From DNA to Social Cognition

FROM DNA TO SOCIAL COGNITION

ffirs01.indd i

7/25/2011 10:10:27 AM

FROM DNA TO SOCIAL COGNITION

Edited by

RICHARD EBSTEIN SIMONE SHAMAY-TSOORY SOO HONG CHEW

A JOHN WILEY & SONS, INC., PUBLICATION

ffirs02.indd iii

7/25/2011 10:10:28 AM

Copyright © 2011 by Wiley-Blackwell. 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) 750-4470, 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, or online at http://www.wiley.com/go/permissions. 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. For general information on our other products and services or for technical support, please contact our Customer Care Department within the United States at (800) 762-2974, outside the United States at (317) 572-3993 or fax (317) 572-4002. Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic formats. For more information about Wiley products, visit our web site at www.wiley.com. Library of Congress Cataloging-in-Publication Data: Ebstein, Richard P., 1943From DNA to social cognition / Richard Ebstein, Simone Shamay-Tsoory, Soo Hong Chew. p. cm. Includes index. ISBN 978-0-470-54396-2 (cloth) 1. Cognitive neuroscience. 2. Affective neuroscience. 3. Social perception. 4. Decision making. 5. Genetic psychology. I. Shamay-Tsoory, Simone. II. Chew, Soo Hong, 1954- III. Title. QP360.5.E27 2012 612.8′233–dc22 2011008254 Printed in the United States of America oBook ISBN: 978-1-118-10180-3 ePDF ISBN: 978-1-118-10178-0 ePub ISBN: 978-1-118-10179-7 10

ffirs03.indd iv

9

8

7

6

5

4

3

2

1

7/25/2011 10:10:28 AM

CONTENTS

Contributors

vii

Introduction

1

Richard P. Ebstein, Mikhail Monakhov, Poh San Lai, and Simone G. Shamay-Tsoory

PART 1 EMPATHY: NEURAL BASES AND GENETIC CORRELATES

19

1.1 Genes Related to Autistic Traits and Empathy

21

Bhismadev Chakrabarti and Simon Baron-Cohen

1.2 The Behavioral Genetics of Human Pair Bonding

37

Hasse Walum and Lars Westberg

1.3 Brain Networks Supporting Empathy

47

Martin Schulte-Rüther and Ellen Greimel

1.4 The Human Mirror Neuron System and Social Cognition

63

Sook-Lei Liew and Lisa Aziz-Zadeh

1.5 Motivational Aspects of Future Thinking in the Ventromedial Prefrontal Cortex

81

Arnaud D’Argembeau

PART 2 MORAL NEUROSCIENCE AND EMOTION

91

2.1 Contributions of the Prefrontal Cortex to Social Cognition and Moral Judgment Processes

93

Chad E. Forbes, Joshua C. Poore, and Jordan Grafman v

ftoc.indd v

7/25/2011 10:10:29 AM

vi

CONTENTS

2.2 Emotion and Moral Cognition

111

Michael Koenigs

2.3 The Neuroanatomical Basis of Moral Cognition and Emotion

123

Roland Zahn, Ricardo de Oliveira-Souza, and Jorge Moll

2.4 Envy and Schadenfreude: The Neural Correlates of Competitive Emotions

139

Jonathan Dvash and Simone G. Shamay-Tsoory

PART 3 Genes and Decision Making

157

3.1 The Somatic Marker Framework and the Neurological Basis of Decision Making

159

Antoine Bechara

3.2 A Model of the Initial Stages of Drug Abuse: From Reinforcement Learning to Social Contagion

185

Gilly Koritzky, Adi Luria, and Eldad Yechiam

3.3 Extrinsic Effects and Models of Dominance Hierarchy Formation

203

Matthew Druen and Lee Alan Dugatkin

3.4 Complex Social Cognition and the Appreciation of Social Norms in Psychiatric Disorders: Insights from Evolutionary Game Theory

215

Martin Brüne and Julia Wischniewski

3.5 From Neuroeconomics to Genetics: The Intertemporal Choices Case as an Example

233

Itzhak Aharon and Sacha Bourgeois-Gironde

Index

ftoc.indd vi

245

7/25/2011 10:10:29 AM

CONTRIBUTORS

Itzhak Aharon, Interdisciplinary Centre, Herzliya, Israel Liza Aziz-Zadeh, The Brain and Creativity Institute, University of Southern California, Los Angeles, California Simon Baron-Cohen, Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, U.K. Antoine Bechara, Department of Psychiatry, Faculty of Medicine, and Desautels Faculty of Management, McGill University, Montreal, Quebec, Canada, and Department of Psychology, and Brain and Creativity Institute, University of Southern California, Los Angeles, California Sacha Bourgeois-Gironde, Institut Jean-Nicod (ENS-EHESS), Paris, France Martin Brüne, Research Department of Cognitive Neuropsychiatry and Psychiatric Preventive Medicine, LWL University Hospital, Ruhr-University Bochum, Germany Bhismadev Chakrabarti, Centre for Integrative Neuroscience and Neurodynamics, School of Psychology and Clinical Language Sciences, University of Reading, Reading, U.K., and Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, U.K. Arnaud D’Argembeau, Department of Psychology, University of Liège, Belgium Matthew Druen, Department of Biology, University of Louisville, Louisville, Kentucky Lee Alan Dugatkin, Department of Biology, University of Louisville, Louisville, Kentucky Jonathan Dvash, University of Haifa, Haifa, Israel Chad E. Forbes, Imaging Sciences Training Program, Radiology and Imaging Sciences, Clinical Center and National Institute of Biomedical Imaging and Bioengineering, Bethesda, MD vii

flast.indd vii

7/25/2011 10:10:28 AM

viii

CONTRIBUTORS

Jordan Grafman, Traumatic Brain Injury Research Laboratory, Kessler Foundation Research Center, West Orange, NJ Ellen Greimel, Child Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital of the RWTH, Aachen, Germany, and Cognitive Neurology Section, Institute of Neuroscience and Medicine (INM-3), Research Center, Jülich, Germany, and Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of the RWTH, Aachen, Germany Michael Koenigs, Department of Psychiatry, University of Wisconsin—Madison, Madison, Wisconsin Gilly Koritzky, Technion—Israel Institute of Technology, Haifa, Israel Sook-Lei Liew, The Brain and Creativity Institute, University of Southern California, Los Angeles, California, and The Division of Occupational Science & Occupational Therapy, University of Southern California, Los Angeles, California Adi Luria, Technion—Israel Institute of Technology, Haifa, Israel Jorge Moll, Cognitive and Behavioral Neuroscience Unit, D’Or Institute for Research and Education, Rio de Janeiro, Brazil Ricardo de Oliveira-Souza, Cognitive and Behavioral Neuroscience Unit, D’Or Institute for Research and Education, Rio de Janeiro, RJ, Brazil, and Gaffrée e Guinle University Hospital, Rio de Janeiro, RJ, Brazil Joshua C. Poore, Cognitive Neuroscience Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD Martin Schulte-Rüther, Child Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital of the RWTH Aachen, Germany, and Cognitive Neurology Section, Institute of Neuroscience and Medicine (INM-3), Research Center, Jülich, Germany Simone G. Shamay-Tsoory, University of Haifa, Haifa, Israel Hasse Walum, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden Lars Westberg, Department of Pharmacology, Institute of Neuroscience and Physiology, University of Gothenburg, Gothenburg, Sweden Julia Wischniewski, Research Department of Cognitive Neuropsychiatry and Psychiatric Preventive Medicine, LWL University Hospital, Ruhr-University Bochum, Germany Eldad Yechiam, Technion—Israel Institute of Technology, Haifa, Israel Roland Zahn, The University of Manchester, School of Psychological Sciences, Neuroscience and Aphasia Research Unit, Manchester, U.K., and Cognitive and Behavioral Neuroscience Unit, D’Or Institute for Research and Education, Rio de Janeiro, Brazil

flast.indd viii

7/25/2011 10:10:28 AM

Log of CD38 expression

1.50 1.00 0.50 0.00 −0.50 −1.00 −1.50 Unaffected parents

ASD

Figure 1. Distribution of the expression (log transformed) of the CD38 gene. Lower expression in the ASD group is significant (p = 0.003). See Lerer et al. (2010), Ebstein et al. (2011), and Riebold et al. (2011) for more details.

bins.indd 1

7/25/2011 10:10:04 AM

(a)

OXT

PLACEBO

−0.21

−0.07

0.00

Suppression index

−0.05 −0.10 −0.15 −0.20 −0.25 ∗

−0.30 −0.35

Hormone

(b) 1.2

Log of power

1.15 1.1 1.05 1 0.95 0.9 0.85 0.8 placebo

Oxytocin Hormone

Biological Motion

Non-biological Motion

Figure 2. (a) Suppression in the 8–10-Hz range, OT versus placebo. Both bars show suppression for the biological motion conditions compared with the nonbiological condition, but this suppression is enhanced significantly by OT. Error bars represent standard error (SE). (b) An 8–10-Hz interaction between Treatment × Motion. OT had an opposite effect on EEG for perception of biological versus nonbiological stimuli.

bins.indd 2

7/25/2011 10:10:05 AM

HSD11B1 LHCGR

CYP11B1∗

CYP17A1

ESR1

CYP19A1

ESR2

SCP2

ARNT2

IGF1 NTF3 OXT

NTRK1∗ NTRK1∗

HSD17B4 HSD17B2∗ NLGN4X EN-2∗ HOXA1 OXTR GABRB3∗ AVPR1B∗ CNR1∗ GABRA6 MAOB VIPR WFS1∗

Experiment 2:

Experiment 2:

AS case-control nominal association (uncorrected p<0.05)

EQ/AQ nominal association (uncorrected p<0.05)

Figure 1.1-1. Genes showing nominal association with (1) AS case-control analysis and (2) autistic trait measures (AQ, EQ) in the population sample. Intersections summarize genes that show a nominal association in both experiments. Gene functional groups are color coded: Pink (sex hormone related), Yellow (neural connectivity related), and gray (social-emotional responsivity related). Genes in bold indicate replications of associations reported in earlier studies. * indicates a nominally significant association with EQ. Reproduced from Chakrabarti et al. (2009).

Figure 1.3-1. Regions in the brain which are implicated in the mirror neuron system. IFG: Inferior frontal gyrus, PPC: posterior parietal cortex, STS: superior temporal sulcus. During the observation of movements (e.g. facial expressions) movement perception is processed in the STS and information is relayed to posterior and frontal components of the MNS to form motor representations of the observed action.

bins.indd 3

7/25/2011 10:10:05 AM

bins.indd 4

7/25/2011 10:10:05 AM

Figure 1.3-2. Experimental paradigm to study self- and other-related empathic processing (Schulte-Rüther et al., 2011; Greimel et al., 2010a; Greimel et al., 2010b; for a similar paradigm see Schulte-Rüther et al., 2007, 2008). Subjects were instructed to empathize with the person presented on the screen and to identify the emotional state observed in the face (OTHER) or to evaluate their own emotional response to that face (SELF). As a control task, a perceptual decision on the width of neutral faces was used.

Figure 1.3-3. Brain regions involved in ToM. STS: superior temporal sulcus; TPJ: temporoparietal junction; TP: temporal pole; MPFC: medial prefrontal cortex.

Figure 2.2-1. Depiction of vmPFC (in red) in midline views of each hemisphere.

bins.indd 5

7/25/2011 10:10:06 AM

bins.indd 6

7/25/2011 10:10:06 AM

2

5

1

4

123456

1

2

3

4

5

6

6

3

654321

B

1

7

Figure 2.2-2. Lesion overlaps in moral judgment studies. Two studies of patients with focal vmPFC lesions demonstrate abnormal moral judgment for emotional moral dilemmas after vmPFC damage. (a) vmPFC lesion overlap (Koenigs et al., 2007). (b) vmPFC lesion overlap (Ciaramelli et al., 2007).

A

A

B

C

Figure 2.2-3. Brain degeneration in FTD. vmPFC dysfunction in FTD patients is associated with (a) apathy, indexed by hypometabolism with position emission tomography (Peters et al., 2006), (b) disinhibition, indexed by hypoperfusion with single photon emission computed tomography (Le Ber et al., 2006), and (c) impairment in social judgment, indexed by atrophy with voxel-based morphometry (Eslinger et al., 2007).

bins.indd 7

7/25/2011 10:10:07 AM

bins.indd 8

7/25/2011 10:10:07 AM

B

C

D

E

Figure 2.2-4. Examples of functional magnetic resonance imaging activations associated with moral cognition. Activations in vmPFC are associated with (a) viewing pictures with moral content (Moll et al., 2002a), (b) viewing statements with moral content (Moll et al., 2002a), (c) judgments of simple statements with moral content (Heekeren et al., 2003), (d) judgments of moral dilemmas featuring physical harm (Greene et al., 2001), and (e) regulation of moral emotions (Harenski & Hamann, 2006).

A

Figure 2.3-2. Using fMRI in healthy participants this study aimed to unravel the neuroanatomical basis of abstract moral and social values (Zahn et al., 2009b). Participants had to imagine actions in accordance with or counter to a value described by a written sentence and to decide whether they would feel pleasantly or unpleasantly about the action. After the scan they rated the unpleasantness/pleasantness on a scale and chose labels that best described their feelings (the analysis compares each moral sentiment versus visual fixation and versus two other moral sentiments; only selective effects were reported). There were four experimental conditions: (1) positive self-agency: e.g., “Tom (first name of participant) acts generously towards Sam (first name of best friend)”—pride in this condition was associated with ventral tegmental, septal, and ventral medial FPC activation (not depicted); (2) positive other-agency: e.g., “Sam acts generously towards Tom“—gratitude in this condition was associated with hypothalamic activation; (3) negative self-agency: e.g., “Tom acts stingily towards Sam”—guilt in this condition was associated with the subgenual cingulate cortex as well as ventral medial FPC activation (not depicted and only when modeling individual frequency of guilt trials), and (4) negative other-agency: e.g., “Sam acts stingily towards Tom”— indignation/anger in this condition was associated with lateral orbitofrontal/insular activation. In the center, one can see the right superior aTL region showing equally strong activation during all moral sentiment and agency contexts; this region increased activity with increasing richness of conceptual detail describing social behavior and is identical to the activation found in a semantic judgment task (Zahn et al., 2007). These results confirmed the right superior anterior temporal lobe as a context-independent store of social conceptual knowledge that allows us to understand the core meaning of social and moral values irrespective of what exact feelings or actions we tie to the value.

bins.indd 9

7/25/2011 10:10:07 AM

Figure 2.3-4. Brain regions activated when participants donated or opposed to donate to charitable organizations during fMRI (Moll et al., 2006) (a) Both pure monetary rewards and decisions to donate (with or without personal financial costs) activated the mesolimbic reward system, including the ventral tegmental area (VTA) and the ventral and dorsal striatum. (b) The septal–subgenual region (SG), however, was selectively activated by decisions to donate, as compared with pure monetary rewards (both by costly and noncostly decisions; conjunction analysis). The lateral orbitofrontal cortex (latOFC) was activated by decisions to oppose charities. This activation extended to the anterior insula and to the inferior dorsolateral PFC, and it was present for both costly and noncostly decisions (conjunction analysis). The FPC and ventral medial PFC were activated for costly decisions (when voluntarily sacrificing one’s own money either to donate to a charity or to oppose it (conjunction analysis).

bins.indd 10

7/25/2011 10:10:08 AM

CHOOSE A DOOR 1

2

3 You +4

The other player’s turn please wait... You +4

Other +16

Figure 2.4-2. Schematic depiction of a single trial setting (Dvash et al., 2010).

Motor/Behavioral Systems AntCingCtx/SupMotArea Striatum

Memory Systerms: DorsoLatPrefCtx (DLPC) Hippocampus

OrbitFrontCtx/VMPC

Amygdala

Dopamine Serotonin Ach NE

Emotion Systems: Insula Posterior Cingulate Hypothalamus PAG Brainstem autonomic centers

Computation: In the body---Body loop In Sensory nuclei and neurotransmitter cell bodies of brainstem ----As if body loop

Figure 3.1-1. A schematic of all the brain regions involved in decision making according to the somatic marker hypothesis.

bins.indd 11

7/25/2011 10:10:08 AM

A

B

−1

You got:

−1

Total:

−4

Figure 3.2-1. A schematic image of the experimental display in an experience-based decision task with two alternatives.

Proportion of Risky choices

1 0.9

Task 1/20

0.8

Task 1/2

0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 With exposure

Without exposure

Figure 3.2-2. Proportion of risky choices in two decision tasks (1/20 and 1/2), by social exposure condition (Yechiam, Druyan, & Ert, 2008).

bins.indd 12

7/25/2011 10:10:08 AM

INTRODUCTION RICHARD P. EBSTEIN,1 MIKHAIL MONAKHOV,2 POH SAN LAI,3 4 AND SIMONE G. SHAMAY-TSOORY 1

Psychology Department, National University of Singapore, Singapore & Psychology Department, Hebrew University, Jerusalem, Israel 2 Research Fellow, Department of Economics, Faculty of Arts and Social Sciences, National University of Singapore & Department of Paediatrics, National University Hospital 3 Human Molecular Genetics Laboratory, Department of Paediatrics, National University of Singapore 4 University of Haifa, Haifa, Israel

SOCIAL COGNITION Social cognition refers to the processes that subserve behavior in relating to conspecifics. Although social cognitive processes draw on many of the same brain structures involved in perception, cognition, and behavior more generally, Adolphs (2009) suggests three prominent differences that differentiate human social cognition from other cognitive processes and from that of other species: (1) the ability to shift one’s conscious experience to places and times outside the here-and-now, and into the viewpoint of another mind; (2) the association of our evaluation of others with strong moral emotions that motivate particular aspects of social behavior, such as altruistic punishment; and (3) the ability to use these capabilities flexibly as a function of context, across considerable time intervals, and with the help of a prodigious episodic memory that helps us to keep track of a large number of other individuals and their past behavior. These processes are the core mechanisms of cooperativity, altruism, and other aspects of prosocial behavior, as well as the mechanisms for coercion, deception, and manipulation of conspecifics. In recent years, the neural basis of social cognition has been the subject of intensive study in both human and nonhuman primates. Recent exciting and provocative

From DNA to Social Cognition, First Edition. Edited by Richard Ebstein, Simone Shamay-Tsoory, and Soo Hong Chew. © 2011 Wiley-Blackwell. Published 2011 by John Wiley & Sons, Inc.

1

cintro.indd 1

7/25/2011 10:10:27 AM

2

INTRODUCTION

findings attempt to combine for the first time genetic and social neuroscience by using a translational genetic approach. Integrating social neuroscience with genetics may pave the way for exploring the neural mechanisms for human social behavior and elucidate our understanding regarding the potential genetic risk for disorders that involve dysfunctional social behavior such autism and schizophrenia. Part 1: Empathy: Neural Basis and Genetic Correlates The first part of this book will focus on the neuroanatomical, neurchemical, and genetic basis for empathy. In Chapter 1.1, Chakrabarti and Baron-Cohen provide an overview of genetic studies of empathy and other trait measures of autism spectrum conditions (ASC). Three neurocognitive theories of ASC are presented: foetal androgen theory, neural connectivity theory, and social-emotional responsivity theory. The authors describe two studies conducted in their laboratory, one on autistic traits and empathy and one on Asperger Syndrome (AS). In these studies, nine candidate genes were identified, some of which are associated with autistic traits in the general population and/or AS. These genes fall into the three functional categories related to sex-steroid synthesis and metabolism, neural development and connectivity, and social-emotional responsivity, providing some support for the three theories of autism. Several of these genes have been also found to be involved in pair bonding. In Chapter 1.2, Walum and Westberg review the genetic influences of pair bonding. Specifically, the authors focus on the neuropeptides oxytocin (OT) and vasopressin (AVP), which have been found to correlate with affiliative behavior and social bonding. It is suggested that pair bonds could be induced by similar stimulation as the one facilitating the bond between a mother and her offspring. Furthermore, the authors show evidence that the mesolimbic dopamine reward system is highly involved in pair bonding in animals. Therefore they suggest that the individual motivation to engage in pair bonds is an effect of activation of reward centers. In Chapter 1.3, Schulte-Rüther and Greimel take a neurobiological approach to empathy, investigating the cognitive components and the neural substrates of these components. The authors emphasize three core aspects of the definition of empathy: (1) an affective response, (2) a cognitive mechanism allowing for perspective taking, and (3) the ability to maintain a self–other distinction. The authors review evidence from studies investigating the brain networks mediating these aspects of empathy. Perspective taking was consistently identified with a neural network that entails the medial prefrontal cortex (MPFC), the temporoparietal junction (TPJ), and the temporal pole. Brain networks involved in the ability to differentiate one’s own feelings and mental states from those of other people include dorsal and ventral subregions of the MPFC along with temporoparietal regions, such as the right inferior frontal gyrus and the TPJ. Finally it is suggested that the affective response to another may be mediated by the mirror neuron system MNS— including the inferior frontal gyrus and the posterior parietal cortex. This system is further explored by Liew and Aziz-Zadeh in Chapter 1.4. In this chapter, the role of the putative MNS in human-social cognition is reviewed. Specifically, the authors focus on the role of the frontoparietal neural network known as the (MNS) in empathy through the process of simulation. These neurons are thought to match incoming visual information about another’s actions to one’s own motor representations, possibly allowing the observer to understand the other’s action. Nevertheless,

cintro.indd 2

7/25/2011 10:10:27 AM

SOCIAL COGNITION

3

the authors argue that the function of these regions may extend from understanding the actions of others to developing conceptualizations of actions. As stated, although the MNS is referred to as processing the affective aspects of empathy, frontal regions are suggested to process perspective-taking abilities. These abilities allow an individual to shift one’s conscious experience to places and times outside the here-and-now. Chapter 1.5 focuses on the motivational aspects of episodic future thinking. The ability to simulate possible future events mentally is a complex cognitive activity that has been linked to social cognitive processes such as mentalizing. D’Argembeau reviews recent functional neuroimaging findings that suggest that the ventromedial prefrontal cortex (vmPFC) may play a key role in these motivational aspects of episodic future thinking. Furthermore, evidence shows that the vmPFC is involved especially in imagining future situations that are relevant to people’s personal goals, particularly situations that refer to possible rewarding experiences. Finally, the author suggests that a major function of the vmPFC during episodic future thinking is to assign a personal value to future event representations, thereby promoting personal goal achievement. Part 2: Moral Neuroscience and Emotion One uniquely human form of social cognition is moral cognition. Moral neuroscience is an emerging field in neuroscience that examines the neural networks involved in moral cognition. In chapter 2.1, Forbes, Poore, and Grafman discuss how the subregions of the prefrontal cortex (PFC) interact to guide our moral behavior. The authors review the functional anatomy of the PFC and the basic processing roles of its components with respect to the PFC’s connectivity to regions both inside and outside of the cortex. It is suggested that the MPFC is important for the representation of self and other knowledge, social norms, and the appropriateness of given behaviors, whereas the lateral PFC (LPFC) is necessary for assessing this information concerning current goal states, assessing novel contexts or stimuli, and formulating a successful plan of action accordingly. The role of the PFC in implicit and explicit social cognitive and moral judgment processes is discussed. In chapter 2.2, Mike Koenigs reviews and integrates the psychological and neuroscientific research findings on the role of emotion in moral cognition. It is suggested that at a psychological level, emotional processes drive moral judgment. In regard to the neurobiological level, the author emphasizes the role of the vmPFC showing that this region is consistently involved in a wide range of tasks involving moral cognition. The insula and amygdala are also referred as having an important role in the processing of moral emotions. In chapter 2.3, Zahn, Oliveira-Souza and Moll further review the current knowledge on the neuroanatomical basis of moral motivations and suggest a model of moral cognition. The authors demonstrate the involvement of fronto-mesolimbic subregions within the moral cognition network in moral sentiments. Specifically, frontopolar regions seem to process moral sentiments in general. The authors suggest a fronto-temporo-mesolimbic integration model of moral cognition. The model stresses functional integration of information between subcortical mesolimbic and fronto-temporal cortical areas as the correlate of reasoning and emotion. In Chapter 2.4 Dvash and Shamay-Tsoory focus on two forms of emotions that are considered immoral: envy and schadenfreude. The authors propose a neural model of social

cintro.indd 3

7/25/2011 10:10:27 AM

4

INTRODUCTION

comparison based on emotions—envy, schadenfreude (pleasure at another’s misfortune), negative empathy (sympathy, pity), or positive empathy (happy for). Two main processes underlie the experience of these emotions: (1) the reward/punishment system, which mediates motivation and the experience of pleasure and displeasure; and (2) the system determining the desirability of the other person and constructing a model of the other person’s plans and goals that includes simulation processes that translate congruent actions, sensations, and emotions from the experience of others into the neural language of our own, as well as the mentalizing network that processes the other’s mental and emotional state. The authors suggest that although both competitive and cooperative emotions are mediated by the reward network, they differ in the process of the mental representation of the other. Cooperative emotions may involve more simulation processes based on their congruent nature, whereas the competitive emotions may involve more mentalizing processes. At the neurochemical level, the authors emphasize the role of oxytocine and dopamine in modulating both cooperative and competitive emotions. Part 3: Genes and Decision Making Another process that is highly dependent on social cognition is decision making. Appropriate decision making in social situations depends on our ability to understand the mental and emotional state of others (Frith & Singer, 2008) In chapter 3.1, Antoine Bechara presents the somatic marker hypothesis—the notion that people make judgments not only by evaluating the consequences and their probability of occurring but also, and even sometimes primarily, at a gut or emotional level. Neurological background, core mechanisms, and critiques of the theory are reviewed. The chapter outlines experiments supporting the argument that (a) decision making is a process critically dependent on neural systems important for the processing of emotions; (b) conscious knowledge alone is not sufficient for making advantageous decisions; and (c) emotion is not always beneficial to decision making: Sometimes it can be disruptive. Decision making may also be influenced by learning. In chapter 3.2, Koritzky, Luria, and Yechiam summarize the factors contributing to drug abuse. They suggest that drug use acquisition is adequately described as the product of individual learning and decision-making impairments, along with certain social and environmental influences. The authors suggest a model that integrates the effect of these factors. Under this model, the effect of individual learning may be replaced by social experience that facilitates choice of alternatives with rare (or delayed) negative outcomes, by increasing the salience of the immediate positive outcomes as a result of drug consumption. That is to say that social exposure facilitates the use of drugs that bear favorable outcomes most of the time because the benefit of using these drugs gains salience as one witnesses them being used. Chapter 3.3 also deals with the effect of social experiences. Druen and Dugatkin illustrate the effects of antecedent social experiences on individual’s performance in subsequent fights. The authors present mathematical models that allow for generating explicit predictions about dominance hierarchy formation. These models suggest that fighting experience affects subsequent aggressive interactions. Furthermore, the authors demonstrate that observing an aggressive interaction between two other individuals may influence that formation of hierarchies (i.e., bystander effect), suggesting that

cintro.indd 4

7/25/2011 10:10:27 AM

THE MOLECULAR TOOLBOX FOR RESEARCH IN SOCIAL COGNITION

5

bystanders acquire information about future adversaries before having to fight them directly. These effects are demonstrated in the empirical work on green swordtail fish. Although these models predict behavior in competition for dominance, Chapter 3.4 focuses on cooperative behavior and decision making in health and in psychopathology. Brüne and Wischniewski address the evolutionary and ontogenetic aspects of cooperation, game-theoretical approaches to study cooperation, brain mechanisms involved in the ability to appreciate social rules and norms, and research into psychiatric conditions based on game-theoretical models. The authors postulate that the perceived—or factual—threat of basic human needs for cooperation and trust in others is at the core of many cognitive distortions and abnormal behaviors in patients with psychiatric disorders. Evidence is presented, showing that genetic endowment and gene–environment interaction may convey individual differences in cooperation and trust and even differences in vulnerability to psychiatric disorders. The game-theoretical models presented here are used in the fast growing study of neuroeconomics. This approach combines psychology, economics, and neuroscience and is further presented in Chapter 3.5. Aharon and Bourgeois-Gironde propose a neuroeconomic approach to developing models on decision making. Neuroeconomics research is presented, and the advantages of using imaging techniques in the study of economic phenomenon are outlined. The authors emphasize the role of “rewards” in governing behavior. Finally, the benefits in taking a neuroeconomic approach are illustrated through the case of intertemporal choices.

THE MOLECULAR TOOLBOX FOR RESEARCH IN SOCIAL COGNITION The Toolbox GWAS. The title of the current volume is DNA to Social Cognition, and ipso facto it behooves us to peek inside the toolbox of molecular genetics toward gaining some insight into how deciphering of the human genome is likely to impact research on social cognition. It might be said that we are now in the year of the GWAS (genomewide association study), and a plethora of GWAS studies (Beauchamp, Cesarini, Rosenquist, Fowler, & Christakis, 2009; Chasman, Pare, & Ridker, 2009; Dubois et al., 2010; Franke, Neale, & Faraone, 2009; Gershon, Liu, & Badner, 2008; Gottlieb, O’Connor, & Wilk, 2007; Han, Gelernter, Luo, & Yang, 2010; Lango et al., 2008; Lasky-Su et al., 2008; Loos et al., 2008; McCarthy & Zeggini, 2009; Potkin et al., 2010; Treutlein et al., 2009; Wellcome Trust, 2007; Zeggini et al., 2008) recently published are highlighting both the promise and the pitfalls of high-throughput Single Nucleotide Polymorphism (SNP) genotyping toward deciphering the riddle of complex phenotypes and common diseases. SNPs and Linkage Disequilibrium. The first attempts at mapping disease and complex phenotypes onto the human genome used positional cloning and genetic linkage, and these methods have been largely supplanted by GWAS. The GWAS platform is based on single nucleotide polymorphisms, binary variations, that constitute a single base pair change in DNA, e.g., C→T. It is estimated that SNPs occur

cintro.indd 5

7/25/2011 10:10:27 AM

6

INTRODUCTION

as frequently as every 100–300 bases suggesting that the human genome contains 10 to 30 million potential SNPs. Today, more than 4 million SNPs have been identified, and online databases, e.g., dbSNP (http://www.ncbi.nlm.nih.gov/projects/SNP/), provide this information freely to the scientific community. The assemblage of public SNPs by NCBI has produced a subgroup of SNPs, so-called nonredundant markers that are used for annotation of the reference genome sequence and are thus referred to as reference SNPs (rsSNPs). More than 2.5 million SNPs have currently been referred to as “rsSNPs.” HapMap. The HapMap project has revealed a remarkable degree of linkage disequilibrium (LD: the nonrandom association of genetic markers) between SNPs that extends over large regions (∼30k base pairs) of the human genome. LD suggests that a SNP at one locus can predict with high probability the occurrence of a particular SNP at a second locus. Hence, genotyping a group of subjects characterized by a complex disease or phenotype, and comparing the frequency of SNP variants between groups, allows the identification of SNP-based genetic associations. It is important to emphasize that GWAS identifies associated loci and not necessarily functional variants because the SNP on the chip may be dependent on LD with the true causative (functional) SNP for the observed association. The GWAS approach is best used in the context of the “common disease common variant” hypothesis and is not as suitable for identifying rare variants that may be contributing to either complex phenotypes or diseases. If an associated SNP is not the true functional locus, then fine mapping (interrogating the locus by genotyping additional SNPs in the observed region) can be used to identify the true causative variation. The extended haplotype structure of the genome, with its extensive regions of LD, allows the use of SNP genotyping to examine the human genome for genetic variants that are associated with common phenotypes. The genotyping is typically performed on high-density oligonucleotide microarrays (a.k.a. SNP chips) developed by companies such as Affymetrix and Illumina that are designed for high throughput and accurate calling of binary SNP variations. Using microarrays with SNP densities of 5 × 105 – 2.5 × 106 SNPs, it is possible to examine the genome for association with sample sizes of several thousand individuals. However, the testing of so many SNPs in genome-wide studies creates the need for more sophisticated statistical treatments. Because the number of interrogated loci is large (hundreds of thousands of SNPs or more), consideration of multiple comparisons requires that genome-wide significance levels (10−8 and below) be employed, rather than p < 0.05, as generally used in conventional single-gene studies. A recent publication provides an excellent discussion of this issue (Victor, Elsasser, Hommel, & Blettner, 2010). As noted by Victor et al., instead of only considering the level of each test, the familywise error rate (FWER) has been defined. This approach requires setting a lower level for each p value when multiple comparisons are carried out. Various statistical treatments have been derived to deal with FWER. Employing the chip platform with appropriate statistical treatment, common (>5% of the population) variants that confer a small risk of disease, typically with odds ratios of 1.2 to 5.0, have now been identified for a variety of human diseases (Hardy & Singleton, 2009).

cintro.indd 6

7/25/2011 10:10:27 AM

THE MOLECULAR TOOLBOX FOR RESEARCH IN SOCIAL COGNITION

7

Genomic Dark Matter. Altogether, hundreds of SNPs have now been identified that are associated with complex diseases and phenotypes using the GWAS strategy (Donnelly, 2008; Hindorff et al., 2009). Nevertheless, the proportion of genetic variance explained by any one of these SNPs is quite small suggesting that there is a problem of “missing variance.” The riddle of the so-called genome dark matter is elegantly resolved by a recent analysis of human height (Yang et al., 2010). Yang and his colleagues analyzed genotype information (294,831 SNPs) for 3,925 unrelated individuals and show that 45% of variance can be explained by considering all SNPs simultaneously. Hence, heritability is not missing but has not previously been detected because the individual effects are too small to pass current standards for genome-wide significance (∼10−8). Moreover, they provide evidence that the remaining heritability (height is ∼80% heritable) is a result of incomplete linkage disequilibrium between causal variants and genotyped SNPs, aggravated by causal SNPs characterized by lower minor allele frequency (MAF) than the SNPs so-far examined. Expression. A recent perspective in Nature Genetics (“On beyond GWAS,” 2010) and an excellent review in the New England Journal of Medicine (Hardy & Singleton, 2009) see an important avenue of post-GWAS research in determining how SNPs influence gene expression. Many variants discovered by the GWAS approach contribute only a small percentage of the variance to the phenotype likely because of their location in noncoding regions of the gene loci that are involved in regulation of transcription levels. From this perspective, many causal SNPs for complex traits are QTLs (quantitative trait loci) that on the whole are likely to induce relatively minor increments in modulating gene expression rather than the dramatic changes observed in Mendelian disorders. Hence, follow-up functional studies of SNPs identified in GWAS studies is a high priority for establishing the true role of such variants in conferring vulnerability to disease or explaining phenotypic variance for nonclinical traits. Copy Number Variations (CNVs). The “common variant-common disease” hypothesis is still alive and kicking, but there is increasing evidence that “common disease-rare variants” is also an important concept toward explaining vulnerability to common disorders (Bodmer & Bonilla, 2008). In fact both common variants and rare variants are likely contributing to disease vulnerability (Bodmer & Bonilla, 2008) as well to the range of “normal” phenotypes encountered in complex human traits such as intelligence of empathy. An important source of rare variants in the human genome are so-called copy number variants (CNVs). CNVs are defined as stretches of DNA larger than 1000 base pairs (bp) that are normally found only once on each chromosome in each person, but in some individuals these are presented in two or more copies—i.e., there is a variation in the number of copies of this section of DNA from one individual to another (Sebat et al., 2004; Tuzun et al., 2005). CNVs have been implicated in behavioral disorders such as autism (Maestrini et al., 2009; Sebat et al., 2007), ADHD (Elia et al., 2009), schizophrenia (Schwab & Wildenauer, 2009; Singh, Castellani, & O’Reilly, 2009; Tam, Redon, Carter, & Grant, 2009) and in bipolar disorder (Lachman et al., 2007).

cintro.indd 7

7/25/2011 10:10:27 AM

8

INTRODUCTION

Gene × Environment Interactions—the Epigenome. A turning point in human behavioral genetics took place with the publication of a seminal article by Caspi et al. (2002) demonstrating a Gene × Environment interaction between maltreated children and a promoter-region repeat polymorphism in the monoamine oxidase A (MAOA) gene. A second publication showing an interaction among the serotonin transporter promoter indel (5-HTTLPR), depression, and life events (Caspi et al., 2003) has been somewhat more controversial (Risch et al., 2009, but see Uher & McGuffin, 2010). The importance of these two articles was to show the path to consilience between Nature and Nurture and to demonstrate that both genetic programming and environmental signals together mold complex human behaviors. However, the molecular mechanisms by which environmental signals are translated into ongoing behaviors in part preprogrammed remained to be clarified. The answer to this puzzle was provided by the experimental analyses of Meaney and his colleagues (Zhang & Meaney, 2010). In a seminal article, Weaver, et al. (2004) showed that differential maternal care (high versus low licking-grooming, LG) in rat pups modified the methylation pattern of the hippocampal GR (glucocorticoid receptor) gene exon 17, which led to significant differences in subsequent adult behavior. Importantly, the cytosine residue within the 5′ CpG dinucleotide of the nerve growth factor-inducible protein A (NGFI-A) consensus sequence was always methylated (associated with low GR expression) in the offspring of low caring mothers and rarely methylated (high GR expression) in the offspring of high caring dams explaining the observed differences in HPAA reactivity in the adult offspring. These intriguing studies in rats have been extended by McGowan et al. (2009) to humans. They examined epigenetic differences in a neuron-specific glucocorticoid receptor (GR: NR3C1) promoter between postmortem hippocampus obtained from suicide victims with a history of childhood abuse and those from either suicide victims with no childhood abuse or controls. They found reduced GR expression and increased cytosine methylation of the GR promoter only in hippocampi from abused suicide victims. These findings translate previous results from rats to humans and suggest a common effect of parental care on the epigenetic regulation of hippocampal GR expression. In short, epigenetic mechanisms, such as DNA methylation and changes in chromatin structure, have been implicated as a means by which environmental factors, such as maternal behavior, can influence gene expression and are thought to produce long-term health consequences. Altogether, the studies of both the Caspi and Meaney groups suggest that future genetic studies of social cognition and behavior in humans should include, whenever feasible, analysis of the epigenome. Recent findings suggest the notion that the most objective measure of stressful life events that could be incorporated in G × E studies, such as pioneered by Caspi and his colleagues (Caspi et al., 2002; Caspi et al., 2003), would be to examine methylation patterns of the GR receptor as we (Edelman et al., submitted) and others (Alt et al., 2010; McGowan et al., 2009; Moser et al., 2007; Oberlander et al., 2008) have done. After all, stressful life events need to impact the genome at the molecular level toward effecting behavior mediated presumably by modifying gene expression. Methylation patterns are studied starting with bisulfite treatment followed by DNA sequencing. First, DNA is treated with bisulfite, which converts cytosine to

cintro.indd 8

7/25/2011 10:10:27 AM

THE MOLECULAR TOOLBOX FOR RESEARCH IN SOCIAL COGNITION

9

uracil but spares methylated cytosine. At the next step, Sanger sequencing or pyrosequencing of bisulfite-treated DNA allows the scoring of methylated or nonmethylated CpG loci. Pyrosequencing is a preferred method (Dejeux, El abdalaoui, Gut, & Tost, 2009; Reed, Poulin, Yan, & Parissenti, 2010) Spotlighting Common Approaches and Future Opportunities We thought it of some interest to examine some chapters in the current book and highlight future directions that could profitably take advantage of a molecular genetic approach by dipping into the toolbox just described. We hope to illustrate how “DNA” knowledge can be gainfully employed toward further exploring the neurobiological basis of social cognition. Most DNA studies in social cognition have employed a candidate gene approach. Candidate gene association studies have been a productive strategy but require prior hypotheses by focusing on genes that are known to affect biochemically and physiologically the phenotype of interest under the assumption that these are the genes most likely to play a role in the inventoried behavior. Wallum and Westburg “The Behavioral Genetics of Human Pair Bonding”. For example, in Chapter 1.2, Walum and Westberg take a hypothesis-driven candidate gene approach based on translational evidence from a rodent species such as the vole, but importantly, they also leverage recent studies of the AVPR1a receptor in human behavioral phenotypes, examining allele 334 of the RS3 promoter-region repeat allele. They found a highly significant association between this allele and a pencil-and-paper Partner Bonding Scale. As reviewed by Wallum and Westburg, accumulating evidence in humans suggests a role for this receptor in human affiliative behaviors and social cognition. Peeking into the toolbox suggests that the role of the epigenome in modulating the association between AVPR1a and human affiliative behaviors would be a worthwhile avenue to explore. GeneCards (http://www.genecards.org/) is always a good starting point, and indeed, AVPR1a is characterized by a CpG island. The interested reader is referred to the UCSC Genome Browser (http://genome.ucsc.edu/cgi-bin/ hgTracks?db=hg19&hgt.customText=http://www.genecards.org/all_genecards.txt &position=chr12:63530216-63556590), which shows the CpG island location at the AVPR1a gene. Unlike CpG sites in the coding regions of a gene, in most instances, the CpG sites in the CpG islands of promoters are unmethylated if genes are expressed and, hence, are potential targets for gene expression modulation perhaps mediated by environmental signals. Additionally, beyond the better-studied repeat regions in the AVPR1a receptor, SNP variations are also reported for this gene (HapMap: http:// hapmap.ncbi.nlm.nih.gov/). The identified SNPs across a 6.375-kbp region can be downloaded, and using Haploview (http://www.broadinstitute.org/haploview) tagging, SNPs can be selected for further genotyping. Tagging SNPs are a representative SNP in a region of the genome with high linkage disequilibrium (the nonrandom association of alleles at two or more loci). Use of tagging SNPs (identified using the Tagger program in Haploview) as a genotype strategy is a widely used approach toward extracting the maximum genetic information at a minimum cost when doing an association study. That being said, if other SNPs have already been reported in

cintro.indd 9

7/25/2011 10:10:27 AM

10

INTRODUCTION

the literature that are associated with a particular phenotype, then it is also worthwhile to include those SNPs, regardless of their status vis a vis tagging SNPs, in the analysis. So-called next-generation sequencing (NGS) comprises high-throughput platforms designed to sequence millions of base pairs of DNA rapidly and reliably. As noted in a recent review (Hawkins, Hon, & Ren, 2010, p. 476), “With the advent of high-throughput methods—including genome-wide association (GWA) studies, chromatin immunoprecipitation followed by sequencing (ChIP–seq) and RNA sequencing (RNA–seq)—acquisition of genome-scale data has never been easier.” NGS has very significantly reduced the cost of genotyping making it feasible, for example, to sequence all the exons (the exome [Ng et al., 2009]) of a group of individuals. Ultimately, NGS is likely to replace SNP arrays as the method of choice in genomic analyses. One strategy might be to sequence the exomes of subjects with extremely skewed phenotypes for some facets of social decision making (e.g., super partners or super empathizers). Peripheral Biomarkers. Some genes of interest discussed in the Walum and Westburg chapter are expressed not only in the brain but also in peripheral tissues such as easily accessible circulating lymphocytes. Special interest relates to the expression of the oxytocin receptor (OXTR) as well as of the AVPR1a receptor whose gene expression can be examined in blood (S. B. Hu et al., 2003; Tansey et al., 2010). Indeed, transcriptome analysis of genes in blood cells is a research opportunity that is only beginning to be exploited by investigators interested in social cognition. For example, our own group has examined CD38 (an ectoenzyme crucial to the release of oxytocin in the brain [Jin et al., 2007]) expression in lymphoblastoid cells from subjects with autism and has observed reduced expression compared with their nonaffected parents (Lerer et al., 2010; Ebstein et al., 2011; Riebold et al. 2011). Our results are shown in Figure 1.

Log of CD38 expression

1.50 1.00 0.50 0.00 −0.50 −1.00 −1.50 Unaffected parents

ASD

Figure 1. Distribution of the expression (log transformed) of the CD38 gene. Lower expression in the ASD group is significant (p = 0.003). See Lerer et al. (2010), Ebstein et al. (2011), and Riebold et al. (2011) for more details. (See color insert.)

cintro.indd 10

7/25/2011 10:10:27 AM

THE MOLECULAR TOOLBOX FOR RESEARCH IN SOCIAL COGNITION

11

Peripheral biomarkers could also serve to index not only clinical groupings as has been carried out for behavioral disorders (Gladkevich, Kauffman, & Korf, 2004; Rollins, Martin, Morgan, & Vawter, 2010), such as autism (Nishimura et al., 2007), PTSD (Segman et al., 2005; Yehuda et al., 2009), postpartum depression (Segman et al., 2010), schizophrenia (Martin et al., 2009), and bipolar disorder (Pandey, Rizavi, Dwivedi, & Pavuluri, 2008) but also nonclinical QTL phenotypes such as of interest in social cognition viz. high “emphasizers.” Perhaps the most thorough investigation of the feasibility of using peripheral blood cells as a proxy for gene expression in the brain is the investigation recently published by Rollins et al. (2010). These authors examined genome-wide transcription using an Affymetrix microarray. Similar coexpression levels were observed for 4,103 transcripts of 17,859 (22.9%) RefSeq transcripts. Hence, the present results support the feasibility to probe genes related to neural function in the peripheral blood transcriptome as other researchers have reported gene expression differences in neuropsychiatric disorders in PBMC studies (Bowden et al., 2006; V. W. Hu, Frank, Heine, Lee, & Quackenbush, 2006; Philibert et al., 2007; Tsuang et al., 2005). To our knowledge, use of biomarkers for nonclinical phenotypes relevant to social cognition has yet to be attempted, but there are good reasons to assume that such a strategy is worthwhile. Liew and Aziz-Zadeh, “The Human Mirror Neuron System and Social Cognition” Pharmacology. Intranasal, aka “sniffing,” administration of neuropeptides (Born et al., 2002) has become an important tool for manipulating peptidergic neurotransmission and for revealing the underlying neurochemical pathways mediating many facets of social cognition (Ditzen et al., 2009; Domes, Heinrichs, Michel, Berger, & Herpertz, 2007; Kosfeld, Heinrichs, Zak, Fischbacher, & Fehr, 2005). Such manipulations are especially powerful when combined with neuroimaging techniques (Baumgartner, Heinrichs, Vonlanthen, Fischbacher, & Fehr, 2008; Kirsch et al., 2005). Liew and Aziz-Zadeh review in Chapter 1.4 of this volume the role of the human mirror neuron system (MNS) in social cognition. This remarkable system was discovered in the early 1990s, and most informative studies to date have used brain imaging techniques in exploring the neuroanatomy and functional significance of mirror neurons. Surprisingly few, if any, investigations have looked at the neurochemistry or neurogenetics of mirror neurons. One of the few exceptions is our recent study that examined the effect of intranasal oxytocin on mirror neuron activity monitored by EEG oscillations, specifically mu suppression (Perry et al., 2010). Electrophysiological studies in humans associated the suppression of EEG in the mu/alpha and beta bands with perception of biological motion and social stimuli (Muthukumaraswamy & Johnson, 2004; Muthukumaraswamy, Johnson, & McNair, 2004). It has been suggested that mu and beta suppression over sensory-motor regions reflects a resonance system in the human brain analogous to mirror neurons in the monkey. We therefore hypothesized that OT, a social hormone (Ebstein et al., 2009), would enhance this suppression and, hence, for the first time, link the action of this neuropeptide with a human correlate of mirror neuron activity (Perry et al., 2010). Twenty-four students were administered 24 IU of OT or placebo

cintro.indd 11

7/25/2011 10:10:27 AM

12

INTRODUCTION (a)

OXT

PLACEBO

−0.21

−0.07

0.00

Suppression index

−0.05 −0.10 −0.15 −0.20 −0.25 ∗

−0.30 −0.35

Hormone

(b) 1.2

Log of power

1.15 1.1 1.05 1 0.95 0.9 0.85 0.8 placebo

Oxytocin Hormone

Biological Motion

Non-biological Motion

Figure 2. (a) Suppression in the 8–10-Hz range, OT versus placebo. Both bars show suppression for the biological motion conditions compared with the nonbiological condition, but this suppression is enhanced significantly by OT. Error bars represent standard error (SE). (b) An 8–10-Hz interaction between Treatment × Motion. OT had an opposite effect on EEG for perception of biological versus nonbiological stimuli. (See color insert.)

intranasally in a robust, double-blind, within-subject design. Forty-five minutes later, participants were shown a point-light display of continuous biological motion of a human figure’s walk. In the 8–10-Hz (low alpha/mu band) and in the 15–25-Hz beta band, a significant main effect of treatment showed that suppression was significantly enhanced in the OT versus the placebo conditions and that this suppression was widespread across the scalp (Figure 2). These results are a first step linking OT to the modulation of EEG rhythms in humans, suggesting that OT may have a role in allocating cortical resources to social tasks partly mediated by mirror neuron activity. Pharmacogenetics. In addition to pharmacology, neurogenetic strategies are also an important tool for dissecting apart the complex underlying neurochemical pathways

cintro.indd 12

7/25/2011 10:10:27 AM

REFERENCES

13

mediating social cognition. Moreover, combining drug administration with neurogenetics is a powerful strategy in unraveling the neurobiological architecture of complex behavioral phenotypes. A good example of this approach is provided by the recent study of Eisenegger et al. (2010). Using a pharmacogenetic approach, they investigated how variation in the DRD4 exon3 repeat polymorphism modulates the impact of dopaminergic stimulation on gambling behavior in healthy subjects. Without considering genetic information, L-DOPA administration did not lead to an increase in gambling propensity compared with placebo. As expected, however, an individual’s DRD4 polymorphism accounted for variation in gambling behavior after the administration of L-DOPA. Subjects who carry at least one copy of the 7-repeat allele showed an increased gambling propensity after dopaminergic stimulation. Returning to our discussion of the mirror neuron system, it is presumably now clearer how combining cutting edge techniques from the toolboxes of neuroscience and molecular genetics could be used to elucidate the underlying neurochemical and neurogenetic architecture of the MNS. Indeed, future studies would profitably incorporate in the same experiments genetic, pharmacological, and imaging strategies toward a more comprehensive understanding of the human MNS.

ACKNOWLEDGMENTS We gratefully acknowledge support from the following granting agencies: (1) RPE, “Decision Making Under Urbanization: A Neurobiological and Experimental Economics Approach” (HSS-1001-P02); (2) RPE, The AXA Research Fund for “Biology of Decision Making Under Risk” (R-581-000-115-720); (3) SHC, “Biological Economics and Decision Making” (MOE2010-T2-1-114); (4) SST, Israeli Scientific Foundation (ISF) (489/08); (5) REP, “The Role of Gene CD38 (Cyclic ADP-Ribose Hydrolase) in Human Social Behavior” (R581000108133 NUS Start-up grant); and (6) RPE, “Interplay of Genes and Environment in Modulating Fetal and Infant Stress Response to Prenatal Maternal Smoking” (ISP 0321693).

REFERENCES Adolphs R. (2009). The social brain: Neural basis of social knowledge. Annu Rev Psychol 60, 693–716. Alt, S. R., Turner, J. D., Klok, M. D., Meijer, O. C., Lakke, E. A. et al. (2010). Differential expression of glucocorticoid receptor transcripts in major depressive disorder is not epigenetically programmed. Psychoneuroendocrinology 35(4), 544–556. Baumgartner, T., Heinrichs, M., Vonlanthen, A., Fischbacher, U., & Fehr, E. (2008). Oxytocin shapes the neural circuitry of trust and trust adaptation in humans. Neuron 58(4), 639–650. Beauchamp, J., Cesarini, D., Rosenquist, J., Fowler, J., & Christakis, N. (2009). A Genome Wide Association Study of Educational Attainment: Prepared for the IZA Conference on Genes, Brains, and the Labor Market to be held in Bonn, November 2009. Bodmer, W., & Bonilla, C. (2008). Common and rare variants in multifactorial susceptibility to common diseases. Nat Genet 40(6), 695–701.

cintro.indd 13

7/25/2011 10:10:27 AM

14

INTRODUCTION

Born, J., Lange, T., Kern, W., McGregor, G. P., Bickel, U., & Fehm, H. L. (2002). Sniffing neuropeptides: A transnasal approach to the human brain. Nat Neurosci 5(6), 514–516. Bowden, N. A., Weidenhofer, J., Scott, R. J., Schall, U., Todd, J., et al. (2006). Preliminary investigation of gene expression profiles in peripheral blood lymphocytes in schizophrenia. Schizophr Res 82(2–3), 175–183. Caspi, A., McClay, J., Moffitt, T. E., Mill, J., Martin, J., et al. (2002). Role of genotype in the cycle of violence in maltreated children. Science 297(5582), 851–854. Caspi, A., Sugden, K., Moffitt, T. E., Taylor, A., Craig, I. W., Harrington, H., et al. (2003). Influence of life stress on depression: Moderation by a polymorphism in the 5-HTT gene. Science 301(5631), 386–389. Chasman, D. I., Pare, G., & Ridker, P. M. (2009). Population-based genomewide genetic analysis of common clinical chemistry analytes. Clin Chem 55(1), 39–51. Dejeux, E., El abdalaoui, H., Gut, I. G., & Tost, J. (2009). Identification and quantification of differentially methylated loci by the pyrosequencing technology. Metho Mol Biol 507, 189–205. Ditzen, B., Schaer, M., Gabriel, B., Bodenmann, G., Ehlert, U., & Heinrichs, M. (2009). Intranasal oxytocin increases positive communication and reduces cortisol levels during couple conflict. Biol Psychiatr 65(9), 728–731. Domes, G., Heinrichs, M., Michel, A., Berger, C., & Herpertz, S. C. (2007). Oxytocin improves “mind-reading” in humans. Biol Psychiatr 61(6), 731–733. Donnelly, P. (2008). Progress and challenges in genome-wide association studies in humans. Nature 456(7223), 728–731. Dubois, P. C., Trynka, G., Franke, L., Hunt, K. A., Romanos, J., Curtotti, A., et al. (2010). Multiple common variants for celiac disease influencing immune gene expression. Nat Genet 42(4), 295–302. Ebstein, R. P., Israel, S., Lerer, E., Uzefovsky, F., Shalev, I., Gritsenko, I., et al. (2009). Arginine vasopressin and oxytocin modulate human social behavior. Ann N Y Acad Sci 1167, 87–102. Ebstein, R. P., Mankuta, D., Yirmiya, N., & Malavasi, F. (2011). Are retinoids potential therapeutic agents in disorders of social cognition including autism? FEBS Lett 585, 1529–1536. Edelman, S., Shalev, I., Uzefovsky, F., Israel, S., Knafo, A., Kremer, I., et al. Epigenetic modification of the glucocorticoid receptor gene predicts women’s salivary cortisol following a threat to the social self. Submitted for publication. Eisenegger, C., Knoch, D., Ebstein, R. P., Gianotti, L. R., Sandor, P. S., et al. (2010). Dopamine receptor D4 polymorphism predicts the effect of L-DOPA on gambling behavior. Biol Psychiatr 67(8), 702–706. Elia, J., Gai, X., Xie, H. M., Perin, J. C., Geiger, E., et al. (2009). Rare structural variants found in attention-deficit hyperactivity disorder are preferentially associated with neurodevelopmental genes. Mol Psychiatr 15(6), 637–646. Franke, B., Neale, B. M., & Faraone, S. V. (2009). Genome-wide association studies in ADHD. Hum Genet 126(1), 13–50. Frith, C. D., & Singer T. (2008). The role of social cognition in decision making. Philos Trans R Soc Lond B Biol Sci 363, 3875–3886. Gershon, E. S., Liu, C., & Badner, J. A. (2008). Genome-wide association in bipolar. Mol Psychiatr 13(1), 1–2. Gladkevich, A., Kauffman, H. F., & Korf, J. (2004). Lymphocytes as a neural probe: Potential for studying psychiatric disorders. Prog Neuropsychopharmacol Biol Psychiatr 28(3), 559–576.

cintro.indd 14

7/25/2011 10:10:27 AM

REFERENCES

15

Gottlieb, D. J., O’Connor, G. T., & Wilk, J. B. (2007). Genome-wide association of sleep and circadian phenotypes. BMC Med Genet 8(Suppl 1), S9. Han, S., Gelernter, J., Luo, X., & Yang, B. Z. (2010). Meta-analysis of 15 genome-wide linkage scans of smoking behavior. Biol Psychiatr 67(1), 12–19. Hardy, J., & Singleton, A. (2009). Genomewide association studies and human disease. New Engl J Med 360(17), 1759–1768. Hawkins, R. D., Hon, G. C., & Ren, B. (2010). Next-generation genomics: An integrative approach. Nat Rev Genet 11(7), 476–486. Hindorff, L. A., Sethupathy, P., Junkins, H. A., Ramos, E. M., Mehta, J. P., et al. (2009). Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci U S A 106(23), 9362–9367. Hu, S. B., Zhao, Z. S., Yhap, C., Grinberg, A., Huang, S. P., et al. (2003). Vasopressin receptor 1a-mediated negative regulation of B cell receptor signaling. J Neuroimmunol 135(1–2), 72–81. Hu, V. W., Frank, B. C., Heine, S., Lee, N. H., & Quackenbush, J. (2006). Gene expression profiling of lymphoblastoid cell lines from monozygotic twins discordant in severity of autism reveals differential regulation of neurologically relevant genes. BMC Genom 7, 118. Jin, D., Liu, H. X., Hirai, H., Torashima, T., Nagai, O., et al. (2007). CD38 is critical for social behaviour by regulating oxytocin secretion. Nature 446(7131), 41–45. Kirsch, P., Esslinger, C., Chen, Q., Mier, D., Lis, S., et al. (2005). Oxytocin modulates neural circuitry for social cognition and fear in humans. J Neurosci 25(49), 11489– 11493. Kosfeld, M., Heinrichs, M., Zak, P. J., Fischbacher, U., & Fehr, E. (2005). Oxytocin increases trust in humans. Nature 435(7042), 673–676. Lachman, H. M., Pedrosa, E., Petruolo, O. A., Cockerham, M., Papolos, A., et al. (2007). Increase in GSK3beta gene copy number variation in bipolar disorder. Am J Med Genet B Neuropsychiatr Genet 144(3), 259–265. Lango, H., Palmer, C. N., Morris, A. D., Zeggini, E., Hattersley, A. T., et al. (2008). Assessing the combined impact of 18 common genetic variants of modest effect sizes on type 2 diabetes risk. Diabetes 57(11), 3129–3135. Lasky-Su, J., Anney, R. J., Neale, B. M., Franke, B., Zhou, K., et al. (2008). Genome-wide association scan of the time to onset of attention deficit hyperactivity disorder. Am J Med Genet B Neuropsychiatr Genet 147B(8), 1355–1358. Lerer, E., Levi, S., Israel, S., Yaari, M., Nemanov, L., Mankuta, D., et al. (2010). Low CD38 expression in lymphoblastoid cells and haplotypes are both associated with autism in a family-based study. Autism Res. 3(6), 293–302. Loos, R. J., Lindgren, C. M., Li, S., Wheeler, E., Zhao, J. H., et al. (2008). Common variants near MC4R are associated with fat mass, weight and risk of obesity. Nat Genet 40(6), 768–775. Maestrini, E., Pagnamenta, A. T., Lamb, J. A., Bacchelli, E., Sykes, N. H., et al. (2009). Highdensity SNP association study and copy number variation analysis of the AUTS1 and AUTS5 loci implicate the IMMP2L-DOCK4 gene region in autism susceptibility. Mol Psychiatr 15(9), 954–968. Martin, M. V., Rollins, B., Sequeira, P. A., Mesen, A., Byerley, W., et al. (2009). Exon expression in lymphoblastoid cell lines from subjects with schizophrenia before and after glucose deprivation. BMC Med Genom 2, 62. McCarthy, M. I., & Zeggini, E. (2009). Genome-wide association studies in type 2 diabetes. Curr Diabetes Rep 9(2), 164–171.

cintro.indd 15

7/25/2011 10:10:27 AM

16

INTRODUCTION

McGowan, P. O., Sasaki, A., D’Alessio, A. C., Dymov, S., Labonte, B., et al. (2009). Epigenetic regulation of the glucocorticoid receptor in human brain associates with childhood abuse. Nat Neurosci 12(3), 342–348. Moser, D., Molitor, A., Kumsta, R., Tatschner, T., Riederer, P., et al. (2007). The glucocorticoid receptor gene exon 1-F promoter is not methylated at the NGFI-A binding site in human hippocampus. World J Biol Psychiatr 8(4), 262–268. Muthukumaraswamy, S. D., & Johnson, B. W. (2004). Changes in rolandic mu rhythm during observation of a precision grip. Psychophysiology 41(1), 152–156. Muthukumaraswamy, S. D., Johnson, B. W., & McNair, N. A. (2004). Mu rhythm modulation during observation of an object-directed grasp. Cognit Brain Res 19(2), 195–201. Ng, S. B., Turner, E. H., Robertson, P. D., Flygare, S. D., Bigham, A. W., et al. (2009). Targeted capture and massively parallel sequencing of 12 human exomes. Nature 461(7261), 272–276. Nishimura, Y., Martin, C. L., Vazquez-Lopez, A., Spence, S. J., Alvarez-Retuerto, A. I., et al. (2007). Genome-wide expression profiling of lymphoblastoid cell lines distinguishes different forms of autism and reveals shared pathways. Hum Mol Genet 16(14), 1682–1698. Oberlander, T. F., Weinberg, J., Papsdorf, M., Grunau, R., Misri, S., et al. (2008). Prenatal exposure to maternal depression, neonatal methylation of human glucocorticoid receptor gene (NR3C1) and infant cortisol stress responses. Epigenetics 3(2), 97–106. On beyond GWAS. (2010). Nat Genet 42(7), 551. Pandey, G. N., Rizavi, H. S., Dwivedi, Y., & Pavuluri, M. N. (2008). Brain-derived neurotrophic factor gene expression in pediatric bipolar disorder: Effects of treatment and clinical response. J Am Acad Child Adolesc Psychiatr 47(9), 1077–1085. Perry, A., Bentin, S., Shalev, I., Israel, S., Uzefovsky, F., et al. (2010). Intranasal oxytocin modulates EEG mu/alpha and beta rhythms during perception of biological motion. Psychoneuroendocrinology 35(10), 1446–1453. Philibert, R., Madan, A., Andersen, A., Cadoret, R., Packer, H., et al. (2007). Serotonin transporter mRNA levels are associated with the methylation of an upstream CpG island. Am J Med Genet B Neuropsychiatr Genet 144B(1), 101–105. Potkin, S. G., Macciardi, F., Guffanti, G., Wang, Q., Turner, J. A., et al. (2010). Identifying gene regulatory networks in schizophrenia. Neuroimage 53(3), 839–847. Reed, K., Poulin, M. L., Yan, L., & Parissenti, A. M. (2010). Comparison of bisulfite sequencing PCR with pyrosequencing for measuring differences in DNA methylation. Anal Biochem 397(1), 96–106. Riebold, M., Mankuta, D., Lerer, B., Israel, S., Zhong, S., Nemanov, L., et al. (2011). Alltrans-retinoic-acid (ATRA) upregulates reduced CD38 transcription in lymphoblastoid cell lines from autism spectrum disorder. Mol Med (in press). Risch, N., Herrell, R., Lehner, T., Liang, K. Y., Eaves, L., et al. (2009). Interaction between the serotonin transporter gene (5-HTTLPR), stressful life events, and risk of depression: A meta-analysis. JAMA 301(23), 2462–2471. Rollins, B., Martin, M. V., Morgan, L., & Vawter, M. P. (2010). Analysis of whole genome biomarker expression in blood and brain. Am J Med Genet B Neuropsychiatr Genet 153B(4), 919–936. Schwab, S. G., & Wildenauer, D. B. (2009). Update on key previously proposed candidate genes for schizophrenia. Curr Opin Psychiatr 22(2), 147–153. Sebat, J., Lakshmi, B., Malhotra, D., Troge, J., Lese-Martin, C., et al. (2007). Strong association of de novo copy number mutations with autism. Science 316(5823), 445–449.

cintro.indd 16

7/25/2011 10:10:27 AM

REFERENCES

17

Sebat, J., Lakshmi, B., Troge, J., Alexander, J., Young, J., et al. (2004). Large-scale copy number polymorphism in the human genome. Science 305(5683), 525–528. Segman, R. H., Goltser-Dubner, T., Weiner, I., Canetti, L., Galili-Weisstub, E., et al. (2010). Blood mononuclear cell gene expression signature of postpartum depression. Mol Psychiatr 15(1), 93–100, 102. Segman, R. H., Shefi, N., Goltser-Dubner, T., Friedman, N., Kaminski, N., & et al. (2005). Peripheral blood mononuclear cell gene expression profiles identify emergent posttraumatic stress disorder among trauma survivors. Mol Psychiatr 10(5), 500–513, 425. Singh, S. M., Castellani, C. A., & O’Reilly, R. L. (2009). Copy number variation showers in schizophrenia: An emerging hypothesis. Mol Psychiatr 14(4), 356–358. Tam, G. W., Redon, R., Carter, N. P., & Grant, S. G. (2009). The Role of DNA copy number variation in schizophrenia. Biol Psychiatr 66(11), 1005–1012. Tansey, K. E., Brookes, K. J., Hill, M. J., Cochrane, L. E., Gill, M., et al. (2010). Oxytocin receptor (OXTR) does not play a major role in the aetiology of autism: Genetic and molecular studies. Neurosci Lett 474(3), 163–167. Treutlein, J., Cichon, S., Ridinger, M., Wodarz, N., Soyka, M., et al. (2009). Genome-wide association study of alcohol dependence. Arch Gen Psychiatr 66(7), 773–784. Tsuang, M. T., Nossova, N., Yager, T., Tsuang, M. M., Guo, S. C., et al. (2005). Assessing the validity of blood-based gene expression profiles for the classification of schizophrenia and bipolar disorder: A preliminary report. Am J Med Genet B Neuropsychiatr Genet 133B(1), 1–5. Tuzun, E., Sharp, A. J., Bailey, J. A., Kaul, R., Morrison, V. A., et al. (2005). Fine-scale structural variation of the human genome. Nat Genet 37(7), 727–732. Uher, R., & McGuffin, P. (2010). The moderation by the serotonin transporter gene of environmental adversity in the etiology of depression: 2009 update. Mol Psychiatr 15(1), 18–22. Victor, A., Elsasser, A., Hommel, G., & Blettner, M. (2010). Judging a plethora of p-values: How to contend with the problem of multiple testing—part 10 of a series on evaluation of scientific publications. Dtsch Arztebl Int 107(4), 50–56. Weaver, I. C., Cervoni, N., Champagne, F. A., D’Alessio, A. C., Sharma, S., et al. (2004). Epigenetic programming by maternal behavior. Nat Neurosci 7(8), 847–854. Wellcome Trust, C. C. C. (2007). Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447(7145), 661–678. Yang, J., Benyamin, B., McEvoy, B. P., Gordon, S., Henders, A. K., et al. (2010). Common SNPs explain a large proportion of the heritability for human height. Nat Genet 42(7), 565–569. Yehuda, R., Cai, G., Golier, J. A., Sarapas, C., Galea, S., et al. (2009). Gene expression patterns associated with posttraumatic stress disorder following exposure to the World Trade Center attacks. Biol Psychiatr 66(7), 708–711. Zeggini, E., Scott, L. J., Saxena, R., Voight, B. F., Marchini, J. L., et al. (2008). Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet 40, 638–645. Zhang, T. Y., & Meaney, M. J. (2010). Epigenetics and the environmental regulation of the genome and its function. Annu Rev Psychol 61, 439–466, C431–C433.

cintro.indd 17

7/25/2011 10:10:27 AM

PART 1 EMPATHY: NEURAL BASES AND GENETIC CORRELATES

c01.indd 19

7/25/2011 10:10:09 AM

1.1 GENES RELATED TO AUTISTIC TRAITS AND EMPATHY BHISMADEV CHAKRABARTI1,2 and SIMON BARON-COHEN2 1

Centre for Integrative Neuroscience and Neurodynamics, School of Psychology and Clinical Language Sciences, University of Reading, Reading, U.K. 2 Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, U.K

Empathy is the glue that binds human society together. It is the ability to understand one’s own and others’ mental states and to respond to these appropriately (BaronCohen & Wheelwright, 2004). It is of particular interest to study empathy in relation to autism spectrum conditions (ASC) because these are marked by atypical social behavior. Recent years have witnessed significant advances in understanding the neurobiology of empathy and its individual differences (Chakrabarti & BaronCohen, 2006; Singer & Lamm, 2009). Independently, human molecular genetics has made enormous advances in the past decade, both in delineating the role of specific genes as well as in making it possible to identify a large number of sequence variations (polymorphisms) in the whole human genome at once. It is therefore timely to take a multilevel perspective in the study of empathy and social cognition, encompassing all stages from genes to cognition. In this chapter, we provide a brief overview of genetic studies of empathy and other trait measures of ASC. We then describe a recent study from our group, using dimensional phenotypic measures of empathy and autistic traits. Finally, we discuss some initial studies that relate genetic variation to “intermediate phenotypes” (also known as endophenotypes) relevant to ASC. ASC entail a disability in social and communication development, alongside unusually narrow interests (“obsessions”) and repetitive behavior (APA, 1987; ICD10, 1994). ASC have a genetic basis, which is indicated by signficantly higher concordance rates in monozygotic (MZ) than in dizygotic (DZ) twins, and with From DNA to Social Cognition, First Edition. Edited by Richard Ebstein, Simone Shamay-Tsoory, and Soo Hong Chew. © 2011 Wiley-Blackwell. Published 2011 by John Wiley & Sons, Inc.

21

c01.indd 21

7/25/2011 10:10:09 AM

22

GENES RELATED TO AUTISTIC TRAITS AND EMPATHY

heritability estimates of more then 90% (Bailey et al., 1995; Folstein & Rutter, 1977). During the last three decades, several strategies have been used to discover genes related to ASC. A common feature in most of these studies has been the use of clinical diagnosis of ASC as a categorical phenotype. In these studies, people with a diagnosis of ASC are compared with a group of people without a clinical diagnosis, matched on a variety of measures. This approach has implicated multiple genes, along with environmental (Wagner, Reuhl, Cheh, McRae, & Halladay, 2006) and epigenetic factors (Crespi & Badcock, 2008; Nagarajan et al., 2008). Mixed evidence from genome-wide linkage studies of samples that do not differentiate between classic (low-functioning) autism and Asperger Syndrome (AS) have found linkage peaks in nearly all chromosomes (Abrahams & Geschwind, 2008). Genome-wide association studies (GWAs) are a more recent development, and they use oligonucleotide microarrays that allow for simultaneous genotyping of common polymorphisms from nearly all known human genes. The initial GWAs on autism, using the traditional case-control design, have found significantly associated polymorphisms in genes located on multiple chromosomes (AGPC, 2007; Wang et al., 2009). In addition, recent findings suggest that rare de novo copy number variations (CNVs) can potentially account for up to 10–24% of cases in families that have only one child with ASC (Jacquemont et al., 2006; Pinto et al., 2010; Sebat et al., 2007). In sum, case-control genetic studies of an ASC suggest that: a) ASC is an oligogenic condition (i.e., it is unlikely that there will be a single gene whose malfunction will explain all features of this condition). b) Both rare as well as common sequence variants (single nucleotide polymorphisms [SNPs] and CNVs) are associated with this condition (Arking et al., 2008; Corvin, Craddock, & Sullivan, 2010; Glessner et al., 2009; Pinto et al., 2010; Wang et al., 2009). Although genotyping common and rare sequence variants of the whole human genome has become a routine procedure during the last few years, most studies have continued to use the classic case-control design. This poses some potential problems, particularly for autism research. The heterogeneity within ASC is not captured in this design, as most of these studies group people with classic autism together with those on the broader spectrum (having a diagnosis of HighFunctioning Autism [HFA] or AS). This raises the possibility of potential confounds resulting from factors such as language delay, below average IQ (as observed in classic autism but not in AS) or co-occurring (a term we prefer to the more medical term “comorbid”, for obvious reasons) conditions such as epilepsy and hyperactivity. In addition, a commonly used measure for verifying a current clinical diagnosis of autism (e.g., the Autism Diagnostic Observation Schedule [ADOS] (Lord et al., 1989) is a) Optimized for diagnosing classic autism, and not AS/HFA and b) Does not include one key dimension of the autistic symptomatology (repetitive behavior) in its final scoring algorithm, both of which could result in a biased sampling within the clinical cohorts.

c01.indd 22

7/25/2011 10:10:09 AM

GENES RELATED TO AUTISTIC TRAITS AND EMPATHY

23

In view of the heterogeneity within ASC, and given the existence of the broader autism phenotype (BAP) (Piven, Palmer, Jacobi, Childress, & Arndt, 1997) or subthreshold instances of ASC, an emerging consensus in autism phenotypic studies suggests that autistic traits are distributed on a continuum not just within clinic samples but right across the general population. The Autism Spectrum Quotient (Baron-Cohen, Wheelwright, Skinner, Martin, & Clubley, 2001) is one such trait measure that captures the population variability in autistic traits in both social and repetitive behavior domains. Other trait measures such as the Empathy Quotient (EQ) (Baron-Cohen & Wheelwright, 2004) and the Social Responsiveness Scale (SRS) (Constantino, Przybeck, Friesen, & Todd, 2000) provide a dimensional measure of the social functioning in the general population, and people with ASC tend to cluster toward the low end of the score distribution. Empathy has been a focus of genetic study for several decades. A standard approach so far has been to test for heritability of “trait empathy” (i.e., stable individual differences in empathy) or other aspects of social behavior by comparing MZ and DZ twins. Nearly all of these studies have shown a greater correlation of empathy measures in MZ compared with DZ twins, suggesting a genetic basis for trait empathy (Davis, Luce, & Kraus, 1994; Loehlin & Nichols, 1976; Matthews, Batson, Horn, & Rosenman, 1981) as measured indirectly using the Questionnaire Measure of Emotional Empathy (QMEE) (Mehrabian & Epstein, 1972). Rushton, Fulker, Neale, Nias, and Eysenck (1986), in a large-scale twin study in humans, suggested a large heritability estimate of 68% for emotional empathy. Other twin studies, particularly in children, have used behavioral observation paradigms of empathy in a laboratory situation. These involve simulating scripted situations (e.g., the experimenter tripping on a chair, or the mother of the child getting her finger caught while closing a suitcase), while video-recording the child’s reactions. A study of 14- and 20-month-old twins using this paradigm confirmed a genetic contribution to empathic concern (Zahn-Waxler, RadkeYarrow, Wagner, & Chapman, 1992). A recent twin study on 409 twin pairs by the same group showed that genetic effects on empathy and prosociality (measured using video-recorded behavior in a laboratory setting) increase with age and that shared environmental effects decrease with age (Knafo, Zahn-Waxler, Van Hulle, Robinson, & Rhee, 2008). Self-reported empathy is one of several trait measures of social behavior. Several other trait and performance measures of social behavior have been studied for genetic contributions (Ebstein, Israel, Chew, Zhong, & Knafo, 2010). Among behavioral phenotypes specifically relevant to ASC, performance on the “Reading the Mind in the Eyes” Test (RMET), shows a strong degree of familiality (Baron-Cohen & Hammer, 1997; Losh & Piven, 2007). Questionnaire measures of social functioning using the SRS (Constantino & Todd, 2000, 2005; Sung et al., 2005) and of autistic traits using the Autism Spectrum Quotient (AQ) (Baron-Cohen et al., 2001) reveal strong familiality (Bishop et al., 2004; Wheelwright, Auyeung, Allison, & Baron-Cohen, 2010) as well as heritability in twin studies (Hoekstra, Bartels, Verweij, & Boomsma, 2007). These studies corroborate findings from the early twin studies in suggesting a genetic underpinning for social behavior relevant to ASC. Interestingly, most of these phenotypic measures have been studied not just on family relatives but also primarily on the higher functioning end of the autism spectrum (HFA and/or AS). This is in contrast to the large-scale genetic studies, which

c01.indd 23

7/25/2011 10:10:09 AM

24

GENES RELATED TO AUTISTIC TRAITS AND EMPATHY

have primarily tested the “lower functioning” end of the clinical spectrum, focusing on classic autism. This presents a disconnect between advances at the phenotypic and genotypic ends of the sequence from DNA to cognition. This disconnect manifests itself in two potential problems: (1) Possible confounds resulting from genes related to low IQ, language delay, or co-occuring conditions might influence the inferences we can draw from the large-scale case-control studies of ASC; and (2) by treating ASC as a categorical condition, it does not take into account the continuous nature of the distribution of autistic traits in the general population. A small number of pioneering studies have attempted to study the dimensional phenotypes within ASC using linkage and association studies (Campbell, Warren, Sutcliffe, Lee, & Levitt, 2010; Conciatori et al., 2004; Losh, Sullivan, Trembath, & Piven, 2008). We attempted to bridge this disconnect by conducting two parallel candidate gene association studies in our laboratory, which we describe in the next section. The first is of autistic traits (measured using the AQ) and empathy (measured using the EQ) in the general population. The second is of AS, which is marked by social and behavioral impairments and unusually narrow interests, but it is not associated with language or general cognitive delays during development. A key feature of our studies was in the choice of multiple candidate genes from three groups of genes, defined by gene function. This approach has been used in other conditions (Pharoah, Tyrer, Dunning, Easton, & Ponder, 2007) but not in the study of ASC. Traditionally, genetic association studies of ASC have either studied one or a small number of candidate genes or the whole genome (Losh et al., 2008). We chose 68 candidate genes for these two experiments, derived from three functional categories: (1) sex hormone-related genes; (2) genes involved in neural development and connectivity; and (3) genes involved in social and emotional responsivity (see Table 1.1-1). We searched for common genetic variants (single nucleotide polymorphisms [SNPs]) on the assumption that autistic traits are continuously distributed in the general population so the genetic contributions to individual differences in empathy or autistic traits are likely to be normative variants rather than “disease”causing mutations. Each of the three functional categories derives from a clear neurocognitive theory of ASC, which will be outlined next. The foetal androgen theory (Baron-Cohen, Knickmeyer & Belmonte, 2005) suggests that genes involved in sex steroid synthesis and transport might be related to ASC. Much of the empirical basis of this theory derives from studies that have measured levels of fetal testosterone (fT), measured in amniotic fluid in the general population. fT levels correlate negatively with eye contact at 12 months old, vocabulary size at 24 months old (Lutchmaya, Baron-Cohen, & Raggatt, 2002), social development at 4 years old (Knickmeyer, Baron-Cohen, Raggatt, & Taylor, 2005), and scores on the EQ and the Eyes test at 8 years old (Chapman et al., 2006). FT levels also correlate positively with narrow interests at 4 years old (Knickmeyer et al., 2005), Systemizing Quotient (SQ), AQ at 8 years old (Auyeung et al., 2009; Auyeung et al., 2006), and autistic traits at as young as 18–30 months of age (Auyeung, Taylor, Hackett & Baron-Cohen, 2010). Neural connectivity theory, based on evidence from studies of rat and human brains, suggests that the key abnormality in autism might be related to neural growth and connectivity (Belmonte et al., 2004). ASC has a neurodevelopmental origin, and an emerging body of genetic evidence suggests a crucial role for genes involved in neural growth, synaptic development, and function (Bourgeron, 2009). At the

c01.indd 24

7/25/2011 10:10:09 AM

GENES RELATED TO AUTISTIC TRAITS AND EMPATHY

25

TABLE 1.1-1. List of all genes included in the association study, along with brief functional roles where known. Genes marked in bold indicate those previously linked to ASC through genetic linkage/association studies. Reproduced from Chakrabarti et al. (2009) Neural development and connectivity NGF, BDNF, NTF3, NTF5, NGFR, NTRK1, NTRK2, NTRK3, TAC1,IGF1,IGF2 RAPGEF4 VGF VEGF

ARNT2 NLGN1,NLGN4X,AGRIN

NRCAM EN-2(AUTS1)

HOXA1

Neuronal survival, differentiation and growth.

Growth and differentiation of neurons. Mutations associated with classic autism. Upregulated directly by NGF and expressed in neuroendocrine cells. Promotes cell growth and migration, especially during angiogenesis and vasculogenesis, often observed during hypoxia. Modulated directly by PTEN. Neural response to hypoxia Synapse formation and maintenance in CNS neurons. NLGN4X mutations have been linked to autism. Neuronal adhesion and directional signalling during axonal cone growth. Neuronal migration and cerebellar development. EN-2 has been previously linked to ASCs in several studies. Hindbrain patterning. Mixed evidence suggests a link with ASCs.

Social and emotional responsivity OXT,OXTR,AVPR1A,AVPR1B

CNR1,OPRM1,TRPV1

MAOB WFS1

GABRB3,GABRG3,GABRA6,ABAT

VIPR1

c01.indd 25

Linked to social attachment behaviour in humans and other mammals.AVPR1A and OXTR have previously been associated with ASCs. Mediate endogenous reward circuits, in tandem with dopaminergic pathways. Implicated in underlying rewarding features of social interactions. Synaptic breakdown of dopamine and serotonin. Suggested links with social cognition. Mutations linked to affective disorders. Overexpressed in amygdala during fear response, though exact functional role is not known. Mediate inhibitory (GABA-ergic) neurotransmission as well as play a role in early cortical development. GABRA6 is expressed strongly in the cerebellum; GABRB3, GABRG3, ABAT have all been associated with ASCs. Suggested involvement in neural pathways underlying pheromone processing. Mutations associated with social behavioural abnormalities in mice. Its endogenous ligand (VIP) shows an overexpression in neonatal children with autism. (Continued)

7/25/2011 10:10:09 AM

26

GENES RELATED TO AUTISTIC TRAITS AND EMPATHY

TABLE 1.1-1. (Continued) Sex hormone biosynthesis, metabolism and transport DHCR7

CYP1A1,CYP1B1,CYP3A,CYP7A1, CYP11A,CYP11B1,CYP17A1, CYP19A1,CYP21A2, POR HSD11B1,HSD17B2,HSD17B3, HSD17B4 STS, SULT2A1,SRD5A1,SRD5A2 SHBG,SCP2,TSPO,SLC25A12, SLC25A13

AR ESR1,ESR2 CGA, CGRPR, LHB, LHRHR, LHCGR, FSHB

Metabolism of cholesterol: precursor for sex hormones (mutations associated with nearuniversal presence of ASC) Synthesis of sex hormones such as progesterone, estrogen, cortisol, aldosterone and testosterone. CYP21A2 and POR mutations associated with CAH. Local regulation of sex steroids. Steroid hormone metabolism Intracellular transport of sex steroids as well as their important precursors and/or metabolites. Mixed evidence suggests an association of SLC25A12 with classic autism. Intracellular receptor for testosterone Receptors for estrogen Regulation of reproductive functions.

phenotypic end, several studies show functional (Just, Cherkassky, Keller & Minshew, 2004; Minshew & Williams, 2007; Shih et al., 2010; Villalobos, Mizuno, Dahl, Kemmotsu, & Müller, 2005; Welchew, 2005) and structural underconnectivity in the autistic brain (Barnea-Goraly et al., 2004; Keller, Kana, & Just, 2007; Sahyoun, Belliveau, & Mody, 2010; Sundaram et al., 2008), which is also marked by abnormal growth patterns (Courchesne et al., 2007). We therefore hypothesized that variations in genes governing neural development and synaptic function could contribute to autistic traits. Finally, social-emotional responsivity theory suggests that the atypical social behavior patterns in ASC might be related in part to genes known to modulate social behavior in animals (Chakrabarti, Kent, Suckling, Bullmore, & Baron- Cohen, 2006; Dawson et al., 2002; Insel, O’Brien, & Leckman, 1999). These genes include those involved in the oxytocin and vasopressin systems, as well as other neuropeptides involved in endogenous reward systems, such as opioids and cannabinoids. Some of these genes have been associated with autism in previous genetic studies, and these are shown in Table 1.1-1. These 68 candidate genes were tested in two experiments. A total of 216 SNPs with a minor allele frequency (MAF) ≥ 0.2 in the Caucasian population were chosen from these genes (full list of SNPs are available in Chakrabarti et al., [2009]). This approach, of selecting multiple common SNPs per gene, has the advantage of checking for informative associations both directly and indirectly (Collins, Guyer, & Chakravarti, 1997). The median SNP density across all genes was one SNP per 14.1 kb. Overall, 125 of these SNPs have been genotyped in one or more populations in the HapMap database (Release 23a). All volunteers contributed mouth swabs for DNA extraction. These were anonymized, and DNA was genotyped for the 216 SNPs using standard polymerase chain reaction (PCR)-based assays (TaqMan SNP

c01.indd 26

7/25/2011 10:10:09 AM

GENES RELATED TO AUTISTIC TRAITS AND EMPATHY

27

genotyping assays; Applied Biosystems Inc., California). The genotyping call rate was 93.35% across all samples. The concordance for duplicate samples was 99.8%. No SNP showed a significant deviation from the Hardy–Weinberg equilibrium at p < 0.001. The two experiments conducted were as follows: 1) Experiment 1: An association study for AQ and EQ was conducted on the population sample (n = 349) using nonparametric analysis of variance for each SNP. Chi-square statistics and asymptotic p-values (two-tailed) were generated from this test. A sex-specific analysis was conducted for all X-linked genes. 2) Experiment 2: A case-control association study of Asperger Syndrome was conducted on all cases of AS (n = 174) and a subset of the population sample (n = 155). The controls were selected to be sex-matched with the cases, while having an AQ score < 25. An AQ < 25 cutoff was employed to exclude a small number of individuals who scored high on AQ even though they did not have a formal diagnosis. For each SNP, a Cochrane–Armitage chi-square statistic (1 d.f.) was calculated to test the null hypothesis that the different alleles have the same distribution in cases and controls. Asymptotic p-values (two-tailed) were calculated. To control for multiple testing of SNPs within genes as well as for multiple phenotypes, permutation testing was conducted using UNPHASED (Dudbridge, 2008) for Experiment 1 and using PLINK (Purcell et al., 2007) for Experiment 2. Because each candidate gene was individually selected on the basis of a priori hypothesis, independent of other genes, permutation tests were performed separately for each gene. In each permutation, the phenotypes were randomly reassigned among participants, keeping the genotypes fixed to preserve their correlation structure. The multiple phenotypes for each subject were permuted together so as to preserve the correlation structure among phenotypes. Each SNP was then tested for association to each permuted phenotype and the minimum p-value recorded. The permutation was repeated 1,000 times, and the corrected p-value was the estimated proportion of permutations in which the minimum p-value was less than or equal to the minimum p-value observed in the original data. When the familywise error rate (FWER)-corrected p-value is significant, we may infer that at least one SNP in the gene is associated and that there is gene-wise significance. This gene-wise p-value thus reflects the p-value of the most significant SNP after FWER correction. In Experiment 1, autistic traits and/or empathy (measured on AQ and/or EQ) were nominally associated at p ≤ 0.05 with SNPs from 19 genes. In Experiment 2, SNPs from 14 genes were nominally associated at p ≤ 0.05 with AS. Across both experiments, 6 genes showed nominal significance at p ≤ 0.05. (See Figure 1.1-1 for a distribution of all nominally significant genes across the two experiments). Eight genes in Experiment 1 and 5 genes in Experiment 2 showed gene-wise significance after 1,000 permutations across all phenotypes. Two genes (CYP11B1 and NTRK1) survived FWER correction in both the experiments, and they are therefore strong candidates for future replication studies. Genes in all three functional groups were found to be significantly associated both with empathy and/or with autistic traits, as well as with a diagnosis of AS. This provides further support for the nonunitary nature of autistic traits and AS (Happé, Ronald, & Plomin, 2006).

c01.indd 27

7/25/2011 10:10:09 AM

28

GENES RELATED TO AUTISTIC TRAITS AND EMPATHY

HSD11B1 LHCGR

CYP11B1∗

CYP17A1

ESR1

CYP19A1

ESR2

SCP2

ARNT2

IGF1 NTF3 OXT

NTRK1∗ NTRK1∗

HSD17B4 HSD17B2∗ NLGN4X EN-2∗ HOXA1 OXTR GABRB3∗ AVPR1B∗ CNR1∗ GABRA6 MAOB VIPR WFS1∗

Experiment 2:

Experiment 2:

AS case-control nominal association (uncorrected p<0.05)

EQ/AQ nominal association (uncorrected p<0.05)

Figure 1.1-1. Genes showing nominal association with (1) AS case-control analysis and (2) autistic trait measures (AQ, EQ) in the population sample. Intersections summarize genes that show a nominal association in both experiments. Gene functional groups are color coded: Pink (sex hormone related), Yellow (neural connectivity related), and gray (social-emotional responsivity related). Genes in bold indicate replications of associations reported in earlier studies. * indicates a nominally significant association with EQ. Reproduced from Chakrabarti et al. (2009). (See color insert.)

