Executive functions in children's everyday lives : a handbook for professionals in applied

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Executive Functions

in Children’s Everyday Lives

A Handbook for Professionals in Applied Psychology

E D I T E D B Y

M A U R E E N J . H O S K Y N

G R A C E I A R O C C I

A R L E N E R . Y O U N G

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Oxford University Press is a department of the University of Oxford. It furthersthe University’s objective of excellence in research, scholarship, and educationby publishing worldwide. Oxford is a registered trade mark of Oxford UniversityPress in the UK and certain other countries.

Published in the United States of America by Oxford University Press198 Madison Avenue, New York, NY 10016, United States of America.

© Oxford University Press 2017

All rights reserved. No part of this publication may be reproduced, stored ina retrieval system, or transmitted, in any form or by any means, without theprior permission in writing of Oxford University Press, or as expressly permittedby law, by license, or under terms agreed with the appropriate reproductionrights organization. Inquiries concerning reproduction outside the scope of theabove should be sent to the Rights Department, Oxford University Press, at theaddress above.

You must not circulate this work in any other formand you must impose this same condition on any acquirer.

CIP data is on file at the Library of CongressISBN 978– 0– 19– 998086– 4

9 8 7 6 5 4 3 2 1

Printed by Sheridan Books, Inc., United States of America

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CONTENTS

About the Editors viiContributors ix

1. Introduction 1Maureen J. Hoskyn, Grace Iarocci, and Arlene R. Young

PART ONE Executive Functions and Children’s Development

2. Executive Functioning: A Developmental Cognitive Neuroscience Perspective 9Katie Knapp and J. Bruce Morton

3. Assessing Executive Functions in Young Children 21Arlene R. Young, Mandeep K. Gurm, and Katherine A. O’Donnell

4. Sports as a Metaphor for Understanding the Development of Executive Function and Misfunction 38Jacob A. Burack, Colin Campbell, Oriane Landry, and Mariëtte Huizinga

5. Executive Functioning Helps Children Think About and Learn About Others’ Mental States 54Jeannette E. Benson and Mark A. Sabbagh

6. Parenting and Young Children’s Executive Function Development 70Annie Bernier, Diane St- Laurent, Célia Matte- Gagné, Tristan Milot, Stuart I. Hammond, and Jeremy I. M. Carpendale

7. Transition to School: Executive Function, Emergent Academic Skills, and Early School Achievement 88Ulrich Müller, Michael Miller, Sarah Hutchison, and Kayla Ten Eycke

PART TWO Diversity in the Development of Executive Functions

8. The Bilingual Advantage: Evidence and Alternative Views 111J. Bruce Morton and Stephanie M. Carlson

9. Executive Functions and Plurilingualism in Young Children 118Maureen J. Hoskyn

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10. Executive Functions and the Developing Social Competence of Children With Autism Spectrum Disorder 134Grace Iarocci and Emily Gardiner

11. The Assessment of Executive Functions in Attention- Deficit/ Hyperactivity Disorder: Performance- Based Measures Versus Ratings of Behavior 157Maggie E. Toplak, Richard F. West, and Keith E. Stanovich

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ABOUT THE EDITORS

Maureen J. Hoskyn, Grace Iarocci, and Arlene R. Young are researchers and registered psychologists interested in child development and how this varies for children with autism spectrum disorders (ASD), attention- deficit/ hyperactivity disorder (ADHD), learning, communication, and other childhood disorders.

Maureen J. Hoskyn, PhD, is Associate Professor and Executive Director of the Centre for Research on Early Child Health and Education at Simon Fraser University. Her current research projects focus on the development of executive functions in young, multilingual children.

Grace Iarocci, PhD, is Professor of Psychology and the Director of the Autism and Developmental Disorders Lab at Simon Fraser University. Her research on executive function focuses on the relation between executive function and social development in children with ASD.

Arlene R. Young, PhD, is Associate Professor and the Director of Clinical Training at the University of Guelph, Ontario. Her research focuses on the inter-play between learning and language disorders and mental health in children and adolescents. Executive function is an important component of her research.

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CONTRIBUTORS

Jeannette E. BensonQueen’s University

Annie BernierUniversity of Montreal

Jacob A. BurackMcGill University

Colin CampbellMcGill University

Stephanie M. CarlsonUniversity of Minnesota

Jeremy I. M. CarpendaleSimon Fraser University

Kayla Ten EyckeUniversity of Victoria

Emily GardinerSimon Fraser University

Mandeep K. GurmSimon Fraser University

Stuart I. HammondUniversity of Ottawa

Maureen J. HoskynSimon Fraser University

Mariëtte HuizingaUniversity of Amsterdam

Sarah HutchisonUniversity of Victoria

Grace IarocciSimon Fraser University

Katie KnappUniversity of Western Ontario

Oriane LandryMcMaster University

Célia Matte- GagnéUniversité Laval

Michael MillerVanderbilt University

Tristan MilotUniversité du Québec à Trois- Rivières

J. Bruce MortonUniversity of Western Ontario

Ulrich MüllerUniversity of Victoria

Katherine A. O’DonnellSimon Fraser University

Mark A. SabbaghQueen’s University

Keith E. StanovichUniversity of Toronto

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Diane St- LaurentUniversité du Québec à Trois- Rivières

Maggie E. ToplakYork University

Richard F. WestJames Madison University

Arlene R. YoungUniversity of Guelph

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Executive Functions in Children’s

Everyday Lives

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Introduction

M A U R E E N J . H O S K Y N , G R A C E I A R O C C I ,

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Children are strategic and draw upon an executive system of cognitive resources to achieve their goals, mostly through engaging with others in their social worlds. Even when as young as 7– 10 months of age, infants attend to the actions and intentions of others. As they grow older, the executive system available to young children becomes organized and refined through their social interactions and experiences with others. For example, young children increasingly interpret and take into account the perspective of others, they update and modify their own thinking based on new information, and they ignore irrelevant details while switching their attention to those details that are important for effective social communication. This executive system of cognitive resources, often referred to as “executive functions,” is foundational to children’s early social and communica-tive competencies. By the time they reach school age, most but not all children will show a seamless integration between this emerging capacity to use executive functions and their engagement in complex social, language, and literacy prac-tices. However, adapting to the social and learning demands of a school environ-ment will seem daunting for children whose capacity or use of executive functions is less than optimal.

Given the knowledge that executive functions play a significant role in pre-dicting children’s social and academic outcomes, furthering understandings about the origin and nature of an executive system of cognitive resources has long been a topic of interest to researchers from disciplines that span psychology, education, and cognitive neuroscience. The authors of chapters in this book are representative of this diversity, and all have attempted to provide insights on the implications of their research findings for the practice of professionals who work with children and their families, including educators, psychologists, and health-care professionals. Two themes are interwoven in the chapters of this book. First, children draw on executive functions to support their engagement in complex social and cognitive activity, and children whose capacity for executive functions

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is compromised due to neurological insult or disability encounter a number of related challenges. Second, executive functions are “cognitive tools” that shape and are shaped by social interactions and therefore must be considered within a relational framework that takes into account the transactional relation between these constructs. The primary aim of the book is to highlight for professionals in the field how cognitive activity related to executive functions is observable in children’s everyday lives, how this activity is facilitated by the actions of adults and peers with whom children interact, and how children may adjust when they do not have access to these cognitive resources. Basic research on executive func-tions has flourished in the last decade, yet the bounty of its discoveries has had only a modest impact on practice. Thus, another goal of this book is to provide a much needed bridge of communication between highly specialized research sci-entists who engage in cutting- edge research on executive functions and the pro-fessionals who have practical knowledge on child development accrued through their many and diverse experiences working with children.

The book is organized around two main parts, with chapters in each part high-lighting different aspects of a common theme. Chapters in the first part empha-size the complexity within a system of executive functions and how this system develops in young children during their first years of life until school entry. These chapters examine the interplay between executive functions and human evolution, neurobiology, social, language, and cultural experience. The second part focuses on complexity of the executive system for diverse populations of children, includ-ing children from diverse linguistic and cultural backgrounds, children with diag-noses of attention- deficit/ hyperactivity disorder (ADHD), or children with autism spectrum disorders (ASD).

In the introductory chapter to the first part, Knapp and Morton highlight the changes that occur in brain networks associated with executive functions over early childhood. As the authors point out, brain organization and its devel-opment have long been associated with “genetic hard wiring” and “biological immutability”— a sort of natural endowment that is shielded from the effects of learning and experience. However, recent advances in our ability to study the developing brain have given rise to a different view, one in which highly interconnected networks show moment- to- moment adaptations in function and continued change into early adulthood. Perhaps nowhere is this more evident than in the development of brain networks supporting executive functioning. Understandings about the plasticity of brain networks associated with executive functions have important implications for understanding the nature of children’s behavior in real- world settings.

With this in mind, it is not surprising that measurement of the behavioral cor-relates of a neural network of executive functions is a topic of interest to research-ers and clinicians alike. In the second chapter, Young, Gurm, and O’Donnell describe four components of executive functioning: working memory, response inhibition, set shifting, and planning and the tools that are available to assess chil-dren’s capacities in these areas. Central to this discussion is the idea that although challenges are present when assessing children’s capacity for executive functions,

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diverse measures of these executive functioning constructs yield valuable infor-mation to guide intervention planning.

Burack, Campbell, Landry, and Huizinga propose a developmental framework of executive functions as a system of multiple- component processes to explain performance on executive functioning tasks at any given time. The usefulness of this conceptual framework is highlighted through a sports metaphor, wherein executive functions are linked to cognitive control and physical movements. At times, optimal performance in sports means that children will bypass higher order thinking to execute lower level actions; at other times, stress and fatigue may overload the system. As the chapter proceeds, the benefit of the proposed devel-opmental framework to analyze the complexity of connections among cognitive control, an executive system, and human activity becomes clear. An important feature of the executive system is flexibility. Rather than being fixed, encapsulated, or modular, the executive system is constantly in flux, and components of this executive system interact with other cognitive processes as children engage in human activity.

One aspect of cognition that has been linked to this executive system is theory- of- mind reasoning. Benson and Sabbagh’s chapter discusses what is meant by theory- of- mind reasoning as it applies to early childhood, and how executive functions may be critical for young children to negotiate alternative perspectives associated with this form of reasoning. The general hypotheses presented by the authors posit that an executive system may be necessary for children to capital-ize on relevant experiences to develop theory- of- mind concepts; yet children’s theory- of- mind skills may also be helpful to relieve stress on the executive system, such as in times of high emotional arousal, or when children are cognitively or physically tired. Benson and Sabbagh’s chapter presents a valuable, informational backdrop for clinicians and practitioners interested in leveraging executive func-tions as a means to assist young children to develop their theory- of- mind skills.

In the following chapter, Bernier, St- Laurent, Matte- Gagné, and their colleagues discuss how the actions of significant adults in children’s lives may influence their development of executive functions. Findings from several studies conducted by the authors converge on the view that both emotional responsivity and cognitive scaffolding aspects of parent– child relationships are associated with development of executive functions. Moreover, the authors propose that the ways that parents assist their children in developing their executive function skills will also apply to teacher– child relationships. Teachers are thought to be in a unique position to extend the supportive influence of parenting practices on children’s development. Through carefully crafted curriculums that promote healthy school environ-ments, children are exposed to optimal settings in which children’s self- regulation and executive functions emerge.

Müller, Miller, Hutchison, and Ten Eycke’s chapter further highlights how an executive system influences children’s transition to a school environment. This chapter provides a critical overview of recent curricular approaches that show promise as a means to facilitate development of executive functions as well as to promote academic achievement of young children. Although further research is

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needed to affirm the study findings to date, the authors propose that global inter-ventions that address the emotional and social basis of executive functions or that provide children with material that encourages particular interactions might be more effective than interventions that target specific executive functioning skills directly.

The second part of the book focuses on how the system of executive func-tions available to young children may differ among diverse populations. The part begins with two chapters highlighting the relations between executive functions and multilingualism in early childhood. First, Morton and Carlson present an overview of alternative perspectives on the “bilingual advantage,” a research find-ing that suggests that on average, bilingual children consistently outperform their monolingual peers on measures of executive functions. As Morton and Carslon point out, there are several interpretations of this finding, but most explanations align well with the chapters in this book that propose that significant differences in the language, social, and cultural experiences of young children may account for group variation in executive functioning task performance.

The final two chapters of the book explore the influence of executive functions on the cognitive, social, and affective aspects of development for two groups of children with diagnosed disabilities: children with ASD and children with ADHD.

Iarocci and Gardiner describe the profile of executive function difficulties in children with ASD. They begin with what is known about the developmental tra-jectory of executive functioning processes in children with ASD as compared to their typically developing peers. They then consider the divergent social develop-mental trajectories of children with and without ASD, and the critical role that executive functioning plays in key social developmental tasks during the pre-school years. The research findings underscore the importance of considering the mutual and reciprocal influence of executive functioning and social competence in children with ASD. The chapter ends with a comparison of lab- based, caregiver ratings, and ecological assessments of executive functioning in children with ASD. Each of the three types of assessments provides a different source of information that when integrated helps the clinician or educator to achieve a valid assessment of executive functioning that includes consideration of the complexity and social elements of the task.

In the final chapter, Toplak, West, and Stanovich highlight how constraints in executive functions impact the lives of children with ADHD. An issue explored in depth is how the quality of information provided by performance- based behav-ioral measures of executive functions and from ratings of executive functions by different informants differs for this group of children. While both assessments provide valuable information to support the process of identification and treat-ment planning for children with ADHD, the information available from each assessment process is discrete and does not overlap. This discussion is consistent with a theme that permeates this book where a clinician’s assessment of a young child’s capacity to use executive functions is viewed as a complex, dynamic pro-cess involving strategic use of assessment tools and critical reflection of assess-ment results.

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Taken together, the chapters in this book are meant to provide the clinician with the tools to construct knowledge about a child’s capacity for executive func-tions, and how these abilities are used by the child in everyday life. Each chapter provides the reader with an additional lens through which he or she can critically evaluate how executive functions may be influencing the social or academic life of a child at home, at school, or in their communities. The effect is cumulative; for each chapter shines more light on the impact of a dynamic system of execu-tive functions on children’s early development, their emerging social competen-cies, and their academic achievements. As a clinician or educator, you must often consider different (and at times conflicting) levels of explanation that range from research on executive functioning for different age or disability groups to the specific profile of executive functioning difficulties for a particular child. Written from a developmental and wholistic framework that views the impact of execu-tive functioning on the whole child and his or her social life, we hope that this book can serve as a guide as you conceptualize the problem and choose the most appropriate tools to assess executive functioning. With the vast number of devel-opmental changes in executive functioning during the preschool years, it may be an ideal window for intervention that can have far- reaching and enduring effects.

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PART ONE

Executive Functions and Children’s Development

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Executive Functioning

A Developmental Cognitive Neuroscience Perspective

K A T I E K N A P P A N D J . B R U C E M O R T O N ■

Executive functioning (EF) refers to a set of higher order cognitive processes that are integral to many everyday psychological activities, including planning, inhibition, flexible thinking, and focused attention. Clinical and brain imaging studies suggest these higher order cognitive abilities are supported in part by the prefrontal cortex (PFC), a large area at the front of the brain that has evolved rap-idly over the last 3 to 5 million years and is protracted in its developmental time course. This chapter will examine the close association of PFC development and changes in higher order cognitive functioning that occur in early childhood, and discuss new findings that suggest the PFC is part of a larger executive functioning network.

THE DEVELOPMENT OF EXECUTIVE FUNCTIONING

EF develops into early adulthood, but it shows particularly pronounced change in infancy and childhood (for reviews, see Diamond, 2002; Morton, 2010). These changes can be readily observed when watching infants and children try to solve simple problems that require planning, inhibition, and flexible thinking. In the A not B task, for example, 8- to 12- month- old infants learn to retrieve a toy hidden at a nearby location called “A.” Then, the same toy is hidden in a new “B” location. Even though infants watch as the toy is hidden at B, most perseverate by search-ing for the toy at A. This striking behavior was first observed by Jean Piaget, who maintained it reflected a fragile understanding of objects as things that exist inde-pendently of one’s own actions. However, more nuanced experiments revealed that infants search for the toy at A despite some memory of it being hidden at B, suggesting difficulty inhibiting reaches to A. Preschool- aged children show com-parable behaviors. In the Dimensional Change Card Sort (DCCS), for example,

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3- year- old children sort colored shapes (e.g., red trucks and blue flowers) one way (e.g., by shape) and then are asked to switch and sort the same cards in a new way (i.e., by color). Although children this age can answer basic questions about the new rules (e.g., “Where do the red cards go in the color game?”), most will persist in sorting cards the old way. As was true in the A not B task, children appear to have the requisite knowledge for succeeding, but they cannot voluntarily bring about a change in their behavior.

There is, of course, a very spirited debate about exactly what aspect of EF is changing during this period, and how these changes might be tied to other aspects of development (for discussion, see Morton, 2010). It is conceivable, for example, that infants and children have only a vague recollection of what they were previously shown or told to do, and therefore are prone to falter when such memories compete with stronger habitual responses (Morton & Munakata, 2002; Munakata, 1998). Alternatively, infants and children may remember exactly what they were shown or told, but falter because of an immature ability to inhibit incor-rect behaviors (Kirkham, Cruess, & Diamond, 2003). Finally, children may be able to remember but not reflect on what they know (Zelazo, Mueller, Frye, & Marcovitch, 2003). Regardless, there is some consensus that the ability to plan, inhibit inappropriate behaviors, and focus on what is at hand are aspects of cogni-tive functioning that follow a protracted developmental time course. The prevail-ing question then is why— why do these abilities develop so slowly?

EXECUTIVE FUNCTIONS AND THE DEVELOPMENT OF THE PREFRONTAL CORTEX

One prominent hypothesis has argued that an important constraint on the devel-opment of EF abilities is the maturation of the lateral prefrontal cortex (PFC; Bunge & Zelazo, 2006; Dempster, 1992; Diamond, 2002). The PFC is well suited to perform cognitive control tasks, is important for the completion of EF tasks in adults, and it follows a more protracted course of development than other brain areas. Both lesion and functional magnetic resonance imaging (fMRI) studies suggest that the development of the PFC plays a pivotal role in the acquisition of executive functioning abilities with age.

The lateral PFC is located anterior to the precentral sulcus and comprises one third of the human cortex. It occupies a larger portion of the cortex in humans than in other animals, suggesting it may play a role in implementing those behaviors that make us distinctly human, such as executive functions (Miller, Freedman, & Wallis, 2002). The PFC sends and receives projections from association areas, vir-tually all sensory and motor systems, and from a number of subcortical structures (Miller & Cohen, 2001; Tanji & Hoshi, 2008). Due to its vast range of anatomical connections, the PFC is capable of synthesizing information from a wide array of brain structures, making it a suitable candidate for EF implementation.

An intact PFC is necessary for the successful completion of EF tasks. Patients with lesions to the PFC have greater difficulty performing the Stroop task

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(Perret, 1974; Vendrell et al., 1995), the go/ no- go task (Aron, Fletcher, Bullmore, Sahakian, & Robbins, 2003), and the Wisconsin Card Sorting Task (Milner, 1963) than do controls or patients with lesions to other parts of the cortex. Patients with dorsolateral prefrontal cortex (DLPFC) lesions have great difficulty shifting from one sorting criterion to another as the demands of the task change when performing the WCST (Milner, 1963). Similarly, when virtual lesions are created over the lateral PFC, performance is disrupted on such tasks of switching (Vanderhasselt, De Raedt, Baeken, Leyman, & D’haenen, 2006a), as well as on tasks of inhibition (Vanderhasselt et al., 2006b) and working memory (Koch et al., 2005). These virtual lesions are created using transcranial magnetic stimulation (TMS), which involves the passing of a pulse of current through a coil placed over the head which generates a magnetic field. The magnetic stimulation of a particu-lar brain area, such as the lateral PFC, disrupts whatever processing is occurring in that region (Pascual- Leone, Walsh, & Rothwell, 2000). Finally, the importance of the lateral PFC for EF has also been demonstrated using fMRI. An increase in activation in the PFC is evident during the performance of working memory (Petrides, 2000), task switching (Lie, Specht, Marshall, & Fink, 2006), and inhibi-tion tasks (Aarts, Roelofs, & van Turennout, 2009). These divergent methods all suggest that the lateral PFC plays a vital role in EF performance in adults.

Although it is clear that the PFC plays a role in EF, of particular interest is whether the maturation of this brain region coincides with and influences EF development. Convergent evidence from studies examining developmental changes in synaptogenesis and gray and white matter suggest that this is indeed the case. Synaptogenesis involves the process of synapse formation and mainte-nance and is necessary for the establishment of efficient communication between neurons (Cohen- Cory, 2002). Following birth there is a stage of rapid synapse pro-duction with synaptic density increasing greatly in the first year of life. Synaptic pruning then follows, eliminating unnecessary synapses and resulting in an over-all decrease in synaptic density during childhood and adolescence (Huttenlocher, 1979; Huttenlocher, de Courten, Garey, & Van der Loos, 1982). This maturational process is different in different areas of the cortex. In primary sensory areas, syn-aptogenesis occurs early with the maximum amount of synapses existing between 3 and 6 months of age. Synaptic elimination then begins at age 1 and mature levels of synaptic density are reached in these areas before the age of 12. The PFC, however, acquires synapses at a much slower rate, with synaptic density peak-ing between the age of 2 and 3. Synaptic elimination does not begin in the PFC until age 7 and continues until adult levels of synaptic density are achieved in midadolescence (Huttenlocher, 1999). This protracted developmental trajectory of synaptogenesis in the PFC corresponds nicely to that of the development of EF abilities, suggesting that synaptogenesis may be one factor contributing to the maturation of EF skills.

In concert with these developmental changes in synaptic density, there are also changes in gray matter thickness across development. Gray matter consists pri-marily of densely packed neuronal cell bodies and dendrites (Kalat, 2007; Mason, 2011). To determine the amount of gray matter in each region of the brain, a


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measure of gray matter density is taken. This measure is obtained by creating a sphere with a 15 mm radius at each cortical surface point. The proportion of gray matter in that small region is calculated to index local cortical thickness (O’Hare & Sowell, 2008; Sowell, Thompson, Tessner, & Toga, 2001). The study of gray matter density and thickness can be made in vivo using magnetic resonance imaging (MRI) technology. MRI has the capability of distinguishing between gray and white matter so that they can be measured and studied separately. The differ-ent types of tissues that comprise gray and white matter behave differently when stimulated, and this information can be used to make three- dimensional images of the tissues (Ward, 2010).

A significant amount of gray matter density loss occurs between adolescence and adulthood with a loss of approximately 32% between the ages of 7 and 60 (Sowell et al., 2001, 2003; Takahashi, Ishii, Takigi, & Yokoyama, 2011). Gray matter density follows a nonlinear pattern with age akin to synaptogenesis (Sowell et al., 2001). In most regions of the cortex, gray matter volume increases at younger ages, with gray matter loss commencing around puberty and a stabilization of cortical thickness occurring in adulthood (Giedd et al., 1999; Gogtay et al., 2004; Shaw et al., 2008). This gray matter thinning reflects maturation of the cortex and is beneficial for performance on cognitive tasks. Children with greater cortical thinning in the left dorsal frontal and parietal lobes display better performance on measures of verbal intelligence (Sowell et al., 2004).

Like synaptogenesis, gray matter loss also follows a different trajectory in differ-ent regions of the cortex. Reductions in gray matter density occur in the parietal cortices early on in childhood, between age 4 and 8. As development progresses, these reductions extend into temporal regions and finally reach the dorsal PFC toward the end of adolescence. The frontal lobe, the area of the brain implicated in higher order processing such as EF, is the last area of the brain to mature. In contrast, those areas of the brain associated with more basic functions like early sensory and motor areas begin their maturation much earlier (Gogtay et al., 2004; Sowell et al., 2001).

Concurrent with developmental changes in gray matter are developmen-tal changes in white matter microstructure. White matter consists primarily of myelinated axons. In order for information to travel through the nervous system, it is carried between neurons by these axons (Kalat, 2007; Mason, 2011). White matter in the brain is studied using diffusion tensor imaging (DTI). This tech-nique allows us to look at the movement of water in the brain, allowing for the examination of anatomical connectivity between regions. The integrity of white matter tracts is assessed using fractional anisotropy (FA), which is a measure of the directionality of water diffusion. FA scores offer information on the diam-eter, density, and myelination of white matter, with higher FA values indicating more coherent white matter tracts (Barnea- Goraly et al., 2005; Olesen, Nagy, Westerberg, & Klingberg, 2003; Snook, Paulson, Roy, Phillips, & Beaulieu, 2005).

In contrast to the nonlinear pattern seen across development with synaptic and gray matter density, a linear pattern exists when white matter development is con-sidered. The volume of white matter in the brain increases linearly with age with


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a net increase of 12.4% between the ages of 4 and 22 (Giedd et al., 1999; Sowell et al., 2003). Similarly, FA values increase with age in several cortical regions, including the PFC, basal ganglia, thalamus, and corpus collosum (Barnea- Goraly et al., 2005). Maturation of white matter in the frontal lobe continues well into the second decade of life (Barnea- Goraly et al., 2005; Klingberg, Vaidya, Gabrieli, Moseley, & Hedehus, 1999). This protracted nature of white matter maturation offers the possibility that myelination is also playing an important role in the development of cognitive abilities, which also have a protracted developmental time course.

The development of mature white matter tracts is linked to improvements in EF performance. Diffusion in frontostriatal tracts becomes more restricted with age, and these developmental changes are related to performance on a go/ no- go task. Frontostriatal tracts with more restricted diffusivities are associated with obtaining faster reaction times in the task (Liston et al., 2006). Similarly, higher FA scores in both the left superior and inferior frontal lobe are correlated with better performance on a working memory task (Nagy, Westerberg, & Klingberg, 2004). These results suggest that improvement on EF tasks may in part be due to more myelinated or coherent white matter circuits in the frontal lobes.

It is evident that the maturation of the brain follows a protracted course of development with the frontal lobes taking the longest period of time to mature. Both gray and white matter in the PFC do not fully develop until well into ado-lescence. Similarly, performance on EF tasks continues to improve well into the second decade of life. The existence of this similarity in developmental trajecto-ries has led to a great deal of research dedicated to looking at this relationship in more depth. Studies examining age- related differences in brain functioning during the performance of EF tasks do suggest that these two developmental processes appear to be tightly linked. fMRI is used to elucidate such functional changes. By tracking changes in blood oxygenation, fMRI provides a means of imaging the functioning brain in vivo. This technique is safe and noninvasive and therefore has been used to study brain functioning in infants and young children (Casey, Davidson, & Rosen, 2002; Davidson, Thomas, & Casey, 2003). Studies using this technique have found age- related changes in PFC functioning, with increases in lateral PFC activity with age during Stroop (Adleman et al., 2002) and working memory performance (Bunge & Wright, 2007; Klingberg, Forssberg, & Westerberg, 2002; Kwon, Reiss, & Menon, 2002). Others have found decreases in PFC activity (Casey et al., 1997) or changes in the laterality of PFC activation with development on response inhibition tasks (Bunge, Dudukovic, Thomason, Vaidya, & Gabrieli, 2002).

There is some inconsistency regarding exactly how the PFC is functionally changing across development and how these changes are influencing the devel-opment of EF. Developmental neuroimaging studies face a variety of challenges during data collection which may be impacting the differences that are seen in their results. One of the difficulties these researchers face is accounting for motion artifacts. fMRI images are sensitive to artifacts produced if the participant moves during the experiment. Even small movements of the neck or head can have a


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significant impact on the quality of the images produced. Data collection gener-ally takes at least an hour, and it can be difficult for young children to remain still for so long (Davidson et al., 2003). A useful alternative to these lengthy studies would be to use resting state data to look at developmental changes in the brain as such data takes less than 10 minutes to collect while the child rests in the scanner. This method will be discussed in more detail as a useful alternative to examining developmental brain changes and their relationship to the development of EF.

One criticism of the lateral PFC account of EF development is that it focuses on developmental changes in one brain region while neglecting changes in other potentially important brain areas. The PFC is not the only region of the brain that shows functional changes with development. Coinciding with developmen-tal improvements in response inhibition, brain activation increases progressively with age in parietal, striatal, cerebellar, and thalamic regions in addition to the PFC (Luna et al., 2001). Similarly, task switching is associated with a distributed frontal parietal network rather than a single localized brain region. Differences are seen with age not only in the PFC, where children show an effect of dimen-sion shifting in the right superior frontal sulcus, but also in the superior parietal cortex and thalamus. These regions are more active in adults than children during dimension shifts of attention (Morton, Bosma, & Ansari, 2009). Given that execu-tive functioning is associated with activity in many brain regions, it is possible, in principle, that the development of EF is related to changes in the functional integrity of a distributed network rather than a single brain region.

The lateral PFC clearly plays an important role in EF. It is activated during most executive functioning tasks, and lesions to this area generally result in difficulties on such tasks. However, results from the neuroimaging literature suggest that it is not the PFC alone that is responsible for EF. Developmental functional changes are also seen in other areas of the brain. It is important to move away from this modular view of brain functioning and instead consider the development of brain networks involved in EF. This may help us develop a better understanding of the relationship between brain development and the development of EF.

A NETWORK APPROACH

To explore the relationship between EF and brain development, it is imperative that the entire network of neural regions implicated in EF is examined. A network of brain regions exists that subserves EF. This network is composed of the anterior cingulate cortex, presupplementary motor area, DLPFC, inferior frontal junction, anterior insular cortex, dorsal premotor cortex, and the posterior parietal cor-tex (Cole & Schneider, 2007). During the performance of EF tasks, these regions interact, showing high correlations with each other but not with other regions of the cortex. It is the interaction of these regions that allows us to perform EF tasks successfully.

Activity in these EF regions is coactivated not just during task performance but also at rest (Cole & Schneider, 2007). Examining the correlations between activity in


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brain regions while participants are at rest has become a popular method for exam-ining cortical networks in children and is known as resting state functional con-nectivity MRI (rs- fcMRI). Collecting rs- fcMRI from children is simple: It takes less than 10 minutes and no complicated task instructions are required. The short time period required to collect rs- fcMRI data mitigates the motion issues that accom-pany standard task- related fMRI data collection. Resting state data are of interest because whether one is at rest or engaged in a task, the brain is exhibiting slow blood oxygen level– dependent (BOLD) fluctuations in the range of 0.01– 0.1 Hz (Vogel, Power, Petersen, & Schlagger, 2010). These fluctuations are correlated in brain regions that are functionally connected and are thought to reflect the history of coactivity between the regions (Biswal, Yetkin, Haughton, & Hyde, 1995; Vogel et al., 2010). Looking at the development of this EF network may shed new light on the relationship between brain development and the development of EF.

Along with age- related structural brain changes, changes also take place in functional networks across development (Dosenbach et al., 2010; Fair et al., 2007, 2009; Kelly et al., 2009; Stevens, Kiehl, Pearlson, & Calhoun, 2007; Supekar, Musen, & Menon, 2009; Vogel et al., 2010). A consistent finding across devel-opmental rs- fcMRI studies is that brain networks in children are organized by anatomical proximity, with strong short- range functional connections, whereas in adults these networks are organized in a more distributed way across the brain, showing enhanced long- range connections (Vogel et al., 2010). The development of these networks follows a protracted course, with adolescents showing connec-tivity patterns falling in between those of children and adults (Kelly et al., 2009). This developmental pattern of changing brain connectivity is so reliable that rs- fcMRI data can be used to predict an individual’s brain maturity (Dosenbach et al., 2010). It is clear that, overall, brain networks seem to change across development, but are there developmental changes in the EF network specifically?

To address this question, rs- fcMRI has been used to examine changes in con-nectivity between only those regions involved in the performance of cognitive control tasks. Such control in adults is implemented by two brain networks: the frontoparietal network, which is important for adaptive control; and the cingu-loopercular network, which implements task- set maintenance (Dosenbach et al., 2007). When only those brain areas implicated in cognitive control are exam-ined, developmental changes are seen in both of these networks (Fair et al., 2007). Across development, connections between frontal and parietal regions increase in strength, leading eventually to the frontoparietal control network seen in adults. The cinguloopercular network is relatively incomplete in childhood, with many connections missing and appearing only gradually with age. Overall, the pattern across development involves an increase in long- range connections and integra-tion between regions, and a decrease in short- range connections with age, termed segregation. The development of adult cognitive control networks relies on these two principles of segregation and integration, and this may help explain the pro-tracted course of developmental performance on EF tasks.

The increase in integration with age is likely contributed to by the myelina-tion of long- distance axons allowing information to travel from distant neurons


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at a faster rate. The development of white matter myelination follows a protracted course, and it is only in the second decade of life that these long- range connections are established in the brain. Synaptic pruning may contribute to the segregation which occurs with age as less efficient short- range connections are eliminated. It is evident that the lateral PFC alone is not responsible for performing EF tasks. Instead, a network of regions interacts to successfully accomplish such tasks. These functional networks change dramatically over the course of development. As such, it is important that we do not focus solely on the development of one important region in EF, but rather focus on the development of the network of regions that are involved in making EF possible.

SUMMARY AND CONCLUSION

Performance on EF tasks follows a protracted course of development with improve-ment occurring throughout childhood and adolescence. Following a similar time course is the development of the PFC. Changes in synaptic, gray matter, and white matter density continue to occur with age throughout the brain, but the PFC is the last brain region to reach maturity. Structural maturity in this brain region is not reached until adolescence, around the same time that EF abilities reach full development. Evidence from lesion, fMRI, and TMS studies consistently show the importance of the lateral PFC in the successful completion of EF tasks. Research examining the impact of PFC development on the development of EF abilities has neglected the consideration of developmental changes in other brain regions that are involved in EF. Examination of EF networks has revealed that they undergo extensive developmental changes, showing an increase in long- range connections and a decrease in short- range connections with age. It is likely that these changes in network architecture are contributing to the development of our abilities on EF tasks. Although there are still many research avenues to be explored to elucidate the developmental origins of EF, the study of brain networks may offer a useful avenue for quantifying the relationship between brain development and the matu-ration of executive functioning.

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3

Assessing Executive Functions in Young Children

A R L E N E R . Y O U N G , M A N D E E P K . G U R M ,

A N D K A T H E R I N E A . O’ D O N N E L L ■

The assessment of executive functioning (EF) in young children has recently garnered considerable attention from clinicians, educators, and researchers. This reflects advances in research indicating that EF is important very early in development and has implications for emotion regulation, social development, and academic achievement. The EF system is responsible for insuring that other cognitive systems are functioning in a smooth, efficient, and coordinated man-ner. Deficits in EF are core features of an array of behavioral, developmental, and mental health disorders such as autism and attention- deficit/ hyperactivity disor-der (ADHD). Thus, the assessment of typical and atypical development of EF in young children has important clinical and theoretical implications.

Although there is currently no universally accepted definition of EF, there is consensus that executive functions are higher order cognitive abilities used when we plan and organize, control impulses, and adapt to novel environments. EF is hierarchically organized with a general factor at the apex and second- order sepa-rate (but related) subdomains below. Understanding EF as a global structure with dissociable subcomponents has garnered substantial support in the literature (Garon, Bryson, & Smith, 2008; Miyake, Friedman, Emerson, Witzki, & Howerter, 2000). These subcomponents include, but are not limited to, planning, response inhibition, cognitive flexibility/ set shifting, and working memory.

Until 20 years ago there was a paucity of research on EF in young children, because it was believed that these cognitive abilities developed later in adoles-cence. This notion has been disproven, however, in light of accumulating evidence that the neural underpinnings of EF, situated particularly in the prefrontal cortex, are active and developing from birth (Hermoye et al., 2006). Furthermore, atten-tional capacity and emotion regulation even in infancy are predictive of EF later in development (Garon et al., 2008a; Sethi, Mischel, Aber, Shoda, & Rodriguez,


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2000; Ursache, Blair, Stifter, & Voegtline, 2013). Thus, EF in the early years appears to lay the foundation for future EF and related abilities.

DIFFERING MODELS OF EXECUTIVE FUNCTIONING

Different conceptualizations of EF have been proposed, depending on the theo-retical viewpoint and specific age group or population under study. Given that assessment procedures and the interpretation of results are anchored on the under-lying model of EF one assumes, it is important to consider various models across development. Conceptualizations of EF historically tend to fall within two broad approaches: the unitary view emphasizing common processes among various mea-sures of EF and the modular or componential view, which emphasizes dissociable EF processes and differential developmental patterns for each. There is consider-able evidence supporting the unitary view, including consistently high intercorrela-tions among various measures of EF, especially at younger ages (Carlson, Mandell, & Williams, 2004; Friedman & Miyake, 2004; Garon et al., 2008a) and a general growth spurt in EF performance during the period between 3 and 6 years of age (Carlson, 2005; Rothbart & Posner, 2001). Furthermore, some factor analytic stud-ies of EF in preschool- aged children found that EF is best represented as a single cognitive ability (Hughes, Ensor, Wilson, & Graham, 2010; Wiebe, Espy, & Charak, 2008). The cognitive complexity and control theory (CCC and more recent CCC- revised) proposed by Zelazo and colleagues (Zelazo, Müller, Frye, & Marcovitch, 2003) is a good example of a current developmental theory of EF. This theory pos-its that age- related changes in EF result from increases in the hierarchical complex-ity of the rules that children can formulate, maintain in working memory, and then use in problem solving (Zelazo & Müller, 2010).

In contrast, while recognizing that there is some overlap in performance across EF tasks, some researchers and theorists emphasize dissociable EF skills. This modular view, based primarily on studies of adults and those with brain injuries, assumes that EF can be reliably divided into relatively independent, functionally discrete modules. Thus, neuropsychological batteries typically include multiple tests purported to measure specific aspects of executive capacities (e.g., Tower of Hanoi = planning; Wisconsin Card Sort = cognitive flexibility). Although the unitary or common process view of EF has considerable support as a model for young children, there is also evidence that certain components are dissociable. Working memory, inhibition, and cognitive flexibility, for example, have been shown to have different developmental trajectories (Diamond, 2006).

So what is a thoughtful clinician to conclude? Integrative frameworks, such as the one proposed in a review by Garon, Bryson, and Smith (2008a), offer a prag-matic and empirically grounded conceptualization with clear utility for assess-ment. This model is based on a systematic review of the literature on EF in young children using the model developed by Miyake et al. (2000) for EF in adults. In this model, EF is considered to be a unitary construct with partially dissociable components, including working memory, response inhibition, and shifting. They

Assessing Executive Functions in Young Children 23

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proposed that changes in the development of the attentional system might under-lie the emergence of EF components during the preschool period, and these com-bine to result in gradually more complex EF abilities across development. Thus, assessment of EF should include both measures of attention and other EF com-ponents while understanding that the skills underlying EF are highly interrelated. Furthermore, one should consider that the components of EF develop hierarchi-cally, and the basic skills needed for components of EF emerge in the preschool period (Garon et al., 2008a).

In the current chapter, we will discuss EF as an overarching term and focus on four subdomains that are critical for understanding EF in children, especially within the context of assessment and treatment planning. These include working memory, set shifting, response inhibition, and planning. Working memory is the capacity to hold information in mind and perform a manipulation. Set shifting is the ability to flexibly switch between mental tasks or operations in response to environmental demands. Response inhibition is the ability to suppress a previ-ously learned response in new situations. Finally, planning is the ability to orga-nize thoughts and actions (i.e., motor planning) in goal- directed activities.

