Figure 8.2A). RAs respond poorly to such stimuli and provide no useful signals about curvature (Goodwin et al., 1995; Khalsa, Friedman, Srinivasan, & LaMotte, 1998; LaMotte, Friedman, Lu, Khalsa, & Srinivasan, 1998). Cortical Processing of Spatial Form There is strong evidence that area 3b plays a critical role in processing information related to spatial form. Removal of area 3b produces profound behavioral deficits in the ability to discriminate a large number of somatosensory stimuli (Randolph & Semmes, 1974). Further, the cortical magnification factors (unit cortical area per unit body surface area) are largest in the digit representation areas of 3b, where spatial acuity is highest (Sur, 1980). Recently it has been shown using controlled, scanned stimuli and stimuli composed of randomly indented probes that over 50% of the neurons in areas 3b and 1 have orientation-tuned responses (Bensmaia, Denchev, Dammann, Craig, & Hsiao, 2008; DiCarlo & Johnson, 2000; Hsiao, Lane, & Fitzgerald, 2002). Figure 8.6A shows an example of an orientation-tuned neuron in area 3b. Across the population of area 3b neurons, all orientations are about equally represented with the neurons having mean tuning bandwidths of about 67 degrees. This tuning is similar to the tuning that is observed in primary visual cortex. Figure 8.6B shows the result of humans performing an orientation discrimination task where they were presented with two oriented bars and responded whether the bars were oriented differently. It was found that the mean orientation discrimination threshold was about 20◦ (i.e., halfway between chance and perfect performance), which matched closely with the mean neurometric orientation discrimination function of orientation-tuned neurons in area 3b (Figure 8.6B). These results suggest that area 3b and possibly area 1 play an important role in tactile orientation discrimination (Bensmaia et al., 2008; Bensmaia, Hsiao, Denchev, Killebrew, & Craig, 2008). In studies investigating the neural mechanisms underlying spatial form processing in area 3b, (DiCarlo, Johnson, & Hsiao, 1998) scanned random dot patterns across the distal finger pads of awake-behaving nonhuman primates. They found that the spatial responses of neurons in 3b are shaped by neurons having RFs with inhibition as well as excitation. Previous studies have generally failed

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Figure 8.6 Orientation tuning in Area 3b and psychometric functions. (a) The left panel shows a raster plot for a neuron in area 3b, which is tuned to a particular orientation (i.e. ∼67.5◦ ). The right panel shows the mean tuning curve for the raster plot (adapted from Hsiao et al., 2002). (b) Left panel shows the psychometric curves, as defined by the probability in making correct angular judgment differences between different orientations. Blue trace = 0◦ , green trace = 45◦ and red trace = 90◦ . The threshold for perception is about 20 degrees. Right panel depicts the mean neurometric curve for a population of orientation tuned neurons in area 3b. The data was derived by comparing for each neuron the percentage of time the rates evoked by bars of different orientation were different; the curves for individual neurons were then averaged. (Adapted with permission from Bensmaia et al., 2008a & b.)

to identify this inhibition using a simple probe because it is manifested only as a reduction of the response to a stimulus that simultaneously contacts both the excitatory and inhibitory parts of the RF. DiCarlo et al. (2000, 2002) (DiCarlo & Johnson, 2000, 2002) reported that the RF structure of 95% of neurons in area 3b have three components: (1) a single, central excitatory region of short duration (10 ms at most); (2) one or more inhibitory regions that are adjacent to and synchronous with the excitation; and (3) a larger inhibitory region that overlaps the excitation partially or totally and is delayed with respect to the first two components (by 30 ms on average). The remaining 5% have two or more regions of excitation. The RFs of area 3b neurons are illustrated in Figure 8.7A, which shows that virtually all RFs are characterized by a single central region of excitation with inhibition on one, two, or three sides. Complete surround

inhibition occurred rarely. The inhibitory area was, on average, about 30% larger than the excitatory area (means were 18 and 14 mm2 ) and, like the excitatory area, varied greatly (from 1–47 mm2 ). The inhibitory mass (absolute value of inhibition integrated over the entire inhibitory field) like the excitatory mass (comparable definition), varied by 50 to 1 between neurons (125–6,830 mass units; mean 1,620 mass units). There were a wide range of RF structures with no evidence of clustering into distinct RF types. The distributions of excitatory and inhibitory areas and masses were all Gaussian in logarithmic coordinates (i.e., lognormal). Moreover, the excitatory and inhibitory masses were more closely correlated (ρ = 0.56) than were the areas (ρ = 0.26). RFs mapped in this way accurately predict neuronal responses to stimulus features such as orientation (DiCarlo & Johnson, 2000) (Figure 8.1B). Delayed Inhibition Area 3b neurons have two striking response properties that are not evident in Figure 8.7A. The first is that the spatiotemporal structure of their neuronal responses and the spatial structures of their RFs are virtually unaffected by the velocity that a stimulus moves across the skin, or how rapidly a finger is scanned over a surface for velocities up to at least 80 mm/sec (DiCarlo & Johnson, 1999). In effect, increasing scanning velocity causes a marked increase in the intensities of the excitatory and inhibitory subfields without affecting their geometries. This results in increased firing rates without any loss of the spatial response selectivity conferred by the RF geometry. The mechanism of this increased responsiveness with increased velocity lies in the interactive properties between the excitatory hotspot and the delayed inhibitory responses. Delayed inhibition confers a second property, which causes the geometry of the RF to be strongly dependent on the scanning direction (DiCarlo & Johnson, 2000). Regardless of scanning direction, there is a fixed region of inhibition relative to the central region of excitation, and a region of inhibition that trails the region of excitation in the scanning direction. To visualize this more clearly, each neuron’s response was fitted with a three-component RF model comprising a Gaussian excitatory region and two Gaussian inhibitory regions (one which simulated the region of fixed and synchronous inhibition, while another simulated the region of delayed inhibition). DiCarlo and Johnson (2000) showed that this three-component model explains the effect of scanning direction on the RF shape. The correlation between the model and observed RFs averaged 0.81 in 62 neurons studied with four or more scanning directions.

Central Mechanisms of Form and Texture Perception

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Figure 8.7 Linear RFs of neurons in area 3b. (a) Plot shows typical examples of RF types, confined to the distal finger pads, for neurons in area 3b (n = 247). The numbers inside the boxes illustrate the percentage of neurons in this area exhibiting the type of RF structure. For example, 15% of neurons in area 3b, with RF on the distal finger pad, show a central region of excitation that is flanked by two inhibitory fields on each side. (Black-excitatory, white-inhibitory.) (b) Upper graph illustrates sample RF with orientation tuning properties. The lower graph shows the correlation between the orientation of the excitatory and inhibitory fields for a cell population. (Adapted with permission from DiCarlo et al., 1998, 2000.)

Spatial-Temporal Receptive Fields (STRF) Sripati et al. (2006) investigated the STRF of peripheral afferents and SI neurons using a 400 pin stimulator (see Killebrew et al., 2007) that allowed them to dynamically stimulate the neurons without the confounding effects of scanning direction (Sripati, Yoshioka, Denchev, Hsiao, & Johnson, 2006). Figure 8 shows examples of a typical SA1 and RA afferent and a cortical area 3b neuron’s STRF. The SA1 afferent initially has a strong excitatory region flanked by a region of suppression. The suppression in this case is due to skin mechanics and is a reflection of the MCNC being sensitive to the maximum compressive strain in the tissue. The excitatory response is then followed by a significant period of infield suppression. The response of the RA afferent is similar except, as described earlier, these afferents have larger RF and do not show surround suppression. The STRF for a typical 3b neuron captures the three-component model described above. The

RF is initially dominated by a strong excitatory RF with an inhibitory surround that is spatially displaced from the excitatory center. As the excitatory response decays after about 10 ms, the entire RF is dominated by infield inhibition, which is delayed relative to the excitation. The infield inhibition in this example is spatially located at the same location as the excitation because this RF was obtained without scanning. Functional Implications of the Three-Component Model The wide range of RF geometries of area 3b neurons shows that the initial isomorphic neural representation of spatial form that prevails in the periphery in the SA1 afferents (Figure 8.3) gives way to an altered representation with neurons in cortex representing the presence of specific features. The more complex responses observed in SII cortex (Hsiao, Johnson, Twombly, & DiCarlo, 1996) suggest that

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area 3b is an intermediate step in a series of transformations leading to a more complex representation of form (Bankman, Johnson, & Hsiao, 1990; Hsiao, 2008; Hsiao & Thakur, 2009; Phillips, Johnson, & Hsiao, 1988). We hypothesize that the fixed inhibitory components of each neuron’s RF interact with the central excitation to act as a spatial filter, conferring selectivity for particular spatial features or patterns regardless of scanning direction and velocity. For example, when the fixed inhibition lies on two adjacent sides, the neuron is more responsive to corners that protrude into the excitatory subfield without activating the inhibitory subregions. However, when the fixed inhibitory subfield occupies a single location on one side of the excitatory subfield, both fields tend to be elongated and to lie parallel to one another; and as a result, the neuron is more responsive to edges oriented parallel to the two subregions (Figure 8.7B). The delayed inhibitory component serves three functions. First, it confers sensitivity to stimulus gradients in the scanning direction, regardless of the direction. The delayed inhibition suppresses the response to uniform surfaces and thereby emphasizes the effects of changes in the spatial or temporal pattern. When scanning the finger over a surface, features first activate the regions of excitation and fixed inhibition and approximately 30 ms later they activate the lagged inhibition (Figure 8.8). Second, when the delayed inhibition is centered on the excitation, it produces a progressive increase in discharge rate with increasing scanning velocity. This mechanism accounts for the acquisition of tactile spatial information when scanning one’s finger over a surface. It is clearly an advantage to be able +0.5 SA1

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Figure 8.8 Spatial-temporal RF (STRF). Representative examples of a STRF for an SA1 and RA afferent as well as a representative neuron in 3b. First row shows an SA1 displaying surround suppression at the onset of the response that lasts approximately 15–20 ms. The excitatory field is replaced by inhibition after ∼25 ms (i.e., delayed inhibition). RA afferents only display delayed inhibition, with no surround suppression. Neurons in area 3b display both surround and delayed inhibition. (Adapted with permission from Sripati et al., 2006.)

to scan one’s fingers over an object or a surface rapidly without loss of information. In support of this, psychophysical experiments demonstrate that performance in pattern recognition is unaffected as scanning rate increases from 20 to 40 mm/s, and only a small loss is observed as the rate is increased to 80 mm/s. In the absence of a compensatory mechanism, rapid scanning has a substantial cost. As scanning velocity increases, each stimulus element spends less time within the RF (reduced dwell time) and the element is represented by fewer action potentials. The delayed inhibition provides a compensatory mechanism that increases the firing rate with increasing scanning velocity. As velocity increases, the delayed inhibition lags progressively to expose more excitation. Consequently, the excitatory and inhibitory components of the receptive field grow rapidly in intensity with no effect on the RF geometry. The result is a representation of spatial form that is invariant with scanning velocity and is more intense than it would be without this mechanism. Third, and least significant, is that direction sensitivity occurs when the delayed inhibition is displaced from the center of excitation (DiCarlo & Johnson, 2000; Gardner & Costanzo, 1980). This is because motion in the direction of the displacement exposes progressively more excitation, which produces a progressively greater discharge rate. Motion in the opposite direction shifts the delayed inhibition over the center of excitation, thereby reducing the discharge rate. The center of the delayed inhibition in area 3b is, with few exceptions, close to the center of excitation, which may explain why so few neurons in area 3b exhibit directional selectivity. When the center of the delayed inhibition is displaced from the center of excitation, it predicts the neuron’s directional selectivity accurately (DiCarlo & Johnson, 2000). Convergent Inputs Mechanisms of signal convergence are central for constructing accurate percepts of objects and events within and across sensory modalities. As we mentioned above, sensory information in the periphery and the ascending pathways seems to be represented in modality-specific channels. Specifically, form-features seem to be encoded by SA1 afferents while dynamic features such as motion and/or vibrations are encoded by the RA and PC afferents. A fundamental question arises: at what level of cortex do these sensory inputs begin to merge so that central representations of objects begin to take place. To address this question Hsiao and his colleagues (Pei, Denchev, Hsiao, Craig, & Bensmaia, 2009) recorded the responses of neurons in primary somatosensory cortical areas 3b and 1, and characterized the degree of SA1/RA convergence

Central Mechanisms of Form and Texture Perception

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Figure 8.9 SA1/RA convergent inputs in 3b. Representative examples of an SA1 and RA afferent as well as three neurons in SI with different responses to a bar indented and retracted from the center of the RF. First two rows show a typical SA1 and RA response profile, respectively. SA1 are characterized by an initial high firing rate followed by a sustained firing during sustained indentation (stimulus duration 500 ms). RA afferents are discharged only at the onset and offset of the stimulus. Neurons in SI show response profiles that resemble SA1 and RA afferents and a large fraction that display intermediate responses (∼60%). (Adapted with permission from Pei et al., 2009.)

using sustained indented probes as stimuli. The degree of convergence was estimated by assessing the neuron’s response during the sustained period of the stimulus indentation (indicating inputs from SA1 afferents) relative to the off-response evoked by the retraction of the stimulus (indicating inputs from the RA afferents). They found that about 40% of the neurons in areas 3b and 1 showed modality-pure responses (see Figure 8.9), while the rest of neurons displayed properties indicative of converging inputs from the SA1 and RA afferents The results suggest that modality convergence occurs relatively early sensory cortex, and that tactile-object representations may commence at a very early stage of the sensory processing stream. Further, the results suggest that neurons may play dual functions when processing cutaneous inputs.

Representation of Stimulus Curvature As discussed earlier, areas 2 and SII lie further along the processing pathway leading to tactile form processing,

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and as expected these neurons display selectivity to stimulus features besides orientation. Figure 8.10A shows the response from sample neurons in these areas and illustrates that these neurons show tuned responses to curves oriented in a particular direction (Yau, Pasupathy, Fitzgerald, Hsiao, & Connor, 2009). The neuron on the top left was recorded from area 2 and is tuned to curves pointed up and to the right and the neuron on the left was recorded from SIIc and was tuned to curves pointed down and to the left. These responses were then fitted with a circular normal distribution to estimate their level of tuning. About 30% (area 2) and 20% (SII) of the neurons showed significant fits to curved stimuli. The tuned response to curves was similar to what was observed in neurons in area V4 supporting the notion that the mechanisms underlying local form processing in vision and touch are similar. Figure 8.10B shows the population tuning curves for neurons in area 2 and SII cortex, and illustrates that a wide range of curvature tuning curves are represented in the population response of neurons in somatosensory cortex. Processing of Spatial Form in SII Cortex As reviewed earlier, neurophysiological studies support the idea that SII is further along the pathway responsible for processing tactile form. Neurons in SII tend to have large RF that often span multiple digits on the same hand (receptive fields < 10 cm2 ; Robinson & Burton, 1980) and respond to stimulation of both hands (Burton & Carlson, 1986; Cusick, Wall, Felleman, & Kaas, 1989; Fitzgerald et al., 2004; Whitsel et al., 1969). Stimulus selectivity varies widely in SII cortex, in that some neurons are activated by light touch, whereas others are activated by complex stimulation patterns (Burton & Sinclair, 1990; Chapman, Zompa, Williams, Shenasa, & Jiang, 1996; Fitzgerald, Lane, Thakur, & Hsiao, 2006a, 2006b; Hsiao et al., 1993). Fitzgerald et al. (2006b) recorded activity from approximately 1,000 neurons in SII while systematically stimulating all finger pads of digits 2–5 with bars oriented in eight different directions (Fitzgerald et al., 2006a; Fitzgerald et al., 2006b). They observed that there are three basic kinds of neurons in SII (Figure 8.11). The first two were neurons that were not orientation selective but had excitatory or inhibitory RFs typically spanning multiple finger pads. The third were neurons that had orientation-tuned responses on one or more finger pads. An example of a tuned neuron is shown on the right in Figure 8.11A. What was remarkable for these types of neurons is that

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Figure 8.10 Representation of stimulus curvature in somatosensory cortex. (a) Curvature tuning response for a neuron in area 2 (left panel) and SII cortex (right panel). The neuron in area 2 shows tuning for stimuli pointing towards ∼0◦ (stimuli in fifth column), while the SII neuron shows preference for stimuli pointing towards ∼225–270◦ (stimuli in the second and third columns). Shading inside the boxes with the curved stimuli denote the neural response for that particular stimulus (darker colors signify greater firing). (b) Population tuning curves for neurons in area 2 (left panel) and SII cortex (right panel). The tuning curves show that neurons in both areas can account for curvature stimuli spanning all directions of the circular range. (Adapted with permission from Yau et al., 2010.)

the orientation tuning was highly similar across the finger pads, suggesting that these neurons play a role in integrating information about edges that span multiple fingers. Figure 8.11B shows a sample of 45 RFs of the three types of neurons in the three fields of SII cortex. What stands out about these RF is the diversity in the types and variety of the RFs. For instance, some neurons only responded to a single finger pad, while others responded to stimulation across all finger pads. Further, the RF of neurons that showed orientation-tuned responses often had mixed responses from the different pads with some pads being excitatory and others being inhibitory. There is diversity in the number of pads that displayed tuned responses. About 30% of the neurons in SII had finger pads with one or more orientation-tuned responses. These findings suggest that, like the diversity in RF structures that were observed in area 3b for coding features from single fingers, SII

neurons show a similar diversity with RFs that span multiple fingers. Thakur et al. (2006) investigated the neural mechanisms underlying the orientation tuning properties and assessed whether tuning could be explained by neurons with linear RFs -like area 3b neurons (Thakur, Fitzgerald, Lane, & Hsiao, 2006). Figure 8.12A shows examples of two neurons that showed orientation-tuned responses to bars placed at multiple locations on a single finger pad. The tuning for the neuron shown on the left of Figure 8.12A was explained by the neuron having a local region of excitation, which was called “hot”-spot tuning. In contrast, the neuron shown to the right in Figure 8.12A displayed orientation tuning independent of where the bar was indented in the skin. Figure 8.12B shows an example of RFs of six neurons plotted in the form of orientation vector fields. Each small vector in these plots represents

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Figure 8.11 Types of neurons in SII cortex. (a) Three types of cutaneously driven neurons can be observed in SII cortex. The left panel shows the response profile of an untuned excitatory neuron. This neuron has an excitatory RF over medial and proximal pads of D2, D3, and D4, however it does not respond differentially to a particular orientation. The middle panel has an inhibitory RF over all pads and digits of the hand. Similar to the untuned excitatory neuron, it does not respond preferentially to a particular orientation. The right panel shows an orientation-tuned neuron with its RF over the distal pads of D2, D3, D4, and D5, as well as the medial pads of D2 and D3. This neuron shows a preferential response to a bar oriented ∼90◦ . This tuning diminishes as the bar is positioned away from the RF hotspot (distal pads of D2 and D3). (b) Diagram of the response profile of each neuron type in each subdivision of SII (n = 135). Each box corresponds to the digits (2–5 in the x -direction) and finger pads (proximal-distal in the y-direction). Purely red squares indicate untuned excitatory response, purely blue squares indicate untuned inhibitory response, and squares (red or blue) with a white bar indicate preferential response of that pad to the bar. (Adapted with permission from Fitzgerald et al., 2010.)

the predicted tuning at each location on the skin. Graphs A and B (shown in the inset) are examples of neurons that had position invariant. These neurons exhibit nonlinear responses that cannot be explained by the linear summation of inputs from excitatory and inhibitory subregions. However, neurons shown in Figures 8.12C and D had linear responses with tuning explained by a “hot” (i.e., red, excitatory) or “cold” (i.e., blue, inhibitory) RF. Some neurons had spatially mixed regions of excitation and inhibition (e.g., neuron in Figure 8.12E) and others had mixed inhomogeneous regions of excitation and inhibition that had no simple explanation (Figure 8.12F).

Taken together, these studies suggest that as the information progresses along the somatosensory pathway, the responses of neurons become more complex and nonlinear, with spatial invariance to features, such as oriented bars or curves, placed anywhere within the neuron’s RF. Texture Perception Multidimensional scaling studies show that texture perception with the bare finger is dominated by two independent dimensions (soft-hard and smooth-rough) (Hollins, Bensma¨ıa, Karlof, & Young, 2000; Hollins, Faldowski, Rao, & Young, 1993; Yoshioka, Bensmaia, Craig, &

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Figure 8.12 Vector fields and orientation tuning in SII. (a) Top two graphs show the response of two neurons to oriented bars that are indented at different locations on the finger pad. The scale on the right side of the raster shows the position of the bar relative to the center of the RF. The pattern of stimulation is shown in middle figure. The bars were presented eight orientations on the finger spaced 1 mm apart. Neuron X responded only when the bar was in the center of the pad indicating that it had a central excitatory RF on the finger. Neuron Y was tuned to horizontal bars independent of where it was placed on the finger, illustrating that it had a position invariant response. (b) Orientation tuning in the form of vector fields for six orientation-tuned neurons (labeled A–F). Each subfigure consists of a vector plot showing the predicted orientation tuning at each location on the skin. To the right of each vector plot are three plots showing top: orientation-tuning RF for the neuron across the hand with each small box representing the tuning of a single finger pad as in Figure 8.11; middle: the orientation-tuning curve for the neuron when the bar was centered on the finger pad; and bottom: the best predicted linear RF for the neuron. Graphs A and B show neurons with position-invariant responses. Graph C shows a neuron with an excitatory field marked by the red circle, graph D shows a neuron with a center inhibitory field marked as a blue circle, graph E shows a neuron with regions of excitation and inhibition, graph F shows a neuron with a complex excitatory/inhibitory RF. (Adapted with permission from Thakur et al., 2006.)

Hsiao, 2007). In addition, a third dimension (stickyslippery) has been shown to improve the multidimensional scaling fit, but its contribution to the overall scaling model is significantly less. Roughness Roughness (or its reciprocal, smoothness) perception has been studied extensively (Blake et al., 1997; Connor,

Hsiao, Phillips, & Johnson, 1990; Connor & Johnson, 1992; Hsiao, Johnson, & Twombly, 1993; Lederman, 1974; Meftah, Belingard, & Chapman, 2000; Stevens & Harris, 1962; Yoshioka, Gibb, Dorsch, Hsiao, & Johnson, 2001; Yoshioka et al., 2007). Roughness perception lies along a single dimensional, which means that subjects are capable of making greater than or less than judgments when estimating the roughness of a surface. The relationship

Central Mechanisms of Form and Texture Perception

between roughness and the height, diameter, shape, and density of the individual elements of the surface is complex. Perceived roughness during active scanning is invariant to velocity, and contact force, and friction between the finger and an object’s surface has minor effects on roughness magnitude judgments (Lederman, 1974; Taylor & Lederman, 1975). Perceived roughness during passive scanning (i.e., when the hand is held stationary and the surface is moved) shows a mild increase with scanning velocity (Cascio & Sathian, 2001). However, there is no change in perceived roughness when the hand is moved by the experimenter across the surface at different velocities (Yoshioka, Craig, Beck, & Hsiao, 2011), demonstrating that proprioceptive input interacts with roughness perception. The neural mechanisms of roughness perception have been studied in a series of combined psychophysical and neurophysiological studies (reviewed in Johnson et al., 2000). Those studies show that when surfaces are contacted with the bare finger, the perceived roughness depends solely on the spatial variation (mean absolute difference) in firing rates between SA1 afferents with RF centers separated by 2–3 mm. This conclusion was derived at using the method of successive falsification (Platt, 1964; Popper, 1959),) whereby psychophysical data from human subjects were tested against neurophysiological data recorded from monkeys using the exact same stimuli and stimulus conditions. In this method, multiple working hypotheses were proposed and a hypothesis was rejected only when there is no consistent (one-to-one) relationship between the neural measure and human performance. The favored hypothesis is the one that survived the hypothesis testing. The first study (Connor et al., 1990) rejected neural codes based on mean firing rate; the second study (Connor & Johnson, 1992) rejected all neural codes that depend on the temporal fluctuations in firing rates of the afferent fibers; the third study (Blake et al., 1997) rejected all neural codes based on the firing of RA afferent fibers; the fourth study (Yoshioka et al., 2001) rejected all neural codes based on the firing of PC afferent fibers. The only neural code that consistently accounts for human roughness perception over surfaces with individual elements spaced 0.1 to 6.2 mm apart and with heights ranging from 0.28 to 2.0 mm is one based on spatial variation of firing among SA1 afferent fibers. The correlation between the psychophysical roughness magnitude estimates and spatial variation in the SA1 discharge was greater than 0.97 in all four studies (see Figure 8.13). The range of stimuli that was accounted for by the SA1 spatial variation measure ranged from surfaces that felt smooth to surfaces that felt very rough. Hollins (2000)

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showed that humans are able to discriminate the roughness of fine surfaces below 100 microns, which weakly drive the SA1 afferents, and postulated a duplex theory for roughness with surfaces with structures greater than 100 microns coded by SA1 afferents and finer surfaces mediated by the PC afferents (Hollins & Risner, 2000). However, PC afferents are strongly driven by all of the surfaces that were studied by Johnson and Hsiao, and it is not clear why this signal is ignored in most roughness discrimination tasks, which argues against the duplex theory. An unresolved question is: If roughness is carried by a spatial mechanism then why are there differences in perceived roughness during active and passive scanning? Goodwin and Sathian (1989) showed that SA1 afferents increase their firing rates with increases in scanning velocity. Thus, it is expected that increases in scanning velocity should produce increases in the firing of the SA1 afferents with a corresponding increase in the spatial variation in the SA1 afferents. One explanation for these scanning differences comes from a recent study (Yoshioka et al., 2011) showing that perceived roughness increases with passive scanning when subjects hold their finger stationary or hold a stationary probe and the surface is scanned at increasing velocities. However, in both active and pseudoactive scanning modes in which the experimenter moves the subject’s arm, the perceived roughness is unchanged with changes in scan velocity. The results suggest that proprioceptive input normalizes the effect of self-motion to allow a surface to feel constantly rough independent of whether it is scanned with the bare finger or with a probe. As described earlier, neurons in area 3b have RF composed of spatially separated regions of excitation and inhibition (Figure 8.7). While these neurons act as spatial filters to extract information related to spatial form, a subpopulation of the neurons have discharge rates that are proportional to the spatial variation in firing rates of the SA1 afferents. Indeed, studies have found that the mean firing rates of many 3b neurons account for the perception of roughness (Chapman, Tremblay, Jiang, Belingard, & Meftah, 2002). Studies using active and passive scanning of gratings find that many neurons in SII are sensitive to scanned textured stimuli (Jiang et al., 1997; Pruett, Sinclair, & Burton, 2000; Sinclair & Burton, 1993). These studies demonstrated that the firing rates of many neurons in SII show monotonic increases (or decreases) in firing rate as the spatial period of a grating is increased. One of these studies (Jiang et al., 1997) reported that the majority of the neurons in SII that responded to the textured surfaces showed responses that were more related to the differences

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Figure 8.13 Neural coding of roughness: Spatial variation of SA1 neurons and human psychophysical performance. Human subjective magnitude roughness estimates and measures of spatial variation in firing rates among SA1 afferents of the monkey in four studies that used different textured surfaces. The left ordinate in each graph is the mean reported roughness. The right ordinate is the spatial variation in SA1 firing rates. The surface pattern used in each study is illustrated below the data to which it applies. The top row shows results from Connor et al. (1990), who used 18 raised-dot patterns with different dot spacings and diameters. The middle row shows results from Blake et al. (1997a), who used 18 raised-dot patterns with different dot heights and diameters. The two left graphs in the bottom row show results from Connor and Johnson (1992) who varied pattern geometry to distinguish temporal and spatial neural coding mechanisms. The right graph shows data from Yoshioka et al. (2001), who used fine gratings with spatial periods ranging from 0.1 to 2.0 mm. The lines connect stimulus patterns with constant spatial periods. (Adapted with permission from Blake et al., 1997a; Connor et al., 1990; Connor & Johnson, 1992; Yoshioka et al., 2001.)

Central Mechanisms of Form and Texture Perception

in spatial periods in animals performing a match to sample task than to the spatial period of the gratings, suggesting that SII cortex may also play a cognitive role in perception

Softness Softness (or its reciprocal, hardness) is the second major dimension of texture (Hollins et al., 2000; Yoshioka et al., 2007). The perception of softness is not to be confused with the perception of compliance. Although both are dependent on changes in contact force, compliance is a physical property of the surface being touched, and consequently discrimination of compliance, like dot spacing, is objective (i.e., can be scored for accuracy). In contrast, softness, like roughness, is subjective and is the progressive change in conformation of a surface as it wraps around the finger as the finger is pressed into the surface. Perceived softness does not necessarily depend on the relationship between force and object displacement—for example, the fact that the space bar on a computer keyboard gives way easily (is compliant) does not make it soft. The neural mechanisms of softness perception have not been studied systematically. Except for a study by Harper and Stevens (1964), most psychophysical studies have focused on the objective ability to discriminate compliance. Harper and Stevens showed that subjective softness judgments were related to the compliance of their test objects by a power function with exponent 0.8 and that hardness and softness judgments were reciprocally related. Srinivasan and LaMotte (1995) used cutaneous anesthesia and various modes of stimulus contact to show that cutaneous information alone is sufficient to discriminate the compliance of objects with deformable surfaces. Subjects discriminate softness when an object is applied to the passive, immobile finger as accurately as when they actively palpate the object. Moreover, they showed that this ability is unaffected when the velocity and force of application are randomized. There are no combined psychophysical and neurophysiological experiments that systematically address the neural mechanisms of softness perception. However, we can estimate the putative mechanism from our understanding of the response properties of each of the afferent types. Just as in roughness perception, the a priori possibilities are intensive (linear increase in firing rate), temporal, or spatial neural codes in one or more of the cutaneous afferent populations. Intensive codes are unlikely because random changes in velocity and force, which do not affect discrimination performance, have strong effects on afferent impulse rates (Srinivasan & LaMotte, 1996). Further,

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purely temporal codes seem unlikely because perceived softness (or hardness) is based on perceived changes in object form with changing contact force. Thus, the most likely mechanism is that softness is based on the dynamic changes in the pattern of SA1 afferents that occur when the finger contacts a surface. Neural Mechanisms of Motion Perception Studies indicate that motion directional signals are absent in the peripheral afferents but they arise at the level of SI cortex, with neurons in areas 3b and 1 playing a major role in signaling motion. Whitsel and colleagues (1971) showed that neurons in area 1 exhibit significant sensitivity to motion signals, and that pyramidal neurons in layers III and V are critical components mediating the perceptual image of these signals. The authors further showed that directional-specific responses were not observed in firstorder afferents (See also Pei, Hsiao, Craig, & Bensmaia, 2010) or in layer IV in cortex, which led them to conclude that motion perception might arise from intra and crosscortical interactions. A follow-up study by Costanzo & Gardner (1980) revealed several types of motion directional neurons in the hand (hairy and glabrous skin) and forearm regions of areas 1 and 2. The authors categorized these cells based on their sensitivity to motion stimuli as: (a) multidirectional neurons (∼37%), which responded vigorously to motion stimuli traveling in different directions, but were preferentially excited by one particular direction; (b) unidirectional cells (∼56.5%), which exhibited strong responses to one direction only; and (c) opponent direction neurons (∼6.5%), which were cells that increased their firing rate to the preferred direction and decreased firing rate (below baseline level) to the antipreferred (least-preferred) direction. The authors also reported that tuning was more prominent and tapered in neurons whose RF was confined to the hand, and that neurons within this skin area had preferred directions spanning the entire circular range (i.e., 0–359◦ ). In contrast, the authors showed that neurons in the forearm (∼67%) tended to have direction preferences that were oriented along the distal-proximal axes. In a more recent study, Pei et al. (2010) showed that while a large number of neurons in areas 3b (∼58.5%) and 2 (∼30.6%) responded to motion signals, only those in area 1 (∼58.8%) were invariant to the spatial form of the stimulus and accounted for the psychophysical performance of human subjects. In addition, the authors found that these neurons exhibited properties that were highly similar to neurons in middle-temporal cortex (MT) that are sensitive to visual

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motion. Figure 8.14A shows an example neuron in area 1 that is tuned to a particular direction, with the tuning strength linearly increasing with the coherence of motion between the dots. In a separate study, Pei et al. (Pei,

Hsiao, Craig, & Bensmaia, 2011) investigated the similarities between motion-sensitive neurons in area 1 and MT. The authors probed the responses of area 1 neurons to tactile moving plaid stimuli, which were composed of two component gratings that moved in different directions, but produced the percept of a stimulus moving in one direction only (Figure 8.14B). Resolving pattern motion from component motion is related to the aperture problem whereby neurons with small RF are unable to “see” the entire pattern. The data showed that a population of neurons in area 1 solves the aperture problem. Further, there were three types of neurons based on their firing-rate properties to the plaid stimuli (see Figure 8.14B): (1) Component neurons that responded to the individual components of the plaid, (2) pattern neurons that integrated the motion signals from the two components and had unimodally distributed responses, and (3) mixed neurons, which had intermediate responses. Importantly, similar to visual neurons in MT, the authors found that the responses of these motion-selective tactile neurons were best accounted for by a modified vector-average model (see Pei et al., 2011, for details). Neural imaging and psychophysical studies provide evidence that tactile and visual motion signals interact during perception. This has led to the proposal that motion signals in these two senses may be mediated by similar neural mechanisms and possibly analyzed in overlapping neural areas. In particular, Konkle et al. (2009) implemented a crossmodal motion adaptation paradigm, between vision and touch, to assess whether signals from the visual modality influence processing of tactile motion, and vice versa. The data showed that repeated exposure to visual motion in a particular direction produced the illusion of a tactile object moving in the opponent direction (i.e., a visual-tactile motion aftereffect). Similar results were obtained in vision when subjects were exposed to continuous stimulation of a tactile moving object. Further highlighting the similarities between the visual and tactile systems, Pei et al. (2008) showed that tactile motion perception is initially determined by the local motion detectors (e.g., edges or corners), which are subject to the aperture problem. However, as more information is accumulated across time, the sensory conflict between the terminator cues and other local motion cues is resolved, thus yielding an accurate perception of the direction that the object is moving. Indeed, a suitable candidate region for processing moving stimuli from both senses is MT cortex, which receives inputs from S1 (Cappe & Barone 2005) and V1 (Maunsell & Van Essen 1983), and has been shown in several imaging studies to be sensitive to tactile as well as visual motion (Blake, Sobel, & James,

Central Mechanisms of Form and Texture Perception

2004; Hagen et al., 2002b; Summers, Francis, Bowtell, McGlone, & Clemence, 2009). Neural Mechanism of Global Shape Processing We effortlessly recognize and manipulate objects with our hands. As illustrated in Figure 8.15, object recognition depends on integrating the cutaneous inputs where the hand contacts the object with proprioceptive inputs, which provide information about where the contact points are located in three-dimensional space. As discussed earlier, at each contact point the cutaneous afferents provide a view about the local surface features of the object, including the local form and texture, as well as information about whether the object is stationary or moving. Determining the global properties of size and shape requires that the multiple views be integrated to form a three-dimensional representation of the object. Relatively little is known about how cutaneous input is integrated across fingers and finger pads. Psychophysical studies suggest that the fingers operate independently when integrating cutaneous inputs across fingers. Craig (1985) showed that patterns presented with an Optacon (which activates only RA and PC afferents) to a single finger pad are identified more accurately and rapidly than when the same patterns are divided in half and presented to separate fingers on the same hand. In addition, Loomis and Klatzky (1991) compared the ability of human subjects to identify two-dimensional raised patterns when either viewed through a small aperture or touching the pattern with a single finger. They found that while doubling the size of the visual aperture (simulating touching the surface with two fingers) greatly improved visual recognition, touching the patterns with two fingers had essentially

Figure 8.15 Mechanisms of global shape processing. General framework of how global shape processing operates. Left shows a hand grasping a round object. Right, there is no perception of the object when the hand is in the same conformation without the object. The difference between the two cases is that when the object is present, the cutaneous receptors at each finger pad code for the local features of the object. Each contact point provides a local view of the object. The local features are integrated in cortex with proprioceptive input to represent the object. (Adapted with permission from Hsiao et al., 2008.)

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no effect on tactile recognition. These results demonstrate that there appears to be minimal integration of cutaneous inputs across fingers from the SA1 afferents when performing spatial discrimination tasks and as such the hand can be considered to have five “foveas”—one for each finger that independently process information about local spatial features. Other studies, however, show that information about global features such as the curvature of large surfaces is enhanced when presented to multiple fingers, or when surfaces are scanned (Davidson, 1972). Pont et al. (1999) tested the ability of subjects to detect broad curvatures that spanned multiple fingers using static and dynamic touch. They found that discrimination of curvature using multiple fingers of these broad surfaces is significantly better compared to discrimination using a single finger. We hypothesize that local features, that require input from cutaneous afferents are processed differently than global features that require input from both cutaneous and proprioceptive afferents (Hsiao, 2008). Generalized object recognition involves integrating information about both global and local features of objects. For example, Klatzky (1993) showed that humans can recognize 100 common objects without visual input with an accuracy of ∼96%. Subjects reported that their decisions were based on two to three feature dimensions that included local features, such as texture, and global features, such as the size and shape of the objects. Skilled hand movements during recognition and manipulation of objects are highly purposeful and task specific. Lederman (1996) showed that humans use a characteristic set of eight hand postures and movements, called exploratory procedures (EP), when performing object recognition tasks. Each of these stereotyped movement patterns are postulated to be ideally suited to extracting qualities or features of objects. For example, back and forth lateral movements of the fingers are used to extract information about surface roughness, applying pressure to the surface is used to extract information about hardness and softness, and moving one’s fingers around the object and enclosing the hand around the object are used to extract information about object shape. A different set of hand conformation patterns are used when the hand is used to grasp and manipulate objects. During manipulation tasks it is essential that objects are held with stable hand conformations using grip forces that are balanced across the hand. The hand conformations used during these tasks depend on the size and shape of the object as well as the purpose of the grasp. For example, power grips, where all five fingers are wrapped around objects, are used to manipulate heavy objects; key grips,

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where the distal pads of digits 1, 2, and 3 encapsulate the object, are used for holding keys or turning objects; and pencil grips, where the distal pads of digits 1, 2, and 3 are used to hold objects, are used for gripping and fine manipulation of long cylindrical objects like probes or pens. Several labs have reported that, although the hand has about 22 degrees of freedom, hand movements used during object exploration and manipulation is captured by a smaller set of movement called synergies (Santello, Flanders, & Soechting, 1998; Santello & Soechting, 2000; Thakur, Bastian, & Hsiao, 2008) that capture much of the variance of typical hand motion. For example, Thakur et al. (2008) showed that only seven synergies are needed to capture more than 90% of the variance of movements during object exploration. Furthermore, they show that these synergies are highly conserved across subjects, suggesting that a “language” of characteristic hand motions are used in everyday movements. Psychophysical evidence shows that we have an accurate perception of object size and that the representation of size involves proprioception. In particular, Santello (1997) showed that subjects can accurately match the size of an object that they scanned with their eyes or held in between their index finger and thumb. Furthermore, Berryman et al. (2006) reported that cutaneous and proprioceptive inputs highly interact during perceptual judgments of haptic size. Specifically, the authors showed that by blocking (or altering) nerves that encode cutaneous-like inputs, subjects’ ability to accurately determine the size of objects is severely hampered. Furthermore, the authors found that object size perception is based on, at least in part by, the distance between fingers at the initial contact, and as such compliant objects are perceived as the same size as hard objects. The neural mechanisms of size and shape perception are poorly understood; however, recent studies suggest that it involves neurons in SI and SII cortex that integrate cutaneous and proprioceptive input. As discussed earlier, there is a progressive increase in RF size as information flows from SI to SII. Furthermore neurons in area 2 and in SII cortex are modulated by both cutaneous and proprioceptive input. In Figure 8.11 we showed that a majority of neurons in SII cortex have complex RF composed of excitatory and inhibitory finger pads. Furthermore we showed that about 30% of the neurons had orientation-tuned cutaneous RF. A working hypothesis is that these RF are the processing kernels that underlie tactile shape and size perception. The hypothesis predicts that different combinations of these neurons will be active when the hand is

in contact with different objects and that those neurons are then matched to previously stored representations of objects (Fitzgerald et al., 2006a; Fitzgerald et al., 2006b; Haggard, 2006). Flutter, Vibration, and Texture Perception With a Probe As described earlier in the chapter, Mountcastle (1972) and his colleagues showed that the sense of vibration is carried by the RA afferents for low-frequency vibrations (called flutter) that are below about 100 Hz and by the PC afferents, which carry information about the highfrequency input (which we will refer to as vibration). A long-standing controversy has been to understand how flutter and vibration are coded and represented in the central nervous system. Mountcastle (1990) initially proposed that the brain uses a periodicity code to represent flutter. That is, frequency is represented as a temporal code whereby neurons showed phase-locked responses to the input and the brain decoded the frequency by determining the mean interspike interval between the activities of RA-like neurons in SI cortex (Figure 8.16). More recently, Romo and his colleagues questioned whether periodicity coding was in fact the basis upon which animals make perceptual decisions about vibration frequency (see de Lafuente & Romo, 2006, for a review). In a series of studies they trained nonhuman primates to discriminate flutter stimuli. They find that neurons with RA–like responses linearly-increase (and sometimes decrease) their firing rates with increases in frequency, suggesting that flutter frequency may be represented as a rate code instead of a periodicity code. Further, Salinas et al. (2000) reported that while SI neurons reveal high fidelity to the periodicity model, SII neurons do not. Moreover, they revealed that a number of neurons in SI and SII cortex displayed firing properties that followed the rate code, and that only firing-rate modeled data covaried with the animal’s psychophysical performance. Interestingly, responses to flutter stimuli in SII show that about 40% of neurons decrease their firing with increases in frequency (Figure 8.16). In addition, neurons in SII are more affected by the behavioral relevance of the stimuli (see e.g., Hsiao et al., 1993), but this will be discussed later on in this chapter. The question remains, however, of how intensity is represented if frequency is represented by firing rate. In fact, Johnson (1974) and more recently Muniak et al. (2007) report that vibration intensity is represented as a rate code in the population response of the peripheral afferents, which leads to the question

Central Mechanisms of Form and Texture Perception

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Figure 8.16 Neural mechanisms of flutter perception. Neural responses to vibratory stimuli in the flutter range. Upper graphs show mean firing rates of one neuron that increase its response with higher frequencies (left panel), and a neuron that shows the opposite pattern (right panel). These neurons encode flutter stimuli with a rate-code. The lower panels show neurons that have phased-locked responses to flutter stimuli, while keeping their mean firing rate constant. These neurons encode flutter stimuli with periodic-coding mechanism. (Adapted with permission from Mountcastle et al., 1972, and Salinas et al., 2000.)

of how rate can code for both intensity and frequency in cortex. When we use a tool or probe, we perceive distant events almost as if our fingers are in direct contact with the working surfaces of the tool or probe. An early demonstration of this was made by (Katz, 1925), who showed that humans can discriminate the texture of a surface through the use of a probe or with a finger applied directly to the surface. He showed further that this capacity is lost when vibrations in the probe are damped. A study by Brisben et al. (1999) showed that when subjects grasp a probe, transmitted vibrations with amplitudes below 10 nm can

be detected (the mean is 30 nm). Only the PC afferents can account for this capacity (see e.g., Figure 8.4). Furthermore, Yoshioka (2007) recently investigated the number of perceptual dimensions when subjects made probe-based texture discriminations. They found that, like in the case of bare fingers, texture information is captured by two and possibly three dimensions (rough-smooth, hard-soft, or sticky-slippery). The working hypothesis is that the PC afferents are responsible for the perception of complex vibrations transmitted through objects held in the hand and allow humans to discriminate the texture of surfaces with probes. Indeed, human psychophysical studies

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of the human ability to detect and discriminate complex vibratory stimuli supports this idea hypothesis by showing that humans are sensitive to the temporal structure of high-frequency stimuli that only activate PC afferents (Formby, Morgan, Forrest, & Raney, 1992; Lamore, Muijser, & Keemink, 1986; Weisenberger, 1986). For example, humans can discriminate 250-Hz amplitudemodulated signals for modulation frequencies as high as 60 Hz (Formby et al., 1992). Bensma¨ıa and Hollins (2000) showed that the discrimination of complex waveforms composed of high frequencies is poor and they propose that RA afferents may play a role in temporal coding. There have been several studies examining the responses of neurons in SII to vibration and to textured patterns. Two separate studies that used vibratory stimuli report that many neurons in SII show phase-locking to both low- and high-frequency vibrations (Burton & Sinclair, 1991; Ferrington & Rowe, 1980). The degree of phase-locking was greater for SII neurons than for SI neurons, suggesting that vibratory information may go directly to SII from the thalamus.

MECHANISMS OF ATTENTION IN TOUCH Research shows that selective attention is limited, and that it operates by honing-in on the relevant properties of a given stimulus and extracting those features that are most relevant to one’s current goal. Attention is particularly useful because it allows for dynamic and rapid allocation of neural resources to stimuli that are potentially threatening or rewarding. The leading tenet proposes that attention is able to accomplish such a difficult task by concomitantly enhancing and suppressing the relevant

and irrelevant stimuli, respectively. In laboratory settings this phenomenon has been classically studied by employing cueing paradigms, whereby a symbolic cue instructs the participant to direct their attention to a particular spatial or body location (or modality), and after a short period of time a stimulus is presented. Then, subjects typically perform a detection or discrimination task. The effects of attention are measured by comparing the behavioral and/or neural responses when the target stimulus is presented in the cued versus an uncued location. The Focus of Attention Several studies show that, like in vision and audition, accuracy and reaction time (RT) are significantly improved when the attentional focus is already at the location where the target stimulus is presented, or when the attentional focus is biased to the same modality or feature represented by the target stimulus. Chapman and her colleagues found that attention influenced behavior when subjects discriminated stimuli between different sensory modalities. In their study, subjects were asked to direct or divide their attention between touch and vision, and discriminate a target stimulus that was subsequently presented (vibration or tactile texture, or luminance). They found that the fastest RT was when subjects were cued correctly to the same modality as the target stimulus (see Post & Chapman (1991); Zompa & Chapman, (1995). Sinclair et al. (2000) observed that tactile features also exhibit similar effects of selective attention. In their study, the subjects were cued to attend to particular features of a tactile stimulus (vibration, roughness, or stimulus duration) and observed that validly cued trials resulted in enhanced performance compared to neutrally or invalidly cued trials (Figure 8.17A).

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Figure 8.17 Behavioral and neural effects of attention. (a) This graph shows the effects if cueing with reaction time (RT). Reaction time is the fastest when stimuli are presented in the locus of attention (i.e., valid trials), slowest when subjects are given invalid trials and intermediate for neutral trials (Adapted from Langner et al., 2011). (b) Effects of attention on neural responses. Blue trace show the neural response to a tactile stimulus when the animal correctly identified the letter X that scanned across the neurons RF. Green trace shows the rate when the animal received the same stimulus but performed a visual distraction task. Red trace is when the animal performed the tactile task but did not identify the letter. (Adapted with permission from Hsiao et al., 1993.)

Mechanisms of Attention in Touch

A number of studies indicate that attention may operate as a “spotlight.” This refers to the notion that, within a single timeframe, attention can only be focused on a particular portion of the body or space, yet the spread of the spotlight can be either narrowed or broadened at the subject’s will. Lakatos and Shepard (1997) reported that the time it takes to discriminate the presence or absence of a tactile stimulus depends on the distance between the cued site and the test locations. Interestingly, the authors found that the contraction or expansion of this “spotlight” was not related to the somatotopic distance between the test sites (i.e., the anatomical distance along the body surface across the sites), but rather to the linear distance between the cue and target sites. The size of the focus of attention appears to be modality specific. Craig (1985) used an Optacon to deliver spatial patterns to two fingers on the same or opposite hand. He found that there was a decrement in performance when subjects were asked to attend to a single finger and competing stimuli were presented to other fingers of the same hand, but there was no decrement when stimuli were presented to opposite hands. Similar results were reported by Evans and Craig (1992) who showed that nontarget distracters influence performance when stimuli are presented to fingers on the same but not on different hands, and that this is unaffected by the spatial separation between the two hands. Since the Optacon only activates the RA and PC afferents, these findings provide evidence that the “attentional spotlight” in touch cannot be reduced to regions smaller than the hand for stimuli that only activate these afferents. A corollary to this suggestion is that attention has its greatest influence on neural activity in neural areas with RFs as large as, or larger than, the whole hand (e.g., area 2, SII, or beyond) for tasks that employ the RA or PC systems, and that the size of the focus of attention is taskor modality-specific. Attention Effects on Tactile Neural Responses Attention has been shown to minimally affect sensory activity at very early levels such as the thalamus (see, e.g., (McAlonan, Cavanaugh, & Wurtz, 2008)), SI cortex (see, e.g., (Hsiao et al., 1993), and even in some medullary nuclei in the brainstem (see, e.g., Hayes, Dubner, & Hoffman, 1981). Hayes and colleagues (1981) reported that neurons in the dorsal horn increased their responses to the same tactile stimulus when animals performed a thermal (innocuous and noxious stimuli) versus a visual task. Moreover, they showed that these responses were enhanced at the initial

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volley of afferent activity, thus indicating that attention might operate early on in sensory processing. Studies of the effects of attention in the thalamus indicate that these attentional effects are modality-specific such that selective attention targets these neurons based on the type of sensory inputs that are relevant to the task at hand (i.e., VPL for touch and LGN for vision). For instance, Morrow and Casey (1992) found that a subset of neurons in the VPL increased their neural firing to somatosensory stimuli when attention was deployed to touch compared to vision. A working hypothesis is that attention at the level of the thalamus operates by exercising feedback and feed-forward mechanisms modulating sensory information. Several studies provide strong evidence that the number of neurons affected by attention, as well as the size of the attentional modulation, seems to increase across the sensory hierarchy (i.e., SI–SII). For instance, in a visuotactile crossmodal study, Meftah et al. (2002) revealed that ∼24% of neurons in SI cortex were modulated by the attentional focus, compared to ∼62% of neurons in SII cortex. Similarly, Hsiao et al. (1993) observed that ∼50% of SI-neurons and ∼80% of SII-neurons were modulated by attention in a task that required the animal to discriminate tactile embossed letters. A typical observation in neocortex is that attention tends to modulate activity in anticipation of a target stimulus. This preparatory mechanism, however, seems to be exclusive to neocortical cells. Neuroimaging and electrophysiological studies in humans and nonhuman primates have revealed baseline shifts in activity of primary and secondary sensory cortices, even in the absence of sensory stimulation (Meftah et al., 2009). In the study by Meftah (2009), the authors showed that attention modulated the neural firing of SI and SII neurons between the period of the symbolic cue and target stimulus (i.e., the cue-target interval, CTI). Specifically, it was found that the firing rate of ∼40% of somatosensory neurons was modulated by attention, by either enhancing or suppressing the responses. More important, the authors observed that the majority of cells that displayed modulations in the CTI also exhibited attentional effects in the same direction during the processing of the target stimulus (e.g., suppressed responses during the CTI and target processing periods). Neural Coding of Selective Attention A widely held belief is that the neural representation of information is encoded in the firing rate of neurons. As we previously showed, the direction of motion of a tactile stimulus is encoded by a subset of neurons in area 1 that

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increase their firing rate to stimuli that move in a particular direction. The direction that elicits the highest firing rate is referred to as the preferred direction, whereas the direction that elicits the lowest firing is labeled the antipreferred or least-preferred direction. In the same context, we just showed that the firing rate of neurons is also increased (or decreased) when the locus of attention overlaps with the RF of the neuron. Figure 8.17B shows the responses of a neuron in SII cortex in an animal trained to discriminate embossed letters of the alphabet (Hsiao et al., 1993). This figure shows that the effects of attention are greatest when the animal correctly identified the letter as compared to the rate when the animal missed the target letter or performed a visual distraction task. Figure 8.17B also shows that attention is rapid and switches on and off within about 100 ms. While attention clearly affects firing rate, from the perspective of neural informatics, however, the prospect that attentional selection and feature encoding might share the same neural coding scheme (i.e., a rate code) represents a dilemma. This is illustrated by the following question: How does a higher-order neuron interpret an intermediate firing rate? Is this rate signal representing an “antipreferred” attended stimulus or does it underlie activity for an unattended “preferred” stimulus? To solve this problem, theoretical models have suggested alternative coding models for attentional selectivity (e.g., Niebur et al., 2002; Roy et al., 2008). One such model proposes that attention operates by synchronizing the firing across neural populations. That is, attention can inject binding signals between independent neural pairs in order to increase or decrease the correlation between them. In fact, attentional modulation of synchrony across neuronal pairs has been demonstrated in empirical studies in somatosensory cortex (Figure 8.18, Steinmetz et al., 2000). In particular, Steinmetz et al. (2000) found that in addition to attention eliciting changes in synchronous firing across neural pairs, the percentage of neurons that showed changes in synchrony that was greater than what would be predicted by changes in rate was modulated by task demands. Specifically, they reported that the most attentionally demanding task elicited changes in synchrony in ∼35% of the neural pairs, whereas only 9% of pairs were affected in the animal that performed the easiest task. Indeed, the synchrony model of attention represents a putative and relatively computational inexpensive mechanism for distinguishing attentionally relevant from distracting or irrelevant stimuli. In addition to changes at the single neuron level, a large body of research has shown that attention can also modulate activity of macroscopic measures such as

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Figure 8.18 Synchrony and attention. Effects of attention on synchrony in firing of a typical neuron pair in SII cortex (red and green upper graphs). The raster plots are triggered at the onsets of 50 tactile stimulus periods while the monkey performs the tactile letter discrimination task (left raster) and when they received the same stimulus but performed a visual distraction task (right raster). Each dot represents an AP from two simultaneously recorded neurons: neuron X (green) and neuron Y (red). Blue dots are when the two neurons fired synchronously. Note that more blue dots are observed when the animal performed the tactile than the visual task. The lower panel displays the instantaneous synchrony firing rate for both neurons during attention to touch (blue), attention to vision (orange), and synchronous spikes due to chance (purple). (Adapted with permission from Steinmetz et al., 2000.)

local-field potential and hemodynamic responses. One area of research that stands out is that of neural oscillations. Oscillations represent a powerful mechanism for sensory selection in the tactile, visual, and auditory modalities, and have been shown to be related to the control of attention. In particular, it is hypothesized that oscillatory activity within the 8–14 Hz range (mu or alpha oscillations) is believed to be a mechanism for sensory suppression, whereas oscillations > 30 Hz (gamma oscillations) have been proposed to play a role in feature binding. Moreover, they showed that the increase in alpha power was systematically modulated by the reliability of the cue and that it related to faster RT and improved accuracy. Similar findings have been reported in the visual and auditory modalities (Gomez-Ramirez et al., 2011;

References

Kelly, Gomez-Ramirez, & Foxe, 2009). Neural oscillatory activity in the high gamma band is contended to play a crucial role in the selection of inputs by amplifying the signals located inside the locus of the attentional spotlight. In an electrocorticographic ECoG) study in humans, Ray et al. (2008) instructed subjects to detect a target in the auditory or somatosensory modality and to ignore stimuli presented in the unattended channel (Ray, Crone, Niebur, Franaszczuk, & Hsiao, 2008). The data revealed that gamma power was increased in electrodes over the sensory modalities where attention was apportioned. Namely, the authors found an increase in high gamma power over auditory and somatosensory cortices when attention was apportioned to audition and touch, respectively. In a separate study, Ray et al. (2008) found that high-gamma activity (60–200 Hz) was tightly correlated with increases in neuronal synchrony, suggesting that high-gamma activity is a neural correlate of attention-evoked synchrony. Further studies are needed to fully understand the functional implications of these findings.

SUMMARY In this chapter we review the peripheral neural mechanisms of tactile sensation and perception, the neural mechanisms in SI and SII cortex, and the effects of attention on information processing in the somatosensory pathways. Three decades of combined psychophysical and neurophysiological experiments provide overwhelming evidence of a sharp functional division of function among the four cutaneous afferent systems that innervate the human hand. Specifically, the SA1 system provides a high-quality neural image of the spatial structure of objects and surfaces that contact the skin, which forms the basis of form and texture perception. The RA system provides a neural image of motion signals from the whole hand. These signals provide the CNS with the crucial information needed for grip control, and also with information about the motion of objects contacting the skin. The PC system provides a neural image of vibrations transmitted from objects grasped in the hand. The SA2 system provides a neural image of skin stretch over the whole hand, which is thought to play a key role in relaying information about joint angle and hand conformation. The roles of the deep mechanoreceptors signaling proprioception are not yet fully understood. The Golgi tendon organs provide afferent information about muscle force, the joint afferents about motion at the extremes of joint angles, and muscle spindles (1a and II) give a signal

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about crude joint angle, with the SA2 (and possibly SA3) afferents leading the way as being most responsible for coding joint angle. Cortical processing of spatial stimuli coding for local features from the finger pad now suggest that the mechanisms employed in the somatosensory and visual systems for two-dimensional form and motion are similar. Neurons in area 3b have responses that are selective to the local spatial features of stimuli-like V1 neurons and are selective for oriented bars indented on the skin. Further, area 1 neurons respond well to patterned tactile-motion, and this is accomplished by employing similar mechanisms to those employed by MT neurons in processing visual motion. Area 2 neurons have responses that suggest that they are responsible for curvature similar to curvature processing in area V4. These neurons are also involved in integrating cutaneous and proprioceptive inputs for the processing of global features of stimuli. Neurons in SII cortex have more complex responses and often have receptive fields that span multiple fingers or both hands. Neurons in SII cortex are similar to neurons in SI cortex. SII neurons however have large receptive fields that span multiple fingers and further the orientation selectivity is consistent over multiple fingers on the same hand. These responses may be a mechanism that confers position invariance (i.e., responses to a stimulus are the same regardless of position) or it may be a mechanism that integrates information across multiple fingers, which is necessary for object recognition. Neurophysiological studies show that selective attention affects neuronal responses at the very early levels of processing within the somatosensory pathways (the dorsal horn of the spinal cord) and it progressively influences more neurons (and with greater proportion) at higher levels within the system. In SII cortex, the focus of attention has profound effects on the responses of single neurons and affects both the firing rate and degree of synchronous firing between neurons. The mechanisms selection by attention are not understood but involve enhancing the responses of neurons that are relevant to the task and suppressing the responses of other neurons.

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CHAPTER 9

The Biopsychology of Pain MAGALI MILLECAMPS, DAVID A. SEMINOWICZ, M. CATHERINE BUSHNELL, AND TERENCE J. CODERRE

INTRODUCTION 240 THE PERIPHERAL NERVOUS SYSTEM 241 THE SPINAL CORD DORSAL HORN 246 THE BRAIN AND BRAINSTEM 255

ENDOGENOUS CONTROL OF PAIN 260 FUTURE DIRECTIONS IN PAIN CONTROL 263 REFERENCES 264

INTRODUCTION

healing is complete or the condition is cured. The importance of physiological pain to the health and integrity of the individual is illustrated by the rare syndrome of congenital insensitivity to pain. These individuals lack functional small diameter primary afferent fibers that transmit information about tissue-damaging stimuli. People with this syndrome frequently injure themselves and are unaware of internal injury or disease, when the sole symptom alerting these conditions is pain. These individuals often become disfigured, develop severe joint deformities and have a significantly shortened lifespan. Pathological pain occurs with the development of abnormal sensitivity in the somatosensory system, usually precipitated by inflammatory injury or nerve damage. Pathological pain is characterized by one or more of the following: pain in the absence of a noxious stimulus (spontaneous pain), increased duration of response to brief stimulation (hyperpathia), perception of pain in response to normally nonpainful stimuli such as light touch (allodynia), increased responsiveness to noxious stimulation (hyperalgesia), and spread of pain and hyperalgesia to uninjured tissue (referred pain and secondary hyperalgesia). The abnormality that underlies pathological pain may reside in any of numerous sites along the neuronal pathways that both relay and modulate somatosensory inputs. Indeed, the most intractable pains are those that result from injury to the nerves (neuropathic pain) and central nervous system (CNS) structures (central pain) that subserve somatosensory processing. In this chapter we review the current knowledge concerning the anatomical, physiological, and neurochemical

Pain has been defined by the International Association for the Study of Pain as “an unpleasant sensory and emotional experience associated with actual or potential tissue damage, or described in terms of such damage.” Pain is subjective: Its perceived quality, intensity, and emotional effects depend on the individual. Biologists recognize that those stimuli that cause pain normally are liable to damage tissue. However, an individual learns the application of the word through experiences related to injury in early life and beyond. Pain is unquestionably a sensation within the body, but it is also unpleasant and therefore reflects an emotional experience. Although pain is often directly associated with tissue injury, it is also true that pain cannot be equated with or predicted by the amount of tissue injury. Some individuals with severe injuries experience little pain and some with minor injury experience excruciating pain. Indeed, pain often persists long after the healing of damaged tissue (Merskey & Bogduk, 1994). Pain can be broken down into physiological and pathological pain (Woolf, 1991). Physiological pain reflects a normal reaction of the somatosensory system to noxious stimulation, which alerts the individual to actual or potential tissue damage. It serves a protective function of informing us of injury or disease and usually remits when D.A.S. received funding from a CIHR postdoctoral fellowship. T.J.C. and M.C.B. received grant support from the Canadian Institutes of Health Research (CIHR). 240

The Peripheral Nervous System

substrates that underlie both physiological and pathological pain. We describe the transmission of inputs from the periphery to the CNS, the physiological properties of the neurons activated by painful stimuli, and the neurochemicals that have been found to mediate or modulate synaptic transmission in somatosensory pathways. We have broken down our review into separate sections on the peripheral nervous system and the CNS, with a further breakdown of the CNS into the spinal cord dorsal horn and the brain and brainstem. We end each of these sections with a specific discussion of the pathology in these systems in chronic pain. In the last section of the chapter we discuss pain control, with particular emphasis on brainstem and brain mechanisms.

241

THE PERIPHERAL NERVOUS SYSTEM Primary Afferent Fibers Under normal circumstances nociceptive inputs are transmitted to the spinal cord dorsal horn, or brain stem trigeminal nuclei (for inputs from the neck, face, and head), by primary afferent nerve fibers in skin, muscle, joint, viscera, and vasculature (see Figure 9.1). Each individual fiber extends from its tissue of origin (e.g., skin, joint, etc.) to its CNS target (e.g., spinal cord dorsal horn), and makes its first synapse at that level. The cell bodies of these neurons are grouped together to form the dorsal root ganglia, with the peripheral axonal branch

DC

Posterior root Spinal ganglion

STT

Posterior division

Anterior root Sympatheric ganglion

Viscera Ad, C

la, b

Blood vesseis Aa Ab

C

Tendon bundle

Skeletal muscle Ad

Receptors in skin Muscle spindle

Figure 9.1 Schematic diagram illustrating the projection of primary afferent fibers (Aβ, Aδ, C, group Ia,b) from their site of origin in the skin, muscle, and viscera to their target cells in the dorsal horn of the spinal cord. Primary afferent fibers with their cell bodies (dorsal root ganglion cells) in the spinal ganglion project through the dorsal or posterior roots. The peripheral axon branch originates in skin receptors, muscle spindles, tendon bundles, and so forth. The central axon branch terminates on spinal cord neurons that project to the thalamus and brain through various ascending tracts, including the spinothalamic tract (STT), or ascend to the caudal medulla through the dorsal columns (DC). Also shown is the ventral or anterior root through which efferent motor fibers (Aα) project to skeletal muscle and sympathetic efferent (C) fibers project through the sympathetic ganglion to blood vessels and viscera. (Reprinted with permission from J. J. Bonica [1990], The Management of Pain, Vol. 1, Philadelphia, PA: Lea and Febiger. Copyright 1990 Lea and Febiger.)

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TABLE 9.1

Primary Afferent Fibers

Diameter

Skin and Viscera (velocity m/sec)

Muscle and Joint (velocity m/sec)

Aβ (30–100)

II (24–71)

Very large Large

Endings and Thresholds

Receptors

Sensation

Mechanoreceptor

Skin: Type I, type II, D hair, G1 , G2 hair, T hair, Field, G1, G2, Krause end bulbs, Merkel cells, Meissner’s corpuscles, Pacinian corpuscles

Skin: Indentation, skin or hair movement, vibrations of the skin and hair

I (72–120) low threshold fibers (somatosensory inputs that encode nonnoxious stimulation)

Viscera: mesenteric Pacinian corpuscles

Medium

Aδ (2–6)

III (6–23)

Free Nerve Endings high threshold (higher intensity stimulation is required to activate these fibers)

Small (unmyelinated)

C (0.2–2)

IV (400 μm) in the rostrocaudal axis, with little dorsoventral or mediolateral spread. Central cells are similar, but have much shorter dendritic trees ( C, C > D, and D > E. The critical comparison at testing involved B and D; during training, both B and D could be either the smaller or the larger stimulus, depending on the other stimulus in the comparison, so that their absolute values were not helpful for making a comparison when they were presented together; however, the correct relational inference could be only that B was larger than D. This version of the transitive inference task has been successfully solved by several animal species: rats (e.g., Davis, 1992), pigeons (e.g., von Fersen, Wynne, Delius, & Staddon, 1991; Lazareva & Wasserman, 2006), crows (e.g., Lazareva, Smirnova, Bagozkaja, Zorina, Rayevsky, & Wasserman, 2004), monkeys (e.g., McGonigle & Chalmers, 1977; Merritt & Terrace, 2011), and chimpanzees (Boysen, Berntson, Shreyer, & Quigley, 1993). So, animals can generate appropriate responses to novel combinations of nonadjacent members of an ordered series without previous experience with those combinations.

Transitive Inference

Analogy

Another interesting instance of relational learning in which the absolute values of the training stimuli cannot be used is

To discriminate collections of same and different items involves understanding what are called first-order

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relations. Can animals go to the next level and learn higher-order relational concepts? Can they understand, not only that several identical apples are the same and that several identical balls are the same, but also that the relation between the apples and the balls is sameness? Judging relations between relations is basic to analogical reasoning, and many authors have proposed that analogical competence is the very essence of human intelligence (e.g., Holyoak & Thagard, 1997). The familiar matching-to-sample task can be used to display and evaluate learning between relations. In the analogical or relational matching-to-sample procedure, the animal is given a sample stimulus set (either two or more identical items on some trials or two or more nonidentical items on other trials), and two choice stimulus sets (one containing two or more identical items and the other containing two or more nonidentical items). Critically, none of the items in the sample is presented in the choice sets; so, if the sample is AA, then the choices can be BB or CD. To be successful, the animal must select the set of choice alternatives that instantiates the same relation as the sample set. Given that there is no overlap between the sample and choice items, only attention to the matching relations (same sample to same choice and different sample to different choice) can yield successful performance. When baboons were given a relational matching-tosample task in which the sample and choice arrays contained 16 items, they successfully learned to choose the 16-item choice array instantiating the same relation as sample array (Fagot, Wasserman, & Young, 2001). Accuracy was high when sample arrays containing novel items were presented, thereby attesting to the generality of the relational matching concept; however, when the number of items in the sample arrays was reduced from 16 to 12 to 8 to 4 to 2, baboons’ accuracy progressively fell to chance level. For the baboons, the task proved extremely taxing when too little pictorial information was available (see Cook & Wasserman, 2007, for similar results with pigeons). In contrast to baboons (and pigeons), chimpanzees solve relational matching-to-sample problems even when only two items are presented in the sample and the choice arrays (Gillan, Premack, & Woodruff, 1981; Thompson, Oden, & Boysen, 1997). The first chimpanzee to exhibit a variety of analogical behaviors was Sarah, who could evaluate, complete, and even create analogies. Sarah initially learned to use a plastic token to denote the concept “same” and another plastic token to denote the concept “different” (Premack & Premack, 1972). In a

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later set of experiments, Sarah was given four geometric forms on a display board in a 2 × 2 format. The two items on one side (e.g., left) represented one relation and the two items on the opposite side (e.g., right) represented a second relation. Sarah had to choose the correct plastic token (one for “same” and one for “different”) and place it in the middle of the board to indicate whether the relations between the set on the left and the set on the right were same (thus representing an analogy) or different (thus representing a disanalogy). Sarah chose correctly about 80% of the time (Gillan et al., 1981). In other experiments, Sarah was given two items on the left side and only one item on the right side. The token for “same” was placed in the middle of the board and Sarah now had to choose, from two alternatives, the item that completed the analogy. Sarah chose the correct option most of the time. She even did so when items representing functional relations were presented. For example, when shown a lock and a key on the left side, and a can on the right side, Sarah would choose the can opener to complete the analogy (Gillan et al., 1981). Later, Oden, Thompson, and Premack (2001) explored whether Sarah could also construct novel analogies. She was given an empty board and four or five items that could be used to create a valid analogy. This task was especially challenging because Sarah had to find unspecified relations among the items and arrange them on the board so that they would represent a proper analogical relationship. When only four items were available, Sarah created a valid analogy 76% of the time. Her performance dropped when five items were available, but it was still above chance level. Premack and his colleagues have contended that language training and/or prior experience with arbitrary symbols for the abstract concepts of same and different are needed for animals to exhibit analogical reasoning (Premack, 1983; Thompson & Oden, 2000). Such training may have allowed Sarah (and three other chimpanzees, see Thompson et al., 1997) to display analogical abilities that had been believed to be uniquely human. Language or symbol systems may facilitate relational and analogical behavior because they provide a way for animals to represent abstract relations so that these relations can be encoded and manipulated (Rattermann & Gentner, 1998; Thompson & Oden, 2000). However, the research with baboons (Fagot et al., 2001) and pigeons (Cook & Wasserman, 2007) that was previously described suggests that language or symbol training may not be necessary for disclosing this cognitive capacity. Another possibility exits: Both the baboons and the pigeons in those experiments

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had been trained to discriminate same from different collections of items before training on the relational matchingto-sample task. That prior learning of first-order relations may have provided the cognitive scaffolding required to process second-order relations. This research suggests that animals either have a rudimentary capacity for analogical reasoning or they at least possess the basic mechanisms that evolved into this capacity. These observations have important evolutionary implications. Higher-level cognition was once believed to be the unique province of human beings, but we now know that chimpanzees, baboons, and pigeons show similar intellectual abilities, at least in their most basic form. The roots of abstract thought may thus lie deep in our animal ancestry. Causality The Scottish philosopher David Hume argued that humans do not directly apprehend causality. Instead, we make causal inferences based on a restricted set of experiences. When (1) two events occur together in time and space, (2) one of the events precedes the other, and (3) the two events appear consistently together (that is, they do not occur alone), we normally infer the existence of a causal relationship between them (Hume, 1739/1964). Human causal learning is affected by these primary Humean rules, which are actually the same factors that affect classical conditioning in animals: contiguity, priority, and contingency (e.g., Shanks & Dickinson, 1987). Moreover, both humans and animals exhibit behavioral phenomena such as discounting and augmentation (Kelley, 1973), which appear to implicate a sophisticated causal reasoning process; organisms not only take into account how a potential cause covaries with the effect, but also how this cause competes with rival explanations of the effect. One of the best-known cases of discounting is the relative validity effect, first reported by Wagner, Logan, Haberlandt, and Price (1968). In this study, for all experimental conditions, a light was equally often paired with the delivery of food after pressing a lever; in addition, the light was paired half of the time with a low-frequency tone and half of the time with a high-frequency tone. In one condition, the light and the low-frequency tone were followed by food delivery, but there was no food when the light was paired with the high-frequency tone. In the other condition, each of the pairs (the light with the low tone and the light with the high tone) was followed by food 50% of the time. So, in both conditions, the light was followed by food 50% of the time; however, in the first condition, the

low tone and the high tone were reinforced 100% and 0% time, respectively, whereas in the second condition, each of the tones was reinforced 50% of the time. When the light was later presented alone, animals’ responding to the light was not equivalent in both conditions—as would be expected based on their identical reinforcement histories. Instead, responding to the light varied depending on the predictive value of each of the tones, the other stimuli presented during the training phase. When the low tone had been a good predictor of food and the high tone had been a good predictor of the absence of food, responding in the presence of the light alone was much lower compared to the condition in which all of the stimuli had been partially and equally associated with the food. So, even when the light was followed by food 50% of the time in all of the conditions, its predictive value was discounted when there were strong predictors of food (low tone) and no food (high tone). Not only rats’, but rabbits’ (Wagner et al., 1968) and pigeons’ conditioned responding (Wasserman, 1974), as well as humans’ causal judgments (Wasserman, 1990a) vary in the same way under comparable circumstances. Parallels between human causal learning and animal conditioning speak to a common underlying process. If one assumes that, during a conditioning procedure, animals acquire information about the causal texture of their environment, then the correspondence between animal conditioning and human causal learning can readily be appreciated. However, some deem these parallels to be inadequate to prove causal understanding in animals, because these studies concern merely making predictions about the temporal and spatial relations between observable events. This might be a bogus argument; causal understanding even in humans is based on the observation of temporal and spatial regularities in the environment (e.g., Allan, 1993; Dickinson, 2001; Miller & Matute, 1996; Shanks, 1995; Wasserman, 1990b). Causal knowledge not only allows us to predict, but also to control our environment. We are able to predict an effect on the basis of observed cues, but we are also able to predict the effects that our own actions will have on the environment. If animals understand that there is a causal relationship between events, then one might argue that, when the effect is highly valuable, the animal should work to make the cause occur. Instrumental conditioning relies on the ability of organisms to learn that their own actions can produce certain outcomes. Humans’ and animals’ control of their environment may be based on the inference of a causal relationship between their own behavior and the consequences of this behavior.

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Skinner wrote: “Man’s first experience with causes probably came from his own behavior: things moved because he moved them” (1971, p. 7). It is not difficult to imagine that all mobile organisms go through this same basic experience. Hence, it is reasonable to ask: Are nonhuman animals also able to distinguish between events that are caused by their own behavior from those that are not? Killeen (1981) “asked” pigeons whether or not they were responsible for the offset of a light. The pigeons were able to discriminate whether it was their own behavior or “something else” (in this case, the programmed computer) that caused the change in the light. The rudiments of causal understanding can easily be noted here. Arguably more compelling evidence of causal understanding in instrumental conditioning comes from studies of outcome devaluation. Adams and Dickinson (1981) trained rats to press a lever to get food pellets. Later, an aversion to the food was induced by injecting the animals with a mild toxin that produced gastric illness. During this aversive food-illness conditioning, the lever was not present. The relevant issue here was to what extent this devaluation of food would affect lever pressing when the lever was again available. If the animals had learned that there was a positive causal relation between lever pressing and the receipt of food pellets, then lever pressing should be influenced by this causal knowledge and the current desirability of the food outcome. This is what happened. Because the food pellets were no longer appetizing, the animals decreased their pressing of the lever. Other instances in which people’s causal inferences can differ concern situations in which they merely observe the occurrence of an effect or they know that someone or something else has intervened to produce that effect (e.g., Waldmann & Hagmayer, 2005). To discover whether or not rats also exhibit a similar tendency, animals were presented with a light followed by a tone and the same light followed by sucrose (Blaisdell, Sawa, Leising, & Waldmann, 2006). The light would be the potential cause and the tone and the sucrose would be potential effects. Would the animals consider the light to be the cause of both the tone and the sucrose? To answer this question, after the above training, a lever was inserted into the chamber. In the Intervention group, the tone was presented each time the rats pressed the lever, whereas in the Observation group, the tone’s presentation was not contingent on lever pressing, although the tone was presented the same number of times in each group. Therefore, one of the effects of the light, the

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tone, had only been observed in the Observation group, whereas it had intervened in the Intervention group. If rats were to consider the light to be a common cause of the tone and the sucrose, then the presentation of the tone in the Observation group should lead the animals to infer that the light must have occurred and to expect sucrose as well. On the other hand, if rats in the Intervention group attributed the tone’s presentation to their own lever pressing behavior, then they should not attribute the tone to the presence of the light, because it had been caused by their own behavior. Thus, rats in the Intervention group should not infer that the light had occurred as well, so they should not expect any other effects of the light—specifically, the sucrose—to be present. This is exactly what Blaisdell et al. (2006) observed: When the tone appeared, rats in the Intervention group did not look for sucrose, whereas rats in the Observation group did. Thus, it seems that nonhuman animals can exhibit behavior that suggests some level of causal understanding. We do not deny that humans’ causal understanding is far more advanced than animals’; however, that advancement is likely to be premised on the basic rules of causal association that were proposed centuries ago by David Hume. Whether that advancement is simply a further elaboration of these rudimentary rules or something qualitatively different is a live empirical question. Metacognition Demonstrations of numerical and basic mathematical abilities, temporal control, different types of memory, as well as abstraction, causal learning, and analogical reasoning clearly document that animals possess a broad range of cognitive abilities. All of this research suggests that we can know what animals know. But do animals know what they know? This question is not merely tricky word play, but the core matter of research in the growing field of metacognition. Metacognition in humans is said to be associated with conscious awareness of one’s own cognitive states (e.g., Nelson, 1996). People know whether they can retrieve a specific memory, they can ascertain if they have enough information to make a decision, and they can assess the amount of knowledge they have about a certain topic; in short, people can think about their own cognitive states and processes. In the past decade, several researchers have studied metacognition in animals as well. Metacognition in animals is plausible. Imagine this common scene: you are strolling through a park and you encounter a woman walking her dog; the dog sees you and it then starts looking

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back and forth between you and its owner, as if wondering “Should I stay or should I go?” Or we see a squirrel hesitate at the base of a wall, apparently deciding if the wall is low enough for it to jump up and to land safely. When animals do not know what to do, they might defer their actions and seek help or additional information. These behaviors may be the result of a metacognitive process, but do they really require metacognition? Do animals have access to their own cognitive states and can they use those states to control their behavior? The Uncertainty Paradigm The first attempts to study animal metacognition used what has been dubbed the uncertainty paradigm, in which animals must learn to discriminate between categories of stimuli, for example, between high-pitch and lowpitch sounds (Smith, Schull, Strote, McGee, Egnor, & Erb, 1995) or between pixel-dense and pixel-sparse visual images (Smith, Shields, Schull, & Washburn, 1997) by choosing one of two different responses for each of the categories. Animals receive a food reward for a correct response and a time-out for an incorrect response. When the stimuli are near the extreme values, the task is easy; but the task becomes increasingly difficult the closer the values are to the middle of the continuum. In addition to the two category responses, animals are also given a third option—the uncertainty response—that avoids the target discrimination altogether and takes the animal to another easier task and a smaller amount of food than if they had chosen the correct response for the training categories. If animals can monitor their knowledge, then they might choose the uncertainty response when the values to be discriminated are highly similar and failure is likely. And, so they do. Dolphins and monkeys choose the two category responses when the task is easy, but they choose the third uncertainty response when the task is more difficult (Smith et al., 1995, 1997). Nevertheless, the uncertainty response paradigm has raised several concerns, because alternative explanations based on simple associative learning can explain animals’ apparently metacognitive behavior (Hampton, 2009; Smith, 2009). For example, animals may have learned to select the uncertainty response for a particular range of stimuli (the difficult ones near the middle of the continuum) because of the reinforcement history with those stimuli (animals are consistently rewarded if they choose the uncertainty response, but they are inconsistently rewarded if they choose the category responses), not because of a subjective feeling of uncertainty.

Memory Information Monitoring In order to avoid this problem, other paradigms have been devised. As we saw earlier, animals have excellent memory for rich and varied information; but as in the case of humans, these memories may fade or become difficult to retrieve over time. One interesting possibility is to see if animals can report having good or poor memory for an event that happened some time ago. Hampton (2001) trained rhesus monkeys on a matching-to-sample task in which a delay was introduced between offset of the sample image and onset of four testing stimuli: the sample along with three distractors. On some trials, an intermediate choice was introduced at the end of the delay interval that allowed the monkeys either to accept the memory test and receive a preferred reward if they were successful or to decline the memory test and receive a guaranteed, but less desirable reward. On other trials, at the end of the delay interval, only the option to take the test was given, so that the monkeys had to take the memory test. If monkeys have metamemory, then when given the option to accept or to decline the test, they should accept the test if their memory is strong, but they should decline the test if their memory is weak. As a consequence, the monkeys should be more accurate on those trials in which they are given the choice. They should accept the test on choice-test trials when they know that their memory is good. But the forced-test trials will also include cases in which the monkeys’ memory is poor, thereby lowering their overall accuracy. Monkeys’ performance accorded with this prediction; they were more accurate on trials in which they accepted the test than on trials in which taking the test was the only option, suggesting that the monkeys could distinguish between their different memory states (see also Foote & Crystal, 2007, for similar results with rats). Confidence Judgments Taking a different approach, Kornell, Son, and Terrace (2007) evaluated monkey’s confidence in their memory by allowing them to gamble. Monkeys viewed a series of six pictures, one by one; after the last picture, nine pictures simultaneously appeared on the screen, only one of which had been presented in the prior series. The monkeys’ task was to select this picture. But now, before feedback was provided, the monkeys were given a choice of two icons, representing a high-risk option and a low-risk option. A high-risk choice resulted in the gain of three tokens (that could later be exchanged for food) if the monkeys’ response on the picture memory test had been correct, but

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a loss of three tokens if the monkeys’ response had been incorrect. A low-risk choice resulted in a sure gain of one token. The rationale was that a monkey showing metacognitive capabilities should make a high-risk bet when confident about its prior response, but it should make a low-risk bet when unsure about its prior response. In fact, monkeys chose the high-risk button more often on correct trials than on incorrect trials, suggesting that they knew whether they had responded correctly before the presentation of any feedback. Moreover, monkeys generalized the use of the high- and low-risk options to a variety of different perceptual discrimination and memory tasks. This flexibility further helps to discount any specific associations between the presented stimuli and the alleged metacognitive responses. Metacognition Versus Behavioral Regulation Animals certainly exhibit complex behaviors in these socalled metacognitive tasks; nevertheless, some researchers believe that it is too soon to conclude that those behaviors are the result of access to and evaluation of an internal cognitive state (e.g., Hampton, 2009; Smith, 2009; Smith, Beran, Couchman, & Coutinho, 2008). According to Hampton (2009), metamemory tasks (Foote & Crystal, 2007; Hampton, 2001; Smith, Shields, & Washburn, 1998), the gambling paradigm (Kornell et al., 2007), and other studies (Basile, Hampton, Suomi, & Murray, 2009; Call & Carpenter, 2001; Hampton, Zivin, & Murray, 2004; Redford, 2010) strongly suggest that animals can adaptively regulate their behavior under conditions of uncertainty and perform according to the knowledge they posses. However, that behavior regulation may be achievable by means other than metacognitive processes. For example, animals may use their latency to respond as a cue for subsequent behavior (humans also take into account their speed of coming up with an answer to decide whether or not they really know something). Although metacognition is a plausible mechanism for the animals’ behavioral regulation, it is not yet clear whether it is the best or only possible mechanism. Again, Morgan’s Canon comes into play. Animal Language In his Politics, Aristotle wrote: “Nature . . . makes nothing in vain, and man is the only animal whom she has endowed with the gift of speech” (2004, p. 7). This staunch belief in the uniqueness of human

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language has shaped Western thought for more than two millennia. The famous French philosopher Ren´e Descartes concurred with Aristotle’s pronouncement and further speculated that, “the reason why animals do not speak as we do is not that they lack the organs but that they have no thoughts” (Descartes 1646/1970, p. 207). The celebrated British philosopher John Locke agreed with Descartes’ claim and also proposed that animals “have not the faculty of abstracting, or making general Ideas, since they have no use of Words, or any other general Signs” (Locke, 1690/1975, pp. 159–160; see Wasserman, 2002, for further consideration of the notion of “general signs”). If authority were the sole route to truth, then with such luminaries as Aristotle, Descartes, and Locke arguing against language in animals, who would dare to disagree with them? We will soon see that some intrepid thinkers have indeed disagreed with these authorities; in so doing, they have helped to move the matter of animal language from philosophy to psychology. We cannot do justice to the full range of thought and inquiry into animal language in this chapter. However, we can discuss some especially interesting issues and review some intriguing behavioral evidence that suggests that, at the very least, there are possible evolutionary antecedents to and behavioral substrates for human language in the animal kingdom. Three points will guide our discussion. First, animals may have their own rich and complex languages that we have not yet fully understood. Second, with suitable training, animals can master many aspects of human language. Third, human language may be the product of the fortuitous amalgamation of component skills which are readily observable in nonspeaking animals and which may have evolved to serve needs other than communication. Natural Animal Communication In The Apology for Raymond Sebond of his Essays (1958; originally 1580, 1588, 1595), the French philosopher Michel de Montaigne turned the matter of animal language in a different direction from Aristotle, Descartes, and Locke (see Melehy, 2005 and Serjeantson, 2001, for discussions of the history and philosophy of inquiry into animal language). Instead of intransigently insisting that animals do not have language, Montaigne first acknowledged that a failure of communication exists between humans and animals; he then asked just where this breakdown occurs: “This defect that hinders communication between [beasts] and us, why is it not just as much ours as theirs? It is a matter of guesswork whose fault it is that we

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do not understand one another; for we do not understand them any more than they do us” (Book 2, Chapter 12, p. 331). Perhaps animals do possess their own prolific and intricate languages; we simply do not comprehend them. This brand of reasoning promotes a clear empirical agenda: decipher animal languages and see if they exhibit any of the familiar benchmarks of human language (e.g., O’Grady, Dobrovolsky, & Aronoff, 1997). Given that he was writing in the sixteenth century, Montaigne had only florid anecdotes at his disposal; these possibly fanciful and probably unverifiable stories suggested that animals do communicate with one another. However, we now have voluminous observational and experimental evidence that unequivocally documents that communication is widespread among animals (Balter, 2010). The real issue is whether these communication systems are at all like the languages of humans. One line of work springs from the pioneering studies of primate communication by Seyfarth, Cheney, and their collaborators. Vervet monkeys are Old World primates that live in east Africa. What makes these particular monkeys so interesting is that they perform decidedly different alarm calls to distinctly different predators. Seyfarth, Cheney, and Marler (1980) cleverly demonstrated that audio recordings of the alarm calls played back on a loudspeaker when predators were absent prompted the monkeys to run into the trees for “leopard” alarms, to look up into the sky for “eagle” alarms, and to look down into the grass for “snake” alarms. Because vervets make these distinctive responses immediately upon hearing a call—without themselves seeing or hearing any predators—their behavior suggests that these calls provide the monkeys with detailed information about the presence of a specific predator (Seyfarth & Cheney, 2003). Adults make these calls largely to leopards, martial eagles, and pythons. Infants are less selective; they make leopard alarms to several mammals, eagle alarms to many birds, and snake alarms to several serpentine objects. The reporting of different predators measurably improves with a monkey’s age and experience (Seyfarth et al., 1980). How do these observations relate to the presumably unique attributes of human language? Consider these key features of human language. Interchangeability: All vervets can both send and receive messages. Specialization: The alarm calls appear to serve no other function but to communicate. Semanticity: The three different alarm calls convey meaning through their association with different predators.

Arbitrariness: There is no natural or inherent connection between the vocalizations and their referents (i.e., they are not onomatopoetic). Cultural transmission: The conventions of the alarm call system are likely to be learned or at least to be refined by young individuals interacting with more senior and experienced users. More recently, Ouattara, Lemasson, and Zuberb¨uhler (2009) have obtained suggestive evidence in primate communication of another key attribute of human language. Discreteness: Different sounds can be combined to produce different meanings. Specifically, the alarm calls of Campbell’s monkeys—Old World primates that live in West Africa—are composed of an acoustically variable stem, which can be followed by an acoustically invariable suffix. Such suffixation functions to broaden the calls’ meaning by changing a highly specific eagle alarm call into a general arboreal disturbance call or by changing a highly specific leopard alarm call into a general alert call, thereby increasing the monkeys’ small basic vocal repertoire. This elaboration may represent the clearest example of animal “proto-grammar” so far discovered. Moving to insects, Karl von Frisch first decoded the so-called “dance language” of foraging honeybees in 1946 (and he summarized his findings in English in 1967). This discovery highlighted the sophistication of animal communication, which involved yet another prime attribute of human language: Displacement: Messages conveyed by the honeybee dance appear to refer to food locations that are remote in space (and time, depending on the distance of the food from the hive and the time that it takes the receiving bees to take flight after witnessing the dance). Specifically, von Frisch first taught honeybees to forage at artificial food sources; he later found that these forager bees could communicate the distance and location of these food sources to other bees via a figure-eight dance on the vertical wax combs within the hive. Information about the distance of a food source was based on the duration of the central “waggle” phase of the dance, whereas information about the direction of the food was based on the orientation of the dancer’s body relative to gravity during the “waggle” phase. The notion of a honeybee dance language, however, has had its share of skeptics. Adrian Wenner, for example,

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has expressed doubts that the waggle dance alone communicates the location of a food source. Floral fragrances on a forager’s body may serve as the primary cues that enable the recruit-bees to pinpoint food sources; recruits may fly to the general area represented in the waggle dance, but then home in on the flower patch by using odor cues (Wenner & Wells, 1990). In still other cases, highly experienced foragers may use those odors to fly directly to food sources that they themselves have committed to memory based on prior foraging episodes (Gr¨uter & Farina, 2009), thereby rendering the waggle dance largely irrelevant to successful foraging. Clearly, much more needs to be learned about the nature and significance of the honeybee’s waggle dance and its priority given other possible foraging cues and experiences. The alarm calls of monkeys and the waggle dances of honeybees provide tantalizing parallels with human language. Yet, rather little is known in these cases about the development of these forms of animal communication. The case of birdsong is much clearer. Here, two key attributes that are believed to be unique to human language come into play. 1. Learnability: Songbirds can learn other variants (e.g., dialects). 2. Cultural transmission: The conventions of birdsong are learned by young birds interacting with adult birds. Marler (1970) pointed out that, although only male songbirds sing (thus violating the language feature of interchangeability), learning was central to their doing so. Young males must learn from their fathers just how to shape their initial, awkward “subsongs” into fully functional songs that are capable of attracting responsive females and repelling rival males; without such experience, functional songs fail to develop. Furthermore, careful spectrographic recordings of males’ vocalizations reveal clear dialects among different locales. A great deal of later research has expanded on Marler’s important observations (reviewed by Bolhuis, Okanoya, & Scharff, 2010, and Doupe & Kuhl, 1999). Both humans and songbirds learn their complex vocalizations; they must not only hear the adults that they will imitate, but they must also hear themselves as they practice singing. Here is one more key feature that has been deemed to be unique to human language: Feedback : Young birds not only perceive what they are singing, but they can make necessary corrections to bring their own vocalizations in line with the songs that they hear being sung by adult birds.

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Humans also share with songbirds an initial phase of learning which is primarily perceptual and which then prepares the young for later vocal production. Finally, humans and songbirds have similar “windows of opportunity” for vocal learning, with a much greater ability to learn early than to learn later in life. Still other attributes of human language have yet to be firmly documented in natural animal communication systems. Productivity: Users can create and understand novel messages. Duality of patterning: Meaningless units (e.g., sounds) can be combined to create larger units (e.g., words) that in turn can be recombined to create new, meaningful larger units (e.g., sentences). Prevarication: Messages can be false or deceptive. Reflexiveness: The communication system can be used to discuss itself. Nevertheless, the evidence reviewed above makes it difficult to argue that natural animal communication systems are entirely unlike human language; there are simply too many parallels for them to be accidental. Two-Way Animal-Human Communication Descartes’ intellectual successor and countryman, Julien Offray de La Mettrie was far more sanguine about the cognitive and linguistic potential of animals than his predecessor (for a discussion of Descartes, La Mettrie, and language, see Gunderson, 1964). La Mettrie was especially impressed by the success of the Swiss physician Johann Konrad Ammann in teaching nonverbal deaf persons to speak by deploying what today we would call behavior modification techniques. Might this or other tutelage also succeed with animals? La Mettrie said “yes,” but only if the correct animal were to be chosen. He believed that an ape would be the best selection. In L’Homme machine (1747/1996), La Mettrie explained this choice: [S]uch is the likeness of the structure and functions of the ape to ours that I have very little doubt that if this animal were properly trained he might at last be taught to pronounce, and consequently to know, a language. Then he would no longer be a wild man, nor a defective man, but he would be a perfect man, a little gentleman, with as much matter or muscle as we have, for thinking and profiting by his education. (p. 103)

Two centuries later, innovative efforts were begun in earnest to teach apes spoken human languages (see

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reviews by Hixson, 1998, and Limber, 1982). These initial efforts to teach apes to produce speech were unsuccessful; evidently, the vocal organs are not sufficiently similar in humans and apes for the latter to pronounce speech sounds. This failure prompted Allen and Beatrix Gardner to adopt a different tactic. Reasoning that chimpanzees might be better able to converse with humans using manual gestures than spoken words, these researchers taught an infant chimpanzee, named Washoe, to use American Sign Language. From 10 months of age, Washoe was given intensive training with a variety of teaching techniques to respond to and produce manual signs in Ameslan. Washoe was reported to have mastered over 100 individual signs, which were used to name both familiar and novel objects from the trained categories (Gardner, Gardner, & Van Cantfort, 1989). Few scientists have disputed this claim. But the further contention that Washoe could suitably sequence or spontaneously combine several of these signs proved to be far more controversial. Herbert Terrace (1979) not only took a second look at the behavioral data from Washoe, but also the behavioral data from his own sign-language-trained chimpanzee, named Nim Chimpsky. Terrace concluded from this reexamination that there was no spontaneity and no real use of grammar in either case; both chimps were simply poor conversationalists. Yet a different effort to teach apes a human language was begun by Duane Rumbaugh and his associates (1977). The LANguage Analogue (LANA) Project used a computer-based language training system that also entailed manual responses. Beginning training at the age of 2.5 years, the chimpanzee Lana was able to discriminate and manipulate arbitrary computer symbols, called lexigrams, which she effectively associated with different objects and activities. Lana communicated with her human experimenters via a keyboard that contained over 300 lexigrams. When Lana depressed a key, a computerized voice pronounced the appropriate word and the lexigram was displayed on a video monitor. After extended training, Lana was reported to use and grammatically order the lexigrams, as well as to generate novel sequences in response to novel experiences. These claims too were met with skepticism. Terrace, Pettito, Sanders, and Bever (1979) contended that, although the apes did learn the meaning of many isolated symbols, this fact alone “yielded no evidence of an ape’s ability to use a grammar” (p. 891). Although lawful regularities in symbol order may have been observed in the Washoe and Lana projects, Terrace and colleagues argued

that such sequenced responding “can, in each case, be explained by reference to simpler nonlinguistic processes” (p. 900). They noted that even pigeons can be taught to produce an ordered string of up to five different responses in the absence of external discriminative feedback (Terrace, 1991). Indeed, pigeons can also reliably discriminate different sequences of colors (e.g., Weisman, Wasserman, Dodd, & Larew, 1980, Experiment 3) as well as successfully reproduce two-item stimulus sequences previously shown to them (Parker, 1984). And, no one has yet suggested that pigeons can engage in two-way communication with humans. The first research project to use bonobos in language investigations built upon Rumbaugh’s work with Lana and it began with a mother bonobo and her son, Kanzi. Baby Kanzi was initially believed to be a 9-month-old spectator while Sue Savage-Rumbaugh was assiduously trying to teach the lexigram language to his mother. His mother did not prove to be at all adept at using the lexigram keyboard; yet, a chance opportunity when Kanzi was briefly observed alone proved that he could not only use lexigrams, but that he could also understand spoken English! Kanzi’s ability to both perceive and produce language elements had not been directly taught; their acquisition had been solely via observation and imitation. Later, as Kanzi’s language competency grew as a result of immersion in the lexigram language, Savage-Rumbaugh reported that his utterances suggested aspects of grammar, syntax, and semantics (Savage-Rumbaugh, 2009; SavageRumbaugh & Lewin, 1994). One more language project is of note in connection with animal communication via spoken English. Irene Pepperberg has reported that an African Grey Parrot, named Alex, not only understood, but also spoke English words and phrases (Pepperberg, 1999). Alex is said to have learned more than 100 words for different objects, actions, and colors; he could also identify the material composition of several objects; and, he could count up to six objects. More controversially, Alex is reported to having begun learning to read the sounds of various letters and to acquire a concept of phonemes, the acoustic building blocks from which words are constructed (Pepperberg, 2007). Notwithstanding the skepticism that has inevitably greeted all of these engaging projects (e.g., Anderson, 2004), there is good reason to believe that real advances in our understanding of animal language have come from these innovative investigations. Future work along these lines is bound to bring forth further advances.

Conclusions and Future Directions

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CONCLUSIONS AND FUTURE DIRECTIONS

As the above discussion suggests, it is extremely difficult to make unequivocal claims as to whether animals are capable of exhibiting human-like language behavior. Another different and less controversial approach is to “ask whether animals possess any of the component skills which should be necessary for language behavior” (Wasserman, 1993, p. 221). At the root of this “componential” approach is a bottomup evolutionary hypothesis (for more on this hypothesis, see de Waal & Ferrari, 2010) that has been articulated by W. Tecumseh Fitch (2010): “language must be viewed as a composite system, made up of many partially separable components. Many of these components are widely shared with other animals (such as the capacity for hearing, memory, basic cognition, and vocalization), but a few differentiate humans from our nearest primate cousins (such as vocal learning or complex syntax). Crucially, each of these necessary components of language may conceivably have its own evolutionary history, and rely upon quite separate neural and genetic mechanisms. Although language is a system characterized by seamless interaction among these multiple components, “Language” is not a monolithic whole, and from a biological perspective may be better seen as a “bag of tricks” pieced together via a process of evolutionary tinkering” (pp. 4–5). From this perspective, the ancestry of language becomes less mysterious and better rooted in evolutionary theory. It is undeniable that there are many component cognitive skills that are concatenated in human language. Which individual and combined skills appear in the behavior of different species of animals may provide important clues for evaluating the unique character of human language and its neural substrates. Special insights are likely to result from decoding the natural languages of animals as well as by direct efforts to teach human and other artificial communication systems to animals. Insights may also emerge from less direct inquiries into the many component processes of animal cognition. In any event, the field of comparative cognition should continue to contribute to our understanding the evolutionary origins of this notable form of human behavior. The remarks of Gisiner and Schusterman (1992) provide a firm rationale for the comparative study of language behavior and for a finale to this section of the chapter: “it seems highly implausible that the linguistic abilities of humans have arisen in complete ontogenetic and phylogenetic isolation from nonlinguistic learning abilities” (p. 90).

Are humans not special and superior to animals? If so, then what if anything can we hope to learn about animals that will be at all applicable to humans? The extensive research that we have reviewed and discussed in this chapter strongly suggests that humans do in fact share many cognitive functions with animals. As do humans, animals remember the past, they respond effectively in the present, and they plan for the future. Animals may also monitor their current state of knowledge to control their own behavior in an adaptive way. Finally, animals master abstract and numerical concepts, perform basic arithmetical operations, and even exhibit signs of analogical reasoning and many of the precursors to human symbolic language. Dumb beasts? Certainly not! Human arrogance alone is responsible for anyone being surprised at how intelligent animals actually are. Animals of many different species are highly sensitive to the rich tapestry of events and relationships that are woven into the causal fabric of the environment. How could it be otherwise? Darwin and his successors were keenly aware that animals evolved under most of the same environmental constraints and contingencies as did the human species. That is why these theorists hypothesized that both bodies and minds underwent evolutionary modification. From this evolutionary perspective, a great deal is to be gained by the comparative study of cognition. Why? Because by studying animal cognition, we are effectively studying the mechanisms and functions of cognition without the involvement of human symbolic language or the interpretive biases of anthropomorphism. Such objective, comparative study not only enriches our understanding of cognition in animals, but it also places human cognition into a more complete evolutionary and less exalted perspective. What better way to combat the rampant arrogance of the human animal? As research in the realm of comparative cognition continues, we expect there to be much greater contact with the areas of cognitive science and behavioral neuroscience. Fuller elucidation of the similarities and differences between human and animal cognition plus greater appreciation of the biological mechanisms of cognition will surely come from these contacts. Here it might be noted that research in comparative cognition has resided somewhat uncomfortably between cognitive science and behavioral neuroscience. Cognitive scientists may pay little attention to research in comparative cognition because nonhuman animals are deemed to

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lack language and formal logic. Behavioral neuroscientists may pay little attention to research in comparative cognition because the basic mechanisms of associative learning have yet to be fully explicated; moving to the more advanced processes of cognition that have been considered in this chapter may be judged to be premature. Future work into linguistic and logical processing by animals should help to make comparative cognition more relevant to the interests of cognitive scientists. And, deploying the more elaborate paradigms of comparative cognition to explicate the neural systems of adaptive action should help to make the field more relevant to the interests of behavioral neuroscientists as they tackle increasingly complex behavioral adaptations. Another realm of contact is with developmental psychology. Those who study the development of human cognition face many of the same problems as comparative psychologists. Key among them is that their focal subjects may not speak. Although the absence of speech might at first blush appear to be a serious limitation to elucidating the nature and mechanisms of cognition, it is also an important opportunity to explore the longstanding question of thought without language. If the evidence that we have reviewed in this chapter stands the test of full scientific scrutiny, then thought without language may not only be possible, but it may be the rule rather than the exception—certainly for most animals and possibly for infants as well. We see all of these contacts—with cognitive science, behavioral neuroscience, and developmental psychology—as critical for the longtime vitality of the study of comparative cognition. Comparative cognition is not an isolated field of behavioral science, but one that is intimately interlaced with other cognate areas. All are rooted in evolutionary theory and dedicated to the natural science of the mind. REFERENCES Adam, C. & Tannery, P. (1908). Oeuvres de Descartes. Paris, France: Cerf. Adams, C. D., & Dickinson, A. (1981). Instrumental responding following reinforcer devaluation. Quarterly Journal of Experimental Psychology, 33B, 109–122. Allan, L. G. (1993). Human contingency judgments: Rule based or associative? Psychological Bulletin, 114, 435–448. Anderson, S. R. (2004). A telling difference. Natural History (November), 38–43. Ariew, R. (2000). Ren´e Descartes: Philosophical essays and correspondence. Indianapolis, IN: Hackett. Aristotle. (2004). Politics. Whitefish, MT: Kessinger. Avramides, A. (1996). Descartes and other minds. Teorema, 16, 27–46. Babb, S. J. & Crystal, J. D. (2006). Episodic-like memory in the rat. Current Biology, 16, 1317–1321.

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CHAPTER 19

Biological Models of Associative Learning JEANSOK KIM, RICHARD F. THOMPSON, AND JOSEPH E. STEINMETZ

INVERTEBRATE PREPARATIONS 511 SPINAL CONDITIONING 517 CLASSICAL AND INSTRUMENTAL CONDITIONING OF DISCRETE RESPONSES 525 CLASSICAL JAW-MOVEMENT CONDITIONING 537

NEURAL SUBSTRATES OF THE INSTRUMENTAL CONDITIONING OF DISCRETE RESPONSES 538 CONCLUSION 540 REFERENCES 540

Over the course of the 20th century and continuing in the present century, extraordinary progress has been made in our understanding of the behavioral principles of basic associative learning and memory and the mechanisms by which the nervous system codes, stores, and retrieves these memories. Current views recognize a number of different forms or aspects of learning and memory involving different neural systems (Figure 19.1) (Squire & Knowlton, 1994). Many workers distinguish between “declarative” and “nondeclarative” memory. Declarative memory generally refers to explicit memories of “what,” that is, one’s own previous experiences, recognition of similar scenes and objects, and so forth. That is clearly slanted toward human verbal memory; some workers have even equated it with the information one can be aware of. However, recognition memory clearly occurs in all mammals studied and even in some invertebrate preparations. Here we focus on nondeclarative, implicit or procedural memory, memory of “how.” The vast majority of memory processes in infrahuman animals, and many aspects of memory in humans, are of this sort. Consider all your likes and dislikes, all the skilled movements you perform (tennis, golf, swimming, bicycle riding, not to mention walking and talking), and so on. Procedural or nondeclarative is really a grab-bag category; it even includes some aspects of recognition memory, as in visual priming memory. The categories of memory shown in Figure 19.1 are of course somewhat arbitrary and by no means mutually exclusive. When an organism learns something important,

several of these memory systems can become engaged. At a more general level, all aspects of learning share a common thrust. As Rescorla (1988) has stressed, basic associative learning is the way organisms, including humans, learn about causal relationships in the world. It results from exposure to relations among events in the world. For both modern Pavlovian and cognitive views of learning and memory, the individual learns a representation of the causal structure of the world and adjusts this representation through experience to bring it in tune with the real causal structure of the world, striving to reduce any discrepancies or errors between its internal representation and external reality (see also Dudai, 1989; Rescorla & Wagner, 1972; Wagner & Rescorla, 1972). Nonassociative learning involves the effect of a single event upon response probability and magnitude. The three examples of nonassociative learning that have received the most attention are habituation, dishabituation, and sensitization. Habituation is defined as a reduction in responding to a repeatedly delivered stimulus where adaptation and fatigue do not contribute to the decremented response (see Thompson & Spencer, 1966). Dishabituation refers to the restoration or recovery of a habituated response by the presentation of another, typically strong stimulus to the animal. Sensitization is an enhancement or augmentation of a response produced by the presentation of a strong stimulus. In vertebrate systems, at least, dishabituation appears to be an instance of sensitization (Groves & Thompson, 1970). Associative learning is a very broad category that includes much of the learning we do, from learning to 509

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DECLARATIVE (EXPLICIT)

FACTS

EVENTS

NONDECLARATIVE (IMPLICIT)

SKILLS

PRIMING

BASIC ASSOCIATIVE LEARNING

NONASSOCIATIVE LEARNING

EMOTIONAL SKELETAL RESPONSES MUSCULATURE

HIPPOCAMPUSSTRIATUM; REFLEX NEOCORTEX AMYGDALA CEREBELLUM MEDIAL TEMPORAL LOBE; MOTOR CORTEX; (HIPPOCAMPUS) (HIPPOCAMPUS) PATHWAYS DIENCEPHALON CEREBELLUM

Figure 19.1 Multiple memory systems and their associated brain structures. Memory systems in the brain can be categorized into two major types: declarative and nondeclarative. The nondeclarative system is also referred to as implicit or procedural. Each system is supported by distinct anatomical regions of the brain. (Modified from Squire and Knowlton, 1994.)

be afraid to learning to talk to learning a foreign language to learning to play the piano. In essence, associative learning involves the formation of associations among stimuli and/or responses. It is generally subdivided into classical vs. instrumental conditioning or learning. Classical or Pavlovian conditioning refers to the procedure where a neutral stimulus, termed a conditioned stimulus (CS), is paired with a stimulus that elicits a response, termed an unconditioned stimulus (US), for example, food that elicits salivation or a shock to the foot that elicits limb withdrawal. Instrumental or operant conditioning describes a situation in which the animal or person must perform some response in order to obtain reward or avoid punishment. That is, the subject can control the occurrence of the US. Classical or Pavlovian conditioning according to the traditional view is a procedure that refers to the operation of pairing one stimulus, the CS, with a second stimulus, the US, as noted above. The US reflexively elicits a response prior to pairing with the CS, termed the unconditioned response (UR). Repeated pairings of the CS and US result in the CS eliciting a learned response defined as the conditioned response (CR). Critically important variables for conditioning are order: The CS precedes the US; timing: the interval between CS and US; and contiguity: the pairing of the CS and US. Conditioning procedures where the CS and US overlap in time are called delay conditioning, whereas trace conditioning consists of a procedure where a time interval of no stimulation exists between the CS and US. It is often, but not always, the case that CR

is similar to the UR (i.e., in Pavlov’s experiment both are salivation). A more general and contemporary view of Pavlovian conditioning has emerged that has emphasized the relationship between the CS and US. That is, the information that the CS provides about the occurrence of the US is the critical feature for learning. This perspective on Pavlovian conditioning is consistent with current cognitive views of learning and memory, as noted above. Thus, the generation of a new response to the CS that has properties similar to the US is viewed as less important. Indeed, in some situations the CR is quite different from the UR: Footshock causes an increase in activity (UR) in the rat; fear learned to a tone paired with this same footshock is expressed as freezing (CR). But note that both responses are adaptive. Instead, as noted earlier, conditioning involves learning about the relations between events in the organism’s environment. In this view, the key process is the contingencies among events in the organism’s environment. Consider the following experiment. A group of rats is given a series of paired tone CS-footshock US trials and learn very well to freeze (the CR) when the CS occurs. Another group of rats is given the same number of paired CS-US-trials but is also given a number of US-alone presentations as well. Animals in this group do not learn to freeze to the CS at all. Both groups had the same number of contiguous pairings of CS and US, but the contingency, the probability that US would be signaled by CS, was very much lower in the group given US-alone trials as well (see Rescorla, 1988).

Invertebrate Preparations

Our focus in this chapter is on classical conditioning but we also consider examples of instrumental learning, particularly processes of instrumental avoidance that relate closely to the phenomena of classical conditioning with an aversive US. Analysis of possible mechanisms underlying processes of learning and memory has been greatly facilitated by the use of “model” systems—simplified preparations in animals where cellular and molecular mechanisms underlying behavioral plasticity can be analyzed. A number of invertebrate preparations have been used as model systems; spinal reflexes have served as a vertebrate model system. We review this literature briefly here; the major focus of this chapter is brain substrates of learning and memory in behaving mammals.

INVERTEBRATE PREPARATIONS Certain invertebrate nervous systems have several advantages for analysis of mechanisms of plasticity: They may contain from hundreds to thousands of neurons in contrast to the billions of neurons in vertebrate nervous systems. Many of the neurons are large and can be identified as unique. Circuits can be identified that exhibit plasticity and have only a small number of neurons. As Beggs et al. (1999) noted: “For many years, the general belief was that the small number of neurons found in most invertebrates limits their behavioral capabilities to only the simplest forms of behavioral modifications such as habituation and sensitization. However, it has become clear that even invertebrates exhibit more complex behavioral modifications such as classical conditioning, operant conditioning, and higher-order forms of classical conditioning” (p. 1415). In the chapter on basic mechanisms and systems of learning and memory in the original text on Fundamental Neuroscience (Zigmond et al., 1999), Beggs et al. provide a very helpful summary of invertebrate preparations that have proved useful for providing insights into possible mechanisms underlying learning. We present their summary, unchanged by any editorial comments we might make (Beggs et al., Box 55.1, pp. 1416–1417, in Zigmond et al., 1999). They treat Aplysia and Hermissenda separately, as do we. The focus of their summary is on laboratory studies, where analysis of mechanisms can to some degree be done, as opposed to the often-rich behavioral phenomena exhibited by some invertebrate species in the natural or “ethological” state, as in the dance of the honeybee.

511

Aplysia The marine mollusk Aplysia has a relatively simple nervous system with large, identifiable neurons that are accessible for detailed anatomical, biophysical, and biochemical studies. Neurons and neural circuits that mediate many behaviors in Aplysia have been identified in heroic studies by Eric Kandel and his many associates (Kandel, 1976). In several cases, these behaviors have been shown to be modified by experience. Two preparations have been particularly useful: the siphon-gill withdrawal reflex and the tail-siphon withdrawal reflex (Figure 19.2). In the siphon-gill reflex, tactile or electrical stimulation of the siphon causes withdrawal of the siphon and gill, a simple defensive reflex. Stimulation of the tail of the animal elicits a set of defensive responses, including withdrawal of the tail and siphon. Relatively simple neuronal circuits mediate these reflexes. Indeed the neural circuits subserving these reflexes can be isolated, with siphon-gill and tail connected, or can be completely isolated from body tissues and studied as neural networks. The key feature of these circuits is that the sensory neurons have monosynaptic connections to the motor neurons. These circuits exhibit habituation, sensitization, and classical conditioning (see Byrne & Kandel, 1996). Most of the analytic work on classical conditioning has actually been done with sensitization. Short-term sensitization is induced by a single brief train of shock to the body wall (or appropriate nerves) that causes release of modulatory neurotransmitters (e.g., serotonin) from interneurons onto the sensory neurons to enhance transmitter release. The mechanism involves activation of adenylyl cyclase, which leads to increased cAMP in the sensory neurons. This results in protein phosphorylation, which alters membrane channels in the neurons, resulting in membrane depolarization, enhanced excitability, and an increase in the duration of the action potential (Figure 19.3). Other synergistic processes also occur. The net result of all this is that stimulation of the sensory neurons results in increased probability of transmitter release at their terminals and a larger postsynaptic response in the motor neurons. Note that the plasticity here is a presynaptic phenomenon. Long-term sensitization, unlike short-term sensitization, requires protein synthesis. The repeated sensitizing stimulus leads to more prolonged phosphorylation and activation of nuclear regulatory proteins by protein kinase A. The key step involves translocation of the catalytic subunit of PKA into the nucleus of sensory neurons where it appears to activate CREB (cAMP responsive element binding protein) that results in long-term sensitization

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BOX 55.1 SOME INVERTEBRATES THAT HAVE PROVEN USEFUL FOR PROVIDING INSIGHTS INTO THE MECHANISMS UNDERLYING LEARNING Gastropod Molluscs Pleurobranchaea. The opisthobranch Pleurobranchaea is a voracious marine carnivore. When exposed to food, the animal exhibits a characteristic bite-strike response. After pairing a food stimulus (CS) with a strong electric shock to the oral veil (US), the CS, instead of eliciting a bite-strike response, elicits a withdrawal and suppression of feeding responses (conditioned response, CR). The task is acquired within a few trials and is retained for up to 4 weeks. Neural correlates of associative learning have been analyzed by examining responses of various identified neurons in the circuit to chemosensory inputs in animals that have been conditioned. One correlate is an enhanced inhibition of command neurons for feeding (London & Gillette, 1986). Tritonia. The opisthobranch Tritonia diomedea undergoes a stereotypic rhythmic swimming behavior in an attempt to escape a noxious stimulus. This response exhibits both habituation and sensitization and involves changes in multiple components of swim behavior in each case (Frost et al., 1996). The neural circuit consists of sensory neurons, precentral pattern generating (CPG) neurons, and motor neurons. Habituation appears to involve plasticity at multiple loci, including decrement at the first afferent synapse. Sensitization appears to involve an enhanced excitability and synaptic strength of one of the CPG interneurons. Pond Snail (Lymnaea stagnalis). The pulmonate Lymnaea stagnalis exhibits fairly rapid nonaversive conditioning of feeding behavior. A neutral chemical or mechanical stimulus (CS) to the lips is paired with a strong stimulant of feeding such as sucrose (US) (Kemenes & Benjamin, 1994). Greater levels of rasping, a component of the feeding behavior, can be produced by a single trial, and this response can persist for at least 19 days. The circuit consists of a network of 3 types of CPG neurons, 10 types of motor neurons and a variety of modulatory interneurons. An analogue of the learning occurs in the isolated central nervous system. The enhancement of the feeding motor program appears to be due to an increased activation of the CPG cells by mechanosensory inputs from the lips.