In the sex-steroid group, the estrogen receptor beta (ESR2) was associated significantly in both experiments. Particularly, the C allele in rs1271572 and rs1152582 was associated with higher AQ in the typical population, and they were also found to be more frequent in cases than in controls. ESR2 codes for the main oestrogen receptor are expressed in the brain. In the fetal brain, testosterone is aromatized to oestradiol and exerts its effects on neural development through acting on these receptors and mediating selective cell survival. It promotes the defeminization of the developing male brain in mice (Kudwa, Bodo, Gustafsson, & Rissman, 2005). Estrogen is thought to mediate social interaction in rodents, and this is supported by the presence of estrogen receptors in areas of the brain involved in emotion and affective behavior, such as the amygdala and the hippocampus. CYP17A1 catalyzes the production of dehydroepiandrosterone (DHEA, a precursor of testosterone), as well as androstenedione (a precursor of oestradiol). Polymorphisms of this gene have been associated with Polycystic Ovary Syndrome (PCOS) in women (Park, Lee, Ramakrishna, Cha, & Baek, 2008), a condition known to be elevated in ASC (Ingudomnukul, Baron-Cohen, Wheelwright, & Knickmeyer, 2007). CYP11B1 is cellularly localized in the mitochondria and converts11-deoxycortisol to cortisol. Polymorphisms in this gene and the CYP11A gene are associated with Congenital Adrenal Hyperplasia (CAH) (Kuribayashi et al., 2005) in which fT

c01.indd 28

7/25/2011 10:10:09 AM

GENES RELATED TO AUTISTIC TRAITS AND EMPATHY

29

is elevated. CAH is associated with higher AQ than in the general population (Knickmeyer et al., 2006). Together, these results implicate genes involved in the synthesis and metabolism of sex steroids in the etiology of autistic traits, empathy, and AS, and it provides some of the first genetic evidence in support of the role of sex-steroids in ASC and related trait measures. In the neurodevelopmental group, four genes (HOXA1, NLGN4X, NTRK1, and ARNT2) survived FWER correction. rs10951154 in HOXA1 has been previously associated with head size in ASC as well as with head growth rate (Muscarella et al., 2007). We found that the G-allele carriers had more autistic traits than the AA homozygotes. This is consistent with the finding that the G allele has been found to be associated with larger head size and greater head growth rate (Muscarella et al., 2007). rs12836764 in the NLGN4X UTR was significantly associated with both EQ and AQ in females. This supports earlier findings implicating this gene in autism (Jamain, Quach, Betancur, & et al., 2003). A large-scale association study of autism found a significant association with neurexins (AGPC, 2007) that interact with neuroligins in mediating glutamatergic synaptogenesis. Among the molecules related to neurotrophin function, a strong association was observed in NTRK1 with empathy (in Experiment 1) and with AS (in Experiment 2). NTRK1 is situated within a peak (1q21-22) reported in the first ever linkage study of AS (Ylisaukko-oja et al., 2004) and thus provides an independent validation. The nerve growth factor (NGF), signaling through TrkA (the protein product of NTRK1), mediates most neurotrophic action of NGF (Sofroniew, Howe, & Mobley, 2001). A primary role of the TrkA in the developing brain is in determining the fate and growth of neurites, in whether they become axons or dendrites (Da Silva, Hasegawa, Miyagi, Dotti, & AbadRodriguez, 2005). Additionally, two SNPs in the ARNT2 gene were found to be associated in both experiments. This gene is involved both in the development of the neuroendocrine cells in the hypothalamus (Michaud, DeRossi, May, Holdener, & Fan, 2000) as well as in the neural response to hypoxia (Maltepe, Keith, Arsham, Brorson, & Simon, 2000). These findings point to a key role played by these neurodevelopmental genes in the development of empathy and autistic traits. In the social-emotional responsivity group, four genes (MAOB, GABRB3, WFS1, and OXT) were found to be significant after FWER correction. MAOB was significantly associated in females only, and this is consistent with the earlier studies showing the importance of this locus in social cognition, both in humans and mouse models (Good et al., 2003; Grimsby et al., 1997). The rationale for testing GABArelated genes came from the fact that social behavior has been linked to GABAergic activity in the central nervous system (CNS) (File & Seth, 2003); and that GABA receptors play a crucial role early in cortical development through their effect on neuronal migration as well as on development of excitatory and inhibitory synapses. In this sense, GABA-related genes could have been placed in both the neurodevelopmental group of candidate genes too. We found GABRB3 was significantly associated with empathy (EQ) in the typical sample, thus corroborating a role of this locus (15q11-q13) in autism (Ashley-Koch et al., 2006; Buxbaum et al., 2002). Gabrb3 knockout mice have been shown to demonstrate low social and exploratory behavior as well as smaller cerebellar vermal volumes, pointing to a potential animal model for autism (DeLorey, Sahbaie, Hashemi, Homanics, & Clark, 2008). Another significant association in this functional group of genes was the Wolframin (WFS1) gene. Wolframin is strongly expressed in the amygdala,

c01.indd 29

7/25/2011 10:10:09 AM

30

GENES RELATED TO AUTISTIC TRAITS AND EMPATHY

especially in response to fear-inducing stimuli (Koks, Planken, Luuk, & Vasar, 2002). The amygdala is one of the key brain regions where functional and structural abnormalities have been consistently found in ASC (Baron-Cohen et al., 2000). Two SNPs in WFS1 showed a strong association with both AQ and EQ. One of these, rs734312, is a nonsynonymous coding SNP and belongs to a haplotype that shows an increased risk for affective disorders (Koido et al., 2004). Finally, three genes from the oxytocin-vasopressin system (OXTR, OXT, and AVPR1B) were found to be nominally associated with ASC and/or with AQ and EQ. These genes have suggestive links with autism (Insel et al., 1999; Jacob et al., 2007; Wermter et al., 2010; Wu et al., 2005) and with social behavior in animal models. Of these, OXT survived a FWER correction in Experiment 2. Oxytocin is of particular interest, given the recent reports of oxytocin levels being low in autism, and the treatment effects of both intranasal and intravenous administration of oxytocin (Hollander et al., 2003). Oxytocin levels are also correlated with empathy and prosocial measures, such as the Eyes Test (Domes, Heinrichs, Michel, Berger, & Herpertz, 2007) and trust in neuroeconomics (Kosfeld, Heinrichs, Zak, Fischbacher, & Fehr, 2005). This provides partial support for the involvement of the oxytocin-vasopressin system in autistic traits. Together, these results support the idea that genes implicated in social and emotional responsivity contribute to individual differences in traits related to ASC. In sum, in the two studies described, we identified nine candidate genes, some of which are associated with autistic traits in the general population and/or AS. These genes fall into the three functional categories related to sex-steroid synthesis and metabolism, neural development and connectivity, and social-emotional responsivity, providing some support for the three theories of autism. It is essential that these are replicated in independent samples and validated through molecular genetic techniques such as gene expression measurement. Importantly, these associations should be validated against other relevant endophenotypes. Endophenotypes are defined as measurable intermediate phenotypes that are generally closer to the action of the gene and thus exhibit higher genetic signal-tonoise ratios (Gottesman & Gould, 2003). A range of endophenotypic measures have been suggested for empathy and autistic behavior, and emotion processing ranks highly among these (Losh & Piven, 2007). In our study described above, we did a preliminary test of two such endophenotypic measures (the “Reading the Mind in the Eyes” Test and the Embedded Figures Test) for cross-validation of our trait association results, in a small subset of the general population sample. This found a nominal association in seven genes with these measures that overlapped with the significantly associated genes in either/both of the two main experiments (Chakrabarti et al., 2009). Although this analysis was preliminary, and underpowered, this provides a framework for future studies. Additional endophenotypes that have been put forward to study social behavior in humans involve the use of neuroimaging. Hariri et al. (2005; 2002) showed that variability in the serotonin transporter (SLC6A4) genotype modulates the amygdala response to fear faces. Using the same paradigm (Meyer-Lindenberg et al., 2008) showed that polymorphisms in the arginine vasopressin receptor 1A (AVPR1A) gene (previously linked to autism) are related to the amygdala response to faces displaying fear or anger. Work from our and other groups has shown that variations in the cannabinoid receptor (CNR1) gene modulate the striatal response to happy faces (Chakrabarti et al., 2006;

c01.indd 30

7/25/2011 10:10:09 AM

REFERENCES

31

Domschke et al., 2008). Future research should target such discrete “neural phenotypes” in ASC in combination with ideal candidate genes. In closing, in this chapter we have presented a brief overview of autism genetics research, which explains the rationale of our experimental approach. We have then discussed two recent genetic association experiments from our laboratory, one on autistic traits and empathy and one on Asperger Syndrome. Finally, we have suggested potential avenues for future research, particularly using cross-validation through relevant endophenotypes. This combination of a functional hypothesisdriven search for candidate genes, alongside the development of fine-tuned quantitative phenotypic measures of brain and behavior, will slowly bridge the gap between DNA and social cognition.

ACKNOWLEDGMENTS Parts of this chapter have been reprinted from Chakrabarti et al. (2009). We are grateful to Ian Craig, Frank Dudbridge, and Lindsey Kent for valuable discussions.

REFERENCES Abrahams, B. S., & Geschwind, D. H. (2008). Advances in autism genetics: On the threshold of a new neurobiology. Nat Rev Genet 9(5), 341–355. AGPC. (2007). Mapping autism risk loci using genetic linkage and chromosomal rearrangements. Nat Genet 39, 319–328. APA. (1987). Diagnostic and Statistical Manual of Mental Disorders, 3rd ed., Washington, DC: American Psychiatric Association. Arking, D. E., Cutler, D. J., Brune, C. W., Teslovich, T. M., West, K., et al. (2008). A common genetic variant in the neurexin superfamily member CNTNAP2 increases familial risk of autism. Am J Hum Genet 82(1), 160–164. Ashley-Koch, A., Mei, H., Jaworski, J., Ma, D., Ritchie, M., et al. (2006). An analysis paradigm for investigating multi-locus effects in complex disease: Examination of three GABA A receptor subunit genes on 15q11–q13 as risk factors for Autistic Disorder. Ann Hum Genet 70, 281–292. Auyeung, B., Baron-Cohen, S., Ashwin, E., Knickmeyer, R., Taylor, K., et al. (2009). Fetal testosterone predicts sexually differentiated childhood behavior in girls and in boys. Psychol Sci 20(2), 144. Auyeung, B., Baron- Cohen, S., Chapman, E., Knickmeyer, R., Taylor, K., et al. (2006). Foetal testosterone and the child systemizing quotient. Eur J Endocrinol 155(1), 123–130. Auyeung, B., Taylor, K., Hackett, G., & Baron-Cohen, S. (2010). Foetal testosterone and autistic traits in 18 to 24-month-old children, Molecular Autism 1, 11. Bailey, A., Le Couteur, A., Gottesman, I., Bolton, P., Simmonoff, et al. (1995). Autism as a strongly genetic disorder: Evidence from a British twin study. Psychol Med 25, 63–77. Barnea-Goraly, N., Kwon, H., Menon, V., Eliez, S., Lotspeich, L., et al. (2004). White matter structure in autism: Preliminary evidence from diffusion tensor imaging. Biol Psychiatr 55(3), 323–326.

c01.indd 31

7/25/2011 10:10:09 AM

32

GENES RELATED TO AUTISTIC TRAITS AND EMPATHY

Baron-Cohen, S., & Hammer, J. (1997). Parents of children with Asperger Syndrome: what is the cognitive phenotype? Journal of Cognitive Neuroscience 9, 548–554. Baron-Cohen, S., Knickmeyer, R., & Belmonte, M. (2005). Sex differences in the brain: Implications for explaining autism. Science 310(5749), 819–823. Baron-Cohen, S., Ring, H., Bullmore, E., Wheelwright, S., Ashwin, C., et al. (2000). The amygdala theory of autism. Neurosci Behavi Rev 24, 355–364. Baron-Cohen, S., & Wheelwright, S. (2004). The empathy quotient (EQ). An investigation of adults with Asperger Syndrome or high functioning autism, and normal sex differences. J Autism Dev Disord 34, 163–175. Baron-Cohen, S., Wheelwright, S., Skinner, R., Martin, J., & Clubley, E. (2001). The autism spectrum quotient (AQ): Evidence from Asperger Syndrome/high functioning autism, males and females, scientists and mathematicians. J Autism Dev Disord 31, 5–17. Belmonte, M., Cook, E. Jr., Anderson, G., Rubenstein, J., Greenough, W., et al. (2004). Autism as a disorder of neural information processing: Directions for research and targets for therapy. Mol Psychiatr 9, 646–663. Bishop, D., Maybery, M., Maley, A., Wong, D., Hill, W., et al. (2004). Using self-report to identify the broad phenotype in parents of children with autistic spectrum disorders: A study using the Autism-Spectrum Quotient. J Child Psychol Psychiatr 45(8), 1431–1436. Bourgeron, T. (2009). A synaptic trek to autism. Current Opin neurobiol 19(2), 231–234. Buxbaum, J., Silverman, J., Smith, C., Greenberg, D., Kilfarski, M., et al. (2002). Assocation between a GABRB3 polymorphism and autism. Mol Psychiatr 7, 311–316. Campbell, D. B., Warren, D., Sutcliffe, J. S., Lee, E. B., & Levitt, P. (2010). Association of MET with social and communication phenotypes in individuals with autism spectrum disorder. Am J Med Genet B 153(2), 438–446. Chakrabarti, B., & Baron-Cohen, S. (2006). Empathizing: Neurocognitive developmental mechanisms and individual differences. Progr Brain Res 156(Special issue on “Understanding Emotions”), 406–417. Chakrabarti, B., Dudbridge, F., Kent, L., Wheelwright, S., Hill-Cawthorne, G., et al. (2009). Genes related to sex steroids, neural growth, and social-emotional behavior are associated with autistic traits, empathy, and Asperger syndrome. Autism Res 2(3), 157–177. Chakrabarti, B., Kent, L., Suckling, J., Bullmore, E., & Baron-Cohen, S. (2006). Variations in human cannabinoid receptor (CNR1) gene modulate striatal response to happy faces. Eur J Neurosci 23(7), 1944–1948. Chapman, E., Baron-Cohen, S., Auyeung, B., Knickmeyer, R., Hackett, G., et al. (2006). Foetal testosterone and empathy: Evidence from the Empathy Quotient (EQ) and the “Reading the mind in the Eyes Test”. Soc Neurosci 1, 135–148. Collins, F., Guyer, M., & Chakravarti, A. (1997). Variations on a theme: Cataloging human DNA sequence variation. Science 278(5343), 1580–1581. Conciatori, M., Stodgell, C., Hyman, S., O’Bara, M., Militerni, R., et al. (2004). Association between the HOXA1 A218G polymorphism and increased head circumference in patients with autism. Biol Psychiatry 55(4), 413–419. Constantino, J., Przybeck, T., Friesen, D., & Todd, R. (2000). Reciprocal social behaviour in children with and without pervasive developmental disorders. J Dev Behav Pediatr 1, 2–11. Constantino, J., & Todd, R. (2000). Genetic structure of reciprocal social behaviour. Am J Psychiatr 157, 2043–2045. Constantino, J., & Todd, R. (2005). Intergenerational transmission of subthreshold autistic traits in the general population. Biol Psychiatr 57(6), 655–660.

c01.indd 32

7/25/2011 10:10:09 AM

REFERENCES

33

Corvin, A., Craddock, N., & Sullivan, P. F. (2010). Genome-wide association studies: A primer. Psychol Med 40, 1063–1077. Courchesne, E., Pierce, K., Schumann, C. M., Redcay, E., Buckwalter, J. A. (2007). Mapping early brain development in autism. Neuron 56(2), 399–413. Crespi, B., & Badcock, C. (2008). Psychosis and autism as diametrical disorders of the social brain. Behav Brain sci 31(03), 241–261. Da Silva, J. S., Hasegawa, T., Miyagi, T., Dotti, C. G., & Abad-Rodriguez, J. (2005). Asymmetric membrane ganglioside sialidase activity specifies axonal fate. Nature Neurosci 8(5), 606–615. Davis, M., Luce, C., & Kraus, S. (1994). The heritability of characteristics associated with dispositional empathy. J Pers 62(3), 369–391. Dawson, G., Carver, L., Meltzoff, A., Panagiotides, H., McPartland, J. (2002). Neural correlates of face and object recognition in young children with autism spectrum disorder, developmental delay and typical development. Child Dev 73, 700–717. DeLorey, T., Sahbaie, P., Hashemi, E., Homanics, G., & Clark, J. (2008). Gabrb3 gene deficient mice exhibit impaired social and exploratory behaviors, deficits in non-selective attention and hypoplasia of cerebellar vermal lobules: A potential model of autism spectrum disorder. Behav Brain Res 187(2), 207–220. Domes, G., Heinrichs, M., Michel, A., Berger, C., & Herpertz, S. C. (2007). Oxytocin improves “mind-reading” in humans. Biol Psychiatr 61(6), 731. Domschke, K., Dannlowski, U., Ohrmann, P., Lawford, B., Bauer, J., et al. (2008). Cannabinoid receptor 1 (CNR1) gene: Impact on antidepressant treatment response and emotion processing in major depression. Eur Neuropsychopharmacol 18(10), 751–759. Dudbridge, F. (2008). Likelihood-based association analysis for nuclear families and unrelated subjects with missing genotype data. Hum Hered 66(2), 87–98. Ebstein, R. P., Israel, S., Chew, S. H., Zhong, S., & Knafo, A. (2010). Genetics of human social behavior. Neuron 65(6), 831–844. File, S. E., & Seth, P. (2003). A review of 25 years of the social interaction test. Eur J Pharmacol 463(1–3), 35–53. Folstein, S., & Rutter, M. (1977). Infantile autism: A genetic study of 21 twin pairs. J Child Psycholol Psychiatr 18, 297–321. Glessner, J. T., Wang, K., Cai, G., Korvatska, O., Kim, C. E., et al. (2009). Autism genome-wide copy number variation reveals ubiquitin and neuronal genes. Nature 459(7246), 569–573. Good, C., Lawrence, K., Simon-Thomas, N., Price, C., Ashburner, J., et al. (2003). Dosagesensitive X-linked locus influences the development of amygdala and obitofrontal cortex, and fear recognition in humans. Brain 126, 1–16. Gottesman, I. I., & Gould, T. D. (2003). The endophenotype concept in psychiatry: etymology and strategic intentions. Am J Psychiatr 160(4), 636. Grimsby, J., Toth, M., Chen, K., Kumazawa, T., Klaidman, L., et al. (1997). Increased stress response and bold beta- phenylethylamine in MAOB- deficient mice. Nat Genet 17, 206–210. Happé, F., Ronald, A., & Plomin, R. (2006). Time to give up on a single explanation for autism. Nat Neurosci 9(10), 1218–1220. Hariri, A., Drabant, E., Munoz, K., Kolachana, B., Mattay, V., et al. (2005). A susceptibility gene for affective disorders and the response of the human amygdala. Arch Gen Psychiatr 62(2), 146–152. Hariri, A., Mattay, V., Tessitore, A., Kolachana, B., Fera, F., et al. (2002). Serotonin transporter genetic variation and the response of the human amygdala. Science 297, 400–403.

c01.indd 33

7/25/2011 10:10:09 AM

34

GENES RELATED TO AUTISTIC TRAITS AND EMPATHY

Hoekstra, R., Bartels, M., Verweij, C., & Boomsma, D. (2007). Heritability of autistic traits in the general population. Arch Pediatr Adolesc Med 161, 372–377. Hollander, E., Novotny, S., Hanratty, M., Yaffe, R., DeCaria, C., et al. (2003). Oxytocin infusion reduces repetitive behaviors in adults with autistic and Asperger’s disorders. Neuropsychopharmacology 28(1), 193–198. ICD-10. (1994). International Classification of Diseases, vol. 10. Geneva, Switzerland: World Health Organisation. Ingudomnukul, E., Baron-Cohen, S., Wheelwright, S., & Knickmeyer, R. (2007). Elevated rates of testosterone-related disorders in women with autism spectrum conditions. Horm Behav 51(5), 597–604. Insel, T., O’Brien, D., & Leckman, J. (1999). Oxytocin, vasopressin, and autism: Is there a connection? Biol Psychiatr 45(2), 145–157. Jacob, S., Brune, C. W., Carter, C. S., Leventhal, B. L., Lord, C. (2007). Association of the oxytocin receptor gene (OXTR) in Caucasian children and adolescents with autism. Neurosci Lett 417(1), 6–9. Jacquemont, M. L., Sanlaville, D., Redon, R., Raoul, O., Cormier-Daire, V., et al. (2006). Arraybased comparative genomic hybridisation identifies high frequency of cryptic chromosomal rearrangements in patients with syndromic autism spectrum disorders. J Med Genet 43(11), 843. Jamain, S., Quach, H., Betancur, C., Råstam, M., Colineaux, C., et al. (2003). Mutations of the X-linked genes encoding neuroligins NLGN3 and NLGN4 are associated with autism. Nature Genetics 34(1), 27–29. Just, M., Cherkassky, V., Keller, T., & Minshew, N. (2004). Cortical activation and synchronization during sentence comprehension in high-functioning autism: Evidence of underconnectivity. Brain 127, 1811–1821. Keller, T. A., Kana, R. K., & Just, M. A. (2007). A developmental study of the structural integrity of white matter in autism. Neuroreport 18(1), 23–28. Knafo, A., Zahn-Waxler, C., Van Hulle, C., Robinson, J. L., & Rhee, S. H. (2008). The developmental origins of a disposition toward empathy: Genetic and environmental contributions. Emotion 8(6), 737–752. Knickmeyer, R., Baron-Cohen, S., Fane, B. A., Wheelwright, S., Mathews, G. A., et al. (2006). Androgens and autistic traits: A study of individuals with congenital adrenal hyperplasia. Horm Behav 50(1), 148–153. Knickmeyer, R., Baron-Cohen, S., Raggatt, P., & Taylor, K. (2005). Foetal testosterone, social cognition, and restricted interests in children. J Child Psychol Psychiatr 46(2), 198–210. Koido, K., Kõks, S., Nikopensius, T., Maron, E., Altmäe, S., et al. (2004). Polymorphisms in wolframin (WFS1) gene are possibly related to increased risk for mood disorders. Int J Neuropsychopharmacol 8(2), 235–244. Koks, S., Planken, A., Luuk, H., & Vasar, E. (2002). Cat odour exposure increases the expression of wolframin gene in the amygdaloid area of rat. Neurosci Lett 322(2), 116–120. Kosfeld, M., Heinrichs, M., Zak, P., Fischbacher, U., & Fehr, E. (2005). Oxytocin increases trust in humans. Nature 435(2), 673–676. Kudwa, A. E., Bodo, C., Gustafsson, J., & Rissman, E. F. (2005). A previously uncharacterized role for estrogen receptor: Defeminization of male brain and behavior. Proc Nat Acad Sci U S A 102(12), 4608. Kuribayashi, I., Nomoto, S., Massa, G., Oostdijk, W., Wit, J., et al. (2005). Steroid 11-BetaHydroxylase deficiency caused by compound heterozygosity for a novel mutation, p G314R, in one CYP11B1 allele, and a chimeric CYP11B2/CYP11B1 in the other allele. Horm Res 63, 284–293.

c01.indd 34

7/25/2011 10:10:09 AM

REFERENCES

35

Loehlin, J., & Nichols, R. (1976). Heredity, Environment and Personality. Austin: University of Texas Press. Lord, C., Rutter, M., Goode, S., Heemsbergen, J., Jordan, H., et al. (1989). Autism daignostic observation Schedule: A standard observation of communicative and social behaviour. J Autism Dev Disord 19, 185–212. Losh, M., & Piven, J. (2007). Social-cognition and the broad autism phenotype: identifying genetically meaningful phenotypes. J Child Psychol Psychiatr 48(1), 105–112. Losh, M., Sullivan, P. F., Trembath, D., & Piven, J. (2008). Current developments in the genetics of autism: from phenome to genome. J Neuropathol Exp Neurol 67(9), 829. Lutchmaya, S., Baron-Cohen, S., & Raggatt, P. (2002). Foetal testosterone and vocabulary size in 18- and 24-month-old infants. Infant Behav Dev 24(4), 418–424. Maltepe, E., Keith, B., Arsham, A., Brorson, J., & Simon, M. (2000). The role of ARNT2 in tumor angiogenesis and the neural response to hypoxia. Biochem Biophys Res Commun 273(1), 231–238. Matthews, K., Batson, C., Horn, J., & Rosenman, R. (1981). “Principles in his nature which interest him in the fortune of others . . . ”: The heritability of empathic concern for others. J Pers 49(3), 237–247. Mehrabian, A., & Epstein, N. (1972). A measure of emotional empathy. J Pers 40, 525–543. Meyer-Lindenberg, A., Kolachana, B., Gold, B., Olsh, A., Nicodemus, K. K., et al. (2008). Genetic variants in AVPR1A linked to autism predict amygdala activation and personality traits in healthy humans. Mol Psychiatr 14(10), 968–975. Michaud, J., DeRossi, C., May, N., Holdener, B., & Fan, C. (2000). ARNT2 acts as the dimerization partner of SIM1 for the development of the hypothalamus. Mech Dev 90(2), 253–261. Minshew, N. J., & Williams, D. L. (2007). The new neurobiology of autism: cortex, connectivity, and neuronal organization. Arch Neurol 64(7), 945. Muscarella, L., Guarnieri, V., Sacco, R., Militerni, R., Bravaccio, C., et al. (2007). HOXA1 gene variants influence head growth rates in humans. Am J Med Genet B 144B(3), 388–390. Nagarajan, R. P., Patzel, K. A., Martin, M., Yasui, D. H., Swanberg, S. E., et al. (2008). MECP2 promoter methylation and X chromosome inactivation in autism. Autism Res 1(3), 169–178. Park, J. M., Lee, E. J., Ramakrishna, S., Cha, D. H., & Baek, K. H. (2008). Association study for single nucleotide polymorphisms in the CYP17A1 gene and polycystic ovary syndrome. Int J Mol Med 22(2), 249. Pharoah, P. D., Tyrer, J., Dunning, A. M., Easton, D. F., & Ponder, B. A. (2007). Association between common variation in 120 candidate genes and breast cancer risk. PLoS Genet 3(3), e42. Pinto, D., Pagnamenta, A. T., Klei, L., Anney, R., Merico, D., J., et al. (2010). Functional impact of global rare copy number variation in autism spectrum disorders. Nature 466(7304), 368–372. Piven, J., Palmer, P., Jacobi, D., Childress, D., & Arndt, S. (1997). Broader autism phenotype: evidence from a family history study of multiple-incidence autism familiies. Am J Psychiatr 154, 185–190. Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., et al. (2007). PLINK: A tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81(3), 559–575. Rushton, J., Fulker, D., Neale, M., Nias, D., & Eysenck, H. (1986). Altruism and aggression: The heritability of individual differences. J Pers Soc Psychol 50(6), 1192–1198.

c01.indd 35

7/25/2011 10:10:09 AM

36

GENES RELATED TO AUTISTIC TRAITS AND EMPATHY

Sahyoun, C. P., Belliveau, J. W., & Mody, M. (2010). White matter integrity and pictorial reasoning in high-functioning children with autism. Brain Cognit 73(3), 180–188. Sebat, J., Lakshmi, B., Malhotra, D., Troge, J., Lese-Martin, C., et al. (2007). Strong Association of De Novo Copy Number Mutations with autism. Science 316(5823), 445–449. Shih, P., Shen, M., Öttl, B., Keehn, B., Gaffrey, M. S., et al. (2010). Atypical network connectivity for imitation in autism spectrum disorder. Neuropsychologia 48(10), 2931–2939. Singer, T., & Lamm, C. (2009). The social neuroscience of empathy. Ann N Y Acad Sci 1156, 81–96. Sofroniew, M., Howe, C., & Mobley, W. (2001). Nerve growth factor signaling, neuroprotection, and neural repair. Ann Rev Neurosci 24, 1217–1281. Sundaram, S. K., Kumar, A., Makki, M. I., Behen, M. E., Chugani, H. T., (2008). Diffusion tensor imaging of frontal lobe in autism spectrum disorder. Cerebr Corte 18, 2659–2665. Sung, Y., Dawson, G., Munson, J., Estes, A., Schellenberg, G., (2005). Genetic investigation of quantitative traits related to autism: Use of multivariate polygenic models with ascertainment adjustment. Am J Hum Genet 76(1), 68–81. Villalobos, M. E., Mizuno, A., Dahl, B. C., Kemmotsu, N., & Müller, R. A. (2005). Reduced functional connectivity between V1 and inferior frontal cortex associated with visuomotor performance in autism. Neuroimage 25(3), 916–925. Wagner, G. C., Reuhl, K. R., Cheh, M., McRae, P., & Halladay, A. K. (2006). A new neurobehavioral model of autism in mice: Pre-and postnatal exposure to sodium valproate. J Autism Devel Disord 36(6), 779–793. Wang, K., Zhang, H., Ma, D., Bucan, M., Glessner, J. T., et al. (2009). Common genetic variants on 5p14. 1 associate with autism spectrum disorders. Nature 459(7246), 528–533. Welchew, D. (2005). Functional dysconnectivity of the medial temporal lobe in Asperger’s syndrome. Biol Psychiatr 57, 991–998. Wermter, A. K., Kamp-Becker, I., Hesse, P., Schulte-Körne, G., Strauch, K., & Remschmidt, H. (2010). Evidence for the involvement of genetic variation in the oxytocin receptor gene (OXTR) in the etiology of autistic disorders on high-functioning level. Am J Med Genet B: Neuropsychiatric Genetics 153(2), 629–639. Wheelwright, S., Auyeung, B., Allison, C., & Baron-Cohen, S. (2010). Defining the broader, medium and narrow autism phenotype among parents using the Autism Spectrum Quotient (AQ), Molecular Autism 1:10. Wu, S., Jia, M., Ruan, Y., Liu, J., Guo, Y., et al. (2005). Positive association of the oxytocin receptor gene (OXTR) with autism in the Chinese Han population. Biol Psychiatr 58(1), 74–77. Ylisaukko-oja, T., Nieminen-von Wendt, T., Kempas, E., Sarenius, S., Varilo, T., et al. (2004). Genome-wide scan for loci of Asperger syndrome. Mol Psychiatr 9(2), 161–168. Zahn-Waxler, C., Radke-Yarrow, M., Wagner, E., & Chapman, M. (1992). Development of concern for others. Dev Psychol 28, 126–136.

c01.indd 36

7/25/2011 10:10:09 AM

1.2 THE BEHAVIORAL GENETICS OF HUMAN PAIR BONDING HASSE WALUM1

AND

LARS WESTBERG2

1

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden 2 Department of Pharmacology, Institute of Neuroscience and Physiology, University of Gothenburg, Gothenburg, Sweden

1.2.1

INTRODUCTION

Pair bonds are dyadic coalitions between sexual partners based on selective social attachments, characterized by partner preference, bi-parental care, and occasionally intrasexual aggression. Pair bonding does not necessarily imply sexual monogamy or lifelong mating relationships, but it infers enduring partner interactions in socially monogamous species. Although it is debatable whether humans are typically monogamous considering, among other things, that in more than 80% of all human societies, polygamous marriages were legally accepted before the influence of Western culture (Cartwright, 2008), long lasting bonds between sexual partners are widespread throughout nearly all modern human societies. In humans, as in other species, pair bonding probably evolved to increase paternal provisioning (Lovejoy, 1981; Marlowe, 2003) or as a consequence of male mating competition (Fuentes, 2002; Hawkes, 2004), but furthermore it has recently been suggested that particular demands for pair-bonding behavior triggered the evolution of the primate social brain (Dunbar & Shultz, 2007) and that this behavior shaped the evolution of human society (Chapais, 2008). Quantitative genetics studies of human mating behavior have found evidence of genetic influences on variation in reproductive behavior (Bailey et al., 2000; Bricker et al., 2006; Mustanski et al., 2007) as well as in more pair-bonding–related outcomes, including maintenance of a heterosexual relationship and remarriage From DNA to Social Cognition, First Edition. Edited by Richard Ebstein, Simone Shamay-Tsoory, and Soo Hong Chew. © 2011 Wiley-Blackwell. Published 2011 by John Wiley & Sons, Inc.

37

c02.indd 37

7/25/2011 10:10:10 AM

38

THE BEHAVIORAL GENETICS OF HUMAN PAIR BONDING

after separation (Trumbetta & Gottesman, 2000), marital quality (Spotts et al., 2004), and the likelihood of divorce (Jockin et al., 1996; Mcgue & Lykken, 1992). In recent years, the research field of complex genetics has shown it possible to identify specific genetic variants contributing to inter-individual variation in vulnerability for psychiatric disorders and in other behavioral traits. As the genetic code is far less influenced by environmental factors, the direction of an association between a genetic variant and a behavior is easier to predict compared with associations between behaviors and other measures of biological correlates, such as hormone levels and brain activity. Moreover, in comparison with other biological measures collecting DNA samples and genotyping, large cohorts are relatively cheap and straightforward. We hence consider the genetic association study to be a preferable method when investigating the relevance of particular molecules and networks for human behavior. We will describe here previous studies that have employed this strategy on pair-bonding and related mating behaviors.

1.2.2

BIOLOGY AND GENETICS OF MATING BEHAVIOR

As candidate gene analysis aims to test a priori hypothesis about biological system’s influences on specific phenotypes, a solid theoretical foundation is essential to choose appropriate genetic markers to analyze. Regarding affiliative behavior and social bonding in animals, the neuropeptides vasopressin (AVP) and oxytocin (OT) have been proven to be of great importance. Both are nonapeptides and are mainly synthesized by neurons of the paraventricular nucleus of hypothalamus. They differ structurally only at two amino acids and share the same evolutionary origin (Acher, 1974). OT, best known for its role in peripheral circulation, particularly in contraction of the uterus during labor and ejection of milk during lactation, is implicated in a wide range of social behaviors, including social motivation and approach behavior, when centrally released (Burbach et al., 2006). AVP is well known for its actions as an anti-diuretic hormone, but also it regulates several male-typical behaviors, including intrasexual aggression and paternal care (Boyd et al., 1992; Goodson & Bass, 2001; Wang et al., 1998). Both OT and AVP are important for the formation and expression of social memory, including parent–offspring recognition and mate recognition (Bielsky & Young, 2004). As revealed by a series of studies on closely related vole species, (i.e. montane voles [Microtus montanus], meadow voles [Microtus pennsylvanicus], and prairie voles [Microtus ochrogaster]), these peptides also have central roles in the formation and regulation of pair-bonding behavior (Young & Wang, 2004). 1.2.2.1 Vasopressin In male prairie voles, which in contrast to montane and meadow voles are socially monogamous and highly social, pair-bond formation and related behaviors are facilitated by AVP and prevented by an AVP receptor 1a (V1aR) antagonist (Cho et al., 1999). Furthermore, the neuroanatomical distribution of V1aR differs considerably between these vole species, especially in the ventral pallidum where prairie voles display higher V1aR density (Insel et al., 1994). Moreover, partner preference is enhanced in the nonmonogamous meadow vole when increasing the V1aR density

c02.indd 38

7/25/2011 10:10:10 AM

BIOLOGY AND GENETICS OF MATING BEHAVIOR

39

in ventral pallidum, using viral vector gene transfer (Lim et al., 2004). Although there are no major differences in the coding sequence of the gene encoding V1aR (avpr1a) between prairie and montane or meadow voles, the former species displays a 428 base pair sequence in the 5′ flanking region that is not found in the latter two species. When the avpr1a of the prairie vole, including the sequence in the 5′ region, is transgenically inserted into the brain of mice (Young et al., 1999), this nonmonogamous species also shows partner preference, similar to that displayed by prairie voles. Furthermore, the 5′ flanking sequence of prairie vole avpr1a is polymorphic and specific genetic variants affect the V1aR expression and alter intraspecific variation in partner preference (Hammock & Young, 2002). Although there is no sequence in the human AVPR1A 5′ flanking region homologous to the one found in prairie voles and, as described above, having a large impact on pair bonding in this species, humans do have three repetitive sequences in this region that are polymorphic: a (GT)25 dinucleotide repeat, a complex (CT)4-TT(CT)8-(GT)24 motif (RS3), and a (GATA)14 tetranucleotide repeat (RS1) (Thibonnier et al., 2000). Previous studies have revealed associations among AVPR1A repeat polymorphisms and autism (Kim et al., 2002; Wassink et al., 2004; Yirmiya et al., 2006), age at first sexual intercourse (Prichard et al., 2007), and altruism (Knafo et al., 2008), suggesting that these repetitive sequences may have an impact on human social behavior. We addressed the issue of assessing human pair-bonding behavior by using self-report questionnaire items that correspond to the behavioral patterns observed when measuring features of pair bonds among nonhuman primates. Based on this rational, we conducted a sum score comprising 13 items that we call the Partner Bonding Scale (PBS) (Walum et al., 2008). We found, in a sample comprising approximately 900 adult twins and their spouses, that the RS3 repeat was significantly associated with scores on the PBS in men but not in women, which is consistent with the fact that AVP and its actions through V1aR are most prominent in male voles. Interestingly, this microsatellite has also been shown to associate with the amount of hippocampal mRNA in human postmortem tissue (Knafo et al., 2008). Further analyses showed that one specific allele, allele 334 of the RS3 repeat previously associated with increased activation of the amygdala (Meyer-Lindenberg et al., 2009) and autism (Kim et al., 2002), was strongly associated with PBS scores in men in a dose-dependent manner; those carrying two 334 alleles had the lowest, whereas noncarriers had the highest scores. We could also show that men carrying the 334 allele were more likely to have experienced marital crisis with threat of divorce during the last year and that women married to men carrying this allele reported being less satisfied with their marital relationship than women married to men not carrying it. If replicated in future studies, these findings support the notion that the AVPR1A is of relevance for human pair-bonding behavior, which would indicate that similar neural circuits may be implicated in pair-bond formation in humans as those characterized in voles. 1.2.2.2

Oxytocin

In female prairie voles, central infusions of OT facilitate partner preference similar to how AVP affects males of this species (Williams et al., 1994) and a selective OT receptor (OTR) antagonist inhibits pair-bond formation (Insel & Hulihan, 1995). Comparable with the species differences in the V1aR brain densities mentioned

c02.indd 39

7/25/2011 10:10:10 AM

40

THE BEHAVIORAL GENETICS OF HUMAN PAIR BONDING

earlier, there are notable differences in OTR distribution patterns among monogamous and nonmonogamous vole species mainly in the brain region nucleus accumbens (NAcc) (Insel & Shapiro, 1992), and a OT receptor antagonist applied directly to this region blocks mating-induced partner preference formation (Young et al., 2001). Futhermore, overexpression of the OTR in NAcc accelerates partner preference formation in female prairie voles (Ross et al., 2009). The molecular mechanism behind the differences in OTR expression remains to be described, although differences in potential regulatory elements in the oxytocin receptor gene (OXTR) which could reflect variation in gene expression have been found between prairie and montane voles (Young et al., 1996). Similar to what has been shown in voles, a recent study has found that manipulations of OT activity alter partner-directed social behavior during pair interactions in the pair-bonding primate Callithrix penicillata (Smith et al., 2009). In humans, several pharmacological studies have found the effects of intranasal administrated OT on a wide spectrum of social behaviors, including trust (Kosfeld et al., 2005), generosity (Zak et al., 2007), judgments of facial trustworthiness and attractiveness (Theodoridou et al., 2009), face recognition (Rimmele et al., 2009), emotion perception (Domes et al., 2007), and social behaviors in autism spectrum disorder (Andari et al., 2010) but also more pair-bonding related phenotypes such as communication and behavior in a conflict discussion between couples (Ditzen et al., 2009). There is also evidence that variation in the OXTR is associated with social behaviors in humans such as empathy (Rodrigues et al., 2009) and prosocial decision making (Israel et al., 2009), as well as with risk of autism (Jacob et al., 2007; Lerer et al., 2008; Wu et al., 2005; Yrigollen et al., 2008). To our knowledge, the only study investigating variation in OXTR in relation to behaviors connected to human mating behavior focused on a microsatellite polymorphism in this gene and sexual and reproductive phenotypes (Prichard et al., 2007). In a sample of approximately 2000 individuals, they found that this microsatellite was associated with the likelihood of using oral contraceptives and having children. To what extent OT is involved in human pair-bonding behavior, not least in women, will be revealed by future studies of genetic variants in OXTR but also in OXT as well as in other genes implicated in release and function of OT. 1.2.2.3

Dopamine

Many of the brain regions included in the circuitry regulating pair bonding in voles are also involved in the mesolimbic dopamine reward system (Young & Wang, 2004). This could implicate that the individual motivation to engage in pair bonds is an effect of activation of reward centers. In favor of this theory, dopamine in NAcc is critical for partner preference formation in prairie voles (Aragona et al., 2003; Liu & Wang, 2003). The dopaminergic regulation of pair bonding is receptor subtypespecific. In female prairie voles, activation of D2 receptors in NAcc accelerates partner preference while blockage of this type of receptor antagonizes this behavior (Gingrich et al., 2000). Activation of D2 receptors has similar effects in males as it has in female prairie voles, but in males and not in females, activation of D1 receptors blocks partner preference (Aragona et al., 2003). In a study focusing on sexual monogamy, Gou et al. show evidence for an association between the dopamine transporter gene (DAT1) and the number of sexual

c02.indd 40

7/25/2011 10:10:10 AM

DISCUSSION

41

partners in young adults (Guo et al., 2007). They studied a sample of more than 2,500 individuals from the United States and found that men carrying one or two copies of the 10 repeat of this gene, a variant shown to be associated with increased gene expression (Heinz et al., 2000; VanNess et al., 2005), had an 80–100% increase in number of sexual partners compared with individuals not carrying this repeat. A similar association has been shown by Beaver et al. in a study of approximately 2,500 individuals also from the United States (Beaver et al., 2008). A study by Emanuele et al. has investigated associations between variants in neurotransmitter genes and different typologies of love as assessed by the 24-item Love Attitudes Scale (Emanuele et al., 2007). They studied 350 young adults and found an association between the dopamine D2 receptor gene (DRD2)-Taq1 A polymorphism, a marker shown to be associated with D2 receptor density in the human brain (Noble et al., 1991; Pohjalainen et al., 1998; Thompson et al., 1997), and the loving style “Eros,” characterized by a tendency to develop intense emotional experiences based on the physical attraction to a partner. In another study comprising 400 American men and women, Miller et al. has shown that variants in DRD2, and especially in interaction with variants in DRD1, is associated with age at first intercourse (Miller et al., 1999). Similar results have been found by Guo and Tong, studying the same outcome in 2,500 individuals, showing that variation in the dopamine D4 receptor gene (DRD4) also associates with age at first intercourse (Guo & Tong, 2006). Recently, Garcia et al. studied variation in DRD4 and found, in a sample of 181 young adults from the United States, this gene to be associated with both sexual infidelity and promiscuous sexual behavior (Garcia et al., 2010). Taken together, these results suggest that activity in dopaminergic pathways might be of importance for mating behavior in humans. Further studies of dopamine genes in relation to social bonding behaviors are, however, warranted.

1.2.3

DISCUSSION

The concept of human romantic relationships is a multifaceted process, comprising somewhat chronologically separated events, including initial attraction and matechoice, courtship, early stages of intense romantic love, reproduction, relationship maintenance strategies, parental care, and separation of partners as a consequence of imperfect monogamy or death and the despair that might follow thereof. All of these events that occur subsequent to the preattachment phase of initial attraction and courtship can be viewed as behavioral and emotional subunits of human pair bonding. Thus, the phenotypic complexity of pair bonding is substantial, and when trying to understand the biology of human mating, it is important to adopt a comprehensive view of this phenotype. As described, most of the behavioral genetics research on human mating behavior has focused on either variation in reproductive strategies and life history characteristics or marital outcomes. Although strategies influencing reproductive success in humans are an interesting field of research the focus is often on variation in sexual monogamy, a feature, as noted earlier, not necessarily a part of the concept of pair bonding. Marriage is a pancultural institution so common among our species that most adults will marry a least once during their lifetime (Bjorksten & Stewart, 1984).

c02.indd 41

7/25/2011 10:10:10 AM

42

THE BEHAVIORAL GENETICS OF HUMAN PAIR BONDING

It has been argued that the function of close attachment between sexual partners is as a commitment device helping individuals to determine when to stop searching for a more optimal mate (Frank, 1988), and marriage could be viewed as the ultimate expression of this device. Therefore, it might not be surprising that much of the research on human romantic relationships has focused on marital outcomes. However, considering the complexity of human pair bonding, we argue that a wider phenotypic perspective is warranted. Research in psychology has a tradition of using experimental approaches when assessing human behavior. Recent studies have used interesting methods to measure individual differences regarding, for example, inattention to attractive alternatives (Maner et al., 2008) and jealousy (Harmon-Jones et al., 2009). These kinds of methods can in combination with genetics provide valuable insights into the biology of pair bonding in humans. Recent research in voles has started to offer some insights into neural mechanisms involved in stress-coping behavior after partner loss (Bosch et al., 2009), which may be important also for humans. By studying differences in how people react to widowhood, by assessing depressive symptoms, for example, it would be possible to investigate whether specific genes implicated in relevant systems are associated with variation in reaction to partner loss in our species. The number of species in which individuals engage in pair bonds is relatively low. Maternal care of offspring is in contrast a common feature in nature, especially among mammals. The bond between a mother and her infant is probably to some extent cemented by increased levels of OT as an effect of vaginocervical stimulation during child birth and nipple stimulation during breast feeding, and similar stimulation during sexual activity is known to also elevate OT levels (Carter, 1992). From an evolutionary perspective, it is therefore interesting to hypothesize that pair bonding, at least for females, is an adaptation based on set of relatively conserved physiological features selected to promote parent–infant attachment. If the adaptive function of pair bonding is to reinforce bi-parental care to promote survival and prosperity of offspring, an increased demand for paternal provisioning could imply a selective pressure favoring females that bond to males they repetitively copulate with. Considering the evolutionary necessity of acting on already existing characteristics when shaping new adaptations, it seems reasonable that pair bonds could be induced by similar stimulation as the one facilitating the bond between a mother and her offspring. In humans, levels of OT are elevated during both parturition (Fuchs et al., 1991) and sexual arousal and orgasm (Carmichael et al., 1987). If the neural circuits involved in the regulation of pair bonding are a product of convergent evolution in unrelated lineages originating from conserved biological systems, it makes sense to investigate how variation in genes related to AVP, OT, and dopamine associates with pair bonding in humans. Although some promising studies have started to look into this, more research within this intriguing field is necessary.

REFERENCES Acher, R. (1974). Chemistry of the neurohypophysial hormones: An example of molecular evolution. Handbook of Physiology, vol. 4, edited by E. Knobil and W. Sawyer. Washington, DC: American Physiological Society.

c02.indd 42

7/25/2011 10:10:10 AM

REFERENCES

43

Andari, E., Duhamel, J. R., et al. (2010). Promoting social behavior with oxytocin in high-functioning autism spectrum disorders. Proc Natl Acad Sci U S A 107(9), 4389– 4394. Aragona, B. J., Liu, Y., et al. (2003). A critical role for nucleus accumbens dopamine in partnerpreference formation in male prairie voles. J Neurosci 23(8), 3483–3490. Bailey, J. M., Kirk, K. M., et al. (2000). Do individual differences in sociosexuality represent genetic or environmentally contingent strategies? Evidence from the Australian twin registry. J Pers Soc Psychol 78(3), 537–545. Beaver, K. M., Wright, J. P., et al. (2008). A gene-based evolutionary explanation for the association between criminal involvement and number of sex partners. Biodemography Soc Biol 54(1), 47–55. Bielsky, I. F. & Young, L. J. (2004). Oxytocin, vasopressin, and social recognition in mammals. Peptides 25(9), 1565–1574. Bjorksten, O. & Stewart, T. J. (1984). Contemporary trends in American marriage. Marriage and divorce: A contemporary perspective, edited by C. C. Nadelson and D. C. Polonsky. New York: Guilford Press. Bosch, O. J., Nair, H. P., et al. (2009). The CRF system mediates increased passive stresscoping behavior following the loss of a bonded partner in a monogamous rodent. Neuropsychopharmacology 34(6), 1406–1415. Boyd, S. K., Tyler, C. J., et al. (1992). Sexual dimorphism in the vasotocin system of the bullfrog (Rana catesbeiana). J Comp Neurol 325(2), 313–325. Bricker, J. B., Stallings, M. C., et al. (2006). Genetic and environmental influences on age at sexual initiation in the Colorado Adoption Project. Behav Genet 36(6), 820–832. Burbach, J. P., Young, L. J., et al. (2006). Oxytocin: Synthesis, secretion, and reproductive functions. Knobil and Neill’s Physiology of Reproduction, edited by J. D. Neill. New York: Elsevier. Carmichael, M. S., Humbert, R., et al. (1987). Plasma oxytocin increases in the human sexual response. J Clin Endocrinol Metab 64(1), 27–31. Carter, C. S. (1992). Oxytocin and sexual behavior. Neurosci Biobehav Rev 16(2), 131–144. Cartwright, J. (2008). Evolution and Human Behavior. Cambridge: MIT Press. Chapais, B. (2008). Primeval Kinship: How Pair-Bonding Gave Birth to Human Society. Cambridge, MA: Harvard University Press. Cho, M. M., DeVries, A. C., et al. (1999). The effects of oxytocin and vasopressin on partner preferences in male and female prairie voles (Microtus ochrogaster). Behav Neurosci 113(5), 1071–1079. Ditzen, B., Schaer, M., et al. (2009). Intranasal oxytocin increases positive communication and reduces cortisol levels during couple conflict. Biol Psychiatr 65(9), 728–731. Domes, G., Heinrichs, M., et al. (2007). Oxytocin improves “mind-reading” in humans. Biol Psychiatr 61(6), 731–733. Dunbar, R. I. & Shultz, S. (2007). Evolution in the social brain. Science 317(5843), 1344–1347. Emanuele, E., Brondino, N., et al. (2007). Genetic loading on human loving styles. Neuro Endocrinol Lett 28(6), 815–821. Frank, R. H. (1988). Passions Within Reason: The Strategic Role of the Emotions. New York: Norton. Fuchs, A. R., Romero, R., et al. (1991). Oxytocin secretion and human parturition: pulse frequency and duration increase during spontaneous labor in women. Am J Obstet Gynecol 165(5), 1515–1523.

c02.indd 43

7/25/2011 10:10:10 AM

44

THE BEHAVIORAL GENETICS OF HUMAN PAIR BONDING

Fuentes, A. (2002). Patterns and trends in primate pair bonds. Int J Primatol 23(5), 953–978. Garcia, J. R., MacKillop, J., et al. (2010). Associations between dopamine D4 receptor gene variation with both infidelity and sexual promiscuity. PLoS One 5(11), e14162. Gingrich, B., Liu, Y., et al. (2000). Dopamine D2 receptors in the nucleus accumbens are important for social attachment in female prairie voles (Microtus ochrogaster). Behav Neurosci 114(1), 173–183. Goodson, J. L., & Bass, A. H. (2001). Social behavior functions and related anatomical characteristics of vasotocin/vasopressin systems in vertebrates. Brain Res Brain Res Rev 35(3), 246–265. Guo, G. & Tong, Y. Y. (2006). Age at first sexual intercourse, genes, and social context: Evidence from twins and the dopamine D4 receptor gene. Demography 43(4), 747–769. Guo, G., Tong, Y., et al. (2007). Dopamine transporter, gender, and number of sexual partners among young adults. Eur J Hum Genet 15(3), 279–287. Hammock, E. A. & Young, L. J. (2002). Variation in the vasopressin V1a receptor promoter and expression: implications for inter- and intraspecific variation in social behaviour. Eur J Neurosci 16(3), 399–402. Harmon-Jones, E., Peterson, C. K., et al. (2009). Jealousy: Novel methods and neural correlates. Emotion 9(1), 113–117. Hawkes, K. (2004). Mating, parenting and the evolution of human pair bonds. Kinship and Behavior in Primates, edited by B. Chapais and C. Berman. Oxford: Oxford University Press. Heinz, A., Goldman, D., et al. (2000). Genotype influences in vivo dopamine transporter availability in human striatum. Neuropsychopharmacology 22(2), 133–139. Insel, T. R. & Hulihan, T. J. (1995). A gender-specific mechanism for pair bonding: oxytocin and partner preference formation in monogamous voles. Behav Neurosci 109(4), 782–789. Insel, T. R. & Shapiro, L. E. (1992). Oxytocin receptor distribution reflects social organization in monogamous and polygamous voles. Proc Natl Acad Sci U S A 89(13), 5981–5985. Insel, T. R., Wang, Z. X., et al. (1994). Patterns of brain vasopressin receptor distribution associated with social organization in microtine rodents. J Neurosci 14(9), 5381–5392. Israel, S., Lerer, E., et al. (2009). The oxytocin receptor (OXTR) contributes to prosocial fund allocations in the dictator game and the social value orientations task. PLoS One 4(5), e5535. Jacob, S., Brune, C. W., et al. (2007). Association of the oxytocin receptor gene (OXTR) in Caucasian children and adolescents with autism. Neurosci Lett 417(1), 6–9. Jockin, V., McGue, M., et al. (1996). Personality and divorce: A genetic analysis. J Pers Soc Psychol 71(2), 288–299. Kim, S. J., Young, L. J., et al. (2002). Transmission disequilibrium testing of arginine vasopressin receptor 1A (AVPR1A) polymorphisms in autism. Mol Psychiatr 7(5), 503–507. Knafo, A., Israel, S., et al. (2008). Individual differences in allocation of funds in the dictator game associated with length of the arginine vasopressin 1a receptor RS3 promoter region and correlation between RS3 length and hippocampal mRNA. Gene Brain Behav 7(3), 266–275. Kosfeld, M., Heinrichs, M., et al. (2005). Oxytocin increases trust in humans. Nature 435(7042), 673–676. Lerer, E., Levi, S., et al. (2008). Association between the oxytocin receptor (OXTR) gene and autism: relationship to Vineland Adaptive Behavior Scales and cognition. Mol Psychiatr 13(10), 980–988.

c02.indd 44

7/25/2011 10:10:10 AM

REFERENCES

45

Lim, M. M., Wang, Z., et al. (2004). Enhanced partner preference in a promiscuous species by manipulating the expression of a single gene. Nature 429(6993), 754–757. Liu, Y., & Wang, Z. X. (2003). Nucleus accumbens oxytocin and dopamine interact to regulate pair bond formation in female prairie voles. Neuroscience 121(3), 537–544. Lovejoy, C. O. (1981). The Origin of Man. Science 211(4480), 341–350. Maner, J. K., Rouby, D. A., et al. (2008). Automatic inattention to attractive alternatives: the evolved psychology of relationship maintenance. Evol Hum Behav 29(5), 343–349. Marlowe, F. W. (2003). A critical period for provisioning by Hadza men—Implications for pair bonding. Evol Hum Behav 24(3), 217–229. Mcgue, M., & Lykken, D. T. (1992). Genetic influence on risk of divorce. Psychol Sci 3(6), 368–373. Meyer-Lindenberg, A., Kolachana, B., et al. (2009). Genetic variants in AVPR1A linked to autism predict amygdala activation and personality traits in healthy humans. Mol Psychiatr 14(10), 968–975. Miller, W. B., Pasta, D. J., et al. (1999). Dopamine receptor genes are associated with age at first sexual intercourse. J Biosoc Sci 31(1), 43–54. Mustanski, B., Viken, R. J., et al. (2007). Sexual behavior in young adulthood: A populationbased twin study. Health Psychol 26(5), 610–617. Noble, E. P., Blum, K., et al. (1991). Allelic association of the D2 dopamine receptor gene with receptor-binding characteristics in alcoholism. Arch Gen Psychiatr 48(7), 648–654. Pohjalainen, T., Rinne, J. O., et al. (1998). The A1 allele of the human D2 dopamine receptor gene predicts low D2 receptor availability in healthy volunteers. Mol Psychiatr 3(3), 256–260. Prichard, Z. M., Mackinnon, A. J., et al. (2007). AVPR1A and OXTR polymorphisms are associated with sexual and reproductive behavioral phenotypes in humans. Mutation in brief no. 981. Online. Hum Mutat 28(11), 1150. Rimmele, U., Hediger, K., et al. (2009). Oxytocin makes a face in memory familiar. J Neurosci 29(1), 38–42. Rodrigues, S. M., Saslow, L. R., et al. (2009). Oxytocin receptor genetic variation relates to empathy and stress reactivity in humans. Proc Natl Acad Sci U S A 106(50), 21437–21441. Ross, H. E., Freeman, S. M., et al. (2009). Variation in oxytocin receptor density in the nucleus accumbens has differential effects on affiliative behaviors in monogamous and polygamous voles. J Neurosci 29(5), 1312–1318. Smith, A. S., Agmo, A., et al. (2009). Manipulation of the oxytocin system alters social behavior and attraction in pair-bonding primates, Callithrix penicillata. Horm Behav 57(2), 255–262. Spotts, E. L., Neiderhiser, J. M., et al. (2004). Genetic and environmental influences on marital relationships. J Fam Psychol 18(1), 107–119. Theodoridou, A., Rowe, A. C., et al. (2009). Oxytocin and social perception: Oxytocin increases perceived facial trustworthiness and attractiveness. Horm Behav 56(1), 128–132. Thibonnier, M., Graves, M. K., et al. (2000). Study of V(1)-vascular vasopressin receptor gene microsatellite polymorphisms in human essential hypertension. J Mol Cell Cardiol 32(4), 557–564. Thompson, J., Thomas, N., et al. (1997). D2 dopamine receptor gene (DRD2) Taq1 A polymorphism: Reduced dopamine D2 receptor binding in the human striatum associated with the A1 allele. Pharmacogenetics 7(6), 479–484.

c02.indd 45

7/25/2011 10:10:10 AM

46

THE BEHAVIORAL GENETICS OF HUMAN PAIR BONDING

Trumbetta, S. L., & Gottesman, I. I. (2000). Endophenotypes for marital status in the NASNRC twin registry. Genetic Influences on Human Fertility and Sexuality, edited by J. L. Rodgers, D. C. Rowe, & W. B. Miller. New York: Springer. VanNess, S. H., Owens, M. J., et al. (2005). The variable number of tandem repeats element in DAT1 regulates in vitro dopamine transporter density. BMC Genet 6, 55. Walum, H., Westberg, L., et al. (2008). Genetic variation in the vasopressin receptor 1a gene (AVPR1A) associates with pair-bonding behavior in humans. Proc Natl Acad Sci U S A 105(37), 14153–14156. Wang, Z., Young, L. J., et al. (1998). Voles and vasopressin: A review of molecular, cellular, and behavioral studies of pair bonding and paternal behaviors. Prog Brain Res 119, 483–499. Wassink, T. H., Piven, J., et al. (2004). Examination of AVPR1a as an autism susceptibility gene. Molecular Psychiatr 9(10), 968–972. Williams, J. R., Insel, T. R., et al. (1994). Oxytocin administered centrally facilitates formation of a partner preference in female prairie voles (Microtus ochrogaster). J Neuroendocrinol 6(3), 247–250. Wu, S., Jia, M., et al. (2005). Positive association of the oxytocin receptor gene (OXTR) with autism in the Chinese Han population. Biol Psychiatr 58(1), 74–77. Yirmiya, N., Rosenberg, C., et al. (2006). Association between the arginine vasopressin 1a receptor (AVPR1a) gene and autism in a family-based study: Mediation by socialization skills. Mol Psychiatr 11(5), 488–494. Young, L. J., Huot, B., et al. (1996). Species differences in central oxytocin receptor gene expression: comparative analysis of promoter sequences. J Neuroendocrinol 8(10), 777–783. Young, L. J., Lim, M. M., et al. (2001). Cellular mechanisms of social attachment. Horm Behav 40(2), 133–138. Young, L. J., Nilsen, R., et al. (1999). Increased affiliative response to vasopressin in mice expressing the V1a receptor from a monogamous vole. Nature 400(6746), 766–768. Young, L. J., & Wang, Z. (2004). The neurobiology of pair bonding. Nat Neurosci 7(10), 1048–1054. Yrigollen, C. M., Han, S. S., et al. (2008). Genes controlling affiliative behavior as candidate genes for autism. Biol Psychiatr 63(10), 911–916. Zak, P. J., Stanton, A. A., et al. (2007). Oxytocin increases generosity in humans. PLoS One 2(11), e1128.

c02.indd 46

7/25/2011 10:10:10 AM

1.3 BRAIN NETWORKS SUPPORTING EMPATHY MARTIN SCHULTE-RÜTHER1,2 AND ELLEN GREIMEL1,2,3 1

Child Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital of the RWTH, Aachen, Germany 2 Cognitive Neurology Section, Institute of Neuroscience and Medicine (INM-3), Research Center, Jülich, Germany 3 Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital of the RWTH, Aachen, Germany

In daily life, we are involved in complex social interactions with both family members and close friends as well as with acquaintances, colleagues, and strangers. When interacting with other persons, overt and subtle emotional cues conveyed via gestures, speech, or facial expressions are important sources of information that facilitate interpersonal understanding and give insight into the emotional state of others. Empathy entails the capacity to understand and share others’ emotional states and experiences (Decety & Moriguchi, 2007) and is a crucial prerequisite for successful social interaction and the creation of emotional bonds throughout the lifespan. With the emergence of the new field of social cognitive neuroscience (Ochsner & Lieberman, 2001), an unprecedented interest has emerged for identifying the neural mechanisms that are associated with this unique ability. However, empathy involves several distinct psychological processes that are likely to be associated with distinct brain mechanisms. Thus, a neuroscientific approach of empathy requires breaking down the concept of empathy to its constituent components and investigating the neural substrate of these components (Decety & Jackson, 2004).

From DNA to Social Cognition, First Edition. Edited by Richard Ebstein, Simone Shamay-Tsoory, and Soo Hong Chew. © 2011 Wiley-Blackwell. Published 2011 by John Wiley & Sons, Inc.

47

c03.indd 47

7/25/2011 10:10:11 AM

48

BRAIN NETWORKS SUPPORTING EMPATHY

1.3.1

MULTIPLE PROCESSES SUPPORTING EMPATHY

Defining empathy in a scientific way is a difficult endeavour. There are numerous definitions and concepts of empathy, perhaps nearly as many as there are researchers in the field (see Blair, 2005; Decety & Jackson, 2004; Preston & de Waal, 2002; Singer, 2006, for recent reviews). Some definitions predominantly focus on the cognitive aspects of empathy and emphasize the ability of explicit perspective-taking (Davis, 1996). Other authors stress the vicarious affective response that is elicited by observing other individuals who are experiencing and displaying emotions (Hoffman, 1977; Singer, 2006; Stotland, 1969). Here, a broader definition of empathy is adopted. Empathy is referred to as the result of processes of psychological inferences about other persons’ mental and emotional states occurring within a specific social context and allowing for appropriate emotional responses. The specific context provides the frame of reference for the integration of emotion and cognition and enables the individual to yield insights into the intentions and feelings of other people. Considering the diverse definitions in the literature, three core aspects become apparent that are of particular importance to such a broad concept of empathy (Decety & Jackson, 2004): (1) an affective response, (2) a cognitive mechanism allowing for perspective-taking, and (3) the ability to maintain a self–other distinction in order to be able to track the origin of self-related and other-related emotions. These aspects are referred to either implicitly or explicitly in most of the current definitions of empathy. 1.3.1.1 Affective Responses The occurrence of an affective response is essential to the concept of empathy. However, to be considered purely empathic, an affective response has to occur in the absence of a direct affective stimulation of the “empathic” person (e.g., a painful stimulation). Affective responses can only be called empathic if they are primarily caused by the observation or imagination of someone else’s emotions. Responses can be predominantly automatic (e.g., the contagious laughing in a group of cheerful and hilarious people), or they can be the result of more complex psychological processes (e.g., feeling sorry for someone who just experienced a major disappointment). 1.3.1.2

Perspective-Taking

An empathic response is often accompanied by complex cognitive processes. For example, understanding why somebody is crying is essential to the reaction. A crying child might be suffering, or it might “use” the crying to achieve a goal (e.g., obtaining more sweets). For an appropriate appraisal of the situation followed by an adequate reaction, the child’s motives and experiences need to be considered. In other words, one has to put oneself in the shoes of another person and take his/her perspective. 1.3.1.3

Self-Other Distinction

During empathizing, other persons’ feelings are shared “as if” they were experienced at first hand. At the same time, these feelings are still clearly distinguishable

c03.indd 48

7/25/2011 10:10:11 AM

BRAIN NETWORKS INVOLVED IN SHARED REPRESENTATIONS OF EMOTIONS

49

from our own emotions. Otherwise, we would often be overwhelmed by our emotional experiences and, as a consequence, perhaps incapable of adequate action. For example, truly empathic sadness in response to a friend who mourns the death of a beloved person is different from one’s own sadness in such a situation. Being aware that it is not oneself who is deeply sad is important to be able to comfort the friend’s sorrow. Thus, empathizing requires a “self–other distinction” (Decety & Jackson, 2004). Noteworthy, the development of empathy during childhood is closely related to the ability of self–other distinction. Once children recognize that other persons’ feelings and needs may differ from their owns, empathic responses have been shown to become more fine-tuned and elaborate (Litvack-Miller McDougal, & Romney, 1997). The cognitive components of empathy (i.e., perspective-taking and self–other distinction) are closely related to the concept of theory of mind (ToM), often also referred to as “mentalizing” (Frith & Frith, 2006; Frith & Frith, 2003) or “mind reading” (Baron-Cohen, 1994). ToM can be defined as the ability to represent selfrelated or other-related mental states such as intentions, beliefs, knowledge, thoughts, desires, and emotions (Premack & Woodruff, 1978), and to appreciate that other people’s mental states may differ from one’s own mental states.

1.3.2 BRAIN NETWORKS INVOLVED IN SHARED REPRESENTATIONS OF EMOTIONS Several studies have demonstrated that the degree of empathic feelings is related to the perceived similarity to an observed person (see Preston & de Waal, 2002, for a review). These behavioral findings suggest that empathy relies on the sharing of emotional experiences between self and other. How can this sharing of emotional experience be implemented on the neural level? 1.3.2.1

Shared Representations of Action and Perception

When seeing another person crying, we often cannot help to be moved to tears ourselves. Consistent with this observation, recent neuroscientific approaches to empathy have been strongly influenced by theories of perception–action coupling. The influential common coding theory put forward by Prinz (1997), for example, assumes that action and perception are not two separate entities but share common representations (or “common codes”). According to the model, perceiving an action activates sensory codes in the brain that are commensurate with motor codes relevant for the execution of the perceived action. This concept of common coding is not restricted to simple, physical motor representations but may also entail abstract, symbolic representations. Consequently, it has been suggested that such mechanisms may also account for a broader range of interpersonal and social processes (Gallese, 2003a, 2003b). The discovery of mirror neurons (Pellegrino, Fadiga, Fogassi, Gallese, & Rizzolatti, 1992) has provided the first evidence for shared perception–action mechanisms at the neural level (see Rizzolatti, Fogassi, & Gallese, 2001, for a review). Mirror neurons represent a particular class of neurons that have been identified in inferior frontal (F5) and superior parietal areas (PF) of the monkey brain (Gallese, Fadiga, Fogassi, & Rizzolatti, 1996, 2002; Rizzolatti, Fadiga, Gallese, & Fogassi, 1996). Mirror

c03.indd 49

7/25/2011 10:10:11 AM

50

BRAIN NETWORKS SUPPORTING EMPATHY

Figure 1.3-1. Regions in the brain which are implicated in the mirror neuron system. IFG: Inferior frontal gyrus, PPC: posterior parietal cortex, STS: superior temporal sulcus. During the observation of movements (e.g., facial expressions) movement perception is processed in the STS and information is relayed to posterior and frontal components of the MNS to form motor representations of the observed action. (See color insert.)

neurons are functionally defined as being active both when a particular action is executed by the monkey and when the monkey observes another individual performing a similar action. In concert with the superior temporal sulcus (STS), mirror neurons in the inferior frontal and parietal cortices may provide the neural basis for imitation (Iacoboni et al., 1999; Miall, 2003). 1.3.2.2 The Mirror Neuron System and Empathy Although mirror neurons have not yet been directly confirmed in humans, numerous neuroimaging, electophysiology and lesion studies have provided indirect evidence for a human homologue of the mirror neuron system (MNS), including the inferior frontal gyrus and the posterior parietal cortex (Figure 1.3-1) (see Iacoboni & Mazziotta, 2007, for a recent review). The first studies used grasping observation and execution paradigms (Rizzolatti et al., 1996; Grafton, Arbib, Fadiga, & Rizzolatti, 1996) as well as tasks targeting the imitation of observed hand actions (Iacoboni et al., 1999). Frontal mirror neuron areas also play a role in coding more abstract intentional states that are inferred from an observed action (Iacoboni et al., 2005). Activation in mirror neuron areas has also been reported for the observation and imitation of facial expressions of emotion (Carr, Iacoboni, Dubeau, Mazziotta, & Lenzi, 2003; Dapretto et al., 2006; Leslie, Johnson-Frey, & Grafton, 2004; Pfeifer, Iacoboni, Mazziotta, & Dapretto, 2008; Jabbi, Swart, & Keysers, 2007) suggesting the involvement of the MNS in understanding the emotions of others by mirroring their observed emotional state. Thus, the MNS may also mediate empathic feelings and the understanding of others’ emotional actions via simulation processes. The anterior insula has been suggested to function as an important relay between the action representation

c03.indd 50

7/25/2011 10:10:11 AM

BRAIN NETWORKS INVOLVED IN SHARED REPRESENTATIONS OF EMOTIONS

51

networks and the limbic structures that process emotions (e.g., the amygdala) (Carr et al., 2003; Iacoboni & Dapretto, 2006). In a series of recent functional magnetic resonance imaging (fMRI) studies, we could replicate and extend these findings (Schulte-Rüther et al., 2007, 2008, 2011; Greimel et al., 2010a, 2010b). We investigated self- and other-related emotional processing in the context of empathy (see Figure 1.3-2). Participants were asked to either evaluate the emotional state of a depicted person (other-task) or their own emotional reaction when observing that person (self-task). Using both the self-task and the other-task in a single paradigm, we were able to construct experimentally an interpersonal context in which empathic responses could emerge. We observed activation of the inferior frontal gyrus (IFG) during both self and other conditions. These results suggest that in the context of face-to-face interaction, the MNS is engaged in decoding and understanding the emotional states of other people. Thus, mirror mechanisms may go beyond simple observation–imitation mapping and actually play a vital role in interpersonal interaction. Noteworthy, the ontogeny of the MNS is largely unknown and currently a matter of debate (Del Guidice, Manera, & Keysers, 2009; Gallese et al., 2009). Knowledge of the typical development of the MNS is crucial for a comprehensive understanding of deviant mirroring mechanisms in neurodevelopmental psychiatric disorders, such as autism spectrum disorders (ASD) (e.g., Dapretto et al., 2006; Greimel et al., 2010a; Schulte-Rüther et al., 2011). The MNS is likely to be present already in infants, but it continues to develop beyond childhood (Kilner & Blakemore, 2007), suggesting an experience-dependent plasticity of the MNS (Calvo-Merino, Glaser, Grèzes, Passingham, & Haggard, 2004; Cross, Hamilton, & Grafton, 2006; Lahav, Saltzman, & Schlaug, 2007). Although several studies have delineated the neural bases of empathy in children (Decety, Michalska, & Akitsuki, 2008; Pfeifer et al., 2008) and adults (Carr et al., 2003; Jabbi et al., 2007; SchulteRüther et al., 2007), data on developmental changes in the neural mechanisms supporting empathy, presumably including areas of the MNS, are scarce. In a recent study, we investigated a developmental sample, including children, adolescents, and adults with our empathy paradigm. We observed increased activity in the frontal component of the MNS with increasing age during empathizing (Greimel et al., 2010b). Based on studies in adults (Calvo-Merino et al., 2004; Cross et al., 2006; Lahav et al., 2007), increased activity in the frontal component of the MNS with increasing age may be explained by greater experience and expertise accumulated during socio-emotional interactions. 1.3.2.3

Neural Mechanisms of Shared Affect

Preston and de Waal (2002) have put forward a perception–action model of empathy. They extend the notion of automatic perception–action mapping by the idea that a perceived emotional state automatically activates the respective emotional state in the observer, along with the associated autonomic and somatic responses. Empirical evidence for this model has been provided by several neuroimaging studies, showing overlapping neural responses during the direct experience and the passive observation of emotions and sensations. For example, both observing facial expressions of disgust and experiencing disgust activates the anterior insula and the anterior cingulate cortex (Wicker et al., 2003). Similarly, the observation of

c03.indd 51

7/25/2011 10:10:11 AM

52

c03.indd 52

7/25/2011 10:10:11 AM

Figure 1.3-2. Experimental paradigm to study self- and other-related empathic processing (Schulte-Rüther et al., 2011; Greimel et al., 2010a; Greimel et al., 2010b; for a similar paradigm see Schulte-Rüther et al., 2007, 2008). Subjects were instructed to empathize with the person presented on the screen and to identify the emotional state observed in the face (OTHER) or to evaluate their own emotional response to that face (SELF). As a control task, a perceptual decision on the width of neutral faces was used. (See color insert.)

BRAIN NETWORKS INVOLVED IN THEORY OF MIND

53

someone being touched and the experience of being touched oneself results in overlapping activation patterns in the secondary somatosensory cortex, a brain region that is involved in the perceptual processing and experience of touch (Keysers et al., 2004). Interestingly, it has also been shown that lesions to the somatosensory cortex (especially to those parts where the facial regions are represented) also impair the ability to recognize facial expressions of emotions (Adolphs, Damasio, Tranel, Cooper, & Damasio, 2000). Furthermore, Singer et al. (2004) could demonstrate that brain regions that are associated with the unpleasantness of physical pain (such as the anterior insula and the dorsal anterior cingulate cortex) are engaged when receiving pain, but also when participants observed a beloved partner experiencing a painful stimulation. This pattern of results can also be observed when pictures of body parts in potential painful situations are shown, such as a knife cutting a hand, a foot stuck in the door (Jackson, Meltzoff, & Decety, 2005), or needles inserted into the skin (Cheng et al., 2007). Taken together, these data suggest that in addition to overlapping representations of perception and action associated with the MNS, several other “mirror systems” (in an extended sense) may exist that support empathic processing (Bastiaansen, Thioux, & Keysers, 2009). The observation of emotions, sensations, and feelings in other people, thus, results in a pattern of brain activation that reflects the embodiment of their actions, affective states, and sensations.

1.3.3

BRAIN NETWORKS INVOLVED IN THEORY OF MIND

Several tasks have been used to study the neural substrates of ToM and perspectivetaking. These include the attribution of social meaning to movement patterns of abstract geometric forms (e.g., “Social Attribution Task”; Heider & Simmel, 1944; Klin, 2000), the inference of complex emotional states from photos of the eye region of faces (e.g., “Reading the Mind in the Eyes Test”; Baron-Cohen, Wheelwright, Hill, Raste, & Plumb, 2001), mental state explanations of visual jokes (Gallagher et al., 2000), or questions on the characters’ behavior and experiences in complex social stories (Happé, 1994). Numerous neuroimaging studies have been conducted to identify the neural substrates of ToM (Blakemore et al., 2003; Fletcher et al., 1995; Gallagher et al., 2000; Gallagher, Jack, Roepstorff, & Frith, 2002; Rilling, Sanfey, Aronson, Nystrom, & Cohen, 2004; Saxe & Kanwisher, 2003; see Frith & Frith, 2006; Frith & Frith, 2003; Saxe, Carey, & Kanwisher, 2004, for reviews). Converging evidence suggests that the medial prefrontal cortex (MPFC), temporoparietal areas (temporoparietal junction [TPJ] and superior temporal sulcus [STS]), and the temporal pole play a particular role in ToM reasoning and associated processes (see Figure 1.3-3). 1.3.3.1 Temporal Pole The temporal pole has been implicated in emotional and autobiographical memory retrieval (Dolan, Lane, Chua, & Fletcher, 2000; Fink et al., 1996; Piefke, Weiss, Zilles, Markowitsch, & Fink, 2003) and in the recollection of familiar faces and scenes (Nakamura et al., 2000). It is likely that the ability to attribute mental states to oneself and others is at least to some degree based on memory, in particular, on

c03.indd 53

7/25/2011 10:10:11 AM

54

BRAIN NETWORKS SUPPORTING EMPATHY

Figure 1.3-3. Brain regions involved in Theory of Mind. STS: superior temporal sulcus; TPJ: temporoparietal junction; TP: temporal pole; MPFC: medial prefrontal cortex. (See color insert.)

autobiographical memories. It is conceivable that also during empathizing tasks we use our own past experiences to gain access to other persons’ thoughts and mental states (Frith & Frith, 2003). 1.3.3.2 Temporoparietal Areas Activations occurring during ToM tasks often comprise the STS, but also extend to more posterior aspects of the TPJ and inferior parietal cortex. Several studies have demonstrated activation of the STS when social meaning has to be extracted from external visual stimuli (Allison, Puce, & McCarthy, 2000) (e.g., static and dynamic displays of facial expressions [Narumoto, Okada, Sadato, Fukui, & Yonekura, 2001; Sato, Kochiyama, Yoshikawa, Naito, & Matsumura, 2004], biological motion [Puce & Perrett, 2003], or socially meaningful actions [Pelphrey, Morris, & McCarthy, 2004]). The extraction of social meaning from stimuli can be considered a prerequisite for empathic reasoning. Other areas at the TPJ may be crucial for abstract ToM reasoning itself. Some authors claim a pivotal role for the TPJ in inferring false belief (Saxe et al., 2004; Saxe & Kanwisher, 2003), whereas others suggest a specific role of temporoparietal areas, such as the inferior parietal cortex, in self–other distinction (Decety & Sommerville, 2003). 1.3.3.3

Medial Prefrontal Cortex

The MPFC has been implicated in diverse socio-emotional and social cognitive functions, including ToM (see Ochsner et al., 2004, for a review). It has been suggested that the MPFC plays a central role in the metarepresentation of other people’s mental states (Gallagher & Frith, 2003). Activation within the MPFC may occur when the mental state of an agent is represented “decoupled from reality” (Gallagher & Frith, 2003, p. 79), in the absence of direct cues signaling the agent’s intent. Brain regions like the temporal pole and the STS, however, play a role in decoding such cues, to provide the basis for more abstract mentalizing mechanisms. However, results from lesion studies challenge the conclusion that the MPFC can be considered the “core” mentalizing region in the brain (Bird, Castelli, Malik, Frith, & Husain, 2004). Recently, subdivisions of the MPFC have been suggested along a

c03.indd 54

7/25/2011 10:10:11 AM

BRAIN NETWORKS INVOLVED IN SELF-REFERENTIAL PROCESSING

55

caudal–rostral axis (Amodio & Frith, 2006). The most ventral parts of the MPFC may be primarily involved in monitoring emotions, whereas dorsal (posterior rostral) MPFC areas are considered to be primarily engaged in action monitoring and the evaluation of observed actions. Moreover, Amodio and Frith (2006) argue that processing within the MPFC proceeds from the most dorsal and most ventral parts toward an anterior transition zone. In this transition zone, more abstract metacognitive representations supporting self-reference and mentalizing are supposed to be implemented (see also Schulte-Rüther et al., 2011).

1.3.4 BRAIN NETWORKS INVOLVED IN SELF-REFERENTIAL PROCESSING AND SELF-OTHER DISTINCTION As noted, empathizing depends on the ability to take one’s own and another person’s perspective simultaneously. Thus, reflecting on one’s own internal emotional state is an important component of empathic processing. This ability is closely related to mentalizing about others, as both processes rely on the cognitive metarepresentation of mental states. Therefore, it is not surprising that studies involving self-referential processing report similar areas of activation (e.g., in the MPFC). Self-related MPFC activation has been demonstrated in different tasks requiring recognition of self-identity (Kampe, Frith, & Frith, 2003), reflection on one’s own emotional reactions (Gusnard, Akbudak, Shulman, & Raichle, 2001; Lane, Fink, Chau, & Dolan, 1997; Ochsner et al., 2004), judgments on whether trait adjectives describe oneself (Kelley et al., 2002; Mitchell, Banaji, & Macrae, 2005) or are selfrelevant (Macrae, Moran, Heatherton, Banfield, & Kelley, 2004), and first-person conceptual perspective-taking (Ruby & Decety, 2003). If similar neural mechanisms are recruited during self- and other-related processing of mental and emotional representations, the question then arises of how the brain distinguishes between self and other. Recent evidence suggests that social cognitive judgments about others rely primarily on the dorsal MPFC (dMPFC) regions, whereas self-referential cognitive and emotional processing additionally involve the more ventral aspects (vMPFC) of the MPFC (Mitchell, Banaji, & Macrae, 2005; Mitchell, Macrae, & Banaji, 2006; Schulte-Rüther et al., 2007, 2008). Furthermore, it has been suggested that temporoparietal regions, and in particular the right inferior parietal cortex, play a special role in self–other distinction (Decety & Sommerville, 2003). This region has been shown to differentiate between self-produced actions and actions caused by others (Blakemore & Frith, 2003). For example, during movement observation, this brain region is associated with the awareness of not being the source of the action (Farrer & Frith, 2002), and it is modulated by the degree of mismatch between self-experienced and observed actions (Farrer et al., 2003). Our own fMRI studies on empathy involved both the evaluation of the emotional state of an other person (other-task) and the evaluation of one’s own emotional reaction (self-task) (see Figure 1.3-2). We found differential activation of a brain area in the TPJ (extending into the inferior parietal cortex) during the self-related attribution of emotion (Schulte-Rüther et al., 2007). This brain region may thus allow us to separate our own experiences mentally from the experiences of another person. Such a mechanism may also explain TPJ and inferior parietal activations in

c03.indd 55

7/25/2011 10:10:12 AM

56

BRAIN NETWORKS SUPPORTING EMPATHY

false-belief tasks (Saxe & Kanwisher, 2003). In such scenarios, simultaneous access to one’s own and someone else’s knowledge and their distinction is required. Interestingly, in our studies, the TPJ was more strongly activated in males (relative to females, Schulte-Rüther et al., 2008), and in subjects with autism spectrum disorders (relative to healthy controls; Schulte-Rüther et al., 2011) during self-related attribution of emotion. At the same time, males were less emotionally responsive to the faces than females and subjects with autism spectrum disorders showed less contagious emotional feelings than controls. We concluded that this brain activation pattern might reflect an enhanced cognitive distinction between one’s own emotions and those expressed by the counterpart, which in turn may be associated with less contagious emotional reactions (see also Derntl et al., 2010).

1.3.5

SUMMARY AND OUTLOOK

Empathy is a complex, multifaceted concept that comprises both affective and cognitive aspects. Empathizing requires that we put ourselves in the shoes of another person and at the same time observed feelings may be shared “as if” they were experienced at first hand. However, the ability to reflect on one’s own feelings and to maintain a self–other distinction are also crucial components of this unique ability. In recent years, a growing number of neuroimaging studies has investigated the neural mechanisms associated with core components of empathy. Studies focusing on cognitive aspects of empathy have consistently identified a neural network that entails the MPFC, temporoparietal areas (STS and TJP), and the temporal pole. Brain regions such as the temporal pole and the STS are thought to be implicated in decoding social cues, whereas the MPFC has been suggested to play a role in the metarepresentation of other people’s mental states and in self-referential processes. Brain networks involved in the ability to differentiate one’s own feelings and mental states from those of other people include dorsal and ventral subregions of the MPFC along with temporoparietal regions, such as the right IFG and the TPJ. Based on the first results from gender studies and investigations in populations characterized by deviant empathic responses, an intriguing question for future research is to specify the relationship between the emergence of empathic feelings and activation in brain regions that mediate self–other distinction. Recently, it has been suggested that the human MNS—including the inferior frontal gyrus and the posterior parietal cortex—may not only support shared perception–action mechanisms in the motor domain but also may be an important neural basis for affective empathy. During empathizing, the insula may form a relay between action representation networks and limbic structures that process emotions. Evidence for the role of the MNS in affective empathy comes from an increasing number of neuroimaging studies that have reported activation in components of the MNS during the processing of facial expressions and during explicit empathy tasks. Noteworthy, to date, studies on the typical development of the MNS remain scarce, although such studies would be crucial for our understanding of disturbed mirror neuron mechanisms and deviant empathic responses in psychiatric disorders and conditions, such as autism spectrum disorders.

c03.indd 56

7/25/2011 10:10:12 AM

REFERENCES

57

Based on studies that have identified overlapping (albeit not identical) brain activation patterns during the first-hand experience and mere observation of emotions and sensations (e.g., in the anterior cingulate cortex for observation and sensation of a painful stimulation), the concept of an “extended MNS” has been put forward. Along with the core MNS, this extended MNS is assumed to play a key role in the emergence of shared affect and appropriate empathic reactions during social interactions.