DEVELOPMENT OF EXECUTIVE FUNCTIONING ACROSS DOMAINS

Working Memory

Working memory has been assessed in infants as young as 6 months old (Káldy & Sigala, 2004; Moher, Tuerk, & Feigenson, 2012). By 24 months, verbal work-ing memory may be assessed through the use of span tasks (Dilworth- Bart, Poehlmann, Hilgendorf, Miller, & Lambert, 2010; Garon et al., 2008a). Children are able to hold more items in mind as they become older (Cragg & Nation, 2007; Gathercole, Willis, Baddeley, & Emslie, 1994). Simply holding something in memory over a delay does not change after early childhood (Luciana, Conklin, Hooper, & Yarger, 2005). In the preschool period, children may be assessed with working memory tasks that involve updating and manipulating informa-tion. Children show improvement on these tasks throughout childhood, and full capacity of working memory may be assessed by late adolescence (Gathercole, Pickering, Ambridge, & Wearing, 2004).

Planning

Motor planning may be assessed in infancy (Claxton, Keen, & McCarty, 2003; McCormack & Atance, 2011). In the preschool age, children are able to complete simple planning tasks that do not require searching ahead. During kindergarten, these abilities increase wherein 5- year- old children are able to search ahead while completing assessment tasks such as the Tower of London (Kaller, Rahm, Spreer,


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Mader, & Unterrainer, 2008). It is hypothesized that this change occurs with the development of inhibitory control and working memory (Pennequin, Sorel, & Fontaine, 2010). Planning abilities may reach adult levels by late adolescence (De Luca et al., 2003).

Set Shifting

Children as young as 1.5 years of age have been found to pass tasks of simple set shifting with few errors (Stahl & Pry, 2005). The ability to complete more compli-cated tasks (i.e., with more complex rules), however, does not manifest itself until the early preschool years when children gradually begin demonstrating more cog-nitive flexibility (Hanania & Smith, 2010; Jordan & Morton, 2012; Stahl & Pry, 2005). Further improvements are noted after kindergarten (Luciana & Nelson, 1998), and these improvements carry on until early adolescence, when they reach adult levels.

Response Inhibition/ Inhibitory Control

Recent measures of inhibitory control in toddlers use simple displacement, delay, and compliance tasks in relation to other aspects of life (Baker, Gjersoe, Sibielska- Woch, Leslie, & Hood, 2011; Morasch & Bell, 2011). In early childhood to early adulthood, inhibitory control abilities continue to increase and may be assessed by using tasks including suppression of interference and response inhibition (Bedard et al., 2002; Bryce, Szucs, Soltész, & Whitebread, 2011).

CHALLENGES TO EXECUTIVE FUNCTIONING ASSESSMENT

Several challenges have been identified in assessing EF, and these are briefly outlined next. It is important to keep these challenges and limitations in mind because they have both pragmatic and theoretical implications for assessment and interpretation of assessment results.

a. There is an impurity problem in measures of EF. Tasks designed to measure EF are typically complex and draw on multiple underlying cognitive processes. Thus, they include nonexecutive processes in the task (Toplak, West, & Stanovich, 2013), and a child’s performance may reflect problems with cognitive aspects of the task other than EF. This impurity problem is unlikely to be resolved given the integrative nature of EF.

b. There is a generally weak association between performance- based and rating measures of EF across ages. Toplak and colleagues (2013) examined


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this association across studies explicitly examining both performance- based measures (e.g., WCST, Stroop) and ratings scales of EF (e.g., the BRIEF and the CHExI) and revealed very low correspondence. They concluded that performance and rating- based measures of EF assess different underlying mental constructs and should, therefore, not be considered to be interchangeable.

c. The task demands of various measures of EF vary with development and there are relatively fewer standardized measures of EF for young children. Performance- based tasks typically attempt to include novel, complex, and integrative processing. Given variability in development, children may be utilizing different kinds of information at different stages of development (Anderson & Reidy, 2012a). For example, the Stroop test is often used to assess selective attention or inhibitory control. This is based on an interference effect on reaction time when examinees are asked to name a color presented in a word other than the target color (e.g., the word “red” is printed in blue ink). This interference effect only occurs if reading is relatively automatic. If not, then the interference effect indicative of inhibitory control will not be observed, but one cannot conclude that the child who is less advanced in word recognition has better inhibitory control. Thus, downward extensions of tests developed for older children or adults should not be assumed to measure the same construct across age groups. Tests designed specifically for younger children are often more appealing and developmentally appropriate. Yet they often lack the normative data needed to make sound clinical interpretations.

d. Performance- based measures of EF are known to have questionable ecological validity (Anderson & Reidy, 2012). The discrepancy between performance on EF measures and real- life behavior has been questioned by many (Hughes, 2011). In part, this discrepancy may reflect the uniqueness of the testing situation in which a child is typically assessed in a quiet room and engaged in a one- on- one interaction with an adult who is working to obtain the child’s “best or optimal performance.” The adult usually provides support and encouragement and discontinues tasks when the child appears fatigued or inattentive. In consequence, the resulting performance may not reflect the child’s typical performance under less ideal circumstances. Indeed, the lack of correspondence between performance- based and rating measures of EF described earlier supports this view. It may, however, also provide an indication of how well performance can be enhanced by the provision of structure and additional support. Thus, children who perform better on performance measures of EF than would be predicted from rating measures may be those who may benefit, for example, from the additional supports at school (Toplak et al., 2013). This possibility, however, needs to be demonstrated empirically.

e. The predictive validity of preschool EF test results is unclear. A recent review (Miller, Nevado- Montenegro, & Hinshaw, 2012) showed that


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test- retest reliability was better for working memory and planning tasks than response inhibition tasks. More important, it is not yet known whether preschool EF measures are predictive of EF later in childhood, let alone adulthood (Anderson & Reidy, 2012a). In contrast, Miyake and Friedman (2012) do report stability in a twin sample across development. Thus, the interpretive significance of a poor performance on certain EF tasks in young children is not always clear.

ASSESSING EXECUTIVE FUNCTIONING IN YOUNG CHILDREN

Despite these challenges and complexities, EF is an important part of any compre-hensive assessment, given its clear association with academic, social, behavioral, and adaptive functioning. Furthermore, such assessment can play a critical role in diagnosing certain childhood disorders, such as ADHD, and in developing inter-ventions to help children struggling with deficits in EF. As noted earlier, assessors need to examine both performance- based and associated behavior ratings from teachers and parents and not assume that these assessment tools measure the same underlying constructs. In particular, given that all of these measure types reflect largely nonoverlapping aspects of behavior (Toplak et al., 2013), both are needed to fully assess EF across contexts. Assessors need to also work to determine the child’s best performance by encouraging engagement, managing the testing session to avoid fatigue, and helping to maintain attention as this may provide information on the type of supports that may help to improve EF functioning in real- life situations.

In general, a “good” test of executive function will be one that uses tasks that are novel to a child and require fluid reasoning. These tasks are complex and require the child to integrate various sources of information in order to complete the task (Anderson & Reidy, 2012b). Each facet of EF draws most heavily on a specific aspect of cognition. For example, working memory tasks require the updating of information (Garon et al., 2008a; Gathercole, 1998; Pelphrey et al., 2004). In other words, children must be able to maintain information in their minds and manipu-late it while solving a problem or completing a complex task (Hunter & Sparrow, 2012; Strauss, Sherman, & Spreen, 2006). Tasks assessing set switching have two components. First, the child must learn a rule while performing an activity. Second, the child must learn a new rule and now use this new rule to complete the activity (Garon, Bryson, & Smith, 2008b; Strauss et al., 2006). Inhibitory control tasks require the child to inhibit a dominant response. This may be done while either stopping a response before it starts or stopping a response that has been used in the activity already (Hunter & Sparrow, 2012). In planning tasks, a child must look forward to reach an end goal (Salimpoor & Desrocher, 2006). These tasks are typically scored by how many mistakes a child makes and how much time it takes to complete the goal. All forms of EF tasks require a measurement of the processing efficiency to complete that task, rather than the task completion itself. This is often what differentiates EF tasks from other neuropsychological


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tests (Strauss et al., 2006). For example, in set- shifting tasks, clinicians are not interested in the outcome of the task, but rather in how the child completed it, how many mistakes the child made, and if the child was actually able to switch from one response set to another.

The remainder of this chapter provides an overview of measures available for assessing EF in young children. We highlight commercially available and vali-dated measures that include norms for young children. This is not a comprehen-sive list but rather we highlight tests that have clinical utility for assessing young children. Some excellent measures of EF have been utilized in developmental and clinical research with children but, given the lack of standardized norms, these are not included in the following tables. For example, stop- signal tasks have been extensively studied as measures of inhibition, but there are few normed versions of these behavioral tasks. Consequently, only a commercially available and stan-dardized version of this task is included next.

SUMMARY AND FUTURE DIRECTIONS

Including EF as a domain for evaluation in an assessment can be particularly infor-mative for understanding why a child may be struggling at school or in other aspects of his or her life. It can also provide the basis for developing recommendations for both academic and behavioral interventions. Furthermore, it is important to iden-tify areas of relatively intact EF functioning and strengths that can help compensate for weaker areas. Clinicians are advised to remember that EF is measured within a particular context and a child may function very differently across contexts. As noted earlier, including both performance tasks and ratings and qualitative descrip-tors from those who know the child well in different situations (e.g., parent and teacher ratings, report card reviews) is essential (Toplak et al., 2013). Standardized tests that include appropriate norms and established predictive validity are recom-mended. Nevertheless, certain cautions are in order. First, by their very nature, standardized tests may not be the most sensitive measure of more subtle EF difficul-ties. The highly interactive and examiner- directed characteristics of these measures may enhance performance for children who have difficulty initiating and direct-ing behavior in less structured setting. Thus, it is important to critically examine the task demands of each assessment tool when interpreting a child’s performance. Similarly, it is important to remember that all assessment tasks require a variety of abilities and skills and may require a response across different modalities (e.g., verbal versus written form). As such, they are not pure or isolated measures of any one aspect of EF regardless of the task name or description. Deficits or delays in a component skill are likely to impact a child’s performance, but this may not reflect EF per se. Thus, we recommend that multiple measures be used and that a careful analysis of the task demands be integrated into the interpretation of results.

Recent developmental research has yielded exciting new measures of EF designed for use with very young children (Hughes, 2011; Isquith, Crawford, Espy, & Gioia, 2005). Future EF assessment will benefit from extensions of these

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Table 3.1 Measures Assessing Multiple Facets of Executive Functioning

Name Reference Age Format Admin Time CommentsBehavioral Assessment of the

Dysexecutive Syndrome in Children (BADS- C)

Emslie, Wilson, Burden, Nimmo- Smith, & Wilson (2003)

7– 16 Test battery and 20- item Parent Rating Scale (Dysexecutive Questionnaire for children)

35 to 45 minutes for test battery, 15 minutes for rating scale

Measures inflexibility and perseveration, novel problem solving, impulsivity, planning, the ability to utilize feedback and moderate one’s behavior accordingly

Barkley Deficits in Executive Functioning Scale— Children and Adolescents (BDEFS- CA)

Barkley (2012) 6– 17 Behavior rating scale 10– 15 minutes/ 3– 5 minutes

Short form also available

Behavior Rating Inventory of Executive Functioning (BRIEF)

Gioia, Isquith, Guy, & Kenworthy (2000)

5– 18 Parent, teacher, and self- report rating scale

10– 15 minutes Scores include a total “Global Executive Composite” as well as scores in subdomains: inhibit, shift, emotional control, initiate, working memory, plan/ organize, organization of materials and monitor

BRIEF- Preschool version (BRIEF- P)

Gioia, Espy, & Isquith (2002)

2– 5 Parent/ teacher rating scales

10– 15 minutes Three overlapping indexes: Inhibitory Self- Control; Flexibility; and Emergent Metacognition

Childhood Executive Functioning Inventory (CHExI)

Thorell & Nyberg (2008)

4– 15 Parent/ teacher rating scales

5 minutes Can be downloaded for free from Web site: http:// www.chexi.se/ index.html

Comprehensive Executive Function Inventory

Naglieri & Goldstein (2013)

5– 18 Parent, teacher, and self- report

15 minutes Yields a full scale score and nine scales to direct intervention: attention, emotion regulation, flexibility, inhibitory control, organization, planning, self- monitoring, and working memory

http://www.chexi.se/index.html

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Cambridge Neuropsychological Test Automated Battery (CANTAB)

Sahakian & Owen (1992)

4– 90 Computer- based test battery

Dependent on how many of the 25 tests included in the battery are administered

The CANTAB has a wide variety of EF and memory tasks. It has been widely used for studying EF deficits in children, especially those with ADHD

Cognitive Assessment System (CAS)

Naglieri & Das (1997)

5– 17 Test battery 40 minutes (basic form), 60 minutes (standard form)

Total score and four separate PASS scores: Planning, Attention- Arousal, Simultaneous, and Successive

Delis- Rating of Executive Function(D- REF)

Delis (2012) 5– 18 Parent, teacher, and child reports

5– 10 minutes per form

Available in online or paper format. Total composite score and three index scores available: Behavioral functioning, Emotional functioning, and Cognitive functioning

Delis– Kaplan Executive Function System (D- KEFS)

Delis, Kaplan, & Kramer (2001)

8– 89 Performance test battery

Full battery— 90 minutes

Two forms: the Standard Form includes nine D- KEFS subtests as a complete battery or individual tests; the Alternative Form includes alternate versions of Sorting, Verbal Fluency, and 20 Questions tests

NEPSY- II Korkman, Kirk, & Kemp (2007)

3– 16 Attention and Executive Functioning domain— Performance test battery

Full battery: 45– 60 minutes; EF only— varies by subtest

EF Subtests: Animal Sorting, Auditory Attention and Response Set, Clocks, Design Fluency Inhibition, and Statue. Two forms available: 3– 4 years and 5– 16 years

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Table 3.2 Additional Measures Assessing Specific Facets of Executive Functioning

Set ShiftingName Reference Age Format Admin Time CommentsIntra- Extra Dimensional Set

Shift (IED) (CANTAB subtest)

Cambridge Cognition Ltd. (2014)

4+ Performance- based task 7 minutes This test is a computerized analogue of the WCST

Wisconsin Card Sorting Test (WCST)

Heaton, Chelune, Talley, Kay, & Curtiss (1993)

6+ Performance- based task 15– 30 minutes Also recognized as a measure of concept formation, abstract reasoning, and problem solving

PlanningName Reference Age Format Admin Time CommentsClocks subtest(NEPSY- II)

Korkman et al. (2007) 7– 16 Performance- based task 5– 10 minutes Includes two parts: drawing and visual

Comprehensive Trail Making Test

Reynolds (2002) 8+ Performance- based task 5– 12 minutes Includes five visual search and sequencing tasks

Stockings of Cambridge Cambridge Cognition Ltd. (2014)

4+ Performance- based task 10 minutes CANTAB subtest

Tower Test (D- KEFS subtest) Delis et al. (2001) 8+ Performance- based task 15– 20 minutes A normed version of the Tower of Hanoi using graduated disks

Tower Subtest(NEPSI- II subtest)

Korkman et al. (2007) 4+ Performance- based task 15– 20 minutes A normed test similar to other tower paradigms using colored balls (e.g., Tower of London)

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Working MemoryName Reference Age Format Admin Time CommentsAutomated Working Memory

Assessment- 2Alloway (2012) 4– 22 Computerized test

battery5– 30 minutes Multiple forms, including screener,

short, and long. Four composite scores include verbal and visual short- term and working memory

Children’s Memory Scale (CMS); Numbers and Sequences subtests

Cohen (1997) 5– 16 Performance- based task 5– 10 minutes each

Children’s Test of Non Word Repetition

Gathercole & Baddeley (1996); Gathercole et al. (1994)

4– 8 Performance- based task 5– 10 minutes

Corsi Block Tapping Task Pagulayan, Busch, Medina, Bartok, & Krikorian (2006)

6+ Performance- based task Depends on variation

Some standardized batteries also use this task (e.g., Stanford- Binet batteries); administration time depends on version

Letter Number Sequencing, Digit Span, and Arithmetic(WISC- IV subtests)

Wechsler (2003) 6+ Performance- based task 5– 10 minutes each

From the Wechsler Intelligence Scales subtest

General Working Memory cluster from the WJ- IV

McGrew, LaForte, & Schrank (2014)

6+ Performance- based task 5– 10 minutes each subtest

From the tests of Cognitive Abilities, Woodcock Johnson- IV

Non- Verbal and Verbal Working Memory subtests (Standford- Binet 5 subtests)

Roid (2003) 2+ Performance- based task 10– 15 minutes each

Includes delayed response and block span subtests (nonverbal) and memory for sentences and last word subtests (verbal)

Spatial Working Memory and the Spatial Memory (CANTAB subtests)

Cambridge Cognition Ltd.

5+ Performance- based task Each tests takes 10 minutes

(continued)

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Wide Range Assessment of Memory and Learning- 2

Sheslow & Adams (2003)

5+ Test battery and performance- based task

60 minutes Contains a variety of tasks measuring verbal, auditory, and visual memory; a screener form is also available

Working Memory Rating Scale

Alloway, Gathercole, & Kirkwood (2008)

5– 11 Teacher Rating Scale Untimed22 items

Co- normed with Working Memory Assessment

inhibitory control (ic)Name Reference Age Format Admin Time CommentsConners Continuous

Performance Test, 3rd edition

Conners (2014) 8+ Computer assessment 14 minutes Assesses attention- related problems, including inattention, impulsivity, sustained attention, and vigilance

Conners’ Kiddie CPT- Version 5

Conners (2001) 4– 5 Computer assessment 7.5 minutes Half the length of the CPT- 3, uses pictures of objects instead of letters

Stop Signal Task (from the CANTAB)

Cambridge Cognition Ltd. (2001)

4+ Computer task Up to 20 minutes

Included in the CANTAB ADHD battery

Tasks of Executive Control (TEC)

Isquith, Roth, & Gioia (2005)

5– 18 Computer assessment 20– 30 minutes Assesses inhibitory control and working memory in a single paradigm and includes three equivalent forms

Test of Variables of Attention (T.O.V.A.)

Greenberg, Kindschi, & Corman (2000)

4– 80 Computerized CPT and T.O.V. A. rating scales

23 minutes CPT has several versions, including a visual CPT; auditory CPT; and a screening or preschool version

Note: Many of the measures in Table 3.1 also include tests of the specific aspects of EF listed in Table 3.2.

Table 3.2 continued

Working MemoryName Reference Age Format Admin Time Comments


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tasks to include all the appropriate reliability and validity characteristics for clinical application. In addition, the importance of assessing EF in an everyday context has gained wide acceptance (Isquith, Gioia, & Espy, 2004). Future per-formance measures focused on ecological validity will be a welcome addition to the current toolkit of rating scales and performance tasks.

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4

Sports as a Metaphor for Understanding

the Development of Executive Function and Misfunction

J A C O B A . B U R A C K , C O L I N C A M P B E L L ,

O R I A N E L A N D R Y, A N D M A R I Ë T T E H U I Z I N G A ■

The play in baseball is not solely determined by physics, chance, and the three S’s of athletic ability: size, strength, and speed. It is also heavily influ-enced by the effects of novel game situations on the ability of the player’s nervous system to make good decisions, plans, and executions.

— Milton, Solomon, & Small (pp. 43– 44)

Rule use. Flexibility. Planning. Inhibition. These terms and the functions they represent are all familiar to the readers of this volume as components of the broad construct of executive function and as essential contributors to adaptive development. To scholars, practitioners, and educators, these functions are typi-cally considered in relation to children’s cognitive abilities, school performance, and social adaptation. They are studied individually with meticulously designed experimental tasks with well- documented validity and reliability ratings that are administered in carefully controlled settings. However, these traditional concep-tions and operationalizations of components of executive function in the service of essential developmental tasks do not easily translate to the reality of day- to- day life in real- world contexts that involve ongoing and quickly evolving action and

Sports as a Metaphor 39

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decision making that both constantly affect and are affected by the innumerable stimuli, events, and cues in the environment, all of which also are in constant flux. With their adherence to the parameters of “good science,” these frameworks typically preclude the consideration of the real- world situations in which com-binations of the components of executive function are called upon in situations that lead to disparities in performance on everyday executive function- type tasks even for a single individual. These inconsistencies can arise from any number of factors, including the constantly changing demands of the tasks and the contexts in which they occur but also momentary fluctuations in the developmental level of the processes that are elicited by these demands. This is evident in certain situ-ations or contexts of apparent developmental regressions from higher level cogni-tive functions of which the individual is capable and has attained to lower level reflexive ones that may lead to diminished outcomes in some situations but that may be necessary for success in other contexts. For example, thoughtful reflection is universally considered to be both developmentally more advanced and more adaptive than reflexive action, yet in a situation in which a young child starts to run in front of a car, a regression to the latter by whomever is watching the child or the driver of the car is more likely to save the child’s life.

If the reality of the commission of executive functions in relation to real- world environmental demands or opportunities is so difficult to capture, how do we then conceptualize a developmental framework of these processes that might be useful to practitioners and educators? We suggest that such a framework would need to be based on a context in which multiple components of executive func-tions are recognized as essential to performance at any given time, developmental trajectories are delineated and considered in relation to experience with the task and those like it, optimal responding varies in relation to task and environment, and the environment and its demands are sufficiently dynamic to ensure that responding is ongoing and continually changing. Following from our (Burack, Russo, Dawkins & Huizinga, 2010) suggestion that rule use and other compo-nents of executive function can be linked to performance in sports which inher-ently involves complex and dynamic relations between physical movements and cognitive control, we suggest that sports provides a uniquely compelling and real- world metaphor to conceptualize and explain the unique development of the various components of executive function, especially rule use. Although sporting activities vary in so many ways with regard to the rules, props, equipment, envi-ronments in which they are played, involvement of team or individual competi-tors, level of competition, and age of the participants, they all inevitably involve goal- oriented action sequences, which are the hallmark of executive functions. Thus, sports provides a necessarily multifaceted framework for broadening the understanding of the development of executive function, or any aspect of func-tioning, by transcending the typical emphases on age and “attainment” of one or more executive function components to include the complexities and nuances that are at the heart of a dynamic systemic developmental approach in an ongoing transactional relationship with the environment.


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With sports as a metaphor, we revisit executive functions such as rule use, flex-ibility, planning, and inhibition, all of which are common terms used to describe essential aspects of successful participation in team sports and all of which are essential ingredients to victory. Sports provides a window into understanding real- world complexity in the relationship between developing executive function and performance, such as when higher levels of executive function control lead to superior performance. This complexity is evident when, in apparent examples of regressions, optimal performance is dependent on bypassing higher order func-tions in order to allow for the execution of lower level ones. At other times, we find examples of developmental regressions in which the cognitive system is over-whelmed by factors such as stress and fatigue, leading to suboptimal outcomes. In the current chapter, we consider the ways in which individual and collective, or team, strategies can impact decision making, inhibition, and the adherence to or the violation of different types of rules. Thus, we argue that the metaphor of sports provides a context for delineating the intricacies in understanding situations when executive function– dependent performance might likely be optimal and when it might be diminished even at points in development when the necessary functions have already been attained and are part of the typical cognitive arsenal.

In our (Burack et al., 2010) previous discussion of executive function in sports, we emphasized that the constantly and consistently changing environment and conditions inherent to sports competition provided an opportunity to concep-tualize executive functions in relation to rule use (and misuse) in the dynamic way in which they occur in the real world. In particular, we focused on different types of rules, with a distinction between those that provide the framework for the specific competition (i.e., the rules of the game) and those that are utilized strategically for success in the competition (i.e., the game plan). This notion was framed within the overarching question as to why even experienced, well- trained athletes make bad decisions, in which they violate either or both types of rule in ways that adversely impact them and their team. We addressed these questions within the context of developmental theory, especially as forwarded by Heinz Werner, with regard to the interplay of the developmental level of functioning and the challenges faced in a dynamic complex environment, as in the case of a sports competition, and the reasons for the occurrence of regressions from higher to lower levels of functioning.

In this chapter, we attempt to provide an even more nuanced analysis of the ways that executive function in sports can be used as a metaphor for real- life activity. We emphasize the complexity of delineating the nature of optimal execu-tive function within a developmental context. We highlight that in sports the tasks of the executive functions and the relationship among them are highly dynamic because the conditions that affect behavior and the functions that govern them change continually in relation to a myriad of events or circumstances during the competition, including (but certainly not limited to) the opponents’ immediate actions and long- term strategies (i.e., rules of success), the score, the moment in time during the competition, the conditions of the venue, and even the support from those external to the competition (i.e., the spectators) (Burack et al., 2010).


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Even if the ability to adhere to rules and other executive functions were the deter-mining factors of success, such as the case in which the talent between the com-petitors is identical, the obvious conclusion that behaviors associated with higher developmental levels of executive function would be associated with victory might not be the correct one. This seemingly nonintuitive point was highlighted by John Wooden (University of California at Los Angeles), the coach with the most championship teams (10) in the history of United States college basketball, who noted that “I constantly repeated to my teams the admonition I had learned from Piggie Lambert at Purdue (University) ‘The team that makes the most mis-takes will probably win’ … The doer makes mistakes and I want doers on my team— players who make things happen” (Wooden, 1998, p. 132). This statement reflects the constructivist developmental premise that action, rather than inac-tion, is associated with success, but also the seemingly counterintuitive develop-mental notion that highest ordered developmental functions are not always the most adaptive (Werner, 1957; Zigler, 1963).

In proposing the metaphor of sports as an initial framework for understanding essential contributors to the inherent “messiness” in understanding the develop-ment and efficacy of executive functions in the complexity of the real world, we underscore that the components of executive function that are typically separated in experimental research overlap considerably and affect each other in real life. For example, in sports, the rule use associated with strategy involves both plan-ning prior to and during the competition and inhibition of certain instinctual responses in order to conform to the game plan or preset rules. Both the adherence to and flexibility in switching among strategies or rules are associated with a vari-ety of factors, including the opponents’ abilities and style of play and those that are more personal such as stress and fatigue. As the former heavyweight champion boxer Mike Tyson is purported to have claimed, “Everyone has a plan, until they get hit in the face.” Our goal is to better understand the factors that contribute to the adherence of that original plan and inhibition of more instinctual responses even after that punch but also the abilities to quickly and efficiently modify or switch the plans in relation to the opponents’ course(s) of action and the ongoing events and circumstances (i.e., score, time left to play) of the competition.

DIFFERENT TYPES OF RULES

In our previous chapter, we (Burack et al., 2010) argued that sports are defined by two general types of rules— those that are context specific to the sport and those that might be considered “guideline for success.” Context- specific rules are essen-tially the rules of the game and provide the framework for the competition. For example, in soccer, these include the size and boundaries of the field of competi-tion, the offside rules that delimit where an offensive player may take possession of the ball passed to her from a teammate in relation to the opponent’s defensive players, and the prohibition of a player, other than a goalkeeper, to intention-ally touch the ball with any part of a hand or arm. In contrast, the guidelines


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for success involve both personal and team strategies intended to optimize the opportunity for individual achievement and a team victory. At the individual, or personal, level, rules for success that are common across various sports include catching a ball with two hands to secure possession and keeping one’s head up when advancing on an offensive foray, such as when dribbling the ball in bas-ketball, skating with the puck in hockey, or running with the ball in football or rugby, in order to be able react to the positioning and opportunities allowed by the actions of both teammates and the opposition. At the team level, the rules of success essentially involve general strategies, or game plans, for the particu-lar competitions that might involve the extent to which a team adopts an attack-ing style of play designed to produce offensive chances to score points or goals rather than a more conservative strategy intended to buffer the defense and limit the opposition’s offensive possibilities. These broad- based strategies also involve micro- plans, in some sports referred to as plays, for specific scenarios or time-frames within the competition. These typically, but not inherently, are consistent with the teams’ (or individuals’) more general style of play.

In this iteration of sports as a metaphor for executive function, we acknowledge that the nature and combination of executive functions differ among the context- specific rules of the game and the various types of guidelines for success rules. For example, context- specific and personal rules of success are essential to long- term strategies, and the adherence to them primarily involves both inhibition and if- then rule use, but planning and cognitive flexibility are essential. If the rule states that I, as a defender, cannot impede the progress of the opposing player by grab-bing or tripping him, I should have already planned on how to defend effectively without doing so, but I also need to inhibit the reflexive response to do so as the opposing player passes me, unless maybe by doing so I might help my team pre-vent a goal at the expense of a penalty to myself. At an even more nuanced level, the athletes might have to adjust their rule use to a higher order understanding that is contingent on the context of the competition (for a model of the complexity of this type of decision- making process, see the decision tree in Fig. 4.1). This is evident in the relation to the athletes’ rule use in relation to the style of referee-ing. In a scenario with a relatively strict referee, strict adherence to the rules of the game are most optimal, whereas with a more lenient referee, a more liberal “bending” of the rules would be more likely to facilitate a positive outcome. This ability to modify tactics clearly involves both planning and cognitive flexibility, in addition to the modulation of inhibition in relation to the context. With a referee who is lenient about physicality of play in sports such as soccer or hockey, impul-sive reactions to fouls or unsportsmanlike behavior committed by the opposing might not lead to a penalty and might be useful in intimidating the opposition, whereas the inhibition of these reactions would be essential with a stricter referee who might harshly penalize a player or team.

The rules of success most obviously involve the planning component of execu-tive function as they are the game plan, or strategy, for a competition. The plan is the premise for the rules to which the players must adhere, and plans are often multifaceted and varied. At a most general level, a game plan might restrict each


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player’s movement that would minimize risk, whereas a more liberal one might be associated with more leeway for individuals to improvise and make decisions about their own movements, and thereby allow for more risk. The plans can also be situational with regard to the juncture of the match or game, the particular opponent, and the score of the game. This clearly involves a higher order level of rule use and flexibility among rules as each action must entail some complex thinking along the lines of “if we are at x moment in the match, and the score is Y, and Z from the other team has the ball in such a position, and Z’s teammates A, B, and C are moving in respective directions, and my teammate D is over there, then I should … , but if I do that, and B from the other team moves over there, and D and E from my team react in that way, then …” And, again, inhibition plays a role as athletes must often restrain themselves from an impulse to perform a certain

Will this help our team

score?

No

Don’t grab, the penalty would

cost us.

Yes

Can I impede the other player?

Yes

Grab, a penaltywill be worth it.

No

Don’t grab

Should I grab the other player?

Who is the referee?

Strict—a penalty is sure

to be called

Lenient—I might get away

with it

Can I impede the other player?

Yes

Grab

No

Don’t grab

Figure 4.1 An example of the complexity of an athlete’s decision- making process.


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action, even one that might lead to success, but does not fit the team strategy and, if unsuccessful, leaves the team at risk. An example might be to attempt a difficult pass when an opportunity arises as it might lead to a score by a teammate if com-pleted successfully but might be easily intercepted by the opponents and turned into a scoring opportunity for them.

The complexity of the executive demands partly explain the difficulties that younger children have with team sports. Just as learning to read and write pro-ficiently involves years of basic skill practice in letter recognition, phonological awareness, and fine motor skills, young children spend much of their early years of sports training learning the basic gross and fine motor techniques, as well as rules, but implementing these skills in the dynamic context of a game would be akin to a child engaging in a rapid- fire written debate— more than literacy is required. In addition to the external factors, such as the situation of the game or match, that impact upon the decision, so too do a myriad of individual fac-tors. These include developmental factors such as age and experience in the sport and specific situation but also individual differences, such as propensity for risk taking, and more transient characteristics such as stress at a specific moment in relation to the logistics of the game, specific events or actions at a given time, and level of fatigue. This tension among game plans, the continual implementation and switching among the rules within the game plan, and the impulses to act in certain ways that optimize the game plan reflect the primary challenge in estab-lishing effective rule use for every coach.

COMPLEXITY OF UNDERSTANDING HIGHER AND LOWER ORDER DEVELOPMENTAL FUNCTIONS: ISSUES IN THE COMMISSION AND INHIBITION OF ACTION

The development of executive functions, or any aspect of functioning, is typi-cally thought of in terms of improvements, or higher order processing, associated with increasing age, whether measured in terms of chronology or general level of functioning (e.g., mental age). But development can also be measured in several other ways that involve the unique relation between the individual and the specific function. For example, regardless of chronological or general developmental level, developmental changes can be seen as a function of an individual’s experience with a task or function, and with the amount of time to which he or she is exposed or has to respond (Diamond & Kirkham, 2005; Werner, 1957). In sports, optimal decision making is thought to be linked to experience in general but also in specific situa-tions, such as in the case of championship play, where previous experience in such a situation, even if in a losing case, is thought to prepare the athlete for the stress and other factors that are unique to the situation. In this way, the development of executive functions is not just seen as an inherent characteristic of the individual but also in the relation between the individual and the task at hand.

Despite the steady progression and development toward increasingly complex and integrated levels of ability within executive function, specific situations may


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cause individuals to abandon optimal strategies and instead to rely on earlier, less advantageous strategies (Diamond & Kirkham, 2005). And, contrary to typical conceptions of development, the developmentally highest function is not neces-sarily the most adaptive function. As Werner (1957) highlighted with examples as diverse as decorticate frogs being better able to catch flies than cortically intact frogs and children with intellectual disability performing more accurately on cer-tain tasks than children with average or better IQ of the same chronological age, that lower developmental functioning may be more adaptive in certain situations. Yet Werner clearly delineates the value and adaptiveness of higher order develop-ment as characterized by thought and reflection, over lower order development, as characterized by action and reflexive behavior.

In sports, the advantages of lower order developmental functions are often apparent. Reflexive actions are often necessary and far more adaptive in the fast- paced dynamic nature of the play than higher cognitive thinking through all possible situations and possibilities that would only be completed long after the opportunity had been terminated. For example, sticking out a limb to block or intercept a ball (or hockey puck) is often needed in situations when the ath-lete has no time for higher developmental thought processes to consider all the possible alternatives before acting if the action is to be successful. Yet entirely reflexive behavior precludes any type of strategy or adherence to the rules of the sport, and it must be governed by some type of higher functions. This might be considered in the case of a goalkeeper in soccer or hockey who often needs to stop the ball or puck headed toward the goal with a reflexive reaction of an arm or leg that would be far too late if he or she were to think through the course of action. Goalkeepers in both sports need to control the commission of this type of reflexive behavior. They are clearly left more vulnerable to a goal by the opposition if the attacking offensive player feints or performs some type of action to intimate a shot toward the goal that would elicit such an action and leave them out of position or sufficiently off balance to protect the net for a sub-sequent maneuver or shot on the goal. This commission and control of lower developmental actions are often in concert with higher order ones, such as, in our case, the positioning of the goalkeeper in relation to the position of the ball or puck, in relation to the opposing and defending players. Again, in this case, the higher order and lower order functions are not entirely distinct as even strategic decisions can be accomplished without conscious thought, especially among experienced players.

In this framework, the most optimally adaptive performance in sports would appear to involve the commission of a mix of interrelated higher and lower devel-opmental functions. The task then for coaches, as it is for other educators, is to try to understand what the optimal mix of developmental levels of functioning might look like and how it can then be elicited. In addressing this challenge, coaches, probably somewhat unwittingly, highlight Vygotskian- type development in that they opt for more experienced players, for whom the control of behavior might be established, to serve as role models in practice drills with less experienced players, and for intensive practice that will, theoretically, enable the athlete to automatize


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certain action sequences in order to act reflexively in the most optimizing way within the context of their abilities and the team strategy.

Performance Regression in Sports

The notion that experience is so essential to the inhibition of reflexive behaviors led us (Burack et al., 2010) to ask the question that guided our initial paper: How does one of the most experienced and, arguably, the best soccer player in the world, Zenadine Zidane, commit such an apparently foolish act as head- butting an opposing player in the championship match of the World Cup, also the final match of his illustrious career. We suggested that this incident reflected a type of developmental regression articulated by Werner (1957), who argued that evi-dence of diminished development in relation to certain demands can reflect an essential regression of development. In this case, regression refers to the utiliza-tion of a developmentally lower action or strategy than those available in the individual’s arsenal for a specific task, but it does not imply a permanent low-ering of developmental level or ability. Undoubtedly, subsequent to the event, Zidane could and would articulate that the type of action he committed was inappropriate even if, as reported, it stemmed from insulting remarks made by the opposing player. Thus, that momentary violation of a rule, as essential and basic as it was, does not reflect a permanent, or even significant, diminishment of developmental understanding.

The notion of mistakes as momentary regressions is certainly not limited to incidents as flagrant or shocking as that committed by Zidane. Virtually any tele-cast of a sporting event, even at the highest level, will involve discussions of men-tal errors made by athletes that are distinct from physical mistakes and failures to complete acts that characterize sporting events. For example, basketball players rarely score on more than 50% of their shots, so a missed shot is not seen as a mental mistake, but attempting a very difficult or long shot in certain situations may reflect a mental error or violation of a team strategy or game plan in that strategically the shot should not have been taken in the first place. What are the reasons that moderately experienced children and elite athletes alike commit del-eterious actions when “they should know better” and have, in prior training or practice, even articulated that they should never do such a thing?

In the following sections, we address this question by highlighting notions of the complexity in delineating optimal levels of executive functions and the extent to which they are affected by context, including task demands, stress, and fatigue. Victory in sports is clearly not simply contingent on skill alone but on the abil-ity to utilize the skill in relation to the context in which it is needed at both the macro- level of a competition and the constantly changing micro- levels of specific situations within the competition. And effective skills and decision making at one level of competition do not guarantee the same at a higher level of competition in which decisions and actions are more complex and must be made more rapidly.


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EXECUTIVE FUNCTION AND CONTEXT

The notions that simple skill level or athletic prowess does not predict success in actual competition and that success at one level of competition does not predict success at a more competitive one challenge the commonly held assumption that the successful completion of a task of executive, or any other type of, function reflects the highest level of developmental attainment for that task or even area of ability. This apparently counterintuitive notion was highlighted experimen-tally by Diamond and Kirkham’s (2005) finding of diversity in the performance among adults on a bidimensional card sort task in which participants need to switch the guiding rule (i.e., shape or color) for sorting a small deck of cards after each block of a few trials. Although performance on this task, as measured by accuracy of response, is typically used to assess developmental changes in execu-tive function among 3- to 4- year- olds and to easily be passed at any age after that, Diamond and Kirkham found that when speed rather than accuracy was used as the dependent measure, even adults perseverated on the first rule (i.e., shape or color) that had been used to sort on a block of trials to a second rule in the subsequent block of trials. This decrement in performance among adults on a task thought to be mastered by 4- to 5- year- olds highlights that apparently opti-mal performance may not really be optimal, and that we need to recognize that our conceptualizations of level of performance are largely influenced and con-strained by our methodologies that cannot tap into the subtleties and nuances of real- life performance.