Land Snail (Helix). Food-avoidance conditioning procedures similar to those used with Pleurobranchaea have been adopted for use in the land snail. A food stimulus such as a piece of carrot (CS) is paired with an electric shock to the dorsal surface of the snail (US). After 5–15 pairings, the carrot, instead of eliciting a feeding response, elicits a withdrawal and suppression of feeding responses. The transmitter serotonin appears to have a critical role in learning. Animals injected with a toxin that destroys serotonergic neurons exhibit normal responses to the food and the shocks alone, but are incapable of learning. Helix also exhibit habituation and sensitization of avoidance responses elicited by tactile stimuli (Balaban, 1993). Limax . The pulmonate Limax is an herbivore that locomotes toward desirable food odors, making it wellsuited for food-avoidance conditioning. The slug’s normal attraction to a preferred food odor (CS) is significantly reduced when the preferred odor is paired with a bitter taste (US). In addition to this example of classical conditioning, food-avoidance in Limax exhibits higher-order features of classical conditioning such as blocking and second-order conditioning. An analogue of taste-aversion learning has been shown to occur in the isolated central nervous system, which will facilitate subsequent cellular analyses of learning in Limax . The procerebral (PC) lobe in the cerebral ganglion processes olfactory information and is a likely site for the plasticity (Gelperin, 1994). Arthropods Cockroach (Periplaneta americana) and Locust (Schistocerca gregaria). Learned modifications of leg positioning in the cockroach and locust may serve as a valuable preparation for the cellular analysis of operant conditioning. In this preparation, the animal is suspended over a dish containing a fluid. Initially, the insect makes many movements, including those that cause the leg to come in contact with the liquid surface. If contact with the fluid is paired with an electric shock, it learns rapidly to hold its foot away from the fluid. Neural correlates of the conditioning have been observed in somata of the leg motor neurons. These correlates include changes in intrinsic firing rate and membrane conductance. Crayfish (Procambarus clarkii). The crayfish tailflip response exhibits habituation and sensitization. A key component of the circuit is a pair of large neurons

Invertebrate Preparations

called the Lateral Giants (LGs), which run the length of the animal’s nerve cord. The LGs are the decision and command cells for the tailflip. Learning is related to changes in the strength of synaptic input driving the LGs. Honeybee (Apis mellifera). Honeybees, like other insects, are superb at learning. For example, sensitization of the antenna reflex of Apis mellifera is produced as a result of presenting gustatory stimuli to the antennae. Classical conditioning of feeding behavior can be produced by pairing visual or olfactory CSs with sugar solutions (US) to the antennae. The small size of bee neurons is an obstacle in pursuing detailed cellular analyses of these behavioral modifications. Nevertheless, regions of the brain necessary for associative learning have been identified, and some neural correlates have been described. In particular, intracellular recordings have revealed that one identified cell, the ventral unpaired median (VUM) neuron, is sufficient to mediate the reinforcing effects of the US (Hammer & Menzel, 1995). Drosophila. Since the neural circuitry in the fruit fly is both complex and inaccessible, the fly might seem to be an unpromising subject for studying the neural basis of learning. However, the ease with which genetic studies are performed compensates for the difficulty to perform electrophysiological studies (DeZazzo & Tully, 1995). A frequently used paradigm is a two-stage differential odor-shock avoidance procedure, which is performed on large groups of animals simultaneously rather than on individual animals. Animals learn to avoid odors paired (CS+) with shock but not one explicitly unpaired (CS–). This learning is typically retained for 4–6 hours, but 24 hours to 1week retention can be produced by a spaced training procedure. Several mutants deficient in learning have been identified. Many of these mutants affect elements of the cAMP-signaling pathway. Recent experiments using inducible genes demonstrate a role for cAMPresponsive transcription factors in the induction of long-term memory. These transcription factors are also important for long-term memory in Aplysia, and in vertebrates.

(Figure 19.3). Following this discovery of the key role of CREB, by Dash, Hochner, and Kandel in 1990, much work has been done on the role of CREB in memory processes in mammalian models (see Silva et al., 1998). One of the newly synthesized proteins initiates internalization and degradation of neuronal cell adhesion

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Annelids Leech. Defensive reflexes in the leech (Hirudo medicinalis) exhibit habituation, dishabituation, sensitization, and classical conditioning. For example, the shortening response is enhanced following pairing of a light touch to the head (CS) with electric shock to the tail (US). The identified S cells appear critical for sensitization, as their ablation disrupts sensitization. Interestingly, ablation of the S cells only partly disrupts dishabituation, indicating that separate processes contribute to dishabituation and sensitization (Sahley, 1995). Separate processes also contribute to dishabituation and sensitization in Aplysia. The transmitter serotonin (5-HT) appears to mediate at least part of the reinforcing effects of sensitizing stimuli and the US. Serotonin appears to play similar roles in Aplysia, Helix, Hermissenda, and Tritonia. Nematoda Caenorhabditis elegans. Although analyses in C. elegans are just beginning, this animal promises to be a valuable preparation for the cellular and molecular studies of learning. Its principal advantages are threefold. First, its nervous system is extremely simple. It has a total of 302 neurons, all of which have been described in terms of their locations and synaptic connections. Second, the developmental lineage of each neuron is completely specified. Third, it is amenable to genetic and molecular manipulations. Recently, the animal has been shown to exhibit several forms of learning. When a vibratory stimulus is applied to the medium upon which they locomote, adult C. elegans will swim backward. This reaction, known as the tap withdrawal reflex, exhibits habituation, dishabituation, sensitization, long-term (24-hour) retention of habituation training, and context conditioning. Although the neurons are small and difficult to record, aspects of the neural circuit have been described. The particular role of individual neurons is being elucidated using laser ablation to remove specific neurons from the circuit (Wicks & Rankin, 1995).

molecules (NCAMS), allowing restructuring of the axon terminal arbors. Other synergistic biochemical processes also occur. Eric Kandel received the Nobel Prize in Physiology and Medicine in 2000 in part for his elucidation of these biochemical processes underlying behavioral plasticity in Aplysia.

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Biological Models of Associative Learning

(A)

(B) Abdominal ganglion

IN

SN

Left pleural ganglion

IN

IN

SN

MN MN

SN

MN

MN

Left pedal ganglion

Gill

Right pleural ganglion

Right pedal ganglion

MN

Abdominal ganglion

Siphon

Siphon

Tail

Figure 19.2 Simplified circuit diagrams of the siphon-gill (A) and tail-siphon (B) withdrawal reflexes. Stimuli activate the afferent terminals of the mechanoreceptor sensory neurons (SN) whose somata are located in central ganglia. The sensory neurons make excitatory synaptic connections (triangles) with interneurons (IN) and motor neurons (MN). The excitatory interneurons provide a parallel pathway for excitation of the motor neurons. Action potentials elicited in the motor neurons, triggered by the combined input from the SNs and INs, propagate out peripheral nerves to activate muscle cells and produce the subsequent reflex withdrawal of the organs. Modulatory neurons (not shown here), such as those containing serotonin (5-HT), regulate the properties of the circuit elements and, consequently, the strength of the behavioral responses. (From Beggs et al., 1999.)

Hermissenda Another invertebrate model that has proved amenable to cellular and molecular analysis of mechanisms of behavioral plasticity is the Pacific nudibranch Hermissenda crassicornis, studied in detail by Daniel Alkon and his many associates. The CR study involves pairing a light CS with high-speed rotation (stimulating hair cell gravity receptors) as a US. Conditioning results in a CS-elicited suppression of the normal positive phototaxic response and a foot shortening (the normal response to a light is foot lengthening), lasting for days (Figure 19.4). Since the sensory systems activated by the CS and US are central, the conditioning process can be studied in the isolated nervous system (see Alkon, 1989; Crow, 1988). The eyes of Hermissenda are very simple no-image forming photoreceptors, labeled type A (two) and type B (three).

Cellular correlates of the CR involve a significant increase in CS-elicited spike frequency and enhanced excitability in the type B photoreceptor and similar changes in the type A photoreceptor. Note that, like sensitization in Aplysia, these are changes in sensory neurons. Several mechanisms have been discovered to cause this increased excitability in the photoreceptor neurons, most particularly two potassium currents that are reduced as a result of conditioning. Since outward potassium currents reduce cell excitability, reduction in the currents would increase cell excitability, as seen in Hermissenda conditioning. Note that these charges are intrinsic to the neurons and not necessarily due to any synaptic processes. It appears that the phosphoinositide system is responsible for the reduction in K+ currents in Hermissenda conditioning. Activation of PKC may be initiated by actions of an agonist released by stimulation of the US pathway

Invertebrate Preparations (A1)

(A2) IN 5-HT

Control

515

(A3) After activation of IN

MN

SN

MN

SN ICa

(B) “N” type

5-H

T

Sensitizing stimuli

+

“L” type gCa,Nif gK,V

5-HT

DAG PKC cAMP

− − − gK,S

+

+ IFP + ApTBL-1 − NCAM

Transcription Translation K+

Ca2+

+

PKA +

gK,Ca

gCa

+ CaM

+ Growth

Figure 19.3 Model of heterosynaptic facilitation of the sensorimotor connection that contributes to short- and long-term sensitization of Aplysia. (A1) Sensitizing stimuli activate facilitatory interneurons (IN) that release modulatory transmitters, one of which is 5-HT. The modulator leads to an alteration of the properties of the sensory neuron (SN). (A2, A3) An action potential in SN after the sensitizing stimulus results in greater transmitter release and hence a larger postsynaptic potential in the motor neuron (MN) than an action potential before the sensitizing stimulus. For short-term sensitization, the enhancement of transmitter release is due, at least in part, to broadening of the action potential and an enhanced flow of CA2+ (ICa ) into the sensory neuron. (B) Molecular events in the sensory neuron. 5-HT released from the facilitatory neuron (A1) binds to at least two distinct classes of receptors on the outer surface of the membrane of the sensory neuron, which leads to the transient activation of two intracellular second messengers, DAG and cAMP. The second messengers, acting through their respective protein kinases, affect multiple cellular processes, the combined effects of which lead to enhanced transmitter release when a subsequent action potential is fired in the sensory neuron. Long-term alterations are achieved through regulation of protein synthesis and growth. Positive (+) and negative (−) signs indicate enhancement and suppression of cellular processes, respectively. (From Beggs et al., 1999.)

(Figure 19.5). Serotonergic neurons may provide polysynaptic input to the visual system, acting synergistically (see Alkon, 1989; Crow, 1988; Matzel et al., 1990). It is important to note that a similar decrease in a calcium-dependent slow afterhyperpolarization, mediated by a voltage-gated potassium conductance, results in a learning-induced increase in excitability of pyramidal neurons in the hippocampus of rabbits as a result of eyeblink conditioning (Disterhoft et al., 1986; de Jonge et al., 1990; see discussion further on).

Comment Advances in our understanding of the cellular and molecular mechanisms underlying various forms and aspects of behavioral plasticity and memory in at least some of the invertebrate models, particularly Aplysia and

Hermissenda, have been spectacular. There would appear to be clear points of contact with putative mechanisms of memory formation in mammals, e.g., CREB, and potassium channel mediated afterhyperpolarization. Study of these systems in their own right is exciting and eminently worthwhile. However, as egocentric mammals we must ask to what extent these systems and mechanisms apply to mammals? Note that the key processes in both Aplysia and Hermissenda systems are in the sensory neurons, hence the changes are presynaptic. To date, no such changes have been reported for sensory neurons in mammals. A major putative mechanism in mammalian learning, LTP, appears to be postsynaptic, at least in the CA1 area of the hippocampus. We note that recent studies by Glanzman and associates (Lin & Glanzman, 1994) have shown that postsynaptic changes in the motor neurons may occur in the monosynaptic Aplysia circuit. Most of the Aplysia

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Biological Models of Associative Learning

(a)

(b)

Unconditioned response

Conditioned response 1. Foot length in the dark before presentation of the CS

2. Foot shortening elicited by the presentation of the CS

Figure 19.4 Conditioned foot-shortening of Hermissenda. The unconditioned response (UR) is shown in (a) as the outline of the foot represented by the dashed line in response to rotation of the animal, the unconditioned stimulus (US). Comparison of the length of the foot after Pavlovian conditioning in the dark (b1) and in response to the presentation of the conditioned stimulus (CS) in (b2). The area indicated by the dashed lines represents the foot-shortening conditioned response (CR). Pseudorandom and random presentations of the CS and US do not result in the development of phototactic suppression or foot-shortening. (From Beggs et al., 1999.) Na+ CS (light) GABA Ca2+

US (rotation) ↑ICa(t)

PKA

Kinase? Transcriptional & translational control

ERK 1, 2 DAG

K+ PKC

US (rotation) 5-HT

↓IA ↑ICa(s) ↓IK(Ca) ↓IK(v) ↓ICa(s) ↓Iir

Figure 19.5 Cellular model for the mechanism of Pavlovian conditioning of Hermissenda. A modulatory transmitter released by stimulation of the US pathway binds to 5-HT and/or γ-aminobutyric acid (GABA) receptors. The receptor-activated signal is transmitted through a G protein to the enzyme phospholipase C (not shown). A precursor lipid, PIP2 (phosphatidylinositol 4,5-biphosphate), is cleaved to yield inositol trisphosphate and diacylglycerol (DAG). The DAG and Ca2+ released by inositol trisphosphate from internal stores activate protein kinase C (PKC), which may reduce K+ currents and enhance cellular excitability. The CS results in increased levels of intracellular Ca2+ produced by the depolarizing generator potential and light-induced release of Ca2+ from intracellular stores. Pairing specificity may result from synergistic action of Ca2+ and PKC-dependent phosphorylation by stimulation of the US pathway, or activation of extracellular signal-regulated protein kinases (ERK1,2). Time-dependent activation of second messengers and ionic events have been proposed to account for the reduction of K+ currents and synaptic enhancement and enhanced excitability. This activation may also be responsible for protein synthesis and gene expression necessary for long-term memory. (From Beggs et al., 1999.)

Spinal Conditioning

results on classical conditioning have actually been obtained for sensitization. In mammalian systems great effort is extended to rule out sensitization in classical conditioning studies. Indeed, sensitization appears to play no role in mammalian classical conditioning studies. There are more general issues as well. To what extent do processes of long-term sensitization and classical conditioning play roles in the natural environment in Aplysia, or light-rotation in Hermissenda? That is, are the laboratory studies imposing plasticity that may not normally occur? More generally, are the control procedures used in these invertebrate studies, which are taken from the mammalian literature, entirely appropriate? In some instances the learning procedure itself may have problems. For example, in fruit fly learning, individual animals are not trained, instead the data are single numbers for groups of animals. The degrees of convergence between mechanisms of memory in invertebrates and mammals will become clearer as we learn more about mechanisms in the mammalian brain. There is much to be done.

SPINAL CONDITIONING The early history of spinal conditioning—the possibility that classical or instrumental training procedures could induce associative learning-like phenomena in the vertebrate spinal cord—was somewhat controversial. Pavlov’s dictum to the effect that associative learning required the cerebral cortex did not help matters. Shurrager, working in Culler’s laboratory (where so many pioneering studies of brain substrates of learning and memory were carried out), published the first modern studies of classical conditioning of spinal reflexes (Shurrager & Culler, 1940, 1941). In brief, they used acute spinal dogs, measured the twitch response of a partially dissected flexor muscle, gave paw shock as a US and weak stimulation of the tail as CS. They obtained robust acquisition in about half their animals and demonstrated CS-alone extinction and successively more rapid reacquisition in repeated training and extinction sessions. Unfortunately, adequate controls for sensitization and pseudoconditioning were not run in these studies. A few years later Kellogg and associates reported negative results in attempts at spinal conditioning (e.g., Deese & Kellogg, 1949; Kellogg, 1947; Kellogg et al., 1946). They used chronic spinal dogs and the flexor response of the whole leg. The US was shock to the paw of that leg and the CS was a shock to the opposite hind paw. Kellogg’s

517

choice of CS locus was unfortunate. Paw shock elicits a crossed extension reflex that would work against the development of a conditioned flexion response. Pinto and Bromily (1950) completed an extensive spinal conditioning study with long-term acute spinal animals and found only inconclusive evidence because of passive hindquarter movements caused by anterior limb movements. Patterson, Cegavske, and Thompson (1973) completed a detailed and extensive study of spinal conditioning, using a number of control procedures and conditions, which yielded clear positive results. Animals were anesthetized, spinalized (T-12), given local anesthetics, then paralyzed with flaxedil and given artificial respiration (Figure 19.6). The superficial and deep peroneal motor nerves were dissected out and placed on stimulating (CS—S1n in Figure 19.6) and recording (UR, CR—Rn in Figure 19.6) electrodes. The CS was a weak shock to the superficial peroneal nerve of intensity yielding a motor nerve response to the first pulse. The US was a series of pulses to skin of the left ankle (B in Figure 19.6), yielding a UR (response of deep peroneal nerve). The conditioning group received 75 acquisition trials, 250 msec forward ISI, and 50 CSalone extinction trials. Control groups received explicitly unpaired CS and US trials (75 each). In one series a CS-alone trials group was also included. Two separate experiments were completed; both showed clear evidence of associative learning. These experiments clearly ruled out sensitization as a process responsible for the increase in CS response in the paired group. The fact that the animals were paralyzed ruled out movement artifacts. Acquisition was rapid, as was extinction, just as in the original Shurrager and Culler studies. These results were replicated in careful studies by Durkovic (1975). In a very interesting study, Durkovic and Prokowich (1998) infused intrathecally artificial CSF (vehicle) or the NMDA blocker APV during the conditioning period in acute spinal cats, using procedures described above. Both groups showed normal acquisition of the spinal CR. However, the APV group exhibited no retention of the increased response in the 2.5-hour retention period, in contrast to the CSF alone group. The results suggest that NMDA receptor activation plays a critical role in the establishment of long-term associative plasticity in the spinal cord. A key issue for many is the extent to which this form of spinal Pavlovian conditioning resembles Pavlovian conditioning of discrete responses in the intact animal. Patterson and associates completed a heroic series of parametric studies to address this issue, using the same general procedures as Patterson et al. (1973). In

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Biological Models of Associative Learning Rs A

S1n

Sensory nerve

Dorsal root

T

x

A Ss S2N

?

g

α

Rm Motor nerve B Strong cutaneous stimulation elsewhere on leg

Rn

Y M

Ventral root

Rα

Figure 19.6 Experimental arrangements used to study habituation, sensitization, and classical conditioning of the hindlimb flexion reflex in the acute spinal animal. Electrical stimuli can be delivered to skin (A, S s ; B) or to afferent nerves (S 1n , cutaneous nerve; S 2n , muscle nerve; x, dorsal root). Responses can be recorded from the muscle (Rm), the motor nerve (Rn), the motor neurons (Rα with microelectrode M ), or from ventral root (γ). (From Thompson, 1967.)

brief, spinal conditioning exhibits differential conditioning (Beggs, Steinmetz, & Patterson, 1985), forward but not backward conditioning (Patterson, 1975), retention of the CR over a period of hours (Beggs et. al., 1983), increasingly effective conditioning with increasing US strength (Polenchar et al., 1984) and best learning with a 250 onset forward ISI. (See Patterson, 1976, for a detailed review of all studies to that time on spinal conditioning.) All these properties resemble the properties of classical conditioning of discrete responses in intact mammals. In an interesting series of studies Grau and associates paired shock to a hind leg as a CS with intense tail shock as a US in the spinal rat and then examined effect of CS presentations on antinociception on the tail-flick test (Joynes & Grau, 1996). This paradigm is complex in that the CR is a variation on the US. Intense tail shock would seem to induce massive sensitization. Grau interpreted the results, incidentally, as protection from habituation. The spinal conditioning results reviewed above do resemble classical conditioning of discrete responses in

intact animals in many properties; nonetheless they appear to differ from intact animal learning in several ways. First, acquisition is very rapid; most increases in response to the CS occurring in the first few trials. Second, and perhaps more important, the onset latency of the CR does not appear to move forward in time over the course of learning. Finally, and seemingly most important, spinal conditioning involves an alpha response. In most studies, the CS elicits the UR-CR before training. As a result of training there is an associatively produced increase in the amplitude of the response to the CS. The fact that spinal conditioning may be “alpha” conditioning perhaps accounts for the lack of forward shift of the CR onset with training. The idea that alpha conditioning differs from normal conditioning may be somewhat arbitrary. In one experiment in Patterson et al. (1973), two branches of the deep peroneal nerve were recorded during conditioning. One branch showed responses to the CS (superficial peroneal nerve stimulus) prior to training but the other branch did not. However, by trial 10 the nonresponsive branch

Spinal Conditioning

did show a response to the CS. Is it the case that one branch of the nerve showed alpha conditioning, whereas the other branch did not exhibit alpha conditioning but rather showed only “normal” conditioning? In unpublished pilot studies, Patterson, Thompson, and associates completed some initial analytic studies in an attempt to localize the site(s) of synaptic plasticity that underlie spinal conditioning. The preparation itself, involving paralysis, cutaneous nerve stimulation as the CS, strong cutaneous stimulation of the paw as a US and recording motor nerve responses, ruled out changes in sensory receptors or properties of the muscles. Using a monosynaptic test pathway (Rα in Figure 19.6) they ruled out changes in motor neurons. Similarly, changes in the excitability of the cutaneous afferent fibers were ruled out by stimulation of the terminals (T in Figure 19.6) with antidromic recording (Rs in Figure 19.6). Consequently, the mechanisms of synaptic plasticity must reside within the interneuron circuits (see Figure 19.6) in the spinal gray (see Thompson, 2001). What happens in the spinal cord when the limb flexion response is conditioned in the intact animal? In the otherwise intact animal, lesions in the cerebellar nuclei or rubrospinal tract produce complete and specific abolition of the conditioned limb flexion response (see Krupa & Thompson, 1994; Voneida, 1999; see also the later discussion). In fact, normal animals that undergo leg flexion training prior to spinal transection show no retention or savings of conditioned responses in spinal reflexes following transection (J. Steinmetz, personal communication). The isolated spinal cord is thus capable of mediating a kind of associative neuronal plasticity but does not subserve classical conditioning of the limb flexion response in the intact animal. Spinal conditioning is a useful model to study basic associative plasticity in a simplified neuronal network, but it does not tell us where or how such memories are formed in the intact animal. Fear Conditioning Fear as a scientific term describes a brain state in which a set of adaptive (or defensive) responses is activated in the presence of danger (LeDoux, 1996). While humans and other animals have genetic predispositions to fear certain stimuli, it is also beneficial for animals to have the capacity to learn about new dangers in their environments. For instance, although newborn infants innately exhibit fear to certain stimuli (e.g., loud noises), they do not show inherent fear to flame or heights (two stimuli that most children learn and adults remember to avoid) (Fischer & Lazerson,

519

1984). Accordingly, fear behaviors to certain stimuli and events in the environment appear to be acquired. Classical or Pavlovian fear conditioning has been widely employed for studying the mechanism(s) by which fear is acquired. Fear conditioning occurs when initially neutral conditioned stimuli (CS) are contingently paired with aversive unconditioned stimuli (US) that reflexively activate unconditioned fear responses (URs) (Rescorla, 1967; Watson and Rayner, 1920). Through CS-US association formation, the CS comes to elicit various CRs that share similar characteristics to innate fear responses (Blanchard & Blanchard, 1969; Bolles, 1970; Fanselow, 1984; LeDoux, 1996; Kim & Jung, 2006). A classic example of fear conditioning is the Little Albert experiment by Watson and Rayner (1920). Little Albert was an 11-month-old infant who initially exhibited curiosity (and no fear) to a white rat by touching and playing with it. As Albert’s hand touched the rat, the experimenters banged a steel bar with a hammer behind his head (US), causing him to startle, fall forward, and cry (UR). Afterwards, when the rat (CS) was placed near Little Albert’s hand, he withdrew his hand and began to cry (CR). This exhibition of fear towards the rat was allegedly generalized to other white, furry animals and objects (e.g., rabbits, dogs, fur muffs). Modern investigations of fear conditioning typically employ small mammals (such as rats, mice and rabbits) as subjects and use a tone (or a light or a distinctive environmental setting) as a CS and a mild electric shock (e.g., a footshock) as a US. Under these circumstances a small number of CS-US pairings produce robust fear learning as evidenced by a variety of fear responses exhibited upon subsequent presentations of the CS (Bolles, 1970). In rats, typical fear CR measures include freezing (Blanchard & Blanchard, 1969; Fanselow, 1984), enhancement of musculature reflexes (e.g., startle) (Brown, Kalish, & Farber, 1951; Davis, 1997; Leaton & Borszcz, 1985), analgesia (Helmstetter, 1992), 22 kHz ultrasonic distress vocalization (Blanchard, Blanchard, Agullana, & Weiss, 1991; Lee, Choi, Brown, & Kim, 2001), and alterations in autonomic nervous system activities (e.g., increased heart rate, increased blood pressure, rapid respiration) (Kapp, Frysinger, Gallagher, & Haselton, 1979; Iwata, Chida, & LeDoux, 1987; Stiedl & Spiess, 1997). Because fear conditioning occurs rapidly and with lasting effect, it has become a popular behavioral paradigm for investigating the neurobiological mechanisms of learning and memory (see Davis, 1997; Lavond, Kim, & Thompson, 1993; LeDoux, 1996; Maren & Fanselow, 1996). Fear can also be rapidly acquired through instrumental or operant conditioning in which the presentation of an

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Biological Models of Associative Learning

aversive stimulus is contingent upon the behavior of the animal. A widely employed procedure with rodents is the passive (or inhibitory) avoidance task (Grossman, Grossman, & Walsh, 1975; McGaugh, Introini-Collison, & Nagahara (1988); Nagel & Kemble, 1976), in which the animal’s response (e.g., entering a dark compartment of a box when placed in an adjacent lighted compartment, or stepping down from a platform onto a grid floor) is paired with an aversive footshock experience. As a function of this response-stimulus pairing, the animal learns to avoid making the response that preceded the aversive experience.

Primary sensory cortex

Evidence From Lesion Studies Permanent and reversible lesions of the amygdala, particularly in the central nucleus (ACe), effectively attenuate or abolish a variety of conditioned fear responses in several mammalian species. In rats, amygdalar lesions impair both acquisition (learning) and expression (performance) of conditioned freezing (Blanchard & Blanchard, 1969;

Hippocampus

2 Δ Thalamus

Amygdala as the Locus of Fear Conditioning An accumulating body of evidence from animal and human studies point to the amygdala—an almond-shaped group of nuclei buried deep within the temporal lobes—as the key neural system underlying fear conditioning (see Davis, 1997; Lavond et al., 1993; LeDoux 1996; Maren & Fanselow, 1996). The amygdala, one of the principal structures of the limbic system (Isaacson, 1974), has long been implicated as a crucial emotive brain center in monkey studies (e.g., Kluver & Bucy, 1937; MacLean and Delgado, 1953; Weiskrantz, 1956). Anatomically, the amygdala receives sensory inputs from diverse areas of the brain (e.g., thalamus, hypothalamus, neocortex, olfactory cortex, hippocampus) and sends projections to various autonomic and somatomotor structures that mediate specific fear responses (e.g., bed nucleus of stria terminalis for activating stress hormones, periaqueductal gray matter for defensive behavior, lateral hypothalamus for sympathetic activation) (LeDoux, 1996). It is generally accepted that various sensory information enter the amygdala through its basal and lateral nuclei (Aggleton, 2000; LeDoux, 1996) where CS-US association formation is believed to take place (Figure 19.7). These nuclei are reciprocally interconnected with the central nucleus, which appears to be the main amygdaloid output structure that sends projections to various autonomic and somatomotor centers involved in mediating specific fear responses. Various types of experimental evidence indicate that the amygdala is the locus of fear conditioning.

Higher order association cortex

1

Δ

Amygdala

Autonomic & Somatomotor Structures

Sensory stimuli (CS, US)

SSDRs

Figure 19.7 A simplified putative fear conditioning model. The CS (e.g., tone) information is processed via two separate pathways to the amygdala. One pathway is through (1) the direct thalamo-amygdalar projection. The second pathway is through (2) the indirect thalamo-cortico-amygdalar projection. The footshock US information seems to be conveyed to the amygdala via a diffuse somatosensory pathway. The CS-US association formation is hypothesized to occur in the lateral nucleus of the amygdala.  denotes modifiable connections. Adapted from LeDoux (1996).

Iwata et al., 1987; Kim et al., 1993), potentiated startle (Choi, Lindquist, & Brown, 2001; Hitchcock & Davis, 1986), analgesia (reduction in pain sensitivity) (Helmstetter, 1992), and increase in blood pressure (Iwata et al., 1986). Similarly, reversible inactivation of neurons (prior to fear conditioning) in the basolateral amygdala complex (BLA), via micro-infusing the γ-aminobutyric acid (GABAA ) agonist muscimol, blocks the acquisition of conditioned fear; whereas muscimol infusions (prior to retention testing) in previously fear conditioned rats impair the expression of conditioned fear (Helmstetter & Bellgowan, 1994; Muller et al., 1997). In rabbits, amygdalar lesions have been found to impede conditioned bradycardia (deceleration in heart rate) (Gentile, Jarrell, Teich, McCabe, & Schneiderman, 1986; Kapp et al., 1979); whereas in cats, reversible cryogenic (cooling) inactivation of the ACe reduces conditioned blood pressure and respiratory responses (Zhang, Harper, & Ni, 1986). Besides affecting fear CRs, amygdalar lesions also influence innate unconditioned fear responses (URs). For instance, amygdalectomized rats fail to display normal defensive freezing behavior in the presence of a cat

Spinal Conditioning

predator (Blanchard & Blanchard, 1972). The fact that lesions to the amygdala interfere with both fear CRs and URs indicates that the amygdala receives both CS and US information (Figure 19.7). Lesions restricted to particular structures afferent to the amygdala can impede fear conditioning to specific CSs. For example, lesions to the medial geniculate nucleus (MGN) of the thalamus, which relays auditory information to the amygdala (LeDoux, Farb, & Ruggiero, 1990), block the formation of the tone-footshock association, but not light-footshock association (LeDoux et al., 1986; Campeau & Davis 1995). Similarly, in rabbits, lesions limited to the medial border of the MGN disrupted differential bradycardia CRs to CS+ and CS− tones, even though the magnitude of the bradycardia response was unaffected (Jarrell, Gentile, McCabe, & Schneiderman, 1986). Amygdalar lesions, by contrast, abolished the retention of differential fear conditioning of bradycardia in rabbits (Gentile et al., 1986). These results suggest that the MGN relays auditory CS to the amygdala, where fear conditioning is likely to take place. It has been shown that the MGN sends auditory information to the amygdala both directly (via the thalamo-amygdala pathway) and indirectly (via the thalamo-cortico-amygdala pathway) (Figure 19.7), with either pathway capable of fully supporting auditory fear conditioning when the other pathway is incapacitated (Romanski & LeDoux, 1992). In the intact brain, however, the default auditory CS pathway appears to be the thalamo-cortico-amygdalar pathway (Boatman & Kim, 2006). On the efferent side, the amygdala sends projections to particular hypothalamic and brainstem areas that mediate specific fear CRs (Francis, Hernandez, & Powell, 1981; Hitchcock et al., 1989; Iwata et al., 1986, Kim et al., 1993). For instance, lesions to the lateral hypothalamus selectively impair conditioned blood pressure (but not freezing) response, whereas lesions to the ventrolateral portion of the periaqueductal gray (PAG) matter abolish conditioned freezing (but not blood pressure) response (LeDoux, Iwata, Cicchetti, & Reis, 1988). Lesions to the ventrolateral PAG also do not affect the expression of the conditioned bradycardia response in rabbits (Wilson & Kapp, 1994). These examples of double dissociations of CSs and CRs, as a result of damaging afferent and efferent structures to the amygdala, are consistent with the view that the amygdala is a critical mediator of fear conditioning. Interestingly, other studies suggest that different amygdalar nuclei mediate independent fear learning systems, that is, the ACe controls the classical fear responses, whereas the BLA controls the instrumental fear responses

521

(Amorapanth, LeDoux, & Nader, 2000; Killcross, Robbins, & Everitt, 1997; Nader & LeDoux, 1997). The possible existence of multiple fear learning systems is perhaps not unexpected given the evolutionary importance of fear conditioning in survival. Evidence From Stimulation and Recording Studies Electrical and chemical stimulation of specific regions in the amygdala can evoke conditioned fear-like responses. In rats, amygdala stimulation produces freezing behavior (Weingarten & White, 1978), cardiovascular changes (Iwata et al., 1987), and acoustically enhanced startle responses (Rosen & Davis, 1988). In rabbits, stimulation of the ACe induces bradycardia, pupillodilation, arrest of ongoing behavior (such as movement of the mouth and tongue), and enhanced amplitude of the nictitating membrane reflex (Applegate et al., 1983; Whalen & Kapp, 1991). Stimulation of the lateral hypothalamus, an efferent target structure of the amygdala, elicits cardiovascular responses in anesthetized rabbits (Gellman, Schneiderman, Wallach, & LeBlanc, 1981). In dogs that previously underwent alimentary (or salivary) conditioning, electrical and chemical stimulations of the basolateral area of the amygdala have been found to inhibit conditioned secretory reflexes (Danilova, 1986; Shefer, 1988). These data indicate that the amygdala can directly activate various fear responses and also inhibit those responses incompatible with fear. In some cases, however, stimulation of the amygdala can interfere with aversive learning. For example, immediate posttraining stimulation of the amygdala produces amnesia that impairs the formation of fear memory (Gold, Hankins, Edwards, Chester, & McGaugh, 1975; McDonough & Kesner, 1971). Unit recording studies reveal that neurons in the ACe respond to both CS and US (Pascoe & Kapp, 1985) and undergo learning-related changes during fear conditioning (Applegate, Frysinger, Kapp, & Gallagher, 1982). Using a differential conditioning paradigm, Pascoe and Kapp (1985) reported that ACe neurons exhibited selective increases in single unit activity to a tone (CS+) that signaled the US but not to an uninformative tone (CS–). The behavior paralleled the ACe neuronal response; the conditioned bradycardia was observed preferentially during CS+ and its response magnitude correlated with the amplitude of the unit activity. A very recent study employed a functional imaging technique (Arc cellular compartmental analysis of temporal gene transcription by f luorescence i n situ hybridization, catFISH) to provide visual evidence that a population of BLA neurons receives convergent CS and US information at the time of fear

522

Biological Models of Associative Learning

conditioning (Barot, Chung, Kim, & Bernstein, 2009). It appears that during fear conditioning some form(s) of neurophysiological changes strengthen(s) the CS-amygdala pathway such that the CS now becomes capable of eliciting conditioned fear responses. Long-term potentiation (LTP), which is commonly suggested as a candidate synaptic mnemonic mechanism (Collingridge, Kehl, & McLennan, 1983; Morris, Davis, & Butcher, 1990; Teyler & DiScenna, 1987), has been demonstrated in the amygdala, e.g., the external capsulelateral nucleus of the amygdala (LA) pathway in vitro (Chapman, Kairiss, Keenan, & Brown, 1990), the internal capsule-LA pathway in vitro (Huang & Kandel, 1998), the auditory thalamus-LA pathway in vivo (Clugnet & LeDoux, 1990), and the subiculum-BLA pathway in vivo (Maren & Fanselow, 1995). The auditory inputs from the MGN to the LA—a pathway involved in tone fear conditioning—demonstrate an enhancement in auditoryevoked potentials (or LTP-like changes) after tone fear conditioning (Rogan & LeDoux, 1995; Rogan, Staubli, & LeDoux, 1997). Similarly, amygdalar slices prepared from fear-conditioned rats demonstrate enhanced synaptic transmission in the MGN-amygdala pathway (McKernan & Shinnick-Gallagher, 1997). Thus, it has been postulated that LTP or LTP-like changes in the amygdala are involved in fear conditioning (Clugnet & LeDoux, 1990; Davis, 1997; Fanselow & Kim, 1994; Maren & Fanselow, 1996; Miserendino, Sananes, Melia, & Davis, 1990). Consistent with this view, a recent study employed optogenetics and effectively produced fear conditioning in rats by pairing a tone CS with an optically induced depolarization of LA pyramidal cells (via viral-targeted, tissue-specific expression of the light-activated channelrhodopsins) as a US (Johansen et al., 2010). Evidence From Pharmacological Studies Immediate posttraining drug manipulations in the amygdala can impair or enhance aversive memories. In 1978, Gallagher and Kapp first demonstrated that intra-amygdalar infusions of the opioid receptor antagonist naloxone enhance fear conditioning. In contrast, infusions of the opioid agonist levorphanol reduced fear conditioning (Gallagher, Kapp, McNall, & Pascoe, 1981). Subsequent studies indicate that the memoryenhancing effect of opiate antagonists is induced partly by blocking the endogenously released opioids from inhibiting the release of norepinepherine in the amygdala (McGaugh, 2000). For instance, intra-amygdalar infusions of norepinephrine (Liang, Juler, & McGaugh, 1986) and propranol (a noradrenergic receptor antagonist; Gallagher

et al., 1981) enhance and impair, respectively, inhibitory avoidance memory, with the latter drug also blocking the memory enhancing effect of naloxone (McGaugh, Intronini-Collison, & Nagahara, 1988). Moreover, intraamygdalar infusions of the dopamine (D2) receptor antagonist eticlopride (Guarraci, Frohardt, Falls, & Kapp, 2000) and anxiolytic drugs (such as diazepam; Helmstetter, 1993)) that decrease fear or anxiety in humans have been shown to attenuate conditioned fear in rats. These and other pharmacological studies suggest that interactions of opioid, GABA, noradrenergic, and cholinergic neurochemical systems in the amygdala modulate aversive learning (McGaugh, 2000; McGaugh, Cahill, & Roozendaal, 1996). Several studies suggest that the N -methyl-D-aspartate (NMDA) subtype of the glutamate receptor in the amygdala is involved in the synaptic plasticity process (e.g., LTP) underlying fear conditioning. Because NMDA receptors have been demonstrated to be critical for the induction (but not expression) of LTP in the hippocampus, a similar type of synaptic plasticity in the amygdala has been proposed as a possible cellular mechanism subserving fear conditioning. Consistent with this notion, intra-amygdalar administrations of DL-2-amino5-phosphonovaleric acid (APV or AP5)—a competitive NMDA receptor antagonist—have been found to effectively block the acquisition of conditioned fear, as measured by fear-potentiated startle response (Miserendino et al., 1990) and freezing (Fanselow & Kim, 1994). Other studies, however, also found that APV infusions into the amygdala significantly impair the expression of conditioned fear (in previously fear-conditioned rats), as measured by a variety of fear responses including freezing, 22 kHz ultrasonic vocalization, analgesia, and potentiated startle (Fendt, 2001; Lee, Choi, Brown, & Kim, 2001; Lee & Kim, 1998; Maren, Aharonov, Stote, & Fanselow, 1996). It is evident that amygdalar NMDARs participate in normal synaptic transmission, and therefore, overall functioning of the amygdala. Other studies indicate that the acquisition of fear conditioning in rats requires RNA and protein synthesis in the amygdala. For example, pretraining intra-BLA infusions of the RNA synthesis inhibitor actinomycin-D significantly attenuate fear conditioning (to tone and context CSs) and RNA synthesis in the amygdala (Bailey, Kim, Sun, Thompson & Helmstetter, 1999). Similarly, immediate posttraining infusions of anisomycin (a protein synthesis inhibitor) and Rp-cAMPS (an inhibitor of protein kinase A) into the LA impair fear conditioning (Schafe & LeDoux, 2000). Once fear conditioning has

Spinal Conditioning

been established (or consolidated), intra-amygdalar infusions of actinomycin-D, anisomycin, and Rp-cAMPS do not affect conditioned fear memories (Bailey et al., 1999; Schafe & LeDoux, 2000). Interestingly, previously consolidated fear memories, when reactivated during retrieval (i.e., during a conditioned tone test), may return to a labile state that again requires protein synthesis in the amygdala for reconsolidation (Nader et al., 2000). However, the notion of reconsolidation, originally proposed by Lewis and colleagues (Misanin, Miller, & Lewis, 1968), continues to be debated (e.g., Nader, 2003; Rudy, Biedenkapp, Moineau, & Bolding, 2006; Tronson & Taylor, 2007). Evidence From Human Studies Human neuropsychological and brain imaging studies are also consistent with findings from animal studies. For example, patients with damage to the amygdala display a selective impairment in the recognition of facial expressions of fear (Adolphs, Tranel, Damasio, & Damasio, 1994) and also exhibit deficits in fear conditioning (LaBar, LeDoux, Spencer, & Phelps, 1995). Amygdalar-damaged patients are also impaired in recalling emotionally influenced memory (Cahill, Babinsky, Markowitsch, & McGaugh, 1995). Correspondingly, imaging studies show that there is a significantly increased blood flow to the amygdala (as measured by fMRI, functional magnetic resonance imaging) when normal subjects are presented with pictures of fearful faces (Morris, Frith, Perrett, Rowland, Young, Calder, & Dolan, 1996) or are undergoing fear conditioning (Knight, Smith, Stein, & Helmstetter, 1999; LaBar, Gatenby, Gore, LeDoux, & Phelps, 1998). Functional activation of the amygdala has also been observed (via PET, positron emission topography) during free recall of emotional information (Cahill, Haier, Fallon, Alkire, Tang, Keator, Wu, & McGaugh, 1996). These sources of evidence further support the view that the amygdala is crucially involved in fear conditioning and/or processing fearful information. Brain Areas Other Than the Amygdala Most of the evidence presented so far point to the amygdala as the locus of fear conditioning. It is not clear, however, whether the amygdala is the permanent storage site for long-term fear memory. The site of learning is not necessarily the site of memory storage. For example, fear retention is abolished if the amygdala is lesioned (electrolytically) or reversibly inactivated (via infusions of a local anesthetic agent lidocaine) shortly (1 day) but not long (21 days) after inhibitory avoidance training (Liang,

523

McGaugh, Martinez, Jensen, Vasquez, & Messing, 1982), suggesting that long-term fear memory is not stored in the amygdala. In contrast to inhibitory avoidance, however, amygdalar lesions made either shortly (1 day) or long (7 or 28 days) after training effectively abolish conditioned freezing response (Maren, Aharonov, & Fanselow, 1996). The insular cortex that receives and relays sensory (e.g., visual) information to the amygdala (Turner & Zimmer, 1984) may have some role in the storage of fear memory. Lesions to the most caudal aspect of the insular cortex impair retention of conditioned light-potentiated startle (Rosen, Hitchcock, Miserendino, Falls, Campeau, & Davis, 1992). Similarly, reversible inactivation of the insular cortex by a Na+ -channel blocker tetrodotoxin impairs retention of inhibitory avoidance memory (BermudezRattoni, Intronini-Collison, & McGaugh, 1991). The hippocampus seems to be involved in certain types of conditioned fear memory. In rats, conditioned fear to a diffuse contextual cue, but not to a discrete tone cue, is abolished when the hippocampus is lesioned shortly (1 day) after conditioning (Anagnostaras, Maren, & Fanselow 1999; Kim & Fanselow, 1992; Maren et al., 1997). However, animals retain a considerable amount of contextual fear when a long delay (28 days) is imposed between the time of conditioning and the time of hippocampectomy. Thus, it appears that the hippocampus is transiently involved in storing contextual fear memory. Similarly, pretraining hippocampal lesions selectively block the acquisition of context fear memory, but not tone fear memory (Phillips & LeDoux, 1992). Interestingly, lesions to the nucleus accumbens (a target of hippocampal efferents) also selectively impair contextual fear conditioning without affecting auditory fear conditioning (Riedel, Harrington, Hall, & Macphail, 1997). Hippocampal lesions also impair trace (but not delay) fear conditioning to an auditory CS in rats (as measured by freezing) (McEchron, Bouwmeester, Tseng, Weiss, & Disterhoft, 1998) and rabbits (as measured by heart rate) (McEchron, Tseng, & Disterhoft, 2000). The notion that the hippocampus is involved in contextual fear memory and trace fear conditioning are also supported by various knockout/transgenic mice studies. In brief, mutant mice with deficient LTP in the hippocampus also exhibit impairments in contextual (but not tone) fear conditioning and trace fear conditioning (e.g., Abeliovich, Paylor, Chen, Kim, Wehner, & Tonegawa, 1993; Bourtchuladze et al., 1994; Huerta, Sun, Wilson, & Tonegawa, 2000). The perirhinal cortex, which is reciprocally connected to the hippocampus (both directly and indirectly via the entorhinal cortex), also seems to be involved in consolidation and/or storage of hippocampal-dependent contextual

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Biological Models of Associative Learning

memory. Neurotoxic lesions of the perirhinal cortex made 1 day, but not 28 days, after training produce marked deficits in contextual fear memory (Bucci, Phillips, & Burwell, 2000). However, other studies provide neuroanatomical, pharmacological, and genetic evidence that the anterior cingulate cortex stores the remote contextual fear memory (Frankland, Bontempi, Talton, Kaczmarek, & Silva, 2004; Tang, Ko, Ding, Qiu, Calejesan, & Zhuo, 2005). It appears then that long-term fear memory may be stored in multiple brain regions. Finally, lesions of the cerebellar vermis in rats have been found to abolish the conditioned autonomic response (heart-rate) without affecting the unconditioned autonomic response (Supple & Leaton, 1990). The vermal lesioned rats also exhibit less freezing to a cat predator and fewer signs of fear in an open field (Supple, Leaton, & Fanselow, 1987). In rabbits, during fear conditioning, single unit recordings of Purkinje cells in the vermis demonstrate selective increases in activity to a tone (CS+) that signaled the US, but not to a different tone (CS–) that did not signal the US. The differential unit activities of the Purkinje cells correlated with the behavioral conditioned autonomic response (Supple, Sebastiani, & Kapp, 1993). Furthermore, recent studies have shown that reversible inactivation of the cerebellum impairs consolidation of fear memories (Sacchetti, Baldi, Lorenzini, & Bucherelli, 2002) as well as reconsolidation of recalled long-term fear memories (Sacchetti, Sacco, & Strata, 2007). These results indicate that the cerebellum is an important part of fear processing/conditioning networks. Some Unresolved and Critical Issues Although much is known about the neuroanatomy and neural mechanisms underlying fear conditioning, there are several unresolved and conflicting issues in the field that warrant discussion. This section highlights three major critical issues in fear conditioning. First, while the CS pathway (specifically the auditory projection) to the amygdala is relatively well defined, the footshock (US) pathway to the amygdala has not been adequately delineated. One study reported that combined lesions of the posterior extension of the intralaminar complex (PINT) and caudal insular cortex (INS) block acquisition of fear-potentiated startle, and proposed that PINT-INS projections to the amygdala constitute the essential US pathways involved in fear conditioning (Shi & Davis, 1999). However, another study (Brunzell & Kim, 2001) reported that fear conditioning (as assessed by freezing) was unaffected by either pretraining

or posttraining PINT-INS lesions. Specifically, Brunzell and Kim found that pretraining lesions in na¨ıve animals do not block the acquisition of fear conditioning, and posttraining lesions in previously fear conditioned animals do not lead to extinction of the CR with continued CS-US training (as would be predicted if the US information does not indeed reach the site of learning). Thus, it appears that the footshock (US) pathway is comprised of diffuse, multiple somatosensory pathways to the amygdala. Additional research is required to understand the specific role of the US information—as relayed via tactile vs. nociception pathways—in fear conditioning. Second, as previously mentioned, LTP in the amygdala (demonstrated both in vivo and in vitro) is commonly suggested as a putative synaptic mechanism through which acquired fear is encoded in the amygdala. However, the receptor mechanisms responsible for the induction and expression of amygdalar LTP remain ambiguous and may depend on the particular synapses and input pathway (Chapman et al., 1990; LeDoux, 2000; Weisskopf and LeDoux, 1999) as demonstrated in the hippocampus (Grover & Teyler, 1990; Harris & Cotman, 1986; Johnston, Williams, Jaffe, & Gray, 1992; Zalutsky & Nicoll, 1990). One study (Chapman and Bellavance, 1992) found that APV (an NMDA receptor antagonist) blocks LTP induction in the BLA, but only in such high concentrations that the drug markedly impairs normal synaptic transmission (but see Huang and Kandel, 1998). Similarly, single-unit recordings indicate that normal auditory-evoked responses in the amygdala are considerably attenuated by APV, suggesting that NMDA receptors are involved in normal synaptic transmission of the auditory pathway to the LA that mediates auditory fear conditioning (Li, Phillips, & LeDoux, 1995). Davis and colleagues initially reported that APV (an NMDA receptor antagonist) infusions into the amygdala selectively block acquisition, but not expression, of conditioned fear, as measured by fear-potentiated startle (Campeau, Miserendino, & Davis, 1992; Miserendino et al., 1990). Their finding is remarkably similar to the effects of APV on hippocampal LTP, that is, blocking induction without affecting expression of the Schaffer collateral/commissural-CA1 LTP (Collingridge et al., 1983). However, other studies found that intra-amygdalar infusions of APV dramatically interfere with the expression of multiple measures of conditioned fear, such as freezing (Lee and Kim, 1998; Maren et al., 1996), 22 kHz ultrasonic vocalization, analgesia, defecation (Lee et al., 2001), as well as fear-potentiated startle (Fendt, 2001). These results indicate that amygdalar NMDA receptors participate in normal synaptic transmission and thus the

Classical and Instrumental Conditioning of Discrete Responses

overall functioning of the amygdala. Clearly, additional studies are necessary to understand the receptor mechanisms of synaptic plasticity underlying fear conditioning in the amygdala. Finally, if the notion that the amygdala is the locus of fear learning is correct, then amygdalar damage should completely and permanently block fear conditioning. However, evidence from conditioned fear studies and inhibitory (or passive) avoidance studies provides conflicting results. Recall that Pavlovian fear conditioning and inhibitory avoidance are considered to be two procedurally different fear tasks. McGaugh and colleagues found that although amygdalar lesions affect inhibitory avoidance learning, animals can still learn and retain fear when they are overtrained, which indicates that the amygdala is not necessary for fear learning (Parent, Tomaz, & McGaugh, 1992). Rats that received more training prior to lesions also exhibited far greater retention of inhibitory avoidance memory. Furthermore, retention of inhibitory avoidance memory is abolished if amygdalar lesions are made shortly after training, but not several days after training (Liang et al., 1982). In contrast to inhibitory avoidance results, the retention of conditioned fear (as measured by freezing) is completely abolished whether amygdalar lesions are made shortly or long after training (Maren, Aharonov, & Fanselow, 1996), which indicates that the amygdala is necessary in Pavlovian fear conditioning. Interestingly, amygdalar lesioned rats, exhibiting impairments in conditioned freezing, are capable of demonstrating inhibitory avoidance behavior when both responses are simultaneously assessed in a Ymaze task (Vazdarjanova & McGaugh, 1998). Based on the observation that amygdalar lesions abolish both conditioned and unconditioned freezing but not avoidance behavior, it has been suggested that the amygdala is critical for the expression (or performance) of reflexive fear reactions rather than the actual learning and storage of fear memory (Cahill, Weinberger, Roozendaal, & McGaugh, 1999). Instead, based on a series of immediate posttraining drug injection studies, McGaugh and colleagues propose that the amygdala critically modulates the consolidation of memory occurring in extra-amygdalar structure(s) (McGaugh, 2000; McGaugh, Cahill, & Roozendaal, 1996). It appears then that studies employing classical fear conditioning and inhibitory (passive) avoidance provide different insights into the neuronal substrate(s) underlying fear learning and memory. If a common neural mechanism(s) mediates both conditioned fear and inhibitory avoidance, then pharmacological manipulations influencing inhibitory avoidance learning should also affect fear conditioning in a similar manner. However, several studies

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employing rats and mice found that conditioned fear is not susceptible to memory modulation by various drugs when conducted in the manner described in inhibitory avoidance tasks (e.g., Lee, Berger, Stiedl, Spiess, & Kim, 2001; Wilensky, Schafe, & LeDoux, 2000). Given the discrepant findings between conditioned fear and inhibitory avoidance studies, it is clear that further studies are necessary to clarify the precise role of the amygdala in fear conditioning.

CLASSICAL AND INSTRUMENTAL CONDITIONING OF DISCRETE RESPONSES Over the years, the study of the neurobiology of learning and memory has been significantly advanced when standard brain research techniques have been used together with classical or instrumental conditioning of discrete responses such as eyeblinks, limb flexions, and jaw movements. For example, classical eyeblink conditioning, used in conjunction with brain recording, lesion, stimulation, and neuropharmacological techniques, has arguably advanced our understanding of the brain systems and processes involved in simple associative learning further than any other behavioral procedure. There are several reasons for the relatively high degree of success that has been obtained when classical conditioning of discrete responses has been used as a behavioral tool for understanding brain function. First, the stimuli used in classical conditioning are discrete, well defined, and simpler than other more complicated behavioral procedures. Second, the responses measured (e.g., eyeblinks, limb flexions, and jaw movements) are relatively simple and discrete. This enables the experimenter to easily and accurately measure various properties of the response including variables related to response amplitude and timing. Third, in classical conditioning experiments, the experimenter controls when stimuli are delivered and thus when responses are expected. This has made lesion, stimulation, and recording experiments relatively easy to interpret. Finally, due to a wide variety of studies conducted by Gormezano and his colleagues as well as other researchers, a huge behavioral database exists concerning the classical conditioning of discrete responses, especially classical eyeblink conditioning (see Gormezano, Kehoe, & Marshall, 1983, for review). This behavioral database has proven useful for designing experiments and interpreting data collected from studies that have been conducted to delineate the neural bases of associative learning. In this section, we review the rather large literature that has been

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generated concerning the neural bases of the classical and instrumental conditioning of discrete responses. Classical Eyeblink Conditioning By far, the most popular paradigm for studying associate learning has been classical conditioning of the eyeblink response. For purposes of this chapter, the eyeblink response refers to a constellation of responses that include movement of the nictitating membrane (in species with this third eyelid) and movement of the external eyelid. In classical eyeblink conditioning, a CS (typically a tone or light) is presented shortly before a US (typically a periorbital shock or corneal air puff). The US reliably elicits a reflexive eyeblink UR. After 100 or so pairings of the CS and the US, the organism begins displaying eyeblink CRs to the CS (i.e., the organism has learned that the CS reliably precedes the US and thus can be used as an anticipatory cue). Over the years, a number of parametric features of the conditioning process have been delineated. For example, five features are: (1) the rate of acquisition of the CR generally increases as the intensity of the CS and/or US increases, (2) the rate of acquisition is affected by the length of the interstimulus interval (ISI) between the onsets of the CS and the US, (3) conditioning of discrete responses occurs only when ISIs between about 80 and 3000 msec are used, (4) CS-alone presentations after acquisition training result in extinction of the CR, and (5) unpaired presentations of the CS and the US do not result in CR acquisition. For several reasons, the rabbit has been the favorite subject for classical eyeblink conditioning. The rabbit is docile and adapts well to mild restraint and this has facilitated the collection of behavioral and neural data. Also, it is relatively easy to accurately measure movements of the rabbit nictitating membrane or external eyelids. Eyeblink conditioning studies involving other species have also been successfully undertaken. For example, Patterson, Olah, and Clement (1977) developed a nictitating membrane conditioning procedure for the cat. Also, Hesslow and colleagues have published a series of studies concerning the involvement of the cerebellum and brain stem in classical eyeblink conditioning using ferrets as behavioral subjects (e.g., Hesslow & Ivarsson, 1994, 1996). Recently, several investigators have developed rat eyeblink conditioning preparations (e.g., Green, Rogers, Goodlett, & Steinmetz, 2000; Schmajuk, & Christiansen, 1990; Skelton, 1988; Stanton, Freeman, & Skelton, 1992) and there has been a renewed interest in human eyeblink conditioning (e.g., see Woodruff-Pak & Steinmetz, 2000, for review).

Early Studies of the Brain Correlates of Classical Eyeblink Conditioning Among the earliest studies concerning the neural substrates of classical eyeblink conditioning were those by Oakley and Russell (1972, 1974, 1976, 1977), who examined the possibility that the cerebral cortex was involved in the storage of eyeblink CRs. They showed that rather extensive lesions of cerebral neocortex did not abolish eyeblink CRs that has been established in rabbits trained before the lesions. The cortical lesions had no effect on the acquisition of new CRs when training was delivered to naive rabbits. Subsequently, Mauk and Thompson (1987) used decerebration to separate neocortex from lower brain areas and showed that the decerebrate rabbits retained eyeblink CRs. Together, the decortication and decerebration studies provide solid evidence that the cerebral cortex was not critically involved in acquisition and storage of classical eyeblink CRs. There is evidence that under some circumstances classically conditioned-related plasticity does occur in neocortex. In an extensive series of studies, Woody and colleagues studied the involvement of portions of neocortex in eyeblink conditioning in cats. In their behavioral paradigm, an auditory CS was paired with a blinkproducing glabellar tap US. After several trials, the tone CS produced an eyeblink CR. Cats given unpaired CS and US presentations did not show eyeblinks to the CS. Using extracellular and intracellular recording techniques, Woody and colleagues showed that learning-related patterns of CS-evoked unit activity could be found in cortical motor areas and that persistent differences in neuronal excitability could be found in these regions after conditioning (e.g., Woody & Black-Cleworth, 1973; Woody & Engel, 1972). These data suggest that the excitability of neurons in motor neocortical areas may change during this type of eyeblink conditioning. There are several differences between the cats’ and rabbits’ preparations, however. For example, the cat conditioned eyeblink response was of very short latency (i.e., less than 20 msec) while the rabbit CR is typically longer in latency. Also, many more trials are needed to produce conditioning in the cat preparation. The cerebellum is not critical for the acquisition and performance of the short-latency CR in cats while (as detailed below) the cerebellum is essential for acquisition and performance of the longer-latency CR in rabbits (and other mammalian species, for that matter). In addition, extensive lesions of motor cortex in the rabbit do not affect acquisition or performance of classical eyeblink CRs (Ivkovich & Thompson, 1997). Nevertheless, the data from Woody et al. demonstrate that under

Classical and Instrumental Conditioning of Discrete Responses

some conditions classical conditioning-related plasticity can occur in regions of neocortex. In other early studies, investigators used brain stimulation techniques to study stimulus pathways in the brain that could potentially be involved in eyeblink conditioning. For example, in a pair of studies, Patterson (1970, 1971) implanted stimulating electrodes into the inferior colliculus and substituted microstimulation of the inferior colliculus for the peripheral tone CS. He observed robust conditioning when the collicular stimulation was paired with an US. These early data suggested that the inferior colliculus might be a portion of the auditory pathway that normally conveyed acoustic CSs used in conditioning. Kettner and Thompson (1982) used signal detection methods in a neural recording study to examine further the involvement of the inferior colliculus in eyeblink conditioning. They showed that while the inferior colliculus effectively encoded a tone CS, patterns of activation did not differ on CR versus non-CR trials, thus indicating that the inferior colliculus was not likely a brain region where CRs were critically encoded. This was contrasted with recording from the hippocampus and cerebellum where CR-related responding could be isolated (as we describe below). Early studies also examined the motor components of the basic eyeblink conditioning circuitry, in essence defining the essential cranial nerve nuclei and relay nuclei involved in generating the unconditioned and conditioned eyeblink responses (e.g., Cegavske, Patterson, & Thompson, 1979; Cegavske, Thompson, Patterson, & Gormezano, 1976; Young, Cegavske, & Thompson, 1976). In brief, these studies showed that for the rabbit, activation of motoneurons in the abducens and accessory abducens nuclei produced nictitating membrane movement through activation of the retractor bulbi muscle, which caused eyeball retraction and passive movement of the nictitating membrane. The oculomotor and trochlear nerves were found to also be involved to some extent in the eyeblink response along with the facial nerve, which controlled external eyelid closure via activation of the orbicularis oculi muscles (Figure 19.8). Although species like the rabbit and cat have functional nictitating membranes, other species like the human and rat do not. Nevertheless, control of the reflexive eyeblink involves similar collections of brain stem nuclei across species. Further, McCormick, Lavond, and Thompson (1982) showed that the occurrence of conditioned nictitating membrane movements and conditioned external eyelid movements were highly correlated, a part of a constellation of responses that are produced by the CS-US pairings.

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+

Parallel fiber Climbing fiber Mossy fiber

+

+

Purkinje cell Int.

Cerebellum CS

+ − + US

CR

Red N.

++ + −

CR Behavior UR, CR US (cornea) CS (tone)

+

Reflex + path +

US

N.VI & VII V. Coch. N.

+

+

Pontine N.

I.O. (DAO) CS

− N.V (sp) midline

Figure 19.8 A simplified schematic hypothetical memory trace circuit for discrete behavioral responses learned as adaptations to aversive events. The US (corneal air puff) pathway seems to consist of somatosensory projections to the dorsal accessory portion of the inferior olive (DAO) and its climbing fiber projections to the cerebellum. The tone CS pathway seems to consist of auditory projections to pontine nuclei (Pontine N) and mossy fiber projections to the cerebellum. The efferent (eyelid closure) CR pathway projections from the interpositus nucleus (Int) of the cerebellum to the red nucleus (Red N) and via the descending rubral pathway to act ultimately on motor neurons. The interpositus nucleus sends a direct GABAergic inhibitory projection to the inferior olive so that when a CR occurs (eyelid closes), the interpositus directly inhibits the inferior olive. Evidence is most consistent with storage of the memory traces in localized regions of cerebellar cortex and interpositus nucleus. Pluses indicate excitatory and minuses inhibitory synaptic action. Additional abbreviations: NV(sp), spinal fifth cranial nucleus; N VI, sixth cranial nucleus; N VII, seventh cranial nucleus; V Coch N, ventral cochlear nucleus. (Modified from Thompson, 1986.)

A variety of data demonstrate that the periorbital shock and air puff USs used in classical conditioning activate the reflexive UR rather directly at the level of the brain stem (Figure 19.8). For example, an air puff US activates neurons in the trigeminal complex which projects to nuclei involved in generating eye blinks both directly and indirectly (Hiroaka & Shimamura, 1977). Neural recordings taken from the motor nuclei (e.g., the abducens nucleus) revealed that the nuclei were activated when either a UR or a CR occurred and the amplitude-time course of the unit activity was very highly correlated with the CR or the UR that was executed (Cegavske, Patterson, & Thompson,

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1979; Cegavske, Thompson, Patterson, & Gormezano, 1976). Lesions of the various motor nuclei abolished portions of the CR and UR, but only those features of the eyeblink response activated by the nuclei that were removed by the lesion (Disterhoft, Quinn, Weiss, & Shipley, 1985; Steinmetz, Lavond, Ivkovich, Logan, & Thompson, 1992). For example, lesions of the abducens nucleus abolished nictitating membrane response while preserving external eyelid responses. Lesions of the facial nucleus produced the opposite effect. Studies of the Hippocampus and Limbic System In the 1960s and 1970s, a rapidly growing body of literature suggested that the hippocampus and related limbic systems structures were involved in a variety of learning and memory processes. During this time, Thompson and his colleagues recognized the power of using the classical eye-blink-conditioning paradigm to study hippocampal function during learning and memory. Specifically, Berger, Thompson and their colleagues recorded multiple- and single-unit activity from the hippocampus and other limbic system structures during conditioning (Berger, Alger, & Thompson, 1976; Berger, Rinaldi, Weisz, & Thompson, 1983; Berger & Thompson, 1978a, 1978b). They showed that even before behavioral CRs emerged, pyramidal neurons in the hippocampus were activated. At first pyramidal cell activation was seen during the trial period that was coincident with US presentation. Over time, as additional paired CS-US trials were delivered, the hippocampal activity could be seen during the CS-US interval. Eventually, the pattern of hippocampal activity formed an amplitude–time course model of the CR. Other limbic system structures were also found to be involved in the eyeblink conditioning process. For example, recordings from the medial septum, which sends cholinergic projections to the hippocampus, revealed stimulus-evoked responses to the CS and the US that declined with training (Berger & Thompson, 1978a). Patterns of action potentials recorded from the lateral septum were similar to the patterns seen in the hippocampus. Many studies have supported the idea that cholinergic activity in the septo-hippocampal system may play a very important role in eyeblink conditioning. Solomon, Solomon, Vander Schaaf, and Perry (1983), for example, showed that systemic administration of scopolamine, an anticholinergic drug that alters hippocampal activity, severely impairs delay eyeblink conditioning (and, in fact, is more disruptive than hippocampal ablation). Salvatierra and Berry (1989) later showed that systemic scopolamine suppressed neuronal responses in the hippocampus and lateral septum while slowing the rate

of delay eyeblink conditioning. Kaneko and Thompson (1997) further showed that central cholinergic blockade prevented trace eyeblink conditioning while slowing the rate of delay conditioning. These studies suggest that the brain’s cholinergic system is centrally involved in the modulation of eyeblink conditioning, an idea that is compatible with a large body of research that suggests an important role for the cholinergic system in learning and memory. Interestingly, an earlier study by Schmaltz & Theios (1972) had shown that rabbits could learn and retain the classically conditioned eyeblink response after the hippocampus was removed. Together with the recording data, these lesion results suggest that while the hippocampus was not critically involved in CR acquisition, it likely plays an important modulatory role in classical eyeblink conditioning. Research conducted after the early lesion and recording studies has concentrated mainly on trying to delineate what role the hippocampus plays in simple associative learning. For example, trace conditioning, a variation of the basic classical eyeblink conditioning procedure, has been used to study the possibility that the hippocampus is involved in memory processing associated with the learning. In trace conditioning, the CS is turned on then turned off, a time period is allowed to elapse, and then the US is presented. Unlike delay conditioning, there is no overlap of the CS and the US during individual trials. In essence, the subject must form a memory of the CS that bridges the trace interval before the US is presented. It has been established that the hippocampus is necessary for this variation of training. For example, lesions of the hippocampus have been shown to abolish or significantly impair trace conditioning without affecting basic delay conditioning (Moyer, Deyo, & Disterhoft, 1990; Port, Romano, Steinmetz, Mikhail, & Patterson, 1986; Solomon, Vander Schaaf, Thompson, & Weisz, 1985). Similar to the recording studies of Berger, Thompson and colleagues, pyramidal cells in the hippocampus become active during trace conditioning in a CR-related fashion (see Disterhoft & McEchron, 2000, for review). Also, at a more cellular level of analysis, Disterhoft and colleagues have shown that calcium-dependent afterhyperpolarization potentials recorded from hippocampal pyramidal cells are significantly reduced after trace conditioning training (Coulter, Lo Turco, Kubota, Disterhoft, Moore, & Alkon, 1989; Disterhoft, Golden, Read, Coulter, & Alkon, 1988). A large part of the interest in exploring the involvement of the hippocampus in simple associative learning tasks such as classical eyeblink conditioning was generated by the observation that amnesics with hippocampal damage,

Classical and Instrumental Conditioning of Discrete Responses

such as the well-known H.M., demonstrated rather severe anterograde amnesia and time-limited retrograde amnesia. Kim et al. (1995) demonstrated similar amnesia effects for classical eyeblink conditioning. Rabbits were trained using a trace conditioning procedure then given hippocampal lesions either immediately or one month after training. While lesions delivered immediately after training effectively abolished CRs, lesions given 1 month after training had no effect. If the rabbits were trained with a delay procedure and then immediately lesioned, no decrement in responding was seen. However, if these rabbits were then switched to trace conditioning, CR extinction occurred. Together with the Disterhoft data, these data suggested that the hippocampus is involved in memory processing of eyeblink conditioning when stimulus memory demands on the system are relatively high (such as during trace conditioning). This idea was supported by Walker and Steinmetz (2008), who showed more profound ibotenic acid lesion effects on the hippocampus during trace conditioning for rats trained with a relatively short CS (50 ms) and long trace interval (500 ms) relative to rats trained with a longer CS (500 ms) and shorter trace interval (50 ms). During simple delay conditioning, however, the CS and the US overlap and there appears to be no need to hold the CS in memory in anticipation of the US. While the recording studies show that the hippocampus is engaged during the simpler delay task, apparently the structure is not necessary for learning (and memory) to take place. This implies that critical plasticity for eyeblink conditioning lies in lower brain areas. In recent decades, the conceptualization of memory systems in the brain has been dominated by the view that distinct brain systems exist for processing declarative and nondeclarative memories (e.g., Clark & Squire, 2000; Squire, 1992). In the human literature, declarative memories are those memories of one’s own experience, as is exemplified by one’s memories for events and facts. Nondeclarative memories are essentially all other memories, including memories for skills, habits, procedures, and simple conditioning. Because hippocampal lesions appear to cause amnesia for declarative memories, the hippocampus has therefore been regarded as critically important for the storage of declarative, but not nondeclarative memories. Because hippocampal lesions affect classical eyeblink conditioning in a manner that is very similar to the effects of hippocampal lesions on other memory tasks (i.e., severe anterograde effects with mild, short-term retrograde effects), eyeblink conditioning has provided an excellent model system for exploring the distinction in memory

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systems (as is evidenced by the data cited above). In addition to the nonhuman animal studies, several human eyeblink conditioning studies have been conducted to explore further the multiple memory system idea. For example, McGlinchey-Berroth et al. (1997) demonstrated deficits in long-trace (but not short-trace or delay) eyeblink conditioning in hippocampal amnesics. Squire and colleagues suggested that whether or not the hippocampus is critically engaged in the learning and memory process may depend for the most part on whether or not the subjects are “aware” of the memories they are forming (see Clark & Squire, 2000, for review). In one study, they trained normal and amnesic subjects on both a delay differential (700 and 1250 ms ISIs) conditioning procedure and a long trace differential (500 and 1000 ms trace intervals) conditioning procedure (Clark & Squire, 1998). Subjects in both group learned the delay procedure normally although the amnesics could not recall the experience when questioned about it later. The control subjects could easily learn the long-trace procedure, but the amnesics could not. These results are compatible with previous literature concerning hippocampal involvement in declarative (trace) versus nondeclarative (delay) memory procedures. Interestingly, using data from the control subjects in this study, Clark and Squire (1999) also demonstrated that “awareness” was important for the learning. The control subjects showed a great deal of variability in learning the trace procedure, and in examining the individual data, Clark and Squire noted that the subjects who learned the procedure could verbalize the stimulus contingencies while the subjects who did not learn the procedure could not (i.e., the subjects who learned trace conditioning were aware of the stimulus contingencies). In another study, Clark and Squire (1999) directly manipulated awareness and studied conditioning in normal, older adults. This study involved four groups of subjects, two of which were given a secondary, attentiondemanding task designed to reduce awareness of the conditioning contingencies, a third group given an explicit explanation of the conditioning contingencies, and a fourth group given the explicit explanation and the attentiondemanding task. They showed that those subjects given trace conditioning and the distraction task did not acquire differential CRs while those given the delay procedure with distraction did acquire differential CRs. The group given knowledge of the CS-US contingency and trace conditioning learned the differential CR while subjects given knowledge together with the distraction task did not. These data indicate that awareness of the contingency affects trace conditioning although the individuals

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apparently have to have access to the knowledge during the conditioning session to produce conditioned responses. At the very least, these data provide a wonderful demonstration of the power of analysis afforded by the use of eyeblink conditioning for the study of basic learning and memory phenomena. Future studies will undoubtedly continue to use this paradigm to explore the hippocampus and other brain systems involved in declarative memory. Studies of the Involvement of Other Higher Brain Areas in Eyeblink Conditioning Over the years, a number of studies have examined the involvement of other higher brain areas, such as the cerebral cortex, thalamus, amygdala, and neostriatum, in classical eyeblink conditioning. Even though there is ample evidence that higher brain areas are not necessary for conditioning, many studies have provided evidence that these brain areas are recruited during conditioning. While Oakley and Russell (e.g., 1972) showed that eyeblink conditioning could be achieved without cerebral neocortex, some studies have demonstrated that neocortex is engaged in eyeblink conditioning and may be encoding the learning process. For example, Fox, Eichenbaum, and Butter (1982) showed that lesions of frontal cortex in rabbits decreased the latencies of conditioned eyeblink responses and retarded extinction of the conditioned responses. Through systematic lesions of brainstem nuclei that interact with frontal cortex, they provided evidence that the frontal cortex may normally provide inhibitory effects on conditioned behavior indirectly through brain stem nuclei that make contact with motor neurons responsible for CR formation. Given the central nature of the thalamus in distributing information to higher brain areas, several studies have explored whether or not this structure is involved in eyeblink conditioning. Buchanan and Powell (1988) showed that knife cuts that severed afferent and efferent connections between the prefrontal cortex and the mediodorsal nucleus of the thalamus retarded the rate of conditioning established with a tone CS and periorbital shock US. Interestingly, these lesions abolished the late-occurring tachycardiac component of the conditioned heart rate response that was measured concomitantly. Similar results were obtained when ibotenic acid lesions of the mediodorsal thalamic nuclei were used (Buchanan & Thompson, 1990). These data suggested that the mediodorsal thalamic–prefrontal circuitry was involved in the sympathetic control associated with somatomotor learning. Other

thalamic regions have also been studied. For example, Sears, Logue, and Steinmetz (1996) recorded neuronal activity from the ventrolateral thalamus, which receives input from the cerebellum, among other areas of the brain, and found that this activity was abolished after cerebellar lesions. These data suggested that the ventrolateral thalamus receives an efferent copy of learning-related activity that is generated in the cerebellum, an efferent copy that is perhaps used to integrate the learned movement into the ongoing motor activity of the organism. In agreement with other data suggesting a role for the brain’s dopaminergic system in sensorimotor learning and integration, there is evidence that the dopamine system is activated during eyeblink conditioning. Kao and Powell (1988) bilaterally infused 6-hydroxydopamine into the substantia nigra and observed a retardation of both eyeblink conditioning and heart-rate conditioning. The lesions produced significant norepinephrine depletion in the nucleus accumbens, frontal cortex and the hypothalamus, and also produced dopamine depletion in the caudate nucleus. Furthermore, the rate of conditioning was highly correlated with the level of dopamine found in the caudate. In another study, White et al. (1994) recorded unit activity from the neostriatum during eyeblink conditioning (see also Richardson & Thompson, 1985). Neostriatal neurons were responsive to the tones and air puffs used as CSs and USs, respectively, and some neurons showed a CR-related pattern of discharge with an onset that preceded the behavioral response. Haloperidol, a dopamine antagonist, caused a disruption of behavioral and neural responding that appeared to be related to CS intensity. This observation was consistent with an earlier study that suggested a role for dopamine in CS processing (Sears & Steinmetz, 1990). These data suggest that the neostriatum may be activated during eyeblink conditioning and consistent with other studies (e.g., Schneider, 1987), the data support the idea that the neostriatum may be modulating the access of sensory inputs to motor output. A recent study expanded the role of the striatum (particularly the caudate nucleus) to trace eyeblink conditioning (Flores & Disterhoft, 2009). Finally, there has been a great deal of recent interest in the role of the amygdala in learning and memory, especially in processing emotional aspects of learning and memory (see above). Given that eyeblink conditioning is an aversive conditioning procedure, it was reasonable to assume that the amygdala might be activated during this type of learning. This appears to be the case. Whalen and Kapp (1991) showed that stimulation of the central nucleus of the amygdala increased the amplitude

Classical and Instrumental Conditioning of Discrete Responses

of an eyeblink UR that was subsequently elicited by an air puff US. Amygdala stimulation was also found to increase the amplitude of a periorbital shock-induced EMG response recorded from the rabbit orbicularis oculi muscles (Weisz & Yang, unpublished data). Weisz, Harden and Xiang (1992) also showed that large electrolytic lesions of the amygdala disrupted the maintenance of reflex facilitation of the eyeblink UR and retarded the acquisition of the eyeblink CR. These data suggest that the amygdala may be involved in US processing, perhaps in processing information concerning the aversiveness of the US (see Lee & Kim, 2004; Richardson & Thompson, 1984). This idea has been supported by a recording study in rats by Rorick-Kehn and Steinmetz (2005). In this study, strong learning-related activity was seen in the amygdala and the amount of learning-related activity observed appeared to be directly related to the intensity of the US used to train the animals. These studies are compatible with other studies suggesting a role for the amygdala in emotional processing (e.g., Hitchcock & Davis, 1991; LeDoux, 1995). Consistent with this view, Wagner and associates have developed a theoretical model (AESOP, Wagner, & Brandon, 1989) that incorporates both emotive and sensory aspects of classical conditioning and have presented considerable empirical evidence to support the model (Brandon & Wagner, 1991). In related work, Shors and associates have explored effects of behavioral stress on processes of learning and memory (Shors, 1998). In general, severe stress can markedly impair performance in learning tasks that might be categorized as declarative in rodents (Overmier & Seligman, 1967; see Figure 19.1). This is perhaps consistent with the fact that behavioral stress markedly impairs the subsequent induction of long-term potentiation (LTP) in the CA1 hippocampus in rats (Foy et al., 1987, 1990; Shors et al., 1989, 1990). Indeed, these observations support the view that LTP is a mechanism of declarative memory storage in the hippocampus (Bliss & Collingridge, 1993). Shors et al (1992) discovered that stress actually facilitates classical eyeblink conditioning in rats. Interestingly, a much earlier literature reported a similar effect in humans: High anxious subjects learn eye blink conditioning better than low anxious subjects (Taylor, 1951). Shors subsequently showed that this stress facilitation of conditioning is sexually dimorphic, facilitating learning in males but impairing learning in females. The facilitation in males involves the amygdala, whereas the impairment in females is dependent on activational influences of estrogen (Shors et al., 1998; 2000; Wood & Shors, 1998).

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The Critical Involvement of the Cerebellum in Eyeblink Conditioning The results of lesion studies involving the cerebral neocortex and limbic system strongly suggested that the learning and memory of classical eyeblink conditioned responses was not critically dependent on higher brain areas but more likely critically involved lower, brain stem areas. With this in mind, Thompson and colleagues used a variety of techniques, including systematic lesion and recording methods, in an attempt to find regions of the lower brain that were essential for the acquisition and performance of eyeblink CRs. These experiments suggested a critical role for the cerebellum in eyeblink conditioning and launched 30 years of research that has strongly supported these early results. In early studies, large aspirations of the cerebellum that included cortex and the deep cerebellar nuclei were found to abolish eyeblink CRs in rabbits trained before the lesion and prevent acquisition of eyeblink CRs in rabbits trained after the lesion (Lincoln, McCormick, &Thompson, 1982; McCormick et al., 1982). Subsequent studies showed that small electrolytic lesions (McCormick & Thompson, 1984a; Steinmetz et al., 1992) as well as kainic acid lesions (Lavond, Hembree, & Thompson, 1985) delivered to a dorsolateral region of the anterior interpositus nucleus on the side ipsilateral to the training permanently abolished the learned responses (Figure 19.8). There appears to be no recovery from the cerebellar lesion (Steinmetz, Logue, & Steinmetz, 1992). Lesions delivered to cerebellar cortex have produced mixed results. Some investigators have reported that lesions delivered to cerebellar cortex abolished conditioned responses (Yeo & Hardiman, 1992; Yeo, Hardiman, & Glickstein, 1985), others have reported little or no effect of the lesion (Woodruff-Pak, Lavond, Logan, Steinmetz, & Thompson, 1993), and others have reported effects on the rate of acquisition and CR amplitude (Lavond & Steinmetz, 1989) and CR timing (Perrett, Ruiz, & Mauk, 1993). The role of cerebellar cortex in response timing has been more recently explored using picrotoxin infusions into the interpositus nucleus to temporarily eliminate cerebellar cortical output to the nucleus during an interstimulus interval (ISI) switch task (Vogel, Amundson, Lindquist, & Steinmetz, 2009). The inactivation infusions were shown to severely affect learning when the animals were trained first with a long (750 ms) ISI then switched to a short (250 ms) ISI then when the opposite training order was used. These data suggest that cerebellar cortex become increasingly important when long, nonoptimal ISIs are used for training. Together, these lesion

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studies established that both the interpositus nucleus and cerebellar cortex were critically involved in eyeblink conditioning. Other studies using temporary inactivation techniques such as the GABAA receptor agonist muscimol (Krupa, Thompson, & Thompson, 1993) or cold probe cooling (Clark, Zhang, & Lavond, 1992) have provided compelling evidence that the cerebellum is necessary for eyeblink conditioning (Figure 19.9). In these studies, the region of the interpositus nucleus was temporarily inactivated by either infusing muscimol (which hyperpolarizes affected neurons) or by cooling with a cold probe (which shuts down neural function). Inactivation of the cerebellum after training abolished conditioned responding for the duration of infusion or cooling. More interestingly, when the cerebellum was inactivated during training trials delivered to naive rabbits, the animals showed no signs that paired training had been delivered. That is, after several days of training while the cerebellum was inactivated, no savings in acquisition was seen during subsequent training when the inactivation was removed. One would expect to see savings if essential neuronal plasticity processes were active at other brain sites during training. Because no savings were seen, these studies provided very strong evidence that the basic cellular processes important for plasticity that underlies classical eyeblink conditioning

resided in the region of the cerebellum that was inactivated by the muscimol or cold-probe. These data provide some of the most compelling evidence to date that plasticity in the cerebellum is essential for classical eyeblink conditioning. Neural recording studies have also provided evidence for the involvement of the cerebellum in encoding classical eyeblink conditioning. Multiple- and single-unit recordings made in the dorsolateral anterior interpositus nucleus have shown patterns of neuronal spiking that correlated well with the behavioral CR (Berthier & Moore, 1990; Gould & Steinmetz, 1996; McCormick & Thompson, 1984b). Neurons that respond to the CS and/or the US have been found along with neurons that discharge in a pattern that when summed, formed amplitude-time course models of the behavioral response. Moreover, the onset of interpositus nucleus spiking typically preceded the behavioral response by 30–60 msec, a time interval that can be accounted for by synaptic and neural processing delays between the cerebellum and the motor nuclei responsible for producing the conditioned response. Recordings of Purkinje cell activity in cerebellar cortex have also revealed learning-related patterns of unit discharges (Berthier & Moore, 1986; Gould & Steinmetz, 1996; Katz & Steinmetz, 1997). Some cells show CS and/or US-related activation patterns while other cells seem to

Cerebellar Cortex mossy fibers Tone CS

Auditory Nuclei

climbing fibers

c. Interpositus Nucleus

Pontine Nuclei

d. e.

Inferior Olive

All other targets of the superior cerebellar peduncle b.

Red Nucleus

a.

Cranial Motor Nuclei

Trigeminal Nucleus

Corneal Airpuff US

Reticular Formation

Eye-blink UR & CR

Figure 19.9 Simplified schematic of the essential brain circuitry involved in eyeblink conditioning. Shadowed boxes represents areas that have been reversibly inactivated during training (see text for details). (a) Inactivation of the motor nuclei including facial (seventh) and accessory sixth. (b) Inactivation of magnocellular red nucleus. (c) Inactivation of dorsal aspects of in the interpositus nucleus and overlying cerebellar cortex. (d) Inactivation of ventral interpositus and of white matter ventral to the interpositus. (e) Inactivation of the superior cerebellar peduncle (SCP) after it exits the cerebellar nuclei. (From Thompson & Krupa, 1994.)

Classical and Instrumental Conditioning of Discrete Responses

fire in relation to the behavioral response. Purkinje cells have been isolated that increase their firing rate during the CS-US interval while other Purkinje cells that have been isolated show decreases in their firing rate (see King et al., 2001; Thompson, 2001). Further, Purkinje cells recorded in the region of the anterior cerebellar cortex show firing patterns that are sensitive to the interstimulus interval length thus suggesting that cerebellar cortical cells may be involved in encoding timing aspects of the conditioning processes (Green & Steinmetz, 2005). These recording studies provide additional supportive evidence for the involvement of both cerebellar cortex and the interpositus nucleus in eyeblink conditioning. Specifically, the patterns of action potentials recorded in both the nucleus and the cortex appear to be encoding the delivery of the CS and US used during training as well as the execution and the timing of the learned response. The essential stimulus pathways used in projecting the CS and US used in eyeblink conditioning from the periphery to the cerebellum have been delineated. On the CS side, it appears that CSs are projected to the basilar pontine nuclei, which send mossy fiber projections to the interpositus nucleus as well as to the cerebellar cortex (Figure 19.8). The basic CS pathway was established by stimulation, lesion and recording studies. For example, CS-related responses were evoked in discrete regions of the pontine nuclei and lesions delivered to these regions abolished conditioned responding (Steinmetz, Logan, Rosen, Thompson, Lavond, & Thompson, 1987). Microstimulation delivered to these same regions could be substituted for the peripherally administered CS and robust conditioning was produced (e.g., Steinmetz, 1990; Steinmetz, Rosen, Chapman, Lavond, & Thompson, 1986; Tracy, Thompson, Krupa, & Thompson, 1998). The essential US appears to involve a projection from the region of the eye where the US is delivered to the trigeminal nucleus, which sends projections to a discrete region of the dorsal accessory inferior olive (Gellman, Houk, & Gibson, 1983). The inferior olive, in turn, sends climbing fiber projections to the cerebellar cortex and the deep cerebellar nuclei (Figure 19.8). Again, recording, stimulation and lesion studies were used to establish the connectivity in the US system. Neurons in the dorsal accessory inferior olive were found to be responsive to stimulation of the face, including to presentations of a corneal air puff (Sears & Steinmetz, 1991). Lesions of the inferior olive caused extinction (McCormick, Steinmetz, & Thompson, 1985 and Voneida et al., 1990, see below) or abolition (Yeo, Hardiman, & Glickstein, 1986) of conditioned responding while stimulation of the inferior olive, which produced a variety of discrete responses

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including eyeblinks (depending on precise location of the stimulation), could be substituted for peripheral US to produce robust conditioning (Mauk, Steinmetz, & Thompson, 1986). The most popular models concerning the cerebellar basis of classical eyeblink conditioning hypothesize that plasticity that is crucial for acquisition and performance of the eyeblink CRs occurs in regions of the cerebellar cortex and/or the deep cerebellar nuclei where a convergence of CS and US input occurs (e.g., Steinmetz, Lavond, & Thompson, 1989; Thompson, 1986). There is ample electrophysiological and anatomical evidence for convergence of the CS and US in the cortex and deep nuclei of the cerebellum (e.g., Gould, Sears, & Steinmetz, 1993; Steinmetz & Sengelaub, 1992). At this point, it is assumed that paired CS-US presentations somehow produce changes in the firing rate of interpositus neurons, either independently or with critical input from cerebellar cortex, which in turn affects nuclei downstream. In essence, current models assume that before conditioning, the CS is not capable of activating cerebellar neurons in a manner that would drive brainstem motor nuclei responsible for the CR. After paired training, however, the firing rates of cerebellar neurons are thought to change such that the CS is now capable of activating brainstem motoneurons involved in conditioned blinking. The critical CR pathway between the cerebellum and the peripheral eye-blinking musculature has been worked out. Axons from the principle cells of the interpositus nucleus cross the midline and innervate the neurons in the magnocellular region of the red nucleus via the superior cerebellar peduncle (Figure 19.8). Lesions of the peduncle completely abolish the eyeblink (and limb flexion) CRs (McCormick, Guyer, & Thompson, 1982; Voneida, 2000). Similarly, lesions of the red nucleus have been shown to result in eyeblink CR abolition (e.g., Haley, Thompson, & Madden, 1988) and learning-related neuronal activity has been recorded form the red nucleus (Chapman, Steinmetz, Sears, & Thompson, 1990; Desmond & Moore, 1991). Red nucleus output cells then project axons back across midline and make synaptic contact on neurons in the variety of cranial nerve nuclei (Figure 19.8) involved in generating the eyeblink CR (and UR, for that matter). Wagner and Donegan (1989), incidentally, showed how the theoretical model of classical conditioning developed earlier by Wagner, the Sometimes-Opponent-Process (SOP) model, maps very closely onto the empirical model of the cerebellar circuitry essential for classical conditioning of discrete responses developed by Thompson and associates shown in Figure 19.8.

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Interestingly, there is good evidence that in addition to projecting information to the red nucleus, output from the interpositus nucleus also feeds back on the CS and US pathways. Inhibitory projections from the deep nuclei to the dorsal accessory olive have been found (e.g., Andersson, Garwicz, & Hesslow, 1988) and it is known that when CR-related interpositus activity occurs, the inferior olive is inhibited such that US-related activity is not passed to the cerebellum (Sears & Steinmetz, 1991). On the CS side, there are known projections from the interpositus nucleus to the basilar pontine nuclei (likely excitatory) and that CR-related activation of the interpositus may, for some as of yet undetermined reason, project a copy of the CR to the CS input pathway during conditioning (Bao, Chen, & Thompson, 2000; Clark, Gohl, & Lavond, 1997). It is likely that the projections from the interpositus nucleus to the CS and US input pathways to the cerebellum are important for response timing and response topography and also may be responsible for some higher-order variations of eye blink conditioning, such as blocking (see Kim, Krupa, & Thompson, 1998). Most studies conducted over the past 5 years or so have been designed to test various aspects of the general model of cerebellar involvement in eyeblink conditioning that was presented just above. These studies have examined relationships between cerebellar cortical and nuclear involvement in the acquisition and performance of eyeblink conditioning and have begun the process of delineating neuronal processes that may be involved in establishing and maintaining plasticity that is associated with the learning. Many researchers have suggested that long-term depression (LTD) may play an important role in the acquisition and performance of classically conditioned eyeblink responses (e.g., Thompson, 1986). LTD is a relatively longterm suppression of cerebellar Purkinje-cell excitability that is produced when climbing fibers and parallel fibers (which receive input from mossy fibers) are conjointly activated (e.g., Ekerot & Kano, 1985; Ito, 1989). Evidence suggests that LTD occurs through the desensitization of quisqualate receptors on Purkinje cells that receive synaptic contact from parallel fiber inputs (e.g., Hemart, Daniel, Jaillard, & Crepel, 1994). It has been proposed that the mossy fiber CS and climbing fiber US used in classical eyeblink conditioning converge on Purkinje cells in cerebellar cortex and that LTD results from their coactivation (Thompson, 1986). Because Purkinje cells inhibit deep nuclear cells to which they send axons, the net effect of the Purkinje cell suppression would be an increase in excitability in the deep nuclei that could allow for the

expression of eyeblink CRs through activation of lower motor nuclei. Indirect evidence for a role for LTD in eyeblink conditioning exists. First, convergence of CS and US from mossy fiber and climbing fibers sources, respectively, has been anatomically and electrophysiologically established in regions of cerebellar cortex (Gould, Sears, & Steinmetz, 1993; Steinmetz & Sengelaub, 1992). Second, recordings from Purkinje cells have shown that some of the neurons demonstrate a conditioning-related decrease in firing rate as would be expected if LTD occurred as a result of conditioning (Berthier & Moore, 1986; Gould & Steinmetz, 1996). Schreurs and colleagues intracellularly recorded from rabbit cerebellar slices to study directly the relationship between LTD and conditioning. In an initial study, they showed a conditioning-specific increase in the excitability of Purkinje-cell dendrites in slices taken from cerebellar cortical lobule HVI without significant changes in dendritic membrane potential or input resistance (Schreurs, SanchesAndres, & Alkon, 1991). While this initial result supported the idea that learning-specific plasticity took place in Purkinje cells, a decrease in excitability was not seen as would be expected if LTD occurred. The increase in excitability did, however, explain earlier studies that showed a large number of learning-related excitatory responses recorded extracellularly from Purkinje cells of awake rabbits. Other studies showed that the increase in Purkinje cell dendritic excitability was a result of alternations in a specific potassium channel (Schreurs, Gusev, Tomsic, Alkon, & Shi, 1998; Schreurs, Tomsic, Gusev, & Alkon, 1997). Using direct stimulation of parallel fibers and climbing fibers, Schreurs and colleagues have produced an LTD effect in the cerebellar slice preparation (Schreurs & Alkon, 1993). In the presence of the GABA antagonist bicuculline, LTD was produced when climbing fibers were stimulated before parallel fibers, but no response depression was seen when the parallel fiber stimulation preceded the climbing fiber stimulation (as is hypothesized to occur during eyeblink conditioning). Depression could be obtained in the absence of bicuculline, however, when parallel fibers were stimulated in the presence of a depolarizing current that induced local, calcium-dependent dendritic spikes. Schreurs, Oh, and Alkon (1996) did produce a form of long-term reduction in Purkinje cell EPSPs when parallel fibers were stimulated before climbing fibers. They presented trains of stimulation for durations that mimicked conditioning in the intact rabbit. In fact, the trains were presented in nonoverlapping fashion. Consistent depression of EPSP peak amplitude was seen in the slice recordings when parallel fiber stimulation preceded climbing fiber

Classical and Instrumental Conditioning of Discrete Responses

stimulation but not when unpaired stimulations were delivered. In total, these data demonstrate that depending on the order and parameters of stimulation, both increases and decreases in excitability can be seen in Purkinje cells as a result of conjoint activation of parallel (mossy) fibers and climbing fibers. These results provide some direct evidence that cerebellar neurons are capable of showing associative plasticity that could at least in part account for eyeblink conditioning. Other studies have demonstrated a role for glutamate receptors in plasticity processes associated with eyeblink conditioning. Hauge, Tracy, Baudry, and Thompson (1998) used quantitative autoradiography to examine changes in the ligand binding properties of AMPA receptors following eyeblink conditioning that were established by pairing a pontine stimulation CS with an air puff US. Preincubation at 35◦ C produced significant decreases in AMPA binding while unpaired CS-US presentations produced no significant effect. These data indicated that eyeblink conditioning is associated with a modification of AMPA-receptor properties in the cerebellum, modification in a direction that is compatible with the hypothesis that LTD is involved in the conditioning process. This hypothesis was further supported by Attwell, Rahman, and Yeo (1999) who used CNQX infusions into cerebellar cortical lobule HVI to reversibly block AMPA-kainate receptors. Conditioned responses were reversibly blocked by the infusion thus suggesting that cortical AMPA receptors were important for the expression of eyeblink CRs. Other studies have examined the role of NMDA receptors in the interpositus nucleus in eyeblink conditioning. A number of studies had demonstrated that systemic injections of the noncompetitive NMDA antagonist, MK-801 or PCP, and the competitive NMDA antagonist CGP-39551, impaired the acquisition of classical eyeblink conditioning in rabbits and rats, but did not affect the performance of the learned response (e.g., Robinson, 1993; Servatius & Shors, 1996; Thompson & Disterhoft, 1997). Chen and Steinmetz (2000a) further demonstrated that direct infusions of AP5, an NMDA receptor antagonist, in the region of the interpositus nucleus, retarded the rate of conditioning but had little, if any, effect on the number of CRs emitted when infused after learning had taken place (although there was an indication that response timing in some rabbits was affected by the AP5 infusions). These studies suggest that NMDA receptors may play a role in the acquisition of classically conditioned eyeblink responses, a result that is compatible with a variety of literature suggesting a rather ubiquitous role for NMDA receptors in plasticity processes.

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There is also evidence that protein synthesis in the cerebellum is involved in eyeblink conditioning. Bracha, Irwin, Webster, Wunderlich, Stachowiak and Bloedel (1998) showed that microinjections of anisomycin, a general protein synthesis inhibitor, into the intermediate cerebellum near the interpositus nucleus impaired the acquisition of conditioning. The anisomycin had no effect on the expression of conditioned responses when infused after asymptotic response levels had been reached. Gomi, Sun, Finch, Itohara, Yoshimi, and Thompson (1999) further showed that infusion of the transcription inhibitor, actinomycin D, into the interpositus nucleus of rabbits, reversibly blocked the learning but not the performance of eyeblink CRs. In this study, using differential display PCR analysis of interpositus RNAs from trained and control rabbits, they also demonstrated the existence of a 207bp band that was induced by the conditioning and that training had increased expression of KKIAMRE, a cdc2related kinase, in interpositus neurons. This result was in agreement with a recent report by Chen and Steinmetz (2000b) that provided evidence that direct infusions of H7, a general protein kinas inhibitor, impaired the acquisition but not the retention of eyeblink CRs. Together, these data provide strong support for the idea that protein synthesis and, more specifically, protein kinase activity in the interpositus nucleus is involved in the acquisition of conditioned responses. New behavioral and molecular genetics techniques have proven useful for advancing our understanding of cellular processes that might underlie classical eyeblink conditioning. Chen, Bao Lockard, Kim and Thompson (1996) showed that partial learning could be seen in Purkinje cell-degeneration (pcd ) mutant mice that were given classical eyeblink conditioning trials. Lesions of the interpositus nuclei of the pcd mice, however, produced complete response abolition thus demonstrating that some residual learning appears to take place in the interpositus nucleus (Chen, Bao, & Thompson, 1999). A very recent study utilized a 78kDa-glucose regulated protein/immunoglobulin binding protein conditional knockout mouse model of Purkinje cell atrophy and confirmed impaired but residual learning sans Purkinje cells which was completely abolished with interpositus nucleus lesions (Kim, Miao, Lee, & Thompson, 2011). Qiao, Chen, Bao, Hefti, Thompson, and Knusel (1998) showed a severe deficit in the acquisition of classical eyeblink conditioning in the spontaneous ataxic mutant mouse stargazer that have a selective reduction of brain-derived neurotrophic factor (BDNF) mRNA expression in the cerebellum. Impaired eyeblink conditioning was also observed in the waggler mutant that also has

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a selective deficit in BDNF expression involving cerebellar granule cells (Bao, Chen, Qiao, Knusel, & Thompson, 1998). These data suggest that BDNF may play a role in plasticity processes that underlie eyeblink conditioning. The data concerning cellular processes involved with eyeblink conditioning seem to suggest that important plasticity processes associated with the learning of this simple, discrete response, involve neurons in both cerebellar cortex and the deep cerebellar nuclei. Indeed, it is likely that interactions between cortical and nuclear areas, along with conditioning-related feedback from the cerebellum onto the essential CS and US pathways that project information to the cerebellum, are involved in generating conditioned responses. Using Classical Eyeblink Conditioning to Study Other Behavioral and Neural Phenomena Many investigators have recognized the usefulness of classical eyeblink conditioning for studying other behavioral and neurological phenomena such as development, aging, neurological impairment, and behavioral and psychological pathologies. Given the well-delineated behavioral bases of this simple form of learning as well as the emerging details concerning the neural substrates that underlie the behavioral plasticity, there has been a growing interest in using eyeblink conditioning to explore a host of basic science issues related to learning and memory as well as a number of clinical and applied phenomena. Stanton and his colleagues have used eyeblink conditioning to describe the development of associative learning in rats (see Stanton & Freeman, 2000, for review). Using techniques developed by Skelton (1988) for classically conditioning the eyeblink response of freely moving rats. Stanton and his colleagues have conducted a series of studies that have explored the ontogeny of associative learning in the developing rat. For example, they have shown that the development of conditioning parallels the development of the cerebellum—Postnatal Day (PND)–17 rats do not show evidence of conditioning, PND-20 rats show moderate amounts of learning, while PND-24 rats demonstrate rather robust learning. At the other spectrum of the life cycle, eyeblink conditioning has been effectively used to study aging in humans and animal models. Studies have shown that in both humans and nonhuman animals, age-related changes in conditioning can be seen when either the delay or trace conditioning procedure is used (e.g., Woodruff-Pak, Lavond, Logan, & Thompson, 1987; Woodruff-Pak & Thompson, 1988). Further, age-related deficits in conditioning seem to parallel age-associated changes in brain

structures that are critical for eyeblink conditioning, such as the cerebellum and hippocampus (see Woodruff-Pak & Lemieux, 2001, for review). In addition, research by Woodruff-Pak and her colleagues has shown that classical eyeblink conditioning is a very sensitive indicator and predictor of Alzheimer’s disease, a neurodegenerative disorder that is known to affect brain regions that are important for eyeblink conditioning (see Woodruff-Pak, 2000, for review). Classical eyeblink conditioning has proven useful for the study of neurological and psychological impairments. For example, Daum and colleagues have shown that persons with cerebellar damage show impaired eyeblink conditioning (Daum et al., 1993) but not persons with other neurological impairments including temporal lobe lesions (Daum, Channon, & Gray, 1992), Parkinson’s disease, and Huntington’s disease (see Schugens, Topka, & Daum, 2000, for review). At least some persons with autism are thought to have cerebellar cortical pathologies and, as predicted, alterations in classical eyeblink conditioning have been reported in some persons with autism (Sears, Finn, & Steinmetz, 1994). Specifically, persons with autism show greatly facilitated rates of conditioning and mistimed CRs, which may be due to the pathology in cerebellar cortex and/or the hippocampus that has been reported. Eyeblink conditioning deficits have been observed in schizophrenics that may be related to pathologies involving the cerebellar cortex (Brown et al., 2005; Edwards et al., 2008). Finally, eyeblink conditioning has been used to test the basic idea that persons with obsessive-compulsive disorder generally acquire aversively motivated CRs more rapidly than other persons. Tracy, Ghose, Stetcher, McFall, & Steinmetz (1999) showed that under some contextual situations, persons with OCD show an extremely rapid acquisition of eyeblink CRs, which supports the idea that these individuals may be sensitive to aversive stimulations. All of these examples demonstrate the great utility of eyeblink conditioning in testing hypotheses concerning behavioral and neural function, a utility that is due to the impressive database that has been assembled concerning behavioral and neural correlates of this simple form of associative learning.

Classical Conditioning of Other Discrete Responses While classical eyeblink conditioning has certainly been the most popular paradigm for studying the conditioning of discrete somatic responses, over the years, classical conditioning of other response systems has been attempted.

Classical Jaw-Movement Conditioning

In an early attempt to evaluate the potential role of the inferior colliculus in classical conditioning, Halas and Beardsley (1970) classically conditioned the hindlimb flexion responses of four cats while recording neural activity from the inferior colliculus. Training consisted of paired presentations of a tone CS with a mild electrical shock to the hindpaw as a US. In the same cats, they also delivered some instrumental conditioning trials where a leg flexion resulted in avoidance of the shock US. Halas and Beardsley reported largely negative results: For three cats the CS produced an inhibition of collicular activity that was not modified by training. For one cat, CRrelated activity was observed during instrumental, but not classical conditioning trials. Several researchers have studied forelimb flexion conditioning in the cat. Tsukahara and associates conducted a series of studies aimed at delineating the involvement of the corticorubrospinal system in classical conditioning of forelimb flexion in cats (e.g., Tsukahara, Oda, and Notsu, 1981). In their studies, the CS was electrical stimulation that was delivered to the cerebral peduncle while the US was an electric shock delivered to the skin of the forelimb. Tsukahara and his colleagues used this preparation to study possible conditioning-related plasticity in the corticorubral system. Voneida and colleagues have also classically conditioned the limb-flexion response in cats by pairing a tone CS and a mild electric shock to the forelimb as a US (Voneida, Christie, Bogdanski, & Chopko, 1990). They have explored the involvement of the olivocerebellar system in encoding classical forelimb conditioning by delivering lesions to various regions of the inferior olivary complex. Rostromedial olivary lesions, which included spino-olivary and cortico-olivary forelimb projection zones and the olivocerebellar projection area, produced extinction and severe deficits in conditioned responding. This result parallels nicely the results of olivary lesion studies during eyeblink conditioning in the rabbit (McCormick, Steinmetz, & Thompson, 1985; Yeo, Hardiman, & Glickstein, 1986). Finally, Marchetti-Gauthier, Meziane, Devigne, and Soumereu-Mourat (1990) examined the effects of bilateral lesions of the cerebellar interpositus nucleus on forelimb flexion conditioning in mice. They used lights and tones as CSs and an electric shock delivered to the forelimb as an US. The mice were restrained in Plexiglas restraint boxes and forelimb flexion responses were measured using electromyographic methods. These researchers showed that bilateral lesions of the interpositus nucleus prevented conditioning. However, unlike previous rabbit studies (e.g.,

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Steinmetz et al., 1992) when bilateral lesions of the interpositus nucleus were delivered after training, no effects of the lesions could be detected in their paradigm. They concluded that in the mouse, the interpositus nucleus was necessary for acquisition, but not retention, of classically conditioned forelimb flexion, a most puzzling result and conclusion.

CLASSICAL JAW-MOVEMENT CONDITIONING In the 1960s, Gormezano and colleagues developed a rabbit appetitive classical conditioning procedure that involves recording a relatively discrete jaw-movement response (e.g., Coleman, Patterson, & Gormezano, 1966; Sheafor & Gormezano, 1972; Smith, DiLollo, & Gormezano, 1966). In this procedure, a light or tone CS is paired with a rewarding intraoral water (or saccharin) US. The water US causes a rhythmic jaw-movement that is related to consumption. After repeated pairings with the appetitive US, the CS eventually becomes capable of eliciting the jaw-movement response in the absence of the US. In behavioral studies, this procedure has proven useful for studying motivational factors that are associated with conditioning. For example, water deprivation prior to conditioning can produce a different motivational state for jaw-movement conditioning and hence alter the rate of acquisition (e.g., Mitchell and Gormezano, 1970). More important for our discussion here, classical jaw-movement conditioning has been effectively used to study the neural bases of simple appetitive learning (see Berry, Seager, Asaka, & Borgnis, 2000 and Berry, Seager, Asaka, & Griffin, 2001, for reviews). Critical CS and US pathways for jaw-movement conditioning have not been delineated. While the motor pathways for jaw-movement conditioning have not been worked out in as much detail as eyeblink conditioning, it is assumed that the trigeminal nucleus, which controls the muscles of mastication (Donga, Dubuc, Kolta, & Lund, 1992), is chiefly involved in generating the UR and CR for this type of learning. The trigeminal is thought to be heavily influenced by neocortical and other higher input in the generation of the jaw-movement response, perhaps accounting for the relatively complex rhythmic response pattern that is seen (Dellow & Lund, 1971). Thus important differences between jaw-movement conditioning and eyeblink conditioning are already apparent—the jaw-movement response is relatively more complex and appears not to involve the cerebellum. This was demonstrated in a study published by Gibbs (1992) who showed

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that lesions of the interpositus nucleus completely abolished eyeblink CRs but had no effects on jaw-movement CRs recorded from the same rabbits. Berry and colleagues have conducted an extensive series of studies designed to study the involvement of the hippocampus in this type of appetitive learning. In multiple- and single-unit recording studies, they have shown that pyramidal cells in the CA1 area of the hippocampus increased their firing rates over training (e.g., Oliver, Swain, & Berry, 1993; Seager, Borgnis, & Berry, 1997). Berry and colleagues compared hippocampal firing patterns recorded on eyeblink conditioning and jawmovement conditioning trials by using a discrimination procedure that employed two tones to differentiate air puff US and water US trials (see Berry et al., 2000). While excitatory patterns of responding are generally seen, the patterns of spiking during jaw-movement conditioning (i.e., the magnitude, duration, frequency, and rhythmicity of spiking) were somewhat different from what is seen during eyeblink conditioning. Berry and associates have also successfully used the jaw-movement conditioning procedure to study aging effects as well as cholinergic brain function. Consistent with data from eyeblink conditioning experiments (Woodruff-Pak, Lavond, Logan, & Thompson, 1987), they observed that 40–49-month-old rabbits were slower to acquire the conditioned jaw-movement response than 3–7 month old rabbits (Seager et al., 1997). Deficits in hippocampal unit activity were also seen in the aged rabbits. Early in training, young rabbits showed significantly greater hippocampal activity just prior to US onset and the magnitude of this activity was highly correlated with the rate of learning. The rhythmic CRs recorded in the aging rabbits were found to be of a significantly lower frequency than younger rabbits but no difference in UR rhythmicity was observed. This suggested that the effect of aging was on a neural system that somehow modulated the central pattern generator during CRs, but not URs. Given previous data indicating an involvement of disruptions of cholinergic system in aging effects, Berry and colleagues have also studied the effects of cholinergic impairment on jaw-movement conditioning. They found many parallels between aging effects and cholinergic impairment. For example, subcutaneous injections of cholinergic blockers (e.g., scopolamine hydrobromide) resulted in longer acquisition times and also the suppression of conditioning-related hippocampal activity (Salvatierra & Berry, 1989). Further, the cholinergic blocker

selectively decreased the frequency of CR rhythmicity similar to a level seen in aged rabbits. The hippocampal recording, aging, and pharmacological studies described here illustrate the potential usefulness of the classical jaw-movement conditioning procedure for studying the neural bases of the associative learning of discrete responses. It is clear that among other things, the major use of this procedure may be in delineating similarities and differences between appetitive and aversive conditioning processes.

NEURAL SUBSTRATES OF THE INSTRUMENTAL CONDITIONING OF DISCRETE RESPONSES Instrumental conditioning procedures have also been used to advance our understanding of the neural bases of simple associative learning. While formally similar to classical conditioning procedures (e.g., discrete stimuli are typically used and discrete responses are recorded), instrumental conditioning differs from classical conditioning in one important respect—the response made by the organism affects the delivery of the stimuli used in training. Avoidance conditioning best illustrates the difference between classical and instrumental conditioning. In classical eyeblink conditioning, a US is presented after the CS regardless if the subject generates a conditioned response. This task can easily be turned into an instrumental task by introducing a contingency between the subject’s response and the presentation of the US. For example, in instrumental eyeblink conditioning, the US is withheld if the subject executes a CR prior to when the US would normally occur. Gabriel and colleagues have used an instrumental conditioning procedure to extensively explore the involvement of the forebrain and other structures in simple associative learning (see Gabriel and Talk, 2001, for review). Their procedure, known as discriminative instrumental avoidance learning, is an adaptation of a procedure first described by Brogden and Culler (1936). Rabbits are placed in a large rotating wheel apparatus. Two CSs (typically tones of two different frequencies) are presented. One tone frequency (the CS+) is initially followed by a footshock US while the second tone frequency (the CS–) is presented alone. If the rabbit steps forward (thus turning the wheel) after tone onset but before shock onset, the shock is not delivered (i.e., the rabbit has successfully avoided the shock). Over several trials, the rabbit learns to respond to the CS+ and ignore the CS–. Gabriel and

Neural Substrates of the Instrumental Conditioning of Discrete Responses

colleagues also developed an appetitive procedure that parallels the aversive task. In this task, the rabbits can receive a reward by approaching and making oral contact with a drinking spout that is presented for a period of time after CS+ presentation. The reward is not delivered if spout contact is made after CS– presentation. In an elegant series of studies, Gabriel and colleagues have recorded brain activity from a variety of brain regions during these forms of learning and have thus described to a large extent the neural systems involved in this learning. Gabriel and colleagues have described the neural system involved in discriminative instrumental avoidance (and approach) learning as functional modules (Freeman, Cuppernell, Flannery, & Gabriel, 1996; Gabriel & Talk, 2001). Unlike eyeblink conditioning, where relatively few critical sites for CS-US convergence appear to exist (and most seem to be in the cerebellum), Gabriel and colleagues have proposed that there are many CS-US convergence sites for discriminative instrumental learning, each that has a rather unique and necessary function for learning to take place. Other CS-US convergence sites are thought to have important modulatory functions. These various sites are referred to as modules, where each module is assumed to receive CS and US input during learning as well as input from other modules. In an extensive and comprehensive series of studies conducted over 25 years or so, Gabriel and colleagues have used mainly lesion and neural recording methods to define and study the modules involved in this type of instrumental learning. While an in-depth review of the impressive dataset is beyond the scope of this chapter, a few generalities concerning their findings can be raised. First, the cingulate cortex and associated thalamic nuclei play a very important role in this learning (e.g., Freeman & Gabriel, 1999; Gabriel, 1990; Gabriel, Sporenborg, & Kubota, 1989). These areas seem to comprise a module that is specialized for processing associative attention and also retrieval of information in response to the presentation of task-relevant cues. The involvement of the hippocampus in this type of instrumental learning has also been evaluated (e.g., Kang & Gabriel, 1998). Lesion and recording studies seem to suggest that the hippocampus is a module that is involved in context-based retrieval (i.e., processing the context in which the simple associative cue-based learning occurs). A third major module that has been defined involves the amygdala, which Gabriel and colleagues have identified as important for initiating learning-relevant plasticity in other areas of the brain (e.g., Poremba & Gabriel, 1999). This idea is compatible with the view championed by McGaugh

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and his colleagues that amygdala efferents are involved mainly in the establishment of memory in a host of nonamygdalar brain areas (e.g., McGaugh, 2000; McGaugh & Cahill, 1997). Interestingly, there appears to be little, if any, overlap in the neural circuitry that encodes classical eyeblink conditioning and discriminative avoidance learning. In two collaborative studies Gabriel and colleagues and Steinmetz and colleagues evaluated the effects of cerebellar lesions (Steinmetz, Sears, Gabriel, Kubota, & Poremba, 1991) and limbic thalamus lesions (Gabriel, Kang, Poremba, Kubota, Allen, Miller, & Steinmetz, 1996) on the two procedures. A complete dissociation of lesion effects was observed: Lesions of the interpositus nucleus completely abolished classical eyeblink CRs but had no effect on the discriminative avoidance learning while lesions of the limbic thalamus severely impaired discriminative avoidance learning but had no effect on classical eyeblink conditioning. Differences in the two learning tasks most likely account for the observed dissociation. The avoidance task involves a relatively complex, goal directed movement in a discriminative context. The classical conditioning task involves a relatively discrete movement in a nondiscriminative context. Also, the interstimulus intervals for the two tasks are widely different—the CS-US interval for the instrumental learning task is usually greater than 5 seconds while the CS-US interval for the classical conditioning task is never more than 2 seconds (and more often in the range of 250–500 msec). Steinmetz and colleagues developed an instrumental bar-press conditioning procedure in rats that has been successfully used to study behavioral, neural, and pharmacological phenomena (Steinmetz, Logue, & Miller, 1993). In the aversive version of this task, a tone CS is presented for a period of time and rats are required to press a response bar during the tone presentation to avoid a mild footshock US. A bar-press made during the shock presentation terminates the shock, thus allowing an escape response. In the appetitive version of this task, a tone CS is presented for a period of time and rats receive a food-pellet reinforcement if they press the response bar while the tone is on. This preparation has been used in within-subject design experiments. The same rat is trained in both the aversive and appetitive versions of the task using the same tone CS, the same training context, with the same response requirements (i.e., in essence, the only difference between the appetitive and aversive training is the consequences of the bar press). Variations of the task have also been used, such as training using a discriminative stimulus to signal appetitive and aversive trials, training on partial or delay

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reinforcement schedules, and training in conjunction with autonomic recording. In an initial study (Steinmetz et al., 1993), it was demonstrated that bilateral lesions of the cerebellar interpositus nuclei prevented learning of the aversively motivated bar press learning when relatively short tone presentation intervals were used. The same rats could acquire the appetitive task normally thus demonstrating that the lesion-induced deficit was not sensory or motor in nature. This withinsubject instrumental learning procedure has also been used successfully to study basic approach and avoidance learning in rats that were bred specifically for alcohol preference (e.g., Blankenship, Finn, & Steinmetz, 1998, 2000), to explore the involvement of the amygdala in aversive learning (e.g., Rorick-Kehn & Steinmetz, 2005), as well as in studies by Garraghty and colleagues designed to assess cognitive impairments associated with the administration of antiepileptic compounds (Banks, Mohr, Besheer, Steinmetz, & Garraghty, 1999).