REFERENCES Adolphs, R., Damasio, H., Tranel, D., Cooper, G., & Damasio, A. R. (2000). A role for somatosensory cortices in the visual recognition of emotion as revealed by three-dimensional lesion mapping. J Neurosci 20, 2683–2690. Allison, T., Puce, A., & McCarthy, G. (2000). Social perception from visual cues: Role of the STS region. Trends Cognit Sci 4, 267–278. Amodio, D. M., & Frith, C. D. (2006). Meeting of minds: The medial frontal cortex and social cognition. Nat Rev Neurosci 7, 268–277. Baron-Cohen, S. (1994). How to build a baby that can read minds: Cognitive mechanisms in mindreading. Curr Psychol Cognit 13, 513–552. Baron-Cohen, S., Wheelwright, S., Hill, J., Raste, Y., & Plumb, I. (2001). The “Reading the Mind in the Eyes” Test revised version: A study with normal adults, and adults with Asperger syndrome or high-functioning autism. J Child Psychol Psychiatry 42, 241–251. Bastiaansen, J. A., Thioux, M., & Keysers, C. (2009). Evidence for mirror systems in emotions. Phil Trans Roy Soc Lond B Biol Sci 364(1528), 2391–2404. Bird, C. M., Castelli, F., Malik, O., Frith, U., & Husain, M. (2004). The impact of extensive medial frontal lobe damage on “Theory of Mind” and cognition. Brain 127, 914–928. Blair, R. J. (2005). Responding to the emotions of others: Dissociating forms of empathy through the study of typical and psychiatric populations. Conscious Cognit 14, 698–718. Blakemore, S. J., Boyer, P., Pachot-Clouard, M., Meltzoff, A., Segebarth, C., et al. (2003). The detection of contingency and animacy from simple animations in the human brain. Cerebr Cortex 13, 837–844. Blakemore, S. J., & Frith, C. (2003). Self-awareness and action. Curr Opini Neurobiol 13, 219–224. Calvo-Merino, B., Glaser, D. E., Grèzes, J., Passingham, R. E., & Haggard, P. (2004). Action observation and acquired motor skills: An fMRI study with expert dancers. Cerebr Cortex 15, 1243–1249. Carr, L., Iacoboni, M., Dubeau, M. C., Mazziotta, J. C., & Lenzi, G. L. (2003). Neural mechanisms of empathy in humans: A relay from neural systems for imitation to limbic areas. Proc Nati Acad Sci USA 100, 5497–5502. Cheng, Y., Lin, C. P., Liu, H. L., Hsu, Y. Y., Lim, K. E., et al. (2007). Expertise modulates the per-ception of pain in others. Curr Biol 17, 1708–1713. Cross, E. S., Hamilton, A. F., & Grafton, S. T. (2006). Building a motor simulation de novo: Observation of dance by dancers. Neuroimage 31, 1257–1267. Davis, M. H. (1996). Empathy—A Social Psychological Approach. Boulder, CO: Westview. Dapretto, M., Davies, M. S., Pfeifer, J. H., Scott, A. A., Sigman, M., et al. (2006). Understanding emotions in others: Mirror neuron dysfunction in children with autism spectrum disorders. Nat Neurosci 9, 28–30.

c03.indd 57

7/25/2011 10:10:12 AM

58

BRAIN NETWORKS SUPPORTING EMPATHY

Decety, J., & Jackson, P. L. (2004). The functional architecture of human empathy. Behav Cognit Neurosci Rev 3, 71–100. Decety, J., Michalska, K., & Akitsuki, Y. (2008). Who caused the pain? An fMRI investigation of empathy and intentionality in children. Neuropsychologia 46, 2607–2614. Decety, J., & Moriguchi, Y. (2007). The empathic brain and its dysfunction in psychiatric population: Implications for intervention across different clinical conditions. BioPsychoSocial Med 16, 1–22. Decety, J., & Sommerville, J. A. (2003). Shared representations between self and other: A social cognitive neuroscience view. Trends Cognit Sci 7, 527–533. Del Guidice, M., Manera, V., & Keysers, C. (2009). Programmed to learn? The ontogeny of mirror neurons. Dev Sci 12, 350–363. Derntl, B., Finkelmeyer, A., Eickhoff, S., Kellermann, T., Falkenberg, D. I., et al. (2010). Multidimensional assessment of empathic abilities: Neural correlates and gender differences. Psychoneuroendocrinology 35, 67–82. Dolan, R. J., Lane, R., Chua, P., & Fletcher, P. (2000). Dissociable temporal lobe activations during emotional episodic memory retrieval. Neuroimage 11, 203–209. Farrer, C., Franck, N., Georgieff, N., Frith, C. D., Decety, J., et al. (2003). Modulating the experience of agency: A positron emission tomography study. Neuroimage 18, 324–333. Farrer, C. & Frith, C. D. (2002). Experiencing oneself vs another person as being the cause of an action: The neural correlates of the experience of agency. Neuroimage 15, 596–603. Fink, G. R., Markowitsch, H. J., Reinkemeier, M., Bruckbauer, T., Kessler, J., et al. (1996). Cerebral representation of one’s own past: Neural networks involved in autobiographical memory. J Neurosci, 16, 4275–4282. Fletcher, P. C., Happe, F., Frith, U., Baker, S. C., Dolan, R. J., et al. (1995). Other minds in the brain: A functional imaging study of “theory of mind” in story comprehension. Cognition 57, 109–128. Frith, C. D., & Frith, U. (2006). The neural basis of mentalizing. Neuron 50, 531–534. Frith, U., & Frith, C. D. (2003). Development and neurophysiology of mentalizing. Phil Trans Roy Soc Lond B Biol Sci 358, 459–473. Gallagher, H. L., & Frith, C. D. (2003). Functional imaging of “theory of mind”. Trends Cognit Sci 7, 77–83. Gallagher, H. L., Happe, F., Brunswick, N., Fletcher, P. C., Frith, U., & Frith, C. D. (2000). Reading the mind in cartoons and stories: An fMRI study of “theory of mind” in verbal and nonverbal tasks. Neuropsychologia 38, 11–21. Gallagher, H. L., Jack, A. I., Roepstorff, A., & Frith, C. D. (2002). Imaging the intentional stance in a competitive game. Neuroimage 16, 814–821. Gallese, V. (2003a). The manifold nature of interpersonal relations: The quest for a common mechanism. Phil Trans Roy Soc Lond B Biol Sci 358, 517–528. Gallese, V. (2003b). The roots of empathy: The shared manifold hypothesis and the neural basis of intersubjectivity. Psychopathology 36, 171–180. Gallese, V., Fadiga, L., Fogassi, L., & Rizzolatti, G. (1996). Action recognition in the premotor cortex. Brain 119, 593–609. Gallese, V., Fadiga, L., Fogassi, L., & Rizzolatti, G. (2002). Action representation and the inferior parietal lobule. Attention & Performance XIX. Common Mechanisms in Perception and Action, edited by W. Prinz & B. Hommel. Oxford, U.K.: Oxford University Press. Gallese, V., Rochat, M., Sinigaglia, C., & Cossu, G. (2009). Motor cognition and its role in the phylogeny and ontogeny of action understanding. Dev Psychol 45, 103–113.

c03.indd 58

7/25/2011 10:10:12 AM

REFERENCES

59

Grafton, S. T., Arbib, M. A., Fadiga, L., & Rizzolatti, G. (1996). Localization of grasp representations in humans by positron emission tomography: 2. Observation compared with imagination. Exp Brain Res 112, 103–111. Greimel, E., Schulte-Rüther, M., Kircher, T., Kamp-Becker, I., Remschmidt, H., et al. (2010a). Neural mechanisms of empathy in adolescents with autism spectrum disorder and their fathers. Neuroimage 49, 1055–1065. Greimel, E., Schulte-Rüther, M., Fink, G. R., Piefke, M., Herpertz-Dahlmann, B., et al. (2010b). Development of neural correlates of empathy from childhood to early adulthood: An fMRI study in boys and adult men. J Neural Transm 117, 781–791. Gusnard, D. A., Akbudak, E., Shulman, G. L., & Raichle, M. E. (2001). Medial prefrontal cortex and self-referential mental activity: Relation to a default mode of brain function. Proc Nati Acad Sci USA 98, 4259–4264. Happé, F. G. E. (1994). An advanced test of theory of mind—Understanding of story characters’ thoughts and feelings by able autistic, mentally-handicapped, and normal children and adults. J Autism Dev Dis 24, 129–154. Heider, F., & Simmel, M. (1944). An experimental study of apparent behavior. Am J Psychol 57, 243–259. Hoffman, M. L. (1977). Sex-differences in empathy and related behaviors. Psychol Bull 84, 712–722. Iacoboni, M., & Dapretto, M. (2006). The mirror neuron system and the consequences of its dysfunction. Nat Rev Neurosci 7, 942–951. Iacoboni, M., & Mazziotta, J. C. (2007). Mirror neuron system: Basic findings and clinical applications. Ann Neurol 62, 213–218. Iacoboni, M., Molnar-Szakacs, I., Gallese, V., Buccino, G., Mazziotta, J. C., et al. (2005). Grasping the intentions of others with one’s own mirror neuron system. PLoS Biol 3, e79. Iacoboni, M., Woods, R. P., Brass, M., Bekkering, H., Mazziotta, J. C., et al. (1999). Cortical mechanisms of human imitation. Science 286, 2526–2528. Jabbi, M., Swart, M., & Keysers, C. (2007). Empathy for positive and negative emotions in the gustatory cortex. Neuroimage 34, 1744–1753. Jackson, P. L., Meltzoff, A. N., & Decety, J. (2005). How do we perceive the pain of others? A window into the neural processes involved in empathy. Neuroimage 24, 771–779. Kampe, K. K., Frith, C. D., & Frith, U. (2003). “Hey John”: Signals conveying communicative intention toward the self activate brain regions associated with “mentalizing,” regardless of modality. J Neurosci 23, 5258–5263. Kelley, W. M., Macrae, C. N., Wyland, C. L., Caglar, S., Inati, S., et al. (2002). Finding the self? An event-related fMRI study. J Cogn Neurosci 14, 785–794. Keysers, C., Wicker, B., Gazzola, V., Anton, J. L., Fogassi, L., et al. (2004). A touching sight: Sii/pv activation during the observation and experience of touch. Neuron 42, 335–346. Kilner, J. M., & Blakemore, S.-J. (2007). How does the mirror neuron system changeh during development? Dev Psychol 10, 524–526. Klin, A. (2000). Attributing social meaning to ambiguous visual stimuli in higher-functioning autism and Asperger syndrome: The social attribution task. J Child Psychol Psychiatry 41, 831–846. Lahav, A., Saltzman, E., & Schlaug, G. (2007). Action representation of sound: audiomotor recognition network while listening to newly acquired actions. J Neurosci 10, 308–314. Lane, R. D., Fink, G. R., Chau, P. M., & Dolan, R. J. (1997). Neural activation during selective attention to subjective emotional responses. Neuroreport 8, 3969–3972. Leslie, K. R., Johnson-Frey, S. H., & Grafton, S. T. (2004). Functional imaging of face and hand imitation—Towards a motor theory of empathy. Neuroimage 21, 601–607.

c03.indd 59

7/25/2011 10:10:12 AM

60

BRAIN NETWORKS SUPPORTING EMPATHY

Litvack-Miller, W., McDougal, D., & Romney, D. M. (1997). The structure of empathy during middle childhood and its relationship to prosocial behaviour. Genet Soc Gen Psychol Monogr 123, 303–325. Macrae, C. N., Moran, J. M., Heatherton, T. F., Banfield, J. F., & Kelley, W. M. (2004). Medial prefrontal activity predicts memory for self. Cerebr Cortex 14, 647–654. Miall, R. C. (2003). Connecting mirror neurons and forward models. Neuroreport 14, 2135–2137. Mitchell, J. P., Banaji, M. R., & Macrae, C. N. (2005). The link between social cognition and self-referential thought in the medial prefrontal cortex. J Cognit Neurosci 17, 1306–1315. Mitchell, J. P., Macrae, C. N., & Banaji, M. R. (2006). Dissociable medial prefrontal contributions to judgments of similar and dissimilar others. Neuron 50, 655–663. Nakamura, K., Kawashima, R., Sato, N., Nakamura, A., Sugiura, M., et al. (2000). Functional delineation of the human occipito-temporal areas related to face and scene processing. A PET study. Brain 123, 1903–1912. Narumoto, J., Okada, T., Sadato, N., Fukui, K., & Yonekura, Y. (2001). Attention to emotion modulates fMRI activity in human right superior temporal sulcus. Brain Res Cognit Brain Res 12, 225–231. Ochsner, K. N., Knierim, K., Ludlow, D. H., Hanelin, J., Ramachandran, T., et al. (2004). Reflecting upon feelings: An fMRI study of neural systems supporting the attribution of emotion to self and other. J Cognit Neurosci 16, 1746–1772. Ochsner, K. N., & Lieberman, M. D. (2001). The emergence of social cognitive neuroscience. Am Psycholo 56, 717–734. Pellegrino, G., Fadiga, L., Fogassi, L., Gallese, V., & Rizzolatti, G. (1992). Understanding motor events: A neurophysiological study. Exp Brain Res 91, 176–180. Pelphrey, K. A., Morris, J. P., & McCarthy, G. (2004). Grasping the intentions of others: The perceived intentionality of an action influences activity in the superior temporal sulcus during social perception. J Cognit Neurosci 16, 1706–1716. Pfeifer, J. H., Iacoboni, M., Mazziotta, J. C., & Dapretto, M. (2008). Mirroring others’ emotions relates to empathy and interpersonal competence in children. Neuroimage 39, 2076–2085. Piefke, M., Weiss, P. H., Zilles, K., Markowitsch, H. J., & Fink, G. R. (2003). Differential remoteness and emotional tone modulate the neural correlates of autobiographical memory. Brain 126, 650–668. Premack, D., & Woodruff, G. (1978). Does the chimpanzee have a theory of mind. Behav Brain Sci 1, 515–526. Preston, S. D., & de Waal, F. B. M. (2002). Empathy: Its ultimate and proximate bases. Behav Brain Sci 25, 1–20. Prinz, W. (1997). Perception and action planning. Eur J Cognit Psychol 9, 129–154. Puce, A., & Perrett, D. (2003). Electrophysiology and brain imaging of biological motion. Phil Trans Roy Soc Lond B 358, 435–445. Rilling, J. K., Sanfey, A. G., Aronson, J. A., Nystrom, L. E., & Cohen, J. D. (2004). The neural correlates of theory of mind within interpersonal interactions. Neuroimage 22, 1694–1703. Rizzolatti, G., Fadiga, L., Gallese, V., & Fogassi, L. (1996). Premotor cortex and the recognition of motor actions. Brain Res Cognit Brain Res 3, 131–141. Rizzolatti, G., Fogassi, L., & Gallese, V. (2001). Neurophysiological mechanisms underlying the understanding and imitation of action. Nat Rev Neurosci 2, 661–670. Ruby, P., & Decety, J. (2003). What you believe versus what you think they believe: A neuroimaging study of conceptual perspective-taking. Eur J Neurosci 17, 2475–2480.

c03.indd 60

7/25/2011 10:10:12 AM

REFERENCES

61

Sato, W., Kochiyama, T., Yoshikawa, S., Naito, E., & Matsumura, M. (2004). Enhanced neural activity in response to dynamic facial expressions of emotion: An fMRI study. Brain Res Cognit Brain Res 20, 81–91. Saxe, R., Carey, S., & Kanwisher, N. (2004). Understanding other minds: Linking developmental psychology and functional neuroimaging. Ann Rev Psychol 55, 87–124. Saxe, R., & Kanwisher, N. (2003). People thinking about thinking people—The role of the temporo-parietal junction in “theory of mind.” Neuroimage 19, 1835–1842. Schulte-Rüther, M., Fink, G. R., Markowitsch, H. J., & Piefke, M. (2007). Mirror neuron and theory of mind mechanisms involved in face-to-face interactions: a functional magnetic resonance imaging approach to empathy. J Cogn Neurosci 19, 1354–1372. Schulte-Rüther, M., Markowitsch, H. J., Fink, G. R., Shah, N. J., & Piefke, M. (2008). Gender differences in brain networks supporting empathy. Neuroimage 42, 393–403. Schulte-Rüther, M., Greimel, E., Markowitsch, H. J., Kamp-Becker, I., Remschmidt, H., Fink, G. R., et al. (2011). Dysfunctions in brain networks supporting empathy: An fMRI study in adults with autism spectrum disorders. Soc Neurosc 6(1), 1–21. Singer, T., Seymour, B., O’Doherty, J., Kaube, H., Dolan, R. J., & Frith, C. D. (2004). Empathy for pain involves the affective but not sensory components of pain. Science 303(5661), 1157–1162. Singer, T. (2006). The neuronal basis and ontogeny of empathy and mind reading: Review of literature and implications for future research. Neurosci Biobehav Rev 30, 855–863. Stotland, E. (1969). Exploratory investigations of empathy. Advances in Experimental Social Psychology, edited by L. Berkowitz. New York: Academic Press. Wicker, B., Keysers, C., Plailly, J., Royet, J. P., Gallese, V., et al. (2003). Both of us disgusted in My Insula: The common neural basis of seeing and feeling disgust. Neuron 40(3), 655–664.

c03.indd 61

7/25/2011 10:10:12 AM

1.4 THE HUMAN MIRROR NEURON SYSTEM AND SOCIAL COGNITION SOOK-LEI LIEW1,2

AND

LISA AZIZ-ZADEH1–3

1

The Brain and Creativity Institute, University of Southern California, Los Angeles, California 2 The Division of Occupational Science & Occupational Therapy, University of Southern California, Los Angeles, California 3 The Neuroscience Graduate Program, University of Southern California

1.4.1

INTRODUCTION

Understanding other people’s actions is crucial for smooth social exchanges. Interacting with loved ones, friends, or even strangers requires interpreting their actions and intentions in a dynamic manner and quickly predicting their goals within a matter of seconds. Although achieving this feat may require a combination of neural processes, in this chapter we will focus specifically on the contributions of a frontoparietal neural network known as the mirror neuron system (MNS) to social cognition. The discovery of the MNS in the early 1990s excited the scientific community by providing a possible neural basis for understanding others’ actions. Researchers recording neural activity from single neurons in premotor area F5 and inferior parietal regions of macaque monkeys found that some neurons fired both when the monkey performed an action, like reaching for a piece of food, and when the monkey watched the experimenter perform the same action (di Pellegrino, Fadiga, Fogassi, Gallese, & Rizzolatti, 1992; Gallese, Fadiga, Fogassi, & Rizzolatti, 1996; Rizzolatti, Fadiga, Gallese, & Fogassi, 1996). These neurons, known as mirror neurons, were thought to match incoming visual information about another’s actions to one’s own motor representations, possibly allowing the observer to understand

From DNA to Social Cognition, First Edition. Edited by Richard Ebstein, Simone Shamay-Tsoory, and Soo Hong Chew. © 2011 Wiley-Blackwell. Published 2011 by John Wiley & Sons, Inc.

63

c04.indd 63

7/25/2011 11:35:14 AM

64

MNS & SOCIAL COGNITION

Figure 1.4-1. Example of fMRI results showing components of the MNS (IFG, PP/IPL) active for action observation, action execution, and imitation (taken from Aziz-Zadeh et al., 2006a).

the other’s action (Fadiga, Craighero, & D’Ausilio, 2009; Rizzolatti & Craighero, 2004). Neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), have allowed researchers to explore whether a similar mirror neuron system exists in humans. There is now a great deal of research supporting the existence of a putative human mirror neuron system located in the inferior frontal gyrus (IFG; Brodman area 44) and the rostral inferior parietal lobule (IPL), which are the proposed human homologues to macaque mirror regions in area F5 and IPL (Aziz-Zadeh, Koski, Zaidel, Mazziotta, & Iacoboni, 2006a; Aziz-Zadeh, Maeda, Zaidel, Mazziotta, & Iacoboni, 2002; Gazzola, Aziz-Zadeh & Keysers, 2006; Keysers & Gazzola, 2006; Rizzolatti & Craighero, 2004; Umilta et al., 2001)).Typical activation from an fMRI study is shown in Figure 1.4-1. This putative human MNS is active during both the execution of an action and the observation of the same or a similar action, comparable with that observed in

c04.indd 64

7/25/2011 11:35:14 AM

INTRODUCTION

65

Figure 1.4-2. Examples of still frames from commonly used video stimuli in human MNS experiments. Participants may observe 2–4-second video clips of goal-directed actions, including mouth actions like eating (left panel), grasping hand actions like grasping a bottle cap (middle panel), or hand actions like turning a book page (right panel). Stimuli generally show only one effector at a time, providing a unilateral view.

macaques (see Figure 1.4-2 for examples of commonly used stimuli). Interestingly, in both macaques and humans, components of the MNS respond not only to the observation of actions but also to the sounds of actions, generating a multimodal representation of actions (Aziz-Zadeh, Iacoboni, Zaidel, Wilson, & Mazziotta, 2004; Gazzola, Aziz-Zadeh, & Keysers, 2006; Kohler et al., 2002). The ability to match observed or heard actions with one’s own motor representations for producing the same actions has led researchers to surmise that a function of the human MNS might be to support the motor simulations of other people’s actions (Gallese, Keysers, & Rizzolatti, 2004). Simulating another person’s actions allows us to generate an internal, first-person understanding of the observed action as though we ourselves performed the action and, based on that understanding, predict their next actions. In a sense, motor simulation is a vehicle through which one person can “get into the mind of another.” Motor simulation may have provided evolutionary benefits—for instance, being able to anticipate an enemy’s movements would equip one to react protectively to threats, and being able to anticipate a family member’s movements would allow one to work cooperatively with another. Matching other people’s actions onto one’s own motor representations for actions may also be useful for motor learning, imitation, and emulation (Iacoboni, 2005). Recently, numerous studies have tried to improve understanding of the complexity of the contribution of the MNS to social cognition. Individual differences in social and personality factors such as experience with the observed action (CalvoMerino, Glaser, Grezes, Passingham, & Haggard, 2005; Cross, Hamilton, & Grafton, 2006), ability to empathize (Shamay-Tsoory, Aharon-Peretz, & Perry, 2008; Singer, 2006), and social group affiliations (e.g., racial or cultural groups (Liew, Han, & Aziz-Zadeh, 2010; Molnar-Szakacs, Wu, Robles, & Iacoboni, 2007) can modulate activity in MNS regions when observing other’s movements. Additional evidence suggests that there is a link between the MNS and language abilities supported by the left IFG, a portion of the language region also known as Broca’s area. Thus, the MNS may not only support the ability to understand another’s immediate observed or heard actions but also be involved in developing conceptual representations of a particular action. The MNS is one of several neural networks proposed to be finely integrated to support higher level social cognition and, in particular, to have shared representations for the self and other. Recent work suggests that other types of simulation are

c04.indd 65

7/25/2011 11:35:14 AM

66

MNS & SOCIAL COGNITION

supported in the brain; for example, when we observe others in pain, we activate our own pain network as if we are in pain. Although it is yet unknown how many regions have shared representations, it is possible that these “as-if body loops” are common in many sensory and motor regions (Damasio, 1994; Damasio & Damasio, 2006). It is important to note that the proposed simulation mechanisms are not the only ones used to understand others. In particular, multimodal regions in the brain located in the medial prefrontal cortex (MPFC), precuneus, and bilateral temporoparietal junctions (TPJs), commonly referred to as the mentalizing or “theory of mind” system (Frith & Frith, 2006; Saxe, 2006; Saxe & Powell, 2006), also support many aspects of social cognition. Several studies have attempted to tease apart the relationship between these systems in action and intention understanding, and a current meta-analysis of these studies suggests that they may have distinct and complementary roles in social cognition (Van Overwalle & Baetens, 2009). In this chapter, we review the likely roles of the putative mirror neuron system in human social cognition, particularly as they are related to supporting simulation and empathizing abilities. We first discuss literature that demonstrates modulation of the MNS by social experiences, such as watching familiar actions, learning new actions, and watching novice or impossible actions. We then explore links between activation of the MNS and behavioral measures of empathy. Next we investigate the role of the MNS as it is modulated by social group affiliations and social identity as well as the proposed role of the MNS in verbal communication. This is followed by a discussion of simulation beyond the MNS, reviewing research on shared representations for a variety of sensations, such as touch and pain. In the last section, we explore the interactions between the MNS and other multimodal brain regions such as the MPFC, precuneus, and TPJ, in an attempt to link the MNS to other neural systems involved in social cognition.

1.4.2 THE MNS AND SOCIAL EXPERIENCES Given that the MNS comprises motor-related cortical regions, the ability to map observed actions onto one’s own motor regions may largely depend on one’s prior experience with the actions and one’s existing motor repertoires. In a study by Calvo-Merino and colleagues (2005), experienced dancers showed greater MNS activation when watching someone perform the type of dance they were specifically trained in (e.g., ballet) than when watching someone perform a different type of dance with similar but novel characteristics (e.g., capoeira). Although this study focused on expertise, it does not, however, take long for experience to alter MNS activity. Acquired experience with a skill can modulate activity in the MNS in a matter of days, as indicated by two separate studies in which novices trained in dance sequences for either several days or several weeks demonstrated greater MNS activity when watching trained dance sequences than novel dance sequences (Cross et al., 2006; Cross, Kraemer, Hamilton, Kelley, & Grafton, 2009). Furthermore, the amount of MNS activity when watching trained sequences positively correlated with participants’ own ratings of their ability to perform the sequences, demonstrating a link between perceived motor competence and MNS activity (Cross et al., 2006).

c04.indd 66

7/25/2011 11:35:14 AM

THE MNS AND SOCIAL EXPERIENCES

67

Interestingly, both physical and visual experiences were found to modulate MNS activity: An increase in MNS activity was found for familiar dance sequences whether they had been practiced or simply watched several times (Cross et al., 2009). A separate transcranial magnetic stimulation (TMS) study also indicated that motor evoked potentials (MEPs), thought to be indicative of mirror system activation, could be larger or smaller depending on physical learning or unlearning of a particular action (Catmur, Walsh, & Heyes, 2007). This implies that our own motor and observational experiences, whether long-term or newly acquired, modulate how we use the MNS to process action observation. Thus, it seems that our capacity to understand other people’s actions stems in part from our own ability to generate their actions ourselves. In addition to representing familiar movements, the MNS is thought to be highly involved in imitation of new movements, with a wide body of literature demonstrating increased MNS activity especially when attempting to learn and imitate novel motor patterns (Buccino et al., 2004b; Iacoboni, 2005; Vogt et al., 2007). Indeed imitation is a primary method of human motor learning (Meltzoff & Prinz, 2002), and the mapping of visual representations onto motor representations, as observed in the MNS, may be essential to this process (Heiser, Iacoboni, Maeda, Marcus, & Mazziotta, 2003). Increased activity in mirror regions was found when participants observed specific guitar chord finger patterns with an intent to imitate the patterns shortly thereafter compared with no intent to imitate (Buccino et al., 2004b). In contrast, when theta-burst TMS, a technique that disrupts neural activity, was applied to the IFG, participants showed a decreased ability to imitate finger movements (Catmur, Walsh, & Heyes, 2009). Iacoboni (2005) proposed that imitative learning involves the MNS and the dorsolateral prefrontal cortex (DLPFC), along with additional motor preparation regions such as the superior temporal sulcus (STS) and the dorsal premotor and superior parietal cortices. Supporting this theory, Vogt et al. (2007) demonstrated that the MNS is most involved during the early stages of learning and that the DLPFC may direct attention and integrate information from MNS regions to other brain regions, thus helping to coordinate the learning process. The perspective from which one observes an action may also influence imitative learning. When one intends to imitate and learn a particular action, action observation from a first-person perspective (e.g., it looks like my hand holding the guitar) was shown to elicit greater activity in the MNS than action observation from the third-person perspective (e.g., it looks like I’m watching someone else hold the guitar; Jackson, Meltzoff, & Decety, 2006). In the third-person perspective, the laterality of the hand seems to make a difference in the strength of activity in the MNS (Koski, Iacoboni, Dubeau, Woods, & Mazziotta, 2003). Greater MNS activity occurred when the actions were performed by the actor’s opposite hand (e.g., the mirror perspective; watching someone’s left hand and imitating with my own right hand) compared with when the actions were performed by the same hand as the imitator (e.g., the anatomical match; watching someone’s right hand and imitating with my own right hand; Koski et al., 2003). Once acquired, action representations in the mirror system seem to retain integrity even after individuals are no longer physically able to perform the actions. For example, individuals who have suffered complete spinal cord injuries and lose the ability to move their feet demonstrate activity within the IPL and cerebellum both

c04.indd 67

7/25/2011 11:35:14 AM

68

MNS & SOCIAL COGNITION

when attempting and imagining foot actions, despite their loss of voluntary foot movements (Hotz-Boendermaker et al., 2008). This suggests that cortical motor representations for foot actions may still be engaged by observation, even when motor output from the cortex to the spinal cord is impeded. A related question concerns how action observation is processed by individuals who have never been physically able to perform a particular observed action. In a recent study, participants with congenital aplasia demonstrated an MNS response for hand actions despite being born without hands and, thus, never having performed hand actions (Gazzola et al., 2007b). Interestingly, these individuals mapped observed hand actions onto their own foot or mouth regions in the motor cortex, depending on which effector they used to achieve the goal of the movement (e.g., mapping the observation of someone using their hand to pick up a pen onto their own foot region, if they use their foot to pick up a pen; Gazzola, Rizzolatti, Wicker, & Keysers, 2007a). Similar results were obtained in another recent study of action observation in a participant with congenital aplasia who was born without both arms or legs (Aziz-Zadeh et al., 2011). However, although this participant showed activity in her MNS for actions whose goals were possible for her using another effector (e.g., flipping a book page with her mouth or upper stump instead of fingers), goals that were impossible for her even with another effector (e.g., cutting with scissors) not only activated the MNS, but also they recruited additional brain regions associated with higher level reasoning abilities, such as the medial prefrontal cortex. Similarly, fMRI and TMS studies have demonstrated that MNS activity in the IFG occurs during observation of actions that are both biomechanically possible and biomechanically impossible (Costantini et al., 2005; Romani, Cesari, Urgesi, Facchini, & Aglioti, 2005). For example, the IFG is active during observation of finger movements whether they are within normal physical abilities or beyond them, such as finger hyperextension (Costantini et al., 2005). TMS delivered to the left primary motor cortex when watching finger movements revealed that both possible and impossible finger movements increased corticospinal excitability in the specific muscles needed for the motion (Romani et al., 2005). These data suggest that regions of the mirror system may be invariant to physical feasibility and may instead be active in response to observed movement patterns regardless of whether those patterns can be completed. The researchers proposed that other regions, such as sensorimotor parietal regions, may encode the movement feasibility (Costantini et al., 2005; Romani et al., 2005). Taken together, these findings on familiar, learned, and impossible actions suggest that mirror region activity may be greater for movements that match our individual motor repertoires or for movements that we wish to acquire into our motor repertoires through imitation and learning. Given that action observation and imitation are essential to development and learning (Meltzoff & Prinz, 2002), this may be an important function of the MNS. In addition, the frontal component of the MNS (the IFG) also responds to the goals of observed actions even when the motor means to achieve the goal dos not fall within our motor repertoire, whether we have never performed them before or they are physically impossible to perform. In these instances, regions beyond the MNS may be additionally recruited to provide further processing for the observation of an action outside our motor repertoire.

c04.indd 68

7/25/2011 11:35:14 AM

THE MNS AND EMPATHY

69

1.4.3 THE MNS AND EMPATHY Studies of humans and primates demonstrate that individuals tend to like those who imitate their mannerisms more than those who do not (Chartrand & Bargh, 1999; Paukner, Suomi, Visalberghi, & Ferrari, 2009). In a behavioral study, people preferred actors who copied their mannerisms, such as fidgeting with a pen or bouncing a foot, more than actors who did not (Chartrand & Bargh, 1999). In another study, monkeys preferred humans who copied their movements, for example, humans who played with a ball in the same way as the monkey, more than those who did not (Paukner et al., 2009). People also tended unconsciously to adopt their partners’ facial expressions, postures, and tics as they worked together, with the amount of mimicry increasing in more empathic participants (Chartrand & Bargh, 1999). An interpretation of such findings is that increased implicit mimicry of another’s actions and mannerisms, known as the Chameleon Effect, increases motor simulation between individuals, which may make it easier to understand, relate to, and empathize with others (Chartrand & Bargh, 1999). As discussed earlier, the mirror neuron system seems to play a prominent role in motor imitation and emulation (Heiser et al., 2003; Iacoboni, 2005; Vogt et al., 2007). Considering the behavioral results on implicit imitation as shown in the Chameleon Effect (Chartrand & Bargh, 1999), some researchers began to explore the role of the MNS in social imitation and its relationship to empathy. One study found that the imitation of facial expressions showed increased activity in components of the MNS as well as in the insula, a region associated with emotional processing (Carr, Iacoboni, Dubeau, Mazziotta, & Lenzi, 2003). Similarly, another study demonstrated that observing emotional facial expressions engaged the MNS, particularly in the IFG (Schulte-Rüther, Markowitsch, Fink, & Piefke, 2007). In the same study, activity in MNS correlated with increased scores on the Balanced Emotional Empathy Scale (BEES; Mehrabian, 1996), a behavioral measure of empathy, suggesting that one’s own empathic abilities may be correlated with activity in the MNS during emotion processing (Schulte-Rüther et al., 2007). Several additional studies have found that increases in MNS activity during action perception—whether hearing action-related sounds, seeing emotional facial expressions, interpreting emotional stories, or watching others’ motor actions— correlate with self-reported levels of empathy, suggesting that the MNS may be involved in empathic processing (Aziz-Zadeh et al., 2010; Kaplan & Iacoboni, 2006; Gazzola et al., 2006; Schulte-Rüther et al., 2007). For example, Gazzola et al. (2006) showed that increased MNS activity when listening to human action sounds compared with neutral environmental sounds correlated with increased scores on the Interpersonal Reactivity Index (IRI; Davis, 1983), a self-report measure of empathy. This correlation is shown in Figure 1.4-3 and suggests that there is a positive link between activity in regions that map others’ actions onto one’s own motor repertoire and empathic abilities. Kaplan and Iacoboni (2006) further demonstrated that activity in the right IFG during observation of actions in context—for example, a precision grip when lifting a cup to drink—is correlated with increased scores on the IRI. Similarly, increased empathic accuracy, which is the ability to predict accurately how another person is feeling as he or she is talking, was correlated to regions of the MNS (right IPL and bilateral dorsal premotor cortices) as well as the STS and MPFC (Zaki, Weber, Bolger, & Ochsner, 2009). A study by Shamay-Tsoory and

c04.indd 69

7/25/2011 11:35:14 AM

70

MNS & SOCIAL COGNITION

Figure 1.4-3. Activity in the MNS for action sounds is correlated with measures of trait empathy. (a) Neural activation when listening to human action sounds such as breaking a peanut or ripping paper, compared with environmental sounds such as thunder. (b) The middle panel displays scores on the perspective-taking (PT) portion of an empathy questionnaire correlated with the neural activity during the human action sounds versus environmental sounds in the IPL (green), IFG/BA44 (orange), and dorsal premotor cortex/BA6 (blue). (c) Whole brain correlations between blood–oxygen-level dependent activity and r-scores at the IFG/BA44, dorsal premotor/BA6, and IPL (Gazzola et al, 2006).

colleagues (2008) evaluated patients with lesions in IFG and found that lesions in the IFG compared with a non-MNS region predictably demonstrated impairments in emotional aspects of trait empathy (Shamay-Tsoory et al., 2008). Taken together, these results indicate a relationship between empathic ability and the MNS, such that individuals with increased trait empathy show more activity in the MNS during a variety of action understanding tasks.

1.4.4 THE MNS AND SOCIAL GROUP AFFILIATIONS Although so far we have discussed the MNS as a relatively automatic system, which responds to observed and executed actions, several factors may modulate activity in this system. The most notable of these is social group affiliations such as one’s racial group, cultural group, or social identity. An early study by Buccino et al. (2004a) considered action observation of conspecifics compared with non-conspecifics. Human participants observed humans, monkeys, or dogs perform mouth actions and showed the greatest MNS activity in response to mouth actions performed by humans, followed by monkeys, and then by dogs. That is, MNS activity during the observation of mouth actions decreased as humans observed species that were less and less similar (Buccino et al., 2004a). In terms of motor simulation, this suggests that the MNS may match the visual and kinesthetic features of actions such that the more physically similar one is to the actor, the more MNS activity that occurs. In humans, race is a highly automatic and implicitly encoded social group affiliation (Chiao et al., 2008; Phelps & Thomas, 2003). Experimental modulation of race between the actor and the observer has been shown to affect an array of neural responses depending on the task, including empathy for another’s pain (Xu, Zuo, Wang, & Han, 2009), fear responses to others (Chiao et al., 2008), and social liking (Phelps & Thomas, 2003). These data suggest that racial in-group/out-group associa-

c04.indd 70

7/25/2011 11:35:14 AM

THE MNS, LANGUAGE, AND EMBODIED SEMANTICS

71

tions can powerfully modulate neural responses to others in a variety of contexts. Although little has been studied regarding race and the MNS, several studies suggest that there is a complex race-based modulation of mirror regions (Liew et al., 2010; Molnar-Szakacs et al., 2007). Using TMS, Molnar-Szakacs et al. (2007) found increased corticospinal excitability when Euro-American participants observed actors of their own race compared with another race (e.g., Nicaraguan) perform the same actions, suggesting a racial in-group bias during action observation. Similarly, recent fMRI research in Chinese participants observing both Chinese and Caucasian actors demonstrated that action observation of members of one’s own race compared with the other race led to increased activity in the IPL and the insula, which is associated with emotional processing (Liew et al., 2010). These findings indicate that one may more readily map observed actions onto their own motor representations if the actor is similar to them (Liew et al., 2010; Molnar-Szakacs et al., 2007), which is a conclusion that is consistent with results from the conspecific study discussed previously (Buccino et al., 2004a). The MNS also seems to be modulated, and in some cases increased, by the degree of dissimilarity between the observer and the observed. An fMRI study revealed that when typically developed individuals observed actions performed by an individual without arms or legs, such as turning a book page with an upper arm stump, they had significantly more activity within the MNS than when observing typically developed individuals (Liew, Seckin, & Aziz-Zadeh, 2011). In contrast, when they observed other typically developed individuals carry out the same actions with their hands (e.g., turn a book page), they had significantly less activity in the MNS. Thus, although the MNS is generally a goal-matching system, it seems that in some instances, a great degree of physically dissimilarity between the actor and the observer may enhance mirror activity, as though to learn new body parts or actions. In addition to being modulated by observations of similar and dissimilar others, the MNS also responds differently to observations of the self versus others. Researchers found that repetitive TMS (rTMS) applied to the right IPL significantly decreased the participants’ abilities to distinguish their own face from the faces of others (Uddin, Molnar-Szakacs, Zaidel, & Iacoboni, 2006). Notably, only the right hemisphere demonstrated this effect in decreased self–other distinction, leading researchers to surmise that a possible role for the MNS in self-processing may occur largely in the right hemisphere. Additional support for this hypothesis was found in an fMRI study that found that the right hemisphere MNS was more active for the self even across modalities (Kaplan, Aziz-Zadeh, Uddin, & Iacoboni, 2008). Both observations of one’s own face versus a friend’s face and listening to one’s own voice versus a friend’s voice generated increased activity in the right IFG, suggesting that the MNS may play a role in distinguishing the self from others across both visual and auditory systems.

1.4.5 THE MNS, LANGUAGE, AND EMBODIED SEMANTICS Several researchers have hypothesized that shared representations between individuals may have been one mechanism by which we evolved language. Recall that the IFG is located in Broca’s area (BA 44), which is a primary language area. The dual role of motor planning and expressive language abilities in the IFG has been

c04.indd 71

7/25/2011 11:35:14 AM

72

MNS & SOCIAL COGNITION

interpreted by some researchers to suggest an evolutionary link between action execution/observation and language (Arbib, 2005; Fadiga et al., 2009; Gallese & Lakoff, 2005; Rizzolatti & Arbib, 1998). One proposal posits that language evolved in a series of stages, such that the ability to make goal-oriented actions (i.e., drinking from a cup) may have supported the ability to pantomime actions (i.e., pantomiming drinking from a cup; Arbib, 2005; Rizzolatti & Arbib, 1998). This, in turn, may have supported the ability to make symbolic gestures, such as an abstract gesture communicating the idea of drinking from a cup, and these symbolic gestures then formed the basis for language. A role for the MNS in conceptual representations and semantic processing has also been suggested. This is largely based on the possibility that having multimodal representations (visual, auditory, and motor) in the premotor cortex may lead to the possibility of abstract, conceptual representations. It is furthermore linked to a theory in linguistics known as embodied semantics, which proposes that concepts are represented in the same neural sensory-motor circuits that support the enactment of that concept. For example, embodied semantics predicts that phrases related to foot actions activate cortical motor representations of the foot. This theory extends beyond the motor system, for example, predicting that hearing a phrase about color activates cortical areas responsible for color processing (Aziz-Zadeh & Damasio, 2008; Barsalou, 1999; Damasio, 1989; Damasio & Tranel, 1993; Feldman & Narayanan, 2004; Gallese & Lakoff, 2005; Glenberg & Kaschak, 2002; Lakoff & Johnson, 1999; Pulvermuller, 2005; Pulvermuller & Hauk, 2006; Pulvermuller, Hauk, Nikulin, & Ilmoniemi, 2005; Pulvermuller, Shtyrov, & Ilmoniemi, 2005). Some is available evidence that phrases related to actions also activate the MNS in an effector-specific manner, which is consistent with the theory of embodied semantics for actions. In one study, Aziz-Zadeh et al. (2006b) showed that reading phrases that focused on foot, hand, or mouth actions also activated premotor regions that were most strongly active for foot, hand, or mouth actions, respectively. Furthermore, this activation occurred in the left hemisphere, where language is largely supported (see Figure 1.4-4; Aziz-Zadeh, Wilson, Rizzolatti, & Iacoboni, 2006b). Other studies have also supported this effect with similar paradigms (Pulvermuller, 2005; Pulvermuller et al., 2005; Tettamanti et al., 2005). Thus, it seems that bringing visual, auditory, and motor components of an action together as a multimodal representation may provide for abstract representation of an action, which in turn may facilitate conceptual representation and semantic processing for actions.

1.4.6

SHARED REPRESENTATIONS BEYOND THE MNS

Since the discovery of the MNS, an increasing number of studies have investigated the possibility that other brain regions and neural systems outside the motor system also respond to one’s own sensations as well as to one’s observations of another’s sensations. Overall, these studies suggest that there are shared representations between the self and other for other perceptions and states, such as emotions, somatosensation, and pain processing. It has been proposed that such shared representations are important for simulation of other people’s emotional and mental states, and that these shared representations may be modulated by our experiences

c04.indd 72

7/25/2011 11:35:14 AM

SHARED REPRESENTATIONS BEYOND THE MNS

73

Figure 1.4-4. Regions that respond to action observation for a specific effector also show increased activity for language related to that effector. (a) Observations of foot, hand, and mouth actions generate greater activity in foot, hand, and mouth premotor ROIs, respectively, in the left hemisphere, (b) and in the right hemisphere. (c) Reading foot, hand, and mouth phrases also generates greater activity in foot, hand, and mouth premotor ROIs in the left hemisphere, (d) but not in the right hemisphere (Aziz-Zadeh et al., 2006b).

and by our social groups (e.g., race and political affiliation), in a manner similar to the modulation of the MNS discussed previously (Cheng et al., 2007; Serino, Giovagnoli, & Làdavas, 2009; Singer et al., 2004; Singer & Frith, 2005; Singer et al., 2006; Xu et al., 2009). Although much of this work is beyond the scope of the current chapter, we briefly introduce some of this research here. One novel fMRI study explored shared representations for disgust (Wicker et al., 2003). When participants observed videos of actors with disgusted facial expressions after sniffing presumably noxious odors, they showed increased activation in the same brain regions that were active when they themselves smelt noxious odors (e.g., the anterior insula; Wicker et al., 2003). This suggests that participants may have simulated the feeling of disgust when they simply observed the disgusted facial expressions of another. Similarly, brain regions that are active when one experiences pain may also become active when observing another person in pain. Numerous studies have demonstrated that observing others undergo a painful situation activates regions that are associated with our own painful experiences, namely the “pain matrix,” which consists of the anterior insula and the anterior cingulate cortex (Cheng et al.,

c04.indd 73

7/25/2011 11:35:14 AM

74

MNS & SOCIAL COGNITION

2007; Singer et al., 2004; Singer & Frith, 2005; Xu et al., 2009). Neural activity in regions that may support shared representations for pain empathy is correlated with behavioral measures of empathy such as the IRI and BEES (Singer et al., 2004) and can be modulated by experience (Cheng et al., 2007), race (Xu et al., 2009), and opinions of another (Singer et al., 2006). For example, Cheng et al. (2007) found that experience modulates pain empathy such that understanding the benefits of the pain or knowing that a painful situation is actually not painful (e.g., needles inserted into the skin during acupuncture) decreases the amount of activity within the pain matrix when watching an individual receive painful stimulation. Similarly, there is increased activity in the pain matrix when observing someone of the same race receiving painful stimulation than when observing someone of a different race (Xu et al., 2009). Opinions of the person receiving pain may also modulate pain empathy. In an elegant study by Singer et al. (2006), male and female participants played games with confederates who either acted fairly or unfairly during the course of the game, after which participants observed the confederates receive painful stimulation. Male participants, during observation of painful stimulation received by “fair” confederates, activated the pain matrix. However, when male participants observed “unfair” confederates receive painful stimulation, no significant activity in the pain matrix was found. Instead, significant activity was noted in regions associated with reward and pleasure. Interestingly, these results differed based on the gender of the participant. Female participants demonstrated activity in the pain matrix when observing both “fair” and “unfair” actors in a painful situation, although this activity was decreased during observation of “unfair” confederates (Singer et al., 2006). Observations of tactile sensations may also be processed by one’s own somatosensory cortices. Participants demonstrated activity in the somatosensory cortex both when they were touched as well as when observing others being touched (Blakemore, Bristow, Bird, Frith, & Ward, 2005; Keysers et al., 2004). Interestingly, some individuals have what is termed “mirrored touch” synesthesia and demonstrate significantly greater activity in the somatosensory cortex compared with typical individuals when they observe others being touched (Blakemore et al., 2005). In a task where they received tactile sensation on their faces and simultaneously observed someone else receive tactile sensation to his or her face, participants with mirrored touch synesthesia made more errors when discerning which side of their own face was touched (Banissy & Ward, 2007). Individuals with mirrored touch synesthesia also had higher behavioral measures of empathy than typical individuals, again suggesting that the ability to simulate another’s sensations may be linked to increased empathy for others. In addition, typical individuals may be biased to more strongly represent observed touch when the observed individual is a political leader from one’s own political affiliation than when the individual is a political leader from a different political affiliation, again demonstrating modulation of shared representation systems based on social group membership (Serino et al., 2009).

1.4.7 THE MNS AND OTHER SOCIAL NETWORKS The MNS is clearly not the only neural network involved in social cognition, and much research has attempted to discern the role of the MNS as it works with several

c04.indd 74

7/25/2011 11:35:15 AM

CONCLUSION AND FUTURE DIRECTIONS

75

other networks associated with social cognition. Other multimodal systems have been heavily implicated in social cognition such as the MPFC, posterior cingulate cortex (PCC), and TPJ (commonly known collectively as the mentalizing system; Frith & Frith, 2006; Van Overwalle & Baetens, 2009). These regions are thought to be involved in effortful reasoning about another’s mind, with a role for the MPFC in general mentalizing abilities, the PCC in autobiographical and episodic memory retrieval, and the bilateral TPJ in directing attention and taking others’ perspectives (Decety & Lamm, 2007; Frith & Frith, 2006; Saxe, 2006; Saxe & Powell, 2006; Van Overwalle & Baetens, 2009). Recent studies suggest that the latter brain regions and the MNS work together to promote understanding of others’ intentions from his or her actions. One study showed that instructing participants to attend to the motor kinematics of an action, or “how” the action was completed, activated the MNS (de Lange, Spronk, Willems, Toni, & Bekkering, 2008). In contrast, attending to the intention of the action, or “why” the action was completed, activated the mentalizing system. This suggests that both MNS and mentalizing regions support action understanding; yet the MNS is more active when attending to the visuomotor properties of an observed action, whereas the mentalizing system is more active when trying to infer the higher level goals of an observed action (de Lange et al., 2008). This finding was replicated in another study in which participants watched fingers manipulate a cube (Hesse, Sparing, & Fink, 2009). Again, increased MNS activity was found when participants attended to the means of the action, and increased mentalizing activity was found when participants attended to the end goal of the action (e.g., where the cube was eventually placed). A recent meta-analysis categorized the activity of the MNS and brain regions associated with mentalizing during action understanding tasks in brain imaging research and concluded that the MNS contributes to understanding motor goals and to predicting future motor actions while mentalizing regions contribute to understanding abstract goals based on the observed actions. For example, the MNS may encode the understanding that someone is reaching an outstretched arm with fingers abducted to pick up a glass of milk, whereas the mentalizing system may help to understand that they are reaching for the glass of milk because they are thirsty after eating too many cookies (Van Overwalle & Baetens, 2009). However, more research is needed to continue to tease apart the contributions of the various brain regions and systems supporting social cognition.

1.4.8

CONCLUSION AND FUTURE DIRECTIONS

A large body of data now indicates that the putative MNS in humans processes actions multimodally (including visual, auditory, and motor representations) and perhaps even conceptually, for both the self and the other. This processing has been linked to a variety of aspects of social cognition, such as action understanding, simulation, perspective taking, empathy, and language. This frontoparietal network seems to be modulated by experience as well as by motor learning. It also seems to be more involved in processing familiar actions as well as in imitative learning. Although this network might to be automatically activated in response to observed actions or action sounds, there might be top-down modulation of these regions by race, experience, and self versus other distinctions,

c04.indd 75

7/25/2011 11:35:15 AM

76

MNS & SOCIAL COGNITION

among other social categories. Moreover, the function of these regions may extend from understanding the actions of others to developing conceptualizations of actions. These putative roles make the MNS a crucial part of many social cognitive processes. Since the discovery of the MNS, there has been increased research in whether other brain regions may also have shared representations between the self and other. This research indicates that understanding others’ disgust, pain, and touch sensations seems to rely on forms of shared representations that are similarly modulated by familiarity and social group affiliations (e.g., race and political group). Given the complexity of social interactions, it is likely that social cognition relies on the intricate interweaving of multiple systems and brain regions. The MNS, for instance, seems to work in complement with other multimodal brain regions, such as the medial prefrontal and posterior medial cortices and the bilateral TPJ, along with other regions associated with higher level cognition, in a task-dependent manner. Although the last decade of research on the MNS has brought many new and key insights into understanding social cognition, there is much left to be explored. Connections between the MNS and other neural networks, such as emotion-related brain regions or other higher level multimodal regions, may expand our knowledge of emotional and social processing. Future research may also better uncover how these regions interact under varied circumstances, as well as help reveal whether they function in a hierarchical manner or in parallel, and whether they perform strictly complementary roles or whether these roles might overlap and in which contexts. The answers to these questions will allow us to improve understanding of how the brain supports social cognition both in typically developed individuals as well as in individuals with difficulties in social interactions. Such insights hold great promise for elucidating the possible synergies between mirror neuron regions and various other neural networks involved in social cognition, and this knowledge, in turn, may begin to explain how we are able to communicate our actions, feelings, and thoughts fluidly with one another.

REFERENCES Arbib, M. A. (2005). From monkey-like action recognition to human language: An evolutionary framework for neurolinguistics. Behav Brain Sci, 28(2), 105–124. Aziz-Zadeh, L., & Damasio, A. (2008). Embodied semantics for actions: Findings from functional brain imaging. J Physiol (Paris), 102(1–3), 35–39. Aziz-Zadeh, L., Iacoboni, M., Zaidel, E., Wilson, S., & Mazziotta, J. (2004). Left hemisphere motor facilitation in response to manual action sounds. Eur J Neurosci, 19(9), 2609–2612. Aziz-Zadeh, L., Koski, L., Zaidel, E., Mazziotta, J., & Iacoboni, M. (2006a). Lateralization of the human mirror neuron system. J Neurosci, 26(11), 2964–2970. Aziz-Zadeh, L., Maeda, F., Zaidel, E., Mazziotta, J., & Iacoboni, M. (2002). Lateralization in motor facilitation during action observation: A TMS study. Exp Brain Ress, 144(1), 127–131. Aziz-Zadeh, L., Sheng. T., & Gheyatanchi, A. (2010). Common premotor regions for the perception and production of prosody and correlations with empathy and prosodic ability. PlosONE 5(1), e8759.

c04.indd 76

7/25/2011 11:35:15 AM

REFERENCES

77

Aziz-Zadeh, L., Sheng, T., Liew, S.-L., & Damasio, H. (2011). Understanding otherness: the neural basis of action comprehension and pain empathy in a congenital amputee. Cerebral Cortex (in press). Aziz-Zadeh, L., Wilson, S. M., Rizzolatti, G., & Iacoboni, M. (2006b). Congruent embodied representations for visually presented actions and linguistic phrases describing actions. Curr Biol, 16(18), 1818–1823. Banissy, M. J., & Ward, J. (2007). Mirror-touch synesthesia is linked with empathy. Nat Neurosci, 10(7), 815–816. Barsalou, L. W. (1999). Perceptions of perceptual symbols. Behav Brain Sci, 22(04), 637–660. Blakemore, S. J., Bristow, D., Bird, G., Frith, C., & Ward, J. (2005). Somatosensory activations during the observation of touch and a case of vision-touch synaesthesia. Brain 128(7), 1571. Buccino, G., Lui, F., Canessa, N., Patteri, I., Lagravinese, G., et al. (2004a). Neural circuits involved in the recognition of actions performed by nonconspecifics: An fMRI study. J Cognit Neurosci, 16(1), 114–126. Buccino, G., Vogt, S., Ritzl, A., Fink, G. R., Zilles, K., et al. (2004b). Neural circuits underlying imitation learning of hand actions: An event-related fMRI study. Neuron 42(2), 323–334. Calvo-Merino, B., Glaser, D. E., Grezes, J., Passingham, R. E., & Haggard, P. (2005). Action observation and acquired motor skills: An fMRI study with expert dancers. Cereb Cortex 15(8), 1243. Carr, L., Iacoboni, M., Dubeau, M. C., Mazziotta, J. C., & Lenzi, G. L. (2003). Neural mechanisms of empathy in humans: A relay from neural systems for imitation to limbic areas. Proce Nat Acad Sci, 100(9), 5497. Catmur, C., Walsh, V., & Heyes, C. (2007). Sensorimotor learning configures the human mirror system. Curr Biol, 17(17), 1527–1531. Catmur, C., Walsh, V., & Heyes, C. (2009). Associative sequence learning: The role of experience in the development of imitation and the mirror system. Phil Trans Roy Soc Lond B Biol Sci, 364(1528), 2369–2380. Chartrand, T. L., & Bargh, J. A. (1999). The chameleon effect: The perception-behavior link and social interaction. J Pers Soc Psychol, 76, 893–910. Cheng, Y., Lin, C. P., Liu, H. L., Hsu, Y. Y., Lim, K. E., et al. (2007). Expertise modulates the perception of pain in others. Curr Biol 17(19), 1708–1713. Chiao, J. Y., Iidaka, T., Gordon, H. L., Nogawa, J., Bar, M., et al. (2008). Cultural specificity in amygdala response to fear faces. J Cogniti Neurosci, 20(12), 2167–2174. Costantini, M., Galati, G., Ferretti, A., Caulo, M., Tartaro, A., et al. (2005). Neural systems underlying observation of humanly impossible movements: An fMRI study. Cerebr Cortex 15(11), 1761. Cross, E. S., Hamilton, A. F. C., & Grafton, S. T. (2006). Building a motor simulation de novo: Observation of dance by dancers. Neuroimage 31(3), 1257–1267. Cross, E. S., Kraemer, D. J. M., Hamilton, A. F. C., Kelley, W. M., & Grafton, S. T. (2009). Sensitivity of the action observation network to physical and observational learning. Cerebr Cortex 19(2), 315. Damasio, A. R. (1989). Time-locked multiregional retroactivation: A systems-level proposal for the neural substrates of recall and recognition. Cognition 33(1–2), 25–62. Damasio, A. R. (1994). Descartes’ Error. New York: Penguin. Damasio, A., & Damasio, H. (2006). Minding the body. Daedalus 135(3), 15–22. Damasio, A. R., & Tranel, D. (1993). Nouns and verbs are retrieved with differently distributed neural systems. Proc Nat Acad Sci 90(11), 4957.

c04.indd 77

7/25/2011 11:35:15 AM

78

MNS & SOCIAL COGNITION

Davis, M. H. (1983). Measuring individual differences in empathy: Evidence for a multidimensional approach. J Pers Soc Psychol, 44(1), 113–126. de Lange, F. P., Spronk, M., Willems, R. M., Toni, I., & Bekkering, H. (2008). Complementary systems for understanding action intentions. Curr Biol, 18(6), 454–457. Decety, J., & Lamm, C. (2007). The role of the right temporoparietal junction in social interaction: How low-level computational processes contribute to meta-cognition. Neuroscientist 13(6), 580–593. di Pellegrino, G., Fadiga, L., Fogassi, L., Gallese, V., & Rizzolatti, G. (1992). Understanding motor events: A neurophysiological study. Exp Brain Res, 91(1), 176–180. Fadiga, L., Craighero, L., & D’Ausilio, A. (2009). Broca’s area in language, action, and music. Ann New York Acad Sci 1169, 448–458. Feldman, J., & Narayanan, S. (2004). Embodied meaning in a neural theory of language. Brain Lang 89(2), 385–392. Frith, C. D., & Frith, U. (2006). The neural basis of mentalizing. Neuron 50(4), 531–534. Gallese, V., Fadiga, L., Fogassi, L., & Rizzolatti, G. (1996). Action recognition in the premotor cortex. Brain 119(2), 593. Gallese, V., Keysers, C., & Rizzolatti, G. (2004). A unifying view of the basis of social cognition. Trends Cognit Sci, 8(9), 396–403. Gallese, V., & Lakoff, G. (2005). The brain’s concepts: The role of the sensory-motor system in conceptual knowledge. The Multiple Functions of Sensory-Motor Representations, 22(3/4), 455. Gazzola, V., Aziz-Zadeh, L., & Keysers, C. (2006). Empathy and the somatotopic auditory mirror system in humans. Curr Biol, 16(18), 1824–1829. Gazzola, V., Rizzolatti, G., Wicker, B., & Keysers, C. (2007a). The anthropomorphic brain: The mirror neuron system responds to human and robotic actions. Neuroimage 35(4), 1674–1684. Gazzola, V., van der Worp, H., Mulder, T., Wicker, B., Rizzolatti, G., et al. (2007b). Aplasics born without hands mirror the goal of hand actions with their feet. Curr Biol 17(14), 1235–1240. Glenberg, A. M., & Kaschak, M. P. (2002). Grounding language in action. Psychonomic Bull Rev 9(3), 558–565. Heiser, M., Iacoboni, M., Maeda, F., Marcus, J., & Mazziotta, J. C. (2003). The essential role of Broca’s area in imitation. Eur J Neurosci, 17(5), 1123–1128. Hesse, M. D., Sparing, R., & Fink, G. R. (2009). End or means—the “what” and “how” of observed intentional actions. J Cognit Neurosci 21(4), 776–790. Hotz-Boendermaker, S., Funk, M., Summers, P., Brugger, P., Hepp-Reymond, M. C., et al. (2008). Preservation of motor programs in paraplegics as demonstrated by attempted and imagined foot movements. Neuroimage 39(1), 383–394. Iacoboni, M. (2005). Neural mechanisms of imitation. Curr Opin Neurobiol, 15(6), 632–637. Jackson, P. L., Meltzoff, A. N., & Decety, J. (2006). Neural circuits involved in imitation and perspective-taking. Neuroimage 31(1), 429–439. Kaplan, J. T., Aziz-Zadeh, L., Uddin, L. Q., & Iacoboni, M. (2008). The self across the senses: An fMRI study of self-face and self-voice recognition. Soc Cognit Affect Neurosci 3(3), 218–223. Kaplan, J. T., & Iacoboni, M. (2006). Getting a grip on other minds: Mirror neurons, intention understanding, and cognitive empathy. Soc Neurosci, 1(3–4), 175. Keysers, C., & Gazzola, V. (2006). Towards a unifying neural theory of social cognition. Progr Brain Res, 156, 379–401.

c04.indd 78

7/25/2011 11:35:15 AM

REFERENCES

79

Keysers, C., Wicker, B., Gazzola, V., Anton, J. L., Fogassi, L., et al. (2004). A touching sight: SII/PV activation during the observation and experience of touch. Neuron 42(2), 335–346. Kohler, E., Keysers, C., Umilta, M. A., Fogassi, L., Gallese, V., et al. (2002). Hearing sounds, understanding actions: Action representation in mirror neurons. Science 297(5582), 846–848. Koski, L., Iacoboni, M., Dubeau, M. C., Woods, R. P., & Mazziotta, J. C. (2003). Modulation of cortical activity during different imitative behaviors. J Neurophysiol 89(1), 460. Lakoff, G., & Johnson, M. (1999). Philosophy in the Flesh: The Embodied Mind and its Challenge to Western Thought. New York: Basic Books. Liew, S.-L., Han, S., & Aziz-Zadeh, L. (2010). Familiarity modulates mirror neuron and mentalizing regoins during intention understanding. Human Brain Mapping. doi: 10./002/ hbm.21164. Liew, S.-L., Seckin, M., & Aziz-Zadeh, L. (2011). The effects of experience on the observation of novel effectors and empathy correlations. Cognitive Neurosci Society Abstracts, 18, 124. Mehrabian, A. (1996). Manual for the balanced emotional empathy scale (BEES). (Available from Albert Mehrabian, 1130 Mesa Road, Monterey, CA 93940, USA.) Meltzoff, A. N., & Prinz, W. (2002). The Imitative Mind: Development, Evolution, and Brain Bases. Cambridge, MA: Cambridge University Press. Molnar-Szakacs, I., Wu, A. D., Robles, F. J., & Iacoboni, M. (2007). Do you see what I mean? Corticospinal excitability during observation of culture-specific gestures. PLoS ONE, 2(7). Paukner, A., Suomi, S. J., Visalberghi, E., & Ferrari, P. F. (2009). Capuchin monkeys display affiliation toward humans who imitate them. Science 325(5942), 880. Phelps, E. A., & Thomas, L. A. (2003). Race, behavior, and the brain: The role of neuroimaging in understanding complex social behaviors. Polit Psychol 24(4), 747–758. Pulvermuller, F. (2005). Brain mechanisms linking language and action. Nat Rev Neurosci 6(7), 576–582. Pulvermuller, F., & Hauk, O. (2006). Category-specific conceptual processing of color and form in left fronto-temporal cortex. Cerebr Cortex 16(8), 1193. Pulvermuller, F., Hauk, O., Nikulin, V. V., & Ilmoniemi, R. J. (2005). Functional links between motor and language systems. Eur J Neurosci 21(3), 793–797. Pulvermuller, F., Shtyrov, Y., & Ilmoniemi, R. (2005). Brain signatures of meaning access in action word recognition. J Cogni Neurosci 17(6), 884–892. Rizzolatti, G., & Arbib, M. A. (1998). Language within our grasp. Trends Neurosci 21(5), 188–194. Rizzolatti, G., & Craighero, L. (2004). The mirror-neuron system. Ann Rev Neurosci 27, 169–1932. Rizzolatti, G., Fadiga, L., Gallese, V., & Fogassi, L. (1996). Premotor cortex and the recognition of motor actions. Cognit Brain Res 3(2), 131–141. Romani, M., Cesari, P., Urgesi, C., Facchini, S., & Aglioti, S. M. (2005). Motor facilitation of the human cortico-spinal system during observation of bio-mechanically impossible movements. Neuroimage 26(3), 755–763. Saxe, R. (2006). Uniquely human social cognition. Curr Opin Neurobiol 16(2), 235–239. Saxe, R., & Powell, L. J. (2006). It’s the thought that counts: Specific brain regions for one component of theory of mind. Psychol Sci 17(8), 692–699. Schulte-Rüther, M., Markowitsch, H. J., Fink, G. R., & Piefke, M. (2007). Mirror neuron and theory of mind mechanisms involved in face-to-face interactions: A functional magnetic resonance imaging approach to empathy. J Cognit Neurosci 19(8), 1354–1372.

c04.indd 79

7/25/2011 11:35:15 AM

80

MNS & SOCIAL COGNITION

Serino, A., Giovagnoli, G., & Làdavas, E. (2009). I feel what you feel if you are similar to me. PLoS ONE 4(3). Shamay-Tsoory, S. G., Aharon-Peretz, J., & Perry, D. (2008). Two systems for empathy: A double dissociation between emotional and cognitive empathy in inferior frontal gyrus versus ventromedial prefrontal lesions. Brain 132(3), 617–627. Singer, T. (2006). The neuronal basis and ontogeny of empathy and mind reading: Review of literature and implications for future research. Neurosci Biobehav Rev 30(6), 855–863. Singer, T., & Frith, C. (2005). The painful side of empathy. Nat Neurosci 8(7), 845–846. Singer, T., Seymour, B., O’Doherty, J., Kaube, H., Dolan, R. J., et al. (2004). Empathy for pain involves the affective but not sensory components of pain. Science 303(5661), 1157–1162. Singer, T., Seymour, B., O’Doherty, J. P., Stephan, K. E., Dolan, R. J., et al. (2006). Empathic neural responses are modulated by the perceived fairness of others. Nature 439(7075), 466. Tettamanti, M., Buccino, G., Saccuman, M. C., Gallese, V., Danna, M., et al. (2005). Listening to action-related sentences activates fronto-parietal motor circuits. J Cognit Neurosci 17(2), 273–281. Uddin, L. Q., Molnar-Szakacs, I., Zaidel, E., & Iacoboni, M. (2006). rTMS to the right inferior parietal lobule disrupts self-other discrimination. Soc Cognit Affect Neurosci 1(1), 65. Umilta, M. A., Kohler, E., Gallese, V., Fogassi, L., Fadiga, L., et al. (2001). I know what you are doing: A neurophysiological study. Neuron 31(1), 155–165. Van Overwalle, F., & Baetens, K. (2009). Understanding others’ actions and goals by mirror and mentalizing systems: A meta-analysis. Neuroimage 48(3), 564–584. Vogt, S., Buccino, G., Wohlschlger, A. M., Canessa, N., Shah, N. J., et al. (2007). Prefrontal involvement in imitation learning of hand actions: Effects of practice and expertise. Neuroimage 37(4), 1371–1383. Wicker, B., Keysers, C., Plailly, J., Royet, J. P., Gallese, V., et al. (2003). Both of us disgusted in my insula: The common neural basis of seeing and feeling disgust. Neuron 40(3), 655–664. Xu, X., Zuo, X., Wang, X., & Han, S. (2009). Do you feel my pain? Racial group membership modulates empathic neural responses. J Neurosci 29(26), 8525–8529. Zaki, J., Weber, J., Bolger, N., & Ochsner, K. (2009). The neural bases of empathic accuracy. Proc Nat Acad Sci 106(27), 11382–11387.

c04.indd 80

7/25/2011 11:35:15 AM

1.5 MOTIVATIONAL ASPECTS OF FUTURE THINKING IN THE VENTROMEDIAL PREFRONTAL CORTEX ARNAUD D’ARGEMBEAU Department of Psychology, University of Liège, Belgium

A fascinating achievement of the human mind is the ability to disengage momentarily from the immediate environment in order to “travel mentally through time” and imagine events or states that might happen in the future (Atance & O’Neill, 2001; Buckner & Carroll, 2007; Schacter, Addis, & Buckner, 2008; Suddendorf & Corballis, 2007; Szpunar, 2010; Tulving, 2005). If you close your eyes for a moment and think about what you plan to do next weekend, for example, it is likely that in just a few seconds colorful images will appear in your mind’s eye. You might “see” a totally different place than the one you are currently in; you might move around that place, grab objects, and interact with people; and you might say things to yourself and feel emotions (D’Argembeau & Van der Linden, 2004, 2006). Such images, thoughts, and feelings about the future are frequently experienced in daily life and may serve a variety of important functions, such as action planning, decision making, and emotion regulation (D’Argembeau, Renaud, & Van der Linden, 2011). Recent studies in psychology and cognitive neuroscience have revealed that the imagination of future events (hereafter referred to as “episodic future thinking”) is a complex mental activity that recruits multiple cognitive processes and neural systems (for reviews, see Atance & O’Neill, 2001; Schacter, Addis, & Buckner, 2007; Schacter et al., 2008; Suddendorf & Corballis, 1997, 2007; Szpunar, 2010; Tulving, 2005). The focus of this chapter is on motivational aspects of episodic future thinking. When contemplating our personal future, we often envision events that are somehow important to ourselves, be they positive situations that we would like to

From DNA to Social Cognition, First Edition. Edited by Richard Ebstein, Simone Shamay-Tsoory, and Soo Hong Chew. © 2011 Wiley-Blackwell. Published 2011 by John Wiley & Sons, Inc.

81

c05.indd 81

7/25/2011 10:10:14 AM

82

MOTIVATIONAL ASPECTS OF FUTURE THINKING

attain or negative events that we would rather avoid (Berntsen & Jacobsen, 2008; D’Argembeau et al., 2011; D’Argembeau & Van der Linden, 2004; Gilbert & Wilson, 2007; Newby-Clark & Ross, 2003; Van Boven & Ashworth, 2007). The future events we imagine are thus often endowed with great motivational force, which influences our decisions and behavior (Johnson & Sherman, 1990; Karniol & Ross, 1996). We will review recent functional neuroimaging findings that suggest that the ventromedial prefrontal cortex (vmPFC)1 may play a key role in these motivational aspects of episodic future thinking. More specifically, it will be suggested that the vmPFC may be involved in appraising the personal relevance or value of the various future events we imagine, thereby promoting personal goal achievement.

1.5.1 IMAGINING EMOTIONAL EVENTS IN THE NEAR AND DISTANT FUTURE A wealth of animal and human studies suggest that the vmPFC plays a key role in calculating the value of choice outcomes, which guides decision making in diverse domains (for reviews, see Bechara & Damasio, 2005; Montague, King-Casas, & Cohen, 2006; Wallis, 2007). Bechara and Damasio (Bechara & Damasio, 2005; Bechara, Damasio, & Damasio, 2000; Damasio, 1994) have argued, in particular, that the vmPFC is a critical neural structure for attaching affective/emotional signals to mental representations of future outcomes. These affective/emotional signals would help individuals to know “what it feels like” to be in a given future situation, thus guiding their decisions in advantageous ways.2 Patients with damage to the vmPFC indeed seem oblivious to the consequences of their actions, and accordingly, they present severe impairments of personal and social decision making in their daily life (e.g., the choices they make often lead to loss of reputation, job, and family), despite otherwise largely preserved cognitive abilities (Bechara & Damasio, 2005; Damasio, 1994). Bechara and Damasio (Bechara, 2005; Bechara & Damasio, 2005) have further suggested that anterior parts of the vmPFC may be particularly important for representing distant future outcomes. Cross-species comparisons indicate that major advancement in the size, complexity, and connectivity of the frontal lobes in humans has occurred in relation to the frontal pole, suggesting that functions associated with this part of the cortex have become particularly important during hominid evolution (Semendeferi, Armstrong, Schleicher, Zilles, & Van Hoesen, 2001). Furthermore, there is evidence that during evolution, hominids have developed an increased capacity to anticipate more distant future events, which allowed them to decide and act according to long-term goals rather than to immediate circumstances (Leary & Buttermore, 2003; Sedikides, Skowronski, & Dunbar, 2006; Suddendorf & Corballis, 2007). On the basis of these findings, Bechara and Damasio suggested that more

1

For the purposes of this chapter, we use the term “ventromedial prefrontal cortex” to refer to a broad area in the lower central portion of the prefrontal cortex, encompassing medial sections of Brodmann’s area (BA) 10, 11, 12, and lower BA 32. 2 Note, however, that predictions of the affective consequences of future events are not always accurate and there is indeed evidence that people display several systematic biases in predicting future affective states (for a review, see Gilbert & Wilson, 2007).

c05.indd 82

7/25/2011 10:10:14 AM

EPISODIC FUTURE THINKING AND PERSONAL GOALS

83

anterior parts of the vmPFC may play a key role in representing future outcomes that are more distant in time. We have recently tested this hypothesis in a functional magnetic resonance imaging (fMRI) study, by directly comparing the brain regions recruited for imagining emotional events in the near and distant future (D’Argembeau, Xue, Lu, Van der Linden, & Bechara, 2008b). Participants first took part in a pre-scan interview, in which they were asked to imagine positive and negative events that might happen to them in the near future (i.e., in the next few days or weeks), and positive and negative events that might happen to them in the distant future (i.e., in at least one year from now). They were free to imagine any event, provided that it was a personally meaningful event that could plausibly happen to them during the specified time period (e.g., “going to a friend’s birthday party next Saturday” or “my graduation ceremony in two years from now”). Participants were also asked to think of some affectively neutral routine activities (e.g., showering), which were used as a control condition (imagining such routine activities involved the construction of multimodal representations of complex events but did not require projecting oneself into the future). For each future event and routine activity, a short cue summarizing the essence of the event was created. The next day, participants were presented with each cue and were asked to project themselves mentally into the corresponding future event or routine activity and to imagine the situation in as much detail as possible (i.e., to consider the location where the event would occur, the persons and/or objects that would be present, what they would do, how they would feel, and so forth) while their brain activity was measured with fMRI. In line with other studies of episodic future thinking (e.g., Addis, Wong, & Schacter, 2007; Botzung, Denkova, & Manning, 2008; Okuda et al., 2003; Spreng & Grady, 2010; Szpunar, Watson, & McDermott, 2007), we found that a network of brain regions that included the medial prefrontal cortex, medial posterior regions (posterior cingulate/retrosplenial cortex), the lateral temporal cortex, and the temporoparietal junction was more active during the imagination of future events compared with routine activities. More importantly, part of the neural circuit engaged when imagining future emotional events was modulated by temporal distance. Specifically, the anterior vmPFC was more active when imagining emotional events in the distant future than when imagining emotional events in the near future, thus supporting Bechara and Damasio’s hypothesis (Bechara, 2005; Bechara & Damasio, 2005).

1.5.2

EPISODIC FUTURE THINKING AND PERSONAL GOALS

Extensive research has revealed that temporal distance changes the way people mentally represent future events (for a review, see Trope & Liberman, 2003). It has been demonstrated, in particular, that people use high-level, goal-related knowledge to a greater extent for representing distant future events than for representing near future events. Consider, for example, a study by Liberman and Trope (1998), in which participants were instructed to imagine themselves in different situations either “tomorrow” or “next year” and then were asked to describe these situations. Liberman and Trope found that participants used more high-level goals to describe the situations in the distant future and more concrete details to describe the

c05.indd 83

7/25/2011 10:10:14 AM

84

MOTIVATIONAL ASPECTS OF FUTURE THINKING

situations in the near future. For example, participants described the situation “moving to a new apartment” as “starting a new life” in the distant future condition but as “packing and carrying boxes” in the near future condition. Considering these differences in the way people represent near and distant future events, we speculated that the increased vmPFC activity observed when people imagine distant future events might be related to the processing of personal goals (D’Argembeau et al., 2008b). To test this hypothesis more directly, we conducted another fMRI study in which we explicitly manipulated the relevance of imagined future events with regard to personal goals (D’Argembeau et al., 2010a). In a prescan interview, participants were asked to fill out an adaptation of the Personal Project Analysis Inventory (Little, 1983), which required them to list personal goals in various life domains (e.g., in relation to work, family, and material goods). Then, for each personal goal, participants were instructed to imagine a specific future event (i.e., something specifically located in place and time and lasting less than a day) related to that goal (e.g., for the personal goal “becoming a doctor,” a specific personal future event might be the imagination of one’s graduation ceremony; we hereafter refer to these events as “personal future events”). Participants were also asked to select “nonpersonal” future events, which were defined as events that can be vividly imagined and that could possibly happen in the future, although they are not part of personal projects and are not particularly self-relevant (e.g., taking one’s first golf lesson and handing out leaflets for an ecological organization). A future time period was then assigned to each selected event so as to match personal and nonpersonal future events with regard to temporal distance. Finally, a series of routine activities were also selected and used as a control condition. During the fMRI session, participants were instructed to project themselves in each event and to imagine it in as much detail as possible in order to experience the situation mentally. In line with our previous study (D’Argembeau et al., 2008b), we found that a network of brain regions that included the vmPFC, the posterior cingulate cortex, the inferior parietal lobe, and the lateral temporal lobe was more active when participants imagined future events compared with routine activities. Our main interest was then to contrast directly the imagination of personal and nonpersonal future events to isolate the brain regions that support personal goal processing during episodic future thinking. This comparison revealed greater activation in the vmPFC when imaging personal future events relative to nonpersonal future events. Importantly, the two types of future events were matched for vividness and temporal distance, suggesting that differences in vmPFC activity cannot be accounted by these factors alone. These findings suggest that the vmPFC may be involved in processing the relevance of future events to personal goals. To investigate this hypothesis further, we compared the brain activations observed during the imagination of personal future events with the brain activations associated with explicit judgments of personal relevance in another task. The neural activity associated with judgments of selfrelevance was isolated using a task that has been extensively used in previous fMRI studies (e.g., D’Argembeau et al., 2007; Fossati et al., 2003; Heatherton et al., 2006; Kelley et al., 2002; Schmitz, Kawahara-Baccus, & Johnson, 2004). Participants were presented with a series of trait adjectives (e.g., polite, dependable, daring, and talkative) and were asked to make different types of judgments on those traits. In the “self condition,” they had to judge whether the adjectives described their own

c05.indd 84

7/25/2011 10:10:14 AM

SEEING ONE’S PERSONAL FUTURE THROUGH ROSE-COLORED GLASSES

85

personal traits, and in the comparison condition, they had to judge whether the adjectives referred to a positive trait. We found that explicit judgments of selfrelevance in this task elicited activation in the vmPFC, which overlapped extensively with the vmPFC activation observed during the imagination of personal versus nonpersonal future events. This finding suggests that when we imagine future events, the vmPFC may be involved in appraising (explicitly or implicitly) the personal relevance or value of our mental constructions, such as their significance for personal goal pursuit.

1.5.3 SEEING ONE’S PERSONAL FUTURE THROUGH ROSE-COLORED GLASSES Personal goals can be distinguished along multiple dimensions (for a review of goal constructs in psychology, see Austin & Vancouver, 1996), and recent functional neuroimaging studies suggest that reflecting on different types of goals recruit distinct brain regions. Johnson et al. (2006) investigated two broad classes of goals— hopes and aspirations as well as duties and obligations. Reflecting on these two types of personal goals was associated with greater activity in anterior and posterior medial areas compared with thinking about concrete things such as polar bears fishing. Notably, an area of the vmPFC was more active when participants thought about hopes and aspirations compared with when they thought about duties and obligations. Furthermore, individual differences in chronic promotion orientation (i.e., a focus toward approach-related goals) were positively correlated with activity in the vmPFC when reflecting on hopes and aspirations. Subsequent fMRI studies have detected similar activations in the vmPFC when reflecting on hopes and aspirations (Johnson, Nolen-Hoeksema, Mitchell, & Levin, 2009; Mitchell et al., 2009; Packer & Cunningham, 2009), suggesting that this brain area may be particularly important for representing future rewarding personal experiences. Note, however, that because a relatively unconstrained paradigm that did not explicitly require participants to imagine particular events was used in those studies, it is unclear whether they engaged in episodic future thinking (i.e., whether they imagined specific future situations) or whether they reflected about their hopes and aspirations in a more abstract way. Yet other fMRI studies in which participants were explicitly asked to engage in episodic future thinking have also found that the vmPFC is particularly active when imagining rewarding experiences. For example, we asked participants to project themselves mentally into some specific future situations that could plausibly happen in their personal future and found that the vmPFC was more active when they imagined positive events (i.e., things they are looking forward to), relative to negative events (i.e., things they would prefer to avoid) (D’Argembeau et al., 2008b). Sharot, Riccardi, Raio, and Phelps (2007) obtained similar results when participants imagined positive and negative events in response to descriptions of life episodes such as “winning an award” or “the end of a romantic relationship.” To the extent that the vmPFC is involved in processing self-relevance, these findings suggest that people may feel more personally involved when envisioning positive compared with negative future events, and there is indeed evidence that most people see their personal future through rose-colored glasses (Sedikides &

c05.indd 85

7/25/2011 10:10:14 AM

86

MOTIVATIONAL ASPECTS OF FUTURE THINKING

Gregg, 2008; Taylor & Brown, 1988). In line with this interpretation, it has been found that more optimistic people show greater activity in the vmPFC when imagining positive versus negative events (Sharot et al., 2007). Overall, then, the greater activation of the vmPFC when envisioning positive versus negative future events may reflect people’s tendency to view their personal future in a positive light. Research suggests that this positive view of the personal future can increase one’s motivation and performance (Ruvolo & Markus, 1992) and can provide benefits in terms of mental and physical health (MacLeod & Conway, 2007; Taylor, Kemeny, Reed, Bower, & Gruenewald, 2000).

1.5.4 FEELING CONNECTED TO FUTURE SELVES AND DECISION MAKING Besides the representation of specific life episodes, an important aspect of future thinking relates to people’s views of themselves in the future (e.g., their representation of who they might become, would like to become, or are afraid of becoming; Markus & Nurius, 1986). Social psychological research indicates that people may feel more or less connected to imagined future selves, which affects their decisions, motivation, and behavior (e.g., Libby, Shaeffer, Eibach, & Slemmer, 2007; Peetz, Wilson, & Strahan, 2009; Pronin, Olivola, & Kennedy, 2008). Notably, there is evidence that people sometimes view their future selves as “other persons,” such that their decisions for future selves differ from their decisions for present selves and instead more closely resemble decisions for other people. For example, Pronin et al. (2008) had participants make decisions involving drinking a disgusting liquid supposedly for the benefit of science. Depending on condition, participants were asked to choose how much liquid they will consume during the current experiment, how much they will consume during an experiment early next semester, or how much will be consumed by the next participant in the study. The results showed that participants chose to drink more disgusting liquid in the future than in the present and that their decisions for the future self resembled decisions for the other participant. Recent fMRI studies have revealed that these differences concerning the way people view present versus temporally distant selves are mirrored in vmPFC activity. Specifically, we found that the vmPFC was more active when participants made trait judgments about their present self than when they made trait judgments about temporally distant selves (either in the past or in the future), whereas making judgments about distant selves and making judgments about others were associated with similar degrees of vmPFC activity (D’Argembeau et al., 2008a; D’Argembeau et al., 2010b). Ersner-Hershfield, Wimmer, and Knutson (2009) have also observed more activation in the vmPFC when making judgments about present versus future selves. Most interestingly, these authors found that individual differences in the degree of vmPFC activity when thinking about present versus future selves correlated with the degree to which people valued immediate gains over future gains in a temporal discounting task administered a week after scanning. In other words, individuals who showed fewer differences in vmPFC activity when reflecting on present versus future selves (presumably because they felt more connected to their future selves) showed less propensity to devalue future rewards. Another study indicates that

c05.indd 86

7/25/2011 10:10:14 AM

REFERENCES

87

individual differences in feelings of connectedness to future selves not only predict valuation of future rewards in laboratory tasks, but they are also associated with real-world savings behavior (Ersner-Hershfield, Garton, Ballard, Samanez-Larkin, & Knutson, 2009). Altogether, these findings suggest that differences in degrees of vmPFC activity when thinking about present versus future selves may reflect the extent to which people feel connected to and value their future selves, which holds important implications for decision making.

1.5.5

CONCLUSION

Human beings spend a lot of time imagining all sorts of situations and scenarios that might happen in their personal future, which can serve various functions in daily life, such as action planning, decision making, and emotion regulation (D’Argembeau et al., 2011). This ability to simulate mentally possible future events is a complex cognitive activity that depends on multiple brain systems (Atance & O’Neill, 2001; Schacter et al., 2007, 2008; Suddendorf & Corballis, 1997, 2007; Szpunar, 2010; Tulving, 2005). The functional neuroimagining findings reviewed in this chapter suggest that the vmPFC is instrumental to motivational aspects of episodic future thinking. The vmPFC shows greater activation when people imagine future situations that are relevant to their personal goals, particularly situations that refer to possible rewarding experiences. Furthermore, degrees of vmPFC activity correlate with feelings of connectedness to imagined future selves, which influences important decisions. Considering these findings, we speculate that the vmPFC may be involved in appraising and/or coding the self-relevance or personal value of imagined future events. This process may not only increase our motivation and effort to achieve desired end-states, but it may also prompt the mental simulation of the steps necessary for reaching these states (Karniol & Ross, 1996; Taylor, Pham, Rivkin, & Armor, 1998). Furthermore, valuing possible future outcomes may help us override momentary needs and impulsive behavior, to pursue courses of action that are more advantageous in the long term (Bechara, 2005; Boyer, 2008). We thus suggest that a major function of the vmPFC during episodic future thinking is to assign a personal value to future event representations, thereby promoting personal goal achievement.

ACKNOWLEDGMENTS Arnaud D’Argembeau is supported by the Fund for Scientific Research (F.R.S.FNRS), Belgium.