Diamond and Kirkham’s (2005) findings are consistent with two aspects of developmental methodology emphasized by Werner. First, developmental pro-gression can be viewed according to different timeframes, including ontogenesis, most commonly used in understanding development across the life span, and microgenesis, as in the case of changes in functioning for a specific event. Whereas ontogenic development reflects a broad swath of functioning in which develop-mental progression appears to progress in a consistent way, microgenic develop-ment is characterized by minute differences that reflect considerable fluctuations undetected in the broader framework. These different lenses through which to understand development are reflected in common conceptualizations of competi-tive athletes in which decision making is thought to improve considerably with experience from the athlete’s first competitions at any given level to a peak years later, but it is never immune to mistakes in the interim in specific moments, espe-cially in novel or more difficult situations with a challenging opponent. Second, developmental level of functioning, as reflected by performance, can vary consid-erably in relation to variations in the demands that are associated with the task. Thus, a player who can easily and consistently complete a task, such as passing the ball or puck to a teammate, in one situation may not be seen as effective on the task with better or faster competition, or any situation with more adversity or difficulty related to factors such as stress (Cerqueira, Almeida & Sousa, 2008; McEwen & Sapolsky, 1995) and fatigue (Chuah, Venkatraman, Dinges, & Chee, 2006).


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The Role of Stress in Undermining Executive Functions

Stress is typically thought of as a situation that causes a psychological and physi-cal response that occurs when facing changing or challenging situations and can be measured either by self- report or in terms of the level of hormones detectable in individuals, specifically cortisol (Morgan et al., 2006). Elevated levels of stress have been reported to reduce effectiveness across a wide range of cognitive abili-ties, including executive functions (McEwen & Sapolsky, 1995). The artificially induced stress in laboratory experiments demonstrates proof of principle, but it is unlikely to replicate the actual stress level experienced by athletes, which can be elicited by factors hard to replicate in an experimental setting, including situ-ation- specific reactions to ongoing events and scenarios in the game (Dunn & Nielsen, 1996) and more overarching psychological reactions to contextual fac-tors such as the magnitude of the event and the conceptualizations about one’s own likelihood of success.

The Role of Fatigue on Executive Functions

The deleterious effect of fatigue on executive function has been highlighted by evidence that sleep deprivation negatively affects performance on executive func-tion tasks such as go/ no- go inhibition (Chuah Venkatraman, Dinges, & Chee, 2006) and task switching (Plessow, Kiesel, Petzold, & Kirschbaum, 2011) among participants. However, the fatigue that is linked to a decline in executive function due to a lack of sleep is considerably different from the type of fatigue experienced when playing sports, which can be more accurately described as physical exertion or physical exhaustion.

One of the common truisms in competitive sports is that late in games or matches that are lengthy or extend into overtime sessions, the outcome is less likely decided by an outstanding maneuver by one of the athletes as they are likely too tired to attempt or complete a daring attack, but rather by a “mental mistake” due to fatigue. The notion is that decision making is impaired by mental fatigue arising from excess exertion for at least three related reasons. First, the athlete is unable to efficiently sort through all the possible responses in order to find the most adaptive. Second, the athlete is more likely to revert to lower development response as characterized by action rather than thought, and thereby act more impulsively. Third, the decisions regarding the course of action at any moment are more difficult because the athlete needs to consider his or her own reduced physi-cal capacity to complete a specific physical act, thereby both increasing the likeli-hood of committing an error or mistake and decreasing the likelihood of being able to recover and prevent the opposition from capitalizing on it. The athlete’s own awareness of the increased likelihood of committing potentially deleterious decisions and actions is often underscored by his or her adopting a more cau-tious approach to the competition. For example, tennis players under physiologi-cal stress have been found to maintain their stroke accuracy at the detriment of


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their stroke velocity (Hornery et al., 2007; Kovacs, 2006; Rogers, 1985) as well as a reduction in maximum running velocity (Ferrauti, Pluim, & Weber, 2001), also resulting in a reduction in stroke velocity and a potential shift from attempting to hit “winners” to a strategy in which players merely attempt to avoid errors.

Of course, the effects of fatigue cannot be entirely, or even largely, differentiated from those of stress: Longer lasting games or matches tend to also be associated with increased stress because they tend to be particularly competitive with close or tied scores. In addition, extended overtime sessions are only invoked for playoff games or matches in the quest for a championship. Thus, the athlete’s decisions and actions must be considered in relation to the physical and mental excessive fatigue due to extreme exertion and the stress associated with situation- specific stress of knowing that one mistake can lead to a loss within the context of game or match of immense psychological magnitude. In this scenario, the tendency to a more conservative approach in which the aim is to minimize the chances of error and loss seems warranted, even if its implementation is at the cost of also minimizing the opportunity to score a “winner,” or in Coach Wooden’s terms, “of being a winner.” These types of “calculations” reflect the ways that plans and rule- based decisions are continually modified in relation to intertwined internal and external contexts.

EXPERIENCE AS DEVELOPMENT

As individuals develop, emphasis is shifted from the simple attainment of a spe-cific function at a certain age to the ability to implement these functions across a variety of complex and stressful real- life scenarios (Carlson, 2003). Thus, devel-opment in this case is largely focused on the ontogenic development of exper-tise and high- level performance in relation to experience rather than on a simple passage of time (chronological age). This is evidenced by Vestberg, Gustafson, Maurex, Ingvar, and Petrovic’s (2012) finding that male and female soccer play-ers from Sweden’s highest division scored higher than players from lower divi-sion teams on the Design Fluency (DF), a standardized test from the D- KEFS test battery of executive functions (Homack, Lee, & Riccio, 2005), which is used to measure online multiprocessing such as creativity, response inhibition, and cog-nitive flexibility. Both groups scored higher than the norms of the general popula-tion. Furthermore, the scores on the DF test predicted the numbers of goals and assists the players had scored in two seasons. The latter finding was interpreted by Vestberg et al. as suggesting a causal role for executive functions for sport success, although the essential role of practice in success in sports and especially on the ability to make the correct decision in crucial decisions suggests that the extended practice of high- level soccer players might also provide experience that is relevant to the development of more efficient higher order executive functions.

In sports, practice is one of the main types of experiences that impacts on per-formance, with both skill- based repetitive drills (e.g., shooting practice in bas-ketball) as well as the simulation of game situations (e.g., controlled scrimmages)


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being important. Similarly, participation in past games played is also important, as is it provides experience in parallel activities in similar situations (Abernethy, Baker, & Côté, 2005). The essential role of experience as development was addressed by Berry, Abernethy, and Côté (2008), who examined the time spent in practice, competition, and play throughout the athlete’s life from childhood to adulthood among professional Australian football players, some of whom were rated as expert decision makers and others who were not. The expert and non-expert decision makers did not differ in number of structured athletic activities, but the former spent more hours in structured athletic activities, spent more time practicing their specific sport after 12 years of age, and had more experience in other sports. The experts also performed better than the nonexperts when asked to recall on a video the placement of Australian football players in a game situa-tion, as well as to accurately predict the next move.

Abernethy et al. (2005) also found that expertise in one sport aided rule- guided behavior in other sports in comparison to nonexperts as well as nonathletes, as experts in one sport were able to outperform nonexperts in their recall of defen-sive player positions for sports in which the experts did not participate. For exam-ple, expert basketball players were better able to remember where defenders were located in a brief clip of field hockey players compared to nonexpert field hockey players. Although inconsistent with traditional notions about sport- specific training, Abernethy et al. likened their findings to the transferability of skills as found within perceptual leaning and perceptual training (e.g., Goldstone, 1998; Smeeton, Ward, & Williams, 2004). Thus, these findings suggest the converse of Vesterberg et al.’s claim that executive functioning predicts athletic success, at least in soccer— rather, athletic expertise appears to influence cognitive strategies that generalize beyond the immediate sport of expertise and that can be seen as reflecting a higher order developmental level of understanding.

Participation in sports has also been linked to gains in executive functioning abilities in children (Chan, Wong, Liu, Yu & Yan, 2011; Enns & Richards, 1997; Kubesch et al., 2009; Pesce & Aduiffren, 2011). Mentally engaging practice ses-sions appear to have a greater effect on the development of cognitive ability com-pared to repetitive, mindless motor movements (Carey, Bhatt & Nagpal, 2005; Guadagnoli & Lee, 2004). Thus, a controlled scrimmage or drills in which players have to react to rapidly changing situations and that involve complex motor skills are far better than simple, repetitive actions such as running up and down the field with a soccer ball for developing executive functioning skills that can be gen-eralized to other settings. The benefit of simple exercise has also been shown to be beneficial for the development of executive function among 13- and 14- year- old students (Kubesch et al., 2009). However, these findings are not as robust as the influence of organized athletic activities, especially skill- based activities that require complex movements, on improvements in executive functioning among 13- to 16- year- olds (Budde, Voelcker- Rehage, Pietrabyk- Kendziorra, Ribeiro, & Tidow, 2008) and 16- to 24- year- olds (Pesce et al., 2009). Expertise, experience, and executive and other cognitive functions appear to transact in a facilitative way within the complex and dynamic environment of the sport activity. We suggest


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that this is the same developmental process that is involved in the development of executive functions in the school, with the classroom being the surrogate orga-nized skill- based activity.

SUMMARY

This apparently essential role of ongoing experience in the development of higher order enhanced executive- and cognitive- related functions that are at least sport specific and likely generalizable to other aspects of life was at the heart of the ques-tion that led us to our initial foray into this area of research: Why do even the most experienced athletes at the highest level of sports competition make bad decisions in crucial moments of a game? The answer to that question seems to be a simple one: It is the clichéd notion of the “human element.” As we learned from Heinz Werner (1957) and contrary to common conceptions of cognitive development, the attainment of an ability does not necessarily entail perfect, or optimal, perfor-mance on a given task. Individuals commit errors and display temporary regres-sions even in tasks of cognitive development on which they have demonstrated mastery. In dynamic, complex, stressful, and fatiguing environments that involve a myriad of continual split- second reactions to the decisions and actions of oppo-nents, even the most efficient executive functions can be taxed. Sports present a unique opportunity both to further study this situational regression of cognitive function and to address the more difficult question of how to improve the ability to optimize decision making and related executive functions in the real- world environments with day- to- day challenges faced by us all and particularly by chil-dren whose development is in most flux.

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Carlson, S. M. (2003). Executive function in context: Development, measurement, the-ory, and experience. Monographs of the Society for Research in Child Development, 68, 138– 151.

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5

Executive Functioning Helps Children Think About and Learn

About Others’ Mental States

J E A N N E T T E E . B E N S O N A N D M A R K A . S A B B A G H ■

The social world presents many challenges for young children. Perhaps the most prominent among these challenges is the interpersonal conflict that can arise from the fact that people are inherently subjective beings, with their own idio-syncratic beliefs, desires, and intentions. Our beliefs and desires about the best course of action in a given situation do not always dovetail well with the beliefs and desires of others. For example, two children might have different beliefs and desires about role assignments in a make- believe game of house, or they may both want to have the first try with a special toy. These discrepancies have the poten-tial to lead to conflict, unless the two are able to successfully negotiate a solution that is agreeable to both. The goal of our chapter is to describe two skills that we believe are required to broker successful negotiations of the interpersonal con-flicts that arise in these types of situations. The first is a “theory of mind”— the fundamental conceptual understanding that human behavior is connected with mental states (such as beliefs and desires) (Wellman, 1990). The second, in line with the theme of this volume, is executive functioning— the suite of skills that is recruited whenever one has to follow social rules or plan an advantageous future course of action (Carlson, Zelazo, & Faja, 2013). What we will discuss in par-ticular is that, though theory of mind and executive functioning are conceptually distinct, research suggests that executive functioning is necessary for children to make effective use of their underlying theory- of- mind knowledge. Moreover, in early childhood, executive functioning skills appear to play a role in the underly-ing development of a theory of mind.

In this chapter, we will briefly review research on theory- of- mind develop-ment and its importance in everyday interactions. We will then review literature

Executive Functioning and Others’ Mental States 55

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showing that executive functioning skills are necessary for (1) effectively using a theory of mind in everyday situations and (2) developing underlying theory- of- mind concepts over time from experience. Finally, we will discuss how under-standing the relationship between executive functioning and theory of mind can help psychologists to promote positive social conflict negotiations in educational and caregiving settings.

THEORY- OF- MIND DEVELOPMENT

“Theory of mind” refers to our everyday understanding that humans have subjec-tive mental states (e.g., beliefs, desires, intentions, and emotions), and that these idiosyncratic mental states both influence and are influenced by human behavior (Wellman, 1990). For instance, imagine you see your friend happily eating a snack that you yourself detest, like raw broccoli (Repacholi & Gopnik, 1997). You can make sense of your friend’s behavior by using your theory of mind, and recogniz-ing that he likely has more positive feelings toward broccoli than you— that is, his actions are being driven by his own subjective preferences and desires.

Major transitions in these types of theory- of- mind understandings take place over the preschool period. Although some have argued that even infants show evi-dence of theory- of- mind knowledge in certain circumstances (see e.g., Sabbagh, Benson, & Kuhlmeier, 2013, for a recent review), there is little question that in most cases, young children have limited ability to reason explicitly about the men-tal states of others. This is particularly true when it comes to reasoning about others’ knowledge and beliefs. Research shows that 3- year- old children tend to assume that their own knowledge and beliefs are unanimously shared by others. Between the ages of 3 and 5 years, this “egocentrism” is gradually overturned as children develop the explicit understanding that others’ mental states are based upon personal experiences and, because of this, can vary from person to person (Perner, 1991).

The most commonly used measure of theory- of- mind understanding in preschool- aged children is the false- belief task, which comes in two general varieties: “location change” and “unexpected contents” (see Wellman, Cross, & Watson, 2001, for a summary). In “location change” false- belief tasks, a short scenario is enacted for children using dolls and toy props. Children are intro-duced to a character (e.g., “Sally”) who hides an object in one location and leaves the scene. In her absence, the object is moved to an alternative location. Sally is then shown to return, and children are asked where she will search for her object. Correctly answering this question requires the explicit understanding that Sally has an outdated, “false” belief about her object’s whereabouts. That is, even though children seeing this scenario are privy to the object’s new hiding spot, they have to think explicitly about what Sally knows in order to make a good guess about where she will look. Of course, because Sally did not see her object get moved, she should therefore search for the object in its original, now- empty location (e.g., Wimmer & Perner, 1983).


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In “unexpected contents” false- belief tasks (e.g., Gopnik & Astington, 1988), children are shown a box whose typical contents can be readily identified from the box’s exterior appearance (e.g., a “Smarties” box). Children are then asked what they think is inside the box. Once the children respond with their guess (which is typically in line with the box’s appearance), the box is opened briefly to reveal something unexpected (e.g., pencils). Children are then introduced to a puppet who is explicitly stated to have never seen inside the box, and asked what the puppet will think the box contains (i.e., Smarties or pencils). Here again, chil-dren must recognize that although they saw inside the box and therefore know that it contains something unexpected, the puppet does not have that knowledge and thus should expect that the Smarties box contains its usual contents (i.e., Smarties).

Hundreds of published studies have used false- belief tasks like these in research settings to measure the development of preschoolers’ explicit reasoning about false beliefs (see Wellman et al., 2001, for a meta- analysis). The results have been remarkably consistent across these studies— children 3 years of age and younger typically fail these tasks by answering that the story characters have the same knowledge and beliefs that they themselves have. Thus, in location- change tasks, they predict that the story character will look for her object in its current (rather than original) location, even though she didn’t see it get moved there. In the unex-pected contents task, they predict that the puppet will think the box contains the unexpected, rather than expected contents (i.e., pencils, rather than Smarties), even though he never saw inside the box. However, a drastic shift in false- belief task performance takes place over the preschool years; by the age of 4, children begin to show improvement on these tasks, and by the age of 5, typically devel-oping children correctly answer false- belief test questions with apparent ease. More recent research has shown that this developmental progression is similar in cultures from all over the world (see Callaghan et al., 2005; Oh & Lewis, 2008; Sabbagh, xu, Carlson, Moses, & Lee, 2006).

A large body of research on theory- of- mind transitions in preschool shows that children’s performance on these standard false- belief tasks is related to a number of natural behaviors and abilities that we would expect to require the understanding that beliefs can be false. These include telling a lie to conceal a transgression (Talwar & Lee, 2008), developing peer relationships (Astington & Jenkins, 1995), playing games like hide- and- seek (Peskin & Ardino, 2003), keep-ing secrets (Peskin & Ardino, 2003), and more (see Hughes & Leekam, 2004, for a review). To illustrate, consider a recent study by Killen and colleagues (Killen, Mulvey, Richardson, Jampol, & Woodward, 2011) on the connection between theory of mind and children’s judgments about moral culpability. These research-ers presented children with stories in which someone either intentionally or unintentionally causes something bad to happen. Of particular interest were the “unintentional” stories; in one, for instance, a child is asked to clean up the desks in a classroom and accidently throws away a friend’s treat that was hidden in a paper bag. From the adult perspective, the person who lost his or her treat might be disappointed, but not necessarily act to punish the friend because it is easy to


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recognize the negative action was unintended— the friend had neither the desire to throw away the treat nor the belief that his actions would cause an unwanted outcome. The study’s results showed that this kind of advanced moral reasoning about others’ intentions, particularly in the face of negative outcomes like the one described earlier, rests on having a theory of mind (see also Crick & Dodge, 1994). Moreover, children’s abilities to put theory of mind to use for moral reasoning becomes increasingly sophisticated over childhood (see, e.g., Killen et al., 2011; Zelazo, Helwig, & Lau, 1996), thereby highlighting the role of continued theory- of- mind developments in everyday social reasoning.

Given its importance, many researchers have aimed to better understand how theory- of- mind reasoning relates to other more general aspects of children’s cog-nitive functioning (Moses & Sabbagh, 2007). Of particular interest has been the role that preschool- aged children’s executive functioning skills play in the devel-opment and use of theory- of- mind knowledge. In the following section we out-line research on the relation between these two constructs.

THE RELATION BETWEEN THEORY- OF-MIND UNDERSTANDING AND EXECUTIVE FUNCTIONING SKILLS

A significant body of research has examined the relation between theory- of- mind development and children’s response conflict executive functioning skills (RC- EF) (see Benson & Sabbagh, 2009, for a recent review). RC- EF skills enable children to overcome the urge to respond in a particular (usually habitual or prepotent) way when doing so would not be in line with achieving particular goals (Zelazo, Carlson, & Kesek, 2009). Children’s RC- EF abilities are undoubtedly important for a range of daily activities. For instance, a child may have the urge to immedi-ately start in on something that she finds very enjoyable, but must inhibit or sup-press this urge in favor of achieving goals like homework completion.

One task used by researchers to measure RC- EF skills in the lab is the “grass- snow” task (Carlson & Moses, 2001). In this task, a board with one white corner and one green corner is placed in front of the children. They are first tested on their knowledge that grass is green and snow is white. Then, the experimenter invites children to play a “silly game” in which they must counterintuitively point to the white corner when the experimenter says “grass,” and to the green corner when the experimenter says “snow.” This job is difficult for children because they have to (1) keep in mind the rules of the silly game, and (2) inhibit the urge to point to the more typical corresponding color in order to respond correctly.

In the following paragraphs, we will review research showing two reasons why the executive functioning abilities measured by tasks like the grass- snow task are necessary for theory- of- mind reasoning. First, they appear to help children express what they know about others’ minds. Second, research suggests that exec-utive functioning skills also play a role in the acquisition of theory- of- mind con-cepts over the course of development.


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Executive Functioning Helps Children Express Their Theory of Mind

The connection between executive functioning and theory of mind was first identified by researchers who noted that tasks designed to measure theory- of- mind understanding seem to also require some executive functioning skills (e.g., Chandler, Fritz, & Hala, 1989; Frye, Zelazo, & Palfai, 1995; Russell, 1996). Recall that in the false- belief task, children are shown a scenario in which a story char-acter believes something that is different from what children themselves know to be true. Regardless of children’s underlying theory- of- mind knowledge, executive functioning skills are required for children to simply state that someone will look for something where it is not. After all, in most cases people look for things in locations where they truly are, and it is thus unusual (i.e., not habitual) to have to consider that the story character will look for something somewhere else, espe-cially a place where we know the object is not located (Carlson, Moses, & Hix, 1998; Couillard & Woodward, 1999).

Evidence that executive functioning skills are required for children to demon-strate underlying knowledge on false- belief tasks comes from research showing that when false- belief tasks are modified to diminish surface executive function-ing challenges like the ones described earlier, children’s performance improves. For instance, in the “location change” task, children’s performance improves if the salience of the contrasting real- world object location is reduced (e.g., if the object— for instance, chocolate— is eaten and thus no longer exists, rather than simply moved, see e.g., Wellman et al., 2001, for a review). Similarly, increasing the executive demands of false- belief tasks by, for instance, increasing the number of possible locations where an object could be located causes a decrease in chil-dren’s performance (Friedman & Leslie, 2004).

The connection between demonstrating a good theory of mind and executive functioning skills extends through childhood and into adulthood. For instance, neuroimaging research shows that when adults are given false- belief tasks similar to those described earlier, regions associated with both mental- state reasoning and executive functoning processes are activated (Saxe, Schulz, & Jiang, 2006). Furthermore, Ian Apperly and colleagues have shown that adults with focal brain legions that affect executive functioning abilities show difficulties on false- belief tasks similar to the difficulties shown by 3- year- old children (Samson, Apperly, Kathirgamanathan, & Humphreys, 2005). However, when those same patients are given theory- of- mind tasks designed to minimize executive functioning demands, they show strong performance.

When taken together, these findings make it clear that— regardless of children’s underlying theory- of- mind knowledge— executive functioning skills are required to engage effectively in theory- of- mind reasoning when presented with standard false- belief task scenarios. This conclusion has important real- world implications. Most notably, even if children understand that others’ mental states can theoreti-cally differ from their own, they may often fail to make use of that knowledge if their executive functioning skills are not sufficiently developed. Many real- world


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situations that require false- belief reasoning also likely require executive func-tioning skills to, for instance, overcome the tendency to focus on one’s own mental perspective. Thus, executive functioning skills help children to effectively make use of underlying mental state knowledge in their day- to- day lives.

Executive Functioning Helps Children Acquire Theory- of- Mind Concepts

To this point, we have established that executive functioning is necessary for chil-dren to express the theory- of- mind understandings that they have acquired. We have also provided evidence that theory- of- mind reasoning— even in adults— is a process that recruits executive functioning skills, at least in some cases. Yet, from a developmental perspective, there is also reason to think that executive function-ing skills may be necessary for children to acquire the abstract conceptual under-standings that underlie a theory of mind. Our first hint that the relation between executive functioning and theory- of- mind performance runs beyond the superfi-cial task demands inherent to theory- of- mind tasks comes from research involv-ing tasks designed to minimize these surface demands. One such task involves showing children a character who acts in accordance with an outdated belief by searching for an object in an empty location. Children are then asked to explain the character’s actions. The job of explaining protagonists’ actions arguably does not pose the same executive functioning demands as those that are posed in the standard prediction task. Namely, children do not have to suppress the urge to point to an object’s true whereabouts; after they have seen a character search inac-curately and have had time to reflect on these actions, they are simply required to provide possible reasons for the observed behavior. Yet children with better executive functioning skills outperform children with poorer executive function-ing on these explanation tasks (Perner, Lang, & Kloo, 2002).

A second hint that executive functioning skills are important for reasons beyond negotiating superficial task demands comes from cross- cultural research. Sabbagh et al. (2006) hypothesized that for several reasons, Chinese preschoolers might outperform their North American counterparts on tasks measuring execu-tive functioning skills. For instance, ethnographic research shows that behaviors typically thought of as requiring high levels of executive functioning (i.e., sitting still, acting reflectively rather than impulsively) are highly valued by Chinese par-ents and teachers (see Chen et al., 1998; Tubin, Wu, & Davidson, 1989). Thus, there may be socialization processes that specifically scaffold and foster the development of executive functioning skills early in development (Chen et al., 1998). Also, children (and adults) from mainland China are less likely than North American counterparts to carry some of the genetic risk factors (i.e., long alleles of the DRD4 gene) that have been associated with poor executive functioning skills (e.g., Chang, Kidd, Livak, Pakstis, & Kidd, 1996). The question was this: If execu-tive functioning development is indeed found to be more rapid among Chinese children as compared to US children, might children from the two groups also


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perform differently on theory- of- mind tasks? The study’s results were striking in confirming that Chinese preschoolers did indeed outperform North American preschoolers on executive functioning tasks— overall, the Chinese 3.5- year- olds were performing at the same level as North American 4- year- olds. Yet there was no difference whatsoever in the two groups’ theory- of- mind performance. Given that the North American 4- year- olds were doing well on the theory- of- mind tasks, we have evidence that their executive functioning skills were sufficiently developed to overcome the executive performance demands of those tasks (such as those reviewed earlier). That same level of executive functioning had been reached by the 3.5- year- old Chinese children, yet they did not demonstrate the same good performance on false- belief tasks. In other words, the Chinese children had executive functioning skills beyond what we might deem to be the “thresh-old” level necessary for them to reveal their underlying false- belief knowledge, yet these skills did not translate to success on false- belief tasks. Similar findings have now been shown in a number of Asian populations that seem to have more rapid development of executive functioning skills relative to North American children (Oh & Lewis, 2008; Tardif, Wellman, & Cheung, 2004). The general consensus from this work is that even if a certain level of executive functioning skills is nec-essary to overcome challenges associated with theory- of- mind reasoning, rapid executive functioning development does not alone lead to rapid theory- of- mind development.

Importantly, even in populations that show a more advanced timetable of exec-utive functioning development, there are still noteworthy connections between executive functioning skills and performance on theory- of- mind tasks. For instance, in the study by Sabbagh and colleagues, when examining the sample of Chinese children independently, the Chinese preschoolers who were better at executive functioning relative to other children in their own cultural group showed better performance on theory- of- mind tasks compared to the Chinese preschool-ers who did comparatively worse on executive functioning tasks (Sabbagh et al., 2006). Recall that these children as a group had already surpassed the level of executive functioning skills that appear to be necessary to demonstrate underly-ing false- belief knowledge on standard false- belief tasks, yet a relation between executive functioning and false- belief task performance persisted. These findings provide evidence that there is more to the relation between executive functioning and theory of mind than a simple “expression” account would suggest.

Finally, longitudinal research has demonstrated that early RC- EF skills predict later theory- of- mind knowledge, whereas early theory- of- mind knowledge does not similarly predict later RC- EF skills. Studies have found this to be true in typi-cal populations across time periods of 12 to 15 months (e.g., Carlson, Mandell, & Williams, 2004; Hughes, 1998). Moreover, the same pattern of findings has been demonstrated in low- income (Hughes & Ensor, 2005) and autistic populations (e.g., Pellicano, 2007). If RC- EF and theory of mind were related solely because a certain level of RC- EF skills is necessary for children to reveal their underlying false- belief understanding, then we would not expect to find this kind of uni-directional, predictive relationship over time between the two constructs. Thus,


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these findings provide further evidence that the relation between RC- EF skills and theory- of- mind understanding is more complicated than what is stipulated by simple “expression” accounts.

As we have alluded to previously, one intriguing hypothesis to account for these findings is that, in addition to being related to theory- of- mind performance, executive functioning skills are important for children’s acquisition of underly-ing theory- of- mind concepts. An association between executive functioning and conceptual development has been proposed for scholastic domains, such as math-ematics and reading (e.g., Blair & Razza, 2007; Bull & Scerif, 2001; Espy et al., 2004), and we hypothesize that the same could be true for theory- of- mind devel-opment. Research shows that executive functioning skills play a critical role in a range of processes that are likely important for children to learn from relevant experience, including being able to (1) correctly identify and attend to relevant variables (Diamond, Barnett, Thomas, & Munro, 2007; Garon, Bryson, & Smith, 2008), (2) notice when previously established expectations do not align with sub-sequent outcomes (Zelazo, Carlson, & Kesek, 2008), and (3) flexibly integrate new experiences with existing knowledge to promote adaptive development (Benson, Sabbagh, Carlson, & Zelazo, 2013).

When applied to the context of social development, this hypothesized role for executive functioning skills hinges upon the notion that theory- of- mind devel-opment involves learning from relevant experiences. In fact, some of the most well- replicated findings in the literature are that preschoolers’ theory- of- mind development is affected by experience. For instance, parents’ mental state talk (i.e., the relative frequency with which parents use verbs like “think” and “know”) is positively associated with preschoolers’ theory of mind performance (Ruffman, Slade, & Crowe, 2002), as is the presence of older siblings in the family (Perner, Ruffman, & Leekam, 1994; Ruffman, Perner, Naito, Parkin, & Clements, 1998). With all of these findings in mind, we (among others) hypothesized that executive functioning skills help children to capitalize on relevant experiences in the service of developing an understanding of other minds.

Gaining evidence in direct support of this general theory has proven to be chal-lenging. However, a recent study in our lab begins to provide such evidence. In this study, a group of 3.5- year- old children who initially showed poor performance on theory- of- mind tests but varied in their executive functioning skills were then given an intensive training regimen designed to improve their theory- of- mind skills (Benson et al., 2013). If it is true that executive functioning skills are impor-tant for learning from experience, then we would expect to see a positive cor-relation between these children’s executive functioning skills and the extent that they benefitted from the theory- of- mind training. In line with these expectations, children’s executive functioning was a strong, consistent predictor of theory- of- mind improvement across the testing period. These relations remained significant after controlling for a number of relevant variables, including age, initial theory- of- mind knowledge, language skills, and executive functioning improvement over the course of the study. Beyond showing greater theory- of- mind improve-ments overall, children with higher levels of executive functioning demonstrated


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theory- of- mind improvements more quickly, and they provided explanations for their answers that reflected more success in using the training feedback to advance their subsequent performance. These findings lend support to the theory that executive functioning skills help children capitalize on relevant experiences in the service of developing theory- of- mind concepts.

Summary

There is now evidence that executive functioning skills play two important roles in theory- of- mind reasoning. First, they enable both children and adults to express their underlying theory- of- mind knowledge by helping them to get around the executive challenges inherent to circumstances that require theory- of- mind rea-soning. Second, executive functioning skills are important for the acquisition and development of underlying theory- of- mind concepts in early childhood.

PROMOTING SUCCESSFUL SOCIAL INTERACTIONS

Adults responsible for groups of young children in caregiving or educational set-tings are familiar with the scenario in which they must intervene to deal with the aftermath of an interpersonal conflict. Frequently, the short- term goal of the intervention is to promote what we now might call theory- of- mind- type infer-ences among the parties (e.g., “How did that make him feel?”; “Do you think he meant to hurt you or was it an accident?”; “Did he think it was his turn to play?”), with the long- term goal of having children adopt these theory- of- mind strategies themselves. Given that using theory of mind requires children to marshal their executive functioning skills, those who work with children might want to keep reasonable expectations about when children are best able to use their theory of mind, and when doing so might be particularly challenging. Here, we will briefly describe both developmental and situational factors that can limit preschoolers’ abilities to marshal the executive functioning skills necessary for both expressing their knowledge about others’ mental states and acquiring new information about other minds.

Typical and Atypical Developmental Influences on Executive Functioning Skills

Perhaps the most obvious reason why children fail to recruit the executive func-tioning skills necessary for effective theory- of- mind reasoning is developmental immaturity. In addition to being an important time for theory- of- mind develop-ment, the preschool years see substantial growth in children’s abilities to deploy executive functioning skills in laboratory tasks (see Carlson, 2005). These devel-opmental gains are likely attributable to both endogenous maturational changes


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that happen within the frontal lobes (Bunge & Zelazo, 2006) and experiential fac-tors that encourage the practice and use of executive functions (Diamond & Lee, 2011). It is important to remember, then, that when younger children find them-selves in situations in which they need to make good judgments about others’ mental states, they may not yet be able to reliably marshal the executive function-ing skills that are necessary to make those judgments to the best of their concep-tual abilities. That is, young preschoolers might know a little bit more than they can show. Also, younger preschoolers may not be as able to marshal the executive functioning that is necessary to learn from new experiences, which itself is impor-tant for making better decisions in the future. For each of these reasons, it may help those who work with younger children to keep in mind that using theory- of- mind knowledge to negotiate interpersonal conflict might be especially challeng-ing for a 3- year- old, but more manageable for a 4- or 5- year- old.

It is also noteworthy that executive functioning skills appear to be influenced by relevant experience and practice (e.g., Diamond & Lee, 2011; Diamond et al., 2007). In one study, a group of children were enrolled in the “Tools of the Mind” preschool program, which is specifically designed to give extensive, supported practice with tasks and situations that tax executive functioning skills (Diamond et al., 2007). These children showed marked improvements in their executive functioning skills, over and above a control group of children enrolled in a pro-gram designed to improve language abilities over the same time period. Thus, executive functioning skills do appear to be affected by experience. With this in mind, psychologists whose goal is to promote social- cognitive developments in young children may wish to implement strategies to encourage and support the development of executive functioning skills to this end. As children’s executive functioning skills improve, they will be better able to make use of interventions designed to more directly address social- cognitive concepts and issues.

Although many children show substantial growth in executive functioning over the preschool years, others have more protracted timetables. This is espe-cially true of children with attention- deficit/ hyperactivity disorder (ADHD)— a developmental disorder characterized by executive functioning deficits, including inattention and/ or hyperactivity- impulsivity (American Psychiatric Association, 2013). Although ADHD is associated primarily with executive functioning def-icits, the research reviewed earlier highlights that children with this condition may also be prone to experiencing difficulties in social realms. Specifically, these children may not have the tools necessary to develop underlying theory- of- mind concepts at the same rate as their peers, and they may find it especially difficult to make use of the mental state knowledge that they do have in real- world settings. We would expect this to be especially true in situations where children are chal-lenged to successfully negotiate interpersonal conflicts that arise when two people want or believe different things.

In line with this prediction, a growing body of work suggests that ADHD pop-ulations exhibit deficits in social realms. Studies show deficits in affect percep-tion (e.g., Sinzig, Morsch, & Lehmkuhl, 2008), social functioning (see Nijmeijer et al., 2008, for review), and empathy (e.g., Braaten & Rosen, 2000). Furthermore,


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Builtelaar and colleagues (Buitelaar, Van der Wees, Swaab- Barneveld, & Van der Gaag, 1999) found that a sample of ADHD- diagnosed children had significantly lower second- order false- belief task scores in comparison to controls. Work by Hughes, Dunn, and White (1998) showed that children rated as “hard to manage” by their parents (likely due to impulsive behavior) showed poor performance on false- belief tasks (but see Perner, Kain, & Barchfeld, 2002, for nonsignificant find-ings). Finally, Fahie and Symons (2003) reported that individual differences in an ADHD sample’s performance on measures of attention, memory, and impulsivity significantly predicted performance on theory- of- mind tasks, over and above the effects of age, language, and socioeconomic status. Although the ADHD sample’s performance in this study was not compared to a control group, the children in their study were estimated to be approximately 2 years delayed in their false- belief task performance relative to typically developing children.

Taken together, it is clear that both the development and effective use of theory- of- mind knowledge is highly dependent upon the maturation of executive func-tioning skills, both in typical and atypical populations.

Situational Influences on Executive Functioning Skills

Beyond developmental considerations, there are several situational factors that can impede children’s ability to recruit the executive functioning skills neces-sary in settings that require theory- of- mind reasoning. For instance, being hun-gry (Gailliot, 2008) or physically fatigued because of lack of sleep (Nilsson, et al., 2005) is likely to render theory- of- mind reasoning difficult for children. Most parents have a sense that children are more prone to interpersonal conflicts when they are hungry and physically tired, and this association between theory- of- mind and executive functioning may help us to understand why. In the current context, the association between executive functioning and theory of mind might also have implications for how these situations are handled. Specifically, instruct-ing a tired or hungry child about the importance of considering other people’s feelings may be an ineffectively timed strategy, both with respect to improving the immediate situation and promoting long- term development.

Along with being physically tired or hungry, being “cognitively tired” can also affect children’s executive functioning skills. In one study, Lillard and Peterson (2011) looked at children’s executive functioning skills after they watched two dif-ferent kinds of TV shows— “SpongeBob Squarepants,” a fast- paced high- energy show with a lot of fantastical elements, and “Caillou,” a slower- paced drama about the real- life challenges of a small boy. Results showed that the preschoolers who watched “SpongeBob” performed much worse at the inhibitory control tasks than did the group who watched “Caillou.” In fact, the Caillou group performed no dif-ferently than a group who watched no TV at all, thereby showing that it was the fast- paced fantasy show (and not just watching TV) that affected performance. These researchers suggest that children were cognitively exhausted after watch-ing “SpongeBob,” which made it difficult for them to marshal the effort required


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to perform well on the executive functioning tasks. Although the authors did not measure theory- of- mind performance in their study, an easy extrapolation is that if children are cognitively tired, they may have difficulty expending the effort required to make good judgments about others’ mental states. It is beyond the scope of our chapter to enumerate the kinds of things that can deplete chil-dren’s abilities to show effortful executive control. It seems reasonable to suggest, however, that children who find school especially challenging may have increased susceptibility to cognitive fatigue. Likewise, even very able children might be sus-ceptible to cognitive fatigue after a difficult day of learning. This cognitive fatigue may contribute to further difficulties marshaling the executive functioning nec-essary to use theory- of- mind skills in the service of negotiating interpersonal conflict.

A final situational factor that can influence executive functioning skills is nega-tive emotional arousal— an emotional state that may prevail following a difficult interpersonal interaction (see e.g., Padmala, Bauer, & Pessoa, 2011). Perhaps a good strategy, then, would be to avoid staging theory- of- mind interventions in the immediate aftermath of an interpersonal conflict and instead discuss making good judgments about others’ mental states when children have cooled down and are ready to make use of that information.

CONCLUSION

A cornerstone of everyday social interactions is having a theory of mind. In this chapter we reviewed evidence showing that executive functioning skills are neces-sary to support children’s (and adults’) use of a theory of mind, and for children, executive functioning skills support the development of important theory- of- mind concepts. We noted that caregivers and educators might be tempted to ask children to engage their theory- of- mind skills in situations in which executive functioning skills are most taxed, such as in times of high emotional arousal, or when children are cognitively or physically tired. We hope that by pointing out that theory of mind relies on these executive functioning skills, parents and educators might be better able to develop strategies for supporting theory- of- mind use in both formal and informal educational settings. We also hope that this research highlights the potential value of interventions designed to promote executive functioning skills for the ultimate purpose of advancing social knowl-edge and functioning.

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6

Parenting and Young Children’s Executive Function Development

A N N I E B E R N I E R , D I A N E S T- L A U R E N T,

C É L I A M A T T E - G A G N É , T R I S T A N M I L O T,

S T U A R T I . H A M M O N D , A N D J E R E M Y I . M . C A R P E N D A L E ■

When children first come to school, they are expected to sit still and to direct their attention to tasks, some interesting, some less so. They also need to listen to, and interact with, their teachers and peers, and follow rules that differ from those at home. To thrive in such an environment, children need to harness their attention, suppress impulses, and control their actions. The skills that are crucial to do so are often referred to as executive functioning (EF), which consists of a set of higher order cognitive abilities, such as impulse control, set shifting, plan-ning, and working memory, that are central to children’s capacity to monitor their emotions, thoughts, and behavior deliberately (Zelazo, Carlson, & Kesek, 2008).