CONCLUSION Study of the brain substrates of learning and memory is at a most exciting stage. With the advent of new technologies, we are rapidly gaining an appreciation of the neural circuits and pathways that form the essential substrates for different forms of learning and memory. On the other hand, analysis of the basic mechanisms of synaptic plasticity, long-term potentiation (LTP) and long-term depression (LTD) is proceeding rapidly. At present, these mechanisms are viewed by many as the most likely candidates for memory storage in the brain (Baudry et al., 2000; Bliss & Collingridge, 1993; but see Shors & Matzel, 1997). But these two approaches have yet to bridge. At present LTP and LTD remain as mechanisms in search of behavioral phenomena and the various forms of learning and memory are behavioral phenomena in search of mechanisms. It is our fervent hope that the two will meet. The fundamental problem, posed by Karl Lashley in 1929, remains: In order to analyze mechanisms of memory storage it is first necessary to localize the sites of storage in the brain. This is now close to being accomplished for simpler forms of learning in mammals: classical conditioning of fear (amygdala) and discrete behavioral responses (cerebellum). Only when this has been done can we build a tight causal chain from, for example, LTP in the amygdala or LTD in the cerebellum to the behavioral expression of memory. The problem is more severe in the hippocampus, a structure that prominently displays LTP and LTD

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CHAPTER 20

Memory Systems HOWARD EICHENBAUM

EARLY VIEWS ON MULTIPLE MEMORY SYSTEMS 551 THE EXPERIMENTAL ERA: DEBATES ON THE FUNDAMENTAL BASIS OF MEMORY 552 RECONCILIATION: MULTIPLE MEMORY SYSTEMS 554

THREE MAJOR MEMORY SYSTEMS IN THE BRAIN 556 ELABORATING THE ROLE OF THE THREE MAJOR MEMORY SYSTEMS 560 SUMMARY 571 REFERENCES 571

EARLY VIEWS ON MULTIPLE MEMORY SYSTEMS

or obscure, image without recalling the ideas behind it. Thus, mechanical memory was seen as expressing habits in the form of coordinated actions, and sensitive memory as a habit expressed in the form of an affective component. These two kinds of memory had in common that they could operate without conscious recall and could be the source of the most inflexible and obstinate behaviors. Maine de Biran developed his formulation without experiments or consideration of the anatomy or functions of brain systems. And there is no record that Maine de Biran’s theory had significant influence over successive developments in memory research. Yet, as it turns out, he was prescient in describing a division of memory systems that is, as you will see, strongly supported by modern cognitive neuroscience. There has been much progress, and many detours, in both psychological and biological studies on the brain and memory systems before Maine de Biran’s scheme was rediscovered. The history of this area has largely preserved the notion of an elemental “habit” mechanism, bolstered by the early discoveries about the existence of reflexes and their conditionability, and many theories have preserved a distinction between simple habits and conscious memory, albeit sometimes in the form of debates in which habits and recollection were polarized as alternatives. A century after Maine de Biran, the notion of habit as a fundamental mechanism, and memory as a more complex phenomenon associated with consciousness was widely

The notion that there is more than one kind of memory is an old one, richly woven into the history of theorizing and research in philosophy, psychology, and neuroscience. Although ideas about different kinds of memory can be dated as far back as Aristotle, in 1804 the French philosopher Maine de Biran proposed what may be the first formal theory of multiple memory systems. He viewed all cognition and memory as based on a fundamental mechanism of “habit,” a concept similar to the current term “association.” In his proposal, habits were simple and automatic mechanisms, but they had a broad applicability. Habits were viewed as mediating acquired behaviors that operate independently of conscious control and conscious recollection. In addition, the habit mechanism was also viewed as the basis for more complex, consciously mediated aspects of memory. Main de Biran elaborated his scheme into three distinct forms of memory, each based on the fundamental habit mechanism but also distinct in its contents and properties. One form was called representative memory, characterized as expressed in the conscious recollection of a “well-circumscribed idea.” The second, designated mechanical memory, refers to situations where the habit mechanism does not generate a recalled idea, but instead only a facilitation of the repetition of a movement. Finally, sensitive memory refers to when the habit mechanism generates a feeling or “fantastic,” albeit vague

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held. William James (1890) wrote of them in separate chapters in his treatise Principles of Psychology. James considered habit a very primitive mechanism that is common among biological systems, and due to plasticity of the organic materials. Within the nervous system, habits were viewed as nothing more than the ready discharge of a well-worn reflex path. But James also attributed to habit great importance in the development of more complicated behavioral repertoires. He suggested that well-practiced behaviors and skills, including walking, writing, fencing, and singing, are mediated by concatenated reflex paths, organized to generate the serial production of movements and unconscious sensations leading to other movements and sensations. He thought of habits as eliminating the need for conscious supervision once a behavior becomes routine, and recommended early and frequent reinforcement of good habits as a key exercise in ethical and cognitive development. James distinguished memory as something altogether different from habit, albeit based on that mechanism, a very complicated phenomenon with many facets. James is perhaps best known for having originated the distinction between primary memory and secondary memory. Primary memory is what we today call short-term or working memory. It is a short-lived state where new information has achieved consciousness and belongs to our stream of thought. James viewed primary memory as the gateway by which material would enter secondary memory, or what we now call long-term memory. James defined secondary memory as “the knowledge of an event, or fact, of which meantime we have not been thinking, with the additional consciousness that we have thought or experienced it before” (p. 648). In addition to its personal and temporal aspects, the full characterization of memory was framed in terms of two other properties, its structure as an elaborate network of associations and its basis in habit mechanisms. Thus James theorized a mechanistic basis for how habits could be elaborated for the formation of multiple and linked associations to support the richness of memory. Thus the underlying foundation of recall was a complex, yet systematic set of associations between any particular item and anything co-occurring in one’s previous experiences with the item. He argued that when we search for a memory, we navigate through the elaborate network of the associations, and if successful locate the sought memory among them. The goodness of memory, he believed, was as much dependent on the number of associations in the network as on the strength of those associations.

THE EXPERIMENTAL ERA: DEBATES ON THE FUNDAMENTAL BASIS OF MEMORY At the outset of experimental approaches to memory, reductionism rained. The goal was to identify a basic mechanism of habit as an explanation of memory, eliminating the need for allusions to consciousness. This approach was known as behaviorism, and its origins began separately in the United States and Russia (see Eichenbaum & Cohen, 2001, for review). At the turn of the 20th century, Thorndike had invented his puzzle box, with which he observed cats learning to manipulate a doorlatch to allow escape from a holding chamber. Around the same time, Small introduced the maze to studies of animal learning, inspired by the famous garden maze at Hampton Court in London. By 1907 Watson had published his accounts on maze learning by rats, and by 1913 he had written his behaviorist “manifesto,” formalizing it the next year in his systematic exposition, claiming we need never return to terms such as consciousness. Independently in the early 1900s, Pavlov and Bechterev, physiologists in Russia had been experimenting on autonomic nervous system reflexes in dogs. Pavlov was studying the physiology of digestion, and observed that dogs would secrete saliva not only when given food but also when presented with an arbitrary stimulus following repeated pairings of the arbitrary stimulus and food delivery. He called this phenomenon the “conditioned reflex.” Bechterev studied the respiratory motor reflex by which cold applied to the skin produces a reflexive “catching” of the breath, and he discovered that an arbitrary stimulus applied repeatedly at the same time as the cold would eventually set off the same reflex by itself. The neurology of the conditioned reflex, especially as elaborated by Sherrington, gave biological validity to what behaviorists saw as the elemental mechanism of learned behavior. There were debates about the distinctions between the fundamental association in Pavlovian conditioning versus that in Thorndike’s instrumental learning, specifically whether the critical association was between the stimulus and response or the stimulus and the reinforcer. Despite this difference, the two viewpoints came to be referred to collectively as stimulus-response or “S-R” learning, and we should consider them as offering a physiological instantiation of the habit mechanism. To the theorists of this time, having a full accounting of S-R learning would solve the problem of memory. Yet there were detractors from this prominent theme. Early challenges to behaviorism came from psychologists

The Experimental Era: Debates on the Fundamental Basis of Memory

such as Yerkes and Kohler whose observations on great apes led them to conclude that animals did not learn complex problems by a combination of random trial-and-error attempts and eventual reinforcement of a correct solution, but rather that at least the higher animals had insights into relationships between means and ends. Tolman (1932) was perhaps the most successful in challenging behaviorism, because he developed operational definitions for mentalistic processes including “purposive behavior” and “expectancy.” Tolman’s goal was to get behind the behavior, not by specifying particular elements of habits or their linkage, but by identifying the complex cognitive mechanisms, purposes, expectations, and insights that guided behavior. Tolman’s basic premise was that learning generally involved the acquisition of knowledge about the world, and in particular about relationships among stimuli and between stimuli and their consequences, and that this knowledge led to expectancies when the animal was put in testing situations. He argued that learning involved the creation of a “cognitive map” that organized the relations among stimuli and consequences based on interconnections between groups of stimuli. Moreover, he rigorously tested these ideas using the same species (rats) and maze-learning paradigms that were a major focus of the prominent S-R theorists. In a series of studies he showed that rats were capable of solving maze problems by taking novel detours or shortcuts, and they exhibited a capacity for “latent learning,” in which they acquired problem solutions in the absence of reinforcement. Collectively, in each of these studies rats showed they were capable of learned behaviors that were not previously reinforced and therefore could not be mediated by S-R representations. A parallel debate emerged from studies on human verbal memory. On the side of reductionism was Herman Ebbinghaus (1885), who had admired the mathematical analyses that had been brought to the psychophysics of perception, and sought to develop similarly precise and quantitative methods for the study of memory. Ebbinghaus had rejected the use of introspection as capable of providing evidence on memory. He developed objective assessments of memory in “savings” scores that measured retention in terms of the reduction in trials required to relearn material, and he used statistical analyses to test the reliability of his findings. Furthermore, to create learning materials that were both simple and homogeneous in content Ebbinghaus invented the “nonsense syllable,” a meaningless letter string composed of two consonants with a vowel between. With this invention he avoided

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the confounding influences of “interest,” “beauty,” and other features that he felt might affect the memorability of real words, and he simultaneously equalized the length and meaningfulness of the items, albeit by minimizing the former and eliminating the latter. Ebbinghaus was and is still hailed as a pioneer of systematic scientific methodology in the study of human verbal memory. His studies and those that followed provided a detailed characterization of the acquisition and retention of arbitrary associations, and many phenomena of verbal memory. This approach also had its detractors. Most prominent among these perhaps was the British psychologist Fredric Bartlett (1932), whose work stands in stark contrast to the rigorous methods introduced by Ebbinghaus. Bartlett differed in two major ways. First, his interest was in the mental processes used to recover memories, that is, in remembering more so than in learning. He was not so interested in the probability of recall, as dominated Ebbinghaus’ approach, but in what he called “effort after meaning,” the mental processing taken to search out and ultimately reconstruct memories. Second, Bartlett shuddered at the notion of using nonsense syllables as learning materials. By avoiding meaningful items, he argued, the resulting memories would necessarily lack the rich background of knowledge into which new information is stored. Indeed, the subtitle of Bartlett’s book Remembering is A Study in Experimental and Social Psychology, thus highlighting his view that “real” memory is embedded in the full fabric of a lifetime of experience including prominently one’s culture. Barlett’s main strategy was called the method of repeated reproduction. His most famous material was a short folk tale titled “The War of the Ghosts,” which was adapted from the original translation by the explorer Franz Boaz. He selected this story for several reasons: The syntax and prose were derived from a culture quite different from that of his British experimental subjects, the story contents lacked explicit connections between some of the events described, and the tale contained dramatic and supernatural events that would evoke vivid visual imagery on the part of his subjects. These qualities were, of course, exactly the sort of thing Ebbinghaus worked so hard to avoid with his nonsense syllables. But Bartlett focused on these features because he was primarily interested in the content and structure of the memory obtained, and less interested in the probability of recall of specific items. Barlett made three general observations on this and other reproductions of the story: First, the story was

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considerably shortened, mainly by omissions. Second, the syntax became more modern and taken from the subject’s culture. Third, the story became more coherent and consequential. From these observations Bartlett concluded that remembering was not simply a process of recovery or forgetting of items, but rather that memory seemed to evolve over time. Items were not lost or recovered at random. Rather, material that was more foreign to the subject, or lacked sequence, or was stated in unfamiliar terms, was more likely to be lost or changed substantially in both syntax and meaning, becoming more consistent with the subject’s common experiences. To account for these observations Bartlett developed an account of remembering known as “schema” theory. In his view the simplest schemata were habit-like traces of items in sequential order of experience. But he elaborated this “low-level” mechanism, arguing that our experience of particular sequences builds up en masse, such that particular past events that are more or less dated, or placed, in relation to other associated particular events, in a dynamic organization from which one can construct or infer both specific contents of memories and their logical order. Bartlett proposed that remembering is therefore a reconstructive process and not one of mere reproduction, as Ebbinghaus preferred and as would guide low-level rote memory.

RECONCILIATION: MULTIPLE MEMORY SYSTEMS The evidence provided by Tolman, Barlett, and others did not resolve the debate, but in general led to more complex constuals of S-R models. The issue has now, however, been largely resolved by the introduction of cognitive neuroscience, and evidence that both habit-like and recollective memory exist, and are mediated by distinct neural systems. I will describe here two particularly compelling lines of evidence that support this reconciliation, one from the literature on maze learning in rats and the other a classic study in the field of human neuropsychology. The debate on learning in animals became focused on the central issue of whether rats acquire maze problems by learning specific turning “responses” or by developing an expectancy of the “place” of reward. The issue was addressed using a simple T-maze apparatus where “response” versus “place” strategies could be directly compared by operational definitions (Figure 20.1). The basic task involves the rat beginning each trial at the base of the “T” and being rewarded at the end of only one arm,

Train

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Figure 20.1 Schematic diagram of the T-maze task. The rat is initially trained to turn left in order to obtain rewards at the indicated goal locus. In subsequent testing, the maze is rotated 180 degrees so that the locus of the goal site within the room is identical to that during training, and the rat starts from the opposite end of the room. The rat might continue to turn left, indicating use of the left-turn “response” strategy. Or it might turn right and go to the same locus or reward, indicating use of the “place” strategy.

for example, the one reached by a right turn. The accountings of what was learned in this situation differ strongly by the two theoretical approaches. In this situation then, according to S-R theory, learning involves acquisition of the reinforced left-turn response. By contrast, according to Tolman’s account, learning involves the acquisition of a cognitive map of the environment and the expectancy that food was to be found at a particular location in the test room. The critical test involved effectively rotating the “T” by exactly 180 degrees, so that the choice arms still ended at the same two loci (albeit that the arms that reach those loci are now exchanged), and the start point would now be at the opposite end of the room. The S-R theorist would predict that a rat would continue to make the previously reinforced right-turn response at the choice point, leading it to a goal location different from that where the food was provided during training. By contrast, the prediction of Tolman’s account was that the rat would switch to a left-turn response in order to arrive at the expected location of food in the same place in the room where it was originally rewarded. Tolman provided initial evidence in favor of his prediction, but subsequent efforts to replicate this result were mixed. A decade of these experiments indicated that place learning was more often favored but that there were conditions under which response learning was preferred. His analysis indicated that the nature of the available cues was the primary determining factor for the differences in the results. In general, whenever there were salient extra-maze visual cues that differentiated one goal location from the other, a place representation predominated. Conversely, when differential extramaze cues were not prominent, the response strategy would predominate. Such a pattern of

Reconciliation: Multiple Memory Systems

results did not, of course, declare a “winner” in the place versus response debate. Instead these results suggested that both types of representation are available to the rat, and that it might use either one under conditions of different salient cues or response demands. This story does not end there. A most elegant explanation of how rats could have both strategies was recently provided by Packard and McGaugh (1996). In this experiment, rats were trained for a week on the T-maze task, then given the rotated-maze probe trial. Then they were trained for another week with the maze in its original orientation, and then finally presented with an additional probe trial. Packard and McGaugh found that normal rats initially adopted a place representation, as reflected in their strong preference for the place of the previous goal during the first probe trial. However, after the additional week of overtraining, normal rats switched, now adopting a response strategy on the final probe test. So, under these training circumstances, rats developed both strategies successively. Their initial acquisition was guided by the development of a cognitive map, but subsequent overtraining led to development of the response “habit.” But Packard and McGaugh’s experiment went beyond merely confirming that the same rats can use both learning strategies. In addition to the pure behavioral testing, Packard and McGaugh also examined whether different brain systems supported these different types of representation. Prior to training, all animals had been implanted with indwelling needles that allowed injection on the probe tests of a local anesthetic or saline placebo directly and locally into one of two brain structures, the hippocampus or the striatum. The results on normal animals described above were from those subjects that were injected with placebo on both probe tests. However, the effects of the anesthetic were striking. On the first probe trial, animals that were injected with anesthetic into the striatum behaved just as control subjects had—they were predominantly “place” learners, indicating that place representation did not depend on the striatum. But the animals that had been injected with anesthetic into the hippocampus showed no preference at all, indicating that they relied on their hippocampus for the place representation, and that this was the only representation normally available at that stage of learning. On the second probe test a different pattern emerged. Whereas control subjects had by now acquired the response strategy, animals given an anesthetic in the striatum lost the turning response and instead showed a striking opposite preference for the place strategy. Animals given an injection of anesthetic into the hippocampus maintained their response strategy.

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Combining these data, a clear picture of the evolution of multiple memory representations emerges. Animals normally develop an initial place representation that is mediated by the hippocampus, and no turning-response representation has developed in this initial period. With overtraining, a response representation that is mediated by the striatum is acquired, and indeed predominates over the hippocampal place representation. The latter is not, however, lost—it can be “uncovered” by inactivating the striatum and suppressing the turning response strategy. These findings offer compelling evidence that elements of both the S-R and the cognitive map views were right: There are distinct types of memory for place and response, and they are distinguished by their performance characteristics as well as by the brain pathways that support them. Notably, the Packard and McGaugh experiment was preceded by many other studies demonstrating a specific role for the hippocampus in memory, as well as a few studies showing specificity in the involvement of the striatum in the acquisition of habits. But this particular study is most striking both in the elegance of the dissociation and in its contact with the history of views on habit and cognitive memory. In the field of human memory research, the discovery of multiple memory systems came from two major breakthroughs in the study of patients with pervasive, “global” amnesia. The first of these breakthroughs came with the report by Scoville and Milner (1957) of what has become probably the most famous neurological patient in the literature, the man known by his initials, H.M. This patient had the medial temporal lobe area removed to alleviate his severe epileptic attacks. H.M. consequently suffered what appeared to be a nearly complete loss of the ability to form new long-term memories: His impairment, tested over the last 40-something years, has been shown to extend to verbal and nonverbal memory, spatial and nonspatial memory, and indeed seems to cut across all categories of learning materials. Yet, a second line of discovery about global amnesia revealed a spared domain of learning capacity. Even from the outset a few exceptions to the otherwise pervasive deficit were apparent. H.M. was able to learn new motor skills, and he showed a facilitation of perceptual identification resulting from prior exposure to objects or words (an effect that later came to be understood as reflective of a preserved “priming”). The second breakthrough came in 1980 when Cohen and Squire proposed that these “exceptions” to amnesia were indicative of a large domain of preserved learning capacities in amnesia. Their conclusion was based on the observation of complete preservation of the acquisition

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and retention of a perceptual skill (reading mirror-reversed words) in amnesic patients. These patients showed fully intact skilled performance, yet were markedly impaired both in recognizing the particular words on which they trained and in recollecting their training experiences. These investigators were struck by the dissociation between the ability to benefit or otherwise have performance shaped by a series of training experiences, an ability that appeared fully normal in the amnesic patients, and the capacity to explicitly remember or consciously recollect those training experiences or their contents, which was markedly impaired in the patients. Cohen and Squire attributed the observed dissociation, together with the earlier findings of spared memory in amnesia, to the operation of distinct forms of memory, which they called procedural memory and declarative memory, respectively. These forms of memory were seen as functionally distinct memory systems, one dedicated to the tuning and modification of networks that support skilled performance, and the other to the encoding, storage, and retrieval on demand of memories for specific facts and events. These functionally distinct memory systems were tied to separate brain systems, with declarative memory seen as critically dependent on the medial temporal lobe and midline diencephalic structures damaged in various amnesias. Procedural memory is seen as mediated by various brain systems specialized for particular types of skilled performance.

THREE MAJOR MEMORY SYSTEMS IN THE BRAIN A general, anatomically based framework for some of the major memory systems has emerged from many experiments, like those just described, that provide dissociations among the role of specific brain structures in different forms of memory, combined with the known anatomical pathways of the key structures. In this section I will provide an anatomical framework and a preliminary overview of the functional distinctions among these pathways. Subsequent sections will elaborate on the functional distinctions in greater detail. A sketch of some of the most prominent memory pathways currently under investigation is provided in Figure 20.2 (for a similar outline see Suzuki, 1996). In this scheme, the origin of each of the memory systems is the vast expanse of the cerebral cortex, focusing in particular on the highest stages of the several distinct sensory and motor processing hierarchies, the cortical association areas. The cerebral cortex thus provides major inputs to each of three main pathways of processing related to distinct memory functions. One pathway is to the hippocampus via the parahippocampal region. As introduced above, this pathway supports the cognitive form of memory, Tolman’s cognitive maps, and declarative memory in humans. The main output of hippocampal and parahippocampal

cerebral cortex

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striatum

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Figure 20.2 A schematic diagram of some of the prominent memory systems of the brain. The origins of major inputs to each of these systems involves widespread areas of the neocortex, and in particular the so-called association areas. This network of cortical areas mediates working memory. Outputs of these cortical areas project in parallel via three main routes. One route is through the parahippocampal region and into the hippocampus. The main outputs of hippocampal and parahippocampal processing are back to the same cortical areas that provided the main inputs. These pathways mediate declarative memory. Another route involves projections into two main subsystems via the striatum and cerebellum that mediate different aspects of motor memory. These pathways involve both projections back to the cortex and outputs to brainstem motor nuclei. The third main route from the cortex is to the amygdala. Outputs of the amygdala project in several directions to hormonal and autonomic outputs. This system mediates the expression of emotional memories. Amygdala outputs also return to the cortex and to the other memory systems to modulate the consolidation of other types of memory processing.

Three Major Memory Systems in the Brain

processing is back to the same cortical areas that provided inputs to the hippocampus, and are viewed as the long-term repository of declarative memories. The other two main pathways highlighted here involve cortical inputs to specific subcortical targets as critical nodal points in processing leading to direct output effectors. One of these systems involves the amygdala as a nodal stage in the association of exteroceptive sensory inputs to emotional outputs effected via the hypothalamicpituitary axis and autonomic nervous system, as well as emotional influences over widespread brain areas. The putative involvement of this pathway in such processing functions has led many to consider this system as specialized for “emotional memory.” The other system involves the striatum as a nodal stage in the association of sensory and motor cortical information with voluntary responses via the brainstem motor system. The putative involvement of this pathway in associating cortical representations to specific behavioral responses has led many to consider this system as specialized for habit or skill learning, two forms of “motor memory.” An additional, parallel pathway that mediates different aspects of sensorimotor adaptations involves sensory and motor systems pathways through the cerebellum. The distinct roles of these systems have been compellingly demonstrated in many multiple-dissociation experiments, three of which will be summarized here. The first study involves a triple dissociation of memory functions in rats that showed three different patterns of sparing and impairment of memory following damage to the hippocampus, amygdala, and striatum. The other two studies involve double dissociations of memory functions in humans with specific types of brain damage. Taken together, the findings suggest a similar set of memory functions supported by homologous brain areas in animals and humans. One of the most striking dissociations among memory functions supported by separate brain structures comes from a study by McDonald and White (1993). This study involved multiple experiments in which separate groups of rats were trained on three different versions of the spatial radial maze task (Figure 20.3). Each version of the task used the same maze, the same general spatial cues and approach responses, and the same food rewards. But the stimulus and reward contingencies of each task differed, each focusing on a different kind of memory processing demand. For each task, performance was compared across three separate groups of rats operated to disrupt hippocampal pathways, the amygdala, or the striatum. In addition, different methods of brain damage were

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compared. Hippocampal system disruption was accomplished by a fornix transection or by a neurotoxic lesion of the hippocampus. Damage to the amygdala and the striatum was accomplished by electrolytic or neurotoxic lesions of the lateral nucleus of the amygdala or dorsal part of the neostriatum, where cortical sensory inputs arrive in these structures. One test was the conventional spatial working memory version of the radial maze task (Figure 20.3A). In this version of the task, an eight-arm maze was placed in the midst of a variety of extramaze stimuli in the testing room, providing animals with the opportunity to encode the spatial relations among these stimuli as spatial cues. On every daily trial, a food reward was placed at the end of each of the eight maze arms, and the animal was released from the center and was allowed to retrieve the rewards. Optimal performance would entail entering each arm only once, and subsequently avoiding already visited arms in favor of the remaining unvisited arms. The central memory demand of this task was characterized as a “win-shift” rule; such a rule emphasizes memory for each particular daily episode with specific maze arms. Also, the task requires “flexible” use of memory by using the approach into previously rewarded locations to guide the selection of other new arms to visit. Based on these characteristics of the memory demands, it was expected that performance on this task would require the hippocampal system. They found that normal animals learned the task readily, improving from nearly chance performance (four errors out of their first eight arm choices) on the initial training trial to an average of fewer than half an error by the end of training. Consistent with expectations, damage to the hippocampal system resulted in an impairment on this version of the radial maze task. Compared to normal animals, rats with fornix transections made more errors by entering previously visited maze arms. By contrast, amygdala and striatum lesions had no effect on task performance. The second test involved a variant of the same radial maze task (Figure 20.3B). In this version, the maze was again surrounded by a curtain and lamps were used to cue particular maze arms. On the first trial of each daily training session, four arbitrarily selected arms were illuminated and baited with food, whereas the other four arms were dark and had no food. After the first occasion a lit arm was entered, and that arm was rebaited, so that the animal could return to the arm for a second reward. Subsequently that lamp in that particular arm was turned off and no more food was provided at that arm. Thus, here the task was characterized by a “win-stay” rule in

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Figure 20.3 Illustrations of example trials in different variants of the radial arm maze task. For each, the measure of performance is indicated below the N + 2 trial. See text for description of each variant of the task. (+ = rewarded arm).

which animals could approach any lit arm at any time and could even re-execute the approach to a particular arm for reward one time in each daily trial. This version of the task minimized the availability of spatial cues, and indeed associated rewards with different sets of locations across days. Also, it did not require memory for recent episodes or flexible expression of memory. Thus, performance was not

expected to rely upon the hippocampal system. Instead, this task would seem to require memory processes associated with learning-consistent stimulus-response contingencies, or simple response habits, and so was expected to rely on the striatal system. Results showed that normal control subjects learned the appropriate behavioral responses to the lit arms gradually

Three Major Memory Systems in the Brain

over several training sessions. In the first few sessions they selected lit arms on only 50% of the trials but, by the end of training, they performed at about 80% correct. Consistent with expectations, animals with striatal damage were impaired, barely exceeding chance performance even with extended training. By contrast, animals with fornix transections succeeded in learning and even outperformed the control subjects in learning rate. Animals with amygdala lesions were unimpaired, learning the task at a normal rate. The third test involved yet another variant of the radial maze task in which animals were separately conditioned to be attracted to one maze arm and habituated to another arm without performing specific approach movements to either of the arms (Figure 20.3C). In this version, the maze was surrounded by a curtain to diminish the salience of spatial cues. Six of the maze arms were blocked off to make them inaccessible, and one of the remaining two arms was illuminated by proximal lamps, whereas the other was only dimly illuminated. After a preliminary session in which rats could explore both available arms, conditioning proceeded with daily exposures to one of the two arms. For each rat, either the lit or the dark arm was associated with food by confining the animal in that arm for 30 minutes with a large amount of food on four separate trials. On another four trials, the same animal was confined for the same period of time to the other arm, but with no food. Thus, in half of the rats, the lit arm was associated with food availability and the dark arm was not; for the other half of the rats the opposite association was conditioned. In a final test session, no food was placed on the maze and the access to both the lit and dark arms was allowed. The amount of time spent in each arm for a 20-minute session was recorded to measure the preference for each of the two arms. This version of the radial maze task emphasized the strong and separate associations between food reward or absence of reward with a particular maze arm defined by a salient nonspatial cue. This task minimized the availability of spatial relations among stimuli. Also, because the same lit and dark arms used during training were represented in testing, the task did not require memory for specific episodes and flexible expression of memory, nor did it require reproduction of specific habitual approach responses. Thus it was not expected that either the hippocampal system or the striatum would be critical to learning. Instead, learning would seem to depend on memory processes associated with emotional conditioning, and so was expected to depend upon the amygdala. They found that normal animals showed a strong preference for the arm associated with food, typically

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spending 50–100% more time in the maze arm in which they had been fed compared to the arm where no food was previously provided. Consistent with expectations, rats with amygdala damage showed no conditioned preference for the cue arm associated with food. By contrast, rats with fornix transections or striatal lesions showed robust conditioned cue preferences. A very similar pattern of observations has emerged from analyses of human amnesia. In both studies, the learning and memory capacities of amnesic patients with damage to the medial temporal lobe was compared with that of “nonamnesic” patients, that is, humans with brain pathologies not producing the classic amnesic syndrome. The two studies differ in their focus on comparing classic amnesia with more specific disorders of learning and memory resulting from damage to the amygdala or striatum, respectively. In one study, Bechara and colleagues (1995) examined three patients with selective damage to the hippocampus or amygdala. One patient suffered from Urbach-Wiethe disease, a disorder resulting in selective bilateral calcification of the tissue of the amygdala, sparing the adjacent hippocampus. Another patient experienced multiple cardiac arrests and associated transient hypoxia and ischemia that resulted in selective bilateral hippocampal atrophy, sparing the neighboring amygdala. The third patient suffered herpes simplex encephalitis resulting in bilateral damage to both the amygdala and hippocampus. This study focused on a form of autonomic conditioning involving an association between a neutral stimulus and a loud sound. The conditioning stimulus (CS+) was either a monochrome color slide or a pure tone. Subjects were initially habituated to the CS+ as well as to several like stimuli (different colors or tones) that would be presented as CS– stimuli. Subsequently, during conditioning the CS’s were presented in random order for 2 seconds each. Each presentation of the CS+ was terminated with the unconditioned stimulus (US), a loud boat horn that was sounded briefly. Autonomic responses to these stimuli were measured as skin conductance changes through electrodermal recordings. Normal control subjects showed skin conductance changes to the US, and robust conditioning to the CS+, with smaller responses to the CS– stimuli. The patient with selective amygdala damage showed normal unconditioned responses to the US, but failed to develop conditioned responses to the CS+ stimuli. By contrast, the patient with selective hippocampal damage showed robust skin conductance changes to the US and normal conditioning to the CS+ stimuli. This patient also showed

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responsiveness to the CS– stimuli, but clearly differentiated these from the CS+’s. The subject with combined amygdala and hippocampal damage failed to condition, even though he responded to the US. After the conditioning sessions, the subjects were debriefed with several questions about the stimuli and their relationships. Control subjects and the patient with selective amygdala damage answered most of these questions correctly, but both patients with hippocampal damage were severely impaired in recollecting the task events. These findings demonstrate a clear double dissociation, with a form of emotional conditioning disrupted by amygdala damage and declarative memory for the learning situation impaired by hippocampal damage. The finding that these different forms of memory for the identical stimuli and associations are differentially affected by localized brain damage further supports the notion of multiple memory systems. In another study, Knowlton and colleagues (1996) examined patients in the early stages of Parkinson’s disease, associated with degeneration of neurons in the substantia nigra resulting in a major loss of input to the neostriatum, and amnesic patients with damage to the medial temporal lobe or to associated regions of the diencephalon. Subjects were trained in a probabilistic classification learning task formatted as a weather prediction game. The task involved predicting the one of two outcomes (rain or shine) based on cues from a set of cards. On each trial, one to three cards from a deck of four was presented. Each card was associated with the sunshine outcome only probabilistically, either 75%, 57%, 43%, or 25% of the time, and the outcome with multiple cards was associated with the conjoint probabilities of the cards presented in any of 14 configurations. After presentation of the cards for each trial, the subject was forced to choose between rain and shine, and was then given feedback as to the outcome. The probabilistic nature of the task made it somewhat counterproductive for subjects to attempt to recall specific previous trials, because repetition of any particular configuration of the cues could lead to different outcomes. Instead the most useful information to be learned concerned the probability associated with particular cues and combinations of cues, acquired gradually across trials, much as habits or skills are acquired. Over a block of 50 trials, normal subjects gradually improved from pure guessing (50% correct) to about 70% correct, a level consistent with the optimal probability of accuracy in this task. However, the patients with Parkinson’s disease failed to show significant learning,

and the failure was particularly evident in those patients with more severe Parkinsonian symptoms. By contrast, amnesic patients were successful in learning the task, achieving levels of accuracy not different from that of controls by the end of the 50-trial block. Subsequent to training on the weather prediction task, these subjects were debriefed with a set of multiple-choice questions about the types of stimulus materials and nature of the task. Normal subjects and those with Parkinson’s disease performed very well in recalling the task events. But the amnesic subjects were severely impaired, performing near the chance level of 25% correct. These findings demonstrate a clear double dissociation, with habit or skill learning disrupted by neostriatal damage and declarative memory for the learning events impaired by hippocampal or diencephalic damage, providing further evidence for the view that different forms of memory are represented for the identical learning materials within parallel brain systems.

ELABORATING THE ROLE OF THE THREE MAJOR MEMORY SYSTEMS This analysis so far has offered only a preliminary view into the distinct functions of the hippocampal, striatum, and amygdala as components of separate memory systems. The remainder of this chapter extends these characterizations, offering greater detail on the full anatomy of the pathways involved in these systems and on their different functional roles. The evidence for these characterizations comes mainly from anatomical and neuropsychological studies of the effects of selective damage within these systems and from neurophysiological and brain imaging studies in animals and humans. I will limit the discussion of a few particularly strong examples of experiments that reveal the scope and nature of their roles in memory. The declarative memory system will receive the most attention, because its role has been most debated and we have considerably more data from different approaches on this system. For a more comprehensive treatment of each of these and other memory systems the reader is referred to book-length treatments in Eichenbaum & Cohen (2001), Eichenbaum (2008), and Eichenbaum (2011). The Declarative Memory System Ideally, to the extent that the early analyses of cognitive memory are correct, this system should have all the properties of recollective memory outlined by Maine de Biran,

Elaborating the Role of the Three Major Memory Systems

James, Tolman, and Bartlett. Indeed, it appears their characterizations fit the modern description of hippocampaldependent memory functions quite well. Recall that the common theme in all those theoretical frameworks is that cognitive memory is a network of associations built up from linking the records of many experiences, and the ability to search the network, via the recollective process, for memories and employ those memories to solve a myriad of problems. Beginning in the 1970s several hypotheses about the function of the hippocampus were proposed, and each captured some of these aspects of the earlier views on cognitive memory. The three most prominent early views will be summarized here. Hirsh (1974) proposed that the hippocampus plays a critical role in contextual retrieval . He argued that hippocampal processing comes into play whenever there is significant interference between competing responses to a stimulus depending on the context in which the decision is made. In support of this view, Hirsh showed that rats with hippocampal damage failed to learn to behave in one of two ways in a T-maze dependent on the internal state of the animal. He placed food at one goal in the maze and water at the other. On alternate days animals were either food- or water-deprived. Normal animals learned to approach each motivationally appropriate goal independently, typically learning both response contingencies more or less simultaneously. By contrast, animals with hippocampal damage acted as if confronted by a single problem. On some blocks of trials they would do well on one component of the problem, for example, but correspondingly would perform below chance on the other component by approaching the same goal across the entire series of trials. Hirsh also considered other problems on which rats with hippocampal damage failed as being consistent with a critical demand for using spatial context to determine the correct response on each trial. For example, he explained Kimble’s (1963) mixture of findings on simultaneous and successive versions of visual discrimination learning in terms of differential demands for conditional operations. In his view, the successive discrimination constituted a conditional task in which whether the rat should turn left or right was dependent on whether the entire maze was white or black. However, in the simultaneous discrimination, learning to select a black versus white arm was not conditionalized on the spatial context as defined by the color of the maze, but rather could be solved by approaching one color-defined arm within a single context. In the second prominent theory, O’Keefe and Nadel (1978) assigned Tolman’s notion of cognitive mapping to

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the hippocampus. Their account was based on an interpretation of the accumulated voluminous literature on the behavioral effects of hippocampal damage in animals, showing a preponderance of observed impairments in spatial learning versus inconsistent deficits in nonspatial learning following hippocampal damage, and on O’Keefe’s discovery of “place cells,” hippocampal neurons that fire associated with a rat’s location in its environment. O’Keefe and Nadel’s analysis went well beyond making a simple distinction between “spatial” and “nonspatial” learning modalities. Their proposal about spatial learning involved the acquisition of cognitive maps that corresponded roughly, if not topographically, to the salient features of physical environment. They referred to the domain of memory supported by the hippocampus as a locale system that maintains a molar model of spatial relations among objects in the environment, that is driven by curiosity rather than reinforcement of specific behaviors, and that is capable of very rapid learning. By contrast hippocampal-independent learning was viewed as supported by a taxon system that mediates dispositions of specific stimuli into categories, is driven by reinforcement of approach and avoidance behaviors, and as involving slow and incremental behavioral adaptations. The third prominent theory that emerged in this period was Olton and colleagues’ (1979) distinction between working memory and reference memory. Notably, Olton’s use of the term “working memory” differs in meaning from the same term used in today’s characterizations of a form of short-term memory in humans and animals. The memory process Olton conceived would today be viewed as more similar to “episodic memory,” memory for a particular experience involving one’s own actions, than our current conception of “working memory” as the contents of current consciousness. To investigate this distinction, Olton invented the radial maze, a maze composed of a central start platform with multiple arms radiating in all directions like the spokes of a wheel. In his classic studies, a bait was placed at the end of each of the arms and then a rat was allowed to forage for the food. After several such trials, rats learn to forage efficiently, running down each arm only once without repetition. Good performance requires the animal remember each arm visited in that session, and then before the next trial “erasing” those memories. Olton distinguished working memory from reference memory operationally, using a maze in which many of the arms were never baited. Thus to be maximally efficient in foraging, animals had to simultaneously demonstrate their capacity for working memory, by visiting each of the baited arms only once,

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and for reference memory, by consistently avoiding the never-baited arms. For a comprehensive review of the experimental tests of these theories, as well as other theories, the reader is referred to Cohen and Eichenbaum (1993). For our purposes here, suffice it to say that the each of these theories was supported by specific experimental findings, thus indicating that each captured a critical aspect of hippocampal system function. However, none of these theories could account for all of the findings, including those that formed the major support for the alternative theories. A formulation that seeks to incorporate the central elements of all of these views within the framework of the earlier conceptualizations of cognitive memory is the account espoused by the present author and his colleagues (see Eichenbaum, 2000, 2004; Eichenbaum & Cohen, 2001). According to this view, the hippocampal systems plays a critical role in context dependent memory as proposed by Hirsh, in episodic memory, as proposed by Olton, and in the development of large-scale organized memory representations, similar to the proposal of O’Keefe and Nadel (but not limited to physical space). This view is based on considerable data on the anatomical pathways of the medial temporal lobe and on findings from behavioral and physiological studies in animals and humans. These lines of data are described next. Anatomical Data The hippocampal memory system is composed of three major components: cerebral cortical areas, the parahippocampal region, and the hippocampus itself (Burwell et al., 1995; Suzuki, 1996) and the major pathways of the system are very similar in rats and monkeys (Figure 20.1). The cerebral cortical areas comprise diverse and widespread “association” regions that are both the source of information to the hippocampal region and the targets of hippocampal output. They project in different ways to the parahippocampal region, a set of interconnected cortical areas immediately surrounding the hippocampus, which in turn, project into the hippocampus itself. Individual cortical association areas project differentially along the parahippocampal region, following two main parallel pathways that can be characterized as extensions into the parahippocampal region of the parallel “what” and “where” streams identified by Ungerlieder & Mishkin (1982). In the “what” pathway, inputs about specific objects represented within unimodal sensory and anterior association areas project principally to the perirhinal cortex and thence to the lateral part of the entorhinal cortex (LEC). In the “where” pathway, information about spatial

context represented in multimodal and parietal and retrosplenial cortex projects principally to parahippocampal cortex and thence to medial part of the entorhinal cortex (MEC). The “what” and “where” streams then converge in each subdivision of the hippocampus itself. The main outputs of the hippocampus return to the parahippocampal region, which send back projections broadly to the same cortical association areas that provided the inputs to the parahippocampal region. Behavioral and Physiological Data There is converging evidence from studies on animals and humans that areas within the parahippocampal region and the hippocampus play distinct and complementary roles in this memory system (Eichenbaum et al., 2007). The perirhinal cortex and LEC play a critical role in memory for single stimuli, as observed in several studies on the effects of damage to these areas the delayed nonmatch to sample (DNMS), where subjects must remember single stimulus across a variable memory delay, and these impairments are much more severe than that following damage to the hippocampus (Meunier et al., 1993; Murray & Mishkin, 1998; Zola-Morgan et al., 1989) or its connections via the fornix (Gaffan, 1994a; Otto & Eichenbaum, 1992). In electrophysiological studies on rats, the response properties of neurons in the perirhinal cortex and LEC have been associated with three critical aspects of the coding and memory for single stimuli in animals performing an odor-guided DNMS task. A substantial proportion of cells in the perirhinal cortex and LEC encoded the identity of the odor cues during the odor-sampling period (Young et al., 1997). Some of these cells fired selectively or differentially to odors at odor onset and ceased firing when odor sampling was concluded, much as one would expect of a sensory neuron. Other cells showed striking odorspecific activity at the end of the memory delay period, indicating some form of intermediate-term storage that was still available just before the choice phase of the trials regardless of the length of the delay. Some of these cells fired during odor sampling and then throughout the delay period. Another set of cells showed selective activity that reflected the match and nonmatch qualities of the odor cues during the choice phase. Some of these cells, called “match enhancement cells,” fired at a higher rate when the rat was sampling a repeated (matching) odor, and this differential response was largest for the most preferred odor for that cell. Other cells, called “match suppression cells,” fired at a higher rate when the rat was sampling an odor different from the one on the previous

Elaborating the Role of the Three Major Memory Systems

trial (i.e., a nonmatch), and this differential response was largest for the most preferred odor for that cell. Taken together, neurons in the perirhinal cortex and LEC have all the properties required to support recognition performance. They encode specific odors, hold these representations (either by maintaining their activity or by regenerating activity) during an extended delay period in which an intact parahippocampal region is required, and they detect match versus nonmatch qualities of the presented choice odors. Findings from recording studies of monkeys are entirely consistent with the above observations of hippocampal system activity in rats. Brown and colleagues recorded from single neurons in perirhinal cortex and LEC in monkeys performing a delayed matching task guided by complex visual pattern cues (Brown & Aggleton, 2001; Brown & Xiang, 1998). These cells showed stimulus-specific decrements in response (match suppression) when the choice stimulus was a repetition of the sample. Some cells, called “novelty neurons,” fired only on the first presentation of a novel visual pattern, and did not recover for at least 24 hours. Other cells, called “familiarity neurons,” did not decrement on the choice phase of the first trial in which the stimulus appeared, but showed reduced responses on all subsequent presentations. Yet other cells, called “recency neurons,” showed match suppression only on the choice phase of each trial when a particular stimulus appeared, but recovered fully when the same cue was presented as a sample on a subsequent trial. Brown has argued that all of these recognition-related firing patterns coexist, and may serve different roles in visual recognition. Importantly, no stimulus-specific match suppression responses were observed in the hippocampus in any of his studies. Other recent studies have provided evidence of memory processing by the parahippocampal region in monkeys performing a more complex delayed matching-to-sample task. Miller et al. (1991) trained monkeys to perform a variant of delayed matching-to-sample, where a pattern cue was presented as the “sample” and, followed by several choice stimuli, the monkey had to respond only to the matching choice stimulus. In these studies, cells in the perirhinal cortex of monkeys showed selective responses to the visual cues. Some cells fired persistently during the initial part of the delay, but ceased firing when the first choice item was presented. In a version of the task where each choice stimulus was presented only once per trial, the predominant observation was “match suppression,” where many cells fired less to the matching choice item. In another version of the task, where incorrect (nonmatching) choice items were presented repeatedly, forcing the animal to attend to the designated sample cue,

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a substantial number of “match enhancement” cells were also observed. Similarly, Suzuki et al. (1997) employed the same task to study the firing properties of neurons in the LEC of monkeys. They found a fraction of LEC cells that fired selectively to specific visual cues. In addition, unlike perirhinal cells in the monkey but like cells throughout the perirhinal cortex and LEC in the rat, neurons in the LEC fired throughout each of the delay periods between the sample stimulus and each of the choice items. Finally, LEC neuronal activity also reflected the match and nonmatch qualities of the choice stimulus, by showing match suppression and match enhancement responses. There have been many fewer studies on the “where” stream areas, the parahippocampal cortex (postrhinal cortex in rats) and the MEC, but these studies strongly support the idea that the parahippocampal cortex and MEC process spatial and other contextual information. In monkeys, damage to the parahippocampal cortex lesions disrupts the performance of monkeys on a variation of DNMS where the subjects had to nonmatch locations shown in the sample phase (delayed nonmatching to location or DNML), but had little effect on standard object DNMS (Alvarado & Bachevalier, 2005). Conversely, perirhinal cortex lesions had the opposite effects, producing a severe deficit in DNMS but no impairment on object DNMS. In a study that used immediate early gene expression as a marker for neuronal activation, rats were exposed to computer images of novel and familiar object stimuli, and responses to changes in the familiarity of particular stimuli or the familiarity of stimulus arrangements were measured (Wan et al., 1999). Activation was observed in the perirhinal cortex and LEC, but not in the hippocampus or postrhinal cortex, when rats view novel stimuli. Conversely, rats viewed novel spatial arrangements of familiar stimuli selectively in postrhinal cortex and hippocampus, but not in perirhinal cortex. Electrophysiological studies on rats have shown that, similar to hippocampal place cells, neurons in MEC also fire when rats are in particular places in the environment (Hafting et al., 2005). However, unlike hippocampal place cells that tend to fire when the rat is in just one location in the environment, MEC neurons fire in multiple locations arranged in a triangular grid, and hence are called “grid cells.” Notably, the MEC is quite different from the LEC in that, in contrast to MEC cells, LEC neurons have very poor spatial coding properties (Hargreaves et al., 2005). The combined findings from behavioral and physiological studies on the parahippocampal region in animals strongly support the distinction between the processing of object familiarity in perirhinal and LEC versus processing of spatial (and perhaps

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temporal) context in the postrhinal and medial entorhinal cortex. While the hippocampus itself contributes very little to performance in standard DNMS tasks, in that the deficits observed are modest at most compared to the effects of damage to the cortex or parahippocampal region. However, the hippocampus may play an essential role in other types of simple recognition memory tests (Rampon et al., 2000; Zola et al., 2000; see below) and in recognition memory for configurations of items within scenes or places (Cassaday & Rawlins, 1995; Gaffan, 1994b; Wan et al., 1999). More recently, it has been suggested that the mixture of findings on the hippocampus and recognition memory can be explained by the possibility that two processes may support recognition memory, and the suggestion has emerged that the hippocampus may be particularly important for recollection whereas the surrounding parahippocampal cortical areas may support familiarity (see Brown & Aggleton, 2001; Eichenbaum et al., 2007; Rugg & Yonelinas, 2003). Recollection is described as a controlled, relatively slow search process that, at some threshold, yields qualitative information about items as well as associated information in the context of a specific experience. By contrast, familiarity is characterized as an automatic, fast, perceptual process that yields a continuous and quantitative sense of the strength of matching to previously experienced stimuli. An analytic technique that has been popular in the study of human recognition memory involves the application of signal detection theory to characterize performance in terms of Receiver Operating Characteristics (ROC) for the detection of previously studied (old) items judged against items not studied (new). This method utilized the previously described differences between features of recollection and familiarity to measure the independent influences of these two processes. Studies on human amnesic patients have shown that damage to the hippocampus produces a selective impairment in the contribution of recollection whereas damage to the perirhinal cortex produces a selective impairment in familiarity (Bowles et al., 2010). Recent studies on rats confirm and extend the findings on amnesia with regard to ROC analyses of recognition memory (Eichenbaum et al., 2009). The results of these studies have shown that rats with hippocampal damage have a selective loss of recollection and intact familiarity. Furthermore, studies using functional brain imaging have also indicated selective hippocampal and parahippocampal cortex involvement in recollection as distinguished from involvement of the perirhinal cortex in familiarity. In particular, several studies have reported double dissociations

between hippocampal and parahippocampal cortex activation paralleling threshold component of the ROC function, which characterizes recollection, whereas gradual activation of the perirhinal cortex in the test phase is continuous with recognition strength, as characterizes contribution of familiarity (Daselaar et al., 2006; Ranganath et al., 2003; Rugg & Yonelinas, 2003). Thus, the functional imaging data combined with the findings on amnesia doubly dissociate recollection and familiarity processes within the hippocampal system, and assign key roles for the perirhinal cortex in familiarity and for the hippocampus and parahippocampal cortex in recollection. Several studies using brain imaging in humans provide further evidence consistent with the notion that components of the parahippocampal region and hippocampus are differentially activated in different types of memory processing in line with the studies on animals described above. Early in this line of work Stern et al. (1996) compared activation when subjects viewed a series of novel magazine pictures as compared with repeated presentations of the same picture and found that the area most activated by novel over familiar pictures was centered in the perirhinal cortex. Since then many studies have shown that the perirhinal cortex is activated by pictures of people and objects more than by environmental scenes, and these responses are decreased with familiarity. In contrast, the parahippocampal cortex is activated when subjects view spatial scenes, composed as views of houses or indoor or outdoor scenes (Epstein & Kanwisher, 1998). But the parahippocampal cortex is also activated when subjects view objects that generate both spatial and temporal contextual associations (Bar & Aminoff, 2003). Thus, for example, the parahippocampal area is activated when one sees objects such as a tractor, which makes people think of a farm, than a pencil, which does not generate a contextual association. Furthermore, many studies have shown that the hippocampus is more activated for memory involving associations whereas the peririnal cortex is activated when single items are remembered (Eichenbaum et al., 2007). For example, in one study subjects were scanned as they learned a large number of associations between objects, then subsequently tested their memory for the objects and associations (Qin et al., 2009). In addition, subjects rated the strength of their memories for the items and associations, allowing the investigators to equate the strength of memory of items and associations in their analysis of the data. The main findings were that memory for items was predicted by activation of the perirhinal cortex (as well as temporal cortex and a part of parahippocampal cortex) whereas memory for the associations was predicted by

Elaborating the Role of the Three Major Memory Systems

activation in the hippocampus (and part of prefrontal cortex). Therefore, even when memory strength is taken into account, the perirhinal cortex is specialized for processing memories of single items whereas the hippocampus is specialized for processing item associations. In another study, Davachi et al., (2003) explored activation of medial temporal areas in humans during the study of objects and the general context in which they were imagined to occur as predicting subsequent memory. Subjects initially studied pictures of objects and an adjective and imagined a spatial scene in which the adjective applied. For example, while viewing a teddy bear and the word “dirty,” they might imagine the teddy bear in a garbage dump. Later subjects were shown the objects and asked whether they recognized them from the study period, and if so, whether they could recall the scene in which they were imagined. Activation of the perirhinal cortex was observed when subjects correctly remembered seeing the objects, regardless of whether they remembered the scene. Conversely, activation of the hippocampus and parahippocampal cortex predicted subsequent success in remembering the scene regardless of whether they remembered the object. These studies provide compelling evidence in human subjects of a direct dissociation of memory processing functions in the hippocampal region. The perirhinal cortex is activated during memory processing of objects whereas the parahippocampal cortex is activated during the processing of contextual information, and the hippocampus is activated during the processing of associations. The combined anatomical, behavioral, and physiological evidence suggests the following hypothesis about how information is encoded and retrieved during memory processing in the medial temporal lobe (Eichenbaum et al., 2007). During the encoding of new memories, representations of distinct items (e.g., people, objects, events) are formed in the perirhinal and lateral entorhinal area. These representations along with their projections back to the “what” pathways of the neocortex could then support subsequent judgments of familiarity of those items even without other components of this system. In addition, during memory encoding, contextual (“where”) representations are formed in the parahippocampal and medial entorhinal area. Subsequently, both the “what” and “where” information are combined within the hippocampus and as hippocampal neurons associate items with their context. When an object or event is subsequently presented as a memory cue, the hippocampus completes the full pattern of object and context, and through the projections back to the parahippocampal cortex and medial entorhinal area, supports a recovery of the contextual representation

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in those areas. Hippocampal processing may also recover specific item associates of the cue and reactivate those representations in the perirhinal cortex and lateral entorhinal area through back projections to those areas. Memory for the Order of Events in Specific Episodes There is also strong evidence that the medial temporal lobe system is involved in more than remembering the context in which events occurred (reviewed in Eichenbaum, 2004). There is accumulating evidence that the hippocampus is critical for remembering the flow of events in specific experiences, a key feature of episodic memory. Direct evidence of coding for information specific to particular types of episodes as well as the flow of events that compose temporally extended experiences comes from another experiment where hippocampal cells fired differentially even in situations where the overt behavioral events and the locations in which they occur are identical between multiple types of experience (Wood et al., 2000). In this experiment rats performed a spatial alternation task, a simple version of one of Olton’s episodic memory tasks, performed in a T-maze. Each trial commenced when the rat traversed the stem of the “T” and then selected either the left- or the right-choice arm. To alternate successfully the rats were required to distinguish between their left-turn and right-turn experiences and to use their memory for the most recent previous experience to guide the current choice. Different hippocampal cells fired as the rats passed through the sequence of locations within the maze during each trial. Most important, the firing patterns of many of the cells depended on whether the rat was in the midst of a left- or right-turn episode, even when the rat was on the stem of the T and running similarly on both types of trials—minor variations in the animal’s speed, direction of movement, or position within areas on the stem did not account for the different firing patterns on left-turn and right-turn. Other cells fired when the rat was at the same point in the stem on either trial type. Thus, the hippocampus encoded both the left-turn and right-turn experiences using distinct representations, and included elements that could link them by their common features. These findings indicate that hippocampal place-cell activity reflects the temporal organization of spatial representations that disambiguate memories for routes. The temporal organization of hippocampal neuronal activity was particularly evident in a study where rats ran in a running wheel in between each trial in a T-maze alternation task (Pastalkova et al., 2008). Some hippocampal neurons fired at a sequence of specific brief times during the 10 seconds of wheel running, even when the animal

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was not changing its location in the environment, and the firing rates of some of these cells distinguished subsequent left and right turns. These findings indicate that the hippocampus sequenced the timing of events in each type of episode, even during “empty” periods between key events. Also, in another study on rats performing a task in which they were required to remember the order of a sequence of odors (Fortin et al., 2002), hippocampal neuronal activity carried a signal of changing temporal context throughout the odor sequence (Manns et al., 2007). In this experiment, rats encoded trial-unique sequences of five odors, and sampled the odors alternately at opposite ends of a chamber so that we could also evaluate the role of spatial coding. Later when memory for the sequence was tested, rats were required to distinguish the earlier of two randomly chosen nonadjacent odors that were previously presented in the study list. Analyses of recordings of many simultaneously recorded neurons showed that the pattern of hippocampal activity surrounding the period of stimulus sampling gradually changed over successive stimulus sampling events, even in the same location. In addition, hippocampal neuronal representations differed substantially at the two locations where the stimuli were sampled. A comparison of correct and error trials revealed that a substantial change in the hippocampal neuronal representation over the odor sequence predicted success on the subsequent memory test, but the difference in spatial representations of the same hippocampal neurons was unrelated to performance. The representation of sequential events in specific episodes also extends to studies on hippocampal neuronal activity in humans (Gelbard-Sagiv et al., 2008). In this experiment, human subjects viewed brief movie clips then subsequently were asked to recall them. Hippocampal neurons that had fired during particular scenes when subjects were viewing the clips again fired just as the subjects recalled the same event. These findings, combined with the observations on hippocampal neurons in rats, provide strong evidence that hippocampal neurons encode sequences of events that compose unique episodes, and contain information that is common across episodes, which could support the linking of distinct but related experiences. Networking and Flexible Expression of Memories In addition to memory for events in their context and for the temporal organization of episodic memories, the findings from studies using animal models point to a critical role for the hippocampus itself in one more key characteristic of declarative memory. To understand this

role it is important to consider the fundamental properties of declarative memory, as introduced by Cohen and Squire (1980) and subsequently elaborated by many investigators. We acquire our declarative memories through everyday personal experiences, and the ability to retain and recall these “episodic” memories is highly dependent on the hippocampus in humans (Vargha-Khadem et al., 1997). But the full scope of hippocampal involvement also extends to semantic memory, the body of general knowledge about the world that is accrued from linking multiple experiences that share some of the same information (Squire & Zola, 1998). For example, we learned about our relatives via personal episodes of meeting and talking about family members, and then weaving together this information into a body of knowledge constituting our family tree. Similarly we learned about the geographies of our neighborhood and hometown by taking trips through various areas, and eventually interconnecting them into cognitive maps. In addition, declarative memory for both the episodic and semantic information is special in that the contents of these memories are accessible through various routes. Most commonly in humans declarative memory is expressed through conscious, effortful recollection. This means that one can access and express declarative memories to solve novel problems by making inferences from memory. Thus, even without ever explicitly studying your family tree and its history, you can infer indirect relationships, or the sequence of central events in the family history, from the set of episodic memories about your family. Similarly, without ever studying the map of your neighborhood, you can make navigational inferences from the synthesis of many episodic memories of previous routes taken. Family trees and city layouts are but two examples of the kind of “memory space” proposed to be mediated by the hippocampal system (Eichenbaum et al., 1999). Within this view, a broad range of such networks can be created, with their central organizing principle the linkage of episodic memories by their common events and places, and a consequent capacity to move among related memories within the network. These properties of declarative memory depend on the functions of the hippocampus itself. Several experiments have shown that the hippocampus is required in situations where multiple and distinct, but overlapping experiences must be combined into a larger memory representation that mediates flexible, inferential memory expression. For example, in one experiment rats initially learned a series of distinct but overlapping associations between odor stimuli (Bunsey & Eichenbaum, 1996). On each trial one of two odors was initially presented, followed by a choice

Elaborating the Role of the Three Major Memory Systems

between two odors, one of which was baited as the assigned “associate” for a particular initial odor (A goes with B, not Y; X goes with Y, not B). Following training on two sets of overlapping odor-odor associations (A-B and X-Y, then B-C and Y-Z), subsequent probe tests were used to characterize the extent to which learned representations could be linked to support inferential memory expression. Control rats learned paired associates rapidly and hippocampal damage did not affect acquisition rate on either of the two training sets. Intact rats also showed that they could link the information from overlapping experiences, and employ this information to make inferential judgments in two ways. First, normal rats showed strong transitivity across odor pairings that contained a shared item. For example, having learned that odor A goes with odor B, and B goes with C, they could infer that A goes with C. Second, control rats could infer symmetry in paired associate learning. For example, having learned that B goes with C, they could infer that C goes with B. By contrast, rats with selective hippocampal lesions were severely impaired, showing no evidence of transitivity or symmetry. The importance of the hippocampus in linking experiences into networks of memories that support inferential memory expression has been extended to a more complex network of memories in rodents (Dusek & Eichenbaum, 1997) and to functional imaging studies in humans (Heckers et al., 2004; Preston et al., 2004). For example, in one of these studies, human subjects were trained on sets of overlapping associations between faces and houses (e.g., face 1 goes with house X and face 2 goes with house X). As a control they were also trained directly on face-face pairs. The hippocampus was maximally activated during successful inferences between indirectly related faces, more so than for retrieval of trained face-face or face-house pairings (Preston et al., 2004). A similar characterization accounts for the common observation of deficits in spatial learning and memory following hippocampal damage. For example, in the Morris water maze test, rats or mice learn to escape from submersion in a pool by swimming toward a platform located just underneath the surface. Importantly, training in the conventional version of the task involves an intermixing of four different kinds of trial episodes that differ in the starting point of the swim. Under this condition, animals with hippocampal damage typically fail to acquire the task (Morris et al., 1982). However, if the demand for synthesizing a solution from four different types of episodes is eliminated by allowing the animal to repeatedly start from the same start position, animals with hippocampal damage acquire the task almost as readily as normal rats and use the same distant spatial cues in identifying the escape

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site (Eichenbaum et al., 1990). Nevertheless, even when rats with hippocampal damage are successful in learning to locate the escape platform from a single start position, they are unable to use this information for flexible, inferential memory expression. Thus, once trained to find the platform from a single start position, normal rats readily locate the platform from any of a set of novel start positions. But under these same conditions, rats with hippocampal damage fail to readily locate the platform, often swimming endlessly and unsuccessfully in a highly familiar environment. The view that has emerged from these and many other studies is that the medial temporal lobe system receives information about all manner of experience and the context in which experiences occur. This information converges on the hippocampus, which plays a central role in representing events as objects and actions in the context in which they occur, episodes as sequences of events, and networks of memories, with their central organizing principle the linkage of episodic memories by their common events and places, and a consequent capacity to move among related memories within the network. The output of medial temporal lobe processing is sent back to the cortex, which contains details of memories, and when activated gives rise to the experience of conscious recollection (Eichenbaum, 2004). Motor Memory Systems Among the most prevalent kind of memory we use everyday is “motor memory,” the habits, skills, and sensorimotor adaptations that go on constantly in the background of all of our intentional and planned behavior. Because this kind of memory generally falls outside of consciousness, we take it for granted. Yet, without it we would be forced to “think” our way through virtually every step we take and every motion we make in our daily tasks. Fortunately there is a motor memory system or systems, a circuitry involving structures of the motor systems of the brain whose plasticity accomplishes the myriad of unconscious learned behaviors we engage in almost every waking moment. Motor memory is generally separated into two general subtypes (Figure 20.2). One type involves the acquisition of habits and skills, the capacity for a very broad variety of stereotyped and unconscious behavioral repertoires. These can involve a simple refinement of particular repeated motor patterns and extend to the learning of long action sequences in response to highly complex stimuli. These abilities reflect both the acquisition of general

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skills (writing, piano playing, etc.) and the unique elements of personal style and tempo in the expression of these behaviors. A key structure in this subsystem is the striatum. The striatum receives its cortical inputs from the entire cerebral cortex, and these projections are capable of activity-dependent changes in responsiveness. These projections are topographically organized into divergent and convergent projections into modules within the striatum that could sort and associate somatosensory and motor representations. The striatum projects mainly to other components of the basal ganglia and to the thalamus, which project back to both the premotor and motor cortex, and the prefrontal association cortex (Figure 20.2). Notably, there are minimal projections of this circuit to the brainstem motor nuclei and none to the spinal motor apparatus. The other type of motor memory involves specific sensory-to-motor adaptations, that is, adjustments of reflexes, such as changing the force exerted to compensate for a new load, or acquisition of conditioned reflexes that involve novel motor responses to a new sensory contingency, as characterize many instances of Pavlovian conditioning described earlier. A key structure of this subsystem is the cerebellum. The cerebellum receives cortical input from a much more restricted cortical area than the striatum, including only the strictly sensory and motor areas projecting via brainstem nuclei into the lateral part of the cerebellar cortex. Like the striatal subsystem, the cerebellum has a thalamic output route to the cerebral cortex, although the cortical target is also more restricted than that of the striatum, limited to motor and premotor cortex. In addition, the cerebellum receives somatic sensory inputs directly from the spinal cord and has major bidirectional connections with brainstem nuclei associated with spinal cord functions. The functional roles of these two subsystems are discussed in turn. The Striatal Subsystem The striatal habit system was introduced via experiments that dissociated this system from the hippocampal and amygdala memory systems. Those experiments provided evidence indicating a role for the striatum in the acquisition of specific stimulus-response associations, as contrasted with declarative memory and emotional memory functions of the hippocampal and amygdala systems, respectively. The scope of striatal involvement is not limited to a particular sensory or motivational modality, or to a particular type of response output. One study by Viaud et al. (1989) illustrates some of the range of memory mediated

by this system and shows a particularly striking dissociation of regions within the striatum in their effects on inhibition of approach behavior conditioned by different cues. In this study, thirsty rats with lesions of the posterior-ventral or ventral-lateral regions of the striatum were trained to approach a waterspout over several days. Subsequently, they were given footshocks in the same chamber in the presence of a conditioning cue, which was either a light or an odor. The animals were tested later for their latency to approach the waterspout when the conditioning cue was present versus when it was absent. Animals with lesions of the posterior-ventral striatum failed to show discriminative avoidance of the light cue, but showed good avoidance of the olfactory cue. Conversely, animals with ventral-lateral striatal lesions failed to show discriminative avoidance of the olfactory cue, but showed good avoidance of the light cue. Above, the selective role of the striatum in learning specific turning responses T-maze and approach responses in radial maze were shown. Similar dissociations showing striatal function in stimulus-approach learning have extended this role to aversively motivated learning in the water maze (Packard & McGaugh, 1992). In addition, there is further evidence from maze learning studies that restrict the nature of response learning by this system. In one of these studies, rats were trained on two tasks on different radial mazes (Cook & Kesner, 1988). In a place-learning (allocentric) task, only one arm of an eight-arm maze was consistently baited, and the rat began each trial from any of the remaining arms chosen at random. In a right-left discrimination (egocentric) task, the animal began each trial in the central area of the maze and two randomly chosen adjacent arms were indicated for a choice. The rat had to choose only the left (or, for other rats, the right) of the two arms regardless of its absolute location. Here, too, rats with striatal lesions performed well on the place-learning task but did not learn the right-left discrimination task, indicating a selective role in egocentric response learning. Taken altogether the literature from studies of damage to the striatum suggests that the deficit following striatal damage is, or includes, an impairment in generating behavioral responses toward important environmental stimuli. The deficit extends to both approach and avoidance responses and to both egocentric spatial and nonspatial stimuli across many modalities. Even this characterization is not sufficiently comprehensive to explain the full range of impairments in animals and humans (see Eichenbaum & Cohen, 2001). Thus, it is likely that the deficits in egocentric localization and stimulus-response learning

Elaborating the Role of the Three Major Memory Systems

in animals with striatal damage may reflect only a subset of the forms of behavioral sequence acquisition mediated by the striatum. The Cerebellar Subsystem The anatomy and functions of the cerebellum have long been associated with aspects of motor learning, and most studies have focused on its highly organized circuitry and emphasized its mechanisms for reflex adaptations (for a review, see Ebner et al., 1996). Considerable recent attention has focused on Pavlovian eyeblink-conditioning as a model-learning paradigm in which to study the role of the cerebellum. In this paradigm, rabbits are placed in restraining chambers where they can be presented with a well-controlled tone or light as the conditioning stimulus (the CS), and a photoelectric device records eyeblinks. In classic delay conditioning, this stimulus lasts 250–1000 ms and coterminates with an airpuff or mild electrical shock to the eyelid (the unconditioned stimulus or US) that produces the reflexive, unconditioned eyeblink (the UR). After several pairings of the CS and US, the rabbit begins to produce the eyeblink after onset of the CS and prior to presentation of the US. With more training, this conditioned response (CR) occurs somewhat earlier, and its timing becomes optimized so as to be maximal at the US onset, showing that not only is a CR acquired but also a timing of the CR is established. The role of the cerebellum and associated areas has been studied extensively by Thompson and his colleagues (for a review see Thompson & Kim, 1996). In their studies they found that permanent lesions or reversible inactivation of one particular cerebellar nucleus, the interpositus nucleus, result in impairments in the acquisition and retention of classically conditioned eyeblink reflexes, without affecting reflexive eyeblinks (URs). Additional compelling data indicating a selective role for the interpositus in this kind of motor memory come from studies using reversible inactivations of particular areas during training. These studies showed that drug inactivation of the motor nuclei that are essential for production of the CR and UR prevented the elicitation of behavior during training. However, in trials immediately following removal of the inactivation, CRs appeared in full form, showing that the neural circuit that supports UR production is not critical for learning per se. A similar pattern of results was obtained with inactivation of the axons leaving the interpositus or their target in the red nucleus, showing that the final pathway for CR production is also not required to establish the memory trace. By contrast, inactivation of the anterior interpositus nucleus and overlying cortex

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by drugs (muscimol, lidocaine) or temporary cooling did not affect reflexive blinking, yet resulted in failure of CR development during inactivation and the absence of savings in learning after removal of the inactivation. These results point to a small area of the anterior interpositus nucleus and overlying cerebellar cortex as the essential locus of plasticity in this form of motor learning. The Emotional Memory System Perhaps the best-studied example of emotional memory involves the brain system that mediates Pavlovian fear conditioning as studied by Joseph LeDoux (1992) and by Michael Davis (1992) and their colleagues. This research has focused on the specific elements of the pathways through the amygdala that support the learning of fearful responses to a simple auditory stimulus (Figure 20.1). The critical elements of the relevant amygdala pathways include auditory sensory inputs via the brain stem to circuits through the thalamus. Some of these sensory thalamic areas then project directly to the lateral amygdaloid nucleus. Other thalamic projections follow a route to the primary sensory cortex, then to secondary areas and the perirhinal cortex. Each of these secondary cortical areas are the source of cortical inputs to the amygdala, particularly the lateral and basolateral nuclei of this structure. Those areas of the amygdala project into the central nucleus, which is the source of outputs to subcortical areas controlling a broad range of fear-related behaviors, including autonomic and motor responses. In this chapter, I provide an overview of the work of LeDoux and colleagues. LeDoux’s studies have examined the neuropsychology and neurophysiology of these structures in animals during the course of a simple tone-cued fear conditioning task. Rats are initially habituated to an operant chamber, then presented with multiple pairings of a 10-second pure tone terminating with a brief electric shock through the floor of the cage. Subsequently conditioned fear was assessed by measuring the autonomic response as reflected in changes to the tone only in arterial pressure, and motor responses as reflected in a stereotypic crouching or freezing behavior when the tone is presented, as well as suppression of drinking sweetened water. Unconditioned responses to the tone were evaluated by presenting other animals with unpaired tones and shocks. Their initial experiments were aimed at identifying the critical auditory input pathway to the amygdala. Animals with selective lesions in the lateral amygdala show dramatically reduced conditioned responses to the tone, both in the measures of autonomic and motor responses.

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Unconditioned responses (consequent to unpaired presentations) were not affected by this damage. Also, animals with damage to the adjacent striatum performed normally, showing anatomical specificity and that the striatal system is not involved in emotional learning. Subsequent efforts focused on identifying which of the two prominent auditory input pathways to the lateral amygdala was critical. Broad destruction of all auditory areas of the thalamus eliminated conditioned responses. However, selective ablation of either of the two prominent direct inputs to the lateral amygdala was individually ineffective. Thus lesions of the medial division of the medial geniculate (including all three nuclei that project directly to the lateral amygdala) or of the entire auditory cortex that projects to the amygdala did not reduce either the autonomic or freezing response. However, elimination of both of these inputs produced the full effect seen after lateral amygdala lesions. Thus, for this simple type of conditioning, either the direct thalamic input, which offers a crude identification of a sound, or the thalamocortical input pathway, which provides a sophisticated identification of auditory signal, is sufficient to mediate conditioning. Additional studies by LeDoux’s group have elucidated the physiology of the neurons in the direct thalamic and thalamo-cortical auditory pathways to the amygdala (Schafe et al., 2005). Cells in both the medial geniculate nuclei that project directly to the amygdala and in the thalamic nucleus that projects to the cortex demonstrate a variety of auditory responses. Finer auditory tuning was observed in the ventral medial geniculate than in areas that project directly to the amygdala. However, cells in the ventral nucleus responded only to auditory stimuli whereas neurons in the medial geniculate nuclei that project to the amygdala also responded to foot shock stimulation. Furthermore, some amygdala-projecting cells that responded to somatosensory stimulation but not auditory stimulation showed potentiated responses to simultaneous presentation of both stimuli. Studies that tracked the locus of plasticity showed that neuronal responses to the conditioning stimulus are enhanced by training in both the medial geniculate and lateral amygdala. However, blocking plasticity in the lateral amygdala is sufficient to prevent permanent memory formation, and the fear response is correlated with the magnitude of the evoke response to the conditioning stimulus in the lateral amygdala but not in the medial geniculate. Therefore a critical site of plasticity is in the lateral amygdala itself. Schafe et al. (2005) observed that cells in the lateral nucleus of the amygdala that receive thalamic input were

responsive to auditory stimuli at both short (12–25 msec) and long (60–150 msec) latencies. Some cells had clear tuning curves, whereas others responded to a broad spectrum of sounds. Cells in the lateral amygdala could also be driven by electrical stimulation of the medial geniculate, and their responses were typically shorter than those in the basolateral amygdala. At the level of neuronal firing patterns, fear conditioning selectively enhances the short latency auditory responses of lateral amygdala neurons. Furthermore, some cells that were not responsive to tones prior to training showed postconditioning short latency responses. Repa et al. (2001) identified two different populations of neurons in the lateral amygdala that show learning related plasticity prior to the first conditioned fear responses. Neurons in the dorsal part of the lateral amygdala exhibited the short latency responses (33 years). Their sample has included 9 pairs of maternal halfsiblings, 22 pairs of paternal half-siblings, and many more unrelated individuals (Langergraber, Mitani, & Vigilant, 2007). Bonds among males were measured in a number of ways, including the frequency with which they were

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members of the same party, groomed, formed coalitions, shared meat, and accompanied one another on hunts and border patrols. Bonds varied in length from 1 to 10 years, and 26 of 28 males formed at least one bond lasting 5 years or longer. As among baboons, the formation of stable, enduring relationships among male chimpanzees was correlated with genetic relatedness. In Mitani’s (2009) study of 28 males observed for at least 5 years, strong bonds lasting 1 year or longer were formed in 56% of maternal kin dyads, 68% of paternal kin dyads, 66% of unrelated age-mates, and 48% of unrelated non-age-mates. The distribution of bonds in one year predicted its distribution in the next. Maternal half-brothers had more equally balanced grooming and formed longer-lasting bonds than did unrelated individuals. Males of similar dominance rank had more equitable grooming relations and longerlasting bonds than males of disparate ranks. There was no effect of age (Mitani, 2009). Kinship, however, was by no means the only or even the most important determinant of long-term bonds among males. Indeed, 22 of 28 males formed their longest, closest bond with an unrelated animal, and the majority of cooperative behavior was observed between unrelated or distantly related individuals (Mitani, 2009). In a test of reciprocal exchanges among 22 males, Mitani (2006) found significant positive pairwise correlations among several measures: grooming given and received, support given and received in coalitions, meat sharing, participation in hunts (Watts & Mitani, 2001), and participation in border patrols (Langergraber et al., 2007). In all cases, results remained significant after controlling for rates of association, age, rank differences, and genetic relatedness. In other words, the best predictor of male X’s rate of interaction with male Y by any of the seven measures listed above was male Y’s rate of interaction with X according to either the same behavioral measure or any other measure chosen from the list. These results replicate data from previous, independent studies at Ngogo that found significant positive correlations between grooming and coalitionary support (Watts, 2000a, 2002), meat sharing and coalitionary support, and reciprocal meat sharing (Mitani & Watts, 2001). Subsequent quantitative analysis (Langergraber et al., 2007) revealed no consistent link between genetic relatedness and any of these behaviors. During Mitani’s 10-year study, 7 of 28 males maintained a strong social bond with another male during the entire period. One dyad remained strongly bonded for all 10 years; another dyad did so for 9 years. With two exceptions, every male maintained at least one bond that

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lasted well over half of the time that he was observed (Mitani, 2009). In sum, male chimpanzees formed friendships that lasted for many years, sometimes with maternal kin but more often with unrelated individuals. The data from Ngogo are strongly supported by data from chimpanzee communities elsewhere. In the Kanyawara community, for example, many male-male dyads maintained strong and stable associations for up to 10 years, as measured by spatial proximity, grooming, and alliances (e.g., Gilby & Wrangham, 2008; NewtonFischer, 2004; Nishida & Hosaka, 1996; Watts, 1998). In the Tai Forest, Wittig and Boesch (2003a) assigned adult dyads a relationship benefit index (RBI) according to the frequency with which they shared food (usually meat) and formed coalitions. Nineteen of 105 dyads exchanged these behaviors frequently. Pairs with a high RBI also had high rates of grooming and were more likely than other pairs to exhibit reconciliatory behavior after aggression (Wittig, 2010). Although early reports suggested that female chimpanzees interacted at low rates and were generally asocial (Goodall, 1986), more recent data paint a different picture. In a study of 39 females at Ngogo—the largest sample to date—Langergraber, Mitani, and Vigilant (2009) found that, whereas the average index of dyadic party association among males was higher than the average among females, the strongest dyadic associations were found among females, even though these females were rarely close kin (see also Wittig & Boesch, 2003b). Other Species A growing body of evidence indicates that the friendships found in baboons and chimpanzees are not aberrations, and that similar long-lasting bonds can be found throughout the animal kingdom. For example, long-term studies have revealed stable, enduring social bonds among female African elephants (Loxodonta africana: Moss, Croze, & Lee, 2010), rhesus and Japanese macaques (M. fuscata: Sade, 1965; Yamada, 1963), and capuchin monkeys (Cebus apella: O’Brien & Robinson, 1993; C. capuchinus: Perry, Manson, Muniz, Gros-Louis, & Vigilant, 2008). In all of these species, females are the philopatric sex and the strongest, most enduring social bonds are formed among mother-daughter pairs and sisters. In elephants, bonds between mothers and daughters and between sisters can persist for more than 20 years (see Moss et al., 2010 for review). In rhesus macaques living on Cayo Santiago, an island off the coast of Puerto Rico, females have the opportunity

to form close bonds with many matrilineal kin, including grandmothers and great aunts (Sade, 1972). As among baboons, close maternal kin (mother-daughter and sister pairs) form the closest friendships (Widdig et al., 2001, 2006; see Kapsalis, 2004, for review and Watanabe, 2001, for similar data on Japanese macaques). Examining behavior within the matrilineal families of Cayo Santiago rhesus macaques, Kapsalis and Berman (1996a, 1996b) found that, if degrees of relatedness (r) were less than 0.125 (equivalent to half first cousins), female interactions with matrilineal kin did not differ from their interactions with nonkin. Like baboons, females rhesus macaques were also more likely to groom, approach, and spend time near individuals of similar age and half-sibs to whom they were related through the paternal line (Widdig et al., 2001). In capuchin monkeys, long alpha male tenure can lead to groups containing full siblings and both maternal and paternal half-siblings (Perry et al., 2008). In Perry et al.’s study, paternal half-siblings seemed unable to recognize one another and the strongest, most enduring bonds involved individuals related through the maternal line. Similarity in rank had a small but significant effect, making bonds between these females stronger than those among females of disparate ranks (Perry et al., 2008). In hyena (Crocuta crocuta) society, virtually all males disperse from their natal clan whereas females remain. In this respect hyenas resemble the elephants and monkeys described above. Within a clan, however, individual hyenas do not forage and travel as a group but instead exhibit fission-fusion behavior much like that found in chimpanzees. Clans may contain up to 80 individuals belonging to one or more matrilineal kin groups. The strongest longterm bonds occur among females who are almost certainly close relatives through the maternal line (see Smith et al., 2010, for review). In feral horses (Equus caballus), both males and females disperse from their natal group, later forming stable breeding groups that include one stallion and several unrelated females. In a 4-year study, Cameron, Setsaas, and Linklater (2009) found striking differences in the degree of social integration (as measured by grooming and proximity) among mares in different groups. Mares that interacted at higher rates with each other experienced reduced rates of harassment by males, and higher foal birth rates and survival when compared with mares that interacted with each other less often. Long-term studies of dolphins (Tursiops aduncus), begun in the 1970s and 1980s, are currently underway in Sarasota Bay, Florida (Wells, 2003) and Shark Bay, Western Australia (Mann, Connor, Barre, & Heithaus, 2000).

Primate Social Relationships

At both sites, some males and many females disperse from their natal range as adolescents, while a few individuals of both sexes continue to use their natal range as adults (Connor et al., 2000; Connor & Mann, 2006; Wells, 2003). Within this range, dolphins live in a fission-fusion society in which individuals associate in small groups that change composition often (Connor, 2007; Connor et al., 2000). In Shark Bay, where scientists study a population of 600 individuals, adult males form first order alliances of two or three males who join together to form a sexual consortship with a female (Connor et al., 1996). At a second level of alliances, 4 to 14 males from two or more firstorder alliances join to defend or take over females from other second- or first-order alliances. In addition to their cooperation in aggression, allied males exhibit high rates of spatial association, gentle rubbing (touching or rubbing each other with pectoral fins), and synchronous swimming and surfacing (Connor et al., 2006). Males in both firstand second-order alliances are more closely related to each other than would be expected by chance (Frere et al., 2010; Kr¨utzen et al., 2003). The bonds between individual male members of a first-order (and therefore second-order) alliance may last for up to 20 years (Connor & Mann, 2006). The Evolution of Friendships Demography and kinship constrain the formation of friendships, but they are not the only factors that determine which individuals form close, enduring social bonds. Among female baboons, macaques, hyenas, and elephants, where females remain with their matrilineal kin throughout their lives, individuals preferentially form long-term bonds with close relatives like mothers, daughters, and sisters (see previous citations). In most cases these individuals are readily available and long-term bonds develop naturally from the close bond established at birth between a mother and her daughter. If close kin are not available, however, individuals form long-term bonds with more distant relatives, with age-mates who may be patrilineal siblings, or with unrelated individuals. Regardless of demography, most individuals form at least one enduring social bond (see previous citations). In dolphins and horses (where both sexes disperse from their natal group), chimpanzees (where females disperse but male kin remain with their brothers), and lions (Panthera leo) and Assamese macaques (M. assamensis) (where only males disperse), long-term alliances among males sometimes involve kin. More often, however, they are formed by unrelated individuals (dolphins:

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Kopps et al., 2010; horses: Cameron, Setsaas, & Linklater, 2009; chimpanzees: Mitani, 2009; lions: Packer, Gilbert, Pusey, & O’Brien, 1991; Assamese macaques: Sch¨ulke et al., 2010). In Mitani’s study, for example, despite the presence of many maternal and paternal kin pairs, 22 of 28 male chimpanzees formed their most enduring bond with an unrelated individual. Natural selection therefore appears to have favored individuals who are motivated to form long-term bonds per se, not just bonds with kin. This suggests that longterm bonds (and the motivation to form them) have not evolved simply as an incidental consequence of the close mother-infant relations in species with overlapping generations. Nor can they be explained simply as the result of selection favoring cooperation between any individuals who are close genetic relatives. Instead, long-term bonds have evolved both through inclusive fitness (in species where bonds are formed with kin) and/or through direct fitness (in species where bonds are formed with unrelated individuals). The exact balance between these two selective pathways is likely to be complex. In lions, for example, individuals in small groups of males are more likely to form enduring bonds with unrelated individuals, probably because without such partners they cannot take over a pride of females. As the number of males increases, however, long-term bonds are more likely to be found exclusively among genetic relatives (Packer et al., 1991; see also Smith et al., 2010, for review). But what, exactly, are the benefits? Long-term bonds pose problems for evolutionary theories of behavior because they often involve interactions like grooming that are of relatively low cost and apparently have no direct link to reproduction or survival. Granted, many friendships involve kin, but as we have seen, they are by no means limited to close genetic relatives. In answer to this question, we now have direct evidence that enduring social bonds can reduce stress and increase individuals’ reproductive success. Among both baboons and rhesus macaques, for example, females whose grooming networks are focused on a few partners have lower GC levels than do females whose grooming networks are more diverse (Brent et al., 2011; Crockford et al., 2008). In baboons, females who lose a close companion to predation increase both their rate of grooming and the diversity of their grooming partners. This behavior may allow the females to form a close bond with a new partner (Engh et al., 2006). If a female’s mother dies, her bonds with sisters grow stronger (Silk, Alberts, et al., 2006). Finally, lactating females whose infants are threatened by infanticide decrease the

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diversity of their grooming partners, apparently focusing their interactions on a few preferred individuals (Wittig et al., 2008). All of these data suggest that forming and maintaining a close friendship helps to reduce stress. Female baboons with the most stable, enduring relationships also experience higher infant survival (Silk et al., 2003, 2009) and live longer (Silk et al., 2010b) than individuals without such relationships. Among horses, more closely bonded females exhibit higher birth rates and higher infant survivorship (Cameron et al., 2009). Among male dolphins, the formation of a long-term alliance increases a male’s reproductive success over what it would have been had no such alliance been formed (Connor et al., 2000). Allied males compete for access to females, and males within a successful alliance appear to share paternity relatively equally (Kopps et al., 2010). Among chimpanzees and Assamese macaques, a male’s reproductive success is directly related to his rank, which in turn is directly related to the coalitionary support he receives from others (chimpanzees: Boesch, 2009; Constable et al., 2001; Nishida & Hosaka, 1996; macaques: Sch¨ulke et al., 2010). Long-term bonds are therefore adaptive, but in different ways for females and males. Among females, individuals with the strongest, most enduring social bonds experience less stress, higher infant survival, and live longer. Among males, individuals with the strongest friendships have superior competitive ability, higher dominance rank, and improved reproductive success.

The Mechanism Underlying Friendships Hormonal Mechanisms At present, little is known about the hormonal mechanisms that underlie the friendships described above. It seems likely, however, that many of the genetic and hormonal mechanisms that underlie monogamous pair bonds in birds and mammals (see Carter et al., 2008 for review) might also apply to the friendships described above. It is now clear that the peptide hormones oxytocin and arginine vasopressin are involved in the formation of male-female pair bonds in rodents. Oxytocin is associated with prosocial behaviors in female mammals, and the gene coding for its receptor, OXTR, is heavily expressed in the brains of female rodents (Carter et al., 2008). By contrast, the arginine vasopression pathway, including the V1a receptor gene, is involved in the expression of partner preference in male mammals (see Turner et al., 2010 for review). In monogamously mated pairs, different

levels of oxytocin may be associated with variation in bond strength. For example, in a study of monogamously bonded tamarins (Saguinus oedipus), Snowdon et al., (2010) found that both males and females exhibited a 10-fold variation in levels of oxytocin. Within pairs, however, male and female levels were highly correlated and the pairs that were most strongly bonded exhibited the highest ocytocin levels. Different behavioral variables were correlated with levels of oxytocin in each sex: for females, affiliation duration and affiliation frequency were the best predictors of oxytocin levels; for males, the best predictor was sexual behavior. The variation in mean oxytocin levels across pairs, however, was best explained by a model that included male sexual behavior, male huddle initiation, and female solicitation (Snowdon et al., 2010). In other words, as with Hinde’s study of responses to separation, the mean oxytocin level in a pair was best predicted not by any single property of either individual but by properties of the pair’s relationship. As already noted, the stress response in both human and nonhuman species (as measured by levels of circulating glucocorticoids, GC) can be mitigated by social contact and affiliation (see Carter et al., 2008; Cheney & Seyfarth, 2009; Yee et al., 2008 for review). Increasing GC levels prompt the release of oxytocin, which increases motivation for social bonding and physical contact (Uvnas-Moberg, 1997). Oxytocin both inhibits the further release of GCs and promotes affiliative behavior, including the tendency to associate with other females. And, as briefly reviewed above, female primates who experience stress often behave in ways that suggest they are motivated to establish new relationships, maintain existing bonds, or restore bonds that have been damaged. This behavior resembles that found in humans, where the loss of a close companion is a potent stressor and individuals show an increased tendency to associate with other females when under stress (Kendler, Myers, & Prescott, 2005; Thorsteinsson & James, 1999). In both men and women, the number of “core” individuals on whom people rely for support during times of crisis (3–5 individuals) tends to be significantly smaller than their circle of mutual friends (12–20) or regular acquaintances (30–50) (Zhou, Sornette, Hill, & Dunbar, 2005). In the elderly, strong social networks enhance survival (Giles, Glonek, Luszcz, & Andrews, 2005; Uchino et al., 1996), and when humans perceive future social opportunities to be limited or at risk—either as they age or when they become ill—they tend to contract their social networks and become more selective in their social relationships (Carstensen, 1995).

Primate Social Relationships

Cognitive Mechanisms Henzi & Barrett (2007) argued that female baboons in their study had unstable patterns of grooming and proximity over a 4-year period (Barrett & Henzi, 2002; but see the reanalysis in Silk et al., 2010a). Grooming, however, was often reciprocal within a bout, and often occurred when one female was attempting to touch or handle another’s infant. Because females seemed to be “trading” grooming given for grooming received or access to a female’s infant, Henzi and Barrett (2007) concluded that “female ‘relationships’ . . . need not, and probably do not, take the long-term, temporally consistent form that has been attributed to them . . . ” (2007, p. 73). Instead, they argue for a view, based on biological markets (No¨e & Hammerstein, 1994), in which “each of the behaviors linked to theories of female coexistence . . . can be seen as an independent, contingent response to current need rather than as interlocking components of an overall female strategy to cultivate and enhance relationships in the long term” (2007, p. 46). Much of their criticism is based on what they believe is an overly anthropomorphic conception of nonhuman primate relationships in the minds of those who study them. Current use of the term, they argue, is based on the assumptions that “monkeys can anticipate their future social needs” (p. 52), that “the function of relationships is to ensure unstinting mutual support . . . at unknown, unpredictable future dates” (p. 64), and that the individuals concerned “possess a declarative, explicit knowledge” (p. 64) or an “overt, cognitive understanding” (p. 46) of their relationships with others. This critique is misplaced, for several reasons. Memory of the Past, Not Projection Into the Future. Although relationship (and here friendship) is widely used as a descriptive term, none of those whose research is cited earlier has ever claimed that monkeys, apes, or any other species can anticipate their future social needs. To the contrary, when scientists have speculated about the mechanisms underlying long-term relationships they have typically assumed that current behavior is affected, wholly or in part, by the individuals’ memory of past interactions (Aureli & Schaffner, 2002; Cheney & Seyfarth, 1990, 2007; Schino & Aureli, 2009). Or, as Hinde (1987, pp. 23–24) put it: “When two individuals interact, each will bring preconceptions about the likely behaviour of the other, or about the behaviour appropriate to the situation. In addition, if two individuals have a series of interactions over time, the course of each interaction may be influenced by experience in the preceding ones. We then speak of them as having a relationship. . . . ” Although the ability

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of animals to plan for the future is controversial, there is no doubt about their ability to learn from experience. Implicit Knowledge. Nor has anyone claimed that animals’ knowledge of their own and each other’s relationships is explicit and declarative—indeed, quite the opposite is true. To cite just one example: “when we say that baboons have social theories we do not mean that they have fully conscious, well-worked-out theories that they can describe explicitly. . . . Instead, baboons appear to have implicit expectations about how individuals will interact with one another. Through processes we do not yet understand, they observe the associations among other group members and generate expectations” about how these individuals will behave under different circumstances (Cheney & Seyfarth, 2007, p. 118). Implicit knowledge is widely documented in studies of children and animals. Four-month-old human infants have an implicit knowledge about the behavior of objects in space but they cannot describe what they know (Kellman & Spelke, 1983); children of 17 months can readily understand the meaning of sentences, yet no one claims that their behavior is based on an explicit, declarative knowledge of grammar (see Hirsh-Pasek & Golinkoff, 1996, for review). Nutcrackers (Nucifraga columbiana) remember the locations of thousands of previously hidden seeds (Balda & Kamil, 1992), while Pi˜non jays (Gymnorhinus cyanocephalus) and fish behave in ways that are difficult to explain without assuming that they have some representation of a transitive rank order (Grosenick, Clement, & Fernald, 2007; Paz-y-Mi˜no, Bond, Mail, & Balda, 2004). Yet knowledge in these and other cases is clearly implicit; it influences the animals’ behavior, but is not accessible to them. They cannot describe what they know. Animals’ knowledge of social relationships is no different. Many Behaviors Are Not Contingent Responses to Current Need. Supporting the current needs hypothesis, many behaviors that characterize friendships are closely juxtaposed in time. In perhaps the paradigmatic example, female primates are strongly attracted to newborn infants and invest many minutes grooming a mother in the apparent hope of being able to touch her infant (Silk et al., 2003). Henzi and Barrett (2002) found that female baboons groomed mothers for longer before handling their infants when there were fewer infants present in the group (see also Gumert, 2007). Infants, they argued, were a “commodity” whose value depended on the current supply. Similar data emerged from an experiment in which first one and then a second female vervet monkey were uniquely granted access to a supply of food (Fruteau,

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Voelkl, van Damme, & No¨e, 2009). When only one female had access to the food she received significantly more grooming from others. When a second female gained access to the food the grooming received by the first declined, as predicted by a current benefits, biological market hypothesis. The best data indicating that one beneficial act is contingent upon another—with or without a short delay—come from experiments in which a single prior event differs from one condition to another and this difference affects behavior (Hemelrijk, 1994; Seyfarth & Cheney, 1984; de Waal, 1997a). In one such test, a baboon who heard another individual’s recruitment call responded positively—that is, moved in the direction of the loudspeaker and approached the individual—if she had recently groomed with that individual and the individual had an infant, but showed no such behavior if she had recently behaved aggressively toward the individual. If the subject had groomed with the individual but not heard a recruitment call, she also showed no tendency to approach. Subjects’ responses were therefore dependent upon certain prior and current conditions, suggesting that at least some cooperative interactions depend on a specific, recent, prior interaction (Cheney et al., 2010). Despite these data, several observations argue against the current needs hypothesis as a complete explanation of the mechanisms underlying long-term bonds. First, it has proved difficult to demonstrate contingent, one-for-one exchanges of cooperative behavior in laboratory settings. This may arise because the settings are too unnatural (but see de Waal, 1997b, 2000), or because animals do not keep precise track of favors given and received (see Schino & Aureli, 2009; Silk, 2007, for review). Brosnan et al. (2009) note that laboratory tests depend primarily on the exchange of goods, particularly food, whereas exchanges in the wild are primarily concerned with services, like grooming and support, which may be more suited to economic exchanges. The argument is intriguing, but it cannot account for the striking difference between chimpanzees’ food-sharing behavior in the wild and the lack of it in captivity. But the strongest argument against the current needs hypothesis comes from the distribution of cooperative behaviors in time and their distribution among individuals. Highly correlated behaviors that are separated in time create an asymmetry whenever the current needs hypothesis is compared with one based on the memory of previous interactions. If two behaviors are closely linked in time—grooming and infant handling, for example— results are consistent with current needs but one cannot rule out the possibility that behavior has also been caused by the

individuals’ memories of past interactions. Experiments in captivity get around this problem by testing for cooperation between animals that have never met each other before, but this hardly solves the problem. After all, one goal of such experiments is to explore the conditions under which selection might have favored the evolution of cooperative, long-term bonds under natural conditions—which brings us back to the same problem. By contrast, if two behaviors are widely separated in time—if one male chimpanzee forms a coalition with another, then receives meat from his partner three days later—results can decisively rule out an explanation based on current need; or, at the very least, require that we expand the current need hypothesis to include behaviors that are widely separated in time and linked by the individuals’ memories of past interactions—which brings us back to long-term relationships. In many monkeys, the pairs of females who groom most often are also those most likely to support each other in coalitions, yet grooming and coalition formation are rarely juxtaposed in time (e.g., Kapsalis, 2004; Schino, 2007). Among pairs of male chimpanzees at Ngogo, those who groom most often also have the highest rates of coalition formation and participation in border patrols, yet these behaviors do not necessarily occur together (see previous citations). The same holds for meat sharing and coalition formation in the Tai Forest, and for grooming given and grooming received (see previous citations). In Japanese macaques (Schino, 2007; Schino, Ventura, & Troisi, 2003), chimpanzees (Gomes et al., 2009), baboons (Frank & Silk, 2009), and capuchin monkeys (Schino et al., 2009), grooming within a bout is often very one-sided, yet grooming between the same two partners is much more evenly balanced when it is summed over weeks or months. All of these results suggest that primates “are tolerant of temporary imbalances in services given and received and are able to keep track of the help given and received over substantial periods of time” (Silk et al., 2010a, pp. 1743–1744). This tolerance of temporary imbalances may be particularly evident in closely bonded dyads. For example, in experiments with chimpanzees, vervet monkeys, and baboons, prior grooming had a strong effect on individuals’ willingness to support each other in weakly bonded dyads, but no noticeable effect on their willingness to support in strongly bonded dyads (Cheney et al., 2010; de Waal, 1997a; Seyfarth & Cheney, 1984). Brosnan, Schiff, and de Waal (2005) found that chimpanzees were more tolerant of inequitable reward distributions if they had a close social bond than if they did not.

Social Knowledge: The Recognition of Other Animals’ Relationships

In sum, while the current needs hypothesis may account for some of the cooperative interactions that characterize friendships, it cannot explain the many cooperative interactions that are widely separated in time—unless, of course, we broaden the temporal scope of the hypothesis so that it includes the memory of past interactions, tolerance of temporary inequities, and allows individuals somehow to “sum” their notion of prior benefits over days, weeks, or months. But in this case the hypothesis would no longer be based on current benefit. The current needs hypothesis also fails as an exclusive explanation of long-term bonds because so many immediately beneficial interactions involve individuals who interact often, and whose long history almost certainly affects what they do. Contingent cooperation does occur in animals, but it cannot account for the existence of enduring, long-term friendships. What hypothesis accounts for the existing data? We consider this issue in greater detail further on, in the section “Social Knowledge,” where we examine what individuals know about their own and other animals’ relationships. Summary: Social Relationships We can see in many group-living mammals the evolutionary origins of human friendship. In horses, elephants, hyenas, dolphins, monkeys, and chimpanzees, evolution has favored the motivation to form close, enduring social bonds either among females, or among males, or between males and females. Genetic relatedness affects the formation of friendships. In species like baboons, macaques, and elephants, where males disperse and females remain in their natal group throughout their lives, friendships are more likely among females, who form enduring bonds with the most obvious category of partners: close matrilineal kin who are brought together from the moment a female is born. By contrast, in species like chimpanzees and dolphins, where female dispersal is common and males remain together, long-term bonds are more likely among males. Not all friendships, however, can be traced to kinship. If a female baboon has no mother or daughter present, she forms her strongest bond with a sister or an unrelated animal, often an age-mate or an individual of similar dominance rank. Many male chimpanzees form their strongest bond with an unrelated male. Mares in a herd of horses form stable, enduring bonds despite being unrelated. Natural selection appears to have favored the motivation to form friendships generally, not just friendships with kin. Friendships are striking because they often involve cooperative interactions that are widely separated in time.

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One male chimpanzee supports another in a coalition, 3 days later his partner offers him meat, and over many months the two behaviors are highly correlated. Enduring friendships are thus built, at least in part, on the memory of past interactions and the emotions associated with them. Friendships are adaptive in different ways for males and females. Among males, allies have superior competitive ability, higher dominance rank, and improved reproductive success. Among females, individuals with the strongest, most enduring social bonds experience less stress, have higher infant survival, and live longer. SOCIAL KNOWLEDGE: THE RECOGNITION OF OTHER ANIMALS’ RELATIONSHIPS Clearly, individuals in many animal groups do not interact at random, but behave in predictably different ways with different individuals. Stable dominance relations, for example, allow an observer to predict who will win a competitive interaction; close, enduring friendships allow an observer to predict which individuals will come to another’s aid when that animal receives aggression. The social world, in other words, contains many statistical regularities. What do animals know about them? We take it for granted that classical conditioning allows animals to form an association between two predictable features of their environment, like a tone that is followed by the delivery of food. Does a similar process allow animals to associate a particular behavior by one individual with a specific behavior by another? Similarly, we now know that animals can learn to associate and group together stimuli, thereby forming a category, even when these stimuli do not look alike (e.g., Bloomfield, Sturdy, Phillmore, & Weisman, 2003; Cerella, 1979). Does experience also allow them to form social categories based on the relationships that individuals have with each other (Dasser, 1988)? As already noted, criticism of the term relationship in animal behavior rests on the notion that it is anthropomorphic: The concept of a relationship may exist in the minds of human observers, but nothing like it exists in the minds of the animals themselves (Henzi & Barrett, 2007). This brings us to the second part of our review. Given that animals form close, enduring social bonds, what (if anything) do the animals themselves know about other individuals’ relationships and how does this knowledge affect their behavior? Recognition of Other Animals’ Dominance Relations There is now an extensive literature indicating that animals recognize other individuals’ relationships. Territorial

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birds recognize the dominance relations that exist among their neighbors (e.g., Peake et al., 2002), while fish, hyenas, lions, horses, dolphins, and several species of primates recognize other individuals’ dominance ranks. When joining a coalition, for example, individual hyenas and monkeys selectively support the higher-ranking of two combatants regardless of who is winning at the time (Engh et al., 2005; see also Seyfarth & Cheney, 2012a, for review). When recruiting a coalition partner, male macaques selectively solicit those who rank higher than both their opponent and themselves (Silk, 1999); capuchin monkeys selectively solicit allies who rank higher than their opponents and have a social relationship with the solicitor that is closer (as measured by the ratio of past affiliative to aggressive interactions) than their relationship with the opponent. The preferential solicitation of more closely bonded individuals can be explained only by assuming that solicitors somehow compare the bond between the ally and themselves with the bond between the ally and their opponent (Perry et al., 2004). In playback experiments, a sequence of calls that mimics a higher-ranking opponent threatening a lowerranking animal elicits little response from listeners, but if the individuals’ roles are reversed the response is significantly stronger—presumably because the rank-reversal sequence violates the listener’s expectations (Bergman, Beehner, Cheney, & Seyfarth, 2003; Kitchen, Cheney, & Seyfarth, 2005; for reviews see Cheney & Seyfarth, 2012a; Schino, 2001; Schino, Polizzi di Sorrentino, & Tiddi, 2007). Recognition of Other Animals’ Close Bonds Animals also recognize the close bonds that exist among others. In playback experiments conducted on vervet monkeys and baboons, females who heard a juvenile’s scream were likely to look at the juvenile’s mother (Cheney & Seyfarth, 1990, 2007). Low-ranking male baboons monitor the sexual consortships of males and females, in an apparent attempt to take advantage of “sneaky matings” (Crockford, Wittig, Seyfarth, & Cheney, 2007). In vervets and many macaque species, an individual who has just been involved in an aggressive interaction with another will redirect aggression by attacking a third, previously uninvolved individual. Judge (1982) was the first to note that redirected aggression does not occur at random. He found that rhesus macaques do not simply threaten the nearest lower-ranking individual; instead, they target a close matrilineal relative of their opponent (see Seyfarth & Cheney, 2012a, for review).

If a baboon receives aggression from another and then, minutes later, hears a grunt from a previously uninvolved animal, the listener’s response to the grunt depends on the relationship between the calling animal and the listener’s opponent. If the caller is a close matrilineal relative of the opponent, the listener is subsequently more likely to approach her recent opponent and tolerate her opponent’s approach than if she hears the grunt of an animal unrelated to her opponent or no grunt at all. In other words, she treats the call as a “reconciliatory” signal that functions as a proxy for reconciliation with the opponent herself (Wittig, Crockford, Wikberg, Seyfarth, & Cheney, 2007). A similar phenomenon occurs among chimpanzees, where the behavior of bystanders and victims following aggression depends on both their own relationships with the combatants and their perception of the relationship between the other animals involved (Wittig & Boesch, 2010). To cite another example, chimpanzees often scream when involved in aggressive disputes. Slocombe and Zuberbuhler (2005) found that victims produce acoustically different screams according to the severity of aggression they are receiving. In playback experiments, listeners responded differently to the different scream types (Slocombe, Townsend, & Zuberbuhler, 2009). In cases of severe aggression, victims’ screams sometimes seemed to exaggerate the severity of the attack, but victims only gave exaggerated screams if their foraging party included at least one listener whose dominance rank was equal to or higher than that of their aggressor (Slocombe & Zuberbuhler, 2007). Victims seemed to alter their screams depending upon their perception of the relationship between their opponent and their potential allies. Integrating Knowledge of Kin and Rank Having found that baboons recognize the close bonds among matrilineal kin and individual dominance ranks, Bergman, Beehner, Cheney, and Seyfarth (2003) tested whether individuals integrated their knowledge of other individuals’ kinship and rank to recognize that the female dominance hierarchy is in fact composed of a hierarchy of families (that is, sub-groups of closely bonded females). As background, recall that rank relations among adult female baboons are generally very stable over time, with few rank reversals occurring either within or between families. When rare reversals do occur, however, their consequences differ significantly depending on who is involved. If, for example, the third-ranking female in matriline B (B3 ) rises in rank above her second-ranking sister (B2 ), the reversal

Social Knowledge: The Recognition of Other Animals’ Relationships

affects only the two individuals involved; the family’s rank relative to other families remains unchanged. However, a rare rank reversal between two females from different matrilines (for example, C1 rising in rank above B3 ) is potentially much more momentous, because it can affect entire families, with all the members of one matriline (in this case, the C matriline) rising in rank above all the members of another. Bergman et al. (2003) played sequences of calls mimicking rank reversals to subjects in paired trials. In one set of trials, subjects heard an apparent rank reversal involving two members of the same matriline: for example, female B3 giving threat-grunts while female B2 screamed. In the other set, the same subject heard an apparent rank reversal involving the members of two different matrilines: for example, female C1 giving threat-grunts while female B3 screamed. As a control, subjects also heard a fight sequence that was consistent with the female dominance hierarchy. To control for the rank distance separating the subject and the individuals whose calls were being played, each subject heard a rank-reversal (either within- or between-family) that involved the matriline one step above her own (e.g., Penn, Holyoak, & Povinelli, 2008). Within this constraint, the rank distance separating apparent opponents within- and between-families was systematically varied. As before, listeners responded with apparent surprise to sequences of calls that appear to violate the existing dominance hierarchy. Moreover, between-family rank reversals elicited a consistently stronger response than did within-family rank reversals (Bergman et al., 2003). Subjects acted as if they classify individuals simultaneously according to both kinship and rank. The classification of individuals simultaneously according to two different criteria has also been documented in Japanese macaques (Schino, Tiddi, & Polizzi di Sorrentino, 2006). Recognition of More Transient Social Relations Bonds among matrilineal kin and a linear, transitive female dominance hierarchy are components of monkey social structure that typically remain stable for many years. It is perhaps not surprising, therefore, that primate social cognition has been most well documented in these two domains. There is growing evidence, however, that primates also recognize and monitor more transient social bonds. Hamadryas baboons in Ethiopia are organized into one-male units, each containing a fully adult male and two to nine adult females (Kummer, 1968; Stammbach,

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1987; Chapter 5, this volume). One-male units frequently come into contact with single, unattached males who may attempt to challenge the unit leader in an attempt to take over his females. In the first experimental test of individuals’ ability to recognize other animals’ relations, Bachmann and Kummer (1980) found that the willingness of a male to challenge a unit leader depended not on the challenger dominance rank relative to that of the leader but on the challenger’s perception of the strength of the bond between the leader and his females. Noting that social bonds between adult males and females can change often, Bachmann and Kummer suggested that challengers continually monitor one-male units to assess whether the bonds between a male and his females have weakened. Just this kind of monitoring seems to occur in multimale groups of baboons, where males form sexual consortships with adult females during the week when she is most likely to ovulate. Sexual consortships constitute a form of mate guarding, and typically involve the highest-ranking male. When a consortship has been formed, lower-ranking males can nonetheless gain mating opportunities by taking advantage of temporary separations between a female and her consort to mate “sneakily.” To test whether subordinate males monitor sexual consortships for such opportunities, Crockford et al. (2007) used a two-speaker playback experiment to simulate a temporary separation between the consort pair. One speaker played the consort male’s grunt to signal his location. The other speaker, located approximately 40 meters away, played the female’s copulation call to signal that she was mating with another male and that further mating opportunities might be available. Subordinate males responded immediately to the apparent separation between the female and her consort by approaching the speaker playing the female’s call. By contrast, when the same playback was repeated a few hours after the consortship had ended, subordinate males showed no interest. Apparently, they already knew that the consortship had ended, and the information was therefore redundant. Thus, males appear to monitor the status of these transient consort relationships very closely, even though they typically last for only a few days (see Smuts, 1985, for similar data on animals’ recognition of the friendships between males and lactating females in baboons). Theory of Mind: The Recognition of Other Animals’ Motives, Intent, and Knowledge Although it now seems clear than many animals recognize other group members’ relationships and dominance ranks,

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we still know little about whether they imbue these relationships with emotions and motives, as humans do. In the more than 30 years since Premack and Woodruff (1978) posed the question “Does the ape have a theory of mind?” much progress has been made in the study of mental state attribution in animals. Many questions, however, remain unresolved. The Recognition of Motives and Intent Several lines of evidence suggest that many animals routinely attribute simple mental states, like intentions and motives, to others. This ability is particularly evident in their vocalizations, when animals must make inferences about the intended recipient of someone else’s calls. Monkey groups are noisy, tumultuous societies, and an individual could not manage her social interactions if she interpreted every vocalization she heard as directed at her. Inferences about the directedness of vocalizations are probably often mediated by gaze direction and relatively simple contingencies. Even in the absence of visual signals, however, monkeys are able to make inferences about the intended recipient of a call based on their knowledge of a signaler’s identity and the nature of recent interactions. For example, when female chacma baboons were played the “reconciliatory” grunt of their aggressor within minutes after being threatened, they behaved as if they assumed the call was directed at themselves, as a signal of benign intent. As a result, they were more likely to approach their former opponent and to tolerate their opponent’s approaches than after hearing either no grunt or the grunt of another dominant female unrelated to their opponent (Cheney & Seyfarth, 1997). Call type was also important, because subjects avoided their recent opponent if they heard her threat-grunt rather than her “reconciliatory” grunt (Engh et al., 2006). By contrast, if subjects heard a female’s threat-grunt shortly after grooming with her, they ignored the call and acted as if they assumed that the female was threatening another individual. Thus, baboons use their memory of recent interactions to make inferences about the caller’s intention to communicate with them. In some cases, these inferences are complex and indirect, and call upon baboons’ knowledge of the kinship relationships of other group members. For example, when female baboons were played the threat-grunts of their aggressor’s relative soon after being threatened, they avoided members of their aggressor’s matriline. In contrast, when they heard the same threat-grunts in the absence of aggression, they ignored the call and acted as if they assumed that the call was directed at someone else (Wittig, Crockford, Seyfarth, & Cheney, 2007). Similarly,

as already mentioned, when subjects heard the “reconciliatory” grunt of their aggressor’s relative after a fight, they were more likely to approach both their aggressor and the relative whose grunt they had heard (Wittig, Crockford, Wikberg, et al., 2007). They did not do so, however, if they had heard the “reconciliatory” grunt of another, unrelated female. Here again, subjects behaved as if they believed that a grunt from their aggressor’s relative must be directed at them, as a consequence of the fight. What is especially interesting in these experiments is that subjects inferred that they were the target of the vocalization even though they had not recently interacted with the signaler, but with her relative. They could only have done so if they recognized that close bond that existed between the two females. In primates, faces and voices are the primary means of transmitting social signals, and monkeys recognize the correspondence between facial and vocal expressions (Ghazanfar & Logothetis, 2003). Presumably, visual and auditory signals are somehow combined to form a unified, multimodal precept in the mind of a monkey. In a study using positron emission tomography (PET), Gil da Costa et al. (2004) showed that when rhesus macaques hear one of their own species’ vocalizations, they exhibit neural activity not only in areas associated with auditory processing but also in higher-order visual areas, including STS. Auditory and visual areas also exhibit significant anatomical connections (Poremba et al., 2003). Ghazanfar, Maier, Hoffman, and Logothetis (2005) explored the neural basis of sensory integration using the coos and grunts of rhesus macaques as stimuli. They found clear evidence that cells in certain areas of the auditory cortex are more responsive to bimodal (visual and auditory) presentation of species-specific calls than to unimodal presentation. Although significant integration of visual and auditory information occurred in trials with both vocalizations, the effect of cross-modal presentation was greater with grunts than with coos. The authors speculate that this may occur because grunts are usually directed toward a specific individual in dyadic interactions, whereas coos tend to be broadcast generally to the group at large. The greater cross-modal integration in the processing of grunts may therefore have arisen because, in contrast to listeners who hear a coo, listeners who hear a grunt must determine whether or not the call is directed at them. In sum, when deciding “Who, me?” upon hearing a vocalization, monkeys must take into account the identity of the signaler (who is it?), the type of call given (friendly or aggressive?), the nature of their prior interactions with the signaler (were they aggressive, friendly, or neutral?),

Social Knowledge: The Recognition of Other Animals’ Relationships

and the correlation between past interactions and future ones (does a recent grooming interaction lower or increase the likelihood of aggression?). Learned contingencies doubtless play a role in these assessments. But because listeners’ responses depend on simultaneous consideration of all of these factors, this learning is likely to be both complex and subtle. The Recognition of Knowledge Although baboons and other monkeys may be able to recognize other individuals’ intentions, they seem not to recognize other individuals’ knowledge or beliefs. For example, both monkeys and apes give alarm calls without any apparent recognition of whether listeners are ignorant or already informed about the presence of a predator (reviewed by Cheney & Seyfarth, 2007). Similarly, although the food calls of capuchin monkeys (GrosLouis, 2004) and the pant hoots of chimpanzees (Clark & Wrangham, 1994) attract others to food, signalers show no evidence of recognizing whether their audience is already aware of the presence of food. To provide another example, chacma baboons often give contact barks when separated from others. When several individuals are calling simultaneously, it often appears that they are answering each other’s calls in order to inform others of the group’s location. Playback experiments suggest, however, that baboons call primarily with respect to their own separation from the group, not their audience’s. They answer others when they themselves are separated, and they often fail to respond to the calls of even their offspring when they themselves are in close proximity to other group members (Cheney, Seyfarth, & Palombit, 1996; Rendall, Cheney, & Seyfarth, 2000). In this respect, the vocalizations of monkeys and apes are very different from human speech, where we routinely take into account our audience’s beliefs and knowledge during conversation. The extent to which animals attribute knowledge, ignorance, and beliefs to others is controversial (see Shettleworth, 2010, for review). It is now well established that many animals are highly attentive to other individuals’ direction of gaze. In particular, domestic dogs (Canis familiaris) are adept at using gaze or gestures to determine which of two locations has food. When presented with a human or another dog informant, they reliably choose the location where the informant is looking, pointing, or orienting (e.g., Hare, Call, & Tomasello, 1998; Hare & Tomasello, 1999; Miklosi & Topal, 2004). Indeed, in one direct comparative experiment dogs were more accurate than chimpanzees in their ability to use communicative

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cues like pointing, gazing, and reaching to locate food (Brauer, Kaminski, Riedel, Call, & Tomasello, 2006). In addition to using other individuals’ direction of gaze to gain information, dogs often go out of their way to make eye contact with others before attempting to communicate with them, and they appear to be sensitive to whether a person is attentive or inattentive (Gacsi, Miklosi, Varga, Topal, & Csanyi, 2004). Some investigators have suggested that animals’ attentiveness to gaze direction is an indication that animals recognize what other individuals can and cannot see and hence what they can and cannot know. Rhesus macaques, for example, are more likely to attempt to steal food from a human whose eyes are averted than from one whose eyes are not (Flombaum & Santos, 2005), and captive chimpanzees are more likely to approach food that a competitor cannot see than food it can see (Hare, Call, Agnetta, & Tomasello, 2000). Similarly, when potential competitors are present, ravens (Corvus corax ) and scrub jays (Aphelocoma californica) are more likely to cache food in sites that are out of view or hidden behind barriers than in more open sites (Bugnyar & Heinrich, 2005; Bugnyar & Kotrschal, 2002; Dally, Emery, & Clayton, 2005; Emery, Dally, & Clayton, 2004). These results are certainly consistent with the interpretation that animals recognize the relationship between seeing and knowing. However, they are also consistent with a simpler interpretation that posits that animals use gaze direction to assess not other individuals’ knowledge, but rather their intentions. As a result, they recognize, for example, that other individuals are motivated to defend food that they are looking at, and less likely to defend food in which they show no interest. Some recent experiments have attempted to avoid this confound by eliminating the possibility that subjects are responding only to their rival’s direction of gaze when choosing among food items. Kaminski, Call, & Tomasello (2008) presented chimpanzees with the choice of three buckets, two of which contained food. The first bucket was baited in the presence of both the subject and the rival. The second bucket was baited in the presence only of the subject. In the test condition, the subject’s view of the apparatus was blocked, while the rival was allowed to choose first. In the control condition, the subject chose first. When subjects chose first, they were as likely to choose the bucket that their rival had seen baited as the one he had not. However, when they chose second, they were more likely to choose the bucket that their rival had not seen baited, suggesting they inferred that the rival would have chosen the bucket that he had seen baited. In other words,

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they acted as if they recognized what their rival knew, based on what he had seen. To date, most studies of animals’ theory of mind have been conducted on captive animals, using paradigms and rewards determined by human experimenters. It is to be hoped that future investigations will attempt to address these questions under more natural conditions, on the animals’ own terms. Until such experiments are conducted, we can only speculate about the selective forces that might favor the evolution of a theory of mind, and its function in social interactions (for further discussion see Cheney & Seyfarth, 2012b). The Mechanisms Underlying Social Knowledge Given that individuals recognize the relations that exist among others, what mechanisms underlie this knowledge? One hypothesis argues that memory and classical conditioning are entirely sufficient to explain primates’ social knowledge. As they mature, baboons recognize patterns of behavior that link individuals in predictable ways. Their knowledge cannot be described as conceptual because there is no direct evidence for the existence of such concepts, and social knowledge can just as easily be explained by simpler hypotheses based on learned associations and prodigious memory (e.g., Schusterman & Kastak, 1998). Explanations based on memory and associative learning are powerful and appealing under simplified laboratory conditions, but they strain credulity when applied to behavior in nature, where animals confront more complex sets of stimuli. A young baboon, for example, must learn thousands of dyadic (and tens of thousands of triadic) relations in order to predict other animals’ behavior. The magnitude of the problem makes one wonder whether simple associations, even coupled with prodigious memory, are equal to the task. Faced with the problem of memorizing a huge, ever-changing dataset, humans are predisposed to search for a higher-order rule that makes the task easier (Macuda & Roberts, 1995). Why should animals be any different? In fact, results suggest that the social knowledge of baboons—to cite just one example—is organized into units of thought that resemble our concepts. To begin, consider the speed of their reactions to events. When baboons hear a sequence of vocalizations that violates the dominance hierarchy, they respond within seconds (Cheney & Seyfarth, 2007). When a male macaque involved in a fight tries to recruit an ally, he seems instantly to know which individuals would be the most effective partners (Silk, 1999). The speed of these reactions suggests that animals are not searching through a massive, unstructured database

of associations but have instead—as a kind of cognitive shortcut—organized their knowledge into concepts: what we call dominance hierarchies and matrilineal (family) groups. Social categories qualify as concepts because they cannot be reduced to any one, or even a few, sensory attributes. Family members do not look alike, sound alike, or share any other physical features that make them easy to tell apart. Infants are black whereas juveniles are olive brown, males are larger than females, and many individuals have idiosyncratic wounds or postures, yet none of this variation affects other animals’ classifications: A three-legged member of family X is still a member of family X. Nor is the classification of individuals into family groups based on behavior. The members of high-ranking families are not necessarily more aggressive than others, nor do they range in different areas or groom or play more often. In fact, because mothers generally groom daughters more than sons, grooming within families can be highly variable—yet this has no effect on other animals’ perception of who belongs in which family. Social categories, moreover, persist despite changes in their composition. Among females and juveniles, the recognition of families is unaffected by births and deaths; among adult males, the recognition of a linear, transitive hierarchy persists despite frequent changes in the individuals who occupy each rank. In the mind of a baboon, social categories exist independent of their members. The classification of individuals into families seems to occur not because outsiders treat family members as identical, but because outsiders regard the family as an assemblage of different individuals who share a common attribute. While the individuals within a family can sometimes be substituted for one another—one member of the A matriline, for example, can reconcile “on behalf of” another (Wittig, Crockford, Wikberg, et al., 2007)—they nonetheless retain their distinct identities. In this respect, baboons appear to be “psychological essentialists” (Medin, 1989): They act as if each animal, though a distinct individual, has an “essence or underlying nature” (Gelman, Coley, & Gottfried, 1994) that makes her a member of family X. The same essentialist thinking applies to each family. Finally, the classification of individuals into families and their arrangement into a dominance hierarchy are cognitive operations that affect behavior. When listeners hear vocalizations from two individuals interacting elsewhere, their response depends not just upon the animals’ identities but also upon their ranks and family membership (Bergman et al., 2003). Social categories are units of thought that determine how individuals behave.