REFERENCES Addis, D. R., Wong, A. T., & Schacter, D. L. (2007). Remembering the past and imagining the future: Common and distinct neural substrates during event construction and elaboration. Neuropsychologia 45, 1363–1377. Atance, C. M., & O’Neill, D. K. (2001). Episodic future thinking. Trends Cognit Sci 5(12), 533–539.

c05.indd 87

7/25/2011 10:10:14 AM

88

MOTIVATIONAL ASPECTS OF FUTURE THINKING

Austin, J. T., & Vancouver, J. B. (1996). Goal constructs in psychology: Structure, process, and content. Psychol Bul 120, 338–375. Bechara, A. (2005). Decision making, impulse control and loss of willpower to resist drugs: A neurocognitive perspective. Nat Neurosci 8(11), 1458–1463. Bechara, A., & Damasio, A. R. (2005). The somatic marker hypothesis: A neural theory of economic decision. Game Econ Behav 52, 336–372. Bechara, A., Damasio, H., & Damasio, A. R. (2000). Emotion, decision making and the orbitofrontal cortex. Cerebr Cortex 10(3), 295–307. Berntsen, D., & Jacobsen, A. S. (2008). Involuntary (spontaneous) mental time travel into the past and future. Conscious Cognit 17, 1093–1104. Botzung, A., Denkova, E., & Manning, L. (2008). Experiencing past and future personal events: Functional neuroimaging evidence on the neural bases of mental time travel. Brain Cognit 66(2), 202–212. Boyer, P. (2008). Evolutionary economics of mental time travel? Trends Cognit Sci 12(6), 219–224. Buckner, R. L., & Carroll, D. C. (2007). Self-projection and the brain. Trends Cognit Sci 11(2), 49–57. D’Argembeau, A., Feyers, D., Majerus, S., Collette, F., Van der Linden, M., et al. (2008a). Selfreflection across time: Cortical midline structures differentiate between present and past selves. Soc Cogn Affect Neurosci 3(3), 244–252. D’Argembeau, A., Renaud, O., & Van der Linden, M. (2011). Frequency, characteristics, and functions of future-oriented thoughts in daily life. Appl Cognit Psychol 25(1), 96–103. D’Argembeau, A., Ruby, P., Collette, F., Degueldre, C., Balteau, E., et al. (2007). Distinct regions of the medial prefrontal cortex are associated with self-referential processing and perspective taking. J Cognit Neurosci 19(6), 935–944. D’Argembeau, A., Stawarczyk, D., Majerus, S., Collette, F., Van der Linden, M., et al. (2010a). The neural basis of personal goal processing when envisioning future events. J Cognit Neurosci 22(8), 1701–1713. D’Argembeau, A., Stawarczyk, D., Majerus, S., Collette, F., Van der Linden, M., et al. (2010b). Modulation of medial prefrontal and inferior parietal cortices when thinking about past, present, and future selves. Soc Neurosci 5(2), 187–200. D’Argembeau, A., & Van der Linden, M. (2004). Phenomenal characteristics associated with projecting oneself back into the past and forward into the future: Influence of valence and temporal distance. Conscious Cogn 13(4), 844–858. D’Argembeau, A., & Van der Linden, M. (2006). Individual differences in the phenomenology of mental time travel: The effect of vivid visual imagery and emotion regulation strategies. Conscious Cogn 15(2), 342–350. D’Argembeau, A., Xue, G., Lu, Z. L., Van der Linden, M., & Bechara, A. (2008b). Neural correlates of envisioning emotional events in the near and far future. Neuroimage 40(1), 398– 407. Damasio, A. R. (1994). Descartes’ Error: Emotion, Reason, and the Human Brain. New York: Putnam. Ersner-Hershfield, H., Garton, M. T., Ballard, K., Samanez-Larkin, G. R., & Knutson, B. (2009). Don’t stop thinking about tomorrow: Individual differences in future self-continuity account for saving. Judgm Decis Mak 4(4), 280–286. Ersner-Hershfield, H., Wimmer, G. E., & Knutson, B. (2009). Saving for the future self: Neural measures of future self-continuity predict temporal discounting. Soc Cogn Affect Neurosci 4(1), 85–92.

c05.indd 88

7/25/2011 10:10:14 AM

REFERENCES

89

Fossati, P., Hevenor, S. J., Graham, S. J., Grady, C., Keightley, M. L., et al. (2003). In search of the emotional self: An fMRI study using positive and negative emotional words. Am J Psychiatry 160, 1938–1945. Gilbert, D. T., & Wilson, T. D. (2007). Prospection: Experiencing the future. Science 317(5843), 1351–1354. Heatherton, T. F., Wyland, C. L., Macrae, C. N., Demos, K. E., Denny, B. T., et al. (2006). Medial prefrontal activity differentiates self from close others. Soc Cogn Affect Neurosci 1, 18–25. Johnson, M. K., Nolen-Hoeksema, S., Mitchell, K. J., & Levin, Y. (2009). Medial cortex activity, self-reflection and depression. Soc Cogn Affect Neurosci 4, 313–327. Johnson, M. K., Raye, C. L., Mitchell, K. J., Touryan, S. R., Greene, E. J., & Nolen-Hoeksema, S. (2006). Dissociating medial frontal and posterior cingulate activity during self-reflection. Soc Cogn Affect Neurosci 1(1), 56–64. Johnson, M. K., & Sherman, S. J. (1990). Constructing and reconstructing the past and the future in the present. Handbook of Motivation and Cognition: Fundations of Social Behavior vol. 2, edited by E. T. Higgins & R. M. Sorrentino New York: Guilford Press. Karniol, R., & Ross, M. (1996). The motivational impact of temporal focus: Thinking about the future and the past. Annu Rev Psychol 47, 593–620. Kelley, W. M., Macrae, C. N., Wyland, C. L., Caglar, S., Inati, S., et al. (2002). Finding the self? An event-related fMRI study. J Cognit Neurosci 14, 785–794. Leary, M. R., & Buttermore, N. R. (2003). The evolution of the human self: Tracing the natural history of self-awareness. J Theor Soc Behav 33(4), 365– 404. Libby, L. K., Shaeffer, E. M., Eibach, R. P., & Slemmer, J. A. (2007). Picture yourself at the polls—Visual perspective in mental imagery affects self-perception and behavior. Psychol Sci 18(3), 199–203. Liberman, N., & Trope, Y. (1998). The role of feasibility and desirability considerations in near and distant future decisions: A test of temporal construal theory. J Pers Soc Psychol 75, 5–18. Little, B. R. (1983). Personal projects: A rationale and method for investigation. Environ Behav 15(3), 273–309. MacLeod, A. K., & Conway, C. (2007). Well-being and positive future thinking for self versus others. Cognit Emot 21, 1114–1124. Markus, H., & Nurius, P. (1986). Possible selves. Am Psychol 41, 954–969. Mitchell, K. J., Raye, C. L., Ebner, N. C., Tubridy, S. M., Frankel, H., et al. (2009). Age-group differences in medial cortex activity associated with thinking about self-relevant agendas. Psychol Aging 24(2), 438– 449. Montague, P. R., King-Casas, B., & Cohen, J. D. (2006). Imaging valuation models in human choice. Ann Rev Neurosci 29, 417– 448. Newby-Clark, I. R., & Ross, M. (2003). Conceiving the past and future. Pers Soc Psychol Bull 29(7), 807–818. Okuda, J., Fujii, T., Ohtake, H., Tsukiura, T., Tanji, K., et al. (2003). Thinking of the future and past: The roles of the frontal pole and the medial temporal lobes. Neuroimage 19(4), 1369–1380. Packer, D. J., & Cunningham, W. A. (2009). Neural correlates of reflection on goal states: The role of regulatory focus and temporal distance. Soc Neurosci 4(5), 412–425. Peetz, J., Wilson, A. E., & Strahan, E. J. (2009). So far away: The role of subjective temporal distance to future goals in motivation and behavior. Soc Cognit 27(4), 475–495.

c05.indd 89

7/25/2011 10:10:14 AM

90

MOTIVATIONAL ASPECTS OF FUTURE THINKING

Pronin, E., Olivola, C. Y., & Kennedy, K. A. (2008). Doing unto future selves as you would do unto others: Psychological distance and decision making. Pers Soc Psychol Bull 34(2), 224–236. Ruvolo, A. P., & Markus, H. R. (1992). Possible selves and performance: The power of selfrelevant imagery. Soc Cognit 10, 95–124. Schacter, D. L., Addis, D. R., & Buckner, R. L. (2007). Remembering the past to imagine the future: The prospective brain. Nat Rev Neurosci 8(9), 657–661. Schacter, D. L., Addis, D. R., & Buckner, R. L. (2008). Episodic simulation of future events: Concepts, data, and applications. Ann NY Acad Sci 1124, 39–60. Schmitz, T. W., Kawahara-Baccus, T. N., & Johnson, S. C. (2004). Metacognitive evaluation, self-relevance, and the right prefrontal cortex. Neuroimage 22, 941–947. Sedikides, C., & Gregg, A. P. (2008). Self-enhancement: Food for thought. Curr Dir Psychol Sci 3, 102–116. Sedikides, C., Skowronski, J. J., & Dunbar, R. I. M. (2006). When and why did the human self evolve? Evolution and Social Psychology: Frontiers in Social Psychology, edited by M. Schaller, J. A. Simpson & D. T. Kenrick New York: Psychology Press. Semendeferi, K., Armstrong, E., Schleicher, A., Zilles, K., & Van Hoesen, G. W. (2001). Prefrontal cortex in humans and apes: A comparative study of area 10. Am J Phys Anthropol 114(3), 224–241. Sharot, T., Riccardi, A. M., Raio, C. M., & Phelps, E. A. (2007). Neural mechanisms mediating optimism bias. Nature 450(7166), 102–105. Spreng, R. N., & Grady, C. L. (2010). Patterns of brain activity supporting autobiographical memory, prospection, and theory-of-mind and their relationship to the default mode network. J Cogn Neurosci 22(6), 1112–1223. Suddendorf, T., & Corballis, M. C. (1997). Mental time travel and the evolution of the human mind. Genet Soc Gen Psychol Monogr 123(2), 133–167. Suddendorf, T., & Corballis, M. C. (2007). The evolution of foresight: What is mental time travel and is it unique to humans? Behav Brain Sci 30, 299–351. Szpunar, K. K. (2010). Episodic future thought: An emerging concept. Perspect Psychol Sci 5, 142–162. Szpunar, K. K., Watson, J. M., & McDermott, K. B. (2007). Neural substrates of envisioning the future. Proc Natl Acad Sci USA 104(2), 642–647. Taylor, S. E., & Brown, J. D. (1988). Illusion and well-being: A social psychological perspective on mental health. Psychol Bull 103, 193–210. Taylor, S. E., Kemeny, M. E., Reed, G. M., Bower, J. E., & Gruenewald, T. L. (2000). Psychological resources, positive illusions, and health. Am Psychol 55(1), 99–109. Taylor, S. E., Pham, L. B., Rivkin, I. D., & Armor, D. A. (1998). Harnessing the imagination. Mental simulation, self-regulation, and coping. Am Psychol 53(4), 429– 439. Trope, Y., & Liberman, N. (2003). Temporal construal. Psychol Rev 110(3), 403–421. Tulving, E. (2005). Episodic memory and autonoesis: Uniquely human? The Missing Link in Cognition: Origins of Self-Reflective Consciousness, edited by H. S. Terrace & J. Metcalfe Oxford: Oxford University Press. Van Boven, L., & Ashworth, L. (2007). Looking forward, looking back: Anticipation is more evocative than retrospection. J Exp Psychol Gen 136(2), 289–300. Wallis, J. D. (2007). Orbitofrontal cortex and its contribution to decision-making. Annu Rev Neurosci 30, 31–56.

c05.indd 90

7/25/2011 10:10:14 AM

PART 2 MORAL NEUROSCIENCE AND EMOTION

c06.indd 91

7/25/2011 10:10:15 AM

2.1 CONTRIBUTIONS OF THE PREFRONTAL CORTEX TO SOCIAL COGNITION AND MORAL JUDGMENT PROCESSES CHAD E. FORBES,1* JOSHUA C. POORE,2*

AND JORDAN

GRAFMAN3

1

Imaging Sciences Training Program, Radiology and Imaging Sciences, Clinical Center and National Institute of Biomedical Imaging and Bioengineering; Cognitive Neuroscience Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 2 Cognitive Neuroscience Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 3 Traumatic Brain Injury Research Laboratory, Kessler Foundation Research Center, West Orange, NJ

What separates us from other primates? Many of the most intuitive answers relate to our social nature; we wonder who we are and how we are different from others; we speculate about others’ intentions and desires; we make conscientious efforts to determine whether our behavior (and that of others’) is socially acceptable and exert great effort to alter behavior and ensure our integration into a variety of social groups. Lesion and functional neurophysiological (e.g., functional magnetic resonance imaging [fMRI], and electroencephalograph [EEG]) studies reveal that these social traits are predicated on large contributions from a prefrontal cortex (PFC) that is distinct from other species (for a recent review, see Forbes & Grafman, 2010). Compared with other species, humans have larger PFCs relative to the rest of the * C. E. F. and J. C. P. contributed equally to this work. This work was supported by the Imaging Sciences Training Program, Radiology and Imaging Sciences, Clinical Center and National Institute of Biomedical Imaging and Bioengineering and the Intramural Research Program of the National Institute of Neurological Disorders and Stroke, National Institutes of Health. From DNA to Social Cognition, First Edition. Edited by Richard Ebstein, Simone Shamay-Tsoory, and Soo Hong Chew. © 2011 Wiley-Blackwell. Published 2011 by John Wiley & Sons, Inc.

93

c06.indd 93

7/25/2011 10:10:15 AM

94

PFC, SOCIAL COGNITION, AND MORAL JUDGMENT

brain, more complex cytoarchitecture within the PFC, particularly within the integrative fronto-polar region (BA 10), and more complexity in white-matter connectivity with the rest of the brain (Semendeferi, Armstrong, Schleicher, Zilles, & Van Hoesen, 2001; Wood & Grafman, 2003). This allows for greater integration between higher-order association areas and low-level perceptual systems. Not surprisingly, the PFC plays a prominent role in advanced social cognitive processes and our capacity for moral judgment (Lieberman, 2010; Moll, Zahn, de Oliveira-Souza, Krueger, & Grafman, 2005; Van Overwalle, 2009). In this chapter, we examine the role of the PFC in social cognition and moral judgments, focusing broadly on how the integration between basic functional segregations within major subcomponents of the PFC allow for a vast array of contextually driven social thought that varies from the most basic elements of social cognition to the assessment of complex moral dilemmas. Specifically, we review the functional anatomy of the PFC and the basic processing roles of its components with respect to the PFC’s connectivity to regions both inside and outside of the cortex. We then highlight evidence suggesting that the social and moral mind maps onto the brain in ways consistent with the basic hierarchical functional organization of the cortex. Throughout, we provide an integrative perspective on the relation between social cognition and moral judgment, articulating how the organization and functional connectivity within the PFC allows for dynamic implementations of these operations at both implicit (i.e., fast) and explicit (i.e., slow) processing speeds across diverse social contexts in response to varied situational cues. We end the chapter with a discussion on the role of the PFC in implicit and explicit social cognitive and moral judgment processes and the mediating role context plays.

2.1.1

PFC FUNCTIONAL ANATOMY AND CONNECTIVITY

The PFC can be conveniently subdivided into posterior and anterior regions, dorsal and ventral regions, medial and lateral regions, and an orbitofrontal region. It is widely interconnected with the rest of the brain, but specific regions within the PFC are more densely connected with cortical and subcortical regions with similar dorsoventral and medio-lateral orientations that share common evolutionary origins. The medial PFC (MPFC) can be segmented based on its reciprocal connections with subcortical structures. Both ventral aspects of the MPFC, e.g., the orbitofrontal cortex (OFC) and the ventromedial PFC (VMPFC), and dorsal aspects of the MPFC, e.g., the dorsomedial PFC (DMPFC), share strong connections with regions involved in reward-related processing such as the basal ganglia, as well as with regions involved in processing visceral arousal such as the amygdala, and regions involved in conflict detection such as anterior cingulate cortex (ACC; Fuster, 1997). MPFC regions, including the DMPFC, PFC, and OFC, are also reciprocally connected to visual association areas in the temporal cortex (Fuster, 1997). These circuits receive information pertinent to an organisms internal state as well as sensory information indirectly from the mesencephalic reticular formation and the inferior temporal cortex (e.g., the fusiform face area; FFA) via the magnocellular portion of the mediodorsal nucleus in the thalamus (Fuster, 1997). In turn, MPFC regions send

c06.indd 94

7/25/2011 10:10:15 AM

PFC FUNCTIONAL ANATOMY AND CONNECTIVITY

95

and receive dense axonal projections from the lateral PFC (LPFC), including the dorsolateral PFC (DLPFC) (Amaral & Price, 1984; Ghashghaei & Barbas, 2002; Ghashghaei, Hilgetag, & Barbas, 2007). Taken together, the PFC, DMPFC, and OFC are likely critical for such processes as decision making (Krueger, Grafman, & McCabe, 2008), monitoring internal states (e.g., Gusnard, Akbudak, Shulman, & Raichle, 2001), and suppressing information or emotions that conflict with salient goal-states (e.g., Levesque et al., 2003), by virtue of their ability to monitor and integrate viscerally arousing information and internal motivational states, and transmitting that information to the LPFC (Elliott & Deakin, 2005; Wood & Grafman, 2003). In kind, the LPFC serves as a hub for the convergence and integration of sensory information from all five modalities (Fuster, 1997), suggesting this region is critical for the execution of movement and goal-directed actions (Barbey, Krueger, & Grafman, 2009b; Beauregard, Levesque, & Bourgouin, 2001; MacDonald, Cohen, Stenger, & Carter, 2000; Miller & Cohen, 2001; Wood & Grafman, 2003). The DLPFC, for example, is densely integrated with neural networks supporting goaldirected movement, such as the substantia nigra and striatum (particularly the dorsal aspect), as well as the premotor cortex, supplementary motor area, cingulate cortex, and association areas (Fuster, 1997). The LPFC’s direct or indirect reciprocal connections with most other neural regions suggest that its broad role in human cognition is in serving executive functions underlying cognitive tasks requiring attention allocation, sustained activation of currently required representations, suppression of competing neural responses, and sequencing of representations in a temporally coherent manner (Bodner, Kroger, & Fuster, 1996; Fuster & Alexander, 1971; Levy & Goldman-Rakic, 2000). Specifically, whereas the ventrolateral PFC (VLPFC) seems to be necessary for sustaining representations that are relevant to immediate goal states (Christoff & Gabrieli, 2000), the DLPFC is necessary for monitoring and selecting among these representations based on changes in immediate goal states (Petrides, 2000; Wagner, Maril, Bjork, & Schacter, 2001; Wood & Grafman, 2003). 2.1.1.1

Summary

The PFC sends and receives either direct or indirect axonal projections from every region of the brain and is densely interconnected with the occipital, temporal, and parietal lobes (Fuster, 1997). In turn, it is necessary for higher order functions, allocating attentional resources to goal-relevant stimuli while inhibiting distractions (Badre & Wagner 2004; Botvinick, Cohen, & Carter, 2004; Dolcos, Miller, Kragel, Jha, & McCarthy, 2007; MacDonald et al., 2000; Milham et al., 2001; Miller & D’Esposito, 2005); evaluating information with respect to learned rewards and consequences (Adolphs, 1999; Damásio, 1994; Rolls & Grabenhorst, 2008); storing semantic information pertinent to one’s self-concept as well as representations of others (Johnson et al., 2002; Kelley et al., 2002; Schmitz, Kawahara-Baccus, & Johnson, 2004); and organizing actions consistent with goals in a temporal sequence (Fuster, 1997; Wager & Smith, 2003). Given that all of the aforementioned processes serve as fundamental components of social cognition and moral judgments, the PFC must be integral in these processes.

c06.indd 95

7/25/2011 10:10:15 AM

96

PFC, SOCIAL COGNITION, AND MORAL JUDGMENT

2.1.2 CONTRIBUTIONS OF THE PFC TO SOCIAL COGNITIVE AND MORAL JUDGMENT PROCESSES 2.1.2.1

Social Cognitive and Moral Judgment Processes

“Social cognition” broadly refers to strategies individuals use to make sense of themselves and the social world around them (Fiske, 1993; Macrae & Bodenhausen, 2000). The study of social cognition encompasses social perceptual processes, attributional processes, and social categorizational processes (Table 2.1-1). Whereas social perceptual processes comprise the fundamental mechanisms involved in detecting and interpreting others and their movements (Van Overwalle, 2009), social attribution is an end product of these processes, (i.e., the representation and understanding of both our actions and those of others [Gilbert & Malone, 1995]). Social categorization provides organizing frameworks for social judgment, facilitating efficient information processing within social contexts (Fiske & Taylor, 1991). This is achieved via scripts and schemas for appropriate behaviors in a wide array of social settings, and stereotypes, which provide individuals with general heuristic knowledge and subsequent predictive power for well-learned or novel social entities (Fiske, 1998; Grafman, 1989; Macrae, Milne, & Bodenhausen, 1994). Additionally, given the breadth of social processes accounted for by social cognitive faculties, it is likely that moral judgments—evaluative assessments of how appropriate one’s behavior is with respect to social or cultural norms (Knutson et al., 2010; Moll et al., 2005)—rely on interactions between multiple basic social cognitive processes. Indeed, there is significant conceptual overlap between basic social cognitive processes and those upon which moral judgments depend; at a fundamental level, moral judgments are assessments that rely on the evaluation of targets’ behaviors within the context of a specific situation. These evaluations, or judgments, are likely products of attributional processes, which are in and of

TABLE 2.1-1. A summary of key PFC regions likely involved in social cognition and moral judgment and the tendency for a given process to occur implicitly (fast) or explicitly (deliberative). MENT = Mentalizing, TOM = Theory of Mind, BA = Brodmann’s Area Key PFC Regions Involved Social Perceptual Processes Attributional Processes

Social Categorization Processes

c06.indd 96

Other Individual Identification Association Value Movement Intentions Self Related Processing Other-Processing/ MENT/TOM Moral Judgments Stereotypes/Schemas/ Scripts Impression-formation

BA9, BA10, BA11, BA47 BA11, BA12 BA9, BA11 BA9, BA11, BA12 BA9, BA11, BA12, BA46 BA9, BA10, BA11, BA12, BA46, BA47 BA9, BA11, BA12, BA46, BA47 BA9, BA11, BA12

Implicit or Explicit Process Implicit Implicit Implicit Both Both Both Implicit Implicit

7/25/2011 10:10:15 AM

CONTRIBUTIONS OF THE PFC TO SOCIAL COGNITIVE

97

themselves predicated on social perceptual processes such as what information is attended to in a given context with respect to salient goal states, and are susceptible to social categorizational processes that may bias perceptions with respect to preexisting, salient knowledge representations (e.g., stereotypes). The interaction between these multiple social cognitive processes invariably engender moral judgments that can vary as a function of one’s context. Finally, most (if not all) social cognitive processes (including moral judgments) occur implicitly (i.e., nonconsciously, habitually, rapidly, or effortlessly), but they can also be accessed or executed explicitly (i.e., consciously, deliberately, or slowly), depending on the nature of available information and the situational context in which social cognition is nested. Whether implicit or explicit processes are engaged will depend heavily on factors like the amount of time an individual has to make a judgment or decision, their motivation to fully consider social information, or their cognitive capacity to process and interpret complex information (Blair, 2002; Devine, Plant, Amodio, Harmon-Jones, & Vance, 2002; Devine & Sharp, 2009; Payne, 2001; Payne, 2005). It is highly likely that the emergence of social perception, attribution, social categorization, and moral judgment in day-to-day life depends heavily on the integrated functioning of the PFC (Forbes & Grafman, 2010). Below, we review evidence supporting this observation as well as how the combined contribution of functionally distinct PFC regions and systems might provide more sufficient accounts of social cognition and moral judgments, their conceptual overlap, and whether such processes are engaged implicitly or explicitly in a situationally bounded manner. 2.1.2.2 The Medial PFC in Social Cognition The MPFC is frequently implicated in a wide variety of social perception, social attribution, and social categorization tasks, including emotion recognition, facial recognition, identification of in-group members, and representation of self and others (Amodio & Frith, 2006; Lieberman, 2010). Given the diversity of the social cognitive processes in which the MPFC seems to be involved, it is not abundantly clear exactly what kind of role the MPFC actually plays in social cognition: Does it play a singular broad role in each of these social phenomena, or does it contribute selective functions unique to distinct social cognitive processes? Regarding more global social cognitive processes, one possibility is that social perception, social attribution, and social categorization play important roles in social affiliation (i.e., establishing and nurturing social bonds). This is likely an associative learning process, with a foundation in reward-based learning (Behrens, Hunt, Woolrich, & Rushmore, 2008; Depue & Morrone-Strupinsky, 2005; Poore et al., under review). In this respect, the medial aspects of the PFC may play a broad role in representing the social “utility,” or Association Value (Tooby & Cosmides, 1996), of those with whom we interact as it is learned through repeated interaction (Behrens et al., 2008; Poore et al., under review), and representing, integrating, and regulating these associations with respect to one’s self-concept, mood, current context, and salient goal states. This possibility is based on a series of findings in the social cognitive literature. First, many of the primary afferent/efferent structures in the reward system connected to the MPFC, especially the VMPFC and the OFC, track social stimuli and

c06.indd 97

7/25/2011 10:10:15 AM

98

PFC, SOCIAL COGNITION, AND MORAL JUDGMENT

moral outcomes that possess reward/punishment-related implications (Lieberman & Eisenberger, 2009), for example, attractive faces (Kampe, Frith, Dolan, & Frith, 2001), approving faces (Rademacher et al., 2009), verbal praise (Kirsch et al., 2003), social equity (Tabibnia, Lieberman, & Craske, 2008), reputational gains (Izuma, Saito, & Sadato, 2010), and charitable donations (Moll et al., 2006). Second, evidence suggests that the reward system, including the MPFC, processes social reward in ways similar to its processing of conditioned and unconditioned rewards (Poore et al., under review; Rademacher et al., 2009). Thus, it is likely that the MPFC receives information pertaining to the psychophysiological impact of social stimuli much like it does somatosensory information. This places the MPFC in a convenient position to integrate and update this information across social interactions for use in social decision making, which may explain why the MPFC, and particularly the VMPFC, has been implicated in a wide array of socioeconomic decisions requiring social perceptual and attributional processes (Moll et al., 2005; Rilling et al., 2002). Given dense connections among the limbic system, the MPFC, and the OFC, well-learned information about social value represented in the medial aspects of the PFC may regulate social behavior by modulating innate, visceral reactions to novel, and/or threatening, stimuli. For instance, human lesion studies suggest that damage to the MPFC has deleterious effects on social judgment that coincide with a wide variety of socially inappropriate behavior (Damásio, 1994; Krueger, Barbey, McCabe, Strenziok, & Zamboni, 2009). Interestingly, damage to the VMPFC has also been associated with increases in utilitarian moral judgments (i.e., rational judgments made in response to difficult moral dilemmas that engender a viscerally arousing emotional response), suggesting this region is critical for the representation, regulation, and subsequent integration of emotion involved in making moral judgments (Koenigs, Young, Adolphs, Tranel, & Cushman, 2007). In this respect, the MPFC, particularly the VMPFC, plays a key regulatory role in moral judgments and the sentiments on which they are based (Moll & de Oliveira-Souza, 2007; Moll & Schulkin, 2009). Several studies suggest that the MPFC also serves as a hub for self-related or introspective processing (Amodio & Frith, 2006; Gusnard et al., 2001; Johnson et al., 2002), well-learned social knowledge (e.g., stereotypes; Berthoz, Armony, Blair, & Dolan, 2002; Gozzi, Raymont, Solomon, Koenigs, & Grafman, 2009; Knutson, Mah, Manly, & Grafman, 2007), and possibly attributional processes such as inferring the thoughts and intentions of one’s self and others (Carrington & Bailey, 2009; Spunt, Satpute, & Lieberman, 2011). These findings are supported by evidence that individuals with autism, who have been shown to have abnormally functioning MPFCs, seem to have considerable difficulty with self-related and mentalizing tasks (i.e., tasks that require individuals to infer intent in other’s behaviors) (BaronCohen, Wheelwright, Hill, Raste, & Plumb, 2001; Gilbert, Meuwese, Towgood, Frith, & Burgess, 2009). Emerging evidence suggests performing specific types of mentalizing tasks may be contingent on a dorsal-ventral segregation in the functional roles of the MPFC in social inferences. For instance, although the DMPFC is important for inferring the intentions of others across a variety of tasks, including those involving empathy or empathic concern (Lieberman, 2010; Van Overwalle & Baetens, 2009), the VMPFC may play an exclusive role in mentalizing about “specific minds,” especially the self (Lieberman, 2010; Lieberman, 2007; Van Overwalle, 2009; Van Overwalle &

c06.indd 98

7/25/2011 10:10:15 AM

CONTRIBUTIONS OF THE PFC TO SOCIAL COGNITIVE

99

Baetens, 2009). The relationships among VMPFC, DMPFC, self, and other processing are likely predicated on the situation however. Whereas increases in DMPFC activity have been found in tasks where individuals infer the intentions or traits of others (Mitchell, Macrae, & Banaji, 2006; Van Overwalle, 2009), DMPFC activation has also been found when individuals are engaged in self-related processing in relation to an in-group they identify with (Volz, Kessler, & von Cramon, 2009). Such findings suggest that representations for self and others may be dorsally and ventrally distinct in the MPFC, however, self and other processing likely requires both regions to engage efficaciously in mentalizing tasks in general. Given the plurality of social cognitive functions the MPFC seems critical for, it is unsurprising that it also plays a key role in many theories of advanced social cognitive processing, especially moral judgments. In addition to a possible role in nonutilitarian moral judgments, or those decisions that are likely to engender an emotional response that overrides a more rational one (Greene, Sommerville, Nystrom, Darley, & Cohen, 2001), Moll and colleagues (Moll et al., 2005) outline how the role of the VMPFC in various social cognitive processing can be integrated to engender more complex moral judgment processing. Among other things, the VMPFC and frontopolar cortex (FPC) are likely critical for enabling an individual to adhere to social and cultural norms derived through the socialization process and to assess their own, as well as other’s, behavior with respect to those social norms. Given the dense interconnectivity between these regions, and their connectivity with subcortical and limbic learning regions, it may be the case that the process of socialization itself relies partially on associative learning functions, with the MPFC and FPC being integral for interpretation and regulation of information received from these regions (Barbey, Srivastava, Reynolds, & Grafman, submitted). Aside from conceptual similarities among associative learning, decision making, and moral judgments, such judgments necessarily involve representations of social knowledge, attributions of others, and comparisons of behaviors with respect to socially derived norms of appropriateness (i.e., processes that have all been shown to recruit neural networks within the MPFC). It is clear that the MPFC is necessary for social perception, attribution, categorization, and moral judgments, but the MPFC cannot, alone, account for the mental capacities necessary for social cognition. As we have established, the MPFC likely serves as a hub for self- and other- related processing, as well as for well-learned social knowledge, that integrates emotional- and reward-based cues within a context of socially derived norms. However, in light of dense reciprocal connections between the MPFC and the LPFC, the MPFC necessarily relays this information on to the LPFC, where it is processed and subsequently regulated within the context of current goal-related behaviors. The manifestation of these interactions is a topic we turn to next. 2.1.2.3 The Lateral PFC in Social Cognition One primary distinction between MPFC and LPFC is the nature of representations stored in each region: Whereas the MPFC stores well-learned representations (e.g., stereotypes and social norms), the LPFC stores flexible representations capable of adapting to novel contexts (Barbey et al., 2009b; Forbes & Grafman, 2010). Like the MPFC, the LPFC plays fundamentally important roles in supporting social cognitive

c06.indd 99

7/25/2011 10:10:15 AM

100

PFC, SOCIAL COGNITION, AND MORAL JUDGMENT

processes and moral judgments (Barbey, Krueger, & Grafman, 2009a; Forbes & Grafman, 2010); yet the LPFC’s role in these functions may not be exclusive to any given social cognitive process. Comprehensive reviews of social cognitive neuroscience research find reliable involvement of the LPFC in self-control/regulation, social outcome reappraisals, and effortful mentalizing, (Lieberman, 2010), which is consistent with preeminent hypotheses about the generalized function of LPFC (e.g., Wood & Grafman, 2003) and functional distinctions between ventral and dorsal aspects of the LPFC (Wagner et al., 2001). Given the basic executive functions the LPFC is necessary for, it is likely particularly active in novel situations that prime, and subsequently necessitates suppression of, information that is inconsistent with overarching goal states. Research on stereotype activation provides a good forum for assessing these conjectures. For instance, individuals with reduced executive function capacity, a function heavily dependent on the DLPFC, are more likely to demonstrate implicit race bias, conceivably because they have greater difficulty downregulating activation inconsistent with a socially desirable response (Payne, 2005). Furthermore, increased DLPFC activity is frequently reported in studies where individuals are confronted with information that is incongruent with implicit attitudes, conceivably because this region is necessary for overriding stereotype activation that is elicited by the presentation of stereotypic information (Knutson et al., 2007). DLPFC activity has also been shown to increase in situations where individuals who have a chronic motivation to respond without prejudice are primed with negative outgroup stereotypes and presented with novel outgroup faces (Cunningham et al., 2004; Forbes et al., under review). This suggests that one fundamental role DLPFC plays in social cognition and moral judgment is to monitor predictable, or well-learned, goal-relevant social representations activated in the MPFC (e.g., information consistent with chronic egalitarian goals) and to integrate or inhibit this information within the context of situational demands or goals. In contrast, the VLPFC seems to be critical for maintaining information or representations that are concurrently monitored and selected by the DLPFC (Wagner et al., 2001). Accordingly, the VLPFC is one of the most frequently reported regions in social cognitive tasks involving self-control, reappraisals of mostly novel social outcomes, and emotion regulation (Lieberman, 2010; Van Overwalle, 2009). The VLPFC is also considered a key component in attitude selection or evaluative interpretations of stimuli in ambiguous situations (Cunningham & Zelazo, 2007). For instance, when white individuals demonstrate more positive explicit attitudes but also more negative implicit attitudes toward blacks, and are then presented with novel black faces, activity in the VLPFC has been shown to increase (Cunningham et al., 2004). It is possible that priming of negative visceral feelings conflicts with overt egalitarian goals engendering ambiguity, and the VLPFC must keep both bits of information accessible so that the DLPFC can select the desired information and resolve ambiguity accordingly. In conjunction with the role the VLPFC plays in self-control and emotional regulation, the role DLPFC plays in evaluating and/or reinterpreting implicit responses, and the role FPC plays in future planning, it is clear that these regions are integral for allowing individuals to assess long-term consequences of a given behavior as well as to predict the type of reaction a given behavior will elicit with respect to known norms (Forbes & Grafman, 2010; Moll et al., 2005). Evidence from the moral

c06.indd 100

7/25/2011 10:10:15 AM

THE INTERACTION BETWEEN IMPLICIT AND EXPLICIT PROCESSES IN THE PFC

101

judgment literature is consistent with this interpretation (e.g., the DLPFC seems to be necessary for overriding emotional reactions to personal moral dilemmas in lieu of rational responses that serve the greater good) (Greene & Haidt, 2002; Greene et al., 2001). Together, these regions interact with MPFC regions as well as subcortical regions to engender complex moral emotions such as shame, guilt, and empathy that facilitate compensatory moral behaviors accordingly (Moll et al., 2005). The broad role of the LPFC in social cognition and moral judgments, much like that of the MPFC, has not been adequately tested and therefore may not be specific to any particular social process. It is likely that in conjunction with other key social cognitive regions such as the anterior temporal lobe and MPFC, LPFC functions generalize to many social cognitive and moral judgment processes as dictated by the situation (Zahn et al., 2007). This would allow for flexible responses to both novel and well-learned contexts on a moment-to-moment basis. Overall, the LPFC serves as an executive processor in the social domain, focusing attention on goalrelevant information and/or detecting aberrations in one’s social context and providing the means for the consideration of a wide range of information during attribution and categorization processes.

2.1.3 THE INTERACTION BETWEEN IMPLICIT AND EXPLICIT PROCESSES IN THE PFC Although it is clear that medial and lateral aspects of the PFC have varied roles in social cognition and moral judgment, the extent to which there are dedicated PFC networks underlying implicit and explicit social cognitive and moral judgment processes and/or whether cortical networks are involved in both implicit and explicit processes remains unclear. One likely possibility is that the functional connectivity within the PFC plays an important role in determining whether social cognitive and moral judgment processes manifest implicitly or explicitly (figure 2.1-1). How these regions dynamically contribute to implicit or explicit processes in turn is likely mediated by the context. For example, whereas well-learned contexts use more implicit processing that arise out of medial regions in the PFC (Krueger, Moll, Zahn, Heinecke, & Grafman, 2007), novel contexts elicit explicit processing that is LPFC intensive (Barbey et al., 2009a). However, novel contexts are not devoid of influences from implicit processing and medial PFC regions, nor are well-learned contexts devoid of influences from explicit processing and lateral PFC regions (i.e., it is possible these processes occur in parallel and interact to influence behavior and perception accordingly). For instance, when navigating through a novel social context, it would be necessary for regions in the medial and lateral PFC to interact dynamically in order to allow for accurate predictions about how events will unfurl and ensure reliable expectations that are consistent with overarching goal states. Regions in the medial PFC would be necessary to retrieve representations pertaining to what has happened in similar situations in the past, and regions in the lateral PFC would be necessary for integrating these representations with unexpected information to update expectations and form a socially appropriate plan of action. Attributional processes provide excellent examples of these interactions as they can be derived through both deliberative (i.e., explicit) and heuristic (i.e., implicit)

c06.indd 101

7/25/2011 10:10:15 AM

102

PFC, SOCIAL COGNITION, AND MORAL JUDGMENT

Explicit

Frontopolar Cortex

Dorsolateral PFC

Ventrolateral PFC

Medial PFC

Orbitofrontal Cortex Implicit

Figure 2.1-1. Diagram of direct neural connectivity between regions critical for implicit and explicit social cognitive and moral judgment processes. Solid bi-directional arrows denote direct reciprocal neural connectivity between two regions within a given processing system, i.e. implicit or explicit processing. Dashed bi-directional arrows represent direct reciprocal connectivity between two regions typically involved in either implicit or explicit processing. These regions are not exclusively involved in implicit or explicit processing however. This conjecture is represented by the shaded boxes and large arrow on the right. The darkest gray box highlights neural regions largely involved in explicit processes, whereas the lighter box represents a region that has been shown to be recruited during both implicit and explicit processing. PFC = Prefrontal Cortex.

routes. According to Kelley’s covariation model (Kelley, 1973), when attempting to infer intentions for behavior, one is likely to assess whether the behavior is consistent across situations, whether it is common among others in the same situations, or whether the behavior is uniquely expressed by the self or another. This model implies extensive deductive processing burdens. Thus, attributions are likely derived via explicit processing, which in turn are likely to require the efforts of executive control centers, such as the lateral PFC. Not surprisingly, past research finds that when individuals are asked to make attributions for a novel other’s emotional state, regions in the lateral PFC show enhanced activation regardless of how close the other’s emotional state resembles their own at the time (Ochsner et al., 2004). Other attributional processes do not follow from such careful, deliberate analyses of covariance in behaviors. Rather, in many cases—particularly when information or time is lacking—implicit, heuristically driven judgments are used to form attributions for behavior. To illustrate, in many day-to-day judgments of others, people default to explaining others’ behaviors in a way that places emphasis on dispositional factors in lieu of situational factors, but they demonstrate the opposite pattern when explaining their own behavior, which is a phenomenon known as the fundamental attribution error (Gilbert & Malone, 1995; Ross, 1977). In addition to representing the default response under normal circumstances, these fundamental attribution errors have been shown to recruit medial PFC regions specifically, denot-

c06.indd 102

7/25/2011 10:10:15 AM

THE INTERACTION BETWEEN IMPLICIT AND EXPLICIT PROCESSES IN THE PFC

103

ing the automaticity inherent in the process (Harris, Todorov, & Fiske, 2005). Thus, depending on the time and motivation one has in any given context, individuals can resort to implicit, explicit, or both forms of processing to inform representations of self and others. Taken together, depending on the time and motivation one has in any given context, individuals resort to implicit, explicit, or both forms of processing to inform representations of self and others. Actions consistent with well-learned associations elicit attributions that are based on implicit processes (and thus likely lead to the fundamental attribution error). However, information that conflicts with expectations would necessitate reevaluation of the target and engender attributions that are based on more explicit processes. For instance, whereas exposure to close others is likely to activate implicitly well-learned associations in the medial PFC and have greater influence on attributions for their actions accordingly, situational factors can require updates or modifications of those default perceptions (and inhibition of the implicit response), recruiting lateral PFC regions. The resultant interpretation is likely a blending of the implicit response with explicit updated information. Similarly, evaluating an unfamilar other’s actions is likely to involve the implicit activation of representations in the medial PFC associated with readily observable characteristics of that individual (e.g., well-learned associations predicated on their gender, ethnicity, or style of clothing), but the lateral PFC will be required to process explicitly new, unanticipated information the individual provides or demonstrates during the social interaction. Dynamic interactions between the MPFC and the LPFC, and thus implicit and explicit processes, would be necessary to provide individuals with the ability to form attributions that are accurate yet efficient in a diverse range of social contexts. The extent to which moral judgments are based on implicit or explicit processes likely depends on dynamic interactions between the MPFC and the LPFC as well. For instance, at the base of every moral judgment is the requirement that behaviors are interpreted or judged within a context of culturally derived norms for right and wrong. Given that these norms are established and fortified early in the developmental process, it is likely that representations for these norms can be activated implicitly, and certainly retrieved explicitly, and are stored in medial PFC regions. This notion is consistent with past research indicating the medial PFC is a hub for representations of social norms and beliefs such as stereotypes (Gozzi et al., 2009; Milne & Grafman, 2001). Likewise, when individuals are given ample time, are motivated, or are presented with novel dilemmas, more complex moral judgments can be derived via similar deductive processes to those circumscribed by Kelley’s covariation model (see above). Such deductive reasoning would likely require an interaction between medial and lateral PFC regions where pertinent information would need to be retrieved, kept accessible, and organized in a manner that allows for logical deduction. As we have discussed in detail, moral judgment processes have regularly been shown to activate simultaneously both medial and lateral PFC regions (Borg, Hynes, Van Horn, Grafton, & Sinnott-Armstrong, 2006; Fiddick, Spampinato, & Grafman, 2005; Greene et al., 2001; Moll, Eslinger, & Oliveira-Souza, 2001). Inherent in this interpretation is the likelihood that moral judgments can ultimately be derived from implicit representations stored in the MPFC or from explicit representations stored in the LPFC. Regardless, both forms of judgment are likely influenced by implicit

c06.indd 103

7/25/2011 10:10:15 AM

104

PFC, SOCIAL COGNITION, AND MORAL JUDGMENT

and explicit processes, which are in and of themselves influenced by contextual stimuli in any given situation, that bias overt perceptions accordingly. Assessing these inherently complex social interactions is a daunting endeavor, requiring astute methodological practice to bridge the gap between the laboratory and the social world. To disentangle the role of implicit and explicit processes in these interactions, it will be necessary to combine the strengths of both EEG and fMRI (i.e., the optimal temporal and spatial resolution each provides respectively), as well as phase coherence analyses that use continuous EEG activity, to assess the extent to which different cortical regions communicate with one another on the order of milliseconds. It will also be necessary to illuminate the role that subcortical regions (e.g., limbic system, the reward system, etc.) play in implicit processes, and how these regions subsequently bias explicit processes, to appreciate fully the dynamic relationship between bottom-up and top-down processing. All of these challenges must be faced while accounting for recent critiques on methodological practices levied on the field of social neuroscience itself (Vul, Harris, Winkielman, & Pashler, 2009; but also see Poldrack & Mumford, 2009) and by striving to better incorporate findings from other valuable neuroscience methodologies such as lesion studies.

2.1.4

CONCLUSIONS

In this chapter we have discussed how medial, lateral, dorsal, and ventral regions of the PFC interact to guide behavior with respect to current motivational and emotional states, situational cues, past experiences, and salient goals. Whereas the MPFC is integral for the representation of self and other knowledge, social norms, and the appropriateness of given behaviors, the LPFC is necessary for assessing the aforementioned information with respect to current goal states, novel contexts or stimuli, and formulating a successful plan of action accordingly. As these MPFC and LPFC interactions underlie implicit and explicit processes necessary for attributional processes and the planning of strategies that facilitate successful interactions with others, they are essential for complex social cognitive processes that enable individuals to function, contribute, and gain acceptance in both small and large social groups in an adaptable, morally just way.

REFERENCES Adolphs, R. (1999). Social cognition and the human brain. Trends Cognit Sci 3, 469–479. Amaral, D. G., & Price, J. L. (1984). Amygdalo-cortical projections in the monkey (Macaca fascicularis). J Comp Neurol 230, 465– 496. Amodio, D. M., & Frith, C. D. (2006). Meeting of minds: The medial frontal cortex and social cognition. Nat Rev Neurosci 7, 268–277. Badre, D., & Wagner, A. D. (2004). Selection, integration, and conflict monitoring: Assessing the nature and generality of prefrontal cognitive control mechanisms. Neuron 41, 473–487. Barbey, A. K., Krueger, F., & Grafman, J. (2009a). An evolutionarily adaptive neural architecture for social reasoning. Trends Neurosci 32, 603–610.

c06.indd 104

7/25/2011 10:10:15 AM

REFERENCES

105

Barbey, A. K., Krueger, F., & Grafman, J. (2009b). Structured event complexes in the medial prefrontal cortex support counterfactual representations for future planning. Phil Trans Roy Soc Lond B Biol Sci 364, 1291–1300. Barbey, A. K., Srivastava, A., Reynolds, R., & Grafman, J. (submitted). Neural architecture of normative social reasoning in the human prefrontal cortex. Baron-Cohen S., Wheelwright, S., Hill, J., Raste, Y., & Plumb, I. (2001). The “Reading the Mind in the Eyes” Test revised version: A study with normal adults, and adults with Asperger syndrome or high-functioning autism. J Child Psychol Psychiatr 42, 241–251. Beauregard, M., Levesque, J., & Bourgouin, P. (2001). Neural correlates of conscious selfregulation of emotion. J Neurosci 21, RC165. Behrens, T. E., Hunt, L. T., Woolrich, M. W., & Rushmore, M. F. (2008). Associative learning of social value. Nature 456, 245–249. Berthoz, S., Armony, J. L., Blair, R. J., & Dolan, R. J. (2002). An fMRI study of intentional and unintentional (embarrassing) violations of social norms. Brain 125, 1696–1708. Blair, I. V. (2002). The malleability of automatic stereotypes and prejudice. Pers Soc Psychol Rev 6, 242–261. Bodner, M., Kroger, J., & Fuster, J. M. (1996). Auditory memory cells in dorsolateral prefrontal cortex. Neuroreport 7, 1905–1908. Borg, J. S., Hynes, C., Van Horn, J., Grafton, S., & Sinnott-Armstrong, W. (2006). Consequences, action, and intention as factors in moral judgments: An fMRI investigation. J Cognit Neurosci 18, 803–817. Botvinick, M. M., Cohen, J. D., & Carter, C. S. (2004). Conflict monitoring and anterior cingulate cortex: An update. Trends Cognit Sci 8, 539–546. Carrington, S. J., & Bailey, A. J. (2009). Are there theory of mind regions in the brain? A review of the neuroimaging literature. Hum Brain Mapp 30, 2313–2335. Christoff, K., & Gabrieli, J. D. (2000). The frontopolar cortex and human cognition: Evidence for a rostrocaudal hierarchical organization within the human prefrontal cortex. Psychobiology 28, 168–186. Cunningham, W. A., Johnson, M. K., Raye, C. L., Chris Gatenby, J., Gore, J. C., & Banaji, M. R. (2004). Separable neural components in the processing of black and white faces. Psychol Sci 15, 806–813. Cunningham, W. A., & Zelazo, P. D. (2007). Attitudes and evaluations: a social cognitive neuroscience perspective. Trends Cogn Sci 11, 97–104. Damásio, A. R. (1994). Descartes’ Error: Emotion, Reason, and the Human Brain. New York: Quill. Depue, R. A., & Morrone-Strupinsky, J. V. (2005). A neurobehavioral model of affiliative bonding: Implications for conceptualizing a human trait of affiliation. Behavi Brain Sci 28, 82. Devine, P. G., Plant, E. A., Amodio, D. M., Harmon-Jones, E., & Vance, S. L. (2002). The regulation of explicit and implicit race bias: The role of motivations to respond without prejudice. J Pers Soc Psycho 82, 835–848. Devine, P. G., & Sharp, L. B. (2009). Automaticity and control in stereotyping and prejudice. Handbook of Prejudice, Stereotyping, and Discrimination, edited by T. D. Nelson. New York: Psychology Press. Dolcos, F., Miller, B., Kragel, P., Jha, A., & McCarthy, G. (2007). Regional brain differences in the effect of distraction during the delay interval of a working memory task. Brain Res 1152, 171–181.

c06.indd 105

7/25/2011 10:10:15 AM

106

PFC, SOCIAL COGNITION, AND MORAL JUDGMENT

Elliott, R., & Deakin, B. (2005). Role of the orbitofrontal cortex in reinforcement processing and inhibitory control: Evidence from functional magnetic resonance imaging studies in healthy human subjects. Int Rev Neurobiol 65, 89–116. Fiddick, L., Spampinato, M. V., & Grafman, J. (2005). Social contracts and precautions activate different neurological systems: An fMRI investigation of deontic reasoning. Neuroimage 28, 778–786. Fiske, S. T. (1993). Social cognition and social perception. Annu Rev Psychol 44, 155–194. Fiske, S. T. (1998). Stereotyping, prejudice, and discrimination. The Handbook of Social Psychology, vols. 1 and 2, (4th ed.). edited by D. T. Gilbert, S. T. Fiske, G. Lindzey, New York: McGraw-Hill. Fiske, S. T, Taylor, S. E. (1991). Social Cognition (2nd ed.) New York: McGraw-Hill. Forbes, C., Cox, C., Schmader, T., & Ryan, L. (under review). Negative stereotype activation alters interaction between neural correlates of arousal, inhibition and cognitive control. Forbes, C. E., & Grafman, J. (2010). The role of the human prefrontal cortex in social cognition and moral judgment. Ann Rev Neurosci 33, 299–324. Fuster, J. M. (1997). The Prefrontal Cortex: Anatomy, Physiology, and Neuropsychology of the Frontal Lobe. New York: Raven. Fuster, J. M., & Alexander, G. E. (1971). Neuron activity related to short-term memory. Science 173, 652–654. Ghashghaei, H. T., & Barbas, H. (2002). Pathways for emotion: Interactions of prefrontal and anterior temporal pathways in the amygdala of the rhesus monkey. Neuroscience 115, 1261–1279. Ghashghaei, H. T., Hilgetag, C. C., & Barbas, H. (2007). Sequence of information processing for emotions based on the anatomic dialogue between prefrontal cortex and amygdala. Neuroimage 34, 905–923. Gilbert, D. T., & Malone, P. S. (1995). The correspondence bias. Psychol Bull 117, 21–38. Gilbert, S. J., Meuwese, J. D., Towgood, K. J., Frith, C. D., & Burgess, P. W. (2009). Abnormal functional specialization within medial prefrontal cortex in high-functioning autism: A multi-voxel similarity analysis. Brain 132, 869–878. Gozzi, M., Raymont, V., Solomon, J., Koenigs, M., & Grafman, J. (2009). Dissociable effects of prefrontal and anterior temporal cortical lesions on stereotypical gender attitudes. Neuropsychologia 47, 2125–2132. Grafman, J. (1989). Plans, Actions, and Mental Sets: The Role of the Frontal Lobes. Hillsdale, NJ: Lawrence Erlbaum Associates. Greene, J., & Haidt, J. (2002). How (and where) does moral judgment work? Trends Cognit Sci 6, 517–523. Greene, J. D., Sommerville, R. B., Nystrom, L. E., Darley, J. M., & Cohen, J. D. (2001). An fMRI investigation of emotional engagement in moral judgment. Science 293, 2105–2108. Gusnard, D. A., Akbudak, E., Shulman, G. L., & Raichle, M. E. (2001). Medial prefrontal cortex and self-referential mental activity: Relation to a default mode of brain function. Proc Natl Acad Sci U S A 98, 4259– 4264. Harris, L. T., Todorov, A., & Fiske, S. T. (2005). Attributions on the brain: Neuro-imaging dispositional inferences, beyond theory of mind. Neuroimage 28, 763–769. Izuma, K., Saito, D. N., & Sadato, N. (2010). Processing of the incentive for social approval in the ventral striatum during charitable donation. J Cognit Neurosci 22, 621–631.

c06.indd 106

7/25/2011 10:10:15 AM

REFERENCES

107

Johnson, S. C., Baxter, L. C., Wilder, L. S., Pipe, J. G., Heiserman, J. E., Prigatano, G. P. (2002). Neural correlates of self-reflection. Brain 125, 1808–1814. Kampe, K. K. W., Frith, C. D., Dolan, R. J., & Frith, U. (2001). Reward value of attractiveness and gaze. Nature 413, 589. Kelley, H. H. (1973). The processes of causal attribution. Am Psychol 28, 107–128. Kelley, W. M., Macrae, C. N., Wyland, C. L., Caglar, S., Inati, S., et al. (2002). Finding the self? An event-related fMRI study. J Cognit Neurosci 14, 785–794. Kirsch, P., Schienle, A., Stark, R., Sammer, G., Blecker, C., et al. (2003). Anticipation of reward in nonaversive differential conditioning paradigm and the brains reward system: An eventrelated fMRI study. NeuroImage 20, 1086–1095. Knutson, K. M., Krueger, F., Koenigs, M., Hawley, A., Escobedo, J. R., et al. (2010). Behavioral norms for condensed moral vignettes. Soc Cogn Affect Neurosci 5, 378–384. Knutson, K. M., Mah, L., Manly, C. F., & Grafman, J. (2007). Neural correlates of automatic beliefs about gender and race. Hum Brain Mapp 28, 915–930. Koenigs, M., Young, L., Adolphs, R., Tranel, D., Cushman, F., et al. (2007). Damage to the prefrontal cortex increases utilitarian moral judgements. Nature 446, 908–911. Krueger, F., Barbey, A. K., McCabe, K., Strenziok, M., Zamboni, G., et al. (2009). The neural bases of key competencies of emotional intelligent. Proc Nat Acad Sci 106, 22486–22491. Krueger, F., Grafman, J., & McCabe, K. (2008). Neural correlates of economic game playing. Phil Trans Roy Soc 263, 3859–3874. Krueger, F., Moll, J., Zahn, R., Heinecke, A., & Grafman. J. (2007). Event frequency modulates the processing of daily life activities in human medial prefrontal cortex. CerebCortex 17, 2346–2353. Levesque, J., Eugene, F., Joanette, Y., Paquette, V., Mensour, B., et al. (2003). Neural circuitry underlying voluntary suppression of sadness. Biol Psychiatry 53, 502–510. Levy, R., & Goldman-Rakic, P. S. (2000). Segregation of working memory functions within the dorsolateral prefrontal cortex. Exp Brain Res 133, 23–32. Lieberman, M. (2010). Social cogntive neuroscience. Handbook of Social Psychology, edited by S. Fiske, D. Gilbert, & G. Lindzey, New York: McGraw-Hill. Lieberman, M. D. (2007). Social cognitive neuroscience: A review of core processes. Annu Rev Psychol 58, 259–289. Lieberman, M. D., Eisenberger, N. I. (2009). Pains and pleasures of social life. Science 323, 890–891. MacDonald, A. W., 3rd, Cohen, J. D., Stenger, V. A., & Carter, C. S. (2000). Dissociating the role of the dorsolateral prefrontal and anterior cingulate cortex in cognitive control. Science 288, 1835–1838. Macrae, C. N., & Bodenhausen, G. V. (2000). Social cognition: thinking categorically about others. Annu Rev Psychol 51, 93–120. Macrae, C. N., Milne, A. B., & Bodenhausen, G. V. (1994). Stereotypes as energy-saving devices: A peek inside the cognitive toolbox. J Pers Soci Psychol 66, 37– 47. Milham, M. P., Banich, M. T., Webb, A., Barad, V., Cohen, N. J., et al. (2001). The relative involvement of anterior cingulate and prefrontal cortex in attentional control depends on nature of conflict. Brain Res Cognit Brain Res 12, 467– 473. Miller, B. T., & D’Esposito, M. (2005). Searching for “the top” in top-down control. Neuron 48, 535–538. Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annu Rev Neurosci 24, 167–202.

c06.indd 107

7/25/2011 10:10:15 AM

108

PFC, SOCIAL COGNITION, AND MORAL JUDGMENT

Milne, E., & Grafman, J. (2001). Ventromedial prefrontal cortex lesions in humans eliminate implicit gender stereotyping. J Neurosci 21, RC150. Mitchell, J. P., Macrae, C. N., & Banaji, M. R. (2006). Dissociable medial prefrontal contributions to judgments of similar and dissimilar others. Neuron 50, 655–663. Moll, J., & de Oliveira-Souza, R. (2007). Moral judgments, emotions and the utilitarian brain. Trends Cognit Sci 11, 319–321. Moll, J., Eslinger, P. J., & Oliveira-Souza, R. (2001). Frontopolar and anterior temporal cortex activation in a moral judgment task: preliminary functional MRI results in normal subjects. Arq Neuropsiquiatr 59, 657–664. Moll, J., Krueger, F., Zahn, R., Pardini, M., de Oliveira-Souza, R., & Grafman, J. (2006). Human fronto-mesolimbic networks guide decisions about charitable donation. Proc Natl Acad Sci U S A 103, 15623–15628. Moll, J., & Schulkin, J. (2009). Social attachment and aversion in human moral cognition. Neurosci Biobehav Rev 33, 9. Moll, J., Zahn, R., de Oliveira-Souza, R., Krueger, F., & Grafman, J. (2005). Opinion: The neural basis of human moral cognition. Nat Rev Neurosci 6, 799–809. Ochsner, K. N., Knierim, K., Ludlow, D. H., Hanelin, J., Ramachandran, T., et al. (2004). Reflecting upon feelings: An fMRI study of neural systems supporting the attribution of emotion to self and other. J Cognit Neurosci 16, 1746–1772. Payne, B. K. (2001). Prejudice and perception: The role of automatic and controlled processes in misperceiving a weapon. J Pers Soc Psychol 81, 181–192. Payne, B. K. (2005). Conceptualizing control in social cognition: How executive functioning modulates the expression of automatic stereotyping. J Pers Soc Psychol 89, 488–503. Petrides, M. (2000). Dissociable roles of mid-dorsolateral prefrontal and anterior inferotemporal cortex in visual working memory. J Neurophysiol 20, 7496–7503. Poldrack, R. A., & Mumford, J. A. (2009). Independence in ROI analysis: Where is the voodoo? Soc Cognit Affect Neurosci 4, 208–213. Poore, J. C., Pfeifer, J. H., Berkman, E. T., Inagaki, T., Welborne, L., et al. (under review). Prediction-error in the context of real social relationships modulates the reward system. Rademacher, L., Krach, S., Kohls, G., Irmak, A., Grunde, G., et al. (2009). Dissociation of neural networks for anticipation and consumption of monetary and social rewards. Neuroimage 15, 3276–3285. Rilling, J., Gutman, D., Zeh, T., Pagnoni, G., Berns, G., et al. (2002). A neural basis for social cooperation. Neuron 35, 395– 405. Rolls, E. T., & Grabenhorst, F. (2008). The orbitofrontal cortex and beyond: From affect to decision-making. Prog Neurobiol 86, 216–244. Ross, L., ed. (1977). The Intuitive Psychologist and His Shortcomings, vol. 10. New York: Academic. Schmitz, T. W., Kawahara-Baccus, T. N., & Johnson S. C. (2004). Metacognitive evaluation, self-relevance, and the right prefrontal cortex. Neuroimage 22, 941–947. Semendeferi, K., Armstrong, E., Schleicher, A., Zilles, K., & Van Hoesen, G. W. (2001). Prefrontal cortex in humans and apes: A comparative study of area 10. Am J Phys Anthropol 114, 224–241. Spunt, R. P., Satpute, A. B., & Lieberman, M. D. (2011). Identifying the what, why, and how of an observed action: An fMRI study of mentalizing and mechanizing during action observation. J Cognit Neurosci 23, 63–74. Tabibnia, G., Lieberman, M. D, & Craske, M. G. (2008). The lasting effect of words on feelings: Words may facilitate exposure effects to threatening images. Emotion 8, 307–317.

c06.indd 108

7/25/2011 10:10:15 AM

REFERENCES

109

Tooby, J., & Cosmides, L. (1996). Friendship and the Banker’s Paradox: Other pathways to the evolution of adaptations for altruism. Proc Br Acad 88, 44. Van Overwalle, F. (2009). Social cognition and the brain: A meta-analysis. Hum Brain Mapp 30, 829–858. Van Overwalle, F., & Baetens, K. (2009). Understanding others’ actions and goals by mirror and mentalizing systems: A meta-analysis. Neuroimage 48, 564–584. Volz, K. G., Kessler, T., & von Cramon, D. Y. (2009). In-group as part of the self: In-group favoritism is mediated by medial prefrontal cortex activation. Soc Neurosci 4, 244–260. Vul, E., Harris, C., Winkielman, P., & Pashler, H. (2009). Puzzlingly high correlations in fMRI studies of emotion, personality, and social cognition. Perspec Psychol Sci 4, 274–290. Wager, T. D., & Smith, E. E. (2003). Neuroimaging studies of working memory: a metaanalysis. Cogn Affect Behav Neurosci 3, 255–274. Wagner, A. D., Maril, A., Bjork, R. A., & Schacter, D. L. (2001). Prefrontal contributions to executive control: fMRI evidence for functional distinctions within lateral prefrontal cortex. Neuroimage 14, 1337–1347. Wood, J. N., & Grafman, J. (2003). Human prefrontal cortex: processing and representational perspectives. Nat Rev Neurosci 4, 139–147. Zahn, R., Moll, J., Krueger, F., Huey, E. D., Garrido, G., et al. (2007). Social concepts are represented in the superior anterior temporal cortex. Proc Natl Acad Sci U S A 104, 6430–6435.

c06.indd 109

7/25/2011 10:10:15 AM

2.2 EMOTION AND MORAL COGNITION MICHAEL KOENIGS Department of Psychiatry, University of Wisconsin-Madison, Madison, Wisconsin

The basis of human morality has been a matter of debate for centuries. Philosophers, psychologists, anthropologists, and neuroscientists have contributed a variety of perspectives on this issue. Despite the seemingly infinite complexity and nuance of human social interaction, the debate seems to hinge on a handful of central themes. One of the enduring themes is considered here: To what extent does emotion underlie human morality? Among philosophers, David Hume boldly espoused the primacy of emotion in human decision making, declaring, “Reason is, and ought only to be, the slave of the passions” (Hume, 1777). This theoretical stance contrasted sharply with that of several “rationalist” thinkers, notably Immanuel Kant, who proposed, “All our knowledge begins with the senses, proceeds then to the understanding, and ends with reason. There is nothing higher than reason” (Kant, 1785). The rationalist perspective on morality—that moral judgments are the product of reason and reflection—was widely espoused among 20th-century psychologists (Kohlberg, 1969; Piaget, 1932; Turiel, 1983); however, more recent theoretical accounts, such as Antonio Damasio’s “Somatic Marker Hypothesis” (Damasio, 1994) and Jonathan Haidt’s “Social Intuitionist Approach” (Haidt, 2001), have again emphasized the role of affect and intuition in moral decision making. Importantly, the contemporary theories are not simply a rehashing of old arguments; they are formulized as hypotheses to be tested with modern tools of experimental psychology and neuroscience. Thus, the debate over the basis of morality has now moved from the armchair to the laboratory. The goal of this chapter is to summarize and integrate psychological and neuroscientific research findings that are beginning to clarify the role of emotion in moral cognition.

From DNA to Social Cognition, First Edition. Edited by Richard Ebstein, Simone Shamay-Tsoory, and Soo Hong Chew. © 2011 Wiley-Blackwell. Published 2011 by John Wiley & Sons, Inc.

111

c07.indd 111

7/25/2011 10:10:16 AM

112

EMOTION AND MORAL COGNITION

2.2.1

PSYCHOLOGICAL STUDIES

First we consider data from behavioral studies of healthy adults. If emotion is a critical determinant of morality, then we should observe correlations and interactions between affective processing and moral cognition. One way to parse the relative contributions of affective intuition versus deliberate reasoning in morality is to determine which of the two is a better predictor of moral judgment. To address this question experimentally, Haidt and colleagues asked subjects to consider harmless yet offensive taboo social behaviors, such as using the national flag to clean a toilet (Haidt & Hersh, 2001; Haidt, Koller, & Dias, 1993). Subjects were asked to report a judgment of the moral appropriateness of the action, as well as their affective reaction to the behavior and a justification for their moral judgment. Despite offering quick condemnations of the behavior, subjects could seldom articulate any coherent rationalization for their judgment. Moreover, negative emotional reactions were a significant predictor of moral condemnation. These data suggest that, at a psychological level, emotional processes drive moral judgment. Psychophysiological data can also be used to establish a link between emotion and morality. Several studies suggest that the sentiment of “moral disgust” is derived from, or at least psychologically and physiologically related to, the feeling of “physical disgust.” For example, one study demonstrates that the muscle activity that characterizes the facial expression of physical disgust (raising the upper lip and wrinkling the nose), is elicited not only by drinking unpleasant-tasting liquids and viewing pictures of uncleanliness and contamination, but also when experiencing unfair treatment, which represents a more abstract moral transgression (Chapman, Kim, Susskind, & Anderson, 2009). Another group of studies underscores the connection between disgust and morality, demonstrating that experimental manipulations of the feeling of disgust can in fact influence the severity of moral judgment. In one such study, subjects underwent hypnosis and were given a posthypnotic suggestion to feel a brief sensation of sickening disgust when reading a particular word. Subjects later read vignettes describing morally inappropriate behavior (some containing the hypnotic disgust word, and others not containing the word) and judged the moral severity of the behavior in the vignette. The moral violations accompanied by the hypnotic disgust word were judged to be significantly more severe (Wheatley & Haidt, 2005). In a related study, experimenters induced disgust in a subset of subjects (e.g., with aversive smells or viewing disgusting pictures) and found that disgust-induction was associated with more severe moral judgments on a subsequent task (Schnall, Haidt, Clore, & Jordan, 2008b). A third study demonstrated the reverse effect; subjects who were primed with concepts of cleanliness and physical purity made less severe moral judgments (Schnall, Benton, & Harvey, 2008a). Another study indicates that emotions other than disgust may influence moral judgment. Subjects who underwent positive mood induction (i.e., watching comedy clips) made less severe moral judgments (Valdesolo & DeSteno, 2006). Taken together, these studies demonstrate that manipulations of emotional state are sufficient to yield systematic changes in moral judgment and, by extension, that emotion plays a critical role in moral cognition. In this section we considered whether emotion and morality are connected at the psychological and behavioral level. In the following two sections we consider

c07.indd 112

7/25/2011 10:10:16 AM

NEUROLOGICAL PATIENT STUDIES

113

whether emotion and morality are connected at the neurobiological level. In other words, do emotion and morality share a common biological substrate in the brain?

2.2.2

NEUROLOGICAL PATIENT STUDIES

For decades, the study of brain-damaged individuals has yielded unique insight into the areas of the brain that are necessary for normal social and emotional function. Perhaps the first such case was that of Phineas Gage (Harlow, 1868). While working as the foreman of a railroad construction crew in Vermont in 1848, Gage was involved in a freak accident in which an iron rod over an inch thick was blasted through his face and out the top of his head, destroying the medial sector of the anterior frontal lobe of his brain (Damasio, Grabowski, Frank, Galaburda, & Damasio, 1994). Remarkably, Gage survived the blast, but his personality was permanently altered. Whereas before the accident Gage had been a dependable and trustworthy man, afterward he became disrespectful, profane, and unreliable. Throughout the 20th century, Several similar cases were reported. In 1975, Blumer and Benson coined the term “pseudopsychopathy” to summarize the personality changes (“the lack of adult tact and restraints”) observed in a subset of neurological patients with brain damage involving the lower frontal cortex (Blumer & Benson, 1975). The connection between frontal brain injury and deficits in emotion and social behavior was subsequently elaborated on by Antonio Damasio and colleagues. Clinical reports associated damage to a specific sector of the frontal lobe— the ventromedial prefrontal cortex (vmPFC) (Figure 2.2-1)—with an array of emotion and decision-making deficits, including conspicuously diminished guilt, shame, empathy, and embarrassment (Anderson, Barrash, Bechara, & Tranel, 2006; Anderson, Bechara, Damasio, Tranel, & Damasio, 1999; Barrash, Tranel, & Anderson, 2000; Damasio, 1994; Eslinger & Damasio, 1985). The importance of vmPFC in emotion and moral behavior has been demonstrated in several laboratory experiments. One of the earliest such studies shows that patients with vmPFC damage exhibit diminished autonomic activity while viewing emotionally charged pictures (Damasio, Tranel, & Damasio, 1990). Subsequent studies demonstrate that damage to vmPFC is associated with abnormalities in moral decision making. Two of these studies employed the same design;

Figure 2.2-1. Depiction of vmPFC (in red) in midline views of each hemisphere. (See color insert.)

c07.indd 113

7/25/2011 10:10:16 AM

114

EMOTION AND MORAL COGNITION

patients with vmPFC lesions evaluated a series of moral dilemmas in which they had to choose whether it is appropriate to sacrifice one person (e.g., push someone off a bridge over a train track) to save several other people (e.g., stop the train from running over five unsuspecting workmen) (Ciaramelli, Muccioli, Ladavas, & di Pellegrino, 2007; Koenigs et al., 2007). Theoretically, the decision to harm one person in order to maximize aggregate welfare (the “utilitarian” response) requires the subject to overcome an emotional aversion toward directly harming another (Greene, Sommerville, Nystrom, Darley, & Cohen, 2001). Thus, the responses to the dilemmas indicate the extent to which emotional considerations (as opposed to rational utilitarian calculations) influence moral judgment. In both studies, patients with vmPFC damage (Figure 2.2-2) were more likely to endorse the utilitarian response. A similar result was found for patients with frontotemporal dementia (FTD), which involves deterioration of anterior brain areas including vmPFC (Figure 2.2-3). FTD patients exhibit blunted emotion and diminished regard for others early in the disease course. Behavioral changes associated with FTD include moral transgressions such as stealing, physical assault, and unsolicited or inappropriate sexual advances (Mendez, Chen, Shapira, & Miller, 2005b). In the test of moral dilemmas, FTD patients, like vmPFC patients, were more likely to endorse the utilitarian response (Mendez, Anderson, & Shapira, 2005a). The vmPFC patients’ moral deficit has also been demonstrated through economic decision-making tasks. In the Dictator Game, a subject is given an initial sum of money (e.g., $10) and is allowed to split the money however he or she chooses with an unfamiliar partner. The amount of the Dictator offer is presumed to reflect some prosocial concern, such as fairness or empathy for the other player. Whereas neurologically healthy adults typically offered a substantial portion to the other player (roughly 40% of the total sum, on average), vmPFC patients offered significantly less (only approximately 10%, on average) (Krajbich, Adolphs, Tranel, Denburg, & Camerer, 2009). The Trust Game is another two-player laboratory test of economic decision making. In the Trust Game, the first player has a chance to “invest” a portion of his money with the second player, in which case, the invested sum would be multiplied. The second player has the option to repay any amount to the first player. Compared with neurologically healthy adults, vmPFC patients repaid a significantly lower amount (i.e., were less trustworthy) when acting as the second player (Krajbich et al., 2009). A noteworthy point is that the vmPFC patients described in the previous studies all acquired their brain damage as middle-aged or elderly adults. Thus, these studies involve patients who had experienced largely normal psychosocial development, and so could perhaps draw on their normal pre-injury experiences to some extent to help guide their moral decision making. An individual who suffers vmPFC damage much earlier in life may not have the benefit of normal social and emotional development. Although focal damage involving vmPFC in early childhood is exceedingly rare, one study clearly demonstrates that early vmPFC damage (within the first two years of life) results in markedly impaired moral and emotional function throughout childhood and adolescence. Unlike the adult-onset vmPFC lesion patients, the early-onset patients exhibit flagrant antisocial behaviors such as petty theft, physical assaults, sexual promiscuity, and chronic lying (Anderson et al., 1999). Furthermore, in a standardized assessment of moral reasoning (Kohlberg, 1981), adult-onset vmPFC patients were able to articulate justifications for moral

c07.indd 114

7/25/2011 10:10:16 AM

115

c07.indd 115

7/25/2011 10:10:16 AM

2

5

1

4

123456

1

2

3

4

5

6

6

3

654321

B

1

7

Figure 2.2-2. Lesion overlaps in moral judgment studies. Two studies of patients with focal vmPFC lesions demonstrate abnormal moral judgment for emotional moral dilemmas after vmPFC damage. (a) vmPFC lesion overlap (Koenigs et al., 2007). (b) vmPFC lesion overlap (Ciaramelli et al., 2007). (See color insert.)

A

116 A

EMOTION AND MORAL COGNITION B

C

Figure 2.2-3. Brain degeneration in FTD. vmPFC dysfunction in FTD patients is associated with (a) apathy, indexed by hypometabolism with position emission tomography (Peters et al., 2006), (b) disinhibition, indexed by hypoperfusion with single photon emission computed tomography (Le Ber et al., 2006), and (c) impairment in social judgment, indexed by atrophy with voxel-based morphometry (Eslinger et al., 2007). (See color insert.)

decisions in a manner consistent with neurologically healthy adults (Saver & Damasio, 1991). By contrast, the early-onset vmPFC cases presented justifications that suggested stilted moral development—an early or “preconventional” stage of moral reasoning, in which moral dilemmas are approached from an egocentric perspective of punishment-avoidance. In sum, the neurological patient data described here point to the vmPFC as a critical brain region involved in emotion and moral cognition. However, the brainbehavior inferences that may be drawn from human lesion studies are inherently imprecise and incomplete, as naturally occurring lesions may encompass multiple functionally heterogeneous regions, and lesion studies will only reveal when a particular function is impaired by damage to a particular brain area. To appreciate more fully the vmPFC’s role in different aspects of emotion and moral cognition, we turn to studies that measure functional brain activity in healthy subjects.

2.2.3

BRAIN IMAGING STUDIES

Recent years have witnessed a rapid increase in the number of neuroimaging studies investigating the neural correlates of moral cognition. Many of these studies have used paradigms contrasting neural responses to stimuli involving moral versus nonmoral content. In one study, subjects viewed a series of emotionally evocative pictures, some depicting moral violations (e.g., physical assaults and war scenes) and others without a clear moral connotation (e.g., body lesions and dangerous animals)

c07.indd 116

7/25/2011 10:10:16 AM

BRAIN IMAGING STUDIES

117

(Moll, de Oliveira-Souza, Bramati, & Grafman, 2002a). Areas of vmPFC (Figure 2.2-4A) were more active during the passive viewing of the moral relative to the nonmoral pictures. A later study demonstrated that vmPFC activity was also elicited during the deliberate evaluation of moral versus nonmoral pictures (Harenski, Antonenko, Shane, & Kiehl, 2009). Comparable vmPFC activations (Figure 2.2-4E) were observed in a similar study that additionally matched moral and nonmoral pictures for social content and required subjects to regulate their own emotional responses to the moral pictures (Harenski & Hamann, 2006). An analogous set of studies investigated subjects’ neural responses while viewing verbal stimuli rather than pictures. Again greater activation within vmPFC (Figure 2.2-4B) was observed for morally relevant statements (e.g., “He shot the victim to death”) than for statements with social and emotional content but comparatively less moral content (e.g., “He licked the dirty toilet”) (Moll et al., 2002b). Another study used deliberately sparse statements in an attempt to ascertain neural responses to moral content in the absence of explicit emotional content, such as bodily harm or violence (e.g., “He steals a car”) (Heekeren, Wartenburger, Schmidt, Schwintowski, & Villringer, 2003). vmPFC activity (Figure 2.2-4C) was greater when subjects made moral judgments about the statements compared with semantic judgments. Likewise, a study that required subjects to make simple “right” or “wrong” judgments of general moral statements (e.g., “We break the law when necessary”) and nonmoral statements (e.g., “Stones are made of water”) again revealed greater vmPFC activity for the moral statements (Moll, Eslinger, & de Oliveira-Souza, 2001). A later study that asked subjects to evaluate more complex verbal content (moral and nonmoral scenarios) found greater vmPFC activation in the moral condition (Schaich Borg, Hynes, Van Horn, Grafton, & Sinnott-Armstrong, 2006). A second group of studies has investigated the neural activations associated with different types of moral content (rather than just comparing activations to moral versus nonmoral content). The first such study examined the distinction between “personal” and “impersonal” harm as subjects evaluated a series of moral dilemmas (Greene et al., 2001). In the personal dilemmas, subjects decided whether to commit direct, intimate (“up close and personal”) harm to another person (e.g., push someone off a bridge over a train track) to save several other people (e.g., stop the train from running over five unsuspecting workmen). In impersonal dilemmas, subjects could use a more indirect means of preventing the five deaths (e.g., pull a lever to switch tracks), although in this case one other person would still be killed (a single unsuspecting workman on the other track). Even though the outcomes for each choice are the same (sacrifice one to save five), the patterns of neural activity differed between the two types of scenario; in particular, vmPFC activation (Figure 2.2-4D) was greater for the personal compared with the impersonal scenarios. A later study demonstrated that moral scenarios involving intentional harm activated vmPFC to a greater extent that did moral scenarios involving unintentional harm (Schaich Borg et al., 2006). Considered together, the functional imaging studies described here have yielded a remarkably convergent result: vmPFC is consistently involved in a wide range of tasks involving moral cognition. Given the emotion-related deficits of patients with vmPFC lesions (described in the previous section), researchers have commonly interpreted these vmPFC activations as evidence for a fundamental role for emotion and affect in moral cognition. Although this process of “reverse inference” is not

c07.indd 117

7/25/2011 10:10:16 AM

118

c07.indd 118

7/25/2011 10:10:16 AM

B

C

D

E

Figure 2.2-4. Examples of functional magnetic resonance imaging activations associated with moral cognition. Activations in vmPFC are associated with (a) viewing pictures with moral content (Moll et al., 2002a), (b) viewing statements with moral content (Moll et al., 2002a), (c) judgments of simple statements with moral content (Heekeren et al., 2003), (d) judgments of moral dilemmas featuring physical harm (Greene et al., 2001), and (e) regulation of moral emotions (Harenski & Hamann, 2006). (See color insert.)

A

CONCLUSION

119

deductively valid (Poldrack, 2006), the imaging data, coupled with the psychological studies and neurological patient studies, suggest that affective processes mediated by vmPFC are a central component of moral cognition. Of course, the vmPFC is not the sole neurobiological substrate for all aspects of human morality. With respect to emotion, several other brain regions should be considered. The preceding section on psychological studies describes evidence for a connection between physical disgust and moral disgust. Neuroimaging studies point to the insula as the principal brain area involved in mediating physical disgust (Phillips et al., 1998; Phillips et al., 1997; Wicker et al., 2003). A corollary hypothesis, then, is that insula activation would also be associated with moral disgust and condemnation. However, the evidence for this idea is equivocal. Although one study demonstrates insula activity in subjects who were being treated unfairly in a test of economic decision making (Sanfey, Rilling, Aronson, Nystrom, & Cohen, 2003), at least two studies specifically designed to detect neural activations associated with both physical and moral disgust failed to find common activity in the insula (Moll et al., 2005; Schaich Borg, Lieberman, & Kiehl, 2008). A second emotion-related brain structure that warrants consideration is the amygdala. A wealth of human and animal data implicate the amygdala in emotional processing, particularly fearrelated learning (Bechara et al., 1995; LeDoux, 2000) and the recognition of facial expressions of emotion (Adolphs, 2002). But within the functional imaging literature, there is no consistent association between moral cognition and amygdala activity (Heekeren et al., 2005). At least two studies have reported amygdala activity during the processing of moral content (Luo et al., 2006; Moll et al., 2002b), but in these studies, either moral severity was conflated with emotional intensity (Luo et al., 2006) or the amygdala activity was similar for both moral and nonmoral emotions (Moll et al., 2002b). Thus, despite well-established roles in emotional processing, the insula and amygdala do not seem to play a pervasive role in moral cognition. It is also germane to point out that tasks of moral cognition have been associated with areas of the brain that are not primarily associated with emotional or affective processes. For example, the dorsolateral prefrontal cortex, an area associated with cognitive control and executive function, is more active when subjects make utilitarian (compared with nonutilitarian) moral judgments (Greene, Nystrom, Engell, Darley, & Cohen, 2004). Similarly, a host of recent studies highlight activity in the temporoparietal junction (TPJ) during tests of moral judgment (Kliemann, Young, Scholz, & Saxe, 2008; Young, Cushman, Hauser, & Saxe, 2007; Young & Saxe, 2008; Young & Saxe, 2009a, 2009b). In these studies, the involvement of TPJ is presumed to reflect a higher cognitive process related to social judgment, such as mental state attribution, rather than primarily affective functions. Overall, the functional imaging literature implicates a distributed network of brain areas, integrating a variety of emotional and cognitive factors, in the service of moral cognition.

2.2.4

CONCLUSION

This chapter has reviewed a large and growing body of experimental evidence related to the role of emotion in moral cognition. This line of research is motivated

c07.indd 119

7/25/2011 10:10:16 AM

120

EMOTION AND MORAL COGNITION

by the possibility that, equipped with a better understanding of the psychological and biological processes that underlie our moral (and immoral) behavior, we can identify and pursue effective means of intervention, whether it be at the individual level (such as developing therapies for those with pathological antisocial disorders) or at the societal level (such as recognizing policies and campaigns that manipulate and distort, rather than cultivate, our collective decision-making capacity). Clearly, brain science has much to offer.

REFERENCES Adolphs, R. (2002). Recognizing emotion from facial expressions: Psychological and neurological mechanisms. Behav Cognit Neurosci Rev 1, 21–62. Anderson, S. W., Barrash, J., Bechara, A., & Tranel, D. (2006). Impairments of emotion and real-world complex behavior following childhood- or adult-onset damage to ventromedial prefrontal cortex. J Int Neuropsychol Soc 12, 224–235. Anderson, S. W., Bechara, A., Damasio, H., Tranel, D., & Damasio, A. R. (1999). Impairment of social and moral behavior related to early damage in human prefrontal cortex. Nat Neurosci 2, 1032–1037. Barrash, J., Tranel, D., & Anderson, S. W. (2000). Acquired personality disturbances associated with bilateral damage to the ventromedial prefrontal region. Dev Neuropsychol 18, 355–381. Bechara, A., Tranel, D., Damasio, H., Adolphs, R., Rockland, C., et al. (1995). Double dissociation of conditioning and declarative knowledge relative to the amygdala and hippocampus in humans. Science 269, 1115–1118. Blumer, D., & Benson, D. F. (1975). Personality changes with frontal and temporal lesions. Psychiatric Aspects of Neurological Disease, edited by D. F. Benson & D. Blumer. New York: Stratton. Chapman, H. A., Kim, D. A., Susskind, J. M., & Anderson, A. K. (2009). In bad taste: Evidence for the oral origins of moral disgust. Science 323, 1222–1226. Ciaramelli, E., Muccioli, M., Ladavas, E., & di Pellegrino, G. (2007). Selective deficit in personal moral judgment following damage to ventromedial prefrontal cortex. Soc Cogn Affect Neurosci 2, 84–92. Damasio, A. R. (1994). Descartes’ Error. New York: Putnam. Damasio, A. R., Tranel, D., & Damasio, H. (1990). Individuals with sociopathic behavior caused by frontal damage fail to respond autonomically to social stimuli. Behav Brain Res 41, 81–94. Damasio, H., Grabowski, T., Frank, R., Galaburda, A. M., & Damasio, A. R. (1994). The return of Phineas Gage: Clues about the brain from the skull of a famous patient. Science 264, 1102–1105. Eslinger, P. J., & Damasio, A. R. (1985). Severe disturbance of higher cognition after bilateral frontal lobe ablation: Patient EVR. Neurology 35, 1731–1741. Eslinger, P. J., Moore, P., Troiani, V., Antani, S., Cross, K., et al. (2007). Oops! Resolving social dilemmas in frontotemporal dementia. J Neurol Neurosurg Psychiatry 78, 457– 460. Greene, J. D., Nystrom, L. E., Engell, A. D., Darley, J. M., & Cohen, J. D. (2004). The neural bases of cognitive conflict and control in moral judgment. Neuron 44, 389– 400. Greene, J. D., Sommerville, R. B., Nystrom, L. E., Darley, J. M., & Cohen, J. D. (2001). An fMRI investigation of emotional engagement in moral judgment. Science 293, 2105–2108.

c07.indd 120

7/25/2011 10:10:16 AM

REFERENCES

121

Haidt, J. (2001). The emotional dog and its rational tail: A social intuitionist approach to moral judgment. Psychol Rev 108, 814–834. Haidt, J., & Hersh, M. A. (2001). Sexual morality: The cultures and emotions of conservatives and liberals. J Appl Soc Psychol 31, 191–221. Haidt, J., Koller, S. H., & Dias, M. G. (1993). Affect, culture, and morality, or is it wrong to eat your dog? J Pers Soc Psychol 65, 613–628. Harenski, C. L., Antonenko, O., Shane, M. S., & Kiehl, K. A. (2009). A functional imaging investigation of moral deliberation and moral intuition. Neuroimage 49, 2707–2716. Harenski, C. L., & Hamann, S. (2006). Neural correlates of regulating negative emotions related to moral violations. Neuroimage 30, 313–324. Harlow, J. M. (1868). Recovery from the passage of an iron bar through the head. Publ Massachusetts Medical Soc 2, 327–347. Heekeren, H. R., Wartenburger, I., Schmidt, H., Prehn, K., Schwintowski, H. P., et al. (2005). Influence of bodily harm on neural correlates of semantic and moral decision-making. Neuroimage 24, 887–897. Heekeren, H. R., Wartenburger, I., Schmidt, H., Schwintowski, H. P., & Villringer, A. (2003). An fMRI study of simple ethical decision-making. Neuroreport 14, 1215–1219. Hume, D. (1777). An Enquiry Concerning the Principles of Morals. Kant, I. (1785). The Foundations of the Metaphysics of Morals. Kliemann, D., Young, L., Scholz, J., & Saxe, R. (2008). The influence of prior record on moral judgment. Neuropsychologia 46, 2949–2957. Koenigs, M., Young, L., Adolphs, R., Tranel, D., Cushman, F., et al. (2007). Damage to the prefrontal cortex increases utilitarian moral judgements. Nature 446, 908–911. Kohlberg, L. (1969). Stage and sequence: The cognitive-developmental approach to socialization. Handbook of Socialization Theory and Research, edited by D. A. Goslin. Chicago, IL: Rand McNally. Kohlberg, L. (1981). Essays on Moral Development, vol 1: The Philosophy of Moral Development. New York: Harper Row. Krajbich, I., Adolphs, R., Tranel, D., Denburg, N. L., & Camerer, C. F. (2009). Economic games quantify diminished sense of guilt in patients with damage to the prefrontal cortex. J Neurosci 29, 2188–2192. Le Ber, I., Guedj, E., Gabelle, A., Verpillat, P., Volteau, M., et al. (2006). Demographic, neurological and behavioural characteristics and brain perfusion SPECT in frontal variant of frontotemporal dementia. Brain 129, 3051–3065. LeDoux, J. E. (2000). Emotion circuits in the brain. Annu Rev Neurosci 23, 155–184. Luo, Q., Nakic, M., Wheatley, T., Richell, R., Martin, A., et al. (2006). The neural basis of implicit moral attitude—an IAT study using event-related fMRI. Neuroimage 30, 1449–1457. Mendez, M. F., Anderson, E., & Shapira, J. S. (2005a). An investigation of moral judgement in frontotemporal dementia. Cognit Behav Neurol 18, 193–197. Mendez, M. F., Chen, A. K., Shapira, J. S., & Miller, B. L. (2005b). Acquired sociopathy and frontotemporal dementia. Dement Geriatr Cognit Disord 20, 99–104. Moll, J., de Oliveira-Souza, R., Bramati, I. E., & Grafman, J. (2002a). Functional networks in emotional moral and nonmoral social judgments. Neuroimage 16, 696–703. Moll, J., de Oliveira-Souza, R., Eslinger, P. J., Bramati, I. E., Mourao-Miranda, J., et al. (2002b). The neural correlates of moral sensitivity: A functional magnetic resonance imaging investigation of basic and moral emotions. J Neurosci 22, 2730–2736.

c07.indd 121

7/25/2011 10:10:16 AM

122

EMOTION AND MORAL COGNITION

Moll, J., de Oliveira-Souza, R., Moll, F. T., Ignacio, F. A., Bramati, I. E., et al. (2005). The moral affiliations of disgust: A functional MRI study. Cognit Behav Neurol 18, 68–78. Moll, J., Eslinger, P. J., & de Oliveira-Souza, R. (2001). Frontopolar and anterior temporal cortex activation in a moral judgment task: Preliminary functional MRI results in normal subjects. Arq Neuropsiquiatr 59, 657–664. Peters, F., Perani, D., Herholz, K., Holthoff, V., Beuthien-Baumann, B., et al. (2006). Orbitofrontal dysfunction related to both apathy and disinhibition in frontotemporal dementia. Dement Geriatr Cognit Disord 21, 373–379. Phillips, M. L., Young, A. W., Scott, S. K., Calder, A. J., Andrew, C., et al. (1998). Neural responses to facial and vocal expressions of fear and disgust. Proc Biol Sci 265, 1809–1817. Phillips, M. L., Young, A. W., Senior, C., Brammer, M., Andrew, C., et al. (1997). A specific neural substrate for perceiving facial expressions of disgust. Nature 389, 495–498. Piaget, J. (1932). The Moral Judgment of the Child. New York: Free Press. Poldrack, R. A. (2006). Can cognitive processes be inferred from neuroimaging data? Trends Cognit Sci 10, 59–63. Sanfey, A. G., Rilling, J. K., Aronson, J. A., Nystrom, L. E., & Cohen, J. D. (2003). The neural basis of economic decision-making in the Ultimatum Game. Science 300, 1755–1758. Saver, J. L., & Damasio, A. R. (1991). Preserved access and processing of social knowledge in a patient with acquired sociopathy due to ventromedial frontal damage. Neuropsychologia 29, 1241–1249. Schaich, B. J., Hynes, C., Van Horn, J., Grafton, S., & Sinnott-Armstrong, W. (2006). Consequences, action, and intention as factors in moral judgments: an FMRI investigation. J Cognit Neurosci 18, 803–817. Schaich, B. J., Lieberman, D., & Kiehl, K. A. (2008). Infection, incest, and iniquity: Investigating the neural correlates of disgust and morality. J Cognit Neurosci 20, 1529–1546. Schnall, S., Benton, J., & Harvey, S. (2008a). With a clean conscience: Cleanliness reduces the severity of moral judgments. Psychol Sci 19, 1219–1222. Schnall, S., Haidt, J., Clore, G. L., & Jordan, A. H. (2008b). Disgust as embodied moral judgment. Pers Soc Psychol Bull 34, 1096–1109. Turiel, E. (1983). The Development of Social Knowledge: Morality and Convention. Cambridge, MA: Cambridge University Press. Valdesolo, P., & DeSteno, D. (2006). Manipulations of emotional context shape moral judgment. Psychol Sci 17, 476– 477. Wheatley, T., & Haidt, J. (2005). Hypnotic disgust makes moral judgments more severe. Psychol Sci 16, 780–784. Wicker, B., Keysers, C., Plailly, J., Royet, J. P., Gallese, V., et al. (2003). Both of us disgusted in my insula: The common neural basis of seeing and feeling disgust. Neuron 40, 655–664. Young, L., Cushman, F., Hauser, M., & Saxe, R. (2007). The neural basis of the interaction between theory of mind and moral judgment. Proc Natl Acad Sci U S A 104, 8235–8240. Young, L., & Saxe, R. (2008). The neural basis of belief encoding and integration in moral judgment. Neuroimage 40, 1912–1920. Young, L., & Saxe, R. (2009a). An FMRI investigation of spontaneous mental state inference for moral judgment. J Cognit Neurosci 21, 1396–1405. Young, L., & Saxe, R. (2009b). Innocent intentions: A correlation between forgiveness for accidental harm and neural activity. Neuropsychologia 47, 2065–2072.

c07.indd 122

7/25/2011 10:10:16 AM

2.3 THE NEUROANATOMICAL BASIS OF MORAL COGNITION AND EMOTION ROLAND ZAHN,1,2 RICARDO 2 AND JORGE MOLL

DE

OLIVEIRA-SOUZA,2,3

1

The University of Manchester, School of Psychological Sciences, Neuroscience and Aphasia Research Unit, U.K. 2 Cognitive and Behavioral Neuroscience Unit, D’Or Institute for Research and Education, Rio de Janeiro, Brazil 3 Gaffrée e Guinle University Hospital, Rio de Janeiro, Brazil

2.3.1

INTRODUCTION

The ability to know what is morally “right” and “wrong” and to act upon this knowledge has developed in humans to a degree of sophistication that cannot be demonstrated in any other species (Nowak & Sigmund, 2005). Human moral abilities may have been the result of evolutionary pressures not only on survival of the individual but also on that of small groups for whom altruistically behaving in-group members may have conferred a competitive advantage (Gintis, Henrich, Bowles, Boyd, & Fehr, 2008). One key question is how to define moral abilities and how to distinguish them from general social cognitive faculties. The Latin word moralis can be literally translated as in accordance with societal customs. Philosophical definitions of morality, however, often seek to establish what is morally right or wrong independently of the sociocultural context and beyond customary behavior. Cognitive neuroscience cannot help in settling philosophical and societal debates about how to establish what is morally right or wrong, but it can identify the cognitive-anatomical components that enable humans to represent moral knowledge and motivations to act upon this knowledge. Social behavior could be defined as behavior that involves more than one person or as behavior that has consequences for more than one person. The latter From DNA to Social Cognition, First Edition. Edited by Richard Ebstein, Simone Shamay-Tsoory, and Soo Hong Chew. © 2011 Wiley-Blackwell. Published 2011 by John Wiley & Sons, Inc.