These skills are foundational for children’s successful functioning in classroom and school contexts, in that they underlie many aspects of cognition and behav-ior that are deemed crucial for academic success (Blair, 2002). As summarized by Bierman, Nix, Greenberg, Blair, and Domitrovich (2008), EF supports the actions of planning (generating and following mental guides, sequencing actions, maintaining a behavioral set), problem solving (generating, evaluating, flexibly altering strategies for goal attainment), and intentional learning (sustained atten-tion, resistance to interference and distraction). Thus, EF can support acquisi-tion of knowledge because it is central to the organization of information and to the problem- solving processes that subsume active learning. In addition, the self- regulatory aspects of EF support the behavioral skills necessary for learn-ing, such as following classroom rules, sitting still, or resisting distraction (Blair, 2002). Finally, when facing social situations with peers or teachers, children must attend selectively to relevant aspects of the encounter; inhibit automatic, prepotent responses; choose and implement a socially appropriate response; and monitor the outcome. These skills are at the core of executive competence, which

Parenting and Young Children’s Executive Function Development 71

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should thus play a primary role in the relationships that children form with their teachers and classmates.

Hence, EF is influential not only in acquisition of knowledge, but it also con-tributes to establishing a behavioral predisposition for learning and for interact-ing appropriately with peers and adults. It enables children to approach learning tasks effectively and enhances their capacity to coordinate social behavior in a way that sustains collaborative and mutually rewarding relationships with teach-ers and peers alike (Bierman et al., 2008; Diamond, 2002). Overall, there can be no doubt that EF is a critical factor for young children’s functioning in school. Teachers would surely agree, however, that children vary widely in their execu-tive capacities. It is part of this variation that this chapter will attempt to explain. Specifically, we focus on parent– child relationships as one possible candidate to explain individual differences in child EF.

SOCIAL INFLUENCES ON CHILD EXECUTIVE FUNCTIONING: A THEORETICAL ACCOUNT

One reason to believe that parent– child relationships that take place during the first years of life could impact the development of young children’s EF refers to brain development. Indeed, it is increasingly believed that early environmental experiences have a direct impact on brain development (De Bellis, 2001; Nelson, 2000). General support for the role of social experience on brain development comes from studies of grossly inadequate environments, characterized by neglect or abuse, which show that such unfavorable experiences are related to problem-atic brain development (Chugani et al., 2001; Curtis & Cicchetti, 2007; Marshall & Fox, 2004).

Conversely, it is proposed that favorable environmental experiences, especially those embedded within early caregiving relationships, may have a positive impact on brain development (Nelson & Bloom, 1997; Schore, 1996). A putative impact of parent– child relations on child EF is especially likely given the close connec-tions between EF and the brain’s frontal lobes. Indeed, EF is inextricably linked to the prefrontal cortex, which shows protracted postnatal development (e.g., Giedd et al., 1999; Sowell, Trauner, Gamst, & Jernigan, 2002). Many have argued that this largely postnatal development leaves a substantial window of opportunity for environmental input to impact the development of frontal brain systems (Kolb, Forgie, Gibb, Gorny, & Rowntree, 1998) and related executive functions (Noble, Norman, & Farah, 2005). According to Glaser (2000), the orderly development of the frontal lobes is dependent on “appropriate input and sensitive interaction with the primary caregivers” (p. 101).

Hence, their potential impact on the frontal cortical structures that underlie EF is one way through which parent– child relations could contribute to the devel-opment of EF among young children. Most likely, any change in brain structure would be accounted for by the exact kinds of experience through which children learn, that is, how exactly adults help them master new skills. One way in which


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the role of adult– child interaction in the development of EF has been discussed is with the notion of sociogenesis, or the development of executive skills through interactions between parent and child (e.g., Fernyhough, 2010; Luria, 1961; Vygotsky, 1934). According to Vygotksy (1934, 1978, 1981), higher mental func-tions first appear on the interpersonal plane through other- regulated activities, before gradually being transferred to the intrapersonal plane. It is in the context of social interactions with more competent individuals that the young child gradu-ally internalizes the regulatory function of these social exchanges and develops the ability to exercise deliberate and autonomous control over his or her behavior and cognitive activity.

This occurs in part through the process of scaffolding, an idea that is likely familiar to educators. Scaffolding is a process in which an educator, tutor, or par-ent breaks problems into manageable parts and modulates support in accordance with the child’s level of competencies demonstrated during the accomplishment of the task (Wood, Bruner, & Ross, 1976). During joint problem solving, the adult structures the activity so as to encourage the child’s active and autonomous involvement in the task, gradually transferring the responsibility of regulating the activity over to the child. Scaffolding includes maintaining the learner’s atten-tion, managing his or her frustration, and organizing the problem space in such a way that children’s existing skills can be applied to the problem. In other words, the scaffolding process between tutor and learner runs parallel to how EF pro-cesses structure lower cognitive functions (Bibok, Carpendale, & Müller, 2009). Sociogenesis is paradoxically both the oldest way of understanding the relation of parent– child interaction and EF, dating back to the origins of the concept of EF in the work of Vygotsky and Luria in the early part of the 20th century, and a way of conceptualizing EF that is only recently becoming the subject of researchers’ (renewed) attention (e.g., Sokol, Müller, Carpendale, Young, & Iarocci, 2010).

The relational context in which scaffolding takes place is likely to be critical as well. It is proposed that the quality of the affective bond characterizing harmoni-ous parent– child relationships provides a safe and orderly relational context in which children can gradually learn to master new skills (Kochanska & Aksan, 1995; Lewis & Carpendale, 2009; Moss, 1992), for instance through harmoni-ous joint play or problem- solving activities that facilitate children’s practice and integration of executive skills (Landry & Smith, 2010; Moss, Parent, Gosselin, & Dumont, 1993; Perez & Gauvain, 2010). Calkins (2004) further underscores that a central feature of parent– child attachment processes is that they are often acti-vated in emotionally evocative contexts. Indeed, attachment relationships are especially salient when children are negotiating emotionally or cognitively chal-lenging situations, such as coping with frustration toward a difficult task, delaying gratification based on trust in the parent who explains that the desired activity will be performed soon, and so on. Attachment research indicates that within secure attachment relationships, the strategies taught by the parent are more often appropriate (e.g., distract oneself with a pleasant activity while waiting, step back and consider other options to solve the puzzle, etc.), and interactions take place in a more harmonious emotional climate (De Wolff & Van IJzendoorn, 1997).


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This makes successful reduction of child negative emotional arousal more likely. Through repeated experiences of successful regulation in such emotionally tax-ing situations, children who are securely attached to their parents are thought to master the skills acquired and to gradually integrate them in their own repertoire of independent self- regulation skills (Calkins, 2004). Hence, a key idea is that regulatory strategies are first practiced in the context of parent– child relation-ships (Sroufe, 1996), and that the strategies learned are then generalized and used outside of these dyadic relationships, such as during tasks requiring independent self- regulation (Calkins, 2004; Cole et al., 2004; Moss, 1992), a defining feature of EF tasks.

Although a great deal of research on EF has focused on the maturation of the prefrontal cortex as the primary driver of growth of EF (e.g., Diamond, 2002; Friedman et al., 2008), the experience- dependent development of the brains of young children admits an important role for the sociogenesis of EF. Overall then, there are several reasons to believe that parent– child relationships may affect young children’s executive capacities. We now turn to empirical research that has examined these putative links, indirectly and directly.

PARENT– CHILD RELATIONSHIPS AND CHILD EXECUTIVE FUNCTIONING: PRIOR RESEARCH

Indirect support for a presumed role of parent– child relationships in child EF development stems from studies that have found parenting to relate to constructs bearing similarities to some components of EF, labeled for instance as metacogni-tion (Moss et al., 1993; Moss, St- Laurent, & Parent, 1999), self- regulation (e.g., Jennings et al., 2008; Piotrowski, Lapierre, & Linebarger, 2013), planning, atten-tion and memory (Gauvain, 2001; NICHD ECCRN, 2005; St- Laurent & Moss, 2002), executive attention (Mezzacappa, Buckner, & Earls, 2011), behavioral reg-ulation (Clark, Woodward, Horwood, & Moor, 2008), or effortful control (e.g., Kochanska, Murray, & Harlan, 2000; Poehlmann et al., 2010; Taylor, Eisenberg, Spinrad, & Widaman, 2013). Although most studies have examined maternal behavior, others have also considered paternal behavior (e.g., Eiden, Edwards, & Leonard, 2004; Kochanska, Aksan, Prisco, & Adams, 2008) or even coparent-ing during triadic mother– father– child interactions (Karreman, van Tuijl, van Aken, & Dekovic, 2008). Across developmental periods and approaches to the assessment of child regulatory abilities, these studies converge to suggest that children exposed to higher quality interactions with their parents demonstrate better skills in aspects of behavioral and cognitive regulation showing substantial overlap with EF.

When considering EF per se, however, the evidence is more limited, although growing. A pioneering study by Landry, Miller- Loncar, Smith, and Swank (2002) revealed that mothers’ verbal scaffolding (i.e., verbalizations that provided concep-tual links between objects, persons, activities, or functions) during routine daily activities and toy play at 3 years was indirectly related to child EF skills at 6 years,


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through the intervening effect of child language and nonverbal problem- solving skills at 4 years of age. Hughes and Ensor (2009) provided further support for this link, reporting that maternal verbal scaffolding of the child’s activity (utterances that elaborated on the child’s course of action) during play at 2 years predicted children’s EF at 4 years of age, above and beyond 2- year EF. Following up the same sample, Hughes, Roman, Hart, and Ensor (2013) found that maternal positive control at age 2 predicted child EF performance at age 6. Other dimensions of parenting with documented connections to subsequent child EF include positive engagement and low negative intrusiveness (Rhoades, Greenberg, Lanza, & Blair, 2011) as well as maternal positive affect (Kraybill & Bell, 2013).

Inspired by these studies, and with the aim of providing further support for the often proposed link between the quality of parent– child interactions and child EF, our three laboratories have been conducting independent research programs addressing these issues. The next sections summarize the main results of each of these programs.

THE UNIVERSITY OF MONTREAL STUDY

Methods

One of the core aims of this project is to document the prospective longitudinal associations between the quality of early parent– child relationships and children’s subsequent executive development. We therefore observed parent– child interac-tions at several points between 1 and 3 years of age, and assessed child EF first at 18 months, and then annually from 2 to 5 years of age. The assessment of child EF at each age is based on Carlson’s (2005) empirically derived measurement guide-lines and aims to maximize reliable detection of individual differences in three dimensions of EF: working memory, inhibitory control, and set shifting. We focus here on results obtained with EF assessments at 18 months, 2 years, and 3 years, given that data analysis on the subsequent time points is still underway.

At 18 months, we used a search task that taps into working memory. At 2 and 3 years, we used batteries of tasks on which children’s performance, as in previous studies of toddlers and preschoolers (e.g., Carlson, Mandell, & Williams, 2004; Carlson & Moses, 2001), loaded on two factors. The first factor, called “Impulse Control,” refers to children’s ability to delay or suppress an impulsive response (e.g., Kochanska, Murray, Jacques, Koenig, & Vandegeest, 1996). The second fac-tor, named “Conflict- EF,” consists of children’s ability to respond appropriately in the face of a salient conflicting response option (Carlson & Moses, 2001). On these “conflict” tasks, the child is not only to suppress a dominant response but also to provide a novel response that is incompatible with the prepotent one (Carlson & Beck, 2009). In our sample, the Impulse Control factor is composed of tasks involving delay of gratification, whereas the Conflict- EF factor consists of tasks entailing different degrees of working memory, set shifting, and inhibitory control.


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With respect to parent– child relationships, we assessed mother– child attach-ment security at 15 months and 2 years, using Waters’s (1995) Attachment Behavior Q- Sort completed by trained observers following a 1.5- hour home visit. We also assessed three dimensions of maternal interactive behavior: sensitivity and mind- mindedness at 12 months, and autonomy support at both 15 months and 3 years. Sensitivity consists of appropriate, warm, and consistent responses to infants’ signals, especially in the context of infant distress. It was assessed with the Maternal Behaviour Q- Sort (MBQS; Pederson & Moran, 1995) based on obser-vations performed throughout a 1.5- hour home visit. Mind- mindedness refers to parents’ tendency to comment appropriately on their children’s mental states, such as cognitions and desires, while interacting with them. This behavior was assessed with Meins, Fernyhough, Fradley, and Tuckey’s (2001) rating system applied to a 10- minute free- play sequence between mother and infant. Finally, autonomy support is often assessed during tasks that are challenging or unattract-ive to children, and consists of intervening according to the child’s needs, adapting the task to create an optimal challenge, and ensuring that the child plays an active role in the completion of the task, thus showing substantial overlap with scaf-folding. We measured autonomy support with a coding system developed in our lab (Whipple, Bernier, & Mageau, 2011), during a problem- solving sequence at 15 months and a clean- up task at 3 years. Finally, we assessed the quality of father– child interactions during a 10- minute free- play sequence at 18 months. The vid-eotaped interactions were coded with an adaptation of the Mutually Responsive Orientation scale (MRO; Aksan, Kochanska, & Ortmann, 2006), which focuses on communication, cooperation and emotional ambiance of the dyad.

Results

In a first set of papers, we examined the direct links between different aspects of parent– child relationships and child subsequent EF. We first investigated whether maternal sensitivity, mind- mindedness, and autonomy support, assessed between 12 and 15 months, were related to child EF performance at 18 months and 2 years (Bernier, Carlson, & Whipple, 2010). We found that both mind- mindedness and autonomy support were significantly related to child working memory at 18 months, whereas sensitivity and autonomy support were related to 2- year conflict- EF. Overall, autonomy support was the most robust predictor of both 18- month working memory and 2- year conflict- EF, while no convincing predictive relations were found with impulse control. When the same children reached 3 years, we sought to expand on these first findings by (a) predicting 3- year EF while partialling out 2- year EF performance to pinpoint increments in EF; and (b) adding assessments of father– child interactions and mother– child attachment security (Bernier, Carlson, Deschênes, & Matte- Gagné, 2012). We found that a composite score of parenting, integrating the three measures of maternal behavior used in the previous paper with the quality of father– child interactions at 18 months, was related to both impulse control and conflict- EF at


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3 years. Likewise, a mean score of attachment security at 15 months and 2 years related to both aspects of child EF at 3 years. However, when controlling for family socioeconomic status, child verbal ability, and 2- year EF performance, parenting and attachment were no longer related to impulse control. In contrast, parenting and attachment made successive incremental contributions to the pre-diction of conflict- EF over and above these controls. Overall, these two papers support the idea that higher quality interactions between parents and their young children may favor children’s executive development, although particularly so with conflict- EF.

A second set of papers attempts to shed light on the unconvincing relations between our measures of parenting and child impulse control. A first consider-ation pertains to when and how many times parenting is assessed. Indeed, par-enting behavior is often assessed only once and assumed to have an enduring influence on children. However, while high levels of early parental competence may establish a positive developmental course for children’s development, later parenting behaviors may change this developmental course, and stability of par-enting behaviors is believed to exert an important influence on children’s devel-opmental trajectories (e.g., Bornstein, 2002). We found evidence that this may be so with maternal autonomy support and child impulse control. Hence, when considering maternal autonomy support at either 15 months or 3 years in isola-tion, no relation emerged with child impulse control at 3 years when controlling for maternal education. However, we found that the average score of autonomy support at 15 months and 3 years did relate to impulse control, and that children of mothers who displayed high autonomy support at both 15 months and 3 years performed the best on impulse control (Matte- Gagné, Bernier, & Lalonde, 2015). This tentatively suggests that impulse control may require not only high- quality parenting but also consistency in this quality.

Still with the aim of better understanding the links between parenting and child impulse control, we examined whether high- quality parent– child interac-tions could be especially beneficial for children’s impulse control among lower socioeconomic status families (Rochette & Bernier, 2014). This hypothesis was based on prior research that suggests that higher quality parenting is associated with lower levels of children’s behavior problems, particularly among children from lower socioeconomic status backgrounds (see Schonberg & Shaw, 2007, for a review). Addressing this question with respect to impulse control at 3 years, we found the expected interaction effects, such that the hypothesized positive links between different aspects of maternal interactive behavior (assessed at 1 year with the MBQS described earlier) and child impulse control were significant only among children living in lower socioeconomic status homes. Specifically, the results pointed to a protective effect of high- quality parenting against the disadvantage normally associated with lower socioeconomic status with respect to child EF. Results showed that when mothers are positive in their interactions with their infants, are responsive to their signals, are often physically close and affectionate to them, show them interesting things in the environment, and are instructive during interactions, infants from less affluent families catch up with


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their more advantaged counterparts, and grow up to show similar impulse con-trol performance at 3 years. However, the same children exposed to low- quality maternal behaviors end up performing the worst on impulse control.

These findings are important in light of the unfortunate but very robust find-ing that children growing up in less affluent families are disadvantaged relative to their peers with respect to academic skills at school entry and subsequent rate progress once in school, which in turn widens the “achievement gap” placing them at risk for school failure and school dropout (e.g., Booth & Crouter, 2008; McLoyd, 1998; Ryan, Fauth, & Brooks- Gunn, 2006). The findings of this paper shed hopeful light on this phenomenon, suggesting that these more vulnerable children may, in fact, benefit to a greater degree from quality interactions with adults, which could in turn help narrow the achievement gap by enhancing their executive skills. Note, however, that this finding was specific to the impulse con-trol component of EF, and that when examining conflict- EF, we rather found that higher quality maternal behavior was related to enhanced child performance across socioeconomic status levels.

Finally, we are attempting to explore pathways that could account for the links we are increasingly finding between parenting and child EF. One promising can-didate is child language. Several authors recently proposed that social interaction should impact child EF through the mediating role of child language (Fernyhough, 2010; Hughes & Ensor, 2009; Lewis & Carpendale, 2009). Verbal exchange is a pri-mary way in which adults structure interactions, allowing children to learn about a rich body of knowledge and strategies that can be used to develop executive control. By explaining rules, providing verbal rationales, and thinking through problems aloud, caregivers supply children with the vocabulary to verbally medi-ate their behavior and solve problems (Carlson, 2003). Based on these proposi-tions, we investigated whether child language, assessed at 2 years, could explain the association between maternal autonomy support at 15 months and child EF at 3 years (Matte- Gagné & Bernier, 2011). Again, the findings varied according to the aspect of EF considered: Child language did explain the relation between maternal autonomy support and child EF, but only in the case of impulse con-trol. These findings, along with others presented later in this chapter, suggest that higher quality parenting may enhance the development of child impulse control by providing children with verbal tools to organize and regulate their thoughts and actions, and thereby to control their behavior in an executive fashion.

THE SIMON FRASER UNIVERSITY—UNIVERSITY OF VICTORIA STUDY

Research at Simon Fraser University and the University of Victoria study focused on the relation between parent– child social interaction and children’s EF, as well as the relation between EF and children’s social understanding in 82 parent– child dyads. To study parental interaction in relation to EF, we examined the ways in which parents and their children solved a puzzle task together at ages 2 and 3 and


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children’s EF on a battery of tasks at 2, 3, and 4 years of age (Hammond, Müller, Carpendale, Bibok, & Liebermann- Finestone, 2012). The circular white puzzle consisted of four concentric rings around a solid center, where each ring had a different curvature compared to those of other rings and was cut into equally sized pieces (Carpendale, 1999). The visual difference in curvature is relatively subtle (anecdotal evidence suggests that university undergraduates initially find the puzzle somewhat difficult). At each time point, participants were asked to solve an increasing number of the puzzle’s rings, from two rings at age 2, to three rings at age 3, allowing researchers to examine parental scaffolding in a relatively consistent context over time.

Children had to discover the difference of curvature between the rings through making mistakes; that is, when the child tried to fit a piece into the wrong level of ring, gaps appear between the pieces. Children may fail to dis-cover these features and become frustrated with the puzzle. These differences can be detected and managed in a variety of ways, and, importantly, with input from parents (Bibok, Carpendale, & Müller, 2009; Schmid- Schönbein & Thiel, 2010). Parents could scaffold their child’s problem solving in a variety of ways, and good scaffolding generally involved the type of autonomy sup-port described earlier in the University of Montreal study. For example, parents could suggest that children compare two pieces by stacking them one on top of the other so that the difference in curvature is evident. Or parents might simply solve the puzzle for the child, which had the effect of preventing the child from making or reflecting on his or her errors and constructing an understanding from these errors.

In general, parents who scaffolded well waited until their child encountered problems before intervening, and they intervened in ways that marshalled the child’s resources in solving the problem. For example, good scaffolding was asso-ciated with a greater use of elaborative over directive utterances (Bibok et al., 2009). Elaboratives are utterances wherein parents ask the child a question (e.g., “Where does this piece go?”). Directives are commands or orders (e.g., “Put the pieces over here”). In general, parents who used more elaboratives scored higher on the global scaffolding score.

The SFU- UVic study found that parental scaffolding at age 3 had a direct effect on EF at age 4, whereas parental scaffolding at age 2 had an indirect effect on EF at age 4 through the child’s verbal ability at 3 years of age (similar to what was observed in the Montreal study earlier). These findings suggest that good scaffold-ing fosters the development of children’s EF, and some of this influence is through children’s language development. In addition, we also examined the relation between children’s EF and their performance on a series of tasks assessing social understanding, a critical skill for children’s relationships with peers and adults alike. Executive functioning at ages 2 and 3 was linked to children’s social under-standing at 3 and 4, respectively (Müller, Liebermann- Finestone, Carpendale, Hammond, & Bibok, 2012), suggesting that EF may facilitate the development of social understanding.


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THE UNIVERSITY OF QUEBEC IN TROIS- RIVIERES STUDY

We are reporting preliminary results from an ongoing longitudinal study con-ducted at the University of Quebec in Trois- Rivieres, which examines associations between mother– child interactions, child EF, and school performance in a sample of economically disadvantaged families (St- Laurent, Milot, Lorent, & Lafantaisie, 2012). Dyadic interactions and child EF were evaluated during the preschool period when children were between 4 and 6 years old, and child academic per-formance was assessed 3 years later when children where between 7 and 9 years old. Seventy- two mother– child dyads from low- income families (annual family income lower than $25,000 CND) completed evaluations at both time points. At the preschool assessment (4– 6 years old), children performed two EF tests that tap inhibitory control: the Day- Night Stroop Task (Gerstadt, Hong, & Diamond, 1994) and the Tapping Task (Diamond & Taylor, 1996). In these tests, children must suppress a dominant response in order to produce another response that conflicts with the prepotent one (e.g., say “day” when shown a card depicting the moon, and say “night” when shown a card depicting the sun). In addition to these test- based measures, we also included assessment of children’s EF in the context of everyday life. Children’s behavioral manifestations of EF in the preschool/ day-care setting were assessed by the child’s preschool teacher using the Behaviour Rating Inventory of Executive Function— Preschool Version (BRIEF- P; Gioia et al., 2002). This questionnaire measures five aspects of children’s EF in the every-day, real- world context: Inhibit, Shift, Emotional Control, Working Memory, and Plan/ Organize.

To evaluate the quality of mother– child interactions, we observed mother– child dyads perform a joint errand- planning task (the Model Grocery Store Task; Gauvain & Rogoff, 1989; adapted by Moss and colleagues [Moss et al., 1993; Moss & St- Laurent, 2001]). The goal of this task is to find a series of items within the model grocery store using a plastic figurine. Grocery items to purchase were presented as a picture card set provided by the experimenter. The dyads were instructed to respect the following rules while performing the task: (a) the figurine must enter through the door, collect the grocery items, and exit through the door; (b) the figurine must walk along the aisles and not fly through the air; (c) once an item has been located, the figurine must remain stationed in front of it while the item is placed in the shopping basket; (d) the figurine must take the shortest route in order to retrieve the items on the list. Mother and child were asked by the experimenter to work together in doing the task. This is a challenging task for preschoolers: They require assistance in order to successfully perform it and abide by the set of rules provided by the experimenter. Consequently, it constitutes an adequate interactional context to observe maternal scaffolding of children’s devel-oping problem- solving skills. Maternal scaffolding was coded from filmed inter-actions, using a 7- point scale, with higher scores reflecting adequate scaffolding (parent provides guidance and support that is consistent with the child’s level of


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ability; i.e., encourages the child’s autonomous problem solving and provides help when the child encounters difficulties that are beyond his or her capabilities), and lower scores reflecting poor scaffolding quality (e.g., parent consistently intrud-ing and interfering with child’s autonomous problem solving or, conversely, parent mostly uninvolved, providing little or no structure and support to the child when needed).

Analyses showed that maternal scaffolding was associated with child EF, both measured in the lab (day- night and tapping tasks) and manifested in the educational preschool setting (BRIEF- P). After controlling for maternal educa-tion and child verbal abilities, maternal scaffolding was still significantly related to children’s behavioral manifestations of EF in the preschool/ daycare context. Associations between maternal scaffolding and EF remained significant on four of the five EF dimensions assessed by the BRIEF- P: Inhibit, Shift, Working Memory, and Plan/ Organize. However, there was no longer a significant relation between maternal scaffolding and lab measures of child EF when control variables were entered in the model.

We next examined whether EF assessed in the preschool period was a signifi-cant predictor of child academic performance during middle childhood (7– 9 years old). Results revealed that, after having controlled for maternal education and child verbal ability, child EF manifested in the everyday, real- world context of the preschool setting was associated with child academic performance 3 years later. No significant association emerged between test- based measures of EF and aca-demic performance. In summary, these findings suggest that parental scaffolding may contribute to the development of EF, particularly the EF skills that the child must rely on and call upon in real- world everyday life when faced with various learning and social situations. In addition, and as proposed by several researchers (e.g., Blair, 2002; Blair & Diamond, 2008; Bull, Espy, & Wiebe, 2008), it appears that the development of these executive skills during the preschool period lays the ground for children’s later academic success.

IMPLICATIONS FOR EDUCATORS

Taken together, the findings of our respective studies indicate that both emotional (warmth, sensitivity) and cognitive (adequate scaffolding) aspects of parent– child relationships are associated with the development of child EF. It is worthy to note that these associations were found in middle- class and low– socioeconomic status families, with children of various ages (spanning the toddler and preschool peri-ods), using diverse measures of child EF, and evaluating parent– child interactions in different observational contexts. So what does this all mean for school teach-ers? In short, there is every reason to believe that many of the ways in which par-ents help their children develop their executive skills, described herein, apply to teacher– child relationships as well. Although there is no doubt that parent– child and teacher– child relationships are very different in several ways, once children


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reach school this becomes a central context in which they can practice, and con-tinue to develop, their executive skills.

Through their daily presence in school and classroom contexts, teachers are in a unique position to provide further adult supportive influence on children’s EF, which in fact might be especially beneficial for those children getting less at home (Bierman et al., 2008). Several of the “parenting” behaviors mentioned herein, which we have examined in our studies of parenting and child EF, can be and are in fact displayed by teachers when they interact with children. For instance, numerous studies show that scaffolding (e.g., Zuniga & Howes, 2009) and auton-omy- support (e.g., Reeve, 2006) are frequently used by teachers in classrooms. When disseminating research results to educators and policymakers, we should put special emphasis on the importance of the relational context in which learn-ing occurs and its potential impact on the development of children’s self- regula-tory skills— which are so closely linked to better school adaptation (in both the socioemotional and academic spheres). Applying our results to the school setting, it appears important, for the development of children’s self- regulation, that teach-ers create a positive classroom climate that is supportive of children’s autonomous efforts at problem solving.

Our results further suggest that, in addition to the overall classroom climate, the quality of the relationships— both in terms of instructional and emotional support— that teachers build with each individual student might contribute to favor or, conversely, hinder the development of children’s EF. Considering that children come from various socioeconomic status backgrounds and arrive at school with diverse family experiences and different levels of EF, teachers should flexibly modulate their instructional support (scaffolding) to adapt to each child’s needs and competencies. Additionally, in their efforts to positively influence the development of self- regulation in children, teachers should also focus on estab-lishing with each individual child a warm, harmonious, and emotionally support-ive relationship.

In addition to reaffirming the call to recognize that academic learning occurs best when children are in relationally healthy school environments (Diamond, 2010), another area that our research supports is the need to design curricula that seek to enhance school readiness of students with low EF. There is increas-ing research on targeted EF- enhancing classroom experiences, such as the Head Start REDI program in the United States (Bierman et al., 2008; Bodrova & Leong, 2007). Such programs are based on the idea that EF skills can in fact be improved by training and practice (see also Diamond, Barnett, Thomas, & Munro, 2007; Dowsett & Livesey, 2000; Kloo & Perner, 2003; Rueda, Rothbart, McCandliss, Saccomanno, & Posner, 2005), a notion which our research on parenting certainly supports.

Finally, outreach to parents appears to be in order. Parents contribute to their children’s academic success not only by fostering skills such as reading but also by providing models of regulation and organization that will help their children suc-ceed in school. We would argue that parents’ continuing involvement, once their


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children reach school, remains one of the most potent influences on children’s EF and school success.

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7

Transition to School

Executive Function, Emergent Academic Skills, and Early School Achievement

U L R I C H M Ü L L E R , M I C H A E L M I L L E R ,

S A R A H H U T C H I S O N , A N D K A Y L A T E N E Y C K E ■

The transition to school brings along a number of changes for young children. For one, it marks the beginning of formal instruction. The classroom environment is more structured than the preschool environment, requiring children to adhere to classroom rules, to control and focus attention, and to follow directions. The social interactions between children and teacher also change, with the teacher emphasizing the instruction of academic skills rather than guidance in unstruc-tured activities as in preschool (Rimm- Kaufman & Pianta, 2000). Children often find it difficult to adjust to the new school environment; many have problems following directions, and 16% of children have a difficult entry into kindergarten in general (Rimm- Kaufman, Pianta, & Cox, 2000). At the same time, research has shown that the way in which children adjust to the school environment pre-dicts later school success, with individual differences in academic achievement remaining stable after the first few years in school (Alexander & Entwistle, 1988). There are a number of characteristics of the child, the context, and the interaction between child and context that have been suggested to facilitate a successful tran-sition to school (Rimm- Kaufman & Pianta, 2000). Along these lines, executive function (EF) is the child characteristic that in all likelihood has recently received the most attention from researchers (Blair et al., 2007).

In this chapter, we will review research on EF and its relations to emergent academic skills and early academic achievement in typically developing children. Before we undertake the review, we will establish a working definition of EF and discuss the relation between EF and self- regulation. In the final section, we raise the issue of the causal relation between EF, emergent academic skills, and school achievement, and discuss approaches that have started to tackle this issue.

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DEFINING EXECUTIVE FUNCTION

EF remains an elusive term; there are many definitions of EF that differ in subtle but theoretically important ways (Dick & Overton, 2010; Zelazo & Müller, 2010). What complicates matters further is that EF overlaps with other similarly elusive theoretical constructs, such as self- regulation, self- control, and effortful control. These terms share a family resemblance, but we would argue that they refer to somewhat different processes. Broadly defined, self- regulation refers to tempo-rally extended goal- directed behavior (Hofmann, Schmeichel, & Baddeley, 2012). Specifically, it has been defined as the “deliberate attempt to modulate, modify, or inhibit actions and reactions toward a more adaptive end” (McClelland, Ponitz, Messersmith, & Tominey, 2010, p. 510; italics in original). Prototypical examples of self- regulation include achievement- related behaviors, personal growth and health strivings, and interpersonal and intimacy strivings (Hofmann et al., 2012). The construct of self- control is often used to refer to a narrower and less complex subset of regulatory processes, including compliance and the delay of gratification (Hofmann et al., 2012; Kopp, 1982). Effortful control refers to a higher order tem-peramental trait that involves both inhibition of prepotent responses and atten-tional control (Rothbart & Rueda, 2005). We mention these terms here not as an intention to settle definitional issues, but rather as an aid to provide a working definition of EF for our literature review.

One useful way of approaching the relation between self- regulation and EF is to conceptualize EF as a process mediator, that is, as contributing to and supporting self- regulatory outcomes. Following Hofmann and colleagues (2012), successful self- regulation involves three main components:

(i) standards of thought, feeling, or behavior that individuals endorse, men-tally represent, and monitor; (ii) sufficient motivation to invest effort into reducing discrepancies between standards and actual states; and (iii) suffi-cient capacity to achieve this (i.e., reduce the discrepancy) in light of obsta-cles and temptations along the way. (p. 174)

Self- regulation failure may occur due to a lack of (monitoring of) standards, a lack of motivation, or a lack of capacity (Hofmann et al., 2012). Thus, as a pro-cess mediator to self- regulation, EF would be involved in the representation and monitoring of standards and goals, the motivation of behavior, and the capacity to remove obstacles and withstand temptations. Frequently, EF is conceived in a way that limits its range to “cool” cognitive processes, such as holding in mind and monitoring standards and goals (Roberts & Pennington, 1996). These cool processes, in turn, are assumed to regulate “hot” (i.e., affective and emotional) processes (Hofmann et al., 2012; Metcalfe & Mischel, 1999). This is not to say that “hot” processes are not essential for successful self- regulation (e.g., motivation, goal identification, and strength). However, the interplay and relation between cool and hot processes in self- regulation has received relatively little attention in


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developmental science (but see Carlson & Wang, 2007; Zelazo & Cunningham, 2007). In this chapter, we focus on cool EF processes.

Historically, the concept of EF originated in neuropsychology and was used to summarize the consequences of lesions to the prefrontal cortex (i.e., the anterior portion of the frontal lobes). Because the prefrontal cortex is a large and heteroge-neous region that comprises several specialized subregions, consequences of pre-frontal lesions are numerous and diverse (Fuster, 2009). The description of these impairments has resulted in open- ended lists of ill- defined processes that include broad constructs such as planning, concept formation, use of feedback, and deci-sion making (Tranel, Anderson, & Benton, 1994). Studies using exploratory fac-tor analysis and principal component analysis of EF task batteries have not been helpful in elucidating the composition of EF because EF tasks are usually complex and involve a mixture of executive and nonexecutive processes (Zelazo & Müller, 2010). As a consequence, different tasks may load on a factor not because they make similar executive demands, but because they share nonexecutive processes (e.g., reading speed). A more promising approach to clarifying the structure of EF consists of using confirmatory factor analysis (CFA). In CFA, the researcher uses a priori hypothesis in order to stipulate that specific tasks load on an underlying latent variable; the stipulated structure or model is then evaluated to determine how well it fits the data (Bryant & Yarnold, 1994). Because CFA extracts only the variance that is common to the tasks that are supposed to measure the same executive process, the common factor underlying performance on these tasks has been suggested to be a better measure of EF than the individual tasks indicating the factor.

A seminal CFA study conducted by Miyake and colleagues (2000) has served as a template for many recent studies on EF in children. Based on a prior litera-ture review, Miyake and colleagues (2000) stipulated three basic EF component processes: inhibition of prepotent responses, shifting between mental sets, and updating and monitoring representations in working memory. They designed relatively simple tasks to measure each component and administered these tasks to adults. For example, they used the Stroop test to measure response inhibition; in this task, participants were presented with color words written in an incongru-ent ink color (e.g., the word “blue” written in red), and they had to name the ink color, thereby inhibiting the automatic tendency to read the word. Shifting was measured, among others, by the Number– Letter task, in which participants were instructed to shift between judging digits (odd vs. even) and letters (consonant vs. vowel), depending on where these symbols were located on a computer monitor. Finally, one updating task used in the study was the Letter Memory task, which required participants to remember the last four letters in a list. The CFA showed that, as theoretically predicted, the different elementary tasks loaded on the inhi-bition, shifting, and updating factors, and that these factors were moderately cor-related, “thus indicating both unity and diversity of executive functions” (Miyake et al., 2000, p. 87).

In the last few years, the CFA approach to EF has gained increasing popu-larity among developmental psychologists. Attempts to replicate the tripartite


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structure of EF in school- aged children, however, have been inconsistent, with some researchers replicating the three- factor model (Lehto, Juujärvi, Kooistra, & Pulkkinen, 2003; Rose, Feldman, & Jankowski, 2011) and other researchers find-ing two- factor solutions (working memory and shifting; Huizinga, Dolan, & van der Molen, 2006; van der Sluis, de Jong, & van der Leij, 2007). In contrast, CFA studies in preschool children have tended to support a unitary EF factor struc-ture, suggesting that these component processes still may be relatively undiffer-entiated in early childhood (Hughes, Ensor, Wilson, & Graham, 2010; Wiebe, Espy, & Charak, 2008; Wiebe et al., 2011; Willoughby, Blair, Wirth, & Greenberg, 2010, 2012; Willoughby, Wirth, & Blair, 2012in press). However, in most of these studies, a two- factor EF structure consisting of working memory and inhibition still fits the data well, but was rejected in favor of a unitary structure for reasons of parsimony. Moreover, other CFA studies have found that a two- component EF structure with working memory and inhibition as latent factors fit the data better than a unitary EF structure both in typically (Miller, Giesbrecht, Müller, McInerney, & Kerns, 2012) and atypically developing preschoolers (Schoemaker et al., 2012). Overall, the structure of EF remains an active area of investigation, but the importance of working memory and inhibition processes is well recog-nized in the development of EF in preschool children (e.g., Garon et al., 2008).

The CFA approach to EF informs about the structure of EF and the develop-mental changes in structure, and it provides a purer and more reliable measure of EF, which is particularly important given the poor test- retest reliability of some EF components (Müller, Kerns, & Konkin, 2012). Therefore, CFA is particularly useful when the antecedents and consequences of individual differences in EF are examined. At the same time, the measurement and individual difference approach to EF cannot replace a comprehensive developmental theory of EF, which cur-rently is lacking. As a consequence, there is virtually no discussion of particular developmental levels of EF being a prerequisite for specific school- related skills (for an exception, see Gropen, Clark- Chiarelli, Hoisington, & Ehrlich, 2011). Instead, research focuses on how individual differences in EF predict emergent academic skills and school achievement. We now turn to this line of research.

EXECUTIVE FUNCTION, EMERGENT ACADEMIC SKILLS, AND SCHOOL ACHIEVEMENT

The skills that provide the foundation of a successful transition to school are commonly referred to as school readiness skills. School readiness, however, is a complex and multifaceted construct that is probably best conceived within a systems framework as “an aspect of child functioning that is determined by the quality and type of interactions that a child engages in with others” (Mashburn & Pianta, 2006, p. 169). Thus, school readiness skills should not be viewed as the property of the child alone but are defined by societal and institutional expec-tations and emerge in the context of social interactions. Preschoolers’ emergent academic skills are one important competence that has been suggested to create a


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strong motivational basis for future learning and facilitate the acquisition of addi-tional academic skills (Duncan et al., 2007). Empirical evidence in support of this claim comes from a growing body of research that shows that preschoolers’ emer-gent academic skills predict later school achievement (Lemelin et al., 2007; Pagani, Fitzpatrick, Archambault, & Janosz, 2010; Romano, Babchishin, Pagani, & Kohen, 2010; for a meta- analysis, see La Paro & Pianta, 2001). The majority of these stud-ies have focused on young children’s early literacy and math skills, which are often measured in terms of identifying letters, words, numbers, counting, and shapes. For example, Duncan et al. (2007) analyzed the findings of six longitudinal data sets and found that preschoolers’ math skills, such as their knowledge of numbers, were the most powerful predictors of later learning (average β = .34), followed by preliteracy skills, such as letter knowledge (average β = .17).