Social Knowledge: The Recognition of Other Animals’ Relationships

Bound up in the baboons’ concepts are expectations: If a member of the A family threatens the member of another matriline, listeners expect that other family members will come to the threatener’s aid (Wittig, Crockford, Seyfarth, et al., 2007). Baboons’ concepts thus concern not only which entities “go together” but also how category membership affects behavior. Indeed, the baboons’ concepts and their expectations about behavior are intimately entwined: They use their observations of behavior to create concepts, and, having done so, use their concepts to predict behavior. For baboons, it is difficult if not impossible to separate concepts from the theory-like relations that underlie them (for further discussion see Seyfarth & Cheney, 2012b). Summary, Implications, and Directions for Future Research We began this review by noting that, during the past 20 years, the focus of research on primate cognition has shifted from the laboratory to the field, from knowledge about objects to knowledge about individuals, their motives and their behavior. What have we learned from this new perspective? First, within social groups primates and many other animals form differentiated social relationships: Some bonds are close and enduring, while others are more transient. Some individuals rarely interact with each other. The result is a rich, heterogeneous social environment in which there are predictable patterns of interaction: statistical regularities that an individual must recognize if she is to predict others’ behavior. Second, natural selection has favored the formation of close, enduring social bonds. Long-term bonds are adaptive in different ways for males and females. Among males, allies have superior competitive ability, higher dominance rank, and improved reproductive success. Among females, individuals with the strongest, most enduring social bonds experience less stress, higher infant survival, and live longer. Third, results suggest that, because the formation of long-term bonds is adaptive and individuals need to know about other animals’ relationships in order to form those bonds that return the greatest benefit, natural selection has also favored the evolution of social cognition— knowledge about other animals’ motives, behavior, and knowledge. Fourth, whatever its evolutionary origins, nonhuman primate social cognition has several properties that are directly relevant to theories about the evolution of human cognition. Specifically:

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• Social knowledge involves the formation of concepts. The recognition of individuals, for example, is widespread, multimodal, and cannot be reduced to or defined in terms of any single sensory attribute. Primates, and perhaps many other animals, also classify individuals into groups—families, dominance hierarchies, mating pairs. These, too, cannot be reduced to a few sensory attributes but are based instead on the relations among their members (Seyfarth & Cheney 2012b). • Social knowledge—at least in baboons—is computational. Individuals recognize others based on properties that have discrete values (dominance rank, membership in a specific kin group) then combine this knowledge to classify others along two dimensions simultaneously: a hierarchy of matrilineal families. • Social knowledge—at least in baboons—is rulegoverned and open-ended. Individuals recognize, for example, that certain vocalizations follow rules of directionality that must correspond to the current dominance hierarchy: threat-grunts are given only by dominant animals to subordinates, fear barks are given only by subordinates to dominants. Individuals react strongly to the violation of these rules. Knowledge is open-ended because, if an individual can recognize that A threat-grunts and B screams is different from B threat-grunts and A screams, then she can make the same judgment for all possible pairs, including any new individuals who join the group. Baboons have a system of social cognition in which animals comprehend a huge number of messages from a finite number of signals. • Social knowledge involves the attribution of motives and implicit theories of causality. A baboon, for example, knows when another individual is vocalizing to her and when an animal’s grunt signals reconciliation after a fight. B threat-grunts and A screams violates expectation only if the listener assumes that the threat-grunt caused the scream. At present, we do not know whether primate social knowledge is qualitatively different from that in other species. Primates (and some other mammals) may differ from other species in their ability to monitor the relationships of many individuals or to classify individuals along multiple dimensions simultaneously. Alternatively, the societies of birds, fish, and other nonprimate species—often superficially simpler than those of primates—may have led us to underestimate the information that individuals acquire about others. Finally, there may be qualitative differences in “social intelligence”

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between different taxonomic groups, but within each group social knowledge may increase in sophistication with increasing social complexity. The comparative study of social cognition remains a work in progress (Cheney & Seyfarth, 2005). Nonhuman primates recognize the motives and intent of others, but compared with humans their knowledge of other individuals’ mental states is rudimentary. They do not seem to recognize what others know, or to distinguish knowledgeable from ignorant individuals (Cheney & Seyfarth, 2007). Nor do they seem motivated to share with others their intentions, motivation, and knowledge (Tomasello, Carpenter, Call, Behne, & Moll, 2005). For all the advances in our understanding of knowledge and cognition among monkeys and apes, these contrasts with humans remain striking. Given these conclusions, future research might well concentrate on some of the following unresolved issues. • What are the proximate mechanisms that underlie the formation of close, enduring social bonds? Reduced stress? Decreased vulnerability to predation as a result of becoming less peripheral? In males, greater access to mates? • What are the evolutionary benefits? In female primates at least, they appear not to include greater defense against predators, greater access to food, or increased rank. They may include better infant survival and increased longevity. How do these benefits arise? • What behavioral traits are most closely correlated with the formation of long-term bonds? Answers to each of these questions can come only from the kind of detailed, long-term, observational field studies that are increasingly rare (and rarely funded!) in comparative psychology. With regard to cognition: • How widespread in the animal kingdom are the cognitive abilities shown thus far in a few primates, particularly baboons? We urge scientists to pay particular attention to cognitive skills other than those concerning theory of mind, which has to date received an inordinate amount of attention. • Does the organization of knowledge about other animals into discrete social categories (individuals, dominance ranks, kin groups, pair bonds) recur throughout the animal kingdom? • Do animals other than primates classify other individuals along two dimensions simultaneously, as demonstrated by Bergman et al. (2003)? Might this be a

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CHAPTER 22

The Neural Basis of Language Faculties CHANTEL S. PRAT

OVERVIEW 595 METHODS 596 THE NEURAL BASES OF LINGUISTIC REPRESENTATION AND PROCESSES

CONTEMPORARY APPLICATIONS SUMMARY 613 REFERENCES 614

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then build up a representation of the idea units, using information about word order, morphology, and previous experience (among other things) to decide who is doing what to whom. Add to this scenario the frequent condition in which what a speaker says (e.g., Can you pass the salt?) and what a speaker means (e.g., I would like you to pass the salt.) are different, requiring the listener to draw upon his understanding of social norms in a culture to infer the true intentions of the speaker, and you begin to get a picture of how complex language processing can be. Keeping in mind that natural language production and comprehension occurs at a rate of about three words per second (e.g., Levelt, 1999), one can begin to understand the difficulties in disentangling these processes in the brain. It is understandable then, that although researchers have known that certain brain regions (e.g., Broca’s and Wernicke’s areas) are critical for language for well over a century, many questions about the intricacies of brain systems supporting language still exist. However, as methods for investigating brain-behavior relationships advance, a growing body of work constrained by psycholinguistic theory is providing interesting insights about the brain basis of linguistic processes. This chapter will begin with an overview of the contemporary methods used for studying cognitive neuroscience in general, and the strengths and weaknesses of each method for uncovering the neural basis of language, specifically. Results from converging methods will then be incorporated into a discussion of our current understanding of the brain basis of language representation and processes. The chapter concludes with descriptions of some important

OVERVIEW Language is a term that has been used to loosely define the array of methods that humans use for communicating with one another. Such communication can occur in person, through vocalizations and gestures, or through written symbolic systems, giving us the unique advantage of being able to share information across distance and time. Though many of the core linguistic computational processes are shared across modalities, essential differences in development and evolution of spoken and written languages are reflected in somewhat separable brain systems. Thus, to truly understand the biological basis of language, one must account for an extremely diverse set of phenomena, ranging from how we learn to categorically perceive the speech sounds in our own native languages to how we reason about the thoughts and intentions of others in a written passage. Understanding the neural basis of language is complicated by the fact that multiple subcomponent processes are executed incredibly rapidly and largely in parallel. Take, for instance, the example of a conversation over dinner. To comprehend the conversation, each listener must use selective attention to filter out the speaker’s stream of speech from the cacophony of background noise. From the selected signal, the listeners must then match patterns of speech sounds with stored representations to segment meaningful units (words or morphemes) in the nearly continuous stream of acoustic information that reaches the ear. This match results in activation of the mental lexicon, which includes information about each word’s meaning or meanings and grammatical roles. The listener must 595

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real-world applications of the brain-behavior links discussed throughout.

METHODS As language is often considered one of the quintessential cognitive abilities that separates humans from other intelligent animal species, the vast majority of research on the cognitive neuroscience of language has come from human studies. Recently, cognitive neuroscientists studying language have been able to capitalize on rapidly advancing technologies for making inferences about brainbehavior relationships. In the interest of space, this section describes only the most popular methods that are currently being used to study the neural basis of language. Lesion Studies Most of what we know about language faculties in the brain comes from the systematic observation of linguistic deficits following brain damage. The extent to which inferences about brain-behavior relationships can be drawn from studies of such patients is dependent on two factors: (1) the ability to accurately localize the region(s) of the brain damaged or impaired, and (2) the ability to measure and characterize the specific nature of the resulting deficit(s). Over the past 50 years, improvements in technology, theory, and experimental design have greatly improved the utility of information gleaned from such lesion studies. Before the early 1970s, techniques for imaging the brain in vivo were rudimentary, at best. To gain precise information about the location of brain damage, it was necessary to examine the brain postmortem, or to rely on descriptions of tissue removed during surgery. In the 1970s, a series of techniques for recording threedimensional images of the brain in living patients were developed, culminating with the introduction of MRI (magnetic resonance imaging), which has the greatest advances in spatial resolution and sensitivity for differentiating tissue types, and does not require any exposure to radiation. This technique has allowed researchers to describe and categorize lesion size and locations more thoroughly, allowing for more powerful inferences about lesion-deficit relations. Recently, methods for compiling lesion-imaging data across groups of patients are proving particularly useful. For instance, VLSM (voxel-based lesion-symptom mapping) is a method for statistically analyzing the relation

between lesion site and behavioral deficits observed on a fine-grained, voxel-by-voxel basis (see Bates et al., 2003, for a complete description). The major advantage of VLSM over previous lesion grouping methods is that it allows researchers to compile large groups of data without grouping them by gross sites of interest (e.g., frontal lesions versus parietal regions) or strict behavioral cutoffs (e.g., naming impaired versus intact). The body of work resulting from such analyses is providing important information about the neurological basis of language (Dronkers, Wilkins, Van Valin, Redfern, & Jaeger, 2004; Wilson & Saygin, 2004) and in some cases has even caused us to reexamine some previous assumptions about brain-behavior relationships (e.g., Borovsky, Saygin, Bates, & Dronkers, 2007). In parallel, researchers have developed a battery of increasingly sensitive tools for measuring and characterizing the nature of language deficits observed in patients following brain injury. Early neuropsychological investigations largely employed interview techniques to assess linguistic deficits. For instance, a patient might be asked to read a short story and then be verbally questioned about the main ideas or themes of the story. Failure to answer such questions can arise as the result of reading difficulties, inferential difficulties, memory difficulties, or pragmatic difficulties (e.g., failure to understand what the interviewer is asking for) to name a few. Over the past two decades, however, neuropsychological investigations have increasingly employed the tools standard in psycholinguistics to better characterize the psychological/computational nature of deficits observed following brain damage (see Long & Baynes, 2002, for an example). Although research on patients with focal brain damage has yielded a plethora of information about language processes in the brain, some limitations in the inferences we can draw from this data must be acknowledged. First, the damage that results from disease or trauma varies in size and location, and is often extensive, making it difficult to isolate the regions responsible for specific language functions. In addition, some researchers have tried to interpret aphasic deficits as purely subtractive, taking the residual behavior to represent normalcy minus the damaged component (e.g., Caramazza, 1984). This view, which has been called the locality assumption, is theoretically appealing, but untenable (Farah, 1994). In short, a functional deficit, even if consistently related to the same locus of injury, may not directly reflect the localization of the impaired function. Much of the brain’s activity depends on connections between regions, and the deficit may reflect disruption of connectivity patterns as opposed to localization of the

Methods

function at the site of damage per se. In addition, it is often unknowable what a patient’s language faculties were like prior to damage, and recent research suggests that large individual differences in language capabilities and brain organization exist in healthy populations (e.g., Prat & Just, 2011; Prat, Keller, & Just, 2007; Prat, Mason, & Just, 2011). Finally, patients with language disorders tend to struggle to communicate. In doing so, they may employ compensatory strategies, and these strategies may differ between participants. In summary, although studies of patients with brain injury continue to provide important insight into our understanding of the neural basis of linguistic abilities, caution must be exercised when drawing inferences about their relevance for understanding language processes in the intact brain. Virtual Lesion Studies Over the past two decades, transcranial magnetic stimulation (TMS) has been increasingly used to create “virtual lesions” in healthy individuals (Pascual-Leone, BartresFaz, & Keenan, 1999). TMS allows researchers to noninvasively stimulate the brain by applying a strong, rapidly changing magnetic field to the scalp, which is capable of inducing a weak electrical current in the cortex. Applied as a single pulse during a critical information processing point, or as a series of pulses repeated across time, such stimulation can safely and temporarily disrupt the natural function of a cortical target (e.g., Pascual-Leone et al., 1999; Wassermann, 1998; Wassermann et al., 1999). The use of this virtual lesion technique can overcome several of the weaknesses described above with respect to studying patients with brain damage. First, researchers can precisely control the location where the virtual lesion will occur, creating focal disruptions. In fact, with new image-guided TMS techniques, researchers can use MRI images to apply target stimulation to anatomically or functionally defined regions of interest on a subject-by-subject basis. Additionally, by using stimulation parameters modified for each individual’s physiology, researchers can roughly equate the “size” of the virtual lesion induced across participants. Individual differences in linguistic abilities can also be accounted for by measuring the behavior of interest before and after the creation of the virtual lesion, allowing for an accurate assessment of the behavioral impairment that manifests as a function of the lesion. Finally, unlike the neuroimaging techniques discussed herein, which are correlational in nature, TMS designs allow researchers to experimentally manipulate brain function, providing a degree of power for generating

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causal inferences not afforded by other methods. Thus, such virtual lesion techniques have become increasingly popular in the past decade for cognitive neuroscience in general, and for language research in particular (see Devlin & Watkins, 2007, for a review of TMS investigations of language). The virtual lesion paradigm is not without limitations, however. First, TMS can currently only be used to stimulate shallow cortical regions, and thus is not a practical tool for studying the function of deeper structures. In addition, virtual lesion studies are subject to the same critiques of the “locality assumption,” namely that disruptions of important circuits can yield to disruption of function, even if that function is not underpinned by the area being stimulated. Despite these limitations, the utility of TMS investigations of language can be seen by the exponentially increasing number of research articles appearing in peer-reviewed journals today. Biologically Inspired Computational Models and Simulated Lesions One important step in investigating the neural basis of language processes is to specify how the transformations in information (or code) from input through output (or vice versa) are related to specific areas of the brain. Computational models of language outline such transformation, with the aim of reproducing human behavior by simulating the functional and structural properties of the underlying biological elements—in this case, single neurons or brain regions (O’Reilly & Munakata, 2000). These models can also be lesioned —that is, they can be altered in some way (e.g., by increasing noise levels, weakening connections, and so forth) to simulate effects of brain damage (see Saffran, Dell, & Schwartz, 2000, for a discussion of several of such models). As such, they provide a great means to test theories and hypothesis about the biological substrates of language. Connectionist models, the most popular type of biologically inspired computational models, are made of simple, interconnected processing units, each of which behaves like a simplified neuron within a neural network (e.g., O’Reilly & Munakata, 2000). The network can be trained using biologically plausible algorithms (e.g., O’Reilly, 1996) until it “learns” to perform the desired behavior. Then, researchers can examine the patterns of connectivity within the network to study how input-output relationships are represented (McClelland & Elman, 1986), or to compare the performance of the model to that of alternative models with differing assumptions (Seidenberg,

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Plaut, Petersen, McClelland, & McRae, 1994). Connectionist models have been extremely successful at modeling complex phenomena that defy simple rules, such as the phonetic rendition of written English words (McClelland & Elman, 1986), the development of the lexicon (Plunkett & Marchman, 1991), the serial unfolding of words in sentences (Elman, 1990), and the processes behind single-word reading (Coltheart, Rastle, Perry, Langdon, & Ziegler, 2001; Plaut, McClelland, Seidenberg, & Patterson, 1996). They have also been instrumental in understanding the sources of language-related disorders such as dyslexia (Plaut, 2002). Connectionist models have even challenged some of the core assumptions of neuropsychological research (Patterson & Plaut, 2009). For example, several models have shown that behavioral double-dissociations do not imply the existence of separate, modular systems within the brain, but only of spatially separated representations (e.g., Farah & McClelland, 1991). Such connectionist models are typically domainspecific, and account only for the phenomena they are designed to simulate. Recently, however, a novel approach has successfully modeled language processes within a more general set of cognitive functions and brain architecture. This approach consists of building language models on biologically inspired cognitive architectures, which attempt to create a map between the architecture of the brain and its general computational functions (Anderson et al., 2004; Just & Varma, 2007). Such models are more flexible in the breadth of behaviors they can explain, and can account for complex linguistic behaviors in terms general computational functions provided by the underlying neural hardware (Lewis & Vasishth, 2005; Stocco & Crescentini, 2005; Taatgen & Anderson, 2002). In summary, computational models represent a useful methodology for investigating the relationship between brain organization and language, and can be used to successfully account for a variety of data and to evaluate alternative hypotheses. Imaging Brain Connectivity Recent research has highlighted the importance of the integrity of white-matter tracts for linguistic function (e.g., Keller & Just, 2009; Klingberg et al., 2000; Niogi & McCandliss, 2006). Advances in diffusion MRI have greatly enhanced researchers’ abilities to investigate whitematter microstructure in healthy and impaired patients. Diffusion weighted imaging (a type of MRI) records the movement of water molecules in the brain, utilizing the known diffusion characteristics of water to generate

inferences about boundaries that restrict water movement. For instance, in a region of the brain such as the ventricles, water can move relatively freely, resulting in isotropic diffusion (sphere-shaped or equal in all directions). In contrast, water molecules that are bound by myelinated axons can diffuse much more easily along the axon than across cell walls, resulting in anisotropic diffusion (ellipsoidshaped or greater in one direction). Diffusion tensor imaging (DTI) research has generally shown that individuals with more organized white-matter structures (resulting in more anisotropic diffusion) in left temporal and parietal areas tend to have better linguistic function (Klingberg et al., 2000; Niogi & McCandliss, 2006) and that increased training in impaired populations can lead both to improvements in white matter organization and to improved language abilities (Keller & Just, 2009). These studies primarily use fractional anisotropy (FA), an index of isotropic versus anisotropic diffusion on a voxel-by-voxel basis to estimate white-matter integrity. Higher FA values reflect more linear diffusion and are assumed to reflect greater myelination, axonal diameter, and/or fiber density in the voxel being investigated. In addition, newer methods for collecting diffusion data and algorithms for combining the data to generate tractographic maps of fiber bundles are advancing at a rapid rate (e.g., Wassermann, Descoteaux, & Deriche, 2008), providing a promising new direction for investigating the structural connectivity underpinning language processes in the brain. Imaging Brain Function Some of the most dramatic developments in methods for studying brain-behavior relationships involve techniques for characterizing brain function in healthy, thinking and/or behaving individuals. While no existing technique can perfectly capture both the spatial and temporal resolution necessary to pinpoint the neural substrates of component linguistic processes, rapidly advancing technologies and converging methods are providing important information for generating inferences about brain-behavior relationships. fMRI A major innovation in cognitive neuroscience has been the extension of MRI methods to functional magnetic resonance imaging (fMRI) in the early 1990s (for review see Friston, 1997). Over the past 20 years, fMRI has largely replaced other metabolic measures of brain function (e.g., SPECT and PET) as the preferred method for studying healthy individuals due to improved patient safety and

Methods

better spatial and temporal resolutions. fMRI capitalizes on the coupling between the changes in activity level of a region of brain and metabolic changes in its blood supply. Specifically, the dynamic changes in the ratio of oxygenated to deoxygenated hemoglobin in the blood is measured as a cognitive task is performed, providing an index of changes in neuronal activity level called the blood-oxygen-level dependence (BOLD). The response of this BOLD signal is somewhat sluggish, peaking between 6 and 10 seconds after the onset of the increased neural activation in a region; however, modern neuroimaging analysis software takes the shape and timing of this response into account, allowing researchers to model neural events with an average of a 2-second time sampling rate (which can be faster or slower depending on data acquisition parameters). There are some important limitations in the application of fMRI for studying language processes. For instance, the method is very sensitive to artifacts due to motion and changes in breathing rate, both of which are especially problematic during speech production paradigms. Recent advances using sparse sampling, a technique that capitalizes on the delayed hemodynamic response, recording images immediately after the response has been emitted, have helped to meet this limitation (see Gracco, Pascale, Pike, & Bruce, 2005, for a detailed description). In addition, the scanning environment is quite noisy. Noise reduction headphones have been designed to deliver auditory stimuli to participants; nevertheless, some level of scanner noise reaches the subject even through the headphones. This acoustic stimulation is present during all scans and thus gets subtracted out of “baseline” conditions, reducing the ability to detect activation related to primary auditory processes. Finally, it takes approximately 2 seconds to record one complete brain volume of fMRI data, though this number can be pushed below 1 second by reducing coverage (e.g., only recording data from a few brain slices and/or increasing the gaps between slices). Most linguistic processes occur at a much faster temporal resolution, and therefore cannot easily be studied in isolation using fMRI. Keeping in mind that a single word is comprehended in about 1/3 of a second (Levelt, 1999), it is difficult to determine hemodynamic changes in response to a particular word embedded within a sentence or discourse context, let alone to isolate the subcomponent processes (e.g., phonological processing) that precede comprehension of the word. Subcomponent linguistic processes are often measured in fMRI through manipulations of stimuli (e.g., reading syntactically complex vs. simple sentences), but these manipulations often assume that the

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various temporally overlapping hemodynamic responses to component processes sum linearly. Despite these limitations, an enormous body of fMRI research has contributed essential information about language processes in the intact brain (see Price, 2010 for a review of 100 fMRI studies of language published in 2009 alone). Electrophysiology While fMRI measures the metabolic byproducts of brain function, the complimentary technique of electrophysiology records the electrical signals emitted by neuronal firing directly. Although the changes in electrical signal resulting from a single neuron are minute, the coordinated firings of large populations of neurons result in signals that are detectable at the scalp, and thus may be recorded noninvasively. Typical electrophysiological experiments sample the signal at the rate of 256–512 Hz, or about once every 2–4 milliseconds (although faster sampling rates upwards of 5kHz are possible), and collect data from between 16 and 256 scalp locations. Thus, unlike the metabolic imaging methods, direct measurements are able to detect fluctuations in neural events at the temporal resolution of component linguistic processes. Electrophysiological research on language takes two forms. The first form, electroencephalography (EEG), involves the collection of a continuous stream of electrical changes while participants are at rest, or are engaged in some sort of ongoing task. Such EEG recordings detect the synchronous firings of populations of neurons and result in oscillations at various frequencies. One method of EEG research involves measuring the event-related desynchronization (ERD) or synchronization (ERS), primarily in the alpha frequency band, as an index of how mentally active or engaged an individual is (e.g., Pfurtscheller, 1977). In general, greater synchronization of EEG between disparate electrode sites is taken as an indicator that a larger underlying neural substrate is engaged in the same process (less differentiation). Thus, greater ERD is presumed to reflect more specialized or efficient neural processes. In fact, such indices of efficiency have been shown to relate to intelligence in general (Neubauer, Freudenthaler, & Pfurtscheller, 1995) and more specifically to language abilities (Maxwell, Fenwick, Fenton, & Dollimore, 1974). The majority of electrophysiological research on language, however, has employed a second technique that looks at the electrophysiological response to a specific type of stimuli. Such event-related potentials (ERPs) are calculated by averaging EEG signals for the precise time periods over which many of the same stimuli of interest have been delivered. ERP investigations of language processes have

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identified several distinguishable “components” of wave forms that correspond to linguistic processes. Among the most widely studied of these components are the N400 and the P600. The nomenclature of waveforms generally refers to the direction of the change in polarity of the waveform (N = negative and P = positive) and the time at which this change occurs. Thus, the N400 is a negative deflection in the ERP waveform that peaks about 400 milliseconds after the onset of a word, and the P600 is a positive deflection in the waveform that peaks about 600 milliseconds after the onset of a word. Thirty years of research supports the idea that the size of an N400 component is an indication of the difficulty of semantic processing or integration of a word into context (see Kutas & Federmeier, 2011, for a review). Thus, a larger N400 would be observed in the word “pepper” in the semantically strange sentence “Bring your socks and pepper” than in the much more predictable sentence “Please pass the salt and pepper” (Kutas & Hillyard, 1980a,b). The P600, on the other hand, seems to be sensitive to grammatical/structural violations of a sentence (e.g., Hagoort, Brown, & Groothusen, 1993). For instance, a larger P600 would be observed corresponding to the word “pass” when it occurs in the grammatically incorrect sentence “She pass the ball” than in the grammatically correct sentence “We pass the ball.” Differences in polarity, timing, and scalp distribution of these components provide multidimensional evidence about how semantic and syntactic information are processed in the brain; however, the correlation between changes in the N400 and P600 with changes in semantic and syntactic processing demands does not imply that these effects are direct manifestations of linguistic processing (Osterhout, Kim, & Kuperberg, 2007; Osterhout, McLaughlin, Kim, Greenwald, & Inoue, 2004). In fact, these components are also produced by nonlinguistic stimuli; for example, the N400 can be elicited by incongruous visual scenes (e.g., Ganis & Kutas, 2003) and the P600 by phrase violations in music (e.g., Besson, Faita, Peretz, Bonnel, & Requin, 1998; Patel, Gibson, Ratner, & Holcomb, 1998). Nonetheless, the sensitivity of these components to unique facets of language provides insight into the nature and timing of linguistic computations. ERPs excel at detecting linguistic computations as they unfold in real time in the brain; however, localization of the neural underpinnings of such computations is problematic (see Kutas, Federmeier, & Sereno, 1999, for a more extensive treatment of this topic). Several advanced mathematical “source localization” models attempt to use the distribution of ERPs on the scalp to infer the underlying neural source(s); however, all are plagued by the “inverse problem.” Simply put, the physics of electrical

signals in the brain are such that if one knows the source or sources of neural activation in the brain, then a unique scalp distribution can be predicted. But there is no unique solution for the inverse process, and a given scalp distribution can be generated by a large number of possible source configurations. Recent advances in models for localizing sources use certain assumptions about the brain to constrain the number of solutions to the inverse problem (e.g., LORETA; Pascual-Marqui, Michel, & Lehmann, 1994) and are capable of generating unique neural source models, but these models have much lower spatial resolution than that observed in fMRI investigations. Thus, while fMRI research provides data with excellent spatial resolution but at a temporal sampling frequency that is suboptimal for language processes, electrophysiological research has an excellent temporal sampling frequency for studying language but the sources of signal change detected at the scalp cannot be precisely located in the brain. Magnetoencephalography Neuronal firing in the brain also generates magnetic changes that can be recorded from the surface of the skull using SQUIDs (superconducting quantum interference devices). The analysis of these local changes in the surrounding magnetic field resulting from neuronal processing is called magnetoencephalography (MEG). The signals recorded using MEG are similar in form and temporal resolution to those recorded using EEG. However, MEG has some important advantages including a typically higher spatial sampling rate, usually 300+ SQUIDs (see Cohen & Cuffin, 1983 for more details). With respect to generating inferences about the source of the neural activity, MEG is more restrictive than EEG, due to the fact that the strength of the magnetic field drops off more sharply with increasing distance than does that of the electrical field. This does not eliminate the inverse problem, but does dramatically constrain the number of possible neural sources to be considered, since the sources can be assumed to be local. As a result, although the technology is more expensive and less widely available than EEG, it is rapidly gaining popularity as a tool for localizing brain activity related to language function (e.g., Levelt, Praamstra, Meyer, Helenius, & Salmelin, 1998; Pulverm¨uller, Shtyrov, & Ilmoniemi, 2005; Shtyrov & Pulverm¨uller, 2007). In summary, it can easily be seen that each method for studying brain function underlying language faculties is significantly limited in some aspect. Some of these limitations can be overcome by using more than one method simultaneously, and almost all can be subverted by conducting converging experiments using complementary

The Neural Bases of Linguistic Representation and Processes

methods. For example, fMRI activations can be used to constrain the inverse solution for EEG or MEG effects (e.g., Dale & Halgren, 2001), and it is currently possible to conduct simultaneous fMRI and EEG, fMRI and TMS, or MEG and EEG experiments (e.g., Liebenthal et al., 2003). Similarly, TMS and patient data can be used to constrain interpretations of fMRI, ERP, and MEG results. Until a new technique becomes available, such combined and converging experiments offer the best possibilities for understanding the neural basis of language processes.

THE NEURAL BASES OF LINGUISTIC REPRESENTATION AND PROCESSES As illustrated by the dinner conversation example, comprehending language involves organizing perceptual information into phonological processing units, accessing word-level information (including information about word meaning, morphology, and syntactic category), parsing word streams into meaningful units (using semantic and syntactic information to build propositional representations), and integrating incoming propositional units into a larger discourse level context (often using real-world knowledge and inferential process to elaborate on inputs and link them to active representations of the conversation or text). Production can be conceptualized, to a certain extent, as comprehension in reverse. A speaker’s task is to formulate a message that specifies the thematic content of the larger message he or she would like to communicate, selecting the words and syntactic forms suitable for expressing this idea content, ordering the words in a manner dictated by the syntax, and activating the motor program for articulating the stream of words. Although most theories of the neural basis of language processing account for these various component processes, the extent to which the processes are functionally and/or structurally isolated in the brain is controversial. These debates are not restricted to language processes (see Fodor, 1985, for the original debates on “modularity” of the mind), but the tension is especially prevalent in language research, where the idea that mental processes could be localized in the brain first gained traction (Broca, 1865). To a large extent, polar views—componential or modular versus holistic or integrated—continue to characterize the debate language researchers. In view of the complexity of language processes, it is not surprising that these issues remain controversial (to sample contemporary arguments for and against the modular approach, see Dick et al., 2001; Fadiga, Craighero, & D’Ausilio,

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2009; Fedorenko, Behr, & Kanwisher, 2011). This review will touch upon issues of functional or structural modularity, or (more often) interactionism, in light of the current research. One difficulty in understanding the biological basis of language is that researchers tend to look for explanations of behaviors at a level that is higher than what is consistent with the known computations being executed at the neural level. For instance, over the past century, researchers have attempted to uncover the neural basis of syntactic processing, but it seems plausible that the neural circuitry we employ for syntactic processes is composed of a system or systems that have evolved for more general computational specialties. In other words, syntactic processing may recruit regions with evolutionary specialization for sequence processing (e.g., Osterhout et al., 2007), for forming associations or “binding” information (e.g., Hagoort, 2005), and/or for modulating, updating, and maintaining information in working memory (e.g., Novick, Trueswell, & Thompson-Schill, 2005). A summary of such research, suggesting a great degree of overlap between linguistic computations and more general executive and memory, is discussed herein. Any theory of the neural basis of language must account for two qualitatively different types of phenomena: representation and processing. Questions about representation address how and where our knowledge about words and word combinations is stored in the brain. On the other hand, questions of processing must account for the computations that occur when we select and activate such representations, and use them to construct models of meaning. The following sections will summarize current research on the biological bases of such construction processes, and on the representation of meaning. Semantic Representation in the Cortex In 1972, psychologist Endel Tulving introduced the term semantic memory to describe the plethora of information representing one’s knowledge of the world. The majority of this knowledge is verbalizeable, and for that portion language serves as our code for accessing and transmitting information about our thoughts and knowledge between one another. Investigations of representation must address both how we store our knowledge about the world in a linguistic format, and how we represent dynamically constructed “models” of the meanings of multiple word utterances. The current section will describe how the brain represents our vast, ever evolving knowledge about words and word meanings. In addition, this section will discuss

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how the meanings of multiword utterances are represented in the two hemispheres. The Mental Lexicon Mental lexicon is a term generally used to refer to our mental store of information about words. Such representations include information about a word’s meaning or meanings, its syntactic roles, and the codes for communicating the word (information about spelling and phonological patterns). A typical adult speaker has knowledge of between 30,000 and 50,000 words (depending on what you count as a unique word). Given this amount of knowledge, the fact that word comprehension and production occurs in such a fast and often effortless manner suggests that the mental lexicon is organized in a highly efficient manner. Researchers generally agree that the mental lexicon is organized as a complex network that contains information about abstract knowledge and concepts as well as information about the word forms or codes necessary for transmitting the ideas or concepts. It is worth noting that this network is not language specific, but rather that lexical information (e.g., how a word is pronounced, signed, or spelled) becomes integrated into our more general semantic knowledge representation (e.g., how an object is used or what color it is). In fact, neuroimaging research shows highly overlapping patterns of activation when individuals are asked to make semantic judgments based on words or pictures of objects (e.g., Vandenberghe, Price, Wise, Josephs, & Frackowiak, 1996). In addition, patients with semantic dementia, a progressive erosion of semantic memory with relative sparing of other cognitive functions (Snowden, Goulding, & Neary, 1989), lose not only the ability to remember the names of things but eventually lose the ability to answer questions about real or depicted objects (e.g., color, size, place of origin). In other words, the semantic impairment affects nonverbal as well as verbal concepts. Converging evidence from neuroimaging research (e.g., Just, Cherkassky, Aryal, & Mitchell, 2010; Shinkareva, Malave, Mason, Mitchell, & Just, 2011; Vandenberghe et al., 1996) and studies of patients with semantic dementia (e.g., Hodges, Patterson, Oxbury, & Funnell, 1992) suggests that this semantic knowledge is represented in a distributed fashion, primarily in bilateral temporal regions. Although bilaterally distributed, these representations tend to be left lateralized, and some researchers argue that different representation characteristics in the two hemispheres give rise to computational differences that have important implications for language. Most notably, Jung-Beeman and

colleagues (e.g., Beeman, Bowden, & Gernbacher, 2000; Jung-Beeman, 2005) propose that the right hemisphere has a coarse representation of semantic information, which gives it advantages over the left hemisphere when activation of diffuse semantic fields is advantageous (e.g., during metaphor comprehension). It is not surprising that our knowledge is represented in the temporal lobes, which encapsulate the brain structures that are necessary for encoding new memories (e.g., the hippocampi and entorhinal cortices). But how might such representations be organized? Converging evidence from behavioral research, patient studies, and neuroimaging investigations suggest that words might be organized in a multidimensional fashion, clustered together by categories, semantic features, or grammatical roles (e.g., Just et al., 2010; Saffran & Schwartz, 1994). Descriptions of the deficits of patients with semantic dementia have provided important insights into the organization of semantic information in the brain. For instance, initial paraphasias (a condition in which an individual substitutes an incorrect word for a correct one) tend to be within the same category, and involve items that share many features; for instance a patient might call a fork a spoon, a horse a cow, or a watch a clock (Schwartz, Marin, & Saffran, 1979). In the following patient (who was formerly an artist)’s drawings, one can see the general preservation of categorical information although the identifying details are largely missing (see Figure 22.1). Additional information about the organization of semantic representation comes from investigations of patients with various types of “category specific” deficits. For instance, some patients exhibit a disproportionate deficit in naming or describing living entities (see Saffran & Schwartz, 1994, for a review), while others show the reverse pattern, disproportionate deficits for manmade artifacts over naturally occurring ones (Breedin, Saffran, & Coslett, 1994; Warrington & McCarthy, 1983; Warrington & McCarthy, 1987). One current debate is whether or not our representations of word meanings are truly abstract or amodal, or whether, instead, they are intertwined with the sensory and motor programs that activate when we encounter the concepts in the real world. The field of embodied cognition takes the later approach and has collected mounting evidence and support over the past two decades (e.g., Barsalou, 2008; Farah & McClelland, 1991; Gallese & Lakoff, 2005; Plaut, 2002; Vigliocco, Vinson, Lewis, & Garrett, 2004). Proponents of the embodied cognition perspective suggest that categorical deficits in patients can be explained by perception and action-based systems. For instance tools, one of the frequently used categories in investigations of

The Neural Bases of Linguistic Representation and Processes

(a)

(b)

(d)

(c)

(e)

Figure 22.1 Drawings by a patient with degenerative dementia featuring semantic loss. The patient, a former artist, attempted to draw each item as it was named by the examiner. (a) Moose, (b) frog, (c) guitar, (d) telephone, (e) cat. From “Deterioration of Language in Progressive Aphasia: A Case Study,” by M. F. Schwartz and J. B. Chawluk, 1990, Modular Deficits in Alzheimer-Type Dementia, Cambridge, MA: MIT Press, ed. M. F. Schwartz, p. 264. Copyright 1990 by MIT Press. Reprinted with permission.

man-made artifacts, tend to be things that have action representations involving the hands or arms. In fact, a seminal neuroimaging study conducted by Martin, Wiggs, Ungerleider, and Haxby (1996) showed that naming of tools produced activation in premotor areas, whereas naming of animals did not. Interestingly, the region of the premotor area that was activated with tool naming was highly overlapping with a region that activated in another study where participants imagined grasping objects (Martin, Haxby, Lalonde, Wiggs, & Ungerleider, 1995). Thus, representational categories may arise due to differences in the way we perceive and interact with concepts (e.g., things you eat, things you pet, things you manipulate). Recent advances in neuroimaging methods have used machine learning algorithms (see Mitchell et al., 2003 for

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a description of methods) to further investigate the factors related to the organization of semantic representation in the brain (Chang, Mitchell, & Just, 2011; Just et al., 2010; Mitchell et al., 2008; Shinkareva et al., 2008; Shinkareva et al., 2011). Each of these studies has highlighted the distributed nature of semantic representation in the brain (including the temporal lobes bilaterally as well as sensory and motor regions) and has contributed to our understanding of the relevant factors that link information about concepts to patterns of activation in the brain. For instance, factor analysis of 60 concrete nouns resulted in four factors (manipulability, edibility, shelter, and word length) that allowed for the prediction of patterns of activation observed when subjects thought about each word (Just et al., 2010). Other studies have used co-occurrence matrices (highlighting the importance of shared lexical contexts in semantic organization) to predict patterns of activation for nouns (Mitchell et al., 2008). Although machine learning investigations of semantic representation are relatively new and have been largely limited to concrete, imageable nouns, the combination of advancing technologies, sophisticated models of semantic representation, and converging investigations of patients and healthy individuals provides incredible promise for the future of a more fine-grained understanding the organization of semantic representations in the brain.

Sentence- and Discourse-Level Representation Thorough explanations of language representation must account not only for how words are represented in isolation, but also for how they are represented in contexts. The majority of research on multiword utterances has focused on comprehension processes rather than representation, which will be discussed in detail in subsequent sections. This section will focus on the representation of texts during reading, as the most extensive research on representation of multiword utterances has originated in this field. Psycholinguists generally agree that readers construct at least two interrelated representations during comprehension of a text: a propositional representation (also called a textbase) and a discourse (or situation) model (Gernsbacher, 1990; Kintsch, 1974, 1988). The propositional representation is composed of a network of the explicit ideas or “propositions” derived from the text and the relations among them (Kintsch, 1974). The propositional representation serves as a foundation for the discourse model (e.g., McKoon & Ratcliff, 1980, 1998). The discourse model is an elaborated representation of what a text is about.

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In the discourse model, explicit text information is integrated with relevant world knowledge to reflect the important features of the real or imaginary situation depicted in the text. Investigations of discourse representation have primarily involved priming studies that explore the organization of a reader’s memory for texts at the various levels. One reason for using priming studies is that online neuroimaging studies cannot easily separate the representation of discourse models from the construction processes involved. Nonetheless, discourse representation researchers assume that the representation of discourse in the brain will be anatomically tied to the construction processes involved, and existing evidence of discourse representation in the two hemispheres is consistent with this assumption. To explore how discourse models are represented in the brain, Long and colleagues developed a lateralized version of the standard item-priming-in-recognition paradigm employed by psycholinguists to study such models (Long & Baynes, 2002; Long, Baynes, & Prat, 2005; Prat, Long, & Baynes, 2007). In these studies, participants received a series of study-test trials composed of four short “to-be-remembered” passages and lateralized, single-word recognition items. The logic behind the itempriming-in-recognition paradigm is that memory for an item will be facilitated to the extent that it is preceded by an item to which it is linked in memory. Words in the various recognition lists were related to each other either thematically (at the level of the discourse model) or propositionally (at the level of the propositional representation). They found that in general, the left hemisphere was sensitive to fine-grained propositional relations (e.g., showing linear priming decreases between ideas in the same proposition, ideas in different propositions in the same sentences, and ideas in different sentences; Long, Prat, & Baynes, 2005), whereas both hemispheres were equally sensitive to discourse-model relations (however, see Prat, Long, & Baynes, 2007, for important individual differences in the lateralization of representations). On the basis of these findings alone, one could conclude that right hemisphere discourse representation is a less-detailed reflection of the left hemisphere’s representation. One problem with this conclusion is that the two hemispheres interact during the construction of discourse models. Thus it is possible that the right hemisphere contributes importantly to the global representation of the discourse model, and that the left hemisphere contributes to propositional analysis, but reflects the right hemisphere’s representation at the discourse level. To explore these various accounts, Long and Baynes (2002)

presented the same stimuli to a small group of callosotomy patients, who have the major connections between hemispheres surgically severed to control epilepsy. By allowing the patients to respond only with their right hands, they found that callosotomy patients showed both propositional and discourse-level priming in the left hemisphere, even when it could be reasonably well assumed that the right hemisphere could not contribute to construction of such models (due to severed communication pathways). Thus, existing literature on representation of multiword utterances provides support that at least two levels of representation exist (explicit propositional representations and elaborated discourse models), and that differences in the processes involved in constructing such representations give rise to differences in the distribution of the representations across the cerebral cortices. In summary, explicit, syntactically derived propositional representations seem to be distributed primarily in the left hemisphere, whereas elaborated discourse models seem to be represented across the two hemispheres (although the right hemisphere does not appear to be necessary for constructing such a representation). The Neural Bases of Language Processes To fully account for the biological basis of language, one must not only understand how meaning is represented in the brain, but must also understand the neural processes involved in constructing and accessing representations of meaning. The majority of research on the cognitive neuroscience of language has focused on such questions of processing. As discussed previously, early attempts to understand processing phenomena were somewhat unsuccessful, in part because attempts to link brain and behavior were made without careful consideration of the computational characteristics of underlying neural substrates. The current section will summarize research on some of the most widely studied linguistic processes: lexical access and retrieval, syntactic parsing, text integration, and discourse elaboration. This list is by no means exhaustive, but it illustrates the field’s attempt to characterize key linguistic processes from the word level through discourse comprehension. Lexical Access and Retrieval In order to comprehend and produce language, one must be able to make selective contact with (or activate) the relevant portion or portions of the semantic network. Although it is generally agreed that both comprehension

The Neural Bases of Linguistic Representation and Processes

and production rely on access to a shared semantic representation, the processes involved in accessing word meaning during comprehension and those involved in retrieving a word to use during production are computationally and neurally separable. To access a word during comprehension, a match must be achieved between perceptual inputs and stored representations of word forms. Neuropsychological research suggests that auditory and visual pathways to the mental lexicon are separable, as some patients acquire pure worddeafness (Howard & Franklin, 1988; Takahashi et al., 1992) that selectively impairs their ability to access wordmeaning using audition. In other words, although these individuals can comprehend written language and can hear (and repeat what they hear) perfectly well, they cannot understand spoken language. Conversely, for cases of pure alexia, patients can comprehend spoken language but can no longer read words, despite having accurate vision and being able to name individual letters (e.g., Coltheart, 1998). Thus the neural mechanisms underpinning auditory and visual access to the semantic network must be separable. A recent comparative review of spoken language comprehension by Rauschecker and Scott (2009) proposed that acoustic information enters primary auditory cortex (BA 41/42) and is subsequently processed in parallel in two streams: an antero-ventral stream that hierarchically processes acoustic information and a postero-dorsal stream that is involved in generating internal models including the motor programs used for producing the stream being processed. Such a model helps with top-down predictions and error-detection of bottom-up comprehension process. Support for the role of the antero-ventral stream, including primary auditory cortex, anterior superior temporal regions, and the inferior frontal gyrus, in hierarchical acoustic processing is robust, but the idea of a parallel “embodied” postero-dorsal modeling stream is somewhat novel. This idea is consistent, however, with the popular “motor theory” of speech comprehension (Liberman, Cooper, Shankweiler, & Studdert-Kennedy, 1967), and provides a promising framework for mapping what we know about primate auditory processing streams on to biologically plausible models of human speech comprehension. With respect to word reading, most researchers generally agree that there are at least two routes by which written words can interact with the mental lexicon: a direct route in which a letter string is recognized as a word and directly accesses that word’s entry in the mental lexicon, and an indirect route, in which letter strings are converted into phonological representations that then interact with

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the mental lexicon. Neuroimaging research has provided evidence that these two access routes are also partially separable in the brain. In particular, a great deal of attention has been placed on the role of the so-called visual wordform area, in the left fusiform gyrus. Many researchers have argued that this strip of cortex in the left inferior temporal lobe is dedicated to recognizing visual word forms (Cohen & Dehaene, 2004; Dehaene & Cohen, 2011; McCandliss, Cohen, & Dehaene, 2003; Vinckier et al., 2007) although others have refuted this claim (Price & Devlin, 2003; Price, Noppeney, Phillips, & Devlin, 2003). One fMRI experiment designed specifically to investigate the neural bases of dual-route word reading compared patterns of activation to frequent words, infrequent words, and pronounceable nonwords (Fiebach, Friederici, M¨uller, & von Cramon, 2002). They found that both types of words resulted in reliably greater activation in bilateral fusiform regions (including the so-called word form area) and in the posterior middle temporal region than did nonwords. On the other hand, low-frequency words and nonwords both elicited greater activation than frequent words in the superior portion of Broca’s area (BA 44), consistent with the terminating region of the antero-ventral stream of auditory speech processing, as well as in the insula, and in subcortical regions. With respect to speech production, researchers generally agree that retrieval of a word for language production involves at least two stages: retrieval of a lemma (or word form that includes semantic and syntactic information), and retrieval of the phonological and orthographic information necessary for producing a specific word form (e.g., Kempen & Huijbers, 1983). It is somewhat controversial whether these processes interact (Dell, 1986; Harley, 2004) or are modular (Levelt, Roelofs, & Meyer, 1999). In everyday speech, we are rarely conscious of the effort involved in selecting the appropriate word for communicating our thoughts, unless that process goes awry. Such mishaps can occur in the form of uttering the wrong word or in a failed retrieval of any word at all. The latter instance, when the typically automatic search process becomes effortful, is often referred to as the tip-of-thetongue (TOT) state. The two-stage theory explains the TOT instances commonly experienced when an individual has a good idea about what he or she wants to say, but cannot retrieve the appropriate word. In this circumstance, it is likely that the lemma has been retrieved but phonological retrieval has failed, often only partially, which leaves the speaker feeling as though they know the first sound or letter of the word they are searching for (see Brown, 1991, for a review).

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To investigate the neural underpinnings of word retrieval, researchers attempt to control for phonological production processes by comparing conditions in which participants must activate, retrieve, and produce a word (e.g., picture naming or tasks) to conditions in which participants must read aloud a word presented (which also involves activation and production). In a recent review of 100 neuroimaging investigations of speech comprehension and production, Price (2010) concluded that lexical retrieval processes resulted in activation of the left inferior frontal gyrus (including BA 44/45 or Broca’s Area) and the inferior frontal sulcus. On the one hand, this is not surprising given over a century’s worth of data showing the importance of Broca’s area for speech production; however, this refines our understanding of the processes that are subserved by this region, noting that it is responsible not merely for articulation, but for retrieval of the word forms necessary for articulation. In another review of the neuroanatomy of lexical retrieval processes, Indefrey and Levelt (2000) looked at combined evidence from neuroimaging, neuropsychological, and electrophysiological investigations. They concluded that the left middle temporal gyrus was critical for lemma selection in particular. They also concluded that phonological encoding and/or assembly processes were most likely occurring in Broca’s area, an idea that was reiterated in a contemporary theory on the role of Broca’s area and binding processes (Hagoort, 2005). Thus, one possible way to integrate the research is to assume that lexical retrieval involves the activation of relevant portions of the semantic network at the lemma level (likely represented in the temporal lobes) followed by selection and translation of this information into a phonological code necessary for producing the desired word. In summary, converging evidence suggests that auditory and visual access to the mental lexicon occur in separate processing streams, with the former beginning in the inferior temporal regions and the latter beginning in the fusiform gyrus. Phonological integration, grapheme-phoneme conversions, and lexical selection processes have all been related to Broca’s area (BA 44/45), which will be discussed in more detail in the next section. Ultimately each of these processes, if successful, makes contact with the semantic network, typically resulting in patterns of activation in the middle and posterior temporal regions. Syntax The history of research on the neural locus of syntactic processes provides a nice illustration of how our attempts

to match linguistic processes with brain processes have evolved with increased understanding of the computational characteristics of neural circuits. Syntax itself is a rather general term used to refer to a host of processes involved in constructing meaning out of multiword utterances. These processes include structure building, or binding words into meaningful phrases (or propositions), agreement checking, and thematic role assignment (e.g., deciding who is the actor and who is the recipient in the sentence “Andy liked Jasmine”; Kaan & Swaab, 2002, Box 1). In 1887, Kussmaul first coined the term “agrammatism” to describe patients who could not form grammatical utterances (Kussmaul, 1887). Subsequent attention was turned to this disorder in the 1970s, when neuropsychologists noted that patients with damage to the inferior frontal gyrus (Broca’s area) exhibited deficits with syntactic processing, including not only speech production that lacked inflections and function words (though these patients tend to have very minimal, labored, “telegraphic” speech production in general), but also receptive deficits in understanding syntactically complex sentences such as “Jasmine was liked by Andy” (Caramazza & Zurif, 1976). In contrast, another group of patients widely studied with damage to the posterior superior region of the left temporal lobe (Wernicke’s area) produced fluent and grammatical speech, although their utterances lacked semantic coherence. This double dissociation was taken as classic evidence that semantic and syntactic processes were separable in the brain, and that the locus of syntactic processes was in Broca’s area (BA 44/45) in the inferior frontal gyrus. A brief look today through most introductory psychology books will still show “syntax” and “speech production” labeled in this area, but new neuropsychological and neuroimaging investigations suggest that the story is not that simple. For instance, recent neuropsychological investigations suggest that damage to Broca’s area is neither necessary for, nor sufficient on its own to result in syntactic processing deficits (e.g., Dronkers, Wilkins, Van Valin Jr., Redfern, & Jaeger, 1994). In addition, more careful explorations of syntactic deficits in aphasic patients suggested that they were particularly impaired at judging complex, noncanonical sentence structures, while not all syntactic processes were disrupted (e.g., Linebarger, Schwartz, & Saffran, 1983). Finally, some Broca’s aphasics also exhibit semantic impairments (e.g., Bushell, 1996), suggesting that Broca’s area is not only involved (consistent with research discussed in the previous section about its role in lexical retrieval and phonological binding processes). In parallel, neuroimaging research has suggested a more distributed network involved in syntactic processes.

The Neural Bases of Linguistic Representation and Processes

Although it is difficult, if not impossible, to manipulate syntax without simultaneously manipulating semantic and integration demands, neuroimaging researchers have investigated syntactic processes using the following paradigms: (1) comparing syntactically complex sentences (e.g., object relative clauses such as “The dog that the woman loved smiled”) to syntactically simpler sentences (e.g., subject relative clauses such as “The dog that loved the woman smiled.”) (Just, Carpenter, Keller, Eddy, & Thulborn, 1996), (2) comparing sentence comprehension to a list of unrelated words (Xu, Kemeny, Park, Frattali, & Braun, 2005), and (3) by comparing sentences with grammatical violations to sentences without grammatical violations (Ni et al., 2000). Broca’s area typically activates more in syntactically complex, or anomalous sentences than it does in simpler, congruent sentences; however, it does not typically activate for comparisons of simple sentences versus word lists (see Kaan & Swaab, 2002, for a review). In addition, complexity manipulations cause more general increases in activation throughout a bilaterally distributed language network (Just et al., 1996; Prat & Just, 2011) highlighting the increase in general computational demands that co-occur with increasing syntactic complexity. Electrophysiological research has also contributed importantly to our understanding of the neural basis of syntactic processes (see Osterhout et al., 2007 for a review). As described in the methods section of this chapter, the sensitivity of the P600 component to syntactic processes has been widely demonstrated (e.g., Hagoort et al., 1993; Osterhout & Holcomb, 1992, 1993). In attempts to localize the P600 effect in the brain, researchers have conducted electrophysiological experiments on patients with lesions in various areas and have found that damage to the basal ganglia can disrupt the P600 effect (Friederici & Kotz, 2003; Friederici, von Cramon, & Kotz, 1999; Friederici, Kotz, Werheid, Hein, & von Cramon, 2003; Kotz, Frisch, von Cramon, & Friederici, 2003). These findings are interesting in light of new theories on the role of the basal ganglia in linguistic computations, which are discussed in more detail in the next section of this chapter. On the basis of combined evidence across methodologies, several researchers have proposed that Broca’s area is involved in syntactic processes, but is not the seat of syntax, per se. Specifically, it has been suggested that several, more general, higher-level cognitive computations are executed in Broca’s area, and that these processes support syntax, especially when processing demands are complex. One example is the postulation that Broca’s area

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becomes activated in syntactic processes when parsing places a particularly large demand on working memory or storage processes (e.g., Kaan & Swaab, 2002; Stowe et al., 1998), for instance, when long-distance dependences are computed. Another proposal is that Broca’s area is involved in error detection and reanalysis processes (Novick et al., 2005). A third proposal is that Broca’s area is involved in binding linguistic representations, at the phonological, semantic, and syntactic levels (Hagoort, 2005). Many researchers have also emphasized that the region identified as Broca’s area encompasses quite a large cortical area, and that this area can easily be divided into several functionally separable subcomponents (e.g., Hagoort, 2005; Newman, Just, Keller, Roth, & Carpenter, 2003). Thus, it is plausible that slightly separate regions in the left frontolateral cortex are responsible for maintenance, binding, and control. In summary, these findings and theoretical approaches suggest that localizing “syntax” in the brain is not possible. Instead, researchers should attempt to localize the component computations involved in syntax, such as variable binding, concept maintenance, and sequential rule learning. While it is likely that the first two components are largely executed in the frontal structures, the latter may rely upon an older neural system, the basal ganglia. Rules and Control in the Basal Ganglia The basal ganglia are a set of interconnected gray matter nuclei located in the middle of the brain that form a complex circuit that controls the thalamic inputs to the frontal lobe (Albin, Young, & Penney, 1989; DeLong, 1990). One subset of these nuclei, the striatum (composed of the caudate nucleus and the putamen) constitutes the input station of the circuit. The striatum receives organized projections from the entire cortex (Alexander, DeLong, & Strick, 1986), and projects to and modulates the activity of lower-level nuclei of the basal ganglia, controlling the output of thalamic neurons to prefrontal cortex. Thus, the striatum is in an ideal position to gather information from all the cortical areas in the brain, and use this information to modulate inputs to the prefrontal cortex. In turn, the prefrontal cortex is the part of the brain that is primarily responsible for higher-level behavior (Miller, 2000), including working memory (Cohen et al., 1997), planning (Shallice & Burgess, 1991), rule-based behavior (Strange, Henson, Friston, & Dolan, 2001), and, of course, language (e.g., Just et al., 1996). Recently, the role of the basal ganglia in language processes has received increasing attention.

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Neuropsychological studies have shown that language impairments such as aphasia, normally associated with cortical lesions, can also originate from basal ganglia damage (Brunner, Kornhuber, Seem¨uller, Suger, & Wallesch, 1982; D’Esposito & Alexander, 1995) or from basal ganglia abnormalities of genetic origin (e.g., Watkins et al., 2002). Also, an increasing number of contemporary neuroimaging studies have discussed the relevancy of the basal ganglia, suggesting that this region is substantial for the control of language (Friederici, 2006; Prat & Just, 2011; Stocco, Yamasaki, Natalenko, & Prat, under review). To explain the different contributions of cortical and subcortical structures to language processes, Ullman and colleagues (Ullman et al., 1997; Ullman, 2001a, 2001b) have proposed a framework in which language is underpinned by two processes with distinct neural instantiations: a semantic representation system and a grammatical (or rule composition) system. This theory largely overlaps with the ideas of language representation and processing discussed herein, only using constructs rooted in a memory framework. Within this dual framework, lexical information is represented as part of the declarative memory system, while syntactic knowledge is stored as part of the procedural memory system. In the human brain, declarative memory is typically associated with cortical structures, and in particular with the temporal lobe (see representation section above). Procedural memory, on the other hand, is typically associated with the striatum and the basal ganglia circuit (Cohen & Squire, 1980; Packard & Knowlton, 2002); thus, Ullman’s framework establishes a link between linguistic rule representation and the basal ganglia. The idea that syntactic rules can be encoded in the basal ganglia is supported by this circuit’s involvement in acquiring procedural knowledge (Knowlton, Mangels, & Squire, 1996) and its importance in sequence learning (Jackson, Jackson, Harrison, Henderson, & Kennard, 1995). Experiments with animals have shown, for instance, that basal ganglia impairments prevent the acquisition of stimulus-response associations (Packard & McGaugh, 1992). In humans, diseases affecting the basal ganglia (e.g., Parkinson’s or Huntington’s disease) impair the acquisition of new perceptual-motor skills such as mirror reading (Cohen & Squire, 1980) and the acquisition of complex stimulus-response associations (Knowlton et al., 1996). Finally, the basal ganglia play an important role in controlling how the avian brain learns and produces songs—which is arguably the type of animal vocalization that most closely resembles human language in terms of structure and complexity (Brainard & Doupe, 2002;

Olveczky, Andalman, & Fee, 2005). Thus, experimental and neuropsychological evidence suggest that the striatal circuitry is responsible for learning and applying complex stimulus-response transformations—a function that is consistent with the application of grammatical rules. In summary, attempts to find a region of the brain that houses syntactic processing have been rather unsuccessful. Nevertheless, it is likely that regions of the inferior frontal gyrus, in combination with the basal ganglia, support the component processes necessary for syntax as well as other, higher-level cognitive processes. Integration Processes To comprehend language, individuals must integrate incoming information on multiple levels. Such integrative processes are essential in the construction of syntactically derived propositional models, and discourse-level representations of texts. Typical investigations of integrative processes involve comparing comprehension of unrelated word strings to related word strings, comparing comprehension of word strings to sentences, and comparing comprehension of sets of unrelated sentences to passages (see Stowe, Haverkort, & Zwarts, 2005, for a review of text integration research). In a neuroimaging study aimed at investigating the emergent properties of increasingly complex text comprehension, Xu et al. (2005) compared patterns of activation during reading of unrelated word strings, sentences, and narrative passages. They found that sentence-level comprehension involved greater activation in Broca’s area (consistent with the research on syntax discussed herein) and in the left posterior medial temporal region, as well as in bilateral anterior temporal regions. Comprehension of narratives also resulted in greater activation of the anterior temporal regions, as well as increased activation in Wernicke’s area and the medial frontal lobes, than did comprehension of sentences. Thus, these results suggest distributed increases in activation resulting from the processing of increasingly complex stimuli. Of course, these three conditions varied not only in integrative demands, but also on linguistic processes involved. As previously discussed, comprehension of sentences requires a variety of subcomponent processes (e.g., parsing, binding, maintenance) in addition to integration. Similarly, comprehension of discourse requires elaborative processes (discussed in the next section) as well as integrative ones. How then, might one separate out the neural substrates related to integration, per se? One approach would be to investigate the region or regions in which activation

The Neural Bases of Linguistic Representation and Processes

increases parametrically as the integrational demands of a task increase. In their recent review of the neural basis of language processes, Stowe et al. (2005) concluded that the bilateral anterior temporal lobes showed this pattern of increased activation with increased integrational complexity. Consistent with the view that the anterior temporal lobes support integrational processes, the N400 ERP component, thought to reflect ease of semantic integration, has been shown to be delayed or missing in patients with damage to the anterior temporal regions (Kotz & Friederici, 2003). In addition Dronkers et al. (1994) found that a large number of patients with morpho-syntactic deficits (which may manifest, in part, from integrational difficulties) had lesions that included the anterior temporal lobes. Research on patients with anterior temporal lobectomies, which are frequently performed to treat epileptics, suggests that the anterior temporal lobes may not actually perform integrative processes (Hermann, Wyler, & Somes, 1991) but instead support integrative processes through more general memory maintenance processes (Rugg, Roberts, Potter, Pickles, & Nagy, 1991). In a subsequent review of neuroimaging research on discourse-level comprehension processes, a consistent role for the anterior temporal lobes was observed across all discourse conditions examined (Ferstl, Neumann, Bogler, & von Cramon, 2008b). Ferstl et al. suggested that the specific role of the anterior temporal lobes might be propositionalization, or the process of combining words into meaningful content units (or constructing the propositional representation). They propose that the fact that the temporal lobes are multimodal association areas make them a likely location for the integration of semantic, syntactic, and episodic information into a meaningful representation. In summary, converging evidence from the memory and text processing literatures suggest that the anterior temporal roles support the process necessary for integrating semantic information into meaningful representations of texts. Inferential Processes As discussed in the “Sentence- and Discourse-Level Representation” section, psycholinguists generally agree that readers construct a model of texts that includes not only the information presented in the text, but also elaborations of that information based on inferences and background knowledge. Inferential processes are central for establishing coherent discourse representations and thus have been the focus of numerous neuroimaging investigations of discourse comprehension. The brain regions reported,

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however, vary across investigations. This is not surprising due in part to the differences in types of inferences drawn, conditions supporting inferential processes, type of texts, and methods used. Perhaps as a result of these complexities, both commonalities and inconsistencies emerge from the research. For instance, the left hemisphere perisylvian language regions (including Broca’s and Wernicke’s areas) and medial prefrontal cortex have been repeatedly implicated in studies of inferencing (Chow, Kaup, Raabe, & Greenlee, 2008; Ferstl & von Cramon, 2001, 2007; Kuperberg, Lakshmanan, Caplan, & Holcomb, 2006; Shinkareva et al., 2011; Sieb¨orger, Ferstl, & von Cramon, 2007). The consensus on the role of medial prefrontal cortex in inferencing is that it plays a general role in coherence monitoring during comprehension (Ferstl, Neumann, Bogler, & von Cramon, 2008; Mason & Just, 2006), which becomes important during inferential processes because it signals a coherence break (Chow et al., 2008; Ferstl & von Cramon, 2001; Mason & Just, 2006; Sieborger et al., 2007), initiating the inference generation process. In contrast, the role of the right hemisphere in inferential processes remains controversial. Some neuropsychological research on right-hemisphere-damaged patients suggests that the right hemisphere is involved in various types of inferential processes (Beeman, 1993; Brownell, Potter, Bihrle, & Gardner, 1986). Other investigations, however, fail to find deficits in RH-damaged patients, even when attempting to replicate previous studies (McDonald & Wales, 1986; Tompkins, 1991; Tompkins, Fassbinder, Lehman Blake, Baumgaertner, & Jayaram, 2004). Similarly, some neuroimaging studies of healthy controls also report right hemisphere contributions to inferential processes, especially in the inferior frontal gyrus and in middle and superior temporal gyri (Kuperberg et al., 2006; Mason & Just, 2004; Sieb¨orger et al., 2007; Virtue, Parrish, & Jung-Beeman, 2008), while others do not (e.g., Ferstl & von Cramon, 2001). In addition, a recent metaanalysis of discourse comprehension studies found no unique right hemisphere contributions to inferential processes (Ferstl et al., 2008). Accounts of right hemisphere function vary across studies. For example, ask discussed in the semantic representation section previously, Jung-Beeman et al. (Beeman et al., 2000; Jung-Beeman, 2005) propose that the right hemisphere’s unique “coarse coding” processing style gives it advantages over the left hemisphere when activation of diffuse semantic fields is advantageous (e.g., during unconstrained predictive inferences). Mason and Just (2004), on the other hand, proposed that the RH becomes

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increasingly involved when successfully drawn inferences are integrated into text representations. They found that moderately related two-sentence passages (with sufficient constraints to allow successful inference selection and integration) resulted in higher RH activation than both highly related passages (where no inference was required) and distantly related passages (where lack of sufficient constraints may have prevented successful inference selection). One alternate hypothesis described by Prat et al. (e.g., Prat et al., 2011; Prat, Mason, & Just, 2012) is that the right hemisphere serves as a resource reserve for language processing, with similar but coarser-grained and less efficient capabilities than the dominant left hemisphere homologues. According to this view, the role of the right hemisphere in inferential processes (as well as all other linguistic processes) varies as a function of the conditions that place demands on the dominant left hemisphere. According to Prat, one of the difficulties in characterizing right hemisphere contributions to inferential processes specifically, or language processes in general, stems from the lack of consideration of an adaptive, as-needed role of the right hemisphere in language processes. Future attempts to characterize the neural underpinnings of inferential processes that quantify computational demands of the task, as well as the comprehension abilities of the reader (e.g., Prat et al., 2011) will allow us to further explore the neural underpinnings of such processes. In summary, contemporary research on the neural basis of language faculties highlights the overlap between general information processing and the computational specialties of various brain regions. For instance, the regions involved in memory, rule learning, control, and hierarchical binding all play key roles in language comprehension processes. In addition, research on the neural underpinnings of language is beginning to account for the fact that seemingly isolated manipulations of linguistic complexity have general consequences on processing demands, and that these demands may account for some of the changes in neural activation observed, especially in the right hemisphere.

CONTEMPORARY APPLICATIONS To ground the research discussed in this chapter in realworld contexts, the following section summarizes some contemporary applications of the knowledge we have acquired on the neural underpinnings of linguistic processes. Both of the examples discussed reiterate the importance of shared neural and computational bases of

language processes and more general cognitive control mechanisms. The first section describes the biological basis of individual differences in reading comprehension abilities in healthy, monolingual adults. The subsequent section summarizes research on the bilingual brain, describing a new theory for interpreting the neural basis of improved executive functioning in bilinguals. The Neural Basis of Individual Differences in Language Comprehension Abilities Individual differences in language abilities are present and prevalent throughout the lifespan, and must ultimately reflect differences in brain functioning. A comprehensive theory of the biological substrates of language comprehension must account for such variability (e.g., Bates, Dale, & Thal, 1995; Cohen, 1994). Based on the fact that language processes results from the collaborative efforts of a distributed network, it is not surprising that individual differences in language abilities primarily manifest at the network level (see Prat, 2011 for a review). In short, this research has shown that good comprehension, like effective cognition, is related to the efficient use of individual components of the language network (e.g., Broca’s and Wernicke’s areas), to effective communication between components, and to dynamic reconfiguration of the network with changing task demands. Thus, at least three facets of function have been shown to underpin good comprehension ability: neural efficiency, neural adaptability, and neural synchronization. Neural Efficiency One of the best understood links between individual differences in cognition and brain function is that moreskilled individuals generally accomplish a task using fewer mental resources than less-skilled individuals (Haier et al., 1988; Maxwell et al., 1974; Neubauer & Fink, 2009). The relation between increased neural efficiency and improved mental function has been replicated for language comprehension abilities in adults (Prat et al., 2007, 2010, 2012; Prat & Just, 2011; Reichle, Carpenter, & Just, 2000). Neuroimaging research has addressed the question of whether general improvements in neural efficiency give rise to improved language abilities, or whether the match between neural efficiency and skill is specific to a particular domain (e.g., Reichle et al., 2000), and have found that the predictive power of skill in a particular domain (verbal versus visuo-spatial) is tied to conditions that evoked

Contemporary Applications

that particular type of processing, and to brain regions that subserved the type of process. To investigate the factors related to increased efficiency in good readers, Prat, Mason, and Just (2010) conducted a multiple-experiment investigation of individual differences in neural efficiency. Using indices of vocabulary size, working-memory capacity, age, handedness, and sex to predict patterns of activation in a multiple regression analysis of 84 readers, they found that the best predictor of efficiency was vocabulary size. Results from this study as well as others suggest that the increase in neural efficiency that is observed in higher-vocabulary individuals may be related to increased linguistic experience (see Prat, 2011, for details). Neural Adaptability A cortical network engaged in performing a complex task must be able to adapt to changing information processing demands (e.g., Garlick, 2002; Schafer, 1982). Language comprehension is the quintessential example of a dynamic task, requiring varying demands (e.g., thematic role assignment, inferencing, integration) on-line, as semantic representations are updated on a word-by-word basis. Thus, characterizing differences in language comprehension abilities must involve descriptions of the interaction among individual characteristics and varying task characteristics. This intersection has been addressed in investigations of individual differences in neural adaptability to changing task demands. Although a modal set of areas activates for any given task, additional areas are recruited on as asneeded basis to deal with changes in cognitive demands. For instance, dorsolateral prefrontal cortex becomes activated when comprehension of a sentence requires problemsolving (Newman, Just, & Carpenter, 2002a). Research on individual differences in reading comprehension has shown that good comprehenders show greater neural adaptability in the face of changing task demands under a variety of sentence processing conditions (Prat & Just, 2011; Prat et al., 2007; Yeatman, Ben-Shachar, Glover, & Feldman, 2010). Specifically, good comprehenders showed greater increases in activation as a function of increased syntactic complexity (Prat et al., 2007; Prat & Just, 2011; Yeatman et al., 2010), decreased lexical frequency (Prat et al., 2007) and increased sentence length (Yeatman et al., 2010) than did poorer comprehenders. Neural Synchronization As discussed previously, language researchers have highlighted the importance of effective connections between

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language processing centers for comprehension processes. To function optimally, the various anatomically distinct, but functionally integrated, nodes of a language network must be able to communicate effectively and to synchronize their processes. Such collaboration can be studied in fMRI investigations by examining the correlation of activation time series in a given region with activation time series of another region. The extent to which the activation levels of two regions rise and fall in tandem is taken as a reflection of the degree to which the two regions are functionally connected (Friston, 1994). Resulting indices of functional connectivity provide useful characterizations of network-level activity. For example, functional connectivity increases with learning, at the same pace as increases in performance, indicating that system coordination is an important facet of its effectiveness (Buchel, Coull, & Friston, 1999). Research on functional connectivity has provided new insight into the nature of individual differences in comprehension ability. In a working memory task, Otsuka and Osaka (2005) found that younger individuals had higher functional connectivity and performed better on the task than did older individuals. Prat et al.’s (2007) investigation of individual differences in sentence comprehension extended these results to young adults, showing that higher-working-memory-capacity readers had better language network synchronization than did lower-capacity readers between the key regions in the left hemisphere language network (including Broca’s and Wernicke’s areas). Reduced connectivity between components of the language comprehension network has also been implicated as a source of processing difficulty in individuals with developmental language impairments. For example, reduced functional connectivity between the angular gyrus and the left temporal lobe has been observed in individuals with dyslexia (e.g., Horwitz, Rumsey, & Donohue, 1998; Pugh et al., 2000), especially during phonologically demanding tasks (Pugh et al., 2000). Reductions in functional connectivity between frontal and posterior regions have also been observed in individuals with autism during a variety of language comprehension tasks (e.g., Just, Cherkassky, Keller, & Minshew, 2004; Kana, Keller, Cherkassky, Minshew, & Just, 2006). Taken together, increased connectivity in high-capacity comprehenders and reduced connectivity in language-impaired populations highlight the importance of effective communication between regions of the language network for intact comprehension. The importance of synchronization between components of the language network has also been demonstrated in investigations of the microstructure of white

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The Neural Basis of Language Faculties Increased FA With Increased Vocabulary (yellow) and Working Memory (blue)

Superior Longitudinal and Inferior FrontoOccipital Fasciculi

Anterior Thalamic Radiation

Cingulum, Forceps Minor

Figure 22.2 Increased FA with increased vocabulary (yellow) and working memory (blue)

matter tracts connecting the language network. As discussed in the methods section, such investigations often use FA as an index of the directional coherence of diffusion of water in the brain, with higher FA values reflecting greater integrity or directional organization of white matter tracts. A recent DTI investigation of individual differences in reading comprehension abilities in healthy adults showed that increased vocabulary size was associated with increased FA in left hemisphere regions of superior longitudinal and inferior fronto-occipital fasciculi (connecting Broca’s and Wernicke’s areas) and in the anterior thalamic radiation (connecting the thalamus to medial prefrontal regions), and better working-memory capacity was associated with increased FA in a right hemisphere region of the forceps minor (connecting left and right frontal regions) and the cingulum (connecting the frontal, parietal, and temporal lobes; Prat, Schipul, Keller, & Just, 2010). Figure 22.2 depicts the correlation between increased FA and increasing vocabulary size (in yellow) and working memory capacity (in blue). In summary, research on the brain basis of comprehension ability shows that individual differences are reflected by the amount of activation necessary for executing a task (efficiency), by fluctuations in activation with changing task demands (adaptability), and by integration of processing between brain regions (synchronization). The research converges to show that network-level descriptions of brain function are necessary for accurately characterizing comprehension ability. In addition, preliminary results suggest that indices of efficiency are tied to experience with specific processes or types of stimuli, whereas indices of adaptability and synchronization are more characteristic of general mental functioning (e.g., working memory). The different sensitivities of these various measures are of interest for future investigations, as they may ultimately

be useful in diagnosing the source or sources of difficulties in individuals with language impairments.

The Bilingual Brain While the majority of the world is bilingual, investigations of the brain basis of bilingualism unfortunately constitute a minority of the research. Existing investigations have centered around two problems: how multiple languages are represented in the brain, and how they are controlled in the brain (see Abutalebi & Green, 2007, for a review). Recent neuroimaging research suggests a great deal of overlap in the representation of multiple languages (see Abutalebi & Green, 2007, p. 256, Table 1). An abundance of studies showed that second languages (L2) have a more distributed representation than do first ones (L1; e.g., Abutalebi, 2008; Buchweitz, Mason, Hasegawa, & Just, 2009; Chee, Tan, & Thiel, 1999) although most of these differences can be accounted for by differences in proficiency levels in the two languages (Abutalebi, 2008; Yokoyama et al., 2006). Thus, the general view is that the same network of regions represents both languages, and that eventual differences depend on the recruitment of additional regions (either prefrontal regions or right hemisphere homologues) to compensate for the increased demands of less-proficient language processing. Because the representation of semantic codes in L1 and L2 is so highly overlapping, a control mechanism must be operating in the bilingual brain to monitor and select the appropriate language to use. In our opinion, the neural substrates of this mechanism are tied to the selecting and shifting components of executive functions (Stocco et al., revison under review), which are executed by the basal ganglia’s selective information routing processes. Investigations of bilingual language control typically involve

Summary

translation paradigms, language switching paradigms, or language selection paradigms (see Jubin, Abutalebi & Green, 2008, for a review). Research on bilingual language control has yielded regions of activation that highly overlap with those observed in nonlinguistic cognitive control tasks, such as the prefrontal cortex and the anterior cingulate (Hernandez, Martinez, & Kohnert, 2000; Rodriguez-Fornells et al., 2005). Of particular interest to our hypothesis, a series of investigations have reported basal ganglia involvement during switching or translating paradigms (e.g., Price et al., 2003). The Basal Ganglia and Bilingual Brain Training The dual framework described by Ullman (previously discussed in the “Rules and Control in the Basal Ganglia” section) can be applied to explain the proficiency-related differences between L1 and L2 in bilinguals. While learning a second language, grammatical rules are more likely to be encoded explicitly, and thus retrieved and held in working memory during language tasks. This additional processing would result in greater activation of prefrontal regions for L2 compared to L1. With increasing practice, however, grammatical rules for L2 would be eventually stored in the basal ganglia in the form of procedural rules, thus reducing the difference between L1 and L2 (Ullman, 2001b). This view is supported by the rather counterintuitive finding that in bilingual individuals with degenerative disorders of the basal ganglia (e.g., Parkinson’s disease), greater impairments are observed in the most proficient of the two languages spoken (see Fabbro, 2001, for a review). In addition, experimental and neuropsychological evidence suggest that in the bilingual brain, the basal ganglia play the additional role of controlling which language to use. For instance, bilingual patients with injuries including the basal ganglia circuit show a pathological tendency to switch back and forth between languages (Fabbro, 2001). This neuropsychological evidence is also corroborated by experiments showing that direct stimulation of the left striatum during open-skull surgery causes spontaneous language switching (Robles, Gatignol, Capelle, Mitchell, & Duffau, 2005). Further support for the role of the basal ganglia in language control comes from a neuroimaging investigation conducted by Crinion et al, (2006; see also Friederici, 2006, for a discussion) that used a crosslinguistic priming paradigm to examine the nature of automatic language switching. In the experiment, bilinguals responded to words that were preceded by either semantically related or unrelated primes. More important, the

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prime word was presented in either the same language as the target word, or in a different language. The authors were specifically interested in the neural activation resulting from a language switch (L1 or L2). Behaviorally, semantic priming crossed the language boundary, so that seeing the prime word “salmon” in English still results in a decreased response time for the target word “trout” in German (forelle). The cross-language priming effect was also significant in a distributed cortical network involving most of the brain regions that were recruited by the task. The only exception to this rule was a region in the head of the left caudate nucleus (e.g., the striatum) in the basal ganglia. Semantically related words reduced activation in this region only when they were in the same language. In other words, the priming effect in the striatum was selectively modulated by the specific language input. Crinion et al.’s (2006) finding suggests that the striatum is involved in detecting which language is in use. It is conceivable that damage to this structure impairs a specific brain system’s ability to keep track of (or control) language in use, thus explaining the pathological symptoms outlined above. Friederici (2006) recognized the connection between this putative function of language selection to establish the role of the striatum and the basal ganglia circuit in selecting motor programs (e.g., Albin et al., 1989; Fabbro, 2001). In a recent review, Stocco, Yamasaki, Natalenko, and Prat (revision under review) use a biologically based model of information routing in basal ganglia (Stocco, Lebiere, & Anderson, 2010) as a mechanism for explaining the improvements in executive functions observed in bilingual individuals. Specifically, they propose that bilinguals have increased demands for controlling languages and for switching between sets of stimuli and sets of rules. These increased demands result in a more “expert” (more experienced) system for routing information selectively to the frontal cortex (through the basal ganglia); and this strengthened system gives rise to better general executive function, especially in cases where switching between tasks or top-down control are necessary. Thus, according to this theory, the linguistic experience of bilinguals “trains the brain” in a way that has general implications for improved functioning in the face of distractions or modulation of tasks.

SUMMARY Language processes are diverse and require a broad range of dynamic computations. This chapter summarizes contemporary theories of language in the brain, highlighting

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the overlap between neural regions involved in representing and processing language, and neural regions executing more general cognitive functions (e.g., rule or sequence learning, memory storage and retrieval, and hierarchical binding). This relation between language and general information processing can also be seen in individual differences investigations, in particular by an examination of those regions where higher-working-memory-capacity readers show more efficient, more adaptable, and more synchronized neural processes during language comprehension. Finally, the overlap between language processing and general cognitive function is evinced by research on the brain basis of improved executive functioning in bilinguals. Taken together, these findings suggest that language processes, like other complex cognitive tasks, are supported by the integrative and emergent properties of a distributed neural network, which considerably overlaps and interacts with general sensory/motor, memory, and control systems in the brain.

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CHAPTER 23

Neurally Inspired Models of Psychological Processes EDUARDO MERCADO III AND CYNTHIA M. HENDERSON

USING CONNECTIONIST MODELS TO UNDERSTAND BRAINS AND BEHAVIOR 622 VISUAL OBJECT RECOGNITION AND CLASSIFICATION 626 PERCEPTUAL LEARNING 629 MEMORY FOR EVENTS 632

AGING AND PSYCHOLOGICAL DECLINE 634 EMERGING TRENDS 637 GENERAL DISCUSSION 638 REFERENCES 638

Models are tools that enable psychologists and neuroscientists to study a subset of facts and rules related to behavior and brain function, and to thereby develop a more complete understanding of how and why individuals behave the way they do. Sometimes, models can help to explain empirical observations by identifying potential mechanisms and principles that give rise to the observed events. Models can also provide a link between verbal statements and scientific measurements of events. In the field of psychology, the measurements of interest are of individual thoughts and behaviors. In neuroscience, they relate to structural and dynamic properties of nervous systems. Across these domains, modeling provides a way for researchers to simplify ideas, quantify and test theories, and generate new scientific predictions. Like any tool, the ultimate purpose of models is to help people solve problems. The kinds of scientific problems that psychologists and neuroscientists are trying to solve are quite complex. Consequently, a wide variety of models have been developed

to attack these problems. The most ubiquitous models in psychology and neuroscience are qualitative models. Qualitative models are essentially summaries of the neural or mental processes that researchers think underlie a particular phenomenon, expressed in narratives or figures (e.g., boxes with arrows between them). Qualitative models serve to simplify explanations for complex phenomena in ways that makes these explanations more accessible. Pavlov’s (1927) descriptions of conditioned reflexes are a classic example of a qualitative model of learning processes and cortical function. Many scientific theories start out as qualitative models. Such models play an important role in the development of psychological constructs and conceptual frameworks. Qualitative models tend to be more constrained, however, when it comes to making precise, unambiguous predictions. Historically, quantitative models have proved to be more effective for generating such predictions (Mazur, 2006). As more data emerges in a particular area of research, and as researchers try to make more precise predictions or discriminate between competing theories, the value of quantitative models for developing theories increases. Quantitative models usually consist of formalized mathematical equations, computer programs, or both. They are typically less intuitive and communicable than qualitative models, but their rigor, explicitness, and testability make up for these limitations. Early efforts to

This research was supported by a National Science Foundation Science of Learning Center Grant, SBE 0542013 to the Temporal Dynamics of Learning Center, and by a fellowship from the Center for Advanced Studies in the Behavioral Sciences at Stanford. The authors thank Barbara Church, Shu-Chen Li, Jay McClelland, Catherine Myers, and Andrew Saxe for commenting on earlier versions of this chapter. 620

Neurally Inspired Models of Psychological Processes

quantitatively model psychological processes focused on mathematical models that instantiated learning theories (Atkinson, 1960; Estes, 1957). More recently, emphasis has shifted to using computational models to explore the neural mechanisms underlying perception, memory, and cognition (e.g., Heinke & Mavritsaki, 2009; O’Reilly & Munakata, 2000; Sun, 2008; Trappenberg, 2010). Computational models are essentially computer programs or hardware that simulate phenomena mathematically. Modern modeling efforts focus less on describing patterns of learning and behavior and more on understanding links between cognition, behavior, and brain function, as well as on developing systems that can replicate human performance. Computational models often build upon critical constructs that have previously been identified for particular behavioral or neural phenomena. In seeking to rigorously embed these theoretical constructs within quantitative frameworks, models can generate a clearer understanding of which factors are truly important for understanding these phenomena. Computational models provide a way to quantitatively explore theories about how brains give rise to cognition and behavior. In this chapter, we review recent computational models of object recognition, perceptual learning, episodic memory, and age-related cognitive decline to illustrate modern, brain-based approaches to quantitatively modeling psychological functions. We begin by describing various ways in which computers can be used to simulate both behavioral patterns and neural processing. Approaches to Simulating Psychological Processes Computational models could in principle be used to simulate any aspect of mental or physical behavior. In practice, they have only occasionally been applied in social psychology (e.g., Freeman & Ambady, 2011; Mischel & Shoda, 1995; Monroe & Read, 2008), clinical psychology (e.g., Gustafsson & Paplinski, 2004; Redish & Johnson, 2007), and developmental psychology (e.g., Mareschal & Thomas, 2007; Munakata & Stedron, 2001; Purser, Thomas, Snoxall, & Mareschal, 2009; Thelen & Smith, 1994). They are used much more extensively in cognitive psychology (see Sun, 2008, for a recent review), learning theory (Gluck & Myers, 2001; Schmajuk, 2010), and behavioral neuroscience (reviewed by Heinke & Mavritsaki, 2009). Cognitive psychologists use computational models to simulate mental actions related to perception, attention, memory, and thinking. Learning theorists use them to model experience-dependent changes in directly observable response patterns. Behavioral neuroscientists (including cognitive neuroscientists) use computational

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models to link behavioral patterns to variations in neural activity or structure. Despite differing domains of interest, these psychological subfields typically use comparable computational models. Many diverse computational approaches to modeling psychological and neural processes are currently available (Shiffrin, 2010), most of which build upon existing mathematical and analytical techniques (Figure 23.1). For example, Bayesian models of cognition and neural information processing are an offshoot of probability theory that have been used extensively to model visual processing and language development (Griffiths, Kemp, & Tenenbaum, 2008; Rao, Olshausen, & Lewicki, 2001). Dynamical systems approaches, which have been used to simulate perception-action interactions, derive from differential calculus (Kelso, 1995; Schoner, 2008). Symbolic modeling is related to logical systems and is useful for modeling human reasoning (Bringsjord, 2008). Finally, connectionist models are broadly used to simulate the acquisition and generalization of perceptual and cognitive skills, and are closely tied to matrix algebra (Haykin, 1994). Each of these approaches can be used to characterize psychological and neural phenomena, and arguments for the advantages and disadvantages of using one approach versus another abound (Fodor, 2001; Heinke & Mavritsaki, 2009; McClelland et al., 2010; Shiffrin, 2010). The usefulness of any particular modeling approach depends critically on the type of question being addressed, as different computational frameworks can provide different perspectives and insights into psychological processes (Marr, 1971). Computational models differ in the extent to which they incorporate knowledge about brain structure and function. Some modern theories of cognition and learning do not mention neural mechanisms. For instance, symbolic models, which focus on the use and manipulation of discrete, structured representations, generally do not incorporate neural constraints in their descriptions of mental processes. Other frameworks, such as connectionist models, were inspired by the structure of neural circuits and theories of neural plasticity mechanisms. In general, the usefulness of a computational model is not contingent on its correspondence to biological facts (Thomas & McClelland, 2008). The realism of a neurally inspired model of psychological function is primarily an issue when the goal is to recreate a neural circuit. If the goal is to identify functional principles, then increasing the biological fidelity of a model can in some cases actually hinder understanding, because as realism increases, so does the complexity of the model.

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P(b \ a) =

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Inferring features of objects from limited information

∀x(Fx → Gx), ∃x(Gx ∧ Wx) Rationally declaring facts

Y′−u(x,t) = −u(x,t) + h + S(x,t) + ∫ dx′w(x –x′)s(u(x′,t))

Finding stability despite fluctuations

y(n) = sgn[wT (n)x(n)] w(n + 1) = w(n) + h[d(n) − y(n)]x(n) Detecting patterns

Figure 23.1 Kinds of computational models. A diverse array of computational models of psychological and neural processes are currently available, each of which builds upon existing mathematical frameworks. Bayesian models simulate how the brain can formulate predictions using probability theory (upper left). Dynamic systems models use differential calculus to identify states of equilibrium (upper right). Logic-based models use propositional and predicate calculus to simulate human reasoning (lower left). Connectionist models can emulate pattern recognition by networks of neurons using principles of matrix algebra (lower right). Each approach emphasizes different ways of conceptualizing the mental functions that give rise to percepts, actions, and thoughts.

USING CONNECTIONIST MODELS TO UNDERSTAND BRAINS AND BEHAVIOR One prevalent connectionist model that encapsulates several features of neural processing is the artificial neural network. Some artificial neural networks (hereafter referred to as neural networks) attempt to replicate known features of neural circuits, whereas others simulate psychological phenomena in ways that cannot be mapped onto actual brain circuits. Even neural networks that attempt to link neural activity with behavior differ in the degree of realism with which they attempt to emulate neural mechanisms. Neural networks are neurally inspired in that collections of interconnected simple processors that can accomplish little alone, can together achieve impressive feats (as is seen in brains), and in that their behavior often depends on past learning. Neural networks are somewhat brainlike in form, and in some simulations, they can also be brainlike in function. When it comes to modeling links between brains and mental abilities, a major complication is that there is a gap between observing the outcomes of brain function (as revealed through the behavior of individuals, variations in brain activity, and subjective awareness) and understanding how brains are actually functioning. Neural network

models attempt to bridge this gap by using mathematical functions to mimic patterns observed in brains and behavior. Most neural networks consist of sets of computational units that can be thought of as mini-calculators. These calculators (hereafter referred to as nodes) take in a set of numbers and perform mathematical operations on those numbers to produce a new number. The calculations performed by each node are relatively simple (Minsky & Papert, 1988). However, when the numbers that sets of nodes generate serve as inputs to other nodes, control the computations applied to future sets of numbers, or are fed back either directly or indirectly as inputs to the nodes that generated them, networks of nodes can exhibit much more complex and interesting behavior (Rumelhart, Hinton, & Williams, 1986; Rumelhart & McClelland, 1986). At first glance, sets of interacting calculators might seem wholly dissociated from the sorts of complex biochemical processes occurring within a neural circuit (not to mention one’s own subjective experiences). The link between neural networks and neural processing comes from the kinds of computations that nodes perform, how nodes interact through their connections, and how their interactions are modified as a function of experience. Similar to a neuron receiving inputs from other neurons, a node within a neural network may aggregate inputs from

Using Connectionist Models to Understand Brains and Behavior

other nodes that are connected to it. Although each node within a network exhibits this neuron-like processing, individual nodes generally are not used to simulate the activity of individual neurons. Instead, they may be used to simulate the activity of a large population of neurons. In other cases, as noted above, the model is intended as an abstraction rather than as a simulation of brain activity. In the most detailed neurocomputational models, even a single neuron might be modeled as a whole network of highly specialized nodes. Consequently, neural network models vary greatly in terms of the complexity of the processes they attempt to simulate, as well as in the complexity of computations that nodes perform (Figure 23.2). One key feature that distinguishes many neural networks from several other computational approaches is their emphasis on quantitatively defining basic algorithms (called learning algorithms) that can control experiencedependent changes in computations (Figure 23.3). Learning algorithms can modify processing in a neural network either based on external guidance (called supervised learning) or without direction (called unsupervised learning or self-organization). Most learning algorithms consist of mathematical rules about how the strengths of connections change between nodes within the neural network; the type of learning algorithm used is a defining feature of different neural network approaches. Many supervised learning algorithms focus on reducing inappropriate outputs

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by changing connection strengths to either reduce errors (called error-correction learning) or to better predict the “goodness” of an output (sometimes called reinforcement learning). Unsupervised learning algorithms instead identify relationships between inputs (e.g., Kohonen, 1995); these algorithms are often associated with Hebbian learning, a process in which connections between neurons that fire simultaneously are strengthened. In some neural networks, certain properties of the learning algorithms themselves can vary with experience (Kohonen, 1995; Luo, 1987; Rokers, Mercado, Allen, Myers, & Gluck, 2002), and even the basic structure of the model can become reorganized over time (reviewed by Heinke & Hamker, 1998). Consequently, the operational features of neural networks depend in part on the inputs, outputs, and learning algorithms that are used, and on how the networks are constructed. As such, developing a specific neural network simulation entails choosing the number of nodes, how they compute outputs, how the nodes are clustered and interconnected, and how connections between nodes change over time. Details of neural network architectures and learning algorithms have been described extensively elsewhere (Dawson, 2004; Dayan & Abbott, 2001; Hanson & Burr, 1990; Haykin, 1994; O’Reilly & Munakata, 2003; Trappenberg, 2010), and thus are only briefly summarized here. Numerous existing learning algorithms and network

Cognitorium Attitude Object

Synaptic connections in the lamprey spinal cord

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Figure 23.2 Examples of neural network models. Neural network models vary greatly in terms of the complexity of the processes they attempt to simulate and the complexity/realism of the processing nodes involved. A recent connectionist model of attitude (left panel) used sets of nodes to simulate basic hypothetical cognitive subprocesses thought to contribute to attitudes (Monroe & Read, 2008), whereas a recent simulation of swimming by a lamprey (right panel) attempted to replicate the actual neural circuits within the spinal cord using biophysically detailed nodes containing thousands of cellular processes (Koslov et al., 2009).

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y(n) = sgn[wT (n)x(n)]

max[wT (n)x(n)]

w(n + 1) = w(n) + h[d(n) − y(n)]x(n)

wj(n + 1) = wj(n) + h[x(n) − wj(n)] One node is the winner

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Figure 23.3 Supervised and unsupervised learning algorithms. Learning algorithms define how neural networks change as a function of their inputs and outputs. Supervised learning algorithms (left panel) use the difference between the actual output y(n) and a desired output d (n) to incrementally adjust connection strengths (the weights, w (n)). Unsupervised learning algorithms (right panel) use the differences between the inputs and the existing connections to adjust weights in the network. Every node in the square grid is associated with a weight vector. Which weights change during learning depends on the network activity rather than on a desired output.

architectures have been refined and standardized in the past two decades, greatly easing the implementation of a variety of complex networks. Nodes within neural networks are typically designed to process inputs consisting of numerical sequences (or vectors). A node monitors the activity of each element of a sequence and effectively filters each one. Filtering of inputs is achieved by numerically weighting each element as it comes in. This process is analogous to how the transmission of information between neurons depends on the synaptic connections between them. The main parameters that determine how a node filters inputs are called its weights (see Figure 23.3). Numerically large weights correspond to strong connections between nodes and small weights represent weak connections. How a node reacts to a particular input thus depends on both the values within the sequence as well as the node’s weight values. Learning algorithms typically change a node’s reactivity by changing its weights. Nodes are typically grouped into sets with similar input and output connections, and these groupings are used to define consecutive stages, or layers, of processing within a neural network. Generally, each layer of nodes is associated with one or more weight vectors describing the strengths of its connections to other nodes. When two layers are fully connected, it means that a node in one layer is connected (sends input to, receives input from,

or both) to every unit in the other layer. In many cases, a node in one layer will only be connected to a subset of the nodes in another layer, with that unit’s particular subset often predetermined by design. Different layers within a neural network can be used to model processing in different brain regions, or to simulate sequential stages of processing within a brain region, but they generally do not simulate the specific neural circuits associated with particular brain regions. While nodes can receive inputs from other nodes, they can also directly receive external inputs that are predetermined. For example, a network designed to identify the objects in an image might have an input layer that encodes the pixels of the image. Other layers may be treated as the network’s output. From the previous example, nodes within an output layer might signify the potential identities of the objects in an image. Within networks that contain multiple layers, layers that neither directly receive external inputs nor directly produce network outputs are often called hidden layers. In some simple neural networks, activations in the input layer are filtered through the connection weights and activation functions of successive hidden layers until reaching an output layer, and the transformation of information from the input to the output can be thought of as the network computing a complicated function on the values of the input.

Using Connectionist Models to Understand Brains and Behavior

Once a node has filtered its inputs, it then performs a calculation using those filtered (weighted) inputs to generate an output value (or activation level). Typically, this output is a single number that reflects the input sequence the node received, as well as the weights associated with the node. Note the difference between a node’s output value, which is the activation value that every node computes, and the output layer referred to above, which is the set of nodes whose outputs represent the final output of the network. Standard node calculations include summing the weighted inputs and then choosing the activation level of the node based on a function (called the activation function) that maps different sums to particular output values. Nodes of this sort can simulate the accumulation of synaptic potentials by a neuron, as well as the all-or-none threshold-based initiation of action potentials (often called spikes) by neurons. How a node processes inputs determines not only what functions networks of those nodes can achieve, but also the kinds of learning algorithms that can be used to modify that processing. Figure 23.4 illustrates several types of nodes that have

been used in neural network models of psychological functions. To summarize, neural networks capture some features of neural structures and function that other computational models do not. These include the use of relatively simple interconnected processing nodes that are organized into sets of processing stages (layers). These nodes that interact in ways that are often experience-dependent, and that reflect interactions between multiple similar nodes. The construction of neural network models of psychological processes involves developing computer programs that transform quantitative descriptions of inputs into patterns of network activity and simulated outputs that are analogous to phenomena observed by psychologists. The following sections describe several neural network models that attempt to simulate the impacts of neural processing on perception and cognition, in order to illustrate how neurally inspired computational models can contribute to the understanding of neural and psychological phenomena. We turn first to computational models of visual object recognition.

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Figure 23.4 Processing nodes within neural networks. A key feature of processing nodes within neural networks is the activation function used to transform weighted sums of inputs into output values. These functions determine not only how the network responds to inputs, but also the form of the learning algorithms used to train a network. Each circle above corresponds to a single node with a different kind of activation function; functions used in past and current connectionist models include a linear function (upper left), a logistic or sigmoid function (upper right), a step function (lower left), and a Gaussian function (lower right).

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VISUAL OBJECT RECOGNITION AND CLASSIFICATION In the summer of 1966, two MIT undergraduates embarked upon a research project to create computer programs that could analyze images. Although these two undergraduates doubtless worked diligently, by the end of the summer they were still grappling with the problem. Hindsight allows us to appreciate the enormity of their project. More than 40 years later, many researchers in psychology, neuroscience, and the entire field of computer vision still work to understand and appreciate how visual inputs are transformed into a coherent understanding of the world around us. To get a sense of how challenging it is to extract meaning from images, consider that half of the human brain is involved in some form of visual processing. A major goal of visual processing is to identify and characterize the objects around us (for an introduction to the neural systems underlying vision, see Baker, this volume; Milner & Goodale, 2006). Similarly, many computational modelers in vision focus on creating systems that can detect or characterize objects. They have developed models to recognize handwritten numbers, objects such as mugs and giraffes, faces, human body movements, and a wide range of other images (e.g., Everingham, Van-Gool, Williams, Winn, & Zisserman, 2010; Griffin, Holub, & Perona, 2007). Although computer programs can achieve impressively high levels of accuracy classifying some images, complex visual scenes continue to present challenges. For instance, when faced with the 2010 Pascal’s Challenge (a competition in which programs attempt to detect and recognize objects within complex images; see Figure 23.5), the best programs had less than 40% median accuracy in detecting prespecified object categories (Everingham et al., 2010).

Object Recognition Is Hard Why is object recognition so difficult? One of the biggest challenges derives from variability in the appearance of an object within an image. Because objects can change in position, size, rotation, and other aspects, there isn’t a straightforward relationship between the values of particular pixels in an image and the objects that are present. The pixels that help identify an object in one image may not be useful in another. For example, many algorithms detect discontinuities in an image, such as edges or curved surfaces. After a shift in the position or size of an object, these discontinuities may appear in completely new parts of the image. Somehow, algorithms must capture the similarities between images that contain a particular object, despite such variations. Furthermore, large changes in the appearance of an object can occur when a viewer (or camera) changes position. Imagine how the image of an airplane changes as you approach it and move around it (see Figure 23.5). Given the variability in the image produced by an object at different visual angles and ranges, finding an algorithm that can identify any view of an airplane as belonging to the category of “airplanes” is quite challenging (Pinto, Cox, & DiCarlo, 2008). This process becomes even more complicated when objects can change their basic shape, such as a dog sitting, standing, or jumping. How might the responses of individual receptors in the retina be used to identify objects when an object’s projection onto those receptors is so variable? It has long been a goal of computer vision to reproduce what is called invariant object recognition; that is, to create an algorithm that can identify objects as the same type regardless of changes in position, size, or viewing angle. For example, a program that can learn about an object or category at one point in space, and then still recognize that object or category when it occurs in other locations is said to

Figure 23.5 Difficulties in recognizing objects. Computational models have difficulty detecting and recognizing objects of a particular type (in this case airplanes) when they are viewed from different angles or are embedded in complex scenes. These images are from the 2010 Pascal’s Challenge, a competition involving object detection and recognition. Images replicated with permission from Abreu, copyright © 2007 (left), and Hymes, copyright © 2000 (right).

Visual Object Recognition and Classification

be translation-invariant. Some researchers argue that the ideal system should be tolerant to translation rather than invariant to it (DiCarlo & Cox, 2007). Computer programs that learn translation-invariance in a fully biologically feasible way have yet to be fully realized (see, however, Serre et al., 2007, discussed later), although many modern programmers artificially introduce this property into their algorithms by aggregating information from multiple copies of an object filter, applied to each location in an image. Another major challenge for object recognition is that images may contain multiple overlapping objects and confusing background clutter (Figure 23.5). In general, adding clutter increases the variability of images, and greater variability increases errors. In many cases, it may be difficult for a computer program to correctly segment the different parts of an image into appropriate subregions (foreground versus background) or to segregate objects. When one object lies partially in front of another in an image, this introduces an additional problem in that the object in front can obscure critical portions of the hidden object, making it more difficult to identify. Shadows cast by other objects can also alter the appearance of objects, blurring existing edges or introducing new ones, or simply changing the visual intensity of large regions of the image. Such complications have increased interest in finding computational methods that can successfully segment an image into its constituent parts. While many different approaches have been developed in computer vision to address these problems, only a subset of these are biologically constrained models relating to how brains might process images. It is definitely possible to develop useful image recognition algorithms without emulating brain function. Nevertheless, modeling efforts that have considered neural mechanisms of visual processing have also proved effective, and have helped inform research about how humans process visual information. The next section describes some classic and modern advances in computational models of object recognition as well as recent techniques for segmenting images.

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Neurons earliest in a chain of visual cortical processing, termed simple cells, generally responded best to edges or bars of light oriented at a specific angle and appearing within highly specific regions of space. Neurons in what was theorized to be the next stage of visual processing (termed complex cells) responded to these same types of visual features, but with less sensitivity to the exact positions of the bars. Other studies (e.g., Hubel & Wiesel, 1965) revealed additional hypercomplex cells that showed selectivity for features such as the length of bars. Recent research supports many aspects of the multiple stages of visual processing identified by Hubel and Wiesel, with successive cortical stages exhibiting selectivity for increasingly complicated visual inputs (Gross, 2005; Yau, Pasupathy, Fitzgerald, Hsiao, & Connor, 2009). These multiple stages of visual processing have several implications for object recognition. Specifically, early visual processing stages may deconstruct images using a library of basic visual features, such as edges, combinations of edges, and quasi-shapes (Serre, Wolf, Bileschi, Riesenhuber, & Poggio, 2007; Yau et al., 2009). Rather than associating an object with the entire image in which it appears, such a system might engage sets of basic feature detectors each time a relevant feature appears in a particular location. The identification of a given object could be represented as a conjunction of complex features, which could be detected regardless of where those complex features appeared. That is, although the object itself may appear in a different location, the same combination of detector types might still be activated, allowing the system to recognize familiar objects in novel positions. Generally speaking, neurons later in this hierarchy of visual processing pool information across a wider range of locations, such that cells in each successive processing stage are less affected by small changes in an object’s position or scale (Figure 23.6). Many current models include several aspects of this approach.

Hierarchical Processing and Image Segmentation in Visual Cortex Starting in the mid-20th century, visual neurophysiologists began outlining a process by which objects can be recognized despite changes in their position, size, and possibly rotation. In an influential series of experiments, Hubel and Wiesel (1962, 1968) segregated neurons in cat and primate visual cortex into various types that differed in selectivity.

Figure 23.6 Subdivision of images by a Neocognitron. Neocognitron subdivides images using spatially limited feature detectors distributed across multiple processing layers. Feature detectors early in processing respond in a more spatially selective way than detectors in later stages of processing. Adapted with kind permission from Fukushima, Cybernetics, 36, 1980, p. 198, Figure 5. Copyright © 1980, Springer Science+Business Media.

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Fukushima’s (1980) landmark Neocognitron model incorporated many of the ideas from Hubel and Wiesel by breaking letter recognition down into multiple processing stages. In the first stage, a set of feature detectors for lines of different orientations is applied to each section of an image (Figure 23.6). The activation of each feature detector by a given subregion of the image depends on how closely the pixels in that region of space match the selected-for feature. The second stage pools responses over a set of nearby, similarly responding feature detectors from the first stage (meant to mimic the responses of complex cells). In the Fukushima model, these pooling nodes become active when at least one associated feature detector in the first stage is strongly active. Because these pooling feature detectors collect information from multiple feature detectors within local regions of space (all selective for the same feature), the pooling nodes end up with larger receptive fields. Fukushima’s Neocognitron includes four additional stages, which similarly alternate increasingly sophisticated feature detectors with nodes pooling the prior stage’s results. In this way, the selectivity and complexity of preferred stimuli increases with each stage. The sizes of each subregion analyzed, and the robustness of responses despite spatial variations of object positions, also increase with each pooling stage. This model’s translationinvariance is partially built in—when a node in the network is tuned to a particular feature through learning, its selectivity is copied onto neighboring feature detectors and applied to all spatial subregions, which allows the pooling stage to collect information from multiple copies of the same filter at slightly different positions. Though this is unlikely to occur biologically, it may replicate the overall result of a process that learns feature detectors for each point in space. Many newer visual object recognition algorithms have been influenced by this hierarchical processing approach as well as by the Neocognitron model itself. Poggio and colleagues developed a similar model to the Neocognitron, but with modified activation functions and modified algorithms for learning to detect features (Poggio & Edelman, 1990; Riesenhuber & Poggio, 1999; Serre et al., 2007; Vetter, Hurlbert, & Poggio, 1995). In the Poggio models, the pooling stages mimic the activity level of the most active simple feature detector, an approach that they suggest is both neurally plausible and leads to good robustness to noise (Knoblich, Bouvrie, & Poggio, 2007; Riesenhuber & Poggio, 1999). Their models also pool responses across slight changes in the size of detected features, making them tolerant to changes in the size of objects. This model incorporates two methods of learning. First, the model learns

a large library of features for each feature-detection stage by selecting the features activated by random patches of random images. After a set of feature detectors has been selected, the responses in the model’s final output layer is then used to recognize objects. Given a set of images with the target object either present or not, this training method detects which of the model’s library of output-level feature detectors typically correspond to the presence of a particular object. This image processing strategy performed in the range of several state-of-the-art object recognition and image segmentation programs (Serre et al., 2007). Like the Neocognitron, this model copies its feature detectors for all positions in space, and achieves translation- and size-invariance through built-in pooling stages. Other researchers have developed neural networks to recognize images using more standard learning algorithms such as backpropagation. The backpropagation algorithm is a supervised learning algorithm that uses differences between the current outputs of network nodes and target outputs to adjust weights within a multilayer network (Rumelhart et al., 1986). This approach provides networks with greater flexibility in learning features suitable for a given recognition task. Hinton and colleagues (2007; 2006) explored how backpropagation-trained neural networks containing large numbers of nodes distributed across many layers can effectively learn features. When multiple hidden layers lie between the inputs and outputs of a neural network (e.g., within hierarchical processing models), weights between the initial processing stages change very slowly, and these backpropagation-trained models often are nonfunctional. To avoid such problems, Hinton and colleagues propose a two-step learning algorithm in which networks are first pretrained to recognize the basic elements of variability within images (e.g., edges, lines, etc.), after which backpropagation is used to train the networks to make finer distinctions between object categories, effectively shifting the features detected in order to separate representations for different objects. Overall, treating object recognition as a hierarchical, multi-stage process has shown great potential for identifying objects within images. There are, however, several aspects of visual cortical processing that are not widely incorporated into Neocognitron-like models of object recognition. For instance, there are many projections in visual cortex from neurons in “higher” levels back down to “lower” levels. These feedback projections are thought to play an important role in object recognition (see Epshtein, Lifshitz, & Ullman, 2008 for an example of a model that does incorporate such feedback). Furthermore, processing within regions of the brain other than

Perceptual Learning

visual cortex may control how visual attention and eye movements are directed to particular regions of space (e.g., Corbetta & Shulman, 2002; Moore, Armstrong, & Fallah, 2003). The capacity to look at and selectively attend to particular subregions of an image is likely critical for parsing cluttered visual scenes. More recent computational models of vision have attempted to incorporate these aspects of neural processing. In humans, this type of selective attention is often considered to be a top-down process, meaning that information from later stages of visual cortical processing, or from other brain regions such as the parietal or frontal cortices, selectively increases the amount of processing devoted to one region of visual space (Corbetta & Shulman, 2002; Desimone & Duncan, 1995). These top-down attentional biases have been found to emphasize regions of space containing objects (Desimone & Duncan, 1995). Recent computational models have attempted to selectively segment regions of an image for processing (Gould, Fulton, & Koller, 2009; Itti & Koch, 2001), for example by identifying boundaries that mimic the outline of a target object or a region in an image. Correct segmentation of an image requires some knowledge about the object being searched for, however, leading to a chicken-and-egg problem. Some researchers have proposed that salient regions of an image might serve to initially guide attention through bottom-up (that is, stimulus-driven) mechanisms (Itti & Koch, 2001; Kanan, Tong, Zhang, & Cottrell, 2009). For example, Itti and Koch propose that there are features such as a flashing light or red square in a field of green squares that are “intrinsically conspicuous or salient in a given context” (p. 194). Maps of the saliency of different parts of an image can be constructed to predict the eye movements of people examining the images quite accurately (Chikkerur, Serre, Tan, & Poggio, 2010; Kanan et al., 2009). Computational approaches to recognizing objects that incorporate saliency-based image segmentation have achieved stateof-the-art performance levels (Kanan & Cottrell, 2010), and show great potential for future explorations. The discussion above provides just a glimpse into the mathematical and conceptual advances that have driven progress in computational modeling of visual object recognition. Currently, one of the great divides in computer vision lies between approaches that use predefined mathematical functions for each stage of processing (an analytical approach), and techniques that acquire mathematical functions through the application of learning algorithms to network inputs and outputs (a dynamic learning approach). Both approaches have had success in recognizing and detecting objects within images, and each has its

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advantages. The analytical approach offers mathematical elegance, as well as the capacity to transform and analyze images in ways that mimic current theories of neural processing. On the other hand, we are far from knowing what neural transformations underlie object recognition by humans. Models of object recognition that dynamically learn to recognize objects offer the advantage that new principles of image processing can potentially be discovered, some of which might not be easily revealed by more analytical approaches. Overall, recognizing that visual object recognition is a process that is strongly affected by the availability, organization, and control of multifaceted feature-detecting operations is a major advance in understanding that was made possible by past efforts to computationally recreate this deceivingly simple mental capacity. The computational frameworks developed to emulate object recognition within cortical circuits set the stage for more recent empirical and computational studies, which increasingly suggest that at least some of the “feature detectors” involved in visual processing may adapt across multiple time scales (Wong, Palmeri, Rogers, Gore, & Gauthier, 2009), such that no object is ever recognized in quite the same way twice by the brain. This facet of perceptual processing is described in the next section.