123

c08.indd 123

7/25/2011 10:10:17 AM

124

THE NEUROANATOMICAL BASIS OF MORAL COGNITION AND EMOTION

definition could apply to actions carried out by a single person with no other people observing or being direct recipients. For example, imagine a person secretly throwing paper into the general waste bin at night in a town with easily available paper recycling. This behavior has potential implications for others when considering how making “throwing paper into the general waste” a general global rule could affect the environment and thereby other people. The other social aspect of this behavior is the anticipation of other people potentially discovering this act. Thus, a trivial and harmless action per se that is carried out alone can nevertheless have morally relevant consequences for others. However, the moral significance of the action depends on the sociocultural value system. For example, in many countries and sociocultural groups of the world, until recently, it has been perfectly acceptable to throw paper into the general waste. Only recently there is a more socioculturally widespread moral value of environmentally sustainable behavior and paper recycling as one important associated implementation. If even a trivial action such as paper recycling can acquire moral significance, how could one justify a meaningful distinction between moral and social behavior and the cognitive systems enabling them? Any kind of social behavior may have implications for other people and could therefore be judged as morally right or wrong. We have therefore argued for a working definition of moral behavior as a distinct form of social behavior with respect to morality-specific motivations rather than to morality-specific effects on others (Moll, de Oliveira-Souza, & Zahn, 2008). Moral motivations have been claimed by 18th-century philosophers of the Scottish enlightenment, such as Francis Hutcheson, who stressed that “benevolence” is what motivates humans to act morally (Bishop, 1996). Moral sentiments, such as “sympathy,” were considered core moral motivations by his successor Adam Smith (Lamb, 1974). The 18th-century German philosopher Immanuel Kant opposed the view that moral actions could be defined on the basis of experiences such as moral sentiments that were conceived of as originating from the external senses (Kant, 1786, pp. 15–17). According to Kant, moral actions are motivated directly by respect (“Achtung”) for the moral law (Kant, 1786, p. 17). The experience of this respect is a direct effect of the moral law on the individual’s will. This respect for the moral law is self-generated and an act of free will rather than determined by moral sentiments as externally evoked experiences. Kant further stressed that respect for the moral law is the impression of a moral value that opposes our selfishness (“self-love”; Kant, 1786, p. 17). Moral actions are those guided by a principle that the individual wants to become a general law (Kant, 1786, p. 52). Thus, moral motivations as defined by these opposing schools of moral philosophy are either the respect for moral rules (Kant) or altruistic moral sentiments (Hutcheson/Smith). Both philosophical schools agree on motivations that can overcome self-interest as the key to defining morality. As empirical neuroscientists, we can describe neural correlates of subjective experiences of moral motivations such as “respect for moral principles” or “moral sentiments.” The causal relationships between these neural correlates of moral motivations and real-life actions are much more difficult to establish. Beyond the realm of pure empirical science is the philosophical debate about free will that is at the core of the dispute between Kant and the moral sentimentalists (for an extensive discussion of the neurophilosophy of free will and its relationship with morality, see Walter, [2001]).

c08.indd 124

7/25/2011 10:10:17 AM

THE NEUROANATOMICAL BASIS OF MORAL MOTIVATIONS

125

The advent of moral cognitive neuroscience was the 19th-century discovery that patients with lesions to ventral parts of the frontal cortex could show marked changes in moral behavior and attitudes (Welt, 1888). Observation and neuropsychological test examination of patients with brain lesions continues to be one of the most important methods of moral cognitive neuroscience. The other main methodological approach is to chart the regional distribution of brain activation during morally relevant stimuli or tasks using functional activation imaging methods such as functional magnetic resonance imaging (fMRI). This chapter will start with an overview of current knowledge on the neuroanatomical basis of moral motivations that we deem to be the most distinctive component of moral neuroscience. We will then describe evidence on the neuroanatomy of sociomoral knowledge and briefly contrast current models of moral reasoning and decision making. We will close with conclusions and delineate future directions for the field.

2.3.2 THE NEUROANATOMICAL BASIS OF MORAL MOTIVATIONS 2.3.2.1

Moral Sentiments and Values as the Basis of Moral Motivations

As mentioned in the Introduction, there is agreement across philosophical schools that motivations that oppose our pure self-interest are at the core of defining moral actions. We will conceive of moral motivations as altruistic motivations throughout this chapter, although moral motivations such as the Kantian respect for or duty to follow a moral principle can go beyond interpersonal altruism, but we think it may have evolved from interpersonal altruistic abilities. Altruistic motivations can be experienced in the form of moral sentiments such as pity/sympathy when observing the suffering of others or as the feeling of guilt for causing harm to another person. The same moral sentiments can be tied to abstract moral values or virtues (e.g., “honesty” and “generosity”), and both interpersonal and value-related moral sentiments can be experienced as motivators of altruistic behavior. Social and moral values have been defined by social psychologists (Schwartz, 1992) as goals that consist of abstract concepts or beliefs that transcend specific situations (contextindependent component), but that also contain emotional salience and guide our behaviour in specific situations (context-dependent component, see also Figures 2.3-2 and 2.3-3). 2.3.2.2

Evolutionary Precursors of Moral Sentiments

Research on pair bonding and attachment in animals has led to new insights into the potential evolutionary precursors of moral sentiments (Moll & Schulkin, 2009). Some species form monogamous partner relationships that depend on neuropeptides such as oxytocin (Insel & Young, 2001). Altruism toward sexual partners or offspring found in nonhuman primates may be the precursors of more remote forms of altruism such as charity donation found in modern humans (Moll & Schulkin, 2009). The brain systems linked to attachment across species were partly overlapping with systems involved in other types of rewards and included the cingulate gyrus,

c08.indd 125

7/25/2011 10:10:17 AM

126

THE NEUROANATOMICAL BASIS OF MORAL COGNITION AND EMOTION

lateral septal nuclei, medial preoptic area, mediobasal hypothalamus, and ventral tegmental area (Depue & Morrone-Strupinsky, 2005; Insel & Young, 2001). 2.3.2.3

Evidence from Patient Lesion Studies

Moral sentiments such as guilt or sympathy/pity have often been observed to be lacking in patients with lesions to the ventral parts of the frontal cortex (Eslinger & Damasio, 1985; Koenigs et al., 2007; Snowden et al., 2001; Welt, 1888) but also in patients with neurodegeneration of the anterior temporal lobes (Rankin et al., 2006; Snowden et al., 2001). It is difficult to interpret these observations as different cognitive deficits could explain a lack of concern for others and direct experimental neuropsychological evidence on disruption of specific moral sentiments but not others is lacking so far. Abnormal social behavior that may be attributable to changes in moral motivations was also observed in patients with subcortical mesolimbic lesions, and there is evidence for an extended fronto-temporo-mesolimbic network of brain regions necessary to support moral behavior (reviewed in (Moll, Zahn, de Oliveira-Souza, Krueger, & Grafman, 2005; see Figure 2.3-1). Gray-matter volumes in key areas of this brain network (bilateral frontopolar, medial orbitofrontal cortex, subgenual frontal region, and bilateral posterior superior temporal sulcus) were associated with the degree of callousness in individuals with developmental psychopathy, who are known to show reduced compassion and guilt (de Oliveira-Souza et al., 2008). 2.3.2.4

Evidence from Functional Imaging Studies

The first functional neuroimaging studies of moral feelings have used pictorial or verbal materials to evoke feelings in different conditions. Participants had to rate the moral relevance of stimuli, and studies compared activation patterns in response to morally relevant compared with morally irrelevant stimuli while controlling for emotional intensity and by using explicit moral judgments (morally right or wrong) or tasks without explicit moral decisions (i.e., implicitly moral tasks). Taken together, the evidence suggests involvement of frontopolar, ventral frontal, anterior temporal, posterior superior temporal sulcus, and mesolimbic regions in morally relevant tasks independently of task demands (reviewed in Moll et al. [2005]). Recently, studies have investigated differences in activation patterns associated with specific moral sentiments. Here, we only report brain regions that have been also associated with changes in moral behavior in patient lesion studies (see Figure 2.3-1) and that were systematically investigated in more than one study. Guilt. Despite some shared components, evidence from social and personality psychology suggests guilt, shame, and embarrassment serve different interpersonal functions (Eisenberg, 2000; O’Connor, Berry, Weiss, & Gilbert, 2002; Tangney, Stuewig, & Mashek, 2007; Tracy & Robins, 2006). Most neuroimaging evidence is available on guilt. Frontopolar cortex activation was consistently observed for guilt when using other-critical feelings (e.g., indignation) as a control condition (Moll et al., 2007; Zahn et al., 2009b), when subtracting embarrassment-evoking

c08.indd 126

7/25/2011 10:10:17 AM

THE NEUROANATOMICAL BASIS OF MORAL MOTIVATIONS

Sequences of actions/events (complex branching, long-term) Sequences actionmotivational state associationsattachmentrelated?

127

Motivational/ Emotional states e.g.“free-floating” anger, anxiety, attachment, fear, hunger Social sensory features: face, voice, body posture, gestures, gaze, etc Sequences of actions/events (complex branching, long-term)

Moral sentiments, values, and “rational” beliefs

Sequential actionmotivational state associationsaversion-related?

Abstract conceptual knowledge of social behaviours: e.g. stingy, honourable qualities of behaviour

Figure 2.3-1. Network of brain regions that show activation during morally relevant tasks and stimuli when compared with morally irrelevant material and that when lesioned may lead to morally inappropriate behavior (adapted from Moll et al., [2005] and (Moll and Schulkin, [2009]). Cortical region: frontopolar cortex (FPC), medial and lateral ventral prefrontal cortex (PFC), right anterior dorsolateral PFC, anterior temporal lobes (aTL), and posterior superior temporal sulcus (pSTS). Subcortical structures include the extended amygdalae, hypothalamus, basal forebrain (especially the preoptic and septal regions), basal ganglia, and midbrain regions. One hypothesis is that integration across these corticolimbic structures gives rise to event–feature–emotion complexes (EFEC) by temporal binding (Moll et al., 2005). The hypothesized neurocognitive components are (1) Sequential knowledge of actions/events represented within PFC subregions. FPC: complex branching of consequences of actions and ventral PFC regions representing associative knowledge of motivational/ emotional states embedded into sequential event/action contexts; (2) social sensory features stored in pSTS (the role of the temporoparietal junction in social spatial representations (Decety & Grezes, 2006) needs to be addressed in future extensions of the model) and abstract (i.e., context-independent) conceptual knowledge of social behavior stored in the anterior temporal cortex, especially in the superior sectors; (3) central motive or basic emotional states, such as “free-floating” anger, attachment, sadness, and sexual arousal (represented by the subcortical limbic structures listed above). The figure also illustrates how moral sentiments, values, and “rational” beliefs can emerge through integration across specific subcomponents of the moral cognition network.

c08.indd 127

7/25/2011 10:10:17 AM

128

THE NEUROANATOMICAL BASIS OF MORAL COGNITION AND EMOTION

statements (Takahashi et al., 2004), or when using anger toward oneself as a control condition (Kedia, Berthoz, Wessa, Hilton, & Martinot, 2008). The first neuroimaging study investigating guilt found anterior cingulate activation dorsally to the genu of the corpus callosum when compared with a neutral condition (Shin et al., 2000). The subgenual portion of the cingulate gyrus was detected as guilt-selective but only when modeling individual differences in either frequency of guilt experience (Zahn et al., 2009b) or empathic concern (Zahn, de Oliveira-Souza, Bramati, Garrido, & Moll, 2009). Subgenual cingulate activation was selective for guilt versus othercritical feelings (indignation) when controlling for valence and conceptual detail (Zahn et al., 2009b). Compassion. Sympathy, pity, and compassion are treated here as synonyms and are closely related to emotional empathy that is, however, usually measured differently and often applied to empathic simulation rather than to feeling pity (for the neural basis of emotional and cognitive empathy, see de Vignemont & Singer, 2006; Decety & Jackson, 2004; Eslinger, 1998; Shamay-Tsoory, Aharon-Peretz, & Perry, 2009). Frontopolar activation was observed while participants felt compassion when compared with neutral conditions (Immordino-Yang, McColl, Damasio, & Damasio, 2009), with self-directed anger (Kedia et al., 2008) or indignation toward others (Moll et al., 2007). The ventral striatum and ventral tegmental area showed higher activity for empathic moral sentiments (compassion and guilt) than for other-critical feelings (disgust and indignation) (Moll et al., 2007). Other-critical (Other-Blaming) Moral Sentiments. When being the victim or observer of other people’s moral violations, humans experience indignation, moral anger, contempt, or moral disgust toward others. These other-critical feelings are vital to enforcing moral rules in societies (Gintis et al., 2008). Contempt was found to be more strongly associated with social exclusion of others in the short and long term, whereas moral anger was associated with short-term attacks but long-term reconciliation in behavioral studies (Fischer & Roseman, 2007). In fMRI studies, moral indignation/anger and contempt/disgust toward others were associated with overlapping activation patterns: lateral orbitofrontal and anterior insular cortex (Moll et al., 2007; Zahn et al., 2009b). Bilateral orbitofrontal cortex activation was more pronounced for indignation and moral disgust than for nonmoral disgust in one study that showed the right amygdala to be more strongly activated for nonmoral than for moral disgust (Moll et al., 2005). Different forms of moral disgust and disgust for behaviors related to health risks for the agent were compared with a morally and emotionally neutral condition in another study (Borg, Lieberman, & Kiehl, 2008). All disgust conditions were associated with activity in the anterior temporal lobes, left lateral orbitofrontal and frontopolar cortices, bilateral amygdalae, and basal ganglia. There was no direct comparison with prosocial moral sentiments such as guilt to investigate whether frontopolar activation was specific for disgust. Another study also reported frontopolar activity for anger toward others, but compared with a condition evoking anger toward oneself (Kedia et al., 2008). Left lateral orbitofrontal and right dorsolateral frontal activations have also been reported in fMRI participants expecting punishment for violating social norms relative to a condition in which they did not expect punishment for those norm violations (Spitzer, Fischbacher, Herrnberger, Gron, & Fehr, 2007). These findings may

c08.indd 128

7/25/2011 10:10:17 AM

THE NEUROANATOMICAL BASIS OF SOCIOMORAL KNOWLEDGE

129

be explained by shared lateral orbitofrontal representations of other people’s indignation toward ourselves and our own indignation toward others. Pride. Pride was deemed to be a self-interested feeling by Adam Smith who distinguished a striving for “dignity” as leading to “ethical improvement” (Lamb, 1974). David Hume recognized “good and bad forms of pride” (Hume, 1777). Neuroimaging studies of pride have investigated the more altruistic variant of pride. Pride-evoking stimuli in one study were found to be related with right posterior superior temporal sulcus and left anterior temporal lobe activation relative to a neutral condition (Takahashi et al., 2008). Activations within the mesolimbic reward system (ventral tegmental area) with its projections to the basal forebrain (posterior septum) and within the ventral frontopolar cortex were reported for pride compared with gratitude and guilt (Zahn et al., 2009b; see Figure 2.3-2). In summary, there is growing evidence for certain fronto-mesolimbic subregions within the moral cognition network to be more strongly activated for some moral sentiments relative to others. Frontopolar activations were among the most consistent for moral sentiments in general (Moll et al., 2005). Those studies that compared prosocial moral sentiments (especially guilt and compassion) with other-critical moral sentiments have demonstrated selective activation for prosocial moral sentiments within the frontopolar cortex. Other-critical (other-blaming) moral sentiments were most consistently associated with lateral orbitofrontal and anterior insular activations when compared with prosocial moral sentiments. Few studies have investigated pride and gratitude, but mesolimbic and basal forebrain regions were found in one study (Figure 2.3-2). A recent fMRI study has shown that moral and social values are associated with activation of fronto-temporo-mesolimbic networks representing the abstract context-independent conceptual detail (within the anterior temporal lobe) and context-dependent moral sentiments (within different fronto-mesolimbic subregions) tied to the same concept (Zahn et al., 2009b; see Figure 2.3-2). This neural architecture may enable us to communicate about moral values such as “honor” across sociocultural groups, even if the feelings and actions we associate with the same value vary. For example, a soldier may associate the action of dying in war as an act of honor and anticipate feeling pride, whereas a pacificist may associate dying during a hunger strike to stop war as an act of honor and anticipate feeling pride as well. Some people may despise the value of honor altogether and feel contempt for honor-driven behavior. All these different people are nevertheless able to understand the common core (context-independent) meaning of the quality of honorable behavior.

2.3.3 THE NEUROANATOMICAL BASIS OF SOCIOMORAL KNOWLEDGE As we illustrated in the Introduction, there is no clear distinction between social and moral knowledge. Social knowledge can be used to promote self-interest or for moral (altruistic) purposes. Social knowledge has been defined as knowledge of one’s own and other people’s minds (Adolphs, 2009). Because of the uncertainties of what comprises “mind” or “mental states,” we prefer to restrict the term social

c08.indd 129

7/25/2011 10:10:18 AM

130

THE NEUROANATOMICAL BASIS OF MORAL COGNITION AND EMOTION

Figure 2.3-2. Using fMRI in healthy participants this study aimed to unravel the neuroanatomical basis of abstract moral and social values (Zahn et al., 2009b). Participants had to imagine actions in accordance with or counter to a value described by a written sentence and to decide whether they would feel pleasantly or unpleasantly about the action. After the scan they rated the unpleasantness/pleasantness on a scale and chose labels that best described their feelings (the analysis compares each moral sentiment versus visual fixation and versus two other moral sentiments; only selective effects were reported). There were four experimental conditions: (1) positive self-agency: e.g., “Tom (first name of participant) acts generously towards Sam (first name of best friend)”—pride in this condition was associated with ventral tegmental, septal, and ventral medial FPC activation (not depicted); (2) positive otheragency: e.g., “Sam acts generously towards Tom“—gratitude in this condition was associated with hypothalamic activation; (3) negative self-agency: e.g., “Tom acts stingily towards Sam”—guilt in this condition was associated with the subgenual cingulate cortex as well as ventral medial FPC activation (not depicted and only when modeling individual frequency of guilt trials), and (4) negative other-agency: e.g., “Sam acts stingily towards Tom”— indignation/anger in this condition was associated with lateral orbitofrontal/insular activation. In the center, one can see the right superior aTL region showing equally strong activation during all moral sentiment and agency contexts; this region increased activity with increasing richness of conceptual detail describing social behavior and is identical to the activation found in a semantic judgment task (Zahn et al., 2007). These results confirmed the right superior anterior temporal lobe as a context-independent store of social conceptual knowledge that allows us to understand the core meaning of social and moral values irrespective of what exact feelings or actions we tie to the value. (See color insert.)

knowledge to denoting nonepisodic (i.e., semantic) knowledge of social sensory properties and social behavior (i.e., functions; see Figure 2.3-3). Wood and Grafman (2003) have pointed to the importance of knowledge of sequences of events and actions for social knowledge and have hypothesized that the ventral portion of the frontal cortex stores this information which could explain abnormal behavior in patients with lesions to this area. Contrary to this view, some studies found intact social knowledge in patients with ventral lesions and have therefore claimed a distributed widespread representation of social knowledge in other cortical areas (Bechara, Damasio, & Damasio, 2000; Eslinger & Damasio, 1985; Saver & Damasio, 1991).

c08.indd 130

7/25/2011 10:10:18 AM

THE NEUROANATOMICAL BASIS OF SOCIOMORAL KNOWLEDGE

131

Social and Moral knowledge

Sensory social knowledge

Knowledge of social behaviours (i.e. functions)

Associative = context-dependent

Sequences of actions/events

Abstract conceptual = context-independent

Motivational/ emotional states

Figure 2.3-3. Hypothetical cognitive-anatomical components of social and moral knowledge are depicted (Moll et al., 2008; Moll et al., 2005; Zahn et al., 2009a; Zahn et al., 2007; Zahn et al., 2009b; see also Figure 2.3-1 for anatomical regions). All components are part of nonepisodic long-term memory (i.e., semantic memory). Sensory social knowledge is hypothesized to be represented in pSTS. Knowledge of social behaviors (i.e., functions) is hypothesized to be separable from sensory social knowledge. The former comprises two anatomically dissociable systems: (1) abstract conceptual (i.e., context-independent) knowledge of social behavior represented in the aTL and (2) associative (i.e., context-dependent) knowledge of social behavior represented in fronto-mesolimbic networks. Associative knowledge of social behaviour comprises (1) knowledge of sequences of social actions (events) within frontopolar and ventral PFC (representing associations of action/event sequences with motivational/ emotional states) and (2) “free-floating” motivational/emotional states represented in subcortical mesolimbic and basal forebrain areas.

Using fMRI, we have recently identified a superior sector within the anterior temporal lobes (especially on the right side) that shows more activation for abstract concepts describing social behavior than concepts describing less socially relevant animal behavior. A subsequent study has found a more middle anterior temporal lobe activation for social concepts compared with concepts describing animal behavior close to the superior temporal sulcus (Ross & Olson, 2010). Previous work has shown that neurodegeneration of the anterior temporal lobes, especially the inferior portions, leads to loss of conceptual knowledge for animate and inanimate objects irrespective of the modality of input (pictorial, olfactory, verbal, and auditory; Patterson, Nestor, & Rogers, 2007). Models of the anterior temporal lobe conceptual representation have therefore argued for an amodal representation of meaning in the anterior temporal lobes (Ralph & Patterson, 2008). The notion that social conceptual knowledge is also important for nonverbal experiences such as feelings was confirmed in a study showing right superior anterior temporal cortex activation for moral sentiments independent of the context of the feeling (pride, guilt, indignation, and gratitude; Zahn et al., 2009b; see Figure 2.3-2). Neurodegeneration of the right superior anterior temporal lobe was associated with loss of social conceptual knowledge with relatively intact conceptual knowledge for less socially relevant material in a study in patients with frontotemporal lobar degeneration (Zahn et al., 2009a).

c08.indd 131

7/25/2011 10:10:18 AM

132

THE NEUROANATOMICAL BASIS OF MORAL COGNITION AND EMOTION

These results confirmed the hypothesis that the social conceptual knowledge representation is independent of the contexts of feelings and probably of the context of sequences of actions/events. Intact context-independent social conceptual knowledge as represented in the superior anterior temporal lobes may explain that patients with ventral frontal lesions have partially retained knowledge of appropriate behavior as long as the gist of the context of the sequence of actions and events is summarized by the person who is testing them (Eslinger & Damasio, 1985). In real life, we are faced with complex parallel chains of possible future events and consequences of our actions. It has been demonstrated that patients with lesions that often involved the frontopolar cortex (compromised in most patients with ventral frontal lobe lesions) were impaired on ordering events into the correct sequence (Sirigu et al., 1996). For example, they could not recognize that one needs to choose the flavor before ordering in the ice cream parlor. The medial frontopolar cortex (Brodmann Area 10) showed activation for sequential judgments of events (which one comes first), and the subregion most activated within this area was dependent on the frequency with which event sequences occurred in real life (Krueger, Moll, Zahn, Heinecke, & Grafman, 2007) and was independent of autobiographical memory retrieval, response time, or emotional intensity. This finding of differences in topographic distribution of activation irrespective of the task, but dependent on the type of content, had been predicted by Wood & Grafman’s (2003) model of the frontal cortex as a store of sequential knowledge of events and actions. Figure 2.3-3 summarizes the hypothesized components of sociomoral knowledge discussed here.

2.3.4 THE NEUROANATOMICAL BASIS OF MORAL REASONING AND DECISION MAKING Functional imaging studies of moral judgment and reasoning found consistent activation of anterior dorsolateral prefrontal cortex (PFC) and frontopolar cortex (Greene, Nystrom, Engell, Darley, & Cohen, 2004; Greene, Sommerville, Nystrom, Darley, & Cohen, 2001; Moll, Eslinger, & de Oliveira-Souza, 2001). The current controversy is how to interpret these activations and what functional role different frontal subregions have in moral cognition. One view is that reasoning (assumed to be purely “cognitive”) and emotion (equated with subjective experience of feelings) depend on anatomically distinct systems (cognition in the PFC and parietal areas, and emotion in limbic regions), and that cognition and emotion can be placed in conflict and can compete with each other during decisions (McClure, Botvinick, Yeung, & Cohen, 2006). Difficult moral decisions (moral dilemmata) are usually used to test the predictions of this dual-process model of moral cognition. For example, when faced with the decision to push an innocent man to death on the tracks of a runaway trolley to save five other individuals, the emotional system is predicted to prefer avoiding this choice, usually the more intuitive response; the cognitive system in contrast is predicted to enforce the “utilitarian” choice, the one that leads to the maximum overall benefit, the rational choice (Greene et al., 2004). The “dual-process model” thus predicts that the decision to push the man on the tracks is the result of cognitive brain areas successfully

c08.indd 132

7/25/2011 10:10:18 AM

THE NEUROANATOMICAL BASIS OF MORAL REASONING

133

overcoming or suppressing the emotional bias of refraining from this action—an extension of the influential cognitive control model (Miller & Cohen, 2001) to moral cognition. Patients with focal brain lesions involving the ventral medial PFC bilaterally were recently tested (Ciaramelli, Muccioli, Ladavas, & di Pellegrino, 2007; Koenigs et al., 2007) using these “trolley-type” dilemmas. Patients more frequently exhibited “utilitarian” decisions than healthy controls in high-conflict scenarios (highly emotionally aversive decisions that result in greater overall benefit, e.g., more lives saved). This increased tendency to make “utilitarian” (rational) decisions in patients could be interpreted in different ways. One possibility is that general emotional blunting and reduced autonomic signaling attributed to ventral medial PFC damage may have led to an increased proneness to “rational” decisions—an interpretation in keeping with the somatic-marker hypothesis (Bechara et al., 2000). This possibility, however, is not supported by the results of another study (Koenigs & Tranel, 2007) investigating the same group of patients using the two-person ultimatum game. In this game (participants anonymously interact only once), participants must decide between accepting an unfair but financially rewarding proposal (the economically “rational” choice), or rejecting the offer to punish the unfair player (the “emotional” choice). Patients with ventral medial PFC lesions decided as if they were more “emotional” than controls because they rejected unfair offers more often, a response normally accompanied by anger. In summary, patients with lesions to the ventral medial PFC seemed to decide more “rationally” in moral dilemmas and more “emotionally” in economic interactions. We therefore conclude that the pattern of abnormalities in these patients can neither be accounted for by a single mechanism of overall emotional blunting as predicted by the somatic marker model, nor by the dual-process model, in which dorsal cortical cognition suppresses ventral cortical–subcortical emotion (Greene et al., 2004) . A more coherent explanation of these findings is offered by a representational model of the frontal cortex (Wood & Grafman, 2003) adopted in our extension of this model to moral cognition (Moll et al., 2005). If the frontal cortex is conceived of as a store of information such as any other cortical area, then the prediction follows that there should be a topographic coding depending on the contents and/ or format of represented information as can be found, for example, in the motor cortex (Wood & Grafman, 2003). The fMRI findings reviewed earlier of stronger activation of the frontopolar cortex and subgenual region (parts of the ventral medial PFC) for prosocial moral sentiments (guilt and compassion) than for othercritical moral sentiments (indignation and contempt toward others) could explain a selective decrease in the ability to experience guilt and compassion with preserved other-critical feelings in patients with ventral medial PFC lesions. This would explain increased anger in the context of unfair offers and decreased guilt/compassion when faced with moral dilemmata. Our model of moral cognition has stressed functional integration of information between subcortical mesolimbic and fronto-temporal cortical areas as the correlate of reasoning and emotion (Moll et al., 2005). According to this fronto-temporomesolimbic integration model of moral cognition, there is no opposition between “emotions” and “rational thoughts,” but there is competition between different fronto-temporo-mesolimbic association complexes representing subjective

c08.indd 133

7/25/2011 10:10:18 AM

134

THE NEUROANATOMICAL BASIS OF MORAL COGNITION AND EMOTION

experiences that we may sometimes call “feeling” or “rational thought,” “rule,” or “value” depending on how vividly we experience emotional/motivational states represented in subcortical regions or how long we have activated this representation or how detailed and vividly we have activated abstract rule and conceptual representations in fronto-temporal cortical areas. The fronto-temporo-mesolimbic integration view of moral cognition draws on research on the neural correlates of consciousness that have demonstrated that subjective experiences depend on temporal binding of neural activity in large-scale distributed networks rather than on activity in isolated areas (Tononi & Koch, 2008). We have argued that the subcortical mesolimbic system is most likely to represent motivational states, such as attachment or hunger, in a “free-floating” rather than in a contextualized fashion in which we almost always experience them (e.g., “attachment to our parents” “or hunger for Belgian chocolate”). The contextualization of a motivational/emotional state with a goal is necessary to motivate behavior, and if we assume that goal representations require fronto-temporal areas, motivations will be represented in fronto-temporomesolimbic circuits rather than solely in subcortical mesolimbic brain areas. One important shortcoming of the models of moral cognition and emotion presented so far is that patients with impaired moral behavior are often able to serve their own self-interest relatively well; this is especially apparent in individuals with developmental psychopathy. Although some of these dissociations may be accounted for by higher cognitive complexity of moral behavior, these observations strongly suggest at least partly dissociable representations of moral and selfish motivations in the human brain. Therefore, the question of what brain areas are relevant to moral decision making is about competition between brain circuits coding for an altruistic sentiment or for the respect of a moral principle versus those circuits coding for a selfish motivation. Building on earlier work on cooperation in neuroeconomic games, the first study to probe altruistic motivations beyond the direct interpersonal sphere by using charity donation found indeed support for brain regions that were selectively activated for altruistic decisions to donate compared with decisions leading to pure monetary gain for oneself (Moll et al., 2006; see Figure 2.3-4). There were two larger sectors showing selectivity for altruistic decisions, the septal-subgenual cingulate and the anterior orbitofrontal/frontopolar region. Subsequent studies have confirmed activation of the septal region and septal part of the nucleus accumbens for donation behavior (Harbaugh, Mayr, & Burghart, 2007; Hsu, Anen, & Quartz, 2008). This region has also been found to be activated for unconditional trust in economic interactions (Krueger, McCabe, Moll, et al., 2007). Future studies are needed to tease apart functional specializations within these brain regions and whether there are indeed subsectors within these areas selective for moral motivations.

2.3.5

CONCLUSIONS AND OUTLOOK

In this chapter, we have outlined the consistent involvement of fronto-temporomesolimbic networks in moral cognition and emotion. Major controversies surround the exact functions and subdivisions of brain areas within these networks. One shortcoming of early research has been the lack of neuropsychological and

c08.indd 134

7/25/2011 10:10:18 AM

CONCLUSIONS AND OUTLOOK

135

Figure 2.3-4. Brain regions activated when participants donated or opposed to donate to charitable organizations during fMRI (Moll et al., 2006) (a) Both pure monetary rewards and decisions to donate (with or without personal financial costs) activated the mesolimbic reward system, including the ventral tegmental area (VTA) and the ventral and dorsal striatum. (b) The septal–subgenual region (SG), however, was selectively activated by decisions to donate, as compared with pure monetary rewards (both by costly and noncostly decisions; conjunction analysis). The lateral orbitofrontal cortex (latOFC) was activated by decisions to oppose charities. This activation extended to the anterior insula and to the inferior dorsolateral PFC, and it was present for both costly and noncostly decisions (conjunction analysis). The FPC and ventral medial PFC were activated for costly decisions (when voluntarily sacrificing one’s own money either to donate to a charity or to oppose it (conjunction analysis). (See color insert.)

functional imaging paradigms that controlled sufficiently for nuisance variables that may account for the variability of relative activations of subregions within the network. Future research needs to design well-controlled experiments that test specific functional hypotheses about the role of moral cognition subregions. Patient lesion studies, are indispensable to validate results from functional imaging studies, and new methods such as repetitive transcranial magnetic stimulation may provide additional insights. Electrophysiological methods such as magnetic or electroencephalography are needed to test the temporal dynamics of moral cognition and emotion. Future applications in moral education as well as the understanding and treatment of neuropsychiatric disorders are immediate areas of societal impact for this rapidly expanding and stimulating area of investigation.

c08.indd 135

7/25/2011 10:10:18 AM

136

THE NEUROANATOMICAL BASIS OF MORAL COGNITION AND EMOTION

REFERENCES Adolphs, R. (2009). The social brain: Neural basis of social knowledge. Annu Rev Psychol 60, 693–716. Bechara, A., Damasio, H., & Damasio, A. R. (2000). Emotion, decision making and the orbitofrontal cortex. Cerebr Cortex 10(3), 295–307. Bishop, J. D. (1996). Moral motivation and the development of Francis Hutcheson’s philosophy. J Hist Ideas 57(2), 277–295. Borg, J. S., Lieberman, D., & Kiehl, K. A. (2008). Infection, incest, and iniquity: Investigating the neural correlates of disgust and morality. J Cognit Neurosci 20(9), 1529–1546. Ciaramelli, E., Muccioli, M., Ladavas, E., & di Pellegrino, G. (2007). Selective deficit in personal moral judgment following damage to ventromedial prefrontal cortex. Soc Cognit Affect Neurosci 2(2), 84–92. de Oliveira-Souza, R., Hare, R. D., Bramati, I. E., Garrido, G. J., Ignácio, F. A., et al. (2008). Psychopathy as a disorder of the moral brain: Fronto-temporo-limbic grey matter reductions demonstrated by voxel-based morphometry. Neuroimage 40(3), 1202–1213. de Vignemont, F., & Singer, T. (2006). The empathic brain: How, when and why? Trends Cognit Sci 10(10), 435– 441. Decety, J., & Grezes, J. (2006). The power of simulation: Imagining one’s own and other’s behavior. Brain Res 1079, 4–14. Decety, J., & Jackson, P. L. (2004). The functional architecture of human empathy. Behav Cognit Neurosci Rev 3, 71–100. Depue, R. A., & Morrone-Strupinsky, J. V. (2005). A neurobehavioral model of affiliative bonding: Implications for conceptualizing a human trait of affiliation. Behav Brain Sci 28(3), 313–350. Eisenberg, N. (2000). Emotion, regulation, and moral development. Annu Rev Psychol 51, 665–697. Eslinger, P. J. (1998). Neurological and neuropsychological bases of empathy. Eur Neurol 39(4), 193–199. Eslinger, P. J., & Damasio, A. R. (1985). Severe disturbance of higher cognition after bilateral frontal lobe ablation: Patient EVR. Neurology 35(12), 1731–1741. Fischer, A. H., & Roseman, I. J. (2007). Beat them or ban them: The characteristics and social functions of anger and contempt. J Pers Soc Psychol 93(1), 103–115. Gintis, H., Henrich, J., Bowles, S., Boyd, R., & Fehr, E. (2008). Strong reciprocity and the roots of human morality. Soc Justice Res 21(2), 241–253. Greene, J. D., Nystrom, L. E., Engell, A. D., Darley, J. M., & Cohen, J. D. (2004). The neural bases of cognitive conflict and control in moral judgment. Neuron 44(2), 389–400. Greene, J. D., Sommerville, R. B., Nystrom, L. E., Darley, J. M., & Cohen, J. D. (2001). An fMRI investigation of emotional engagement in moral judgment. Science 293(5537), 2105–2108. Harbaugh, W. T., Mayr, U., & Burghart, D. R. (2007). Neural responses to taxation and voluntary giving reveal motives for charitable donations. Science 316(5831), 1622–1625. Hsu, M., Anen, C., & Quartz, S. R. (2008). The right and the good: Distributive justice and neural encoding of equity and efficiency. Science 320(5879), 1092–1095. Hume, D. (1777). An Enquiry into the Principles of Morals, vol. 2. London: T. Cadell. Immordino-Yang, M. H., McColl, A., Damasio, H., & Damasio, A. (2009). Neural correlates of admiration and compassion. Proc Nat Acad Sci U S A 106(19), 8021–8026. Insel, T. R., & Young, L. J. (2001). The neurobiology of attachment. Nat Rev Neurosci 2(2), 129–136.

c08.indd 136

7/25/2011 10:10:18 AM

REFERENCES

137

Kant, I. (1786). Grundlegung zur Metaphysik der Sitten, 2nd ed. Riga, Latvia: Johann Friedrich Hartknoch. Kedia, G., Berthoz, S., Wessa, M., Hilton, D., & Martinot, J. L. (2008). An agent harms a victim: A functional magnetic resonance imaging study on specific moral emotions. J Cognit Neurosci 20(10), 1788–1798. Koenigs, M., & Tranel, D. (2007). Irrational economic decision-making after ventromedial prefrontal damage: Evidence from the Ultimatum Game. J Neurosci 27(4), 951–956. Koenigs, M., Young, L., Adolphs, R., Tranel, D., Cushman, F., et al. (2007). Damage to the prefrontal cortex increases utilitarian moral judgements. Nature 446(7138), 908–911. Krueger, F., McCabe, K., Moll, J., Kriegeskorte, N., Zahn, R., Strenziok, M., Heinecke, A., & Grafman J. (2007). Neural correlates of trust. Proc Natl Acad Sci U S A 104(50), 20084–20089. Krueger, F., Moll, J., Zahn, R., Heinecke, A., & Grafman, J. (2007). Event frequency modulates the processing of daily life activities in human medial prefrontal cortex. Cereb Cortex 17(10), 2346–2353. Lamb, R. B. (1974). Adam Smith’s system: Sympathy not self-interest. J Hist Ideas 35(4), 671–682. McClure, S. M., Botvinick, M. M., Yeung, J. D., & Cohen, J. D. (2006). Conflict monitoring in cognition-emotion competition. Handbook of Emotion Regulation, edited by J. J. Gross. New York: Guilford Press. Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annu Rev Neurosci 24, 167–202. Moll, J., de Oliveira-Souza, R., Garrido, G. J., Bramati, I. E., Caparelli-Daquer, E. M. A., et al. (2007). The self as a moral agent: Linking the neural bases of social agency and moral sensitivity. Soc Neurosci 2(3 & 4), 336–352. Moll, J., de Oliveira-Souza, R., Moll, F. T., Ignacio, F. A., Bramati, I. E., et al. (2005). The moral affiliations of disgust: A functional MRI study. Cognit Behav Neurol 18(1), 68–78. Moll, J., de Oliveira-Souza, R., & Zahn, R. (2008). The neural basis of moral cognition: Sentiments, concepts, and values. Ann N Y Acad Sci 1124(1), 161–180. Moll, J., Eslinger, P. J., & de Oliveira-Souza, R. (2001). Frontopolar and anterior temporal cortex activation in a moral judgment task: Preliminary functional MRI results in normal subjects. Arq Neuropsiquiatr 59(3-B), 657–664. Moll, J., Krueger, F., Zahn, R., Pardini, M., De Oliveira-Souza, R., & Grafman, J. (2006). Human fronto-mesolimbic networks guide decisions about charitable donation. Proc Natl Acad Sci U S A 103, 15623–15628. Moll, J., & Schulkin, J. (2009). Social attachment and aversion in human moral cognition. Neurosci Biobehav Rev 33(3), 456– 465. Moll, J., Zahn, R., de Oliveira-Souza, R., Krueger, F., & Grafman, J. (2005). Opinion: The neural basis of human moral cognition. Nat Rev Neurosci 6(10), 799–809. Nowak, M. A., & Sigmund, K. (2005). Evolution of indirect reciprocity. Nature 437(7063), 1291–1298. O’Connor, L. E., Berry, J. W., Weiss, J., & Gilbert, P. (2002). Guilt, fear, submission, and empathy in depression. J Affect Disord 71(1–3), 19–27. Patterson, K., Nestor, P. J., & Rogers, T. T. (2007). Where do you know what you know? The representation of semantic knowledge in the human brain. Nat Rev Neurosci 8(12), 976–987. Ralph, M. A. L., & Patterson, K. (2008). Generalization and differentiation in semantic memory—Insights from semantic dementia. Year in Cognitive Neuroscience 2008 1124, 61–76. Rankin, K. P., Gorno-Tempini, M. L., Allison, S. C., Stanley, C. M., Glenn, S., et al. (2006). Structural anatomy of empathy in neurodegenerative disease. Brain 129(11), 2945–2956.

c08.indd 137

7/25/2011 10:10:18 AM

138

THE NEUROANATOMICAL BASIS OF MORAL COGNITION AND EMOTION

Ross, L. A., & Olson, I. R. (2010). Social cognition and the anterior temporal lobes. NeuroImage 49(4), 3452–3462. Saver, J. L., & Damasio, A. R. (1991). Preserved access and processing of social knowledge in a patient with acquired sociopathy due to ventromedial frontal damage. Neuropsychologia 29(12), 1241–1249. Schwartz, S. H. (1992). Universals in the content and structure of values—theoretical advances and empirical tests in 20 countries. Adv Exp Soc Psychol 25, 1–65. Shamay-Tsoory, S. G., Aharon-Peretz, J., & Perry, D. (2009). Two systems for empathy: A double dissociation between emotional and cognitive empathy in inferior frontal gyrus versus ventromedial prefrontal lesions. Brain 132, 617–627. Shin, L. M., Dougherty, D. D., Orr, S. P., Pitman, R. K., Lasko, M., et al. (2000). Activation of anterior paralimbic structures during guilt-related script-driven imagery. Biol Psychiatry 48(1), 43–50. Sirigu, A., Zalla, T., Pillon, B., Grafman, J., Agid, Y., et al. (1996). Encoding of sequence and boundaries of scripts following prefrontal lesions. Cortex 32(2), 297–310. Snowden, J. S., Bathgate, D., Varma, A., Blackshaw, A., Gibbons, Z. C., et al. (2001). Distinct behavioural profiles in frontotemporal dementia and semantic dementia. J Neurol Neurosurg Psychiatr 70(3), 323–332. Spitzer, M., Fischbacher, U., Herrnberger, B., Gron, G., & Fehr, E. (2007). The neural signature of social norm compliance. Neuron 56(1), 185–196. Takahashi, H., Matsuura, M., Koeda, M., Yahata, N., Suhara, T., et al. (2008). Brain activations during judgments of positive self-conscious emotion and positive basic emotion: Pride and joy. Cerebr Cortex 18(4), 898–903. Takahashi, H., Yahata, N., Koeda, M., Matsuda, T., Asai, K., et al. (2004). Brain activation associated with evaluative processes of guilt and embarrassment: An fMRI study. Neuroimage 23(3), 967–974. Tangney, J. P., Stuewig, J., & Mashek, D. J. (2007). Moral emotions and moral behavior. Annu Rev Psychol 58, 345–372. Tononi, G., & Koch, C. (2008). The neural correlates of consciousness—An update. Year in Cognitive Neuroscience 2008 1124, 239–261. Tracy, J. L., & Robins, R. W. (2006). Appraisal antecedents of shame and guilt: Support for a theoretical model. Pers Soc Psychol Bull 32(10), 1339–1351. Walter, H. (2001). Neurophilosophy of Free Will: From Libertarian Illusions to a Concept of Natural Autonomy. Boston, MA: MIT Press. Welt, L. (1888). Über Charakterveränderungen des Menschen. Dtsch Arch Klin Med 42, 339–390. Wood, J. N., & Grafman, J. (2003). Human prefrontal cortex: Processing and representational perspectives. Nat Rev Neurosci 4(2), 139–147. Zahn, R., de Oliveira-Souza, R., Bramati, I., Garrido, G., & Moll, J. (2009). Subgenual cingulate activity reflects individual differences in empathic concern. Neurosci Lett 457(2), 107–110. Zahn, R., Moll, J., Iyengar, V., Huey, E. D., Tierney, M., et al. (2009a). Social conceptual impairments in frontotemporal lobar degeneration with right anterior temporal hypometabolism. Brain 132(3), 604–616. Zahn, R., Moll, J., Krueger, F., Huey, E. D., Garrido, G., et al. (2007). Social concepts are represented in the superior anterior temporal cortex. Proc Natl Acad Sci U S A 104(15), 6430–6435. Zahn, R., Moll, J., Paiva, M. M. F., Garrido, G., Krueger, F., et al. (2009b). The neural basis of human social values: Evidence from functional MRI. Cereb. Cortex 19(2), 276–283.

c08.indd 138

7/25/2011 10:10:18 AM

2.4 ENVY AND SCHADENFREUDE: THE NEURAL CORRELATES OF COMPETITIVE EMOTIONS JONATHAN DVASH

AND

SIMONE G. SHAMAY-TSOORY

University of Haifa, Haifa, Israel

2.4.1 THE EMOTIONAL REACTIONS TO SOCIAL COMPARISON A large body of evidence concerning social comparison processes indicates that the emotional state may be affected not only by one’s actual state but also by one’s relative state as compared with others (Adams, 1963; Fehr & Schmidt, 1999; Festinger, 1954; Homans, 1961; Stouffer, 1949). The emotional reactions to the success or failure of others can vary greatly toward different people as well as toward the same person. When another person experiences a failure or success, our emotional reaction can take various courses, ranging from envy about the other’s possessions to sympathy for the other’s loss of possessions or life savings, and even to pleasure at seeing an arrogant leader fall (Ben-Ze’ev, 2000). These emotions, involving twoperson situations in which one’s emotion depends on the other’s lot, are classified by some as fortune-of-others emotions (Ortony & Collins, 1988) or as social comparison based emotions (Smith, 2000). It has been suggested that when facing a social agent’s emotional or mental state, four major types of emotional reactions may emerge: envy, schadenfreude (pleasure at another’s misfortune), negative empathy (sympathy, pity) or positive empathy (happy for). These four social comparison based emotions can be divided into cooperative-oriented emotions, referring to both positive and negative empathy, and competitive-oriented emotions, including envy and schadenfreude. In regard to empathy, the affective tone of the response to another person’s fortune may be either negative or positive (Royzman & Kumar, 2001), but it is in any case congruent

From DNA to Social Cognition, First Edition. Edited by Richard Ebstein, Simone Shamay-Tsoory, and Soo Hong Chew. © 2011 Wiley-Blackwell. Published 2011 by John Wiley & Sons, Inc.

139

c09.indd 139

7/25/2011 10:10:19 AM

140

ENVY AND SCHADENFREUDE Positive

Positive Empathy

Schadenfreude

Cooperative

Competitive Negative Empathy

Envy

Negative

Figure 2.4-1. The relationship between valence and level of cooperativeness in social comparison based emotions.

with that of the other person’s perceived emotional response. Congruence refers to the valence of the emotion, that is, negative when the other’s fortune is negative and positive when the other’s fortune is positive (Batson, 1991). Thus, we propose here a distinction between the social comparison based emotions in accordance with the valence of one’s emotional reaction (positive/negative) and its level of cooperativeness (cooperative/competitive). A schematic depiction of this distinction is illustrated in Figure 2.4-1. The neural correlates of basic emotions, such as fear, anger, happiness and disgust, have been extensively documented in the literature (for a review, see Phan, Wager, Taylor, & Liberzon, 2002), but the neuroanatomical correlates of social comparison based emotions have only recently started to receive attention. Moreover, to date, most of the studies concerning social comparison based emotions have been focused on empathic cooperative emotions. However, these emotions will receive little attention in this chapter and will be described only as a framework for the competitive social comparison subtype. Rather, the focus of this chapter will be on the neuroanatomical and biological bases of envy and schadenfreude within the larger framework of social comparison based emotions.

2.4.2 ENVY AND SCHADENFREUDE—AN EVOLUTIONARY PERSPECTIVE It has been suggested that the evolution of social understanding may have emerged partly from competitive situations in which resources are limited and the understanding of competitors provides individuals with a selective advantage (Hare, Call, & Tomasello, 2001). Throughout the evolution of social behavior, it may have become essential for individuals to compare their own state and pay-offs with those of others. This self–other comparison and competitive behavior may be critical for various purposes, such as social cooperation (Brosnan, Schiff, & de Waal, 2005) and

c09.indd 140

7/25/2011 10:10:19 AM

SOCIAL COMPARISON BASED EMOTIONS

141

social ranking (Garcia, Tor, & Gonzalez, 2006). The adaptive nature of social comparison stems from the ability to recognize by it where one stands in the social hierarchy and to act accordingly. Competitive emotions are markers for the position of oneself in the social hierarchy and are suggested to have very early phylogenetic roots (Gilbert, Price, & Allan, 1995; Smith, 2000). The ability to compare one’s own rewards, efforts, and abilities with those of others is not unique to humans and can be found even in capuchin monkeys. In a study by Brosnan and de Waal (2003), the results showed that capuchin monkeys respond negatively when they are treated inequitably as compared with a group mate. Specifically, the monkeys were less willing to participate in a simple task if their peers received a greater reward for the same effort. These interesting results were further examined in a later study (van Wolkenten, Brosnan, & de Waal, 2007), which showed that the monkeys’ rejection of unequal pay was dependent on the effort required to invest in the task. Monkeys were willing to participate as long as a slight inequality was taking place, just as long as they received a reward. Nevertheless, they refused to participate when required to invest greater effort for the same reward. Even lower performance was observed when their partners received greater rewards for less effort (Brosnan & de Waal, 2003; van Wolkenten, et al., 2007). Rudimentary social comparison processes and a sense of fairness may even be found in dogs (Range, Horn, Viranyi, & Huber, 2009) and mice (Langford et al., 2006). In Range et al.’s (2009) recent study, it was found that trained dogs performed a trick regardless of whether they received a reward. However, lower participation rates were evident when they saw another dog receiving a reward while they received none. These results suggest the presence of sensitivity toward unequal reward distribution in a nonprimate species. Furthermore, the dogs showed more symptoms of stress in the inequality condition, that is, when another dog receives a reward while the onlooker dog receives none. Contrary to primates and monkeys, in dogs there was no effect of inequality when it was a matter of different value rewards, such as a sausage versus a piece of bread. Therefore, the authors propose that dogs have a more primitive version of sensitivity to inequality. Given the importance of these emotions in social interactions among humans, as well as in phylogenetically earlier species, we can speculate that social comparison based emotions have an evolutionary necessity that may be mediated by specialized neural networks.

2.4.3

SOCIAL COMPARISON BASED EMOTIONS

Empathy allows an automatic sharing of the emotional states of others, which is essential for the regulation of coordinated activity and cooperation toward shared goals (de Waal, 2008). The results of research on cooperative emotions suggest that they are related to parts of the neuronal network involved in processing that same state in oneself, namely simulation processes (Jackson, Meltzoff, & Decety, 2005; Jackson, Rainville, & Decety, 2006; Preston & de Waal, 2003). These “shared circuits” include such regions as the inferior frontal gyrus (IFG), the frontal operculum (IFO), the anterior insula (AI), and the anterior cingulate cortex (ACC). It has been suggested that these regions act as a translator for actions, sensations, and emotions

c09.indd 141

7/25/2011 10:10:19 AM

142

ENVY AND SCHADENFREUDE

from the experience of others into the neural language of our own actions, sensations, and emotions (Keysers & Gazzola, 2006). Nevertheless, it has been demonstrated that these empathic neural responses may be modulated by the perceived fairness of the protagonist (Singer et al., 2006). In Singer et al.’s (2006) study, participants engaged in a Prisoner’s Dilemma game with opponents who either played fairly or unfairly. According to the activation in particular brain regions, including the AI and ACC, the results demonstrate at the neurobiological level that the appraisal of other people’s social behavior may affect the emotional reaction to their fortune and may suggest a positive affective response to watching justice being done. Indeed, those men who exhibited a stronger desire for revenge also showed greater activation in the ventral striatum, a region associated with reward processing. This is in line with previous findings demonstrating the role of the striatum in altruistic punishment (De Quervain et al., 2004). Although Singer et al. (2006) linked the elevated striatal activation in their study to the desire for revenge, it may be plausible to interpret these findings as the social comparison emotional reaction of schadenfreude. Contrary to de Quervain et al.’s (2004) study, which measured an actual action of revenge, the subjects in Singer et al’s (2006) study were passive in generating the other’s misfortune, as characteristically experienced in schadenfreude. Thus, this study shows how certain contexts can transform one emotion in the group of emotions into another—in this case, negative empathy to schadenfreude. 2.4.3.1

Competitive Emotions

Contrary to cooperative emotions, which are linked with prosociality, competitive emotions, such as envy and schadenfreude, often prompt aggressive behavior (Schoeck, 1987; Smith & Kim, 2007) and are linked with a motivation to sacrifice one’s own outcomes in order to diminish another’s relative advantage (Berke, 1988; Parks, Rumble, & Posey, 2002). These emotions often emerge in competitive situations, including invidious comparisons (Ortony & Collins, 1988; Smith et al., 1996). Envy is the negative reaction to another person’s good fortune. It comprises the wish to have another person’s possession or success and/or the wish that the other person did not possess the desired characteristic or object (Ben-Ze’ev, 2000; Parrott, 1991). Envy is characterized by a blend of accompanying emotions, such as inferiority, hostility, and resentment for the other person (Smith & Kim, 2007). The resentment is inherent in envy as a reaction to an advantage that feels unfair, although it may be fair according to social standards (Ben-Ze’ev, 2000; Smith & Kim, 2007). However, when a person judges an event to be undesirable for another person and is pleased about it, that the person may be described as gloating (Ortony & Collins, 1988). This particular type of emotional reaction is also termed as “pleasure at another’s misfortune” or schadenfreude, a German word literally denoting joy about the shame or misfortune of another (Ben-Ze’ev, 1992, 2000; Smith et al., 1996). The relationship between envy and schadenfreude has been empirically documented in the literature (Ben-Ze’ev, 2000; Smith & Kim, 2007; Smith et al., 1996). Some have even suggested that schadenfreude is one facet of envy (Brigham, 1997; Smith et al., 1996). In several studies conducted by Smith and his colleagues, participants reported more pleasure in witnessing a misfortune befalling an advantaged person rather

c09.indd 142

7/25/2011 10:10:19 AM

NEURAL SUBSTRATES OF COMPETITIVE EMOTIONS

143

than a less advantaged person. This effect on schadenfreude was found to be mediated by participants’ envy toward the target (e.g., Smith & Kim, 2007).

2.4.4 NEURAL SUBSTRATES OF COMPETITIVE EMOTIONS: ENVY AND SCHADENFREUDE It may be proposed that two neural networks participate in the experience of competitive emotions, such as envy and schadenfreude. The first is the reward system, which mediates motivation and the experience of pleasure and displeasure; the second is the mentalizing network, which processes the other’s mental and emotional state. Hence, an interaction between these two systems is at the heart of the experience of competitive emotions. 2.4.4.1 The Role of the Reward and Punishment Networks The role of rewards and punishments in emotions is crucial (Rolls, 2005). Several studies have shown that when people gain valuable goods (such as money), activation can be observed in the brain areas described as “reward areas” (Erk, Spitzer, Wunderlich, Galley, & Walter, 2002; Kenning & Plassmann, 2005). Recent studies show that the reward system is involved in situations concerning not merely one’s own but also another’s gain (Dvash, Gilam, Ben-Ze’ev, Hendler, & Shamay-Tsoory, 2010; Fliessbach et al., 2007; Qiu et al., 2010; Zink et al., 2008) or pain (Han et al., 2009; Singer et al., 2006; Xu, Zuo, Wang, & Han, 2009). These are compatible with various characterizations of social comparison based emotions (see Ben-Ze’ev, 2000), indicating the participation of the reward system in mediating these emotions. As mentioned, recent studies suggest a correlation between the role of the ventral striatum and the value of a reward in social interactions (Fliessbach et al., 2007; Lieberman & Eisenberger, 2009; Singer et al., 2006). A recent study by Fliessbach and colleagues (2007) provided the first neurophysiological evidence for the importance of social comparison to reward processing in the human brain. The study investigated the impact of social comparisons on reward-related brain activity using functional magnetic resonance imaging (fMRI). Pairs of subjects were scanned while performing an estimation task that entailed monetary rewards in the case of correct performance. Results showed that a variation in the comparison subject’s payment affected blood oxygenation level-dependent (BOLD) responses in the ventral striatum, with responses varying according to the ratio between one subject’s reward and the other subject’s reward. The BOLD response was the lowest for the conditions in which less money was earned, followed by the conditions with equal payment, whereas the conditions in which more money was earned yielded the greatest response. Furthermore, relative comparisons seemed to be even more important than the absolute amounts of money earned (Fliessbach et al., 2007). Qiu and colleagues (2010) used a variation of the task used by Fliessbach et al. (2007) in their recording of event-related potentials (ERPs) to investigate the electrophysiological correlates of the impact of social comparisons on the neural substrates of reward processing. The results point to a role for several ERP components (N350–550, LNC) and brain areas (the medial

c09.indd 143

7/25/2011 10:10:19 AM

144

ENVY AND SCHADENFREUDE

CHOOSE A DOOR 1

2

3 You +4

The other player’s turn please wait... You +4

Other +16

Figure 2.4-2. Schematic depiction of a single trial setting (Dvash et al., 2010). (See color insert.)

frontal/ACC and the caudate nucleus) in reward processing under social comparison conditions. Focusing on the social aspect of social comparison, Zink and colleagues (2008) used fMRI to investigate the neural mechanisms that process social superiority and inferiority in humans. Participants performed a simple task for monetary reward simultaneously with one of two other players. Before the scanning session, an initial test run was performed to create a social hierarchy. The performance of one other player was identified as better (“three-star player”) and one other player as worse (“one-star player”) than the participant (“two-star player”). When viewing a more superior player, as compared with viewing a more inferior player, brain activity was found to be significantly greater in the occipital/parietal cortex, the ventral striatum, and the parahippocampal cortex, thereby highlighting the role of these brain regions in the neural encoding of hierarchical rank. A recent study by Dvash and colleagues (2010) investigated the role of the reward network in envy and schadenfreude. The study examined the emotional and neural correlates of upward social comparison (comparison with those who have more) and downward social comparison (comparison with those who have less). Two experiments were conducted with subjects in an interactive game of chance, in which a putative player won or lost more money than the participant. This gameof-chance task, which was designed and validated as evoking social comparison and its accompanying emotional outcomes, is depicted in Figure 2.4-2. In this task, three doors were presented from which subjects had to choose one. This decision was followed by presentation of the participant’s outcome. After a short interval, the other (putative) player’s outcome was presented adjacent to that of the participant. In this example, a relative loss condition is shown. An absolute gain of 4 NIS (New Israeli Shekel), equivalent to $1, was followed by the other’s gain of 16 NIS ($4), thus producing a relative loss. The results showed that even when participants lost money, they expressed joy and schadenfreude if the other player had lost more money. Interestingly, when they actually won money, but the other player had won more, they expressed envy. This

c09.indd 144

7/25/2011 10:10:19 AM

NEURAL SUBSTRATES OF COMPETITIVE EMOTIONS

145

pattern was also demonstrated in a differential BOLD response in the ventral striatum. Comparing the activations between an actual gain and a relative gain (when the subject won more or lost less money than the other putative player) suggests that even when a person loses money, merely adding information about another person’s greater loss may increase ventral striatum activations to a point where they resemble those of an actual gain. In other words, even a loss may seem like a gain when compared with another’s greater loss. Likewise, winning money elicited activations in the ventral striatum that resembled those of an actual loss when compared with another’s greater gain. That is, even a gain may seem like a loss when compared with the other’s greater gain. These results further support the role of the ventral striatum in social comparison based emotions and suggest that an absolute gain is processed in a similar pattern as relative gain and that an absolute loss is processed similarly to a relative loss. Others have differentiated between positive and negative affective responses, linking the ventral striatum to the positive affective response of schadenfreude and the ACC to the negative affective response of envy (Singer et al., 2006; Takahashi et al., 2009). Two fMRI experiments by Takahashi and colleagues (2009) directly investigated the neurocognitive mechanisms underlying the link between schadenfreude and envy. In the first study, participants were presented with a scenario involving the protagonist and three other characters. The participants were asked to imagine themselves as the protagonist, whereas the other three target persons differed in their level of possessions and in the self-relevance of the comparison domains. The results showed that envy ratings were modulated by the quality of possessions and by the self-relevance of the comparison domain. Specifically, when a target person was perceived as being superior and self-relevant, higher envy ratings were found. Activations in the dorsal anterior cingulate cortex (dACC), a region associated with social pain in previous studies (Eisenberger, Lieberman, & Williams, 2003; Singer et al., 2004), were observed when subjects felt stronger envy. In the second study, the target persons were the victims of successive misfortunes. The schadenfreude ratings were found to be higher when the target was considered to be superior and self-relevant. These ratings were correlated with stronger activations of the ventral striatum, which were also found to be stronger toward an envied other. The role of the ACC in negative social comparison based emotions was also recently demonstrated in Beer and Hughes (2010). Exploring the “above-average” effect, the results of the study indicated that the more participants viewed themselves as being more desirable than other people, the less they recruited medial and lateral orbitofrontal cortex (OFC), and to a lesser extent, dACC. 2.4.4.2 The Role of Mentalizing Processes in Envy and Schadenfreude The emotional reaction to competitive situations concerning social comparison depends in part on the presumed desirability of an event for another person. Determining the desirability for the other person requires that one construct, at least a partial model of the other person’s plans and goals (Ortony & Collins, 1988). Therefore, these emotions are likely to involve empathy-related abilities, such as mentalizing and/or simulation. Linking mentalizing with social comparison and the experience of competition, Decety, Jackson, Sommerville, Chaminade, and Meltzoff (2004) demonstrated that the medial prefrontal cortex (mPFC) is activated more in

c09.indd 145

7/25/2011 10:10:19 AM

146

ENVY AND SCHADENFREUDE

competitive than in cooperative conditions. The authors reasoned that in competitive situations, the opponent’s upcoming behavior is less predictable and may require additional mentalizing processes (Decety et al., 2004). To identify brain activity specifically associated with the adoption of an intentional stance in a competitive setting, Gallagher et al. (2002) examined participants playing a computerized version of the game “rock, paper, scissors” against either another human player or the computer. Activations were found mainly in the paracingulate cortex, including the mPFC, which suggests that the mPFC is involved in mentalizing in social competitive games (Gallagher, Jack, Roepstorff, & Frith, 2002). Likewise, results of the recent study by Dvash and colleagues (2010) indicate increased activation in the medial prefrontal cortex and temporal poles in relative outcome events as compared with absolute outcome events. The importance of these areas for mentalizing about other people’s minds has previously been demonstrated (Gallagher & Frith, 2003), and their involvement in relative reward points to a role for the mentalizing network in competitive emotions. In sum, it may be suggested that although the medial and superior prefrontal cortex are responsible for processing another person’s state of mind, the ventral striatum is responsible for calculating the subjective value of absolute and relative rewards. Evidence for the role of the mentalizing network in envy and schadenfreude can also be found in lesion studies. In a neuropsychological study, Shamay-Tsoory and colleagues (2007) reported that the understanding of envy and schadenfreude requires mentalizing abilities and is impaired in patients with lesions in the ventromedial (VM) prefrontal cortex. The authors speculated that the ability to understand competitive emotions, such as schadenfreude and envy, is related to broader mentalizing and perspective-taking capacities and, therefore, that lesions in the VM prefrontal cortex may impair the ability to understand these emotions. To examine this hypothesis, patients with lesions in the VM prefrontal cortex completed the “Yoni” task (Figure 2.4-3). This computerized task is based on another task previously described by Baron-Cohen et al. (1995), which involves the ability to judge mental states on the basis of verbal and eye gaze cues (the “Charlie” task) (BaronCohen, Campbell, Karmiloff-Smith, Grant, & Walker, 1995). The task was modified by adding “fortune of others” conditions for envy, schadenfreude, and identification. In the envy condition, Yoni’s facial expression was negative (sad and frowning),

Y oni gloats over

Y oni envies

Figure 2.4-3. The “Yoni” task (Shamay-Tsoory, 2008; Shamay-Tsoory et al., 2007).

c09.indd 146

7/25/2011 10:10:19 AM

THE NEUROCHEMICAL BASES OF SOCIAL COMPARISON BASED EMOTIONS

147

whereas the protagonist’s facial expression was positive (happy and smiling). In the schadenfreude condition, Yoni’s facial expression was positive, whereas the protagonist’s facial expression was negative. In the identification condition, Yoni’s expression matched the protagonist’s expression (both sad or both happy). The results supported the aforementioned hypothesis by showing selective impairment in recognizing schadenfreude and envy, but not identification, in patients with VM prefrontal cortex damage (Shamay-Tsoory et al., 2007). A later study by Shamay-Tsoory (2008) examined the ability of individuals with Asperger Syndrome (AS) and high functioning autism (HFA) to recognize social comparison based emotions that involve the perception of an emotional interaction between two characters. To test the ability of individuals with AS/HFA to understand schadenfreude, envy, and identification, the aforementioned “Yoni” task was used. The results showed that although individuals with AS and HFA showed no difficulty on basic theory of mind (ToM) conditions, they were impaired in their ability to identify envy and schadenfreude. In addition, the ability to recognize these emotions was related to their scores on a self-rating scale of perspective-taking ability and the ToM task. Additional validation for the role of the mentalizing network in envy comes from a recent study conducted in our laboratory (Dvash et al., unpublished results). A positive correlation was found between the participants’ self-reported dispositional empathy (measured using the Interpersonal Reactivity Index, IRI) and their selfreported tendency to envy (measured using the Dispositional Envy Scale, DES). A correlation analysis revealed a positive correlation between the DES and the Fantasy subscale, which measures the tendency to transpose oneself imaginatively into fictional situations and is linked with mentalizing abilities (Davis, 1980, 1983; Davis, Luce, & Kraus, 1994). These results may provide more evidence for the role of the mentalizing network in competitive emotions. Contrary to the congruent nature of empathy, competitive emotions are incongruent. Envy and schadenfreude are characterized by incongruence between the valence of the emotion of the perceiver and the perceived other (Ortony & Collins, 1988). This notion may explain why regions involved in simulation processes are not found in studies examining competitive emotions. Unlike cooperative emotions, competitive emotions may rely mainly on higher empathic processes, such as mentalizing, in order to represent another’s presumed desirability of an event.

2.4.5 THE NEUROCHEMICAL BASES OF SOCIAL COMPARISON BASED EMOTIONS 2.4.5.1

Oxytonergic System

One of the hormones most associated with social cognition is oxytocin (Bartz & Hollander, 2006; Baumgartner, Heinrichs, Vonlanthen, Fischbacher, & Fehr, 2008; Domes et al., 2007; Domes, Heinrichs, Michel, Berger, & Herpertz, 2007; Guastella, Carson, Dadds, Mitchell, & Cox, 2009; Heinrichs, Baumgartner, Kirschbaum, & Ehlert, 2003; Kirsch et al., 2005; Kosfeld, Heinrichs, Zak, Fischbacher, & Fehr, 2005; Meyer-Lindenberg, 2008; Zak, Stanton, & Ahmadi, 2007). A growing number of studies suggest that oxytocin enhances cooperation and plays a pivotal role in

c09.indd 147

7/25/2011 10:10:19 AM

148

ENVY AND SCHADENFREUDE

various complex prosocial behaviors, such as overcoming fear of betrayal (Baumgartner et al., 2008) and reinforcing altruism (Zak et al., 2007). Using a risk-taking paradigm, Kosfeld et al. (2005) found that the intranasal administration of oxytocin in humans leads to a pronounced elevation in trust. Oxytocin elevated their subjects’ readiness to accept risks that may develop in an interpersonal interaction in a calculated manner. Heinrichs et al. (2003) also found that social support has a buffering effect in stressful situations, which oxytocin seems to mediate. Interestingly, subjects who were administered oxytocin and had their best friend for support showed the lowest cortisol level in their saliva after a stressinducing task and reported being more calm and relaxed. A recent study by Barraza and Zak (2009) examined the association among oxytocin, empathy, and generosity. Subjects were presented with short clips of emotional or unemotional scenes and were asked to rate their emotions toward the scenes using empathic words (sympathetic, compassionate etc.). The results showed a positive correlation between the degree of experienced sympathy and the change in oxytocin levels. Subsequently, they played an ultimatum game with a stranger, and higher levels of sympathy were associated with a more generous monetary response toward the stranger. The authors suggest that oxytocin may be perceived as physiological evidence for empathy, which seems to affect behavior prosocially. Thus, oxytocin is associated with prosocial behavior, possibly by increasing the empathic mechanisms that augment one’s understanding of the mental state of the other. One possible hypothesis emerging from this line of study regarding the role of oxytocin (OT) in social behavior is that it mainly contributes to increasing positive social comparison based emotions. If this is indeed the case, then we would expect a differential effect of oxytocin between cooperative and competitive emotions. Specifically, according to this hypothesis, elevated oxytocin would be expected to increase the levels of negative and positive empathy, while diminishing the levels of envy and schadenfreude. Contrary to the prosocial notion that oxytocin has positive effects on social behavior, the results of animal studies have demonstrated the role of oxytocin in territoriality and aggressive behavior, particularly in maternal aggression (Bosch, Meddle, Beiderbeck, Douglas, & Neumann, 2005). For example, a study with lactating hamsters by Ferris (1992) showed that a repeated infusion of oxytocin into the central amygdala increased maternal aggression toward a male intruder (Ferris, 1992). Another study demonstrated that after cohabitation with a male, female prairie voles became increasingly aggressive to other females (Bowler, Cushing, & Carter, 2002). However, this effect was significantly enhanced by intraperitoneal OT injections within 24 hours of birth (Bales & Carter, 2003). In an attempt to reconcile these contradictory findings, Pedersen (2004) and Debiec (2005) have suggested that oxytocin has a dual effect on parental behavior: inhibiting aggression directed toward the offspring, while enhancing aggressive behavior toward intruders (Debiec, 2005; Pedersen, 2004). Other interpretations have argued that oxytocin may generally enhance social motivation and affiliative behaviors (e.g., Depue & Morrone-Strupinsky, 2005; Leng, Meddle, & Douglas, 2008). Nevertheless, it is possible that oxytocin plays a broader role in modulating social emotions. Contrary to the prevailing belief that the oxytocinergic system is involved solely in positive cooperative behaviors, a recent study (Shamay-Tsoory

c09.indd 148

7/25/2011 10:10:19 AM

CONCLUSION

149

et al., 2009) showed that oxytocin plays a key role in a wider range of social emotionrelated behaviors. Fifty-nine subjects participated in this double-blind, placebocontrolled, within-subjects study. Subjects played the game of chance described earlier (Dvash et al., 2010), after the administration of oxytocin or a placebo. Although the oxytocin had no effect on feelings toward colors or on general mood, increased ratings of envy and schadenfreude were reported. These results suggest that the oxytocinergic system is involved in modulating envy and schadenfreude. Thus, it was proposed that the oxytocinergic system is responsible for modulating the salience of social agents in social contexts. As such, the administration of oxytocin may evoke a wide range of emotions and behaviors related to social behavior and parenting, such as trusting collaborators, attacking potential intruders, and competing with rivals. 2.4.5.2

Dopaminergic System

Dopamine seems to play an essential role in reward processing, which, as stated, plays a crucial role in emotions (Rolls, 2005). Mesolimbic and neostriatal dopamine projections have been suggested to serve as a “common neural currency” for rewards sought by animals and humans (Montague & Berns, 2002). In primates, the regions found to be involved in reward processing are the nucleus accumbens, the ventral putamen, and the medial portion of the caudate head, which receive projections from the amygdala and the orbital and medial prefrontal cortex (Ongur & Price, 2000). Recent studies show that the dopaminergic system is also involved in social ranking (Morgan et al., 2002; Paulhus 2008; Zink et al., 2008), thus linking it to feelings of inferiority and superiority in competitive emotions. For example, D2 dopamine receptor binding sites in monkeys were found to change as a function of hierarchical rank when the animals moved from individual to social housing. Low social rank was found to correspond to low D2-receptor binding (Morgan et al., 2002). Evidence for the role of dopamine in social ranking in humans can be found in studies investigating personality traits, such as dominant/submissive selfpresentation (Paulhus, 2008). Moreover, as mentioned, Zink et al. (2008) have demonstrated that social superiority is correlated with higher activations in the ventral striatum. Taken together, the striatal dopamine system is involved in the dominant-submissive social hierarchy, with low levels of DA markers corresponding with submissive behavior and high levels with dominant behavior. Superiority and inferiority in humans play a crucial role in competitive emotions, thus linking the dopaminergic network with envy and schadenfreude.

2.4.6 CONCLUSION: A NEURAL LINK BETWEEN SOCIAL COMPARISON BASED EMOTIONS The psychological and philosophical literature posits that emotions toward the fortune of others constitute a common group (Ben-Ze’ev, 2000; Ortony & Collins, 1988). Based on the evidence presented earlier, a connection between the social comparison based emotions can also be made from a neuroscientific perspective. Evidence for this supposition may be derived from various studies, including several

c09.indd 149

7/25/2011 10:10:19 AM

150

ENVY AND SCHADENFREUDE

Oxytonergic Systerm

Dopaminergic Systerm

Reward striatum

Positive

Simulation insula IFG ACC

Positive Empathy Cooperative

Schadenfreude Competitive

Negative Empathy

Envy

Mentalizing mPFC TP

Negative

Punishment dACC

Figure 2.4-4. A tentative neural model of social comparison based emotions in humans.

that examined patients with deficits in empathic abilities as well as in competitive emotions (Shamay-Tsoory, 2008; Shamay-Tsoory et al., 2007). Additional reinforcement comes from imaging studies that show that in certain contexts, one emotion from this group can change to another. For example, negative empathy may change to schadenfreude (Singer et al., 2006), and envy may facilitate schadenfreude (Takahashi et al., 2009). It is proposed here that two main processes underlie the experience of social comparison based emotions (see Figure 2.4-4): The first is the reward/punishment system, which mediates motivation and the experience of pleasure and displeasure. The second is a system determining the desirability of the other person and constructing a model of the other person’s plans and goals, which includes simulation processes that translate congruent actions, sensations, and emotions from the experience of others into the neural language of our own, as well as the mentalizing network that processes the other’s mental and emotional state. Both cooperative and competitive social comparison based emotions recruit the reward network.

c09.indd 150

7/25/2011 10:10:19 AM

REFERENCES

151

Nevertheless, these emotions may differ in the process of the mental representation of the other. Thus, the cooperative emotions may involve more simulation processes based on their congruent nature, whereas the competitive emotions may involve more mentalizing processes. Hence, an interaction between the mentalizing and simulation networks and the reward system is proposed to be at the heart of the experience of social comparison based emotions. The four major social comparison based emotions are differentiated here according to the degree to which they promote cooperation or competition and to which they produce positive or negative experience for the self. Although the schematic depiction seems dichotomous in the recruitment of mentalizing or simulation processes for emotions, the two processes may be used for both cooperative and competitive emotions. Nevertheless, competitive emotions seem to rely mainly on mentalizing processes through the mPFC and TP, whereas cooperative emotions rely mainly on simulation processes through the insula, IFG, and ACC. The oxytonergic and dopaminergic systems may modulate social comparison based emotions by enhancing social agents. At the neurochemical level, the role of oxytocin in social functioning makes it a major candidate in mediating social comparison based emotions. In relation to the model proposed here, the involvement of oxytocin in empathy, as well as in envy and schadenfeurde, has been well documented (Domes et al., 2007; Shamay-Tsoory et al., 2009). Given the involvement of the dopaminergic system in the social hierarchy, it is plausible to assume its role in social comparison based emotions. In addition, oxytocin binding sites can be found in the frontal lobe as well as in the mesolymbic systems, which are identified with neural reward mechanisms and dopamine (Lim, Murphy, & Young, 2004). This suggests that oxytocin is associated with motivation and reward, linking it to the reward systems found to be involved in social comparison based emotions. Future directions in the study of the neural bases of social comparison based emotions should investigate the relation between the mentalizing network and the reward system in these emotions. A dysfunctional interaction between these systems may account for the social behavioral disturbances found in patients with brain injuries and different psychopathologies, such as autism spectrum disorders. Comparing patients and healthy controls on the performance and brain mechanisms involved in these emotions might offer new insight regarding the neuroanatomical bases of social behavior.

REFERENCES Adams, J. S. (1963). Toward an understanding of inequity. J Abnorm Psychol 67, 422– 436. Bales, K. L., & Carter, C. S. (2003). Sex differences and developmental effects of oxytocin on aggression and social behavior in prairie voles (microtus ochrogaster). Horm Behav 44(3), 178–184. Baron-Cohen, S., Campbell, R., Karmiloff-Smith, A., Grant, J., & Walker, J. (1995). Are children with autism blind to the mentalistic significance of the eyes? Bri J Dev Psychol 13(4), 379–398. Barraza, J. A., & Zak, P. J. (2009). Empathy toward strangers triggers oxytocin release and subsequent generosity. Ann N Y Acad Sci 1167(1), 182–189.

c09.indd 151

7/25/2011 10:10:19 AM

152

ENVY AND SCHADENFREUDE

Bartz, J. A., & Hollander, E. (2006). The neuroscience of affiliation: Forging links between basic and clinical research on neuropeptides and social behavior. Horm Behav 50(4), 518–528. Batson, C. D. (1991). The Altruism Question: Toward a Social Psychological Answer. Hillsdale, NJ: Erlbaum. Baumgartner, T., Heinrichs, M., Vonlanthen, A., Fischbacher, U., & Fehr, E. (2008). Oxytocin shapes the neural circuitry of trust and trust adaptation in humans. Neuron 58(4), 639–650. Beer, J. S., & Hughes, B. L. (2010). Neural systems of social comparison and the “aboveaverage” effect. Neuroimage 49(3), 2671–2679. Ben-Ze’ev, A. (1992). Envy and inequality. J Philo 89(10), 551–581. Ben-Ze’ev, A. (2000). The Subtlety of Emotions. Cambridge, MA: MIT Press. Berke, J. H. (1988). The Tyranny of Malice: Exploring the Dark Side of Character and Culture. New York: Summit Books. Bosch, O. J., Meddle, S. L., Beiderbeck, D. I., Douglas, A. J., & Neumann, I. D. (2005). Brain oxytocin correlates with maternal aggression: Link to anxiety. J Neurosci 25(29), 6807. Bowler, C. M., Cushing, B. S., & Sue Carter, C. (2002). Social factors regulate female-female aggression and affiliation in prairie voles. Physiol Behav 76(4), 559–566. Brigham, N. L. (1997). The roles of invidious comparisons and deservingness in sympathy and schadenfreude. Basic Appl Soc Psychol 19(3), 363–380. Brosnan, S. F., & de Waal, F. B. (2003). Monkeys reject unequal pay. Nature 425(6955), 297–299. Brosnan, S. F., Schiff, H. C., & de Waal, F. B. (2005). Tolerance for inequity may increase with social closeness in chimpanzees. Proc Biol Sci 272(1560), 253–258. Davis, M. H. (1980). A multidimensional approach to individual differences in empathy. JSAS Catalog of Selected Documents in Psychology 10(4), 85. Davis, M. H. (1983). Measuring individual differences in empathy: Evidence for a multidimensional approach. J Pers Soc Psychol 44(1), 113–126. Davis, M. H., Luce, C., & Kraus, S. J. (1994). The heritability of characteristics associated with dispositional empathy. J Pers 62, 369–369. De Quervain, D. J. F., Fischbacher, U., Treyer, V., Schellhammer, M., Schnyder, U., et al. (2004). The neural basis of altruistic punishment. Science 305(5688), 1254. de Waal, F. B. M. (2008). Putting the altruism back into altruism: The evolution of empathy. Annu Rev Psychol 59, 279–300. Debiec, J. (2005). Peptides of love and fear: Vasopressin and oxytocin modulate the integration of information in the amygdala. Bioessays 27(9), 869–873. Decety, J., Jackson, P. L., Sommerville, J. A., Chaminade, T., & Meltzoff, A. N. (2004). The neural bases of cooperation and competition: An fMRI investigation. Neuroimage 23(2), 744–751. Depue, R. A., & Morrone-Strupinsky, J. V. (2005). A neurobehavioral model of affiliative bonding: Implications for conceptualizing a human trait of affiliation. Behav Brain Sci 28(03), 313–350. Domes, G., Heinrichs, M., Gläscher, J., Büchel, C., Braus, D. F., et al. (2007). Oxytocin attenuates amygdala responses to emotional faces regardless of valence. Biol Psychiatry 62(10), 1187–1190. Domes, G., Heinrichs, M., Michel, A., Berger, C., & Herpertz, S. C. (2007). Oxytocin improves “mind-reading” in humans. Biol Psychiatry 61(6), 731–733.

c09.indd 152

7/25/2011 10:10:19 AM

REFERENCES

153

Dvash, J., Gilam, G., Ben-Ze’ev, A., Hendler, T., & Shamay-Tsoory, S. G. (2010). The envious brain: The neural basis of social comparison. Hum Brain Mapp 31(11), 1741– 1750. Eisenberger, N. I., Lieberman, M. D., & Williams, K. D. (2003). Does rejection hurt? An fMRI study of social exclusion. Science 302(5643), 290. Erk, S., Spitzer, M., Wunderlich, A. P., Galley, L., & Walter, H. (2002). Cultural objects modulate reward circuitry. Neuroreport 13(18), 2499. Fehr, E., & Schmidt, K. M. (1999). A theory of fairness, competition, and cooperation. Q J Econ 114(3), 817–868. Ferris, C. (1992). Role of vasopressin in aggressive and dominant/subordinate behaviors. Ann N Y Acad Sci 652, 212–226. Festinger, L. (1954). A theory of social comparison. Hum Relat 7(1), 17– 40. Fliessbach, K., Weber, B., Trautner, P., Dohmen, T., Sunde, U., et al. (2007). Social comparison affects reward-related brain activity in the human ventral striatum. Science 318(5854), 1305–1308. Gallagher, H. L., & Frith, C. D. (2003). Functional imaging of “theory of mind”. Trends Cogniti Sci 7(2), 77–83. Gallagher, H. L., Jack, A. I., Roepstorff, A., & Frith, C. D. (2002). Imaging the intentional stance in a competitive game. Neuroimage 16(3), 814–821. Garcia, S. M., Tor, A., & Gonzalez, R. (2006). Ranks and rivals: A theory of competition. Pers Soc Psychol Bull 32(7), 970–982. Gilbert, P., Price, J., & Allan, S. (1995). Social comparison, social attractiveness and evolution: How might they be related? New Ideas Psychol 13(2), 149–165. Guastella, A. J., Carson, D. S., Dadds, M. R., Mitchell, P. B., & Cox, R. E. (2009). Does oxytocin influence the early detection of angry and happy faces? Psychoneuroendocrinology 34(2), 220–225. Han, S., Fan, Y., Xu, X., Qin, J., Wu, B., et al. (2009). Empathic neural responses to others’ pain are modulated by emotional contexts. Hum Brain Mapp 30(10), 3227–3237. Hare, B., Call, J., & Tomasello, M. (2001). Do chimpanzees know what conspecifics know? Anim Behav 61(1), 139–151. Heinrichs, M., Baumgartner, T., Kirschbaum, C., & Ehlert, U. (2003). Social support and oxytocin interact to suppress cortisol and subjective responses to psychosocial stress. Biol Psychiatry 54(12), 1389–1398. Homans, G. C. (1961). Social Behaviour: Its Elementary Forms. London: Routledge & Kegan Paul PLC. Jackson, P. L., Meltzoff, A. N., & Decety, J. (2005). How do we perceive the pain of others? A window into the neural processes involved in empathy. Neuroimage 24(3), 771–779. Jackson, P. L., Rainville, P., & Decety, J. (2006). To what extent do we share the pain of others? Insight from the neural bases of pain empathy. Pain 125(1–2), 5–9. Kenning, P., & Plassmann, H. (2005). NeuroEconomics: An overview from an economic perspective. Brain Res Bull 67(5), 343–354. Keysers, C., & Gazzola, V. (2006). Towards a unifying neural theory of social cognition. Progr Brain Res 156, 379– 401. Kirsch, P., Esslinger, C., Chen, Q., Mier, D., Lis, S., et al. (2005). Oxytocin modulates neural circuitry for social cognition and fear in humans. J Neurosci 25(49), 11489. Kosfeld, M., Heinrichs, M., Zak, P. J., Fischbacher, U., & Fehr, E. (2005). Oxytocin increases trust in humans. Nature 435(7042), 673–676. Langford, D. J., Crager, S. E., Shehzad, Z., Smith, S. B., Sotocinal, S. G., et al. (2006). Social modulation of pain as evidence for empathy in mice. Science 312(5782), 1967.

c09.indd 153

7/25/2011 10:10:19 AM

154

ENVY AND SCHADENFREUDE

Leng, G., Meddle, S. L., & Douglas, A. J. (2008). Oxytocin and the maternal brain. Curr Opin Pharmacol 8(6), 731–734. Lieberman, M. D., & Eisenberger, N. I. (2009). Neuroscience. pains and pleasures of social life. Science 323(5916), 890–891. Lim, M. M., Murphy, A. Z., & Young, L. J. (2004). Ventral striatopallidal oxytocin and vasopressin V1a receptors in the monogamous prairie vole (microtus ochrogaster). J Comp Neurol 468(4), 555–570. Meyer-Lindenberg, A. (2008). Impact of prosocial neuropeptides on human brain function. Progr Brain Res 170, 463–470. Montague, P. R., & Berns, G. S. (2002). Neural economics and the biological substrates of valuation. Neuron 36(2), 265–284. Morgan, D., Grant, K. A., Gage, H. D., Mach, R. H., Kaplan, J. R., Prioleau, O., Nader, S. H., Buchheimer, N., Ehrenkaufer, R. L., & Nader, M. A. (2002). Social dominance in monkeys: Dopamine D2 receptors and cocaine self-administration. Nature Neuroscience 5, 169–174. Ongur, D., & Price, J. L. (2000). The organization of networks within the orbital and medial prefrontal cortex of rats, monkeys and humans. Cerebr Cortex 10(3), 206. Ortony, A. A., & Collins, A. A. (1988). The Cognitive Structure of Emotions. Cambridge, MA: Cambridge University Press. Parks, C. D., Rumble, A. C., & Posey, D. C. (2002). The effects of envy on reciprocation in a social dilemma. Pers Soc Psychol Bull 28(4), 509. Parrott, W. G. (1991). The emotional experiences of envy and jealousy. The Psychology of Jealousy and Envy, edited by P. Salovey. New York: Guilford Press. Paulhus D. L., & Trapnell, P. D. (2008). Self-presentation of personality: An agency-communion framework. Handbook of Personality Psychology: Theory and Research, edited by O. P. John, R. W. Robins, & L. A. Pervin. New York: Guilford Press. Pedersen, C. A. (2004). Biological aspects of social bonding and the roots of human violence. Ann N Y Acad Sci 1036, 106–127. Phan, K. L., Wager, T., Taylor, S. F., & Liberzon, I. (2002). Functional neuroanatomy of emotion: A meta-analysis of emotion activation studies in PET and fMRI. Neuroimage 16(2), 331–348. Preston, S. D., & de Waal, F. B. M. (2003). Empathy: Its ultimate and proximate bases. Behavi Brain Sci 25(01), 1–20. Qiu, J., Yu, C., Li, H., Jou, J., Tu, S., et al. (2010). The impact of social comparison on the neural substrates of reward processing: An event-related potential study. Neuroimage 49(1), 956–962. Range, F., Horn, L., Viranyi, Z., & Huber, L. (2009). The absence of reward induces inequity aversion in dogs. Proc Nat Acad Sci 106(1), 340. Rolls, E. T. (2005). Emotion Explained. Oxford: Oxford University Press. Royzman, E. B., & Kumar, R. (2001). On the relative preponderance of empathic sorrow and its relation to commonsense morality. New Ideas Psychol 19(2), 131–144. Schoeck, H. (1987). Envy: A Theory of Social Behaviour. Indianapolis, In: Liberty Fund Inc. Shamay-Tsoory, S. G. (2008). Recognition of “Fortune of others” emotions in Asperger syndrome and high functioning autism. J Autism Deve Disord 38(8), 1451–1461. Shamay-Tsoory, S. G., Fischer, M., Dvash, J., Harari, H., Perach-Bloom, N., et al. (2009). Intranasal administration of oxytocin increases envy and schadenfreude (gloating). Biol Psychiatr 66(9), 864–870.

c09.indd 154

7/25/2011 10:10:19 AM

REFERENCES

155

Shamay-Tsoory, S. G., Tibi-Elhanany, Y., & Aharon-Peretz, J. (2007). The green-eyed monster and malicious joy: The neuroanatomical bases of envy and gloating (schadenfreude). Brain 130(6), 1663–1678. Singer, T., Seymour, B., O’Doherty, J., Kaube, H., Dolan, R. J., et al. (2004). Empathy for pain involves the affective but not sensory components of pain. Science 303(5661), 1157. Singer, T., Seymour, B., O’Doherty, J. P., Stephan, K. E., Dolan, R. J., et al. (2006). Empathic neural responses are modulated by the perceived fairness of others. Nature 439(7075), 466– 469. Smith, R. H. (2000). Assimilative and contrastive emotional reactions to upward and downward social comparisons. Handbook of Social Comparison: Theory and Research. New York: Springer. Smith, R. H., & Kim, S. H. (2007). Comprehending envy. Psychol Bull 133(1), 46. Smith, R. H., Turner, T. J., Garonzik, R., Leach, C. W., Urch-Druskat, V., et al. (1996). Envy and schadenfreude. Pers Soc Psychol Bull 22(2), 158. Stouffer, S. A. (1949). The American Soldier. Princeton, NJ: Princeton University Press. Takahashi, H., Kato, M., Matsuura, M., Mobbs, D., Suhara, T., et al. (2009). When your gain is my pain and your pain is my gain: Neural correlates of envy and schadenfreude. Science 323(5916), 937–939. van Wolkenten, M., Brosnan, S. F., & de Waal, F. (2007). Inequity responses of monkeys modified by effort. Proc Nat Acad Sci 104(47), 18854. Xu, X., Zuo, X., Wang, X., & Han, S. (2009). Do you feel my pain? Racial group membership modulates empathic neural responses. J Neurosci 29(26), 8525. Zak, P. J., Stanton, A. A., & Ahmadi, S. (2007). Oxytocin increases generosity in humans. PLoS One 2(11), e1128. Zink, C. F., Tong, Y., Chen, Q., Bassett, D. S., Stein, J. L., et al. (2008). Know your place: Neural processing of social hierarchy in humans. Neuron 58(2), 273–283.

c09.indd 155

7/25/2011 10:10:19 AM

PART 3 GENES AND DECISION MAKING

c10.indd 157

7/25/2011 10:10:20 AM

3.1 THE SOMATIC MARKER FRAMEWORK AND THE NEUROLOGICAL BASIS OF DECISION MAKING ANTOINE BECHARA Department of Psychiatry, Faculty of Medicine, and Desautels Faculty of Management, McGill University, Montreal, Quebec, Canada Department of Psychology, and Brain and Creativity Institute, University of Southern California, Los Angeles, California

3.1.1

INTRODUCTION

A great deal of contemporary decision research in economics, business, psychology, and neuroscience now accepts the idea that emotions play a significant role in influencing decision making. Almost 20 years ago, insights from studies on brain lesion patients set the cornerstone for this stream of research and led to the formulation of the somatic marker hypothesis. Despite some debate, the somatic marker framework is still providing a unique neuroanatomical and cognitive framework that helps explain the role of emotion in decision making. This chapter reviews the neurological background, core mechanisms, and critiques of the somatic marker theory; puts into perspective conceptually related approaches that link emotion to decision making; and presents an outlook for future research. Furthermore, this chapter will outline experiments supporting the argument that (1) decision making is a process critically dependent on neural systems important for the processing of emotions; (2) conscious knowledge alone is not sufficient for making advantageous decisions;

The research of this study was supported by the following grants from the National Institute on Drug Abuse (NIDA): DA11779, DA12487, and DA16708; by the National Science Foundation (NSF) Grant IIS 04-42586; and by NINDS Program Project Grant P01 NS19632.