Recent research has examined the contribution of EF to preschoolers’ academic skills and achievement (Blair & Razza, 2007; Bodrova & Leong, 2006; Monette, Bigras, & Guay, 2011; Müller, Liebermann, Frye, & Zelazo, 2008; Welsh, Nix, Blair, Bierman, & Nelson, 2010). Composite measures of EF and different components of EF both have been linked to a variety of indicators of academic achievement in school- aged children (e.g., Best, Miller, & Naglieri, 2011; Visu- Petra, Cheie, Benga, & Miclea, 2011; Waber, Gerber, Turcios, Wagner, & Forbes, 2006; for a review, see Müller et al., 2008). Less research has been conducted with preschool-ers and kindergartners, but there is increasing evidence that emerging academic skills are significantly correlated with composite measures and individual com-ponents of EF in younger children (Alloway et al., 2005; Bull, Espy, Wiebe, Sheffield, & Nelson, 2011; Espy et al., 2004; Kroesbergen, Van Luit, Van Lieshout, Van Loosbroek, & Van de Rijt, 2009; Lan, Legare, Ponitz, Li, & Morrison, 2011). Whereas several of these studies used a cross- sectional design and assessed only one EF component, few studies have used longitudinal designs, comprehensive EF batteries, or CFA to control for the task impurity problem. Moreover, only a few studies have systematically assessed the relative contribution of different com-ponents of EF to emergent academic skills and school achievement. We review this research in terms of both concurrent and predictive findings.

Concurrent Relations

Several cross- sectional studies have demonstrated a concurrent relation between EF and different aspects of children’s early academic achievement. Alloway and colleagues (2005) have shown that different aspects of working memory were associated with teacher- based assessments of children’s progress toward learning goals at school entry. For example, phonological short- term memory tasks (e.g., forward digit and word spans) and complex working memory tasks (i.e., tasks that require the storage and manipulation of information such as the backward digit span task) were related to teacher- assessed reading, writing, speaking and listen-ing, and mathematics skills in preschoolers. Furthermore, after controlling for a number of background variables (e.g., maternal educational level), phonological


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short- term memory accounted for a unique amount of variance in reading as well as speaking and listening skills, and complex working memory accounted for a unique amount of variance in writing skills (Alloway et al., 2005). Unique relations between complex working memory and different aspects of early writing in 6- and 7- year- olds were also found in a study by Bourke and Adams (2003), and work-ing memory was the only component of EF that uniquely predicted mathematical ability in 6- to 8- year- olds over and above reading ability and other components of EF (Bull & Scerif, 2001; see also Lan, Legare, Ponitz, Li, & Morrison, 2011).

Further research has examined the combined and unique influence of different EF components on young children’s academic skills. For instance, Kroesbergen and colleagues (2009) found that working memory and inhibition explained a sig-nificant amount of variance in 5- and 6- year- old’s counting skills, even after con-trolling for language skills, fluid intelligence, and subitizing skills (i.e., the ability to perceive at a glance the number of items presented). Espy and colleagues (2004) went a step further and gauged the relative contribution of inhibition, working memory, and shifting to preschoolers’ math skills (i.e., subitizing, counting, and simple arithmetic). Both inhibition and working memory accounted for unique variance in math skills after controlling for age, language skills, and maternal edu-cation. However, after controlling for the other EF components as well, only inhi-bition accounted for a unique portion of variance in math skills, suggesting that inhibition is particularly important for early math skills. In a large- scale study, Willoughby and colleagues (2012) administered a newly developed battery of EF tasks and a number of early literacy and math assessments to 5- year- olds. CFAs established that a single- factor model best fit both the EF data and the school achievement data. Children’s performance on the EF battery was strongly cor-related with the latent academic achievement variable (ф = .70). In addition, the latent EF variable was significantly correlated with all individual academic achieve-ment tests, with stronger correlations between the latent EF variable and math performance (rs ≥ .62) than (pre- ) reading achievement tests (rs = .39 and .56). The correlations between the latent EF variable and academic achievement were stronger than correlations between individual EF tasks and academic achieve-ment, demonstrating the value of CFA in controlling for measurement error and nonexecutive task demands (Willoughby et al., 2012). Furthermore, Bull and col-leagues (2011) showed that a latent EF variable predicted math achievement in 2- to 6- year- olds even after controlling for crystallized IQ.

Recently, we (Miller, Müller, Giesbrecht, Carpendale, & Kerns, in press) con-ducted a latent variable study that aimed to clarify the relative contributions of EF as well as social understanding to individual differences in emergent academic skills in 3- to 5- year- olds. All children were tested on a battery of tasks designed to measure working memory, inhibition, social understanding (e.g., false- belief understanding), and verbal ability. In addition, children were administered the Bracken School Readiness Measure (Bracken, 2002), which is a standardized measure of emergent academic skills that includes six subscales designed to test children’s knowledge of colors, letters, numbers and counting, sizes, object com-parisons, and shapes. We evaluated a series of latent variable models that examined


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the joint and unique influences of EF and social understanding on preschool-ers’ emergent academic skills. Our findings indicated that working memory was a unique predictor of early letter (β = .52) and math (β = .75) skills, even after controlling for inhibition, social understanding, verbal ability, and age. Inhibition and social understanding did not uniquely contribute to preschoolers’ academic skills, even though both were positively correlated with subscales on the Bracken School Readiness Measure. Our findings indicate that preschool children with strong working memory skills are at an advantage in terms of acquiring early let-ter and math skills.

Predictive Relations

A number of longitudinal studies suggest that EF facilitates the acquisition of emerging academic skills (Blair & Razza, 2007; Bull, Espy, & Wiebe, 2008; Clark, Pritchard, & Woodward, 2010; McClelland et al., 2007; Monette et al., 2011; NICHD, 2003; Welsh et al., 2010). Welsh and colleagues (2010) found that growth in a composite measure of EF (i.e., working memory, inhibition, and attentional shifting) over the course of the prekindergarten year predicted (a) growth in liter-acy (i.e., identifying and saying letters and words) and math skills (i.e., numbers, quantities, counting, and simple arithmetic) between the beginning and end of the prekindergarten year, and (b) kindergarten reading and math achievement after controlling for growth in literacy skills, math skills, and verbal ability during the prekindergarten year. Similarly, Clark and colleagues (2010) found that an EF composite at age 4 explained about 30% of variance in mathematical achievement at age 6, and remained significant even after controlling for reading comprehen-sion and IQ. In addition, using the Head- Toes- Knees- Shoulder task, a complex EF task that requires working memory, inhibition, and attentional control, Ponitz and colleagues (2009) found that performance on the EF task in the fall predicted kindergartners’ mathematics, literacy, and vocabulary achievement as well as teacher ratings of classroom functioning in the following spring. Moreover, per-formance on the EF task predicted only gains in mathematic achievement, but not gains in literacy or vocabulary achievement.

Even though research suggests that EF is predictive of emergent academic skills, findings concerning the relative contribution of different components of EF to these skills are somewhat inconsistent. For instance, there is evidence that individual differences in preschoolers’ inhibition skills explain variance in their academic skills later on (Blair & Razza, 2007; Espy et al., 2004; McClelland et al., 2007; NICHD, 2003). Researchers at the National Institute of Child Health and Human Development (NICHD, 2003) examined the relations between perfor-mance on the Continuous Performance Task (CPT) and emergent academic skills (derived from the Woodcock- Johnson Psycho- Educational Battery; Woodcock, McGrew, & Mather, 2001) in 4½- year- olds. In the CPT, children are asked to respond to a target stimulus by pressing a button and to withhold a response to nontarget stimuli. The CPT measures sustained attention (errors of omission, i.e.,


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failing to respond to target stimuli) and inhibitory control (errors of commis-sion, i.e., responding to nontarget stimuli). The research team found that both omission and commission errors were significantly correlated with emergent aca-demic skills, even after controlling for gender, family income, mothers’ receptive vocabulary score, children’s temperament, and children’s earlier attention skills. Furthermore, performance on the CPT mediated the relation between family characteristics (e.g., home environment) and emergent academic skills, suggesting that parenting affects emergent academic skills indirectly via sustained attention and inhibitory control. A follow- up of this sample, conducted when children were in the sixth grade, showed that an EF composite measure (assessed at 54 months and during late elementary school) explained 13.5% of variance of teacher rat-ings of sixth- grade academic skills and behaviors (e.g., work habits), after control-ling for the contributions of family socioeconomic status, ethnicity, gender, and early cognitive ability. CPT commission errors as assessed at 54 months made a unique contribution to academic behavior in sixth- grade children (Jacobson, Williford, & Pianta, 2011).

Evidence for the importance of inhibition also emerged in a study by Blair and Razza (2007). In this study, inhibition and shifting were measured in a sample of children in preschool and again a year later in kindergarten, when they also were tested on letter and math skills (i.e., numbers, quantities, sizes, shapes, and basic arithmetic and graphic relations). After controlling for shifting, IQ, and verbal ability, inhibition uniquely predicted (a) concurrent letter knowledge in kinder-garten and (b) both concurrent and prospective math knowledge in kindergarten.

Other longitudinal studies, however, point to the importance of working memory for emergent academic skills and achievement. For example, Gathercole, Brown, and Pickering (2003) found that performance on working memory mea-sures at school entry, when children were between 4 and 5 years old, predicted reading, writing, and spelling scores but not mathematics scores 2 years later, even after controlling for the baseline assessment of the academic skills when children were 4 years old. However, in contrast to Gathercole and colleagues, other studies have shown that working memory in preschoolers and primary school children makes a unique contribution to later math achievement and number knowledge (e.g., Bull et al., 2008; Fitzpatrick, & Pagani, 2012; Lee et al., 2012; Sabol & Pianta, 2012; van der Ven, Kroesbergen, Boom, & Leseman, 2012). For example, Monette and colleagues (2011) found that kindergarteners’ working memory, but not their inhibition or shifting, predicted their math achievement at the end of first grade, even after controlling for earlier emergent academic skills (e.g., colors, letters, numbers), age, maternal education, and family income. However, none of the EF components directly predicted reading and writing achievement at the end of first grade.

Few researchers have examined the relative contribution of shifting to pre-schoolers’ school readiness, and most researchers who have included measures of shifting in their studies have not found significant relations between preschoolers’ shifting and school readiness (Espy et al., 2004; Monette et al., 2011). One excep-tion involves a recent study by Vitiello, Greenfield, Munis, and George (2011; see


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also George & Greenfiled, 2005), who found that teachers’ ratings of preschoolers’ attention and persistence mediated the relation between shifting and emergent academic skills in preschoolers of Head Start classrooms. However, the zero- order correlation between shifting and school readiness skills was relatively small (r = .19, p < .01) in this study. This finding suggests that shifting may have some influence on emergent academic skills, but that the influence is small or indirect. It may be the case that, during the preschool years, shifting is a relatively indis-tinct component of EF, as suggested by recent latent variable studies of EF that do not support a unique shifting component in preschoolers (Miller et al., 2012; Willoughby et al., 2010, 2012) or elementary school children (Lee et al., 2012; Van der Ven et al., 2012).

To summarize, there is ample evidence from cross- sectional and longitudinal studies that EF is associated with emergent academic skills and academic achieve-ment. However, findings with respect to the relative contribution of different EF components to early literacy and math skills as well as to school achievement are inconsistent, probably due to differences in the measurement of EF and academic outcomes. It is also possible that the inconsistent findings reflect a lack of dif-ferentiation in EF in young children. Clearly, more studies are required that use longitudinal design and latent variables to account for error variance.

DOES EXECUTIVE FUNCTIONING CAUSE ADVANCES IN EMERGENT ACADEMIC SKILLS AND ACADEMIC ACHIEVEMENT?

As we summarized earlier, there is robust evidence from cross- sectional and lon-gitudinal studies that EF is associated with emergent academic skills and aca-demic achievement. Particularly the findings that EF predicts future academic skills and achievement even after controlling earlier academic skills suggests that EF does cause changes in academic skills. However, the possibility that a third variable is responsible for changes in both cannot be excluded. Corroborating evidence for a causal relation can be established using two other methods. First, EF training studies allow researchers to examine whether changes in EF lead to improvements in academic outcomes. Second, novel analytic strategies, including fixed effects model analysis, attend to sample selection effects that may contrib-ute to the strong association between EF and performance on tests of academic achievement and thus provide a stronger test of the causal relation between EF and academic outcomes (Willoughby, Kupersmidt, & Voegler- Lee, 2012). We will discuss both approaches in turn.

Recent evidence suggests that EF is amenable to training, even in the preschool years (Diamond et al., 2007). A number of factors have been demonstrated to affect EF, including distal social factors such as socioeconomic status (Hackman, Farah, & Meaney, 2010) and proximal social factors such as parental scaffold-ing (e.g., Hammond et al., 2012) and attachment (Bernier, Carlson, Deschênes, & Matte- Gagnè, 2012), aerobic exercise (Hillman, Erickson, & Kramer, 2008),


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mindfulness training (Zelazo & Lyons, 2012), and specifically designed EF train-ing programs (e.g., Diamond et al., 2007; Dowsett & Livesey, 2000; Kerns et al., 1999; Rothlisberger et al., 2012). One approach to training EF has been to apply massed practice, and this approach has resulted in significant positive effects in working memory, mental flexibility (Kloo & Perner, 2003; Thorell et al., 2009), and to a lesser degree, inhibitory control tasks (Dowsett & Livesey, 2000). However, such training programs have been criticized for narrow transfer to other cognitive skills or lack of transfer to other aspects of EF (e.g., training in working memory skills does not transfer to inhibitory control skills) (Thorell, Lindqvist, Bergman Nutley, Bohlin, & Klingberg, 2009). Even though some studies found evidence that children who received working memory training improved their math skills (Holmes, Gathercole, & Dunning, 2009) and reading skills (Loosli, Buschkuehl, Perrig, & Jaeggi, 2012), these studies did not establish that the changes in working memory skills mediated changes in academic skills (i.e., no meditational analy-sis was conducted). Furthermore, in a meta- analytic review, Melby- Lervåg and Hulme (2013; see also Shipstead, Redick, & Engle, 2012) found no convincing evidence for the transfer of working memory training to other skills (including nonverbal and verbal ability or arithmetic). It is possible that aspects of the train-ing (e.g., the length of the training program) and participant characteristics (e.g., age, clinical status) affect transfer effects (Thorell et al., 2009). Moreover, it is dif-ficult to increase the difficulty of some training programs, resulting in a ceiling effect for the trained skills and poor transfer to other skills (Klingberg et al., 2002). Clearly, these issues need to be addressed by future research. Future research also should statistically test the question of whether changes in EF mediate changes in academic outcomes.

Another way of training EF is through school- based curricula and intervention programs that integrate EF training into the child’s everyday life by providing challenging opportunities. One such EF training program is the Tools of the Mind Program (Bodrova & Leong, 2007). The theoretical framework of the program is derived from the theory of the Russian psychologist Lev Vygotsky, particularly his ideas that the internalization of semiotic tools such as self- directed speech and symbol systems transforms human cognition, and that sociodramatic play (i.e., social or collaborative make- believe play) is important for development of self- regulation (Vygotsky, 1978). The main goals of the program are (a) to foster foundational skills, such as children’s ability to regulate their own social behav-ior, attention, and cognition, and to use symbolic representation, and (b) to pro-mote specific literacy prerequisites for reading and writing (e.g., oral language, phonemic awareness, knowledge of letters, and familiarity with the conventions of print) and specific prerequisites for mathematics (e.g., counting meaning-fully, one- to- one correspondence, patterns, numeral recognition; see Bodrova & Leong, 2007). To achieve these goals, particular activities have been designed to promote sociodramatic play and the use of self- regulatory speech, and to teach the use of semiotic tools (i.e., external aids) to facilitate attention and memory. For example, activities that illustrate semiotic mediation and the playful teaching of self- regulation skills are “Buddy Reading” (i.e., children take turns reading to


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each other and one child [listener] receives a picture depicting an ear, and the other child [reader] receives a picture depicting a mouth) and the “Freeze Game” (i.e., children dance to music and have to take the pose represented by a stick fig-ure once the music stops). In the Tools of the Mind curriculum, teachers do not just let children play but actively support more complex, planned play through special instructional strategies, such as suggestions for themes, props to sustain the imaginary situation, prompts to increase the number of roles within a theme, and initializing the transition from using realistic props to using a minimal num-ber of props.

Program evaluation studies that examine impact of the Tools of the Mind pro-gram on EF and academic outcomes have generated mixed findings. Bodrova and Leong (2001) report that children participating in the program scored significantly better on many preliteracy variables closely associated with reading achievement in elementary school than a control group of nonproject kindergartners (Bodrova & Leong, 2001). Similar findings emerged for preschool children participating in the program. In addition, in first and second grade, teachers reported that pro-gram children were better self- regulators and expressed more interest in literacy activities (Bodrova & Leong, 2001).

In a widely cited study, Diamond and colleagues (2007) evaluated the effects of the Tools of the Mind program on EF skills. One- hundred forty- seven preschool-ers from low- income, urban families were randomly assigned to the Tools of the Mind program or to a balanced literacy curriculum (i.e., a program developed by the school district). Data were based on participants who had spent either 1 or 2 years in their particular program. After participating in the programs, children were tested on two measures of EF. In both tasks children received simple trials that did not make inhibitory demands (e.g., in one task, a heart appeared on a computer screen and children were required to press a button on the same side as the heart) and more complex trials that made considerable inhibitory demands (e.g., children had to press a button on the side opposite to the heart). Diamond and colleagues (2007) found that children in the Tools of the Mind program out-performed controls on both tasks. The largest effect of the Tools program was evident on those trials that made inhibition demands. These findings were also consistent with teachers’ observations (Diamond et al., 2007).

Using a randomized controlled trial, Barnett and colleagues (2008) found that 3- and 4- year- old children assigned to the Tools of the Mind program had fewer behavior problems— presumably due to better self- regulation skills promoted in the Tools program— and moderately more advanced general language develop-ment than children assigned to the balanced literacy curriculum. However, sur-prisingly, Barnett and colleagues did not find any advantage for children who participated in the Tools of the Mind program on measures of literacy and intelli-gence. More recent attempts to replicate the very positive effects of the Tools pro-gram on EF and emergent academic skills have been rather disappointing. Wilson and Farran (2012) found that children enrolled in the Tools of the Mind program did differ from children enrolled in control programs on language measures, mea-sures of emergent academic skills as measured (Woodcock et al., 2001), different


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components of EF (including measures of attention, inhibitory control, and work-ing memory), or teacher ratings of interpersonal, work- related, and adaptive lan-guage skills. Lillard and colleagues (2012) report two further studies that failed to find a positive effect of the Tools program on EF. It is currently unclear why more recent studies fail to replicate the promising effects of earlier studies. It is possible that fidelity of program implementation is an issue; due to the popularity of the Tools program, control programs might also have started to incorporate elements into their own program, making it difficult to find proper controls for the Tools program. However, the balance of current evidence does not support the claim that the Tools program promotes EF or emergent academic skills.

Preliminary findings from recent evaluations of the Montessori curriculum are more promising than findings from evaluations of the Tools program. Lillard and Else- Quest (2006) found that 5- year- olds attending Montessori schools performed significantly better than controls on emergent literacy and math skills, cognitive flexibility, perspective taking, and social reasoning. It is unlikely that these find-ings were due to differences in parental background or parenting because chil-dren in both the experimental and the control group had entered the Montessori school lottery; those children who did not win the lottery were assigned to the control group. Whereas 12- year- old children in Montessori schools wrote more creative essays with more complex sentence structures, showed better social problem- solving skills, and reported feeling more of a sense of community at their school than children in control schools, there were no differences in perfor-mance on standardized measures of academic skills. A further study suggests that benefits of Montessori schools are partly due to the material used in the classic Montessori program (e.g., the Pink Tower engages planning, working memory, inhibition, and flexibility; see Lillard, 2012). Lillard (2012) compared school- year gains (fall to spring) in 3- to 5- year- old preschoolers in classic Montessori programs, lower fidelity Montessori programs, and conventional programs on a variety of measures of cognitive, academic, and social skills. The main difference between classic Montessori programs and lower fidelity Montessori programs was that the latter supplemented Montessori materials with other conventional materials and activities. Consequently, children in classic Montessori programs were far more likely to be engaged with Montessori materials than children in supplemented Montessori programs. In spring, children in the classic Montessori program showed significantly greater gains on measures of emergent academic skills, vocabulary, EF, and social problem solving than children in supplemented Montessori and conventional programs. Somewhat surprisingly, however, perfor-mance of children in the classic Montessori program did not differ from that of children in the other schools, even though many of them had already spent 1 year or even 2 years in the program. Clearly, the long- term effects of Montessori pro-grams on EF and academic achievement need further study.

The PATHS (Promoting Alternative Thinking Strategies) intervention program takes a more global approach to improving school readiness and achievement. The PATHS program was originally designed for school- aged children (Greenberg, Kusche, Cook, & Quamma, 1995; Kam, Greenberg, & Kusche, 2004). It focuses


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on developing the expression, understanding, and regulation of emotions, as well as social problem- solving skills, and it is based on an “affective- behavioral- cognitive- dynamic model of development,” which emphasizes the developmen-tal integration of affect, behavior, and cognition (Greenberg & Kusche, 1993). PATHS has been shown to promote EF among second- and third- graders, which, in turn, mediated intervention effects on reduced problem behaviors (Riggs, Greenberg, Kusche, & Pentz, 2006). In a more recent study, 4- year- old children were randomly assigned to an enriched intervention Head Start REDI program that included the preschool PATHS curriculum or a control “usual practice” Head Start classroom (Bierman et al., 2008). Controlling for preintervention scores and a number of other variables, the results showed that children in the REDI pro-gram made significant gains on some measures of EF (cognitive flexibility and task orientation). Furthermore, intervention was particularly beneficial to the social competence and control of aggressive behaviors of those children who had started the school year with lower levels of EF. Finally, improvements in EF skills, particularly task orientation, partially mediated intervention effects on emergent literacy and social– emotional competencies. Bierman and colleagues (2008) sug-gest that the creation of a supportive interpersonal environment in the REDI program (e.g., establishing rules and routines in classrooms, promoting emotion regulation) positively affected EF.

The findings from longitudinal studies are consistent with the claim that individ-ual differences in EF lead to differences in emergent academic skills and academic achievement. However, longitudinal studies cannot rule out that an unmeasured third variable drives the relation between EF and academic outcomes. To address this problem, Willoughby, Kupersmidt, and Voegler- Lee (2012) have suggested the use of fixed effects models to account statistically for time- invariant (stable) influences on academic outcomes that are not directly attributable to changes in EF. Fixed effects models are used on repeated measures data and are created by regressing differences in the outcome (e.g., change in academic achievement) on differences in the predictor (e.g., change in EF). This use of within- person com-parisons allows a statistical basis for establishing causal associations that cannot be achieved with correlational or lagged analysis (Willoughby et al., 2012). In an example of the use of fixed effects models, Willoughby and colleagues (2012) administered two measures of inhibitory control and measures of emergent aca-demic skills (Woodcock et al., 2001) to preschoolers at the beginning (fall) and end (spring) of the school year. Using the standard statistical approach to test-ing the relation between EF and emergent academic achievement, Willoughby and colleagues found that inhibition predicted preschoolers’ reading, writing, and math skills, thus replicating findings from previous longitudinal studies. However, when fixed effects models were used, these associations became nonsig-nificant, suggesting that the evidence for an association between EF and academic achievement is masked by other time- stable influences on academic achievement (Willoughby et al., 2012). Furthermore, the results using fixed effects analyses raise the question of whether the associations between EF and academic out-comes may be spurious, due to the presence of unmeasured third variables.


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CONCLUSION

In this chapter, we reviewed evidence from cross- sectional and longitudinal stud-ies on the relation between EF and emergent academic skills and school achieve-ment. Overall, research shows that EF is strongly associated with and predictive of academic outcomes, even though it is not entirely clear whether particular aspects of EF are more important for academic outcomes than others. However, there is no strong empirical basis to support a causal inference. Interventions that target EF directly and that train particular aspects of EF (e.g., working memory) stud-ies have been criticized for lack of generalization and their long- term effects are unclear. Furthermore, few training studies have assessed whether changes in EF mediate changes in academic outcomes. More holistic programs and curricula such as Tools of the Mind, Montessori education, and PATHS were not designed to promote EF but rather target self- regulation, emotion regulation, and social competence. Both Montessori programs and PATHS programs appear to promote EF and emergent academic skills, and the study by Bierman and colleagues (2008) demonstrated that, consistent with a casual interpretation of the link between EF and academic outcomes, changes in academic skills in the PATHS program are mediated by intervention- based changes in EF. Even though in need of replication, these findings suggest that more global interventions that address the emotional and social basis of EF (PATHS) or provide children with material that encourages particular interactions (Montessori) might be more effective than interventions that target specific EF skills directly.

Further research on the relation between EF and emergent academic skills and academic achievement is needed to determine whether a causal interpretation of this well- document association between EF and academic outcomes is warranted. This research should more thoroughly assess whether intervention- based changes in EF mediate changes in academic outcomes, and it should also use more com-plex statistical models that control for potential confounds. Finally, this research must ultimately also clarify the exact mechanism by means of which EF affects emergent academic skills and school achievement.

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PART TWO

Diversity in the Development of Executive Functions

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8

The Bilingual Advantage

Evidence and Alternative Views

J . B R U C E M O R T O N A N D S T E P H A N I E M . C A R L S O N ■

Parents and educators in Canada are often faced with complicated questions concerning the costs and benefits of second— or even third— language acquisi-tion in young children. How does acquisition of a second language in the school context, as for example through a French immersion program, impact the development of a child’s primary language? If a student is struggling in French immersion, should she or he be switched to a first- language curriculum? And what are the costs and benefits of second- language education outside of the domain of language? This chapter focuses on one facet of these larger issues— namely the potential benefits of bilingualism on the development of executive function. There is evidence that growing up bilingual bestows lifelong advan-tages on a child’s ability to focus attention and think flexibly (Bialystok, Craik, Klein, & Viswanathan, 2004). The basic idea is that to produce utterances in one language, the bilingual child needs to inhibit words and phrases from her or his second language. Over time, the child’s inhibitory abilities become highly practiced and outpace those of monolingual age- mates. This “bilingual advan-tage,” as it is sometimes termed, can be observed as early as 7 months of age and extends well into retirement age, where it may afford protection against age- related cognitive decline (Luk, Bialystok, Craik, & Grady, 2011). It would seem reasonable therefore to promote bilingual education whenever possible, given the importance of executive functioning for academic and social achieve-ment (Bull, Espy, Wiebe, Sheffield, & Nelson, 2011; Mischel, Shoda, & Peake, 1988). There is, however, uncertainty about the origins of the bilingual advan-tage. Although bilingual and monolingual children may differ in how prac-ticed they are at regulating their attention, they may also differ in cultural and socioeconomic background, factors that are known to influence the develop-ment of executive functioning (Mezzacappa, 2004; Noble, Norman, & Farah, 2005). In fact, there is some evidence that properly controlling for cultural and

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socioeconomic differences between monolingual and bilingual children can attenuate or even eliminate the bilingual advantage. Our goal in this chapter is to provide an overview of the evidence concerning the nature and origins of the bilingual advantage and offer some cautionary notes about the interpretation of this evidence.

EVIDENCE

The ability to regulate one’s own thoughts and actions is an important predic-tor of later psychological, social, and physical well- being (Mischel et al., 1988). The development of these abilities, often referred to as the executive functions, was long thought to be governed by constitutional factors and to be largely immutable to experience (Dempster, 1992). Evidence that children who grow up learning two languages show an advantage in executive functioning relative to children who grow up learning only one language is among the more compelling challenges to this view, and it suggests the potential importance of experience in shaping the development of executive functioning.

Among the earliest observations of the bilingual advantage come from studies of bilingual and monolingual children’s reasoning about spoken utterances. In one study (Bialystok, 1988), groups of 5- , 7- , and 9- year- old children were asked to listen to a series of short utterances and, for each utterance, decide whether or not the utterance was grammatically correct. Half of the utterances contained grammatical errors (e.g., “I have two pencil”) and half did not. The task was made additionally challenging, however, by including utterances that were grammati-cally correct but anomalous in meaning (e.g., “Apples grow on noses”). Children therefore had to selectively monitor for the presence of grammatical errors while at the same time ignoring salient but irrelevant factual errors. Although bilingual and monolingual children performed comparably on utterances that were both grammatically and semantically correct, bilingual children performed better than monolinguals on grammatically correct but semantically anomalous utterances— that is, they were better able to block out salient but unusual meanings and remain focused on grammaticality.

This advantage has since been observed across the life span in a wide vari-ety of tasks, including tasks devoid of any obvious connection to language (Bialystok, 1999; Martin- Rhee & Bialystok, 2008). In the Simon task, for exam-ple, participants respond to the color of a stimulus in the face of spatial dis-traction. Participants are instructed to press a button with their right hand whenever a red square appears on a computer monitor, for example, and their left hand whenever a green square appears. Red and green squares are then presented on either the left or the right side of the monitor. Responses are typically faster and more accurate on compatible trials, when, for example, red squares appear on the right (or green squares on the left), than they are on incompatible trials when red squares appear on the left (or green squares on the right). This difference, referred to as the Simon effect, measures how much

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a participant is distracted by irrelevant information (in this case, the spatial location of the squares), and it is typically smaller in bilinguals compared to monolinguals (Bialystok et al., 2004). Another example is eye- movement con-trol tasks, in which participants need to look away from peripherally presented visual targets. Although this is highly demanding of executive control, given that most individuals are reflexively compelled to look toward rather than away from such peripheral stimuli, it is not related in any obvious way with lan-guage. Nevertheless, bilinguals show an advantage in eye movement control tasks relative to monolinguals. Finally, in task- switching paradigms, partici-pants are presented with stimuli that can be categorized in different ways. In the Dimensional Change Card Sort (DCCS; Zelazo, 2006), for example, chil-dren are given test cards that can be sorted either by shape or color. In pre-switch trials, they sort them one way (e.g., by color). Then, in postswitch trials, children have to switch and sort the same cards in a new way (i.e., by shape). Performance can be assessed either by measuring the proportion of partici-pants in a group that correctly switch sorting criteria (used in studies of young children) or by measuring the difference in response time on switch versus repeat trials (used in studies of older children and adults). Bilinguals show an advantage relative to monolinguals in the DCCS, with a greater propor-tion of bilinguals correctly switching relative to monolinguals (Bialystok, 1999; Bialystok & Martin, 2004). Thus, it would appear that across a wide variety of tasks, including those devoid of any obvious connection to language, children who grow up learning two languages are better able to filter out distracting information and remain focused on what is relevant.

More recent studies have examined whether the bilingual advantage extends to other aspects of executive functioning, such as the ability to delay immediate gratification in the interest of larger long- term rewards. In one study, for example, 50 children (12 full bilinguals, 21 partial bilinguals, and 17 monolinguals) aged 58 through 83 months were administered a battery of nine executive functioning tasks (Carlson & Meltzoff, 2008). Factor analysis of scores on these tasks con-verged on a two- factor solution, with six tasks that involved attention conflict (e.g., the DCCS) loading on one factor, and three tasks that involved delay (e.g., Delay of Gratification) loading on a second factor. Scores on individual tasks were then averaged to create separate Conflict and Delay scores for each participant. After controlling for differences in social background, full bilin-guals had higher scores relative to partial bilinguals and monolinguals on the Conflict scale, but they showed no advantage on the Delay scale. The second study (Poulin- Dubois, Blaye, Coutya, & Bialystok, 2011) extended these findings by administering a battery of three conflict tasks and two delay tasks to sixty- three 24- month- olds (33 bilinguals, 30 monolinguals). Consistent with the find-ings of the first study, bilingual toddlers were advantaged relative to monolinguals in managing attention conflict, but they showed no advantage in the ability to sustain delay. It would appear, therefore, that the bilingual advantage does not generalize to all aspects of executive functioning, but it is most pronounced in the context of managing attentional conflict.


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ORIGINS AND ALTERNATIVE VIEWS

Although evidence of the bilingual advantage in executive control is relatively well established, questions concerning its origin remain largely unanswered. The pre-vailing interpretation is that the bilingual advantage originates in the practice of attention control (Bialystok & Craik, 2010). On this account, a bilingual’s two lan-guages are constantly active during everyday language use, and therefore compre-hending and producing utterances in a target language requires the suppression of lexical entries (i.e., translation equivalents) from the nontarget language. Over time, this leads to massive amounts of practice managing competing demands on attention, the benefits of which transfer to standard attention control tasks. This practice of attention control account accommodates much of the extant evidence, and it has motivated widespread interest among scientists, educators, and layper-sons in cognitive benefits associated with bilingualism.

There are, however, a number of findings that are clearly inconsistent with pre-dictions of the practice of attention control account. For example, the practice of attention control account claims that the bilingual advantage derives from practic-ing attention control and the ongoing need to suppress translation equivalents (i.e., words from the other language) in the course of daily language use. One predic-tion that follows from this account is that there should be no evidence of a bilin-gual advantage very early in development, when infants have merely been exposed to two languages but understand few if any words, and produce none. However, inconsistent with this prediction, the bilingual advantage has been observed in 7- month- old infants. In one study, 7- month- old monolingual infants (i.e., infant exposed to one language at home) and bilingual infants (i.e., infants exposed to two languages at home) learned that a cue predicted where an interesting visual stimulus would appear a second or two later. In initial trials, the cue predicted the stimulus would appear in one location (e.g., the right side of a screen); in later trials, the cue changed, and it predicted the stimulus would appear at a different location (i.e., the left side of the screen). Monolingual and bilingual infants learned comparably in initial trials. However, in later trials, when infants had to suppress looking to the first location (i.e., the right side of the screen), bilinguals learned more quickly than monolinguals (Kovács & Mehler, 2009). Whatever contributed to the bilingual advantage among these infants, it clearly could not have been prac-tice derived from everyday language use, as these infants were all preverbal (i.e., they did not speak) and prelexical (i.e., they understood few, if any, words).

A second, related prediction of the practice of attention control account is that the magnitude of the bilingual advantage should increase as children grow older. Assuming younger and older bilinguals are equally proficient in both languages, the older should exhibit a greater advantage in attention control compared to the younger, given greater opportunities for practice. There is, however, no clear evi-dence of age- related change in the magnitude of the bilingual advantage— if any-thing, the advantage appears to be smaller among college- aged adults (Bialystok et al., 2004) than among children (Bialystok, 1999) and infants (Kovács & Mehler, 2009).


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Over and above inconsistencies in the practice of attention control account, there are a number of alternative explanations for the bilingual advantage that deserve attention. Bilingual and monolingual children can, for example, differ in many ways beyond their knowledge of languages, including cultural background and socioeconomic status. As an illustration, one very influential Canadian study found greater cognitive flexibility among immigrant children who spoke English and Chinese as compared to nonimmigrant children who spoke only English (Bialystok, 1999). This difference was attributed to a greater opportunity for bilin-gual children to practice attention control, but it could have just as easily been attributed to differences in the cultural and socioeconomic backgrounds of Asian immigrant and Caucasian nonimmigrant children. Asian families adhere to the values of collectivist cultures that place a premium on self- regulation and control, whereas Caucasian North American families adhere to the values of individualist cultures that place a premium on self- assertion (Sabbagh, xu, Carlson, Moses, & Lee, 2006). Furthermore, owing to an immigration policy that favors highly skilled applicants, Canadian immigrants are more educated than their nonim-migrant counterparts (for discussion, see Morton & Harper, 2007). These cultural and socioeconomic factors are not mere nuisance variables but strongly influence children’s executive functioning. Therefore, at face value, it is unclear whether the bilingual advantage observed in this study derived from different levels of atten-tion control practice among bilinguals and monolinguals, or from differences in their cultural and socioeconomic backgrounds.

Given that cultural and socioeconomic factors are an important influence on the development of executive control, and are frequently confounded with differ-ences in language status in studies of the bilingual advantage, several studies have examined what happens to the bilingual advantage when cultural and socioeco-nomic differences between bilingual and monolingual children are properly con-trolled. One such study compared executive functioning in English monolinguals (living in the United Strates), Korean- English bilinguals (living in the United States), and Korean monolinguals (living in South Korea). As expected, Korean- English bilinguals scored higher on measures of executive functioning scores than English monolinguals. Of course, it was unclear whether this difference reflected a difference in language status (bilingual vs. monolingual) or culture (collectiv-ist vs. individualist). To clarify, the authors compared executive functioning in the English monolinguals (individualist culture) and Korean monolinguals (col-lectivist culture). The results were clear and compelling: Executive functioning scores were higher in Korean monolinguals than English monolinguals, but indistinguishable from executive functioning scores in Korean- English bilinguals (Carlson & Choi, 2009). These data are important because they suggest that dif-ferences that appear to relate to language status may derive from differences in cultural background. A second such study compared attention control among monolingual and bilingual children of identical socioeconomic backgrounds (i.e., socioeconomic status) by means of the Simon task (Morton & Harper, 2007). As expected, socioeconomic status was associated with Simon performance, with children from higher socioeconomic status families showing fewer errors relative


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to children from lower socioeconomic status families. Interestingly, however, monolingual and bilingual children performed identically. These data are impor-tant because they suggest that properly controlling for differences in socioeco-nomic status can attenuate the bilingual advantage.

In sum, although evidence of the bilingual advantage in executive control is well established in the literature, there is considerable uncertainty about the ori-gin of these effects. The prevailing account emphasizes increased opportunities for bilinguals to practice attention control owing to their need to manage two competing language systems. However, other data suggest alternative accounts may also need to be considered.

IMPLICATIONS FOR EDUCATORS AND PARENTS

Although nuanced debates about the effects of bilingualism on cognitive devel-opment are likely to continue for years, parents and educators in Canada face immediate questions concerning the costs and benefits of second- or even third- language acquisition in young children. Regrettably, it is beyond the scope of this chapter (and our expertise) to advise on these matters. What we can offer, how-ever, is a measured assessment of the literature on the bilingual advantage. Thus, from our vantage point, it seems likely that (1) children who grow up learning two languages will show a small advantage in executive control relative to chil-dren who grow up learning one; (2) these benefits will not extend to all aspects of executive functioning, but they will be most pronounced in the domain of attention control; and (3) these benefits will be apparent long before children show up for the first day of school and will persist for long after children finish formal education. What remains unclear is why these differences arise in the first place. It is quite possible that managing two languages makes ongoing demands on attention control, so that over the course of time, bilinguals become more proficient selecting relevant information from the environment and filtering out distractions. However, it is also quite possible that some of these differences reflect differences in the social, cultural, and economic backgrounds of bilingual and monolingual children.

REFERENCES

Bialystok, E. (1988). Factors in the growth of linguistic awareness. Child Development, 57, 498– 510.

Bialystok, E. (1999). Cognitive complexity and attentional control in the bilingual mind. Child Development, 70(3), 636–644.

Bialystok, E., & Craik, F. I. M. (2010). Cognitive and linguistic processing in the bilin-gual mind. Current Directions in Psychological Science, 19(1), 19– 23.

Bialystok, E., Craik, F., Klein, R., & Viswanathan, M. (2004). Bilingualism, aging, and cog-nitive control: Evidence from the Simon task. Psychology and Aging, 19(2), 290–303.


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Bialystok, E., & Martin, M. (2004). Attention and inhibition in bilingual children: evidence from the dimensional change card sort task. Developmental Science, 7(2), 325–339.

Bull, R., Espy, K. A., Wiebe, S. A., Sheffield, T. D., & Nelson, J. M. (2011). Using confir-matory factor analysis to understand executive control in preschool children: Sources of variation in emergent mathematic achievement. Developmental Science, 14(4), 679– 692.