PERCEPTUAL LEARNING Many models of visual object recognition characterize the computational mechanisms that enable pattern recognition and localization as well as the structural and functional organization of the neural circuits involved. Some also attempt to describe how visual recognition circuits develop within the brain (Bienenstock, Cooper, & Munro, 1982) or how learning experiences affect recognition abilities (Norman, O’Reilly, & Huber, 2000; Obermayer & Sejnowski, 2001). Historically, attempts to model general learning mechanisms have progressed independently from models of both perception and neural plasticity. Recent computational models of perceptual learning, however, are beginning to bring these two domains closer together. Expertise in Making Subtle Distinctions Perceptual learning is a process in which repeated experiences with sounds, objects, odors, tastes, or textures increase an individual’s ability to make fine distinctions between highly similar percepts (Lu & Dosher, 2009; Palmeri, Wong, & Gauthier, 2004). This phenomenon was

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noted early on by James (1890) and others, but did not become a focus of experimental studies until the 1950s (Gibson & Gibson, 1955). Although initially identified in humans, early experiments established that other animals show similar improvements after either extended stimulus exposure or discrimination training. Increases in discrimination performance after mere exposure to objects increased theoretical interest in this phenomenon, because most early theories of learning proposed that predictive associations between stimuli, responses, and outcomes (including reinforcement or punishment of responses) were a necessary prerequisite for learning. Several qualitative (Ahissar & Hochstein, 2004; Seitz & Watanabe, 2005) and computational models (Liu, Lu, & Dosher, 2010; Petrov, Dosher, & Lu, 2005, 2006; Saksida, 1999; Tsodyks & Gilbert, 2004) have been proposed to account for the improvements in discrimination performance associated with perceptual learning. In some cases, these models build upon existing learning theories and are equally applicable to humans and other animals. Figure 23.7 shows the architecture of a representative neural network model developed to account for perceptual learning that includes a combination of unsupervised learning by a spatially organized grid (or map) of nodes, followed by supervised learning of associations Outcome

∑ Supervised learning Competitive layer

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Figure 23.7 Saksida’s (1999) neural network model of perceptual learning. Saksida’s (1999) model includes an input layer, a spatially organized layer in which nodes compete to become active, and a node that controls the systems response to stimuli. As learning progresses, weights feeding into the competitive layer are modified based on the frequency and similarity of different stimuli using an unsupervised learning algorithm. Learned representations of stimuli become associated with specific responses via a supervised learning algorithm, to produce specified response outcomes.

between inputs and desired responses (Saksida, 1999). This model explains exposure-based perceptual learning as the result of spatial reorganization within a map of nodes that responds to sensory inputs. Map reorganization is driven by the properties of inputs received by the map—frequently recurring inputs are represented by larger numbers of spatially contiguous nodes. When the map is repeatedly presented with two similar inputs, those inputs compete for space in the map, causing the nodes that best represent differences between the stimuli to become spatially separated. In this model, improvements in the discriminability of perceptual events are a side effect of competitions between nodes that are all attempting to tune their responses to match the properties of frequent inputs. This model focuses on characterizing mechanisms of experience-dependent representational change without accounting for the actual properties of perceptual circuits within the brain. Nevertheless, it retains some neurally inspired features, including representing stimulus similarity in terms of the spatial contiguity of nodes—a feature commonly observed in the primary sensory cortices. Representational Change or Shifts in Attention? One notable property of perceptual learning in the visual domain is that it is often highly specific to the images that participants experience during training. For instance, improvements in the ability to distinguish slightly tilted lines may disappear if the images are rotated 90 degrees (Fiorentini & Berardi, 1980). This has led some researchers to suggest that neural changes associated with visual perceptual learning are constrained to circuits in primary visual cortex (V1). Consequently, efforts to model the neural mechanisms of perceptual learning have often targeted this region (however, see Ahissar & Hochstein, 2004, for an alternative perspective). Current models of visual perceptual learning emphasize the role of feedback (e.g., Herzog & Fahle, 1998, 2002), and focus on isolating connections within visual cortical regions that change with experience. Unlike Saksida’s (1999) model, several of these models propose that the response properties of networks of cortical neurons that represent visual inputs are stable, and that what changes during learning are the associative connections between these cortical neurons and other brain regions. For example, Petrov and colleagues (2005) developed a neural network model of perceptual learning in which elements of inputs are selectively emphasized based on feedback about the accuracy of responding by the network (Figure 23.8). In their model, increases in acuity reflect selective processing of stimulus components

Perceptual Learning Outcome Criterion

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Figure 23.8 Petrov et al.’s (2005) neural network model of perceptual learning. Petrov et al.’s (2005) model consists of a single layer of nodes feeding into one node that modulates the outcomes associated with inputs based on feedback. Unlike Saksida’s (1999) model, shown in Figure 23.7, this model assumes that stimulus representations are unaffected by learning experiences.

to emphasize those that are most relevant. The approach of this model is quite similar to earlier theories proposed by animal learning theorists to explain how animals learn to discriminate stimuli (Blough, 1975; Mackintosh, 1965). A major challenge for current computational models of perceptual learning is to distinguish situations in which behavioral/perceptual improvements reflect changes in the representation of sensory inputs (as in Saksida’s 1999 model) versus shifts in attention or learned associations between events (as in Petrov, Dosher, and Lu’s model [2005]). More neurally constrained models of perceptual learning have been developed to account for auditory perceptual learning, especially in birds (Larson, Perrone, Sen, & Billimoria, 2010; Troyer & Bottjer, 2001). These models of auditory processing converge with models of visual object recognition, because both emphasize the encoding and differentiation of incoming spike trains by cortical networks. The main difference is that auditory perceptual learning models typically focus on simulating recognition of temporally dynamic sound sequences (the equivalent of silent movies in the visual domain). Computational models of auditory perceptual learning have been constrained by the types of inputs being distinguished (spike trains), as well as the kinds of learning algorithms used to modify connections (based on empirically derived rules about how asynchronies in spike-timing impact synaptic plasticity). A second modeling approach not specifically focused on perceptual learning, but closely related to it, examines

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how changes in cortical receptive fields occur as an individual learns to perform various auditory tasks (detection, discrimination, and recognition of sounds within noise). Most auditory learning tasks have been associated with changes in auditory cortical receptive fields (Weinberger, 2004), including habituation (in which animals are repeatedly exposed to sounds with no consequence, as in exposure-based perceptual learning), and frequency discrimination training (in which animals are trained to distinguish similarly pitched tones). Auditory learning leads to various shifts in the response sensitivities of cortical neurons, depending on the task and the stimuli used during training. Neurally based qualitative (Suga & Ma, 2003; Weinberger et al., 1990), mathematical (Mesgarani, Fritz, & Shamma, 2010), and computational models (Mercado, Myers, & Gluck, 2001) have been developed to predict and explain experience-dependent changes in auditory receptive fields. In these models, the focus is on identifying how and when cortical representations of sound features are adjusted based on experience. Thus, these models attempt to predict changes in cortical response patterns that would occur during auditory learning. Computational models of auditory cortical plasticity complement recent efforts to simulate the mechanisms that determine the form of visual cortical receptive fields (Karklin & Lewicki, 2009; Olshausen & Field, 1996), described below. Efficient Encoding of Natural Scenes Attneave (1954) and Barlow (Barlow, 1961, 2001) introduced the idea that one of the main goals of sensory systems is to identify statistically redundant information, so that it might be represented more efficiently. This encoding approach emphasizes a smaller set of more general features that are shared across inputs, rather than veridical replication of the idiosyncratic features of any particular image. From this perspective, perceptual learning may serve to encapsulate critical features that help discriminate between underlying components of a perceived event, such as individual objects. Redundant information within inputs can be represented in several ways. Neural networks trained with backpropagation, for example, can use hidden layers with few nodes, forcing the network to compress its representation of inputs. Processing in such a network is analogous to limiting the number of factors in a regression equation—although a regression equation with fewer factors may not reproduce patterns in a data set as precisely, reduced-factor equations are more likely to generalize well to novel data sets. Dimensional compression of this sort

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is also associated with principal components analysis and self-supervised learning algorithms (Edelman & Intrator, 2002). Another popular technique for forcing a neural network to efficiently encode the most relevant information is sparse coding. In sparse coding, a large number of possible input “filters” (or regression equation factors) exist, but only a small number of them can become simultaneously active. Sparse coding can be implemented in a neural network by including the amount of node activity as part of the error signal, with greater activity being penalized. Unlike the compressed representations generated by small hidden layers, sparse coding produces representations that are more similar to independent components analysis (Bell & Sejnowski, 2002). Some sparse coding algorithms naturally generate receptive field properties similar to those observed in primary visual cortex (Olshausen & Field, 1996), and in peripheral auditory processing (Smith & Lewicki, 2006). Each of the neurally inspired models described above was developed to address different aspects of learning, perception, and object recognition. Nevertheless, they collectively capture overlapping neural and psychological mechanisms of the mental processing of perceived events. These computational models have generated new predictions about how learning experiences may change brain and behavioral patterns. Experiments that test these predictions can clarify not only how brain activity enables perceptual processing, but also what factors determine when and how existing perceptual systems adapt to new requirements (e.g., when sensory receptors or the brain are damaged).

MEMORY FOR EVENTS Computational models of perceptual learning emphasize gradual experience-dependent adjustments to processing of sensory inputs that enhance perceptual acuity, whereas models of visual object recognition focus on the decomposition of inputs into components that facilitate pattern recognition. In neural network models simulating either of these perceptual processes, connections between nodes are incrementally modified by learning algorithms in ways that roughly mimic cumulative synaptic changes. This approach reflects a widely held view that experience-dependent changes in synaptic connections between neurons are a fundamental neural substrate of learning and memory (Martin, Grimwood, & Morris, 2000). The “synaptic plasticity and memory”

hypothesis, proposed a century ago by Ramon y Cajal (1937, 1937/1996) and James (1890), among others, is arguably the foundation for modern, neurally motivated models of psychological processes. It is believed to hold true not just for perceptual processes, but for all of the mental abilities that organisms possess. Despite the dominance of this viewpoint in neuroscience, many computational models of memory processes developed by psychologists do not incorporate incremental changes into either memory representations or perceptual processes (e.g., Yonelinas & Parks, 2007). Instead, most models of memory currently focus on replicating behavioral patterns observed in laboratory experiments that were designed to produce memories for specific events that occurred during the experiment. The few models that do consider how neural processes relate to memory phenomena typically focus on how different brain regions are involved in different kinds of memories. Specifically, neurally inspired models of memory attempt to account for how brains store, maintain, and retrieve (or forget) information across various time scales. The memory phenomena emphasized in these models can be glossed as “things you can do,” “things you know about,” and “things you’ve experienced that you can remember.” In this taxonomy, object recognition can be viewed as something that you’ve learned to do—you’ve acquired perceptual and conceptual skills for segregating and classifying experienced objects. Perceptual learning would then be part of the process that enables you to learn to recognize objects. Objects are also “things you know about,” in the sense that you can verbally describe them and understand their implications. Models of memories for facts about the world—semantic memories—have only occasionally been inspired by findings from neuroscience (Rogers, 2008). Consequently, in the following sections we focus on recent neurocognitive models related to “things you’ve experienced that you can remember,” often referred to as episodic memories. Recognition, Recall, and Recurrence Memories for relatively detailed events (e.g., a recently watched movie) can be formed quite rapidly and have the potential to last for years. This property appears to differentiate event memories from the kinds of incremental changes associated with perceptual learning. Additionally, behavioral data from brain-damaged individuals shows that it is possible to lose the ability to recall events, while retaining some ability to learn skills (e.g., Vann, Aggleton, & Maguire, 2009; Wang & Morris, 2010), and neuroimaging data shows that the formation of event memories

Memory for Events

is associated with unique distributions of brain activity (e.g., Ranganath, 2010). Consequently, many researchers have proposed that the brain regions that process event memories are functionally and anatomically distinct from those used to learn and recall skills. Several recent computational models of episodic memory formation suggest that a small set of brain regions are critically involved: the hippocampus, entorhinal cortex, perirhinal cortex, and prefrontal cortex (Meeter, Myers, & Gluck, 2005; Norman, 2010; Norman & O’Reilly, 2003). Although there is consensus that each of these brain regions plays a unique role in the encoding and retrieval of event memories, modelers do not always agree about what those roles are. These brain regions are composed of several subregions, each of which may be separately accounted for within a computational model. Connectionist models of episodic memory have often incorporated differences in processing within hippocampal subregions (e.g., in the dentate gyrus, CA1, and CA3; see Figure 23.9). Most neurally inspired models of episodic memory attempt to capture basic behavioral phenomena observed in humans with and without brain damage, and some also attempt to account for data from nonhumans. A few incorporate what is known about the temporal dynamics of neural oscillations within the hippocampus (Norman, Newman, & Perotte, 2005), but most simply focus on encoding and retrieving sets of vectors representing particular events using interconnected neural networks. Because EC_out

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Figure 23.9 A neural network model of corticohippocampal interactions. A neural network model of interactions between cortical fields (the entorhinal cortex = EC) and hippocampal subregions (CA1, CA3, and the dentate gyrus = DG), called the complementary learning systems network, attempts to account for how memories can be stored such that they can be retrieved with minimal interference (adapted from Norman, Detre, & Polyn, 2008). More recent models of hippocampal processing in memory formation emphasize the unique role that newly added neurons (depicted here as diamonds) in the dentate gyrus may play during encoding.

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input representations are distributed, changing the connections to encode one vector can also potentially change connections that were previously modified during the encoding of a different event. Such counterproductive changes can interfere with encoding and disrupt later recall. As noted earlier, changes in connections between nodes within neural networks serve to simulate learning-related changes in synaptic strengths, and thus many current connectionist models implicitly endorse the proposal that the neural substrates of memories are sets of adaptive synapses (modeled as weight vectors). These models also often assume that encoding a memory involves modifying synapses, whereas retrieval consists of reinstating a pattern similar to the one that was previously experienced, using the modified synapses. There is a second way, however, that memory processes can be incorporated into connectionist models. This involves making the outputs of some nodes the inputs for other nodes that provide inputs to the original nodes (forming a loop). By creating loops of processing nodes, patterns of activity across nodes can be sustained (Cohen & Grossberg, 1983; Elman, 1990; Hopfield, 1982); such networks are called recurrent neural networks. Figures 23.3 and 23.9 show three different examples of recurrent neural networks with nodes connected in loops. Several computational models of event memory allocate the job of maintaining patterns of activity to the prefrontal cortex (Moustafa & Gluck, 2011; O’Reilly & Frank, 2006). The hippocampus is then associated with rapid synaptic encoding of events, whereas cortical circuits in the parietal and temporal lobe are constrained to slower encoding of events (with intermittent recoding assistance from hippocampal networks). The idea that different brain regions are segregated based on their ability to maintain activity patterns and on how rapidly they can encode new patterns is referred to as the complementary learning systems model (Marr, 1971; McClelland, McNaughton, & O’Reilly, 1995; Norman, 2010). This model attempts to incorporate both connectionist principles and general knowledge about the involvement of different brain regions in different memory tasks to create a global model of how brains solve the task of memory storage and retrieval. As such, this approach represents an attempt to merge circuit-level studies of experience-dependent synaptic plasticity with neuropsychological and cognitive neuroscience studies of brain systems. New Neurons and New Memories In all the models of object recognition, perception, and memory described earlier in the chapter, the main features

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of brain circuits that are simulated characterize interactions between neurons as well as experience-dependent changes in those interactions. Recently, however, a new class of models has emerged inspired by the relatively recent discovery of neurogenesis in adult brains. For many years, researchers believed that neurogenesis (the birth and maturation of new neurons) occurred only during early stages of brain development. This belief was so entrenched that the first reports of neurogenesis in adult animals were largely ignored or disparaged. In the last decade, however, definitive evidence of neurogenesis in several brain regions from multiple species has been collected. These findings immediately raise the question: what exactly are the new neurons doing? One place where neurogenesis occurs in adult mammals is within a subregion of the hippocampus known as the dentate gyrus. You may recall from the discussion above that computational models of the dentate gyrus have been a component of several neurally inspired models of episodic memory. Although some neural network models have the capacity to add nodes as needed (Heinke & Hamker, 1998), these have rarely been used to explain memory abilities, and never as models of hippocampal function. In principle, neurogenesis within the hippocampus might not have any impact on memory processes (e.g., if the new neurons are not incorporated into existing networks). However, training experiences and environmental enrichment that are known to affect learning abilities also modulate the rate of neurogenesis within the hippocampus (Aimone, Deng, & Gage, 2010), suggesting that new neurons contribute to learning processes. The main role hypothesized for the dentate gyrus in memory processing is as a segregator of encoded representations (Rolls, 1996). Most computational models of memory have assumed that stable sets of neurons within the dentate gyrus are involved (Myers & Scharman, 2009; O’Reilly & McClelland, 1994). In contrast, several recent connectionist models of episodic memory suggest that the availability of neurons of different ages within the dentate gyrus may have multiple impacts on memory encoding. First, young neurons may facilitate time-dependent linkages between event representations, as might be expected for chronologically organized episodic memories (Aimone, Gage, & Wiles, 2009). Additionally, the availability of young neurons may increase encoding capacity (Becker, 2005; Weisz & Argibay, 2009), or reduce interference by having new neurons encode new information (Appleby & Wiskott, 2009). Collectively these simulations illustrate how new findings from neuroscience research can drive new approaches to modeling psychological processes.

Computational models can in turn generate new predictions that neuroscientists can subsequently test in further experiments. Like synaptic plasticity, neurogenesis may affect neural and psychological functions in complex ways. Computational models provide a way to systematically explore the various hypothesized functions of the dentate gyrus and neurogenesis in memory processes, and to assess how differences in neural processing afforded by younger neurons might affect one’s ability to encode and recall events.

AGING AND PSYCHOLOGICAL DECLINE Neurally based computational models of psychological processes often simulate the mechanisms that enable brain functions. They can also be used, however, to explore how brain disorders, damage, and deterioration can lead to psychological dysfunction. For example, researchers can simulate psychological deficits resulting from cortical lesions by removing components of a computational model or by constraining model parameters that affect learning or processing of inputs. Research on Parkinson’s and Alzheimer’s disease has revealed that deteriorating neuromodulatory systems within the brain contribute to the mental deficits associated with these disorders. Computational models have been used to simulate the psychological impacts of these age-related disorders (Finkel, 2000; Hasselmo & Sarter, 2011), and to characterize decreases in neural and psychological function associated with aging and neuromodulation more generally (Fellous & Linster, 1998; Li, Lindenberger, & Sikstrom, 2001; Li & Sikstrom, 2002). Dopamine and Age-Related Mental Deterioration Dopamine is a neurotransmitter that is released throughout the brain, especially in association with rewarding events. Computational models of dopaminergic modulation of behavior have focused on capturing experimentally measured properties of neurophysiology in monkeys and behavioral performance in humans. Specifically, the models have emphasized how fluctuations in dopaminergic levels can either facilitate or impede learning (Joel, Niv, & Ruppin, 2002; Suri, Bargas, & Arbib, 2001). Recent simulations have also looked at how variations in dopaminergic modulation might affect the neural representation of ongoing events. Normal aging is associated with a parallel decline in sensory and cognitive functions (Backman, Lindenberger, Li, & Nyberg, 2010; Backman,

Aging and Psychological Decline

Nyberg, Lindenberger, Li, & Farde, 2006; Lindenberger & Ghisletta, 2009). This deterioration is attributed to the loss of neurons and connections throughout the brain (but especially in prefrontal cortex), as well as systematic neurochemical changes. One recent computational model suggests that age-related decrements in dopamine levels contribute to memory deficits and sensory processing in the elderly (Li, Brehmer, Shing, Werkle-Bergner, & Lindenberger, 2006; Li, Naveh-Benjamin, & Lindenberger, 2005; Li, von Oertzen, & Lindenberger, 2006). In this model, a lack of dopamine increases the noisiness of neural representations of events, thereby decreasing the distinctiveness of those representations. The effect was simulated by adjusting the gain parameter of the activation function used by nodes within a neural network (see Figure 23.10). Models of dopaminergic modulation have also been used to characterize processing deficits associated with agerelated disorders such as Parkinson’s disease. Parkinson’s disease destroys neuromodulatory neurons that release dopamine into both the basal ganglia and the cerebral cortex. This disruption of dopaminergic modulation is associated with deficits in cognitive and motor performance.

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Drugs developed to treat Parkinson’s disease attempt to counteract the lower availability of dopamine and can alleviate some symptoms. Over time, however, these drugs can also lead to cognitive and motor dysfunction. A popular network architecture for simulating dopaminergic function in motor learning and performance is the actor-critic model (Joel et al., 2002). In actor-critic models, one network (the actor) learns to act in ways that maximize future rewards. A second network (the critic) measures rewards and adaptively learns to predict rewards. Learning in the critic network is based on a learning algorithm referred to as temporal difference learning, in which errors between sequential predictions are used to adjust weights in the critic network. Recent simulations of behavioral deficits associated with Parkinson’s disease characterize dysfunctional dopaminergic modulation as reductions in the learning rate used to modify connection weights in an actor-critic model (Moustafa & Gluck, 2011; Moustafa, Keri, Herzallah, Myers, & Gluck, 2010). These simulations capture basic learning and generalization deficits seen in Parkinson’s patients. Neurally inspired approaches to modeling dopaminergic involvement in learning and memory in individuals with intact or dysfunctional brains can generate specific predictions about how different treatments might affect performance, as well as predictions about how neural activity should be affected by variations in dopamine levels, which can potentially clarify when (and if) different models’ predictions are most informative. Cholinergic Modulation of Corticohippocampal Learning and Encoding

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Figure 23.10 Response properties of node activation functions. Parameters controlling the response properties of node activation functions can be used to simulate the effects of variations in neuromodulators, such as dopamine on activation patterns. Li and colleagues (2006) modeled decreases in dopaminergic modulation as decreases in the gain of a sigmoidal activation function to simulate stimulus processing in the elderly.

Like Parkinson’s disease, Alzheimer’s disease is associated with a dysfunctional neuromodulatory system. In the case of Alzheimer’s, however, it is cholinergic disruption rather than dopaminergic dysfunction that contributes to psychological deficits. The basal forebrain cholinergic system modulates activity in the hippocampal region as well as cortex by controlling levels of the neuromodulator acetylcholine. Like dopamine, acetylcholine is a neurotransmitter that is released throughout the brain. However, whereas dopamine is typically released in association with rewarding events, acetylcholine is more closely linked to attentional and memory encoding processes. In Alzheimer’s disease, cholinergic neurons in the basal forebrain gradually die off, effectively reducing cholinergic modulation. Many of the drugs developed to treat Alzheimer’s patients were designed to counteract this neuromodulatory deficit. It is known from animal studies that

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Figure 23.11 Simulating cholinergic modulation in corticohippocampal processing. In a multi-network model of interactions between cortical and hippocampal processing during learning developed by Myers et al. (1996), the effects of acetylcholine on encoding are simulated using variations in a learning rate parameter.

basal forebrain neurons can control when cortical circuits are modified by experience (Suga & Ma, 2003; Weinberger, 2004). For example, pairing electrical stimulation of basal forebrain neurons with the presentation of sounds can dramatically shift the response properties of neurons in auditory cortex (Kilgard & Merzenich, 1998). This region is also known to be critical to memory abilities in humans, because strokes destroying the basal forebrain lead to severe amnesia (Deluca, Bryant, & Myers, 2003). Neural network models of corticohippocampal involvement in learning processes within normally functioning brains have been used to simulate the possible impacts of cholinergic disruption in ways that parallel the computational approaches used to simulate dopaminergic modulation. For example, Myers and colleagues developed a model of corticohippocampal involvement in associative learning to simulate effects of deficient cholinergic modulation on hippocampal processing (Myers et al., 1996; Myers, Ermita, Hasselmo, & Gluck, 1998; Rokers et al., 2002). In this connectionist model, a neural network simulating hippocampal processing modifies inputs to compress redundant information while maintaining information predictive of likely consequences (Figure 23.11). In the basic model, hippocampal representations supplement more constrained cortical representations (Gluck & Myers, 1993). Within this framework, changes in cholinergic modulation produced by brain damage or other disruptions (e.g., drugs affecting cholinergic functions) were modeled as differences in the amount of time the hippocampal network spent encoding inputs (implemented as variations in the learning rate). Myers and colleagues (1996, 1998) found that this simple manipulation captured several effects of septal and cholinergic disruption on classically conditioned

responses. More recent extensions of this model found that varying encoding time could also capture impacts of cholinergic levels on instrumental conditioning (Rokers et al., 2002), and category learning (Moustafa et al., 2010). The above computational models all focus on describing the impacts of global variations in neuromodulation on behavioral performance. It is also possible to model neuromodulatory effects at a much more detailed neurophysiological level. For instance, Hasselmo and Barkai (1995) developed biophysical simulations in which the effects of acetylcholine on synaptic modifications were investigated (Figure 23.12). In this biophysical model, the effects of cholinergic modulation on associative memory function were simulated by controlling different stages of synaptic transmission—learning in this model depended on strengthening excitatory synapses using a Hebbian learning rule. They found that to maintain the storage capacity of a neural network, the rates of synaptic potentiation (strengthening) and depression (weakening) had to be simultaneously regulated, and that there was an optimal balance for maximizing encoding. Hasselmo and Barkai’s simulations of cholinergic modulation also predict that neural circuits need to oscillate between states of encoding and recall to prevent disruptive synaptic changes, and that cholinergic modulation by neurons in the basal forebrain may be an important controller of how and when synaptic changes should occur. These predictions are consistent with neurophysiological experiments showing that cortical networks can be rapidly redesigned by externally controlling cholinergic neurons with electrical neurostimulation (Kilgard & Merzenich, 1998). Extensions of this model of cholinergic function were used to simulate the breakdown of memory abilities

Emerging Trends Synaptic Na+ conductance (Afferent)

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Figure 23.12 Biophysical model of sensory processing in cortex. Hasselmo and Barkai’s (1995) biophysical model of sensory processing in cortex simulated the effects of acetylcholine on memory formation using networks of nodes that characterized the membrane potentials and synaptic conductance of individual neurons (top). With such models, it becomes possible to simulate the electrical dynamics of individual neurons as well as networks of neurons (bottom).

associated with Alzheimer’s disease (Hasselmo, 1994, 1997). Psychological problems arise in individuals with Alzheimer’s disease, according to the model, because decreases in cholinergic modulation may cause synaptic transmission to interfere with synaptic modification. Hasselmo’s simulations also suggest that dysfunctional consolidation mechanisms in Alzheimer’s that are engaged during sleep could exacerbate the memory problems experienced by patients with Alzheimer’s disease.

EMERGING TRENDS The history of interactions between the fields of neuroscience, psychology, and mathematics over the past century reveals a cyclical waxing and waning of contact and progress. Similarly, neurally inspired models of psychological processes have periodically risen and fallen in popularity. In the late 1800s, advances in neurochemistry and neurophysiology provided the impetus for developing neurophilosophical theories of the mind (James, 1890). In the early 2000s, advances in neuroimaging and synaptic physiology have played a similar role in driving the development of computational neuroscience. Most recently, computational models based on the mathematics of complex systems, information theory, and Bayesian statistics

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have gained in popularity (though see also, McClelland et al., 2010). These developing technologies will likely provide new ways of thinking about the operations that neural circuits perform, and potentially new ways of modeling psychological functions that merge multiple computational approaches. Over the past decade, computational modelers have increasingly focused on a few core topics pertinent to understanding brain function, including: • simulations examining the role of plasticity mechanisms (Nelson, McKenzie, Cottrell, & Sejnowski, 2010; Poggio, 2007) • algorithms for constructing efficient representations of sensory inputs (Bell & Sejnowski, 2002; Karklin & Lewicki, 2009) • models that incorporate precise spiking patterns and spike-timing dependent learning (Bohte & Mozer, 2007; Larson, Billimoria, & Sen, 2009) • systems-level models of categorization, working memory, decision processes, and cognitive control (Ashby, Ennis, & Spiering, 2007; Krueger & Dayan, 2009; O’Reilly, Herd, & Pauli, 2010; Purcell et al., 2010) • subregion-level models of reinforcement learning and neuromodulation (Frank, Santamaria, O’Reilly, & Willcutt, 2007; Suri et al., 2001) • realistic models that recreate simple motor patterns (Boothe, Cohen, & Troyer, 2006; Degallier & Ijspeert, 2010) • connectionist models of age-related brain disorders (Li et al., 2005; Moustafa et al., 2010) • theoretical models that describe learning as probabilistic interpolation or model construction (Bell, 2007; Berkes, Orban, Lengyel, & Fiser, 2011). These focal areas are likely to be emphasized over the next decade as well, although new techniques and foci will undoubtedly arise. Development of new and existing models reflects, in part, the discovery of new empirical phenomena in need of modeling, as well as the availability of techniques for constructing models. Before spatially organized receptive fields in primary sensory cortices were identified, models that incorporated topographical constraints on processing were rare. Before the role of neuromodulatory systems in age-related disorders was discovered, there was little motivation or need to consider their potential effects on behavior or brain activity in computational models of learning and memory. New discoveries in neuroscience and psychology will undoubtedly lead to more powerful

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computational models. Mathematical methods and engineering techniques generally have advanced more quickly than psychological science (or neuroscience), and many existing computational tools have yet to be applied to the simulation of brains or behavior. Consequently, crossdisciplinary interactions will likely play an important role in the future development of neural and psychological simulations. Increasing computational resources will also broaden opportunities for developing and testing a variety of models (McClelland, 2009). This will likely lead to larger-scale, more detailed simulations of neural systems (Koslov, Huss, Lansner, Kotaleski, & Grillner, 2009).

GENERAL DISCUSSION Our review focused on computational models of visual processing, learning-related changes in perception, memory storage and retrieval, and age-related deterioration of behavioral capacities. Modeling efforts in these domains provide snapshots of the progress and potential of neurally inspired computational approaches to modeling psychological processes. We make no claims about the validity of these models as simulacra of brains, or about whether they represent the best of breed. The value of neurally based simulations of psychological processes does not necessarily lie in establishing which models are the “truest,” or even in identifying which models replicate the most patterns in neural and behavioral data. Instead, computational models can prove their worth through their ability to expose gaps in our current understanding of how brain structure and function relate, by inspiring new research to test models’ predictions, and by highlighting the many hidden assumptions underlying verbal claims about what brains are like and about how people think and behave. The promise of neurally inspired models to bridge the gap between neuroscience and psychology is as yet only partially fulfilled. Modelers tend to follow the trends of the experimentalists whose data they are modeling. Driven by advances in technology and theoretical understanding, current experimental trends are primarily in the direction of disciplinary specialization. Behavioral neuroscientists are moving more toward cellular-level studies of particular brain regions, or subregions, or even subsystems of subregions (e.g., a subset of neurons that release a specific type of neurotransmitter). Cognitive psychologists, in contrast, are increasingly focusing on processes that involve multiple interacting brain regions, and in some cases, on multiple interacting brains (e.g., social cognition research, mother-infant interactions in development).

This divergence has led to parallel divisions in modeling efforts. For instance, a recent collection of papers reviewing computational models of cognitive processes (Sun, 2008) showed little overlap in authors or topics with a similar volume reviewing computational models in behavioral neuroscience (Heinke & Mavritsaki, 2009). With increasing specialization, the models developed by computational neuroscientists, behavioral neuroscientists, cognitive neuroscientists, and experimental psychologists run the risk of becoming increasingly insular, reducing their potential to span the divide between psychology and neuroscience. In adapting to this trend, some researchers are beginning to form interdisciplinary teams in which modelers, human experimentalists, and physiologists work together to develop integrative theories of mental and neural functions. It remains to be seen whether current modeling approaches will eventually be able to converge on a common computational framework, or whether a diversity of separate approaches may be better suited to effectively modeling different neural and psychological processes. The range of modeling approaches continues to diversify as researchers gain access to increasingly powerful algorithms and computers. Undoubtedly, as more is learned about brains and behavior, new tools will be added to the current arsenal. Current computational models need to be pushed to their limits and tested rigorously in a variety of contexts to assess their adequacy for simulating different neural and psychological processes. In this way, the neurally inspired models of today will become the building blocks for future computational, psychological, and neural explorations. Who knows what new computational discoveries are waiting around the corner?

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Sun, R. (Ed.). (2008). The Cambridge handbook of computational psychology. New York, NY: Cambridge University Press. Suri, R. E., Bargas, J., & Arbib, M. A. (2001). Modeling functions of striatal dopamine modulation in learning and planning. Neuroscience, 103 (1), 65–85. Thelen, E., & Smith, L. B. (1994). A dynamic systems approach to the development of cognition and action. Cambridge, MA: MIT Press. Thomas, M. S. C., & McClelland, J. L. (2008). Connectionist models of cognition. In R. Sun (Ed.), The Cambridge handbook of computational psychology (pp. 23–58). New York, NY: Cambridge University Press. Trappenberg, T. P. (2010). Fundamentals of computational neuroscience. Oxford, UK: Oxford University Press. Troyer, T. W., & Bottjer, S. W. (2001). Birdsong: Models and mechanisms. Current Opinion in Neurobiology, 11 (6), 721–726. Tsodyks, M., & Gilbert, C. (2004). Neural networks and perceptual learning. Nature, 431 (7010), 775–781. Vann, S. D., Aggleton, J. P., & Maguire, E. A. (2009). What does the retrosplenial cortex do? Nature Reviews Neuroscience, 10, 792–802. Vetter, T., Hurlbert, A., & Poggio, T. (1995). View-based models of 3D object recognition: Invariance to imaging transformations. Cerebral Cortex, 5, 261–269. Wang, S. H., & Morris, R. G. M. (2010). Hippocampal-neocortical interactions in memory formation, consolidation, and reconsolidation. Annual Review of Psychology, 61, 49–79. Weinberger, N. M. (2004). Specific long-term memory traces in primary auditory cortex. Nature Reviews Neuroscience, 5 (4), 279–290. Weinberger, N. M., Ashe, J. H., Metherate, R., McKenna, T. M., Diamond, D. M., & Bakin, J. (1990). Retuning auditory cortex by learning: A preliminary model of receptive field plasticity. Concepts in Neuroscience, 1, 91–132. Weisz, V. I., & Argibay, P. F. (2009). A putative role for neurogenesis in neurocomputational terms: Inferences from a hippocampal model. Cognition, 112, 229–240. Wong, A. C.-N., Palmeri, T. J., Rogers, B. P., Gore, J. C., & Gauthier, I. (2009). Beyond shape: How you learn about objects affects how they are represented in visual cortex. PLoS One, 4, e8405. Yau, J. M., Pasupathy, A., Fitzgerald, P. J., Hsiao, S. S., & Connor, C. E. (2009). Analogous intermediate shape coding in vision and touch. Proceedings of the National Academy of Sciences, USA, 106, 16457–16462. Yonelinas, A. P., & Parks, C. M. (2007). Receiver operating characteristics (ROCs) in recognition memory: A review. Psychological Bulletin, 133, 800–832.

CHAPTER 24

Normal Neurocognitive Aging BONNIE R. FLETCHER AND PETER R. RAPP

INTRODUCTION 643 A FOCUS ON NORMAL NEUROCOGNITIVE AGING 644 ANIMAL MODELS: METHODOLOGICAL CONSIDERATIONS 644 NEUROPSYCHOLOGICAL PERSPECTIVE ON NORMAL COGNITIVE AGING 645 FRONTAL LOBE FUNCTION IN COGNITIVE AGING 646 MEDIAL TEMPORAL LOBE FUNCTION IN COGNITIVE AGING 648

COGNITIVE AGING BEYOND THE PREFRONTAL CORTEX AND HIPPOCAMPUS: ADDITIONAL DOMAINS, COGNITIVE ADAPTATION, NETWORK INTERACTIONS, AND OTHER CHALLENGES 650 STRUCTURAL CHANGES 650 NEUROPHYSIOLOGY 653 CELL BIOLOGY: OXIDATIVE STRESS 655 CURRENT CHALLENGES AND FUTURE DIRECTIONS 656 REFERENCES 657

INTRODUCTION

to echo throughout the oldest segments of the population. From 2030 to 2050 the number of people over 85 years of age will increase from 8.7 million to a projected 19 million. Representing 4.3% of the population at that time, up from 2.3% in 2030, this projected expansion in the segment of the population most at risk for many of the disabilities of aging clearly presents a significant challenge. As the nation ages, the rich diversity of contributions from older people can be expected to emerge as an increasingly prominent feature of society. The percentage of individuals who continue to participate in the labor force past age 65, for example, has increased steadily in recent decades, with a particularly sharp rise among women (Federal Interagency Forum on Aging-Related Statistics, 2010). This shift is presumably a result of financial necessity for many people and it seems likely that broader economic pressures on entitlement programs and other social support mechanisms will reinforce the trend. Older people also support productivity of the labor force indirectly, with grandparents frequently providing regular, noncustodial care for their grandchildren. Although the prevalence of grandparents serving as adjunct, nonprimary caregivers has declined steadily in the United States since the 1960s, the number of children living in households headed by a grandparent, with or without a parent present, has increased dramatically (Fuller-Thomson & Minkler,

At the beginning of the 20th century people born in the United States could anticipate living for approximately 50 years. Average expected lifespan now approaches 85 in many industrialized countries, and although decreases in infant mortality were an important contributor early in the century, current and projected increases in longevity are now driven predominantly by gains at the end of the lifespan (Kirkwood, 2008). Demographic trends and analysis by the Administration on Aging (www.aoa.gov/) indicates that the number of people over 65 years of age will increase sharply in the coming decades, more than doubling between the years 2000 and 2030, from 35 million to well over 70 million people. Driven by the aging of the baby boom generation, this represents a tremendous shift in the overall age structure of the nation in which the population over age 65 will expand in correspondence with a decline in the proportion of younger individuals. Although the rate of demographic change is less pronounced after 2030, aging of the baby boom generation will continue The authors would like to thank members of the Neurocognitive Aging Section of the Laboratory of Experimental Gerontology for helpful discussion. Manuscript preparation and original research was supported by the Intramural Research Program of the National Institue on Aging. 643

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2001). The reasons are complex, but considered alongside demographic projections for the coming decades (see Federal Interagency Forum on Aging-Related Statistics, 2010), the evidence points to a continued expansion of avenues through which older segments of the population will remain actively engaged as contributing, valued members of the family and society. For some, growing older represents a satisfying period of intellectual engagement, a return to interests set aside earlier in life, or the discovery of new pursuits. Few need to look beyond their own friends and relatives to find examples of aged people deeply connected in society, vibrant and independent, enjoying life. All too often, however, aging is associated with increasing disability, a restriction in daily activities, and a gradual narrowing in the scope and richness of day-to-day experience. Although cardiovascular disease and cancer pose far greater mortality risks, comprising the leading causes of death for those 65 and older (see Federal Interagency Forum on AgingRelated Statistics, 2010), Alzhiemer’s disease is frequently cited as the most feared consequence of aging (Wortmann, Andrieu, Mackell, & Knox, 2010). A total of 4.5 million people in the United States suffered with the cognitive devastation of this disorder in 2000, and the number is estimated to nearly triple by 2050 (Hebert, Scherr, Bienias, Bennett, & Evans, 2003). Through an inexorable erosion of memory and other cognitive capacities, Alzheimer’s disease robs individuals of the unique mental record built over a lifetime—the record that fundamentally defines who we are as individuals. As alarming as these statistics are, they sometimes overshadow the fact that a much larger segment of the population will experience age-related cognitive change that, while modest by comparison, poses a significant risk to independence and the quality of life.

Alzheimer’s disease and other neurodegenerative conditions. Perhaps more important, however, there is increasing recognition from both cognitive and neurobiological investigation that aging can proceed without dramatic cognitive decline. Growing evidence suggests that such outcomes, sometimes termed successful aging, do not simply reflect the absence of decline, or the perpetuation of a youthful condition, but that preserved function is instead supported by an active process of adaptation. By this perspective successful cognitive aging represents a unique neurobiological condition, distinct from that of both young adults and aged individuals with impairment. This view also encourages the translational goal that, alongside traditional efforts to prevent or slow age-related dysfunction, research might profitably be directed at identifying and engaging the mechanisms responsible for successful adaptations to aging. Our review centers on studies in animal models, where effects of normal and pathological aging can be clearly distinguished, drawing on findings in people where the research allows. In the next section we adopt a neuropsychological framework to describe the cognitive effects of normal aging, with particular emphasis on learning and memory. Subsequent sections consider the corresponding neurobiological consequences of aging across multiple levels of analysis, from the interactions among brain networks to the epigenetic regulation of gene transcription. Taken together, the aim is to provide an extensive yet targeted summary of current research on neurocognitive aging, and alongside continued progress toward maximizing longevity, to draw attention to the critical challenge of enabling optimally healthy mindspan, that is, the preservation of cognitive function over the lifespan (Gallagher, Stocker, & Koh, 2011).

A FOCUS ON NORMAL NEUROCOGNITIVE AGING

ANIMAL MODELS: METHODOLOGICAL CONSIDERATIONS

Considerable interest surrounds research on the consequences and causes of normal cognitive aging, that is, changes in memory and other domains over the course of aging in the absence of neurodegenerative disease or other clinically diagnosed conditions that affect cognitive function. The focus of this chapter derives, in part, from the perspective that normal aging comprises a unique and necessary condition for the development of pathological disorders of aging, and in turn, that understanding the impact of aging on the brain in the absence of disease will valuably inform efforts to identify the basis of

The study of normal cognitive aging in people presents a number of challenges. The overall goal is to understand how cognitive function changes over the course of the lifespan, from young adulthood through old age, in the absence of disease. Although tracking individuals over time can provide a detailed characterization of change, within-subject, longitudinal assessment is less efficient than cross-sectional comparisons between age groups, and both designs involve a number of assumptions that typically go untested (Salthouse, 2000, 2010). It has been argued, for example, that cross-sectional data provide

Neuropsychological Perspective on Normal Cognitive Aging

only a weak basis for inferring the process of age-related cognitive change because differences observed between young and aged groups might reflect the influence of multiple variables operating independent of age (e.g., secular changes, differences in educational attainment or other situational and environmental factors). Longitudinal assessment, by comparison, directly tracks change over time, but this approach also rests on assumptions that can be difficult to validate. The selective attrition of poor performing individuals, and the influence of repeated assessment itself on test scores, for example, are among the factors positioned to skew the outcome of longitudinal investigation (Salthouse, 2010). Animal models circumvent many of the methodological challenges of human investigation into neurocognitive aging. A key advantage is that laboratory rodents and nonhuman primates fail to spontaneously develop the pathognomonic features of Alzheimer’s disease and other neurodegenerative conditions. As a consequence the effects of normal aging can be examined in isolation, uncomplicated by the potential contribution of pathological processes that cloud the interpretation of findings in people. By providing exacting control over a wide range of environmental factors, and precluding the influence of other variables unique to human investigation (e.g., educational attainment), studies in animals also mitigate some of the issues surrounding the relative merits of longitudinal and crosssectional approaches to cognitive aging. Alongside these benefits, animal research presents its own hurdles toward a translational account of normal neurocognitive aging. Laboratory rodents are typically maintained with unlimited access to food and little opportunity for physical activity. Rearing under these conditions is associated with a constellation of negative outcomes that includes insulin resistance, obesity, and inflammation, while conversely, calorie restriction and physical exercise benefit metabolic signs of aging (Mattson, 2010). Thus, a potentially significant concern is that overweight, sedentary subjects provide a misleading or skewed baseline for studies of aging, and this may be among the factors contributing to the translational failure of experimental interventions identified as promising in preclinical animal studies (Martin, Ji, Maudsley, & Mattson, 2010). Although diet, exercise, and other rearing conditions have attracted particular attention, in fact these are just a few of the factors that constrain the interpretation of results from animal models of neurocognitive aging. Current evidence derives, to a large degree, from studies of inbred strains of mice and rats maintained in colonies free of many common pathogens, including endemic microbes people regularly

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encounter and that pose little or no risk among immunocompetent individuals. Although this convention ostensibly minimizes variability, a priori it is unknown if the data obtained by this approach reflect fundamental principles of aging that are broadly applicable, or alternatively, findings peculiar to a single genotype maintained under artificial conditions. Indeed, recognizing that “inbred stocks are not only homogeneous, they are also weird, debilitated, and short-lived” (R. A. Miller & Nadon, 2000), a number of research teams have launched cross-breeding initiatives with the specific goal of generating genetically diverse but well defined and reproducible lines of mice for biomedical research (Churchill et al., 2004; R. A. Miller et al., 2007). A substantial body of research has also been directed at documenting the neurocognitive effects of aging in controlled or naturally outbred species, including canines and nonhuman primates (Baxter, 2009a; Studzinski, Araujo, & Milgram, 2005). Although experimental control is decreased to some degree, these models offer the potential of enhanced relevance to human aging. Ultimately, no single strategy is equally suitable for addressing all questions of interest, and it will be important to remain mindful of their particular strengths and limitations in guiding the design and implementation of translational research efforts.

NEUROPSYCHOLOGICAL PERSPECTIVE ON NORMAL COGNITIVE AGING Broad agreement has emerged around key concepts in the neuropsychology of normal memory, establishing a powerful framework for exploring the effects of aging. Once viewed as a unitary, monolithic capacity, it is now understood that memory functions in multiple forms, distinguished along a number of dimensions including the particular qualities of the information remembered, their endurance characteristics, and the flexibility of memory representation (Dickerson & Eichenbaum, 2010; Squire & Wixted, 2011). A significant insight to emerge from studies in humans and animal models since the late 1970s is that the existence of multiple forms of memory—demonstrable at cognitive and behavioral levels of analysis—directly reflects the organization of brain systems that mediate these capacities. In rats, monkeys, and humans, normal memory for the facts and events of daily life, for example, critically requires a collection of anatomically interconnected regions of the medial temporal lobe that includes the hippocampus and adjacent parahippocampal cortical areas (Dickerson & Eichenbaum, 2010; Squire & Wixted, 2011). Damage to the prefrontal cortex, by comparison,

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fails to yield the dense anterograde amnesia for episodic information characteristic of medial temporal lobe damage, instead giving rise to impairment in a constellation of other memory related processes. Prominent cognitive signatures of frontal lobe damage include impairment in the strategic use of remembered information when confronted with changing circumstances, and deficits in recall for the temporal order and source of acquired information (E. K. Miller, Freedman, & Wallis, 2002). Adopting this neuropsychological framework, research detailing the specific nature of deficits that emerge during aging can provide a window on the functional status of the neural systems that mediate memory and other cognitive processes. The following sections update related treatments in our earlier reviews (Gallagher & Rapp, 1997; P. Rapp & Gallagher, 1997; P. R. Rapp, 2009) and those by others (Baxter, 2009a; S. N. Burke & Barnes, 2006).

FRONTAL LOBE FUNCTION IN COGNITIVE AGING Although establishing a unified consensus has proved challenging, current theories emphasize the functional heterogeneity of the prefrontal cortex, supporting multiple information processing capacities that influence the strategic use of memory (for extensive discussion and alternate perspectives see Roberts, Robbins, & Weiskrantz, 1996). Whether the cytoarchitectonically distinct subdivisions of the prefrontal cortex are more appropriately characterized by the specific cognitive processes they mediate, or according to the particular modalities of information on which they operate, however, is the subject of continuing debate (E. K. Miller, 2000). Indeed the degree to which structural or functional homologies exist across the prefrontal cortex of rats and primates remains contested. These unresolved issues notwithstanding, a theme shared among a number of accounts is that the prefrontal cortex comprises an “executive” system, importantly involved in the online manipulation of recently acquired information, and that supports the selection of impending actions among competing options (Robbins & Arnsten, 2009). Conceptualized in this way, a fundamentally similar processing function may underlie the reported role of the dorsolateral prefrontal cortex in spatiotemporal working memory (Ma, Hu, & Wilson, 2011; Tsujimoto, Genovesio, & Wise, 2011), the contribution of the orbitofrontal cortex to predicting future outcomes (Schoenbaum & Esber, 2010), and the broader involvement of the prefrontal cortex in cognitive flexibility and

the control of attention (Kehagia, Murray, & Robbins, 2010; Robbins & Roberts, 2007). A neuropsychological perspective on the prefrontal cortex is illuminating with respect to the behavioral consequences of aging observed in animal models. A number of authors have noted that deficits in capacities requiring prefrontal cortex integrity are among the most consistently reported features of normal aging (A. F. Arnsten, Paspalas, Gamo, Yang, & Wang, 2010; Bartus & Dean, 2009; Baxter, 2009a; Gallagher et al., 2011). Findings from nonhuman primate studies are particularly compelling, demonstrating reliable deficits in aged monkeys on testing procedures that, like the classic delayed response task, emphasize spatiotemporal working memory (A. F. T. Arnsten & Goldman-Rakic, 1985; Bartus, Fleming, & Johnson, 1978; Nagahara, Bernot, & Tuszynski, 2010; O’Donnell, Rapp, & Hof, 1999; P. R. Rapp, Morrison, & Roberts, 2003). Average lifespan in rhesus monkeys is less that 25 years (Tigges, Gordon, McClure, Hall, & Peters, 1988), and subjects over 20 years are typically considered aged. In the traditional, manual version of the delayed response procedure, reward requires subjects to remember the location of a preferred food hidden in one of two possible locations just prior to a delay. Because the rewarded location varies between just two possibilities across trials within a session, the opportunity for proactive interference is substantial, and accuracy depends in part on memory for temporal order, guiding the selection of the location baited most recently. The delayed response procedure also involves an explicit spatial component that is known to engage task-related neuronal firing in the dorsolateral prefrontal cortex (area 46) in monkeys (Ichihara-Takeda, Takeda, & Funahashi, 2010; F. A. Wilson, Scalaidhe, & GoldmanRakic, 1993), and results from a long history of lesion and pharmacological studies confirm that accurate performance requires the integrity of the dorsolateral prefrontal cortex (Goldman-Rakic, 1987). Consistent with the interpretation that age-related delayed response deficits reflect prefrontal cortical dysfunction, recent findings confirm that such impairment occurs together with blunting of memory-related neuronal activity in the dorsolateral prefrontal cortex, and that normal task-dependent firing is at least partially restored by pharmacological treatments that are known to benefit delayed response performance in aged monkeys (Wang et al., 2011). Aged monkeys also exhibit corresponding impairment on other tests emphasizing executive function (Bartus & Dean, 1979; T. L. Moore, Killiany, Herndon, Rosene, & Moss, 2005, 2006; M.B. Moss, Killiany, Lai, Rosene, & Herndon, 1997; P. Rapp & Amaral, 1989), including a nonhuman primate adaptation

Frontal Lobe Function in Cognitive Aging

of the Wisconsin Card Sorting task that requires the dorsolateral prefrontal cortex in young monkeys (T. L. Moore, Schettler, Killiany, Rosene, & Moss, 2009). Aging in the rat is also accompanied by a number of behavioral abnormalities that resemble the effects of discrete frontal lobe lesions (for recent reviews see Baxter, 2009a; Gallagher et al., 2011). Considerable attention has focused on the functional organization of the orbitofrontal cortex (OFC), emphasizing its role in cognitive flexibility and the selection of actions on the basis of expected outcomes. Deficits in reversal learning are a widely reported signature of OFC damage, and although results have not been entirely consistent across studies (Baxter, 2009a), qualitatively similar impairment is sometimes a feature of normal aging. In a study using a reversal procedure known to require the OFC, for example, young and aged rats first learned a series of odor discrimination problems (Schoenbaum, Nugent, Saddoris, & Gallagher, 2002). Although initial acquisition was intact, aged subjects displayed significant deficits when the task contingencies were reversed and previously negative stimuli were rewarded. Interestingly, this impairment was unrelated to individual differences in the status of spatial learning and memory mediated by the hippocampal system (Schoenbaum et al., 2002), suggesting that these systems are independently vulnerable to age-related decline. A corresponding neurophysiological investigation also found that reversal deficits in aged rats are coupled with abnormal encoding among OFC neurons such that cue-selective firing fails to exhibit the reversal-induced shift observed in young and aged subjects with normal reversal learning (Schoenbaum, Setlow, Saddoris, & Gallagher, 2006). Although not all capacities that depend on the OFC appear equally sensitive (Singh et al., 2011), current evidence is consistent with the conclusion that age-related decline in cognitive flexibility derives, at least in part, from functional compromise in the prefrontal cortex. Whereas traditional reversal learning of the sort just described involves behavioral adaptation to new contingencies among stimuli within a single perceptual dimension (i.e., an intradimensional shift), other procedures that are sensitive to aging and frontal lobe damage in people and monkeys, such as the Wisconsin Card Sorting test, emphasize extradimensional set shifting (EDS), that is, the ability to direct attention away from a previously relevant perceptual attribute (e.g., from color to shape). Barense, Fox, & Baxter (2002) examined this capacity in rats using a procedure in which the dimension of a compound stimulus that predicted reward (the odor or texture of digging medium) was modified after initial acquisition.

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Aged rats displayed significant deficits selectively on the EDS component of the task, and similar to the results from standard reversal learning, the impairment was unrelated to the status of hippocampal learning and memory measured in the same subjects. Parallel lesion experiments in young subjects demonstrate that EDS requires the medial frontal cortex (Barense, Fox, & Baxter, 2002), consistent with the possibility that age-related impairments in intra- and extradimensional set-shifting arise independently, reflecting the functional status of the OFC and medial frontal cortex, respectively. Identifying the basis of these and other signs of aging in prefrontal systems (Winocur, 1991; Zyzak, Otto, Eichenbaum, & Gallagher, 1995) is an important area for exploration in rat models. Neuropsychological studies of normal aging in people reveal a profile congruent with findings in animal models (Buckner, 2004; Hedden & Gabrieli, 2004). In some cases it is possible to directly compare results from investigations using operationally similar testing procedures, and in this regard it is noteworthy that, like the results for monkeys described earlier, humans display significant decrements in delayed response accuracy with advancing aging (Lyons-Warren, Lillie, & Hershey, 2010). Although these findings alone provide only limited insight into the specific nature of impairment, other studies have focused greater attention on identifying the underlying information processing functions vulnerable to age-related decline. Noteworthy findings in this context include the observation that normal aging is associated with significant impairment in memory for the temporal order, that is, a capacity emphasized in traditional delayed response procedures (Daigneault & Braun, 1993; Parkin, Walter, & Hunkin, 1995). Evidence that these deficits occur coincident with altered patterns of regional brain activation confirm the likely involvement of the prefrontal cortex (Cabeza, Anderson, Houle, Mangels, & Nyberg, 2000). Other signs of prefrontal compromise in normal human aging include impaired memory for the source of acquired information, independent of the status of memory for the target items themselves (Cabeza et al., 2000; Glisky, Polster, & Routhieaux, 1995). Consistent with results from animal models, these findings imply that aging progresses independently across the neural systems responsible for different aspects of memory. Glisky et al. (1995), for example, addressed this issue in an analysis of neuropsychological test results from a sample of 48 healthy people over 65 years of age. The key finding was that although item and source memory were largely unrelated to each other, they were strongly coupled with performance on a battery of other assessments targeting medial temporal

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lobe and prefrontal cortex integrity, respectively. Taken together, the available evidence indicates that impaired executive function and deficits in other capacities supported by the prefrontal cortex are significant features of normal aging (Buckner, 2004; Hedden & Gabrieli, 2004), positioned to influence planning and decisionmaking capacities critical for independence and the quality of life as we grow older.

MEDIAL TEMPORAL LOBE FUNCTION IN COGNITIVE AGING Establishing a mental record of the unfolding events that comprise our daily lives depends on a collection of anatomically related brain structures in the medial temporal lobe including the hippocampal formation and adjacent parahippocampal region. Sufficiently extensive damage to this system produces deficits in the ability to establish memory for new events, whereas remote memory, for experience prior to the onset of medial temporal lobe dysfunction, is relatively preserved (Squire & Wixted, 2011). A qualitatively similar pattern of impairment and sparing emerges over the course of Alzheimer’s disease (Addis, Sacchetti, Ally, Budson, & Schacter, 2009), consistent with the known pathophysiological progression of the disorder. Anterograde amnesia for episodic information is not a feature typical of the impairment that arises from damage to the prefrontal cortex, revealing a dissociation that can be usefully exploited in the neuropsychological analysis of cognitive aging. Recent efforts have gone beyond a simple neurological accounting of brain areas involved, however, toward understanding how aging influences the underlying component processing functions of memory mediated by the medial temporal lobe system. The following sections review this background with an emphasis on findings that have set the stage for research aimed at defining the neurobiological basis of normal cognitive aging. The type of memory supported by the hippocampal system has been examined most extensively in monkeys using tests of visual recognition memory developed in the 1970s (Gaffan, 1974; Mishkin & Delacour, 1975). In the now classic delayed nonmatching-to-sample (DNMS) procedure, monkeys tested in a manual apparatus are initially presented with a sample “junk” item that can be displaced to reveal a food reward. After a delay during which the sample is hidden from view, recognition memory is probed by re-presenting the previously viewed object together with a novel item. In this latter phase reward

is contingent on a selection of the new, or “nonmatching,” stimulus. The overall pattern of results from lesion studies in young monkeys, conducted across decades of research in multiple laboratories, is that extensive damage substantially encroaching on parahippocampal cortical areas causes marked DNMS impairment, including both dramatic deficits in learning the task initially with a short retention interval, and significant impairment in accuracy across longer memory delays (for reviews, see Squire & Zola-Morgan, 1991; Suzuki, 1996). Lesions restricted to the hippocampus, by comparison, spare the rate of task acquisition, and cause at most relatively mild impairment on the delay component of the procedure (Beason-Held, Rosene, Killiany, & Moss, 1999; Murray & Mishkin, 1998; Zola et al., 2000). Although debate surrounds the underlying nature of the deficit (for differing perspectives see, Baxter, 2009b; Suzuki, 2009), the pattern is informative as a neuropsychological window on cognitive aging. Deficits in DNMS performance are a reliable feature of aging in rhesus monkeys (M. B. Moss, Moore, Schettler, Killiany, & Rosene, 2007; Presty et al., 1987; P. R. Rapp & Amaral, 1991; Shamy et al., 2011). Task acquisition with a short delay is reliably impaired, and indeed the magnitude of this deficit is substantially greater than would be predicted on the basis of accuracy aged subjects achieve when the memory demands of the procedure are increased by imposing longer retention intervals (i.e., acquisition is disproportionately impaired relative to delay performance in aging). Because damage restricted to the hippocampus fails to affect DNMS acquisition, these findings suggests that the effects of aging are more widely distributed, involving cortical components of the medial temporal lobe system. Given extended training, however, aged monkeys achieve criterion levels of accuracy fully comparable to young adults. In addition to ensuring that performance is matched across groups before more challenging delays are imposed, these observations confirm that older subjects are sufficiently motivated and attentive to score as well as younger monkeys. The delay component of DNMS typically involves testing with retention intervals ranging from 10s of seconds to several minutes or more, and under these conditions, aged monkeys score worse than young adults (M. B. Moss et al., 2007; Presty et al., 1987; P. R. Rapp & Amaral, 1991; Shamy et al., 2011). The absolute magnitude of impairment is relatively mild and varies considerably across older individuals, confirming that in contrast to the consequences of neurodegenerative disease or experimental lesions, the cognitive consequences of normal aging are relatively subtle. Age-related impairment has also been reported when

Medial Temporal Lobe Function in Cognitive Aging

recognition is measured in the absence of explicitly reinforced responding, using a visual-paired comparison task (Insel et al., 2008). Results from this assessment add to growing evidence that multiple tests of medial temporal lobe integrity are sensitive to aging in monkeys (Erickson & Barnes, 2003). Whereas investigations in monkeys have relied heavily on DNMS, parallel research on cognitive aging in rats has focused predominantly on spatial learning and memory (Gallagher et al., 2011; P. Rapp & Gallagher, 1997; Rosenzweig & Barnes, 2003). The Morris water maze has been widely used in this context, capitalizing on a substantial background of evidence demonstrating the critical role of the hippocampus in normal task performance. In basic form, across multiple trials animals learn the spatial location of an escape platform hidden just below the surface in a pool of opaque water. Navigation is guided by the distribution of cues surrounding the apparatus, and under these conditions, lesions or functional disruption of the hippocampus cause dramatic impairment in young rats (Guzowski & McGaugh, 1997; Morris, Garrud, Rawlins, & O’Keefe, 1982). Aging is associated with qualitatively similar but numerical modest deficits across multiples strains of rats (reviewed in Gallagher et al., 2011), revealed as poor spatial bias for the escape location when the platform is removed from the pool. In some strains, aged rats perform on par with young subjects when testing involves swimming directly to a visible escape platform (Gallagher, Burwell, & Burchinal, 1993; Markowska & Savonenko, 2002; P. R. Rapp, Rosenberg, & Gallagher, 1987). In addition to documenting that performance can be spared when demands on hippocampus-dependent processing are eliminated, these results suggest that sensorimotor function and motivational factors are unlikely to account for the deficits aged animals display on the hidden platform variant of testing. The observation that age-related water maze impairment co-occurs with deficits on other tests of spatial learning and memory (Barnes, 1979; Gallagher & Burwell, 1989) has established this vulnerability as a key feature of normal cognitive aging in rodent models (Gallagher et al., 2011; Rosenzweig & Barnes, 2003). Alongside a continuing focus on spatial information processing, advances in defining the normative structure and function of memory in the hippocampal system have fueled related investigation into the effects of aging. Considerable attention, for example, has focused on the component processes of recognition memory. It has long been appreciated that recognition can be supported by two qualitatively distinct mechanisms, one involving the conscious

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recollection of target items together with a rich network of related detail about the context in which they were encountered, and a second that mediates a general sense of familiarity, independent of any obligatory link to surrounding events (Yonelinas, 2001). Although well established at a cognitive level of description, the novel concept to emerge from recent neurobiological investigation is that recollection and familiarity contributions to recognition memory may be differentially mediated by the component structures of the medial temporal lobe system. Studies of patients with hippocampal damage (Yonelinas et al., 2002), together with in vivo brain imaging in normal subjects (Ranganath et al., 2004), converge on the view that the hippocampus critically supports recollection. Judgments of relative familiarity, in contrast, can be mediated by adjacent rhinal cortical areas. Experimental lesion studies in rats have reported findings consistent with this conclusion, demonstrating that damage restricted to the hippocampus selectively impairs the recollective component of recognition memory for odors (Eichenbaum, Fortin, Sauvage, Robitsek, & Farovik, 2010; Sauvage, Fortin, Owens, Yonelinas, & Eichenbaum, 2008). Some investigators have challenged current interpretations on analytic grounds, arguing that available evidence fails to demonstrate specialization of function across the constituent structures of the medial temporal lobe system, (Squire & Wixted, 2011; Wixted & Squire, 2011). Nonetheless, research adopting a dual-process framework has provided an illuminating window on cognitive aging, revealing a pattern of deficits and sparing that maps onto other, well-established features of decline. In a recent study of this sort, for example, the effects of aging on familiarity and recollection for odor stimuli were directly compared with the status of spatial memory assessed in the water maze (Robitsek, Fortin, Koh, Gallagher, & Eichenbaum, 2008). The key finding was that aged rats displayed a selective loss of recollection, and the magnitude of this effect was correlated with individual variability in spatial memory. The familiarity component of odor recognition, in contrast, was relatively spared and unrelated to the status of water maze performance. These findings extend the scope of cognitive vulnerability in rat models of normal aging to explicitly nonspatial assessments, providing independent support for the conclusion that processing dependent on the hippocampus is sensitive to decline. As is the case in monkeys, the effects of aging appear to extend to multiple components of the medial temporal lobe system, including rhinal cortical areas implicated in certain aspects of nonspatial object recognition (S. N. Burke, Wallace, Nematollahi, Uprety, & Barnes, 2010).

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Recent findings confirm that the overall pattern of results from animal models is compatible with observations in people. Specifically, regional brain activation measured by functional MRI revealed an age-dependent decrease in the hippocampus associated with recollection, together with a corresponding, familiarity-related increase in adjacent rhinal cortical areas (Daselaar, Fleck, Dobbins, Madden, & Cabeza, 2006). This cross-species agreement might be profitably exploited in future translational efforts targeting a neurobiological account of impaired recollection and a platform for testing potential interventions.

COGNITIVE AGING BEYOND THE PREFRONTAL CORTEX AND HIPPOCAMPUS: ADDITIONAL DOMAINS, COGNITIVE ADAPTATION, NETWORK INTERACTIONS, AND OTHER CHALLENGES A casual survey of coverage in the current animal literature might suggest that cognitive processes mediated by the prefrontal cortex and hippocampus are selectively vulnerable to normal aging. The fabric of human cognitive aging is actually far more textured and includes nuanced change across a range of capacities including implicit memory (Dennis & Cabeza, 2010), the component processes of attention (Parasuraman & Jiang, 2012), and cognitive control and inhibition (Braver et al., 2001; R. West & Craik, 2001). Growing interest also surrounds the concept of cognitive resilience (Resnick & Sojkova, 2011), or reserve (Tucker & Stern, 2011), referring to individual differences in successful adaption that support normal function against a background of cumulative brain aging. By this view cognitive reserve accounts for the surprising observation that many older people who appear free of clinical symptoms nonetheless exhibit brain pathology on postmortem inspection or in vivo imaging characteristic of Alzheimer’s disease (e.g., Sojkova et al., 2011). Whereas a conventional neuropsychological perspective on the known functional organization of relevant brain areas has fueled many advances in cognitive aging research, emerging evidence points to the interaction between systems as a key mediator of successful and impaired outcomes. In vivo imaging studies demonstrate that the balance of activations in the prefrontal cortex and temporal lobe, for example, is reliably shifted in aged individuals in relation to deficits in memory (Cabeza et al., 2004; Cabeza et al., 1997). Even in cases where memory scores are matched across young adults and aged subjects, the distribution of neural systems activation is

significantly altered (Della-Maggiore et al., 2000), implying a successful adaptation or compensatory process. Indeed it is increasingly clear that normal cognitive function critically depends on large-scale network dynamics, prominently involving interactions between the multiple anatomical areas comprising a default mode network (Raichle et al., 2001). Growing evidence documents that functional connectivity in the DMN and other networks is compromised during aging and coupled with cognitive outcome (Grady et al., 2010; Sambataro et al., 2010). Research in animal models has only recently begun to tackle how the interactions between neural systems might contribute to or modify the nature of age-related decline. Systematic effort is also needed to rigorously examine the structure and organization of cognitive function in the setting of successful outcomes, under conditions where standard assessments reveal substantial sparing. Although such preservation might arise simply as a consequence of slower aging, and thus the perpetuation of a youthful phenotype, current evidence favors the alternative account that successful aging reflects an active process involving effective adaptation (Gallagher et al., 2006). The significant implication is that, alongside a traditional emphasis on the prevention and treatment of impairment, effort might be profitably directed at understanding and promoting adaptive strategies that enable optimally healthy cognitive outcomes in aging. A complete neurocognitive account of normal aging, and establishing maximally predictive translational strategies, hinges on further creative elaboration in animal models.

STRUCTURAL CHANGES Brain structural changes that accompany aging and cognitive decline occur across multiple levels of analysis. Noninvasive imaging technologies, such as magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) allow the study of brain macro-structure and function in vivo, offering several advantages over other approaches. Relative to labor intensive histological analyses, for example, studying much larger numbers of subjects is feasible, and the availability of young adult controls—a rare resource in many tissue banks—is substantially greater. Cognitive testing also can be performed in temporal coordination with image acquisition, at defined intervals before, during, or after testing. While postmortem analyses provide greater anatomical precision than even the most advanced imaging techniques, they are by definition cross-sectional and thus cannot control for the

Structural Changes

influence of improved nutrition, education, and medical care across study populations. Individual variability poses an additional challenge in cross-sectional designs, making it difficult to detect subtle effects. In vivo imaging, in contrast, allows within-subject comparisons, providing a sensitive window on individual trajectories of macrostructural brain change in relation to cognitive outcomes in aging. Early MRI imaging lacked the resolution needed to differentiate between cortical regions or to identify specific brain nuclei. Intracranial CSF volume was instead used as a proxy, with increased CSF signal interpreted as indicative of generalized brain atrophy (Grant et al., 1987). Later studies compared sulcal and ventricular CSF volumes and found relatively greater increases in the former parameter with increasing age, suggesting that cortical structures are more susceptible to atrophy than subcortical regions. These early studies also tested for potential sex differences in age effects, often finding more pronounced change in men than women, prompting the suggestion that this difference might reflect the neuroprotective actions of ovarian hormones (Gur et al., 1991). Rapidly advancing imaging technology yielded increased resolution and the ability to differentiate CSF, grey and white matter, and at least major regional anatomical borders. Cross-sectional studies taking advantage of these improvements revealed significant decreases in grey-matter volume with age against a background of relative white-matter preservation (Jernigan et al., 1991; Pfefferbaum et al., 1994). More sensitive analyses of longitudinal change, however, revealed ageassociated atrophy of both grey and white matter (Resnick, Pham, Kraut, Zonderman, & Davatzikos, 2003). Regional analysis suggested that associational cortices are more susceptible to age-related atrophy than primary sensory areas in the neocortex. Consistent with this account of differential vulnerability, the most substantial decline has generally been found in the prefrontal cortex (Raz et al., 1997). Interestingly, atrophy of limbic structures tends to be more restricted and smaller in magnitude than among prefrontal areas (Raz et al., 1997). Although once viewed as especially vulnerable to structural decline, volumetric preservation and stereological evidence for limited neuron death in the hippocampus (M. J. West, 1993) have prompted significant revision in this perspective. While relative sparing of hippocampal volume is evident in both MRI and postmortem findings, these techniques yield divergent results for the effects of aging on grey and white-matter volumes. The pattern reported in MRI studies is that regional gray matter atrophy follows a linear trajectory beginning in young adulthood,

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whereas white-matter volume plateaus in middle age, followed by later decline (Madden, Bennett, & Song, 2009). Postmortem findings, in contrast, demonstrate age-related white-matter atrophy together with preservation of greymatter volume (Piguet et al., 2009). Additionally, the disproportionate sensitivity of frontal cortical regions to aging reported in many neuroimaging studies is less consistently observed in postmortem analyses (Double et al., 1996; Piguet et al., 2009). The inclusion of subjects with preclinical, undiagnosed dementing illness may contribute to this discordance. These individuals can be identified with confidence on the basis of postmortem histopathological findings, but accurate exclusion in imaging studies, where cognitive measures typically serve as a coarse proxy, is challenging. Indeed, when only the healthiest subjects are considered, MRI analysis reveals minimal age-related change (Mueller et al., 1998; Resnick et al., 2003), consistent with the interpretation that widely distributed, robust atrophy is a more reliable indication of pathology than normal aging. Diffusion tensor imaging (DTI) has provided a valuable adjunct to volumetric analysis for examining white-matter integrity and its relationship to cognitive functioning in aging. DTI is a relatively new MRI modality that reflects the direction and rate of water molecule diffusion in different tissue components. Diffusion in grey-matter tends to be relatively uniform in all directions, while organized white-matter tracts of similarly oriented fibers restrict the movement of water molecules along one dimension. As the integrity of white matter is compromised, diffusion is less restricted, and decreased directionality together with increased diffusion rate are hallmarks of white-matter damage. Similar to data from structural MRI studies, frontal regions display particular vulnerability to age-related decline in white-matter integrity (Sullivan & Pfefferbaum, 2006; Zahr, Rohlfing, Pfefferbaum, & Sullivan, 2009). Across multiple studies, compromised whitematter integrity, independent of age, correlates with poor performance on tasks emphasizing cognitive processing speed and executive function (Madden et al., 2004; Tuch et al., 2005). Indeed there is considerable evidence for coupling between regional white-matter integrity and cortical functional organization throughout adulthood. Reaction time for lexical decision making in young adults, for example, is reliably correlated with DTI measures of white-matter integrity in language regions, but not motor or visual areas (Gold, Powell, Xuan, Jiang, & Hardy, 2007). Although findings of this sort point to a strong function link, the extent to which altered white-matter integrity, as assessed by DTI, contributes to age-related

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cognitive decline remains unclear (for review, see Madden et al., 2009). At present, age-related slowing in information processing speed is perhaps the most reliable correlate of compromised white matter revealed by DTI (Gold, Powell, Xuan, Jicha, & Smith, 2010). Postmortem light microscopic examination provides substantial precedent for the conclusion that white matter is vulnerable to age-related compromise. In an early study of histological preparations from human visual cortex, for example, Lintl and Braak (1983) reported that myelinated fiber staining comprising the line of Gennari declines linearly from the third decade of life onward (Lintl & Braak, 1983). What was not clear from these findings alone, however, is whether aging is associated with an actual loss of myelinated fibers, or simply a change in staining characteristics such that labeling intensity among surviving fibers is diminished. Stereological analyses quantifying fiber length, size and density address this issue, confirming that a loss in overall fiber length likely contributes to the change observed by nonspecific histochemical methods. This loss of inter-cortical connectivity may provide a basis for the effects of aging on large-scale network dynamics, and the vulnerability of cognitive capacities mediated by the interaction between multiple brain regions. While considerable research in nonhuman primates and other animal models has examined the effects of brain aging on glia, related efforts in human studies have focused predominantly on neuronal integrity. Early studies using density as a measure of potential neuron loss generally reported a decline with age (Brizzee, Ordy, & Bartus, 1980; Issa, Rowe, Gauthier, & Meaney, 1990). Although the magnitude of effect varied widely across investigations, these findings prompted a long-standing consensus that widespread neuron death is an inevitable consequence of growing older. This perspective has been substantially revised over the past decade on the basis of results derived using stereological methods of quantification. The key advance of these techniques is that they provided, for the first time, a practical and efficient means of directly estimating total neuron number in any neuroanatomically defined region of interest, independent of processing related tissue shrinkage and other factors that confound studies of neuron density. Across multiple species, including rodents, nonhuman primates and people, investigations using stereological methods of quantification have demonstrated that neuron number in the aged hippocampus can be substantially preserved in the absence of neurodegenerative disease (Keuker, de Biurrun, Luiten, & Fuchs, 2004; Rasmussen, Schliemann, Sørensen, Zimmer, & West, 1996; M. J. West, 1993; M. J. West &

Slomianka, 1998). In contrast, current understanding is that significant age-related neuron loss in these regions, particularly in the entorhinal cortex, is a signature of Alzheimer’s disease. Indeed patients who display even very mild cognitive impairment indicative of Alzheimer’s disease exhibit up to 60% neuron loss in layer II of the entorhinal cortex, with degeneration approaching 90% at more advanced stages of disease (G´omez-Isla et al., 1996). These findings are consistent with the view that the disproportionate vulnerability of memory to Alzheimer’s disease arises from a disconnection in the bidirectional exchange of information between the hippocampus and multimodal association areas of the neocortex. While frank degeneration of cortical and hippocampal neurons is no longer considered a necessary consequence of normal aging, more subtle changes in cell morphology and connectivity have been strongly implicated in agerelated cognitive decline. These alterations tend to be highly selective, targeting some regions and circuits over others, and affecting multiple aspects of dendritic and spine morphology (e.g., arborization, spine length, and shape). Using Golgi staining, early studies documented age-related decreases in dendritic arbor length among pyramidal cells in layer V of the dorsolateral frontal and primary motor cortices, in layer III cells in Wernicke’s speech area, and in Broadman areas 10 and 18 (Scheibel, Lindsay, Tomiyasu, & Scheibel, 1975). Using the same methods the dendritic fields of pyramidal neurons in hippocampal subregions CA1, CA2, and CA3 and layer III in the subiculum were shown to be stable with age (for review, see Scheibel et al., 1975; Scheibel, Lindsay, Tomiyasu, & Scheibel, 1976; Uylings & de Brabander, 2002). It is difficult to confirm that the overall quality and completeness of Golgi staining is equivalent in young and aged brains, and many studies using this approach were conducted prior to the wide availability of stereological methods of quantification. Nonetheless, the results are generally in agreement with more recent work in nonhuman primates demonstrating age-related morphological changes in the hippocampus and PFC. Studies combining retrograde tract-tracing with intracellular dye filling, for example, have been used to label and quantify the dendritic arbors and spines of layer III pyramidal neurons forming long corticocortical projections from the superior temporal cortex to PFC area 46. Relative to young adults, aged monkeys display a 31% decrease in apical dendritic length, a 43% loss of spines from apical dendrites, and a 27% loss of basal dendritic spines (Duan et al., 2003; Kabaso, Coskren, Henry, Hof, & Wearne, 2009). Ultrastructural analysis by electron microscopy reveals that in area 46 layer I of the

Neurophysiology

monkey PFC aging results in a 30-60% decrease in synapse density, and notably, that the magnitude of this effect is correlated with multiple measures of cognitive function, including acquisition and retention performance on the DNMS task (Peters, Sethares, & Moss, 1998). Layers II/III of area 46 also exhibits a 30% decrease in synapse density with age (Peters, Sethares, & Luebke, 2008). While both asymmetric (excitatory) and symmetric (inhibitory) synapses are lost at the same rate, only asymmetric synapse density is robustly correlated with measures of cognition (Peters et al., 2008). In layer V of area 46, there is a 20% age-related decrease in synapse density that is due entirely to a loss of asymmetric synapses (Peters et al., 2008). In contrast to the effects observed in superficial layers, however, synapse density in layer V fails to correlate with behavioral performance in young and aged monkeys (Peters et al., 2008). The overall reduction in synapses across multiple layers of the PFC suggests that local circuit dynamics and interactions with distal structures maybe be substantially disrupted, potentially contributing to the effects of normal aging on executive function and wide range of other cognitive capacities. By comparison with the vulnerability of the PFC, hippocampal synaptic morphology displays substantial stability during aging. The total number of axospinous synapses in the CA1 stratum radiatum, as well as the number perforated and nonperforated contacts, is similar in young and aged rats regardless of cognitive status (Geinisman et al., 2004). The area of the postsynaptic density among perforated synapses, however, is significantly decreased in aged rats with memory impairment relative to either age-matched subjects with intact memory or young adults (Nicholson, Yoshida, Berry, Gallagher, & Geinisman, 2004). In monkeys, axospinous synapse density and postsynaptic density (PSD) length in the molecular layer of the monkey dentate gyrus are unchanged with age and fail to correlate with cognitive function (Hara et al., 2012; Tigges, Herndon, & Rosene, 1995, 1996). While aged monkeys do exhibit lower axodendritic synapse density in outer portions of the molecular layer, this decline represents only a 3% overall reduction that seems unlikely to have a major impact on hippocampal information processing (Tigges et al., 1995). The subiculum is the other hippocampal region where synaptic change in the context of normal aging has been examined in monkeys. Very old animals (27–28 years old) exhibit markedly decreased dendritic length and branching among subicular pyramidal neurons compared to young and middle-aged adults, together with significantly lower spine and synapse density (Uemura, 1985). Whether the effects of aging on

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memory mediated by the medial temporal lobe are more tightly coupled with morphological change in cortical components of this system than the hippocampus proper is a key focus of current investigation.

NEUROPHYSIOLOGY Considerable interest centers on the basic biophysical properties of aged neurons as an additional window on integrity critical for normal cognitive function. Multiple aspects of dendritic architecture influence a neuron’s passive electrotonic properties, and these properties in turn are fundamental determinants of synaptic integration and neuronal firing patterns. Thus, age-related changes in neuronal morphology such as dendritic regression, loss of dendritic spines and synapses, and myelin dystrophy would be predicted to substantially impact the functional properties of individual neurons. Neuronal physiology remains relatively stable in the aged hippocampus and prefrontal cortex as measured by a variety of basic parameters, including resting membrane potential, input resistance, membrane time constant, and excitatory postsynaptic potentials (EPSPs) (Barnes, 1994; Luebke & Chang, 2007). In area 46 layer II/III pyramidal neurons, however, the frequency of spontaneous EPSCs declines in aged monkeys, while inhibitory postsynaptic currents (IPSC) increase (Luebke, Chang, Moore, & Rosene, 2004). Perhaps most interesting is that action potential firing rates and input resistance exhibit a Ushaped relationship with measures of cognitive function, suggesting that there is an optimal range for capacities that depend on the PFC (Chang, Rosene, Killiany, Mangiamele, & Luebke, 2005). In contrast, layer V neurons in area 46 show no age-related change in basic membrane or repetitive action potential firing properties (Luebke & Chang, 2007). Although action potential amplitude, duration, and fall time decline, none of these electrophysiological parameters are coupled with cognitive outcome measures (Luebke & Chang, 2007). Thus, within PFC area 46, the integrity of layer II/III cortico-cortical projecting pyramidal neurons appears more directly linked to normal cognitive aging than the status of deep layer corticosubcortical projection neurons (for review, see Luebke, Barbas, & Peters, 2010). The aged rat hippocampus also exhibits substantial electrophysiological stability, with many baseline parameters (e.g., resting membrane potential, membrane time constant, input resistance, and spike depolarization threshold) remaining similar to young adults (for review, see

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Barnes, 1994). The density of L-type voltage gated Ca2+ channels (VGCCs) and calcium conductance, however, increases with age in CA1 pyramidal neurons (Thibault & Landfield, 1996). Calcium influx following a burst of action potentials, in turn, increases outward potassium currents, yielding an age-related increase in the amplitude of the after-hyperpolarizing potential (AHP) in both CA1 and CA3 neurons (Disterhoft, Thompson, Moyer, & Mogul, 1996; Landfield & Pitler, 1984). In young animals, the amplitude of AHPs is modulated by behavioral training such that after successful associative learning the AHP of hippocampal pyramidal neurons is significantly reduced (Oh, 2003). Aged animals that learn successfully exhibit AHPs resembling those in young animals, while aged animals that fail display significantly larger AHPs (Moyer, Power, Thompson, & Disterhoft, 2000; Tombaugh, 2005). Furthermore, compounds that decrease the amplitude of AHPs also ameliorate learning deficits in aged subjects (for review, see Disterhoft & Oh, 2006). Larger AHPs would suggest that pyramidal neurons in the aged hippocampus are generally less excitable and might exhibit correspondingly lower firing rates as a consequence. When recorded in vivo, however, baseline firing rates appear relatively stable with age in CA1 and are increased in CA3 (for review, see S. N. Burke & Barnes, 2006; S. N. Burke & Barnes, 2010). Indeed changes in neuron excitability and the balance of excitatory and inhibitory tone have emerged as important themes in research on the neurobiology of both normal (I. Wilson, Gallagher, Eichenbaum, & Tanila, 2006) and pathological aging (Bero et al., 2011; Palop et al., 2007). The effects of aging on memory-related synaptic plasticity have also been examined using long-term potentiation (LTP) and long-term depression (LTD). Although independent studies have variously reported that aged rats exhibit deficits in both LTP induction and maintenance, and an increased probability of LTD, results vary according to experimental protocol and region (for review, see S. N. Burke & Barnes, 2006). Strong stimulation protocols induce LTP equally in young and aged animals, but over time, potentiation declines toward baseline more rapidly in the aged hippocampus in both the dentate gyrus and CA3 (Barnes, 1979; Dieguez & Barea-Rodriguez, 2004). Use of weaker, perithreshold stimulation protocols reveal age-related induction deficits in the dentate gyrus and CA1 (Deupree, Turner, & Watters, 1991; Moore, Browning, & Rose, 1993). Finally, although the maximal amplitude of LTD is unaffected at the Schaffer collateral–CA1 synapse, aged rats are more susceptible to LTD induction at these synapses (for review, see Barnes, 2003).

A current perspective is that normal aging is associated with an overall shift in neural plasticity favoring decreased synaptic transmission (LTD) and a reduction in the capacity for LTP-mediated enhancement of synaptic transmission (for review, see S. N. Burke & Barnes, 2006). Calcium dysregulation has been proposed as a possible mechanism underlying this age-related shift in the balance between LTP and LTD. Increased intracellular calcium concentrations lead to increased activity of the Ca2+ -dependent phosphatase, calcineurin, which can act on NMDA receptors to reduce Ca2+ influx. Interestingly, increased calcineurin activity has been documented in aged animals (Kumar & Foster, 2004). Impaired LTP induction as a result of a decreased calcium influx through NMDA receptors is supported by studies showing that induction deficits can be overcome by strong postsynaptic depolarization. Additionally, it has been proposed that age-related increases in AHPs compound depolarization deficits in the aged hippocampus. By this account, large AHPs disrupt the integration of depolarizing postsynaptic potentials, increasing the stimulation intensity needed to induce LTP. Consistent with this view, in addition to improving learning, pharmacological manipulations that reduce the AHP enable the induction of LTP even at lower stimulation frequencies than would normally elicit LTP in young or aged animals (Disterhoft et al., 1996). The process can be reversed such that an increase in the AHP following the addition of an L-channel agonist inhibits LTP induction (for review, see Kumar & Foster, 2004). Alongside evidence that increased L-type calcium channel density and altered intracellular calcium homeostasis contribute to deficits in neurocognitive aging, other findings suggest these changes might reflect compensatory adaptations. Pharmacological tools can distinguish two distinct components of LTP, that is, NMDA receptor dependent and voltage-gated calcium channel dependent potentiation (NMDAR-LTP and VGCC-LTP) (Grover & Teyler, 1990). The overall magnitude of LTP in the aged rat hippocampus is similar to young animals, but there is a decrease in the contribution of NMDA receptor dependent mechanisms, together with a corresponding increase in VGCC mediated potentiation (Shankar, Teyler, & Robbins, 1998). More recently it has been shown that while NMDAR-LTP is reduced in relation to chronological age, VGCC-LTP is increased among cognitively intact aged rats, and reliably coupled with the status of spatial memory mediated by the hippocampus (Boric, Mu˜noz, Gallagher, & Kirkwood, 2008). Similarly, the magnitude of NMDAR independent LTD is correlated with better spatial memory in aged rats (Lee, Min, Gallagher, & Kirkwood,

Cell Biology: Oxidative Stress

2005). These findings suggest that successful cognitive outcomes in normal aging might be enabled by a shift in the mechanisms that support memory related synaptic plasticity, from NMDAR-dependent to -independent forms. This switch might also serve a neuroprotective function, mitigating the excitotoxic effects of excessive NMDA receptor activation (Lee et al., 2005). Alterations in the mechanisms underlying synaptic plasticity and changes in the circuitry of the hippocampal system may ultimately lead to faulty information coding. As rats explore an environment, pyramidal neurons in CA1 and CA3 fire selectively when the animal visits specific locations within that setting (O’Keefe & Dostrovsky, 1971). Neurons exhibiting location specific firing of this sort are commonly termed “place cells” and are thought to reflect encoding for the relationships between spatial cues and other salient events subjects experience during navigation. Altered place cell coding in the aged rat hippocampus has been documented in a variety of experimental settings. In CA1, the scope of information encoded is reduced during aging while vulnerability to interference increases (Barnes, Suster, Shen, & McNaughton, 1997; Redish, McNaughton, & Barnes, 1998; Tanila, Shapiro, Gallagher, & Eichenbaum, 1997; Tanila, Sipila, Shapiro, & Eichenbaum, 1997). Parallel results for aged CA3 cells suggest that information encoding is more rigid and less easily updated. The place field remapping typically observed when young subjects are transferred from a familiar to a novel environment, for example, is significantly blunted in the aged hippocampus. Moreover, the degree of rigidity or failure to remap under these conditions is significantly correlated with individual variability in spatial memory (I. A. Wilson et al., 2004; I. A. Wilson et al., 2003). These observations suggest that the aged hippocampus fails to encode new information with optimal fidelity, and that the representation of recent experience can interfere with the encoding for current events (for review, see I. Wilson et al., 2006).

CELL BIOLOGY: OXIDATIVE STRESS Reactive oxygen species (ROS) are continuously formed as a consequence of normal metabolism and in response to environmental factors such as UV light, ionizing radiation, heat and pollution. If the level of ROS overwhelms the capacity of cells to counteract these harmful species, oxidative stress can induce various types of cell damage involving modifications of proteins, lipids, and DNA, ultimately leading to mitochondrial and cellular dysfunction.

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Neurons are vulnerable to oxidative stress as a consequence their substantial oxygen demands, and the relative low activity of antioxidant defense mechanisms. Although a systemic consequence of aging, oxidative damage poses a particular risk to terminally differentiated cells such as neurons, where the capacity for replacement is limited (for review, see Bishop, Lu, & Yankner, 2010; Brasnjevic, Hof, Steinbusch, & Schmitz, 2008; Weissman, de SouzaPinto, Stevnsner, & Bohr, 2007). For over a half century, oxidative stress has been considered one of the principle mediators of the progressive decline in cellular function observed during normal aging (Harman, 1956). Age-related increases in levels of oxidized proteins, nucleic acids, and lipids have been documented across many studies as the cellular manifestations of oxidative damage. Protein oxidation affects many enzymes that control a wide array of cellular functions including protein synthesis and degradation, mitochondrial and cytoskeletal function, and cell signaling (Keller et al., 2004; Sohal, Mockett, & Orr, 2002). Additionally, oxidation of proteins increases protein misfolding and hydrophobicity, promoting nonspecific protein-protein interactions and protein aggregation. While it has been postulated that protein aggregates may be a cellular defense mechanism for sequestering cytotoxic proteins, it is clear that their long-term presence negatively impacts cellular functions (Goldberg, 2003). Cell viability, for example, is compromised by impairing trafficking necessary for the maintenance of long cellular processes such as neuronal axon and dendrites, and by sequestering beneficial proteins. To degrade oxidized proteins and aggregates, cells rely on proteasomal- and lysosomal-proteolytic pathways. Excessively oxidized proteins and protein aggregates strongly inhibit proteolysis, however, setting the stage for a feed-forward pathway driving further protein oxidation and aggregation (Goldberg, 2003). Interestingly, loss of proteasome activity alone is sufficient to increase levels of oxidized proteins, in turn leading to further inhibition of this regulatory pathway (Keller, Hanni, & Markesbery, 2000; Merker & Grune, 2000). While protein aggregates and impaired proteasome function are reliable features of normal aging, they are exacerbated in many age-related neurodegenerative disorders, including Parkinson’s and Alzheimer’s disease (Keller et al., 2000). By comparison with research on protein oxidation, the cell biology of aging with respect to RNA oxidation remains relatively unexplored. Unlike DNA, the majority of RNA is single stranded and therefore unprotected by hydrogen bonding, leaving it more susceptible to cellular oxidizers. All forms of RNA, both coding and noncoding,

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are vulnerable to oxidative damage resulting in translation errors and altered regulation of gene expression. Damage to noncoding RNAs (ncRNA) is likely to have the most wide ranging effect as these transcripts control the rate, timing and location of transcription and translation. It is noteworthy in this context that ncRNA’s are postulated to play a key role in controlling dendritic protein translation necessary for learning and memory (Konopka, Schutz, & Kaczmarek, 2011). While the precise biological outcomes in normal aging remain unknown, accumulating evidence indicates that RNA oxidation is a marker of neuronal vulnerability preceding cell death in many neurodegenerative conditions (for review, see Kong & Lin, 2010; Kong, Shan, Chang, Tashiro, & Lin, 2008; Nunomura et al., 2009). Increasing evidence suggests that both nuclear (nDNA) and mitochondrial DNA (mtDNA) damage accumulate differentially across cell types and contribute substantially to the aging process. Improperly or unrepaired nDNA lesions alter DNA structure and transcriptional regulation, with deleterious consequences for cell function and survival. The accumulation of oxidative DNA damage during aging is well documented in the mammalian brain, and perhaps more important, this damage preferentially targets the promoter regions of genes involved in synaptic plasticity, vesicular transport and mitochondrial function (Lu et al., 2004). Combined with evidence that some brain areas are more prone to oxidative DNA modification than others (Cardozo-Pelaez, Stedeford, Brooks, Song, & S´anchez-Ramos, 2002; Giovannelli, 2003), the contribution of these effects to the cognitive outcome of aging clearly merits systematic investigation. It is well established that damage to mtDNA also accrues over the course of aging (for review, see M¨uller, Eckert, Kurz, Eckert, & Leuner, 2010). Although most mitochondrial proteins are encoded by the nuclear genome, mitochondria contain many copies of their own DNA, e.g., encoding for 13 polypeptide complexes of the respiratory chain. That mitochondrial DNA is especially affected by oxidative agents may result from several factors including its proximity to the inner membrane where oxidants are formed, the lack of protective histone protein structures, and a less efficient repair capacity. Mitochondria are essential components of the synapse where, by virtue of their role in bioenergetic control, they are positioned to potently influence synaptic function and transmission. Impaired mitochondrial metabolism, respiratory chain dysfunction, and oxidative stress are considered major pathological mechanisms in a multitude of neurodegenerative diseases (M¨uller et al., 2010). The role of mtDNA damage

in normal neurocognitive aging, however, remains to be examined.

CURRENT CHALLENGES AND FUTURE DIRECTIONS Learning and memory are understood to require a coordinated pattern of gene expression, and considerable interest centers on the possibility that age-related cognitive decline ultimately arises from deficits at the level of transcriptional control. An exhaustive treatment is beyond the scope of the current review, but in overview, a growing literature indicates that normal cognitive aging in rat models is associated with altered profiles of gene expression in the hippocampus under both baseline, resting conditions, and in settings intended to measure the dynamic regulation of gene expression important for memory related synaptic plasticity (Blalock et al., 2003; Haberman et al., 2011; Rowe et al., 2007). Key themes from this emerging area include the recognition that, like the morphological effects of aging reviewed earlier, gene expression changes in the aged hippocampus are region specific. In this context, recent findings suggest that the CA3 field is disproportionately affected and distinguishes aged rats that differ according to the status of hippocampal memory among aged rats (Haberman et al., 2011). Initial results are also consistent with the view that successful cognitive outcomes, measured in rats as preserved hippocampal memory capacity, reflect an active adaptation to aging, not simply the passive perpetuation of a youthful condition (Gallagher et al., 2006). The important implication is that, in addition to a focus on ameliorating impairment, translational efforts might be profitably directed at the development of strategies to promote successful neurobiological adaptions to aging. Research in the rapidly advancing field of neuroepigenetics, targeting mechanisms positioned to potently influence broad scale transcriptional regulation, is a promising direction in this regard (Castellano et al., 2012; Peleg et al., 2010; Penner, Roth, Barnes, & Sweatt, 2010). Individual differences in the cognitive outcome of aging are likely to reflect the operation of both environmental and genetic factors. Whereas genetic influences on cognitive trajectories and risk for age-related disorders may be difficult to modify, we can, at least in principle, exercise substantially greater control over the wide variety of lifestyle and environmental factors that might slow the aging process. In this context considerable interest has centered on the potential benefits of relatively

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Author Index

Abayev, Y., 312 Abbas, A. K., 452 Abbas, P. J., 137 Abbott, C. R., 315 Abbott, L. F., 623 Abbs, J. H., 211 Abeliovich, A., 523 Abeyesinghe, S. M., 70 Ableitner, A., 409 Abraham, L. D., 189 Abrams, M., 276 Abutalebi, J., 612, 613 Acciavatti, T., 387 Accolla, R., 277, 283 Acebo, C., 377 Acevedo, L. D., 190 Achaval, M., 348 Achermann, P., 378 Achiraman, S., 345 Acker, J. D., 651 Ackerman, A. E., 350 Ackerman, K. D., 454 Ackermann, H., 536 Ackroff, K., 311, 312 Acquas, E., 403 Acuna, C., 213 Adachi, H., 351 Adachi, Y., 103 Adam, C., 481 Adamason, S., 34 Adame, D. D., 455 Adams, C. D., 497 Adams, D. L., 93, 95 Adan, R. A., 308 Adcock, I. M., 450 Addis, D. R., 648 Adkin, A., 185 Adkins, S., 388 Adkinson, N. F., 452 Adler, E., 276 Adler, N. T., 335 Adolph, E. F., 318 Adolphs, R., 426, 523, 559

Aeschbach, D., 389 Afonso, V. M., 339 Afraz, A., 98 Afshin-Pour, B., 650 Aggleton, J. P., 423, 428, 520, 563, 564, 632 Agis-Balboa, R. C., 656 Aglioti, S. M., 100, 258 Agmo, A., 9, 339, 346 Agnetta, B., 587 Agster, K. L., 566 Aguiler, G., 320 Aguirre, G. K., 100 Agullana, R., 519 Ahad, P., 171 Aharonov, G., 522–525 Ahern, J., 443, 446 Ahima, R. S., 307 Ahissar, M., 630 Ahlbeck, K., 263 Ahmad, K. M., 88 Ahmat, A., 70 Ahmed, S., 388 Ahnelt, P. K., 57, 63 Aho, A. C., 70 Aiello, A. E., 450 Aimone, J. B., 634 Air, E. L., 314 Airey, D. C., 645 Aitkin, L. M., 162, 163 Aizawa-Abe, M., 315 Aja, S., 310, 316 Akaike, T., 389 Akasaka, S., 346 Akay, T., 179, 184, 186, 189–192 Akerstedt, T., 382 Akerstrom, V., 315 Akil, H., 261, 309 Akira, S., 310, 447 Akitsuki, Y., 258 Alam, R. I., 275 Al-Amin, H. A., 262 Alarc´on, L. K., 277 665

Albano, J. E., 162 Albeck, Y., 141 Albers, H. E., 344, 374 Albert, D., 12 Alberts, S., 576, 579–581 Alberts, S. C., 576 Albin, R. L., 409, 607, 613 Albouny, G., 170 Albouy, G., 372 Albrecht, P. J., 207 Albrecht, S., 346 Albrecht, U., 373, 376 Albright, T. D., 98, 102 Alcock, J., 575 Aldaq, J. M., 9 Alden, M., 280 Alder, T. B., 139 Aldridge, J. W., 401, 402, 411 Aldwin, C. M., 443 Aleman, A., 434 Alexander, A. L., 424, 426, 429, 432, 433 Alexander, G. E., 607 Alexander, G. M., 345 Alexander, M., 608 Alexandre, L., 346, 354 Alexis, D. M., 488 Alger, B., 528 Alia-Klein, N., 16 Alitto, H. J., 90 Al-Khater, K. M., 251 Alkire, M., 523 Alkon, D. L., 514, 515, 528, 534 Allan, J. S., 381 Allan, L. G., 496 Allard, J., 354 Allayee, H., 645 Allee, W. C., 15 Allegrante, J. P., 445 Allen, C. N., 374 Allen, G. C., 374 Allen, J. J. B., 426 Allen, K. A., 84

666

Author Index

Allen, M. T., 539, 623, 636 Allender, S., 446 Allison, A. C., 293 Allison, D. B., 657 Allison, J. D., 90 Allison, T., 100 Allman, J., 103, 157 Allman, J. M., 89, 93 Alloway, K. D., 213 Alluin, O., 194 Ally, B. A., 648 Almarestani, L., 246 Alonso, A., 340, 451 Alquier, T., 307, 310 Althaus, J., 314 Altmann, J., 576, 579–581 Altschuler, S. M., 312 Altshuler, L. L., 431 Alvarado, M. C., 563 Alvarez, V. A., 467 Amano, K., 92 Amanzio, M., 263 Amara, D. A., 10 Amaral, D., 423, 428, 646 Amaral, D. G., 423, 428, 431, 433, 562, 648 Amarasingham, A., 565 Ambady, N., 621 Ambroggi, F., 406 Amedi, A., 214, 472 Amemiya, A., 309 Aminoff, E., 564 Amir, Y., 91 Amodio, D. M., 451 Amoore, J. E., 294 Amorapanth, P., 521 Amouyel, P., 475 Amsler, S. J., 577 Amundson, J. C., 531 Amunts, K., 213 An, Y., 650, 657 Anagnostars, S. G., 523 Anand, B. K., 307 Anane, L. H., 454 Andalman, A. S., 608 Andersen, J. B., 179 Andersen, O. K., 256 Andersen, R. A., 93, 103, 104, 163 Anderson, D. J., 162 Anderson, E., 405 Anderson, J. C., 67 Anderson, J. R., 598, 613 Anderson, K. D., 315 Anderson, L. A., 163 Anderson, N. D., 647 Anderson, S. R., 502 Andersson, B., 318 Andersson, G., 534 Andersson, J., 262

Andersson, M., 30, 36, 37 Andersson, O., 187, 189 Andree, T. H., 346 Andreini, I., 291, 293 Andres, K. H., 210, 289 Andrew, C., 412 Andrew, D., 251 Andrews, B., 129 Andrews, G. R., 580 Andrews, J., 214 Andrews, M. M., 408 Andrews, P. L., 312 Andrews, P. W., 30, 46 Andrieu, S., 644 Anegon, I., 3 Angel, J. M., 645 Angel, M. J., 188 Angeles-Castellanos, M., 386 Angelucci, F., 474 Angstadt, J. D., 191 Angstadt, M., 426, 429, 432, 434 Ano, G. G., 445 Ansberry, M., 469 Ansseau, M., 387 Anthony, B., 407 Antin, J., 313 Antle, M. C., 379 Antognini, J., 256 Ant´on, B., 346 Antoni, M., 452 Antoni, M. H., 450–453 Anwander, A., 171 Anzai, A., 96 Ao, Y., 196 Aparicio, J. M., 39 Apergis, J., 425, 570 Apicella, P., 402 Apkarian, A. V., 257–260, 262 Apkarian-Steilau, P., 215 Apple, D., 195 Appleby, P. A., 634 Applegate, C. D., 521 Aragona, B. J., 404 Arahujo, J., 475 Arai, A., 405 Arampatzi, K., 383 Araujo, J. A., 645 Araujo, J. F., 377, 386 Arbib, M. A., 634, 637 Arcaro, M., 100 Arcaro, M. J., 92, 98, 101 Arcaya, J., 261 Arch, J. R., 308, 309 Archer, M., 70 Archibald, S. L., 651 Archunan, G., 345 Arcizet, F., 103 Arehole, S., 126, 127 Arendash, G. W., 348

Arendt-Nielsen, L., 244, 256 Arezzo, J. C., 132 Arfanakis, K., 434 Argibay, P. F., 634 Argiolas, A., 351, 352 Ariew, R., 481 Arii, T., 354 Aristotle, 499 Arjomand, J., 354 Arletti, R., 309 Armanini, M. P., 250 Armijo-Prewitt, T., 41 Armitage, R., 387 Armony, J. L., 423, 433 Armstrong, D. M., 178 Armstrong, K. M., 629 Arnason, U., 74 Arnold, A. P., 14 Arnold, L. L., 166 Arnold, M., 315 Arnsten, A. F., 646 Arnsten, A. F. T., 646 Arntz, D., 387 Aronoff, M., 500 Arrese, C., 70 Arrese, C. A., 70 Arsenault, M., 259 Arshavsky, Y., 177, 179, 180, 184 Arthur, R. J., 442 Artieres, H., 293 Arvidson, K., 277 Aryal, S., 602, 603 Asadollahi, A., 141 Asaka, Y., 537, 538 Asakawa, A., 315 Asan, E., 398 Aschkenasi, C., 307 Aschkenasi, C. J., 309 Aschoff, A., 145 Asensio, N., 75 Aserinsky, E., 366, 367 Asgari, M., 100 Ashburner, J., 260, 608 Ashby, F. G., 637 Ashe, J. H., 631 Ashley, M., 580 Assad, J. A., 104, 105 As-Sanie, S., 259 Assenmacher, I., 447 Assheuer, J., 423, 424 Asteggiano, G., 263 Aster, J. C., 452 Asthana, D., 450 Astle, C. M., 645 Aston-Jones, G., 407 Astruc, J., 428 Atkinson, L. J., 347, 349 Atkinson, R. C., 621 Atlas, L. Y., 432

Author Index

Attie, A. D., 645 Attneave, F., 631 Attrep, J., 450 Attwell, P. J. E., 535 Atweh, S. F., 262 Atzori, E., 274 Audero, E., 18 Augath, M., 164, 171, 172, 214 Auger, A. A., 336 Auger, A. P., 12, 333 Augustinack, J. C., 93 Augustine, G. J., 212 Ault, S. J., 95 Aureli, F., 581, 582 Auriacombe, S., 475 Ausborn, J., 187 Austin, M. C., 408 Austin, P. J., 252, 255 Autrum, H., 131 Avenanti, A., 258 Avendano, C., 90 Averbeck, B. B., 93, 170 Aversa, A., 345 Avidan, G., 101 Avila, C., 261 Avramides, A., 481 Awad, T. A., 12 Awaya, S., 467 Axel, R., 289, 290, 292–294 Axelrod, J., 447 Ayabe-Kanamura, S., 283 Ayers, J. L., 185 Aylward, R. L., 399 Ayroles, J. F., 2 Azevedo-Lopes, M. A. D., 75 Aziz, T. Z., 258 Azpiroz, A., 451 Azuma, S., 280 Azzara, A. V., 312 Baamonde, C., 474 Baan, R., 386 Baba, V. V., 446 Babb, S. J., 487 Babinsky, R., 523 Babu, K. S., 213, 214 Baccus, S. A., 86, 105 Bach, S. M., 211 Bachand, K., 255 Bachen, E. A., 449 Bachevalier, J., 562, 563, 648 Bachmann, C., 585 Bachmann, M. F., 452 Bachmanov, A. A., 275, 277 Backhaus, J., 387 Backhaus, W., 72 Backhaus, W. G. K., 72 Backlund, H., 207 Backman, L., 634, 635

Backonja, M., 264 Backstrom, J. R., 354 Bacon, S., 120 Bacon, S. J., 399 Bader, C. R., 136 Bagot, R. C., 19 Bagozkaja, M. S., 494 Bahari-Javan, S., 656 Bailey, D. J., 522, 523 Bailey, W. J., 131 Baird, R. A., 116, 119, 125 Bajo, V. M., 161 Baker, C. I., 97–100, 105 Baker, C. L., 102 Baker, F. H., 89 Baker, J. R., 564 Baker, R. A., 70 Bakermans-Kranenburg, M. J., 15 Bakin, J., 631 Bakker, J., 333, 340 Bakker, T. C., 5 Bakos, N., 12 Bakshi, V. P., 409 Bal, R., 144 Balaban, P., 512 Balan, P. F., 103 Balasubramanian, M., 93 Balciuniene, J., 16 Balda, R. P., 581 Baldessarini, A. R., 313 Baldessarini, R. J., 388 Baldi, E., 524 Baldo, B. A., 403 Baldwin, A. E., 403 Baldwin, P. J., 129, 130 Balestrieri, A., 346 Balfour, M. E., 350, 352 Balian, H., 261 Baliki, M., 262 Baliki, M. N., 257–260 Balkin, T. J., 384 Ball, G. F., 332, 350 Ballermann, M., 194, 196 Balliet, R. F., 70 Ballou, E. W., 192 Bally-Cuif, L., 2 Bals-Kubik, R., 409 Balter, M., 500 Balthasar, N., 309, 315 Balthazart, J., 332, 349 Bamford, A., 403 Bancila, M., 354 Bancroft, J., 345 Bandettini, P., 92 Bandiera, F. C., 451 Bandini, E., 346 Bandy, D., 258 Bandyopadhyay, S., 146 Banis, P. L., 445

667

Bankman, I. N., 218 Banks, G. C., 41 Banks, M. K., 540 Banks, S., 384 Banks, S. J., 429, 432 Banse, R., 427 Bao, S., 534–536 Bao, X. M., 312 Baquero, A. F., 277 B¨ar, K. J., 262 Bar, M., 564 Baraban, S. C., 308 Barbarossa, I. T., 274 Barbas, H., 104, 423, 428, 429, 431, 433 Barbeau, H., 191, 192, 194, 195 Barbini, B., 388, 389 Barbosa, A., 39 Barch, D. M., 650 Barclay, R. M. R., 119 Bard, C., 129 Bard, J., 9 Barde, Y. A., 464 Bardi, M., 343 Bardin, C. W., 336 Barea-Rodriguez, E. J., 654 Barense, M. D., 647 Bareyre, F. M., 196 Barfield, R. J., 345, 346 Bargas, J., 404, 634, 637 Bargh, J. A., 446 Barhight, M. F., 411 Barkai, E., 636, 637 Barker, C., 388 Barker, G. J., 314 Barker, J. L., 337 Barker, J. P., 318 Barkin, A., 445 Barlow, H., 631 Barnes, C. A., 646, 649, 653–656 Barnes, P. J., 450 Barnes, P. M., 445 Baron, A., 490 Baron, A. D., 316 Baron, J., 162 Baron, J. C., 472 Baron, R., 246 Barone, M., 307 Barone, P., 170, 226 Barone, S., 464, 465 Barot, S. K., 522 Barr, C. S., 17 Barrachina, M. D., 316 Barr´e, L. M., 579 Barre, J. M., 578 Barrera, J. G., 316 Barreto-Estrada, J. L., 337, 340 Barrett, C., 584 Barrett, D., 371 Barrett, G. M., 343

668

Author Index

Barrett, H. C., 33, 47 Barrett, J. E., 656 Barrett, L., 581, 583 Barri`ere, G., 194, 195 Barro-Soria, R., 278 Barrow, J., 405 Barry, M. A., 282 Barsalou, L. W., 602 Barsh, G. S., 309, 310 Barth´elemy, D., 194 Bartlett, F. C., 553 Bartlin, C., 180 Bartol, S. M., 70 Barton, B. E., 448, 450 Barton, G. M., 448 Barton, J. J., 100 Barton, R. A., 65 Bartoshuk, L. M., 272, 274, 277, 282, 287 Bartres-Faz, D., 597 Bartus, R. T., 646, 652 Bartz-Schmidt, K. U., 83 Basbaum, A. I., 13, 252, 256, 260 Basile, B. M., 499 Basili, A. G., 488 Baskerville, T. A., 354 Baskin, D. G., 307–310, 315, 316 Bass, J., 383 Basser, P. J., 429 B¨assler, U., 177, 179, 180, 183, 184, 187, 188 Basso, A. M., 403 Basso, D. M., 194 Basso, M., 195 Bastian, A. J., 228 Batchelor, J. H., 41 Bates, E., 596, 601, 610 Bates, J. E., 45 Bates, J. F., 169, 170 Bates, T. C., 41 Bateson, G., 463 Bathellier, B., 277 Batkin, S., 469 Batra, R., 145, 161 Batteau, D. W., 129 Batterham, R. L., 314 Battey, J. F., 276 Baud, P., 408 Baudry, M., 405, 535, 540 Bauer, V. K., 388 Bauer, W. R., 260 Baum, A., 441–443, 446, 449–451, 454, 455 Baum, M. J., 333, 340, 345, 347–349, 353 Baumbauer, K. M., 195 Baumeister, R. F., 47 Baumgaertner, A., 609 Baumgartner, G., 96

Baumgartner, U., 261 Bavelas, J. B., 427 Bavelier, D., 475 Baxter, M. G., 645–648 Bayliss, D. A., 191 Baylor, D. A., 59, 84, 85 Baynes, K., 596, 604 Bazhenov, M., 404 Bazzett, T. J., 352 Bealer, S. L., 277 Bear, M. F., 467 Beardsley, J. V., 537 Beart, P. M., 399 Beasely, L., 293 Beason-Held, L. L., 648 Beattie, M. S., 194 Beatty, J., 645 Beauchamp, G. K., 9, 10, 273, 276, 277, 287, 469 Beauchemin, K. M., 371 Beazley, L. D., 70 Becchio, C., 469 Becerra, L., 260, 262 Bechard, A. R., 10 Bechera, A., 559 Beck, A., 291 Beck, G. C., 223 Beck, P. D., 93 Becker, A. J., 344 Becker, J., 443 Becker, J. B., 337, 340 Becker, J. T., 561 Becker, M. L., 17 Becker, N., 346 Becker, S., 634 Becker, T. C., 310 Becker, T. S., 2 Beckius, G. E., 161 Beckmann, M., 260 Beckstead, R. M., 398, 404, 409, 411 Beczkowska, I. W., 409 Bedny, M., 170 Bee, M. A., 116, 117, 132, 133 Beedie, A., 185 Beehner, J. C., 576, 579–582, 584–586, 588, 590 Beeman, M., 609 Beeman, M. J., 602, 609 Beer, J. S., 428, 431, 432 Beersma, D. G., 388 Beets, M. G. J., 290 Begg, D. P., 322 Beggs, A. L., 518 Beggs, J. M., 511, 514–516 Beggs, S., 255 Beglinger, L. J., 411 Behbehani, M. M., 279, 353 Behets, C., 210 Behles, R. R., 309, 316

Behne, T., 590 Behnia, R., 522 Behr, M. K., 601 Behrens, M., 276 Behrens, T. E., 260, 429 Behrman, A., 195 Behrman, A. L., 195 Behrmann, M., 99–102 Beijer, A. V., 399 Beitel, R. E., 129, 164, 244 Bejder, L., 579 B´ek´esy von, G., 116, 122, 125 Belay, A. T., 2 Belenky, G., 384 Belfrage, M., 259 Belin, D., 397 Belingard, L., 222, 223 Bell, A. H., 100 Bell, A. J., 632, 637 Bell, C., 287 Bell, C. G., 13 Bell, F. R., 365 Bell, J., 209 Bellavance, L. L., 524 Bellen, H. J., 2 Bellgowan, P. S., 520 Bellingrath, S., 451 Belliveau, J. W., 92, 93 Bello, M., 259 Bellosevich, A., 261 Bell-Pedersen, D., 13 Belmaker, R. H., 14 Beloozerova, I. N., 180, 184 Belova, M. A., 423, 425 Belsky, J., 15, 18, 34, 41, 44, 45 Beltz, B. S., 190 Beltz, T. G., 320 Benca, R. M., 387 Bence, K. K., 310 Bender, D. B., 98 Bendor, D., 164, 166 Benedek, C., 163 Benedetti, F., 263, 388, 389 Ben-Eliyahu, S., 260, 452 Benelli, A., 309 Bengel, D., 12 Benham, R. S., 340 BenHamed, S., 93, 103 Beninger, R. J., 402, 403 Benjamin, J., 14 Benjamin, P. R., 512 Benner, T., 100 Bennet, E. L., 473, 474 Bennett, A. J., 17 Bennett, A. T. D., 72, 75 Bennett, D. A., 644 Bennett, D. J., 179, 183, 184, 192, 193, 196 Bennett, G. G., 444