From DNA to Social Cognition, First Edition. Edited by Richard Ebstein, Simone Shamay-Tsoory, and Soo Hong Chew. © 2011 Wiley-Blackwell. Published 2011 by John Wiley & Sons, Inc.

159

c10.indd 159

7/25/2011 10:10:20 AM

160

THE NEUROLOGICAL BASIS OF DECISION MAKING

and (3) emotion is not always beneficial to decision making: sometimes it can be disruptive.

3.1.2 A BRIEF HISTORICAL PERSPECTIVE The terms ventromedial prefrontal cortex (vmPFC) and orbitofrontal cortex (OFC) are often used interchangeably in the literature, even though these do not refer to identical regions. For this reason, it is necessary to clarify exactly what we mean when we use these terms. The OFC is the entire cortex occupying the ventral surface of the frontal lobe, dorsal to the orbital plate of the frontal bone. We have used the term vmPFC to designate a region that encompasses medial portions of the OFC along with ventral portions of the medial prefrontal cortex. The vmPFC is an anatomical designation that has developed because lesions that occur in the basal portions of the anterior fossa, which include meningiomas of the cribiriform plate and falx cerebri, and aneurysms of the anterior communicating and anterior cerebral arteries, frequently lead to damage in this area. Often, this damage is bilateral. With respect to the cytoarchitectonic fields identified in the human orbitofrontal and medial prefrontal cortices by Price and colleagues (Ongur & Price, 2000), the vmPFC comprises Brodmann area (BA) 14 and medial portions of BA 11 and 13 on the orbital surface and BA 25, 32, and caudal portions of BA 10 on the mesial surface. The vmPFC excludes lateral portions of the OFC, namely BA 47/12, as well as more dorsal and posterior regions of BA 24 and 32 of the medial prefrontal cortex. The vmPFC is, thus, a relatively large and heterogenous area. One of the first and most famous cases of the so-called “frontal lobe syndrome” was the patient Phineas Gage, who was a railroad construction worker who survived an explosion that blasted an iron tamping bar through the front of his head (Harlow, 1848). Before the accident, Gage was a man of normal intelligence, energetic, and persistent in executing his plans of operation. He was responsible, sociable, and popular among peers and friends. After the accident, his medical recovery was remarkable. He survived the accident with normal intelligence, memory, speech, sensation, and movement. However, his behavior changed completely. He became irresponsible, untrustworthy, and impatient of restraint or advice when it conflicted with his desires. Using modern neuroimaging techniques, Damasio and colleagues have reconstituted the accident by relying on measurements taken from Gage’s skull (Damasio, Grabowski, Frank, Galburda, & Damasio, 1994). The key finding of this neuroimaging study was that the most likely placement of Gage’s lesion included the vmPFC region, bilaterally. The case of Phineas Gage paved the way for the notion that the frontal lobes were linked to social conduct, judgement, decision making, and personality. Several instances similar to the case of Phineas Gage have since appeared in the literature (Ackerly & Benton, 1948; Brickner, 1932; Welt, 1888). Interestingly, all these cases received little attention for many years. Over the years, we have studied numerous patients with this type of lesion. Such patients with damage to the vmPFC develop severe impairments in personal and social decision making, despite otherwise largely preserved intellectual abilities. These patients had normal intelligence and creativity before their brain damage. After the damage, they begin to have

c10.indd 160

7/25/2011 10:10:20 AM

A BRIEF HISTORICAL PERSPECTIVE

161

difficulties planning their workday and future as well as difficulties in choosing friends, partners, and activities. The actions they elect to pursue often lead to losses of diverse order (e.g., financial losses, losses in social standing, and losses of family and friends). The choices they make are no longer advantageous and are remarkably different from the kinds of choices they were known to make in the premorbid period. These patients often decide against their best interests. They cannot learn from previous mistakes as reflected by repeated engagement in decisions that lead to negative consequences. In striking contrast to this real-life decision-making impairment, problem-solving abilities in laboratory settings remain largely normal. As noted, the patients have normal intellect, as measured by a variety of conventional neuropsychological tests (Bechara, Damasio, Tranel, & Anderson, 1998; Damasio, Tranel, & Damasio, 1990; Eslinger & Damasio, 1985; Saver & Damasio, 1991). The question then originated as to whether the decision-making deficits caused by vmPFC damage were related to its visceromotor functions. Nauta (1971) had by then proposed that the guidance of behavior by the frontal lobes was linked to the interoceptive and visceromotor functions of this area. Specifically, he proposed that the prefrontal cortex, broadly defined, functioned to compare the affective responses evoked by the various choices for behavior and to select the option that “passed censure by an interoceptive sensorium.” According to Nauta, the “interoceptive agnosia” suffered by patients with frontal lobe damage could explain their impairments in real life, as well as their poor performance on various tests of executive function, including the Wisconsin card sort task. This model was meant to explain the function of the prefrontal cortex as a whole. Furthermore, it was meant as a broad explanation of executive function deficits, not of a specific deficit in decision making within the social and personal domains. However, the deficits of patients with damage in the vmPFC were limited to the personal and social domains; patients with focal vmPFC damage showed marked impairments in their real-life personal and social functioning but had intact intelligence. Indeed, these patients performed normally on standard laboratory tests of executive function such as the Wisconsin card sort task. This background helped to shape a more specific formulation, deemed the “somatic marker hypothesis” (Damasio, 1994; Damasio, Tranel, & Damasio, 1991). According to this hypothesis, patients with damage in the vmPFC make poor decisions in part because they are unable to elicit somatic (visceral) responses that “mark” the consequences of their actions as positive or negative. In this framework, the vmPFC functions to elicit visceral responses that reflect the anticipated value of the choices. Although this function is specific to the vmPFC, it draws on information about the external world that is represented in multiple higher order sensory cortices. Furthermore, this function is limited to specific types of decision making, in particular, those situations where the meaning of events is implied and the consequences of behavior are uncertain. These are situations, such as social interactions and decisions about one’s personal and financial life, where the consequences of behavior have emotional value (i.e., they can be experienced as subjective feelings and can increase or decrease the likelihood of similar behavior in the future [they are rewarding or punishing]). Furthermore, these are situations where the rules of behavior are not explicit, but yet they require some form of mental deliberation in real time in order to navigate them successfully. This form of reasoning is

c10.indd 161

7/25/2011 10:10:20 AM

162

THE NEUROLOGICAL BASIS OF DECISION MAKING

distinct from reasoning that does not require the weighing of positive and negative consequences, or in which the outcomes of decisions are known with a high degree of certainty. Thus, the somatic marker hypothesis proposed by Antonio R. Damasio attributes the inability of certain patients with frontal lobe damage to make advantageous decisions in real life to a defect in an emotional (somatic) mechanism that rapidly signals the prospective consequences of an action and, accordingly, assists in the selection of an advantageous response option. However, this somatic marker signal is actually derived from a prior life experience with reward and/or punishment. As such, the “affective” or “emotional” past is actually used to anticipate or forecast the future.

3.1.3

OUTLINE OF THE SOMATIC MARKER HYPOTHESIS

Several neural structures have been shown to be key components of the neural circuitry underlying somatic state activation. The amygdala as well as the medial orbitofrontal cortex/ventromedial prefrontal cortex region are critical structures for triggering somatic states, but the amygdala seems more important for triggering somatic states from emotional events that occur in the environment (that is, primary inducers), whereas the medial orbitofrontal cortex/ventromedial prefrontal cortex region seems more important for triggering somatic states from memories, knowledge, and cognition (that is, secondary inducers) (Bechara & Damasio, 2005). Decision making is a complex process that relies on the integrity of at least two sets of neural systems: (1) one set is important for memory (e.g., the hippocampus), and especially working memory (e.g., the dorsolateral prefrontal cortex), in order to bring online knowledge and information used during the deliberation of a decision; and (2) another set is important for triggering emotional responses. This set includes effector structures such as the hypothalamus and autonomic brainstem nuclei that produce changes in internal milieu and visceral structures along with other effector structures such as the ventral striatum, periacqueductal gray, and other brainstem nuclei, which produce changes in facial expression and specific approach or withdrawal behaviors. It also includes cortical structures that receive afferent input from the viscera and internal milieu, such as the insular cortex and the posterior cingulate gyrus (and adjacent retrosplenial cortex), and pre-cuneus region (i.e., medial area of the parietal cortex) (Figure 3.1-1). During the process of pondering decisions, the immediate prospects of an option may be driven by more subcortical mechanisms (e.g., via the amygdala) that do not require a prefrontal cortex. However, weighing the future consequences requires a prefrontal cortex for triggering somatic responses about possible future consequences. Specifically, when pondering the decision, the immediate and future prospects of an option may trigger numerous somatic responses that conflict with each other (i.e., positive and negative somatic responses). The end result, though, is that an overall positive or negative signal emerges (a “go” or “stop” signal, as it were). There is a debate as to where this overall somatic state may be computed. We have argued that this computation occurs in the body proper (via the so-called body loop), but it can also occur in the brain itself, in areas that represent “body” states

c10.indd 162

7/25/2011 10:10:20 AM

OUTLINE OF THE SOMATIC MARKER HYPOTHESIS

163

Motor/Behavioral Systems AntCingCtx/SupMotArea Striatum

Memory Systerms: DorsoLatPrefCtx (DLPC) Hippocampus

OrbitFrontCtx/VMPC

Amygdala

Dopamine Serotonin Ach NE

Emotion Systems: Insula Posterior Cingulate Hypothalamus PAG Brainstem autonomic centers

Computation: In the body---Body loop In Sensory nuclei and neurotransmitter cell bodies of brainstem ----As if body loop

Figure 3.1-1. A schematic of all the brain regions involved in decision making according to the somatic marker hypothesis. (See color insert.)

such as the dorsal tegmentum of the midbrain, or areas such as the insula and posterior cingulate (via the so-called as-if-body loop). The controversy has largely been in relation to the body loop, with certain investigators arguing that decision making is not necessarily dependent on “somatic markers” expressed in the body (e.g., see Maia & McClelland [2004])—but also see Bechara, Damasio, Tranel, & Damasio [2005] and (Persaud, McLeod, & Cowey [2007]) for counter arguments—irrespective of whether this computation occurs in the body itself, or within the brain, we have proposed that the emergence of this overall somatic state is consistent with the principles of natural selection. In other words, numerous and conflicting signals may be triggered simultaneously, but stronger ones gain selective advantage over weaker ones, until a winner takes all emerges, a positive or negative somatic state emerges, and consequently biases the decision one way or the other (Bechara & Damasio, 2005). For somatic signals to influence cognition and behavior, they must act on the appropriate neural systems. One target for somatic state action is the striatum. A large number of channels conveys body information (that is, somatic signals) to the central nervous system (e.g., spinal cord, vagus nerve, and humoral signals). Evidence suggests that the vagal route is especially critical for relaying somatic signals (Martin, Denburg, Tranel, Granner, & Bechara, 2004). Furthermore, it was proposed that the next link in this body-brain channel involves neurotransmitter systems (Bechara &

c10.indd 163

7/25/2011 10:10:20 AM

164

THE NEUROLOGICAL BASIS OF DECISION MAKING

Damasio, 2005; Damasio, 1994, 1996). Indeed, the cell bodies of the neurotransmitter dopamine, serotonin, noreadrenaline, and acetylcholine are located in the brainstem; the axon terminals of these neurotransmitter neurons synapse on cells and/or terminals all over the cortex and striatum (Blessing, 1997). When somatic state signals are transmitted to the cell bodies of dopamine or serotonin neurons, for example, the signaling influences the pattern of dopamine or serotonin release at the terminals. In turn, changes in dopamine or serotonin release will modulate the synaptic activities of neuron subserving behavior and cognition within the cortex. This chain of neural mechanisms provides a way for somatic states to exert a biasing effect on decisions. At the cellular, and more recently the functional, neuroimaging level, the pioneering work of Schultz, Dayan, and Montague (1997) on the role of dopamine in reward processing and error prediction provides a strong validity for the proposed neural framework. Thus, all the work related to dopamine and the ventral striatum is consistent with the somatic marker framework. The key difference is that the dopamine mechanism addresses only one specific component of a larger neural network that is important for implementing decisions. The somatic marker hypothesis is a neural framework that incorporates all the different neural steps involved in decision making, including the dopamine link, such as the one initially studied by Schultz et al. (1997). We note that one of the clear predictions of the somatic marker hypothesis is that working memory (and other executive processes of working memory such as response inhibition and reversal learning) is a key process in decision making. Consequently, damage to neural structures that impair working memory, such as the dorsolateral prefrontal cortex, also lead to impaired decision making. Nonetheless, some criticisms of the theory were made on the basis that deficits in decision making as measured by the Iowa Gambling Task (IGT) may not be specific to the ventromedial prefrontal cortex (Manes et al., 2002), or it may be explained by deficits in other processes, such as reversal learning (Fellows & Farah, 2003). However, research has demonstrated that the relationship between decision making on the one hand and working memory or reversal learning on the other hand are asymmetrical in nature (e.g., see Bechara [2004] and Bechara & Damasio [2005] for reviews). In other words, working memory and/or reversal learning are not dependent on the intactness of decision making (that is, subjects can have normal working memory and normal reversal learning in the presence or absence of deficits in decision making). Some patients with ventromedial prefrontal cortex lesions who were severely impaired in decision making (that is, abnormal in the Iowa Gambling Task) had superior working memory, and they are perfectly normal on simple reversal learning tasks. In contrast, decision making seems to be influenced by the intactness or impairment of working memory and/or reversal learning (that is, decision making is worse in the presence of abnormal working memory and/or poor reversal learning). Patients with right dorsolateral prefrontal cortex lesions and severe working memory impairments showed low normal results in the Iowa Gambling Task (Bechara et al., 1998). Patients with damage to the more posterior sector of the ventromedial prefrontal cortex (which includes the basal forebrain), such as the patients who were included in the study by Fellows and Farah (2003), showed impairments on reversal learning tasks, but similar patients with similar lesions also showed poor performance on the Iowa Gambling Task (Bechara et al., 1998).

c10.indd 164

7/25/2011 10:10:20 AM

TESTING THE SOMATIC MARKER HYPOTHESIS

165

3.1.4 TESTING THE SOMATIC MARKER HYPOTHESIS At the core of somatic marker hypothesis lays the insight that decision makers encode the consequences of alternative choices affectively. For many years, these vmPFC patients presented a puzzling defect. Although the decision-making impairment was obvious in the real-world behavior life of these patients, there was no effective laboratory probe to detect and measure this impairment. Bechara’s development of what became known as the “Iowa Gambling Task” enabled the detection of these patients’ elusive impairment in the laboratory for the first time, it to be measured, and its possible causes to be investigated (Bechara, Damasio, Damasio, & Anderson, 1994). Such work using the Iowa Gambling Task has provided the key empirical support for the proposal that somatic markers significantly influence decision making (Bechara & Damasio, 2005). 3.1.4.1 The Iowa Gambling Task (IGT) (Bechara et al., 1994; Bechara, Tranel, & Damasio, 2000): The IGT mimics real-life decisions so closely. The task is carried out in real-time, and it resembles real-world contingencies. It factors reward and punishment (i.e., winning and losing money) in such a way that it creates a conflict between an immediate, luring reward and a delayed, probabilistic punishment. Therefore, the task engages the subject in a quest to make advantageous choices. As in real-life choices, the task offers choices that may be risky, and there is no obvious explanation of how, when, or what to choose. Each choice is full of uncertainty because a precise calculation or prediction of the outcome of a given choice is not possible. The way that one can do well on this task is to follow one’s “hunches” and “gut feelings.” More specifically, this task involves four decks of cards. The goal in the task is to maximize profit on a loan of play money. Subjects are required to make a series of 100 card selections. However, they are not told ahead of time how many card selections they are going to make. Subjects can select one card at a time from any deck they choose, and they are free to switch from any deck to another at any time, and as often as they wish. However, the subject’s decision to select from one deck versus another is largely influenced by various schedules of immediate reward and future punishment. These schedules are preprogrammed and known to the examiner, but not to the subject, and they entail the following principles: Every time the subject selects a card from two of decks (decks A and B), the subject gets $100. Every time the subject selects a card from the two other decks (C or D), the subject gets $50. However, in each of the four decks, subjects encounter unpredictable punishments (money loss). The punishment is set to be higher in the high-paying decks A and B and lower in the low-paying decks C and D. For example, if 10 cards were picked from deck A, one would earn $1000. However, in those 10 card picks, 5 unpredictable punishments would be encountered, ranging from $150 to $350, bringing a total cost of $1250. Deck B is similar: Every 10 cards that were picked from deck B would earn $1000; however, these 10 card picks would encounter one high punishment of $1250. Nevertheless, every 10 cards from deck C or D earn only $500, but they only cost $250 in punishment. Hence, decks A and B are disadvantageous because they cost more in the long run (i.e., one loses $250 every 10 cards). Decks C and D are advantageous

c10.indd 165

7/25/2011 10:10:20 AM

166

THE NEUROLOGICAL BASIS OF DECISION MAKING

because they result in an overall gain in the long run (i.e., one wins $250 every 10 cards). We investigated the performance of normal controls and patients with vmPFC lesions on this task. Normal subjects avoided the bad decks A and B and preferred the good decks C and D. In sharp contrast, the vmPFC patients did not avoid the bad decks A and B; indeed, they preferred decks A and B. From these results, we suggested that the patients’ performance profile is comparable with their real-life inability to decide advantageously. This is especially true in personal and social matters, a domain for which in life, as in the task, an exact calculation of the future outcomes is not possible and choices must be based on hunches and gut feelings. 3.1.4.2

Evidence that Emotional Signals Guide Decisions

In light of the finding that the IGT is an instrument that detects the decision-making impairment of vmPFC patients in the laboratory, we went on to address the next question of whether the impairment is linked to a failure in somatic signaling (Bechara, Tranel, Damasio, & Damasio, 1996). To address this question, we added a physiological measure to the IGT. The goal was to assess somatic state activation while subjects were making decisions during performance of the task. We studied 2 groups: normal subjects and vmPFC patients. We had them perform the IGT while we recorded their electrodermal activity (skin conductance responses, SCRs). As the body begins to change after a thought, and as a given somatic state begins to be enacted, the autonomic nervous system begins to increase the activity in the skin’s sweat glands. Although this sweating activity is relatively small and not observable by the naked eye, it can be amplified and recorded by a polygraph as a wave. The amplitude of this wave can be measured and, thus, provide an indirect measure of the somatic state experienced by the subject. Both normal subjects and vmPFC patients generated SCRs after they had picked a card and were told that they won or lost money. The most important difference, however, was that normal subjects, as they became experienced with the task, began to generate SCRs prior to the selection of any cards (i.e., during the time when they were pondering from which deck to choose). These anticipatory SCRs were more pronounced before picking a card from the risky decks A and B when compared with the safe decks C and D. In other words, these anticipatory SCRs were like “gut feelings” that warned the subject against picking from the bad decks. Patients with vmPFC damage failed to generate such SCRs, before picking a card. This failure to generate anticipatory SCRs before picking cards from the bad decks correlates with their failure to avoid these bad decks and choose advantageously in this task. These results provide strong support for the notion that decision making is guided by emotional signals (gut feelings) that are generated in anticipation of future events. An important question regards the information content of visceral responses that are elicited by the vmPFC. If somatic markers are to be useful in guiding decisionmaking processes involving uncertain reward and punishment, then they should provide information about both the valence of an anticipated outcome (e.g., whether a choice will result in winning or losing money) as well as the information about the magnitude of the anticipated outcome (e.g., how much money will be won or lost). Our results using the IGT show that the vmPFC triggers anticipatory visceral

c10.indd 166

7/25/2011 10:10:20 AM

TESTING THE SOMATIC MARKER HYPOTHESIS

167

responses to both advantageous and disadvantageous decks. These responses are larger for disadvantageous decks than for advantageous decks, although they are still deployed for the advantageous decks. Additional experiments from our laboratory (Bechara, Dolan, & Hindes, 2002) and others (Tomb, Hauser, Deldin, & Caramazza, 2002) have shown that when the reward–punishment contingencies are reversed, with the disadvantageous decks paying out a lower quantity of reward, rather than doling out a higher punishment, the SCRs are now greater to the advantageous decks than to the disadvantageous decks. This suggests that SCR is not merely an index of the potential “badness” of choices. Rather, SCR can index the magnitude of both the anticipated negative outcome of a choice as well as the magnitude of the anticipated positive outcome of a choice. It seems, however, that SCR does not differentiate the anticipated valence of the outcomes. This is consistent with work by others (Lang, Bradley, Cuthbert, & Patrick, 1993) showing that SCR does not differentiate the hedonic valence of emotional stimuli, but it does index the magnitude of the arousal that they elicit. This would mean that some other signal is required to assess the valence of the anticipated outcome. Although our laboratory (Rainville et al., 2006) has provided preliminary evidence that cardiovascular responses, such as changes in heart rate, can provide information that distinguishes between positive and negative emotional states, the fact remains that the preponderance of evidence speaks to the lack of such a distinction at the peripheral visceral level (Cacioppo, Berntson, Larsen, Poehlmann, & Ito, 2000; Cacioppo, Klein, Berntson, & Hatfield, 1993). Although it is possible that such signals can combine with those reflected in the SCR to provide information about both the perceived valence of the future outcome of a choice, as well as its perceived magnitude, there is a strong likelihood that this discrimination is not achieved until the signals reach the central nervous system. Indeed, the somatovisceral afference model of emotion— SAME—does provide an explanation for how undifferentiated visceral responses might produce distinguishable emotions (Cacioppo et al., 2000; Cacioppo et al., 1993). Perhaps somatic markers operate in a fashion that is consistent with that model. Future experiments may examine at what level of the brain the visceral signals reflecting different channels of autonomic outflow become differentiated in such a manner to exert influence on decision making. 3.1.4.3

Emotional Signals Need not be Conscious

According to the somatic marker hypothesis, the vmPFC mediates an implicit representation of the anticipated value of choices that is distinct from an explicit awareness of the correct strategy. To test this idea, we performed a study (Bechara, Damasio, Tranel, & Damasio, 1997) in which we examined the development of SCRs over time in relation to subjects’ knowledge of the advantageous strategy in the IGT. In this study, the IGT was administered as before, but this time, the task was interrupted at regular intervals and the subjects were asked to describe their knowledge about “what was going on” in the task and about their “feelings” about the task. Here, it was shown that normal subjects began to choose preferentially from the advantageous decks before they were able to report why these decks were preferred over the disadvantageous decks. They then began to form “hunches” about the correct strategy, which corresponded to their choosing more from the advantageous decks than from the disadvantageous decks. Finally, some subjects reached a

c10.indd 167

7/25/2011 10:10:20 AM

168

THE NEUROLOGICAL BASIS OF DECISION MAKING

“conceptual” stage, where they possessed explicit knowledge about the correct strategy (i.e., to choose from decks C and D because they pay less but also result in less punishment). As before, normal subjects developed SCRs preceding their choices that were larger for the disadvantageous decks than for the advantageous decks. This time, it was also found that the SCR discrimination between advantageous and disadvantageous decks preceded the development of conceptual knowledge of the correct strategy. In fact, the SCR discrimination between advantageous and disadvantageous decks even preceded the development of hunches about the correct strategy. In contrast to the normal subjects, subjects with damage in the vmPFC failed to switch from the disadvantageous decks to the advantageous decks, as was previously shown. In addition, as previously shown, subjects in this group failed to develop anticipatory responses that discriminated between the disadvantageous and advantageous decks. Furthermore, patients with vmPFC damage never developed “hunches” about the correct strategy. Together, they suggest that anticipatory visceral responses that are governed by the vmPFC precede emergence of advantageous choice behavior, which itself precedes explicit knowledge of the advantageous strategy. This suggests that signals generated by the vmPFC, reflected in visceral states, may function as a nonconscious bias toward the advantageous strategy. An especially intriguing observation was that not all the normal control subjects were able to figure out the task, explicitly, in the sense that they did not reach the conceptual period. Only 70% of them were able to do so. Although 30% of controls did not reach the conceptual period, they still performed advantageously. However, 50% of the VM patients were able to reach the conceptual period and state explicitly which decks were good and which ones were bad and why. Although 50% of the VM patients did reach the conceptual period, they still performed disadvantageously. After the experiment, when these VM patients were confronted with the question, why did you continue to pick from the decks you thought were bad, these patients would resort to excuses such as “I was trying to figure out what happens if I kept playing the $100 decks” or “I wanted to recover my losses fast, and the $50 decks are too slow.” These results show that VM patients continue to choose disadvantageously in the IGT, even after realizing explicitly the consequences of their action. This suggests that the anticipatory SCRs represent unconscious biases derived from prior experiences with reward and punishment. These biases (or gut feelings) help deter the normal subject from pursuing a course of action that is disadvantageous in the future. This occurs even before the subject becomes aware of the goodness or badness of the choice he or she is about to make. Without these biases, the knowledge of what is right and what is wrong may still become available. However, by itself, this knowledge is not sufficient to ensure an advantageous behavior. Therefore, although the VM patient may manifest declarative knowledge of what is right and what is wrong, he or she fails to act accordingly. The VM patients may “say” the right thing, but they “do” the wrong thing. Thus, “knowledge” without “emotion/somatic signaling” leads to dissociation between what one knows or says and how one decides to act. This dissociation is not restricted to neurological patients, but it also applies to neuropsychiatric conditions with suspected pathology in the VM cortex or other components of the neural circuitry that process emotion. Addiction is one example, where patients know the

c10.indd 168

7/25/2011 10:10:20 AM

TESTING THE SOMATIC MARKER HYPOTHESIS

169

consequences of their drug-seeking behavior, but they still take the drug! Psychopathy is another example, where the psychopaths can be fully aware of the consequences of their actions, but they still go ahead and plan the killing or the rape of a victim! More recently, other investigators have questioned whether it is necessary to invoke visceral responses as constituting nonconscious biasing signals (Maia & McClelland, 2004). By using more detailed questions to probe subjects’ awareness of the attributes of each of the decks in the IGT, this study showed that subjects possess explicit knowledge of the advantageous strategy at an earlier stage in the task than was shown in the Bechara et al. (1997) study. Furthermore, the Maia and McClelland study found that subjects began to make advantageous choices at around the same time that they reported knowledge of the correct strategy. Based on these findings, it was argued that nonconscious somatic marker processes are not required to explain how decision making occurs. A response to this study has been published elsewhere (Bechara et al., 2005), along with a rebuttal by Maia and McClelland (2005). Two points bear discussion here. First, because this study did not measure visceral responses nor examine the effects of brain damage, it does not disprove the hypothesis that somatic markers mediated by the vmPFC play a role in decision making; it only shows that conscious awareness of the correct strategy occurs at around the same time as advantageous decision making. Second, both the Bechara et al. (1997) study and the Maia and McClelland (2005) study found that some subjects continue to make disadvantageous choices despite being able to report the correct strategy. This pattern bears an uncanny resemblance to the way in which subjects with lesions in the vmPFC are able to report the correct strategies for personal and social decision making, despite their severe deficits in the actual execution of personal and social behavior in real life. Indeed, this clinical observation provided the initial impetus to hypothesize a role for covert biasing processes in decision making in the first place. This indicates that, in both the IGT and in real life, conscious knowledge of the correct strategy may not be enough to guide advantageous decision making. Thus, some process that operates independently of conscious knowledge of the correct strategy (i.e., somatic markers) must be invoked to explain fully how individuals make advantageous decisions. Indeed, it seems likely that this process can sometimes bias behavior that goes against what a person consciously thinks to be the correct strategy. That nonconscious biasing processes may not precede conscious knowledge in time is potentially an important finding, but it does not provide a basis for rejection of the fundamental role of somatic markers as nonconscious biases of behavior. 3.1.4.4 The Biological Nature of These “Somatic Markers” is Related to Neurotransmitters Systems in the Brain Somatic states do not cause or produce behavior; they only bias or modulate behavior through changes in neurotransmitter release. A preliminary study supports this basic notion that decisions made during the IGT are influenced by manipulations of these neurotransmitter systems with agonist/antagonist drugs. We studied normal subjects under a serotonin manipulation with either: (1) a placebo (Vitamin C); (2) a selective serotonin reuptake inhibitor (fluvoxamine); and, (3) a serotonin 5HT2A receptor antagonist (cyproheptadine) with H1 histamine receptor

c10.indd 169

7/25/2011 10:10:20 AM

170

THE NEUROLOGICAL BASIS OF DECISION MAKING

antagonist properties. We studied other normal subjects under a dopamine manipulation involving the use of either (1) a placebo; (2) a psychostimulant (dextroamphetamine) resulting in a net increase in dopaminergic neurotransmission; and (3) a dopamine D2-like receptor antagonist (haloperidol). For each drug condition, the subjects were tested with a different version of the IGT for assessing decision making, with the order of the three drug conditions counterbalanced within each group. The blockade of both dopamine and serotonin interfered with the selection of advantageous choices, but the dopamine effect seemed restricted to the earlier part of the GT, when decisions are still guided by covert knowledge. The stimulation of both dopamine and serotonin improved the selection of advantageous choices but only in specific parts of the task. Serotonin improved only the latter part of the task when decisions are guided by conscious knowledge of which choices are good or bad. By contrast, dopamine improved only the early part of the task when guidance is covert. The results suggest that covert biasing of decisions might be dopaminergic, whereas overt biasing might be serotonergic. This also suggests that different types of decision making (e.g., risk vs. ambiguity) can be manipulated by different pharmacological mechanisms. It is more difficult to manipulate noreadrenaline (NA) and acetylcholine (Ach) in similar laboratory settings, but unquestionably these neurotransmitters do play a role in decision-making, which remains to be determined. 3.1.4.5

Functional Neuroimaging Support of the Somatic Marker Hypothesis

Studies that have looked at neural activation while participants performed the Iowa Gambling Task remain relatively scarce. One study had individuals perform the Iowa Gambling Task while situated in a positron emission tomography (PET) scanner (Ernst et al., 2002). The control task in this experiment involved the examiner signaling the participant to select cards from the four decks in a specified order, instead of allowing the participant to select decks. A predominantly right-sided network of prefrontal and posterior cortical regions was activated, which included the medial orbitofrontal cortex/ventromedial prefrontal cortex region, adjacent anterior cingulate cortex, dorsolateral prefrontal cortex, insula, and adjacent inferior parietal cortex (Ernst et al., 2002). This neural network overlaps considerably with that known from lesion studies to interfere with Iowa Gambling Task performance, as outlined earlier. Similar neural correlates underlying Iowa Gambling Task performance were revealed using functional magnetic resonance imaging (fMRI). Fifteen healthy volunteers performed the Iowa Gambling Task while having their brain activity scanned using event-related fMRI (Fukui, Murai, Fukuyama, Hayashi, & Hanakawa, 2005). When the neural activity occurring during selections from the advantageous decks was compared with the neural activity occurring during selections from the disadvantageous decks, it was found that activity during the anticipatory period (that is, the time spent pondering which deck to choose) engaged the superior part of the anterior cingulate and the neighboring medial frontal gyrus. This activity occurred in an area that is relatively superior to the medial orbitofrontal cortex/ventromedial prefrontal cortex area, although it still lies within the overall region known for housing decision-making impairments in patients with prefrontal cortex lesions. It is unclear whether the medial orbitofrontal cortex/ventromedial prefrontal cortex

c10.indd 170

7/25/2011 10:10:20 AM

EMOTION MAY NOT ALWAYS BE BENEFICIAL TO DECISION MAKING

171

area was precluded in this fMRI study because of signal dropout attributed to distortion artifacts. Northoff et al. (2006) analyzed the relationship among the ventromedial prefrontal cortex, emotionally accentuated affective judgment (that is, unexpected), cognitively accentuated affective judgment (that is, expected), and performance on the Iowa Gambling Task. Neuronal activity in the ventromedial prefrontal cortex during unexpected affective judgment significantly correlated with Iowa Gambling Task performance. The authors posit that the degree to which subjects recruit the ventromedial prefrontal cortex during affective judgments is related to beneficial performance on the Iowa Gambling Task (Northoff et al., 2006). These findings support the claim of somatic marker theory that not only cognitive but also affective mechanisms are crucial for decision making. Because affective judgments require an interaction between affective and cognitive components, it might be considered a key process in decision making that has been linked to neural activity in the ventromedial prefrontal cortex. Research by Windmann et al. (2006) used the original and inverted versions of the Iowa Gambling Task in healthy controls and suggested that the tendency to choose from the bad decks for longer in the original, relative to the inverted, task activated the medial orbitofrontal cortex more, which is consistent with the notion that the medial orbitofrontal cortex is involved in maintaining a behavioral strategy. Conversely, the inverted task activated more the lateral orbitofrontal cortex subregions, consistent with the notion that the ability to shift from the initially preferred choice option to alternative options is the relevant variable determining lateral orbitofrontal cortex activation, as well as performance on the Iowa Gambling Task, and not the ability to look into the future. Finally, Lawrence et al. (2009) used the Iowa Gambling Task to analyze decision making under initially ambiguous circumstances. Using a version of the Iowa Gambling Task that was modified for event-related fMRI, the authors find involvement of several prefrontal cortical regions in task performance. Decision making in healthy subjects resulted in ventromedial prefrontal cortex activation. The findings of this study not only replicate but also add to prior research in that they disclose that deciding advantageously under initially ambiguous circumstances may require both continuous and dynamic processes involving the ventral and dorsal prefrontal cortex (Lawrence et al., 2009). As such, this research adds more validity to the Iowa Gambling Task in terms of eliciting dorsal and ventral prefrontal cortex activation. In summary, the ventromedial prefrontal cortex, the dorsolateral prefrontal cortex, the medial orbitofrontal cortex, and the amygdala emerge as key brain areas related to emotion and decision making from the aforementioned research and, as such, form the neuroanatomical basis of the somatic marker framework.

3.1.5 EMOTION MAY NOT ALWAYS BE BENEFICIAL TO DECISION MAKING Although the somatic marker view argues that emotions are an important factor in the process of decision making, there is a popular notion that “emotions cloud the mind and interfere with good judgment” and that “wise decisions and

c10.indd 171

7/25/2011 10:10:20 AM

172

THE NEUROLOGICAL BASIS OF DECISION MAKING

judgments come only from cool heads.” How can we reconcile these seemingly conflicting views? Do emotions help the process of making advantageous decisions or disrupt it? The somatic marker hypothesis concerns emotion that is integral to the decisionmaking task at hand. For instance, when deciding to speed on a highway because you are late for an interview, the “thought” of being stopped by a police, or the “thought” of getting into an accident will trigger somatic states (e.g., some form of a fear response). However, these somatic states are integral to the decision-making task at hand (i.e., the decision on whether to speed or not). These somatic states are indeed beneficial because they consciously or nonconsciously bias the decision in an advantageous manner. However, the induction of somatic states that are unrelated to the decision task at hand (for example, receiving a cell phone call about someone dying in the family while driving) may become disruptive. Support of this hypothesis comes indirectly from clinical observations of neuropsychiatric patients with bipolar disorders, who show disturbances in decision making that include indecisiveness (during depression) or impulsiveness (during mania) (First, Spitzer, Gibbon, & Williams, 1997). Experimental evidence also suggests that the presence of such unrelated emotions shifts decisions in the direction of short-term goals (Gray, 1999). Also we have obtained preliminary evidence in normal subjects suggesting that the induction of strong emotional states (e.g., by the recall of personal emotional experiences) prior to the performance of the IGT, reduced the number of choices from the advantageous decks (Preston, Buchanan, Stansfield, & Bechara, 2007). However, this emotion-related or unrelated distinction does not always hold true. For instance, we have shown instances where emotions that are integral to the task can sometimes be disruptive (Shiv, Loewenstein, Bechara, Damasio, & Damasio, 2005). In a study, we developed a “risky decision-making task” closely modeled on a paradigm developed in previous economic research to demonstrate “myopic loss aversion” (Shiv et al., 2005). We studied normal participants and patients with focal lesions in the VM cortex, but we also studied patients with damage in other neuroal components known to be critical for processing emotions, namely the amygdala and insula. We endowed each participant with $20 of play money, which they were told to treat as real because they would cash the amount they were left with at the end of the study. Participants were told that they would be making several rounds of investment decisions and that, in each round, they had to make a decision between two options: invest $1 or not invest. If the decision were not to invest, the task would advance to the next round. If the decision were to invest, they would hand over a dollar bill to the experimenter. The experimenter would then toss a coin in plain view of the subject. If the outcome of the toss was heads (50% chance), they would lose the $1 that was invested; if the outcome of the toss was tails (50% chance), $2.50 would be added to the participant’s account. The task would then advance to the next round. The task consisted of 20 rounds of investment decisions. We designed the investment task so that it would behoove participants to invest in all the 20 rounds because the expected value on each round is higher if one invests ($1.25) than if one does not ($1). Examination of the proportion of the 20 rounds in which participants decided to invest revealed that the target patients made decisions that were closer to a profit-maximizing viewpoint. Specifically, target patients invested in 83.7% of the rounds on average, as compared with normal participants who

c10.indd 172

7/25/2011 10:10:20 AM

CRITIQUE OF THE SOMATIC MARKER FRAMEWORK

173

invested in 57.6% of the rounds, and patient-controls who invested in 60.7% of the rounds (Shiv et al., 2005). Furthermore, target patients earned more money during the 20 rounds of the experiment ($25.70, on average) than did normal participants ($22.8) or patient-controls ($20.07; the average amount earned by normal participants was no different than that earned by patient-controls). Even though the normal subjects intellectually knew what to do, loss aversion took over: Normal subjects know that the right thing to do is to invest in every single round, but when they actually get into the game, they just start reacting to the outcomes of previous rounds. These results suggest that taking away neural systems that are important for processing emotions render decisions more optimal, which is in contrast to what we have shown earlier in the IGT experiments. Two key differences between the two types of experiments should be noted. First, in the IGT experiments, subjects always have to make a choice, whereas in the investment task experiments, subjects can opt out making a choice. Second, in the IGT, the expected values (or long-term consequences) of the luring decks were negative, whereas in the investment task, the expected values were positive. Had the expected values in the investment task turned negative, the target patients may have performed suboptimally. Indeed, in subsequent experiments where the expected values of a risky decision were rendered negative, then VM lesion patients, as well as amygdala lesion patients, performed suboptimally and took risks when they were not supposed to, which led to long-term losses (Weller, Levin, Shiv, & Bechara, 2007). Irrespective of these methodological differences, the fact remains that there are instances when emotions can be disruptive to decision making, and the ability to suppress or control these emotions can be advantageous in the long term. Thus, it is not a simple issue of trusting biases and emotions as the necessary arbiter of good and bad decisions. It is a matter of discovering the circumstances in which biases and emotions can be useful or disruptive.

3.1.6 3.1.6.1

CRITIQUE OF THE SOMATIC MARKER FRAMEWORK Somatic Markers as Executive Processes

According to the somatic marker hypothesis, the visceral responses elicited during decision making, both during the contemplation of the future outcome of a choice and after the outcome of a choice has been signaled, aid in guiding decisions toward advantageous choices and away from disadvantageous choices. The process that is assessed by the IGT is ultimately a learning process, one in which knowledge of the correct strategy evolves over time. In this view, visceral responses to receiving reward and punishment, which are mediated by the amygdala, contribute to the encoding of the predictive value of the sensory cues and actions that preceded reward and punishment. Over time, through this encoding, subjects learn the association between a given choice and its outcome. This learning may precede explicit awareness of the contingencies between specific choices and their outcome. This learning is expressed by the vmPFC, which evokes learned representations of the predictive value of a choice in the period before a choice is made, when the outcomes of various choices are weighed against each other as they are held “in mind.”

c10.indd 173

7/25/2011 10:10:20 AM

174

THE NEUROLOGICAL BASIS OF DECISION MAKING

The representation of predictive value is based on the visceral response that is triggered within the vmPFC, an emotional response that “marks” the value of options for behavior based on past experience. Within the somatic marker framework, then, the vmPFC functions as a system that holds the affective-visceral properties of objects in mind during the planning and organization of behavior that is directed toward courses of action that are in the overall best interests of the organism. This function falls into the broader executive role of the prefrontal cortex, of which the vmPFC is a part. This role is supported by connections between the vmPFC and higher order sensory cortices, as well as connections between the vmPFC and the dorsolateral prefrontal cortex (both of which are mediated by the orbital network). The connections with higher order sensory cortices provide a route for highly processed information about the sensory properties of biologically relevant stimuli to reach the vmPFC. The connections with the dorsolateral prefrontal cortex link the functions of the vmPFC to executive processes that guide attention and prioritize action, allowing the vmPFC to serve as a buffer for the maintenance of information pertaining to the homeostatic value of goal objects (i.e., predictive value). Thus, the vmPFC is not involved in regulating global working memory processes, as indicated by the finding that damage to the vmPFC does not disrupt performance on broad tasks of working memory (Bechara et al., 1998). However, the vmPFC function does require intact working memory processes, as indicated by the finding that damage in regions of the prefrontal cortex that play a global role in working memory impairs performance on the IGT (Bechara, 2004; Clark, Cools, & Robbins, 2004). Some tasks that call upon representations of predictive reward value but that do not require this information to be held in working memory may also engage the vmPFC. For example, one study has shown that damage to the vmPFC disrupts both reversal learning and IGT performance (Fellows & Farah, 2005). In contrast, damage to the dorsolateral prefrontal cortex impairs performance upon the IGT but does not impair reversal learning. An important caveat in the comparison of this study with studies from our laboratory is that the Farah and Fellows study examined damage in posterior regions of vmPFC that also impinged on basal forebrain structures, such as the nucleus accumbens. Our studies, in contrast, have found that lesions restricted to more anterior regions of the vmPFC that do not include the basal forebrain can alter performance on the IGT (Bechara et al., 1998). Thus, it is possible that the reversal learning deficits found in the Fellows and Farah study are attributable to damage in the basal forebrain, rather than to damage in the vmPFC. Notwithstanding this, it is possible that both reversal learning and the IGT require an ability to register that the predictive reward value of a stimulus has changed, as well as an ability to inhibit a previously rewarded response. However, unlike the IGT, reversal learning does not require that information about the predictive reward value of a stimulus be held in working memory. Thus, the vmPFC may be engaged by processes that invoke representations of predictive reward value, as well as by processes that require inhibition of a previously rewarded response. Such process, which may themselves rely on somatic markers, could operate independently of working memory under certain experimental situations such as reversal learning. In real life, however, where decision making usually requires holding representations of predictive reward value in mind over a delay, they are likely to work in concert with working memory processes.

c10.indd 174

7/25/2011 10:10:20 AM

CRITIQUE OF THE SOMATIC MARKER FRAMEWORK

175

3.1.6.2 The Role of Feedback of Somatic Markers in Decision Making According to the somatic marker hypothesis, the afferent feedback of visceral responses is an important component of the decision-making process. In other words, the visceral responses during the contemplation of choices are necessary for biasing behavior in the advantageous direction, as well as for “gut feelings” and “hunches” related to choices. The question originates, then, as to whether the visceral responses induced by the vmPFC are actually necessary for decision making, or whether they are merely an epiphenomenal bodily reflection of the operation of certain mental processes. One way to address this question is to manipulate directly the sensory feedback of the visceral state during performance of the IGT. Several studies have attempted to do this. For example, one study has examined how cervical transection of the spinal cord affects performance on the IGT (North & O’Carroll, 2001). This study found no effect of the manipulation on performance on the IGT. Because the spinal cord carries somatosensory and interoceptive information from the body to the brain (Craig, 2002), this may be taken as evidence that the sensory feedback of bodily states does not contribute to decision making. However, a great deal of the information about visceral states is conveyed to the central nervous system via the vagus nerve, which is spared by spinal transection. If visceral states play a special role in signaling homeostatic processes, which we believe that they do, then it is not surprising that spinal transection has a limited effect on decision making. Another study (Heims, Critchley, Dolan, Mathias, & Cipolotti, 2004) examined more specifically the role of visceral states in decision making. They showed that patients with pure autonomic failure, a peripheral nervous disorder that broadly disrupts the ability to deploy visceral responses, do not demonstrate impaired performance on the IGT. This can also be taken as evidence that visceral responses are not necessary for decision making. However, this study did not actually measure visceral responses during the IGT, so it is possible that subjects still produced some form of visceral response during the task. Also, it is possible that, because pure autonomic failure develops slowly and manifests later in life, significant neural reorganization may take place in subjects with this disease, altering the normal mechanism of decision making. Another study (Martin et al., 2004) showed that electrical stimulation of the vagus nerve during the IGT, which largely affects visceral afferent signaling, can actually improve performance. This can be taken as evidence that visceral states do play a role in decision making. However, this study was limited by the fact that most of the subjects studied suffered long-standing epilepsy and many of them had lower than normal decision making to begin with. Functional imaging studies have provided circumstantial evidence of the role of visceral states in decision making. As discussed, the insular cortex is a visceral sensory region that has been hypothesized to play a role in decision making by mapping the visceral responses that are induced by the vmPFC and the amygdala. Several studies (Craig, 2002) have shown that activity in the insular cortex is correlated with changes in the visceral state. The insular cortex is also activated by decision-making tasks that involve uncertain reward and punishment and an evaluation of emotional information. For example, a PET study (Ernst et al., 2002) has shown that performance of the IGT, in addition to activating the vmPFC, also activates the insular cortex. Moreover, this study found that activity in the insular

c10.indd 175

7/25/2011 10:10:20 AM

176

THE NEUROLOGICAL BASIS OF DECISION MAKING

cortex was correlated with the performance on the IGT. The insular cortex has also been shown using fMRI to be activated during other decision-making tasks that involve uncertain reward and punishment (Critchley, Mathias, & Dolan, 2001). In addition, another study (Sanfey, Hastie, Colvin, & Grafman, 2003) found that the insular cortex is activated by the evaluation of the fairness of offers of money. Here, activity in the insular cortex was shown to be correlated with the tendency to reject unfair offers. Although these studies did not examine visceral responses directly, they show that the insular cortex, an area that has previously established as a visceral sensory representation area, is engaged during decision making, particularly when the decisions require an evaluation of emotional consequences that are uncertain. Thus, on the balance, the evidence seems to favor the role of visceral states in decision making; however, more definitive evidence is required to establish exactly how and under what circumstances visceral states contribute to decision making. Although certain forms of decision making may engage somatic marker processes, it may be that not all forms of decision making require the elicitation and sensory mapping of visceral states. Indeed, the somatic marker hypothesis maintains that, under some conditions, “as-if” representations of the visceral state, mediated by direct connections between the vmPFC and brainstem neurotransmitter nuclei, may be sufficient to guide decision making in the advantageous direction. Also, decisions that do not require the weighing of rewarding or punishing consequence or in which the outcome is relatively certain may not engage somatic marker processes at all. 3.1.6.3 Why Somatic Markers? A strictly computational approach to decision making may not require that the brain represent signals that are expressed within the body in order to compute the anticipated value of options for behavior (Maia & McClelland, 2004, 2005; Rolls, 1999). It is important to keep in mind, however, that the human brains differ from computers in many ways, not the least of which is a concern for the promotion of survival through regulation of the internal milieu—which is the regulation of bodily process. All nervous systems contain representations of basic bodily processes, such as those that regulate energy demands, reproduction, fluid balance, temperature, and the response to sickness and injury. Survival requires precise control over the state of these processes in order to maintain them within the narrow range that is compatible with life (i.e., homeostasis). The autonomic nervous system functions to make relatively rapid adjustments in the visceral state that maximize the survival value of events in the world that have the potential to impact homeostasis. It can be argued that visceral responses operate merely as reflexes, acting independently of higher order cognitive processes. Indeed, visceral reflexes that are implemented at the level of the spinal cord and brainstem do provide some benefit after the fact for reacting to events that challenge homeostasis. However, it is more advantageous to be able to predict the impact of events upon the internal milieu before they occur. To do this would require the brain to connect sensory and motor representations of the viscera with processes that govern perception, learning, memory, and goal-directed behavior. It is clear, based on a multitude of studies, many of which are reported in this volume, that the vmPFC plays a role in several

c10.indd 176

7/25/2011 10:10:20 AM

CRITIQUE OF THE SOMATIC MARKER FRAMEWORK

177

cognitive processes. It is also clear that the vmPFC plays a role in the control and mapping of visceral states. The most parsimonious explanation would seem to be that the cognitive processes that are mediated by the vmPFC and the visceral functions mediated by this area are somehow linked. According to the somatic marker hypothesis, the integration of visceral states into higher cognitive functions, such as decision making, is the function of the vmPFC. This function has expanded in evolution, allowing for the planning of behaviors that are executed further into the future and for which the outcomes of behavior in terms of rewards and punishments are more abstract. In humans, as well as in nonhuman primates and rodents, the vmPFC is involved in the planning of behaviors related to the most immediate and basic needs, such as food, water, and sex. In humans, the vmPFC also plays a role in guiding behaviors for which choosing advantageously requires a deliberate concern for one’s long-term well-being as well as knowledge of cultural norms and expectations. In this way, the vmPFC may function to link highly evolved human faculties, such as moral behavior, altruism, financial reasoning, creativity, and a sense of purpose in one’s work life and social relationships, to the basic mechanisms that govern survival and the maintenance of homeostasis. James (1884) initially proposed that visceral responses to biologically relevant stimuli are a necessary component of the subjective experience of emotion. Specifically, James (1884) proposed that subjective feelings—the hedonic meaning that is attributed to objects and events in the world—originates from the sensory feedback of the visceral responses that are elicited by those objects and events. According to the somatic marker hypothesis (Damasio, 1994, 1996; Damasio et al., 1991), the sensory mapping of visceral responses not only contributes to feelings, but this mapping is also important for the execution of highly complex, goal-oriented behaviors. In this view, visceral responses function to “mark” potential choices as being advantageous or disadvantageous. This process aids in decision making in which there is a need to weigh positive and negative outcomes that may not be predicted decisively through “cold” rationality alone. Both the Jamesian view and the somatic marker hypothesis hold that the brain must contain a system that translates the sensory properties of external stimuli into changes in the visceral state that reflect their biological relevance. The somatic marker hypothesis argues that this is the essential function of the ventromedial prefrontal cortex—a function that ties control of the visceral state to decision making and affect. Despite the fact that the original studies on somatic markers and their impact on the decision-making field (Bechara & Damasio, 2005; Bechara et al., 1994; Bechara, Damasio, Tranel, & Damasio, 1997) have inspired many subsequent studies, and at least in part, may have driven the emergence of the new fields of neuroeconomics, decision neuroscience, and consumer neuroscience (Hansen & Christensen, 2007; Kenning & Plassmann, 2005; Shiv et al., 2005), some views have critiqued the concept of somatic markers and questioned its utility as an explanatory framework for decision making (Maia & McClelland, 2004, 2005). Although the somatic marker framework incorporates the roles of many different brain structures relevant to decision making, whose neuroanatomical arrangement has been validated by a multitude of lesion and functional neuroimaging studies (Dunn, Dalgleish, & Lawrence, 2006), most of the critiques have been leveled on only one specific component of the somatic marker neural circuitry, namely the role of peripheral body

c10.indd 177

7/25/2011 10:10:20 AM

178

THE NEUROLOGICAL BASIS OF DECISION MAKING

signals in decision making. Although the somatic marker framework postulates an “as-if-body loop,” which bypasses the peripheral route altogether, these criticisms are fair and should be considered seriously because these body signals are one of the key hallmarks of the somatic marker theory. However, the fact remains that no currently available neurological theories provide an alternative to the somatic marker theory. Most often, the somatic marker theory is critiqued and replaced by models that are not neurological in perspective. More specifically, seemingly competing frameworks and models—mostly psychological, cognitive, or behavioral— derive their critique from the viewpoints of their own domains and schools of thought, whereas somatic marker theory was established on neurological evidence based on the comparison of decision-making strategies of patients with specific and focal brain lesions to healthy subjects. Because of the theoretical origin of the different approaches—neurology and neuroscience for the somatic marker theory and psychology, cognitive science, and behaviorism for other models of decision making—a conceptual comparison simply leads to a comparison of “apples and oranges” and, therefore, does not yield meaningful insights. To date, there is no other currently available neurological theory of decision making, which specifically details the different neural steps that take place inside the brain before the execution of a decision. Certainly, there are currently numerous neuroscientific studies on decision making that describe a whole variety of events such as expectation, conflict monitoring, gains, losses, and error detection. However, none of these theories are comprehensive neurological theories, and they rather focus on only one specific process of the more complex phenomenon of decision making. For example, the dopaminergic system has been implicated in decision making (D’Ardenne, McClure, Nystrom, & Cohen, 2008; Montague, Dayan, & Sejnowski, 1996; Schultz, 1998) and is clearly considered a neuroscientific approach to decision making. Yet, the dopamine story is very specific and constrained and does not explain decision making and its influence by emotions in a comprehensive manner as somatic marker theory does. Moreover, the somatic marker model is inclusive of the role of dopamine in decision making (Bechara & Damasio, 2005). Therefore, the dopamine link to reward, error prediction, and decision making (Bayer & Glimcher, 2005; Bayer, Lau, & Glimcher, 2007; Pessiglione, Seymour, Flandin, Dolan, & Frith, 2006) is not an alternative view to the somatic marker hypothesis, but rather one specific link in a broader neural model of decision making described under the somatic marker framework. In summary, criticisms of the somatic marker theory seem to originate from two common sources. One source is contrasting the somatic marker theory with other available affect-based theories of decision making, the majority of which are psychological or behavioral in perspective. Drawing such contrasts, however, might be like comparing “apples to oranges” because the somatic marker theory is neurological in perspective and addresses specific brain mechanisms of decision, whereas other psychological and behavioral theories do not. Another source of criticism is primarily directed against the “body” link in the theory, which argues that somatic states can be triggered in the body itself before exerting an influence on brain systems involved in decision making. Although the somatic marker theory is a comprehensive neurological theory that encompass numerous brain regions that are called on during the pondering of a decision, and that the peripheral (body) loop is only one link in a much broader neural framework, it is often assumed that the

c10.indd 178

7/25/2011 10:10:20 AM

REFERENCES

179

theory is only about these peripheral (body) signals that play a role in biasing decisions. Admittedly, this peripheral link remains the weakest link in the theory because of the impossibility of getting patients that have a complete disconnection between their body and their brain. No matter which neurological condition involves the peripheral nervous system (e.g., spinal cord transection or peripheral neuropathies), the condition never provides a complete disconnection between the body and the brain—there will always be spared routes for the body to provide neural signaling to the brain. The only condition that leads to almost complete disconnection is a lesion in the dorsal tegmentum of the brainstem, but unfortunately such damage leads to a coma or it is incompatible with life. For these practical reasons, the tests of the hypothesis that body signals influence decision making have remained inconclusive. Regardless, the somatic marker theory is not contingent on this peripheral link, and its description of the various roles that many other different brain regions play in emotional processing and decision making have been tested over time and have received robust support from numerous studies, including functional neuroimaging (e.g., see Dunn et al. [2006] for a review). Thus, the somatic marker theory remains the only neurological theory of decision, which unlike other theories that focus on very specific process of decision making, provides a comprehensive description of the neural events that take place during decision making all the way from perception to the execution of a motor response.

3.1.7

CONCLUSION

Emotions are a major factor in the interaction between environmental conditions and human decision processes, with these emotional systems (underlying somatic state activation) providing valuable implicit or explicit knowledge for making fast and advantageous decisions. Historically, most theories of choice have been cognitive in perspective, and they assumed that decisions derive from an assessment of the future outcomes of various options and alternatives through some type of cost– benefit analyses (see Loewenstein, Weber, Hsee, & Welch [2001] for a review). The somatic marker hypothesis of Damasio (1994) provides a neurobiological support for the notion that people make judgments not only by evaluating the consequences and their probability of occurring, but also and even sometimes primarily at a gut or emotional level (Damasio, 1994; Loewenstein et al., 2001; Schwartz & Clore, 1983; Zajonc, 1984). However, it is important to note that emotion is not always beneficial to decision making, and sometimes it can be disruptive. Therefore, it is important to discover the various conditions and circumstances under which emotion can be helpful or disruptive. Thus, the process of decision making depends in many important ways on neural substrates that regulate homeostasis, emotion, and feeling. In other words, the process of deciding is not just logical and computational but also emotional.

REFERENCES Ackerly, S. S., & Benton, A. L. (1948). Report of a case of bilateral frontal lobe defect. Proc Assoc Res Nerv Ment Dis 27, 479–504.

c10.indd 179

7/25/2011 10:10:20 AM

180

THE NEUROLOGICAL BASIS OF DECISION MAKING

Bayer, H. M., & Glimcher, P. W. (2005). Midbrain dopamine neurons encode a quantitative reward prediction error signal. Neuron 47(1), 129–141. Bayer, H. M., Lau, B., & Glimcher, P. W. (2007). Statistics of midbrain dopamine neuron spike trains in the awake primate. J Neurophysiol 98(3), 1428–1439. Bechara, A. (2004). The role of emotion in decision-making: Evidence from neurological patients with orbitofrontal damage. Brain Cognit 55, 30–40. Bechara, A., & Damasio, A. (2005). The somatic marker hypothesis: A neural theory of economic decision. Games Econ Behav 52(2), 336–372. Bechara, A., Damasio, A. R., Damasio, H., & Anderson, S. W. (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition 50, 7–15. Bechara, A., Damasio, H., Tranel, D., & Anderson, S. W. (1998). Dissociation of working memory from decision making within the human prefrontal cortex. T J Neurosci 18, 428– 437. Bechara, A., Damasio, H., Tranel, D., & Damasio, A. R. (1997). Deciding advantageously before knowing the advantageous strategy. Science 275(5304), 1293–1295. Bechara, A., Damasio, H., Tranel, D., & Damasio, A. R. (1997). Deciding advantageously before knowing the advantageous strategy. Science 275, 1293–1295. Bechara, A., Damasio, H., Tranel, D., & Damasio, A. R. (2005). The Iowa gambling task (IGT) and the somatic marker hypothesis (SMH): Some questions and answers. Trends Cognit Sci 9(4), 159–162. Bechara, A., Dolan, S., & Hindes, A. (2002). Decision-making and addiction (Part II): Myopia for the future or hypersensitivity to reward? Neuropsychologia 40(10), 1690–1705. Bechara, A., Tranel, D., & Damasio, H. (2000). Characterization of the decision-making impairment of patients with bilateral lesions of the ventromedial prefrontal cortex. Brain 123, 2189–2202. Bechara, A., Tranel, D., Damasio, H., & Damasio, A. R. (1996). Failure to respond autonomically to anticipated future outcomes following damage to prefrontal cortex. Cerebr Cortex 6, 215–225. Blessing, W. W. (1997). Anatomy of the lower brainstem. The Lower Brainstem and Bodily Homeostasis. New York: Oxford University Press. Brickner, R. M. (1932). An interpretation of frontal lobe function based upon the study of a case of partial bilateral frontal lobectomy. Localization of function in the cerebral cortex. Proc Assoc Res Nerv Ment Dis 13, 259–351. Cacioppo, J., Berntson, G., Larsen, J., Poehlmann, K., & Ito, T. (2000). The psychophysiology of emotion. The Handbook of Emotion, 2nd ed, edited by M. Lewis & J. Haviland-Jones. New York: Guiford Press. Cacioppo, J., Klein, D. J., Berntson, G. G., & Hatfield, E. (1993). The psychophysiology of emotion. The Handbook of Emotion, 1st ed, edited by M. Lewis & J. M. Haviland. New York: The Guiford Press. Clark, L., Cools, R., & Robbins, T. (2004). The neuropsychology of ventral prefrontal cortex: Decision-making and reversal learning. Brain Cognit 55, 41–53. Craig, A. D. (2002). How do you feel? Interoception: The sense of the physiological condition of the body. Nat Rev Neurosci 3, 655–666. Critchley, H., Mathias, C., & Dolan, R. (2001). Neuroanatomical basis for first- and secondorder representations of bodily states. Nat Neurosci 4(2), 207–212. D’Ardenne, K., McClure, S. M., Nystrom, L. E., & Cohen, J. D. (2008). BOLD responses reflecting dopaminergic signals in the human ventral tegmental area. Science 319(5867), 1264–1267.

c10.indd 180

7/25/2011 10:10:20 AM

REFERENCES

181

Damasio, A. R. (1994). Descartes’ Error: Emotion, Reason, and the Human Brain. New York: Grosset/Putnam. Damasio, A. R. (1996). The somatic marker hypothesis and the possible functions of the prefrontal cortex. Philo Trans Roy Soc Lond 351, 1413–1420. Damasio, A. R., Tranel, D., & Damasio, H. (1990). Individuals with sociopathic behavior caused by frontal damage fail to respond autonomically to social stimuli. Behav Brain Res 41, 81–94. Damasio, A. R., Tranel, D., & Damasio, H. (1991). Somatic markers and the guidance of behavior: Theory and preliminary testing. Frontal Lobe Function and Dysfunction, edited by H. S. Levin, H. M. Eisenberg, & A. L. Benton. New York: Oxford University Press. Damasio, H., Grabowski, T., Frank, R., Galburda, A. M., & Damasio, A. R. (1994). The return of Phineas Gage: Clues about the brain from the skull of a famous patient. Science 264, 1102–1104. Dunn, B. D., Dalgleish, T., & Lawrence, A. D. (2006). The somatic marker hypothesis: A critical evaluation. Neurosci Biobehav Rev 30(2), 239–271. Ernst, M., Bolla, K., Moratidis, M., Contoreggi, C. S., Matochick, J. A., et al. (2002). Decisionmaking in a risk taking task. Neuropsychopharmacology 26, 682–691. Eslinger, P. J., & Damasio, A. R. (1985). Severe disturbance of higher cognition after bilateral frontal lobe ablation: patient EVR. Neurology 35, 1731–1741. Fellows, L. K., & Farah, M. J. (2003). Ventromedial frontal cortex mediates affective shifting in humans: Evidence from a reversal learning paradigm. Brain 126, 1830–1837. Fellows, L. K., & Farah, M. J. (2005). Different underlying impairments in decision making following ventromedial and dorsolateral frontal lobe damage in humans. Cerebr Cortex 15, 58–63. First, M. B., Spitzer, R. L., Gibbon, M., & Williams, J. B. W. (1997). Structured Clinical Interview for DSM-IVAxis I Disorders, Research Version, Non-patient Edition. (SCID-I/NP). New York: Biometrics Research, New York State Psychiatric Institute. Fukui, H., Murai, T., Fukuyama, H., Hayashi, T., & Hanakawa, T. (2005). Functional activity related to risk anticipation during performance of the Iowa Gambling Task. Neuroimage 24, 253–259. Gray, J. R. (1999). A bias toward short-term thinking in threat-related negative emotional states. Pers Soc Psychol Bull 25(1), 65–75. Hansen, F., & Christensen, S. R. (2007). Emotions, Advertising and Consumer Choice. Copenhagen, Denmark: Copenhagen Business School Press. Harlow, J. M. (1848). Passage of an iron bar through the head. Boston Medical and Surgical Journal 39, 389–393. Heims, H. C., Critchley, H. D., Dolan, R., Mathias, C. J., & Cipolotti, L. (2004). Social and motivational functioning is not critically dependent on feedback of autonomic responses: Neuropsychological evidence from patients with pure autonomic failure. Neuropsychologia 42, 1979–1988. James, W. (1884). What is an emotion? Mind 9(34), 188–205. Kenning, P., & Plassmann, H. (2005). NeuroEconomics: An overview from an economic perspective. Brain Res Bull 67(5), 343–354. Lang, P. J., Bradley, M. M., Cuthbert, B. N., & Patrick, C. J. (1993). Emotion and psychopathology: A startle probe analysis. Experimental Personality and Psychopathology Research, vol. 16, edited by L. J. Chapman, J. P. Chapman & D. C. Fowles. New York: Springer. Lawrence, N. S., Jollant, F., O’Daly, O., Zelaya, F., & Phillips, M. L. (2009). Distinct roles of prefrontal cortical subregions in the Iowa gambling task. Cerebral Cortex 19, 1134–1143.

c10.indd 181

7/25/2011 10:10:20 AM

182

THE NEUROLOGICAL BASIS OF DECISION MAKING

Loewenstein, G. F., Weber, E. U., Hsee, C. K., & Welch, N. (2001). Risk as feelings. Psychol Bull 127(2), 267–286. Maia, T. V., & McClelland, J. L. (2004). A reexamination of the evidence for the somatic marker hypothesis: What participants really know in the Iowa gambling task. Proc Nat Acad Sci 101(45), 16075–16080. Maia, T. V., & McClelland, J. L. (2005). The somatic marker hypothesis: Still many questions but no answers: Response to Bechara et al. Trends Cogit Sci 9(4), 162–164. Manes, F., Sahakian, B., Clark, L., Rogers, R., Antoun, N., et al. (2002). Decision-making processes following damage to the prefrontal cortex. Brain 125, 624–639. Martin, C., Denburg, N., Tranel, D., Granner, M., & Bechara, A. (2004). The effects of vagal nerve stimulation on decision-making. Cortex 40, 1–8. Montague, P. R., Dayan, P., & Sejnowski, T. J. (1996). A framework for mesencephalic dopamine systems based on predictive Hebbian learning. J Neurosci 16(5), 1936–1947. Nauta, W. J. H. (1971). The problem of the frontal lobes: A reinterpretation. J Psychiatr Res 8, 167–187. North, N. T., & O’Carroll, R. E. (2001). Decision making in patients with spinal cord damage: Afferent feedback and the somatic marker hypothesis. Neuropsychologia 39(5), 521–524. Northoff, G., Grimm, S., Boeker, H., Schmidt, C., Bermpohl, F., et al. (2006). Affective judgment and beneficial decision making: Ventromedial prefrontal activity correlates with performance in the iowa gambling task. Hum Brain Mapp 27, 572–587. Ongur, D., & Price, J. L. (2000). The organization of networks within the orbital and medial prefrontal cortex of rats, monkeys and humans. Cerebr Cortex 10(3), 206–219. Persaud, N., McLeod, P., & Cowey, A. (2007). Post-decision wagering objectively measures awareness. Nat Neurosci 10(2), 257–261. Pessiglione, M., Seymour, B., Flandin, G., Dolan, R. J., & Frith, C. D. (2006). Dopaminedependent prediction errors underpin reward-seeking behaviour in humans. Nature 442(7106), 1042–1045. Preston, S. D., Buchanan, T. W., Stansfield, R. B., & Bechara, A. (2007). Effects of anticipatory stress on decision-making in a gambling task. J Behav Neurosci 121(2), 257–263. Rainville, P., Bechara, A., Naqvi, N., Virasith, A., Bilodeau, M., et al. (2006). Basic emotions are associated with distinct patterns of cardiorespiratory activity. Int J Psychophysiol 61(1), 5–18. Rolls, E. T. (1999). The Brain and Emotion. Oxford: Oxford University Press. Sanfey, A., Hastie, R., Colvin, M., & Grafman, J. (2003). Phineas guaged: Decision-making and the human prefrontal cortex. Neuropsychologia 41, 1218–1229. Saver, J. L., & Damasio, A. R. (1991). Preserved access and processing of social knowledge in a patient with acquired sociopathy due to ventromedial frontal damage. Neuropsychologia 29, 1241–1249. Schultz, W. (1998). Predictive reward signal of dopamine neurons. J Neurophysiol 80(1), 1–27. Schultz, W., Dayan, P., & Montague, P. R. (1997). A neural substrate of prediction and reward. Science 275, 1593–1599. Schwartz, N., & Clore, G. L. (1983). Mood, misattribution, and judgements of wellbeing: Information and directive functions of affective states. J Pers Soc Psychol 45, 513–523. Shiv, B., Bechara, A., Levin, I., Alba, J., Bettman, J., et al. (2005). Decision neuroscience. Market Lett 16(3/4), 375–386.

c10.indd 182

7/25/2011 10:10:20 AM

REFERENCES

183

Shiv, B., Loewenstein, G., Bechara, A., Damasio, H., & Damasio, A. R. (2005). Investment behavior and the negative side of emotion. Psychol Sci 16(6), 435– 439. Tomb, I., Hauser, M., Deldin, P., & Caramazza, A. (2002). Do somatic markers mediate decisions on the gambling task? Nat Neurosci 5(11), 1103–1104. Weller, J. A., Levin, I. P., Shiv, B., & Bechara, A. (2007). Neural correlates of adaptive decisionmaking in risky gains and losses. Psychol Sci 18(11), 958–964. Welt, L. (1888). Uber Charaktervaranderungen des Menschen infoldge von Lasionen des Stirnhirns. Dtsch Arch Klin Med 42, 339–390. Windmann, S., Kirsch, P., Mier, D., Stark, R., Walter, B., et al. (2006). On framing effects in decision making: Linking lateral versus medial orbitofrontal cortex activation to choice outcome processing. J Cognit Neurosci 18(7), 1198–1211. Zajonc, R. B. (1984). On the primacy of affect. Am Psychol 39, 117–123.

c10.indd 183

7/25/2011 10:10:21 AM

3.2 A MODEL OF THE INITIAL STAGES OF DRUG ABUSE: FROM REINFORCEMENT LEARNING TO SOCIAL CONTAGION GILLY KORITZKY, ADI LURIA, AND ELDAD YECHIAM Technion—Israel Institute of Technology, Haifa, Israel

A great deal has been written about the antecedents of drug use and abuse. The importance of the issue is of course incontestable: Drug use and abuse carry severe consequences for the individuals themselves as well as for society as a whole. For individuals, drug abuse affects mood and cognitive processes (Adams, Blanken, Ferguson, & Kopstein, 1990); is a factor in contracting various diseases such as lung cancer (Blum, 1987), coronary heart disease (Blum, 1987), and AIDS (Beyrer et al. 2000; Chu & Levy, 2005; Sorensen & Copeland, 2000), and is related to various negative situations such as child abuse, violent crime, unemployment (Hawkins, Catalano, & Miller, 1992), and adolescent and adult delinquency (Brook, Whiteman, Finch, & Cohen, 1996). For society in general, drug abuse has consequences for the cost of health care and mental health services, crime rates, educational systems, and more (Adams et al., 1990). The issue has been studied from many different perspectives and approaches, and an array of risk factors—biological predisposition, societal and cultural variables, and personality variables (such as impulsiveness or sensation seeking)—has been suggested. It is important to note that various definitions of substance or drug use and abuse have been used in the literature. In the current chapter, we will rely on the definition of substance abuse adopted by Hawkins et al. (1992), and that is the frequent use of alcohol, drugs, or other substances in a manner that is associated

From DNA to Social Cognition, First Edition. Edited by Richard Ebstein, Simone Shamay-Tsoory, and Soo Hong Chew. © 2011 Wiley-Blackwell. Published 2011 by John Wiley & Sons, Inc.

185

c11.indd 185

7/25/2011 10:10:24 AM

186

A MODEL OF THE INITIAL STAGES OF DRUG ABUSE

with problems and dysfunctions. Taking this perspective, drug abuse is a habit that certain individuals acquire despite its detrimental consequences. In the current chapter, we suggest that drug use acquisition is adequately described as the product of individual learning and decision-making impairments, along with certain social and environmental influences. In the next sections we will summarize several factors contributing to drug abuse. These findings show the influence of individuals’ decision characteristics and their social environment (peer and family influence) on drug abuse risk, but most current theories present these factors as separate mechanisms. Later in the chapter we will present a model that integrates the effect of these factors.

3.2.1

INDIVIDUAL FACTORS LEADING TO RISK OF ADDICTION

It has been found that certain individual characteristics, such as sensation seeking and impulsivity, are related to drug use (Cloninger, Sigvardsson, & Bohman, 1988; Shedler & Block, 1990). In a series of studies on alcohol use, Donohew and colleagues found the use of alcohol to be twice as high among individuals high in sensation seeking than those low in the measure (Donohew, Helm, Lawrence, & Shatzer, 1990; Donohew, Lorch, & Palmgreen, 1991; Donohew, Palmgreen & Lorch, 1994). Findings such as these lend support to the idea that individual traits such as sensation seeking and impulsivity may be related to risk taking and poor decision making in real-world situations. Furthermore, impulsivity and sensation seeking have been linked to neurotransmitter activity in certain brain structures, suggesting that some drug-related behavior may in part be affected by biological characteristics (see Hawkins et al., 1992, for a review). For example, there is some research to suggest that sensation seeking is linked biochemically to platelet monoamine oxidase (MAO) activity (Zukerman, 1987), which has also been found to be associated with early-onset alcoholism (see Hawkins et al., 1992). The enzyme ALDH (aldehyde dehydrogenase) has also been linked to alcoholism. Studies on Asian populations showed that Asians lacking the ALDH enzyme drink less and have lower rates of alcoholism than controls (Hawkins et al., 1992). Studies of individual decision making also lend support to the idea that drug abusers may be adopting different decision-making patterns than those of nonusers. Such studies usually have subjects complete an abstract decision-making task and then compare performance between different subject groups. The differences between choice alternatives along with the subjects’ choice patterns allow the researchers to reach different conclusions about the emphasis subjects place on variables such as the relative magnitude of winning or losing, the immediacy of the payoff, and its probability. For example, in Grant, Contoreggi, and London (2000) and in Bechara, et al. (2001), groups of substance abusers and controls completed the decision-making task known as the Iowa Gambling Task (Bechara, Damasio, Damasio, & Anderson, 1994). The drug abusers demonstrated impaired decision making compared with controls, preferring choices that yielded high immediate gains but warranted greater future losses. We will return to the Iowa Gambling Task later in the chapter and elaborate on its potential in telling drug abusers from nonabusers.

c11.indd 186

7/25/2011 10:10:24 AM

SOCIAL FACTOR LEADING TO RISK OF ADDICTION

3.2.2

187

SOCIAL FACTOR LEADING TO RISK OF ADDICTION

The most prominent antecedent of drug use, however, is simply the exposure to drugs being used. Among the factors associated with drug use and abuse are the prevalence of drug use in one’s social environment and the general availability of drugs in one’s surroundings, as well as social norms favoring drug-related behaviors. Social learning theory (Bandura, 1977) suggests that behavior is primarily learned through a process of operant conditioning, in which behavior is shaped by consequences that follow it. The social aspect of the theory stems from the assumption that behavior is learned through this direct process of conditioning but also through the imitation or modeling of others’ behaviors. Social groups are so significant because they affect the individual’s main sources of reinforcement and punishment, expose the individual to behavioral models, and help form conceptualizations of normative behavior. This suggests, for example, that individuals who are raised in a home where alcohol is frequently consumed without major punishment come to perceive frequent drinking as normative. According to reviews of the literature, the most important of these social groups with which one is associated are the peerfriendship groups and the family (Akers, Krohn, Lanza-Kaduce, & Radosevich, 1979). Social learning theory proposes that drug use and abuse develop through a process of observation of and experience with the associated problematic behaviors and their consequences. In brief, individuals form a sense of belonging to a social group, which influences their definitions of norms and expectation and provides models for imitation. From this perspective, Akers and his colleagues (1979) suggest that individuals both experience and observe reinforcement or punishment for drug use, and their norms and expectations with regard to drug use are shaped accordingly. The interaction of these variables leads either to abstinence from, or to initial use of, various substances. After the initial use, consequences (also partially influenced by the social group) determine whether this behavior will continue, and to what extent. Notice that the effect of social norms implies that in environments in which drug use is generally accepted, abstinence from drugs may bear aversive consequences, just as much as drug use does in environments that condemn the behavior. Indeed, strong support for social learning theory in the context of drug abuse has been reported. In their classic investigation of social learning theory and deviant behavior, Akers and colleagues (1979) found that social group association variables explain the largest amount of variance for marijuana and alcohol use and, specifically, that peer association was the most important predictor. The researchers also indicated that modeling of peer behavior was considered to have its greatest effect on first use and in the initial stages of behavior acquisition, suggesting that social learning effects may be particularly important in the early stages of drug abuse. The social learning aspect of drug abuse is also related to parental modeling of drug-related behaviors. A large body of literature has shown that parental use of alcohol and drugs is strongly related to juvenile use and misuse of substances (see Hawkins et al., 1992, for a review). Parental drug use has been shown to predict initiation of drug use by adolescents, frequency of adolescent marijuana use, and

c11.indd 187

7/25/2011 10:10:24 AM

188

A MODEL OF THE INITIAL STAGES OF DRUG ABUSE

adolescent use of additional illegal drugs, such as cocaine and barbiturates. In a study examining the effects of parental modeling of drug-related behavior, Ahmed, Bush, Davidson, and Iannotti (1984) investigated children’s expectations to use drugs as well as their actual drug use. The researchers found that the strongest predictors of children’s expectations to use alcohol and of their actual alcohol use were the number of household drug users and the degree of the child’s involvement in parental drug-taking. Parental modeling of drug-related behavior is assumed to influence children and adolescent’s behavior indirectly as well. Several studies have found that parental substance abuse is related to friends’ use of drugs, which in turn is related to adolescent substance abuse (e.g., Hansen et al., 1987, in Hawkins et al., 1992). Still, of the various drug abuse antecedents, the strongest predictor of drug use in youth remains drug abuse by peers. A substantial body of research has consistently shown that the influence of peers on adolescent drug use is robust for different ethnic groups and surmounts the effect of parental drug modeling (Brook, Whiteman, Gordon, & Brook, 1988; Hawkins et al., 1992). In some drugs of abuse, often referred to as social drugs, this situation is particularly striking. Certain drugs of abuse such as alcohol and cannabis are more often than not consumed in groups (Skog, 2006; Wilkins, Casswell, Bhatta, & Pledger, 2002; WDR, 2006), and their use is positively associated with the amount of time spent with friends (Duncan, Duncan, & Strycker, 2000; McMorris & Uggen, 2000; Peretti-Watel & Lorente, 2004; Svenson, 2000). These findings support the key role that social learning processes play in drug use and abuse.

3.2.3 A UNIFIED MODEL FOR THE JOINT EFFECT OF INDIVIDUAL SENSITIVITY AND SOCIAL EXPOSURE The current chapter proposes a unified model for the joint effect of individual learning and social influence on drug abuse. Under this model, social experience acts as a substitute to individual learning that facilitates choice of alternatives with rare (or delayed) negative outcomes, by increasing the salience of the immediate positive outcomes as a result of drug consumption. Social experiences, therefore, accelerate risky behavior under certain conditions. The current model further suggests that there is a particular decision-making style that prompts the acquisition of drug use in certain individuals. It therefore indicates an interaction between a particular decision style and environmental factors implicated in addiction. We first present the models’ main assumptions verbally and follow in the next section with a quantitative summary. 3.2.3.1 Observing What Could Have Been—Social Influence and Risk Taking in the Laboratory Because drug use is learned from experience—acquired through repeated encounters of initially unknown consequences—we consider the most suitable experimental environment for simulating drug use acquisition to be experience-based decision tasks. These tasks involve repeated selection between alternatives while receiving little or no initial information as to the nature of the payoffs associated with them.

c11.indd 188

7/25/2011 10:10:24 AM

A UNIFIED MODEL FOR THE JOINT EFFECT

189

As the task proceeds, the accumulated knowledge of past outcomes gradually shapes one’s pattern of responses. As their phrasing and interface do not reveal what traits or tendencies they measure, experience-based tasks are also relatively immune to response biases, such as attempts to impress observers favorably (Koritzky & Yechiam, 2010). This contributes to their capability of capturing real-world behavior in the laboratory. Laboratory settings of this kind can be made even more suitable for modeling the initial acquisition of drug use if social influence is taken into account, for instance, by letting decision makers observe the outcomes of others’ choices. The effect of observing others’ behavior in decisions from experience has been studied by Yechiam, Druyan, and Ert (2008). They used decision tasks that included a safe alternative (yielding a constant payoff) and a risky one (yielding payoffs with some variance). In one task, labeled 1/2, the safe alternative produced superior outcomes in half of the trials, and in the other task, labeled 1/20, the safe alternative was superior in only 5% of the trials. However, the safe alternative was always the advantageous one in terms of expectancy, as follows: Task 1/20

S (Safe) R (Risky)

Lose 2 Ag.1 Lose 30 Ag with a probability of 0.05 (1 in 20). Lose 1 Ag otherwise.

Task 1/2

S (Safe) R (Risky)

Lose 2 Ag. Lose 4 Ag with a probability of 0.5 (1 in 2). Lose 1 Ag otherwise.

Participants performed the tasks on a computer, with the two alternatives displayed as two unmarked buttons that could be selected by mouse clicking (see Figure 3.2-1). Participants were encouraged to maximize their earnings, which were converted into real money at the end of the experiment.

A

B

−1

You got:

−1

Total:

−4

Figure 3.2-1. A schematic image of the experimental display in an experience-based decision task with two alternatives. (See color insert.) 1

c11.indd 189

Ag.- abbreviation for “Agora,” an Israeli currency. 1 NIS = 100 Ag. 1 Ag = approximately. 0.24 U.S. cents.

7/25/2011 10:10:24 AM

190

A MODEL OF THE INITIAL STAGES OF DRUG ABUSE

Proportion of Risky choices

1 0.9

Task 1/20

0.8

Task 1/2

0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 With exposure

Without exposure

Figure 3.2-2. Proportion of risky choices in two decision tasks (1/20 and 1/2), by social exposure condition (Yechiam, Druyan, & Ert, 2008). (See color insert.)