Carlson, S. M., & Choi, H. P. (2009). Bilingual and bicultural: Executive function in Korean and American children. Talk presented at the 2009 biennial conferences ofthe Society for Research in Child Development, Denver, CO, April 2–4. SRCD, 2009.

Carlson, S. M., & Meltzoff, A. N. (2008). Bilingual experience and executive functioning in young children. Developmental Science, 11(2), 282– 298.

Dempster, F. N. (1992). The rise and fall of the inhibitory mechanism: Toward a unified theory of cognitive development and aging. Developmental Review, 12, 45– 75.

Kovács, A. M., & Mehler, J. (2009). Cognitive gains in 7- month- old bilingual infants. Proceedings of the National Academy of Sciences USA, 106(16), 6556– 6560.

Luk, G., Bialystok, E., Craik, F. I. M., & Grady, C. L. (2011). Lifelong bilingualism maintains white matter integrity in older adults. Journal of Neuroscience, 31(46), 16808– 16813.

Martin- Rhee, M., & Bialystok, E. (2008). The development of two types of inhibitory control in monolingual and bilingual children. Bilingualism, 11(1), 81– 93.

Mezzacappa, E. (2004). Alerting, orienting, and executive attention: Developmental properties and sociodemographic correlates in an epidemiological sample of young, urban children. Child Development, 75(5), 1373– 1386.

Mischel, W., Shoda, Y., & Peake, P. K. (1988). The nature of adolescent competencies pre-dicted by preschool delay of gratification. Journal of Personality and Social Psychology, 54(4), 687– 696.

Morton, J. B., & Harper, S. (2007). What did Simon say? Revisiting the bilingual advan-tage. Developmental Science, 10(6), 719–726.

Noble, K. G., Norman, M. F., & Farah, M. J. (2005). Neurocognitive correlates of socio-economic status in kindergarten children. Developmental Science, 8(1), 74– 87.

Poulin- Dubois, D., Blaye, A., Coutya, J., & Bialystok, E. (2011). The effects of bilingual-ism on toddlers’ executive functioning. Journal of Experimental Child Psychology, 108(3), 567– 579.

Sabbagh, M., xu, F., Carlson, S., Moses, L., & Lee, K. (2006). The development of execu-tive functioning and theory of mind: A comparison of Chinese and US preschoolers. Psychological Science, 17(1), 74– 81.

Zelazo, P. D. (2006). The Dimensional Change Card Sort (DCCS): A method of assessing executive function in children. Nature Protocols, 1(1), 297– 301.

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9

Executive Functions and Plurilingualism in Young Children

M A U R E E N J . H O S K Y N ■

Young plurilingual children access an array of cognitive and linguistic resources as they construct meanings from their social, emotional, and cultural experience. Even in infancy, they not only attend to their own moment- to- moment actions as they unfold; they also attend to the actions of others, which include the use of spoken sounds/ words, gestures, and other linguistic and cultural symbols. As they grow older and as their engagement with languages in multilingual practices increases, children come to know how linguistic symbols, social experience, and meanings are connected and shared among individuals. In the words of Mead, “What language seems to carry is a set of symbols answering to certain content which is measurably identical in the experience of the different individuals. If there is to be communication as such, the symbol has to mean the same thing to all individuals involved” (Mead, 1934, p. 54). From this perspective, the signifi-cance of linguistic symbols rests in their use during the construction of meaning in multilingual practices. This is a dynamic process, for people and the languages they use are always changing; common points of reference are constantly in flux; and multiple perspectives and absolute meanings are unavailable (Cenoz, 2013; De Bot, Lowie, Cenoz, & Vespoor, 2005; Herdina & Jessner, 2013). To engage in these highly dynamic activities, children must learn to regulate their attention efficiently using a system of cognitive resources, collectively known as executive functions.

To illustrate how children monitor attention to linguistic symbols (spoken words) as they engage with others in multilingual practices, consider a 6- year- old child who interacts with her French- English bilingual parents. French is a lan-guage of cultural origin of the child’s mother, and strong feelings of intimacy and attachment emerge as the child communicates using spoken French words with her mother during everyday activities (e.g., as they share a meal, read a bedtime

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story, discuss the day at school, and so forth). In contrast to the sense of warmth and personal connectedness that emerges when using spoken French words with her mother, more reflective thought processes about the cultural significance of the French language emerges when she communicates using French with her father. That is, French is spoken on rare occasions by her father, and always in a didactic manner to impress upon her a need to preserve the family’s cultural her-itage (e.g., as he assists her with French homework or with writing letters to her monolingual French- speaking grandparents). As a result of these differences in how the French language is used, a simple phrase such as “Regardez ici” (“Look here”) evokes different social and connotative meanings for the child, depend-ing on which parent is speaking to her. Subtle variations in connotative mean-ings arise as children engage with family, friends, and acquaintances who use languages differentially in multilingual practices. From this perspective, mean-ings associated with language use can only be created through social experience; meanings are not universal or embedded in a hierarchy of preexisting language forms or structures.

Not surprisingly, attending to people and the languages they speak in multi-lingual practices can be cognitively demanding. These cognitive demands are thought to increase markedly as children learn to self- regulate their language use. For instance, cognitive flexibility may be necessary to switch automatically among known languages, at times within the same sentence, phrase, or word; inhibitory control may facilitate the suppression of attention to a language or social action that is familiar while attending to a language or action that is less familiar; working memory may update and refresh the executive system as new linguistic, emotional, or social information becomes available over time. The nature of this executive system of cognitive resources and how it is used to sup-port children’s engagement in multilingual practices is dynamic and complex. In this chapter, this complexity is explored through a lens that views access to an executive system as a valuable resource to support children’s emerging plu-rilingualism; however, the reverse is also true. As children monitor and control their use of languages in dynamic multilingual practices, the executive sys-tem available to them is constantly reconfigured to adjust to these fluctuating demands.

Two arguments are discussed in the chapter. One position posits an executive system is recruited when actions within multilingual practices are either novel or challenging for children. Executive functions are important assets to support construction of social meanings in multilingual practices. When this engagement is familiar and routine, such as when children communicate with caregivers using languages heard since birth, meanings are easily co- constructed and demands on an executive system are likely minimal. Alternatively, when children communi-cate with people who speak unfamiliar languages, construction of meanings is far from routine or predictable, and cognitive demands are markedly increased. The distinction between meanings constructed easily and meanings that arise only with cognitive effort is not categorical or fixed; rather, the relations between


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meaning construction and cognition are constantly in flux, as children’s social and cultural networks expand to include an increasingly diverse group of people who share one or more languages with them.

The second argument that weaves throughout the chapter proposes that chil-dren are active social agents, and they access their repertoire of executive functions to self- regulate their use of languages as they accomplish things as they speak: They persuade others, describe actions, make requests, and so forth. A question that arises for clinicians is: “What effects occur for young children when access to this executive system of cognitive resources is less than optimal?” One possibility is that social communication breaks down when children are not able to access sufficient resources to manage the complex attentional demands associated with constructing meanings in multilingual practices. Yet many young children are able to engage effectively in multilingual practices, even when their capacity for executive functions is developmentally and/ or biologically constrained and long before they have attained adult like spoken proficiency in one or more of their lan-guages (Grosjean, 2010). This social communication occurs because children use the set of linguistic and cognitive assets available to them to meet their intents or social goals. The implication is that intervention practices that focus on optimiz-ing engagement and construction of meanings in multilingual practices will have lasting effects on children’s emerging language competencies and, at the same time, configuration and growth of an executive system will occur. This approach requires that clinicians become aware of how plurilingual children use their lan-guages intentionally as they co- construct meanings in multilingual practices. In this way, intervention efforts build from a focal area of personal strength, rather than weakness.

To make these arguments, the chapter begins with an overview of current con-ceptions of plurilingualism and plurilingual competencies in young children. As executive functions involve the control of attention and action, the next section discusses how young children intentionally regulate their attention to languages, actions, and interactions in multilingual practices. Three child- specific factors thought to mediate children’s use of executive functions in multilingual practices are reviewed: children’s age when they first encounter their languages, their exist-ing linguistic competencies, and their agency social actors. The chapter concludes with the proposal that the executive system in use by children can be represented on a continuum that parallels the quality of their active engagement in multilin-gual practices. Whenever possible, research findings related to clinical practices that support social communication of plurilingual children with limited access to executive functions are discussed.

PLURILINGUALISM AND PLURILINGUAL COMPETENCE

The conceptions of plurilingualism and plurilingual competence discussed here take their inspiration from Herder (2002), Sapir (1949), Whorf (2012), and other theorists who propose that language influences thought, and speakers of different


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languages think about the world in different ways. It follows, then, that how young children experience and use their languages influences how they come to know and think about their social worlds. Plurilingual competence in this context refers to children’s ability to negotiate meanings with others using the multiple linguistic and cognitive resources available to them. This interpretation acknowledges that children’s proficiencies in their languages will vary, but all languages in their rep-ertoire have potential to support the process of constructing meaning. The suite of language resources available to children is used to navigate social interactions; speakers are social actors, and the structural boundaries of language systems are outcomes of social action (Giddens, 2009). Plurilingualism implies a fluidity of signification and meaning, provisional and contingent upon when languages are used, where they are used, and for what purposes in multilingual practices.

Multilingual practices also embody the societal, cultural, and institutional dimensions of language use (Lüdi, 2006), in addition to those at the interactional level. Children are members of cultural groups, and as such, they are expected to come to know the cultural tools and expectations that are shared among indi-viduals in their multilingual practices. Even when children have never interacted directly with particular individuals in a cultural group, they share an indirect, common cultural ground that is transmitted intergenerationally and beyond the immediate social context. As they switch from one language to another, children adapt their language use to the social context at hand, but they also flexibly cross borders between cultures and share different perspectives on the world (Cenoz, 2013). Lüdi (2006) describes such activity as intercultural communication, where cultural values are compared and evaluated in relation to each other and inte-grated into an overall metasystem of values— a process that contributes to chil-dren’s emerging social identities and plurilingual personalities.

The conceptualization of plurilingualism and plurilingual competencies described here aligns well with “holistic views of multilingualism,” where the coexistence and constant interaction of multiple languages in the same mind is conceived as a complete language system (Cook & Bassetti, 2011; Grosjean, 2010; Lüdi & Py, 2009). Common to both plurilingual and holistic multilingual per-spectives is the idea that children do not need to be fully proficient in all of their languages to negotiate meanings with others. In support of this view, Grosjean (2010) notes that multilingual children and adults rarely attain native- like profi-ciencies in all of their languages, yet they are often able to communicate effectively with individuals who share even partial knowledge of diverse language systems and/ or their related social and cultural conventions. This communication is made possible because social meanings do not preexist in either children’s minds or in a static, universal system of rules that governs language use. Instead, mean-ings emerge as plurilingual children learn what they can accomplish through their interactions in the world. They learn to predict how others will react to their actions, and in this way, they learn the meanings that their actions and their use of multiple languages have for others.

The linguistic, social, and cultural dimensions of interacting with others in multilingual practices are complex and differ markedly from those found through


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engagement in monolingual practices, where a single language is in use. For this reason, there is a general consensus that plurlilingual children are individuals in their own right and cannot be represented as a composite of monolinguals who speak their languages (Cook & Basetti, 2011; Grosjean, 2010). It follows, then, that describing the language proficiencies of plurilingual children in relation to a monolingual normative standard in each of their languages is likely to fall well short of providing an overview of their emerging plurlingual competencies in multilingual practices.

Comparisons between the language proficiencies of plurilingual children and monolingual peers are typically made to address concerns about the pos-sible deleterious effects of learning more than one language on children’s over-all language development and social communication. Bialystok, Luk, Peets, and Yang’s (2010) study of receptive vocabulary growth in bilingual and monolin-gual children between the ages of 3 and 10 years is illustrative. A standardized measure of English receptive vocabulary (English Peabody Picture Vocabulary Test; PPVT; Dunn & Dunn, 2007) was used to document age- related change in vocabulary development. Monolingual children at each age outperformed their bilingual age peers, a finding that was interpreted as evidence that learning two languages simultaneously results in a cost to vocabulary development in each language. However, follow- up studies that compared the combined vocabulary knowledge in two languages of bilingual children with vocabulary of monolin-gual children in a single language found no difference in the total number of words produced by each group (e.g., Hoff et al., 2012). Furthermore, Akhtar and Menjivar (2012) reanalyzed data from Bialystok, Luk, Peet, and Yang’s (2010) study to show that although bilingual children had, on average, somewhat lower English vocabulary scores than their monolingual peers, the mean score of the bilingual group was still well within the average range for English vocabulary development. Moreover, while vocabulary knowledge of bilingual groups rela-tive to their monolingual peers was slightly depressed, this had no deleterious effects on academic achievement beyond factors that are thought to play a more significant role, such as socioeconomic status, prenatal nutrition, exposure to sleep, and environmental toxins. Plurilingual children are not confused by dual- language input (Hakuta, 1987), and their knowledge of the phonological, lexical, and grammatical systems in any one language evolves along similar trajectories as monolingual children who speak the language, albeit at a slower pace (Hoff, Rumitche, Burridge, Ribot, & Welsh, 2014). In summary, reliance upon stan-dardized measures of language proficiency normed on samples of monolingual, native speakers will likely produce an underestimate of plurilingual children’s long- term academic, social, and linguistic competencies.

Similarly, comparing plurilingual children’s performance on executive function tasks to a monolingual normative standard may also be a poor predictor of long- term developmental outcomes. It is well documented that plurilingual children typically outperform their monolingual peers on measures of executive functions (see Chapter 8 in this volume for a review of research on the “bilingual advan-tage”). However, although cognitive benefits accrue for young children who receive


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intensive and sustained exposure to additional languages, this cognitive advantage appears to have little substantive effect on plurilingual children’s long- term academic or social outcomes beyond that explained by socioeconomic status or cultural par-enting practices. In general, comparisons between plurilingual and monolingual children’s language or cognitive development do little to further understandings of how plurilingual children access executive functions to support their use of languages to construct meanings in multilingual practices. It is important to acknowledge that plurilingual children are social actors in their own right. To capture the significance of this statement, the next section operationalizes the terms “social actions,” “interac-tions,” and “intentionality” within the context of multilingual practices.

SOCIAL ACTIONS, INTERACTIONS, AND INTENTIONALITY

An action can be rendered social simply because the action of an individual is intended to influence others. For instance, a plurilingual preschooler who shouts “Swing!” as he runs to a swing set on a playground, but who remains oblivious to the play of children about him is engaging in action that is not social. On the other hand, if the child initiates eye contact with another child nearby and points to the swing as he shouts and runs to it, the action becomes social. Whether the social act becomes an interaction depends on whether the child nearby notices and responds to the speech and/ or gesture of the child running. That is, a social interaction occurs when attention to languages and/ or other cultural symbols (i.e., gesture, pointing, speech) is jointly shared with another individual in an action sequence.

Several levels of intentionality are associated with plurilingual children’s lan-guage use within social actions. For example, consider a young, plurilingual child as she plays with several toys with her mother. The child first looks at her French- English bilingual mother, smiles, and utters the English phrase “for me” as she reaches for a ball nearby. When her mother doesn’t respond, the child looks at her again and uses French words “pour moi” as she reaches for the ball a second time. If a question is asked about the “why” of the repeated social action of reach-ing for the ball and the use of languages during this action, a chain of causes could be implied ranging from the transparent (“to hold the ball”) to proximal, more opaque reasons (“to claim the ball as my own”) to more distal, esoteric rea-sons (“to be happy” or “to play with the ball with my mother”). At which point in this social exchange can one determine the child’s intention? Pezzulo and Castelfranchi (2009) suggest that the “how” of an action sequence, rather than the “why,” addresses this question; specifically, what is important is the process of “steering and controlling actions until the intended goal is achieved” (p. 562). Related questions that ask why a child speaks words become replaced with the question: “How did the controlled use of words influence a social outcome?” Language use in a social exchange (the child looks and speaks with her mother as she reaches for the ball) is predictive of a range of intentions that are further


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refined as the child engages in ongoing social activity. For instance, the final out-come of speaking English or French phrases while reaching for the ball could be that the child’s mother assists her, and the child successfully grasps the ball, or it could result in a failed action where the child’s mother assists, but the ball remains out of reach, or the action may result in another unintended effect, such as the child’s mother helps, but the ball rolls further away. As the child repeatedly engages in the social action of communicating with her mother while reach-ing for the ball, the language embedded in the action takes on a routine, shared meaning.

Demands on an executive system of resources is likely minimal when meanings associated with social actions are routine and when the use of languages aligns well with an intended effect. However, when a social action and/ or language use results in a failed or unintended effect, access to a suite of executive resources theoretically functions to support a shift in the course of events (such as in the previous example, where the child switches from English to spoken French to garner her mother’s attention and to influence her). Social actions provide a window to observe how plurilingual children access languages to construct shared meanings that culminate in either intended or unintended effects. Observations of social activity, in turn, lead to predictions about how an executive system of cognitive resources has been accessed to shift, plan, update, and/ or modify existing social and linguistic behavior.

A related question to consider when observing children’s social activity in mul-tilingual practices is: “What child- specific factors may be affecting access to this integrated cognitive and language system?” How an executive system emerges in early childhood among plurilingual children is not well understood; however, the research currently available suggests that this executive system is influenced by three factors: children’s age when they first encounter their languages, their exist-ing plurilingual competencies, and their agency to regulate their choice and use of languages as social actors.

AGE AND LANGUAGE ENGAGEMENT

Findings from neuroimaging studies show the human brain changes both func-tionally (Kroll, Bobb, & Hoshino, 2014; Noort et al., 2014) and structurally (Mechelli et al., 2004) in response to the timing of plurilingual children’s first exposure to their languages. The magnitude and quality of this change have been shown to differ, based on the age at which dual (or multiple) language exposure first occurs, for babies living in homes where multiple languages are spoken experience their languages at an age when neural plasticity is most robust (May, Byers- Heinlein, Gervain, & Werker, 2011). Findings from brain- imaging stud-ies on infants exposed to either one or two languages show that regardless of the number of spoken languages heard about them, babies recruit the same language- dedicated neural tissue during processing of the sounds in speech. However, when the first intensive exposure to another spoken language occurs after the child has spoken in a language heard since birth (usually after a child has reached the age of


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5 years), more bililateral and distributed activation occurs in frontal areas associ-ated with executive functions such as working memory and inhibitory control (Weber- Fox & Neville, 1999). That is, the neural organization that emerges for language processing is altered among plurilingual children who learn to use an additional, unfamiliar language as they reach school age, particularly in frontal brain regions affiliated with an executive system (Costa & Sabastián- Gallés, 2014). Although some researchers point to the age when children first engage with their languages as the critical determinant of these differences in brain activity, other findings suggest that variation in children’s emerging plurilingual competencies accounts for the involvement of the regions of the brain associated with executive functioning.

EMERGING PLURILINGUAL COMPETENCIES

An awareness of how languages and other cultural symbols are used in social actions begins early in development long before infants start to speak, for it is well established that infants attend to and imitate the actions of others in their early social encounters. At birth, infants have broad sensitivities to the sounds that make up any human language (Aslin, Pisoni, Hennessy, & Perey, 1981) and to differences in spoken utterances from languages from different rhythmic classes (Tincoff et al., 2005). By the age of 4 months, infants are sensitive to the regu-larities in patterns of caregiver speech (Kovács & Mehler, 2009), and they can discriminate spoken languages from the same rhythmic class if they have had experience with these spoken languages in their social environments (Byers- Heinlein & Fennell, 2014). At this age, infants are perceptually attentive to sounds in isolation (Werker & Tees, 1984), as well as sounds within spoken word forms (Byers- Heinlein, Burns, & Werker, 2010) that differentiate spoken language sys-tems, including languages they have never heard before. Although preverbal infants maintain the ability to discriminate sounds in unfamiliar spoken lan-guages at 4.5 months (Bosch & Sebastián- Gallés, 2003), this capacity declines between the ages of 6 and 10 months. By their first birthday, infants are no longer able to discriminate sounds in unfamiliar spoken languages, irrespective of the number of languages they have heard spoken about them (Werker & Tees, 1984). The perceptual narrowing observed for speech sounds in languages also extends to other properties of the speech signal: lexical stress (Abboub, Bijeljac- Babic, Serres, Heohle, & Nazzi, 2015), lexical tone (Mattock, Molnar, Polka, & Burnham, 2008), and speech prosody (Bosch & Sebastián- Gallés, 2003). Perceptual narrow-ing has also been observed for visual features of languages, specifically how visual facial cues are utilized to discriminate spoken languages (Sebastián- Gallés & Diaz, 2012; Weikum et al., 2007). Weikum and collegues found that infants between the ages of 4 and 6 months who lived in homes where only English was spoken reliably discriminated faces speaking either English or French languages in silent videos; however, by the age of 8 months, they were unable to make these discriminations. In contrast, infants raised in homes where both English and French languages


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were spoken maintained their ability to discriminate these spoken languages from facial cues at 8 months.

Whether attention to the perceptual features of diverse spoken language sys-tems invokes a significant cognitive cost for infants is a matter of unresolved, theoretical debate. One possibility is that infants automatically separate languages they have heard spoken to them from birth; therefore, attending to diverse lan-guage systems for this group of children involves little cognitive effort. An alterna-tive explanation suggests that although infants are able to differentiate the speech signal of diverse languages, this language system is highly integrated; therefore, inhibiting the sounds of one spoken language to attend to speech in another lan-guage involves a cognitive cost. Regardless of the mechanism that underlies infant detection of spoken language systems, the perception of a speech or visual signal is only one aspect of plurilingual communication in infancy. Around the age of 8 to 10 months, infants are able to anticipate the actions of those with whom they interact (Buresh & Woodward, 2007), and they begin to use spoken languages themselves within embodied, social experiences (Bates, Camaioni, & Volterra, 1975, Daum, Sommerville, & Prinz, 2009). By their second year of life, toddlers have a growing sense of intentionality, and they respond to what people mean to say and do, rather than simply what they do. Children of this age are beginning to understand how actions, language use, and mental states of others are conveyed in social communication. As they grow older, young children come to know that the exact same phenomenon may be construed in many different ways depending on the languages in use and how they are used by different people.

At times, interacting with others using multiple languages is routine, and inten-tional goals are highly predictable. In this case, demands on an executive system of cognitive resources is likely minimal; however, at other times, when language use within social activities is novel and/ or when intentional goals are less clear, demands on an executive system are likely increased. It follows that observation of plurilingual children’s engagement with their languages during social activ-ity provides a valuable lens through which to observe the functionality of their executive system of cognitive resources. Because engagement with languages in multilingual practices is associated with reconfiguration and growth of an execu-tive system, optimizing children’s awareness of diverse languages and their use of these languages in multilingual practices may also benefit development of execu-tive functions.

PLURILINGUAL CHILDREN AS SOCIAL ACTORS

Plurilingual children move flexibly among their languages as meanings shift and evolve through engagement in social actions. This movement can occur intra-sententially or intersententially within the same conversation, and it is often described as “code switching” or “code mixing” (Gumperz, 1973). Originally, code switching among autonomous language systems was thought to function as a way to compensate for a lack of vocabulary knowledge (Ribot & Hoff, 2014). However,


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more recent study findings show that code switching serves a number of functions in multilingual practices beyond filling a gap for unknown vocabulary (Ferguson, 2009; Gajo, 2007; Lewis, Jones, & Baker, 2012). That is, language selection and choice are controlled actions, even among preschoolers (Greene, Peña, & Bedore, 2013), and movement among languages is thought to be important for children to assume cultural identities (Kramsch & Whiteside, 2007), to communicate inten-tions (Yow & Markman, 2015) or emotions more accurately (Yow & Markman, 2011), and to assist in the monitoring and accommodation to the language use of others. Terms such as “shuttling among languages” (Creese & Blackledge, 2010), “code meshing” (Canagarajah, 2011), or “translanguaging” (Garcia and Wei, 2013) have been used in place of “code switching” to reflect the recursive ebb and flow of resources in a unified language system.

The reasons for moving among language systems in social activity are complex. It is well documented in sociological investigations that languages are anything but neutral, and some languages are legitimized or valorized more than others at any given point in time in a multilingual society (Pavlenko & Blackledge, 2004). As a result, plurilingual children are both socially positioned and assert their own social positions through the status and power reflected in the languages they use in communication with others (Pratt & Hardy, 2014). High- status languages are chosen by children either to claim identities they value or to resist identities that socially position them in undesirable ways. Norton (2000) proposes that children “invest” in their languages to the extent needed as they negotiate their identi-ties within social groups. Languages in this sense are a form of cultural capital (Bourdieu, 1977). Unlike the concept of motivation, the notion of “investment” considers factors related to power and identity to account for children’s use of languages in a particular social context. Although there is no direct evidence to link children’s emerging plurilingual identities with executive functions, it seems plausible that the investment children place in their languages also reflects a will-ingness to allocate the cognitive effort necessary to attend to, monitor, and move among these languages in social activity.

To date, most research that has examined the influence of executive func-tions on children’s movement among languages has adopted a psycholinguis-tic approach. It is well documented that when one language is spoken, all other known languages of the individual are also activated in the brain (Misra, Guo, Bobb, & Kroll, 2012); therefore, interference from competing language systems must be controlled (Costa, Albareda, & Santesteban, 2008; Green & Abutalebi, 2013; Runnqvist et al., 2012). How this language interference is managed is a matter of some theoretical debate. One possibility is that words in the nonin-tended language are actively inhibited (e.g., Kroll, Bobb, Misra, & Guo, 2008). For example, when a French- English bilingual child intends to use the English word “book,” the French translation “livre” competes for selection, and therefore must be inhibited in order for the child to say “book.” Another possibile explanation is when a child switches to speaking words in a new language, words in the lan-guage previously used must be inhibited. Empirical evidence for this idea comes from studies that report a delay in speech production when the word or sentence


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spoken is in a language that has been previously used (Philipp & Koch, 2009). The delay in speech production associated with switching out of a language may represent inhibition, to suppress spoken words that remain activated, or work-ing memory, to integrate a code- switched sentence with a sentence in the lan-guage previously used (Moreno, Federmeier, & Kutas, 2002). On the other hand, Costa and his colleagues (e.g., Costa, Miozzo, & Caramazza, 1999) propose that the word selection involves only the activation of the language in use; words in the language not in use are ignored; therefore, they cannot compete during word selection. Therefore, general attentional control is thought to underlie intended word selection. A third explanation, described previously in this chapter, is young infants who have repeated and intense exposure to multiple languages from birth have a perceptual advantage in these languages, and moving among them repre-sents very little cognitive cost to the child.

Clearly, more studies are needed to clarify how access to executive functions influence and/ or are influenced by children’s movement among languages; how-ever, the evidence available suggests that observation of code switching is helpful to assess the functionality of the language and cognitive resources available to young, plurilingual children.

EXECUTIVE FUNCTIONS AS A CONTINUUM OF FUNCTIONAL, COGNITIVE RESOURCES

One way to conceptualize the executive system available to a plurlingual child is on a continuum of complexity that parallels the complexity of the child’s active engagement in multilingual practices. At one end of the continuum, social actions and multiple language use are familiar and functional, in that they satisfy the child’s immediate, basic interests or needs in social activity (e.g., speaking a pre-ferred language to convey intimacy and attachment to a caregiver; weighing the personal significance of languages in use, independently of whether use of these languages follows; code switching among highly familiar languages to describe a shared event). At this endpoint on the continuum, social actions, interactions and multiple language use are routine and familiar to the child. Predictably, the cogni-tive cost of constructing meanings at this end of the continuum is much less than at the far opposite point on the continuum where actions and multiple language use are not scripted or predictable. he child who interacts with people who share some, but not all of his languages must consider the culturally diverse perspec-tives of others as he intentionally adjusts and adapts his own use of languages to meet functional goals (e.g., to argue, persuade, cajole, explain, reason, and so forth). Code switching at this endpoint is reflexive, as the child moves among his languages and accesses a range of executive resources to bias and shape unpredict-able social actions. Access to an executive system occurs flexibly and recursively in response to rapidly shifting changes in multilingual practices, representing dif-ferent points along this continuum.


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To illustrate the two endpoints of this continuum, consider the following exam-ple where a child has spoken competencies in English and French, but prefers to use English because he knows more English vocabulary and feels more con-fident speaking in English. After coming home from school, the child opens the refrigerator and uses English words to ask his mother: “Is there anything to eat?” Opening the refrigerator door and asking his mother about the food available is a routine action that has been repeated many times in the past by the child, and the child easily predicts his mother’s response, depending upon what food he sees in the refrigerator. This routine use of English embedded in social action is highly functional and involves little cognitive cost to the child. Access to a repertoire of executive functions is likely minimal and therefore represents one end of this pro-posed continuum. Alternatively, when the child leaves the kitchen and goes to his room to play a computer game with his monolingual, French- speaking friend from school, this collaborative social activity is far from routine and unpredictable. Yet the child is highly invested in using French, a language that is less familiar to him, to accomplish the shared goal of winning the game. In this case, the child struggles with retrieving French words and phrases to signal his future actions (e.g., which avatar he is pursuing as his target, where he plans to go next in the game) while at the same time, he suppresses his use of English words, which are more familiar to him. Controlling thought and action while suppressing automatic responses is a defining feature of an executive system of cognitive resources, and at this end of the continuum, the control of attention to language use in these social actions is high.

Conceptualizing executive resources on a continuum that parallels the com-plexity of language use in social activity provides clinicians with a framework to view how children optimize the executive system available to them to engage in multilingual practices. For instance, a plurlingual child with executive function capacity that is less than optimal may compensate for this limitation by respond-ing to a question posed to her in an unfamiliar language with a language in which she has greater proficiency; she may rely more upon gesture to support her use of speech in unfamiliar languages; she may match a single language with adults about her, even though she shares a number of languages. Even when young chil-dren have access to adequate executive resources to speak confidently in the lan-guage in use, they may be highly invested in using another language that places more demands on their executive system. As argued throughout this chapter, taking into account the dynamic complexity of constructing meanings in multi-lingual practices is foundational to creating understandings about the emerging executive system available to young, plurilingual children.

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10

Executive Functions and the Developing Social

Competence of Children With Autism Spectrum Disorder

G R A C E I A R O C C I A N D E M I LY G A R D I N E R ■

Clinical and anecdotal reports often characterize the behavior of children with autism spectrum disorder (ASD) as elicited by salient yet seemingly irrelevant and often nonsocial cues (e.g., earrings, watches) in their environment. Compared to their mental age– matched peers who show more deliberate control over their thoughts and actions, children with ASD, even those who are highly intelligent and verbal, appear to react as if controlled by their environment and show a range of difficulties with executive function (EF), such as inhibiting socially inappro-priate actions, shifting attention flexibly, generating goal- directed behavior, and planning for the future. Although deficits in EF are found in several clinical dis-orders, in children with ASD, performance on EF tasks may be specifically related to aspects of social communication and adaptation. In this chapter we highlight the unique profile of EF difficulties in children with ASD and discuss the relation to their developing social competence.

ASD is a neurodevelopmental disorder that is typically evident early in devel-opment with regard to delayed or atypical development in several domains, including cognitive, language, and social- emotional functioning. The core symp-toms are social- communication deficits and repetitive behaviors and restricted interests. The majority of children do not receive a diagnosis until preschool or school age, as reliable early indicators have been difficult to identify in infants and toddlers. Rather than the typical pattern of the presence or absence of symptoms as stipulated in The Diagnostic and Statistical Manual of Mental Disorders (DSM; APA, 2013), infants and toddlers and, to some extent, preschoolers with ASD may

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show more subtle qualitative differences in behavior as compared to typically developing children. For example, their social- communicative difficulties may be reflected in a pattern of inconsistency in responsiveness (e.g., responds to name occasionally) and a lack of synchrony across different communicative skills (e.g., gestures are poorly integrated with eye contact). Thus, although the behavior may be present, the quality of the behavior (in this case the communicative signal) is poor.

Nonverbal behaviors such as attentional and perceptual atypicalities are emerg-ing as important early predictors of ASD in young children. A few examples include decreased interest in people and, to some extent, objects as a whole (e.g., may be more interested in parts of objects, such as the wheels of a toy car), a lack of integration of the exploration of objects and interactions with others (e.g., poor joint attention and sharing of interests), a lack of alerting response to own name, and a tendency to fixate on seemingly irrelevant features of the environment (e.g., horizontal blinds and gratings). For example, infants later diagnosed with ASD showed a decreased ability to pay attention to complex social scenes. They also spent less time looking at persons and their faces, as well as less time looking at the toys in the scene (Chawarska, Macari, & Shic, 2012). Early visual/ attentional differences may, in part, relate to later impairments in social perception. A recent study found that adults with ASD showed significant variation in visual percep-tion ranging from typical to superior in their ability to visually discriminate the angle of tilt of patterns and detecting the difference in brightness between objects within the same field of view, suggesting that some individuals with ASD may have fundamental differences in the way they visualize objects in their environ-ment. However, all participants with ASD showed impairments in high- level social perception such as emotion and face recognition, regardless of whether they had typical or superior low- level visual abilities. Thus, visual abilities alone did not account for the differences in social perception among the groups (Shafai, Armstrong, Iarocci, & Oruc, 2013).

Whether, and to what extent, low- level attentional/ perceptual atypicalities are related to higher level processing difficulties in ASD is still an open ques-tion; however, the role of high- level executive control problems has been con-sidered in its own right in theories of ASD. Executive functions encompass a variety of key skills, such as sustaining attention over a period of time or in spite of distraction, organizing one’s thoughts and behavior before acting, uti-lizing feedback in order to succeed in a given task, juggling multiple pieces of information at once (mental flexibility), and using planning and organizational skills when solving a task. It was Damasio and Maurer’s (1978) seminal paper that first linked executive dysfunction to ASD symptom presentation, and much research has since explored the link between the EF impairments char-acterizing those with ASD and different areas of functioning. In children with ASD, mental inflexibility, perseveration, and planning difficulties are particu-larly prevalent (see Hill, 2004a, 2004b; Kenworthy, Yerys, Anthony, & Wallace, 2008; Rajendran & Mitchell, 2007), and they have been posited to be related to ASD- characteristic impairments, including poor false- belief understanding


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(e.g., Russell, Saltmarsh, & Hill, 1999), restricted interests and rigid patterns of thinking (e.g., Low, Goddard, & Melser, 2009), pragmatic language diffi-culties (e.g., Kissine, 2012), and repetitive behaviors (e.g., South, Ozonoff, & McMahon, 2007).

DEVELOPMENT OF EXECUTIVE FUNCTIONS IN CHILDREN WITH AUTISM SPECTRUM DISORDER

EF encompasses multiple processes that mature at different times across devel-opment. The maturation of EF during typical development follows a protracted course, which varies by the specific process being measured and the level of com-plexity of the task. Therefore, it is essential to consider the developmental tra-jectories of these interdependent yet distinguishable processes. We focus on the “foundational EFs” (Best & Miller, 2010, p. 1643) of inhibition, flexibility, and working memory, as identified by Miyake et al. (2000).

In typically developing children, the ability to control or prevent an auto-matic behavioral response (i.e., inhibition) develops earlier than other execu-tive processes, with much improvement evident quite early in development, and minimal improvement throughout adolescence (Best & Miller, 2010; Gerstadt, Hong, & Diamond, 1994). In contrast, both working memory and flexibility follow a more protracted developmental course. Efficient switching on com-plex tasks is typically achieved by middle adolescence (e.g., Huizinga, Dolan, & van der Molen, 2006), and working memory improves in a gradual fashion from preschool through childhood and adolescence (e.g., Conklin, Luciana, Hooper, & Yarger, 2007).

Studies comparing individuals with and without ASD on different EF domains have generally found inhibition to be unimpaired, although conclusions differ depending on task type. For example, children and adolescents with ASD often demonstrate intact performance on the classic Stroop task (Goldberg et al., 2005; Ozonoff & Jensen, 1999). However, findings are mixed on other types of tasks, such as the go/ no- go and windows tasks (Happé, Booth, Charlton, & Hughes, 2006; Ozonoff, Strayer, McMahon, & Filloux, 1994; Russell, Mauthner, Sharpe, & Tidswell, 1991), leading some to suggest that impairment lies in the area of prepotent inhibition, but not in neutral inhibition (Hill, 2004b). Both working memory and flexibility appear to be more consistent areas of impairment for school- aged children with ASD (Geurts, Verté, Oosterlaan, Roeyers, & Sergeant, 2004; Joseph, McGrath, & Tager- Flusberg, 2005; Ozonoff et al., 2004). With regard to the development of EF skills in children with ASD, a few studies have reported age- related improvements in EF skills (Happé et al., 2006; Pellicano, 2010), whereas others report no improvements with age (Ozonoff & McEvoy, 1994). One study of school- aged children with ASD found that inhibition and working memory skills improved over time, yet they were slower to mature and lagged behind skills in same- aged typical peers (Luna, Doll, Hegedus, Minshew, & Sweeney, 2007). Generally, EF skills appear to improve throughout childhood


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and adolescence in persons with ASD, yet they develop at a much slower rate and remain impaired into adulthood.

EXECUTIVE FUNCTIONS AND SOCIAL DEVELOPMENT

The surge in EF development during the preschool years coincides with advance-ments in several social- cognitive processes, such as theory of mind, joint atten-tion, perspective taking, and pretense. We now consider the divergent social developmental trajectories of children with and without ASD, and the critical role that EF plays in important social developmental tasks.

Social competence has been defined as “the active and skillful coordination of multiple processes and resources available to the [individual] to meet social demands and achieve social goals in a particular type of social interaction and within a specific context” (Iarocci, Yager, & Elfers, 2007, p. 113). As applied to young children, the social processes, goals, and contexts pivotal to this stage of development reflect the emerging differentiation of the social environment, and in particular, the new importance of peer relationships (Saunders & Green, 1993). Children at this stage are developing and refining the skills necessary to successfully confront novel and more complex social demands that involve both interpersonal (e.g., self- regulation and emotion regulation) and intrapersonal social competencies (e.g., effective communication; Rose- Krasnor & Denham, 2009). Rose- Krasnor and Denham (2009) identify four interrelated skills as foundational to social- emotional competence: self- regulation, social awareness, social problem solving, and prosocial orientation. The role of EF is critical in each of these social skills.

Self- regulation refers to emotional and behavioural processes related to impulse control as applied across a variety of contexts. EF has similarly been parsed based on the circumstance in which it is executed, with “hot” EF referring to emotional effortful control, and “cool” EF referring to situations requiring sustained attention and prepotent inhibition (Willoughby, Kupersmidt, Voegler- Lee, & Bryant, 2011). The former is thought to emerge in tasks that involve the provision and removal of high- value rewards (e.g., Iowa Gambling Task), whereas the latter is assessed with the more traditional lab- based measures discussed earlier (e.g., Stroop). This distinction can also be understood as referring to the EF skills that are applied in affectively significant (i.e., “hot”) versus affectively neutral (i.e., “cool”) contexts (Zelazo & Carlson, 2012). Likewise, self- regulation may be applied in social- emo-tional or strictly cognitive ways (Liebermann, Giesbrecht, & Müller, 2007), such as when a child refrains from pushing another child in anger or speaking out of turn. Regardless of the context in which it is applied, self- regulation is a skill requiring multiple EF components, including inhibition and self- monitoring.