Author Index

Bennett, G. J., 246, 247, 251, 259 Bennett, I. J., 651, 652 Bennett, J., 95, 316 Bennett, M. R., 177 Bennett, P., 650 Benoit, G., 349, 350 Benoit, S. C., 316 Bensch, S., 3 Benschop, R. J., 449 Ben-Shachar, M., 611 Bensma¨ıa, S. J., 221, 223, 225 Bensmaia, S. J., 208, 209, 215–219, 222, 225, 226, 228–230 Benson, C., 144 Benson, M., 443, 444 Benson, P. J., 97 Benson, R. R., 100 Benton, D., 449, 469 Ben-Zeev, A., 446 Benzer, S., 1 Ben-Zur, H., 443, 449 Beran, M. J., 488, 489, 499 Beranek, L., 116 Berardi, N., 473, 474, 630 Bercovitch, F., 578 Bereiter, D. A., 280 Berendse, H. W., 399 Berger, M., 387, 388, 475 Berger, S. Y., 525 Berger, T. W., 528 Berger, W., 185 Berger-Sweeney, J., 474 Bergman, J., 410 Bergman, T., 576, 579–582, 584–586, 588, 590 Bergmann, B. M., 383 Bergmann, P., 382 Bergua, V., 475 Bergula, A., 280 Berhow, M. T., 651 Berkes, P., 637 Berkley, K. J., 256 Berlin, P., 171 Berman, C. M., 578 Berman, K. F., 259 Berman, R. A., 105 Berman, S., 263 Bermudez-Rattoni, F., 523 Berna, C., 262 Bernab´e, J., 349, 350, 354 Bernal, S., 312 Bernard, C., 306 Bernard, J. F., 256, 280 Bernardi, G., 406–408, 472 Bernasconi, A., 389 Bernot, T., 646 Bernstein, I. L., 522 Bernstein, L. A., 473 Berntson, G. G., 489, 494

Bero, A. W., 654 Berra, H. H., 262 Berrebi, A. S., 144 Berridge, K. C., 34, 311, 401, 402, 406, 410, 411 Berry, C. D., 597 Berry, R. W., 653 Berry, S. D., 528, 537, 538 Berryman, L. J., 228 Berson, D. M., 88 Berthier, N. E., 532, 534 Berthoud, H. R., 280, 313 Bertolini, A., 309 Bertrand, D., 136 Berzon, Y., 449 Besheer, J., 540 Besnard, P., 277 Besson, J. M., 247, 256, 280 Besson, M., 600 Bester, H., 280 Betti, C. A., 491 Betti, V., 258 Bettica, P., 346 Betzig, L. L., 41 Bevan, S. J., 243 Bever, T. G., 502 Beylin, A. V., 531 Beynon, R. J., 10 Bhagavatula, J., 579, 580 Bhagwandin, A., 57 Bharaj, S. S., 312 Bhatt, R. S., 493 Bhugra, D., 45 Bi, S., 308, 309, 315, 316 Biagiotti, G., 344 Biala, G., 402 Bialy, M., 348 Bianchi, L., 213 Bicca-Marques, J. C., 75 Bickford, M. E., 90 Bieber, S. L., 281, 282 Biedenkapp, J. C., 523 Biederman, I., 99 Bieger, D., 312 Biello, S., 374 Biello, S. M., 374 Bienenstock, E. L., 629 Bienias, J. L., 644 Bien-Ly, N., 654 Bigatti, S. M., 444 Bigbee, A. J., 194 Bihrle, A. M., 609 Bileschi, S., 627, 628 Billes, S. K., 307, 308 Billimoria, C. P., 631, 637 Billington, C. J., 308, 309, 311, 317 Binder, J. R., 601 Binder, M., 564 Bindra, D., 395

Bingel, U., 259, 262, 263 Birbaumer, N., 536 Birch, D. G., 70 Bird, K. D., 452 Birklein, F., 246 Birnbaum, S., 316 Birrell, J. M., 412 Bishop, D. C., 161 Bishop, G. H., 242 Bishop, N. A., 655 Bishop, P. J., 131 Bishop, S. J., 428, 433 Bisley, J. W., 103, 210 Bismark, A., 536 Bissiere, S., 404 Bisti, S., 70 Biswal, B., 429 Biswal, B. B., 430 Bitran, D., 354 Bittar, R. G., 102 Bixby, J. L., 102 Bjorbaek, C., 307, 310 Bj¨ork, C., 7 Bjork, R. A., 486 Bjorklund, D. F., 32 Bjornsdotter, M., 207 Black, A., 427 Black, J., 463, 464, 472, 515 Black, J. E., 463, 465, 466, 474 Black, J. M., 575 Blackburn, J. R., 352 Black-Cleworth, P., 526 Blagovechtchenski, E., 191, 193 Blaha, C. D., 405 Blair, H. T., 522 Blair-West, J. R., 320 Blaisdell, A., 497 Blaiss, C. A., 410 Blake, D. T., 209, 215, 222–224 Blake, R., 65, 70, 226, 227 Blakemore, C., 466, 467, 475 Blakeslee, B., 65, 70 Blalock, E. M., 656 Blanchard, D. C., 519–521 Blanchard, R. J., 519–521 Blank, T. O., 446 Blankenship, J. E., 179 Blankenship, M. R., 540 Blasberg, M. E., 337 Blasco, B., 90 Blasdel, G. G., 91, 94, 102 Blasi, G., 17 Blason, L., 469 Blass, E. M., 295, 319 Blatt, G. J., 103, 104 Blatter, K., 380 Blaustein, J. D., 336, 337 Blaxton, T. A., 597 Blendy, J., 523

669

670

Author Index

Bleske, A. L., 29, 30 Blessing, E. M., 88 Blevins, J. E., 309, 316 Bliesener, N., 346 Bliss, J. C., 214 Bliss, T. V., 405, 472 Bliss, T. V. P., 531, 540 Bloch, B., 409 Bloedel, J. R., 195, 535 Bloise, S. M., 431 Blokhuis, H. J., 5 Blomberg, B., 452 Blomqvist, A., 256 Blonde, G., 282 Bloom, E. T., 449 Bloom, F. E., 403 Bloom, S. R., 309, 314, 315 Bloomfield, L. L., 583 Blough, D. S., 485, 631 Blount, R., 443 Blow, N. S., 64 Blumell, S., 163, 166–168, 170 Blutstein, T., 340 Boatman, J. A., 521 Bochner, B., 452 Bock, G. R., 121 Boddi, V., 346 Bodnar, J. C., 450 Bodnar, R. J., 261, 312, 409 Bodner, M., 214 Bodo, C., 333 Boe, C. Y., 580 Boecker, H., 261 Boehm, T., 293 Boeke, S., 443 Boekoff, I., 292 Boers, F., 230 Boesch, C., 577, 578, 580, 582, 584 Boettger, M. K., 262 Bogdanski, R., 533, 537 Bogduk, N., 240 Bogler, C., 609 Bohbot, V. D., 571 Bohr, V. A., 655 Bohte, S. M., 637 Bohus, B., 5 Boi, A., 351, 352 Boivin, D., 255, 388 Boivin, D. B., 375, 380, 386 Bok, M. J., 54, 58 Bola˜nos-Guzm´an, C. A., 352 Bolanowski, S. J., 209, 210 Bolding, K., 523 Boldry, R. C., 405 Bolhuis, J. J., 143, 501 Bolles, R. C., 311, 519 Bolliger, M., 194 Bolnick, D. A., 74 Boly, M., 260

Bombois, S., 475 Bonal, R., 39 Bonci, A., 406, 407 Bond, A. B., 581 Bonds, A. B., 90 Bone, I., 651 Bonham, B. H., 164 Bonhoeffer, T., 467 Bonica, J. J., 241, 242, 253 Bonicalzi, V., 259 Bonilla-Jaime, H., 346 Bonke, B., 443 Bonke, D., 142 Bonnel, A., 600 Bonnet, M., 383 Bonsall, R. W., 343 Bontempi, B., 524 Bookheimer, S. Y., 431 Boothe, D. L., 637 Borbely, A. A., 378, 383 Borde, M., 184 Bordi, F., 163 Borgdorff, A. J., 354 Borgius, L., 191, 193 Borgland, S. L., 401, 407 Borgmann, A., 179 Borgnis, R. L., 537, 538 Boric, K., 654 Born, J., 449 Born, R. T., 93, 102, 103 Bornhovd, K., 259 Borovsky, A., 596 Borras, M. C., 262 Borsook, D., 260, 262 Borst, A., 186 Borszcz, G. S., 259, 519 Bortolato, M., 11 Bos, R., 196 Bosch, J. A., 451, 454 Bossi, T., 39 Bosson, D., 389 Bostock, M. E., 409 Boston, B. A., 307, 308 Botbol, M., 14 Bothell, D., 598 Bottjer, S. W., 144, 631 Botvinick, M., 432 Botvinick, M. M., 621, 637 Boudreau, D., 255 Boughter, J. D., 285 Bouknight, A., 407 Boulant, J. A., 337 Boulenguez, P., 196 Boulware, M. I., 341 Bourdelat-Parks, B. N., 12 Bourgeais, L., 280 Bourgeon, S., 231 Bourke-Taylor, H., 470 Bourtchuladze, R., 523

Bouton, M. E., 425 Boutrel, B., 401, 407 Bouvard, V., 386 Bouvrie, J., 628 Bouwmeester, H., 523 Bouyer, L., 194 Bouyer, L. J., 195 Bovbjerg, D. H., 450, 451, 454 Bovens, A., 339 Bovet, D., 6 Bowden, E. M., 602, 609 Bowerman, R. F., 179 Bowers, M. S., 407 Bowlby, J., 34, 471 Bowles, B., 564 Bowles, C. A., 449 Bowmaker, J. K., 58, 72 Bowman, K. C., 99 Bowtell, R. W., 227 Boycott, B. B., 86 Boyd, E., 434 Boyd, J. D., 90 Boyd, R., 47 Boyer, L., 191 Boyle, A. G., 292 Boyle, M. P., 309 Boyse, E. A., 9 Boysen, S. T., 489, 494, 495 Boytim, M., 403 Bozarth, M. A., 402 Braak, H., 652 Braaten, R. F., 132 Brabant, G., 382 Bracha, V., 535 Bracht, T., 429 Brack, K. E., 346, 354 Brackett, N. L., 352, 353 Brackevelt, C. R., 70 Bradbury, A. G., 190 Bradley, D. C., 102 Bradley, M., 426, 427 Bradley, M. M., 427 Bradley, R. M., 279, 285, 465–467, 469 Brady, P. M., 493 Brady, T. J., 92, 93 Brailly-Tabard, S., 14 Brain, P. F., 451 Brainard, M. S., 143, 608 Brand, J. B., 275 Brandon, S. E., 531 Brandt, C., 115 Brannon, E. M., 104, 488–490 Brantl, V., 409 Bras, H., 196 Brasnjevic, I., 655 Brauer, J., 171, 587 Braun, A., 586, 607, 608 Braun, C. M., 647 Braver, T. S., 607, 650

Author Index

Brawn, C. M., 529 Braz, J. M., 256 Brebner, K., 408 Brecht, M., 467 Bredart, S., 260 Bredy, T. W., 19 Breedin, S. D., 602 Breedlove, S. M., 354 Breen, P. A., 315 Breer, H., 291–293 Bregman, A., 144 Bregman, A. S., 132 Brehmer, Y., 635 Breifert, M., 180 Breininger, J., 316 Breininger, J. F., 308 Breiter, H., 426 Breivik, H., 263 Bremen, P., 141 Bremmer, F., 93, 103 Bremner, A. J., 468 Brennan, M. B., 261 Brennan, P. A., 9 Brennand, P. A., 340 Brenner, G. F., 444 Brenner, S., 1 Brenowitz, E., 143 Brent, L. J. N., 579 Breslin, P. A., 277, 311 Bresnahan, J. C., 194 Bressler, S. C., 349 Brett, M., 428, 433 Brewer, A. A., 92, 98, 101 Breznitz, S., 449 Brief, D. J., 446 Briggs, F., 89–91 Briggs, J., 317 Briggs, S. D., 651 Brincat, S. L., 99 Bringsjord, S., 621 Brinley-Reed, M., 399 Brisben, A. J., 210, 229 Briscoe, A. D., 58, 61 Britten, K. H., 103 Britti, B., 613 Britton, J. C., 434 Brizzee, K. R., 652 Brobeck, J. R., 307 Broberger, C., 309 Broca, P., 601 Brocard, C., 196 Brock, O., 333, 340 Brock, R. L., 445 Brodfuehrer, P. D., 179 Brodie, D. A., 41 Brodin, L., 194 Brody, C. D., 214 Broe, G. A., 651 Broere, C. A., 607

Brog, J. S., 400 Brogden, W. J., 538 Brombacher, F., 452 Bromberg, N. M., 70 Bromberg-Martin, E. S., 401, 402 Bromet, E. J., 446 Bromiley, R. B., 517 Bromley, L. M., 254 Bromm, B., 259 Bronikowski, A. M., 12 Brooks, P. J., 656 Brooks, R., 579 Brooks, W. S., 651 Brooks-Gunn, J., 44 Brookshire, B. R., 407 Br¨osamle, C., 196 Brosnan, S. F., 582 Brotchie, P., 93 Brouwer, G. J., 95 Brown, A. C., 428, 434 Brown, A. S., 213, 605 Brown, C., 600, 607 Brown, C. H., 129 Brown, D., 262 Brown, D. L., 385 Brown, E. N., 381 Brown, G. D., 512 Brown, G. K., 443 Brown, G. T., 177, 180, 186 Brown, J. L., 350, 353 Brown, J. S., 519 Brown, K., 349, 350 Brown, M. W., 563, 564 Brown, R. E., 287, 407 Brown, S. J., 2 Brown, S. M., 536 Brown, T. D., 455 Brown, T. H., 511, 514–516, 519, 520, 522, 524 Brown, V. J., 412 Brown, W. D., 256 Browne, H. M., 214, 215 Brownell, H. H., 609 Brownell, W. E., 136 Browning, A. S., 208, 215 Browning, L. J., 449 Browning, M. D., 654 Brownstone, R. M., 178, 191, 193 Bruandet, A., 475 Bruce, C., 98 Bruce, L., 142 Bruce Pike, G., 102 Brudzynski, S. M., 349, 350, 406 Bruel-Jungerman, E., 474 Brugge, J., 136, 137 Brugge, J. F., 164, 166 Bruijnzeel, A. W., 410 Brummett, B., 18 Brunelli, S. A., 471

671

Brunet, L. J., 292 Brunn, D. E., 180 Brunner, D. P., 383 Brunner, R. J., 608 Bruns, V., 117, 123 Brunstein, E., 191 Brunzell, D. H., 524 Brush, F. R., 2, 6 Brustrom, J., 443 Bryant, D., 636 Bryant, P. E., 494 Bucci, D. J., 425, 524 Buchanan, J. T., 180, 181 Buchanan, S. L., 530 Buchanan-Smith, H. M., 75 Buchel, C., 259, 262, 263, 423, 429, 611 Bucherelli, C., 524 Buchholz, H. G., 261 Buchsbaum, M. S., 610 Buchwald, J., 163 Buchweitz, A., 612 Buck, L., 289, 290, 292, 294 Buck, L. B., 276, 291–293 Buckholtz, J. W., 17 Buckingham, R., 308 Buckley, P. B., 489 Buckner, R., 426 Buckner, R. L., 430, 647, 648 Bucy, P. C., 396, 520 Budde, M., 650 Budson, A. E., 648 Bueller, J. A., 260–262 Bueschges, A., 181 Bueti, D., 258 Buffalo, E. A., 564, 648 Bugajski, J., 447, 449 Bugnyar, T., 587 Buhot, M. C., 10 Buijs, R. M., 386 Buitelaar, J., 261 Bulkin, D. A., 162 Bullier, J., 91 Bullivant, S., 287 Bullmore, E. T., 412 Bult, A., 5 Bult, C., 3 Bunch, C. C., 116, 122 Bunge, S. A., 431 Bunney, B. S., 399, 408 Bunsey, M., 566 Buoniviso, N., 293 Buonocore, M., 256 Buonocore, M. H., 648 Burchinal, M., 649 Burda, H., 116 Burdette, J. H., 258 Burger, R. M., 161 Burgess, P., 446, 607 Burgess, P. R., 211, 242

672

Author Index

Burgess, S. E., 261 Burgoyne, P. S., 14 Buritova, J., 256 Burke, D., 195 Burke, R. E., 189 Burke, S. N., 646, 649, 654 Burkhalter, A., 95 Burkhardt, S., 656 Burks, C. A., 275, 277 Burn, P., 307, 308 Burns, P., 320 Burns, V. E., 454 Burr, D. J., 623 Burris, R. P., 43 Burt, M., 43 Burton, B. G., 67 Burton, H., 212, 213, 219, 223, 230 Burton, M. J., 423, 428 Burwell, R., 649 Burwell, R. B., 524 Burwell, R. D., 562, 649 B¨uschges, A., 177, 179, 180, 183, 184, 186–188, 195 Buschman, T. J., 103 Buser, P., 191, 192 Bush, B. M. H., 177, 188 Bush, G., 426, 433 Bushell, C. M., 606 Bushnell, M. C., 207, 256, 258–260, 262 Buss, D. M., 26, 27, 29, 30, 32–34, 36–38, 41, 43, 45–47 Busse, W., 452 Bussel, B., 191 Bussey, T. J., 412 Butcher, S. P., 522 Buti, A. L., 388 Butler, A. A., 308 Butt, S. J., 191, 193 Buttenhoff, P., 129 Butter, C. M., 530 Buwalda, B., 5 Buzas, P., 88 Buzsaki, G., 565 Byas-Smith, M. G., 259 Byrne, J. H., 511, 514–516 Byrne, M. D., 598 Byrne, M. J., 54 Byrnes, D. M., 450 Caan, W., 98 Cabana, T., 189 Cabanac, M., 317 Cabeza, R., 564, 647, 650 Cabib, S., 18 Cacioppo, J. T., 426, 427, 580 Cadoni, C., 403 Cador, M., 403 Caggiula, A. R., 352

Cagiula, A. R., 451 Cagle, S., 411 Cagniard, B., 401 Cahill, C., 405 Cahill, E., 405 Cahill, L., 13, 522, 523, 525, 539, 571 Cai, X., 309 Cain, S. W., 347, 348 Cain, W. S., 288 Caine, N. G., 75 Caine, S. B., 410 Cajochen, C., 380 Calabrese, R. L., 180, 183 Calabresi, P., 406–408, 472 Calancie, B., 181 Calas, A., 354 Calder, A. J., 426, 523 Calderone, J. B., 56 Caldwell, H. K., 9 Calejesan, A. A., 524 Calford, M., 135 Calford, M. B., 163, 213 Calkins, D. J., 88 Call, J., 488, 499, 574, 587, 590 Callaghan, C. K., 405 Callaway, E. M., 83, 93, 102 Callicott, J. H., 650 Cal`o, C., 274 Caltagirone, C., 474 Camak, L., 342 Camardo, J. S., 319 Cameron, E. Z., 578–580 Cameron, M. D., 407 Cameron Liles, L., 12 Camicioli, R. M., 651 Caminiti, R., 213 Camp, D. M., 340 Campana, E., 399 Campbell, B. C., 45 Campbell, D. H., 213 Campbell, G., 462 Campbell, H., 64 Campbell, J., 132 Campbell, J. N., 244 Campbell, J. S., 171 Campbell, L., 30, 46 Campbell, S. L., 19 Campbell, S. S., 378 Campeau, S., 521, 523, 524 Campenhausen, von, M., 141 Campfield, L. A., 307, 308, 315 Campori, E., 388, 389 Canastar, A., 4 Canavero, S., 259 Cangiano, L., 179–181, 183, 184 Canli, T., 434 Cannella, N., 401, 407 Cannistraro, P. A., 434 Cannon, J. T., 260

Cannon, T. A., 316 Cannon, T. D., 7 Cannon, W. B., 306, 318, 422, 441, 442, 446 Cant, N., 144 Cantlon, J. F., 488–490 Cao, Y., 278 Capelle, L., 613 Caplan, D. N., 609 Cappe, C., 226 Cappuccio, F. P., 385 Capranica, R. R., 116, 125, 127, 131 Capretta, P. J., 469 Capshew, J. H., 484 Capulong, E., 474 Caracciolo, S., 344 Caramaschi, D., 5 Caramazza, A., 488, 596, 606 Carandini, M., 93 Carani, C., 346 Carboo, A., 58 Card, J. P., 374, 404 Cardello, A. V., 275 Carder, B., 261 Cardinal, R. N., 403 Cardozo-Pelaez, F., 656 Carelli, R. M., 403, 404, 410, 411 Caretta, N., 343 Carey, D. P., 97, 185 Carey, M. P., 453 Carle-Florence, T. L., 352 Carleton, A., 277, 283 Carleton, K. L., 58 Carlezon, W. A., 410, 411 Carli, G., 209, 228, 229 Carlier, M., 14 Carlson, A., 603 Carlson, E. T., 98, 99 Carlson, L. E., 452 Carlson, M., 219 Carlyon, R. P., 132 Carman, G. J., 92 Carmichael, S. T., 428, 429, 431, 433 Carneiro, B. T., 377, 386 Carola, V., 18 Caron, M. G., 12 Carota, A., 260 Carp, J. S., 195 Carpenter, M., 499, 590 Carpenter, P. A., 607, 610, 611 Carr, A. J., 260 Carr, C. A., 564 Carr, C. E., 115, 133, 137, 140–142 Carr, D. B., 398, 399, 404 Carr, M. F., 399 Carrasco, P., 310 Carre, G., 185 Carrico, A. W., 452, 453 Carrier, B., 259, 262

Author Index

Carrier, J., 385 Carrillo, M. C., 529 Carro-Ju´arez, M., 346, 354 Carroll, C. A., 536 Carroll, J., 58, 59, 85 Carroll, J. E., 454 Carskadon, M. A., 370, 377 Carson, R. E., 586 Carstens, E., 256 Carstensen, L. L., 580 Carter, C. S., 262, 335, 432, 580, 650 Carter, D. E., 485, 493 Cartoni, C., 277 Caruana, F., 170 Carvalho, L. S., 61 Carvell, G. E., 468 Casagrande, V. A., 64, 68, 70, 89–91, 93 Casals, N., 310 Casanueva, F. F., 309, 315 Cascio, C. J., 223 Cases, O., 4, 10, 11, 17 Casey, B. J., 433 Casey, K. L., 231, 256, 262, 263 Caspi, A., 3, 15–17, 41, 44, 45 Cassaday, H. J., 564 Casseday, J. H., 115, 130, 133 Cassell, M. D., 280 Cassone, V. M., 13 Castellani, R. J., 656 Castellano, G., 259 Castellano, J. F., 656 Castellanos, F. X., 430 Castelli, M. P., 351 Castiello, U., 469 Castles, D., 43 Catalanotto, F. C., 282 Catania, A. C., 490, 491 Catena, M., 16 Catherine Bushnell, M., 258 Catt, K. J., 320 Cattaert, D., 179, 186, 188 Cauda, F., 259 Caulo, M., 259 Cavada, C., 90 Cavallini, G., 344 Cavanagh, P., 98 Cavanaugh, J., 91, 231 Cavigelli, S. A., 580 Caviness, V. S., 93 Caywood, M., 215 Cazalets, J. R., 184, 191, 192 Cebrian, C., 400 Cecconi, F., 463 Cegavske, C. F., 517, 518, 527, 528 Celenza, M. A., 70 Celnik, P., 170 Censi, S., 339 Centeno, M. V., 262

Centonze, D., 406, 472 Cerdan, M., 307, 309 Cerella, J., 583 Cerf, B., 284 Cermakian, N., 386 Cerri, D. H., 647 Cervoni, N., 462 Cesaro, P., 259 Chabert, C., 13 Chacko, V. P., 429 Chafee, M. V., 93 Chakravarty, S., 340, 352 Challis, B. G., 308 Chalmers, M., 494 Chamas, L., 339 Chamberland, J., 383 Chambers, J., 10 Chambers, M. R., 210 Chambers, R. E., 130 Chambers, W. H., 454 Chamero, P., 10 Champagne, F. A., 19, 462 Champion, J. E., 443, 444 Champoux, M., 17 Chan, A. W., 99 Chan, J., 339, 656 Chan, J. C. K., 161 Chan, J. L., 383 Chan, J. S., 337, 339, 346 Chan, T. L., 86 Chan, Y. S., 163 Chandrashekar, J., 274–276, 278, 284 Chang, E., 7 Chang, F.-C. T., 284 Chang, J. Y., 256, 412 Chang, K. M., 603 Chang, R. B., 275 Chang, S. J., 407 Chang, Y., 656 Chang, Y.-M., 653 Channon, S., 536 Chao, C. C., 259 Chao, C. R., 468 Chao, S. K., 293 Chaouch, A., 247 Chaplin, D. D., 447 Chapman, C. E., 214, 219, 222, 223, 230, 231 Chapman, C. J., 123, 131 Chapman, H., 309 Chapman, P. F., 522, 524, 533 Chappelle, A. M., 408 Chaput, M. A., 293 Charbonneau, G., 170 Charles, L., 100 Charles-Dominique, P., 75 Charlton, B. D., 37 Charman, W. N., 378 Chartoff, E. H., 411

673

Chatila, T. A., 448 Chatlosh, D. L., 491, 492 Chau, C., 191, 192 Chaudhari, N., 274–278, 285 Chaudhury, D., 411, 413 Chavez, M., 313 Chee, M. W. L., 612 Cheer, J. F., 404 Cheetham, S. A., 10 Chelikani, P. K., 315 Chemelli, R. M., 309 Chen, C., 4, 523 Chen, C. C., 214 Chen, G., 95, 535 Chen, J., 213 Chen, J. Y., 285 Chen, J.-Y., 285 Chen, K., 4, 10, 11, 14, 258, 316 Chen, K. C., 656 Chen, L., 195, 534–536 Chen, M. C., 276 Chen, Q., 655 Chen, T., 259 Chen, W., 90 Chen, W. J., 468 Chen, X., 275, 284 Chen, X. Y., 195 Chen, Y., 98, 195 Cheney, D L., 584, 585 Cheney, D. L., 157, 500, 576, 579–582, 584–590 Cheng, C., 443 Cheng, J., 179, 183, 184 Cheng, K., 93, 98, 99 Cheng, L. H., 277 Cherfouh, A., 13 Cherkasova, M., 100 Cherkassky, V. L., 602, 603, 611 Chernenko, G., 466 Chernock, M. L., 162 Cherny, S. S., 2 Chersa, T., 191 Chester, J., 521 Chetana, M., 259 Cheung, S. W., 162, 164 Chialvo, D. R., 259 Chiang, C. W., 14 Chiasson, M., 446 Chiba, S., 309 Chichilnisky, E. J., 86–89, 92 Chida, K., 519–521 Chida, Y., 451 Chikkerur, S., 629 Childs, P. A., 388 Chillotti, C., 274 Chin, A. S., 308, 309 Chin, J., 654 Chin, S. M., 65 Chircus, L. M., 58

674

Author Index

Chisholm, J. S., 34 Chittka, L., 58, 61, 72 Chiu, C., 105 Chmielewski, J., 446 Cho, Y. K., 276, 279–281, 283 Choe, E., 451 Choi, B. H., 465 Choi, E., 402 Choi, J.-S., 519, 520, 522, 524 Choi, S. J., 309 Chole, R. A., 278 Choleris, E., 9 Choo, K. K., 214 Choong, M. F., 75 Chopko, B., 533, 537 Chorover, S. L., 287 Chou, T. C., 374 Chow, H. M., 609 Chow, S. S., 214 Choy, M., 470 Chrachri, A., 183 Christ, G. J., 351 Christensen, A., 341 Christensen, B. N., 247 Christensen, L. O., 179 Christensen, P. N., 42 Christensen-Dalsgaard, J., 115, 116, 131, 133–135 Christian, L. M., 450, 452, 454 Christiansen, B. A., 526 Christiansen, E. H., 446 Christie, D., 533, 537 Christie, K. J., 191 Christie, M. J., 399 Christmann, C., 258 Chronwall, B. M., 308 Chrousos, G. P., 382 Chu, R., 12 Chu, X., 346 Chua, S. C., 307 Chuang, J. C., 316 Chuang, Y. W., 468 Chugani, H. T., 461 Chun, M. M., 100 Chung, A., 522 Chung, J. M., 246 Chung, K., 246 Chung, S. K., 336, 342 Chung, W. K., 307 Church, R. M., 491 Churchill, G. A., 645 Churchill, L., 398, 399, 405, 406 Ciaramidaro, A., 431, 432 Cicchetti, P., 423, 521 Cichy, R. M., 98 Ciliax, B. J., 409 Cinelli, A. R., 293 Cinotti, L., 343 Cioffi, D., 523

Cirelli, C., 13, 373 Ciriello, J., 400 Cirrito, J. R., 654 Cirulli, E. T., 16 Claassen, C. A., 446 Clack, J. A., 115, 133 Clancy, A. N., 343, 344, 348 Clapcote, S. J., 10 Clapp, T. R., 278, 288 Clarac, F., 177, 183, 184, 186–188, 191, 192 Clarey, J., 213 Clark, A., 41, 587 Clark, A. S., 337, 339, 340 Clark, D., 408 Clark, F. J., 211 Clark, J., 308, 571 Clark, J. J., 413 Clark, R. E., 529, 532, 534, 564, 648 Clarke, J. D., 180 Clauw, D. J., 259, 263 Clavagnier, S., 170 Clayton, A., 346 Clayton, D. F., 2 Clayton, N. S., 487, 488, 587 Cleary, J. P., 311 Clegg, K. E., 308 Clemence, M., 227 Clemens, L. G., 350 Cl´ement, P., 346 Clement, J., 526 Clement, T. S., 581 Clipperton-Allen, A. E., 9 Cliquet, R. L., 44 Clodi, M., 447 Clugnet, M.-C., 522 Clunas, N., 86 Clutton-Brock, T. H., 31, 36 Coats, J. K., 8 Coccaro, E. F., 434 Cocco, C., 352 Cochran, G., 33, 47 Cocito, D., 259 Code, R. A., 140–142 Coderre, T. J., 243, 248, 249, 251, 254, 260 Coffey, R. J., 258 Coffey, T., 316 Coggeshall, R. E., 242, 246, 251 Coghill, R. C., 256–259, 262 Cogliano, V., 386 Cohen, A. H., 191, 192, 637 Cohen, D., 600 Cohen, F., 450 Cohen, J. D., 262, 263, 432, 607, 650 Cohen, J. Y., 637 Cohen, L., 100, 443, 446, 449, 454, 605 Cohen, L. G., 170 Cohen, M. A., 314, 315, 633

Cohen, M. X., 564, 649 Cohen, N. J., 552, 560, 562, 566, 568, 571, 608 Cohen, R., 263, 443 Cohen, R. L., 610 Cohen, S., 444, 445, 449–451, 453, 455 Cohen-Salmon, C., 4–6 Cohn, J. F., 471 Coimbra, A., 251 Colantuoni, C., 650, 656, 657 Colarelli, S. M., 26 Colautti, C., 472 Colby, C. L., 81, 103, 104 Cole, A. R., 578, 579 Cole, R., 316 Cole, R. L., 316 Cole, S. P., 455 Cole, S. W., 450 Colecchia, E. F., 384 Coleman, D. L., 307 Coleman, G. T., 213 Coleman, S. R., 537 Coley, J. D., 588 Colin, A., 387 Coll, A. P., 308 Collett, B., 263 Collett, T. F., 66 Collignon, O., 170 Collin, S. P., 56, 61, 65, 69 Collingridge, G. L., 405, 522, 524, 531, 540 Collings, V. B., 278 Collins, D. F., 211 Colombari, D. S. A., 320 Colombo, C., 388, 389 Colombo, G., 195 Coltheart, M., 598, 605 Comar, D., 343 Comas, D., 2 Comis, S. D., 123 Compan, V., 10 Compas, B. E., 443, 444 Compton, D., 310 Conant, M. A., 450 Condon, B., 651 Cone, R. D., 307, 308 Confer, J. C., 33 Conlee, J. W., 142 Connelly, A., 566, 608 Connolly, M., 93 Connor, C. E., 98, 99, 219, 222–224, 627 Connor, R. C., 578–580 Connor-Smith, J. K., 444 Constable, J., 580 Constable, R. T., 607, 611 Conti, C., 387 Contreras, R., 281 Contreras, R. J., 282, 284, 321

Author Index

Convers, P., 258 Conway, B. A., 187, 191, 192 Conway, B. R., 98 Conway, C. A., 338 Cook, A. P., 119 Cook, C. J., 399 Cook, D., 568 Cook, M., 168, 586 Cook, P., 487 Cook, P. F., 444 Cook, R. G., 486, 495 Cool, S. J., 70 Coolen, L. M., 345–350, 352–354 Cools, A. R., 346 Coon, H., 274 Cooney, R. E., 432 Cooper, C., 446 Cooper, D., 385 Cooper, F. S., 605 Cooper, G. F., 466 Cooper, L. N., 629 Cooper, R. M., 15 Cooper, S. J., 262, 409 Cooper, T. B., 388 Cooper, T. T., 344 Coopersmith, R., 295 Coote, J. H., 346, 354 Copinschi, G., 381, 384 Coppari, R., 315 Coppola, M., 608 Corbett, D., 396, 402 Corbetta, M., 259, 629 Corda, M. G., 2, 6 Cordery, P. M., 475 Coria-Avila, G., 339 Cork, L. C., 648 Corkin, S., 564, 608 Cornelissen, P. L., 258 Cornish, J. L., 405, 407 Cornsweet, J. C., 66 Cornsweet, T. N., 66 Cornu, O., 210 Corodimas, K. P., 520 Corona, G., 346 Corona, K., 340 Corp, E. S., 310 Cortis, L., 351 Corvaro, M., 463 Coskren, P. J., 652 Coslett, H. B., 602 Cosmides, L., 26, 30, 32, 33, 43, 47 Costa, P. T., 434 Costalupes, J. A., 122, 123 Costanzo, R. M., 218, 225, 289 Costes, N., 343 Costigan, M., 245 Cota, D., 310 Cotman, C. W., 524 Cotton, J. A., 62

Cottrell, G. W., 629, 637 Couchman, J. J., 499 Coull, J. A., 255 Coull, J. T., 611 Coulter, D. A., 515, 528 Coureaud, G., 469 Courjon, J., 367 Courtine, G., 196 Coury, A., 353 Cousins, A. J., 42, 43 Cousins, M. S., 402 Coussens, L. M., 452 Coutinho, M. V. C., 499 Covey, E., 162, 286 Covington, H. E., 411, 413 Cowan, W. M., 463 Cowey, A., 89 Cowing, J. A., 61 Cowley, K. C., 191 Cowley, M. A., 307–309, 314 Cox, D., 451 Cox, D. D., 97, 98, 626, 627 Cox, J. E., 312 Cox, R., 92 Coy, D. H., 309 Coyle, J. T., 313 Crabtree, J. W., 466 Craddock, N., 7 Craddock, N. J., 7 Cragg, B. G., 466 Craig, A. D., 170, 212, 256–259 Craig, I. W., 15–17 Craig, J. C., 215–219, 222, 223, 225–227, 229, 231 Craig, K. J., 446, 450 Craighero, L., 601 Craik, F. I., 650 Craik, F. I. M., 650 Crain, B. J., 429 Crandall, C., 311 Crane, A., 168 Crane, A. M., 586 Crasson, M., 387 Crawford, A. C., 125 Crawford, D., 33 Crawford, J. D., 138 Crawford, M. L. J., 70 Crawley, A. P., 256 Crawley, J. N., 3, 10, 11, 14 Creac’h, C., 258 Creamer, M., 446 Creemers, H. E., 18 Cremers, H. R., 434 Crepaz, N., 453 Crepel, F., 534 Crescentini, C., 598 Creso Moyano, J., 571 Crews, D., 19, 332, 336 Crewther, D., 162

675

Crewther, S., 162 Crill, W. E., 183 Crinion, J., 613 Cristaudo, S., 162 Cristobal-Azkarate, J., 75 Crocker, A., 401, 407 Crockett, M. J., 431 Crockford, C., 576, 579–582, 584–586, 588, 589 Crognale, M. A., 61, 74 Crombag, H. S., 407 Crombez, G., 262 Cromwell, H. C., 395 Crone, N. E., 233 Cronin, K. A., 580 Cronin, T. W., 54, 58 Crook, J. D., 86 Crosby, R. J., 313 Croucher, P. J. P., 578 Crow, T., 511, 514–516 Crowder, R. G., 486 Crowe, D. A., 93 Crowe, S. J., 116, 122 Crowley, S. J., 377 Crowley, W., 308 Crown, E. D., 195 Croxson, P. L., 429 Croze, H., 578 Cruess, D., 452, 453 Cruess, D. G., 452, 453 Cruess, S., 452, 453 Crumling, A. J., 410, 411 Crupi, C., 564 Cruse, H., 179, 180, 184, 186, 188 Cruz, J. R., 10 Cruz, S. L., 354 Crystal, J. D., 487, 490, 498, 499 Csanyi, V., 587 Csillag, A., 142 Csizy, M. L., 129, 130 Cuello, A. C., 251 Cuervo, J. J., 39 Cueva-Rol´on, R., 346, 354 Cuffin, B. N., 600 Culham, J., 97 Culham, J. C., 102 Culig, Z., 448, 450 Cullen, K., 651 Cullen, K. E., 286 Cullen, M. J., 309, 310 Culler, E., 517, 538 Culpepper, J., 307 Cumming, B. G., 93, 95, 96, 103 Cummings, D. E., 315 Cummings, N., 450 Cummins, R. A., 473 Cunha, F. Q., 243 Cupini, L., 472 Cuppen, E., 3, 11, 339, 346

676

Author Index

Cuppernell, C., 539 Curatolo, M., 244 Curcio, C. A., 84, 85 Curio, E., 129 Curran, M., 10, 425 Curtis, J. T., 340 Curtis, K. S., 321 Cusick, C. G., 170, 213, 219 Cusin, I., 308 Cuthbert, B. N., 427 Cuthill, I. C., 72, 75 Cutler, N. L., 388 Cutrona, C. E., 445 Cutuli, D., 474, 475 Cynader, M., 466 Czeisler, C. A., 373, 375, 378–381 Daabees, T. T., 346 Dabelsteen, T., 584 Dacey, D. M., 85–88 Dacheux, R. F., 86 Dacke, M., 54 d’Adamo, P., 274 Daffner, K. R., 657 D’Agata, F., 259 Dagher, A., 571 Dahl, C. D., 170 Dahms, P., 314 Daigneault, S., 647 Dakin, C. L., 314, 315 Dalal, M. A., 383 Dale, A. M., 92, 93, 601 Dale, N., 180 Dale, P. S., 610 D’Alessio, A. C., 19, 462 D’Alessio, D. A., 314, 315 Dalgaard, L. T., 309 Dalley, J. W., 403 Dallos, P., 116, 136 Dally, J. M., 587 Dalrymple, R. M., 494 Dalton, J. T., 337 Dalton, L. W., 124 Daly, M., 35, 38, 41, 45 Daly, M. D., 288 Damak, S., 276, 277 Damasceno, F., 346 Damasio, A., 523 Damasio, A. R., 426, 559 Damasio, H., 426, 523 Damez-Werno, D., 411, 413 D’Amico, J., 192, 193, 196 Dammann, J. F., 215–217, 228 Damme, S. V., 262 Damsma, G., 340, 353 Dancer, C., 214, 227 Dandekar, K., 208 Dando, R., 278 Dang, S., 98

Dang-Vu, T., 372 Dang-Vu, T. T., 372 Daniel, D. R., 308, 309 Daniel, H., 534 Daniel, K. M., 118, 119 Daniels, D., 321 Danilova, L. K., 521 Danilova, V., 281 Dann, J., 70 Dannals, R. F., 261 Dantzer, R., 448, 450 Danziger, N., 258 Darby, E. M., 287 Darian-Smith, I., 208, 209, 223 Darnell, J. E., 307 Darrow, B., 292 Dartigues, J. F., 475 Darvell, B. W., 75 Darwin, C., 29, 30, 422 Das, A., 95 Das, S., 650 Daselaar, S. M., 564, 650 Dash, P. K., 513 DaSilva, A., 262 da Silva, B., 307 Dasser, V., 583 Date, Y., 315 Datta, S. R., 6 Daum, I., 536 D’Ausilio, A., 601 Dauvilliers, Y., 389 Davachi, L., 565 Davatzikos, C., 651 Davidson, D. L. W., 97 Davidson, J. M., 354 Davidson, L. M., 454 Davidson, M. L., 431 Davidson, P. W., 227 Davidson, R. J., 262, 263, 431 Davidson, T., 345 Davies, A. O., 447 Davies, W. L., 61 Davis, B. A., 350 Davis, B. J., 279, 280 Davis, C., 102 Davis, C. M., 405 Davis, F. C., 374, 425–428, 430, 432–434 Davis, H., 494 Davis, J. D., 311, 312, 403 Davis, J. F., 322 Davis, J. L., 540 Davis, K. D., 256, 258, 259, 262, 263 Davis, L. M., 321 Davis, M., 423, 425, 430, 433, 519–524, 531, 569 Davis, M. C., 450 Davis, M. H., 171 Davis, S., 522

Davis, W. J., 185 Davis-Singh, D., 388 Davisson, M. T., 474 Dawkins, R., 28, 31 Dawkins, T. E., 339 Dawson, D., 384 Dawson, M. R. W., 623 Dawson, W. W., 70 Day, J. J., 403 Day, M., 409, 410 Dayan, P., 373, 623, 637 De, W. D., 261 de, Q. D., 260 Dean, J., 180 Dean, R. L., 646 DeAngelis, G. C., 95, 102, 103 Dear, T. N., 293 Deary, I. J., 475, 476 De Baene, W., 99 De Bartolo, P., 474, 475 De Berardis, D., 387 de Biurrun, G., 652 Deboer, T., 379 de Boer, P., 279 de Boer, S. F., 5, 11 de Brabander, J. M., 652 De Brito, S. A., 472 DeBruine, L. M., 338 Debski, E. A., 179 de Cabo, R., 657 DeCasper, A. J., 471 de Castilhos, J., 348 Decety, J., 258, 343 deCharms, R. C., 263, 264 DeCola, J. P., 430 Dedert, E. A., 445 Deeb, S. S., 85 Deegan, J. F., 74 Deegan II, J. F., 61, 73, 74 Deese, J., 517 Deese, J. E., 517 DeFazio, R. A., 278 Defer, G., 259 Deforge, D., 195 DeFries, J., 6 DeFries, J. C., 1, 6 Degallier, S., 637 de Gelder, B., 426 De Gennaro, L., 371 Deghenghi, R., 351 Degos, J. D., 259 DeGroot, K. I., 443 Degtyarenko, A. M., 189 Dehaene, S., 100, 489, 605 de Ibarra, N. H., 75 Deininger, W., 60 Deisseroth, K., 522 de Jong, T. R., 337, 339, 346 De Jonge, M. C., 515

Author Index

de Jonge, F. H., 347 de Kloet, A. D., 322 De Koninck, Y., 255 Delacour, J., 648 de Lafuente, V., 228 Delahanty, D. L., 446, 449, 450 Delamater, A. R., 312 de la Mothe, L., 163, 166–168, 170 de la Mothe, L. A., 164 Delano, P., 144, 145 de la Torre, B., 447 Delaunay-El Allam, M., 469 Delay, E., 275 Delcomyn, F., 177 Del Corpo, A., 339–341 de Lecea, L., 309, 401, 407 de Leon, R. D., 194, 195 Delgado, B., 580 Delgado, J. M. R., 520 Delgado, M. R., 423, 431 D’Elia, L., 385 Deliagina, T., 179–181, 183, 192 Deliagina, T. G., 178–180, 184, 185 Delic, S., 3 Delius, J. D., 494 Dell, G. S., 597, 605 Della-Maggiore, V., 650 Dell’Anna, M. E., 163, 164, 166 Dellinger, E. P., 315 Dellow, P. G., 537 DeLong, M., 397 DeLong, M. R., 214, 607 DeLong, R. E., 492 de Lope, F., 39 Del Prete, E., 314 Del Punta, K., 8 Deluca, J., 636 Delville, Y., 444 de Maeyer, E., 10, 11 Dembski, M., 307 Demenescu, L. R., 434 Demertzi, A., 260 De Miguel, Z., 451 DeMonasterio, F. M., 85 De Murtas, M., 407, 408 den Boer, J. A., 388 Denchev, P., 217 Denchev, P. V., 215, 216, 218, 219 Deneris, E. S., 10 Deng, N., 307 Deng, P., 407 Deng, W., 634 Deniau, J. M., 400 Denk, W., 86 Dennis, J., 378 Dennis, N. A., 650 Denny, R. M., 121 Dent, M. L., 117, 140, 141 Denton, D. A., 320

Denys, P., 354 De Olmos, J., 429 De Panfilis, C., 388 DePriest, D. D., 214 Derbyshire, S. W., 258 Derbyshire, S. W. G., 258 de Ribaupierre, F., 146, 163 de Ribaupierre, Y., 136 Deriche, R., 598 Derrington, A. M., 90 De Ruiter, A. J., 5 De Sanctis, P., 132 Descalzi, G., 259 Descartes, R., 499 Descoteaux, M., 598 DeSimone, J. A., 275, 276 DeSimone, K., 100 Desimone, R., 98, 102, 563, 629 Des Marais, D. J., 60 Desmond, J. E., 533 de Souza-Pinto, N. C., 655 D’Esposito, M., 100, 564, 608, 649 Desrochers, B., 70 Desrochers, T. M., 425, 570 Desseilles, M., 372 DeSteno, D. A., 46 Detari, L., 379 Detre, G., 633 Detrick, S., 412 Detwiler, P. B., 86 Deupree, D. L., 654 Deurveilher, S., 374 Deussing, J. M., 3 Deutch, A. Y., 399 Deutsch, J. A., 101 De Valois, R. L., 64, 70, 92, 95, 96 Devigne, D., 537 de Visser, K. E., 452 Devlin, J., 613 Devlin, J. T., 100, 597, 605, 613 Devor, M., 287 Devos, R., 307 De Vries, G. J., 336 DeVries, A. C., 335 de Vries, H., 5 de Waal, F. B. M., 26, 503, 582 Dewing, P., 14, 341 De Wit, H., 402 Dews, P. B., 490 Dewsbury, D. A., 1 Deyo, R. A., 515, 528 DeYoe, E. A., 92, 95 Deyrup, L. D., 282 DeZazzo, J., 513 De Zio, D., 463 de Zubicaray, G. I., 412 Dhabhar, F. S., 448–451 Dhillo, W. S., 315 di, P. G., 258

677

Diamond, D. M., 631 Diamond, J., 207 Diamond, M., 332 Diamond, M. C., 473 Diano, S., 307, 309 Dias, P. A. D., 75 Diaz, A., 452 Dibner, C., 373, 376 DiCaprio, R. A., 188 DiCarlo, J. J., 97, 98, 101, 215–218, 626, 627 Dichgans, J., 536 Di Chiara, G., 403 Di Ciano, P., 406 Dick, D. M., 18 Dick, F., 596, 601 Dickenson, A., 250 Dickerson, B. C., 645 Dickerson, L., 163 Dickie, E. W., 433 Dickinson, A., 397, 487, 488, 496, 497 Dickson, B. J., 2 Dickson, S. L., 308, 315 Didier-Erickson, A., 14 Dieguez, C., 309, 315 Dieguez, D., 654 Diener, S., 261 Dierich, A., 10 Diers, M., 258, 261 Dietz, V., 179, 184, 187–189, 194–196 DiFilippi, J., 446 DiFiore, A., 576 Di Giannantonio, M., 387 Di Iorio, G., 387 Dijk, D. J., 375, 378–380, 383 Dijkgraaf, S., 124 Dike, G. L., 530 DiLeone, R. J., 310 Dilger, S., 264 Diller, L., 88 Diller, L. C., 85 DiLollo, V., 537 Di Lorenzo, P. M., 273, 277, 279–281, 283–286 Dilts, R. P., 398, 399 Diltz, M., 170 Diltz, M. D., 170 Di Martino, A., 430 DiMartino, V., 446 Dimayuga, E., 655 Dimberg, U., 426, 427 Dimitrijevic, M. R., 181 Dimitriou, M., 211 Dimitrov, S., 449 Dimoulas, E., 427 Ding, H.-K., 524 Ding, J., 409, 410 Ding, Q., 655 Dinges, D. F., 373, 380, 384

678

Author Index

Dinh, T. T., 306 Dinkins, M. E., 282 Dionne, V. E., 293 Diorio, J., 19, 462 di Pellegrino, G., 213 Di Salle, F., 164 Disbrow, E., 213, 256 DiScenna, P., 522 Distel, H., 287 Disterhoft, J. F., 515, 523, 528–530, 535, 653, 654 Ditman, T., 567 Diukova, A., 258 Divac, I., 399 Dixon, C. E., 474 Dixson, A. F., 39, 348 Dizon, A., 61 Dobberfuhl, A. P., 70 Dobbins, I. G., 650 Dobelle, W. H., 93 Dobkin, B., 195 Dobrovolsky, M., 500 Docherty, R., 245 Dodd, P. W. D., 492, 502 Dodds, C. M., 99 Dodell-Feder, D., 170 Dodge, K. A., 45 Dodson, M. J., 215 Dodson, S. E., 282 Dodsworth, R. O., 471 Doetsch, G. S., 285 Doherty, J. A., 125 Doherty, M. D., 402 Dolan, R. J., 258, 262, 423, 426, 429, 523, 607 Dolcos, F., 650 Dollimore, J., 599, 610 Dom´ınguez, P. R., 469 Domesick, V. B., 398, 399, 404 Dominguez, J., 347, 350 Dominguez, J. M., 338, 350–352 Dominy, N. J., 75 Domjan, M., 482 Donaldson, D. D., 448 Donegan, N., 533 Donevan, S., 141 Dong, X. W., 191 Donga, R., 537 Donner, K., 59 Donny, E. C., 451 Donohue, B. C., 611 Donzanti, B. A., 405 Dooling, R. J., 116–118, 122–124, 126, 127, 129, 130, 140, 141 Dorian, B., 451 Dorrian, J., 380 Dorsa, D. M., 310 Dorsch, A. K., 222–224 Dosher, B. A., 629–631

Dostrovsky, J., 655 Dostrovsky, J. O., 212, 258 Doty, R. L., 287 Douaud, G., 260 Doubell, T. P., 162 Double, K. L., 651 Doucet, S., 469 Dougall, A. L., 446, 449, 450 Dougherty, K. J., 191, 193 Dougherty, P. M., 256 Dougherty, R. F., 92, 102 Douglas, A. J., 354 Douglas, R. H., 62 Douglas, R. M., 70 Douglass, J., 64 Douglass, S., 598 Doupe, A. J., 143, 469, 501, 608 Downar, J., 258 Downing, J., 118 Downing, P. E., 99, 100 Downs, D. L., 47 Doyere, V., 570 Doyle, T. G., 311 Doyle, W. J., 451 Drabant, E. M., 17, 428, 429, 431, 433, 434 Dragunow, M., 425 Draper, P., 44, 45 Dray, A., 243, 249 Drayna, D., 274 Drayson, M. T., 454 Dreher, B., 90 Drevets, W. C., 428, 433 Drew, T., 186, 189 Dreyer, D. A., 225 Dreyfus, L. R., 492 Driesang, R., 195 Driscoll, P., 2, 6 Driver, J., 262 Dronkers, N., 596, 601 Dronkers, N. F., 596, 606, 609 Droupy, S., 349, 350 Drury, H. A., 81 Du, J., 343, 350 Dua, R., 352 Duan, H., 652 Duarte, C., 402 Dube, M. G., 308 Dubner, R., 231, 244, 246, 247, 251, 256 Dubuc, C., 579 Dubuc, R., 537 Dubus, J. P., 605 Duca, S., 259 Ducci, F., 16 Duch, C., 180 Duchaine, B., 100 Duchamp, A., 292 Duchon, A. P., 185

Dudai, Y., 509 Dudchenko, P., 565, 566 Dudman, J. T., 433 Dudukovic, N. M., 567 Duffau, H., 613 Duffy, J. F., 375, 378, 380, 381 Duffy, P., 405 Duffy, V. B., 274 Dugina, J. LK., 346 Dugovic, C., 383 Duhamel, J. R., 93, 103 Duke, C. B., 337 Dulac, C., 7, 8, 293 du Lac, S., 142, 162 Dumont, Y., 316 Dumoulin, S. O., 92, 102 Dun, N. J., 250 Dun, S. L., 250 Dunbar, R. I. M., 580 Duncan, G. H., 256, 259, 262 Duncan, J., 428, 433, 629 Duncan, W. C., 389 Dunlop, S. A., 70 Dunn, K. W., 407 Dunn-Meynell, A. A., 310 Dunphy, G., 14 Duntley, J. D., 26 Dupoux, E., 489 Dupr´e, C., 342 Dupuis, J. H., 651 Dur´an, R. E., 451, 452, 453 Durkovic, R. G., 517 D¨urr, V., 180 Durrant, R., 30, 34, 46 Dusek, J. A., 567 Dussor, G. O., 260 Dutar, P., 408 Dutilleul, S., 468 Duysens, J., 184, 186–189 Dvorak, C. A., 70 Dvoryanchikov, G., 278 Dvoryanchikov, G. A., 275 Dwinell, M. R., 3 Dwyer, S., 7 Dy, C. J., 564, 649 Dymov, S., 19 Dzaja, A., 383 Dziurawiec, S., 471 Early, A. H., 336 Earnest, D. J., 13 Easton, A., 340–342 Easton, J. A., 33 Eastwood, C., 312, 313 Eaton, R. C., 351, 352 Ebbinghaus, H., 553 Ebert, E., 195 Ebihara, K., 315 Ebiko, M., 346

Author Index

Ebina, T., 467 Ebner, T. J., 569 Ebstein, R. P., 14 Eccleston, C., 262 Echeverri, F., 278 Echteler, S. M., 116 Eckel, L. A., 315 Eckert, A., 656 Eckert, G. P., 656 Eckert, H., 67 Eddington, D. K., 93 Edds, J., 10 Edds-Walton, P. L., 131 Eddy, K. T., 429, 432 Eddy, W. F., 607 Edelman, S., 98, 632 Edgerton, V. R., 183, 194–196 Edin, B. B., 210, 211 Edmondson, J., 293 Edwards, C. A., 494 Edwards, C. J., 139 Edwards, C. R., 536 Edwards, D. A., 342, 348, 352, 353 Edwards, E. M., 317 Edwards, K. M., 454 Edwards, R. M., 521 Edwards, R. R., 262 Edwards, S., 3 Efantis-Potter, J., 450 Egan, J. P., 120 Egelhaaf, M., 186 Eggenberger, E., 314 Egnor, R., 498 Ehrensperger, F., 314 Ehret, G., 116, 122–124, 145, 146 Ehrman, L., 2 Ehrmann, D. A., 384 Eibergen, R. D., 352 Eichenabaum, H., 655 Eichenbaum, H., 530, 552, 560, 562, 564–568, 571, 645, 647, 649, 650, 654–657 Eichler, V. B., 373 Eichten, A., 452 Eickhoff, S., 259 Eickhoff, S. B., 213, 230 Eide, E. J., 389 Eiki, J., 310 Eimas, P. D., 468 Einhorn, L. C., 348 Einstein, G., 91 Eippert, F., 262 Eisenberger, N. I., 431 Eisenstein, T. K., 451 ¨ , 187 Ekeberg, O Ekeberg, O., 179–181, 183, 187, 192 Ekerot, C. F., 534 Ekstedt, M., 382 Ekstrom, L. B., 95

Elde, R., 209 El-Deredy, W., 263 El-Din, M. M., 346 El Ghissassi, F., 386 Elhilali, M., 132 Elias, C. F., 307 Elizalde, G., 311 Ellingson, M. L., 601 Elliott, D., 124 Ellis, B. J., 27, 28, 30, 32, 34–38, 41, 45–47 Ellis, C. B., 431 Ellis, H., 471 Ellis, S. M., 314 Ellis, S. P., 16 Elman, J. L., 597, 598, 628, 633 El Manira, A., 179–181, 183, 188, 192, 193 El Manira, E., 177, 186 Elmehed, K., 427 Elmquist, J., 309 Elmquist, J. K., 307, 309, 315 Elsenbruch, S., 259 Elson, R. C., 188 Elston, G., 213 Ely, D., 14 Ely, T. D., 425 El Yacoubi, M., 12 Emens, J., 387 Emens, J. S., 388 Emery, D. E., 348 Emery, N. J., 587 Emilsson, L., 16 Emond, M., 316 Emrich, H. M., 409 Emson, P., 351 Emson, P. C., 409 Enck, P., 263 Endepols, H., 139 Endler, F., 141 Endler, J. A., 53 Endo, H., 283 Endoh, K., 4 Engel, J., 526 Engel, S. A., 92 Engh, A. L., 576, 579–582, 584, 586 England, J. D., 245 Engquist, G., 262 Enguehard-Gueiffier, C., 351 Ennis, J. M., 637 Ennis, M., 353 Enns, M. P., 290 Enoch, M. A., 16 Enquist, L., 282 Enriori, P. J., 307, 308 Epel, E. S., 452 Epshtein, B., 628 Epstein, A. N., 318, 319

679

Epstein, O. I., 346 Epstein, R., 100, 564 Epstein, R. P., 3 Erb, L., 498 Erickson, C. A., 649 Erickson, J. C., 308 Erickson, R. P., 273, 274, 280, 283, 285 Eriksson, K. S., 407 Erion, G. J., 481 Erisir, A., 90 Erk, S., 431, 432 Erlenbach, I., 276, 284 Ermita, B. R., 636 Erni, T., 184 Erren, T. C., 386 Erskine, M. S., 342, 348 Erwin, H., 129 Esber, G. R., 646 Escobar, C., 386 Escudie, B., 129 Eskelson, C., 451 Eslinger, P. J., 284 Espana, R. A., 401, 407 Esposito, R. U., 396 Essex, M. J., 45 Essick, G., 214, 227 Essick, G. K., 214 Essock, E. A., 69 Esswein, L. A., 448 Esterling, B. A., 450 Esterly, S. D., 142, 162 Estes, W. K., 621 Etcoff, N., 426 Etgen, A. M., 339, 342 Etkin, A., 433 Ettenberg, A., 402, 403 Ettlinger, G., 213 Eugene, F., 432 Euler, T., 86 Evans, A., 259 Evans, A. C., 102, 256 Evans, A. E., 307, 308 Evans, D. A., 644 Evans, J. D., 2 Evans, L. D., 279 Evans, M., 136 Evans, P. M., 231 Evans, R. H., 248, 249 Evans, S. B., 316 Evans, T. A., 488, 489 Evans-Campbell, T., 446 Everingham, M., 626 Everitt, B. J., 347–349, 352, 353, 397, 402, 403, 406, 410, 412, 521 Evoniuk, H., 381 Evoy, W. H., 180 Eyl, C., 408 Eysenck, M., 433

680

Author Index

Fabbri, A., 345 Fabbro, F., 613 Fabiani, G., 346 Fabre-Thorpe, M., 97 Fabri, M., 213 Fabrigoule, C., 475 Fadiga, L., 601 Fagioli, I., 371 Fagot, J., 493, 495 Fahey, B., 405 Fahey, J., 450 Fahey, J. L., 449 Fahle, M., 630 Fahrenkrug, J., 250 Faillenot, I., 258 Fair, P. L., 427 Faita, F., 600 Falchier, A., 170 Falcon, L. M., 443, 446 Faldowski, R., 221 Fallah, M., 629 Fallon, J., 523 Fallon, J. H., 398, 404, 409 Falls, W. A., 522, 523 Famous, K. R., 406 Fan, C., 262 Fan, G., 19 Fan, J., 258 Fan, W., 307, 308 Fan, X., 260 Fang, H., 291, 293 Fano, E., 451 Fanselow, M. S., 261, 430, 519–525 Fantino, M., 311, 317 Faraco, J., 369 Farah, M. J., 596, 598, 602 Farb, C., 521 Farber, I. E., 519 Farber, N. B., 256 Farde, L., 635 Fargnoli, J., 383 Farina, W. M., 501 Faris, P., 452 Faris, P. L., 261 Farooqi, I. S., 308 Farovik, A., 564, 649 Farr, A. G., 448 Fassbinder, W., 609 Fast, K., 274 Fattori, P., 97 Faure, A., 406 Faurion, A., 284 Fausto, N., 452 Fausto-Sterling, A., 332 Fay, R. R., 115, 116, 118, 122–128, 131–135, 137, 138 Febbraio, M., 277 Federico, F., 474, 475 Federmeier, K. D., 600

Fedigan, L. M., 75 Fedirchuck, J. R., 181 Fedorenko, E., 170, 601 Fee, M. S., 608 Feeney, M. C., 487, 488 Fehnert, B., 403 Feig, S., 162 Feinstein, J. S., 428, 433 Fekete, E. M., 451 Fekete, T., 63 Feldman, D. E., 467 Feldman, H. M., 611 Feldman, J. L., 191 Felleman, D. J., 91, 93, 95, 213, 219 Fellous, J. M., 634 Fendt, M., 522, 524 Feng, A. S., 131, 135, 138–140 Feng, J., 19 Feng, L., 276 Feng, Z., 310 Fenton, G. W., 599, 610 Fenwick, P. B., 599, 610 Fera, F., 426, 431, 433 Feraboli-Lohnherr, D., 191 Ferboli-Lohnherr, D., 192 Ferguson, A. R., 195 Ferguson, J. N., 9 Ferlazzo, F., 474 Ferlin, A., 343 Fernagut, P. O., 14 Fernald, R., 581 Fernald, R. D., 2, 54 Fernandes, C., 6 Fernandez, F. M., 474 Fernandez, G., 564 Fernandez-Fewell, G. D., 345, 348 Fern´andez-Guasti, A., 348, 349 Fernandez-Teruel, A., 2, 6 Ferraina, S., 213 Ferrara, M., 371 Ferrari, P. F., 503 Ferraro, E., 463 Ferraz, M. M., 346 Ferraz, M. R., 346 Ferreira, S. H., 243 Ferrell, F., 278 Ferretti, A., 259 Ferri, G. L., 352 Ferri-Kolwicz, S. L., 342 Ferrington, D. G., 230 Ferris, C. F., 374 Ferrucci, L., 650, 657 Ferster, C. B., 490 Ferster, D., 93, 94 Ferstl, E. C., 609 Feskanich, D., 385 Fetterman, J. G., 490–492 Fettiplace, R., 125 Fey, D., 313

Ffytche, D. H., 314 Fiala, S. C., 388 Fibiger, H. C., 340, 347, 349, 353, 396 Fiebach, C. J., 605 Field, C. A., 446 Field, D. J., 631, 632 Field, G. D., 86–89 Fielder, R. L., 453 Fields, H. L., 252, 258–260, 262, 311, 406, 407, 410, 411 Fietz, H., 83 Fifer, W. P., 468, 471 Fig, L. M., 434 Figlewicz, D. P., 310, 316 Figueiredo, H. F., 315 Filipini, D., 385 Fillipini, N., 260 Finch, C. E., 535 Finger, T. E., 288 Fink, A., 610 Fink, G. R., 103, 429 Finkel, L. H., 634 Finkelstein, J. A., 308 Finn, P. R., 536, 540 Finsterbusch, J., 262 Fiorentini, A., 70, 630 Fiorino, D. F., 353 Fireman, M. J., 388 Firestein, S., 290, 292 Fischer, B., 92 Fischer, D., 88 Fischer, F., 116 Fischer, H., 192, 195, 320, 423, 424, 426 Fischer, J., 576 Fischer, J. C., 451 Fischer, J. E., 451 Fischer, K. W., 519 Fiser, J., 637 Fisher, K., 249 Fisher, P. B., 450 Fisher, S. L., 307 Fishman, Y. I., 132 Fissell, K., 432 Fitch, W. T., 503 Fitzgerald, D. A., 426, 431 Fitzgerald, P., 215, 216 Fitzgerald, P. J., 212, 213, 219, 220, 222, 228, 232, 627 Fitzgibbons, P. F., 125–127 Fitzpatrick, D., 91, 102, 212 Fitzpatrick, D. C., 145 Fitzpatrick, K. A., 162 Fitzsimmons, J. T., 275 Fitzsimons, J. T., 318, 319 Flachsbart, C., 444 Flament, D., 569 Flanagan, K., 10 Flanagan-Cato, L. M., 342

Author Index

Flanders, M., 228 Flannery, K., 539 Flaxman, S. M., 44 Fleck, M. S., 564, 650 Fleischman, D. S., 33 Fleming, D., 646 Fletcher, B. R., 656 Fletcher, G. J. O., 46 Fletcher, H., 120, 122 Fletcher, H. J., 485 Fletcher, M. A., 450–453, 455 Fletcher, P., 405 Flier, J. S., 307, 309 Flinn, M. V., 45 Flint, J., 6 Flint, M. S., 454 Flombaum, J. I., 587 Floody, O. R., 346, 350 Flor, H., 258, 261 Flores, L. C., 530 Floresco, S. B., 405–408, 412 Florez, J., 474 Flottorp, G., 120–122 Floyd, R. A., 645 Fluharty, S. J., 321 Flurkey, K., 645 Fluxe, K., 399, 404 Flynn, M. C., 315 Fodor, J., 601 Fodor, J. A., 621 Foerster, B., 260 Foerster, S., 276 Foland-Ross, L. C., 431 Foley, R. A., 33 Folkard, S., 375, 380 Folkman, S., 440, 442, 443 Fong, S. G., 648 Fonnum, F., 399, 405 Fontanella, J. C., 346 Fontanini, A., 283, 286 Fontenot, D. T., 277 Foote, A. L., 498, 499 Foote, W., 91 Forehand, R. L., 45 Foresta, C., 343 Forestner, D. M., 90 Forgie, M., 474 Forlenza, M., 446, 449 Formby, C., 230 Formisano, E., 164 Forrest, T. G., 230 Forss, N., 259 Forssberg, H., 189, 191, 192 Forsting, M., 259 Fortin, N., 564, 649 Fortin, N. J., 566, 649 Fortin, S. M., 315 Foster, C., 446 Foster, D. S., 651

Foster, M. T., 322 Foster, R. G., 374 Foster, T. C., 654, 656 Foti, F., 474, 475 Fouad, K., 187, 192–196 Foulkes, D., 371 Fouriezos, G., 402 Fournier, A., 316 Fowler, J. S., 16 Fox, G. D., 562 Fox, K., 467 Fox, M. D., 259 Fox, M. T., 647 Fox, M. W., 461 Fox, P., 259 Fox, P. C., 530 Fox, R., 68, 70 Foxe, J. J., 132, 214, 232, 233 Foxhall, J. S., 316 Foy, J. G., 531 Foy, M. R., 531, 533 Frackowiak, R. S., 256, 263, 602 Franaszczuk, P. J., 233 Francis, D., 462 Francis, D. D., 462 Francis, J., 521 Francis, S. T., 227 Frank, E., 451 Frank, L. M., 399 Frank, M., 281 Frank, M. E., 279, 281, 282, 284 Frank, M. J., 633, 637 Frank, R. E., 582 Frankel, W. N., 3 Franken, P., 378, 379 Frankenreiter, M., 116 Frankland, P. W., 513, 524 Franklin, M., 43 Franklin, S., 605 Fransson, P., 262 Frantz, A. G., 381 Franzen, O., 214, 227 Frascella, J., 409 Frattali, C., 607, 608 Frayo, R. S., 315 Frazzetto, G., 18 Fredrikson, M., 423 Freedman, D. J., 104, 105, 646 Freeman, J., 95 Freeman, J. B., 621 Freeman, J. H., 526, 536, 539 Freeman, R. D., 466 Freese, J., 423, 428 Fregni, F., 472 Freiwald, W. A., 100, 101 Fremouw, T., 142 French, K. L., 374 Frenguelli, B., 523 Frere, C. H., 579

681

Freud, S., 371 Freudenthaler, H. H., 599 Freund, W., 259 Frey, S., 284 Frey, S. H., 171 Frick, K. M., 474 Fridel, Z., 520 Fridman, E. A., 170 Fried, I., 566 Fried, Y., 446 Friederich, M. W., 409 Friederici, A., 608, 613 Friederici, A. D., 171, 605, 607, 609 Friedman, A., 261 Friedman, A. K., 411, 413 Friedman, D. P., 212–214 Friedman, H., 451 Friedman, H. S., 96 Friedman, J. M., 307 Friedman, M. I., 306, 309 Friedman, R. D., 351 Friedman, R. M., 215 Friedrich, M. F., 448 Friesen, W. O., 179, 180, 183 Frigon, A., 194 Frihauf, J. B., 100 Frings, S., 54 Frisch, S., 607 Friston, K. J., 256, 260, 405, 423, 429, 598, 607, 608, 611 Frith, C. D., 258, 405, 426, 523 Fritz, J., 166, 169, 171, 631 Fritzsch, B., 115, 133 Froguel, P., 13 Frohardt, R. J., 522 Frost, B. J., 129, 130, 142 Frost, G. S., 314 Frost, J. J., 261 Frost, W. N., 180, 181, 191, 512 Fruteau, C., 582 Frye, C. A., 337, 339, 340 Frysinger, R., 519, 520 Frysinger, R. C., 521 Fu, K. M., 166 Fu, S., 315 Fu, Y. H., 389 Fuchs, E., 461, 652 Fuchs, P., 136 Fudge, J. L., 399, 400 Fuhrmann, M., 60 Fujikawa, T., 385 Fujimiya, M., 315 Fujimura, Y., 314 Fujino, M. A., 315 Fujita, I., 98, 99 Fujita, N., 227 Fukada, Y., 84, 98 Fukuda, A., 467 Fukuda, H., 284

682

Author Index

Fukui, M. M., 91 Fukunishi, I., 451 Fukushima, K., 627, 628 Fukuyama, H., 262 Fulbright, R. K., 607, 611 Fulgosi, M. C., 388 Fullard, J. H., 119 Fuller, J. L., 2 Fuller, P. M., 368, 369 Fuller-Thomson, E., 644 Fulton, R., 629 Fulton, S., 316 Funahashi, H., 309 Funahashi, S., 646 Funakoshi, A., 314 Fundytus, M. E., 249 Fung, J., 184 Funk, G. D., 191 Funnell, E., 602 Furey, M. L., 100 Furlow, F. B., 41 Furmark, T., 423 Furst, A., 262 Furuichi, T., 4 Furukawa, N., 307, 310 Fuzessery, Z. M., 129 Fyhn, M., 563 Fyhrquist, N., 59 Fyodorov, D. V., 10 Gabbay, H., 191, 192 Gabbinai, F., 186 Gabbott, P. L., 399 Gabriel, J. P., 179, 184, 186 Gabriel, M., 538, 539 Gabrieli, J. D., 431, 598, 647, 648 Gabrieli, J. D. E., 529, 567 Gacsi, M., 587 Gad, H., 194 Gadek-Michalska, A., 447, 449 Gadian, D. G., 608 Gadin, D. G., 566 Gaebler-Spira, D., 470 Gaffan, D., 562, 564, 648 Gage, F. H., 474, 634 Gahr, M., 462 Galaburda, A., 164 Galaburda, A. M., 93 Galarraga, E., 404 Galati, G., 258 Galbo, H., 449 Gale, R., 452 Galea, L. A., 261, 461 Galea, L. A. M., 337 Galea, S., 443, 446, 450 Galey, F., 278 Galhardo, V., 256 Galis-de Graaf, Y., 399, 400 Gallacher, D., 263

Gallagher, M., 407, 423, 425, 519–522, 644, 646, 647, 649, 650, 653–657 Gallaher, T. K., 4, 11 Gallant, J. L., 96, 98 Gallese, V., 170, 469, 602 Galletti, C., 97 Gallistel, C. R., 403, 488, 489 Galloway, M. T., 451 Galsworthy, M. J., 6 Galvan, A., 433 Galvin, K. E., 282 Gamberini, M., 97 Gamble, G. D., 260 Gamble, K. L., 374 Gamboni, F., 245 Gamkrelidze, G. N., 179 Gamlin, P. D., 86–88 Gammeltoft, S., 309, 310 Gammie, S. C., 8, 12 Gamo, N. J., 646 Gamoletti, R., 278 Ganchrow, D., 280, 283, 285 Gandevia, S. C., 211 Gandhi, N. J., 162 Ganesh, D. S., 345 Ganeshina, O., 653 Gangestad, S. S., 28 Gangestad, S. W., 30, 34, 37–39, 41–43, 45–47, 288 Ganis, G., 600 Ganten, D., 320 Ganten, U., 320 Ganzevles, P. G. J., 275 Gao, E., 146 Gao, N., 278 Gao, S., 310 Gao, X., 310 Gao, X. B., 351 Gapp, V., 431, 432 Garavan, H., 425 Garber, J., 45 Garber, P. A., 75 Garcea, M., 282 Garcha, H. S., 213 Garcia, J., 32 Garcia, K., 315 Garcia, M. C., 309 Garcia-Hidalgo, A. A., 346 Garcia-Larrea, L., 258 Gardell, L. R., 261 Gardner, E. P., 213–215, 218, 225 Gardner, H., 609 Gardner, K. A., 445 Gardner, S. M., 144, 160 Garey, L. J., 467 Garfinkel, P. E., 451 Garland, J., 400 Garlick, D., 611 Garolla, A., 343

Garraghty, P. E., 163, 164, 166, 213, 540 Garrett, M. F., 602 Garrud, P., 567, 649 Garver-Apgar, C. E., 43 Garwicz, M., 534 Gasbarri, A., 398, 399 Gaska, J. P., 91 Gaspar, P., 10, 11, 17 Gasparini, P., 274 Gatehouse, R. W., 129, 130 Gatenby, C., 523 Gatenby, J. C., 423 Gatewood, J. D., 333 Gati, J. S., 214 Gatignol, P., 613 Gattass, R., 95, 98 Gaus, S. E., 374 Gauthier, I., 629 Gauthier, J. L., 86–89 Gauthier, S., 652 Gawltney, J. M., 451 Gawne, T. J., 70 Gaysinkaya, L., 346 Gazzola, V., 170 Geary, D. C., 35 Geary, N., 314, 315 Gee, C. E., 195 Gee, D. G., 428, 430, 432–434 Gee, J., 655 Geha, P. Y., 258–260 Gehlert, D. R., 308 Gehring, H. M., 215 Geiger, J. R., 406 Geinisman, Y., 653 Geisler, D. C., 116 Geisser, M. E., 259, 263 Gelbard-Sagiv, H., 566 Gelfo, F., 474, 475 Gelling, R. W., 316 Gellman, M. D., 521 Gellman, R., 533 Gelman, R., 488, 489 Gelman, S., 588 Gelperin, A., 512 Geminiani, G., 259 Geminiani, G. C., 259 Geniec, P., 162 Genovese, C. R., 103, 104 Genovesio, A., 646 Gentile, C. G., 520, 521 Gentilucci, M., 97 Gentner, D., 495 Gentner, T. Q., 132 Georgescu, M., 339–341 Georgopoulos, A. P., 213 G´erard, D., 343 Gerasimenko, Y., 181 Gerber, G. J., 402, 403

Author Index

Gerfen, C. R., 398, 409 Gerges, M., 312 Gerhardt, H. C., 118, 119, 125, 131 Gerlai, R., 13 Gerling, G. J., 210 Gernbacher, M. A., 602, 609 Gernsbacher, M., 603 Gernsbacher, M. A., 601 Gervais, M. C., 6 Geschwind, N., 171 Gesteland, R. C., 292 Getchell, M. L., 289, 290 Getchell, T. V., 289, 290, 292 Getting, P. A., 180, 181, 183, 512 Getz, L. L., 335 Geurts, A. M., 3 Geyer, L. A., 346 Geyeregger, R., 447 Ghasemzadeh, M. B., 409 Ghashghaei, H. T., 423, 428, 429, 431, 433 Ghatei, M. A., 309 Ghazanfar, A. A., 170–172, 586 Gheusi, G., 295 Ghidini, S., 388 Ghilardi, M. F., 373 Ghim, M. M., 70 Ghisletta, P., 635 Ghose, S. S., 536 Ghosh, S., 213, 214, 275 Gibaud, R., 447 Gibb, B., 222–224 Gibb, R., 474 Gibbon, J., 491 Gibbs, C. M., 537 Gibbs, J., 313 Gibson, A. R., 533 Gibson, E., 600 Gibson, E. J., 185, 630 Gibson, J. J., 185, 630 Giedke, H., 387 Giesecke, T., 259, 263 Gil, M., 350 Gilbar, O., 443 Gilbert, A. N., 10, 288 Gilbert, C., 630 Gilbert, C. D., 95 Gilbert, D. A., 579 Gilbertson, T. A., 275, 277 Gilboa, S., 446 Gilby, C., 578 Gil da Costa, R., 586 Giles, L. C., 580 Gill, C. F., 279 Gill, C. J., 347 Gill, P. R., 142 Gillan, D. D., 495 Gillette, R., 512 Gillies, J. D., 195

Gillin, J. C., 387 Gillman, C. B., 489 Gilman, A., 318 Gilmore, S. L., 449 Gimenez y Ribotta, M., 191 Ginsburg, B. E., 1, 13–15, 20 Giorgi, O., 2, 6 Giovannelli, L., 656 Giovine, A., 474 Giraudi, D. M., 126 Girgis, J., 196 Giroux, N., 191 Gisiner, R., 503 Gispen, W. H., 261 Gitelman, D. R., 260 Giuliano, F., 346, 349, 350, 354 Giunta, L., 463 Givens, B. S., 408 Giza, B., 284, 285 Giza, B. K., 283–285, 312 Gizewski, E. R., 259 Glanzman, D. L., 280, 515 Glaser, R., 447, 450–454 Glass, D. C., 446 Glass, M. J., 311, 317 Glasser, M. F., 171 Glauche, V., 259, 429 Glauer, M., 264 Gleich, O., 116, 125, 140 Glendenning, K. K., 161, 162 Glendinning, J. I., 295 Glickman, S., 92 Glickstein, M., 531, 533, 537 Glimcher, P., 92 Glisky, E. L., 647 Globus, A., 466 Glonek, G. F. V., 580 Glover, G. H., 92, 259, 263, 264, 433, 611 Gluck, M. A., 571, 621, 623, 631, 633, 635–637 Gmeiner, P., 351 Gnadt, J. W., 103 Go, Y., 276 Gobbini, M. I., 100 Gobbo, O., 405 Godinho, S. I. H., 3 Godinot, N., 277 Goebel, R., 164 Goehler, L. E., 243 Goetz, A. T., 41 Goetz, C. D., 33 Gogas, K. R., 245, 260 Gogos, J. A., 12 Gohl, E. B., 534 Goke, R., 315 Gold, A. L., 431 Gold, B. T., 651, 652 Gold, E., 14

683

Gold, G. H., 292 Gold, J. I., 103 Gold, P. E., 521 Goldberg, A. L., 655 Goldberg, M. E., 103 Golden, D. T., 528 Golden, S. S., 13 Goldfine, I. D., 313 Goldin, P. R., 434 Goldman, D., 16, 426 Goldman-Rakic, P. S., 166, 169, 170, 404, 646 Goldowitz, D., 3 Goldreich, D., 214 Goldsmith, M. R., 2 Goldsmith, T. H., 54, 55, 59 Goldstein, B. J., 289 Goldstein, D. B., 16 Golinkoff, R., 581 Gollisch, T., 83, 88 Gollub, R. L., 263 Golombek, D., 374 Gomes, C. M., 582 G´omez-Isla, T., 652 G´omez-Martinez, L. E., 346, 354 Gomez-Pinilla, F., 195 Gomez-Ramirez, M., 214, 232, 233 Gomi, H., 535 Gomita, Y., 403 Gonzaga, W. J., 400, 405, 407 Gonzalez, R. G., 262, 564 Gonz´alez-Cadavid, N. F., 344 Gonz´alez-Flores, O., 339 Gonzalez-Lim, F., 429 Gonz´alez-Mora, J. L., 352 Goodale, M., 626 Goodale, M. A., 97, 102, 185, 214 Goodall, J., 577, 578, 580 Goodhouse, J., 564 Goodkin, K., 450 Goodlett, C. R., 526 Goodwin, A. W., 208, 210, 215, 223 Goodwin, G. M., 211, 262 Gooley, J. J., 88, 368, 369, 374 Gopfert, M. C., 134 Gorassini, M., 179, 188 Gorassini, M. A., 192, 193 Gordon, F., 342 Gordon, I. E., 70 Gordon, I. T., 191, 192 Gordon, J. A., 13 Gordon, N. C., 262 Gordon, T. P., 646 Gore, J. C., 100, 423, 523, 611, 629 Gorelova, N., 404 Gorgoni, M., 371 Gorlach, A., 70 Gormezano, I., 525, 527, 528, 537 Gorny, G., 407, 474

684

Author Index

Gorski, R. A., 348 Gosnell, B. A., 311, 409 Gossard, J.-P., 188 Gostic, J. M., 262 Gotimer, K., 430 Gotlib, I. H., 432 Goto, F., 258 Goto, Y., 404 Gottfried, G., 588 Gottlieb, B., 444, 445 Gottlieb, G., 465 Gottlieb, G. L., 651 Gottlieb, J., 102, 103 Gottlieb, J. P., 103 Gottsch, M. L., 333 Gouin, J., 455 Gould, E., 461, 531 Gould, S., 629 Gould, S. J., 29, 30 Gould, T. J., 532–534 Goulding, M., 179 Goulding, P. J., 602 Gourevitch, H. C., 123 Govardovskii, V. I., 59 Goy, R. W., 333, 348 Gozal, E. A., 190 Graber, J. A., 44 Gracco, V. L. T., 599 Grace, A. A., 404, 408 Grace, M., 309, 311 Grace, M. K., 308, 317 Gracely, R. H., 256, 259, 263 Graczyk-Milbrandt, G., 306 Grady, C., 650 Grady, C. L., 650 Graessle, M., 17 Graf, W., 93, 103 Grafton, S. T., 425 Graham, J. E., 450, 452, 454 Graham, K. E., 578, 579 Graham, M. D., 339 Grammar, K., 43 Granata, A. R., 346 Granda, A. M., 70 Grandt, D., 314 Granger, R., 405 Granot, M., 259 Granovsky, Y., 259 Grant, D. S., 485, 486 Grant, M. A., 259 Grant, M. A. B., 263 Grant, R., 651 Gratton, A., 402 Grau, J. W., 195, 518 Gray, C. M., 406 Gray, J. A., 536 Gray, J. R., 431 Gray, R., 524 Gray, R. W., 409

Gr´eco, B., 337, 348 Green, A. L., 258 Green, B. A., 181 Green, C. S., 475 Green, D., 124, 612, 613 Green, J., 214 Green, J. T., 526, 533 Greenberg, D. A., 584 Greenberg, M. A., 446 Greenberg, S., 144 Greene, J., 404 Greene, R. W., 4 Greenhouse, J. B., 444 Greenlee, M. W., 95, 609 Greenough, W., 463, 464, 472 Greenough, W. T., 463, 465, 466, 474 Greenspan, J. D., 256, 258 Greenspan, R. J., 2 Greenwald, M., 426, 427 Greenwald, R., 600 Greenwood, D., 451, 455 Greenwood, D. D., 120, 122, 123 Greer, C. A., 289 Grefkes, C., 103 Gr´egoire, M. C., 343 Gregor, R. J., 195 Gregoryan, G., 6 Greicius, M. D., 259, 433 Greig, A., 141 Grenier, F., 404 Greschner, M., 86–89 Gridi-Papp, M., 135 Grieve, K. L., 93 Griffin, A. L., 537 Griffin, D., 127 Griffin, G., 626 Griffin, G. D., 342 Griffiths, P. E., 29, 30 Griffiths, T. L., 621 Grigson, P. S., 411 Grill, H. J., 280, 282, 310, 312, 315, 316 Grillner, S., 177–181, 183–185, 187, 189, 191–194, 197, 623, 638 Grillner, S. J., 184 Grimm, K., 181 Grimsby, J., 10, 11 Grimwood, P. D., 632 Grinvald, A., 91, 95 Grippo, A. J., 580 Gritsenko, I., 14 Gritsenko, V., 195 Grivich, M. I., 88 Groenewegen, H. J., 398–400, 404 Groer, M., 450 Grogan, A., 613 Groh, J. M., 162 Grohr, P., 450 Groothusen, J., 600, 607 Grosenick, L., 581

Gros-Louis, J., 578, 587 Gross, C., 10, 18 Gross, C. G., 95, 98, 100, 102, 429, 627 Gross, D., 427 Gross, J., 263 Gross, J. J., 428, 431, 432, 434 Gross, M. R., 37 Grossberg, S., 633 Grosse, Y., 386 Grossman, H., 6 Grossman, H. C., 6 Grossman, L., 520 Grossman, S. E., 283, 286 Grossman, S. P., 520 Groth, G., 36 Groth, G. E., 46 Groth, H., 469 Grothe, B., 115, 133, 141, 145 Grove, E. A., 399, 400 Grover, L. M., 524, 654 Groves, P. M., 509 Growdon, J., 608 Growdon, J. H., 652 Gruenewald, T. L., 447 Gruhn, M., 184, 186, 187 Grundy, D., 312, 313 Grune, T., 655 Grunert, U., 85, 86 Gr¨uter, C., 501 Gu, X., 258 Guadalupe, T., 352 Guarnieri, D. J., 310 Guarraci, F. A., 522 Guay, A. T., 345 Gubellini, P., 406 Guenot, M., 258 Guertin, P., 188 Guertin, P. A., 192 Guezard, B., 191 Guigon, E., 213 Guild, S. R., 116, 122 Guillaume, I., 2 Guilleminault, C., 370 Guillen, A. K. Z., 343 Guillermet, C., 208 Guillery, R. W., 89 Guillot, P. V., 4–6 Guimaraes, A. R., 564 Guinan, J. J., 116 Guinard, D., 208 Guise, K. G., 258 Gulbransena, B. D., 288 Gullberg, A., 74 Gumert, M. D., 581 Gunderson, K., 481, 482, 501 Gunnel, S., 315 Gunning, F. M., 651 Gunter, T. D., 16 Gunturkun, O., 142

Author Index

Guo, L., 399 Guo, W., 275, 276, 284 Guptarak, J., 339, 342 Gur, R. C., 651 Gur, R. E., 651 Gureviciene, I., 655 Gurfinkel, V. S., 179 Gurung, R. A. R., 447 Gusev, P. A., 534 Gusnard, D. A., 259, 428, 433, 650 Gustafson, B., 472 Gustafsson, J. A., 9 Gustafsson, L., 621 Gutekunst, C. A., 409 Gutierrez, R., 283, 286 Gutknecht, L., 14 Gutknecht, V., 275 Gutman, D. A., 429 Gutschalk, A., 132 Guy, E. G., 402 Guyer, P. E., 533 Guzowski, J. F., 649 Gwilym, S. E., 260 Gysling, K., 409 Haagensen, M., 57 Haas, H. L., 407 Habeck, C., 648 Haber, S. N., 399, 400 Haberlandt, K., 496 Haberman, R. P., 650, 656, 657 Hackeman, E., 214 Hackett, K. J., 2 Hackett, T. A., 83, 162–164, 166–171 H¨adicke, A., 449 Hadj-Bouziane, F., 100 Hadjimarkou, M. M., 340 Hadley, D. M., 651 Haesler, S., 244 Hafter, E. R., 128 Hafting, T., 563 Hagan, M. M., 308, 314 Hagar, J., 33 Hagelsrum, L. J., 348 Hagen, M. C., 214, 227 Hagenauer, M. H., 370 Hager-Ross, C., 210 Haggard, P., 228 Hagglund, M., 191, 193 Hagmayer, Y., 497 Hagoort, P., 600, 601, 606, 607 Hahn, B. L., 403 Hahn, J., 445 Hahn, T. M., 308 Haier, R., 523 Haier, R. J., 610 Haig, B. D., 30, 46 Hajnal, A., 311 Hakan, R. L., 408

Hake, H. W., 120 Hakimi, S., 434 Halaas, J., 307 Halaas, J. L., 307 Halas, E. S., 537 Hale, C., 129 Hale, G., 6 Haley, D. A., 533 Halgren, E., 601 Halko, M. A., 92, 214 Hall, A., 374 Hall, E. C., 646 Hall, F. S., 11 Hall, G., 425, 523 Hall, M., 446, 454 Hall, M. H., 449 Hall, N. J., 103 Hall, W. C., 212 Hall, W. G., 312 Haller, J., 12 Hallett, M., 19 Halliday, G. M., 651 Hallock, R. M., 285, 286 Halpern, B. P., 274, 275, 282, 286 Halsell, C. B., 279 Haluk, D. M., 406, 408 Hamanaka, H., 522 Hamani, C., 258 Hamann, S., 425 Hamernik, R. P., 126, 127 Hamilton, R. B., 283 Hamilton, S. L., 95 Hamilton, W. D., 28, 31, 37 Hamker, F. H., 623, 634 Hamm, A., 426, 427 Hammar, I., 191 Hammer, M., 513 Hammerstein, P., 581 Hammond, D. L., 252 Hamon, M., 402 Hampton, R. R., 498, 499 Hamshere, M. L., 7 Han, J. S., 256 Han, M. L., 454 Han, S., 258 Han, V. K., 315 Han, Y., 179, 183, 184 Han, Y. R., 6 Hanakawa, T., 262, 613 Hanamori, T., 281 Handa, R. J., 337, 340 Handlemann, G. E., 561 Handwerger, K., 434 Haney, R. Z., 647 Hankins, L., 521 Hanks, T. D., 103 Hanlon, R., 134 Hann, N. E., 446 Hanna, D., 559

Hannan, A. J., 473–475, 657 Hanni, K. B., 655 Hansen, D. R., 275, 277 Hansen, P. C., 258 Hansen, S., 348 Hanson, S. J., 623 Hansson, B., 3 Hansson, P., 259, 402 Hantsoo, L., 455 Happel, L. T., 245 Hara, Y., 653 Haraguchi, M., 245, 408 Harano, K., 346 Harasty, J., 651 Harasty, J. A., 651 Harden, D. G., 531 Harden, R. N., 260 Harden-Jones, F. R., 287 Hardiman, M. J., 531, 533, 537 Hardin, P. E., 13 Harding, C. F., 346 Harding, S. M., 343, 346 Hardy, P. A., 651 Hardy, R., 475 Hare, B., 587 Hare, T. A., 433 Harel, M., 91, 95, 566 Hargreaves, E. L., 563 Hargreaves, R., 260 Hari, R., 258, 259 Hariri, A., 17 Hariri, A. R., 17, 426, 431, 433 Harkavy-Friedman, J. M., 16 Harkema, S. J., 195 Harlan, R. E., 341 Harley, T. A., 605 Harlow, H. F., 471, 574 Harlow, M. K., 471, 574 Harman, A., 70 Harman, A. M., 65, 70 Harman, D., 655 Harmar, A. J., 374 Harmening, W. M., 69, 70 Harmon-Jones, E., 426 Harpending, H., 33, 44, 45, 47 Harper, R., 225 Harper, R. M., 520 Harrington, H., 17 Harrington, M. E., 374 Harrington, N. R., 523 Harris, E. H., 488, 489 Harris, E. W., 524 Harris, G. C., 407 Harris, J. R., 222 Harris, K., 636 Harris, M. G., 185 Harris, R. E., 259, 260 Harris, T., 445 Harris, W. A., 462, 466

685

686

Author Index

Harrison, D. E., 645 Harrison, J., 163, 608 Harrison, L., 429 Harrison, M., 124 Harrison, N. L., 337 Harrison, Y., 373 Harris-Warrick, R. M., 191–194 Hart, B. L., 348 Hart, N. S., 61, 62, 72 Harte, S. E., 259 Harting, J. K., 162 Hartley, C. A., 431 Hartley, R., 191, 193 Hartmann, B. R., 451 Hartmann, U., 344 Harvey, M. A., 185 Harvey, P. J., 192, 193, 196 Hasegawa, I., 103 Hasegawa, K., 283 Hasegawa, M., 612 Hasegawa, S., 4 Hasegawa, T., 98 Haselton, J., 519, 520 Haselton, M. G., 29, 30, 45, 47 Hasen, N. S., 8, 12 Hashikawa, T., 163, 164, 166 Hashimoto, M., 290 Hashimoto, S., 351 Hashmi, J. A., 257 Hashmonay, R., 346 Hasselmo, M., 636 Hasselmo, M. E., 634, 636, 637 Hasselquist, D., 3 Hasson, U., 101 Hastings, M. C., 118, 119 Hastings, M. H., 374 Hatsopoulos, N., 186 Hauge, S. A., 535 Haugen, C. M., 348 Hauger, R. L., 450 Haughton, V. M., 429 Haugtvedt, C., 262 Haun, T., 86 Haun, T. J., 86 Hauser, H., 382 Hauser, M., 167 Hauser, M. D., 157, 489, 586 Haushofer, J., 101 Hausler, D., 2 Hausselt, S. E., 86 Havel, P. J., 316 Havenaar, J. M., 446 Haver, A. C., 315 Haverkamp, S., 86 Haverkort, M., 608, 609 Hawcock, A. B., 339 Hawk, L., 449 Hawken, M., 94 Hawken, M. J., 95

Hawkins, A. D., 118, 123 Hawkins, J., 120, 122 Haworth, C. M., 2 Hawryshyn, C. W., 54 Hawwa, N., 262 Haxby, J. V., 100, 603 Hayama, T., 279, 283 Hayes, M. R., 310, 315 Hayes, R. L., 231 Haykin, S., 621, 623 Haynes, A. C., 309 Haynes, J. D., 94, 98 Hays, P., 371 Hayward, L., 311 Hayward, M., 449 Hayward, V., 226 Hazlett, E., 610 Hazrati, L. N., 406 He, J., 163, 164 He, Y., 337, 389 Head, D., 651 Head, E., 475 Headlam, A. J., 399 Hearn, E. F., 9 Heatherington, A. W., 307 Heatherton, T. F., 432 Hebb, D. O., 473 Hebert, L. E., 644 Hecht, G. S., 286 Heck, G. L., 275 Heckers, S., 567 Heckman, C. J., 192 Hedden, T., 647, 648 Hedehus, M., 598 Hedges, V. L., 340 Hedlund, P. B., 189–192 Heeb, M. M., 347–349, 353 Heeger, D. J., 92, 95, 98, 101, 102 Heeley, D. W., 97 Heeren, D., 346 Heesen, M., 582 Heffner, H., 116, 128 Heffner, H. E., 117, 118, 124, 128–130 Heffner, R. S., 117, 118, 124, 128–130 Hegde, J., 96, 98 Hegemann, P., 60 Hegvik, D. K., 486 Heidbreder, C., 346 Heidbreder, C. A., 403 Heiduschka, P., 83 Heien, M. L., 404 Heilman, C. J., 409 Heils, A., 17 Heim, N., 343 Heiman, J. R., 337 Heiman, M. L., 315 Heimer, L., 348, 398, 399, 423 Hein, A., 470 Hein, G., 607

Heinemann, S. F., 293 Heinke, D., 623, 634 Heinrich, B., 587 Heinricher, M. M., 260 Heinz, R. D., 118 Heinze, H.-J., 613 Heiser, M. A., 215 Heisler, J. A., 446 Heistermann, M., 579 Heithaus, M. R., 578 Heitler, W. J., 186 Heitz, R. P., 637 Held, R., 470 Helenius, P., 600 Helfert, R. H., 145 Helfrich, T., 291 Helgason, A. R., 445 Hellekant, G., 281 Heller, H. C., 309 Hellgren, J., 192 Helmstetter, F. J., 519, 520, 522, 523 Hemart, N., 534 Hembree, T. L., 531 Hemelrijk, C., 582 Hemmi, J. M., 70 Hen, R., 10, 13, 433 Henderson, D., 126, 127 Henderson, L., 308, 608 Henderson, N. D., 6, 15 Hendler, T., 101 Hendricks, S. E., 353 Hendrickson, A. E., 84, 85 Hendriks, T. J., 10 Hendry, S. H., 90, 93 Henkin, R. I., 287 Hennessey, A. C., 342 Hennig, T., 12 Henning, G. B., 120 Henning, H., 294 Henrich, J., 582 Henriksen, G., 261 Henry, B. I., 652 Hensch, T. K., 467 Hensley, K. L., 645 Henson, R. N. A., 607 Henter, I. D., 388 Henzi, P. S., 581 Henzi, S. P., 576, 581, 583 Hepner, B. C., 354 Herberman, R., 450 Herbert, T. B., 449, 450 Herbst, J. H., 453 Herd, S. A., 637 Herkenham, M., 398 Herman, J. P., 315, 322, 656 Herman, L. M., 124, 125 Hermane, J. P., 288 Hermann, B. P., 609 Hermans, E. J., 564

Author Index

Hermans-Borgmeyer, I., 293 Hernandez, A., 228, 229 Hernandez, A. E., 613 Hern´andez, L., 311, 337, 340 Hernandez, L. L., 521 Hernandez, O., 162 Hernandez-Andres, J., 53 Hernandez-Lopez, S., 404 Herndon, J. G., 646, 653 Herness, S., 278 Herold, S., 405 Herr, S., 88 Herrmann, J. E., 196 Herrnstein, R. J., 493 Hersch, S. M., 409 Hershey, G. K., 448 Hershey, T., 647 Hershman, J. M., 382 Herskovitch, P., 586 Hertwig, R., 26 Herz, A., 409 Herz, R. S., 403 Herzallah, M. M., 635–637 Herzog, E. D., 375 Herzog, H., 314 Herzog, M. H., 630 Hesch, R. D., 382 Hess, D., 183 Hess, U., 427 Hesselink, J. R., 651 Hesslow, G., 526, 534 Hetrick, W. P., 536 Hettes, S. R., 400, 405, 407 Hettinger, T., 279, 281 Heussy, J. K., 86 Hevelone, N. D., 651 Hewson, A. K., 315 Heyman, R., 454 Heyming, T. W., 400, 405, 407 Hickok, G., 171, 608 Hiebert, G. W., 187, 188, 195, 196 Hiegel, C., 339, 342 Higgins, C., 64 Higgins, N. C., 146 Highland, L., 348 Hightower, A., 41 Higley, J. D., 17 Hikosaka, O., 214, 401, 402, 404 Hilburger, M. E., 451 Hilgetag, C. C., 423, 428, 429, 431, 433 Hill, D. L., 276, 279, 281 Hill, K., 577 Hill, R., 179, 180, 183, 192 Hill, R. A., 580 Hill, R. H., 179–181, 183, 192 Hille, B., 182, 183 Hillebrand, J. J., 315 Hillery, C. M., 125 Hillis, A. E., 102

Hillyard, S. A., 600 Hilmert, C. J., 451 Hiltl, D. M., 344 Hilton, M. F., 381, 386 Hiltunen, J., 258 Himes, B. T., 192 Himmerich, H., 383 Hinckley, C., 191, 193 Hinckley, C. A., 191, 193 Hinde, R. A., 493, 574, 575, 581 Hinds, O. P., 93 Hines, M., 345 Hinkle, D. A., 98 Hinman, C., 163 Hinton, G. E., 622, 628 Hinz, W. A., 389 Hiramatsu, C., 75 Hirano, J., 292, 293 Hiraoka, M., 527 Hirayama, K., 605 Hirsch, H. V., 466 Hirsch, J., 433 Hirsh, R., 561 Hirshfield, S., 446 Hirsh-Pasek, K., 581 Hirst, G. D., 192 Hisatomi, O., 61 Hitchcock, C., 428, 433 Hitchcock, J. M., 520, 521, 523, 531 Hivley, R., 278 Hixson, M. D., 502 Hlushchuk, Y., 259 Hnasko, T. S., 308 Ho, C. W., 403 Hoang, T. X., 195 Hobfoll, S. E., 442 Hobisch, A., 448, 450 Hobson, J. A., 369, 370 Hochgeschwender, U., 261 Hochman, S., 183, 190, 195 Hochner, B., 513 Hochstein, S., 630 Hocker, C. G., 180 Hodge, C. W., 408 Hodges, J. R., 602 Hodgkinson, C., 16 Hodgson, J. A., 194, 195 Hodos, W., 70 Hoebel, B. G., 311 Hoeft, F., 258 Hoekstra, H. E., 580 Hoen, M., 316 Hoenders, B. J., 59 Hof, P., 655 Hof, P. R., 258, 646, 648, 652 Hofbauer, R. K., 259, 262 Hofer, H., 85 Hofer, M. A., 471 Hofer, S. B., 467

Hofer, T., 656 Hoffman, D. S., 231 Hoffman, E. A., 597 Hoffman, G. E., 353 Hoffman, K. L., 171, 586 Hoffmann, M. L., 320 Hoffrage, U., 26 Hofmann, C. M., 58 Hofmann, E., 381 Hogness, D. S., 73 Hohagen, F., 387 Hohmann, J. G., 333 Hokfelt, T., 245, 250, 309, 351, 399, 404 Hokoc, J. N., 63 Holahan, C. J., 443 Holahan, M. R., 403, 407 Holcomb, P. J., 600, 607, 609 Holden, G. W., 444 Holden, R., 185 Holder, M. K., 340 Holekamp, K., 578, 579, 584 Holgate, S., 452 Holland, P., 425 Holland, P. C., 407, 423, 425 Hollander, J. A., 407 Hollands, M. A., 185 Holley, A., 292, 469 Holley, H., 452 Holliday, J. E., 453 Holliday, K., 280 Hollins, M., 221, 223, 225, 230 Hollopeter, G., 308 Holloway, K. S., 349 Holmes, A., 10, 11, 14, 17 Holmes, E. A., 262 Holmes, G. M., 344 Holmes, M. H., 209 Holmes, N. P., 93 Holter, S. M., 3 Holtman, B., 12 Holtzman, D. M., 654 Holtz-Vitaterna, M., 3 Holub, A. D., 626 Holy, T. E., 8 Holyoak, K., 585 Holyoak, K. J., 495 Homberg, J. R., 11 Honda, E., 292 Honda, M., 262 Hong, S. T., 261 Hook, M. A., 195 Hooks, M. S., 412 Hoon, M. A., 274–276, 278, 284 Hooper, S. L., 179 Hopfield, J. J., 633 Hopkins, D. A., 312 Hopster, H., 5 Hoque, S., 374

687

688

Author Index

Horch, K. W., 211 Horiguchi, H., 92 Horita, H., 351 Horn, J. M., 374 Horne, J. A., 373 Hornstein, E. P., 67, 86 Hornung, D. E., 290, 292, 294 Horowitz, M. J., 446 Horridge, A., 71 Horst, G. J. T., 279 Horstmann, G. A., 189 Horton, J. C., 92–95, 102 Horvath, C. M., 307 Horvath, T., 307, 309 Horvitz, J. C., 402, 403 Horwitz, B., 611 Hosaka, K., 578, 580 Hosoya, T., 105 Hosoya, Y., 280 Hostetler, A. M., 314 Houk, J. C., 533 Houle, S., 647 Housgaard, J., 180, 183 Howard, D., 605 Howard, M. W., 566 Howard, R. V., 350 Howells, R. D., 341 Howieson, D. B., 651 Howland, J. G., 405 Hoy, P. A., 318 Hoy, R. R., 119, 125–127 Hoyle, G., 190 Hoyt, W. F., 92 Hrdy, S. B., 33 Hruby, V. J., 307, 308 Hsiao, S., 214 Hsiao, S. S., 83, 207, 209, 210, 212–220, 222–233, 627 Hsieh, I. H., 171 Hsieh, J. C., 259 Hsieh, S. T., 259 Hu, B., 162 Hu, D., 90 Hu, G., 179, 183 Hu, K., 381, 388 Hu, M., 95 Hu, X., 171, 214, 646 Huang, A. L., 275, 284 Huang, B., 446 Huang, L., 277 Huang, R. S., 93 Huang, T. C., 19 Huang, Y. A., 275, 278 Huang, Y. Y., 16, 522, 524 Huard, J. M. T., 289 Hubbard, A. M., 116 Hubbard, S. J., 210 Hubel, D., 95, 465 Hubel, D. H., 92–95, 466, 467, 627

H¨ubener, M., 467 Huber, D. E., 629 Huber, F., 119, 125 Huber, R., 373 Hubli, M., 194 H¨ubner, H., 351 Hudsen, R., 287 Hudson, R., 295 Hudspeth, A. J., 134–136, 158 Hueletl-Soto, M. E., 346 Huerta, M., 145 Huerta, M. F., 89, 163 Huerta, P. T., 523 Huettel, S. A., 651 Huganir, R. L., 407 Hugart, J. A., 494 Hughes, A., 55–57 Hughes, B. L., 431 Hughes, G. M., 196 Hughes, J., 261 Hughes, N. P., 258 Hughes, P., 425 Hughes, Z. A., 346 Huie, J. R., 195 Huijbers, P., 605 Huisman, A. M., 189 Huizink, A. C., 18 Huk, A. C., 102 Hulihan, T. J., 347 Hull, E. M., 334–336, 338, 343–345, 347–352, 354 Hulley, S. B., 384, 385 Hulse, S., 118 Hulse, S. H., 132 Hultborn, H., 181, 187, 191, 192 Hultman, C. M., 7 Humberstone, M., 446 Hume, D., 496 Hume, R. I., 180, 181 Humeau, Y., 404 Hummler, E., 275, 284 Humphrey, G. K., 97, 214 Humphrey, N. K., 574 Humphreys, C. T., 411 Hunkin, N. M., 647 Hunt, D. M., 58, 61, 72 Hunter, C., 132 Hunter, J. C., 245 Hunter, J. N., 103 Hunter, W. S., 485 Huo, L., 310 Hupka, R. B., 45 Hurlbert, A., 628 Hursch, C. J., 343 Hurst, J. L., 10 Huss, M., 623, 638 Hussain, Z., 399 Hutchings, M. E., 162 Hutchinson, J. S., 320

Hutchinson, L., 446 Hutchinson, R., 603 Hutson, K. A., 161, 162 Hwang, D. J., 337 Hyde, J. S., 429 Hyman, B. T., 652 Hyman, K. B., 449, 455 Hyman, S. E., 13 Hynes, M. A., 315 Hyun, J., 308, 315 Iadarola, M. J., 259 Iannetti, G. D., 258 Iannucci, U., 351 Iaria, G., 571 Ichida, J. M., 90, 91 Ichihara-Takeda, S., 646 Ide, C., 208 Iggo, A., 210 Ihle, K., 260 Iimori, H., 451 Iitaka, C., 389 Ijames, S. G., 410, 411 Ijspeert, A., 637 Ikawa, M., 288 Ikeda, A., 385 Ikeda, K., 276 Ikeda, T., 288 Ikemoto, S., 400 Ikonen, S., 655 Iles, J. N., 191 Ilmoniemi, R., 600 Imai, T., 288 Imaizumi, K., 119 Imamura, K., 292, 293 Imaoka, H., 411 Imig, T. J., 162, 164 Imoto, T., 279, 283 Inada, H., 275 Inati, S. J., 100 Indefrey, P., 606 Inglis, B., 262 Ingram, C. D., 447 Ingvar, M., 259, 262, 263 Innocenzi, R., 399 Inoue, K., 255, 600 Inoue, M., 275 Inoue, S., 486 Insausti, R., 171 Insel, N., 649 Insel, T. R., 9, 347, 462 Inslicht, S. S., 455 Intrator, N., 632 Introini-Collison, I. B., 520, 522 Intronini-Collison, I. B., 523 Inui, A., 315 Inui, K., 258 Irani, B. G., 310 Irons, W., 33

Author Index

Ironson, G., 451–453, 455 Irvin, G. E., 90 Irvine, D. R., 162 Irvine, D. R. F., 162 Irwin, K. B., 535 Irwin, M. R., 450 Isa, T., 180 Isaacson, R. L., 520 Ise, S. N., 346 Ishai, A., 100 Ishida, N., 389 Ishida, Y., 275 Ishii, M., 309, 385 Ishii, R., 273 Ishikawa, A., 406 Ishikawa, T., 451 Ishimaru, Y., 275 Isidori, A. M., 345 Islam, A. A., 273 Isnard, J., 258 Ison, J. R., 126, 127 Isowa, T., 450 Israel, S., 3 Issa, A. M., 652 Ito, K., 450 Ito, M., 96, 98, 99, 534 Ito, R., 410 Ito, S., 283 Itoh, M., 284 Itohara, S., 288, 535 Itskov, V., 565 Itti, L., 629 Iuvone, P. M., 352, 353 Ivarsson, M., 526, 535 Ivic, L., 290 Ivkovich, D., 526, 528, 531, 537 Iwamura, Y., 213, 214 Iwasa, Y., 30, 36 Iwata, J., 519–521 Iwata, K., 612 Iwema, C. L., 291, 293 Izadnegahdar, R., 40 Izard, V., 489 Izquierdo, M. A., 162 Jabbur, S. J., 262 Jaber, M., 262 Jack, C. R., 657 Jacks, T., 3 Jackson, B., 309 Jackson, G. M., 608 Jackson, J. M., 388 Jackson, L. R., 262 Jackson, M. E., 405 Jackson, P. L., 258 Jackson, S. R., 608 Jacob, H. J., 3 Jacob, S., 287, 288 Jacobs, G. H., 56–65, 70, 72–74, 95

Jacobs, K. M., 284 Jacobs, P., 181 Jacobs, R., 184, 449 Jacobsen, F., 40 Jacob-Vadakot, S., 192 Jacoby, R., 86 Jadhav, S. P., 399 Jaeger, J. J., 596, 606, 609 Jafek, B. W., 289 Jaffe, D., 524 Jager, L., 260 Jagielo, J. A., 485, 491, 494 Jaillard, D., 534 Jakeman, L. B., 195 Jakobson, L. S., 97 Jakupovic, J., 345 Jamal, M., 446 James, F. O., 386 James, J. E., 288, 445, 580 James, K. B., 188 James, T. W., 97, 214, 226, 227 James, W., 422, 552, 630, 632, 637 Jamon, M., 4–6 Janak, P. H., 410, 412 Jang, T., 279 Janik, D., 374 Janke, A., 74 Jankowska, E., 177, 191 Jansen, G., 2 Janson, C. H., 488 Janssen, M. C., 11 Janssen, W. G. M., 653 Jansson, L., 446 Janzen, L., 564 Jarcho, J. M., 263 Jarrell, T. W., 520, 521 Jarvis, C. D., 97 Jarvis, E. D., 142 Jarvis, J. R., 70 Jaser, S. S., 443, 444 Jastreboff, P. J., 289 Jastrow, H., 278 Javel, E., 164 Javitt, D. C., 166 Jayakumar, J., 90 Jayaram, N., 609 Jazin, E., 13, 16 Jeanrenaud, B., 280, 308 Jean-Xavier, C., 196 Jeffress, L. A., 120 Jelsone, L. M., 426 Jenike, M. A., 426, 433 Jenkins, F., 455 Jenkins, F. J., 450, 454 Jenkins, W. J., 340 Jenkinson, N., 258 Jennings, P. J., 564 Jensen, L., 195

689

Jensen, R. A., 523, 525 Jeon, D., 259 Jepson, L. H., 88, 89 Jerlov, N. G., 53 Jernigan, T. L., 651 Jerome, C., 313 Jessell, T. M., 197 Jessen, H. M., 12 Jesteadt, W., 120, 124 Jewett, D. M., 261 Jewett, M. E., 380 Jezzini, A., 170 Ji, H., 306, 309 Ji, S., 645 Jiang, D., 141 Jiang, E., 282 Jiang, H., 100 Jiang, W., 161, 162, 186, 214, 219, 223 Jiang, Y., 100, 650, 651 Jicha, G. A., 652 Jin, C., 315 Jin, L. E., 646 Jin, Z., 279 Jirtle, R. L., 19 Jo, D., 259 Jo, Y. H., 316 Jobert, A., 408, 605 Jobst, E. E., 307, 308 Jocson, C. M., 95 Joel, D., 634, 635 Johansen, J. P., 522 Johansen-Berg, H., 260, 429 Johansson, O., 399, 404 Johansson, R. S., 208–210 John, K. T., 215, 223 Johns, A., 309 Johnsen, S., 53 Johnson, A., 621 Johnson, A. K., 320 Johnson, B. A., 293 Johnson, C. S., 118, 122 Johnson, E. N., 95 Johnson, F., 144 Johnson, H. R., 646 Johnson, J. M., 3 Johnson, K. O., 83, 208–210, 213–219, 222–224, 228–232 Johnson, L. R., 399 Johnson, M. H., 471 Johnson, R. D., 345 Johnson, S., 576 Johnsrude, I. S., 171 Johnston, B. R., 194 Johnston, D., 524 Johnston, K., 70 Johnston, R. S., 97 Johnston, T. A., 166 Johnstone, A. D. F., 118, 131

690

Author Index

Johnstone, T., 424, 426, 427, 429, 431–433 Joiner, W. M., 105 Jois, M., 322 Jokl, P., 451 Jolly, A., 574 Jolt, J., 313 Jonas, P., 406 Jonas, U., 344 Jonassaint, C., 18 Jones, A., 337 Jones, A. K., 256 Jones, A. K. P., 263 Jones, B., 396 Jones, B. C., 1, 43, 338 Jones, B. E., 368, 369 Jones, C. R., 389 Jones, D. L., 396, 408 Jones, E. G., 163, 164, 166, 212 Jones, E. P., 7 Jones, J. E., 315 Jones, J. L., 411, 647 Jones, L. M., 286 Jones, M., 379 Jones, M. W., 408 Jones, S. R., 407 Jones-Gotman, M., 284 Jonides, J., 607 Jonkman, S., 397 Jonsson, G., 399, 404 Joo, H. R., 86 Joormann, J., 432 Joosten, H. W., 189 Jordan, L. M., 178, 189–192 Jordan, T. R., 97 Jorge, J. C., 337, 340 Jorgenson, E., 274 Jorgenson, M., 131 Jørgenson, M. B., 131 Joris, P. X., 141, 144, 145, 160 Jorum, E., 261 Jos Dederen, P., 346 Josephs, O., 602 Jouvet, M., 367 Jovanovic, K., 179, 183, 184 Joynes, R. L., 518 Judge, P., 584 Jukam, D., 8 Jukes, M. G., 177, 191 Juler, R., 522 Julien, S., 83 Julliot, C., 75 Jung, M. W., 519 Jung-Beeman, M., 602, 609 Jury, F., 10 Just, M. A., 597, 598, 602, 603, 607–612 Just, W., 14 Juster, R., 442