Social influence came into play as follows. For half of the participants, the display was twofold: while they were performing the tasks, their monitors also displayed the course of the same task as simultaneously performed by another, independent participant. As shown in Figure 3.2-2, these participants made more risky choices than control participants, who did not have the opportunity to observe others. However, this difference was larger in the 1/20 task in which the risky alternative was superior most of the time, indicating that exposure to others’ behavior prompts risky choices especially in the case of seemingly attractive alternatives with rare—but severe— negative consequences (see Figure 3.2-2). This pattern is consistent with the effect of social exposure on drug use. Evidently, the exposure to the outcomes experienced by others as they act and make decisions influences the behavior of the observers. This influence is stronger when the behavior displayed by the social models (which could be risky) yields better outcomes than its alternative most of the time. This can explain why effects such as that of social drugs are only observed in certain risky behaviors and not in others: Social exposure will enhance risk taking in contexts where the risky alternative is better most of the time. In other contexts, social exposure may have a negative effect, or no effect at all, on risk taking. For example, some behaviors associated with risk, such as Internet gambling, tend to be solitary activities, and were found to be negatively associated with time spent with friends (Wood, Griffiths, & Parke, 2007; see also Miltenberger, Fuqua, & Woods, 1998). One possible contributor for the difference between these behaviors and the consumption of the so-called social drugs may lie in the frequent penalties associated with Internet gambling and similar activities. When a behavior often yields negative outcomes, this effect would only be emphasized if one is exposed to others performing that same behavior. Therefore, frequent punishments may make the social context less conducive of risky behavior.

c11.indd 190

7/25/2011 10:10:24 AM

A UNIFIED MODEL FOR THE JOINT EFFECT OF INDIVIDUAL SENSITIVITY

191

Another related phenomenon involves the difference between drugs of abuse. In a nationwide Canadian study, the use of drugs such as heroin and hallucinogens was not found to be associated with the amount of time spent with friends (HBSC, 2004). Possibly, social exposure in a situation where drugs have common negative consequences, along with their positive impact on affect, does not increase consumption to the extent it does when the outcomes are typically favorable (e.g., in marijuana). It could be argued that in the experimental model of Yechiam et al. (2008) being observed by others, rather than the observation of the payoffs others received, accounts for an elevated tendency to take risk in social contexts. However, in a second study (Yechiam et al., 2008), no similar pattern emerged for individuals who were simply observed by others. We assert that the same logic applies to the issue of social influence in initial drug use: The sometimes favorable outcomes of others’ drug use behavior can prompt people into using drugs. Next we will consider whether individual differences in drug abuse are indeed associated with a tendency to pick an alternative that is pertinently better most of the time. 3.2.3.2

Sensitivity to Favorable Outcomes and Drug Abuse

The reward learning of drug abusers can be studied using experience-based decision tasks, such as the commonly used Iowa Gambling Task (IGT; Bechara et al., 1994). In this task, respondents choose repeatedly among four decks of cards without initial information as to the payoffs they yield, and with the goal of maximizing their profit. Each card selection yields a gain, but occasionally losses occur too (gains and losses are given in points, which are converted into real money when the task ends). Two decks are disadvantageous in that they yield relatively high gains along with losses that are even higher, resulting in net loss. The two advantageous decks yield smaller gains but also much smaller losses, resulting in net gain (see Table 3.2-1). To perform well in this task, one must learn to avoid the disadvantageous decks and prefer the advantageous ones. Indeed, such a pattern of learning is usually found in normal samples of task performers. Another distinction among the decks that is less commonly made (see Yechiam, Stout, Busemeyer, Rock, & Finn, 2005b) is between decks that accrue positive outcomes most of the time (B and D, where outcomes are gains 90% of the time) and those that accrue positive outcomes only half of the time (A and C). This distinction can allow the assessment of the sensitivity of performers to a typically favorable event.

TABLE 3.2-1 The Distribution of Payoffs in the Iowa Gambling Task Deck A B C D

c11.indd 191

Type

Gains

Losses

Disadvantageous Disadvantageous Advantageous Advantageous

100 for sure 100 for sure 50 for sure 50 for sure

50% to lose 250 10% to lose 1250 50% to lose 50 10% to lose 250

7/25/2011 10:10:24 AM

192

A MODEL OF THE INITIAL STAGES OF DRUG ABUSE

The IGT was originally proposed to provide insight into the real-world maladaptive decision making of persons with lesions in a particular area of the forebrain— the ventromedial prefrontal cortex (PFC). Despite having their intellectual skills intact, these individuals are known to be reckless and impulsive in their daily lives. Bechara et al. (1994) conjectured that it takes more than cognitive ability for successful decision making. As PFC lesion patients perform poorly on the IGT, these authors suggested that motivational and affective factors are involved in responding to losses and gains, and in learning to avoid needless risk and prefer the alternatives that would be better in the long run (Bechara et al., 1994). When applying the IGT as a means of distinction between drug abusers and nonabusers in terms of decision-making style, a noteworthy pattern is observed. Although chronic drug abusers tend to perform poorly on the task (Bechara et al., 2001; Stout, Busemeyer, Lin, Grant, & Bonson, 2004; Yechiam, Busemeyer, Stout, & Bechara, 2005a), high-functioning drug abusers are hardly any different from controls (Stout, Rock, Campbell, Busemeyer, & Finn, 2005; Yechiam et al., 2005a, 2005b). High-functioning drug abusers are “individuals with high substance use levels and/or substance use problems identified by self-report on substance use questionnaires, but who have not received a formal substance abuse diagnosis, have not been treated, and who remain either employed or in school” (Yechiam et al., 2005b, p. 98). It may not sound surprising if indeed these individuals demonstrated adaptive decision making. After all, by definition they are individuals whose use of drugs does not hinder their coping with the demands of everyday life, and this may suggest that their decision-making style is just as adaptive as any healthy nonuser’s. However, this does not seem to be the case. Notice that in half of the trials of the task, disadvantageous deck A is superior to the advantageous decks (see Table 3.2-1), and disadvantageous deck B is superior in as much as 90% of the trials. This misleading, apparent advantage is sufficient to attract chronic drug abusers. It may be, though, that high-functioning drug abusers are not immune to these effects either. Rather, it might simply take a higher salience of the bogus rewards to lead these individuals astray. Yechiam et al. (2005b) hypothesized that high-functioning drug abusers would differ from nonabusers when favorable outcomes are more saliently presented in the form of foregone payoffs: knowledge of the outcomes of alternatives that were not selected by the participant. To test the effects of foregone payoffs, the IGT can be modified so that with each card selection, all decks will be revealed, and decision makers will see not only the payoff for their choice, but also the payoffs they could have obtained if they had chosen differently. This addition of foregone payoffs provides that regardless of one’s last choice, most of the time larger payoffs will be displayed by the disadvantageous decks. This state of affairs may increase temptation to choose from these decks, despite the bad consequences. Indeed, this was the finding in Yechiam et al. (2005b): High-functioning drug abusers and nonabusers performed about equally in the standard version of the IGT, but the high-functioning drug abusers made significantly more disadvantageous choices when foregone payoffs were presented. Of the two disadvantageous decks, the deck most sensitive to the addition of foregone payoffs was deck B, which yielded favorable outcomes in the majority of trials (see Table 3.2-1). Highfunctioning drug abusers chose from this deck more frequently, and this effect lasted as the task proceeded, in contrast to the decrease in deck B choices found without

c11.indd 192

7/25/2011 10:10:24 AM

ACCOUNTING FOR DRUG ABUSE IN FORMULAS

193

foregone payoffs (in fact, in the standard task, drug abusers showed a much more rapid decrease than controls). Nonabusers were also affected by the addition of foregone payoffs in the same direction, although to a much smaller extent (Yechiam et al., 2005b). It seems therefore that however well-adjusted they are, high-functioning drug abusers are more easily distracted by seemingly attractive (yet hazardous) alternatives when these are made salient enough. Notice the similarity of these results to the findings of Yechiam et al. (2008) regarding the effect of observing others’ behavior. As described in the first sections of this chapter, drug use is almost always acquired upon observing others in one’s environment consume drugs and obtain desired outcomes. We suggest that the social experience functions as foregone payoffs, which increase the salience of commonly favorable outcomes from risky alternatives.

3.2.4 ACCOUNTING FOR DRUG ABUSE IN FORMULAS: THE EXPECTANCY-VALENCE MODEL Drug abuse is acquired through learning, and so it can be described with a quantitative learning model (i.e., a set of equations representing the stages of learning). An appropriate model should encompass the initial exposure to drugs (or to the possibility of using them), the evaluation of the pros and cons of this action in comparison with its alternatives, the principle determining what choice will be made, and the influence of accumulated experience on future choices made. All these features can be implemented in reinforcement learning models (e.g., Busemeyer & Myung, 1992; Erev & Roth, 1998; Sarin & Vahid, 1999), which explain the behavior of a person who makes a series of choices from a set of alternatives. For instance, a reinforcement learning model called the expectancy-valence (EV) model (Busemeyer & Stout, 2002; Yechiam et al., 2005a) was proposed to account for the decision making of individuals taking the Iowa Gambling Task. In this section we examine its suitability for the process of drug use acquisition. Upon encountering the available alternatives, choices lead to outcomes. These are modeled as gains if they are favorable and as losses if they are aversive. Each alternative may convey either gains, losses, or both. In an analogy to drug use, this simulates a situation where positive and negative outcomes can follow either from using the drug or from not using it. Although the decision maker is not informed in advance about the exact magnitudes of gains and losses and probabilities of obtaining them, he or she can rely on subjective experience when making the next choice. According to the model, each experienced outcome evokes an affective reaction, referred to as valence. As valences are experienced, individual expectancies are formed with respect to each alternative, and in turn, they may affect the following choices. These expectancies are constantly updated as further choices are made, and so forth, shaping one’s behavior through a process of reinforcement learning. Adaptive learning will take place if the subjective expectancies are closely related to the objective ones, as the individual will then tend to select the more beneficial alternatives. In decisions regarding drug use, learning to stay away from drugs is usually adaptive and desired. However—as described earlier in this chapter— several individual and social factors might stand in the way of such learning.

c11.indd 193

7/25/2011 10:10:24 AM

194

A MODEL OF THE INITIAL STAGES OF DRUG ABUSE

The updating of the expectancy of an alternative is not determined only by the outcomes it has produced, but also by individual properties of the person making the decision. As suggested by the expectancy-valence model, these are weighting of losses versus gains, weighting of recent outcomes, and consistency of choosing according to expectancy. These three properties appear in the model as parameters that are assessed through computation. Next we will consider these components and the roles they are assumed to play in drug use acquisition. 3.2.4.1 The Motivation Parameter: Weight to Losses and/or Gains When people make choices, they experience gains and losses that they do not necessarily weigh equally. If an environmental stimulus leads to gains and losses of great magnitudes (and hence is risky according to the classic economic definition of the variance of outcomes; see Markowitz, 1952; Pratt, 1964; Sharpe, 1964), then individuals who tend to assign a heavier weight to gains can be tempted to take the risk despite potential losses.2 This seems to be relevant to the situation of a person deciding whether to use a drug because taking drugs can lead to exhilaration and pleasurable experiences, but it can also be harmful in many ways. In formal terms, the evaluation of the gains and losses results in a cognitiveaffective response called a valence, and it is represented by a utility function that allows for gains and losses to be weighted differently (see Kahneman & Tversky, 1979). The valence is denoted v(t), and it is calculated as a weighted average of gains and losses for the chosen alternative in trial t. v(t ) = W × gain(t ) − (1 − W ) × loss(t )

(1)

where gain(t) is the gain on trial t, loss(t) is the loss on trial t, and W is a parameter indicating the weight given to gains versus losses. This parameter varies among decision makers, hence, reflecting individual differences in the weight assigned to different outcomes. The parameter ranges from 0, denoting complete focus on losses, to 1, denoting complete focus on gains. Values between 0 and 1 indicate the comparative weight of gains versus losses. Chronic drug abuse is associated with high weighting to gains compared with losses (Stout et al., 2004; Yechiam et al., 2005a)—a motivation bias that is consistent with the attraction to the enjoyment the drug provides while ignoring the damages it causes. We assert that in a high-functioning individual, drug use may also be prompted by attraction to gains and lower sensitivity to losses. Yet, in the case of high functioning, this bias is only activated in certain conditions. These conditions, as shown earlier, occur when the gains are made to be more salient by (a) their being the typical outcome and (b) such means as foregone payoffs and social exposure. This suggestion is consistent with the results of studies of self-control showing the influence of salient temptations. For example, in the classic study of Mischel (1974), children were asked to wait and not take a prize (a

2

Ert and Yechiam (2010) have shown that the weighting of gains and losses is not a construct independent from risk taking. Consistent individual differences in this parameter only appear when the individual has clear signals that he or she faces a risky alternative containing gains and losses, and an alternative where risk can be avoided.

c11.indd 194

7/25/2011 10:10:24 AM

ACCOUNTING FOR DRUG ABUSE IN FORMULAS

195

small teddy bear) in order to receive a larger prize (e.g., a larger teddy bear). Waiting was more difficult when the smaller prize was in front of the children, compared with when it was covered. In real life, the enjoyment and popularity of drug users in one’s environment can serve as salient temptations. By attracting attention to the advantages associated with drug use, they might weaken self-control while one makes his or her decisions. The result is an increased tendency to choose poorly, which can be modeled as an increase in the weight assigned to gains in the presence of foregone payoffs. We suggest that this tendency is a most influential factor in the initial stages of drug abuse acquisition.

3.2.4.2 The Recency Parameter: Weighting Recent versus Distant Valences In the course of repeated decisions, individuals can rely on previous experience by making choices based on recent outcomes, on outcomes observed over longer periods of time (within memory limitations), or both. The balance between recent and distant experiences (or prior information) is highly relevant to the decision of whether to use drugs because drugs typically produce outcomes that are better in the immediate range than in the long run. Moreover, seeing others use drugs vicariously demonstrates their immediate positive effect. To make the “right” decision, the individual must balance this positive information with previously experienced negative consequences of drug use, or with her commitment not to use. In the EV model, the expectancy term Ej denotes the accumulated expected utility for alternative j (i.e., the experience one has with this alternative). High relative expectancy implies a large propensity to select the corresponding alternative. Expectancies are updated as a function of both the new valence and the old expectancy from previous trials. A delta learning rule (see, e.g., Busemeyer & Myung, 1992; Sarin & Vahid, 1999) is used for updating the expectancy after each choice, as follows: E j (t ) = E j (t − 1) + φ[u(t ) − E j (t − 1)] × δ j (t )

(2)

The term δj(t), which denotes the weight associated with the chosen alternative, equals 1 if alternative j is chosen on trial t, and 0 otherwise. On any trial t, the present expectancy Ej(t) is equal to the preceding expectancy Ej(t − 1), unless this alternative has been chosen on that trial. In such a case, the expectancy is updated according to the outcomes newly obtained, and the change occurs in the direction of the prediction error given by [u(t) − Ej(t)]. That is, if the new outcome from alternative j is higher than the preceding outcome (i.e., the old expectancy), this increases the expectancy as well as the propensity to select the alternative again. If the new outcome is lower, then the new expectancy is lower, and the propensity of choosing the alternative again decreases. The recency parameter ϕ describes the effect of recent outcomes relative to the overall past experience with the associated alternative. This parameter is also limited from 0 to 1. Large values of ϕ indicate strong recency effects such that the most recent trials are more prominent when updating the expectancy, whereas past outcomes are discounted. In contrast, small values of ϕ indicate the persistence of prior

c11.indd 195

7/25/2011 10:10:24 AM

196

A MODEL OF THE INITIAL STAGES OF DRUG ABUSE

outcomes in memory over longer spans of selections and slower incorporation of new outcomes into expectancies.3 In drug use, some outcomes are more frequent than others and have a better chance of having been recently experienced. For instance, a “high” feeling can come with every use, but aversive aftereffects—pain, failure, or the consequences of neglecting other causes while under influence—may only appear once in a while. However, refraining from the drug may not be tempting in the short run, but it would be beneficial over time. The effect of the individuals’ predispositions to use drugs can also be implemented formally. It can be assumed that the expectancies in the initial learning period are not neutral and favor a certain alternative (e.g., not using a certain drugs) over others. Again, high recency would be expected to be associated with a greater tendency to succumb to addiction as a result of the positive weight of average recent experiences. Indeed, chronic drug abusers display a high level of recency when assessed by the EV model. For instance, Yechiam et al. (2005a) found that chronic cannabis and cocaine abusers were greatly affected by recent outcomes in the Iowa Gambling Task. However, such an effect was not found for high-functioning users of drugs and alcohol, and hence, it does not seem that high recency is a major risk factor in the initial stages of drug abuse. 3.2.4.3 The Choice Consistency Parameter: Reliability of Choice Behavior Another possible factor involved in decisions regarding drug use is the ability to follow one’s resolves and opinions consistently, rather than to choose randomly. Under the expectancy-valence model, the expectancies formed in the learning process do not have a deterministic effect on the next choice of the decision maker. Rather, in the expectancy-valence model, the probability of choosing an alternative is a strength ratio of that alternative’s expectancy relative to the sum of strengths of all alternatives’ expectancies. The formula yields a probability for choosing each alternative, and the probabilities sum up to one (these probabilities can be later examined for their predictive value). Pr[Gj (t )] =

eθ ( t )⋅E j ( t )

∑e

θ ( t )⋅Ek ( t )

(3)

k

The parameter θ denotes the choice consistency of the decision maker. Low values (close to zero) are associated with erratic patterns of choice, whereas high values induce choosing according to maximal expectancy. With regard to drugs, we expect that low consistency—making choices unrelated to updated expectancies— will hinder the convergence to beneficial alternatives (the learning pattern usually found in the general population) and will result in more incidences of drug use. 3

According to some approaches, if foregone payoffs are assumed to be weighted differently than obtained payoffs, the delta rule should be modified, and a parameter denoting the weight of foregone payoffs should be assessed separately (see Yechiam & Busemeyer, 2005).

c11.indd 196

7/25/2011 10:10:24 AM

CONCLUSIONS AND IMPLICATIONS

197

Indeed, low consistency was found among chronic abusers of cocaine (Stout et al., 2004; Stout et al., 2005) and cannabis (Yechiam et al., 2005a). However, as in the case of the recency parameter, consistency does not seem to be exceptionally low among regular, high-functioning drug users (Yechiam et al., 2005a). Therefore, it does not seem that consistency is a major factor in the initial formation of drug abuse. The recency and low consistency effects in chronic cocaine and marijuana abusers may be from the impact of drugs of abuse or may constitute an important selection bias that predisposes individuals to abuse these drugs. A study by Lane, Yechiam, and Busemeyer (2006) supports the former view, at least with respect to marijuana. These authors showed that an acute administration of marijuana led to increased recency and decreased consistency while being intoxicated, compared with a placebo condition. This suggests that the pattern of high recency in marijuana abusers may be attributed to memory problems that result from cannabanoid administration (e.g., Bolla, Eldreth, Matochik, & Cadet, 2005). Altogether, the EV model embeds cognitive and affective elements and has the potential to account for different phenomena occurring in adaptive as well as maladaptive reinforcement learning in complex environments.

3.2.5

CONCLUSIONS AND IMPLICATIONS

This chapter proposed a decision-making model of sensitivity to drug abuse among high-functioning individuals. The model suggests that individuals at risk for drug abuse have an increased tendency to respond to gains when they are salient. We presented two factors that increase this salience, both of which have ecological similarity to features of typical drug use situations: (1) risky alternatives that produce favorable outcomes for the majority of the time and (2) foregone payoffs that highlight the positive counterfactuals of such risky alternatives. These factors are involved in situations of social exposure to drug use, where individuals observe salient gains that are favorable in the immediate range. Either alone or interactively, they increase individuals’ propensity to use drugs. Social exposure is therefore suggested as a replacement process by which a person receives information about the consequences of drug use even if he or she has sufficient resolution to avoid the particular drug. This is consistent with the effect of the drugs that are commonly consumed in the company of other people. The most popular explanation for why certain drugs are “social” involves their low price (WDR, 2006). We propose an alternative explanation based on the fact that the typical outcomes from social drugs, such as cocaine and marijuana, tend to be rewarding. Furthermore, notice that unlike other drugs, these drugs do not involve negative effects that are immediately observable (e.g., skin abrasion or pain). Hence, social drugs fall into the category of behaviors that, although risky and maladaptive, result in favorable outcomes most of the time. According to the model presented in this chapter, these are the behaviors most prone to being induced by observing others. In terms of cognitive processes, results using the IGT and the quantitative EV model show that high-functioning drug users are described as having heightened sensitivity to gains (compared with losses), which is triggered in situations where

c11.indd 197

7/25/2011 10:10:24 AM

198

A MODEL OF THE INITIAL STAGES OF DRUG ABUSE

potential gains have relatively high salience. Thus, individual tendencies are triggered in contexts that are typically induced by the social environment. The current model implies that an effective way to reduce drug use, at least among high-functioning users, is to prevent them from socializing frequently with drug-using individuals. Indeed, this implication is consistent with the observations of sociologists on the effect of social exposure to drug use in American universities. A survey conducted by NIDA showed that marijuana is used by approximately 30% to 35% of college students (USDE, 2008). An interesting phenomenon, however, is that many students who use drugs in college stop doing so afterward (Merline, O’malley, Schulenberg, Bachman, & Johnston, 2004). For instance, the rate of marijuana use among college graduates is reported to be only 7% for those who finished college and 11% for those who dropped out (Merline et al., 2004). A simple explanation for this effect is that marijuana smoking drops when one is no longer frequently surrounded by other users. More specifically, we assert that social exposure facilitates the use of drugs that bears favorable outcomes most of the time because the benefit of using these drugs gains salience as one witnesses them being used. As drugs such as marijuana are prohibited in most countries, one potential way of reducing the effect of social exposure on drug use is by higher levels of enforcement. This is in line with certain law enforcement policies, which proclaim that the extended presence of police forces in public places reduces crime. Under the current model, such enforcement has added value because it reduces the contagious effect of drug using; yet it should focus not on single and isolated users but on hubs of social activity where many individuals are exposed to the use of drugs by their peers. The negative consequences of drug use can also be made more salient by drawing attention to them as consistently and as often as possible. The extended presence of authorized officials in venues of social gathering can be helpful in itself, as it can serve as a salient reminder of the possibility of getting caught. From another perspective, the idea of elevated enforcement can be applied in intervention programs and other methods of education: According to our suggested model, such endeavors will be more effective if they take place frequently rather than once in a while.

REFERENCES Adams, E. H., Blanken, A. J., Ferguson, L. EX, & Kopstein, A. (1990). Overview of Selected Drug Trends. Rockville, MD: National Institute on Drug Abuse. Ahmed, S. W., Bush, P. J., Davidson, F. R., & Iannotti, R. J. (1984). Predicting Children’s Use and Intentions to Use Abusable Substances. Paper presented at the Annual Meeting of the American Public Health Association, Anaheim, CA. Akers, R. L., Krohn, M. D., Lanza-Kaduce, L., & Radosevich, M. (1979). Social learning and deviant behavior: A specific test of a general theory. Am Psychol Rev 44, 636–655. Bandura, A. (1977). Social Learning Theory. Englewood Cliffs, NJ: Prentice Hall. Bechara, A., Damasio, A. R., Damasio, H., & Anderson, S. W. (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition 50, 7–15. Bechara, A., Dolan, S., Denburg, N., Hindes, A., Anderson, S. W., et al. (2001). Decisionmaking deficits, linked to a dysfunctional ventromedial prefrontal cortex, revealed in alcohol and stimulant abusers. Neurosychologica 39, 376–389.

c11.indd 198

7/25/2011 10:10:24 AM

REFERENCES

199

Beyrer, C., Razak, M. H., Khomdon, L., Jie, C., Wei, L., et al. (2000). Overland heroin trafficking routes and HIV-1 spread in south and south-east Asia. AIDS 14, 75–83. Blum, R. (1987). Contemporary threats to adolescent health in the United States. JAMA 257, 3390–3395. Bolla, K. I., Eldreth, D. A., Matochik, J. A., & Cadet, J. L. (2005). Neural substrates of faulty decision-making in abstinent marijuana users. Neuroimage 26, 480–492. Brook, J. S., Whiteman, M., Finch, S. J., & Cohen, P. C. (1996). Young adult drug use and delinquency: Childhood antecedents and adolescent mediators. J Am Acad Child Adolesc Psychiatr 35, 1584–1592. Brook, J. S., Whiteman, M., Gordon, A. S., & Brook, D. W. (1988). The role of older brothers in younger brother’s drug use viewed in the context of parent and peer influences. J Genet Psycho 151, 59–75. Busemeyer, R., & Myung, I. J. (1992). An adaptive approach to human decision making: Learning theory, decision theory, and human performance. J Exp Psychol 121, 177–194. Busemeyer, J. R., & Stout, J. C. (2002). A contribution of cognitive decision models to clinical assessment: Decomposing performance on the Bechara gambling task. Psychol Assess 14, 253–262. Chu, T. X., & Levy, J. A. (2005). Injection drug use and HIV/AIDS transmission in China. Cell Res 15, 865–869. Cloninger, C. R., Sigvardsson, S., & Bohman, M. (1988). Childhood personality predicts alcohol abuse in young adults. Alcoholism 12, 494–503. Donohew, L., Helm, D., Lawrence, P., & Shatzer, M. (1990). Sensation seeking, marijuana use, and responses to drug abuse prevention messages. Drug and Alcohol Abuse Prevention, edited by R. Watson. Hillsdale, NJ: Lawrence Erlbaum. Donohew, L., Lorch, E. P., & Palmgreen, P. (1991). Sensation seeking and targeting of televised anti-drug PSAs. Persuasive Communication and Drug Abuse Prevention, edited by L. Donohew, H. E. Sypher, & W. J. Bukoski. Hillsdale, NJ: Lawrence Erlbaum. Donohew, L., Palmgreen, P., & Lorch, E. P. (1994). Attention, sensation seeking, and health communication campaigns. Am Behav Sci 38, 310–332. Duncan, S. C., Duncan, T. E., & Strycker, L. A. (2000). Risk and protective factors influencing adolescent problem behavior: A multivariate latent growth curve analysis. Ann Behav Med 22, 103–109. Erev, I., & Roth, A. E. (1998). Predicting how people play games: Reinforcement learning in experimental games with unique, mixed strategy equilibria. Am Econ Rev 88, 848–881. Ert, E., & Yechiam, E. (2010). Consistent constructs in individuals’ risk taking in decisions from experience. Acta Psychologica 134, 225–232. Grant, S., Contoreggi, C., & London, E. D. (2000). Drug abusers show impaired performance in a laboratory test of decision making. Neurosychologica 38, 1180–1187. Hansen, W. B., Graham, J. W., Sobel, J. L., Shelton, D. R., Flay, B. R., et al. (1987). The consistency of peer and parent influences on tobacco, alcohol, and marijuana use among young adolescents. J Behav Med 10, 559–579. Hawkins, J. D., Catalano, R. F., & Miller, J. Y. (1992). Risk and protective factors for alcohol and other drug problems in adolescence and early adulthood: Implications for substance abuse prevention. Psychol Bull 112, 64–105. Health Behaviour of School-Aged Children (HBSC). (2004). Young people in Canada: Their health and well-being. Ottawa, Ontario: Health Canada. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decisions under risk. Econometrica 47, 263–291.

c11.indd 199

7/25/2011 10:10:24 AM

200

A MODEL OF THE INITIAL STAGES OF DRUG ABUSE

Koritzky, G., & Yechiam, E. (2010). On the robustness of description and experience based decision tasks to social desirability. J Behav Decis Making 23, 83–99. Lane, S., Yechiam, E., & Busemeyer, J. R. (2006). Application of a computational decision model to examine acute drug effects on human risk taking. Exp Clin Psychopharmacol 14, 254–264. Markowitz, H. M. (1952). Portfolio selection. J Finance 7, 77–91. McMorris, B., & Uggen, C. (2000). Alcohol and employment in the transition to adulthood. J Health Soc Behav 41, 276–294. Merline, A. C., O’Malley, P. M., Schulenberg, J. E., Bachman, J. G., & Johnston, L. D. (2004) Substance use among adults 35 years of age: Prevalence, adulthood predictors, and impact of adolescent substance use. Am J Pub Health 94, 96–102. Miltenberger, R. G., Fuqua, R. W., & Woods, D. W. (1998). Applying behavior analysis to clinical problems: Review and analysis of habit reversal. J Appl Behav Anal 31, 447–469. Mischel, W. (1974). Processes in delay of gratification. Advances in Experimental Social Psychology, vol. 7, edited by L. Berkowitz. San Diego, CA: Academic Press. Peretti-Watel, P., & Lorente, F. O. (2004). Cannabis use, sport practice and other leisure activities at the end of adolescence. Drug Alcohol Depend 73, 251–257. Pratt, J. W. (1964). Risk aversion in the small and in the large. Econometrica 32, 122–136. Sarin, R., & Vahid, F. (1999). Payoff assessments without probabilities: A simple dynamic model of choice. Games Econ Behav 28, 294–309. Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. J Finance 19, 425–442. Shedler, J., & Block, J. (1990). Adolescent drug use and psychological health: A longitudinal inquiry. Am Psychol 45, 612–630. Skog, O-J. (2006). Studying cultural change: Were the changes in alcohol and coffee consumption in the nineteenth century a case of beverage substitution? Acta Sociologica 49, 287–302. Sorensen, J. L., & Copeland, A. L. (2000). Drug abuse treatment as an HIV prevention strategy: A review. Drug Alcohol Depend 59, 17–31. Stout, J. C., Busemeyer, J. R., Lin, A., Grant, S. R., & Bonson, K. R. (2004). Cognitive modeling analysis of the decision-making processes used by cocaine abusers. Psychonomic Bull Rev 11, 742–747. Stout, J. C., Rock, S. L., Campbell, M. C., Busemeyer, J. R., & Finn, P. R. (2005): Psychological processes underlying risky decisions in drug abusers. Psychol Addict Behav 19, 148–157. Svenson, R. (2000). Risk factors for different dimensions of adolescent drug use. J Child Adolesc Subst Abuse 9, 67–90. United States Department of Education (USDE). (2008). Marijuana use among students at institutions of higher education. Wilkins, C., Casswell, S., Bhatta, K., Pledger, M. (2002). Drug use in New Zealand: National surveys comparison 1998 and 2001. Alcohol and Public Health Research Unit, University of Auckland. Wood, R. T. A., Griffiths, M. D. & Parke, J. (2007). Acquisition, development, and maintenance of online poker playing in a student sample. CyberPsychol Behav 10, 354–361. World Drug Report (WDR). (2006). United Nations Publication (Vol. 1). Yechiam, E., & Busemeyer, J. R. (2005). Comparison of basic assumptions embedded in learning models for experience based decision-making. Psychonomic Bull Rev 12, 387–402.

c11.indd 200

7/25/2011 10:10:24 AM

REFERENCES

201

Yechiam, E., Busemeyer, J. R, Stout, J. C., & Bechara, A. (2005a) Using cognitive models to map relations between neuropsychological disorders and human decision making deficits. Psychol Sci 16, 973–978. Yechiam, E., Druyan, M., & Ert, E. (2008). Observing others’ behavior and risk taking in decisions from experience. Judgment Decision Making 3, 493–500. Yechiam, E., Stout, J. C., Busemeyer, J. R., Rock, S. L., & Finn, P. R. (2005b). Individual differences in the response to forgone payoffs: An examination of high functioning drug abusers. J Behavi Decis Making 18, 97–110. Zukerman, M. (1987). Biological connection between sensation seeking and drug abuse. Brain Reward Systems and Abuse, edited by J. Engel & L. Oreland. New York: Raven Press.

c11.indd 201

7/25/2011 10:10:24 AM

3.3 EXTRINSIC EFFECTS AND MODELS OF DOMINANCE HIERARCHY FORMATION MATTHEW DRUEN

AND

LEE ALAN DUGATKIN

Department of Biology, University of Louisville, Louisville, Kentucky

One of the most striking elements of the social structure of many species is the presence of dominance hierarchies, which often emerge as the result of repeated bouts of aggression in group-living animals. We can measure the social status of individuals in a hierarchy by noting the proportion of times they defeat, or are defeated by, other group members. The process of hierarchy formation often, but not always, leads to stable differences in social status. Consider the simplest case where a hierarchy is possible—when animals live in groups of three. Suppose that individual 1 wins the majority of its fights with individuals 2 and 3, individual 2 wins the majority of its fights with individual 3, and individual 3 loses the majority of its fights with individuals 1 and 2. In such a case, individual 1 would be considered the top-ranked or alpha (α) member of the hierarchy, individual 2 would be the second ranked or beta (β) member, and individual 3 would be the third ranked or gamma (γ) member of the hierarchy. Hierarchies with this form are called linear hierarchies. A strictly linear hierarchy—such as the case in the example just described—is characterized by exclusively transitive relationships among group members (e.g., 1 > 2 and 2 > 3; therefore, 1 > 3). If all possible relationships in a group exhibit transitivity, the hierarchy is said to be strictly linear; if intransitive relationships exist (e.g., 1 beats 2, 2 beats 3, but 3 beats 1), the hierarchy is considered less linear or nonlinear (Chase, 1980). In animal populations, individuals at the top of a hierarchy often typically have access to more resources, better mates, or larger territories than others in a group. For example, in groups of male green swordtail fish (Xiphophorus helleri),

From DNA to Social Cognition, First Edition. Edited by Richard Ebstein, Simone Shamay-Tsoory, and Soo Hong Chew. © 2011 Wiley-Blackwell. Published 2011 by John Wiley & Sons, Inc.

203

c12.indd 203

7/25/2011 10:10:24 AM

204

EXTRINSIC EFFECTS AND MODELS OF DOMINANCE HIERARCHY FORMATION

top-ranked individuals are responsible for up to 75% of paternity in the next generation (Luo, Sanetra, Scharti, & Meyer, 2005). Variables that determine fighting ability—and ultimately the structure of a dominance hierarchy—fall into two categories: intrinsic and extrinsic factors. Intrinsic factors describe an animal’s physical fighting prowess or resource holding power (RHP) (Parker, 1974). The most frequently studied intrinsic traits are those related to body size or weaponry. Generally, such traits reliably predict the outcome of fights when large differences exist between opponents at the start of a contest. Extrinsic effects capture how types of antecedent social experiences—winning a fight, losing a fight, watching a fight—alter an individual’s performance in subsequent fights (Dugatkin & Dugatkin, 2007; Fawcettt & Johnstone, 2010; Fuxjager & Marler, 2010; Hock & Huber, 2009; Hsu et al., 2005, 2009; Landau, 1951b). Winner and loser effects are defined as an increased probability of winning at time T, based on victories at times T − 1, T − 2, and so on, and an increased probability of losing at time T, based on losing at time T − 1, T − 2, and so on, respectively. Although winner effects seem to be less common than loser effects, both have been documented in a variety of animal types including freshwater fish, crustaceans, and mammals (Chase, Bartolomeo, & Dugatkin, 1994; Dugatkin & Druen, 2004; Dugatkin, 2009; Fawcettt & Johnstone, 2010; Fuxjager & Marler, 2010; Hsu et al., 2005, 2009). Winner and loser effects occur when an individual’s direct involvement in aggressive contests increases or decreases that individual’s assessment of its own RHP. It is important to note that winner and loser effects are not necessarily flip sides of a coin, as one effect can exist in the absence of the other. For example, animal A may be more likely to defeat animal B if it has just won a fight with animal C (winner effect), but this does not necessarily mean that animal C is more likely to be defeated during a subsequent fight when there is an absence of loser effects. A related type of extrinsic factor, the bystander effect—also known as the eavesdropper effect—occurs when the observer of an aggressive interaction between two other individuals changes its estimate of the RHP of the individuals it has observed (Earley & Dugatkin, 2002; McGregor, 2005). That is, bystanders acquire information about future adversaries before having to fight them directly (Coultier, Beaugrand, & Lague, 1996; Johnsson & Akerman, 1998; Oliveira, McGregor, & Latruffe, 1998). Bystander effects come in two flavors. Bystander-winner effects occur when a bystander increases its appraisal of the RHP of an individual it observed defeating a rival. Bystander-loser effects occur if a bystander devalues its appraisal of an individual’s RHP it has observed lose a fight. Experimental studies have identified the presence of bystander effects in birds, mammals, and fish (Dugatkin, 2009). To date, however, most researchers have focused more on intrinsic traits and less on bystander, winner, and loser effects. There is, though, mounting evidence that extrinsic effects change behaviors in important ways that strongly determine the final structure of dominance hierarchies (Chase, 1982b, 1985; Dugatkin, 2001; Fawcettt & Johnstone, 2010; Fuxjager & Marler, 2010; Hock & Huber, 2009; Hsu et al., 2005, 2009). Pioneering work on the impact of winner and loser effects on hierarchy formation was initiated by H. G. Landau in the early 1950s (Landau, 1951a, 1951b). Landau was puzzled that the linear hierarchies often found in nature did not at first emerge from his mathematical models. Initially, his models were driven by individual differ-

c12.indd 204

7/25/2011 10:10:24 AM

MODELS OF EXTRINSIC EFFECTS

205

ences in inherent fighting abilities—conceptually analogous with intrinsic traits. However, after Landau added winner and loser effects—extrinsic effects—hierarchy structures became more linear and were thus more similar to hierarchies observed in animals. One of the earliest efforts to incorporate bystander effects into a theoretical framework was Ivan Chase’s “jigsaw model” (Chase, 1980, 1982a). This model begins with three players and in this group, a bystander (3) observes an aggressive exchange between the other two players (1 and 2). For the purposes of illustration, now suppose that 1 defeats 2. Next, the bystander is allowed to fight either 1 or 2. Chase described the four possible outcomes of dyadic fights: (1) double dominance in which 1 defeats 3, (2) double subordinance in which 3 defeats 2, (3) bystander dominance in which 3 defeats 1, and (4) the case in which 2 defeats 3 (initial subordinate defeats the bystander). Chase found that in his model, double dominance and double subordinance always led to linear hierarchies, whereas the other two outcomes always led to substantially nonlinear structures. Results from experiments with domesticated chickens supported his model’s finding that double dominance and double subordinance were indeed associated with the linear hierarchies found in chickens (Chase, 1982a, 1982b). Work by Chase and Landau has provided new ways to understand how experience through repeated aggressive encounters influences the structure of dominance hierarchies. Yet, despite the importance of their papers on dominance hierarchy research, several fundamental issues concerning extrinsic effects were not addressed until several decades later. For example, although animals are known to be able to “size up” their opponents, neither researcher included this ability in their models. Is it possible that when RHP is assessed, different kinds or combinations of extrinsic effects have different implications for hierarchy formation? In addition, Landau did not examine winner and loser effects independently but only hierarchy formation when both were at work. Similarly, Chase did not isolate the consequences of different kinds of bystander effects. For example, it could easily be the case that bystander effects might act only via losers or winners of fights but not through both fighters. In the last decade or so, the study of extrinsic effects on dominance hierarchy formation has received renewed interest from theoreticians and empiricists alike. Because each research domain has witnessed significant developments, we will highlight mathematical models as well as experimental work. We will first examine a set of related models developed by one of the authors and his colleagues that simulates the formation of dominance hierarchies when pure winner and loser effects, bystander effects, and several other salient factors are evaluated independently and in combination (Dugatkin, 1997, 2001; Dugatkin & Earley, 2003, 2004). Next, we will review a body of empirical work on extrinsic effects in green swordtail fish. We conclude with a discussion and recommendations for future work.

3.3.1

MODELS OF EXTRINSIC EFFECTS

The basic model begins with randomly chosen individuals paired in potentially aggressive contests (Dugatkin, 1997). Before the start of a simulation, each player is assigned a score that denotes the individual’s assessment of its own fighting ability.

c12.indd 205

7/25/2011 10:10:24 AM

206

EXTRINSIC EFFECTS AND MODELS OF DOMINANCE HIERARCHY FORMATION

This is analogous to a player’s estimate of its own RHP. In encounters with other group members, players can opt either to be aggressive and initiate a fight or to retreat from the contest. To provide a rule for deciding whether to fight or to flee, an “aggression threshold” variable was incorporated into the model. Lowering the aggression threshold creates conditions in which individuals are more likely to choose to fight individuals whose RHP is significantly greater than their own. Conversely, higher aggression thresholds produce conditions in which individuals are more likely to submit to an individual whose RHP is only slightly greater than their own. As a result, there are three potential outcomes when two players meet: (1) both players meet the aggression threshold and both decide to fight, (2) one player meets the threshold and fights while the other player does not meet the threshold and retreats, and (3) neither player meets the aggression threshold and so neither individual initiates a fight (a double kowtow).

3.3.2 WINNER/LOSER EFFECTS In simulations in which winner effects alone were examined, linear hierarchies emerged in which it was possible to unambiguously determine the relative rank of each individual. A different pattern emerged when loser effects were examined in the absence of winner effects. Under these conditions, a clear α (top-ranked) individual always emerged in the group, but the relationships of all other individuals were indeterminate. When both winner and loser effects were considered, increasing winner effects for a given value of the loser effect raised the number of individuals with clearly delineated positions in a hierarchy, and the converse was also true. These results suggest that the type of hierarchy predicted depends fundamentally on whether winner or loser effects operate in isolation or in combination. Winner effects alone produce structured hierarchies in which group members fall into unambiguous positions within the hierarchy. This is because when only winner effects are in play, pairs of individuals are more likely to interact by fighting, rendering it relatively easy to determine each individual’s rank. Loser effects alone produce hierarchies in which a clear α individual is detectable, but the rank-order among other group members remains unclear. This occurs because loser effects quickly produce individuals that refrain from aggression because of their low estimate of their own RHP after a few losses, leading to an abundance of double kowtows and to a limited number of actual fights or attack/retreat interactions.

3.3.3 WINNER AND LOSER EFFECTS AND INDIVIDUAL RECOGNITION Individual recognition is generally assumed to stabilize hierarchies. Yet so far we have only discussed models in which individuals do not recognize the identity of others in their group. To mimic individual recognition, the basic model described above was modified so that at the start of a contest, individuals were aware of their own starting RHP value as well as of the starting RHP score of all other group members (Dugatkin & Earley, 2004). Individuals, however, were not privy to changes in a group member’s RHP that occurred because of winner and loser effects

c12.indd 206

7/25/2011 10:10:24 AM

BYSTANDER EFFECTS

207

experience by other group members. This simulation always generated nonlinear, nontransitive hierarchies. These results are strikingly different from the results described earlier in which winner and loser effects operated in the absence of individual recognition. From an evolutionary perspective, the inclusion of individual recognition results in a somewhat perplexing set of predictions. Individual recognition is believed to have evolved to enable the detection of cheaters—individuals that are likely to bluff in aggressive interactions—and to increase the stability of dominance hierarchies (Barnard & Burk, 1979; Pagel & Dawkins, 1997). As such, the absence of individual recognition might be regarded as the primitive state in animal social groups that may already have dominance hierarchies and winner and loser effects. However, individual recognition nullifies the impact that winner and loser effects have on the probability of linear, transitive hierarchies forming when it is introduced. Much more work needs to be done to understand why this is so.

3.3.4

BYSTANDER EFFECTS

To model bystander effects, the simulation was altered so that all fights in a group were observed by all other group members (Dugatkin, 2001). At the conclusion of a particular round of fighting, all individuals in the group altered their estimates of each group member’s RHP. When bystander-winner effects were in operation, a bystander raised its estimation of the fighting ability of another in the group when it observed that individual win a fight. Conversely, when bystander-loser effects were in play, if a bystander witnessed another individual lose a fight or retreat, that individual’s RHP was devalued. Hierarchies were first simulated when bystander effects alone were in operation— that is, in the absence of pure winner and loser effects. When only bystander-winner effects are at work, each group has a clear bottom-ranking (omega) individual, but the ranking of other group members is difficult to determine. Why? Recall that all individuals begin with the same RHP values. During early rounds, one individual within the group will, by chance, lose a majority of its initial fights, but because only bystander-winner effects are in play, other group members react to this individual— let’s call it X, as if its initial RHP is unchanged. However, because most group members will have emerged victorious in some of their early contests, and such victories would have been observed by others, group members view everyone except X as having a high RHP, and so there are few attacks on anyone except X. As a result, most aggressive interactions are of the “attack” (the omega individual) rather than of the “both individuals opt to fight” variety. Hierarchy structures are dramatically different when only bystander-loser effects are in operation. Now, victories and defeats are randomly distributed throughout the group. In contrast to the bystander-winner case in which most aggressive interactions are attack-retreat, now individuals always fight when they meet. The lack of a clear hierarchy in such groups is because individuals change the RHP value they assign to others, but only by devaluing it while self-estimates of RHP do not change. When two players confront one another, each determines the other to have an RHP lower than their own and they both initiate a fight, but who wins such fights is random as the real RHP of the fighters has not changed.

c12.indd 207

7/25/2011 10:10:24 AM

208

EXTRINSIC EFFECTS AND MODELS OF DOMINANCE HIERARCHY FORMATION

3.3.5

JOINT EFFECTS

When pure winner and bystander-winner effects are in play simultaneously, a linear hierarchy—in which the rank order of individuals is clearly defined emerges—with most meetings resulting in attack–retreat exchanges. The same result is found when pure loser and bystander-loser effects are combined and when all four effects are in play simultaneously. In these scenarios, all individuals have the same information regarding RHPs. That is, if individual A assesses its own RHP as R, and any of the situations just described are in play, all other individuals also assess A’s RHP as R, so that there is agreement about the value of each and every individual’s RHP. This in turn creates a social environment in which individuals respond to asymmetries in RHPs, making most interactions take the form of attack–retreat.

3.3.6

EXTRINSIC EFFECTS AND MERGING HIERARCHIES

We have discussed how winner/loser effects and bystander effects might alter the ultimate structure of dominance hierarchies in single groups. But what happens when two smaller hierarchies merge to form a single larger one? In other words, would an individual’s social rank in an initial hierarchy have any predictive power with respect to the social rank that is attained when that hierarchy is merged with another—when hierarchies are fused (Dugatkin & Earley, 2003)? Imagine the case where hierarchies are formed in two groups of four, initially identical, individuals. In these groups, as in previous models, winner, loser, bystanderwinner, and bystander-loser effects then influence RHP, and the eventual rank-order of the group members. These smaller groups are then merged into one larger assemblage. Hierarchy formation redevelops in this larger group and can be influenced by the same suite of extrinsic factors, as well as by any preexisting status asymmetries generated by interactions in the small groups. When only pure winner effects were at play, a clear linear hierarchy with all positions delineated occurred both in the four-member groups and in the fused eightmember hierarchy. Rank position within the large hierarchy is strongly correlated to rank position in an individual’s former hierarchy. That is, the two top-ranked individuals in small groups were also the two top-ranked members in the large group, the two β individuals from the smaller groups became the third- and fourthranked members of the larger group, and so on. Moreover, individuals in the fused group were equally likely to engage in aggressive interactions with the three individuals that were in their initial hierarchy and the four individuals (newly encountered) from the other small group. When loser effects alone were at play, only the top-ranked member in each of the four-member groups was delineated. When groups were merged, only a single α individual could be identified. That is, for each fusion, one of the α individuals from the four-member groups retained its alpha status, and one of the alphas from the four-member group dropped in rank and was part of a group of seven others with ambiguous ranks. In both pre- and post fusion groups, the majority of interactions were either attack–retreats or double kowtows (rather than fights). When only bystander-loser effects were at play, in both prefusion and postfusion groups, the majority of interactions were fights, and wins and losses were randomly distributed among group members, producing no discernable hierarchy. When

c12.indd 208

7/25/2011 10:10:24 AM

EMPIRICAL STUDIES: THE CASE OF THE GREEN SWORDTAIL

209

bystander-winner effects alone were in operation, a clear bottom-ranking (omega) individual emerged but the rank of other individuals was ambiguous in the fourmember groups. In the fused group, most interactions were attack–retreats and double kowtows, but it was difficult to rank any of the eight individuals. Two intriguing patterns, however, did emerge. First, individuals in the eight-member groups interacted almost strictly with those that were not in their original four-member group. Second, within each of the four-member groups (now merged into a group of eight), one individual clearly ranked below the other three, and yet there was no correlation between this individual’s identity and the identity of the bottom-ranked individual from the prefused groups. An interesting pattern also emerged when loser and bystander-loser effects acted together. In the prefusion, four-member hierarchies, results were similar to the case of loser/bystander-loser simulations described earlier. That is, a clear linear hierarchy emerged with all positions being definitive and most interactions taking the form of attack–retreat. When smaller groups were merged, two top-ranking individuals were detected, but the relative ranks of the other players were difficult to assign. As before, most interactions remained attacks–retreats. The reason is that only the two top-ranked individuals interacted with all other group members. The remaining six individuals interacted, but only with those in their former, prefusion group. As such, their rank was only clear with respect to their former groupmates, and it was consistent with previous ranks in the four-member hierarchies. When winner and bystander-winner effects operated simultaneously, the results were similar to the case of pure winner effects—a clear linear hierarchy with all positions occupied existed in the both the small and large hierarchies. Moreover, an individual’s rank in the eight-member hierarchy was strongly related to its rank position in the original four-member group. Again, as in the case when just winner effects were operating, individuals in the fused group were equally likely to undertake aggressive interactions with all postfusion group members.

3.3.7

EMPIRICAL STUDIES: THE CASE OF THE GREEN SWORDTAIL

One of the most frequently used species for experiments on aggression and dominance hierarchies is the green swordtail fish (Xiphophorus helleri). Green swordtails are a member of the family Poeciliidae (e.g., livebearers), and they live primarily in small-order freshwater streams and rivers in Honduras and Mexico. Fights between nonterritorial males often progress through four stages: initiation, escalation, resolution, and reinforcement. During the initiation stage, individuals signal hostile intentions to one another using relatively low-cost, noncontact, frontal and lateral sigmoid displays in which the fins are fully spread and their opercula (gill coverings) are flared. Escalated fighting includes numerous bouts of mouth-wrestling (gripping the upper or lower jaw), circling, chasing, and repeated bites to the body, including the opercula and gonopodium (reproductive organ) (Franck & Ribowski, 1989). Swordtails readily form linear hierarchies in field and laboratory settings making them ideal for work on aggression and dominance. In an empirical test of models that describe how winner and loser affects determine the rank orders of individuals within hierarchies, Dugatkin and Druen (2004) used a random-selection procedure in which individuals within triads of male green swordtails (matched for size to control for intrinsic attributes) were

c12.indd 209

7/25/2011 10:10:25 AM

210

EXTRINSIC EFFECTS AND MODELS OF DOMINANCE HIERARCHY FORMATION

provided an experience of winning or losing a fight, or else a “neutral” experience. These initial contests took place out of view of other triad members. Subsequently, individuals were placed within the same arena and allowed to establish dominance relationships. Consistent with the notion that winner and loser effects can alter the structure of hierarchies, it was found that previous winners most often obtained the α rank, previous losers fell to the γ position, and neutral individuals occupied the β position. In addition, rank orders tended to be stable over relatively long periods. Within a group of male swordtails, an individual may alternately assume one of several roles in an emerging hierarchy. It may be actively fighting with another, acting as a bystander within sensory range of a fight between two other group members, or remaining solitary. Several intriguing questions related to bystander effects can be examined in such a system. For example, will bystanders respond differently to individuals that were observed winning versus individuals that were observed losing a fight? Are bystanders sensitive to the dynamics of an observed fight? Does watching fights affect aggressive tendencies in a general way or is behavior altered toward specific individuals? In a series of experiments using groups of swordtails, Earley and his colleagues tested for the presence of bystander effects by using experimental arenas that allowed for the control of the flow of information in staged fights between pairs of males and a bystander (Earley, Tinsley, & Dugatkin, 2003; Earley, Druen, & Dugatkin, 2005; Earley & Dugatkin, 2002). Subsequently, bystanders could be pitted against observed winners or observed losers to test whether information acquired in this manner affected the outcomes of fights. Observing fights had a significant impact on swordtail aggressive behavior. These effects were most evident when bystanders faced individuals that were observed winning an initial fight. In such cases, bystanders exhibited a reduced probability of initiating fights compared with individuals that had not viewed a fight. A bystander’s assessment of a winner’s fighting ability did not depend on the particular progression of an observed fight (e.g., whether it had witnessed an escalated or a nonescalated match). Fight intensity, however, altered bystander behavior when individuals were pitted against individuals who they saw defeated in a contest. Here, bystanders were less likely to initiate aggression or win against previous losers that had persisted for long periods. These findings suggest that bystanders avoid engaging in potentially costly fights by making decisions about fighting particular types of individuals. Earley and his colleagues next examined “aggressive priming” as a possible alternative explanation for changes in bystander aggression brought about by watching fights. Priming occurs when bystanders exhibit a generalized agonistic response that is insensitive to a group member’s identity (Hollis, Dumas, Singh, & Fackelman, 1995). To test for priming effects, fights were first staged in view of a bystander. Bystanders then faced naïve, unfamiliar individuals. Watching and then confronting such individuals increased a bystander’s probability of initiating aggression and winning the fight only when their opponents were slightly larger in body size. This result was opposite to the diminished aggression response detected in the first experiment (wherein eavesdroppers fought previous winners). In other words, bystander responses seemed to rest on familiarity rather than on the observation of aggression per se. Overall, these findings suggest that male swordtails acquire

c12.indd 210

7/25/2011 10:10:25 AM

REFERENCES

211

specific information that enables them to make distinctions between winners and losers of observed fights—a finding that is consistent with bystander effects.

3.3.8

DISCUSSION AND FUTURE DIRECTIONS

Given the preponderance of dominance hierarchies in nature, the growing evidence that fighting experience affects subsequent aggressive interactions, and the strong support for individual recognition in many social systems, models such as those described in this chapter can be used to generate explicit predictions about hierarchy formation. Depending on which effects described in this chapter are in play, a variety of hierarchy forms are possible. For example, we have observed that one might expect only a clear bottom-ranking individual, a clearly defined hierarchy, or no hierarchy at all (where wins and losses are randomly distributed among individuals) based on different combinations of extrinsic effects. Winner, loser, and bystander effects also have implications for hierarchy formation when two groups merge. Again, the precise nature of these implications depends on which effects are operating. Nevertheless, it is important to mention two limitations of the models discussed earlier. First, individuals randomly interact with others in their group. Although this may be the case for some animal social groups, in others, subordinates may actively avoid confrontations with dominant individuals, thus rendering interactions in such systems nonrandom. Second, although such models can examine different strengths of winner, loser, and bystander effects, they do not manipulate how long such effects last. It is certainly possible that the duration of different extrinsic effects may be unequal (i.e., in some systems, loser effects may decay more slowly than winner effects; Drummond & Cannales, 1998). Currently, theoretical work outpaces empirical efforts, and both laboratory and field studies that explicitly test predictions made by mathematical models are needed. Although bystander-winner, bystander-loser, pure winner, and pure loser effects are evident in a wide variety of taxa including insects (Alexander, 1961), mollusks (Zack, 1975), fish (Beaugrand & Zyan, 1985), birds (Drummond & Osorno, 1992), reptiles (Schuett, 1996), and rodents (van de Poll & Smets, 1982), no work has yet definitively documented winner, loser, and bystander effects, and the detailed aggressive interactions when individuals are in groups, in any single species. In an ambitious research program, hierarchy formation could be examined in different species that had documented combinations of bystander-winner, bystander-loser, pure winner, and pure loser effects. The types of hierarchies that emerged could then be compared with the various hierarchy structures from the models discussed earlier.

REFERENCES Alexander, R. D. (1961). Aggressiveness, territoriality and sexual behavior in field crickets. Behaviour 17, 131–223. Barnard, C., & Burk, T. (1979). Dominance hierarchies and the evolution of individual recognition. J Theor Biol 81, 65–73.

c12.indd 211

7/25/2011 10:10:25 AM

212

EXTRINSIC EFFECTS AND MODELS OF DOMINANCE HIERARCHY FORMATION

Beaugrand, J. P., & Zyan, R. (1985). An experimental model of aggressive dominance in Xiphophorus helleri. Behav Process 10, 1–52. Chase, I. 1980. Social process and hierarchy formation in small groups: A comparative perspective. Am Soc Rev 45, 905–924. Chase, I. (1982a). Behavioral sequences during dominance hierarchy formation in chickens. Science 216, 439–440. Chase, I. D. (1982b). Dynamics of hierarchy formation: The sequential development of dominance relationships. Behaviour 80, 218–240. Chase, I. D. (1985). The sequential analysis of aggressive acts during hierarchy formation: An application of the “jigsaw puzzle” approach. J An Behav 33, 86–100. Chase, I. D., Bartolomeo, C., & Dugatkin, L. A. (1994). Aggressive interactions and intercontest interval: How long do winners keep winning? J An Behav 48, 393–400. Coultier, S., Beaugrand, J. P., & Lague, P. C. (1996). The role of individual differences and patterns of resolution in the formation of dominance orders in domestic hen triads. Behav Process 38, 227–239. Drummond, H., & Canales, C. (1998). Dominance between booby nestlings involves winner and loser effects. Anim Behav 55, 1669–1676. Drummond, H., & Osorno, J. L. (1992). Training siblings to be submissive losers: Dominance between booby nestlings. J An Behav 44, 881–893. Dugatkin, L. A. (1997). Winner effects, loser effects and the structure of dominance hierarchies. Behav Ecol 8, 583–587. Dugatkin, L. A. (2001). Bystander effects and the structure of dominance hierarchies. Behav Ecol 12, 348–352. Dugatkin, L. A. (2009). Principles of Animal Behavior, 2nd ed. New York: W.W. Norton. Dugatkin, L. A., & Druen, M. (2004). The social implications of winner and loser effects. Proc Roy Soc Lond Biol Lett 271, S488–S489. Dugatkin, L. A., & Dugatkin, A. D. (2007). Extrinsic effects, estimating opponents’ RHP, and the structure of dominance hierarchies. Biol Lett 3, 614–616. Dugatkin, L. A., & Earley, R. L. (2003). Group fusion: The impact of winner, loser, and bystander effects on hierarchy formation in large groups. Behav Ecol 14, 367–373. Dugatkin, L. A., & Earley, R. L. (2004). Individual recognition, dominance hierarchies and winner and loser effects. Proc Roy Soc Lond 271, 1537–1540. Earley, R. L., & Dugatkin, L. A. (2002). Eavesdropping on visual cues in green swordtails (Xiphophorus helleri): A case for networking. Proc Roy Soc Lond 269, 943–952. Earley, R. L., Tinsley, M., & Dugatkin, L. A. (2003). To see or not to see: Does previewing a future opponent affect the contest behavior of green swordtail males (Xiphophorus helleri)? Naturwissenschaften 90, 226–230. Earley, R. L., Druen, M., & Dugatkin, L. A. (2005). Watching fights does not alter a bystander’s response towards naïve conspecifics in male green swordtail fish (Xiphophorus helleri). J An Behav 69, 1139–1145. Fawcett, T. W., & Johnstone, R. A. (2010). Learning your own strength: Winner and loser effects should change with age and experience. Proc Roy Soc Lond 277, 1427–1434. Fuxjager, M. J., & Marler, C. A. (2010). How and why the winner effect forms: Influences of contest environment and species differences. Behav Ecol 21, 37–45. Franck, D., & Ribowski, A. (1989). Escalating fights for rank-order position between male swordtails (Xiphophorus helleri): Effects of prior rank-order experience and information transfer. Behavioral Ecology and Sociobiology 24, 133–143.

c12.indd 212

7/25/2011 10:10:25 AM

REFERENCES

213

Hock, K., & Huber, R. (2009). Models of winner and loser effects: a cost-benefit analysis. Behaviour 146, 69–87. Hollis, K. L., Dumas, M., Singh, P., & Fackelman, P. (1995). Pavlovian conditioning of aggressive behavior in blue gourami fish (Trichogaster trichopterus): Winners become winners and losers stay losers. J Comp Psychol 109, 123–133. Hsu, Y., Earley, R. L., & Wolf, L. (2005). Modulation of aggressive behavior by fighting experience: Mechanisms and contest outcomes. Biol Rev 80, 1–42. Hsu, Y. Y., Lee, I. H., & Lu, C. K. (2009). Prior contest information: Mechanisms underlying winner and loser effects. Behav Ecol Sociobiol 63, 1247–1257. Johnsson, J., & Akerman, A. (1998). Watch and learn: Preview of the fighting ability of opponents alters contest behaviour in rainbow trout. J An Behav 56, 771–776. Landau, H. G. (1951a). On dominance relations and the structure of animal societies: I. Effects of inherent characteristics. Bull Math Biophys 13, 1–19. Landau, H. G. (1951b). On dominance relations and the structure of animal societies: II. Some effects of possible social causes. Bull Math Biophys 13, 245–262. Luo, J., Sanetra, M., Schartl, M., & Meyer, A. (2005). Strong reproductive skew among males in the multiply mated swordtail Xiphophorus multilineatus (Teleostei). J Hered 96(4), 346–355. McGregor, P. (2005). Animal Communication Networks. Cambridge: Cambridge University Press. McGregor, P. K., & Peake, T. (2000). Communication networks: Social environments for receiving and signalling behaviour. Acta Ethologica 2, 71–81. Oliveira, R. F., McGregor, P. K., & Latruffe, C. (1998). Know thine enemy: Fighting fish gather information from observing conspecific interactions. Proc Roy Soc Lond 265, 1045–1049. Pagel, M., & Dawkins, M. S. (1997). Peck orders and group size in laying hens: “Futures contracts” for non-aggression. Behav Process 40, 13–25. Parker, G. A. (1974). Assessment strategy and the evolution of fighting behaviour. J Theor Biol 47, 223–243. Schuett, G. W. (1996). Fighting dynamics of male copperheads, Agkistrodon contortrix (Serpentes, Viperidae): Stress-induced inhibition of sexual behavior in losers. Zoo Biol 15, 209–221. van de Poll, N. E., & Smeets, J. (1982). Behavioral consequences of agonistic experiences in rats: Sex differences and the effect of testosterone. J Comp Physiol Psychol 96, 893–903. Zack, S. (1975). A description and analysis of agonistic patterns in an opisthobranch mollusc, Hermissenda crassicornis. Behaviour 53, 238–267.

c12.indd 213

7/25/2011 10:10:25 AM

3.4 COMPLEX SOCIAL COGNITION AND THE APPRECIATION OF SOCIAL NORMS IN PSYCHIATRIC DISORDERS: INSIGHTS FROM EVOLUTIONARY GAME THEORY MARTIN BRÜNE AND JULIA WISCHNIEWSKI Research Department of Cognitive Neuropsychiatry and Psychiatric Preventive Medicine, LWL University Hospital, Ruhr-University Bochum, Germany

3.4.1

INTRODUCTION

If there is anything like a common denominator of psychiatric disorders and abnormal psychological conditions, it is perhaps the maladaptive way affected individuals get along with other people. For example, withdrawal from the social environment is typical of depression; avoidance of encounters with strangers characterizes social anxiety disorder; mistrust to the degree of paranoia and uncooperative behavior is part of the schizophrenia phenotype but may also occur in borderline personality disorder; and opportunistic exploitation of others is at the core of psychopathy. This listing is certainly not exhaustive. It may, however, illustrate that across diagnostic categories, social interaction is compromised in psychiatric disorders, sometimes to the extent that basic rules of social conduct are violated and moral values disregarded, sometimes in more subtle ways in that suspicion or anxiety is only elicited in situations that are experienced as extremely stressful—with vague boundaries to normal psychological functioning. In any event, except for the case of psychopathy, the above-mentioned examples have in common that patients assume such interpersonal attitudes in defense of perceived threat, however improbable the actual danger may be (Gilbert, 2001). More specifically, cognitive defense mechanisms are

From DNA to Social Cognition, First Edition. Edited by Richard Ebstein, Simone Shamay-Tsoory, and Soo Hong Chew. © 2011 Wiley-Blackwell. Published 2011 by John Wiley & Sons, Inc.

215

c13.indd 215

7/25/2011 10:10:25 AM

216

INSIGHTS FROM EVOLUTIONARY GAME THEORY

often abnormally activated in psychiatric disorders to protect the self from or anticipated violations of rules of cooperation, reciprocity, and trust by significant others (conversely, in the case of psychopathy, the affected individual is the perpetrator of rule violations; Mealey, 1995; Troisi, 2005). To date, however, there has been little empirical research on that matter partly because reliable tools for the assessment of how psychiatric patients appreciate social rules and values such as cooperation, reciprocity, and trust were unavailable. In the last 20 years or so, research into the underlying mechanisms of maladaptive social interaction has shown that impaired social cognition is critical in this regard. The term “social cognition” concerns the perception and interpretation of social signals to construct representations of the relation between oneself and others and to use those representations flexibly to guide social behavior (Lysaker et al., 2005). The concept has somewhat blurry boundaries to the domains of motivation, emotion, attention, memory, and decision making (Adolphs, 2001). It is nevertheless useful in that social cognition emphasizes those cognitive domains that have a direct link to social interaction such as emotion recognition from facial expressions, prosody and body posture, as well as more sophisticated cognitive capacities like conscious reflection on one’s own and others’ mental states in terms of desires, feelings, intentions, knowledge, and so forth. The latter aspect of social cognition has been termed “theory of mind” or “mentalizing,” which broadly overlaps with the concept of “empathy” in terms of both its cognitive and affective aspects (Singer et al., 2006; Shamay-Tsoory, Aharon-Peretz, & Perry, 2009). A wealth of studies have demonstrated that social cognition is compromised in many—if not all—psychiatric conditions, ranging from childhood autism to dementia (Brüne and Brüne-Cohrs, 2006). However, the exact patterns of manifestation of social cognitive deficits differ between psychiatric disorders—some patients may particularly have difficulties in deciphering the emotional content of facial expressions, whereas others may be lost when asked to reason about the mental life of others or may be unable to empathize with others. Comparable variation also exists with regard to the underlying proximate mechanisms (i.e., gene–environment interaction) that cause social cognitive impairment (Ebstein, Israel, Chew, Zhong, & Knafo, 2010). In any case, it has now become increasingly clear that impoverished social cognitive capacities have consequences in many areas of social functioning, as has been demonstrated, for example, in schizophrenia patients (Brüne, Schaub, Juckel, & Langdon, in press). From an evolutionary point of view, this association is logical simply because the core functions of social cognition—mentalizing and empathy—evolved in humans in the first place to maintain complex relationships and to reinforce cooperation among individuals within a social group—which critically entails the ability to detect violations of the rules of cooperative interaction (Trivers, 1971), but also to build and sustain reliable and enduring relationships based on reciprocality and trust. Accordingly, it can be predicted that dysfunctional social cognition, in turn, has behavioral consequences for social interaction as, for instance, expressed in the form of mistrust, and heightened vigilance toward cooperativeness and reciprocity or conversely as violations of social norms and values. The question why cooperation between genetically unrelated individuals exists at all has long puzzled evolutionary theorists. It has been argued that altruistic behavior and reciprocity would not be able to spread in a population because

c13.indd 216

7/25/2011 10:10:25 AM

EVOLUTIONARY ASPECTS OF COOPERATION

217

they easily fell prey to exploitative counter-strategies of free-riders, thus producing a group of egotists who had greater reproductive success than those prosocial individuals who freely shared resources with others—akin to the concept of homo oeconomicus, who exploits others to his own benefit while disregarding the needs of others (Axelrod & Hamilton, 1981). However, humans seem to have evolved a set of rules of social norms that reinforce cooperation and help to keep rule violations at bay. These social norms form the basis of what we call moral values, which are sensitive to what is fair or unfair, and whom to trust or distrust. They constitute human universals, such that unfairness is rejected or even punished at one’s own expense in very similar ways across cultures (Henrich & Boyd, 2001). The conditions under which people choose, or not choose, cooperative strategies have experimentally been modeled by game theorists. A substantial literature exists on how psychologically healthy humans behave in game-theoretical scenarios, in which they assume the role of a proposer or a recipient in the distribution of (virtual) goods (Camerer, 2003a). This approach has only recently begun to be applied to research into psychiatric disorders. Central to this chapter is the hypothesis that the study of patients with psychiatric conditions using game-theoretical approaches can greatly improve our understanding of the nature to psychopathological signs and symptoms because the perceived—or factual—threat of basic human needs for cooperation and trust in others is at the core of many cognitive distortions and abnormal behaviors in patients with psychiatric disorders. The chapter, therefore, aims to summarize evolutionary and ontogenetic aspects of cooperation, game-theoretical approaches to study cooperation, brain mechanisms involved in the ability to appreciate social rules and norms, and research into psychiatric conditions based on game-theoretical models.

3.4.2

EVOLUTIONARY ASPECTS OF COOPERATION

In a seminal paper published in 1971, Robert Trivers highlighted the evolutionary problem of cooperative behavior between genetically unrelated organisms, as well as the evolutionary contingencies under which cooperation to different degrees of altruism could evolve. Accordingly, the simplest form of cooperation involves direct reciprocity, which implies repeated interactions between the same two individuals (or groups of individuals) and resources that are attractive to one another (Trivers, 1971). This primitive form of exchange, termed “conditional cooperation,” usually leads to mutual cooperation; if, however, one of the parties defects, the other one will turn to defection as well. The efficacy of such reciprocity can be experienced by both parties within relatively short periods of time and can frequently be observed in nonhuman animals. Indirect reciprocity is more complex because the benefit to the cooperating or reciprocating individual may be associated with improved reputation (Nowak, 2006) rather than a net increase in resources—a potential pay-off that may lie in the more distant future and may not be experienced instantaneously. Indirect reciprocity can be frequently observed in human societies (and perhaps other primate species) because helpful deeds are usually approved by significant others or the community as a whole, and therefore, they may raise one’s social status (Nowak & Sigmund,

c13.indd 217

7/25/2011 10:10:25 AM

218

INSIGHTS FROM EVOLUTIONARY GAME THEORY

1998). In evolutionary terms, indirect reciprocity may also serve as an “honest signal”: Individuals who are willing to take costs without the (direct) prospect of getting anything in return may in fact be showing that they can afford giving away “surplus” resources. Beyond direct and indirect reciprocity, genuinely altruistic behavior is characterized by the lack of reliable predictors that anything will ever be gained in return of a cooperative act. This so-called “strong” altruism has caused problems for evolutionary theorists because strong altruism may benefit the social group rather than the individual expressing strong altruism; moreover, selfish individuals can easily exploit the altruism of others and increase their own reproductive fitness at the expense of the altruist or the entire group, which eventually would lead to the extinction of strong altruism. To counteract such “free-riding” strategies and keep the level of altruism within social groups high, humans evolved cognitive mechanisms that evaluate the behavior of others in terms of their willingness to cooperate or intentions to defect, including mechanisms of cheating detection, vigilance toward defection, and emotional mechanisms such as friendship, sympathy, trust, but also mistrust and moralistic aggression (Trivers, 1971). In other words, sanctioning noncooperative behavior became critical at some point in human evolution. As far as is known, all human societies have established social rules and standards that enforce cooperative behavior within the group, including obedience to norms of fairness and equity. In fact, most people even feel uncomfortable when witnessing somebody being cheated on by another person, and most experience satisfaction when observing punishment of norm-violators or impute punishment at their own expense (de Quervain et al., 2004; Singer et al., 2006). Thus, groups of highly cooperative individuals can be assumed to have the highest average individual fitness, which declines with the number of defectors in a particular population (Nowak, 2006). Sustaining mutual cooperation at a high level may also have promoted new levels of societal organization with increasing specialization and diversity both biologically and culturally (Tooby & Devore, 1987), and it may have substantially contributed to populating the whole planet by homo sapiens. Outside our species, strong altruism seems to be rare. However, a look at our closest extant relatives, the great apes, may be enlightening with regard to the evolutionary roots of cooperation. Observations in wild populations suggest that chimpanzees share food and even care, to some extent, for wounded or frail conspecifics (Goodall, 1986). In experimental conditions, it was demonstrated that chimpanzees helped others in altruistic ways that were similar to the degree of cooperation of 18-month-old human infants. That is, they aided an unfamiliar individual by handing an object to the recipient that was out of reach for the recipient but within reach of the test subject. This was even observable when the cooperation was “costly” (i.e., required some physical effort on the side of the test subject) (Warneken, Hare, Melis, Hanus, & Tomasello, 2007). However, in an experiment that examined chimpanzees’ sensitivity to unfairness, in that a chimpanzee proposer suggested how to share food items between him and a chimpanzee recipient (where, in case the recipient rejected the offer, neither of the two received any food), the recipient actually accepted any offer, irrespective of the degree of fairness of the proposer. Moreover, when given the choice, chimpanzee proposers did not make fair offers more often than unfair offers (Jensen, Call, & Tomasello, 2007). This suggests that both proposer and recipient had no sense of fairness or aversion of inequity, as is typical for

c13.indd 218

7/25/2011 10:10:25 AM

ONTOGENETIC ISSUES OF COOPERATION

219

humans. Thus, it seems that chimpanzees are more “rational” in their choices, rather than altruistic.

3.4.3

ONTOGENETIC ISSUES OF COOPERATION

In general, social cognitive abilities such as empathy and mentalizing emerge during the first years of life. They critically depend on brain maturation (Singer et al., 2006) as well as on environmental input such as the quality of attachment to caregivers, family relations, and proximity to significant others. Under normal conditions, the ability to understand that others can have beliefs, knowledge, and intentions that differ from one’s own emerges around the age of 3 to 4 years. At that time, children can discriminate between their own and others’ mental states, especially with regard to the possibility that others can have false beliefs about facts such as the location of an object. There is an extensive literature demonstrating that a continuous refinement of the “mentalizing” abilities occurs throughout childhood and adolescence, such that complex mental states associated with the expression of irony, sarcasm, or “faux-pas” can reliably be understood when children have achieved higher levels of mental state representations (“he thinks that she believes that I know . . . ”). Empathetic abilities emerge even earlier, with emotional contagion being already present in young infants. However, higher levels of empathy that require the ability to put oneself into the shoes of others appear later and parallel, to some degree, the development of mentalizing. Arguably, both empathy and mentalizing are prerequisites for the evaluation of more complex social interactions involved in mutual cooperation. Although infants between 14 and 18 months of age already make use of “instrumental helping,” as described, other-regarding preferences to the degree of active avoidance of inequality take several years to develop and may not be observable until the age of 7 to 8 years. In an ingenious series of experiments, children aged 3 to 4, 5 to 6, and 7 to 8, respectively, were given the choice to share an amount of jelly beans between themselves and another child with different degrees of costs to the sharer, and inequality between the two parties. It turned out that 3- to 4-year-olds behaved selfishly in all conditions suggesting little other-regarding preferences. This changed in 5- to 6-yearolds, with the strongest effects for 7- to 8-year-olds, who showed a strong aversion of inequality and a high preference for egalitarian outcomes. This effect was pronounced when children’s choices were analyzed with regard to in-group versus out-group membership of the recipient, with higher preferences for egalitarian allocations for in-group members as compared with out-group members (Fehr, Bernhard, & Rockenbach, 2008). In support of the assumption that social cognitive abilities play a role in the comprehension of cooperation scenarios and fairness, children who assumed the role of a proposer in a game, in which several candies had to be shared with another child, tended to make more fair-split offers, if they had acquired mentalizing abilities such as false belief understanding. In contrast, children with less mentalizing abilities made more unfair offers and, hence, ran the risk to get nothing because, in case of an unfair offer, the recipient had the opportunity to reject the offer such that neither received anything. Interestingly, mentalizing abilities had no influence on the rejection rate on the side of the recipient, although good mentalizers anticipated anger

c13.indd 219

7/25/2011 10:10:25 AM

220

INSIGHTS FROM EVOLUTIONARY GAME THEORY

in the recipient of unfair offers better than poor mentalizers (Takagishi, Kameshima, Schug, Koizumi, & Yamagishi, 2010). The link of social cognitive abilities with people’s behavior in cooperative scenarios is further buttressed by studies in adults that revealed a link between emotional intelligence (a concept similar to empathy) and social exchange reasoning that was designed to detect violations of social rules and norms (Reis et al., 2007), as well as by imaging and neurophysiological studies showing an overlap of brain activation during mentalizing, cooperation, and contemplation of the degree of unfairness in the allocation of resources (Elliott et al., 2006; Polezzi et al., 2008).

3.4.4 ASSESSMENT OF COOPERATION USING GAME-THEORETICAL APPROACHES To understand the environmental contingencies in which individuals cooperate or not cooperate, it is vital to manipulate conditions experimentally that may prompt cooperation, rejection of cooperation, and punishment of noncooperative behavior. Although transferring the evolutionary scenarios of social exchange into empirical settings is difficult in light of the complexity of real-life interactions between individuals, groups, or larger societal organizations, various evolutionary gametheoretical scenarios have been developed to examine behavior in (virtual) social interactions (Axelrod & Hamilton, 1981). Generally, these scenarios deal with the distribution of resources between two or more parties (Nowak, 2006). They differ in complexity according to the number of participants and repetitions of social exchange. The Prisoner’s Dilemma (PD), for example, involves the decision to cooperate or to defect in a scenario played by two parties (Axelrod, 1984). The idea is that each player gains when both cooperate, but if only one of them cooperates, the other one, who defects, will gain more. If both defect, both lose (or gain very little), but not as much as the “cheated” cooperator whose cooperation is not returned. The game is named after a hypothetical situation of two criminals who are under the suspicion of having committed a crime together. The two are interrogated separately and offered a deal, according to which the one who provides evidence against the other one will be freed. If both reject the offer, and instead, cooperate against the police, either one will get only a small punishment. However, if one of them is disloyal to the other, the defector will gain more, whereas the other will receive the full punishment. If both betray, both will be punished but less severely than if they had refused to talk. Theoretically, cooperation is not the best strategy in this scenario because without knowing the strategy chosen by the other player, noncooperation is associated with a higher expectancy value. Interestingly, most people nevertheless cooperate in the PD at a considerable rate (Rilling, Sanfey, Aronson, Nystrom, & Cohen, 2004). The Trust Game (TG) is similar to the PD in that it involves reciprocity. One player, called the investor, is endowed with a sum of money, of which he/she can pass some amount to another player (the trustee). The investment is multiplied by the experimenter. The trustee decides how much of the multiplied money he/she wants to return to the investor. Selfish trustees would not reciprocate, particularly if there is no sanction in place for noncooperation; moreover, the investor, if suspi-

c13.indd 220

7/25/2011 10:10:25 AM

ASSESSMENT OF COOPERATION USING GAME-THEORETICAL APPROACHES

221

cious, would not invest a significant amount of money. However, in the TG, the investor usually sends a significant proportion of his/her endowment to the trustee, who in most instances reciprocates. One reason for trustful behavior in both the TG and the PD resides in implicitly accepted social norms and moral values. People tend to cooperate with the expectation that their counterpart is willing to cooperate in return. In contrast, most people would consider it appropriate to defect when the other player defects (a shorthand for this kind of interaction is “tit-for-tat”; Axelrod & Hamilton, 1981). Indeed, if repetitively played, the willingness to cooperate can change over time. The Public Goods (PG) game illustrates this. It is played by an optional number of players who initially receive a defined amount of money or tokens, which participants are asked to invest simultaneously in a common pool (the public good) without knowing the contribution of the other players. The experimenter multiplies the whole sum by a factor that is larger than one and smaller than the number of players, and experimenter returns an equal share of that money to each player. This suggests that all players benefit from the public goods, irrespective of how much they have invested before. Accordingly, the PG game examines the extent to which players are tempted to choose an exploitative or “free-riding” strategy because players who refuse to invest will gain more than the other players who contributed (Fehr & Fischbacher, 2004a). If played repetitively, players of the PG game tend to reduce their investment over successive rounds, unless non-cooperation is sanctioned. Punishment of noncooperators, in turn, leads to a steep increase of investment (Fehr and Gächter, 2002). In one-shot PG games the degree of punishment is largely determined by the magnitude by which a noncooperator’s contribution deviates from the average investment. This suggests that the recognition of “free-riding” induces negative emotions in cooperators, including a sense of unfairness, which increases the likelihood of sanctioning noncooperative behavior, even in singular events (Fehr & Gächter, 2002). The PG game imitates “real-life” situations associated with the distribution of goods within social groups such as taxes, donations, fees, and so on, with relatively little differences in performance across cultures (Okada & Riedl, 1999). However, PG scenarios have also revealed the dark side of human cooperation that may surface, if people need not be afraid of sanctions of noncooperators. In such situations, “antisocial” punishment may occur; that is, altruistic behavior is punished, particularly if the punishment can be pursued anonymously (Herrmann, Thöni, & Gächter, 2008). In games such as the PD, TG, and PG, players do not have the option to reject unfair offers straightaway. This option is modeled in a scenario, called the Ultimatum Game (UG), where two players have to split up a sum of money (e.g., 10 MU). One player (“A”) is asked to propose how to distribute the money. In contrast to the PD, Player “B” has the option to either accept or decline the offer. If B agrees, the sum will be split according to A’s proposal. If, however, B rejects, both receive nothing (Falk & Fischbacher, 2000). Rejecting an offer, however unfair it may be, is therefore costly not only for A but also for B. Therefore, the UG involves a mild form of costly punishment (Camerer, 2003b; Falk & Fischbacher, 2000; Güth, Schmittberger, & Schwarze, 1982; Sanfey, Rilling, Aronson, Nystrom, & Cohen, 2003). From a strictly rational point of view, B should to be happy with every offer and should not reject offers larger than zero. Practically, however, offers smaller than 50% of the total amount are perceived as unfair and are usually rejected (Sanfey et al., 2003).

c13.indd 221

7/25/2011 10:10:25 AM

222

INSIGHTS FROM EVOLUTIONARY GAME THEORY

As player A in the UG knows in advance that his or her offer can be rejected by B, an interesting question is how people would behave if they did not have to fear any sanctions. The Dictator Game (DG) represents such a scenario, in which the recipient is forced to accept all offers (Falk & Fischbacher, 2000). Accordingly, from the perspective of A, the DG is a more direct measure of altruism (Camerer, 2003a). Not unexpectedly, in the DG, player A usually makes offers of around 15% of the whole amount (Charness & Gneezy, 2003). Altruistic behavior, therefore, seems to be highly context dependent, which includes predictions of possible sanctions. Interestingly, if the DG is modeled such that a third party (player “C”) has the option to reinforce reciprocity and fairness by investing his or her own money—a selfless cost to the punisher, hence, a case of strong altruism (Seymour, Singer, & Dolan, 2007)—the common observation is that people are willing to invest in punishing uncooperative behavior, even when they just witness an unfair interaction between others (de Quervain et al., 2004; Fehr & Fischbacher, 2004b). Normally, about 60% of nonparticipating observers of a DG sanction dictators who are proposing less than 50% of the whole sum. Taken together, these game-theoretical approaches suggest that a delicate balance exists between cooperative and noncooperative strategies in social exchange situations. Specifically, individuals seem to have a clear motivation to punish noncooperative behavior within their social in-group—exactly what Trivers (1971) proposed would be expected for the establishment of reciprocal altruism and cooperation between genetically unrelated individuals of long-lived social animals like humans. This complexity of social behavior almost certainly drove human brain evolution because large computational resources for dealing with the manifold behavioral options of coalition formation, reciprocity, or detection of exploitative strategies were likely associated with the odds of individual reproductive success (Brothers, 1990; Dunbar, 2003).

3.4.5 NEURONAL CORRELATES OF SOCIAL COGNITION AND COOPERATION Social cognitive processes—especially mentalizing and empathy—are associated with activity in a neural network that includes cortical midline structures such as the medial prefrontal cortex (mPFC), the anterior cingulate cortex (ACC) and paracingulate gyrus, the medial part of the orbitofrontal cortex (mOFC), the precuneus, as well as lateral portions of the middle temporal lobes (MTLs), the temporoparietal junction (TPJ), the superior temporal sulcus (STS), and the temporal poles (reviewed in Brüne & Brüne-Cohrs, 2006; Hynes, Baird, & Grafton, 2006; Saxe, 2006; Saxe, Carey, & Kanwisher, 2004; Völlm et al., 2007). The area extending from the ACC to the anterior frontal poles, particularly the paracingulate cortex, is supposed to be engaged in self-reflection, person perception, and inferring others’ thoughts, feelings, and intentions (Amodio & Frith, 2006; Völlm et al., 2006). Furthermore, the TPJ is involved in reasoning about the contents of another person’s mind (Saxe & Kanwisher, 2003; Saxe & Wexler, 2005), the attribution of a character’s true and false beliefs, or deceptive intentions (Lissek et al., 2008; Saxe, 2006; Sommer et al., 2007). Moreover, the TPJ comprises an area that contributes to the discrimination of self and other (Gallagher et al., 2000). The role of the

c13.indd 222

7/25/2011 10:10:25 AM

COOPERATION IN PSYCHOPATHOLOGICAL CONDITIONS

223

precuneus is less well known, but this brain area seems to be important for the experience of agency and self-consciousness (Cavanna & Trimble, 2006; Schilbach et al., 2006). Amygdalar and orbitofrontal activity contribute the affective “tone” to the evaluation of thoughts and intentions (Baron-Cohen et al., 1999), and the mOFC is selectively active when subjects engage in emotional perspective-taking as compared with cognitive perspective-taking (Hynes et al., 2006). This neural network broadly overlaps with that found active during performance of economic games (Elliott et al., 2006). Functional brain imaging studies suggest that the dorsolateral prefrontal cortex (dlPFC) is active during processing of unfair offers, irrespective of the degree of unfairness, which is consistent with its role in goal-maintenance, working-memory, and executive control (Bechara, Damasio, Tranel, & Anderson, 1998; Miller & Cohen, 2001; Sanfey et al., 2003). Similarly, the mPFC, temporal pole, and TPJ were activated in a guessing-game in which cooperation with another player was simulated (Elliott et al., 2006). Moreover, a functional magnetic resonance imaging (fMRI) study using versions of the UG and PD revealed activations in the paracingulate gyrus and the posterior STS during cooperation relative to defection (Rilling et al., 2004). The perception of unfairness seems to be specifically associated with bilateral activation of the anterior insula, and the ACC that seems to be “dose dependent.” The strength of activation in the anterior insula and ACC correlated with the degree of unfairness of offers and in the insula alone with the number of rejected offers in the UG (Sanfey et al., 2003). Interestingly, other brain structures such as the striatum including the caudate nucleus, the nucleus accumbens, and the thalamus are active when people are engaged in costly punishment (de Quervain et al., 2004). In particular, the activation of the caudate nucleus, which is known to be involved in reward processing (Knutson, Adams, Fong, & Hommer, 2001), was positively correlated with the investment in punishment. Conversely, if the desire to punish could not be satisfied, caudate activation was below average. In addition, increased activation in the thalamus, the mPFC, and the mOFC was observed in conditions in which subjects verbalized a strong desire to punish (de Quervain et al., 2004). This study illustrates, therefore, that people may experience punishment of unfair behavior as rewarding, even if costly to oneself. In summary, it can be pointed out that an extended neural network is involved in the evaluation of costs and benefits of social exchange and socioeconomic decision making, including cooperation and altruistic punishment. Importantly, this network overlaps with that involved in more basic social cognitive processes such as mentalizing and empathy, which suggests that intact social cognition is essential for the understanding of the more complex rules of cooperation and exchange.