Social awareness involves emotion understanding, perspective taking, and theory of mind, all of which are interdependent and undergo rapid development during the preschool years. Early emotion understanding involves young chil-dren becoming able to recognize emotions within themselves and others, which


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relies strongly on theory of mind, or the understanding of others as intentional, as well as perspective taking to effectively “put themselves in another’s shoes” and consider how a peer may be feeling. These advanced social awareness processes require monitoring and inhibition, as children must put aside their own perspec-tives in favor of another’s (Denham et al., 2003; Hughes, Dunn, & White, 1998). For example, Carlson, Mandell, and Williams (2004) found that 24- month- old children’s EF, as assessed by a number of tasks, predicted the extent to which they spoke about mental, emotional, and motivational states, and that EF and theory of mind were highly correlated in 39- month- olds.

Social problem solving is another foundational skill that involves executing multiple EF components in complex ways, as the child must correctly iden-tify the presenting problem and consider how surrounding constraints impact the availability of solutions. For example, if a young child encounters a peer who refuses to share a desired classroom toy, that child must consider the best approach to achieving the relevant social goal (i.e., getting the peer to share). However, the choice of problem- solving strategy is affected by existing knowl-edge of how that peer has reacted previously to requests for sharing, as well as previous experience with successful and unsuccessful strategies. Given these parameters, children who effectively resolve social problems will not only choose and implement appropriate strategies, but they will evaluate the good-ness of fit of the solution to the problem and potentially adapt future problem- solving strategies accordingly (Rose- Krasnor & Denham, 2009). As such, social problem solving involves planning, initiation, flexibility, self- monitoring, and evaluation.

Prosocial orientation, defined as “the ability and motivation to act in ways that benefit others … helping, cooperating, sharing, comforting” (Rose- Krasnor & Denham, 2009, p. 169), represents another foundational skill. For example, chil-dren must demonstrate the necessary ability to generate prosocial thoughts (i.e., generativity) and to subsequently carry those ideas out (i.e., initiation). Moreover, prosocial children are better able to inhibit antisocial or socially disruptive behav-ior, thereby acting in ways that promote and maintain positive peer relationships. It is these underlying mechanisms that help to explain why impulsive children (i.e., who demonstrate poor inhibition) receive low sociometric preference ratings by their peers (Gomes & Livesey, 2008).

EXECUTIVE FUNCTIONS AND SOCIAL COMPETENCE IN AUTISM SPECTRUM DISORDER

The foundational skills reviewed are emerging, and they represent critical social developmental tasks characterizing early childhood development. Children with ASD, however, demonstrate pervasive and enduring social impairment from an early age. We now highlight research that examines the role of EF in social com-petence for young children with ASD.


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Self- Regulation

Children with ASD have difficulty self- regulating, in both cognitive and emo-tional domains, and this significantly impacts their abilities to engage across social contexts, such as in school and with peers (Hill, 2004a; Mazefsky et al., 2013). Jahromi, Bryce, and Swanson (2013) examined the role of EF in such social outcomes for young children with and without ASD (mean age: 54.57 months). For both groups, inhibition, as assessed by the Day- Night Task and the Behavior Rating Inventory of Executive Function, Preschool ver-sion (BRIEF- P; Gioa, Espy, & Isquith, 1996) Inhibitory Self- Control Index, emerged as a uniquely significant predictor of emotion regulation, which sub-sequently predicted children’s school engagement, both behavioral (i.e., extent to which children could behave cooperatively or independently during school activities depending on task requirements) and emotional (i.e., extent to which children liked or disliked school). EF was also positively associated with pro-social peer engagement.

Social Awareness

As discussed, social awareness relies on emotion understanding, perspective tak-ing, and theory of mind, all of which are impaired in individuals with ASD (Baron- Cohen, Leslie, & Frith, 1985). In comparison to research examining EF and theory of mind, the relationship between EF and emotion understanding has received relatively less attention in the literature. Oerlemans et al. (2013) examined this relationship in older children (mean age: 12.4 years) with ASD and their siblings, and in families unaffected by ASD. Across participants, EF, specifically inhibition, cognitive flexibility, and verbal working memory, was positively associated with emotion understanding, including face recognition, facial emotion recognition, and recognition of voice emotion. More research is needed, however, to elucidate the nature of these developing relationships in young children with ASD.

The closely integrated nature of EF and theory of mind has been researched extensively in typical development, and some have suggested that EF abilities may actually lay the foundation upon which theory of mind develops (Forgeot d’Arc & Mottron, 2012; Russell, 1996, 1997). Studies examining this relation-ship in children with ASD highlight the highly correlated nature of the two constructs, although different EF components have emerged as important. Pellicano’s (2007) research identified that for young children with ASD (mean age: 67.6 months), cognitive flexibility remained significantly associated with theory of mind after age, and verbal and nonverbal ability, were accounted for. This is in contrast to Joseph and Tager- Flusberg (2004), who found a significant association between theory of mind and performance on a task tapping inhibi-tion and working memory abilities (Knock- Tap task) after accounting for verbal and nonverbal mental age, and to Pellicano (2010), whose work highlighted the


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role of planning. The latter study, however, underscores the multicomponent nature of planning tasks, in this case the Tower of London, which requires coor-dinating multiple EF abilities, including working memory (remembering rules), inhibition (rule adherence), and flexibility (solution generation and initiation of alternate strategies).

Moreover, research with comparison groups indicates that the nature of the relationship between EF and theory of mind may differ across individuals with and without ASD. Pellicano (2007), for example, found a different pattern of sig-nificance when examining correlations between EF and theory- of- mind mea-sures, as planning was highlighted for typical individuals and flexibility for those with ASD. EF may also be differentially related to particular aspects of theory of mind, as Ozonoff, Pennington, and Rogers (1991) found that EF was correlated with both first- and second- order theory- of- mind tasks in individuals with ASD, but only with second- order theory of mind in the comparison group. Functioning level may also be an important consideration, as EF and theory of mind are highly correlated in mildly impaired individuals, but unrelated in those who are severely impaired (Colvert, Custance, & Swettenham, 2002; Zelazo, Jacques, Burack, & Frye, 2002).

Moreover, the interrelated nature of EF and theory- of- mind development appears to hold over time, as Pellicano’s (2010) longitudinal study identified that EF, and in particular planning, uniquely predicted change in theory- of- mind abil-ities 3 years later. Thus, the reviewed research demonstrates that theory of mind and EF are strongly linked across development, from the early years (Pellicano, 2007, 2010) through childhood (Colvert et al., 2002; Joseph & Tager- Flusberg, 2004) and adolescence (Ozonoff et al., 1991; Zelazo et al., 2002).

Another index of social awareness is joint attention, an important precursor to theory of mind (Baron- Cohen, 1991). Joint attention is the ability to focus simultaneously on both another person and an object in order to share interest and engagement, and it is an extremely important part of typical social develop-ment that is uniquely impaired in ASD (Charman et al., 1997) and one of the earliest indicators of the disorder (Wetherby et al., 2004). In preschoolers (aged 3– 5 years) with ASD, EF plays an important role in both current and future joint attention abilities. Dawson et al. (2002) found that tasks (delayed nonmatching to sample and object discrimination reversal) tapping into the medial tempo-ral lobe and the ventromedial cortex were associated with joint attention. These tasks assessed rule- learning (pertaining to the association between novel stimuli and reinforcement), and the authors hypothesized that these kinds of skills more closely relate to affective and social processes mediated by this pathway. Others have implicated dorsolateral prefrontal functioning, as performance on a task of working memory and inhibition was associated with joint attention (McEvoy, Rogers, and Pennington,1993; Griffith, Pennington, Wehner, and Rogers,1999). EF performance predicted joint attention approximately 1 year later for indi-viduals with ASD (Griffith et al.,1999) demonstrating the long- term importance of these abilities. However, the same relationship did not emerge for develop-mentally delayed participants.


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Prosocial Orientation

Fewer studies have examined how EF influences prosocial orientation for young children with ASD, although a number of studies have found positive correlations between EF tasks and related social skills. In Dawson, Meltzoff, Osterling, and Rinaldi’s (1998) study, for example, performance on the delayed nonmatching to sample task was associated with social orienting, immediate and deferred imita-tion, shared attention, response to distress, and symbolic play, all of which act as building blocks for prosocial behavior at this developmental stage. McEvoy et al. (1993) also found that spatial reversal (assessing working memory and inhibition) performance was associated with participants’ abilities to maintain the kinds of simple interactions that are important for effective peer relationships at this stage (e.g., turn taking and object sharing).

Most research has included lab- based tasks; however, this methodology often lacks ecological validity and precludes examination of how such abili-ties are coordinated in day- to- day life. Rating scales provide an opportunity to gauge these complex relationships, and research outside of the lab confirms the important role of EF. Gilotty, Kenworthy, Sirian, Black, and Wagner (2002) utilized the Behavior Rating Inventory of Executive Function (BRIEF; Gioia, Isquith, Guy, & Kenworthy, 2000) and the Vineland Adaptive Behavior Scales (VABS; Sparrow, Balla, & Cicchetti, 1984) with children and adolescents with ASD (mean age: 10.5 years), and they found significant relationships across a number of the scales. For the purposes of this chapter, we will only review those associated with the VABS socialization domain, which was found to be associ-ated with the BRIEF metacognition, initiate, and working memory subscales, indicating that greater executive dysfunction was associated with poorer adap-tive functioning.

Our research also supports this work. We have examined these relationships using adaptive scales and lab- based tasks in young children with ASD (mean age: 63.2 months) (Gardiner, Hutchison, Müller, Kerns, & Iarocci submitted). In addition to administering lab- based EF tasks assessing working memory, inhibition, flexibility, and planning, parents completed the Behavior Assessment System for Children (BASC- 2; Reynolds & Kamphaus, 2004) and Vineland Adaptive Behavior Scales- II (Vineland- II; Sparrow, Cicchetti, & Balla, 2005). A correlation analysis revealed that go/ no- go performance was associated with the Vineland- II socialization scale (r = – .56), and Stroop performance was associ-ated with the BASC- 2 social skills domain (r = – .61). In both cases, better inhi-bition was associated with parents rating their children as more socially adept. Stroop performance was also associated with the negative emotionality (r = .69) and emotional self- control (r = .60) content scales, indicating that poorer inhibi-tion was associated with a greater tendency to react in overly negative ways and greater self- control problems.

The findings underscore the importance of considering the mutual and recip-rocal influence of EF and social competence. The majority of the existing research takes a correlational approach, and although this provides insight into important


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EF– social associations, we cannot determine directionality or developmental course. Long- term longitudinal studies that enable a detailed examination of developmental trajectories are needed to help clinicians understand how best to focus their interventions in order to optimally impact multiple domains of functioning.

MEASUREMENT OF EXECUTIVE FUNCTIONING: LAB- BASED AND RATING SCALES

In addition to developmental considerations, methods of EF assessment must be considered when interpreting EF research as well as clinical findings. Research results vary according to a number of factors, such as type of task and perfor-mance indicator examined, administration methodology (i.e., manual versus computerized), choice of comparison group, and age group examined (O’Hearn, Asato, Ordaz, & Luna, 2008; Russo et al., 2007). Regarding task choice, it is impor-tant to consider the EF domain that a given test is purported to measure, as dif-ferent aspects of the same task may map onto different EF domains. For example, tower tasks are usually conceptualized as complex measures of planning, yet they require the application of inhibition, working memory, and flexibility in order to succeed. Miller, Giesbrecht, Müller, McInerney, and Kerns’ (2012) research also supports this point, as their confirmatory factor analysis revealed that for the same measure, a Continuous Performance Test, commission errors loaded on inhibition, whereas omission errors loaded on working memory.

Administration methodology appears to be particularly critical, as research indicates that individuals with ASD tend to perform better on computer- adminis-tered EF tasks, as opposed to manual ones (Ozonoff & Strayer, 2001). For example, Ozonoff (1995) compared children and adolescents with ASD to age- and IQ- matched typically developing peers (aged 7– 16 years) on a computerized version of the Wisconsin Card Sorting Test and found no significant performance dif-ferences, although there were differences on a manual version. The findings are similar with tower tasks, as studies using the Cambridge Neuropsychological Test Automated Battery’s Stockings of Cambridge (Robbins et al., 1994), a computer-ized task designed to be similar to the Tower of London and Tower of Hanoi, report no ASD- specific impairment (Goldberg et al., 2005; Happé et al., 2006; Ozonoff et al., 2004; Sinzig, Morsch, Bruning, Schmidt, & Lehmkuhl, 2008), whereas others utilizing manual versions of the Tower of London (Geurts et al., 2004; Joseph et al., 2005; Pellicano, Maybery, Durkin, & Maley, 2006; Pellicano, 2007; Robinson, Goddard, Dritschel, Wisley, & Howlin, 2009) and Tower of Hanoi (Bennetto, Pennington, & Rogers, 1996; Ozonoff & Jensen, 1999; Ozonoff & McEvoy, 1994; Ozonoff et al., 1991) find ASD- specific impairment.

The selection of appropriate comparison groups is also an important consider-ation, as conclusions may differ depending on whether children are matched on chronological age or IQ, and whether presence of learning disabilities, language


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impairments, or comorbidities also characterized by EF difficulties (e.g., attention- deficit or conduct disorders, and Tourette’s syndrome) have been accounted for (Burack, Iarocci, Bowler, & Mottron, 2002; Russo et al., 2007).

Finally, executive dysfunction in individuals with ASD appears to become more apparent with increasing age, as most studies with school- aged children identify ASD- specific impairment (Hill, 2004b; Russo et al., 2007), whereas studies with preschool- aged children generally do not. A few studies comparing preschoolers with ASD to CA and mental age– matched typically developing (Lam & Yeung, 2012; Stahl & Pry, 2002) and developmentally delayed comparisons (Griffith et al., 1999) on a variety of tasks found no differences in EF (Dawson et al., 2002; Yerys, Hepburn, Pennington, & Roger, 2007). One study found cross- domain EF impair-ment in the preschool- aged ASD sample, as compared to children with Down syndrome and to those who were typically developing (Dawson et al., 1998). Still others showed that impairments were demonstrated on some tasks but not others (McEvoy et al., 1993; Pellicano et al., 2006; Pellicano, 2007). See Table 10.1 for an overview of studies pertaining to this young age group.

Although EF deficits do not always emerge when assessed with highly struc-tured lab- based tasks, most clinicians, teachers, and parents would agree that individuals with ASD have difficulties with various aspects of executive control in their daily lives. Behavior rating scales consider EF from a global perspective that includes multiple components of EF simultaneously and assess these abilities as applied across different contexts in the child’s everyday activities (Kenworthy et al., 2008; Toplak, West, & Stanovich, 2013). Thus, behavior rating scales capture an important facet of EF in children with ASD that may supplement the highly controlled lab- based tests of EF discussed previously. Moreover, many rating scales have both parent and teacher versions, which facilitates the clinician’s abil-ity to obtain different perspectives and gain insight into how a child’s behavior may change across different contexts.

The BRIEF is the most commonly used EF rating scale (Toplak et al., 2013). This informant- report questionnaire was developed to identify everyday EF abilities in children between the ages of 5 and 18 years. The BRIEF measures two broad areas of EF: behavioral regulation, which is the ability to shift and modulate emotions and behavior via appropriate inhibitory control; and metacognition, referring to the ability to cognitively self- manage tasks and monitor performance. There is also a preschool version, the BRIEF- P, that is appropriate for children between the ages of 2 and 5 years, and measures EF skills across three broad areas: inhibitory self- control, referring to the child’s ability to appropriately modulate his or her behavior given the confines of the situation; flexibility, referring to a child’s ability to shift strategies given the specific demands of the task; and emergent metacog-nition, which refers to coordinated abilities such as working memory, problem solving, and planning.

Another behavior scale, the BASC (Reynolds & Kamphaus, 1992, 2004), con-tains a supplemental index called the Executive Function content scale (EFCS)1 that assesses day- to- day instances of planning, inhibition, and other EF- related

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Table 10.1 Studies Examining Lab- Based Executive Functioning in Young Children With and Without Autism Spectrum Disorder

Reference Groups/ Ages (M) Matching Criteria Executive Functioning Measures ASD Impairments?

Dawson et al. (1998) ASD: 64.6 monthsDS: 65.3TD: 30.9

Receptive language MA; and ASD and DS matched on CA and VIQ

DNMSDelayed Response

Yes

Dawson et al. (2002) ASD: 43.5 monthsDD: 44.8TD: 27.1

MA (composite score) A- not- BA- not- B with Invisible ReplacementSpatial ReversalObject Discrimination ReversalDNMS- objectsDNMS- pictures

No

Griffith et al. (1999) ASD: 50.6 monthsDD: 50.7 months

NVMA A- not- B3- Boxes StationaryObject Retrieval6- Boxes Stationary3- Boxes ScrambledA- not- B with Invisible Replacement6- Boxes ScrambledSpatial Reversal

No

Lam & Yeung (2012) ASD: 6.11 yearsTD: 5.64

Sex, CA, NVIQ, VIQ WCST No

McEvoy et al. (1993) ASD: 60.65 monthsDD: 50.38TD: 37.94

ASD and DD groups matched on CA, SES, nonverbal ability

ASD and TD groups matched on SES and verbal MA

Piagetian AB ErrorDelayed ResponseSpatial ReversalAlternation

NoNoYesNo

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Pellicano et al. (2006) ASD: 67.15 monthsTD: 65.70

CA, gender, verbal ability, nonverbal ability

Luria’s handgameMazesToLMCST

YesNoYesYes

Pellicano (2007) ASD: 67.60 monthsTD: 65.70

Age, VIQ, NVIQ MazesToLMCSTLuria’s handgame

NoYesYesYes

Stahl & Pry (2002) ASD: 60.71 monthsTD: 25.23

ASD MA with TD- CA (MA not assessed in TD group)

Multilocation Search task No

Yerys et al. (2007) ASD: 34.8 monthsDD: 35.5MA- TD: 22.2CA- TD: 32.6

Age (except MA- TD)VMA and NVMA (except CA- TD)

WindowsSpatial ReversalA- not- B

No

ASD, autism spectrum disorder; CA, chronological age; DNMS, delayed nonmatching to sample; DS, Down syndrome; MA, mental age; MCST, modified card sorting test; NVIQ, nonverbal IQ; NVMA, nonverbal mental age; SES, socioeconomic status; TD, typically developing; ToL, Tower of London; VIQ, verbal IQ; VMA, verbal mental age; WCST, Wisconsin Card Sorting Task.


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abilities. The EFCS is derived from the second edition of the BASC Parent form (BASC- 2; Reynolds & Kamphaus, 2004), and it includes items related to atten-tion, control, and motivation. Example items include “Is easily distracted” and “Is a ‘self- starter.’ ” Item scores are combined, and a t- score is assigned. The BASC- 2 is a commonly utilized measure in the assessment of adaptive skills and maladaptive behavior, and it covers a broad age span (ages 2– 21 years). The abil-ity to measure day- to- day EF skills without the addition of another instrument is appealing to clinicians, and research has begun to evaluate the utility of the scale. The EFCS is internally consistent (alpha = .84), and it is significantly cor-related with other validated measures of EF, including the BRIEF and Conners’ Parent Rating Scales Revised (CPRS; Conners, 1997; Sullivan & Riccio, 2006). Moreover, it reliably differentiates groups with and without frontal lobe impair-ment (Reynolds & Kamphaus, 2002; Sullivan & Riccio, 2006). This may be par-ticularly important for the assessment of preschool- aged children, as this is a developmental period during which the profile of EF in young children with ASD as compared to same- aged typically developing peers is especially unclear (Gardiner et al., submitted).

Other potentially useful measures are the Childhood Executive Functioning Inventory (CHExI; Thorell & Nyberg, 2008) and the Behavior Flexibility Rating Scale- Revised (BFRS- R; Green et al., 2006). The CHExI is intended for children between the ages of 4 and 7 years, and it measures working memory and inhibi-tion. This measure is somewhat unique as it aims to assess these foundational EF components while excluding behaviors characteristic of those with attention- deficit/ hyperactivity disorder (ADHD), such as hyperactivity, impulsivity, and inattention, which may confound EF difficulties and attention problems. This measure has good test- retest reliability (r = .84), and it reliably differentiates those with and without ADHD (Thorell, Eninger, Brocki, & Bohlin, 2010). Given the considerable overlap that exists between ADHD and ASD (Hattori et al., 2006), this measure may be appropriate for use with individuals with ASD. Future research is needed to assess the psychometric properties when utilized with this population.

The BFRS- R was developed specifically for children with ASD, and it assesses inflexibility toward objects, persons, and the environment, across a wide age span (age 2– 17 years). It taps into the insistence on sameness and resistance to change often observed in those with ASD. The measure is internally consistent (alpha = .90), significantly correlated with the BRIEF Shift scale (Teunisse et al., 2012), and found to reliably distinguish those with Angelman syndrome, non-specific intellectual disability, and ASD (Didden et al., 2008). This measure has also been used as part of a functional assessment, and it can support intervention planning (Ollington, Green, O’Reilly, Lancioni, & Didden, 2012).

In typically developing children, parent- reported EF problems generally decrease as their children age. Specifically, 5- to 8- year- olds showed significantly more difficulties than 9- to 11- year- olds in emotional control, inhibition, shift-ing, and working memory, and 12- to 14- year- olds were rated as having more difficulty than 15- to 18- year- olds on scales of inhibition and emotional control,


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as assessed with the Dutch BRIEF (Huizinga & Smidts, 2011). However, in chil-dren with ASD, parent- reported EF problems seem to increase with age. Using a similar methodology to Huizinga and Smidts, Rosenthal et al. (2013) utilized the BRIEF to examine age- related differences in a large sample (n = 185) of children and adolescents with ASD. In contrast to the typical EF trajectory, children with ASD demonstrated increasing impairment with age on task and self- initiation, working memory, and organization of materials. Moreover, shifting emerged as the most significant area of impairment across the ages examined, as this scale received the highest scores (indicating greatest dysfunction) in both the youngest and oldest age groups. This is consistent with lab studies demonstrating that flex-ibility poses great difficulty for children and adolescents with ASD (Geurts et al., 2004; Ozonoff et al., 2004).

In a recent study we examined different facets of EF among young children (mean age: 5 years) with ASD by employing multiple methods, including both lab- based tasks and a parent- rating scale of EF (Gardiner et al., submitted). The findings indicate that working memory, inhibition, and flexibility were not deficient in young children with ASD when assessed with lab- based comput-erized tasks, even when compared to typically developing peers with higher assessed IQs. Children with ASD also demonstrated superiority in planning efficiency (as indexed by a Tower of Hanoi task), although typically develop-ing children were able to solve more levels. Despite this age- appropriate and, in some aspects, superior EF, young children with ASD were rated by their parents as demonstrating greater behavioral manifestations of EF impairment in their everyday lives, as assessed with the BASC- 2 EFCS. The findings indi-cate that EF problems may be less evident in young children with ASD when a single EF ability is assessed in a highly controlled lab setting, but they may become more evident when assessed with tasks (e.g., Tower of Hanoi) that involve coordinating multiple EF abilities, or with parent report measures of EF that capture how well the child applies EF in less structured settings. Thus, an accurate evaluation of EF requires consideration of the task and context within which it is executed.

Young children with ASD show age- appropriate performance and age- related improvements on lab- based measures, yet their ratings on performance- based scales indicate that they may not be able to apply their abilities to perform age- appropriate behaviors within more demanding everyday contexts such as the home, school, and playground. Over time, this initial difficulty with coordinat-ing and applying EF skills in everyday contexts may lead to fewer opportunities and successes in employing EF abilities to achieve age- appropriate goals. This dis-advantage may, in part, explain why the gap between environmental demands and the EF ability of a child with ASD widens over the course of development. Specifically, according to parent reports, flexibility remains impaired across ages in ASD (i.e., representing a developmental deficit), whereas working memory, initiation, and organization become increasingly problematic over time (i.e., rep-resenting developmental deterioration). The implications of these findings are that children with ASD will need support with following multistep directions,


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transitioning between activities, keeping school materials organized, and initiat-ing activities and social interaction.

In sum, lab- based and rating scale measures of EF provide different yet comple-mentary sources of information for the EF assessment of preschool- aged children with ASD. The former may provide estimates of the functioning of specific EF abilities, and the latter may gauge how children adapt to, and keep pace with, real- world behavioral expectations (see Chapter 11 in this volume).

ASSESSMENT OF EXECUTIVE FUNCTIONING IN CHILDREN WITH AUTISM SPECTRUM DISORDER: CONSIDERING COMPLEXITY AND SOCIAL DEMANDS

Traditional laboratory tasks of EF typically assess EF components in a unitary fashion, in structured, minimally distracting, paced environments with relatively low social demands. The standardized behavior rating measures of EF inquire about the perspective of the caregiver on how well the child is able to manage his or her behavior in less structured, often distracting, fast- paced contexts that generally have greater social demands. Another method, ecological assessment of EF, involves the direct observation of EFs as they are applied to solve a task while following rules. The complexity of the task may vary as well as the social demands. Ecological assessments of EF that simulate real- world tasks and the measurement of the real- world behaviors needed to perform those tasks have been adapted for use with children. For example, multitasking, defined as the ability to work on several and varied tasks, each being suspended and then resumed after appro-priate intervals to reach completion at the same time (Rajendran et al., 2011), is one way to manipulate complexity of EF. That is, multiple processes of EF are needed simultaneously to successfully accomplish multitasking. Researchers have adapted tasks for children such as the Six Parts Test, in which the children (mean age: 9 years) were asked to complete six tasks in 5 minutes with the order of task performance limited by rules. The ASD group had the lowest mean scaled scores of any group on this subtest (Emslie et al., 2003). The Battersea Multitask Paradigm composed of three interleaved tasks (sorting, counting, and coloring) that must be completed in 3 minutes was also used with children. The children with ASD (mean age: 12 years), as compared to their age- and IQ- matched peers, showed significantly poorer performance in Plan and Perform, but not in Rule Learn, Plan Follow, Monitor, or Rule Memory (although the trend was in the direction of poorer performance in the ASD group; Mackinlay, Charman, & Karmiloff- Smith, 2006). Rajendran and colleagues (2011) examined multitasking in a computer-ized virtual environment. A virtual environment simulates a real environment and has the advantage of scientific control for testing participants. The Virtual Errands Task (VET; McGeorge et al. 2001), wherein children are asked to run errands for a schoolteacher, was used to assess multitasking. When the tasks were presented sequentially during the training phase, the children with ASD (mean age: 13.9 years) were able to perform well. However, when these tasks had to be


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interleaved during the task phase, the children completed fewer tasks and made more errors in the allocated time.

The social demands of multitasking tasks vary and need to be considered when assessing EF in children with ASD. White, Burgess, and Hill (2009) suggest a dis-tinction between “open- ended’ ” and “constrained” multitasking tasks. Their find-ings suggest that EF performance deficits in children with ASD might be due as much to the social- communication difficulties in this group as to any EF problems per se. That is, open- ended tasks are relatively unstructured and require the par-ticipant to infer what the experimenter wants from him or her (i.e., the participant is not explicitly told to do the task in the most efficient way). Multitasking tasks are intrinsically open- ended, and therefore, performance on such tasks might be influenced by social- communication as well as executive abilities (Rajendran et al., 2011). For example, the Children’s Cooking Task (CCT) is an open- ended cook-ing task designed to be sufficiently complex to involve multitasking abilities, and with minimal external control and structure. The task was shown to be sensitive to EF problems in children with traumatic brain injury (Chevignard et al., 2000), and performance on the CCT was correlated with cognitive and behavioral tests of EF such as the BRIEF (Chevignard et al., 2008). These ecological assessments provide insight into the domains where individuals struggle, not at the construct level (i.e., working memory, inhibition, etc.), but at the clinically relevant func-tional level (i.e., skills like turn taking or distractibility; see Burgess et al., 2006). Moreover, they assess several of the components of EF simultaneously. However, one must consider that the currently available tasks need to be modified for use with preschoolers and that the tasks may vary with regard to their social demands on the child.

Lab- based, caregiver ratings, and ecological assessments each provide a differ-ent source of information that when integrated helps one achieve a valid assess-ment of EF that includes consideration of the complexity and social elements of the task. Employing multiple methods also has the added advantage of captur-ing EF in multiple settings. For example, traditional EF tests assess EF in stan-dardized and structured environments and identify the child’s cognitive profile; caregiver ratings provide a caregiver’s perspective on how the child applies EF to regulate his or her behavior in various settings; and direct observation provides one opportunity to evaluate directly how the child uses his or her EF to accom-plish conventional tasks with both structured and unstructured components with varying social demands.

CONCLUSION

The complex profile of EF abilities and disabilities of children with ASD must be understood within the context of developmental changes, assessment tools used, and the complexity of the environment within which they are assessed. Generally, EF skills appear to improve throughout childhood and adolescence in persons with ASD, yet they develop at a much slower rate and remain impaired


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into adulthood. Methods of EF assessment, whether lab- based, informant- based, or ecologically valid, provide useful, complementary information on different aspects of EF in preschool- aged children with ASD. Together they provide valid estimates of the functioning of specific EF abilities, and they gauge how children adapt to, and keep pace with, real- world behavioral and social demands.

Note1. Sullivan and Riccio (2006) refer to this scale as the Frontal Lobe/ Executive Control

(FLEC) scale.

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11

The Assessment of Executive Functions in Attention- Deficit/

Hyperactivity Disorder

Performance- Based Measures Versus Ratings of Behavior

M A G G I E E . T O P L A K , R I C H A R D F. W E S T,

A N D K E I T H E . S T A N O V I C H ■

Attention- Deficit/ Hyperactivity Disorder (ADHD) has been characterized as a neurodevelopmental disorder including problems with attention, impulsivity, and hyperactivity. The actual diagnosis derives from 18 symptoms that have been used to define these three behavioral domains in the Diagnostic and Statistical Manual of Mental Disorders (DSM- IV- TR: American Psychiatric Association [APA], 2000; DSM- 5: APA, 2013). Executive functions have been identified as a key domain for understanding the behavioral manifestation and impairments observed in ADHD (Barkley, 2006; Nigg, 2006; Sagvolden, Johansen, Aase, & Russell, 2005; Sonuga- Barke, 2002, 2003; Sonuga- Barke, Biksakou, & Thompson, 2010). Individuals with ADHD tend to perform less well than controls on measures of executive function (Toplak, Bucciarelli, Jain, & Tannock, 2009; Willcutt, Doyle, Nigg, Faraone, & Pennington, 2005). The purpose of this chapter is to examine performance- based measures and rating scales of executive function in ADHD based on the existing empirical evidence. The relationship between performance- based measures and rating scales of executive function is discussed, as well as the clinical implications for assessing ADHD.


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THE RELEVANCE OF EXECUTIVE FUNCTIONS IN CLINICAL ASSESSMENT

Executive functions are assumed to play an important role in the efficiency of goal- directed behavior (Miyake et al., 2000; Pennington & Ozonoff, 1996; Salthouse et al., 2003; Strauss, Sherman, & Spreen, 2006), especially in novel contexts where there are no well- learned behaviors to draw upon (Shallice, 1990). Several elements of executive function have been articulated as key com-ponents, including anticipation and deployment of attention, impulse control and self- regulation, initiation of activity, working memory, mental flexibility and utilization of feedback, planning ability and organization, and selection of effi-cient problem- solving strategies (Anderson, 2008). However, the most typical domains assessed are updating (constant monitoring and rapid addition/ dele-tion of working memory contents), shifting (switching flexibly between tasks or mental sets), and inhibition (deliberate overriding of dominant or prepotent responses; Miyake & Friedman, 2012).

Executive functions have become an important construct for understanding the cognitive development of typically developing preschoolers (Anderson & Reidy, 2012), school- aged children (Davidson, Amso, Anderson, & Diamond, 2006), aging adults (Salthouse, Atkinson, & Berish, 2003), and special popula-tions (Nigg, 2006; Pennington, 2002). Executive processes develop and change over the life span (Davidson et al., 2006; Lamm, Zelazo, & Lewis, 2006; Williams, Ponesse, Schachar, Logan, & Tannock, 1999), but individual differences in execu-tive functions show relative stability over the course of development (Miyake & Friedman, 2012). Inhibition or inhibitory control seems to be disproportionately difficult for young children (ages 4– 9 years) relative to older children (Davidson et al., 2006; Diamond, 2013). It has been demonstrated in 4- to 9- year- olds that updating develops once children are able to temporarily store this information with some consistency (Davidson et al., 2006; Diamond, 2013). Shifting or cogni-tive flexibility has been reported to develop later than inhibition and updating (Davidson et al., 2006; Diamond, 2013). The developmental trajectory of execu-tive functions is long and complex.

Deficits in executive functions are of particular relevance for clinicians (see Chapter 3 in this volume). Such deficits may result in inappropriate social behav-ior; problems with decision making and judgment; and difficulties with initiat-ing, following, shifting, and organizing plans (Damasio, 1994, 1996; Strauss et al., 2006). Difficulties with executive functions have been implicated in ADHD, and in several other neurological and psychiatric conditions such as traumatic brain injuries (Clark, Manes, Antoun, Sahakian, & Robbins, 2003; Labudda et al., 2009); schizophrenia (Cavallaro et al., 2003; Kester et al., 2006; Nakamura et al., 2008); substance use (Barry & Petry, 2008; Ernst et al., 2003); obsessive- compulsive dis-order (Lawrence et al., 2006); psychopathy (Mahmut, Homewood, & Stevenson, 2008); and pathological gambling (Toplak, Liu, MacPherson, Toneatto, & Stanovich, 2007). As deficits in executive functions have been implicated in sev-eral psychiatric and neurological conditions, the assessment of these abilities may

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be important in the context of a clinical assessment. Two major classes of mea-sures have been used to assess executive functions, including performance- based and rating scale measures.

PERFORMANCE- BASED MEASURES VERSUS RATINGS OF EXECUTIVE FUNCTION

Performance- Based Measures of Executive Function

The conventional measurement of executive function has been based on cogni-tive performance- based tests (Pennington & Ozonoff, 1996). Performance- based tests are administered in highly standardized conditions. Stimulus presentation is carefully controlled so that each examinee experiences and completes the task in precisely the same way as other examinees. In addition, the measures of per-formance are typically based on the examinee’s accuracy, response time, and/ or speeded responding under a time constraint. There are several performance- based measures of executive function, such as the Wisconsin Card Sorting Test (WCST; Heaton et al., 1993), the Stroop test (Jensen & Rohwer, 1966; MacLeod, 1991; Stroop, 1935), and tests of verbal fluency (Strauss et al., 2006). The WCST requires the maintenance of a task set, flexibility in response to feedback, avoid-ing perseverative tendencies, and inhibiting a prior response that is no longer appropriate (Salthouse et al., 2003). The Stroop effect (MacLeod, 1991; Stroop, 1935) is a demonstration of interference control. In the Stroop test’s key condi-tion, the participant must inhibit an overlearned response (reading a word that names a color) in order to respond with another dimension that is incongruent and “interfering” (naming the ink color of the word, instead of the actual color word). Verbal fluency tests require the maintenance of a task set (generating items that fit a particular criteria or category), generating multiple responses, monitoring and avoiding repetitions, and using different retrieval strategies (Salthouse et al., 2003).

Although this is only a sample of available performance- based tests of executive function, these tests share the same general characteristics (see Strauss et al., 2006, for a comprehensive list of performance- based measures of executive function). They are all administered under highly standardized conditions with a single examiner who provides specific feedback or direct prompts to the examinee in order to direct performance. Accuracy and response time are the typical depen-dent measures on these tests. A key dependent measure on the WCST is the total number of sets of 10 consecutive correct pairings. On the Stroop, the typical mea-sure is the difference between the response time for naming the ink colors in the incongruent condition minus the response time for naming the actual ink colors. The key dependent measure on the verbal fluency test is the total number of items given by the examinee in the period of 1 minute. These highly structured features characterize and define performance- based measures of executive function. Such measures have been well studied in the field of ADHD.


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Attention- Deficit/ Hyperactivity Disorder and Performance- Based Measures of Executive Function

Several studies have now been published documenting significant differences between individuals with ADHD and controls on performance- based measures of executive function (see Barkley, 2006; Nigg, 2006), including reviews and meta- analyses (Pennington & Ozonoff, 1996; Willcutt et al., 2005). Across stud-ies that have included children, adolescents, and adults with ADHD, individu-als with ADHD tend to display lower performance than comparison controls on performance- based measures of executive function.

The most comprehensive review of executive function performance in chil-dren and adolescents with ADHD was reported in a meta- analysis by Willcutt et al. (2005). In a set of 83 empirical articles, these authors identified 13 specific measures of executive function that were consistently used in the ADHD litera-ture. They included the following domains of executive function: response inhi-bition, set shifting, verbal working memory, spatial working memory, planning/ organization, and vigilance. Across all of these measures, the weighted mean effect size was .54, which is considered a medium effect size. Significant group differences were most consistently observed on stop- signal reaction time (mea-sure of response inhibition) and Continuous Performance Test (CPT) omis-sion errors (measure of vigilance). Planning and spatial working memory also showed relatively consistent group differences. Participants with ADHD consis-tently demonstrated lower performance on all of these measures compared to controls.

In addition to behavioral studies, most theories of ADHD include executive function performance as a critical domain in explaining the deficits observed in ADHD (Barkley, 2006; Nigg, 2006; Sagvolden et al., 2005; Sonuga- Barke, 2002, 2003; Sonuga- Barke et al., 2010). Altered patterns of neural activity have also been shown in participants with ADHD relative to control participants dur-ing the administration of executive function tasks (Bush, Valera, & Seidman, 2005; Castellanos & Proal, 2012; Cubillo, Halari, Smith, Tayler, & Rubia, 2012; Liston, Cohen, Teslovich, Levenson, & Casey, 2011; Schneider et al., 2010; Valera, Faraone, Biederman, Poldrack, & Seidman, 2005).

Given the substantial research literature that has investigated performance- based measures of executive function in individuals with ADHD, it is important to examine the clinical utility of these measures. That is, does performance on measures of executive function accurately identify children who meet diagnostic criteria for ADHD? Lambek et al. (2011) examined the prevalence of executive function deficits on eight performance- based measures of executive function tasks in a sample of children diagnosed with ADHD and a community comparison group. The community sample was screened for the presence of psychopathology based on parent report in order to rule out the possibility of ADHD. Community controls outperformed children with ADHD on all of the performance- based measures of executive function (including response inhibition, set shifting, work-ing memory, and planning tasks). Executive function deficits were defined as a


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score below the 10th percentile of scores in the community sample. Eighty- three percent of children with ADHD were impaired on one or more executive func-tion tasks, and 49% of the community controls were impaired on one or more of the executive function tasks. Impairment on six or more executive function tasks resulted in identifying 6% of children with ADHD and none of the community controls. A similar pattern of findings was reported by Nigg, Willcutt, Doyle, and Sonuga- Barke (2005).

There are several challenges in using performance- based measures in the clini-cal assessment of ADHD. One issue pertains to identifying the optimal cutoff on continuous measures for defining impairment on executive functions in indi-viduals with ADHD. For example, accuracy and response time are continuous measures. Identifying what should be considered impaired is a judgment, as no clear cutoff has been established based on the research literature. Second, no spe-cific domain or measure of executive function has consistently been implicated with ADHD. Third, not all children with ADHD display deficits on performance- based measures of executive function (Nigg et al., 2005). Although the research literature has implicated performance- based measures of executive function in individuals with ADHD, there is more work to be done to establish the clinical validity of these measures.