Kaan, E., 606, 607 Kaas, J., 146 Kaas, J. H., 64, 83, 89, 93, 102, 162–164, 166, 168–171, 213, 214, 219 Kabani, N. J., 102 Kabaso, D., 652 Kably, B., 186 Kachele, D. L., 279 Kaczmarek, L., 524, 656 Kaczmarek, L. K., 189 Kadish, I., 656 Kadotani, H., 369 Kaelin, C. B., 310 Kaga, K., 605 Kaga, T., 315 Kahler, A., 315 Kahn, A., 349 Kahn, B. B., 307, 310 Kahn, D. F., 343 Kai, N., 403 Kairiss, E. W., 522, 524 Kaisaka, J., 279, 283 Kaisaku, J., 279 Kaiyala, K., 316 Kajikawa, Y., 163, 166–168, 170 Kakigi, R., 258 Kakuda, N., 210 Kakuma, T., 309 Kalabat, D., 278 Kalata, U., 348 Kali, S., 373 Kalichman, S. C., 453 Kalick, M., 41 Kalin, N. H., 431 Kalina, R. E., 84, 85 Kalinchuk, A. V., 369 Kalinoski, D. L., 292 Kalisch, R., 262 Kalish, H. I., 519 Kalivas, P. W., 398, 399, 405–409, 411, 412 Kalkus, J. F., 100 Kalra, P., 308 Kalra, P. S., 308 Kalra, S., 308 Kalra, S. P., 308 Kalso, E., 262, 263 Kamarck, T. W., 445 Kamenecka, T. M., 407 Kameyama, K., 467 Kamil, A. C., 581 Kaminski, J., 587 Kamitani, Y., 94 Kana, R. K., 611 Kanan, C., 629 Kanarek, R. B., 311 Kanazawa, H., 278 Kanda, M., 262

Kandel, E. R., 433, 511, 513, 522, 524 Kandova, E., 312 Kaneda, H., 283 Kaneko, T., 528 Kang, E., 539 Kang, N., 345 Kang, Y., 281 Kangawa, K., 315 Kannan, V., 102 Kano, M., 534 Kanold, P. O., 146 Kanoski, S. E., 315 Kanwisher, N., 98–100, 564, 601 Kanwisher, N. G., 101, 102 Kao, K.-T., 530 Kao, S.-Y., 656 Kao, T., 192 Kapen, S., 381 Kaplan, E., 90 Kaplan, J. M., 310, 312, 316 Kapp, B. S., 423–425, 519–522, 524, 530 Kappas, A., 427 Kappel, M., 449 Kappers, A. M. L., 227 Kapsalis, E., 578, 582 Kaptchuk, T. J., 263 Kapur, S., 650 Karam, P., 348 Karayiorgou, M., 12 Karimnamazi, H., 280 Karkanias, G., 310 Karklin, Y., 631, 637 Karlof, K., 221, 223, 225 Karom, M., 344 Karp, C. L., 448 Karten, H., 142 Karten, H. J., 142, 146 Kashiwadani, H., 293 Kastak, D. A., 588 Kastin, A. J., 315 Kastner, S., 89–92, 98, 100–102 Kasuga, M., 315 Kataoka, K., 314 Kataoka, S., 277 Kato, A., 341 Kato, K., 4 Kato, M., 246 Kato, T., 285 Katoh, K., 293 Katoh, Y. Y., 163 Katsuura, G., 315 Katsuura, Y., 409 Katz, D., 229 Katz, D. B., 283, 285, 286, 532 Katz, E., 429 Katz, J., 254 Katz, L. F., 314 Katz, P. S., 180, 181, 189, 191, 193

Author Index

Kaube, H., 258 Kauer, J. S., 293 Kauffman, A. S., 333, 334 Kaufman, J. M., 346 Kaup, B., 609 Kavaliers, M., 9, 261 Kawaguchi, H., 275 Kawaguchi, Y., 409 Kawai, K., 316 Kawamura, M., 605 Kawamura, N., 451 Kawamura, S., 74, 75 Kawamura, Y., 280, 285 Kawano, S., 277 Kawarasaki, A., 213 Kawashima, R., 284, 612 Kawata, M., 333, 354 Kay, B. A., 185 Kay, J., 100 Kay, R. F., 69 Kayaert, G., 99 Kaye, A. N., 213 Kaye, J. A., 650, 651 Kayser, C., 164, 170–172, 214 Kazennikov, O. V., 179 Keane, T. M., 446 Kearns, D., 129 Keator, D., 523 Keehn, J. D., 311 Keemink, C. J., 230 Keenan, C. L., 522, 524 Keenan, J. P., 597 Kehagia, A. A., 646 Kehl, S. J., 522, 524 Kehoe, E. J., 525 Keil, K., 70 Keire, D. A., 314 Kelber, A., 53, 63, 65, 72 Keller, E. T., 448, 450 Keller, F. S., 493 Keller, G., 443, 444 Keller, J., 259, 433 Keller, J. N., 655 Keller, M., 340 Keller, T. A., 597, 598, 607, 610–612 Kellermann, T., 230 Kelley, A. E., 399, 401–403, 405, 407–409, 411 Kelley, H. H., 496 Kelley, K. W., 448, 450 Kelley-Bell, B., 656, 657 Kelliher, K. R., 345, 348, 349 Kellman, P. J., 581 Kellmeyer, P., 171 Kellogg, W. N., 517 Kelly, A. M., 405, 430 Kelly, C., 430 Kelly, D. D., 261 Kelly, D. G., 214

Kelly, E. E., 381 Kelly, J., 307 Kelly, L., 313 Kelly, S., 14, 258, 310 Kelly, S. P., 132, 232, 233 Kelm, G. R., 312 Kelso, J. A. S., 621 Keltner, J. R., 262 Kemble, E. D., 520 Kemenes, G., 512 Kemeny, M., 450 Kemeny, M. E., 450 Kemeny, S., 607, 608 Kemner, C., 426 Kemp, C., 621 Kempen, G., 605 Kempermann, G., 474 Kendall-Tackett, K., 450 Kendler, K. S., 2, 580 Kendrick, D. F., 486 Kendrick, K. M., 9 Kenins, P., 208, 209 Kenna, H., 259, 433 Kennard, C., 608 Kennedy, C., 97 Kennedy, D. P., 286 Kennedy, G., 306 Kennedy, H., 170 Kennedy, J. M., 129 Kennedy, S. H., 258 Kennedy, W., 426 Kennedy, W. A., 100 Kenny, E. T., 411 Kenny, P. J., 407 Kenrick, D. T., 26, 36, 46 Kent, P. F., 292, 294, 295 Keri, S., 635–637 Kerkhofs, M., 389 Kerr, D. C., 651 Kerschensteiner, M., 196 Kersey, K. S., 409, 411 Kershaw, M., 308 Kershaw, Y. M., 447 Keshishian, H., 190 Kesner, R. P., 412, 521, 568 Kessing, L. V., 387 Kesterson, R. A., 307, 308 Kesteven, S., 292 Ketelaar, T., 27, 28, 30, 34, 41 Kettner, R. E., 527 Keuker, J. I. H., 652 Keung, W., 310 Kevan, P. G., 72 Keverne, E. B., 293 Keys, B. A., 650 Keysers, C., 170 Khalsa, P. S., 215 Khan, N. A., 277 Khan, Q., 13

691

Kheyfets, I. A., 346 Khotib, J., 351 Kiani, R., 103 Kibbe, W. A., 3 Kick, S. A., 129 Kida, S., 4, 513 Kiecolt-Glaser, J. K., 450–455, 580 Kieffaber, P. D., 536 Kieffer, T. J., 315 Kiehn, O., 180, 183, 184, 187, 191–193 Kiemen, A., 387 Kieser, J., 41 Kikusui, T., 288 Kilbourn, M. R., 261 Kilduff, T. S., 309 Kilgard, M. P., 636 Killcross, S., 521 Killebrew, J. H., 215, 217 Killeen, P. R., 491, 492, 497 Killiany, R., 648 Killiany, R. J., 646–648, 653 Kilpatrick, D., 443, 446 Kilpatrick, I. C., 408 Kilts, C. D., 425 Kim, A., 600, 601, 607 Kim, B. W., 95 Kim, D. H., 656 Kim, D. S., 164 Kim, E. M., 308 Kim, H., 424–429, 432, 433 Kim, H. S., 426, 427 Kim, I., 277 Kim, J., 612 Kim, J. J., 430, 519–525, 529, 531, 534, 535, 569 Kim, J. W., 278 Kim, M. J., 425–428, 430, 432–434 Kim, S., 259, 535 Kim, U. K., 274 Kim, Y., 1 Kim, Y. B., 307, 310 Kim, Y. H., 3 Kim, Y. I., 468 Kimble, D. P., 295, 561 Kimchi, T., 8 Kim-Cohen, J., 16 Kimura, K., 450 Kindermann, T., 180 Kindon, H. A., 348 King, A. J., 162, 163 King, C. T., 282 King, D. A. T., 533 King, J. E., 2 King, M. S., 279 Kinnamon, S., 275 Kinnamon, S. C., 273, 275, 277, 278 Kinomura, S., 284 Kintsch, W., 603 Kinzie, J. M., 387

692

Author Index

Kinzig, K. P., 310, 314, 315 Kiper, D. C., 95 Kippin, T. E., 347, 348 Kirchner, H., 315 Kirk, E. C., 69, 70 Kirkpatrick, L., 47 Kirkpatrick, M. E., 337 Kirkpatrick-Steger, K., 491, 494 Kirkup, A. J., 312, 313 Kirkwood, A., 654, 655 Kirkwood, T. B., 643 Kirsch, I., 263 Kirschfeld, K., 56 Kishi, K., 400 Kishi, T., 309 Kishimoto, T., 447 Kishitake, M., 339 Kisley, L. R., 321 Kissileff, H. R., 313, 318 Kita, H., 280 Kitada, R., 214 Kitchell, R. L., 345 Kitchen, D. M., 576, 584 Kito, T., 214 Kitzes, L. M., 164 Kiyatkin, E. A., 403, 404 Kjaerulff, O., 184, 191–193 Kjiri, K., 387 Kjiri, S., 387 Klann, E., 475 Klatzky, R. L., 227 Klebanoff, L., 403 Kleber, H. D., 411 Klein, D. C., 373 Klein, L. C., 447 Klein, T. W., 451 Kleindienst, H. U., 119, 125 Kleinhaus, A. L., 191 Kleinman, J. T., 102 Kleitman, N., 366, 367, 379 Kleitz-Nelson, H. K., 350 Klemenhagen, K. C., 433 Klerman, G. L., 427 Kleven, O., 40 Klimas, N., 452, 453 Kline, A. E., 474 Kline, D. G., 245 Klingberg, T., 598 Klingm¨uller, D., 346 Klisz, C., 58 Klitenick, M. A., 398, 399, 406 Klug, K., 88 Klug, R., 259 Klump, G. M., 126, 127, 129, 132, 135, 140, 141 Kl¨uver, H., 396, 520 Knapp, W. H., 344 Knauss, K. S., 493 Kneip, J., 309

Knierim, J. J., 563 Knight, D. C., 523 Knight, R. F., 649 Knight, R. T., 596 Knipp, S., 312, 314 Knoblauch, V., 380 Knoblich, U., 628 Knopf, S., 451 Knopman, D. S., 657 Knorr, U., 387 Knowlton, B., 608 Knowlton, B. J., 397, 509, 510, 560, 608 Knox, S., 644 Knudsen, E. I., 129, 131, 142, 162, 163 Knusel, B., 535, 536 Knutsen, T. A., 100 Knutson, K. L., 384, 385 Ko, H. G., 259 Ko, S., 524 Koay, G., 129 Kobatake, E., 97, 98 Kobayakawa, K., 288 Kobayakawa, R., 288 Kobayakawa, T., 283 Kobayashi, H., 292 Kobayashi, K., 403 Kobayashi, M., 314 Kobayashi, Y., 178 Koblin, B., 446 Kobor, M. S., 19 Koch, C., 629 Koch, J. E., 409 Koch, V. M., 92 Kochiyama, T., 214 Koda, J. E., 316 Koehler, S. D., 161 Koelliker, Y., 274 Koelling, R. A., 32 Koenderink, J. J., 227 Koenig, H. G., 444, 445 Koentges, G., 8 Koeppe, C., 258 Koeppe, R. A., 256, 262 Koester, J., 346 Koga, K., 259 Kogan, J. H., 513 Koh, D. S., 406 Koh, E. T., 279 Koh, K. B., 451 Koh, M. T., 644, 646, 647, 649 Kohane, I., 656 Kohler, S., 564 Kohn, A., 105 Kohn, M., 651 Kohnert, K., 613 Kohonen, T., 623 Kohyama, J., 178 Kojima, D., 84 Kojima, M., 315

Kokkotou, E., 309 Kokrashvili, Z., 276 Kolachana, B., 426 Kolachana, B. R., 17 Kolachana, B. S., 17, 428, 429, 433, 434 Kolata, S., 6 Kolb, B., 194, 407, 474 Kolb, H., 57 Kollack-Walker, S., 347, 349, 353 Kollar, E. J., 383 Koller, D., 629 Kolli, T., 278 Kolta, A., 537 Koltzenburg, M., 262 Komaki, G., 258, 451 Komatsu, H., 96 Komisaruk, B. R., 261 Komori, M., 284 Kompier, M. A., 446 Konczak, J., 185 Kondo, Y., 347 Kondrashov, A. S., 331 Konen, C. S., 102 Kong, J., 263 Kong, Q., 656 Konig, A., 387 Konishi, M., 118, 124, 129, 131, 137, 138, 140–143, 162 Konkle, T., 226 Kono, A., 314 Konopka, W., 656 Konorski, J., 485 Konradi, C., 3 Koob, G. F., 402, 403 Koolhaas, J. M., 5 Koorengevel, K. M., 388 Kopf, M., 452 K¨oppl, C., 115, 116, 133, 135, 141 Kopps, A. M., 579, 580 Kops, C. E., 215 Korach, K. S., 9, 336 Korman, M., 95 Kornell, N., 498, 499 Kornetsky, C., 396 Kornhuber, H. H., 209, 608 Kornstein, S., 346 Koroshetz, W., 608 Korotkova, T. M., 407 Korte, S. M., 5 Korzus, E., 19 Kosaki, H., 164 Kosar, E., 283 Koshimoto, H., 293 Kosinski, E. C., 337 Koskinen, M., 259 Koslov, A., 623, 638 Kotaleski, J. H., 623, 638 Kotler, M., 14 Kotrschal, K., 587

Author Index

Kotz, C. M., 309, 317 Kotz, S. A., 607, 609 Koukourakis, K., 41 Kourtzi, Z., 100 Kouyama, N., 86 Kovacevic, N., 650 Kovelowski, C. J., 261 Kow, L.-M., 340–342 Koyabashi, T., 43 Koyama, H., 116, 119, 125 Koyama, M., 103 Koyama, T., 262 Kozma, S. C., 310 Kraemer, P. J., 486 Kraft, R. A., 257 Krahn, D. D., 311 Krapp, H. G., 186 Krasnow, B., 259 Kraus, J. E. M., 135 Krause, C. D., 450 Krause, E. G., 321, 322 Krauseneck, T., 260 Kraut, M. A., 650, 651, 657 Kravitz, D. J., 97, 98 Kravitz, E. A., 190 Krawczak, M., 578 Krech, D., 473 Kreher, B., 429 Kreher, B. W., 171 Kreithen, M. L., 124, 125 Kreitzer, A. C., 397 Krieger, J., 291, 293 Kriegeskorte, N., 98 Kril, J. J., 651 Kringelbach, M. L., 258 Kriplani, A., 16 Kristan, W. B., 180 Kristiansen, O. P., 448, 450 Kristjansson, A. L., 445 Kroeze, J. H., 282 Kroeze, J. H. A., 275 Kroll, N. E., 649 Kronauer, R. E., 380 Kronberg, E., 295 Krout, K. E., 280 Krubitzer, L., 146, 213 Krubitzer, L. A., 162–164, 213 Krueger, G. P., 446 Krueger, J. M., 373 Krueger, K. A., 637 Kruger, R., 314 Krupa, D. J., 519, 532–534 Kr¨utzen, M., 579, 580 Kr¨utzfeldt, N. O. E., 141, 142 Ku, Y., 214 Kubick, S., 291, 293 Kubie, J. L., 292 Kubke, M. F., 140–142 Kubota, M., 275

Kubota, Y., 539 Kubotera, N., 74 Kuchinad, A., 260 Kuczynski, M., 116, 117 Kudielka, B. M., 451 Kudo, M., 162 Kudwa, A. E., 333, 337, 340 Kuenzel, W., 142 Kuffler, S. W., 86 Kuhar, M. J., 313 Kuhl, P. K., 143, 469, 501 Kuhle, B. X., 38 Kuhn, C., 275, 284 Kuhn, H. G., 474 Kuhn, R., 3 K¨uhne, R., 129 Kuijper, J. L., 316 Kulikov, A. V., 10 Kulikovsky, O. T., 312 Kulkarni, R., 309 Kumar, A., 19, 352, 654 Kumar, M., 452, 453 Kumar, N., 336 Kumar, V., 375, 452 Kummer, H., 585 K¨ummerer, D., 171 Kunishio, K., 399 Kunzle, H., 146 Kuo, J. J., 230 Kuperberg, G., 600, 601, 607 Kuperberg, G. R., 609 Kupfermann, I., 179, 189 Kura, N., 385 Kurian, A. V., 580 Kurian, J. R., 333 Kuroda, M., 400 Kurohata, T., 351 Kurth, F., 259 Kurth, P. A., 402 Kurtz, D. B., 291, 293 Kurz, C., 656 Kurzban, R., 33, 47 Kussmaul, A., 606 Kusuhara, Y., 277 Kusunoki, M., 103 Kutas, M., 600 Kutz, D. F., 97 Kuwada, S., 145 Kuypers, H. G. J. M., 189 Kuzmin, D. G., 59 Kveton, J. F., 272, 282 Kwan, C. L., 256 Kwok, E. H., 250 Kwong, K. K., 92, 93, 100 Kyin, M., 564 Kyrkouli, S. E., 308 LaBar, K. S., 423, 523 Labhart, T., 54

693

Labonte, B., 19 Labus, J. S., 260 Lachenal, G., 403 Lachica, E. A., 93 Laconte, S., 214 LaCount, L., 259 Lacquaniti, F., 213 Ladenheim, E. E., 307, 309, 313, 314, 316 Ladouceur, M., 179 Lafaille, P., 171 Lafarge, E., 343 Laferriere, A. L., 260 la Fleur, S. E., 308 Lafrance, L., 317 Lage, R., 315 Lago, F., 309 Lagoda, G., 350 Lagoda, G. A., 350 Lahiri, D. K., 388 Lahr, G., 14 Lai, C. H., 163 Lai, J., 261 Lai, Z. C., 646 Laird, A., 259 Laita, B., 278 Lakatos, I., 28, 34 Lakatos, P., 170 Lakatos, S., 231 Lakoff, G., 602 Lakshmanan, B. M., 609 Laland, K. N., 47 Lalonde, F. M., 603 LaLumiere, R. T., 402, 405, 406, 410 LaMantia, A. S., 212 LaMarre, Y., 207 Lamb, G. D., 214 Lamb, T. D., 59, 84 Lambert, T. J., 474 Lambertino, S., 388 Lambeth, S. P., 582 La Mettrie, J. O. D., 501 Lamore, P. J. J., 230 LaMotte, R. H., 209, 215, 225, 228, 229 Lamour, Y., 408 Lampert, S., 308 Lance, J. W., 195 Land, M. F., 54–56, 66–68, 71 Landauer, T. K., 489 Landeira-Fernandez, J., 430 Landfield, P. W., 654, 656 Landin, A. M., 277 Landisman, C. E., 95 Landry, E. S., 192 Landwehrmeyer, B., 259 Lane, C. E., 120 Lane, J. W., 212, 213, 215, 216, 219, 220, 222, 228 Lanfranchi, P. A., 385

694

Author Index

Lang, A. E., 346 Lang, P. D., 311 Lang, P. J., 426, 427 Langbauer, W. R., 117, 144 Langdon, R., 598 Lange, G. M., 350 Lange, T., 449 Langer, G., 142 Langergraber, K., 577, 578 Langford, L. A., 246 Langhans, W., 314, 315 Langner, G., 162 Langner, R., 230 Langston, A., 70 Lanotte, M., 263 Lansford, J. E., 18 Lanska, M., 446 Lansner, A., 179–181, 183, 192, 623, 638 Lanteri, P., 100 Lanzara, C., 274 LaPerriere, A. R., 453 Lapish, C. C., 405 Lapointe, N. P., 192 Laposky, A. D., 383 Larew, M. B., 492, 502 Laricchiuta, D., 474 Larimer, J. L., 179 Larkin, A. E., 405 Larkin, G. L., 455 Laroche, S., 474 Larriva-Sahd, J., 340 Larsen, J. T., 426, 427 Larsen, P. J., 315 Larsen, R., 38 Larsen, R. J., 41 Larson, C. L., 431 Larson, D. B., 444, 445 Larson, D. C., 282 Larson, E., 631, 637 Larson, G. E., 444 Larson, G. F., 211 Larson, L., 450 Larsson, J., 92, 98, 101 Larsson, K., 348, 349 Larsson, M., 423 Larue, D. T., 162 Lashley, K. S., 318, 395 Lasiter, P. S., 279, 280 Lasko, N. B., 431 Lassonde, M., 170 Latendresse, S. J., 18 Latimer, W., 131 Latta, F., 381 Lau, Y. F., 14 Laubach, M., 646 Lauder, J. M., 315 Lauderdale, D. S., 384, 385 Laugerette, F., 277

Laugero, K. D., 443, 446 Laughlin, S. B., 67, 73 Laurendi, K., 406 Laurent, A., 447 Laurent, B., 258 Laurent, G., 186 Laureys, S., 260, 372 Laurie, A. L., 388 Laurienti, P. J., 179, 262 Lavarello, S., 262 Lavenne, F., 343 Lavielle, G., 354 Lavine, M. L., 286 Laviola, G., 465 LaViolette, P., 263 Lavoie, S., 186 Lavond, D. G., 519, 520, 527, 528, 531–534, 536–538 Lawrence, A., 651 Lawrence, A. D., 428, 433 Lawrence, B. D., 129 Lawrence, E., 445 Law Smith, M. J., 338 Lazar, J., 3 Lazareva, O. F., 494 Lazarus, R. S., 440, 442–444 Lazerson, A., 519 Lazeyras, F., 260 Lazzara, M. M., 649 Le, T., 19 Leary, M. R., 47 Leaton, R. N., 519, 524 LeBar, K., 511, 514–516 Le Bars, D., 343 Lebiere, C., 598, 613 Le Bihan, C., 402 Le Bihan, D., 284, 489 LeBlanc, W., 521 Leblond, H., 195 Le Boeuf, B. J., 39 Lecanuet, J. P., 468 Le Carret, N., 475 Lechner, S., 452 Leck, K., 42 le Coutre, J., 277 Ledehendler, I. I., 515 Ledent, C., 12 Lederman, S. J., 222, 223, 227 Ledersman, S. J., 214 LeDoux, J., 472 LeDoux, J. E., 163, 423, 425, 428–431, 433, 511, 514–516, 519–525, 531, 569, 570 Ledoux, J. E., 431 Lee, A., 307, 310 Lee, A. S., 535 Lee, A. T., 92 Lee, A. W. L., 340–342 Lee, B. B., 88

Lee, B. H., 451 Lee, B. S., 311 Lee, C., 307 Lee, C. E., 309, 315 Lee, D. N., 185 Lee, G. H., 307 Lee, H., 522, 524 Lee, H. H., 292 Lee, H. J., 9, 10, 519, 522, 524, 525 Lee, H. K., 407 Lee, H. S., 350 Lee, H.-K., 655 Lee, I., 563 Lee, L. J., 468 Lee, L. S., 311 Lee, M. C., 258 Lee, P. C., 578 Lee, P. K. D., 75 Lee, R. L., 53, 354 Lee, R. R., 258 Lee, T., 531 Lee, T. M., 370 Lee, Y. S., 13 Lefkowitz, R. J., 447 Lefler, B. J., 388 Le Foll, C., 310 Leggio, M. G., 163, 166, 474, 475 Le Goualher, G., 102 Legrain, V., 258, 262 Legros, J. J., 387 Lehman, C. D., 282 Lehman, J. R., 120 Lehman, M. N., 347, 375 Lehman Blake, M., 609 Lehmann, D., 600 Lehmann, J., 580 Lehmann, M. L., 348 Lehmkule, S., 69 Lei, C., 316 Leibel, R. L., 307 Leibenluft, E., 388 Leibowitz, S. F., 308, 309, 409 Leichner, T. M., 310 Leichnetz, G. R., 428 Leifer, D., 258 Leighty, K. A., 488, 489 Leinders-Zufall, T., 8 Leinenweber, A., 490 Leinninger, G. M., 316 Leipheimer, R. E., 337, 343, 354 Leising, K., 497 Leknes, S., 262 Lelutiu, N. B., 10 Lemanske, R., 452 Lemasson, A., 500 Lemery, C. R., 427 LeMeur, M., 10 Lemieux, S. K., 536 Le Moal, M., 402, 408

Author Index

Le Moine, C., 409 Lemon, C. H., 284, 285 Lengyel, M., 637 Lennard, P. R., 180, 181 Lenneberg, E. H., 468 Lennie, P., 84, 85, 90 Lenz, F. A., 214, 256, 258 Lenzi, A., 345 Leon, M., 293, 295 Leon, R. D., 195 Leopold, L., 307 Lepore, F., 170 Leppert, M., 274 Leproult, R., 381, 384 Le Ray, D., 179, 186 Le Roux, C. W., 314 LeRoy, I., 4, 13 Le Sauter, J., 314, 375 Lesch, K. P., 2, 11, 12, 14, 17 Leserman, J., 455 Leshan, R. L., 316 Leshchinskiy, S., 286 Leslie, F. M., 409 Lesniak, D. R., 210 Lester, N., 443 Letenneur, L., 475 Lettvin, J. Y., 292 Leuner, K., 656 Lev, R., 259 Lev, S., 443 LeVay, S., 467 Levelt, W. J. M., 595, 599, 600, 605, 606 Levenson, D. H., 61 Levenson, R. W., 427 Leventhal, A. G., 95 Leventhal, H., 262, 443 Leverenz, E. L., 116, 119, 125 Levesque, D., 192 Levik, Y. S., 179 Levin, B., 310 Levin, B. E., 310 Levine, A. S., 308, 309, 311, 317 Levine, J. D., 260, 262 Levine, S., 531 Levinson, S. R., 245 Levitan, G., 449 Levitan, I. B., 189 Levitt, J. B., 91, 95 Lev-Tov, A., 191, 192 Levy, I., 101 Levy, R. M., 260 Levy, S., 450 Lewczyk, C., 531 Lewicki, M. S., 621, 631, 632, 637 Lewin, R., 502 Lewinsohn, P. M., 44 Lewis, B., 129 Lewis, B. L., 404

Lewis, B. P., 447 Lewis, D. B., 131 Lewis, D. J., 523 Lewis, D. M. G., 33 Lewis, E. R., 116, 119, 125, 133–135 Lewis, J. W., 98, 104, 260 Lewis, R. L., 598 Lewis, T., 243 Lewis, W., 602 Lewis-Jones, D. I., 41 Lewith, G., 263 Lewitus, G. M., 451 Lewontin, R. C., 29, 46 Lewy, A., 387, 388 Lewy, A. J., 388 Leybaert, J., 468 L’Hermite-Baleriaux, M., 381, 384, 387 Lhote, M., 468 Li, A. A., 306 Li, C., 62, 656 Li, C. S., 279, 281 Li, C.-S., 276, 279–281, 283 Li, E., 19 Li, J., 250, 316 Li, L., 563 Li, N. P., 26 Li, P. H., 86 Li, R., 369 Li, S. C., 634, 635, 637 Li, W.-H., 60, 74 Li, X., 92, 193 Li, X. Y., 259 Li, Y., 14, 193, 351 Liabeuf, S., 196 Liang, K. C., 522, 523, 525 Liang, N. C., 311 Liang, P., 348 Liao, H. W., 88 Liberles, S. D., 291 Liberman, A. M., 605 Liberzon, I., 434 Lichtenstein, P., 7 Lichtermann, D., 346 Lickliter, R., 469 Lidbrink, P., 399, 404 Liddle, P. F., 405 Liddle, R. A., 313 Liebenthal, E., 601 Lieberman, M. D., 263, 431 Liebeskind, J., 452 Liebeskind, J. C., 260, 261 Lieblich, S. E., 337 Lifjeld, J. T., 40 Lifshitz, I., 628 Light, A. R., 251 Light, K., 6 Lightman, S. L., 447 Lillie, R., 647 Lily, R., 442

695

Lim, H., 95 Lim, H. H., 162 Lim, J. E., 17 Lim, K. O., 651 Liman, E. R., 275 Limber, J., 502 Lin, C.-L. G., 656 Lin, D. L., 448, 450 Lin, H. E., 259 Lin, L., 369 Lin, S. Y., 259 Lin, W., 275 Lin, X., 369 Lin, X. Y., 515 Lincoln, J. S., 531 Lindell, S., 17 Lindemeier, J., 276 Lindenberger, U., 634, 635, 637 Lindhorst, T., 446 Lindquist, D. H., 520, 531 Lindquist, M., 189 Lindquist, M. A., 431 Lindquist, W. B., 407 Lindsay, R. D., 652 Lindsley, R. W., 166 Linebarger, M. C., 606 Ling, L. H., 250 Linklater, W. L., 578–580 Linkowski, P., 389 Linster, C., 634 Lintl, P., 652 Lipina, T., 10 Liposits, Z., 12 Lisabeth, L. D., 385 Lit, Q., 14 Litinas, E., 213 Litke, A. M., 86–88 Little, A. C., 37, 43, 338 Liu, D., 95, 462 Liu, H., 279, 285, 309 Liu, J., 92, 98, 100, 101, 103, 189–192 Liu, J. J., 630 Liu, K., 384, 385 Liu, L., 6, 277 Liu, R., 315 Liu, S. M., 307 Liu, X., 258 Liu, Y., 105 Liu, Y. C., 345, 348–350, 352 Liu, Z. P., 245 Livingstone, M., 95 Livingstone, M. S., 95, 100 Ljungberg, T., 402 Ljungdahl, A., 399, 404 Lledo, P. M., 295 Lloyd, D., 258 Lloyd, D. M., 258 Lloyd, J. E., 28 Lloyd, S. A., 348

696

Author Index

Lloyd-Fox, S., 468 Lo, E. S., 388 Lobel, E., 284 Lobo, M. K., 411, 413 Lockard, J. M., 535 Locke, J., 499 Locke, J. L., 407 Locket, N. A., 64 Loconto, J., 7 Loeb, G. E., 189 Loeffler, M., 371 Loewenstein, W. R., 210 Loewy, A. D., 279, 280 Loftus, W. C., 161 Logan, C. G., 528, 531, 533, 536–538 Logan, D. W., 10 Logan, F. A., 496 Logan, G. D., 637 Logan, J., 16 Logerot, P., 141, 142 Loggia, M. L., 258 Logothetis, N., 93, 586 Logothetis, N. K., 92, 98, 164, 170–172, 214 Logue, S. F., 530, 531, 539, 540 Lohelin, J. C., 1 Lohman, A. H., 399 Lohr, B., 116, 117, 140, 141 Loken, L., 207 Lomber, S. G., 103 Lomo, T., 472 London, J. A., 512 London, N., 195 Long, D. L., 596, 604 Long, G. R., 116, 118, 122, 123 Long, J., 657 Long, J. C., 17 Long, J. H., 166 Long, P. J., 45 Longo, G., 246 Longueville, F., 354 Lonstein, J. S., 347 Loomis, J. M., 214, 215, 227 Loop, M. S., 70 Lopaschuk, G. D., 310 Lopes, M., 586 Lopes da Silva, F. H., 404 L´opez, F. J., 337, 340 Lopez, C., 451 Lopez, D. E., 161 Lopez, M., 309, 315 Lopez, S. E., 166 Lopezjimenez, N. D., 275 Lopiano, L., 263 Lorenz, J., 256, 262, 263 Lorenz, J. G., 17 Lorenz, K., 463 Lorenzetti, B. B., 243 Lorenzi, C., 389

Lorenzini, C. A., 524 Lorrain, D. S., 338, 350–352 Losch, M. E., 426, 427 Lotter, E. C., 310 Lotti, F., 346 Lou, J. S., 195 Loucks, J. A., 352 Loucks, R. A., 426, 428, 430, 432–434 Loughlin, S. E., 398 Louilot, A., 352, 402 Louis, G. W., 316 Louwerse, A. L., 347 Loveland, D. H., 493 Lovely, R. G., 195 Lovett-Barron, M., 342 Low, M., 307, 309 Lowe, C. H., 388 Lowe, D., 260 Lowe, G., 292 Lowe, K., 455 Lowe, V. J., 657 Lowell, B. B., 309 Lowery, T., 313 Lowlocht, R. A., 282 Lozano, A. M., 258 Lu, C., 215 Lu, H. D., 95, 98 Lu, J., 88, 374 Lu, M., 278 Lu, Q., 407 Lu, T., 655, 656 Lu, X. Y., 309, 398, 399, 409 Lu, Y., 252 Lu, Z., 118, 119 Lu, Z. L., 630, 631 Lu, Z.-L., 629 Lubahn, D. B., 336 Lucan, J. N., 214 Lucas, F., 311 Lucas, P. W., 75 Lucchina, L. A., 274 Ludlow, D., 263, 264 Ludwig, D. S., 309, 310 Luebke, J. I., 653 Lueschow, A., 98 Luethke, L. E., 162–164 Luger, A., 447 Lugg, J. A., 344 Luijendijk, M. C., 308 Luine, V., 12 Luiten, P. G. M., 279, 652 Lukats, A., 63 Luksch, H., 139 Lumberas, M., 474 Lumia, A. R., 345 Lumley, L., 352 Lumley, L. A., 349, 351 Lumpkin, E. A., 210 Lund, J. P., 537

Lund, J. S., 91, 102 Lund, P. K., 315 Lund, S., 177, 191 Lundberg, A., 177, 191 Lundberg, U., 447 Lundin, T., 446 Lundy, R. F., 278, 281, 282, 284 Luo, F., 256 Luo, Z.-Q., 623 Lupien, S. J., 442 Luppino, G., 97 Luquet, S., 308 Luskin, M. B., 289 Luszcz, M. A., 580 Lutgendorf, S., 450, 452, 453 Lutgendorf, S. K., 445 Luthi, A., 404 Lutz, J., 260 Lutz, T. A., 314 Lutzenberger, W., 536 Luyimbazi, J., 448 Lv, B., 316 Lyall, V., 275 Lycett, J. C., 576 Lydon, J. P., 336 Lydon-Lam, J. R., 444 Lynch, C. B., 5 Lynch, G., 405 Lynch, G. S., 405 Lynch, J. C., 213 Lyon, D. C., 102, 163 Lyons-Warren, A., 647 Mφller, A. R., 116 Ma, L., 132 Ma, L. Y., 322 Ma, M. K., 315 Ma, X., 146, 171, 631, 636 Ma, Y., 646 Macaluso, D. A., 284 Macbeth, A. H., 9, 10 MacCallum, R. C., 450 Macdonald, V., 651 MacDougall-Shackleton, S. A., 132 Macedo, A., 652 Macefield, V. G., 210 MacFall, J. R., 651 Machado, T. A., 88, 89 Machado-Vieira, R., 388 Machtens, S. A., 344 MacIver, K., 258 MacKay, T. F., 2 MacKay-Sim, A., 292 Mackell, J., 644 Mackey, S. C., 256, 263, 264 Mackie, P. D., 213 Mackintosh, N. J., 631 Macko, K. A., 97 MacLarnon, A., 579

Author Index

MacLean, P. D., 520 Mac Leod, P., 284 MacLeod, A. M., 259, 650 MacLeod, J. E., 425 MacNeil, M. A., 86 MacNeilage, P., 489 Macperson, J. M., 184 Macphail, E. M., 523 MacPherson, K., 487 Macr, S., 465 Macuda, T., 70, 588 Madden, D. J., 650–652 Madden, J., 533 Madden, J. M., 279 Madeo, B., 346 Madriaga, M. A., 191 Madsen, P. T., 115 Maeda, F., 263, 264 Maeda, M., 258, 451 Maeng, L., 310 Mafessoni, R., 344 Maffei, L., 70, 473, 474 Maffei, M., 307 Magdalin, W., 308, 309 Magerl, W., 261 Maggi, M., 346 Maggini, C., 388 Magnan, C., 310 Magnee, M. J. C. M., 426 Magnin, M., 258 Magnusson, A., 388 Mague, S. D., 411 Maguire, E. A., 632 Mahaut, S., 316 Maher, K., 452, 453 Mahler, S. V., 410 Mahon, L. E., 96 Mai, J., 423, 424 Maier, E. H., 126, 127 Maier, J. X., 171, 586 Maier, S. F., 243, 260, 261 Mail, A. C., 581 Main, M., 346 Maione, S., 256 Maislin, G., 373, 384 Maisog, J. M., 259 Maiuro, R. D., 443 Maixner, W., 261 Majchrzak, M. J., 311 Majewska, M. D., 337 Maki, W. S., 486 Makin, T. R., 93 Malach, R., 91, 95, 100, 101, 566 Malan, P., 261 Malan, T. P., 261 Malarkey, W. B., 450 Malaval, F., 447 Malave, V. L., 602, 603, 609 Malberg, J. E., 346

Malcolm, W. B., 563, 564 Maldonado, A., 346 Maldonado, P. E., 406 Maldonado-Irizarry, C. S., 405 Maleki, N., 260 Malenka, R. C., 13, 397, 406 Malhotra, A., 381 Malinen, S., 259 Malinova, L., 315 Mallinckrodt, C., 346 Malmierca, M. S., 162 Malnic, B., 292, 293 Malow, R., 446 Malow, R. M., 453 Malpeli, J. G., 89 Malsbury, C. W., 349 Manber, T., 434 Manchester, L., 386 Mandel, A., 10 Mandel, M. R., 427 Mandelkern, M., 263 Mandeville, J. B., 100 Mandiyan, V. S., 8 Mandolesi, L., 474, 475 Mandrup-Poulsen, T., 448, 450 Maner, J. K., 26 Mangels, J. A., 560, 608, 647 Manger, P. R., 57 Mangiamele, L. A., 653 Mani, S. K., 336 Manis, P. B., 161 Manji, H. K., 388 Manley, G., 116, 135, 140 Manley, G. A., 116, 125, 135, 140, 158 Mann, D., 139 Mann, D. A., 118, 119 Mann, J., 578–580 Mann, J. J., 16 Mann, K. A., 119 Mann, M. W., 259 Manning, J. T., 41 Mannon, P., 314 Manns, J. R., 566 Mannucci, E., 346 Manookin, M. B., 86 Manresa, J. B., 244 Manson, J., 578, 584 Mansour, H., 191 Mantini, D., 259 Mantzoros, C. S., 383, 386 Manuck, S. B., 431, 449 Manzo, J., 340 Maquet, P., 372 Maras, P. M., 347 Maratos-Flier, E., 309, 310 Maravich, C., 446, 449 Marceglia, A. H., 215 Marchante, A. N., 428, 434 Marchetti-Gauthier, E., 537

Marchman, V., 598 Marco, E. M., 465 Marcovici, A., 166 Marcus, J. N., 315 Marder, E., 183 Marecek, J. F., 292 Marek, P., 260, 261 Maren, S., 519, 520, 522–525 Mareno, M. C., 582 Mareschal, D., 468, 621 Maret, S., 389 Marfia, G. A., 406 Margoliash, D., 142, 143 Margolis, F. L., 290 Margolskee, R. F., 276–278 Margulies, D. S., 430 Maricich, S. M., 210 Marin, O. S. M., 602 Marino, M. D., 12 Mariola, E., 214 Mark, G. P., 284 Mark, R. F., 70 Markert, L. M., 294 Markesbery, W. R., 655 Marketon, J. I., 447 Markham, A. E., 444 Markison, S., 282 Markowitsch, H., 523 Markowska, A. L., 649 Markowski, V. P., 351, 352 Marks, I. M., 33 Marks, J. L., 310 Marks, W. B., 189 Marks-Kaufman, R., 311 Marler, P., 500, 501 Marlier, L., 295 Maroteaux, L., 17 Marple-Horvat, D. E., 185 Marple-Hovat, D. E., 185 Marques, D. M., 345 Marr, D., 621, 633 Marrett, S., 256 Marsan, L., 339–341 Marsh, D. J., 308 Marshak, D., 86 Marshall, B. S., 525 Marshall, M., 657 Marshall, N. J., 62, 72, 75 Marsland, A. L., 449 Marson, L., 342, 349, 350, 353, 354 Mart´ınez-Garcia, R., 346 Martel, P., 311 Martin, A., 586, 603 Martin, B., 645 Martin, C., 185 Martin, G. R., 70 Martin, J., 15–17 Martin, J. H., 470 Martin, N. J., 388

697

698

Author Index

Martin, P. R., 85, 86, 88, 90 Martin, R. J., 310 Martin, R. L., 129 Martin, S. J., 632 Martin, W. D., 462 Martinez, A., 92, 613 Martinez, J. L., 523, 525 Martinez, M., 194 Martinez, S., 142 Martinez, V., 316 Martinez-Cue, C., 474 Martinez-de-la-Torre, M., 142 Martis, B., 434 Marton, T. F., 10 Maruoka, T., 451 Maruyama, Y., 275, 278 Marzano, C., 371 Mas, M., 352, 354 Masaki, T., 309 Mascagni, F., 399 Mascia, M. S., 351 Masland, R. H., 83, 86 Mason, J. W., 441 Mason, P., 260 Mason, R. A., 597, 602, 603, 609–612 Mason, W. A., 484 Masse, I., 475 Massimini, M., 373 Mastaitis, J. W., 309 Master, S. L., 451 Masters, W. M., 129 Masterton, B., 116–118, 128–130 Masterton, R. B., 124, 161, 162 Mataga, N., 292, 293 Matchin, W., 171 Matelli, M., 97 Mathalon, D. H., 651 Mathew, P. R., 531 Mathews, H. L., 449 Mathis, C., 312 Matson, C. A., 316 Matsuda, K. I., 333 Matsukura, S., 315 Matsumoto, H., 288 Matsumoto, M., 401, 402, 404 Matsumura, H., 347, 349 Matsumura, M., 214 Matsunaga, M., 450 Matsunami, H., 275, 276 Matsuo, H., 315 Matsuo, R., 280 Matsushita, M., 280 Matsuura, M., 246 Matsuyama, K., 178 Matsuyama, T., 58, 61 Matsuzaki, I., 309 Matsuzawa, T., 486, 488 Mattay, V. S., 426, 431, 433 Mattes, R. D., 273, 277

Matthews, B. J., 332 Matthews, D., 30, 46 Matthews, E. E., 444 Matthews, P. B. C., 211 Matthews, P. M., 429 Matthews, S. C., 428, 433 Mattick, J. S., 3 Mattson, M. P., 645, 657 Matuszewich, L., 338, 350–352 Matute, H., 496 Matzel, L. D., 6, 515, 540 Matzuk, M. M., 9 Maubach, K., 312, 313 Maudoux, A., 372 Maudsley, S., 645 Maugui`ere, F., 258 Mauk, M. D., 526, 531, 533 Maunsell, J. H., 98, 102, 104, 214, 226 Maurage, C. A., 475 Mauri, A., 351 Mauro, F., 371 Mavrocordatos, P., 263 Max, M. B., 259 Maximov, V. V., 71 Maxson, S. C., 1, 2, 4, 9, 10, 13, 14, 20 Maxwell, A. E., 599, 610 May, A., 260 May, B. J., 145 May, O. L., 279 Mayberg, H. S., 258, 429 Mayeda, A., 388 Mayer, A., 14 Mayer, D. J., 261 Mayer, E. A., 260, 263 Mayer, J., 306 Mayo, W., 408 Maywood, E. S., 374 Mazer, J. A., 646 Mazmanian, D. S., 493 Mazmanian, D. W., 486 Mazur, J. E., 620 Mazurkiewicz, J. E., 209 Mazziotta, J. C., 431 Mazzola, L., 258 Mazzoni, P., 411 Mazzucco, C. A., 337 Mazzulla, E. C., 426, 427, 433 McAllister-Sistilli, C. G., 451 McAlonan, K., 91, 231 McAlpine, D., 141, 145 McBurney, D. H., 274 McCabe, P. M., 520, 521 McCall, A. L., 307 McCandliss, B. D., 598, 605 McCarley, R. W., 369, 370 McCarthy, G., 100 McCarthy, M. M., 333, 340 McCarthy, R., 602 McCarthy, R. A., 602

McCaughey, S., 277 McCaughey, S. A., 284, 312 McCaul, K. D., 262 McClay, J., 15–17 McClearn, G. E., 1 McClelland, J. L., 597, 598, 602, 621, 633, 634, 637, 638 McClintick, J., 450 McClintock, M., 287, 580 McClintock, M. K., 287, 288 McCloskey, D. I., 211 McCloskey, M., 488 McClure, H. M., 646 McCollough, J. K., 43 McCormack, S., 401, 407 McCormick, C. A., 138, 139 McCormick, D. A., 527, 531–533, 537 McCourt, M. E., 65, 70 McCrae, R. R., 434 McCrane, E. P., 85 McCrea, D. A., 183, 188, 195 McCrory, E., 472 McCullogh, M., 444, 445 McCullough, L. D., 402 McDaniel, M. A., 41 McDannald, M. A., 647 McDearmid, J. R., 191–194 McDermott, J., 100, 157 McDonald, A. J., 347, 399 McDonald, P. G., 450 McDonald, R. J., 557 McDonald, S., 609 McDonald, S. E., 493 McDonald, V., 345 McDonough, J. H., 521 McEchron, M. D., 523, 528 McEwen, B., 448, 451 McEwen, B. S., 333, 442, 461, 474 McFall, R. M., 536 McFann, K., 445 McFarland, K., 402, 405, 411 McFarland, N. R., 400 McFayden-Ketchum, S., 45 McGaugh, J. L., 397, 399, 402, 410, 520–523, 525, 539, 555, 568, 571, 608, 649 McGee, K., 498 McGinnis, M. Y., 343, 344 McGivern, J. G., 245 McGivern, R. F., 337, 340 McGlinchey-Berroth, R., 529 McGlone, F., 214, 227 McGlone, F. P., 227 McGonigle, B. O., 494 McGorry, M., 260 McGowan, P. O., 19 McGrath, P. J., 388, 389 McGregor, P. K., 584 McGuffin, P., 1

Author Index

McGuinness, E., 103 McHaffie, J. G., 257, 262 McHenry, J. A., 350 McHugh, P. R., 312–314 McInerney, S. C., 424, 426, 433 McIntosh, A., 650 McIntosh, A. R., 259, 429, 650 McIntosh, C. H., 315 McKay, G., 444, 445 McKay, L. D., 310 McKay, R., 41 McKeel, D. W., 652 McKenna, K., 354 McKenna, K. E., 336, 342, 345, 349, 350, 352–354 McKenna, T. M., 631 McKenzie, C. R., 637 McKernan, M. G., 522 Mckibbin, W. F., 41 McKinnon, W., 449 McKoon, G., 603 McLaren, D. G., 213 McLaughlin, J., 600 McLaughlin, R. J., 406, 408 McLennan, H., 522, 524 McMahan, R. W., 655 McMahon, C., 59 McMahon, C. E., 70 McMahon, D. G., 374 McMahon, M. J., 88 McMahon, S. B., 246, 250 McMains, S. A., 92, 98, 101, 214 McMillan, N., 487 McMinn, J. E., 316 McMullin, K., 434 McNall, C. L., 521, 522 McNally, R. J., 426, 433 McNamara, J., 212 McNaughton, B., 406 McNaughton, B. L., 633, 655 McNeely, H. E., 258 McPhee, L. C., 191 McPheeters, M., 279 McPhie-Lalmansingh, A. A., 333 McQuade, D. B., 354 McQuain, J., 651 McQueary-Flynn, S., 451 McRae, K., 431, 598 McRitchie, D. A., 651 McVary, K. T., 336, 342 Meagher, M. W., 450 Meaney, M. J., 19, 462, 652 Mearow, K. M., 207 Mech, L. D., 287 Mechler, F., 93 Meck, W. H., 491 Medalla, M., 104 Mediavilla, M. D., 386 Medin, D., 588

Medin, D. L., 493 Medina, J., 102 Medzhitov, R., 448 Meenderink, S. W., 159 Meerts, S. H., 339, 340 Meeter, F., 5 Meeter, M., 633 Meftah, E., 223 Meftah, E.-M., 222, 231 Megela, A. L., 118 Megela-Simmons, A., 118, 119 Mehler, L., 489 Mehrotra, N., 260 Meijer, J. H., 379 Meinertzhagen, I. A., 194 Meisel, R. L., 337, 340, 343, 345 Meister, M., 8, 83, 86, 88, 105 Melamed, B. G., 444 Melanson, K. J., 307 Melcher, D., 81 Melcher, J. R. M., 132 Melcher, T., 406 Melehy, H., 481, 499 Melhorn, S. J., 322 Melia, K. R., 522, 524 Melin, A. D., 75 Melis, M. R., 351, 352 Melis, T., 351, 352 Meller, S. T., 312 Mellman, T. A., 446 Mello, N. K., 410 Mellon, S. H., 452 Melotto, S., 346 Melrose, R. J., 571 Meltzoff, A. N., 258, 471 Melzack, R., 254, 256 Memenis, S. K., 578, 579 Menaker, M., 374 Menani, J. V., 320 Mencl, W. E., 607, 611 Mendell, L. M., 195, 243 Mendelsohn, F. A. O., 320 Mendelsohn, M., 293 Mendelson, S. D., 335 Mendlewicz, J., 389 Meng, Z., 470 Mengual, E., 400 Menini, A., 292 Mennella, J. A., 287, 469 Menon, R. S., 214 Menon, V., 259, 433 Menoret, S., 3 Mensah, P. A., 280 Mentel, T., 181 Mentlein, R., 314 Mentzel, H. J., 264, 428, 433 Menzel, R., 72, 513 Merabet, L., 214 Merabet, L. B., 92, 214, 472

699

Mercado, E., 623, 631, 636 Merchan, M. A., 161 Merchant, M. J., 443, 444 Mercuri, N. B., 408 Meredith, M., 345, 348 Merigan, W. H., 70 Merino, S., 39 Merker, K., 655 Mermelstein, P. G., 340, 341 Merriam, E. P., 95, 103, 104 Merritt, D. J., 494 Merritt, M. M., 444 Merrywest, S. D., 192 Merskey, H., 240 Merzenich, M. M., 145, 146, 162–164, 166, 215, 636 Mesce, K. A., 183 Meschi, M. R., 344 Mesgarani, N., 631 Mesoudi, A., 47 Messing, R. B., 523, 525 Mesulam, M. M., 428 Metherate, R., 631 Mettenleiter, T. C., 196 Metzger, R. R., 162 Meunier, M., 562 Meyer, A. S., 600, 605 Meyer, E., 256 Meyer, E. P., 54 Meyer, M., 464 Meyer, R. A., 244 Meyerhof, W., 276 Meyer-Lindenberg, A., 17, 428, 429, 433, 434 Meyers, J. L., 18 Meziane, H., 537 Mezza, R., 289 Micevych, P., 341 Michael, R. P., 343 Michaelides, M., 403 Michel, C. M., 600 Michel, F., 367 Michelsen, A., 119, 125, 128, 131, 135 Michelsen, A. M., 131 Micheyl, C., 132, 133 Michkin, M., 586 Michl, P., 307 Michopoulos, V., 333 Middlebrooks, J. C., 163 Mietlicki, E. G., 321 Miezin, F., 103 Mignot, E., 369 Mikhail, A. A., 528 Miki, A., 451 Mikkelsen, J. D., 315 Miklosi, A., 587 Mikoshiba, K., 290 Mikschl, A., 431, 432 Mikulis, D. J., 256, 258, 263

700

Author Index

Milad, M. R., 431, 433 Miles, G. B., 191, 193 Milgram, N. W., 475, 645 Milham, M. P., 430 Mill, J., 15–17 Millan, M. J., 244, 245, 249, 251, 254, 261, 354 Millecamps, M., 260, 262 Miller, A. L., 451 Miller, D. D., 337, 405 Miller, D. P., 530, 539, 540 Miller, E. A., 563 Miller, E. K., 98, 103, 432, 563, 571, 607, 646 Miller, G. E., 445, 448–451, 453 Miller, I. J., 274, 277, 281 Miller, J. D., 118, 123 Miller, J. L., 468 Miller, J. M., 411 Miller, J. S., 485, 491 Miller, K. D., 444, 491 Miller, L. A., 119 Miller, M. A., 385 Miller, M. R., 135 Miller, P., 286 Miller, R., 43 Miller, R. A., 645 Miller, R. R., 496, 523 Miller, S. A., 354 Miller, S. S., 469, 491 Milleri, S., 346 Mills, A. W., 128 Mills, L. R., 207 Milne, B. J., 41 Milne, R. J., 260 Milner, A. D., 97, 102 Milner, B., 555 Milner, D., 626 Milsted, A., 14 Miltner, W. H., 264, 428, 433 Min, S. S., 655 Mineka, S., 33 Miner, P., 312 Minkler, M., 644 Minokoshi, Y., 307, 310 Minor, R. K., 657 Minors, D. S., 375, 380 Minoshima, S., 256, 262, 263 Minshew, N. J., 611 Minsky, M. L., 622 Miquel, M., 340 Miranda, R. C., 195 Mirenowicz, J., 404 Mirpour, K., 103 Mirth, M. C., 344 Misanin, J. R., 523 Mischel, W., 621 Miselis, R. R., 312, 320 Miserendino, M. J., 523

Miserendino, M. J. D., 522, 524 Mishkin, M., 83, 97, 166, 168, 169, 171, 212–214, 396, 562, 566, 648 Misiaszek, J. E., 195 Mistlberger, R. E., 379 Mistretta, C. M., 277, 285, 465–467, 469 Mitani, J. C., 577–579 Mitchell, D. E., 466 Mitchell, D. S., 537 Mitchell, J. M., 260 Mitchell, J. P., 565 Mitchell, M. C., 613 Mitchell, T., 603 Mitchell, T. M., 602, 603, 609 Mitrofanis, J., 399 Mitrovic, I., 398, 399 Miura, H., 316 Miyajima, E., 385 Miyamoto, T., 612 Miyanaga, F., 315 Miyaoka, M., 97 Miyasaka, K., 314 Miyashita, Y., 103 Miyatake, M., 351 Miyazaki, K., 389 Miyazaki, T., 451 Miyoshi, T., 309 Mizoguchi, H., 346 Moalem-Taylor, G., 252, 255 Mobbs, D., 246 Mockett, R. J., 655 Modenini, F., 344 Modersitzki, J., 92 Moeller, S., 98, 100 Moffat, A. J. M., 129 Moffat, A. M., 116 Moffitt, T. E., 3, 15–17, 44, 45 Mogenson, G. J., 396, 399, 400, 404–406, 408 Moghaddam, B., 405 Mogil, J. S., 13, 258, 260, 261 Mogul, D. J., 654 Mohedano-Moriano, A., 171 Mohr, N. L., 540 Mohseni, H. R., 258 Moineau, J., 523 Moiseff, A., 119, 125–127, 131 Molden, S., 563 Molholm, S., 132, 214, 232 Molinari, M., 163, 164, 166 Moll, H., 590 Moller, A. P., 39–41 Mollon, J. D., 53, 54, 75 Molson, M.-P., 2 Mombaerts, P., 8, 293 Momen, N., 444 Monaghan, E. P., 354

Monahan, E., 9 Moncomble, A. S., 469 Monfils, M. H., 522 Mong, J. A., 340 Monleon, S., 6 Monroe, B. M., 621, 623 Monroe, S., 279–281, 283, 284 Monroe, W. T., 277 Montaigne, Michel de, 480 Montez, J. M., 309 Montgomery, P., 378 Montie, E. W., 119 Montigny, D., 469 Montmayeur, J. P., 276, 277 Monyer, H., 406 Moody, D., 129 Mook, D., 310, 312 Mooney, R., 462 Mooney, T., 134 Moont, R., 259 Moore, B. C. J., 120 Moore, B. D., 90 Moore, C. I., 226, 654 Moore, G. J., 431 Moore, J., 349 Moore, J. W., 532–534 Moore, K. A., 309 Moore, M. K., 471 Moore, M. M., 651 Moore, P. W. B., 130 Moore, R. Y., 373, 374, 398, 404 Moore, T., 100, 629 Moore, T. L., 646–648, 653 Moore, T. O., 344 Moore-Ede, M. C., 373 Moos, R., 443 Moos, R. H., 443 Morales, S., 313 Moran, D. T., 289 Moran, G., 70 Moran, T. H., 307–310, 312–316 Morch, C. D., 256 Moreira, P. I., 656 Moreira-Almeida, A., 445 Morel, A., 163, 164, 166 Moreno, C., 3 Moreno, M., 261 Morest, D. K., 161, 162 Moretti, J. L., 259 Morgan, F., 346 Morgan, H. C., 70 Morgan, J. I., 425 Morgan, K., 310 Morgan, L. N., 230 Morgan, M., 12 Morgan, M. M., 260 Mori, A., 343 Mori, H., 333 Mori, K., 288, 291–293

Author Index

Mori, S., 178, 429 Mori, T., 258 Moriguchi, Y., 258 Morin, C., 207 Morin, L. P., 374 Morishita, H., 467 Moriya, M., 98 Morley, J. E., 308, 309, 449 Morlion, B., 263 Mormede, P., 1 Moro, V., 100 Moroney, D. N., 286 Moroni, F., 371 Morris, J., 423, 429 Morris, J. C., 652 Morris, J. S., 423, 426, 523 Morris, R. G., 405, 522, 632 Morris, R. G. M., 567, 632, 649 Morrison, C. D., 310 Morrison, E. E., 289 Morrison, I., 258 Morrison, J. H., 646, 652, 653 Morrison, S. E., 423, 425 Morrow, T. J., 231, 256, 262 Morse, P. A., 468 Morsella, E., 446 Mortaud, M., 13 Mortaud, S., 4–6 Morton, D. L., 263 Morton, G. J., 307, 316 Morton, J., 471 Moscovice, L., 576, 580–582 Moscovitch, M., 100 Moseley, M. E., 598 Moser, E., 563 Moser, M.-B., 563 Moses, J., 351, 352 Mosinger, B., 277 Moskovitz, B., 405 Moskvina, V., 7 Mosler, G., 14 Moss, C. F., 118, 119, 123, 125, 129 Moss, C. J., 578 Moss, M. B., 646–648, 653 Mossner, R., 12, 14 Motivala, S. J., 450 Moulton, D. G., 292 Moulton, E. A., 260 Mountcastle, V. B., 209, 212, 213, 228, 229 Mountjoy, K. G., 309 Mouraux, A., 258 Mourrain, P., 2 Moussawi, K., 407 Moustafa, A. A., 633, 635–637 Movshon, J. A., 95, 102, 103, 105 Moxon, K. A., 192 Moyer, B. D., 278 Moyer, J. R., 528, 654

Moyer, R. S., 489 Moyse, E., 316 Mozell, M. M., 288, 292, 294 Mozer, M. C., 637 Mozley, P. D., 651 Mrosovsky, N., 374 Mrsic-Flogel, T. D., 467 Mu˜noz, P., 654 Mucha, R. F., 409 Mucke, L., 654 Mudumbi, R. V., 346 Mueller, D., 339 Mueller, E. A., 651 Mueller, K. L., 276, 284 Muglia, L. J., 9 Mugnaini, E., 144 Muijser, H., 230 Muir, J. L., 412 Mujica-Parodi, L. R., 444 Mujtaba, T., 141 Mukamel, R., 566 Mukhametov, L. M., 365 Mulcahy, N. J., 488 Mulder, G., 607 Muldoon, M. F., 449 Mullen, P. E., 45, 46 M¨uller, K., 605 M¨uller, W. E., 656 Muller, J., 425, 520, 570 Muller, M., 244, 577 Muller, R., 196 Muller, S., 195 Muller, T., 278 Muller-Schell, W., 314 Muller-Schwartz, D., 287 Muller-Schwefe, G., 263 Mullett, J., 427 Mullingan, J. A., 118 Mullington, J. M., 373, 384 Mulloney, B., 190 Munakata, Y., 597, 621, 623 Munch, M., 380 Mundry, R., 582 Mundy, N. I., 75 Munger, B. L., 208 Munhoz, R. P., 346 Muniak, M. A., 228 Muniz, L., 578 Munoz, A., 39 Munoz, K. E., 17, 428, 429, 433, 434 Munoz-Lopez, M. M., 171 Munro, P. W., 629 M¨unte, T., 613 Murakami, K., 400 Murakami, N., 315 Murashima, S., 450 Murasugi, C. M., 103 Murata, Y., 278 Murayama, N., 283

701

Murphy, A. Z., 353, 354 Murphy, C., 275 Murphy, D. L., 10, 11, 14 Murphy, J., 316 Murphy, K. G., 315 Murphy, S. J., 292 Murray, A. D., 475 Murray, B., 214 Murray, E. A., 212, 213, 499, 562, 648 Murray, G. K., 646 Murray, G. M., 213 Murray, J. D., 315 Murray, K. C., 192, 193, 196 Murray, L. K., 43 Murray, M., 192 Murty, V. P., 650 Murzi, E., 317 Musatov, S., 9, 339 Muschamp, J. W., 350–352 Mushahwar, V., 195 Mushahwar, V. K., 177 Musick, J. A., 70 Musolino, E., 487 Mutso, A., 262 Mycroft, R. H., 100 Myers, C., 571 Myers, C. E., 621, 623, 631, 633–637 Myers, D. G., 46 Myers, J., 580 Myers, M. G., 307, 316 Myers, M. M., 468 Myhrer, T., 405 Nachemson, A., 259 Nadeau, J. H., 19 Nadel, L., 561 Nader, K., 521, 523 Nadol, J. B., 158 Nadon, N. L., 645 Nagahara, A. H., 520, 522, 646 Nagai, T., 136, 285 Nagao, H., 291 Nagao, T., 346 Nagarajan, S. S., 164 Nagatani, S., 345 Nagel, J. A., 520 Nagumo, Y., 351 Nagy, M. E., 609 Nagy, T. R., 657 Nahin, R. L., 445 Naidich, T. P., 258 Naitoh, P., 383 Naka, F., 474 Nakach, N., 348 Nakae, A., 192, 193, 196 Nakahara, K., 103 Nakamura, K., 104, 283 Nakamura, T., 283, 292, 310 Nakamura, Y., 399

702

Author Index

Nakanishi, S., 293 Nakano, Y., 315 Nakao, K., 315 Nakashima, K., 316 Nakata, A., 451 Nakazato, M., 315 Nakazawa, K., 184 Naliboff, B. D., 260, 263, 449 Nanthakumar, E., 287 Napadow, V., 259 Napier, T. C., 398, 399, 408 Napoli, A., 451 Narayanan, S., 408 Narins, P. M., 125, 133–135 Narita, M., 351, 474 Narita, N., 474 Narvanen, S., 258 Nascimento-Silva, S., 170 Nassar, M. A., 256 N¨assel, D. R., 186 Nassi, J. J., 83, 93, 102 Natarajan, D., 5 Nathan, P. J., 426, 429, 431, 432 Nathans, J., 73 Nault, F., 255 Naumenko, V. S., 10 Naurin, S., 3 Nauta, W. J., 398, 400, 404 Naveh-Benjamin, M., 635, 637 Nawar, E. M., 402 Neagu, T. A., 163, 169 Neale, J. M., 451 Nealey, T. A., 214 Nearing, K. I., 423, 431 Neary, D., 602 Neben, T. Y., 448 Nedderman, E. N., 317 Nedzelnitsky, V., 116 Needham-Shropshire, B., 181 Nef, P., 293 Neff, W. D., 130 Negi, L. T., 455 Negus, S. S., 410 Neild, T. O., 192 Neitz, J., 59, 61, 74, 85, 92 Neitz, J. A., 74 Neitz, M., 59, 61, 74, 75, 85 Nelini, C., 469 Nelson, A. M., 210 Nelson, G., 276, 278 Nelson, J. D., 637 Nelson, K. R., 485, 492 Nelson, L. M., 282 Nelson, R. J., 331, 332, 338 Nelson, S. G., 195 Nelson, T. M., 275 Nelson, T. O., 497 Nemanov, L., 14 Nematollahi, S., 649

Nemes, A., 293 Nemoto, H., 258 Nemoto, K., 258 Ner, A., 196 Nesse, R. M., 26, 33, 34 Nestler, E. J., 13, 19, 340, 411, 413 Neta, M., 424–428, 432, 433 Nettle, D., 26 Neubauer, A., 599 Neubauer, A. J., 610 Neugebauer, V., 256 Neumann, I. D., 5 Neumann, J., 609 Neumeyer, C., 72 Neuweiler, G., 117, 123 Newcombe, R., 16 Newhart, M., 102 Newman, A. H., 403 Newman, E. L., 633 Newman, L., 481 Newman, M. L., 444 Newman, S., 536 Newman, S. D., 607, 611 Newman, S. W., 347–349, 353, 355 Newman, T. K., 17 Newsome, W. T., 102, 103 Newton, R., 450 Newton-Fisher, N., 577, 578 Neziri, A. Y., 244 Ngai, J., 292 Nguyen, J. K., 400, 405, 407 Nguyen, J. P., 259 Nguyen, M. A., 405 Nguyen, N., 576 Nguyen, T., 258 Ni, H., 520 Ni, W., 607 Nicassio, P. M., 443, 450 Nicholson, D. A., 653 Nickle, B., 57 Nicola, S. M., 406, 411 Nicoladis, S., 320 Nicolas, L., 13 Nicolelis, M. A., 283, 286 Nicoll, R. A., 524 Nicolopoulos-Stournaras, S., 191 Niculescu, R. S., 603 Nie, Z., 384 Niebur, E., 232, 233 Nielsen, J., 181 Nielsen, J. B., 179 Nielsen, M., 405 Niemeier, A., 260 Nikolaev, E., 348 Nikolaev-Diak, A., 348 Nikolay, P., 69, 70 Nilsson, D.-E., 54, 55 Nilsson, G., 314 Ninomiya, Y., 273, 277, 278, 281, 316

Niogi, S. N., 598 Niot, I., 277 Nir, R. R., 259 Nishida, T., 578, 580 Nishijo, H., 283 Nishikawa, T., 405 Nishimoto, N., 448, 450 Nishizaki, M., 98, 99 Nishizawa, K., 403 Niswender, K. D., 307, 316 Nithianantharajah, J., 473–475, 657 Niv, Y., 634, 635 Niven, J. E., 73 No¨e, R., 581, 582 Noelle, D. C., 621, 637 Noga, B. R., 178 Noguchi, H., 309 Nogueiras, R., 315 Noh, U., 314 Noirhomme, Q., 260 Nolan, L. J., 284 Nolan, P. M., 3 Nolen, T. G., 119 Noll, D. C., 607 Nomura, T., 283 Noppeney, U., 605, 613 Norgren, R., 278–284, 311, 313 Norman, K. A., 629, 633 Normandin, J. J., 353, 354 Norman, M., 568 Normann, C., 475 Norris, C. J., 424, 426, 427, 432 Norsted, E., 311 North, M., 287 Northcutt, K. V., 347 Northcutt, R. G., 138 Northmore, D. P., 70 Northmore, D. P. M., 70 Northrop, L. E., 342 Norton, T. T., 90 Norton, W., 2 Notsu, T., 537 Nottebohm, F., 143 Novakovic, S. D., 245 Novick, J. M., 601, 607 Novin, D., 319 Nowak, E. L., 321 Nuding, S. C., 279 Nuechterlein, K. H., 610 Nugent, B. M., 333 Nugent, S., 647 Numan, M., 13 Numminen, J., 258 Nunez, J. M., 293 Nunn, B. J., 59, 85 Nunomura, A., 656 Nurmikko, T., 258 N¨urnberg, P., 578 Nurnberger, J. I., 388

Author Index

Nusbaum, M. P., 188 Nyberg, L., 634, 635, 647, 650 Nystrom, L. E., 432, 607 Oakley, D. A., 258, 526, 530 Oakley, I. A., 277 Obal, F., 373 Obata, K., 278 Ober, C., 288 Obermayer, K., 94 Obermeyer, W. H., 387 Obici, S., 310 O’Brien, S. J., 2, 579 O’Brien, T. G., 578 O’Carroll, D. C., 67 Ochs, S. L., 70 Ochsner, K. N., 428, 431, 432 O’Connell, J. D., 332 O’Connell, L. A., 332 O’Connell, R. J., 345 O’Connor, D. H., 91 Oda, Y., 537 Oden, D. L., 495 O’Doherty, J., 258, 402 O’Doherty, J. P., 402 Odom, J. V., 70 O’Donahue, T. L., 313 O’Donnell, B. F., 536 O’Donnell, K. A., 646 O’Donnell, P., 404 O’Donohue, T. L., 308 O’Donovan, M. C., 7 Oehler, R., 89 Oertel, D., 144, 160, 161 O’Gara, B. A., 179 Ogawa, H., 207, 279, 283–285 Ogawa, S., 8, 9, 12, 14, 336, 339 Ogawa, Y., 315 Ogino, Y., 258 Ogiwara, Y., 273 O’Gorman, R., 28, 29 O’Grady, W., 500 Oh, D.-J., 70 Oh, M. M., 534, 654 Ohara, S., 214, 256 Ohayon, M. M., 370 Ohira, H., 450 Ohkuri, T., 273, 277 Ohman, A., 423 Ohnishi, T., 258 Ojima, H., 400 Ojima, J., 170 Oka, Y., 275, 284, 288 Okabe, M., 288 Okada, H., 171 Okada, T., 262 Okado, N., 474 Okamoto, H., 612 Okano, T., 84

Okanoya, K., 118, 122, 143, 501 O’Keefe, G., 446 O’Keefe, G. B., 282 O’Keefe, J., 567, 655 O’Keefe, J. A., 561 O’Keefe, J. O., 649 O’Keeffe, M. K., 454 Oksengard, A. R., 405 Okubo, Y., 246 Olah, J., 526 Olausson, H., 207, 210 Olazabal, U. E., 336 Oldenburger, W. P., 347 Oldfield, B. P., 119, 125 O’Leary, A., 451 Oler, J. A., 425–428, 432, 433 Olesen, K. M., 333 Oleson, E. B., 407 Oletsky, H., 597 Oliver, C. G., 538 Oliver, D., 145 Oliver, D. L., 161, 162 Olivier, B., 337, 339, 346, 348, 353 Olivier, J. D., 346 Olivieri, L., 387 Ollier, W. E., 10 Olsen, J. F., 146 Olshausen, B. A., 621, 631, 632 Olson, C. R., 99, 100 Olton, D. S., 561 Olveczky, B. P., 86, 608 O’Mahony, M., 273 O’Malley, B. W., 336 O’Mara, S. M., 405 O’Neill, J. B., 212 Ong, W. S., 103 Ono, S., 284 Oomura, Y., 280, 310 Oosting, R., 339 Oosting, R. S., 337, 339, 346 Op de Beeck, H. P., 99, 101, 105 Opfermann-Rungeler, C., 70 Oppenheim, R. W., 470 Oquendo, M. A., 16 O’Rahilly, S., 308 Orban, G., 637 Ordy, J. M., 652 O’Reilly, R., 597 O’Reilly, R. C., 621, 623, 629, 633, 634, 637 Oreland, L., 16 Orlichenko, A., 434 Orlovsky, G. N., 177–180, 184, 185 Orlowski, J., 69, 70 Oronska, A., 263 Orr, S. P., 431 Orr, W. C., 655 Orsal, D., 191, 192 Ort, C. A., 180

Ortega, S., 450 Ortiz, J. G., 337, 340 Osada, T., 103 Osaka, N., 611 Osborn, J., 258 Osborne, L. R., 10 Osborne-Lawrence, S., 316 O’Shaughnessy, D. M., 213 O’Shea, D., 309 O’Shea, M., 186, 190 O’Shea, R., 470 O’Shea, R. P., 82 Osher, Y., 14 Oshiro, Y., 257, 258 Osindero, S., 628 Osipova, D. V., 10 Osorio, D., 63, 65, 72, 73, 75 Osowiecki, D. M., 443, 444 Ossipov, M. H., 260, 261 Osterhout, L., 600, 601, 607 Ostner, J., 579, 580 Ostrander, E. A., 2 Ostrowsky, K., 258 Oswaldo-Cruz, E., 70 Otake, K., 399 Otaki, J. M., 290 Otsuka, Y., 611 Otte, C., 17 Otto, T., 562, 647 Ouattara, K., 500 Overmier, J. B., 531 Owen, M. J., 7 Owen, S. L., 258 Owens, C. B., 649 Oxbury, S., 602 Oxenham, A. J., 132 Ozdoba, J. M., 286 Paans, A. M., 607 Pabello, N., 404 Pace, T. W. W., 455 Pacitti, C., 399 Pack, A. I., 369 Pack, A. K., 210 Pack, C. C., 103 Packard, M., 608 Packard, M. G., 397, 398, 555, 568, 570, 608 Packer, C. A., 579 Packer, O., 84, 85 Packer, O. S., 86, 88 Pacynski, M., 531 Padberg, F., 260 Padberg, J., 213 Padgett, D. A., 450, 452, 454 Padiglia, A., 274 Padilla, G. A., 444 Paek, J., 610 Page, G. G., 260

703

704

Author Index

Pagni, C. A., 259 Palacios, A. G., 59 Palmer, A. L., 428, 434 Palmer, A. R., 141, 163 Palmer, C. I., 215 Palmeri, T. J., 629, 637 Palmiter, R. D., 308, 309, 311 Palombit, R., 587 Palombit, R. A., 576 Palop, J. J., 654 Pan, J. Y., 474 Pan, W., 315 Pan, Y., 656 Pan, Z. Z., 260 Panchin, Y., 177, 179, 180, 184, 191 Pandya, D. N., 91, 164, 428 Pang, M. Y. C., 187 Panksepp, J., 395 Panksepp, J. B., 12 Panocka, I., 260 Pantel, K., 314 Panush, R. S., 444 Paolasso, I., 259 Papademetris, X., 431 Papert, S., 622 Papes, F., 7 Paplinski, A. P., 621 Papp, A., 17 Parada, M., 339 Paradiso, S., 411 Parasuraman, R., 650 Pardo, J. V., 214, 227, 284 Par´e, M., 209, 210 Par´e-Blagoev, E. J., 571 Paredes, R. G., 340, 346, 348 Paredes, R. J., 348 Paredes-Ramos, P., 340 Parent, A., 406 Parent, M. B., 525 Parfitt, D. B., 348 Pargament, K. I., 445 Parhar, I. S., 336 Pariente, J., 263 Paris, J. J., 340 Park, C. L., 446 Park, C. S., 653 Park, G., 607, 608 Park, J. H., 333 Park, K., 259 Park, K. F., 102 Park, T. J., 129, 130 Parker, A. J., 81, 93, 95, 96 Parker, A. R., 54 Parker, B. K., 502 Parker, D., 193, 194 Parker, D. C., 382 Parker, H. G., 2 Parker, L. A., 311 Parkin, A. J., 647

Parkinson, J. A., 403 Parks, C. M., 632 Parks, T., 141 Parks, T. N., 141, 142, 144 Parmentier, M., 12 Parraga, C. A., 75 Parra-G´amez, L., 346 Parrent, A., 564 Parrish, B. P., 350 Parrish, T., 609 Parrish, T. B., 260 Parry, J. W., 58 Parsons, C. E., 258 Parsons, P. A., 2 Pascale Pike, Bruce, 599 Pascoe, J. P., 423–425, 521, 522 Pascual-Leone, A., 170, 214, 472, 597 Pascual-Marqui, R. D., 600 Pascucci, T., 18 Pashkam, M. V., 98 Pasnau, R. O., 383 Paspalas, C. D., 646 Pasquier, F., 475 Passchier, J., 443 Passilly-Degrace, P., 277 Passin, W. F., 453 Passingham, R. E., 423, 428, 608 Passmore, N. I., 131 Pastalkova, E., 565 Pasupathy, A., 98, 219, 627 Patel, A., 600 Patel, K., 452 Patel, S. B., 332 Patel, S. H., 256 Patel, Z., 58 Patla, A. E., 185 Paton, J. J., 423, 425 Patris, B., 277 Patterson, J., 651 Patterson, K., 598, 602 Patterson, M. M., 517, 518, 526–528, 537 Patterson, T. L., 450 Pattij, T., 11, 346 Pattison, P., 446 Paul, S. M., 337 Pauli, W. M., 637 Pauls, J., 98 Paulsen, R. E., 405 Paulus, M. P., 428, 433 Pauly, J. M., 263, 264 Pauri, F., 472 Pavek, G., 127 Pavlov, I., 310 Pavlov, I. P., 620 Pavlova, G. A., 180, 184 Pawitan, Y., 7 Pawlak, M. A., 102 Pawluski, J. L., 337

Pawson, L., 210 Paxinos, G., 423, 424 Paya-Cano, J. L., 6 Paykel, E. S., 387 Paylor, R., 523 Payne, K. B., 117, 144 Payne, R., 127, 131 Payne, R. S., 127 Paz, J., 474 Paz-y-Mi˜no, G., 581 Pazzaglia, M., 100 Peake, A. M., 584 Peake, W. T., 116 Pearce, J. M., 425 Pearl, D., 521 Pearson, D. L., 119 Pearson, K., 180, 187 Pearson, K. G., 177, 179, 180, 182–192, 194, 195 Pecina, S., 311, 401, 410, 411 Peck, J. H., 194 Peck, J. W., 319 Pecka, M., 141, 145 Pedersen, A. F., 450, 454 Pedersen, B. K., 449 Pedersen, J. T., 656 Pedersen, P. E., 289, 295 Pedersen, V., 196 Pederson, R. A., 315 Pedrazzini, T., 12 Peelen, M. V., 99, 100 Peelle, J. E., 171 Peeters, M., 350 Peglion, J. L., 354 Pehek, E. A., 10, 351, 354 Pei, Y. C., 218, 219, 225, 226 Peichl, L., 62, 63 Peirson, R., 444 Pekary, A. E., 382 Peleg, S., 656 Peleshok, J. C., 246 Pelleymounter, M. A., 309, 310 Pellicer, F., 349, 352 Peltier, S., 214 Pena, J. L., 141, 142 Pendedo, F. J., 451 Pendergrass, J. C., 425 Pendse, G., 260 Penedo, F., 452, 453 Penev, P., 383, 385 Penev, P. D., 382, 384 Penfold, C., 431 Peng, X., 96 Penn, D., 338 Penn, D. C., 585 Pennartz, C. M., 398–400, 404, 410 Penner, M. R., 656 Penney, J. B., 409, 607, 613 Penny, W., 429

Author Index

Penton-Voak, I. S., 43 Pepperberg, I. M., 502 Perchet, C., 258 Pereira, E., 276–278 Pereira, F., 603 Perello, M., 316 Peretz, I., 600 Perez, C., 311 Perez, F. A., 308 Perez, M., 142 Perez, S., 400, 405, 407 Perez-Diaz, F., 4, 14 Perez-Gonzalez, D., 162 Perez-Tilve, D., 315 Pergolizzi, J., 263 Perilloux, C., 33 Perl, E. R., 212, 242, 247, 250–252 Perlstein, W. M., 607 Permenter, M., 649 Pernigo, S., 100 Perona, P., 626 Perotte, A. J., 633 Perreault, M. C., 188 Perrett, D., 43 Perrett, D. I., 43, 97, 98, 426, 523 Perrett, S. P., 531 Perrey, A., 70 Perrin-Monneyron, S., 354 Perrino, N., 383 Perrins, R., 183 Perrins, R. J., 191 Perrone, B. P., 631 Perry, B., 464 Perry, C., 598 Perry, G., 656 Perry, H. E., 528 Perry, S., 578, 584 Perry, V. H., 89 Perryman, J. I., 370 Persico, A. M., 12 Pert, C. B., 261 Pestka, S., 450 Peterhans, E., 96 Peterman, A. H., 445 Peters, A., 646, 653 Peters, H. J., 348, 349, 353 Peters, J., 405, 406 Peters, R. M., 214 Peters, R. P., 287 Peters, S., 83 Petersen, A. S., 598 Petersen, C. C., 277 Petersen, N., 181 Petersen, R. C., 657 Petersen-Felix, S., 244 Peterson, B. B., 86–88 Peterson, D. M., 348 Peterson, J., 449 Peterson, M. J., 387

Petersson, K. M., 262, 263 Petit, S., 3 Petitto, L. A., 502 Petkov, C. I., 164, 171, 172, 214 Petrides, M., 171, 284, 571 Petrie, M., 40 Petrof, I., 163 Petrosini, L., 474, 475 Petrou, M., 260 Petrov, A. A., 630, 631 Petrovic, P., 262, 263 Petrucelli, L. M., 213, 219 Petrulis, A., 347 Petrusca, D., 88 Petry, H. M., 68, 70 Petter, M., 487 Pettersson, U., 16 Petticoffer, A. C., 317 Pettigrew, J. D., 57, 69, 466 Pettit, G. S., 18, 45 Pettit, H. O., 403 Petty, R. E., 426, 427 Petzke, F., 259, 263 Petzke, F. W., 259 Pevsner, J., 290 Peyron, C., 309 Peyron, R., 258 Pezawas, L., 17, 428, 429, 433, 434 Pfaff, D., 12, 13 Pfaff, D. W., 9, 333, 336–342 Pfaffmann, C., 281, 337 Pfaus, J. G., 335, 339–341, 347–349, 352, 353 Pfefferbaum, A., 651 Pfeifer, J. H., 431 Pfeiffer, A., 409 Pfl¨uger, A., 177 Pfluger, P. T., 315 Pfurtscheller, G., 599 Pham, D. L., 651 Phan, A., 9 Phan, K. L., 426, 429, 431, 432, 434 Phan, T. H., 275 Phelan, R., 408 Phelps, C. P., 308 Phelps, E. A., 423, 431, 434, 523 Phelps, S. M., 336, 580 Philibert, B., 164 Philibert, R. A., 16 Philips, A. G., 353 Phillips, A. G., 335, 347, 349, 352, 353, 396, 405, 412 Phillips, D. P., 166 Phillips, H. S., 250 Phillips, J., 605, 613 Phillips, J. R., 208–210, 214, 215, 218, 222–224 Phillips, K., 452 Phillips, P. E., 413

Phillips, R. G., 523, 524 Phillips-Farf´an, B. V., 348 Phillipson, O. T., 408 Phillmore, L. S., 583 Philpot, B. D., 467 Piaget, J., 471 Piazza, M., 489 Picanco, C. W., 70 Picco, C., 292 Piche, M., 259 Pickavance, L., 308 Pickel, V. M., 404 Pickett, J., 531 Pickles, C. D., 609 Pickles, J. O., 123 Piddington, R., 135 Piekarski, D. J., 344 Piekema, C., 564 Pien, G. W., 369 Pieper, B. A., 580 Pierce, A. D., 116 Pierce, J. D., 288 Pierce, R. C., 406 Pieribone, V. A., 194 Pierpaoli, C., 429 Pietrini, P., 100 Pigott, S., 564 Piguet, O., 651 Pike, B., 171, 571 Pike, G. B., 171 Pike, J. L., 450 Pike, L. M., 290 Pilgrim, C., 14 Pillias, A. M., 284 Pillolla, G., 352 Pinel, P., 489 Pinker, S., 30–32, 608 Pinnock, S. B., 308 Pinsk, M. A., 91, 92, 100, 429 Pinsonneault, J., 17 Pinter, M. M., 181 Pinto, N., 626 Pinto, R., 517 Piper, M., 309, 310 Piras, A. P., 351 Pisani, A., 472 Pi-Sunyer, X., 313 Pitcher, D., 100 Pitkanen, A., 423, 428, 431, 433 Pitler, T. A., 654 Pitman, R. K., 431 Pitts, W. H., 292 Pitzalis, S., 92, 97 Pivik, T., 371 Place, N. J., 344 Plaghki, L., 258 Plassmann, H., 402 Plata-Salaman, C. R., 283, 314, 315 Platt, C., 137, 138

705

706

Author Index

Platt, D., 283 Platt, J. R., 223 Platt, M. L., 104 Plaut, 598 Plaut, D. C., 598, 602, 621, 637 Plaza, I., 161 Ploghaus, A., 262 Plomin, R., 1, 2, 6 Ploner, M., 263 Plotkin, H., 43 Pluess, M., 18 Plunkett, K., 598 Pniak, A., 349, 350 Podio, V., 259 Poeppel, D., 171 Poggio, G. F., 212 Poggio, T., 98, 627–629, 637 Pokorny, J., 88 Poldrack, R. A., 571, 598 Polenchar, B. E., 518 Polich, G., 263 Polimeni, J. R., 93 Polis, S., 424, 426, 429, 432 Polizzi di Sorrentino, E., 584, 585 Pollack, G., 137 Pollack, G. S., 119, 125–127, 131, 137 Pollard, R., 464 Pollen, D. A., 91 Pollmacher, T., 383 Pollo, A., 263 Pollock, M. S., 379 Polonsky, K. S., 382, 384 Polson, M. C., 261 Polster, M. R., 647 Polston, E. K., 342 Polvogt, L. L., 116, 122 Polyn, S. M., 633 Pomerantz, S. M., 354 Ponce, C. R., 93, 103 Ponganis, P. J., 61 Ponmanickam, P., 345 Pons, T., 166 Pons, T. P., 213 Pont, S. C., 227 Pontiggia, A., 388, 389 Poole, S., 243 Poon, M., 180, 183 Pope, A., 480 Popova, L. B., 179 Popova, N. K., 10, 11 Popper, A. N., 115, 116, 118, 119, 123, 128–130, 133–135, 137–139 Popper, As., 133 Popper, K., 223 Poremba, A., 168, 539, 586 Porges, S. W., 580 Porkka-Heiskanen, T., 369 Porreca, F., 260, 261 Port, R. L., 528

Porte, D., 310 Porter, K. K., 162 Porter, M. L., 54, 58 Porter, N. M., 656 Porter, R. A., 309 Portillo, W., 336 Post, L. J., 230 Postolache, T. T., 389 Potenza, M. N., 346 Pothion, S., 13 Potter, D. D., 609 Potter, H. H., 609 Potterat, E. G., 444 Poulsen, T. D., 449 Poulton, R., 15–17, 41 Pournajafi-Nazarloo, H., 580 Pournin, S., 10, 11 Povinelli, D. J., 585 Powell, D. A., 521, 530 Powell, D. K., 651, 652 Powell-Griner, E., 445 Power, J. M., 654 Powers, J. B., 347 Powers, W. J., 259, 650 Powley, T. L., 312 Powning, K. S., 578, 579 Pozzato, C., 346 Praamstra, P., 600 Pradhan, S., 161 Prakash, A., 346 Prank, K., 382 Prat, C. S., 597, 604, 607, 608, 610–612 Pratley, R. E., 307 Pratt, W. E., 402 Pratte, M., 4–6 Preat, T., 2 Prechtl, J. C., 312 Premack, A. J., 495 Premack, D., 493, 495, 586 Premereur, E., 99 Prendergast, M. A., 353 Prenger, R. J., 96 Prensa, L., 400 Prentice, S., 185 Prescott, C. A., 580 Prescott, S. A., 255 Preslar, A. J., 277 Presley, R. W., 260 Press, W. A., 92 Pressman, S., 445 Pressnitzer, D., 132 Preston, A. R., 567 Prestwich, G. D., 292 Presty, S. K., 648 Preuss, T. M., 164, 171 Price, C., 602, 613 Price, C. J., 599, 605, 606, 613 Price, D. D., 256, 262 Price, D. L., 648

Price, J. L., 289, 423, 428, 429, 431, 433, 652 Price, L. L., 124 Price, R. A., 287 Price, T., 496 Priebe, N. J., 93, 94 Prieto, T. E., 601 Prietto, G. J., 260 Prince, F., 385 Prince, S. E., 650 Pritchard, T., 279 Pritchard, T. C., 283, 284, 315, 317 Privat, A., 191, 192 Probert, S. P., 258 Prochazka, A., 177, 179, 184–187, 195 Proctor, L., 142 Proenca, R., 307 Profet, M., 44 Prokop, T., 185 Prokowich, L. J., 517 Pronko, N. H., 517 Protzner, A. B., 650 Proulx, K., 310 Provencher, J., 195 Provenzale, J. M., 651 Pruett, J. R., 223 Prusky, G. T., 70 Prutkin, J., 274 Przybyszewski, A. W., 91 Puce, A., 100 Puelles, L., 142 Pugeat, M., 343 Pugh, K. R., 607, 611 Puglisi-Allegra, S., 18 Puhl, J. G., 183 Pujol, J. F., 343 Pukall, C. F., 260 Pulicicchio, C., 346 Pullman, S., 70, 470 Pulverm¨uller, F., 600 Purcell, B. A., 637 Purcell, D. W., 453 Purcell-Miramontes, M., 2 Purnell, J. Q., 315 Purser, H. R. M., 621 Purves, D., 212 Pusey, A. E., 577, 579, 580 Puskar, Z., 251 Putnam, K. M., 431 Putnam, S. K., 343, 344, 350 Puts, D. A., 35, 39, 43 Pyner, S., 346, 354 Qi, H. X., 102 Qi, J., 196 Qi, L. Y., 256 Qiao, X., 535, 536 Qin, S., 564 Qin, Y., 598

Author Index

Qiu, C.-S., 524 Qu, D., 309, 310 Quamme, J. R., 649 Quante, M., 259 Quevedo, A. S., 256–258 Quigley, K. S., 494 Quilliam, T. A., 209 Quine, D. B., 124, 125 Quinn, K. J., 528 Quintero, L., 261 Quirion, R., 316, 320 Quirk, G. J., 428, 430–433 Quiroz, J. A., 388 Quitkin, F. M., 388, 389 Raabe, M., 609 Rabbitts, T. H., 293 Rabin, B. S., 445, 449, 451, 454 Rabinowitz, T., 444 Raby, C. R., 488 Rademacher, J., 93 Radlwimmer, F. B., 64 Rafferty, B., 388, 389 Raffray, T., 377 Ragozzino, M. E., 412 Raguse, J.-D., 276 Rahe, R. H., 442 Rahman, J. E., 166 Rahman, S., 535 Raichle, M. E., 259, 428, 433, 650, 654 Raij, T. T., 258 Rainero, I., 263 Raineteau, O., 194, 196 Rainnie, D. G., 4 Rainville, P., 259, 262 Raison, C. L., 455 Raja, S. N., 244, 246 Rajeevan, N., 431 Rajendran, N., 93 Rajfer, J., 344 Rajimehr, R., 95 Rakic, P., 463 Rakshit, S., 98 Ralph, M. R., 374 Rama, S. M., 453 Ramazan, B., 160 Ramboz, S., 10 Ramer, M. S., 246 Raming, K., 291, 293 Ramirez, J.-M., 182, 183, 185–188, 191, 195 Ramkumar, M., 258 Ramkumar, P., 259 Ramon y Cajal, S., 632 Ramos, M. S., 467 Rampin, O., 349, 350, 354 Rampon, C., 474, 564 Ramsay, D. J., 320 Rance, M., 261

Randall, J. I., 260 Randall-Thompson, J. F., 407 Randich, A., 312 Randolph, M., 215 Raney, J. J., 230 Ranft, U., 382 Rang, H. P., 243 Ranganath, C., 562, 564, 565, 633, 649 Ranganathan, S., 307 Rangel, A., 402 Rank, M., 192, 193, 196 Rank, M. M., 192, 193, 196 Rankin, C. H., 513 Ranson, S. W., 307 Rao, G., 563 Rao, M. S., 141 Rao, R. P. N., 621 Rao, S., 221 Rapp, P., 646, 649 Rapp, P. R., 646, 648–650, 653, 656, 657 Rasche, D., 258 Rascol, O., 408 Rasia-Filho, A., 348 Rasmussen, H. N., 444 Rasmussen, T., 652 Rasquinha, R. J., 98 Rastle, K., 598 Ratcliff, R., 603 Rathbun, D. L., 90 Rathouz, P. J., 384, 385 Ratliff, F., 66 Ratner, B. M., 600 Rattermann, M. J., 495 Rauceo, S., 346 Rauch, S. L., 424, 426, 431, 433, 434 Rauschecker, J. P., 132, 166, 167, 169, 171, 605 Rausell, E., 163, 164, 166 Ravicz, M. E., 117 Raviola, E., 86 Ravizza, R., 116, 128 Ravizza, R. J., 129, 130 Ravussin, E., 307 Rawles, J. M., 651 Rawlins, J. N. P., 564, 649 Rawlins, J. P., 567 Rawls, L. H., 469 Ray, S., 228, 233 Rayevsky, V. V., 494 Rayner, R., 519 Raynor, D. A., 445 Raz, N., 651 Read, A. J., 579, 580 Read, H. L., 146, 528 Read, S. J., 621, 623 Reader, T. A., 191 Reardon, F., 399 Rebec, G. V., 404, 530

Rebrin, I., 4, 11 Recanzone, G. H., 166, 213 Rechtschaffen, A., 383 Redeker, N. S., 444 Redfern, B. B., 596, 606, 609 Redfern, R., 262 Redford, J. S., 499 Redish, A. D., 621, 655 Redman, R., 282 Redmond, D. P., 384 Redout´e, J., 343 Reed, B. R., 650 Reed, D. R., 287 Reed, H., 486 Reed, M. D., 162 Rees, G., 94 Rees, H. D., 409 Reese, B. E., 56 Reeslund, K., 443, 444 Reeve, J. R., 314 Reeves, G., 389 Refsgaard, L. K., 339 Refshauge, K. M., 211 Regan, B. C., 75 Reh, T. A., 462, 466 Rehkamper, G., 70 Reichardt, W., 185 Reichenbach, T., 158 Reichert, H., 179 Reichle, E. D., 610 Reid, D. F., 316 Reid, K., 384 Reid, K. J., 373 Reid, R. C., 88, 90 Reidelberger, R. D., 315 Reif, A., 14 Reiman, E. M., 258, 259 Reinkensmeyer, D. J., 195 Reis, D. J., 521 Reis, D. L., 431 Reisert, I., 14 Reisine, T. D., 447 Reiss, A. L., 259 Reiter, J., 39 Reiter, R. J., 386 Reith, C. A., 193, 194 Reitzen, S. D., 213 Rekling, J. C., 191 Remedios, R., 170 Remien, R., 446 Remy, S., 3 Renaud, D. L., 128–130 Rendall, D., 576, 587 Renehan, W. E., 279 Renken, R., 434 Ren-Patterson, R., 11 Renthal, W., 19 Repa, C., 430 Repa, J. C., 425, 570

707

708

Author Index

Reppas, J. B., 92, 93, 100 Requin, J., 600 Rescorla, R. A., 425, 430, 509, 510, 519 Reser, D. H., 132 Resnick, H., 443, 446 Resnick, S. M., 650, 651 Ress, D., 92 Ressler, K. J., 291 Restrepo, D., 292, 295 Retana-M´arquez, S., 346 Rethelyi, M., 251 Reus, V. I., 452 Reuter, T., 59 Revial, M. F., 292 Rexed, B., 246 Rexrode, K. M., 385 Rey, H. R., 468 Reyes, A., 404 Reyes, R. A., 342 Reymond, L., 69, 70 Reynolds, C. P., 449 Reynolds, D. S., 339 Reynolds, D. V., 261 Reynolds, S. M., 406 Reynolds, W. F., 493 Rezai, A. R., 258 Rhee, S. H., 3 Rheinlaender, J., 131 Rhode, W. S., 144 Rhoden, E. L., 344 Rhodes, C. J., 307, 316 Rhodes, G., 41 Rhodes, M. E., 339 Ribeiro, A., 9, 339 Ribeiro-da-Silva, A., 246, 251 Ribotta, M. G., 192 Ricardo, J. A., 279 Ricca, V., 346 Ricci, B., 474 Rice, D., 464, 465, 488, 489 Rice, F. L., 207, 209 Rice, W. R., 129, 130 Richard, C., 399 Richard, F., 475 Richard, J. M., 401, 406 Richards, A. F., 579 Richards, D. G., 116 Richards, M., 475, 476 Richards, M. H., 36 Richardson, R. C., 46 Richardson, R. T., 530, 531 Richerson, P. J., 47 Richmond, B. J., 98 Richmond, C. E., 254 Richter, C. P., 306, 373 Richter, J. D., 475 Richter, M. C., 90 Richter, S., 449 Richter, T. A., 275

Riddell, N. E., 454 Ridley, R. M., 213 Riedel, G., 523 Riedel, J., 587 Riedl, M., 447 Riel, E., 378 Riemann, D., 387, 388 Riemersma, K. K., 132 Riesen, A. H., 466 Riesenhuber, M., 627, 628 Riggs, L. A., 66 Rigon, P., 348 Rijpkema, M., 564 Rikowski, A., 43 Riley, A., 346 Rilling, J. K., 171, 262, 263 Rilling, M., 486 Rinaldi, P. C., 258, 528 Rind, F. C., 186 Rinker, M. A., 231 Rinkwitz, S., 2 Riolo, J. V., 347, 350–352 Rios, L., 352 Risch, N., 274 Risinger, R. C., 425 Risner, S. R., 223 Rison, R. A., 520, 521 Rissman, E. F., 333, 334, 336, 347 Riters, L. V., 346 Rittenhouse, C. D., 467 Ritter, R. C., 313, 316 Ritter, S., 306 Ritter, W., 132 Ritzmann, R. E., 180 Rivas, D., 346 Rivkees, S. A., 370 Rizvi, T. A., 353 Rizzolatti, G., 97, 170 Ro, J. Y., 213, 214 Roach, A. R., 451 Robbins, A., 336 Robbins, N., 654 Robbins, T. W., 397–399, 402, 403, 406, 410, 412, 521, 646 Roberson, E. D., 654 Robert, D., 134 Roberts, A., 177, 179, 180, 183, 189, 191 Roberts, A. C., 412, 646 Roberts, B. P., 279 Roberts, D. C., 407, 408 Roberts, J. A., 646 Roberts, M. T., 653 Roberts, N., 258 Roberts, R. C., 609 Roberts, S., 491 Roberts, S. C., 37 Roberts, T., 213 Roberts, W. A., 485–488, 493, 588

Robertson, G. S., 347, 349 Robertson, R. J., 40 Robertson, R. M., 179, 180, 186, 195 Robillard, R., 385 Robin, R. W., 16 Robinson, C. J., 213, 219 Robinson, F. R., 86–88 Robinson, G. B., 535 Robinson, G. E., 2 Robinson, J., 578 Robinson, K. R., 444 Robinson, P. P., 277 Robinson, P. R., 54, 57, 58 Robinson, T. E., 340, 401, 402, 407 Robitsek, J. R., 565 Robitsek, R. J., 564, 649 Robles, C., 142 Robles, G., 613 Robles, L., 144, 145 Robson, M. D., 429 Roby-Brami, A., 191 Rocha-Miranda, C. E., 98 Rochefort, C., 295 Rocher, A. B., 652 Rochira, V., 346 Rockland, C., 559 Rockland, K. S., 91, 170 Rodgers, R. J., 260 Rodieck, R. W., 89 Rodier, P. M., 461, 464 Rodin, I., 388 Rodin, J., 450 Rodr´ıguez-Manzo, G., 334–336, 343, 344, 346, 348, 349, 352, 354 Rodriguez, E., 346 Rodriguez, I., 8 Rodriguez, M., 451, 455 Rodriguez-Feuuerhahn, M., 449 Rodriguez-Fornells, A., 613 Rodriguez-Luna, E., 75 Rodriguez-Raecke, R., 260 Rodriguiz, R. M., 12 Roe, A. W., 95, 98 Roeder, K. D., 119, 127, 131 Roelofs, A., 605 Roelofs, D., 434 Rogacz, S., 381 Rogan, M. T., 430, 433, 522 Rogers, B., 309 Rogers, B. P., 629 Rogers, N. L., 380 Rogers, R. F., 526 Rogers, T. T., 621, 632, 637 Rogers, W., 369 Roh, J. H., 654 Rohleder, N., 451 Rohlfing, T., 651 Rohlich, P., 63 Rohner-Jeanrenaud, F., 308

Author Index

Roitblat, H., 493 Roitman, J., 489 Roitman, J. D., 103, 104 Roitman, M. F., 403, 410, 411 Rojas, A., 292 Rokers, B., 623, 636 Roland, B. L., 316 Rolke, R., 261 Rolls, B. J., 312, 316, 317, 320 Rolls, E., 429, 634 Rolls, E. T., 98, 283, 317 Romani, G. L., 259 Romano, A. G., 518, 528 Romano, G. J., 341 Romanski, L. M., 166, 169, 170, 423, 521 Romo, R., 214, 228, 229 R¨ompler, H., 580 Rompre, P. P., 402 Ronacher, B., 179 Rong, F., 171 Rong, M., 276 Ronken, E., 11 Roozendaal, B., 522, 525, 571 Ropella, K. M., 601 Roper, K. L., 486 Roper, S. D., 274–278, 285 Roppolo, J. R., 225 Rorick-Kehn, L. M., 531, 540 Rosa, M. G., 91 Rosas, H. D., 651 Rose, C. A., 451 Rose, G. J., 139 Rose, G. M., 654 Roseberry, A. G., 309 Rosen, A. C., 258 Rosen, A. M., 273, 277, 285 Rosen, B. R., 426, 564 Rosen, D. J., 533 Rosen, J. B., 433, 521, 523 Rosen, M. S., 313 Rosen, R. C., 346 Rosenak, R., 311 Rosenberg, J. S., 32 Rosenberg, R. A., 649 Rosenberger, C., 259 Rosene, D., 648 Rosene, D. L., 646–648, 653 Rosenkranz, J. A., 404 Rosenthal, J. M., 314 Rosenthal, N. E., 389 Rosenzweig, M. R., 473, 474 Rosenzweig-Lipson, S., 346 Rosman, I. S., 263 Rosowski, J. J., 116, 117, 135 Ross, T. J., 425 Ross, W. M., 70 Rossato, M., 343 Rossel, S., 54, 66

Rossetti, L., 310 Rossi, M., 309 Rossignol, S., 185, 187–189, 191, 192, 194, 195 Rossini, P. M., 258, 472 Rossiter, S. J., 62 Rossman, L. G., 382 Roth, G. L., 163 Roth, J., 607 Roth, J. D., 316 Roth, R. H., 399 Roth, T. L., 656 Rothe, C., 14 Rothman, R. B., 261 Rotte, M., 613 Rouald`es, B., 259 Roubertoux, P. L., 4–6, 13, 14 Rough, J., 387 Rouillard, C., 192 Rouiller, E., 144, 163 Rouiller, E. M., 161 Rousselet, G. A., 97 Roussin, A. R., 273, 277 Roussin, A. T., 285 Routh, V. H., 310 Routhieaux, B. C., 647 Routtenberg, A., 319 Rovinsky, S. A., 316 Rowe, M., 230 Rowe, M. J., 213 Rowe, M. P., 62 Rowe, W., 652 Rowe, W. B., 656 Rowell, C. H. F., 186 Rowland, D., 426, 523 Rowland, N. E., 282 Rowley, H. A., 256 Rowley, J. C., 289 Rowntree, S., 474 Roy, A., 232 Roy, A. K., 430 Roy, R. R., 194–196 Roy, S., 90 Royal, D., 89, 91 Rozengurt, E., 276 Rubel, E. W., 141, 161 Rubinstein, M., 307, 309 Ruby, P., 372 Rudick, C. N., 262, 340 Rudy, J. W., 523 Ruef, A. M., 427 Ruether, W., 260 Ruf, M., 258 Ruger, M., 377 Rugg, M. D., 564, 609 Ruggero, M. A., 164 Ruggiero, D. A., 521 Ruiz, B. P., 531 Ruiz, C., 275

Ruiz, O., 89, 91 Ruiz-Luna, M. L., 649 Ruley, H. E., 259 Rumbaugh, D. M., 488 Rumelhart, D. E., 92, 622, 628 Rumsey, J. M., 611 Ruppin, E., 634, 635 Rusch, N., 429 Ruscio, M. G., 580 Rushing, P. A., 308, 314 Rushworth, M. F., 429 Rushworth, M. F. S., 260 Russchen, F. T., 423 Russell, D., 445 Russell, I. S., 526, 530 Russo, G., 7 Russo, J., 443 Rust, N. C., 97, 98 Ruttiger, L., 88 Ruttimann, E. B., 315 Ryan, A., 117 Ryan, M. J., 125 Ryba, N. J., 274–276, 278, 284 Ryba, N. J. P., 276, 284 Rydahl-Hansen, S., 444 Ryder, O. A., 2 Ryugo, D. K., 140, 144 Ryvlin, P., 258 Saad´e, N. E., 262 Saalmann, Y. B., 90 Saaltink, D. J., 5 Saavedra, J. M., 320 Sabatini, B. L., 467 Sabban, E., 14 Saberi, K., 171 Sabongui, C., 339–341 Sacchetti, B., 524 Sacchetti, D. C., 648 Sacco, K., 259 Sacco, T., 524 Sachs, B. D., 335–337, 343–345, 347–350, 352, 354 Sachs, M. B., 118, 137 Sack, R. L., 388 Sadacca, B. F., 286 Sadalla, E. K., 36, 46 Sadato, N., 214 Saddoris, M. P., 647 Sad´e, W., 17 Sade, D. S., 578 Sadeghian, K., 403 Saderi, N., 386 Sadowski, B., 261 Sadreyev, R. I., 179 Safarinejad, M. R., 346 Saffen, D., 17 Saffran, E. M., 597, 602, 606 Saghatelian, A., 10

709

710

Author Index

Sago, M., 4 Saha, A. K., 315 Sahar, S., 386 Sahley, C. L., 513 Sahu, A., 308, 309 Sahuc, S., 185 Saino, N., 39 Saito, D. N., 214 Saito, H., 98 Saito, S., 258, 283 Sakai, R. R., 315, 321, 322 Sakami, S., 451 Sakamoto, H., 354 Sakamoto, M., 214 Sakano, H., 288 Sakata, H., 213 Sakata, I., 316 Sakmann, B., 406 Saksida, L. M., 630, 631 Sakuma, Y., 338, 340–342, 347 Sakurada, S., 346 Sakurai, M., 405 Sakurai, T., 309, 351 Salamone, J. D., 348, 350, 352, 402 Salat, D. H., 651 Salazar-Ju´arez, A., 346 Salcedo, E., 295 Saldana, E., 161 Sale, A., 473, 474 Saleem, K. S., 97, 98 Salgado-Delgado, R., 386 Salimi, N., 196 Salinas, E., 228, 229 Salmelin, R., 600 Salmon, P. G., 445 Salorio, C. F., 313 Salovey, P., 46 Salt, P., 427 Salthouse, T. A., 644, 645 Salvadore, G., 388 Salvatierra, A. T., 528, 538 Salvatore, P., 388 Salvi, R., 127 Salvi, R. J., 126, 127 Salz, T., 598 Salzman, C. D., 103, 423, 425 Sambataro, F., 650 Samson, H. H., 408 Sanacora, G., 308, 310 Sananbenesi, F., 656 Sananes, C. B., 521, 522, 524 Sanchez, B. N., 385 Sanchez-Andres, J. V., 534 Sanchez-Barcelo, E. J., 386 S´anchez Montoya, E. L., 337, 340 S´anchez-Ramos, J. R., 656 Sand, O., 137, 138 Sandell, J. H., 95, 98 Sanders, N. M., 310

Sanders, R. J., 502 Sanders-Bush, E., 354 Sandkuhler, J., 254, 255 Sands, S. F., 486 Sandy, P., 3 Sanelli, L., 192, 196 Sanematsu, K., 273 Sanes, D. H., 462, 466 Sang, C. N., 259 Sangameswaran, L., 245 Sanides, F., 164 Sanna, F., 351, 352 Sannen, K., 336 Santamaria, A., 637 Santarelli, L., 10 Santello, M., 228 Santha, M., 12 Santiago, H. C., 486 Santos, L. R., 587 Saper, C. B., 88, 279, 280, 307, 309, 315, 368, 369, 374, 382 Saris, W. H., 307 Sarkar, D., 450 Sarkar, J., 339 Sarter, M., 634 Sarti, F., 407 Sartori, L., 469 Sary, G., 89, 91 Sasaki, A., 19 Sasaki, K., 346 Sasaki, Y., 95 Sasaki, Y. F., 293 Sassin, J. F., 381 Sassone-Corsi, P., 386 Sathian, K., 214, 223 Sato, M., 284, 285 Sato, S., 343, 344, 350 Sato, S. M., 351, 352 Sato, T., 98, 99, 292, 293 Sato, T. O., 292 Sato, Y., 351 Satta, Y., 276 Satz, P., 475 Saudou, F., 10 Sauer, A. E., 181, 188 Saunders, J. C., 118, 121, 123, 124 Saunders, R., 168 Saunders, R. C., 171, 586 Saur, D., 171, 429 Sauvage, M., 564, 649 Sauvage, M. M., 649 Sauve, M. J., 649 Savage, V. R., 407 Savage-Rumbaugh, S., 502 Savonenko, A. V., 649 Savoy, L. D., 279 Sawa, K., 497 Sawaki, L., 258 Sawamoto, N., 262

Sawatari, A., 102 Sawchuk, M. A., 190 Sawtell, N. B., 467 Sawynok, J., 250 Saxe, R., 170 Saxod, R., 208 Saygin, A. P., 596 Sayles, M., 132 Sayres, R. A., 92 Sbriscia-Fioretti, B., 170 Scalaidhe, S. P., 646 Scammell, T. E., 374, 401, 407 Scarr, S., 468 Schaal, B., 295, 468, 469 Schabus, M., 260 Schachter, S., 422 Schacter, D. L., 430, 571, 648 Schadt, E. E., 3 Schaefer, H. S., 431 Schaen, E., 443 Schaerer, L., 387 Schafe, G. E., 523, 525, 570 Schafer, E. W. P., 611 Schafer, M. P., 308 Schaffer, H., 471 Schaffner, C. M., 581 Schall, J. D., 637 Schapiro, S. J., 582 Scharenberg, A. M., 3 Scharf, B., 120 Scharff, C., 143, 501 Scharman, H. E., 634 Scharrer, E., 314 Schatzberg, A. F., 259, 433 Schedlowski, M., 259, 263, 449 Scheen, A. J., 382, 384 Scheer, F. A., 377, 379, 381, 383, 386 Scheibel, A. B., 652 Scheibel, M. E., 652 Scheich, H., 142 Scheier, M. F., 444 Schein, S., 88 Schein, S. J., 85, 98 Schellart, N. A. M., 138–140 Scheller, F., 344 Schelling, G., 260 Schelling, P., 320 Schendan, H. E., 571 Scheres, A., 430 Schernhammer, E. S., 385 Scherr, P. A., 644 Schessler, T., 129 Schettler, S. P., 647, 648 Scheurink, A., 310 Scheyd, G., 43 Schibler, U., 373, 376 Schichida, Y., 58, 61 Schiff, H. C., 582 Schiffman, S. S., 273

Author Index

Schiffmann, S. N., 12 Schiller, D., 423 Schino, G., 581, 582, 584, 585 Schipper, L. J., 451 Schipul, S. E., 612 Schleicher, A., 213 Schlenker, E. H., 333 Schlereth, T., 246 Schliemann, T., 652 Schluppeck, D., 92 Schmahmann, J. D., 258 Schmajuk, N. A., 526, 621 Schmaltz, L. W., 528 Schmidova, K., 315 Schmidt, A. C., 656 Schmidt, B. J., 189, 191 Schmidt, C., 474 Schmidt, H. D., 406 Schmidt, J., 179, 184, 186 Schmidt, R. E., 449 Schmidt, S., 428, 433 Schmitt, D. P., 35, 36, 38 Schmitz, C., 655 Schmitz, J., 180, 183 Schmuckler, M. A., 185 Schnapf, J. L., 59, 85, 86 Schneider, D., 287 Schneider, D. J., 2 Schneider, J. E., 334 Schneider, J. S., 530 Schneider, K. A., 89–91 Schneider, L. H., 311, 403 Schneider, R. A., 287 Schneider, R. J., 214 Schneiderman, N., 451–453, 455, 520, 521 Schnell, S., 171, 429 Schnider, A., 260 Schnitzler, H. U., 116, 118, 129 Schnupp, J. W., 162 Schoell, E., 263 Schoenbaum, G., 646, 647 Schoenfeld, W. N., 493 Schoffelmeer, A. N., 11 Schofield, B., 448 Schofield, B. R., 91 Scholtz, C. H., 54 Sch¨oneberg, T., 580 Schoner, G., 621 Schouten, J. L., 100 Schraermeyer, U., 83 Schrago, C. G., 74 Schredl, M., 371 Schreiner, C. E., 146, 147, 162, 164 Schreurs, B. G., 534 Schrier, A. M., 493 Schroeder, C. E., 166, 170 Schroeder, M. B., 346 Schrott, B., 315