3.4.6

COOPERATION IN PSYCHOPATHOLOGICAL CONDITIONS

Abundant research has shown that social cognition (i.e., empathy and mentalizing) is compromised in many psychiatric disorders. The broadest empirical evidence for this contention exists for autism spectrum disorders and schizophrenia (reviewed in Brüne and Brüne-Cohrs, 2006), with growing support for abnormal empathy and mentalizing in bipolar affective disorder (Shamay-Tsoori et al., 2009), neurodegenerative disorders (reviewed in Brüne and Brüne-Cohrs, 2006), borderline

c13.indd 223

7/25/2011 10:10:25 AM

224

INSIGHTS FROM EVOLUTIONARY GAME THEORY

personality disorder (Shamay-Tsoori et al., 2009), and psychopathy (Blair, 2005). Certainly, alterations of empathetic responses and mentalizing are not uniform across disorders. In autism, for example, deficits in these domains seem to be associated with a developmental delay, whereas in schizophrenia and, partly, in borderline personality disorder, mentalizing seems to be overactive to the degree of paranoid ideation, at least in some patients. Whether impaired empathy and mentalizing impact on more complex social cognitive processes as expressed in game-theoretical scenarios has only recently begun to be explored. The rationale behind the hypothesis that this might be the case relates to the observation of patients’ behavior, as outlined in the introductory paragraphs. In addition, several studies have revealed abnormal activations of the neural network involved in social cognition in autism, schizophrenia, bipolar disorder and so on, which in turn could lend support to the assumption that economic decision making in psychiatric disorders differs from that of psychologically healthy subjects. 3.4.6.1 Autism Autism is characterized by an impoverishment of social interaction, stereotyped behaviors, and social insecurity. Accordingly, one would expect that violations of cooperative rules occur more often in autism compared with healthy children. In fact, children with autism seem to have a diminished sense of fairness compared with normally developing children. In an UG, autistic children accepted unfair offers more often but also rejected fair offers more frequently than healthy children of the same developmental age (Sally & Hill, 2006). Moreover, in the initial round of the game, autistic children who acted as proposers displayed a balanced preference for even offers or extremely unfair offers. In addition, false-belief understanding predicted, to some degree, cooperation in a PD and whether children made fair offers in the UG (Sally & Hill, 2006). These findings are in line with the literature on the ontogenetic development of economic decision making suggesting that basic social cognitive skills such as false-belief understanding predict children’s behavior in more complex interactions involving the recognition of fairness and rules of cooperation. 3.4.6.2

Schizophrenia

The term “schizophrenia” embraces a group of psychotic disorders that are characterized by delusions, hallucinations, thought disorder, affective flattening, and disorganised behavior. Paranoid patients are highly mistrustful and suspicious, whereas patients with negative symptoms often withdraw form their social environment. Both may be associated with a lack of understanding of rules of social exchange. Indeed, in schizophrenia research, the question of whether patients have difficulties in appreciating social rules and moral values, including those associated with reciprocity and cooperation, has been debated since the 19th century. Kahlbaum, for instance, coined the term “heboidophrenia” for a disorder for which violations of social rules were pathognomonic (Kahlbaum, 1885). A few empirical studies of schizophrenia patients’ ability to appreciate moral values carried out some 50 years ago revealed that schizophrenia patients chose humanitarian responses to moral

c13.indd 224

7/25/2011 10:10:25 AM

COOPERATION IN PSYCHOPATHOLOGICAL CONDITIONS

225

problems less often than controls, indicating that patients disregarded the needs of others (Baruk, 1968; Johnson, 1960). However, this research was conducted in longterm hospitalized patients with chronic schizophrenia. Thus, in cannot be ruled out that artiefacts of chronic hospitalization influenced the outcome of studies. A more recent study revealed that schizophrenic patients made overly fair offers (as proposers) in the UG compared with unaffected control subjects (Agay, Kron, Carmel, Mendlovic, & Levkovitz, 2008). However, schizophrenia patients failed to adjust their proposals to the response they received from the recipient in the previous trial. For example, they raised their offer after trials in which recipients had accepted the proposal. Interestingly, when schizophrenic patients took the role of the recipient in the UG, their behavior did not differ from that of control participants (Agay et al., 2007). In our own study of schizophrenia patients’ performance in the UG and in a DG with the option to punish observed unfair behavior, patients accepted more often unfair offers in the role of the recipient in the UG. When assigned the role of a third-party punisher in the DG, however, patients with schizophrenia performed similarly to healthy controls. Interestingly, excitement and disorganization scores on the Positive and Negative Syndrome Scale (Kay, Opler, & Lindenmayer, 1989) correlated with the acceptance rates of unfair offers. Moreover, we found correlations between the amount of negative symptoms and the rejection of fair offers, as well as with the punishment investment in the most unfair DG condition. These findings suggest that the severity of psychopathology influences patients’ economic decision making in ways that seem inconsistent or incongruent. No correlation emerged between patients’ empathetic ability as measured using the Reading the Mind in the Eyes Task (Baron-Cohen, Wheelwright, Hill, Raste, & Plumb, 2001) and performance in the economic games, which may reside in the fact that the patient group was exceptionally good at this and did not differ from controls (Wischniewski & Brüne, submitted). In summary, the studies available so far show that patients with schizophrenia are able to appreciate rules of cooperation, but they deviate from normals in their appreciation of fairness norms. 3.4.6.3

Borderline Personality Disorder

Borderline personality disorder (BPD) is characterized by unstable interpersonal relationships, emotional dysregulation, and rapid changes of over-idealization and devaluation of close others. Thus, it is conceivable that patients with BPD differ from controls in their ability to appreciate social rules and norms. In support of this hypothesis, a recent study demonstrated that in a TG that was played repetitively, patients with BPD were unable to maintain high levels of investment by an investor, when assuming the role of the recipient. In fact, patients’ behavior induced a downward shift in investment by healthy investors, which was not observed when the TG was played by two healthy subjects. In addition, whereas healthy trustees (recipients) were able to increase the investment in situations of impending break of cooperation by returning amounts larger than 50% of the tripled money to the investor, thus signaling their trustworthiness, patients with BPD failed to rescue cooperation. In other words, BPD subjects were unable to show generosity to repair a breakdown of reciprocity (King-Casas et al., 2008). Notably, neural activity in the anterior insula during functional brain imaging was similar in BPD and controls upon repayment. Unlike healthy controls, however, patients with BPD did not show

c13.indd 225

7/25/2011 10:10:25 AM

226

INSIGHTS FROM EVOLUTIONARY GAME THEORY

insular activation upon receipt of offers from the investor, which could reflect the difficulty individuals with BPD have in deciphering social signals of cooperation (King-Casas et al., 2008). 3.4.6.4

Psychopathy

In contrast to the aforementioned disorders that reflect distorted defense mechanisms, psychopathy involves exploitation of others associated with an inability to empathize. Accordingly, psychopaths lack feelings of guilt and remorse and tend to manipulate others to their own advantage. In game-theoretical scenarios, psychopaths are therefore expected to behave selfishly and to violate rules of cooperation. Indeed, in a PD scenario, individuals with high psychopathy scores were more likely than low scorers to defect and discontinue cooperation more often. High psychopathy scorers were also more likely to experience defection, probably as retaliation of their former defection. However, unlike low psychopathy scorers, high scorers showed less amygdala activity upon being cheated. In contrast, when choosing to defect, psychopaths displayed less activation in the dlPFC and ACC, which could indicate that they experienced less cognitive conflict when cheating the other player. Conversely, psychopathic individuals activated the dlPFC more strongly when choosing to cooperate, suggesting that they had to override cognitively a tendency to defect (Rilling et al., 2007). These findings are in line with another fMRI study that examined the relationship of Machiavellianism to economic decision making. Machiavellianism is often considered part of the psychopathy phenotype because it involves a tendency to deceive and manipulate others for personal benefit. In a DG with the option of being punished for uncooperativeness, subjects scoring high on Machiavellianism activated the left anterior OFC, which has been shown activated during evaluation of punishment threats, more strongly than low Machs. Impending punishment led to an increase of transferred money, whereas in a nonpunishment condition, the actual transfer level was negatively correlated with Machiavellianism (Spitzer, Fischbacher, Herrnberger, Grön, & Fehr, 2007). These findings suggest that high Machs seem to pretend increased cooperation when threatened with punishment, whereas they behaved selfishly when no punishment was expected.

3.4.7

SUMMARY AND FUTURE DIRECTIONS

Clinical observation suggests that social cognitive deficits are core features of many, if not all, psychiatric disorders. Patients with psychopathological conditions seem to assume defensive attitudes in the fear of being victimized by others who refuse cooperation and reciprocity, however unrealistic this perception may be. This misperception may induce mistrust, paranoid ideation, or social withdrawal on the side of the patients. As empathetic abilities and mentalizing skills are frequently distorted in psychiatric disorders, a vicious circle may emerge from continuing misinterpretation of social signals as malevolent. Although to date sparse, data on economic decision making in psychiatric disorders support this view. Patients with psychiatric disorders seem to have difficulties in appreciating social rules of exchange and reciprocality, partly, as may be the case

c13.indd 226

7/25/2011 10:10:25 AM

REFERENCES

227

in autism and schizophrenia, because of problems in empathizing and mental state attribution. Moreover, a failure to establish and maintain trust and enduring relationships based on reciprocity is at the core of the psychopathology of personality disorders. For many other psychiatric disorders such as depression, drug addiction, or anxiety disorders, no data are available. It is, however, conceivable, that patients with any one of these disorders also differ from unaffected controls in performance on economic games. Little is known about the underlying mechanisms that may cause these differences. It is likely that gene–environment interactions play a role. For example, the quality of early attachment determines whether an individual can accept the (social) world as a safe place or, conversely, whether interpersonal relationships are perceived as unreliable and untrustworthy. The hormone oxytocin may be a decisive proxy that mediates social attachment. For example, it has been shown that the administration of oxytocin increases trust in healthy individuals (Kosfeld et al., 2005). Conversely, individuals who as children had experienced emotional abuse have smaller concentrations of oxytocin in their cerebrospinal fluid (Heim et al., 2009). In a similar vein, an oxytocin receptor polymorphism may be associated with an increased risk for depression that is possibly mediated by the quality of adult attachment (Costa et al., 2009). With regard to economic decision making, individual differences in the length of the vasopressin 1a receptor RS3 (AVRP1) as well as an oxytocin receptor polymorphism have been shown to predict behavior in a DG. Like oxytocin, AVRP1a plays an important role in affiliative behaviors in mammals (Hammock & Young, 2006). Individuals with the short AVRP1a allele offered significantly less money to recipients than participants with a longer version of the allele (Knafo et al., 2008). Similarly, a single nucleotide polymorphism at the oxytocin receptor was found to predict higher allocations in the DG (Israel et al., 2009). Other candidates may be variations of the dopamine and serotonin transporter coding genes that have been shown to mediate the level of risk aversion and risk tolerance (Zhong et al., 2009). Similarly, antisocial behavior has been found linked to genetic variations of the MAOA and serotonin transporter coding genes. However, this tendency toward antisocial behavior only manifests if associated with adverse childhood experiences (Caspi et al., 2002). These examples clearly suggest that differences in genetic endowment and gene– environment interaction not only convey individual differences in cooperation and trust but also differences in vulnerability to psychiatric disorders (Israel et al., 2008). Future studies into the nature of psychiatric disorders may make use of these insights from evolutionary game theory.

REFERENCES Adolphs, R. (2001). The neurobiology of social cognition. Curr Opin in Neurobiol 11(2), 231–239. Agay, N., Kron, S., Carmel, Z., Mendlovic, S., & Levkovitz, Y. (2008). Ultimatum bargaining behavior of people affected by schizophrenia. Psychiatr Res 157(1–3), 39–46. Amodio, D. M., & Frith, C. D. (2006). Meeting of minds: The medial frontal cortex and social cognition. Nat Rev Neurosci 7(4), 268–277.

c13.indd 227

7/25/2011 10:10:25 AM

228

INSIGHTS FROM EVOLUTIONARY GAME THEORY

Axelrod, R. (1984). The Evolution of Cooperation. New York: Basic Books. Axelrod, R., & Hamilton, W. D. (1981). The evolution of cooperation. Science 211(4489), 1390–1396. Baron-Cohen, S., Ring, H. A., Wheelwright, S., Bullmore, E. T., Brammer, M. J., et al. (1999). Social intelligence in the normal and autistic brain: An fMRI study. Eur J Neurosci 11(6), 1891–1898. Baron-Cohen, S., Wheelwright, S., Hill, J., Raste, Y., & Plumb, I. (2001). The “Reading the Mind in the Eyes” test revised version: A study with normal adults, and adults with Asperger syndrome or high-functioning autism. J Child Psychol and Psychiatr 42(2), 241–251. Baruk, H. (1968). La schizophrénie morale. Annales Medico-Psychologique 2, 221–227. Bechara, A., Damasio, H., Tranel, D., & Anderson, S. W. (1998). Dissociation of working memory from decision making within the human prefrontal cortex. J Neurosci 18(1), 428–437. Blair, R. (2005). Responding to the emotions of others: Dissociating forms of empathy through the study of typical and psychiatric populations. Conscious Cognit 14, 698–718. Brothers, L. (1990). The neural basis of primate social communication. Motiv Emot 14(2), 81–91. Brüne, M., & Brüne-Cohrs, U. (2006). Theory of mind-evolution, ontogeny, brain mechanisms and psychopathology. Neurosci Biobehav Rev 30(4), 437–455. Brüne, M., Schaub, D., Juckel, G., & Langdon, R. (in press). Social skills and behavioral problems in schizophrenia: The role of mental state attribution, neurocognition and clinical symptomatology. Psychiatr Res. Camerer, C. F. (2003a). Behavioral studies of strategic thinking in games. Trends Cognit Sci 7(5), 225–231. Camerer, C. F. (2003b). Psychology and economics. Strategizing in the brain. Science 300, 1673–1675. Caspi, A., McClay, J., Moffitt, T. E., Mill, J., Martin, J., et al. (2002). Role of genotype in the cycle of violence in maltreated children. Science 297(5582), 851–854. Cavanna, A. E., & Trimble, M. R. (2006). The precuneus: A review of its functional anatomy and behavioural correlates. Brain 129(3), 564–583. Charness, G., & Gneezy, U. (2003). Whats in a Name? Anonymity and Social Distance in Dictator and Ultimatum Games. Technical report, University of California, Santa Barbara, Department of Economics working paper series. Costa, B., Pini, S., Gabelloni, P., Abelli, M., Lari, L., et al. (2009). Oxytocin receptor polymorphisms and adult attachment style in patients with depression. Psychoneuroendocrinology 34(10), 1506–1514. Dunbar, R. (2003). The social brain: Mind, language, and society in evolutionary perspective. Ann Rev Anthropol 32, 163–181. Ebstein, R., Israel, S., Chew, S., Zhong S., & Knafo, A. (2010). Genetics of human social behavior. Neuron 65(6), 831–844. Elliott, R., Völlm, B., Drury, A., McKie, S., Richardson, P., et al. (2006). Co-operaton with another player in a financially rewarded guessing game activates regions implicated in theory of mind. Soc Neurosci 1, 385–395. Falk, A., & Fischbacher, U. (2000). A Theory of Reciprocity, Technical Report, Institute for Empirical Economic Research. Fehr, E., Bernhard, H., & Rockenbach, B. (2008). Egalitarianism in young children. Nature 454(7208), 1079–1083.

c13.indd 228

7/25/2011 10:10:25 AM

REFERENCES

229

Fehr, E., & Fischbacher, U. (2004a). Social norms and human cooperation. Trends Cognit Sci 8(4), 185–190. Fehr, E., & Fischbacher, U. (2004b). Third-party punishment and social norms. Evol Hum Behav 25, 63–87. Fehr, E., & Gächter, S. (2002). Altruistic punishment in humans. Nature 415(6868), 137–140. Gallagher, H. L., Happé, F., Brunswick, N., Fletcher, P. C., Frith, U. et al. (2000). Reading the mind in cartoons and stories: An fMRI study of theory of mind in verbal and nonverbal tasks. Neuropsychologia 38(1), 11–21. Gilbert, P. (2001). Evolutionary approaches to psychopathology: The role of natural defences. Austr New Zeal J Psychiatr 35(1), 17–27. Goodall, J. (1986). The Chimpanzees of Gombe: Patterns of Behavior. Cambridge, MA: Harvard University Press. Güth, W., Schmittberger, R., & Schwarze, B. (1982). An experimental analysis of ultimatum bargaining. J Econo Behav Organ 3(4), 367–388. Hammock, E. A. D., & Young, L. J. (2006). Oxytocin, vasopressin and pair bonding: Implications for autism. Phil Trans Roy Soc B Biol Sci 361(1476), 2187–2198. Heim, C., Young, L. J., Newport, D. J., Mletzko, T., Miller, A. H., et al. (2009). Lower CSF oxytocin concentrations in women with a history of childhood abuse. Mol Psychiatr 14(10), 954–958. Henrich, J., & Boyd, R. (2001). Why people punish defectors. Weak conformist transmission can stabilize costly enforcement of norms in cooperative dilemmas. J Theor Biol 208(1), 79–89. Herrmann, B., Thöni, C., & Gächter, S. (2008). Antisocial punishment across societies. Science 319, 1362–1367. Hynes, C. A., Baird, A. A., & Grafton, S. T. (2006). Differential role of the orbital frontal lobe in emotional versus cognitive perspective-taking. Neuropsychologia 44(3), 374–383. Israel, S., Lerer, E., Shalev, I., Uzefovsky, F., Reibold, M., et al. (2008). Molecular genetic studies of the arginine vasopressin 1a receptor (AVPR1a) and the oxytocin receptor (OXTR) in human behaviour: From autism to altruism with some notes in between. Progr Brain Res 170, 435–449. Israel, S., Lerer, E., Shalev, I., Uzefovsky, F., Riebold, M., et al. (2009). The oxytocin receptor (OXTR) contributes to prosocial fund allocations in the dictator game and the social value orientations task. PLoS One 4(5), e5535. Jensen, K., Call, J., & Tomasello, M. (2007). Chimpanzees are rational Maximizers in an ultimatum game. Science 318, 107–109. Johnson, D. L. (1960). The moral judgment of schizophrenics. J Nerv Ment Dis 130, 278–285. Kahlbaum, K. (1885). Über eine klinische Form des moralischen Irreseins. Allgemeine Zeitschrift für Psychiatrie 41, 711. Kay, S. R., Opler, L. A., & Lindenmayer, J. P. (1989). The positive and negative syndrome scale (PANSS): Rationale and standardisation. Br J Psychiatr Suppl 155(7), 59–67. King-Casas, B., Sharp, C., Lomax-Bream, L., Lohrenz, T., Fonagy, P., et al. (2008). The rupture and repair of cooperation in borderline personality disorder. Science 321(5890), 806–810. Knafo, A., Israel, S., Darvasi, A., Bachner-Melman, R., Uzefovsky, F., et al. (2008). Individual differences in allocation of funds in the dictator game associated with length of the arginine vasopressin 1a receptor RS3 promoter region and correlation between RS3 length and hippocampal mRNA. Gene Brain Behav 7(3), 266–275.

c13.indd 229

7/25/2011 10:10:25 AM

230

INSIGHTS FROM EVOLUTIONARY GAME THEORY

Knutson, B., Adams, C. M., Fong, G. W., & Hommer, D. (2001). Anticipation of increasing monetary reward selectively recruits nucleus accumbens. J Neurosci 21(16), RC159. Kosfeld, M., Heinrichs, M., Zak, P. J., Fischbacher, U., & Fehr, E. (2005). Oxytocin increases trust in humans. Nature 435, 673–676. Lissek, S., Peters, S., Fuchs, N., Witthaus, H., Nicolas, V., et al. (2008). Cooperation and deception recruit different subsets of the theory-of-mind network. PLoS ONE 3(4), e2023. Lysaker, P. H., Carcione, A., Dimaggio, G., Johannesen, J. K., Nicolò, G., et al. (2005). Metacognition amidst narratives of self and illness in schizophrenia: Associations with neurocognition, symptoms, insight and quality of life. Acta Psychiatr Scandi 112(1), 64–71. Mealey, L. (1995). The sociobiology of sociopathy: An integrated evolutionary model. Behavior and Brain Sci 18(3), 523–599. Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annu Rev Neurosci 24, 167–202. Nowak, M. A. (2006). Five rules for the evolution of cooperation. Science 314(5805), 1560–1563. Nowak, M. A., & Sigmund, K. (1998). Evolution of indirect reciprocity by image scoring. Nature 393(6685), 573–577. Okada, A., & Riedl, A. (1999). When culture does not matter: Experimental evidence from coalition formation. Ultimatum Games in Austria and Japan, University of Amsterdam (CREED), Working paper. Polezzi, D., Daum, I., Rubaltelli, E., Lotto, L., Civai, C., et al. (2008). Mentalizing in economic decision-making. Behav Brain Res 190(2), 218–223. de Quervain, D. J.-F., Fischbacher, U., Treyer, V., Schellhammer, M., Schnyder, U., et al. (2004). The neural basis of altruistic punishment. Science 305(5688), 1254–1258. Reis, D. L., Brackett, M. A., Shamosh, N. A., Kiehl, K. A., Salovey, P. et al. (2007). Emotional intelligence predicts individual differences in social exchange reasoning. Neuroimage 35(3), 1385–1391. Rilling, J. K., Glenn, A. L., Jairam, M. R., Pagnoni, G., Goldsmith, D. R., et al. (2007). Neural correlates of social cooperation and non-cooperation as a function of psychopathy. Biol Psychiatr 61(11), 1260–1271. Rilling, J. K., Sanfey, A. G., Aronson, J. A., Nystrom, L. E., & Cohen, J. D. (2004). The neural correlates of theory of mind within interpersonal interactions. Neuroimage 22(4), 1694–1703. Sally, D., & Hill, E. (2006). The development of interpersonal strategy: autism, theory-of-mind, cooperation and fairness. J Econ Psychol 27(1), 73–97. Sanfey, A. G., Rilling, J. K., Aronson, J. A., Nystrom, L. E., & Cohen, J. D. (2003). The neural basis of economic decision-making in the ultimatum game. Science 300(5626), 1755–1758. Saxe, R. (2006). Uniquely human social cognition. Curr Opin Neurobiol 16(2), 235–239. Saxe, R., Carey, S., & Kanwisher, N. (2004). Understanding other minds: Linking developmental psychology and functional neuroimaging. Annu Rev Psychol 55, 87–124. Saxe, R., & Kanwisher, N. (2003). People thinking about thinking people. The role of the temporo-parietal junction in “theory of mind”. Neuroimage 19(4), 1835–1842. Saxe, R., & Wexler, A. (2005). Making sense of another mind: The role of the right temporoparietal junction. Neuropsychologia 43(10), 1391–1399. Schilbach, L., Wohlschlaeger, A. M., Kraemer, N. C., Newen, A., Shah, N. J., et al. (2006). Being with virtual others: Neural correlates of social interaction. Neuropsychologia 44(5), 718–730.

c13.indd 230

7/25/2011 10:10:25 AM

REFERENCES

231

Seymour, B., Singer, T., & Dolan, R. (2007). The neurobiology of punishment. Nat Rev Neurosci 8(4), 300–311. Shamay-Tsoory, S. G., Aharon-Peretz, J., & Perry, D. (2009). Two systems for empathy: A double dissociation between emotional and cognitive empathy in inferior frontal gyrus versus ventromedial prefrontal lesions. Brain 132(Pt 3), 617–627. Singer, T., Seymour, B., O’Doherty, J. P., Stephan, K. E., Dolan, R. J., et al. (2006). Empathic neural responses are modulated by the perceived fairness of others. Nature 439(7075), 466–469. Sommer, M., Döhnel, K., Sodian, B., Meinhardt, J., Thoermer, C., et al. (2007). Neural correlates of true and false belief reasoning. Neuroimage 35(3), 1378–1384. Spitzer, M., Fischbacher, U., Herrnberger, B., Grön, G., & Fehr, E. (2007). The neural signature of social norm compliance. Neuron 56(1), 185–196. Takagishi, H., Kameshima, S., Schug, J., Koizumi, M., & Yamagishi, T. (2010). Theory of mind enhances preference for fairness. J Exp Child Psychol 105(1–2), 130–137. Tooby, J., & Devore, I. (1987). The reconstruction of hominid behavioral evolution through strategic modeling, The Evolution of Human Behavior: Primate Models, edited by W.G. Kinzey. Albany: SUNY Press. Trivers, R. L. (1971). The evolution of reciprocal altruism. Q Rev Biol 46, 35–57. Troisi, A. (2005). The concept of alternative strategies and its relevance to psychiatry and clinical psychology. Neurosci Biobehav Rev 29(1), 159–168. Völlm, B., Richardson, P., McKie, S., Elliott, R., Dolan, M. et al. (2007). Neuronal correlates of reward and loss in Cluster B personality disorders: A functional magnetic resonance imaging study. Psychiatr Res 156(2), 151–167. Warneken, F., Hare, B., Melis, A. P., Hanus, D., & Tomasello, M. (2007). Spontaneous altruism by chimpanzees and young children. PLoS Biol 5(7), e184. Wischniewski, J., & Brüne, M. (submitted). Moral reasoning in schizophrenia: An explorativestudy into economic decision-making. Zhong, S., Israel, S., Xue, H., Sham, P. C., Ebstein, R. P., et al. (2009). A neurochemical approach to valuation sensitivity over gains and losses. Proc Biol Sci 276(1676), 4181–4188.

c13.indd 231

7/25/2011 10:10:25 AM

3.5 FROM NEUROECONOMICS TO GENETICS: THE INTERTEMPORAL CHOICES CASE AS AN EXAMPLE ITZHAK AHARON1 and SACHA BOURGEOIS-GIRONDE2 1

Interdisciplinary Centre, Herzliya, Israel Institut Jean-Nicod (ENS-EHESS), Paris, France

2

Despite substantial advances, the question of how we make decisions and judgments continues to pose important challenges for scientific research. Historically, different disciplines have approached this problem using different techniques and assumptions, with few unifying efforts made (Sanfey, Loewenstein, McClure, & Cohen, 2006). Previous behavioral and economic research has focused on stimulus input and behavioral output, often ignoring the intermediary steps by which information is processed and decisions are made. There is now an extensive literature describing the many ways in which human decision making violates the principles of rationality as defined by the expected utility (EU) (Starmer, 2000) and discounted utility (DU) (Frederick et al., 2002) models (Sanfey et al., 2006). Early work in economics revealed situations (e.g., Ellsberg & Allais paradoxes) whereby behavior violated key axioms of the EU model. More recently, the “heuristics and biases” approach in psychology has documented many instances of deviations from economic rationality (Kahneman et al., 1982). For example, most people are reluctant to take a gamble with 50% chance of winning $25 and 50% chance of losing $20, despite the gamble’s overall positive expected value. This illustrates the phenomena of “loss aversion,” whereby people often place disproportionate weight on losses relative to gains of similar absolute value (Kahneman et al., 1991).

From DNA to Social Cognition, First Edition. Edited by Richard Ebstein, Simone Shamay-Tsoory, and Soo Hong Chew. © 2011 Wiley-Blackwell. Published 2011 by John Wiley & Sons, Inc.

233

c14.indd 233

7/25/2011 10:10:26 AM

234

FROM NEUROECONOMICS TO GENETICS

3.5.1 WHAT IS NEUROECONOMICS A very recent approach, popularly known as neuroeconomics, has sought to integrate ideas from the fields of psychology, neuroscience, and economics in an effort to specify more accurate models. Brain imaging techniques such as functional magnetic resonance imaging (fMRI) or positron emission tomography (PET) can be used to determine areas of relative brain activation and to deduce the specific portions of the brain used, during decision-making tasks. Functional neuroimaging doing the past decade has provided a new way to examine brain behavior relationships (Mandzia & Black, 2001). As human beings, we live in an unpredictable and complex world. Yet, we have learned to adapt and to make choices that ensure our well-being. Evolution has endowed organisms with various mechanisms that ensure its behaviors maintain Darwinian fitness. Specifically, the brain’s reward system encodes information about the value of the potential outcomes of our actions. This information is highly relevant to other cognitive brain systems because it motivates our actions and, in an abstract sense, adds purpose to our behavior. Neuroeconomics research has identified a biological mechanism mediating behavior motivated by events commonly associated with pleasure in humans. These events are termed “rewards” and are viewed as primary factors governing normal behavior. The subjective impact of rewards (e.g., pleasure) can be considered essential (e.g., Young, 1959) or irrelevant (e.g., Skinner, 1953) to their effect on behavior, but the motivational effect of rewards on behavior is universally acknowledged by experimental psychologists. Neuroeconomics provides an important scientific approach to the study of behavior because of the ability to improve understanding of the role of the human brain in decision making (Clithero, Tankersley, & Huettel, 2008). Research in the field of neuroeconomics suggests a common neural currency (i.e., utility) in the judgment of various reward types as well as other factors that affect value (e.g., reward size,

BOX 3.5-1

Neuroimaging

Neuroimaging refers to the methodology that provides a visualization of the structure or function of elements of the nervous system. It includes the use of various techniques to image either directly or indirectly the structure and/or function/pharmacology of the nervous system. In addition to being used to diagnose disease and assess brain health, neuroimaging is also valuable in the study of brain dynamics, aiming to understand how the brain works and how various activities impact the brain. In a classic example of functional neuroimaging, fMRI can generate images in which different sections of the brain light up as they become active. Structural neuroimaging is static and concerned with the physical structure of the brain. Other neuroimaging modalities include electroencephalography (EEG), magnetoencephalography (MEG), PET, or optical imaging. For review, please see R.J. Dolan (2008).

c14.indd 234

7/25/2011 10:10:26 AM

WHAT IS NEUROECONOMICS

235

BOX 3.5-2 The Brain Reward System The brain reward system is a collection of brain structures that attempts to regulate and control behavior by inducing pleasurable effects. A psychological reward is a process that reinforces behavior. In neuroeconomics, reward and social interaction are central concepts to understand what motivates human behavior. Both concepts are investigated in humans using neuroimaging and physiological methods. For review, please see Schultz (2002).

BOX 3.5-3

Criticizing Neuroeconomics

Recently the use of neuroimaging techniques in research procedures has come under fire. According to one line of criticism, much of this use involves fundamental errors in data handling and statistics (Vul, Harris, Winkielman, & Pashler, 2009). According to another line, much of it involves inappropriate experimental protocols that ignore basic features of the circuitry and functional organization of the brain (Logothetis, 2008).

Breiter et al., 2001; temporal delay to reward, McClure et al., 2004; Montague & Berns, 2002). Neuroeconomics also suggests that decision making is an emergent process that results from the interaction (synergistically or competitively) of independent neuronal subsystems (Sanfey et al., 2006). Among the topics that neuroeconomic methods may be best suited to examine, in combination with imaging techniques, is brain functioning in optimal and suboptimal decision making. Clithero and his coauthors (2008) report that the neuroscience evidences that improves our understanding of economic phenomena (Camerer, Loewenstein, & Prelec, 2005; Camerer 2007; Glimcher, 2003; Sanfey et al., 2006) come from a broad array of novel experimental findings, including demonstrations of brain regions that guide responses to fair (King-Casas et al., 2005; Singer et al., 2006) and unfair (Sanfey et al., 2003) social interactions, that resolve uncertainty during decision making (Yoshida & Ishai, 2006), that track loss aversion (Tom, Fox, Trepel, & Poldrack, 2007) and subjective value (Padoa-Schioppa & Assad, 2006), and that encode willingness to pay (Knutson, Rick, Wirnmer, Prelec, & Loewenstein, 2007; Plassmann, O’Doherty, & Rangel, 2007) and reward error signals (Donchin, 2006). Yet, neuroeconomics has been characterized as a faddish juxtaposition, not integration, of disparate domains. In addition, critics have charged that neuroscience and economics are fundamentally incompatible (Gul & Pesendorfer, 2008), an argument that resonates with many social scientists. Economics thrived for centuries in the absence of neuroscience, and some economists argue that existing neuroeconomics research is not useful to mainstream economics (Harrison, 2008). Neuroeconomics is at a crossroads, poised to demonstrate that neuroscience can provide the same types of benefits it has long received from the social sciences (Clithero et al., 2008). Ideas from game theory and expected utility theory can

c14.indd 235

7/25/2011 10:10:26 AM

236

FROM NEUROECONOMICS TO GENETICS

explain the responses of individual neurons to incoming information (Glimcher, 2003). Similarly, aspects of utility theory can be used to describe the activity of populations of neurons within the brain’s reward system (Schultz, 2006). There is also an opportunity for the axiomatic approach of decision theory to explain decision-making mechanisms, such as building from the response properties of dopaminergic neurons (Caplin & Dean, 2008). Without comparable examples of neuroscience data contributing to economic models, critics could argue that neuroeconomics research is a brain-centric enterprise that incorporates ideas from the social sciences without reciprocation (Harrison, 2008). Neuroeconomics draws attention to motive forces that are ignored in the standard framework of economic theory. For example, impulsivity and inconsistency in intertemporal choice have been attracting attention in economics. Although loss of self-control by substance abusers is strongly related to their inconsistency in intertemporal choice, researchers in economics have usually studied impulsivity in intertemporal choice using a discount rate (e.g., hyperbolic k), with little effort being expended on motive forces. In the rest of the chapter, we will describe and discuss intertemporal research from the neuroeconomics perspective.

3.5.2

MODELING INTERTEMPORAL CHOICES

When it comes to choice over time, there is also ample evidence of violations of the DU model. Perhaps most importantly, there is strong evidence that discounting is much steeper for shorter time delays than for longer delays, a phenomenon known as “hyperbolic time discounting” (Ainslie & Haslam, 1992). For example, offered a choice between $10 today and $11 in a week, many people are likely to choose the immediate $10. However, offered the choice between $10 in a year and $11 in a year and a week, most people would chose the $11, now considering the extra week of wait inconsequential. From the economist’s perspective, however, this implies a reversal of preference (i.e., whether an extra dollar is worth a week’s wait) and, therefore, does not conform to the rational model (Frederick, Loewenstein, O’Donoghue, 2002). The idea that the value of a good depends on the timing of its consumption was already present in the economic thought of the 18th century but discussed in more details by John Rae (1834, 1905) who is considered the “father” of intertemporal choices modeling. According to Rae, someone’s time preferences are explained by his “effective desire of accumulation.” In 1884, Eugen von Böhm-Bawerk (1890) claimed that this systematic tendency to underestimate future pleasures may be attributed to humans lacking the capacity to make a complete picture of their future wants, especially when it comes to remotely distant ones. Fisher (1930)—who announced the basic economic relations in intertemporal choice—continued this approach, suggesting that every person has his own rate of “impatience,” one that depends on objective factors (size and risk of future income) and subjective factors (foresight, strength of will, habit, uncertainty, selfishness, and influence of fashion). Paul Samuelson (1937) was the first economist who suggested the DU model, a mathematical function describing time preferences in general assuming that “the individual behaves so as to maximize the sum of all future utilities” (p. 156).

c14.indd 236

7/25/2011 10:10:26 AM

NEUROECONOMICS AND INTERTEMPORAL CHOICES

237

Many economic decisions involve outcomes that occur at different points in time. To model such decisions, discounted utility models are typically used. These models combine a utility function that reflects attitudes toward outcomes and a discount function that captures the effect of the passage of time. The most widely used discounted utility model in economics is constant discounting in which the discount function is determined by a constant rate of discount. However, much empirical research has underlined “various inadequacies of the DU model as a descriptive model of behavior” which is a phenomenon referred to as decreasing impatience (Frederick et al., 2002; Read, 2001). One of the major anomalies is that discount rates seem not to be invariant over different horizons. Indeed, as noted by Strotz (1956), agents seem to discount the future relative to the present more rapidly than they discount between different dates in the future. According to this hypothesis, “people are impatient at present, but claim to be patient in the future” (Nir, 2004). These findings have led to the development of alternative discounted utility models, which are commonly referred to as hyperbolic discounting. The hyperbolic discounting models are consistent with decreasing impatience and have become quickly popular in economics. Today many applications are based on hyperbolic discounting, in particular on quasi-hyperbolic discounting a model that was first proposed by Phelps and Pollak (1968) and made popular by Laibson (1997). In his work, Laibson (1997) suggested a discrete time discount function, the “quasihyperbolic,” which captures the key property of hyperbolic discounting in a more tractable functional form: preferences at time t are inconsistent with preferences at time t + 1. Thus, a gap occurs between one’s long-run goals and short-run behavior. This gap may elicit some type of behavior that should be invested by policy makers (for example, retirement issues and procrastination).

3.5.3

NEUROECONOMICS AND INTERTEMPORAL CHOICES

The tendency to choose lesser immediate benefits over greater long-term benefits characterizes alcoholism and other addictive disorders. However, despite its medical and socioeconomic importance, little is known about its neurobiological mechanisms. Brain regions that are activated when deciding between immediate or delayed rewards have been identified (McClure et al., 2004; McClure, Ericson, Laibson, Loewenstein, & Cohen, 2007), as have areas in which responses to reward stimuli predict a paper-and-pencil measure of temporal discounting (Hariri et al., 2006). These studies assume “hot” and “cool” response selection systems, with the hot system proposed to generate impulsive choices in the presence of a proximate reward. However, to date, brain regions in which the magnitude of activity during decision making reliably predicts intertemporal choice behavior have not been identified (Boettiger et al., 2007). Like humans, nonhuman animals can be run in experimental paradigms in which they choose between smaller earlier rewards and larger later rewards (although animals need to learn about the rewards through multiple trials, whereas humans can simply be informed of the contingencies). Monterosso and Ainslie (1999) note that “people and less cognitively sophisticated animals do not differ in the hyperbolic form of their discount curves” (p. 343). Some researchers (e.g., Herrnstein, 1997; Rachlin, 2000) hold the view that hyperbolic time discounting is effectively

c14.indd 237

7/25/2011 10:10:26 AM

238

FROM NEUROECONOMICS TO GENETICS

“hardwired” into our evolutionary apparatus. However, there is considerable evidence that the time discounting of humans and other animals relies on qualitatively different mechanisms (e.g., Loewenstein, 1996; Shefrin & Thaler, 1988). In support of this evidence, Rangel, Camerer, & Montague, (2008) arguer that although both humans and animals discount the future at dramatically different rates, both humans and animals display a common pattern of time discounting commonly referred to as “hyperbolic time discounting.” However, they believe that although such findings do not rule out the possibility that humans and animals discount the future similarly, the quantitative discontinuity is indicative of a qualitative discontinuity. There is, in fact, considerable evidence that the time discounting of humans and other animals relies on qualitatively different mechanisms. Specifically, human time discounting reflects the operation of two fundamentally different systems, one that heavily values the present and cares little about the future (which we share with other animals), and another that discounts outcomes more consistently across time (which is uniquely human) (e.g., Loewenstein, 1996; Shefrin & Thaler, 1988). Although (some) animals display far-sighted behaviors (e.g., storing nuts for winter), these are typically preprogrammed and distinct from the type of spontaneous selfcontrol observed in humans (e.g., deciding to go on a diet). The almost uniquely human capacity to take the delayed consequences of our behavior into account seems to be directly attributable to the prefrontal cortex, the part of the brain that was the most recent to expand in the evolutionary process that produced humans (Manuck, Flory, Muldoon, & Ferrell, 2003), and that is also the latest part of the brain to develop with age. Patients with damage to prefrontal regions tend to behave myopically, placing little weight on the delayed consequences of their behavior (Damasio, Grabowski, Frank, Galaburda, & Damasio, 1994). Many different microfoundations have been proposed to explain the preference patterns captured by the hyperbolic and quasi-hyperbolic discount functions. The most prominent examples include temptation models and dual-brain neuroeconomic models (Bernheim & Rangel, 2004; Gul & Pesendorfer, 2001; McClure et al., 2004; Thaler & Shefrin, 1981). However, both the properties and the mechanisms of time preferences remain in dispute. For example, using fMRI, McClure et al. (2004) examined the brain activity of participants while they made a series of intertemporal choices between smaller proximal rewards ($R available at delay d) and larger delayed rewards ($R’ available at delay d’), where $R < $R’ and d < d’. Rewards ranged from $5 to $40 Amazon.com gift certificates, and the delay ranged from the day of the experiment to 6 weeks later. McClure et al. (2004) found that time discounting is associated with the engagement of two neural systems: Limbic and paralimbic cortical structures are preferentially recruited for choices involving immediately available rewards; and frontoparietal regions, which support higher cognitive functions, are recruited for all intertemporal choices. Moreover, the authors find that when choices involved an opportunity for immediate reward, thus engaging both systems, greater activity in frontoparietal regions than in limbic regions is associated with choosing larger delayed rewards. A subsequent fMRI study that replaced gift certificates with primary rewards (juice and water) that could be delivered instantly in the scanner replicated this pattern (McClure et al., 2007). Yet another study by a different set

c14.indd 238

7/25/2011 10:10:26 AM

FUTURE RESEARCH SHOULD USE DISCOUNT RATES AS PHENOTYPES

239

of authors (Hariri et al., 2006) found a similar pattern in a between- rather than within-subject study. These studies seemed to support, at a neurobiological level, the dual-self β−δ model of David Laibson (1997), which was developed to explain the hyperbolic-like (quasi-hyperbolic) discounting behavior mentioned previously. However, contradictory to McClure studies (2004, 2007), Kable and Glimcher (2007) found no evidence of separable neural agents that could account for the multiple-selves theories. Current knowledge regarding the neural basis of temporal discounting derives primarily from lesion studies (Boettiger et al., 2007). For example, damage to the human orbitofrontal cortex (OFC) (Berlin, Rolls, & Kischka, 2004), but not to the ventromedial frontal lobe (Fellows & Farah, 2005), increases immediate reward bias, whereas in rats, lesions of the OFC (Mobini et al., 2002; Rudebeck, Walton, Smyth, Bannerman, & Rushworth, 2006), basolateral amygdala (Winstanley et al., 2004), nucleus accumbens (Cardinal, Pennicott, Sugathapala, Robbins, & Everitt, 2001), or hippocampus (Cheung & Cardinal, 2005) bias selection toward immediate rewards. Such selection bias may be viewed as a form of impulsivity (Evenden, 1999). Correspondingly, polymorphisms (a genetic variant that appears in at least 1% of a population) in several genes in the dopaminergic system (related with the brain reward system and the previously mentioned brain regions) have been identified as likely contributors to impulsivity (Kreek, Nielsen, Butelman, & LaForge, 2005). Following the studies by McClure and his colleagues (McClure et al., 2004, 2007), Boettiger and his group (Boettiger et al., 2007) used fMRI and a modified delay discounting task (Mitchell, Fields, D’Esposito, Boettiger, 2005; Mitchell, Tavares, Fields, D’Esposito, Boettiger, 2007) to identify brain regions associated with immediate reward bias or anti-bias. To maximize the range of individual differences across subjects, participants were either abstinent alcoholics (AA; n = 9) or age-matched controls with no history of substance abuse (CS; n = 10). In each trial of the task, subjects were instructed to choose between two amounts of money, a smaller amount available “Now” (e.g., “$80 TODAY”) or a larger amount available “Later” (e.g., “$100 in 1 month”). The authors report that the tendency of an individual to wait for a larger, delayed reward correlates directly with the blood oxygenation leveldependent (BOLD) signal in the lateral orbitofrontal cortex. In addition, a genotype at the Val158Met polymorphism of the catechol-O-methyltransferase gene (an enzyme playing an important role in prefrontal cortex dopamine metabolism) predicts both impulsive choice behavior and activity levels in the prefrontal cortex during decision making.

3.5.4 FUTURE RESEARCH SHOULD USE DISCOUNT RATES AS PHENOTYPES IN GENETIC STUDIES Different people are likely to have different discount rates (Chabris et al., 2009) as some people are more patient (low discount rate) and others are more impatient (high discount rate). Do individuals’ discount rates help to explain their decisions about behaviors like saving, health, and smoking? The authors use a laboratory task to compute an individual-specific discount rate and then estimate the effect of the discount rate and demographic factors on

c14.indd 239

7/25/2011 10:10:26 AM

240

FROM NEUROECONOMICS TO GENETICS

behaviors such as saving and smoking. Although other studies have demonstrated a relationship between laboratory measures of discounting and various behaviors (Setlow, Mendez, Mitchell, & Simon, 2009), this study is unique for its use of a large, diverse sample to examine a wide range of behaviors (15 in all) and to compare the predictive strength of the discount rate with that of demographic variables in explaining these behaviors. The authors begin by estimating discount rates for more than 500 subjects using a laboratory task. The subjects engaged in three different substudies examining a large set of behaviors and health-related variables associated with making tradeoffs between the present and future. Next the authors present a theoretical framework to explore how much of the variation in behavior we would expect discounting to explain. The paper’s results support two broad conclusions. First, “there exists a domaingeneral behavioral disposition towards impatience/impulsivity,” and second, “a discount rate estimated through a set of intertemporal monetary choice questions constitutes a useful, though noisy, measure of this disposition.” Therefore, the authors of the study (Chabris et al., 2009, p. 17) suggest that future research could use discount rates as phenotypes in genetic studies design to indentify biological intertemporal choices mechanisms.

3.5.5 OTHER FACTORS AFFECTING INDIVIDUAL’S TIME PREFERENCES Independently of subjects’ discount rates, there are many other factors affecting individual’s time preferences. Using data collected by questionnaires, Van der Pol and Cairns (2000, 2001) argue that younger people expect to have more responsibilities in the future. They are thus more likely to have negative discount rates. Older people are more likely to have positive discount rates because of their reduced life expectancy. Another factor impacting on individual’s time preferences is optimism (Berndsen & van der Pligt, 2001). Uncertainty about the future allows for optimism (i.e., the hope that future losses will be avoided), and this optimism can underlie the preferences for present or future care. A study by Berndsen and van der Pligt (2001) found that people are willing to delay gains and to speed up losses. The authors suggest that optimism underlies the strong preference for immediate gains in both the monetary and the health domain. It is argued that optimism has asymmetric effects on time preferences for gains versus losses: One reason why decision makers prefer immediate gains is because they are optimistic that these gains will be followed by additional gains in future. In contrast, decision makers prefer to delay losses because they are optimistic that losses are avoidable in the future. Optimism about outcomes affects time preferences for both gains and losses, such that low optimism reduces the discount rates while increasing optimism. In addition, many other factors affect time preferences, for example, people’s assumptions regarding future technology in the field of health care. However, the evidence for correlation between these factors and the discount rate is weak and their individual effect on time preferences is hard to measure.

c14.indd 240

7/25/2011 10:10:26 AM

REFERENCES

241

REFERENCES Ainslie, G., & Haslam, N. (1992). Hyperbolic discounting. Choice Over Time, edited by G. Loewenstein and J. Elster, New York: Russell Sage. Arnea, A., Cronqvist, H., & Siegel, S. (2010). Nature or nurture: What determines investor behavior? J Financ Econ 98(3), 583–604. Bernheim, D., & Rangel, A. (2004). Addiction and cue-triggered decision processes. Am Econ Rev 94(5), 1558–1590. Berlin, H. A., Rolls, E. T., & Kischka, U. (2004). Impulsivity, time perception, emotion and reinforcement sensitivity in patients with orbitofrontal cortex lesions. Brain 127, 1108–1126. Berndsen, M., & van der Pligt, J. (2001). Time is on my side: Optimism in intertemporal choice. Acta Psychol 108(2), 173–186. Boettiger, C. A., Mitchell, J. M., Tavares, V. C., Robertson, M., Joslyn, G., et al. (2007). Immediate reward bias in humans: Fronto-parietal networks and a role for the catecholO-methyltransferase 158(Val/Val) genotype. J Neurosci 7(52), 14383–14391. Bohm-Bawerk, E.-von. (1890) [1884]. Capital and interest: A critical history of economical Theory. London: Macmillan. Breiter, H. C., Aharon, I., Kahneman, D., Dale, A., & Shizgal, P. (2001). Functional imaging of neural responses to expectancy and experience of monetary gains and losses. Neuron 2001(2), 619–639. Camerer, C., Loewenstein, G., & Prelec, D. (2005). Neuroeconomics: How neuroscience can inform economics. J Econ Lit 43, 9–64. Camerer, C. F. (2007). Neuroeconomics: Using neuroscience to make economic predictions. Econ J 117, C26–C42. Caplin, A., & Dean, M. (2008). Dopamine, reward prediction error, and economics. Quart J Econ 123, 663–701. Cardinal, R. N., Pennicott, D. R., Sugathapala, C. L., Robbins, T. W., & Everitt, B. J. (2001). Impulsive choice induced in rats by lesions of the nucleus accumbens core. Science 292, 2499–2501. Chabris, C. F., Laibson, D., Morris, C. L., Schuldt, J. P., Taubinsky, D. (2009). The allocation of time in decision-making. J Eur Econ Assoc 7(2), 628–637. Cheung, T. H., Cardinal, R. N. (2005). Hippocampal lesions facilitate instrumental learning with delayed reinforcement but induce impulsive choice in rats. BMC Neurosci 6, 36. Clithero, J. A., Tankersley, D., & Huettel, S. A. (2008). Foundations of neuroeconomics: From philosophy to practice. PLoS Biol 6(11), 2348–2353. Damasio, H., Grabowski, T., Frank, R., Galaburda, A. M., & Damasio, A. R. (1994). The return of Phineas Gage: Clues about the brain from the skull of a famous patient. Science 264(5162), 1102–1105. Dolan, R. J. (2008). Neuroimaging of cognition: Past, present, and future. Neuron 60(3), 496–502. Donchin, E. (2006). fMRI: Not the only way to look at the human brain in action. Observer 19(11). Evenden, J. L. (1999). Varieties of impulsivity. Psychopharmacology 146, 348–361. Fellows, L. K., Farah, M. J. (2005). Dissociable elements of human foresight: A role for the ventromedial frontal lobes in framing the future, but not in discounting future rewards. Neuropsychologia 43, 1214–1221.

c14.indd 241

7/25/2011 10:10:26 AM

242

FROM NEUROECONOMICS TO GENETICS

Fisher, I. (1930). The Theory of Interest. New York: Macmillan. Frederick, S. et al. (2002). Time discounting and time preference: A critical review. J. Econ Lit 40, 351–401. Frederick, S., Loewenstein, G., & O’Donoghue, T. (2002). Time discounting and time preferences: A critical review. J Econ Lit 40(2), 351–401. Glimcher, P. W. (2003). Decisions, Uncertainty, and the Brain: The Science of Neuroeconomics. Cambridge: MIT Press. Gul, F. & Pesendorfer, W. (2001). Temptation and self-control. Econometrica 69(6), 1403–1435. Gul, F., & Pesendorfer, W. (2008). The case for mindless economics. Foundations of Positive and Normative Economics, Methodologies of Modern Economics, edited by A. Caplin, & A. Schotter. Oxford: Oxford University Press. Hariri, A.R., Brown, S.M., Williamson, D.E., Flory, J.D., de Wit, H., & Manuck, S.B. (2006). Preference for immediate over delayed rewards is associated with magnitude of ventral striatal activity. J Neurosci 26(51), 13213–13217. Harrison, G. W. (2008). Neuroeconomics: A critical reconsideration. Econ Philo 24, 303–344. Herrnstein, R. J. (1997). The Matching Law: Papers in Psychology and Economics, edited by H. Rachlin & D. Laibson. New York: Sage Publishers. Kable, J. W., & Glimcher, P. W. (2007). The neural correlates of subjective value during intertemporal choice. Nat Neurosci 10(12), 1625–1633. Kahneman, D., et al. (1982). Judgment under uncertainty: Heuristics and biases. Cambridge, MA: Cambridge University Press. Kahneman, D., et al. (1991). The endowment effect, loss aversion, and the status quo bias: Anomalies. J Econ Perspec 5, 193–206. King-Casas, B., Tomlin, D., Anen, C., Camerer, C. F., Quartz, S. R., et al. (2005). Getting to know you: Reputation and trust in a two-person economic exchange. Science 308, 78–83. Knutson, B., Rick, S., Wirnmer, G. E., Prelec, D., & Loewenstein, G. (2007). Neural predictors of purchases. Neuron 53, 147–156. Kreek, M. J., Nielsen, D. A., Butelman, E. R., & LaForge, K. S. (2005) Genetic influences on impulsivity, risk taking, stress responsivity and vulnerability to drug abuse and addiction. Nat Neurosci 8, 1450–1457. Laibson, D. (1997). Golden eggs and hyperbolic discounting. Q J Econ 112(2), 443–477 Loewenstein, G. (1996). Out of control: Visceral influences on behaviour. Organ Behav and Hum Decis Process 65, 272–292. Logothetis, N. K. (2008). What we can do and what we cannot do with fMRI? Nature 435, 869–878. Mandzia, J., & Black, S. E. (2001). Neuroimaging and behavior: Probing brain behavior relationships in the 21st century. Curr Neurol Neurosci Rep 1, 553–561. Mandzia, J., & Black, S. E. (2007). Neuroimaging and behavior: Probing brain behavior relationships in the 21st century. Curr Neurol Neurosci Rep 1528–4042, 553–561. Manuck, S. B., Flory, J. D., Muldoon, M. F., & Ferrell, R. E. (2003). Central nervous system serotonergic responsivity and aggressive disposition in men. Physiol Behav 77(4–5), 705–709. McClure, S. M. et al. (2004). Separate neural systems value immediate and delayed monetary rewards. Science 306, 503–507. McClure, S. M., Ericson, K. M., Laibson, D. I., Loewenstein, G., & Cohen, J. D. (2007). Time discounting for primary rewards. J Neurosci 27(21), 5796–5804.

c14.indd 242

7/25/2011 10:10:26 AM

REFERENCES

243

Mitchell, J. M., Fields, H. L., D’Esposito, M., & Boettiger, C. A. (2005). Impulsive responding in alcoholics. Alcohol Clin Exp Res 29, 2158–2169. Mitchell, J. M., Tavares, V. C., Fields, H. L., D’Esposito, M., & Boettiger, C. A. (2007). Endogenous opioid blockade and impulsive responding in alcoholics and healthy controls. Neuropsychopharmacology 32, 439–449. Mobini, S., Body, S., Ho, M. Y., Bradshaw, C. M., Szabadi, E., et al. (2002). Effects of lesions of the orbitofrontal cortex on sensitivity to delayed and probabilistic reinforcement. Psychopharmacology 160, 290–298. Montague, P. R., Berns, G. S. (2002). Neural economics and the biological substrates of valuation. Neuron 36, 265–284. Monterosso, J. R., Ainslie, G. (1999). Beyond discounting: Possible experimental models of impulse control. Psychopharmacology 146, 339–347. Nir, A. (2004), Cognitive Procedures and Hyperbolic Discounting, Tilburg University, Center for Economic Research, Discussion Paper No. 47. Padoa-Schioppa, C., & Assad, J. A. (2006). Neurons in the orbitofrontal cortex encode economic value. Nature 441, 223–226. Phelps, E. S., & Pollak, R. (1968), On second best national saving and game-equilibrium growth. Rev Econ Stud 35 185–199. Plassmann, H., O’Doherty, J., & Rangel, A. (2007). Orbitofrontal cortex encodes willingness to pay in everyday economic transactions. J Neurosci 27, 9984–9988. Rae, J. (1905) [1834]. The Sociological Theory of Capital. London: Macmillan. Rachlin, H. (2000). The Science of Self-Control Cambridge. London: Harvard University Press. Rangel, A., Camerer, C., & Montague, R. (2008). A framework for studying the neurobiology of value-based decision making. Nat Rev Neurosci 9, 545–556. Read, D. (2001). Is time-discounting hyperbolic or subadditive? J Risk Uncertainty 23(1), 5–32. Rudebeck, P. H., Walton, M. E., Smyth, A. N., Bannerman, D. M., & Rushworth, M. F. (2006). Separate neural pathways process different decision costs. Nat Neurosci 9, 1161–1168. Samuelson, P. (1937). A note on measurement of utility. Rev Econ Stud, 4, 155–161. Sanfey, A. G., Loewenstein, G., McClure, S. M., & Cohen, J. D. (2006). Neuroeconomics: Crosscurrents in research on decision-making. Trends Cogn Sci 10, 108–116. Sanfey, A. G., Rilling, J. K., Aronson, J. A., Nystrom, L. E., & Cohen, J. D. (2003). The neural basis of economic decision-making in the ultimatum game. Science 300, 1755–1758. Schultz, W. (2002). Getting formal with dopamine and reward. Neuron 36(2), 241–263. Schultz, W. (2006). Behavioral theories and the neurophysiology of reward. Annu Rev Psychol 57, 87–115. Setlow, B., Mendez, I. A., Mitchell, M. R., & Simon, N. W. (2009). Effects of chronic administration of drugs of abuse on impulsive choice (delay discounting) in animal models. Behav Pharmacol 20(5–6), 380–389. Shefrin, H. M., & Thaler, R. H. 1988. The behavioral life-cycle hypothesis. Econ Inq 26(4), 609–643. Singer, T., Seymour, B., O’Doherty, J. P., Stephan, K. E., Dolan, R. J., et al. (2006). Empathic neural responses are modulated by the perceived fairness of others. Nature 439, 466–469. Skinner, B. F. (1953). Science and Human Behavior. New York: Macmillan. Starmer, C. (2000). Developments in non-expected utility theory: The hunt for a descriptive theory of choice under risk. J Econ Lit 38, 332–382.

c14.indd 243

7/25/2011 10:10:26 AM

244

FROM NEUROECONOMICS TO GENETICS

Strotz, R. (1956). Myopia and inconsistency in dynamic utility maximization. Rev Econ Stud 23, 165–180. Thaler, R. H., & Shefrin, H. M. (1981). An economic theory of self-control. J Polit Econ 89 (2), 392–406. Tom, S. M., Fox, C. R., Trepel, C., & Poldrack, R. A. (2007). The neural basis of loss aversion in decision-making under risk. Science 315, 515–518. Van der Pol, M., & Cairns, J. (2000). Negative and zero time preferences for health. Health Econ 9, 171–175. Van der Pol, M., & Cairns, J. (2001). Estimating time preferences for health using discrete choice experiments. Soc Sci Med 52, 1459–1470. Vul, E.., Harris, C., Winkielman, P., & Pashler, H. (2009). Puzzlingly high correlations in fMRI studies of emotion, personality, and social cognition. Perspect Psychol Sci, 4(3), 274–290. Winstanley, C. A., Theobald, D. E., Cardinal, R. N., & Robbins, T. W. (2004). Contrasting roles of basolateral amygdala and orbitofrontal cortex in impulsive choice. J Neurosci 24, 4718–4722. Yoshida, W., & Ishii, S. (2006). Resolution of uncertainty in prefrontal cortex. Neuron 50, 781–789. Young, P. T. (1959). The role of affective processes in learning and motivation. Psychol Rev 66, 104–125.

c14.indd 244

7/25/2011 10:10:26 AM

INDEX

ABAT gene, 25 Accuracy, empathic, 69 Acetylcholine, 163–164 Action, generally observation, 66–68, 73 planning, 81, 87 sounds, 69–70 Addiction/addictive behavior, 168–169, 237. See also Alcoholism; Drug abuse Adolescents, drug use in, 187–188 Affect, neural mechanisms of, 51, 53 Affective empathy, 56 Affective processing, 112, 119 Affective response, 8, 48, 145 Affiliation, social/social group, 65–66, 70–71, 97 Affiliative behaviors, 148 Agency, 223 Aggression, generally in hiearchy formation. See Dominant hierarchy formation threshold, 206 Aggressive behavior, 38, 142, 148 Agonist drugs, 169 AGRIN gene, 25 AIDS, 185

Alcoholism, 186, 237 Alcohol use, 188 Aldehyde dehydrogenase (ALDH), 186 Altruism, 1, 39, 124–125, 134, 177, 129 Amygdala, functions of, 3, 94, 119, 128, 162–163, 171, 175, 223 Aneurysms, 160 Anger, 127–128 Antagonist drugs, 169 Anterior cingulate cortex (ACC), 51, 57, 73, 94, 128, 141, 145, 151, 222, 226 Anterior fossa, 160 Anterior insular (AI) cortex, 51, 73, 128, 141 Anterior temporal lobe (ATL), 129–131 Antisocial behavior, 114 Antisocial disorders, 120 Anxiety disorder, 215 Approach behavior, 38 AR gene, 26, 30 ARNT gene, 25, 28–29 As-if body loops, 66. See also Body loops Asperger Syndrome (AS), 2, 22–24, 27–31, 147 Aspirations, 85 Association Value, 97

From DNA to Social Cognition, First Edition. Edited by Richard Ebstein, Simone Shamay-Tsoory, and Soo Hong Chew. © 2011 Wiley-Blackwell. Published 2011 by John Wiley & Sons, Inc.

245

bindex.indd 245

7/25/2011 10:10:03 AM

246

INDEX

Association studies, autism spectrum conditions, 22, 25–26 Attachment characteristics of, 127, 134 in pair bonding, 41–42 parent-infant, 42 Attention deficit hyperactivity disorder (ADHD), 7 Attitude, 100 Attributional processes, social, 96, 101–103 Autism, see specific types of autism characteristics of, 7, 11, 224, 227 classic, 22, 24–26 Autism Diagnostic Observation Schedule (ADOS), 22 Autism Quotient (AQ), 23, 28–30 Autism spectrum conditions (ASC) foetal adrogen theory, 2, 24–25 gene mutations, 25–26 measurement of, 23–24 neurocognitive theories of, 2 overview of, 21–22 Autism spectrum disorders (ASD), 40, 51, 56, 151, 224 Autobiographical memory, 54, 132 Autonomic nervous system, 176 AVPR1 gene, 9–10, 25, 28, 30, 39, 227 Balanced Emotional Empathy Scale (BEES), 69, 74 Barbiturates, 188 Basal forebrain, 129–130, 174 Basal ganglia, 94, 128 Behavioral disorders, 7, 11 Beliefs, rational, 127 Benevolence, 124 Bias covert, 170 emotional, 133 explicit processes, 104 nonconscious signals, 169 overt, 170 racial, 100 unconscious, 168 Bilateral temporoparietal junctions, 66 Biomarkers, peripheral, 10–11 Bipolar disorder, 7, 11, 172 Blood oxygenation level-dependent (BOLD) response, 143, 145 Body-brain channel, 163 Body loop, 162–163, 178

bindex.indd 246

Borderline personality disorder (BPD), 225–226 Brain, anatomy of, see specific parts of brain anatomy Brain imaging, 116–119. See also Neuroimaging; specific types of neuroimaging techniques Brain injury, 113, 151 Brain lesions studies, 114–117, 126, 132–133, 146, 159, 170, 177, 192 Brain networks supporting empathy multiple processes, 48–49 overview of, 47 self-referential processing and self-other distinction, 55–56 shared respresentations of emotions, 49–53 theory of mind, 53–58, 66 Brain reward system, 234–235 Brainstem, 162, 16, 176, 179 Broader autism phenotype (BAP), 23 Broca’s area, 65 Brodman area (BA), 160 Bystander effects, hierarchy formation, 4–5, 204–205, 207–209, 211 Callithrix penicillata, pair-bonding studies, 40 Candidate gene association studies, 9 Canine studies, social comparison-based emotions, 141 Cannabinoids, 26 Cardiovascular responses, 167 Case-control studies, autism spectrum conditions, 22, 24, 28 Categorization processes, social, 96 Central nervous system (CNS), 25, 29, 163, 167 Cerebellum, 67 Cerebrospinal fluid, 227 CGA gene, 26 Chameleon Effect, 69 Children autism spectrum conditions, 23 childhood abuse, 8 cooperation studies, 219–220 crying behavior, 48 drug use in, 187–188 empathy development, 49, 51 Cholesterol, 26 Chromatin, 8 Chronic promotion orientation, 85

7/25/2011 10:10:03 AM

INDEX

Cingulate gyrus, 125, 128 CNR gene, 25, 28, 30 Cocaine, 188, 197. See also Drug abuse Coercion, 1 Cognitive neuroscience, 81 Cognitive distortion, 5 Compassion, 128, 133 Competitive emotions characteristics of, 4 neural substrates of, 143–147 social comparison-based, 142–143 Conflict, 103, 132 Congenital Adrenal Hyperplasia (CAH), 28 Congenital aplasia, 68 Conjunction analysis, 135 Connectedness, 87 Connectivity, 94–95 Contempt, 128 Contraception, oral, 40 Cooperation assessment using game-theoretical approaches, 217, 220–223 conditional, 217–218 evolutionary aspects of, 5, 217–219 ontogenetic aspects of, 219–220 in psychopathological conditions, 223–226 Cooperative emotions, 4, 139–140, 151 Cooperativity, 1 Copy number variations (CNVs), 7–8, 22 Coronary heart disease, 185 Corpus callosum, 128 Covariance analysis, 102 Covariation, 102 CpG sites, 9 Creativity, 177 Crying behavior, 48–49 CYP gene, 26–28 Cyproheptadine, 169 DAT gene, 40–41 Deception, 1 Decision making brain injury, impact on, 160–161 brain regions involved in, 162–163 deficits, 161 economic, 227 emotional signals guiding, 166–169 impaired, 161 influential factors, 4, 82, 86–87, 113–114, 132–134 overview, 159–160

bindex.indd 247

247

prosocial, 40 substance abuse and, 186 Decision theory, 236 Deductive reasoning, 102–103 Depression, 215 Dextroamphetamines, 170 DHCR gene, 26 Dictator Game (DG), 114, 222, 225, 227 Dignity, 129 Dinucleotides, pair bonding, 39 Discount rates, 239–240 Discounted utility (DU) model, 236–237 Disgust, 119, 128 Dispositional Envy Scale (DES), 147 Distant selves, 86–87 Dizygotic twin studies, autism spectrum conditions, 22–23 DNA methylation, 8 Dominance hierarchy formation bystander effects, 204–205, 207 empirical studies, green swordtail case, 209–211 extrinsic effects models, 205–206, 208–209 future research directions, 211 joint effects, 208 overview, 203–205 winner/loser effects, 204–211 Donation behavior studies, 134–135 Dopamine impact of, 2, 4, 25, 163–164, 170, 227 pair-bonding behavior, 38, 40–42 Dopaminergic system, 149, 178, 239 Dorsal anterior cingulate cortex (dACC), 145 Dorsal MPFC (dMPFC), 55–56, 94, 98–99 Dorsolateral frontal cortex, 128 Dorsolateral prefrontal cortex (DLPFC) decision making and, 162, 164, 171, 174 empathy support, 67 moral cognition and emotion, 132, 135 moral judgment studies, 95, 100–101 psychiatric disorders, 223, 226 Dorsomedial prefrontal cortex (DMPFC), 94, 127 Double-blind studies, 12 Drug abuse accounting formulas, 193–197 addiction risk factors, 186–188, 197 chronic, 194, 196–197 defined, 186 high-functioning users, 192–194, 197–198 impact of, 4, 185

7/25/2011 10:10:03 AM

248

INDEX

Drug abuse (cont’d) intervention programs, 198 model for joint effect of individual sensitivity and social exposure, 188–193, 197–198 social drugs, 189 social exposure and, 187, 191, 193, 198 social factors leading to, 187–188 social learning theory, 187 Dual-process model, 132–133 Electrical stimulation, 175 Electroencephalograph (EEG) studies, 93, 104, 234 Electrophysiological studies, 11–12 Embarassment, 126 Embedded Figures Test, 30 Emotional cues, types of, 47 Emotional processing, influential factors, 71 Emotional signals, impact on decisionmaking process, 166–169 Emotional state impact on moral judgment, 102 manipulation of, 112 Emotion(s), see also specific emotions competitive, 139–151 moral cognition and, 111–135 perception, 40 regulation, 81, 87, 100 systems, in decision making, 163 Emotions, shared representations of action and perception, 49–50 mirror neuron system and empathy, 50–51, 56 shared affect, neural mechanisms of, 51, 53 Empathic processing, 69 Empathy ASC and, 21, 23 brain networks, supportive, 47–57 characteristics of, 40, 141 competitive emotion studies, 150 defined, 48, 56 future thinking and, 81–87 genetic correlations, 2–3 human mirror neuron system, 63–68 negative, 4, 140, 142 neural basis, 2–3 pair bonding and, 37–41 positive, 4, 139–140 social comparison-based emotion studies, 148

bindex.indd 248

Empathy Quotient (EQ), 23, 27–28, 30 Emulation, 65 EN-2 gene, 25, 28 Endophenotypes, autism spectrum conditions, 22, 30–31 Envy, 3–4, 143–147 Epigenetics, autism spectrum conditions, 23 Epigenome, 8–9 Epilepsy, 22, 175 Episodic memory, 1 Episodic future thinking cognitive thinking and, 3 defined, 81 personal goals and, 83–85 ESR gene, 26, 28 Estrogen, 26 Event-feature-emotion complexes (EFEC), 127 Event-related potentials (EVPs), 143 Expectancy-valence (EV) learning model, drug abuse studies choice consistency parameter, 196–197 motivation parameter, 194–195 overview, 193–197 recency parameter, 195–196 Expected utility theory, 235 Expressive language, 71–72 External effects, 204 Extrinsic effects, dominant hierarchy formation, 205–206, 208–210 Face recognition, 40, 71 Facial attractiveness, 40 Facial expression, 50, 54, 56, 69 Facial trustworthiness, 40 False-belief tasks, 56 Familywise error rate (FWER), 6, 27, 29–30 Fear, 70 Fluvoxamine, 169 Foregone payoffs, 192–193, 197 Free will, 124 Frontal cortex, 113, 125–126, 133 Frontal lobe, 151, 162 Frontal lobe syndrome, 160 Frontal operculum (IFO), 141 Fronto-mesolimbic subregions, 129 Fronto-temporo-mesolimbic integration model, moral reasoning, 133–134 Fronto-tempero-mesolimbic networks, 129 Frontoparietal neural network, 2. See also Human mirror neuron system; Mirror neuron system

7/25/2011 10:10:03 AM

INDEX

Frontopolar cortex (FPC), 99, 102, 126–127, 128, 132 Frontotemporal dementia (FTD), 114, 116 FSHB gene, 26 Functional magnetic resonance imaging (fMRI) studies characteristics of, 234, 238–239 competitive emotions, 143–145 emotional states, 51 episodic future thinking, 81–86 mirror neuron systems, 64, 68, 71, 73 moral judgment processes, 93 moral motivations, 125 moral sentiments, 126, 128–130 support of somatic marker hypothesis, 170–171, 176 values, moral and social, 129 Fundamental attribution error, 102–103 Fusiform face area (FFA), 94 Future selves, 86–87 GABRB gene, 25, 28–29 Gage, Phineas, 113, 160 Gambling behavior, 13 Game theory, 5 Gender studies, brain networks supporting networks, 56 GeneCards, 9 Gene-environment interaction, 5 Gene expression, 7, 11 Genes, decision making and dominance hierarchy formation, 203–211 drug abuse studies, 185–198 neuroeconomics, 233–240 psychiatric disorders and, 215–227 somatic marker framework, 159–179 Gene x Environment Interaction, 8–9 Generosity, 40, 125, 148 Genetic neuroscience, 2 Genetic studies future research directions, 239–240 large-scale, 24 Genome-wide association studies (GWAS) autism spectrum conditions, 22, 24 overview of, 5–7, 10 Genomic dark matter, 7 Genotyping, 6–7, 9 Glucocorticoid receptor (GR), 8 Goal-directed behavior, 176 Gratitude, 129–130 Gray matter, 126

bindex.indd 249

249

Green swordtail fish, hierarchy formation study, 203, 209–211 Grief, 49 Guilt, 125–126, 128–129, 133, 226 Hallucinogens, 191 Haloperidol, 170 Haplotypes, 6 Haploview, 9 HapMap database, 6, 27 Hardy-Weinberg equilibrium, 27 Heboidophrenia, 224 Heritability, 7 Heroin, 191 High-Functioning Autism (HFA), 22–24, 147 Hippocampus, 8, 163 Homeostasis, 176–177, 179 Honesty, 125 Hopes, 85 Hormones anti-diuretic, 38 sex, 26 steroid, 26 testosterone, 26, 28 HOX gene, 25, 28–29 HSD gene, 26, 28 Human mirror neuron system empathy and, 69–70 function of, 11–13, 65 future research directions, 75–76 language and embodied semantics, 71–72 overview of, 63–66 shared representations beyond, 72–74 social experiences and, 66–68 social group affiliations, 66, 70–71 social networks, 74–75 Human disease, 6–7 Human morality, historical perspectives, 111 Human genome, 5–6 Human mating behavior, influences on, 40–41 Hume, David, 129 Hunger, 134 Hutcheson, Francis, 124 Hyperactivity, 22 Hyperbolic time discounting, 236–238 Hypothalamus, 162–163 IGF gene, 25, 28 Imitation, 65, 67, 69 Imitative learning, 67

7/25/2011 10:10:03 AM

250

INDEX

Impatience, 240 Impulsivity, 186, 236, 240 Indignation, 128–129 Indirect reciprocity, 217–218 Inferior frontal gyrus (IFG), 2, 64, 67–71, 141, 151 Inferior parietal cortex, 54–55 lobule (IPL), 64, 67, 70–71, 84 Inferior temporal cortex, 94 Infidelity, sexual, 41 Instrumental helping, 219 Insula, 3 Insular cortex, 3, 162, 175–176 Intelligence, 160–161. See also IQ; Knowledge Intentional harm, 116–118 Intentional states, 50 Intentions, 102 Interoceptive agnosia, 161 Interpersonal Reactivity Index (IRI), 69, 74, 147 Intertemporal choice models, 236–237 Intrinsic factors, 204 Introspective processes, 98 Iowa Gambling Task (IGT), decision making studies, 164–176, 186 drug abuse studies, 191–193, 196 IQ, autism spectrum conditions (ASC), 22, 24 Kant, Immanuel, 111, 124 Knowledge, 162, 168–169, 179 Language delay, 24 Lateral prefrontal cortex (LPFC), 3, 95, 99–101, 103–104 Lateral orbitofrontal cortex (latOFC), 128, 135 Lateral septal nuclei, 12 L-DOPA, 13 6 Learning, 176 Learning influential factors, 67 individual, 4 reward-based, 97 LHB gene, 26 LHCGR gene, 26, 28 LHRHR gene, 26 Life events, stressful, 8 Limbic system, 51, 56, 98–99 Linear hierarchies, 203–205

bindex.indd 250

Linkage disequilibrium, 6 Linkage studies, autism spectrum conditions (ASC), 22, 25–26 Loser effects, hierarchy formation characteristics of, 204–206, 208–209, 211 empirical study, green swordtail case, 209–211 individual recognition and, 206–207 joint effects, 208–209 Loss aversion, 172–173, 233, 235 Love Attitudes Scale, 41 Lung cancer, 185 Machiavellianism, 226 Magnetic resonance imaging (MRI) functional. See Functional magnetic resonance imaging (fMRI) studies transcranial. See Transcranial magnetic resonance (TMS) studies Magnetoencephalography (MEG), 234 Manipulation, 1, 112 MAO gene, 8, 25, 28–29 Marijuana, 187–188, 197–198 Marriage, 41–42 Maternal care, 8, 42 Mathematical models, 205, 236 Medial frontal gyrus, 170 Medial orbitofrontal cortex (mOFC), 162, 170–171, 223 Medial prefrontal cortices (MPFC) competitive emotions, 145–146, 151 empathy support, 2, 53–55 future thinking, 83 human mirrr neuron system, 66, 76 moral judgment, 94, 97–99, 101–104 psychiatric disorders and, 222–223 Medial preoptic area, 126 Mediobasal hypothalamus, 126 Memory, 53, 132, 162–163, 176, 196–197 Mendelian disorders, 7 Meningiomas, 160 Mentalizing, 3–4, 49, 55, 66, 75, 98, 100, 145–147, 151, 216, 219–220 Merging hierarchies, 208–209 Mesolimbic forebrain, 129–130 Metacognitive representations, 55 Methylation, 8 Microarray studies autism spectrum conditions, 23 features of, 6, 11 Microsatellites, pair-bonding behavior, 39–40

7/25/2011 10:10:03 AM

INDEX

Microtus spp., pair bonding studies, 38–40 Midbrain, dorsal tegmentum, 163 Middle temporal lobes (MTLs), 222 Mind reading, 49 Minor allele frequency (MAL), 7 Mirror neuron system (MNS) brain regions in, 49–50 empathy and, 50–51 extended, 57 functions of, 49–50, 5 historical perspective, 63 human, see Human mirror neuron system ontogeny, 51 racial differences in, 71 Mirrored touch synesthesia, 74 Molecular genetics, 9, 13, 21, 30 Monoamine oxidase (MAO), 186 Monogamy, sexual, 40–41 Monozygotic twins, autism spectrum conditions, 21–23 Moral abilities, 123 Moral behavior, 177 Moral cognition brain imaging studies, 116–119 emotion and, 3 fronto-temporo-mesolimbic integration, 3 neurological patient studies, 113–116 psychological studies, 112–113 Moral condemnation, 112 Moral dilemmas, 114–115, 118 Moral disgust, 112 Moral judgment explicit processes, 96–97, 101–104 influential factors, 3 Moral motivations, 125–130 Moral neuroscience, emotion and competitive emotions, 139–151 moral cognition, 111–135 moral judgment systems, 93–104 Moral reasoning, 114, 116, 132–134 Moral sentiments empathic, 128 evolutionary precursors of, 125–126 prosocial, 128–130, 133 Motivation moral, 124–130 social, 38, 103, 148 Motivational thinking, 81, 87 Motor/behavioral systems, 163 Motor cortex, 68 Motor evoked potentials (MEPs), 67 Motor kinematics, 75

bindex.indd 251

251

Motor learning, 65, 67 Motor planning, 71–72 Motor simulation, 65, 69–70 Mouse studies autism spectrum conditions (ASC), 28, 30 social comparison-based emotions, 141 Movement observation, 55, 65–66 Myopic loss aversion, 172 Nature-nuture debate, 8 Negative affective response, 145 Negative emotional events, impact of, 82–83 Negative emotions, moral cognition and, 112 Negatve empathy, 150 Nerve growth factor (NGF), 29 Neural connectivity direct, 102 significance of, 2 theory, 26 Neural networks, 65–66, 76, 95, 99, 170 Neural phenotypes, 31 Neurexins, 29 Neuroanatomical studies decision making, 132–134 moral motivations, 125–130 moral reasoning, 132–134 sociomoral knowledge, 129–132 Neurobiology, 237, 239 Neurochemical pathways, 11–13 Neurodevelopment, 26, 29 Neuroeconomics characteristics of, 5, 30, 177, 234–236 critique of, 235–236 future research directions, 239–240 individual’s time preferences, 240 intertemporal choices, 236–239 Neuroendocrine cells, 25 Neuroimaging studies characteristics of, 30, 234 mirror neuron systems, 64 motivational aspects of future thinking, 81 theory of mind applications, 53 Neurological studies, 113–116 Neuronal network, 141 Neuropeptides, 11, 26 Neurophilosophy, 124 Neuropsychiatric conditions, 168–169 Neuropsychiatric disorders, 11, 135 Neuropsychological tests, 125, 161 Neuroscience, 13, 125, 235–236

7/25/2011 10:10:03 AM

252

INDEX

Neurotransmitters, 163–164, 169–170, 176, 186 Next-generation sequencing (NGS), 10 NGF gene, 25 NGFR gene, 25 NLGN gene, 25, 28–29 Nonapeptides, 38 Noradrenaline, 163–164 NRCAM gene, 25 NTF gene, 25, 28 NTRK genes, 25, 27–29 Nucleus accumbens, 149 Observation-imitation mapping, 51 Occipital lobe, 95 Occipital/parietal cortex, 144 Oligonucleotides, autism spectrum conditions, 23 Opioids, 26 OPRM gene, 25 Optical imaging, 234 Optimism, 240 Orbitofrontal cortex (OFC), 94–95, 98, 102, 128, 160, 163, 226, 239 Other-Blaming, 128–129 Other-critical moral sentiments, 128–129 OXT gene, 25, 28–30 OXTR gene, 10, 25, 28 Oxytocin (OT) characteristics of, 2, 4, 30, 151, 227 human mirror neuron system, 11–12 pair-bonding behavior, 38–40, 42 social comparison-based emotions, 151 Oxytonergic system, 147–149 Pain empathy, 74 matrix, 73–74 Pair bonding, behavioral genetics of human, see Human mating behavior mating behavior, biology and genetics of, 38–41 overview of, 2, 9, 37–38 Parahippocampal cortex, 144 Parietal lobe, 95 Partner Bonding Scale (PBS), 9, 39 Partner loss, 42 Paternal care behavior, 38 Peer behavior, significance of, 187–188 Perception mirror neuron systems, 66–67 shared representations, 49–50 significance of, 176

bindex.indd 252

Perception-action coupling, 49, 53 mapping, 51 Perceptual processes, social, 96 Periacqueductal gray, 162 Peripheral blood cells, 11 Peripheral nervous disorders, 175 Peripheral nervous system, 179 Permutation testing, 27 Personal goals, 83–86 Personality disorders, 227 Personality factors, 65 Perspective first-person, 55, 67 third-person, 67 Perspective-action coupling, 3 Perspective-taking, 48–49, 223 Pharmacogenetics, 12–13 Phenotypes autism spectrum conditions, 31 characteristics of, 6–7, 11 pair bonding, 40 Pity, 4, 128, 130 Polycystic ovary syndrome (PCOS), 28 Polymerase chain reaction (PCR), 26 Polymorphisms autism spectrum conditions, 21–22, 28–30 pair bonding, 39–40 POR gene, 26 Positive affective response, 145 Positive and Negative Syndrome Scale, 225 Positive emotional events, impact of, 83 Positron emission tomography (PET) studies, 116, 170, 175–176, 234 Posterior cingulate cortex (PCC), 75, 84 Posterior cingulate gyrus, 162 Posterior parietal cortex, 2, 56 Postpartum depression, 11 Prairie voles, see Microtus spp. Prefrontal cortex (PFC) explicit processes, 101–104 functional anatomy and connectivity, 94–95 implicit processes, 101–104 overview of, 3, 93–94, 174 social cognitive and moral judgment processes, 96–101 Premotor cortex, 72 Pride, 129–130 Primate studies cooperation, 218 mirror neuron system, 63–65

7/25/2011 10:10:03 AM

INDEX

pair-bonding studies, 39–40 perception-action coupling, 49–50 social comparison-based emotion, 141 somatic markers and, 177 Prisoner’s Dilemma (PD) game, 220, 223–224, 226 Promiscuity, 41 Prosocial behavior, 1, 148 Pseudopsychopathy, 113 Psychiatric disorders complex social cognition and, 215–217 cooperation and, 217–226 deviant empathic responses, 56 future research directions, 226 game-theoretical studies, 217, 220–223 neurodevelopmental, 5, 51 Psychological studies, 112–113 Psychopathy developmental, 126, 134 types of, 5, 151, 169, 215, 226 Psychophysiological studies, 112 Psychosocial development, 114 Psychostimulants, 170 PTSD, 11 Public Goods (PG) game, 221 Punishment networks, 128–129, 133, 143–145 Pyrosequencing, 8–9 Quantitative trait loci (QTL), 7, 11 Questionnaire Measure of Emotional Empathy (QMEE), 23 Questionnaires drug abuse studies, 192 emotional empathy, 23 self-report, 39 Race bias, 100 Rae, John, 236 RAPGEF gene, 25 Rat studies autism spectrum conditions, 26 gene-environment interactions, 8 Rationality, 177, 233 Rationalization, 112 “Reading the Mind in the Eyes” Test (RMET), 23, 30, 53, 225 Reappraisals, 100 Reasoning. See Deductive reasoning; Moral reasoning decision-making process and, 161–162 empathic, 54

bindex.indd 253

253

financial, 177 influential factors on, 112 Reciprocity, 215, 217, 226 Reinforcement learning models, 191–193 Resource holding pwer (RHP), 204–208 Respect, 124 Retrosplenial cortex, 162 Reversal learning, 164, 174 Reverse inference, 117 Reward/punishment networks, 143–145, 150, 162, 166–167, 175–176, 223 Reward system, 4, 97–98, 104, 129, 144, 237 RNA sequencing, 10 Romantic relationships, 41–42 Sadness, 127 Sanger sequencing, 8 Saving behavior, 239–240 Schadenfreude, 3–4, 142–147, 150 Schizophrenia, 11, 215–216, 224–225, 227 SCP gene, 26, 28 Selective seronin reuptake inhibitors, 169–170 Self-concept, 95 Self-consciousness, 223 Self-control, 100,195, 238 Self-interest, 124, 129, 134 Selfishness, 124 Self-love, 124 Self-other comparison, 140 distinction, 2, 48–49, 54–56, 71 Self-reference, 55 Self-referential processing, 55–56 Self-related processes, 98–99 Self-relevance, 84–86, 145 Self-reported empathy, 23 Self-task, 51–52 Semantics, 71–72 Sensation seeking behavior, 186 Sensory information, 94–95 Sensory mapping, 177 Serotonin, 25, 163–164, 227 Serotonin 5HT2A receptor antagonist, 169–170 Sex steroids, 2, 29 Sexual arousal, 127 Sexual intercourse, 39, 41. See also Pair bonding Shame, 126 Shared representations, 66, 72–74 SHBG gene, 26

7/25/2011 10:10:03 AM

254

INDEX

Simulation processes, 151 Simulation studies extrinsic effects, 205–207, 209 mirror neuron system, 65–66 Single nucleotide polymorphisms (SNPs) autism spectrum conditions (ASC), 22, 24, 2 6–27, 29–30 genomic dark matter, 7 linkage disequilibrium, 5–6 tagging, 9 Skin conductance response (SCR), 166–168 SLC gene, 26 Smith, Adam, 124, 129 Smoking behavior, 239–240 Social behavior, defined, 123–124 Social cognition, generally defined, 1, 96 global, 97 influential factors, 63 molecular toolbox for research, 5–13 neural basis of, 1–2, 47 Social comparison, emotional reactions to envy, 140–141 overview of, 139–140 schadefreude, 140–141 Social comparison-based emotions, 141–143 Social cooperation, 140–141. See also Cooperation Social disorder, 215 Social drugs, 189, 197 Social-emotional responsivity theory, 2, 26, 29–30 Social experiences, effects of, 4 Social factors, 65 Social functioning, measurement of, 23 Social influence, 189–191 Social Intuitionist Approach, 111 Social judgment, 119 Social knowledge conceptual, 131–132 defined, 129 Social learning theory, 187 Social liking, 70 Social memory, 38 Social motivation, 38 Social neuroscience, 2, 104 Social norms, 99, 215–227 Social ranking, 149 Social Responsiveness Scale (SRS), 23 Sociocultural influences, 124, 129 Sociomoral knowledge, 129–132

bindex.indd 254

Somatic marker hypothesis brain regions involved in decision making, 162–163 characteristics of, 111, 133, 161–164 critique of, 173–179 decision making process and, 171–172 development of, 159 functional neuroimaging support of, 170–171 neurotransmitters and, 169–170 testing, 165–171 Somatic markers, in decision-making process as executive processes, 173–174 feedback, impact on, 175–179 framework, 177–178 Somatic marker theory, 178–179. See also Somatic marker hypothesis Somatosensory cortex, 53, 74 Somatovisceral afference model of emotion (SAME), 167 Spinal cord functions of, 175–176, 179 injuries, 67–68 SRD gene, 26 Stereotypes, 96–97, 99–100 Stress-coping behavior, 42 Striatum, 95 STS gene, 26 Substance abuse. See Drug abuse Substantia nigra, 95 Suicide, 8 SULT gene, 26 Superior temporal sulcus (STS), 53–54, 56, 67, 69, 222–223 Sympathy, 4, 124, 128 Systemizing Quotient (SQ), 23 TAC gene, 25 Tactile sensation observations, 74 Temperoparietal area, 54 Temporal discounting, 237 Temporal lobe, 95, 101 Temporal poles (TPs), 53–54, 151, 222 Temporoparietal junction (TPJ), 2, 53–55, 56, 66, 74, 119, 127, 222–223 Testosterone, 26, 28 Thalamus, 94 Theory of mind (ToM), 49, 66, 147, 216 Time discounting, 237–238

7/25/2011 10:10:03 AM

INDEX

Trait empathy, 23, 70 judgments, 86 Transcranial magnetic resonance (TMS) studies episodic future thinking, 86–87 mirror neuron systems, 67–68, 71 moral judgment, 104 repetitive (rTMS), 71 Transcription, 11 Transcriptsome analysis, 10 Transgression, 112 Transitive hierarchy formation, 207 Trolley-type dilemmas, 133 TRPV gene, 25 Trust, 40, 134 Trust Game (TG), 114, 220–221, 225 TSPO gene, 26 Twin studies autism spectrum conditions, 21–24 pair bonding, 39 UCSC Genome Browser, 9 Ultimatum Game (UG), 221–225 Utilitarian response, 114, 132–133 Vagus nerve, 175 Values, 125 Vasopressin functions of, 2, 30, 227 pair-bonding behavior, 38–39, 42 VEGF gene, 25 Ventral frontopolar cortex, 129 Ventral MPFC (vMPFC), 55–56

bindex.indd 255

255

Ventral putamen, 149 Ventral striatum, 144–146, 162 Ventral tegmental area, 126 Ventrolateral PFC (VLPFC), 95, 100, 102 Ventromedial prefrontal cortex (vmPFC) brain injury in, 113 characterized, 2–3, 160–163 decision making process and, 164–171 emotion and moral behavior studies, 113–118 future thinking, 81–82, 84–87, 94 lesions, 192 moral motivations, 127 moral reasoning, 133, 135 moral sentiments, 129 somatic marker hypothesis and, 170–171, 174–177 VGF gene, 25 V1PR gene, 25, 28 Vulnerability, disease, 5, 7–8 WFS gene, 25, 28–30 White matter, 94 Widowhood, 42 Winner effects, hierarchy formation characteristics of, 204–206, 208–209, 211 empirical study, green swordtail case, 209–211 individual recognition and, 206–207 joint effects, 208–209 Wisconsin card sort test, 161 Working memry, 162, 164, 174 Yoni task, 146

7/25/2011 10:10:03 AM