Schubert, M., 185 Schuermeyer, T., 382 Schugens, M. M., 536 Schuijf, A., 131 Schul, J., 132 Schuld, A., 383 Sch¨ulke, O., 579, 580 Schulkin, J., 433 Schull, J., 498 Schuller, G., 117, 123 Schultheiss, D., 344 Schultz, W., 402–404, 406, 412 Schulz, E., 263 Schumm, J., 282 Schusterman, R. J., 70, 503, 588 Schutz, G., 523, 656 Sch¨utzwohl, A., 38 Schwab, M. E., 194, 196 Schwager, A., 346 Schwalb, J. M., 258 Schwartz, B., 260 Schwartz, E. L., 93, 98 Schwartz, G. E., 427 Schwartz, G. J., 282, 283, 307, 312–314, 316, 317 Schwartz, J. J., 116, 117, 125 Schwartz, M., 307, 451 Schwartz, M. F., 597, 602, 606 Schwartz, M. W., 306, 307–310, 315, 316 Schwartz, P. J., 389 Schwartz, R. D., 337 Schwartz, S., 372 Schwartz, T. H., 232 Schwartzbaum, J. S., 285 Schwarzler, F., 387 Schwarzlose, R. F., 98 Schweinhardt, P., 260 Schweitzer, L., 279 Schwier, C., 262 Schwindt, P. C., 183 Schwob, J. E., 289, 291–293 Sclafani, A., 273, 276, 277, 311, 312 Sclar, G., 214 Scott, A. L., 11 Scott, D. J., 262, 263 Scott, J. P., 2, 15 Scott, K., 274 Scott, K. A., 308, 310, 314, 315, 322 Scott, S. K., 171, 605 Scott, T. R., 283–285, 312, 315, 317 Scott-Sheldon, L. A. J., 453 Scoville, W. B., 555 Scratcherd, T., 312 Scrymgeour-Wedderburn, J. F., 192 Scutt, D., 41 Seager, M. A., 537, 538 Seal, L. J., 315 Seamans, J. K., 404, 412

711

Searcy, M. D., 126, 127 Searl, M. M., 571 Sears, L. L., 530, 533, 534, 536, 539 Sebastiani, L., 524 Seckl, J. R., 374, 462 Secretan, B., 386 Seebach, B., 191, 193 Seeburg, P. H., 406 Seeley, J. R., 44 Seeley, R. J., 307–310, 314, 315 Seeley, W. W., 259, 433 Seem¨uller, E., 608 Segal, R. L., 195 Segalat, L., 2 Segerstrom, S. C., 444, 448–451 Seghier, M. L., 260 Segu, L., 10 Sehatpour, P., 232 Seib, T. B., 531 Seidenberg, M. S., 598, 621, 637 Seif, I., 4, 10, 11 Seigel, J., 367 Seipel, A. T., 404 Seirafi, A., 308, 309 Seitz, A., 630 Sejnowski, T. J., 404, 632, 637 Sekiguchi, M., 405 Sekuler, R., 650 Seligman, M., 33 Seligman, M. E. P., 531 Selionov, V. A., 179 Sellergren, S. A., 287 Sellman, D., 34 Selmanoff, M. K., 13 Selye, H., 441, 442, 446 Semba, K., 374 Seminowicz, D., 258 Seminowicz, D. A., 259, 260, 262, 263 Semmelroth, J., 38 Semmes, J., 215 Semple, S., 579 Sen, K., 631, 637 Senaris, R., 309, 315 Senbel, A. M., 346 Sengelaub, D. R., 533, 534 Senjem, M. L., 657 Senn, M., 314 Sento, S., 140 Seoane, L., 309 Seoane, L. M., 315 Sephton, S. E., 445 Ser, J. R., 58 Sereno, A. B., 104 Sereno, M. I., 92, 93, 596, 600 Sergeeva, O. A., 407 Sergeeva, S. A., 346 Serjeanstson, R., 499 Seron-Ferre, M., 377 Serova, L., 14

712

Author Index

Serra, D., 310 Serre, T., 627–629 Serretti, A., 389 Serroni, N., 387 Servatius, R. J., 535 Serviere, J., 162 Servos, P., 214 Sesack, S. R., 398, 399, 404 Sesma, M. A., 90 Seta, Y., 277 Sethares, C., 653 Setlow, B., 647 Setsaas, T. H., 578–580 Sexton, G., 651 Seyfarth, R. M., 157, 500, 576, 579–582, 584–590 Seymour, B., 258, 262 Shacham, S., 262 Shackelford, T. K., 26, 29, 30, 41, 45 Shadlen, M. N., 92, 103 Shaffer, P. A., 445 Shah, A. S., 166 Shah, N. M., 8 Shair, H. N., 471 Shallice, T., 607 Shamma, S., 132, 631 Shamma, S. A., 132, 146 Shamy, J. L., 648 Shan, X., 656 Shanahan, T. L., 378 Shanbhag, S., 142, 569 Shand, J., 65, 70 Shankar, S., 654 Shanks, D. R., 496 Shanks, N., 447 Shankweiler, D., 607 Shankweiler, D. P., 605 Shannon, C., 17 Shao, H., 277 Shapiro, M., 566, 655 Shapley, R., 90, 94, 95 Shapley, R. M., 88, 90 Sharabi, F. M., 346 Sharma, S., 462 Sharrow, K., 656 Shaywitz, B. A., 611 Shaywitz, S. E., 607, 611 Shea, S. A., 381, 383, 386 Shea, T. J., 381 Sheafor, P. J., 537 Shearin, A. L., 2 Sheehe, P. R., 292, 294 Shefchyk, S. J., 188 Shefer, S. I., 521 Shehzad, Z., 430 Sheikh, S. P., 315 Shekhar, A., 536 Sheldon, B., 452 Shelton, B. R., 129, 130

Shelton, J., 383 Shen, H. W., 407 Shen, J., 655 Shen, J. X., 135 Shen, R.-Y., 351, 352 Shen, W., 409, 410 Shenasa, J., 219, 231 Shepard, R. N., 231 Shepherd, G. M., 289, 290, 292, 293 Shepherd, J. K., 260 Sher, A., 86–89 Sher, L., 389 Sherburne, L. M., 486 Sheridan, J., 450 Sheridan, R. A., 132 Sherman, P. W., 44 Sherman, S. M., 89, 90, 163 Sherrington, C. S., 177, 186 Sherwin, W. B., 579, 580 Shettleworth, S. J., 587 Sheward, W. J., 374 Shi, C., 524 Shi, C. J., 280 Shi, T., 534 Shi, Y., 450 Shibasaki, H., 262 Shibasaki, K., 275 Shibutani, H., 213 Shichida, Y., 84 Shields, W. E., 498, 499 Shiffrin, R. M., 621 Shigemoto, R., 277 Shigemura, N., 273, 277 Shih, J. C., 4, 11, 14 Shimada, J. M., 388 Shimada, M., 309 Shimada, S., 275 Shimamura, M., 527 Shimizu, E., 564 Shimizu, K., 343 Shimizu, T., 142, 146 Shimomura, K., 375 Shimura, T., 284, 411 Shin, H. S., 259 Shin, L. M., 424, 426, 429, 431, 432, 434 Shindo, M., 605 Shing, Y. L., 635 Shinkai, M., 400 Shinkareva, S. V., 602, 603, 609 Shinnick-Gallagher, P., 522 Shinohara, M., 97 Shinotou, H., 605 Shintani, M., 315 Shipley, M. T., 353, 528 Shipp, S., 91, 95 Shippenberg, T. S., 409 Shirom, A., 446 Shirosaki, S., 273

Shishelova, A. Y., 468 Shivers, B. D., 341 Shizgal, P., 316 Shlens, J., 86–88 Shmuel, A., 93, 95 Shnayder, L., 314 Shoaf, S. E., 17 Shoda, Y., 621 Shoemaker, D., 3 Shofner, W. P., 131 Shohamy, D., 571 Shomstein, S., 102 Shore, S. E., 161 Shors, T. J., 531, 535, 540 Shrager, Y., 567 Shreve, P. E., 405, 408 Shreyer, T. A., 494 Shtyrov, Y., 600 Shu, X. Q., 243 Shulman, G. L., 259, 629, 650 Shum, D. K., 163 Shuman, M., 100 Shumsky, J. S., 192 Shumway, C. A., 70 Shupliakov, O., 194 Shupnik, M. A., 353 Shurrager, P. S., 517 Shyu, B. C., 259 Sibhatue, H. M., 288 Sicard, G., 292 Siddam, A., 337 Siderowf, A. D., 346 Sieber, W. J., 450 Siebert, A. R., 584 Sieb¨orger, F. T., 609 Siegel, B. V., 610 Siegel, J. M., 369, 370 Sienkiewicz, Z. J., 283 Siessmeier, T., 261 Sigfusdottir, I. D., 445 Sigman, M., 605 Sigmundson, K., 332 Sigvardt, K. A., 183 Sik, A., 255 Sikes, R. W., 259 Sikstrom, S., 634 Silbersweig, D., 405 Silk, J. B., 575, 576, 579–582, 584, 588 Sillar, K. T., 177, 180, 183, 188, 191–194 Sills, T. L., 311 Silva, A. J., 13, 513, 523, 524 Silva, N. L., 337 Silva, P. A., 45 Silva de Almeida, O. M., 346 Silveira, L. C., 70 Silver, B., 481 Silver, M. A., 92 Silver, R., 375

Author Index

Silverman, M. S., 92, 95 Sim, H., 14 Simanek, A. M., 450 Simansky, K. J., 408 Simenauer, L. H., 58 Simerly, R. B., 348, 353 Simmen, B., 75 Simmers, A. J., 183, 191, 192 Simmons, A., 428, 433 Simmons, A. M., 123, 125, 128 Simmons, A. N., 433 Simmons, D. A., 348 Simmons, J. A., 129 Simmons, P. J., 186 Simon, E. S., 189 Simon, H., 402, 408 Simon, S. A., 283, 286 Simon, T., 450 Simons, B. J., 319 Simons, D. J., 468 Simons, F. E., 452 Simpson, J. A., 28, 30, 34, 37–39, 42, 43, 46 Simpson, J. B., 319 Simpson, J. R., 428, 433 Simpson, S. J., 317 Sims, N., 388 Simson, P. E., 408 Sinchak, K., 14, 341 Sincich, L. C., 93–95, 102 Sinclair, R. J., 213, 219, 223, 230 Sindelar, D. K., 316 Sindou, M., 258 Sing, H. C., 384 Sing, J., 402, 403 Singer, B. D., 92, 98, 101 Singer, C. M., 388 Singer, J., 422 Singer, J. E., 446 Singer, T., 258 Singer, W., 464 Singh, K. D., 95 Singh, T., 647 Sinkjaer, T., 179 Sinnadurai, T., 213 Sinnayah, P., 307, 308 Sinnott, J. M., 118 Sipila, P., 655 Sipols, A. J., 310, 316 Sisk, C. L., 333 Sisk, D., 260 Sitcheran, R., 293 Sivian, L. J., 116, 118 Sivilli, T. I., 455 Siwak-Tapp, C. T., 475 Sj¨ostr¨om, A., 183 Skalak, R., 210 Skaliora, I., 162 Skelton, R. W., 526, 536

Skibicka, K. P., 310 Skinhoj, P., 449 Skinner, B. F., 395, 490, 497 Skinner, M. K., 19 Skoglund, C., 313 Skoner, D. P., 451 Skorupski, P., 72, 188, 190 Skosnik, P. D., 536 Skrinskaya, J. A., 11 Skuler, A., 650 Slater, J. M., 411 Sleeman, M. W., 315 Slimp, J. C., 348 Sloan, D. M., 427 Sloan, K. R., 84, 85 Slocombe, K. E., 584 Slomianka, L., 652 Slotnick, B. M., 262 Sluyter, F., 5 Small, A. M., 120 Small, C. J., 314, 315 Small, D. M., 283, 284 Smart, J., 307, 309 Smart, L., 443 Smedh, U., 314 Smedley, K. D., 38 Smeraldi, E., 389 Smiley, D. L., 315 Smiley, J. F., 166, 170, 404 Smirnova, A. A., 494 Smith, A. C., 75 Smith, A. D., 399 Smith, A. M., 209, 411 Smith, A. T., 95 Smith, B. K., 313 Smith, B. P., 288 Smith, C. D., 652 Smith, C. N., 523 Smith, C. R., 2 Smith, D. L., 657 Smith, D. V., 276, 277, 279–283, 285 Smith, D. W., 446 Smith, E. C., 632 Smith, E. E., 262, 263, 493, 607 Smith, E. R., 354 Smith, F. J., 307, 315 Smith, G., 313 Smith, G. P., 311–314, 403 Smith, G. T., 313 Smith, J. C., 124, 282 Smith, J. D., 481, 498, 499 Smith, J. E., 578, 579 Smith, K. A., 310, 448, 449 Smith, K. S., 311, 410, 411 Smith, L. B., 621, 637 Smith, L. D., 492 Smith, M. A., 656 Smith, M. C., 537 Smith, P. C., 448, 450

713

Smith, P. E., 288 Smith, P. H., 141, 144, 145, 160 Smith, R., 262 Smith, T. L., 450 Smith, V. C., 88 Smith, Y. R., 261 Smith-Roe, S. L., 403, 407 Smits, R. P., 59 Smolker, R. A., 579 Smotherman, W. P., 295 Smuts, B. B., 585 Smythe, P. J., 388 Snodderly, D. M., 70 Snoeren, E., 337, 339 Snoeren, E. M., 346 Snowden, J. S., 602 Snowdon, C. T., 580 Snoxall, S., 621 Snyder, A. Z., 259, 428, 433, 650 Snyder, L. H., 93, 102, 105 Snyder, S. H., 261, 290 Sobel, K. V., 226, 227 Sober, E., 28 Soderstrom, M., 382 Soechting, J. F., 228 Soffe, S. R., 177, 179, 180, 189 Sofroniew, M. V., 196 Sohal, R. S., 655 Sohya, K., 467 Sojkova, J., 650, 657 Sokabe, T., 275 Sokol, C. L., 448 Sokolik, A., 262, 263 Sokoloff, L., 168, 586 Sokolowski, J., 474 Sokolowski, J. D., 402 Sokolowski, M. B., 2 Solberg Nes, L. S., 444 Sole, C., 352 Sollars, S. I., 281 Sollers, J. J., 444 Solms, M., 372 Sololoff, L., 97 Solomon, G. F., 449, 450 Solomon, K. M., 428, 434 Solomon, P., 636 Solomon, P. R., 528 Solomon, S. D., 528 Solomon, S. G., 84, 85, 88, 90 Solomon, T. E., 314 Sombati, S., 190 Somers, D. C., 92, 214 Somerville, L. H., 424, 426, 429, 432, 433 Somes, G., 609 Son, L. K., 498, 499 Sondergaard, S. R., 449 Soneji, D., 263, 264 Song, A. W., 651, 652

714

Author Index

Song, B., 196 Song, J. E., 451 Song, S., 656 Songer, J. B., 388 Sonty, S., 260 Sood, A. K., 450 Soodak, R., 90 Sora, I., 11 Sorensen, L., 70 Sørensen, J. C., 652 Sornette, D., 580 Sosa, Y., 260 Soumireu-Mourat, B., 537 Sousa, A. P., 98 Soussignan, R., 295 Souto, C. A., 344 Sowell, E. R., 651 Spady, T., 58 Spanaki, M. V., 601 Spangler, E. L., 657 Spangler, H. G., 119 Spano, M. S., 351 Sparenborg, S., 539 Spark, K. J., 322 Sparks, D. L., 162, 180 Spear, N. E., 469, 485, 491 Speca, M., 452 Specht, C., 166 Specter, S., 451 Spector, A. C., 275, 281, 282, 284, 312 Speicher, C. E., 453 Spelke, E. S., 581 Spence, C., 468 Spencer, D. D., 523 Spencer, N. A., 287 Spencer, W. A., 509 Spera, G., 345 Sphton, S. E., 450 Spiegel, K., 383–385 Spielman, A. I., 278 Spiering, B. J., 637 Spiess, J., 519, 525 Spike, R. C., 251 Spilker, M. E., 261 Spinelli, D. N., 466 Spirito, F., 474 Spiteri, T., 9, 339 Spitz, R. A., 471 Spitzer, S., 452, 453 Sporhase-Eichmann, U., 190 Spray-Watson, K. J., 277 Sprecher, E., 259 Sprenger, T., 261 Spuz, C. A., 259 Sqalli-Houssaini, Y., 191, 192 Squire, L. R., 509, 510, 529, 560, 562, 564, 566, 608, 645, 648, 649 Srinivasan, M. A., 208, 215, 225 Sripati, A. P., 208, 209, 217, 218

St. John, S. J., 282, 285 Stachowiak, M. K., 535 Stack, D. M., 468 Stack, G. P., 346 Stacy, M., 346 Staddon, J. E. R., 494 Staehler, F., 276 Staff, R. T., 475 Stafford, D., 86 Stafford, D. K., 86 Stammbach, E., 585 Stanfa, L., 250 Stanford, T. R., 161, 162 Stangl, D., 455 Stanley, B. G., 308, 309, 400, 405, 407 Stanley, T. K., 322 Stanton, A. L., 451 Stanton, M., 18 Stanton, M. E., 526, 531, 536 Stapleton, J. R., 286 Starck, G., 207 Starr, C. J., 258 Starratt, V. G., 41 Staubli, U. V., 430, 522 Stavenga, D. G., 55, 59 Stearn, N. A., 474 Stearns, W. H., 307 Stebbins, W., 129 Stecina, K., 191 Stedeford, T. J., 656 Stedman, H. M., 285 Stedron, J. M., 621 Steers, W. D., 345 Steeves, J. D., 186 Stefanacci, L., 428, 564, 648 Stefanski, V., 452 Stein, B. E., 161, 162 Stein, D., 310 Stein, E. A., 408, 425, 523 Stein, L., 124 Stein, M. B., 428, 433 Stein, R. B., 179, 183, 184, 188, 189 Stein, W., 187 Steinberg, J. C., 122 Steinberg, L., 44 Steinbusch, H., 655 Steiner, B., 345 Steiner, H., 409 Steiner, J. L., 444 Steinmetz, H., 93 Steinmetz, J. E., 518, 526, 528–537, 539, 540 Steinmetz, M. A., 228 Steinmetz, P. N., 232 Steinmetz, S. S., 531 Steinschneider, M., 132 Stekelenburg, J. J., 426 Stellar, E., 307, 318 Stellar, J. R., 402

Stelmer, T., 2, 6 Stem, F. L., 345 Stenger, V. A., 258 Stensaas, S. S., 93 Stent, G. S., 180, 183 Stephan, F. K., 373 Stephan, K. E., 262 Stephens, M. J., 188, 192, 193, 196 Stephenson, J. A., 399 Stepniewska, I., 102, 163, 164, 166, 168–170 Steptoe, A., 443 Sterbing-D’Angelo, S., 163 Sterelny, K., 29, 30 Steriade, M., 372, 373, 404 Sterkin, A., 95 Sterling, P., 88 Stern, C. E., 564, 571 Stern, K., 287 Stern, Y., 475, 650, 657 Sternberg, W. F., 260 Sterpenich, V., 372 Stetcher, T., 536 Stettler, D. D., 95 Steudel, T., 172 Stevens, J. C., 214 Stevens, S., 120–122 Stevens, S. S., 222, 225 Stevens, W. D., 430 Stevenson, P. A., 190 Stevenson, S. A., 12 Stevnsner, T., 655 Stewart, C., 567 Stewart, F. R., 654 Stewart, J., 339 Stewart, J. W., 388, 389 Stewart, R. E., 276 Stewart, W. B., 289 Stickgold, R., 373 Stickrod, G., 295 Stiedl, O., 519, 525 Stief, C. G., 344 Stier, S., 431, 432 Stilla, R., 214 Stimac, R., 275 Stocco, A., 598, 613 Stocker, A. M., 644, 646, 647, 649, 656 Stoffel, M., 307 Stoffregen, T. A., 185 Stohler, C. S., 261–263 Stokes, B. T., 195 Stol´eru, S., 343 Stone, A. A., 451 Stone, E. A., 2 Stonebraker, T. B., 486 Stone-Elander, S., 259 Stoner, G. R., 103, 104 Storace, D. A., 146 Storm-Mathisen, J., 399

Author Index

Stote, D. L., 522, 524 Stout, J. C., 453 Stowe, J. R., 321 Stowe, L. A., 607–609 Stowell, J. R., 451 Stowers, L., 7, 8, 10 Straif, K., 386 Strain, E. C., 453 Strange, B. A., 607 Strata, P., 524 Stratford, T. R., 401, 408, 411 Stratmann, G., 449 Straube, T., 264, 428, 433 Strausfeld, N., 64 Strausfeld, N. J., 186 Strauss, I., 191 Strazzullo, P., 385 Streich, W. J., 578 Streif, S., 3 Streri, A., 468 Strick, P. L., 607 Stricker, E. M., 318–320 Striedter, G. F., 138 Strobel, A., 14 Strong, R., 645 Strote, J., 498 Strother, S. C., 650 Strotmann, J., 291, 293 Struble, R. G., 648 Struck, J., 447 Stubbs, A., 492 Stubbs, D. A., 492 Stuber, G., 259 Studdert-Kennedy, M., 605 Studts, J. L., 445 Studzinski, C. M., 645 Stulnig, T., 447 Sturdy, C. B., 583 Sturm, W., 230 Suarez, A. V., 2 Suarez-Roca, H., 261 Succu, S., 351, 352 Suderman, M., 19 Suga, N., 146, 631, 636 Sugden, K., 17 Suger, G., 608 Sugihara, T., 170 Sugimori, M., 310 Sugimoto, K., 316 Suhara, T., 246 Sukoff Rizzo, S. J., 346 Sulli, A., 398 Sullivan, E. V., 651 Sullivan, P. F., 7 Sullivan, R. L., 288 Sullivan, R. M., 295 Sullivan, S. L., 275, 291 Sullivan, W. E., 137 Summers, I. R., 227

Summers, R. J., 399 Sumner, P., 53, 54, 75 Sun, H., 261, 411, 413 Sun, H.-J., 185 Sun, L. D., 523 Sun, W., 522, 523, 535 Sun, X., 163, 411 Sun, Y. E., 19 Sundaram, K., 336 Sundgren, P. C., 260 Suo, Y., 316 Suomi, S., 499 Supple, W. F., 423–425, 524 Sur, M., 214, 215 Surbey, M., 44, 45 Suri, R. E., 634, 637 Surmeier, D. J., 404, 409, 410 Surridge, A. K., 75 Sussman, S., 446 Suster, M. S., 655 Sutcliffe, J. G., 309 Suthers, R. A., 70 Sutton, J. M., 407 Suyenobu, B. Y., 263 Suzuki, N., 351 Suzuki, T., 351 Suzuki, W., 562 Suzuki, W. A., 556, 562, 563, 648 Swaab, T. Y., 606, 607 Swaddle, J. P., 41 Swain, R. A., 538 Swanson, C. J., 405 Swanson, D. J., 3 Swanson, L. W., 348, 353, 398 Swartz, M. S., 455 Sweatt, J. D., 19, 656 Sweazy, R. D., 282 Sweeney, A. M., 53 Sweeney, M. I., 250 Sweet, D. C., 309 Swender, P., 288 Swerdloff, R. S., 345 Swisher, J. D., 92, 98, 214 Swithers, S. E., 312 Switkes, E., 92, 95 Syagailo, Y. V., 17 Symons, D., 32, 35, 36, 38 Syzf, M., 19 Szabady, M. M., 314 Szafarczyck, A., 447 Szechtman, H., 401 Szegda, K., 462 Szel, A., 63 Szepessy, Z., 63 Szmajda, B. A., 88 Szosland, D., 385, 386 Szpir, M. R., 140 Szyf, M., 19

Taatgen, N. A., 598 Tache, Y., 316 Tadayyon, M., 308, 309 Taepavarapruk, P., 405 Tafti, M., 389 Taga, T., 447 Tagari, P., 251 Taha, S. A., 311, 406, 407, 409–411 Taheri, S., 315 Takada, T., 7 Takagi, S. F., 293 Takahashi, H., 246 Takahashi, J. S., 3, 375 Takahashi, N., 605 Takahashi, T., 137 Takahashi, T. T., 138, 140, 142 Takahata, N., 276 Takakusaki, K., 178 Takamiya, K., 407 Takaoka, A., 213 Takashima, M., 405 Takata, Y., 314 Takaya, K., 315 Takebayashi, M., 292 Takeda, K., 310, 646 Takenaka, O., 276 Takeo, T., 338 Takiguchi, S., 314 Talbot, J. D., 256 Talbot, W. H., 209 Talk, A. C., 538, 539 Talton, L. E., 524 Tam, E., 10 Tamaki, H., 195 Tamashiro, K. L., 308 Tambor, E. S., 47 Tamura, H., 98 Tamura, R., 284 Tan, C., 629 Tan, D. X., 386 Tan, E. W. L., 612 Tan, H. Y., 650 Tan, Y., 74 Tanabe, S., 98 Tanaka, H., 309 Tanaka, K., 93, 97–99 Tanaka, M., 213, 214 Tanapat, P., 461 Tancer, M. E., 431 Tanda, G., 403 Tang, C., 523 Tang, J., 524 Tang, M., 318, 319 Tang, X. C., 411 Tang, Y.-P., 467, 564 Tani, A., 293 Tanifuji, M., 98, 99 Tanigawa, H., 95, 98 Tanila, H., 566, 650, 654–657

715

716

Author Index

Tannery, P., 481 Taplitz, R. A., 313 Tapper, D. N., 286 Tarakeshwar, N., 445 Tarcic, 449 Tareilus, E., 292 Tarjan, E., 320 Tarokh, L., 377 Tarr, K. L., 453 Tartaglia, L., 307 Tartaglia, L. A., 307 Tartaro, A., 259 Tasali, E., 381, 383–385 Tashiro, H., 656 Tassinary, L. G., 426 Taylor, A., 16, 17, 452 Taylor, A. N., 261 Taylor, J., 336 Taylor, J. A., 336, 531 Taylor, J. E., 309 Taylor, J. L., 211 Taylor, J. R., 403, 523 Taylor, K. S., 259 Taylor, M. K., 444 Taylor, M. M., 223 Taylor, S. E., 447, 451 Taylor, S. F., 434 Taziaux, M., 349 Tecchio, F., 258 Teeling, E. C., 62 Teeter, J. H., 292 Tegelstrom, H., 40 Teh, Y.-W., 628 Teich, A., 520, 521 Teive, H. A., 346 te Kortschot, A., 399 Telford, S. R., 131 Teloken, C., 344 Tempesta, D., 371 Temple, E., 598 Tendolkar, I., 564 ten Donkelaar, H. J., 189 Tenenbaum, J. B., 621 Teng, E., 564, 648 Teo, A. R., 472 Tepper, B. J., 274 Tepper, R. I., 307 Terdal, S. K., 47 Terenius, L., 261 Terhune, J. M., 130 Terman, J. S., 388, 389 Terman, M., 388, 389 Terrace, H. S., 488, 489, 494, 498, 499, 502 Terry, A. M. R., 584 Tesfaye, F., 346 Teskey, G. C., 194 Tessarollo, L., 275 Tessitore, A., 426, 431, 433

Tesson, L., 3 Tetzlaff, W., 196 Tewes, U., 449 Teyler, T. J., 522, 524, 654 Thagard, P., 495 Thakur, P. H., 212, 213, 218–220, 222, 228 Thal, D., 610 Thanos, P. K., 403 Thapar, A., 7 Thayer, J. F., 444 Theios, J., 528 Thelen, E., 621 Theodore, W. H., 597 Theunissen, F., 142 Thiam, K., 3 Thibault, J., 398 Thibault, O., 654 Thiebot, M. H., 402 Thiel, T., 612 Thiele, T. E., 307, 309 Thielman, N. M., 455 Thierry, A. M., 400 Thisted, R. A., 387 Thoits, P. A., 444 Thom, M. D., 10 Thomas, D., 73 Thomas, D. A., 345 Thomas, E. A., 6 Thomas, E. M., 117, 144 Thomas, G., 310 Thomas, K. H., 388 Thomas, L., 9, 10 Thomas, M. J., 406, 410, 411 Thomas, M. L., 384, 448 Thomas, M. S. C., 621 Thomas, O. M., 96 Thomas, T. L., 13 Thomas, W. J., 308, 309 Thompson, A., 195 Thompson, C., 388 Thompson, J. K., 532, 533 Thompson, J. L., 401, 407 Thompson, J. T., 351, 352, 354 Thompson, K. G., 95 Thompson, L. A., 409 Thompson, L. T., 535, 654 Thompson, P. M., 431 Thompson, R., 179, 183 Thompson, R. F., 509, 511, 514–520, 522, 523, 526–538, 540, 569 Thompson, R. H., 530 Thompson, R. K., 124, 125 Thompson, R. K. R., 493, 495 Thompson, S. W., 246 Thompson-Schill, S. L., 601, 607 Thomsen, T. G., 444 Thomson, J. A., 185 Thomson, P., 131

Thomson, W. M., 41 Thorne, D. R., 384 Thornhill, R., 39, 41–43, 288 Thornill, R., 43 Thornton, J., 313 Thornton, S. K., 162 Thornton, T. A., 214 Thorpe, J., 655 Thorpe, S. J., 97, 317 Thorsteinsson, E. B., 580 Thouron, C. L., 245 Thresher, R. R., 308 Thulborn, K. R., 90, 607 Thunberg, M., 427 Thunhorst, R. L., 320 Thurow, M. E., 431 Thut, G., 214 Thwin, M. T., 654 Tian, B., 132, 166, 167, 169 Tiddi, B., 584, 585 Tiemann, L., 263 Tiesjema, B., 308 Tigges, J., 646, 653 Tighe, D. K., 309 Tillakaratne, N. J., 194, 195 Tillisch, K., 260 Timney, B., 70, 466 Timofeev, I., 373, 404 Tindell, A. J., 401, 411 Tither, J. M., 45 Titone, D., 567 Tizzano, M., 288 Tkatch, T., 262 Tobin, A., 195 Tobin, A. J., 195 Tobler, I., 365, 367, 372, 378 Tobler, I. I., 378 Tochikubo, O., 385 Toda, S., 407 Todd, A., 191, 193 Todd, A. J., 191, 193, 251, 254 Todd, C. L., 408 Todd, F., 34 Todd, K., 282 Todd, P. M., 26 Toh, K. L., 389 Tokunaga, F., 61 Tolhurst, D. J., 75 Tolle, T. R., 261 Toller, S. V., 272 Tollin, D. J., 161 Tolman, E. C., 553 Tom, S. M., 431, 564, 649 Tomasello, M., 574, 587, 590 Tomaz, C., 525 Tombaugh, G. C., 654 Tominaga, M., 275 Tomiyasu, U., 652 Tompkins, C. A., 609

Author Index

Tomsic, D., 534 Tonegawa, S., 4, 523 Tonelli-Lemos, L., 307, 308 Toner, J. P., 335 Tong, C., 2 Tong, F., 94 Tong, M. H., 629 Tonoike, M., 292 Tononi, G., 373 Tooby, J., 26, 30, 32, 33, 43, 47 Tootell, R. B., 92, 93, 95, 100 Tootell, R. B. H., 65, 70 Topal, J., 587 Topka, H. R., 536 Tordjman, S., 4–6, 14 Tordoff, M. G., 277 Toros-Morel, A., 163 Torres-Farfan, C., 377 Tortorici, V., 260 Toru, M., 405 Toth, A. L., 2 Toth, L. J., 104 Tottenham, N., 433 Totterdell, S., 399 Tougas, G., 313 Tourigny, L., 446 Touzani, K., 312 Tovar, S., 315 Tov´ee, M. J., 466, 467 Towle, A. C., 315 Townsend, D. A., 6 Townsend, J., 431 Townsend, S. W., 584 Toyono, T., 277 Toyoshima, K., 277 Trabado, S., 14 Trabasso, T., 494 Tracey, I., 260, 262 Trachsel, L., 378 Tracy, J., 536 Tracy, J. A., 533, 535, 536 Traish, A., 345 Tran, B. B., 405 Tranel, D., 426, 523, 559 Tranguch, A. J., 411 Tr¨ankner, D., 275, 284 Trappenberg, T. P., 621, 623 Trattner, A., 14 Travers, J. B., 279, 280 Travers, S. P., 279, 281, 282, 284 Tray, N., 308 Treanor, J. J., 445 Treat, A. E., 119 Treede, R. D., 244, 256, 258 Treesukosol, Y., 275 Tremblay, F., 214, 223 Tremblay, L., 406 Trepp, A., 194

Tresco, P. A., 375 Trevaskis, J. L., 316 Triemstra, J. L., 345 Trillat, A. C., 10 Trinchieri, G., 448, 449 Tripathi, A., 400 Trippel, M., 189 Tritos, N. A., 309 Trivers, R. L., 34, 35, 39 Troisi, A., 582 Tronick, E., 471 Tronnier, V. M., 258 Tronson, N. C., 523 Troscianko, T., 75 Trost, M. R., 36, 46 Troyer, T. W., 631, 637 Trueswell, J. C., 601, 607 Truitt, W. A., 345, 354 Truskett, P. G., 452 Truss, M. C., 344 Trussell, L. O., 141, 144, 145 Tryon, R. C., 6 Tsankova, N., 19 Tsao, D. Y., 98, 100, 101 Tschop, M., 315 Tschop, M. H., 315 Tse, A., 194 Tseng, M. T., 259 Tseng, W., 523 Tseng, W. Y., 259 Tsien, J., 564 Tsien, J. Z., 467 Ts’o, D. Y., 95 Tsodyks, M., 630 Tsonis, M., 468 Tsuda, H., 2 Tsuda, M., 255 Tsuetaki, T., 278 Tsujimoto, S., 646 Tsukahara, N., 537 Tsukamoto, T., 351 Tsumoto, T., 467 Tsunoda, K., 98, 99 Tsutsui, Y., 403 Tuch, D. S., 651 Tucker, A. M., 650 Tucker, K. L., 443, 446, 464 Tucker, V. A., 69 Tulloch, I., 346 Tully, S., 260 Tully, T., 513 Tulving, E., 487, 571, 650 Turek, F. W., 383 Turkish, S., 409 Turman, A. B., 213 Turner, B. H., 523 Turner, D. A., 654 Turner, E. H., 388, 389 Turner, L. M., 580

717

Turner, M., 14 Turner, R., 613 Turri, M. G., 6 Tuscher, O., 429 Tuszynski, M. H., 196, 646 Tweedale, R., 91, 213 Twining, R. C., 411 Twombly, I. A., 214, 217, 219, 222, 228, 230–232 Tyler, W. J., 312 Tzoumaka, E., 245, 292 Tzschentke, T., 348 Uchida, G., 99 Uchida, K., 275 Uchida, N., 293 Uchino, B. N., 580 Uckert, S., 344 Uddin, L., 259 Uddin, L. Q., 430 Udry, J. R., 44, 45 Ueda, K., 285 Ueda, T., 275 Ueffing, M., 3 Uemura, E., 653 Ugawa, S., 275 Ugurbil, K., 90, 93, 164 Uhde, T. W., 431 Uher, R., 17 Uhl, G. R., 11 Uhlrich, D. J., 69 Uhr, M., 383 Uka, T., 103 Ulbert, I., 170 Ullen, F., 185 Ullman, M., 608, 613 Ullman, S., 95, 98, 628 Ullmann, J. F. P., 70 Ullrich, P., 445 Ullum, H., 449 Umeda, K., 98 Umemura, S., 385 Underwood, L., 444, 445 Underwood, M. D., 521 Ungerleider, L. G., 83, 97, 100, 562, 603 Ungerstedt, U., 311 Uotani, S., 307 Updegraff, J. A., 447 Uphouse, L., 339, 342 Uprety, A. R., 649 Urban, L., 243, 249 Uretsky, N. J., 405, 408 Urgesi, C., 100 Urry, H. L., 431 Usal, C., 3 Usdin, T. B., 353 Usrey, W. M., 89–91 Usson, Y., 208 Utman, J. A., 601

718

Author Index

Uvnas-Moberg, K., 580 Uvnas-Wallensten, K., 314 Uylings, H. B. M., 652 Vaalburg, W., 607 Vaca, F., 383 Vaccarino, A. L., 254 Vaccarino, F. J., 311 Vagell, M. E., 344 Vahl, T. P., 315 Vaidya, J. G., 411 Vaisse, C., 307 Valdimarsdottir, H., 451 Valenzuela, G. J., 377 Valenzuela, M. J., 475, 657 Valerius, G., 387 Valet, M., 261 ˚ B., 208–210 Vallbo, A. Vallbo, A., 207 Vallone, J., 406 Valyear, K. F., 102 Van, D. P., 183 Van Baal, G. C., 5 Van Boven, R. W., 214 van Boxtel, R., 3 van Bueren, A., 307 Van Cauter, E., 381, 382, 383–385, 389 van Damme, E., 582 Vande Berg, J. S., 119 Van de Casteele, T., 579 Van Dellen, A., 475 van de Moortele, P. F., 164, 284 Vandenberghe, R., 602 Vandenbeuch, A., 278 Vandenbeucha, A., 288 van den Hoofdakker, R. H., 387, 388 van den Pol, A. N., 309, 351 Van de Poll, N. E., 347, 348 Vanderah, T. W., 261 Vanderhaeghen, J. J., 12 van der Heijden, M., 159 Van der Horst, F. C., 471 van der Lugt, A., 613 Van der Schaaf, E. V., 528 Vanderschuren, L. J., 398, 399 Van der Shaaf, E. V., 528 Van der Veer, R., 471 Van Der Vegt, B. J., 5 Vander Wall, S. B., 487 van der Wee, N. J., 434 Vandewalle, G., 170 van Dijk, G., 307, 309 van Dis, H., 348 Vandiver, R., 388 Van Dongen, H. P., 373, 384 van Dongen, Y. C., 400 Vanduffel, W., 95, 101 Vanelle, J. M., 386 Van Essen, D., 93

Van Essen, D. C., 81, 91, 93, 95, 96, 98, 102, 104, 213, 226, 259 van Furth, W. R., 352 Vangel, M., 263 Vangel, M. G., 263 Van-Gool, L., 626 van Gorp, W. G., 411 Vanhaudenhuyse, A., 260 Van Hoesen, G. W., 423, 428 Van Horn, R. C., 576, 578, 579 Van Horn, S. C., 90 Vania Apkarian, A., 262 van IJzendorn, M. H., 15 van Lieshout, D. P., 162 Vanman, E. J., 426 van Mier, P., 189 Vann, S. D., 632 Van Onderbergen, A., 389 van Oorschot, R., 346 van Oortmerssen, G. A., 5 Van Paesschen, W., 566 van Praag, H., 474, 657 van Ree, J. M., 352 van Reekum, C. M., 431 Van Reen, E., 377 Van Reenen, C. G., 5 van Reenen, K., 5 van Rheden, R., 189 Van Sluyters, R. C., 466 Vansteensel, M. J., 379 van Tol, M. J., 434 Van Valin, R. D., 596, 606, 609 van Veen, V., 262 Van Vugt, M., 28, 29 van Willigen, J. D., 279 van Zijl, P. C., 429 Vanzo, R. J., 12 Varadarajan, V., 276 Varela, L., 315 Varga, O., 587 Vargas, S., 452 Vargha-Khadem, F., 566, 608 Varma, S., 598 Varrassi, G., 263 Vartanian, V., 482 Vartiainen, N., 259 Vasconcelles, E. B., 445 Vasishth, S., 598 Vasquez, B. J., 523, 525 V´asquez-Palacios, G., 346 Vassar, R., 293 Vathy, I., 342 Vauclair, J., 493 Vaugeois, J. M., 12 Vaughn, M. G., 16 Vavrek, R., 192, 196 Vazdarjanova, A., 525 Vazquez, M. J., 315

Vea, J. J., 75 Vedhara, K., 451 Veenema, A. H., 5 Veening, J. G., 346–349, 353 Vega-Bermudez, F., 208–210, 214, 215, 223 Vegas, O., 451 Veilleux, C. C., 70, 74 Veldhuijzen van Zanten, J. J. C. S., 454 Veldman, F., 468 V´elez, A., 116, 117 Velotta, J. P., 343, 346 Veney, S. L., 347 Venezia, J., 171 Ventafridda, V., 263 Vento, P. J., 321 Ventura, A., 3 Ventura, R., 582 Vercelli, A., 259 Verchinski, B. A., 17, 428, 429, 433, 434 Verge, D., 354 Verhagen, J. V., 283 Vermeulen-Van der Zee, E., 399 Verney, C., 399 Versteegh, C. P., 159 Verweij, J., 86, 88 Vetter, T., 628 Vettor, R., 308 Vgontzas, A. N., 382 Viaene, A. N., 163 Viala, D., 191, 192 Vialou, V., 352 Viaud, Marc D., 568 Vickers, J. N., 185 Victor, J. D., 283, 285 Vidal-Puig, A., 315 Viding, E., 472 Vidyasagar, T. R., 90 Vielhauer, M. J., 446 Viemeister, N. F., 126, 127 Vienot, F., 75 Viete, S., 141 Vigdorchik, A., 350 Vigdorchik, A. V., 350 Vighetti, S., 263 Vigilant, L., 577–580 Vigliocco, G., 602 Vijayaraghavan, L., 411 Vila, G., 447 Vilain, E., 14 Villalba, C., 336 Villanueva, L., 280 Villar, M. J., 250 Villemure, C., 262 Vinay, L., 196 Vinberg, M., 387 Vincent, J. D., 295 Vincent, J. L., 259

Author Index

Vincent, R., 446 Vinckier, F., 605 Vinnikova, A. K., 275 Vinson, D. P., 602 Virk, G., 389 Virshup, D. M., 389 Virtue, S., 609 Vishnivetskaya, G. B., 11 Viswanathan, K., 449 Vital-Durand, F., 467 Vitali, G., 344 Vitaliano, P. P., 443 Vitek, J. L., 214 Vitiello, M. V., 370 Vittoz, N., 407 Vlahov, D., 443, 446 Voderholzer, U., 387, 388 Voelkl, B., 582 Vogel, E. R., 75 Vogel, R. W., 531 Vogels, R., 99 Vogt, B. A., 256, 259 Vogt, J., 649 Vogt, L. J., 256, 259 Vogt, M. B., 285 Vogten, L. L. M., 120 Vohs, J. L., 536 Voigt, H. F., 117 Voldrich, L., 116 Volkow, N. D., 403 Vollmer-Conna, U., 452 Volpe, J., 465 Von Bockstaele, E. J., 342 von Cramon, D. Y., 605, 607, 609 von der Heydt, R., 96 Vonderschen, K., 141 von During, M., 210 Voneida, T. J., 519, 533, 537 von Fersen, L., 493, 494 Vongher, J. M., 337 Vonk, J., 493 von Oertzen, T., 635 von zur Muhlen, A., 382 Voon, V., 258, 346 Voorn, P., 398–400 Vorel, S. R., 411 Vorobyev, M., 63, 72, 73, 75 Voss, H. U., 433 Voss, P., 170 Vosshall, L. B., 2, 293 Vrana, S. R., 427 Vry, M.-S., 171 Vuilleumier, P., 260 Wada, K., 405 Wada, S., 339 Wade, A. R., 92, 98, 101 Wadsworth, M. E., 475 Wadsworth, M. E. J., 475

Wager, T. D., 262, 263, 431 Waggoner, R. A., 93 Wagner, A. D., 425, 565 Wagner, A. K., 474 Wagner, A. R., 496, 509, 531, 533 Wagner, C. D., 444 Wagner, D. D., 432 Wagner, H., 66, 69, 70, 137, 141 Wagner, K. D., 446 Wagner, L., 444 Wagner, S., 7, 8 Wagner, T. O., 382 Wahlberg, M., 115 Wakabayashi, K. T., 411 Wake, H., 227 Wakefield, D., 452 Wakefield, J. C., 29, 30 Wald, L. L., 93, 100 Waldinger, M., 339 Waldinger, M. D., 337, 339, 346 Waldman, I. D., 3 Waldmann, M. R., 497 Waldmann, T. A., 448, 449 Waldron, I., 179 Waldstreicher, J., 381 Wales, R., 609 Walker, A. G., 529 Walker, H. A., 337 Walker, J. M., 409 Walker, L. C., 648 Walker, M. P., 373 Walkowiak, W., 139 Wall, J. T., 213, 214, 219 Wall, P. D., 254 Wallace, B., 445 Wallace, C. S., 463, 474 Wallace, D. L., 352 Wallace, J. L., 649 Wallace, L. J., 405, 408 Wallace, M. N., 163 Wallach, J. H., 521 Wallen, K., 338 Wallen, P., 179–181, 183, 185, 192 Wallesch, C. W., 608 Walley, A. J., 13 Wallin, B. G., 207 Wallis, J. D., 412, 646 Wallman, J., 127, 131 Walls, G. L., 63 Walsh, B., 316 Walsh, J. H., 314 Walsh, L., 520 Walsh, P., 399 Walsh, R. N., 473 Walsh, V., 100 Walter, B., 348 Walter, B. M., 647 Walter, H., 431, 432 Walter, M. R., 450

719

Walters, K. L., 446 Wan, H., 563, 564 Wanat, M. J., 413 Wandell, B. A., 92, 98, 101 Wang, C., 345 Wang, D., 656 Wang, F., 293 Wang, G. J., 16, 403 Wang, H., 259 Wang, J., 353, 654 Wang, J. Y., 256 Wang, L., 214, 316 Wang, M., 279, 535, 646 Wang, Q., 226, 316 Wang, R. Y., 409 Wang, S. H., 632 Wang, T., 446, 449 Wang, W., 310, 603 Wang, X., 164, 166, 258, 603 Wang, X. J., 646 Wang, Y., 95, 195 Wang, Y. C., 468 Wang, Z., 99, 261, 340, 347, 409, 410 Wangen, K., 405 Wankerl, M., 17 Wanner, I., 291, 293 Ward, H. L., 315 Ware, M., 489 Warner, R. K., 352 Warr, W. B., 145 Warrant, E., 53 Warrant, E. J., 53, 54, 71 Warren, H. W., 185 Warrington, E. K., 602 Washburn, D. A., 498, 499 Washburn, S. N., 195 Washburn, T. F., 336 Wasserman, E. A., 485, 491–496, 499, 502, 503 Wassermann, D., 598 Wassermann, E. M., 597 Wassle, H., 83, 86, 88 Watanabe, C., 346 Watanabe, K., 578 Watanabe, M., 89, 406 Watanabe, T., 630 Watanabe, Y., 467 Waterhouse, J. M., 375, 380 Waters, E., 13 Waters, H., 275 Wathes, C. M., 70 Watkins, J. C., 248, 249 Watkins, K. E., 566, 597, 608 Watkins, L. R., 243, 260, 261 Watkins, N., 346, 354 Watson, A., 263 Watson, C., 123 Watson, C. A., 311, 403 Watson, H. L., 352

720

Author Index

Watson, J. B., 395, 519 Watson, J. R., 469 Watson, R. R., 451 Watson, S. D., 310 Watson, S. J., 309 Watters, C. L., 654 Watts, D., 577, 578 Watts, D. P., 577 Way, B. M., 431 Wayman, C., 354 Wayne, R. K., 2 Waynforth, D., 41, 42 Wearne, S. L., 652 Weaver, I. C., 19, 462 Webb, I. C., 379 Webb, J. M., 263 Weber, B., 431, 432 Weber, M. S., 352 Weber, P., 3 Webster, M. A., 53 Webster, M. L., 535 Webster, W. R., 129, 162 Weckbecker, K., 346 Wedderburn, J. F., 191, 192 Wedekind, C., 338 Wedig, M. M., 434 Weekes-Shackelford, V. A., 41 Ween, J. E., 472 Wegel, R. L., 120 Weghorst, S. J., 38, 45 Wegman, L. J., 10 Wehner, J. M., 523 Wehner, R., 54 Wehr, T. A., 388, 389 Wei, J. Y., 316 Weidner, N., 196 Weigand, S. D., 657 Weigle, D. S., 315, 316 Weiller, C., 171, 259, 263 Weinberg, J., 379 Weinberger, D. R., 17, 426, 428, 429, 431, 433, 434, 650 Weinberger, N. M., 525, 631, 636 Weiner, B., 486 Weiner, H., 447 Weiner, K. S., 100 Weingarten, H., 521 Weingarten, H. P., 310, 312 Weingrill, T., 576 Weinmann, O., 196 Weinshenker, D., 12 Weintraub, D., 346 Weir, C., 124 Weisberg, M. P., 467 Weisenberger, J. M., 230 Weisinger, H. S., 322 Weisinger, R. S., 320, 322 Weiskrantz, L., 396, 520, 646 Weisman, R. G., 492, 502, 583

Weiss, A., 2 Weiss, A. P., 567 Weiss, C., 523, 528, 531 Weiss, D. W., 449 Weiss, K. R., 179 Weiss, S. M., 519 Weiss, T., 428, 433 Weissbecker, I., 445 Weisse, C. S., 449 Weisskopf, M. G., 524 Weissman, L., 655 Weisz, D. J., 528, 531 Weisz, V. I., 634 Weitzman, E. D., 373, 381 Welborn, B. L., 431 Welch, C. C., 308 Weliky, M., 105 Welling, L. L. M., 338 Wellnitz, S. A., 210 Wells, K. D., 125 Wells, P. H., 501 Wells, R., 578, 579 Wells, R. S., 579, 580 Welsh, R. C., 260 Wendland, J. R., 17 Weng, X., 307 Wenkstern, D., 340, 353 Wenner, A. M., 501 Wenner, M., 451 Werheid, K., 607 Werkle-Bergner, M., 635 Werner, C. W., 70 Werner, G., 213, 219 Werner, T. J., 485, 493 Wernig, A., 195 Werrett, S., 481 Wersinger, S. R., 336, 347 Wertz, J. M., 354 Wesensten, N. J., 384 Wesner, K. A., 445 Wessa, M., 261 Wessberg, J., 207, 210 West, D. B., 313 West, M. J., 651, 652 West, P. W. R., 70 West, R., 650 Westbrook, R. F., 452 Westen, D., 38 Wester, H., 261 Westerterp-Plantenga, M. S., 307 Westheimer, G., 96 Weston, B. J., 189 Wetsel, W. C., 12 Wettendorff, H., 318 Wetterslev, J., 387 Wettschurek, R. G., 129 Wever, E. G., 116, 135 Weyer, C., 316 Whalen, J., 489

Whalen, P. J., 423–430, 432–434, 521, 530 Whalley, L. J., 475 Whalley, M. G., 258 Wheat, H. E., 208, 215 Wheeler, R. A., 410, 411 Whelan, P. J., 187, 188, 191, 192, 195 Whetteckey, J., 346 Whetten, K., 455 White, A. J., 88, 90 White, C. L., 310 White, D., 568 White, D. M., 388 White, E. A., 274 White, F. J., 408 White, G. L., 45, 46 White, I. M., 530 White, J. D., 308, 310 White, L. E., 651 White, N., 521 White, N. M., 557, 570 White, P., 263 White, S. J., 116, 118 White, W., 132, 530 White, W. O., 307, 313 Whitehead, M. C., 279, 280, 282 Whiten, A., 47 Whiting, W. L., 651 Whitsel, B. L., 213, 219, 225 Whitten, P., 576, 579, 586 Whitten, P. L., 580 Whitten, W. K., 287 Whittingstall, K., 172 Wiater, M. F., 316 Wichmann, T., 397 Wichnalek, M., 260 Wickings, E. J., 39 Wicks, S. R., 513 Widaman, K. F., 649 Widder, E. A., 53 Widdig, A., 578 Widdowson, P. S., 308 Wiebe, S., 564 Wiech, K., 262 Wiegand, M., 387 Wiegant, V. M., 261 Wierson, M., 45 Wiertelak, E. P., 260, 261 Wiesel, T., 465 Wiesel, T. N., 92–95, 463, 466, 467, 627 Wiesenfeld-Hallin, Z., 245, 250 Wieser, J., 92 Wieskopf, J. S., 283, 286 Wig, G. S., 430 Wiggs, C. L., 603 Wightman, R. M., 403, 404 Wigstr¨om, H., 472 Wijers, A. A., 607

Author Index

Wik, G., 423 Wikberg, E., 584, 586, 588 Wikstr¨om, M., 192 Wilczynski, W., 137 Wild, J. M., 141, 142 Wilensky, A. E., 525 Wiles, J., 634 Wiley, R. H., 116 Wilhelm, S., 426, 433 Wilkins, D. P., 596, 606, 609 Wilkinson, C. W., 309 Willcutt, E., 637 Willer, K., 181 Williams, A., 40 Williams, A. L., 95 Williams, B., 16 Williams, C. A., 250 Williams, C. K., 626 Williams, D. A., 259, 263 Williams, D. L., 315, 316 Williams, D. R., 85, 88 Williams, E., 400 Williams, G., 308 Williams, G. C., 28–30, 37, 46 Williams, H. J., 7 Williams, J., 577 Williams, J. A., 313 Williams, J. L. D., 186 Williams, K. K., 190 Williams, M. E., 278 Williams, R., 18 Williams, R. B., 444 Williams, R. J., 622, 628 Williams, S., 524 Williams, S. C., 314, 412 Williams, S. R., 219 Williams, T., 309 Willins, D. L., 405, 408 Willis, W. D., 242, 246, 251 Willmes, K., 230 Willmore, B. D., 96 Willoch, F., 261 Wills, T. A., 444, 445 Wills-Karp, M., 448 Willuhn, I., 413 Wilson, A., 521 Wilson, C. J., 409 Wilson, D. M., 177, 196 Wilson, D. S., 28, 29 Wilson, E. C., 132 Wilson, F. A., 646 Wilson, I., 654, 655 Wilson, I. A., 650, 655–657 Wilson, J. M., 191, 193 Wilson, J. R., 90 Wilson, M., 35, 38, 41, 45, 115, 119, 406, 487 Wilson, M. A., 523 Wilson, M. D., 481

Wilson, S. M., 596 Wilson, T., 185 Wilson, W. W., 129 Wimmer, M., 407 Winans, S. S., 347 Winawer, J., 92 Windle, R. J., 447 Windt, W., 129 Winer, J., 146, 147 Winer, J. A., 146, 162 Winn, J., 626 Winocur, G., 100, 647 Winslow, J. T., 9 Winter, M., 132 Wirz, M., 184 Wirz-Justice, A., 380, 387 Wisco, J. J., 651 Wise, R., 602 Wise, R. A., 396, 402, 403 Wise, R. G., 258 Wise, S. P., 646 Wiskott, L., 634 Wisniewski, A. B., 132 Wisse, B. E., 315 Wiste, H. J., 657 Witek-Janusek, L., 449 Withers, D. J., 314 Withington, D. J., 162 Wittenberg, G. F., 258 Witter, M. P., 399, 562 Wittig, R. M., 576–580, 584–586, 588, 589 Wittmer, C., 316 Wixted, J. T., 645, 648, 649 Wohlert, A. B., 214 Wohlgemuth, M. J., 102 Wohlreich, M., 346 Wohltmann, C., 398–400 Wojtowicz, M., 650 Wolf, G., 280 Wolf, H., 179, 187, 188, 195 Wolf, J. B., 54, 58 Wolf, J. M., 259 Wolf, K. M., 471 Wolf, L., 627, 628 Wolf, M. E., 407, 411 Wolf, S., 287 Wolf, S. L., 195 Wolfle, T. L., 261 Wolitski, R. J., 453 Wolkowitz, O. M., 452 Wollman, K., 449 Wolpaw, J. R., 195 Wolpert, R. L., 286 Wolters, J. G., 399 Wong, A. C.-N., 629 Wong, D. F., 650, 657 Woo, C. C., 293 Wood, E., 565, 566

721

Wood, G. E., 531 Wood, J. N., 245, 256 Wood, M. L., 256 Wood, R. I., 345, 348, 350, 352 Wood, R. J., 320 Wood, R. M., 185 Wood, S. A., 447 Woodruff, G., 495, 586 Woodruff-Pak, D. S., 531, 536, 538 Woods, S. C., 308–310, 313, 314, 322 Woods, T. M., 166 Woodside, B., 316 Woodward, D. J., 256, 412 Woodward, K., 36 Woodworth, R. S., 395 Woody, C. D., 526 Woody, E. Z., 401 Woolf, C. J., 240, 245, 254, 264 Woolley, J. D., 311 Woolley, S., 142 Woolston, D. C., 285 Wortley, K. E., 315 Wortmann, M., 644 Wrangham, R., 577 Wrangham, R. W., 578, 587 Wren, A. M., 315 Wright, A. A., 486 Wright, C. I., 399, 424, 426, 431, 434 Wright, P., 409 Wright, T. F., 116, 117 Wu, D., 310 Wu, F. C., 345 Wu, J. B., 14 Wu, J. C., 610 Wu, L., 191, 193 Wu, M., 400, 406 Wu, Q., 309 Wu, S. V., 276 Wu, S. Y., 250 Wu, W., 4, 11 Wulfeck, B., 601 Wunderlich, A. P., 259 Wunderlich, D. A., 535 Wunderlich, G. R., 346 Wunderlich, K., 89, 91 Wu-Peng, X. S., 307 Wurst, W., 3 Wurtz, R. H., 91, 98, 105, 162, 231 W¨ust, S., 17 Wyler, A. R., 609 Wynings, C., 451, 455 Wynn, K., 489 Wynn, T. A., 448 Wynne, C. D. L., 494 Wyrwicka, W., 318 Wysocki, C. J., 8, 10, 288 Wysocki, L. M., 10 Wyzinski, P. W., 369, 370

722

Author Index

Xagoraris, A., 423 Xavier, L. L., 348 Xi, X., 310 Xi, Z. X., 408 Xia, Q., 163 Xiang, J. Z., 563 Xiang, Z., 531 Xiao, L., 337 Xiao, Y., 95 Xing, D., 94 Xu, A. W., 310 Xu, J., 8, 14, 607, 608 Xu, K., 16 Xu, X., 64, 90, 258, 448 Xu, X. J., 250 Xu, Y., 261, 262 Xu, Z., 347, 350 Xu, Z. C., 407 Xuan, L., 651, 652 Xue, B., 307, 310 Yabuta, N. H., 93, 102 Yack, J. E., 119 Yacoub, E., 93 Yagaloff, K. A., 307, 308 Yager, D. D., 116, 119, 127, 131, 134 Yahr, P., 287, 347–349, 353 Yakovleff, A., 191 Yamada, K., 284, 351 Yamada, M., 578 Yamada, S., 339 Yamada, Y., 310, 337 Yamamoto, B., 10 Yamamoto, T., 275, 280, 284, 285, 411 Yamanaka, A., 275 Yamane, Y., 98, 99 Yamanouchi, K., 339 Yamashita, N., 74 Yamashita, S., 284, 285 Yamazaki, K., 9, 10, 287 Yan, J., 316, 317 Yan, L. L., 385 Yan, P., 654 Yanagawa, Y., 278, 467 Yanagisawa, M., 309 Yang, C. R., 399, 404–406 Yang, H., 316 Yang, J. F., 179, 187–189 Yang, L., 308 Yang, Y., 646 Yankner, B. A., 655, 656 Yarmolinsky, D. A., 274, 275, 284 Yarnitsky, D., 259 Yasuda, T., 309 Yasumatsu, K., 276, 277 Yasuo, T., 277, 278 Yau, J. M., 219, 228, 627 Yau, K. W., 84 Yau, W. Y., 263

Yaxley, S., 283, 317 Ye, J., 310 Ye, M. K., 281 Yeatman, J. D., 611 Yee, C. M., 451 Yee, J. R., 580 Yells, D. P., 353 Yen, L. D., 246 Yeo, B. W., 452 Yeo, C. H., 531, 533, 535, 537 Yeo, G. S., 308 Yeomans, M. R., 409 Yermal, S. J., 449 Yetkin, F. Z., 429 Yilmaz, P., 261 Yim, C. Y., 396, 404, 406 Yin, H. H., 397 Yin, T. C., 141, 145 Yin, T. C. T., 161 Ying, Z., 195 Yip, B. H., 7 Yirmiya, R., 261, 452 Ylikoski, J., 278 Yoder, A. D., 74 Yokel, R. A., 402 Yokoi, M., 293 Yokoyama, S., 58, 61, 64, 612 Yonelinas, A. P., 562, 564, 565, 632, 649 Yonezawa, A., 346 York, D. A., 313 Yoshida, K., 179, 183, 184, 401, 407 Yoshida, R., 277, 278, 653 Yoshida, T., 4 Yoshie, S., 278 Yoshihara, Y., 291, 292 Yoshimatsu, H., 309 Yoshimi, K., 535 Yoshimichi, G., 309 Yoshimoto, K., 612 Yoshioka, S., 284 Yoshioka, T., 90, 91, 93, 208–210, 217, 222–225, 229 Yoshizawa, T., 84 Yoshizumi, M., 346 Yost, W. A., 128 Young, A. B., 409, 607, 613 Young, A. R., 580 Young, A. W., 426, 523 Young, B. J., 562 Young, C. E., 185 Young, E. D., 144, 161 Young, E. J., 10 Young, F., 221, 223, 225 Young, K. S., 258 Young, L. J., 9 Young, M. E., 494, 495 Young, R. A., 527 Young, R. C., 313

Young, R. F., 258 Young, W. S., 9, 10 Youngentob, S. L., 289, 291–295 Younger, J., 450 Yttri, E. A., 105 Yu, J., 309 Yu, J. S. C., 260 Yu, L., 13, 352 Yu, Z. L., 135 Yuen, T. D. B., 75 Yun, I. A., 411 Yung, K. K., 409 Yurco, P., 292, 294 Yuyama, N., 285 Zachariea, R., 450, 454 Zaff, J., 443 Zafonte, R. D., 474 Zahavi, A., 37, 38 Zahm, D. S., 398–400, 407 Zahr, N. M., 651 Zaidi, F. N., 282 Zainos, A., 228, 229 Zajonc, R., 422 Zakowski, S. G., 449 Zald, D. H., 214, 284 Zalesak, M., 567 Zaleta, A. K., 651 Zalsman, G., 16 Zalutsky, R. A., 524 Zangaladze, A., 214 Zangger, P., 177, 183 Zappasodi, F., 258 Zappulla, M., 6 Zarate, C. A., 388 Zarjevski, N., 308 Zatorre, R. J., 171, 284 Zautra, A. J., 450 Zebrowitz, L. A., 41 Zee, P. C., 373 Zegans, L. S., 450 Zehr, J. L., 333 Zeki, S., 95, 98, 102 Zeki, S. M., 95, 98, 102 Zelano, B., 288 Zelaya, F. O., 314, 412 Zelick, R., 139 Zelinski, B., 289 Zeng, Y., 14 Zentall, T. R., 485, 486, 494 Zhang, A. A., 532 Zhang, B. B., 310 Zhang, C., 295 Zhang, E. T., 256 Zhang, H., 64, 277 Zhang, H. Q., 213 Zhang, J., 401 Zhang, J. X., 520 Zhang, K., 98

Author Index

Zhang, L., 412 Zhang, L. Y., 629 Zhang, M., 409 Zhang, S., 62 Zhang, X., 13, 245, 250, 279 Zhang, Y., 146, 276, 278, 307 Zhang, Y. F., 276, 284 Zhao, C., 317 Zhao, F. L., 278 Zhao, G. Q., 276, 284 Zhao, H., 62, 290 Zhao, T., 171 Zhavbert, E. S., 346 Zhong, H., 196 Zhou, F. C., 407 Zhou, H., 96 Zhou, Q. Y., 311 Zhou, T., 374 Zhou, W., 487 Zhou, W. X., 580 Zhou, Y., 19, 95, 425, 570, 650, 657 Zhou, Y. D., 214 Zhu, G., 310 Zhu, W., 16

Zhu, X. H., 90 Zhuang, H., 275 Zhuang, X., 10, 401 Zhuo, M., 259, 263, 524 Ziegler, J., 598 Ziegler, T. E., 580 Ziemssen, F., 83 Zigman, J. M., 315, 316 Zilles, K., 213, 259 Zimmer, E. Z., 468 Zimmer, J., 523, 652 Zimmerman, J. C., 373 Zimmerman, R., 651 Zinder, O., 449 Zipursky, R. B., 651 Ziskind-Conhaim, L., 191, 193 Zisserman, A., 626 Zita, G., 388 Zivin, A., 499 Zoghbi, H. Y., 210 Zohary, E., 93 Zoia, S., 469 Zola, S. M., 564, 566, 648 Zola-Morgan, S., 562, 648 Zompa, I. C., 219, 230

Zonderman, A. B., 651 Zonza, A., 274 Zoran, M. J., 13 Zorina, Z. A., 494 Zosh, W. D., 185 Zou, F., 645 Zou, S., 276 Zovoilis, A., 656 Zubek, J. P., 15 Zuberb¨uhler, K., 500, 584 Zubieta, J. K., 258, 261–263 Zucker, I., 344, 373 Zuk, M., 37 Zuker, C. S., 274–276, 278, 284 Zumpe, D., 343, 348 Zuo, X., 258 Zup, S. L., 340 Zurif, E. B., 606 Zushida, K., 405 Zwarts, F., 607–609 Zwicker, E., 120–122 Zwislocki, J. J., 210 Zych, G., 181 Zyzak, D. R., 647

723

Subject Index

Accessory (vomeronasal) olfactory systems, 340 Accessory olfactory bulb (AOB), 7–8, 340 Acetylcholine, 342, 635 ACTH. See Adrenocorticotropic hormone (ACTH) Activation function, 625 AD. See Alzheimer’s disease (AD) Adaptation, 29–30 Adrenocorticotropic hormone (ACTH), 332, 381 Affectional systems, 574 African grey parrot, 502 After-hyperpolarizing potential (AHP), 654 Aging men, 344 Agrammatism, 606 AHP. See After-hyperpolarizing potential Allodynia, 240 Allostatic load theory, 442 Altruism, 31 Alzheimer’s disease (AD), 475, 536, 634, 635 Amacrine cells, 86 Amblyopia, 467 American Sign Language, 502 Ameslan, 502 α-Amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor, 248, 407 Amphetamines, 34, 339 Amygdala, 396 basolateral nuclei, 347 corticomedial nuclei, 347–348 eyeblink conditioning, 530–531 fear conditioning, 520–523 Amygdala-mPFC circuitry functional neuroanatomy, 429 resting state, 429–430 structural neuroanatomy, 428–429 Amygdala-prefrontal circuitry anxiety, 433–434 emotion regulation, 431–434 emotionally ambiguous facial expressions, 432–433 fear conditioning and extinction, 430–431 Anaclitic depression, 471 Analgesia, 341, 519 Analogical reasoning, 495–496

Androgen insensitivity syndrome, 332 3α-Androstanediol, 337 Angiotensin II, 320 Animal learning, 631 Anisogamy, 35 Anisomycin, 337, 343 Anisotropic diffusion, 598 Anterior auditory field, 146 Anterior ectosylvian fields, 146 Anterior lateral hypothalamus, 351 Anterior pituitary, 333 Anthropomorphism, 503 Antisocial behavior, 15–17 Aplysia, 511–513, 515 Apologie de Raymond Sebond, 480 Apomorphine, 339, 350 Appetitive stimuli, 402 Appraisal theory, 46 Arbitrariness, 500 Arcopallium, 142 Arcuate fasciculus, 171 Arcuate nucleus, 341 Arginine vasopressin, 580 Aromatization hypothesis, 344 Associative learning discrete responses, classical/instrumental conditioning of eyeblink conditioning, 526–536 forelimb flexion conditioning, 537 jaw-movement conditioning, 537–538 neural substrates, 538–540 fear conditioning amygdala as locus of, 520–523 caudal insular cortex, 523 cerebellum, 524 critical issues in, 524–525 fear-potentiated startle, 524 hippocampus, 523 Little Albert experiment, 519 memory modulation, 525 vermis, 524 invertebrate preparations Aplysia, 511–513 Caenorhabditis elegans, 513 725

726

Subject Index

Associative learning (continued) cockroach and locust, 512 crayfish, 512 Drosophila, 513 Hermissenda, 514–515 honeybee, 513 land snail (Helix ), 512 leech, 513 Limax, 512 Pleurobranchaea, 512 pond snail (Lymnaea stagnalis), 512 Tritonia, 512 overview, 509–511 spinal conditioning, 517–519 Associative memory function, 636 Attachment theory, 34 Audiogram, 116 Audition absolute auditory thresholds, 116–119 acoustic stimuli and environment, 116–117 auditory scene analysis, 131–132 central auditory pathway, 136–146 frequency difference thresholds, 123–126 future directions, 146–148 localization, 127–131 masked auditory thresholds, 119–123 overview, 115–116 periphery, 133–136 temporal resolution gap detection thresholds, 125–127 temporal modulation transfer function, 126–127 Auditory cortex, 146, 160 Auditory learning, 631 Auditory memory, 171 Auditory processing systems, primate brains auditory belt, 166–168 auditory core, 164–167 auditory cortex, 160 auditory memory, 171 auditory thalamus, 163–164 cortical auditory areas and networks, 164 external, middle, and inner ear, 157–160 frontal cortex, 169–170 hearing, 157 insular cortex, 170 multisensory areas, 162 parabelt, 168–169 peripheral auditory system, 158 speech and language, 157, 171–172 structures, 160–162 superior colliculus, 162–163 superior temporal sulcus, 170 visual cortex and visual activation, 170–171 Auditory scene analysis, 131–132 Auditory streaming, 132–133 Auditory thalamus, 163–164 Autism spectrum disorders, 7 Autoerotic activity, 338

Automatons, 481 Aversive motivation, 401–402 Avoidance learning, 539 Baboons, 576 Backpropagation, 628 Bar-press conditioning, 539 Basal ganglia, 397–399, 607–608, 613 Basilar membrane, 116 Basilar papilla, 116 Basolateral amygdala (BLA), 347, 398, 423, 520 BDNF. See Brain-derived neurotrophic factor (BDNF) Bed nucleus of the stria terminalis (BNST), 340, 348 Behavioral cascade, 344 Behavioral genetics epigenetics, 18–19 future directions, 20 gene expression correlations, 3 gene mutations, 3 genome projects, 2 genotype by environment interactions, 15–18 genotype-by-genotype interaction, 13–15 human psychopathology, 7 individual chemosignals and social recognition, 9–10 male mouse aggression, 4–6 mouse emotionality, 6 mouse offense gene expression, 12–13 genetic variants, 10–12 mouse olfaction, 7–8 natural genetic variants and behavior, 2–3 overview, 1 pedigree of causes, 7 rat/mouse learning and cognition, 6 rodent social recognition, 8–9 subjects, 1–2 translational knockdowns, 3 Belize, 41, 42 Bicuculline, 340 Bilevel apparatus, 335 Bilingual brain, 612 Biopsychology of pain, 263–264 brain and brainstem chronic pain, 259–260 cortical processing, 256–257 DBS, 258 hypnotic suggestion, 258 nociceptive inputs to supraspinal centers, 255–256 endogenous control, 260–263 peripheral nervous system inflammation and peripheral sensitization, 243–244 phenotypic changes, 245–246 primary afferent fibers, 241–242 specific receptors and ion channels, 244–245 sympathetic nervous system, 246 spinal cord dorsal horn afferent input, 246–247 Ca2+ influx, 250 central sensitization, 254

Subject Index

descending fibers, 252–253 dorsal horn neurons, 251 dorsal horn nonneuronal cells, 252 excitatory amino acids, 247–250 Gate Control Theory of Pain, 254 interneurons, 251–252 neurotransmitters and neuromodulators, 247 neurotrophins, 250 phenotype changes, 254–255 purines, 250 second messengers, 250–251 Bipolar cells, 86 Birds, 135–136, 140–143 Birdsong, 501 Bitterness, 275–276 BLA. See Basolateral amygdala (BLA) Blastocyst, 333 Blobs, 93 Blood-oxygen-level dependence (BOLD), 599 B lymphocytes, 448 BNST. See Bed nucleus of the stria terminalis (BNST) Bonobos, 488 Bradycardia, 520 Brain-derived neurotrophic factor (BDNF), 14, 535 Breast cancer, 443, 444, 452 Bulbospongiosus muscle, 344 Bullfrog, 118 Bushy cell, 144 Caenorhabditis elegans, 1, 513 CAH. See Congenital adrenal hyperplasia (CAH) Campbell’s monkeys, 500 cAMP responsive element binding (CREB), 511 Cancer, 452 Canon of parsimony, 483 Capillary chromatography, 352 Capuchin monkeys, 578 Cardiovascular disease, 444 Cat, eyeblink conditioning, 526 Catechol-o-methyl transferase (COMT), 14–15 Catecholamines, 441 Categorical scaling task, 491 Categorization, 493 Category selectivity, 99–101 Caudal parabelt (CPB), 169 Caudate-putamen, 397 Caudomedial area (CM), 166–168 Causality, 496–497 Central auditory pathways birds, 140–143 fishes, 137–138 frogs, 138–139 insects, 137 mammals, 144–146 phase-locked spikes, 136 place code, 136 reptiles, 140 Central nervous system (CNS). See Nervous system development, environmental influences

Central nucleus (Ce), 142, 145, 423 Central tegmental field (CTF), 353 Cephalopods, 134 Cercopithecine primates, 59 Cerebellar subsystem, 569 Cerebellum, 340, 531–536 Cerebral cortex, 91 Cerebral reserves, 475–476 Chimpanzees, 486, 577–578 Cholecystokinin (CCK), 250 Chromophores, 58 Chromosome 7, 73 Chronic pain effect on brain anatomy, 260 effect on brain function, 259–260 phenotype changes, 254–255 phenotypic changes, 245–246 Cichlid fishes, 58 Circadian rhythmicity cardiovascular function, 380–381 development, 377–378 endocrine regulation, 381–383 endogenous rhythm, 375–379 genetic and molecular basis, 375–376 mood, 380 neural basis, 373–375 peripheral oscillators, 376–377 psychiatric illnesses, 386–389 sleep regulation, 378–379 Circadian rhythms, 13 Circumventricular organs (CVOs), 319 Cis-flupenthixol, 352 Climbing fibers, 533, 534 Cnemidophorus inornatus, 332 Cnemidophorus uniparens, 332 Cocaine, 34 Cochlear amplifier, 136 Cockroach, associative learning, 512 Coelacanth, 64 Color vision, 72 Comparative cognition abstract concepts, 493 animal language, 499–503 future direction, 503–504 historical foundations anecdotal method, 483 B. F. Skinner, 484 Charles Darwin, 480, 482–483 C. Lloyd Morgan, 483 Edward Thorndike, 483–484 Ivan Pavlov, 483 Julien Offray de La Mettrie, 481–482 Michel de Montaigne, 480–481 parametric parallels, 484 Ren´e Descartes, 481 William James, 484 memory behavioral mediation, 485 delayed matching-to-sample, 485

727

728

Subject Index

Comparative cognition (continued) delayed-response paradigm, 485 directed forgetting and rehearsal, 485–486 Ebbinghaus, 484 episodic memory, 487–488 planning, 488 serial position function, 486 working, 486–487 metacognition, 497–499 Michel de Montaigne, 480 numerical processing basic arithmetic, 489–490 discrimination, 488–489 linguistic competence, 488 relational concepts and higher-level reasoning, 492–493 same-different concept, 493–497 timing and behavior temporal control procedures, 490–491 temporal discrimination procedures, 491–492 Complex cells, 93 Computational models, 621 COMT. See Catechol-o-methyl transferase (COMT) Conditioned place preference (CPP), 335 Conditioned response (CR), 483, 569 Conditioned stimuli, 426 Conduct disorder, 16 Conductive education, 470 Cone bipolar cells, 86 Cone photoreceptors, 84–85 Congenital adrenal hyperplasia (CAH), 332 Connectionist models, 621 activation function, 625 brains and mental abilities, 622 Hebbian learning, 623 hidden layers, 624 learning algorithms, 623 neural networks, 622 processing nodes, 625 unsupervised learning algorithms, 623 weights, 624 Coolidge effect, 334 Coping, stress, 443–445 Copulatory rate factor, 335 Corpus luteum, 333 Cortical magnification factor (CMF), 92 Cortical processing, pain, 256–257 Corticohippocampal learning, 635–637 Corticomedial nuclei, 347 Corticosterone, 332 Corticotropin-releasing hormone (CRH), 375 CR. See Conditioned response (CR) Crayfish, 512 Cross-maze, 335 Cue-target interval (CTI), 231 Cultural transmission, 500 Cyclic nucleotide-gated channel α2 (CNGA2), 8 Cynomolgus monkey, 83 Cytochrome oxidase (CO), 93–94

D antagonist, 349 Darwin, Charles, 480, 482–483 Decision-making, 395 Declarative memory system, 560–567 anatomical data, 562 behavioral and physiological data, 562–565 cognitive mapping, 561 contextual retrieval, 561 inferences from memory, 566 locale system, 561 networking and expression, 566–567 order of events, 565–566 reference memory, 561 spatial and nonspatial learning, 561 taxon system, 561 working memory, 561 Deep brain stimulation (DBS), 258 Defensive aggression, 4 Delayed inhibition, 216 Delayed matching-to-sample, 485 Delayed nonmatch to sample (DNMS), 562, 563, 648 Delayed-response paradigm, 485 Delta opioid receptor, 409 Dendritic boutons, 342 Dentate gyrus, 633, 634 Depression, 17–18 Depth perception, 95 Descartes, Ren´e, 481 Deuteranopes, 72 Dialects, 501 Dichromatic color vision, 63, 72 Diestrus, 337 Dietary restriction, 657 Diffusion tensor imaging (DTI), 429, 598, 651 Diffusion weighted imaging, 598 Dihydrotestosterone (DHT), 332, 333 5,7-Dihydroxytryptamine (5,7-DHT), 354 Direct phenotypic benefits, 37 Directed-forgetting paradigm, 486 Discreteness, 500 Dishabituation cognition, 493 nonassociative learning, 509 Dizocilpine, 348, 350 DNA methylation, 333 DNMS. See Delayed nonmatch to sample (DNMS) Dolphins, 578 Dopamine, 338, 634–635 dopamine-induced plasticity, 403–404 dopamine-stimulated plasticity, 404 emission of, 402–403 Dopamine receptor D4 (DRD4), 14–15 Dopaminergic modulation, 634 Dorsal cochlear nucleus, 144 Dorsal column nuclei (DCN), 161, 212 Dorsal horn neurons, 251 Dorsal medial geniculate, 146 Dorsal medullary nucleus, 139

Subject Index

Dorsal striatum, 340, 353, 397 Dorsolateral tegmentum (DLT), 353 Dreams neurobiology, 372 psychopathology, 371 D receptors, 350, 351, 409 Drosophila, 513 DTI. See Diffusion tensor imaging (DTI) Duality of patterning, 501 Dunedin Longitudinal Study, 16 Dynamical systems approaches, 621 Eagle, 69–71 Eidetic memory, 487 Ejaculation-specific circuit, 334, 348 Electric vector (e-vector), 54 Electrodermal activity (EDA), 427 Electroencephalography (EEG), 599 Electrophysiology, 599–600 EMG recordings, 344 Emotion amygdala-mPFC circuitry functional neuroanatomy, 429 resting state, 429–430 structural neuroanatomy, 428–429 amygdala-prefrontal circuitry anxiety, 433–434 emotion regulation, 431–434 emotionally ambiguous facial expressions, 432–433 fear conditioning and extinction, 430–431 facial expressions conditioned stimuli, 426 psychophysiological measures, 426–427 human amygdala landscape of, 423–425 uncertainty, importance of, 425 Emotion-focused coping, 443–444 Emotional memory system, 569–571 Endogenous opioid systems, 261 β-Endorphin, 341 Energy balance, 382 Enkephalin, 341, 399, 409 Environment of evolutionary adaptedness (EEA), 33–34 Environmental enrichment (EE), 473–475 Epigenetics, 18–19 Epinephrine, 441, 447 Episodic memory, 171, 487–488, 633 Epstein-Barr virus, 450 Erotic thoughts, 338 Estrogen receptor α (ERα), 332, 333, 336 Estrous cycles, 337 Estrus, 337 Event-related potentials (ERPs), 599 Evoked calling, 118 Evolutionary psychology future directions, 46–48 growth of publications, 26

hierarchical structure of, 27 hypotheses, 38–40 impact of mate selection, 46 puberty, 44–45 sexual jealousy, 45–46 ultimate and proximate explanations, 44 metatheory adaptation, 29–30 inclusive fitness theory, 31 level of analysis, 28 multilevel selection theory, 28 natural selection, 29 psychological mechanisms, 31–34 sexual selection, 30–31 middle-level theories good genes sexual selection theory, 36–38 parental investment theory, 35–36 predictions, 40–43 Ex copula tests, 336 Experience-dependent development, 472–473 Experience-expectant development cerebral reserves, 475–476 early affective and cognitive experiences, 470–472 early motor experiences, 469–470 early sensory experiences auditory stimulation, 468–469 gustatory and olfactory stimulations, 469 tactile system, 467–468 visual system, 466–467 environmental enrichment, 473–475 experience-dependent development, 472–473 Exploratory procedures (EP), 227 Exposure-based perceptual learning, 630 External ears, 117 External nucleus, 142 Extradimensional set shifting (EDS), 647 Eye contact, 335 Eyeblink conditioning amygdala, 530–531 behavioral and neural phenomena, 536 cerebellum, 531–536 dopamine system, 530 features, 526 frontal cortex, 530 hippocampus and limbic system, 528–530 neural substrates of, 526–540 stress, 531 subjects, 526 thalamus, 530 Facial electromyography (EMG), 426 Fadrozole, 344 Fallopian tube, 334 Feedforward, 91 Feral horses, 578 α-Fetoprotein, 333 Figure-eight dance, 500 Filliform papillae, 277

729

730

Subject Index

Fixed interval (FI), 490–491 Fluctuating asymmetry, 41 Flutamide, 348 fMRI. See Functional magnetic resonance imaging (fMRI) Follicle stimulating hormone (FSH), 333, 447 Follicular phase, 333 Food intake energy balance adiposity signal, 310 agouti-related peptide, 308 amylin, 314 anorexigenic arcuate neurons, 309 arcuate nucleus, 307 cholecystokinin, 313 control systems, interactions among, 315–317 dopaminergic mediation, 311 duodenal vagal afferents, 312 examining ingestion rates, 311 feedback mechanisms, 312 flavor conditioning, 312 gastric mechanoreceptive vagal affere, 312 ghrelin, 315 glucagon, 314 glucagon-like peptide 1, 314 hypothalamic neurons, 310 hypothalamic system, 307–310 leptin receptors, 307 meal initiation, 312 meal size, 312, 316 melanocortin, 308 metabolic signals and mediation, 306–307 neuropeptide Y, 308 nucleus accumbens, 311 nutrient conditioning, 311 Ob-Rb receptors, 307 opioid, 311 orexigenic arcuate neurons, 309 peptide YY (3-36), 314 proopiomelanocortin, 307 satiety signaling, 312–316 second-order neurons, 309 sham feeding, 310, 312 taste and palatability, 310 vagus nerve, 312 Food-seeking behavior, 402 Foot stomp, 334 Forebrain module, 340 Forelimb flexion conditioning, 537 Form perception, 214–215 Fractional anisotropy (FA), 598 Fragile X syndrome, 465 Freezing, 519 Frequency selectivity, 123 Frogs, 135, 138–139 Frontal eye field (FEF), 103 Frugivorous, 75 FSH. See Follicle stimulating hormone (FSH) Fukushima model, 628

Functional magnetic resonance imaging (fMRI), 598–599 Fundamental Neuroscience, 511 GABAergic neurons, 467 Galanin, 250 Gamma-amino-butyric acid (GABA), 252, 337 Ganglion cells, 56, 86–88 Gap detection threshold, 125–127 Gastrin-releasing peptide (GRP), 354 Gate Control Theory of Pain, 254 GC. See Glucocorticoids (GC) GenBank, 60 Gene-environment interaction, 462 Genes, Brain, and Behavior, 1 General adaptation syndrome, 441 Genetics as a Tool in the Study of Behavior, 1 Genome projects, 2 Genotype by environment interactions 5HTT, stress and depression, 17–18 MAOA and antisocial behaviors, 15–17 mouse aggression, 15 rat cognition, 15 risk vs. plasticity genotypes, 18 Genotype-by-genotype interaction, 13–15 Gestalt laws, 132 GHRH. See Growth-hormone-releasing hormone (GHRH) Global shape processing, 227–228 Globus pallidus, 397 Glucagon, 447 Glucocorticoid receptor (GR), 19 Glucocorticoids (GC), 447, 465, 580 Glucose, 382, 447 Glutamate, 350, 404–407, 522 GnRH. See Gonadotropin releasing hormone (GnRH) Goal-directed behavior, 402 Goldfish audition, 126 vision, 59–60 Golgi tendon organs (GTO), 211 Gonadotropin releasing hormone (GnRH), 333, 342 Good genes sexual selection theory direct phenotypic benefits, 37 hypotheses, 38–40 overview, 36–38 predictions, 41–43 Granule cells, 340, 536 Green treefrog, 118 Growth hormone (GH), 381, 447 Growth-hormone-releasing hormone (GHRH), 381 Habituation cognition, 493 nonassociative learning, 509 Hair cells, 116 Hamilton’s rule, 31 Handicap principle, 37

Subject Index

HDAC. See Histone deacetylase (HDAC) Hebbian learning, 623 Hedyloidea butterflies, 119 Helicotrema, 116 Hermissenda, 514–515, 517 Herpes Simplex virus, 450 Hidden layers, 624 Hikikomori, 472 Hindlimb flexion reflex, 518 Hippocampal system, 557 Hippocampus, 398, 523, 538 Histamine, 342 Histone deacetylase (HDAC), 333 Homogametic sex, 332 Honeybee associative learning, 513 dance language, 500–501 vision, 59–60 Hopping, 335 Horizontal cells, 86 Horizontal sound localization, 128 Hormone response element, 336 Horseshoe bat, 116 Houseflies, 66–67 HPA. See Hypothalamic-pituitary-adrenal cortical (HPA) axis 5-HT receptors, 342, 354 Human immunodeficiency virus (HIV), 451–453 Hume, David, 496 Huntington’s disease, 536 Huxley, Thomas, 482 Hyena, 56, 578 Hyperalgesia, 240 Hypercomplex cells, 627 Hyperpallium, 142 Hypocretin, 407 Hypogonadal men, 343 Hypothalamic module, 341 Hypothalamic-pituitary-adrenal cortical (HPA) axis, 387, 441, 447 Hypothesis, 38–40 Hypothyroidism, 465 IC. See Inferior colliculus (IC) Immune system, 447–452 Impedance matching ossicles, 135 Implicit knowledge, 581 Incentive motivation, 401 Inclusive fitness theory, 31 Inferior colliculus (IC), 145, 161–162 Inferior olive, 533 Injury-induced plasticity, 194–196 Inner hair cells, 136 Instrumental conditioning, 496 Insular cortex, 170 Insulin, 447 Interchangeability, 500 Inter-intromission interval, 335 Intermediolateral cell column (IML), 375

Internal genitalia, 332 Interneurons, 251–252 Interpositus nucleus, 531 Intersexes, 332 Interstimulus interval (ISI), 531 Intraocular filters, 63 Intromission ratio factor, 335 Inuit, 45 James, William, 484 Japan, 43 Jaw-movement conditioning, 537–538 Joint afferents, 211 Kangaroo rat, 117 Kappa-opioid receptors, 409 Kin-selection theory, 31 Kisspeptin, 333 Koniocellular neurons, 90–91 Land snail (Helix ), 512 Language biologically inspired computational models, 597–598 contemporary applications basal ganglia and bilingual brain training, 613 bilingual brain, 612 individual differences, language abilities, 610 neural adaptability, 611 neural efficiency, 610–611 neural synchronization, 611–612 imaging brain connectivity, 598 imaging brain function electrophysiology, 599–600 fMRI, 598–599 magnetoencephalography, 600 lesion studies, 596–597 linguistic representation and processes basal ganglia, 607–608 inferential processes, 609–610 integration processes, 608–609 lexical access and retrieval, 604–606 semantic representation in cortex, 601 sentence-and discourse-level representation, 603–604 syntax, 606–607 simulated lesions, 597–598 virtual lesion studies, 597 LANguage Analogue (LANA) Project, 502 l-arginine, 354 Lateral geniculate nucleus (LGN), 89–91 Lateral Giants (LGs), 513 Lateral hypothalamus, 400 Lateral intraparietal area (LIP), 103–105 Lateral part of the entorhinal cortex (LEC), 562–563 Lateral septum, 340 Lateral superior olive (LSO), 145, 161 Law of effect, 483 Learning algorithms, 623 associative eyeblink conditioning, 526–536

731

732

Subject Index

Learning (continued) fear conditioning, 519–525 forelimb flexion conditioning, 537 instrumental conditioning, neural substrates of, 538–540 invertebrate preparations, 511–517 jaw-movement conditioning, 537–538 overview, 509–511 spinal conditioning, 517–519 epigenetics, 6, 19 nonassociative, 509 Leech, 513 Lemma, 605 Lemniscal nuclei, 141–142 Letter to the Marquess of Newcastle, 481 Lexigrams, 502 LGN. See Lateral geniculate nucleus (LGN) Life history theory, 34 Ligand-independent fashion, 336 Limax, 512 Lions, 579 Lizards, 135 Lobula giant motion detectors (LGMD), 186 Localization, 127–131 Locomotor systems act of progression, 176–177 central control, 177 central pattern generator, 177 half-center model, 177 modulation of cellular properties and synaptic efficacy, 192–193 constitutive receptor activity, 192 effects of, 191 initiation of, 191 intrinsic cellular properties, 193 ongoing locomotor activity, 191–192 receptors classes, 192 sources of, 189 synaptic transmission, 194 neural basis, 177 pattern-generating networks Clione, 179 basic rhythmic activity, 183 cellular and subcellular level, 180 controllers, 179 decision to locomote, 178 forward excitation, 180–183 functional organization, 178 groups of interneurons, 178 location of, 183–184 locomotor organs, 179 neuronal networks, 180 operational level, 180 power stroke, 178 reciprocal inhibition, 180–182 reticulospinal pathways, 178 return stroke, 178 sense organs, 179

plasticity, 193–196 sensory signals controlling exteroceptive input, 189 muscle activity, 188 phase transition, 186–188 visual regulation, 185–186 Locus coereleus (LC), 368 Locust, associative learning, 512 Long-term depression (LTD), 534–535, 540 Long-term potentiation (LTP), 255, 472, 522, 540, 654 Lordosis behavior, 333, 334 Lordosis quotient, 335 Lower brainstem module, 342 LTD. See Long-term depression (LTD) Lumbosacral spinothalamic (LSt), 354 Luteal phase, 333 Luteinizing hormone (LH), 333, 447 MAA. See Minimum audible angle (MAA) Magnetic resonance imaging (MRI), 650 Magnetoencephalography, 600 Magnocellular neurons, 90–91 Main olfactory epithelium (MOE), 7–8 Major histocompatability complex (MHC), 9–10 Male copulatory behavior, 335 Male-female bonding, 335 Mammalian taste buds, 278 Mantis shrimps, 58 Map reorganization, 630 Masking, 119–123 Matching effect, 46 Mate retention strategies, 41 Matrilineal kinship, 576 Mechanical memory, 551 Mechanical tuning, 125 Medial amygdala (MeA), 337, 340 Medial geniculate nucleus (MGN), 146, 521 Medial/magnocellular nucleus (MGm), 163 Medial nucleus of the trapezoid body (MNTB), 161 Medial part of the entorhinal cortex (MEC), 563 Medial preoptic area (MPOA), 337, 338 Fos studies, 349 lesions effects, 348–349 microdialysis studies, 350 microinjection studies, 349–350 stimulation, 349 Medial superior olive (MSO), 145, 161 Mediodorsal thalamus (MD), 400 Medium-spiny neurons (MSN), 397 Meissner’s corpuscles, 208 Membrane-bound ERα, 341 Memory declarative and nondeclarative, 509 episodic, 487–488 multiple memory systems, 510 neurogenesis, 633 recognition, recall, and recurrence, 632–633 working, 486–487

Subject Index

Memory systems brain, 556–560 amygdala, 557 conditioning stimulus, 559 hippocampus, 557 Parkinson’s disease, 560 probabilistic classification learning, 560 prominent memory systems, 556 radial arm maze task, 558 radial maze task, 559 spatial radial maze task, 557 striatum, 557 contextual fear conditioning, 570 declarative memory system, 560–567 anatomical data, 562 behavioral and physiological data, 562–565 cognitive mapping, 561 contextual retrieval, 561 inferences from memory, 566 locale system, 561 networking and expression, 566–567 order of events, 565–566 reference memory, 561 spatial and nonspatial learning, 561 taxon system, 561 transitivity, 567 working memory, 561 emotional memory system, 569–571 fundamental basis, 552–554 habit, 551 mechanical memory, 551 motor memory systems, 567–569 primary memory, 552 reconciliation, 554–556 representative memory, 551 secondary memory, 552 sensitive memory, 551 Mental continuity, 482 Mental lexicon, 602–603 Merkel-cell-neurite-complex (MCNC), 207 Mesolimbic dopamine tract, 340 Metabotropic glutamate receptor, 341 Metacognition, 498–499 Metatheory, evolutionary psychology adaptation, 29–30 inclusive fitness theory, 31 level of analysis, 28 multilevel selection theory, 28 natural selection, 29 psychological mechanisms domain specificity of, 32–33 environment of evolutionary adaptedness, 33–34 unit of analysis, 31–32 sexual selection, 30–31 Methamphetamine, 340 Methyl mercury, 464 MGN. See Medial geniculate nucleus (MGN) Microdialysis, 352 Midbrain central gray, 342

Midbrain locomotor region, 338 Midbrain module, 342 Middle ear, 116 Middle temporal area (MT), 102–103 Midget ganglion cells, 88–89 Minimum audible angle (MAA), 128–130 Minimum resolvable angle (MRA), 129–131 Mole rat, 117 Molecular clock, 60 Mongolian gerbil, 117 Monogamous pair bonds, 575 Mood, 380 Morning sickness, 44 Morphine, 351 Motion perception, 225–227 Motivated behavior cortical and subcortical circuitry, 412 dopamine dopamine-induced plasticity, 403–404 dopamine-stimulated plasticity, 404 emission of, 402–403 glutamate, 404–407 goal-directed behavior, 412 habitual behavior, 412 hypocretin/orexin system, 407 motive circuit allocortical afferents, 399 aversive motivation, 401–402 basal ganglia, 397–399 historical perspectives, 396 incentive motivation, 401 information processing and translation, 396 lateral hypothalamus, 400 limbic subcircuits, 399–400 neurotransmitters, function of, 412–413 prefrontal cortical input, 399 nucleus accumbens, 410–411 opioid-peptides, 409–410 ventral pallidum, 411–412 Motor memory systems cerebellar subsystem, 569 sensorimotor adaptations, 567 skills, 567 striatal subsystem, 568–569 MPOA. See Medial preoptic area (MPOA) Multilevel selection theory, 28 Mu opioid receptor, 409 Muscarinic receptors, 342 Muscimol, 520 Mutant mice, 523 NAc. See Nucleus accumbens (NAc) NADPH-diaphorase (NADPH-d), 348 Naloxone, 352 Nasal epithelium, 345 National Center for Biotechnology Information (NCBI), 60 Natural killer (NK) cells, 449–450 Natural selection, 29, 35, 484 Neocognitron model, 628

733

734

Subject Index

Nervous system development, environmental influences experience-expectant development cerebral reserves, 475–476 early affective and cognitive experiences, 470–472 early motor experiences, 469–470 early sensory experiences, 465–469 environmental enrichment, 473–475 experience-dependent development, 472–473 gene-driven processes, 462–463 gene-environment interaction, 462 maturation processes, 464–465 overview, 461–462, 476 sensitive periods, 463–464 Neural adaptability, 611 Neural efficiency, 610–611 Neural mechanisms, tactile perception attention, 230–233 cutaneous mechanoreception, 207–210 flutter, vibration, and texture perception, 228–230 form and texture perception 3a, 3b, 1 and 2, 213 area 5, 213 convergent inputs, 218–219 cortical processing, 215–217 curvature perception, 215 DCN, 212 delayed inhibition, 216 dorsal-column medial-lemniscal pathway, 212 3D shape perception, 214 infield suppression, 217 local cutaneous form, 214 medial-leminiscal tract, 212 modality-specific channels, 218 peripheral processing, 215 STRF, 217–218 three-component model, 217–218 VPL, 212 global shape processing, 227–228 motion perception, 206, 225–227 periodicity coding, 228 probe-based texture discriminations, 229 proprioception, 211 rate code, 228 size, shape, and weight, 206 spatial form, SII cortex orientation tuning, 220 roughness, 222–225 softness, 225 spatial variation, 223, 224 types of neurons, 221 vector fields and orientation, 222 stimulus curvature, 219–220 tactile form processing, 219 Neural network model, 622 Neural plasticity, 472 Neural synchronization, 611–612 Neurally inspired models aging and psychological decline, 634–637 connectionist models, 622–625

emerging trends, 637–638 memory, 632–634 perceptual learning, 629–632 Neuro-peptide Y (NPY), 250 Neurocognitive models, 632 Neurogenesis, 634 Neurokinin 1 receptor (NK1R), 251 Neuromodulators cellular properties and synaptic efficacy, 192–193 effects of, 191 sources of, 189 Neuronal cell adhesion molecules (NCAMS), 513 Neuropeptides, 249–250 Neuroplasticity, 254 Neurotensin, 399 Neurotrophins, 250, 464 Nictitating membrane, 526 Nigrostriatal dopamine tract, 353 Nitric oxide (NO), 332 Nitric oxide synthase, 339 N-methyl-D-aspartate (NMDA), 248, 405, 522 Nociceptive-specific (NS) neurons, 251 Nociceptor sensitization, 244 Nocturnal penile tumescence, 344 Nonassociative learning, 509 Noncontact erections, 336 Nonneuronal cells, 252 Nonnociceptive neurons (NON-N), 251 Nonopioid analgesia, 260 Non-rapid-eye movement (NREM) sleep, 369 Norepinephrine, 441, 447, 452 Normal neurocognitive aging adaptation process, 644 Alzhiemer’s disease, 644 attention process, 650 calorie restriction, 645 cognitive control and inhibition, 650 cognitive reserve, 657 default mode network, 650 demographic trends and analysis, 643 dietary restriction, 657 effective adaptation, 650 exercise, 645 frontal lobe function, 646–648 healthy mindspan, 644 hippocampus, 645 implicit memory, 650 medial temporal lobe function, 648–650 methodological considerations, 644–645 neuroepigenetics, 656 neurophysiology, 653–655 neuropsychological perspectives, 645 nutritional intervention, 657 oxidative stress, 655–656 prefrontal cortex, 645 structural changes, 650–653 Nucleus accumbens (NAc), 337, 351–353, 410–411 Nucleus angularis, 142

Subject Index

Nucleus laminaris, 142 Nucleus of the brachium of the inferior colliculus (nBIC), 162 Nucleus of the pons (PbN), 279 Nucleus of the solitary tract (NTS), 278 Nucleus paragigantocellularis (nPGi), 353 Numerical distance effect, 489 Numerical magnitude effect, 489 Numerical reasoning, 489–490 Obsessive-compulsive disorder, 536 Ocular dominance columns, 93 Ocular filtering, 62–63 Offensive aggression coping strategies, 5 measures of, 4–5 types of, 4 Oil droplets, 62–63 Olfaction analytical problem, 289 anatomy, 288–290 odorant stimuli epithelium and olfactory bulb, 292–293 experienced-induced olfactory plasticity and behavior, 295 from molecules to perception, 293–295 signal recognition and transduction, 290–292 role of, 287–288 Olfactory bulbectomy, 345 Olivocochlear, 145 Ommatidia, 67 Oocyte, 333 Opiate analgesia, 261–262 Opioid-peptides, 409–410 δ-Opioid receptors (DOR), 250 μ-Opioid receptors (MOR), 250, 341 Opioids, 447 Opossum, 63–64, 69 Opsins, 58–62 Optic nerve, 88–89 Optogenetics, 522 Orangutans, 488 Orexin system, 407 Orexin/hypocretin (orx/hcrt) neurons, 351 Otoacoustic emissions, 134 Otocysts, 134 Otolith, 133 Outer hair cells, 136 Ovotestes, 331 Ovulation, 333 Owl monkeys, 93 Oxotremorine, 350 Oxytocin, 342, 350, 354, 447, 580 Paced intromissions, 335 Pacinian (PC), 207–210 PAG. See Periaqueductal gray (PAG) Pallium, 138 Parallel fibers, 534 Parallel processing, 83 Parasol cells, 88

Paraventricular nucleus (PVN), 9, 342, 350–352 Parental investment theory, 35–36 Parkinson’s disease, 346, 353, 536, 634 Parvocellular neurons, 90–91 Pattern-generating networks, locomotion Clione, 179 basic rhythmic activity, 183 cellular and subcellular level, 180 controllers, 179 decision to locomote, 178 forward excitation, 180–183 functional organization, 178 groups of interneurons, 178 location of, 183–184 locomotor organs, 179 neuronal networks, 180 operational level, 180 power stroke, 178 reciprocal inhibition, 180–182 reticulospinal pathways, 178 return stroke, 178 sense organs, 179 Pavlov, Ivan, 483 Pavlovian conditioning. See Associative learning Peahens, 40 Peak interval (PI), 491 Penile anteroflexions, 336 Penile function, 335 Penile muscles, 354 Penis, 334 Peptides, 447 Perceptual learning, 629–632 Periaqueductal gray (PAG), 261, 353, 521 Perineal area, 342 Peripheral auditory system, 133–136 Peripheral nervous system inflammation and peripheral sensitization, 243–244 phenotypic changes, 245–246 primary afferent fibers, 241–242 specific receptors and ion channels, 244–245 sympathetic nervous system, 246 Phenylthiocarbamide (PTC), 274 Pheromones, 334, 338 Phonotaxis, 118 Phosphodiesterase type 5 inhibitor, 337, 352 Photopigments, 57–62 Photoreceptors, 84–85 Phylogeny, 61 Piaget’s theory, 471 Picrotoxin, 531 Pigeons, cognition, 486 Place coding, 122 Plasticity afferent pathways, 195–196 injury-induced plasticity, 194–196 Platyrrhine monkeys, 74 Pleistocene era, 33 Pleurobranchaea, 512 Polar bear, 33

735

736

Subject Index

Polarization sensitivity, 54 Pond snail (Lymnaea stagnalis), 512 Pope, Alexander, 480 Positive feedback response, 333 Positron emission tomography (PET), 586 Postejaculatory interval, 335 Posterior ventral cochlear nucleus, 144 Posterodorsal preoptic nucleus, 348 Postmenopausal women, 337 Praying mantis, 66 Predictions, evolutionary psychology, 40–43 Prefrontal cortex, 633 Pregnancy, 335 Premature ejaculation, 346 Pressure gradient receivers, 116, 135 Pressure receivers, 116 Presynaptic cells, 278 Prevarication, 501 Primacy, 486 Primary afferent fibers, 242 neurotransmitters and neuromodulators, 247 projection of, 241 specific receptors and ion channels, 244–245 Primary somatosensory cortex (SI), 213 Primate color vision, 75 Primate social relationships African elephants, 578 baboons, 575–576 capuchin monkeys, 578 chimpanzees, 577–578 cognitive mechanisms, 581–583 dolphins, 578–579 evolution of friendships, 579–580 feral horses, 578 historical background, 574–575 hormonal mechanisms, 580 hyena, 578 Japanese macaques, 578 rhesus macaques, 578 Principles of Psychology, 482 Probability theory, 621 Problem solving, 484 Problem-focused coping, 443–444 Proceptive behaviors, 334 Proenkephalin, 341 Proestrus, 337 Progesterone, 332 Progesterone receptor “knock-out” mice (PRKOs), 336 Prolactin (PRL), 342, 381, 447 Propositionalization, 609 Proprioceptors, 342 6-n-Propylthiouracil (PROP), 274 Protein kinase A (PKA), 403 Protein synthesis, 522 Proto-grammar, 500 Psychiatric illnesses, 386–389 Psychogenic noncontact erections, 348 Psychomotor vigilance task (PVT), 380, 384 Psychoneuroimmunology, 447

Puberty, 44–45 Punishers, 483 Purkinje cells, 340, 524, 534 Puzzle box, 483 PVT. See Psychomotor vigilance task (PVT) Pyramidal cells, 404 Qualitative models, 620 Quantitative models, 620 Quantitative trait loci (QTLs), 2 Quinelorane, 339, 351, 352 Quinpirole, 350 Rabbit, eyeblink conditioning, 526 Rapid eye-movement (REM) sleep, 369 Rapid membrane effects, 337 Rapidly adapting (RA), 207–209, 219 Rats, 510 Reaction time (RT), 230 Reactive oxygen species (ROS), 655 Receiver Operating Characteristics (ROC), 564 Recency, 486 Receptive fields (RF), 95, 216 Receptor coactivators, 336 Reciprocal inhibition, 181 forward excitation, 180–183 group of neurons, 180 Reconsolidation, 523 Recurrent neural networks, 633 Red nucleus, 533 5-Reductase, 332 Red-winged blackbird, 118 Reflexiveness, 501 Reinforcement learning, 637 Reinforcers, 483 Relational matching-to-sample procedure, 495–496 Relative disparity, 95, 98 Relative validity effect, 496 Representative memory, 551 Reptiles, 135, 140 Resident intruder test, 11, 12 Reticulospinal neurons, 342 Retina amacrine cells, 86 bipolar cells, 86 ganglion cells, 86–88 horizontal cells, 86 optic nerve, 88–89 photoreceptors, 84–85 structure of, 83 Retinotopic maps, 92–93 Rhesus macaques, 489, 578 Rod bipolar cells, 86 Rod photoreceptors, 84–85 Romanes, George J., 482 Rostral parabelt (RPB), 169 Rostroventral medulla (RVM), 260 Roughness, 222–225

Subject Index

Saltiness, 275 Same-different concept, 493–497 Samoans, 45 Sample stimulus, 485 Schizophrenia, 7 Scrub jays, 487 Seasonal affective disorder (SAD), 388 Secondary somatosensory cortex (SII), 213 Selective androgen receptor modulators (SARMs), 337 Selective attention, 629 Selective serotonin reuptake inhibitor, 339 Self-esteem, 47 Self-evaluation maintenance theory, 46 Self-organization, 623 Self-supervised learning algorithms, 632 Selfish-gene theory, 31 Semantic dementia, 602 Semantic memory, 601 Semanticity, 500 Seminal emission, 336 Sensitive memory, 551 Sensitization, nonassociative learning, 509 Sensory bias theory, sexual selection, 36 Sensory information processing, 338 Sensory signals controlling exteroceptive input, 189 proprioceptive regulation, 186–188 visual regulation, 185–186 Sensory systems auditory system, 345 chemosensory systems, 345–347 somatosensory systems, 345 Serial position function, 486 Serial-probe-recognition task, 486 Serotonergic influences, 354 Serotonin (5-HT), 191 Serotonin 1A receptor, 339 Sexual behavior evolutionary psychology jealousy, 45–46 psychological mechanisms, 32 sexual selection, 30–31 female brain areas involved, 338–343 drugs affecting, systemically administered, 338–339 gonadal hormones, 337–338 female attractivity, 335 hormonal action, principles of genomic effects, 336 rapid, nongenomic effects, 337 male brain areas involved, 345–355 drugs affecting, systemically administered, 345–346 gonadal hormones, 343–345 male copulatory behavior, 335 patterns of common across species, 334–335 female reproductive cycles, 333–334

penile function, 335 proceptivity and receptivity, 335 sex differentiation, 331–333 sexual motivation, 335 urethrogenital reflex, 336 Sexual quiescence, 334 Sexual satiety, 334 Short hairpin RNA (shRNA), 339 SIA. See Stress-induced analgesia (SIA) Sildenafil, 345 Simple cells, 93, 627 Single nucleotide polymorphisms (SNPs), 2 Single scores, offensive aggression in mice, 4–5 Sinusoidal grating patterns, 68 Skinner, B.F., 484 Sleep across the lifespan, 370–371 dreams neurobiology, 372 psychopathology, 371 function of, 372–373 stages of, 366–367 wakefulness, 368–369 Slow afterhyperpolarization (sAHP), 183 Slowly adapting type 1 (SA1), 207–208, 219 Slowly adapting type 2 (SA2), 210 Slow-wave activity (SWA), 378 Small bistratified ganglion cells, 88 SNS. See Sympathetic nervous system (SNS) Social anxiety disorder (SAD), 434 Social intelligence hypothesis, 574 Social knowledge future research, 589–590 knowledge of kin and rank, 584–585 recognition of other animals’ close bonds, 584 recognition of transient social relations, 585 theory of mind, 585–588 Softness, 225 Somatostatin, 447 Sometimes-Opponent-Process (SOP) model, 533 Sourness, 275 Sparse coding, 632 Spatial acuity, 69–71 Spatial form cortical processing, 215–216 peripheral processing, 215 SII cortex, 221–225 Spatial-temporal receptive fields (STRF), 217–218 Spectral absorption curve, 57–62 Spectrally opponent cells, 71 Spencer, Herbert, 482 Sperm transport, 335 Spike frequency adaptation (SFA), 183 Spike-timing dependent learning, 637 Spinal control of pain, 252–254 Spinal cord, 342, 353–354 Spinal nucleus of the bulbocavernosus (SNB), 354 Spinopontoamygdaloid system, 256 Spinothalamic fibers, 256

737

738

Subject Index

Spontaneous erections, 336 Spontaneous pain, 240 Squirrel, 65 Statocyst, 134 Stimulation-produced analgesia (SPA), 261–262 Stimulus-response (S-R) learning, 552 Strepsirrhines, 74 Stress, 17–18 allostasis, 442 cognitive appraisals, 442 coping, 442–445 criticism, 440 definition, 441 eyeblink conditioning, 531 immune system health outcomes, 451–452 mechanisms of, 447–449 psychosocial factor, 450–451 relationships, 449–450 management interventions, 452–453 neuroendocrine responses, 446–447 overview, 440, 441, 453 psychological and behavioral responses, 446 research, future directions, 453–455 resource loss and conservation, 442 SNS and HPA axis, 441 Stress-induced analgesia (SIA), 261–262 Striatal subsystem, 568–569 Striatum, 397 Subfornical organ (SFO), 319 Sublenticular extended amygdala (SLEA), 423 Subpallium, 138 Subparafascicular nucleus of the thalamus (SPFp), 353 Substance P, 342 Substantia nigra, 353, 397 Superconducting quantum interference devices (SQUIDS), 600 Superior colliculus, 162–163 Superior olive, 139 Supervised learning, 623 Suprageniculate nucleus (Sg), 163 Supraoptic nucleus (SON), 354 Swallow, 39, 40 Sweetness, 276 Symbolic matching-to-sample, 485 Symbolic modeling, 621 Sympathetic nervous system (SNS), 246, 441 Sympathetically maintained pain (SMP), 246 Synaptogenesis, 465 Syntax, 606–607 Syrian golden hamster, 57 Tachykinins, 354 Tapetum, 74 Taste central taste pathways, 278–280 coding theory, 284–286 gustatory system, 280–281 peripheral nervous system, 277–278 physiology, 281–284

psychophysical analysis, 273–274 stimuli bitterness, 275–276 chemical structures, 272 chemosenses, 272 experiments, 273 fat and putative taste qualities, 277 saliva, 272 saltiness, 275 sensation, 272 sourness, 275 sweetness, 276 taste qualities, 272 umami, 276 Taste buds, 277 Tectum, 142 Teleogryllus oceanicus, 125 Teleost fish, 65–66 Temporal control procedures, 490–491 Temporal difference learning, 635 Temporal discrimination procedures, 491–492 Temporal modulation transfer function (TMTF), 126–127 Temporal resolution gap detection, 125–127 TMTF, 126–127 Testing stimuli, 485 Testosterone, 332 Texture perception, 221–222 The Apology for Raymond Sebond, 499 The Descent of Man, 482 Thoracolumbar sympathetic antierectile pathway, 354 Thorndike, Edward, 483–484 Thyroid-stimulating hormone (TSH), 382, 447 Thyrotropin-releasing hormone, 447 Tiger beetles, 119 Time-domain processing, 125 Tip-of-the-tongue (TOT) state, 605 T lymphocytes, 448 Touch-based erections, 336 Transcranial magnetic stimulation (TMS), 597 Transitive inference task, 494 Trichromatic color vision, 72 Tritonia, 512 Tryptophan, 343 TSH. See Thyroid-stimulating hormone (TSH) Tuberoinfundibular peptide of 39 residues (TIP39), 353 Tuberomammillary nucleus (TMN), 369 Turtles, 135 Tympanic ears, 133 Tympanum, 135 Tyrosine hydroxylase, 340 Ultrasonic calls, 334 Ultrasonic vocalizations, 334, 345 Umami, 273, 276 Unsupervised learning, 623 Upper bank of the lateral sulcus (UBLS), 213 Urethrogenital reflex, 336, 342

Subject Index

Uterine horn, 334 Uterus, 333 Vagina, 334 Vaginocervical stimulation (VCS), 339 Vardenafil, 337 Vas deferens nerve, 354 Vasoactive intestinal peptide (VIP), 250 Vasopressin, 354, 447 Ventral cochlear nucleus, 144 Ventral pallidum (VP), 397, 411–412 Ventral pathway category selectivity, 99–101 form selectivity in single neurons, 98–99 position tolerance, 97–98 Ventral subiculum, 351 Ventral tegmental area (VTA), 337, 351–353, 397 Ventromedial hypothalamus (VMH), 338, 339 Ventromedial nucleus, 336 Ventroposterior inferior nucleus (VPI), 212 Ventroposterior lateral nucleus (VPL), 212, 213 Vermis, 524 Vertical localization, 129 Vervet monkeys, 500 Vestibular organs, 342 Violence, 5 Vision animal, 65–66 chromatic cues, 71–75 eyes, design features and evolution of, 54–57 overview, 52, 75 photic environments, 52–54 photosensitivity animal vision, photopigment measurements, 63–64 nervous system, role of, 64–65 ocular filtering, 62–63 photopigments, 57–62 processing in primates cerebral cortex, 91 dorsal pathway, 101–103 lateral geniculate nucleus, 89–91 lateral intraparietal area, 103–105 overview, 105

parallel processing, 82–83 primary visual cortex, 93–95 retina, 83–89 retinotopic maps, 92–93 ventral pathway, 97–101 visual field, 81–82 V2 neurons, 95–97 spatial contrast sensitivity functions, 68–71 stimulus change detection, 66–68 Visual object recognition confusing background clutter, 627 difficulties, 626 hierarchical process and image segmentation, 627–629 overlapping, 627 Visual regulation, 185–186 Visual streaks, 56 Vomeronasal organ (VNO), 7–8 VPL. See Ventroposterior lateral nucleus (VPL) Waggle dance, 501 Wakefulness, 368–369 Water intake brain osmosensors and cellular dehydration, 319 circumventriculater intake, 319–320 control systems, interactions among, 320 osmotic and hypovolemic signals, 317–319 satiety signals, 320 Weber’s law, 125, 489 Weberian ossicle, 134 Whiptail lizards, 332 Wide dynamic range (WDR) neurons, 251 Widowbird, 36–37 Winter depression, 388 Word forms, 602 Working memory, 486–487 World Health Organization (WHO), 386 X chromosome, 73 X-maze, 335 XX chromosomal pattern, 332 Zinc sulfate, 345 ZZ birds, 332

739