Rating scales of executive function have become another commonly used mea-sure to assess executive functions. These scales have also been examined in sam-ples of individuals with ADHD.

RATING SCALES OF EXECUTIVE FUNCTION

Rating scales of executive function were developed to provide an ecologically valid indicator of competence in complex, everyday, problem- solving situations (Roth, Isquith, & Gioia, 2005). An assumption underlying the use of these rating scales is that they are measuring behaviors that are importantly related to processes that are assessed by performance- based measures of executive function. A commonly used rating scale of executive function in children and adolescents has been the Behavior Rating Inventory of Executive Function (BRIEF; Gioia, Isquith, Guy, & Kenworthy, 2000). This instrument is composed of eight individual scales and three composite scores. The Inhibit, Shift, and Emotional Control scales com-pose the Behavioral Regulation Index Composite. The Initiate, Working Memory, Plan/ Organize, Organization of Materials, and Monitor scales compose the Metacognition Index Composite. The Behavior Regulation and Metacognition Indices can be combined to form an overall Global Executive Composite. Each item is rated on whether difficulties are encountered: never, sometimes, or often. A score for each Index and Composite can be used to derive a scaled score that indicates degree of difficulty in each rated domain, with a higher score indicating more difficulty. Informants are asked to report on behaviors that have been prob-lematic in the last 6 months. There are also preschool and adult versions of this particular scale (Gioia, Espy, & Isquith, 2003; Roth et al., 2005). A comprehensive


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review of rating scales measuring executive function can be found in Malloy and Grace (2005). Some of these measures are proprietary tests, whereas others are rating scales described in detail in peer- reviewed papers or books. However, the most commonly used measure tends to be the BRIEF in the research literature (Toplak, West, & Stanovich, 2013).

Ratings of executive function are based on self- ratings or informant ratings, as opposed to measured performance on an actual task. These ratings reflect the perceived success or difficulties based on observable behaviors that are thought to be related to executive functions. Ratings of executive function have been studied in ADHD, but not to the same extent as performance- based measures.

Attention- Deficit/ Hyperactivity Disorder and Ratings of Executive Function

In the relatively few studies that have been conducted, individuals with ADHD have been rated as having problems across all domains of executive function within these surveys. Adolescents with a diagnosis of ADHD were reported to have more difficulties based on BRIEF ratings than comparison controls (Hummer et al., 2010; Toplak et al., 2009). This pattern of findings has also been found in adults using the Deficits in Executive Functioning Scale (Barkley & Fischer, 2011; Barkley & Murphy, 2011). Higher ratings of ADHD behaviors have also been associated with ratings of more difficulties on the BRIEF in a heteroge-neous sample of clinically referred children (Gioia et al., 2000). Both domains of inattention and hyperactivity/ impulsivity were significantly correlated with the Behavior Regulation and Metacognition Indices. This pattern of findings has also been found using the Childhood Executive Functioning Inventory (CHExI) in a community sample of children (Thorell & Nyberg, 2008).

The clinical utility of ratings of executive function has also been examined in a sample of adults with ADHD and adult controls. Biederman et al. (2007) exam-ined whether adults diagnosed with ADHD would also show impairment on a rating scale designed to measure executive function (The Current Behavior Scale). Impairment was defined as cutoffs at the 93rd and 98th percentiles in the control sample, approximating 1.5 and 2 standard deviations from the mean. Using this cutoff, 89.5% and 78.5% of participants with ADHD were identified as having an executive function deficit on this scale.

A somewhat separate but important issue from the perspective of ADHD is that there is some overlap between the symptoms used to assess ADHD and items used to rate executive function behaviors. For example, the behavioral descriptors for short attention span and impulsivity are characteristic of ADHD rating scales, and these items overlap considerably with items on the BRIEF (Thorell, Eninger, Brocki, & Bohlin, 2010; Thorell & Nyberg, 2008). If some of the items do overlap, this will inflate the relationship between ratings of ADHD and ratings of executive function. For example, Barkley and Murphy (2010) reported correlations ranging from r = .68 to .91 between ratings of ADHD and ratings of executive function.


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Similar to the studies that have examined performance- based measures in ADHD, there are no specific domains of executive function ratings that have been implicated in ADHD. Similar diagnostic issues are present with respect to identi-fying individuals with ADHD as with the performance- based measures of execu-tive function. That is, there is not a 1:1 mapping between those individuals who display impairment on ratings of executive function and those who meet criteria for an ADHD diagnosis. The issue of overlap between ratings of ADHD behaviors and ratings of executive function poses a limitation for using these measures as unique diagnostic indicators in a clinical assessment for ADHD. A conservative clinician may choose to use ratings of executive function as information that aug-ments ADHD ratings, as opposed to using it as unique evidence that contributes to the diagnostic assessment of ADHD.

THE RELATIONSHIP BETWEEN PERFORMANCE- BASED VERSUS RATINGS OF EXECUTIVE FUNCTION

Several studies have empirically examined the relationship between performance- based measures and rating scales of executive functions. Toplak et al. (2013) reviewed 20 studies that have examined the association between performance- based measures of executive function and rating scale measures of executive func-tion. These studies included different periods of development and both clinical and nonclinical samples. The most commonly used rating of executive function reported in these studies was the BRIEF (13 studies), the Behavioural Assessment of the Dysexecutive Syndrome (BADS) Dysexecutive questionnaire (DEx; five studies), and ratings of impulsivity (used to index a lack of inhibition; three stud-ies). The performance- based measures of executive function that were included in these studies were diverse and varied, but they primarily assessed the domains of inhibition, working memory, planning, mental flexibility, and verbal fluency. Only 68 (24%) of the 286 relevant correlations reported in these studies were sta-tistically significant, and the overall median correlation was only .19. The median values for each executive function rating measure were as follows: r = .18 for the BRIEF, r = .14 for the BADS- DEx, and r = .25 for the impulsivity ratings. Given that Type 1 error likely contributed to some of the significant relationships given the multiple comparisons conducted in several of these studies and the fact that significant effects are differentially advantaged in the publication process, these median values were interpreted as very modest. Based on this review, it was con-cluded that performance- based and rating scale measures of executive function assess different underlying mental constructs.

The findings reported by Toplak et al. (2013) included studies with ADHD samples, and these studies were consistent with their overall findings and con-clusions. For example, Biederman et al. (2008) examined overlap in impairment on performance- based and ratings measures of executive function in a sample of adults with ADHD. Impairment on the performance- based measures was based on a score 1.5 standard deviations below the mean in a comparison control sample


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(or below the 7th percentile). Impairment on the ratings was based on the same algorithm: 1.5 standard deviations above the mean or above the 93rd percentile in the comparison group. There was little overlap in the impairment identified by these two domains of measures. Only 14% of the participants who were impaired on the performance- based measures also displayed impaired scores on the rating scale measure of executive function.

Barkley and Murphy (2010) further explored whether performance- based and rating scale measures of executive function made overlapping or unique contri-butions to explaining self- reported occupational problems in a sample of adults with ADHD. Occupational impairments were measured using self- reported or employer- reported difficulties in the workplace. Examples of self- reported ratings of occupational impairment included work quality, percent of jobs from which the participant was fired, and percent of jobs from which the participant was rep-rimanded. The two different types of measures of executive function explained separate variance in the occupational success of adults with ADHD.

THEORETICAL PERSPECTIVES ON WHAT PERFORMANCE- BASED MEASURES AND RATING SCALES OF EXECUTIVE FUNCTION MEASURE

Performance- based measures and rating scales are the two major approaches for assessing executive functions, but these two approaches provide different types of assessment information. From the perspective of operationalization, performance- based and rating measures of executive function are different in terms of how they are administered and scored. The existing empirical literature indicates that these two types of measures are only minimally correlated. Performance- based mea-sures and rating scales of executive function assess different aspects of cognitive and behavioral functioning that independently contribute to clinical problems.

We might begin to explain the lack of association between performance and ratings of executive function by drawing an analogy with the field of intelligence. Like the case of executive functioning, the construct of intelligence has also been defined broadly but measured narrowly. This distinction between broad and nar-row theories of intelligence is discussed by Stanovich (2009b), who noted that “broad theories include aspects of functioning that are captured by the vernacular term intelligence (adaptation to the environment, showing wisdom and creativity, etc.), whether or not these aspects are actually measured by existing tests of intelli-gence. Narrow theories, in contrast, confine the concept of intelligence to the set of mental abilities actually tested on extant IQ tests” (p. 12). That is, a full- scale intel-ligence score (narrow sense) does not assess all of the ways that someone may be considered to be “smart” as a layperson might understand that term (broad sense). This is analogous to executive functions. For example, few perseverative errors on the WCST (narrow sense) does not index all of the ways that someone shows com-petence in novel problem solving and goal- directed behavior (broad sense).

Our explanation for the lack of convergence displayed by the performance- based and behavior rating measures involves positing that these measures are


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actually tapping different cognitive levels— specifically, what Stanovich (2009b, 2011) terms the difference between the algorithmic and the reflective mind. Cognitive scientists refer to the level of analysis concerned with efficiency as the algorithmic level of analysis (Anderson, 1990; Marr, 1982; Stanovich, 1999, 2009b). The cognitive psychologist and neuropsychologist work largely at this level by showing that human performance can be explained by information processing mechanisms in the brain, such as input coding mechanisms, per-ceptual registration mechanisms, working memory, long- term memory, and so on. In contrast, the reflective level of analysis is concerned with the goals of the person, beliefs relevant to those goals, and the choice of action that is rational given the goals and beliefs (Bratman, Israel, & Pollack, 1991; Dennett, 1987; Newell, 1982, 1990; Pollock, 1995; Stanovich, 2009b, 2011). In short, the reflective level is concerned with the goals of the system, beliefs relevant to those goals, and the choice of action that is optimal given the system’s goals and beliefs.

The distinction between the algorithmic level of analysis and the reflective level from cognitive science maps analogously onto differentiating performance- based from rating scale measures of executive function. Only the latter assess issues of rational control, which refers to behavior in the real environment that serves to foster goal achievement. Performance measures may indeed be assessing some-thing of genuine importance, namely the efficiency of the processes available to recruit in behavioral control such as inhibition, but performance- based measures bypass the whole issue of rational goal pursuit. This point about the laboratory measures has been made before by Salthouse et al. (2003): “The role of executive functioning may also be rather limited in many laboratory tasks because much of the organization or structure of the tasks is provided by the experimenter and does not need to be discovered or created by the research participant” (p. 569). Performance- based measures of executive function provide important informa-tion regarding efficiency of processing, but rating scales of executive function tell us more about success in rational goal pursuit.

Assessment of indicators at the algorithmic and reflective levels provides dif-ferent information about cognitive functioning. For this reason, Stanovich (2009a) has suggested that the term “executive processes” has been misnamed. The term “executive” conflates these two different levels and “mistakenly implies that everything ‘higher up’ has been taken care of, or that there is no level higher than what these executive functioning tasks measure” (Stanovich, 2009a, p. 67). Performance- based tasks would be better described as supervisory processes, as regulation is directed by an external examiner.

The conceptual differentiation of performance- based and rating measures of executive function is also consistent with an important distinction in psychometrics. Psychometricians have long distinguished typical performance situations from opti-mal or maximal performance situations (see Ackerman, 1994, 1996; Ackerman & Heggestad, 1997; Cronbach, 1949; Matthews, Zeidner, & Roberts, 2002). Typical performance situations are unconstrained in that no overt instructions to maxi-mize performance are given, and the task interpretation is determined to some extent by the participant. The goals to be pursued in the task are left somewhat


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open. The issue is what a person would typically do in such a situation, given few constraints. Typical performance measures assess, in part, goal prioritization. In contrast, optimal performance situations are those where the task interpretation is highly constrained, and the person performing the task is instructed to maximize performance. Thus, optimal performance measures examine questions of the effi-ciency of goal pursuit. All tests of intelligence or cognitive aptitude are optimal per-formance assessments, whereas measures of critical thinking and cognitive styles are often assessed under typical performance conditions. Likewise, many measures of rational thinking and decision making are assessed under typical performance conditions (Stanovich, 2009b; Stanovich, West, & Toplak, 2011).

Performance- based measures and ratings scales of executive function cleave the optimal/ typical distinction in different ways. It is clear that performance- based measures are assessed under optimal/ maximal conditions. This charac-teristic of neuropsychological tests of executive function has been echoed by Gioia, Isquith, and Kenealy (2008), who argue that “individuals with substantial executive dysfunction can often perform adequately on well- structured tests when the examiner is allowed to cue and probe for more information, relieving the individual of the need to be appropriately inhibited, flexible, strategic in planning, and goal directed” (p. 180). Performance- based measures of execu-tive function capture optimal performance situations, because the task inter-pretation is determined externally by the examiner and is not left up to the participant.

In contrast, rating scales of executive function are unlike measures of maximal or optimal performance. When participants are estimating the frequency and typ-icality of how well they perform in day- to- day situations that are likely to engage executive processes, their responses are not constrained by an external examiner and there are no explicit instructions to “maximize” or “optimize” their ratings. The interpretation of the task is left up to the raters, who must decide on instances from their everyday lives that map onto the questions or constructs probed. Their task is to provide an estimate of the frequency of such events. Ratings of behaviors are subject to the limitations associated with informant reports, such as context effects and differences in the way different observers judge behavior (Barkley, 2006). Both performance- based and rating measures of executive function pro-vide important and nonredundant information about an individual’s efficiency and success in achieving goals.

IMPLICATIONS FOR THE USE OF PERFORMANCE- BASED AND RATING SCALE MEASURES OF EXECUTIVE FUNCTION IN THE ASSESSMENT OF ATTENTION- DEFICIT/ HYPERACTIVITY DISORDER

The findings from the empirical studies that have examined the association between these measures indicate that the two different types of measures are


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assessing different aspects of cognitive functioning (Toplak et al., 2013). Studies from the field of ADHD are consistent with this conclusion (Barkley & Fischer, 2011; Barkley & Murphy, 2010; Biederman et al., 2008). As Barkley (1997) has noted, “measures taken in clinics or laboratory assessments over relatively brief temporal durations are going to prove less sensitive to the identification of the disorder and its associated cognitive deficits than will measures collected repeat-edly over longer periods of time” (p. 332). The impairments of ADHD manifest as typical performance in day- to- day activities, across different situations and contexts. An implicit assumption in ratings of typical day- to- day behavior is that the subject’s natural tendencies to internally regulate his or her behavior are being assessed— namely, how well he or she can carry out these activities with-out constant direction or regulation by an external evaluator. Ratings of execu-tive function assess the former, whereas performance- based measures assess the latter.

The implication for the practicing clinician is that there is more separabil-ity than commonality among performance- based measures and rating scales of executive function. These two classes of measures should not be interpreted as equivalent, interchangeable, or as types or subcategories of one another. The fact that both sets of measures are defined as executive functions in name further confuses the issue, suggesting that these measures are alike, when in fact they represent different aspects of cognitive and behavioral functioning. Impairment on performance- based measures of executive function does not translate into impairment on ratings of executive function, or vice- versa, as was demonstrated by Biederman et al. (2008). It remains an empirical question to determine the cor-relates, convergers, and predictive utility of both performance- based and rating measures of executive function. Such research will be useful to properly char-acterize these measures. Some literature has begun to do this, such as examin-ing impairment in major life activities and in occupational functioning in adults (Barkley & Fischer, 2011; Barkley & Murphy, 2010, 2011).

Performance- based measures of executive function provide information regarding performance in highly structured environments where the examiner has set the goals and outcomes for the testing session. If performance is low in this optimal, structured testing environment, this might tell us something about potential processing weaknesses in the individual. If performance in this struc-tured environment is at least average and less variable than in unstructured envi-ronments, this indicates that a structured environment facilitates performance. Better performance in the standardized assessment context should be taken as an indicator of how well a child, for example, would do in the classroom with addi-tional structure and support. The standardized assessment situation has been cri-tiqued for providing a less ecologically valid assessment of how a child performs in “real” or everyday contexts. However, the standardized assessment situation provides a “good test” of how well a child will perform under high structure and direction from an examiner. Instead of regarding the 1:1 behavior testing situa-tion in an assessment as a nonecologically valid indicator of behavior, it should be regarded as an indicator of how well performance is ameliorated under highly


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structured conditions. This is a somewhat novel perspective on the standardized assessment context. From this account, it is not so surprising that some individu-als with ADHD may display impaired performance on ratings of executive func-tion, but not on performance- based measures of executive function.

The different information provided by performance- based and rating measures of executive function should allow us to draw prescriptive recommendations in a clinical assessment for children. The importance of different structured contexts on ADHD behavior has been addressed from the perspective of assessment. For example, the Parental Account of Childhood Symptoms (PACS) interview (Chen et al., 2008; Müller et al., 2011; Taylor et al., 1986a, 1986b, 1987) presents parents with questions about behavior during specific situations. Similarly, the Teacher Interview Probe (TIP; Corkum, Andreou, Schachar, Tannock, & Cunningham, 2007) poses parents and teachers with six problem situations. For example, teach-ers are asked about arrival routines, getting materials ready for lessons, doing group work, doing individual seat work, coming in and settling after morning recess, and getting along with peers. All of these situations represent different lev-els of structure and expectations. Pervasiveness of these symptoms may be appar-ent across these different contexts, but the structure of the situation may impact the degree to which symptoms of ADHD are expressed. There is good reason to expect that more structure is beneficial for children and youth with ADHD, given the effectiveness of behavioral parent training and behavioral classroom manage-ment programs (Pelham & Fabiano, 2008). It is thus likely that performance can be ameliorated with intervention strategies increasing the degree of structure in the environment for children with ADHD.

CONCLUSIONS

ADHD has been characterized by difficulties in executive functions. Two types of measures have been used to assess executive functions in ADHD, including performance- based measures and rating scales of executive function. Based on empirical studies including samples of individuals with ADHD, both performance- based measures and rating scales of executive function have been shown to be affected in individuals with ADHD more than in comparison controls. No spe-cific or unique type of executive function has been associated with ADHD.

Studies that have examined the relationship between performance- based measures and rating scales of executive function indicate that these measures are only modestly related. There is also little overlap in individuals with ADHD who perform less optimally on both types of measures. This has been demon-strated empirically based on reviews, and it has been supported by theoretical distinctions in the cognitive science literature. These two types of measures assess different levels of analysis, and they should not be considered as equiv-alent or interchangeable. By juxtaposing these two types of measures that are used to assess ADHD, this provides the clinician with a different lens with which


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to understand observable behaviors that have been associated with ADHD. For example, performance and behavior during performance- based measures of executive function provide information regarding how well the child behaves and manages in an optimal and highly structured testing environment. Alternatively, ratings of executive function from different informants provide information on how well the child manages in less structured environments relative to the test-ing situation, such as a classroom with several other children and in the home setting where there is likely even less structure than in the classroom. The former provides an assessment of how well the individual performs when the goals are explicitly laid out. The latter provides an assessment of how well the individual executes his or her goals without explicit guidance. Both domains are useful and valuable in the assessment of ADHD, but they provide different types of informa-tion in the context of a clinical assessment.

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INDEX

Page references for figures are indicated by f and for tables by t.

Abernethy, B., 50Academic performance, preschool

executive functioning on, 80Academic skill emergence and school

achievement, transition to school, 91– 100

concurrent relations, 92– 94confirmatory factor analysis, 90– 91executive functioning causation, 96– 100predictive relations, 94– 96

Achievement gap, 77Action

commission and inhibition, 44– 46vs. inaction, 41

Adams, A.- M., 93Adherence, flexibility, 41Adults, significant, on executive functions,

3, 70– 82. See also Parent– child relationships

Akhtar, N., 122Algorithmic level of analysis, 165Alloway, T. P., 92Apperly, I., 58Assessment, executive functions,

2– 3, 21– 33applications and importance, 21challenges, 24– 26inhibitory control, 24measures, 28t– 32tmeasures, inhibitory control, 26, 32tmeasures, multiple facets, 28t– 29tmeasures, planning, 26, 30tmeasures, set shifting, 26– 27, 30t

measures, working memory, 26, 31t– 32tmodels, 22– 23multiple test batteries, 22planning, 23– 24set shifting, 24working memory, 23young children, 26– 27

Athlete's decision- making process, 42, 43fAttachment Behavior Q- Sort, 75Attachment security, parent– child,

72– 73, 75– 76Attention

bilingual advantage, 111, 113, 114shifting, academic achievement, 94, 95–96

Attention- deficit/ hyperactivity disorder (ADHD), 4, 157– 169

diagnosis, 157executive functioning, 63executive functioning, clinical

assessment, 158– 159executive functioning, social

deficits, 63– 64false- belief task, 64performance- based measures, 159performance- based measures, ADHD

and, 160– 161performance- based measures– rating

scales, for ADHD assessment, 166–168performance- based measures– rating

scales, relationship, 163– 164performance- based vs. rating scales,

163– 164rating scales, 161– 163

176 Index

176

Autism spectrum disorders (ASD), 4, 134– 150

assessment, executive functioning, 142–148, 144t– 145t

behaviors, infants, toddlers, and preschoolers, 134– 135

behaviors, nonverbal, 135characteristics, 134development, executive functioning,

136– 137difficulties, 134– 136executive functioning assessment,

complexity and social demands, 148– 149

high- level executive control, 135prosocial orientation, 141– 142self- regulation, 139social awareness, 137– 138, 139– 140social competence, 137, 138– 142social development, 137– 138symptoms and diagnosis, 134– 135theory of mind, 138, 139– 140

Automated Working Memory Assessment- 2, 31t

Autonomy, maternal, language and, 77Autonomy support

scaffolding, 75, 78working memory and conflict- EF, 75

Barkley, R. A., 162– 163, 164, 167Barkley Deficits in Executive Functioning

Scale– Children and Adolescents (BDEFS- CA), 28t

Barnett, W. S., 98Behavioral Assessment of the Dysexecutive

Syndrome in Children (BADS- C), 28t, 163

Behavior Assessment System for Children (BASC- 2)

autism spectrum disorders, 141, 143, 146

measures, 143Behavior Flexibility Rating Scale-

Revised (BFRS- R), autism spectrum disorders, 146

Behavior Rating Inventory of Executive Functioning (BRIEF), 28t, 161– 162, 163

ADHD, 162ASD, 139, 141

Behavior Rating Inventory of Executive Functioning (BRIEF)– Preschool version (BRIEF- P), 28t, 79, 80

Berry, J., 50Bialystok, E., 111– 115, 122Biederman, J., 162, 163– 164, 167Bierman, K. L., 70, 100Bilingual advantage, 4, 111– 116, 122. See

also Plurilingualism, young children

alternative views, 115– 116attention control, 113attention control, inconsistencies, 114Dimensional Change Card Sort, 113evidence, 112– 113eye- movement control tasks, 113flexible thinking, 111focusing attention, 111gratification delay, 113implications, educators and parents, 116inhibition, 111origins, 111, 114Simon task, 112– 113, 115– 116socioeconomic factors, 115– 116utterances, spoken, 112

Biological immutability, 2Black, D. O., 141Blair, C, 95Blair, C., 70, 100Bodrova, E., 98Bourke, L., 93Bracken School Readiness Measure,

93– 94Brain development. See also

Prefrontal cortexsocial experience, 71– 73

BRIEF Shift scale, autism spectrum disorders, 146

Broad theories of intelligence, 164Brown, L., 95Bryce, C. I., 139Bryson, S. E., 22Buddy Reading, 97– 98Buitelaar, J. K., 64Bull, R., 93

Index 177

177

Cambridge Neuropsychological Test Automated Battery (CANTAB), 29t

Carlson, S. M., 74, 138Castelfranchi, C., 123– 124Childhood Executive Functioning

Inventory (CHEXI), 28tADHD, 162ASD, 146

Children's Cooking Task (CCT), autism spectrum disorders, 149

Children's Memory Scale (CMS): Numbers and Sequences subtests, 31t

Children's Test of Non Word Repetition, 31t

Chinese- English bilinguals, 115Cinguloopercular networks, 15Clark, C. A., 94Classroom behavior, 70– 71Clocks subtest (NEPSYS- II), 30tCode meshing, 127Code switching (mixing), 126– 128Cognitive Assessment System (CAS), 29tCognitive complexity and control theory

(CCC and CCC- revised), 22Cognitive scaffolding

parent– child relationship, 3, 72– 80 (See also Scaffolding, parent– child relationships)

teacher– child relationship, 81Commission, action, 44– 46Componential view, 22Comprehensive Executive Functioning

Inventory, 28tComprehensive Trail Making Test, 30tConfirmatory factor analysis (CFA), 90– 91Conflict- EF

autonomy support, 75University of Montreal study, 74– 77

Conners Continuous Performance Test, 3rd ed., 32t

Conners' Kiddle CPT– Version 5, 32tConners' Parent Rating Scales Revised

(CPRS), autism spectrum disorders, 146

Constructivist developmental premise, 41Context- specific rules, 41– 42Continuous Performance Test (CPT), 94– 95

ADHD, 160ASD, 142

Control attention, bilingual advantage, 113, 114effortful, 89eye- movement, bilingual advantage, 113impulse, 74– 77inhibitory, 24, 26, 32t, 90– 91self- , 89tap inhibitory, 79Tasks of Executive Control (TEC), 32t

Cool processes, 89– 90, 137Corsi Block Tapping Task, 31tCôte, J., 50Cultural dimensions

vs. bilingual advantage, 115plurilingualism, 121– 122

Damasio, A. R., 135– 136Dawson, G., 140, 141, 144tDay- Night Stroop Task, 79, 80

autism spectrum disorders, 139Decision- making process, athlete's, 42, 43fDeficits in Executive Functioning Scale,

ADHD, 162Delis- Kaplan Executive Function System

(D- KEFS), 29tDelis- Rating of Executive Function

(D- REF), 29tDenham, S., 137Design Fluency (DF), 49Development. See also specific types

experience as, 49– 51functioning level, 47

Diamond, A., 47, 98Diffusion tensor imaging (DTI), 12Dimensional Change Card Sort

(DCCS), 9– 10bilingual advantage, 113

Directives, 78Dissociable executive functioning

skills, 22Domitrovich, C. E., 70, 100Doyle, A. E., 161Duncan, G. J., 92Dunn, J., 64Dysexecutive questionnaire (DEX), 163

178 Index

178

Effortful control, 89Egocentrism, 3- 5 yr, 55Elaboratives, 87Else- Quest, N., 99Emotional responsivity, parent– child

relationship, 3, 70– 82. See also Parent– child relationships

English Peabody Picture Vocabulary Test (PPVT), 122

Ensor, R., 74Environment, continuous changes, 40Espy, K. A., 93Executive functioning (EF). See also

specific topicsChinese vs. North American

preschoolers, 59– 60classroom and school behavior, 70– 71components, 70conceptual development, 61context, 47– 49continuum of functional, cognitive

resources, 128– 129cool processes, 89– 90, 137deficits, for clinicians, 158– 159definition, 9, 89– 91, 112example, 54experience on, 49– 51, 63factors, 96– 97hot processes, 89– 90, 137origins, historical, 90overlapping terms, 89performance- based measures, 159processes, misnaming, 165rating scales, 161– 162response conflict skill, 57, 60– 61theory- of- mind reasoning, 54, 57– 62

(See also Theory- of- mind reasoning)training programs, 96– 98

Executive functioning, developmental cognitive neuroscience, 9– 16

brain development, other areas, 14brain development, prefrontal cortex,

10– 14, 71, 73 (See also prefrontal cortex)

development, 9– 10network approach, 14– 16

Executive functions, children

clinical assessment, 158– 159cognitive tools, 2complex social and cognitive

activities, 1definition, 1, 21examples, 1neurological insult or disability, 1– 2on outcomes, social and academic, 1research, basic, 2school age, 1subsets, 21

Executive functions, theory of mind and, 54, 57– 62

in acquisition of theory of mind, 59– 62in expression of theory of mind, 58– 59response conflict executive functioning

skill, 57, 60– 61social interactions and, situational

influences, 64– 65social interactions and, typical

and atypical developmental influences, 62– 64

Exercise, on executive function, 50Experience

as development, 49– 51on executive functioning, 63, 71– 73social, 71– 73social, plurilingualism, 121– 122on theory- of- mind reasoning, 61

Eye- movement control tasks, bilingual advantage, 113

Fahie, C. M., 64False- belief tasks, 55– 57

ADHD, 64location- change, 55, 56, 58studies, 56theory- of- mind reasoning, 55unexpected contents, 55, 56unintentional stories, 56– 57

Farran, D. C., 98– 99Fatigue, 40, 48– 49, 64Fernyhough, C., 75Fixed- effects models, 100Flexibility, 3, 40

autism spectrum disorders, 136bilingual advantage, 111

Index 179

179

game plan vs., 42– 44, 43fplurilingualism, 119

Focusing attention, bilingual advantage, 111

Fractional anisotrophy (FAT), 12Fradley, E., 75Freeze Game, 98Frontal lobe

functions, 12maturation, 12

Frontal Lobe/ Executive Control (FLEC) scale, 150n1

Frontoparietal network, 15Functional magnetic resonance imaging

(fMRI) artifacts and research

inconsistencies, 13– 14prefrontal cortex development, 13

Game plan vs. flexibility, 42– 44, 43fGardiner, E., 141Garon, N., 22Gathercole, S. E., 95General Working Memory cluster,

WJ- IV, 31tGenetic hard wiring, 2George, J., 95– 96Giesbrecht, G. F., 142Gilotty, L., 141Gioia, G. A., 166Glaser, D., 71Goal- oriented action sequences, 39“Grass- snow” task, 57Gratification delay, bilingual

advantage, 113Gray matter

contents, 11prefrontal cortex development, 11– 13thickness and density, MRI

measurement, 11– 12thickness and density, thinning, 12

Greenberg, M. T., 70, 100Greenfield, D. B., 95– 96Griffith, E. M., 144tGrosjean, F., 121Guidelines for success, 41, 42– 43Gustafson, R., 49, 50

Hart, J. J., 74Head Start REDI program, 100Head- Toes- Knees- Shoulder task, 94Herder, J. G., 120– 121Higher order developmental

functions, 44– 46Hofmann, W., 89Hot processes, 89– 90, 137Hughes, C., 64, 74Hunger, 64Hutchison, S. M., 141

Iarocci, G., 141Impulse control

definition, 74University of Montreal study, 74– 77

Ingvar, M., 49, 50Inhibition, 40

academic achievement, fixed effects model, 100

academic achievement, longitudinal studies, 94– 95

action, 44– 46autism spectrum disorders, 136bilingual advantage, 111math skills, early, 93

Inhibition + working memory counting skills, early, 93math skills, early, 93– 94

Inhibitory control, 24confirmatory factor analysis, 90– 91measures, 26, 32t

Integration, 15– 16Intentionality, 123– 124Intercultural communication, 121Intra- Extra Dimensional Set Shift

(IED), 30tIsquith, P. K., 166

Jahromi, L. B., 139Joint attention, autism spectrum

disorders, 140Joseph, R. M., 139

Kenealy, L. E., 166Kenworthy, L., 141Kerns, K. A., 141, 142

180 Index

180

Killen, M., 56Kirkham, N., 47Knock- tap task, autism spectrum

disorders, 139Knoweldge acquisition, 70Korean- English bilinguals, 115Kroesbergen, E. H., 93Kupersmidt, J. B., 100

Lam, Y. G., 144tLambek, R., 160Landry, S. H., 73– 74Language, child, 77

autonomy, maternal, 77engagement and plurilingualism, age

and, 124– 125young children, 118

Learning, intentional, 70Leong, D. J., 98Letter Memory task, 90Letter Number Sequencing, Digit

Span, and Arithmetic (WISC- IV subtests), 31t

Letter skills, early, working memory, 94Lillard, A. S., 64, 99Linguistic symbols, 118– 119Location- change false- belief tasks,

55, 56, 58Lower order developmental

functions, 44– 46Lüdi, G., 121Luk, G., 122Luria, A. R., 72

Magnetic resonance imaging (MRI), gray matter, 12

Mandell, D. J., 138Maternal Behaviour Q- Sort

(MBQS), 75, 76Math skills, early

inhibition, 93working memory, 93– 94working memory + inhibition, 93

Maurer, R. G., 135– 136Maurex, L., 49, 50McEvoy, R. E., 141, 144tMcInerney, R. J., 142

Mead, G. H., 118Meins, E., 75Meltzoff, A. N., 141Memory. See specific typesMenjivar, J. A., 122Microgenic development, 47Miller, M. R., 142Miller- Loncar, C. L., 73– 74Mind- mindedness, 75Mistakes, as momentary regressions, 46Miyake, A., 22– 23, 90Modular view, 22Montessori schools, 99Moss, E., 79Müller, U., 141, 142Multilingualism, early childhood, 4,

118–129. See also Bilingual advantage; Plurilingualism, young children

Multiple- component processes, 3, 38– 51. See also Sports metaphor

Munis, P., 95– 96Murphy, K. R., 162– 163, 164Mutually Responsive Orientation (MRO)

scale, 75

Narrow theories of intelligence, 164NEPSYS- II, 29tNetworks

activity, 14– 15children vs. adults, 15cinguloopercular, 15components, 14executive functioning

development, 14– 16frontoparietal, 15protracted development, 15resting state functional connectivity

MRI, 15segregation and integration, 15– 16

Nigg, J. T., 161Nix, R. L., 70, 100Non-Verbal and Verbal Working

Memory subtests (Standford- Binet 5 subtests), 31t

Norton, B., 127Number– Letter rask, 90

Index 181

181

Oerlemans, A. M., 139Ontogenetic development, 47, 49Osterling, J., 141Ozonoff, S., 140, 142

Parental Account of Childhood Symptoms (PACS) interview, 168

Parent– child relationships, 3, 70– 82attachment security, 72– 73, 75– 76educators, implications, 80– 82research, prior, 73– 74research, Simon Fraser University–

University of Victoria study, 77– 78research, University of Montreal

study, 74– 77research, University of Quebec in

Troise- Rivieres study, 79– 80scaffolding, 3, 72– 80 (See also

Scaffolding, parent– child relationships)

scaffolding, emotional responsivity, 3, 72

social influences, 71– 73sociogenesis, 72

PATHS (Promoting Alternative Thinking Strategies), 99– 100

Peets, K. F., 122Pellicano, E., 139– 140, 145tPennington, B. F., 140Performance

optimal, 47variability, task demands, 47

Performance- based measures, 159Performance- based measures– rating scales

ADHD assessment, 166– 168assessment information, 164– 166relationship, 163– 164

Performance regression, sports, 46Peterson, J., 64Petrovic, P., 49, 50Pezzulo, G., 123– 124Piaget, Jean, 9Pickering, L. J., 95Planning, 23– 24, 40

executive functioning, 70measures, 26, 30t

Plasticity, brain network, 2, 124

Plurilingualism, young children, 4, 118–129. See also Bilingual advantage

age and language engagement, 124– 125code switching, 126– 128cognitive demands, 119– 120competence, 120– 123competencies, emerging, 125– 126executive function continuum, 128– 129executive system, 119– 120, 128– 129holistic perspectives, 121intercultural communication, 121linguistic symbols, child's monitoring

attention to, 118– 119shuttling among languages, 127social actions, interactions, and

intentionality, 123– 124social actors, 120, 126– 128societal, cultural, and institutional

dimensions, 121– 122Ponitz, C. C., 94Practice, sports, 49– 50Prefrontal cortex, 9

lesions, 11, 90Prefrontal cortex development, 10– 14

dorsolateral lesions, 11executive functioning, 10– 14, 71, 73functions, 10gray matter, 11– 13gray matter, thickness, 11– 12lateral, 10– 11, 14lateral, criticism, 14projections, 10protracted development, 9– 11, 13, 71synaptogenesis and synaptic pruning, 11task completion, 10– 11white matter, 13– 14

Problem solving. See also specific testsexecutive functioning, 70social, autism spectrum disorders, 138

Prosocial orientation autism spectrum disorders, 138,

141– 142definition, 138

Pry, R., 145t

Rajendran, G., 148Rating scales, 161– 162

182 Index

182

Rating scales– performance- based measures

assessment information, 164– 166relationship, 163– 164

Rationale, 38– 41Razza, R. P., 95Readiness skills, school, 91– 92REDI program, Head Start, 100Reflective level of analysis, 165Regression

momentary, mistakes as, 46performance (sports), 46

Response conflict executive functioning skill (RC- EF), 57, 60– 61

Response inhibition, 24confirmatory factor analysis, 90– 91measures, 26, 32t

Resting state functional connectivity MRI (rs- fcMRI), 15

Rinaldi, J., 141Rogers, S. J., 140Roman, G., 74Rose- Krasnor, L., 137Rosenthal, M., 147Rules, 41– 44, 43f

context- specific, 41– 42guidelines for success, 41, 42– 43use, 39, 40

Sabbagh, M. A., 59, 60Salthouse, T. A., 165Sapir, E., 120– 121Scaffolding, parent– child relationships,

3, 72– 80. See also Parent– child relationships

autonomy support, 75, 78elaborative vs. directive parental

utterances, 78executive functionoing, 78joint errand- planning task, 79– 80verbal, maternal, 73– 74

Scaffolding, teacher– child relationships, 81School behavior, executive

functioning, 70– 71School readiness skills, 91– 92Segregation, 15Self- control, 89

Self- regulation autism spectrum disorders, 137, 139definition, 89, 137examples, 89executive functioning, 70failure, 89language use, plurilingualism, 119

Set shifting, 24confirmatory factor analysis, 90measures, 26– 27, 30t

Shuttling among languages, 127Simon effect, 112– 113Simon Fraser University– University of

Victoria study, 77– 78Simon task, 112– 113, 115– 116Sirian, L., 141Situational influences, theory of mind

and, 64– 65Six Parts Test, autism spectrum

disorders, 148Skill- based activities, 50Smith, I. M., 22Smith, K. E., 73– 74Social actions, plurilingualism, 123– 124Social actors, plurilingual children, 120,

126– 128Social awareness, autism spectrum

disorders, 137– 138, 139– 140Social competence

autism spectrum disorders, 137, 138– 142

definition, 137Social development

ADHD, 63– 64ASD, 137– 138

Social experience and influences brain development, 71executive functioning, 71– 73plurilingualism, 121– 122

Social interactions, theory of mind, 62– 65executive functioning skills, situational

influences on, 64– 65executive functioning skills, typical and

atypical developmental influences on, 62– 64

Social problem solving, autism spectrum disorders, 138

Index 183

183

Socioeconomic factors, vs. bilingual advantage, 115– 116

Sociogenesis, 72Sonuga- Barke, E. J. S., 161Spatial reversal, autism spectrum

disorders, 141Spatial Working Memory and the Spatial

Memory, 31t“SpongeBob Squarepants,” cognition

exhaustion from, 64– 65Sports metaphor, 3, 38– 51

action commission and inhibition, 44– 46

action vs. inaction, 41adherence and flexibility, 41athlete's decision- making process,

42, 43fenvironmental changes, 40executive function and context,

47– 49experience as development, 49– 51fatigue, 40, 48– 49flexibility, 40game plan vs. flexibility, 42– 44, 43fgoal- oriented action sequences, 39inhibition, 40performance regression, 46planning, 40practice, 49– 50rationale, 38– 41rules, 41– 44, 43frules, context- specific, 41– 42rules, guidelines for success, 41,

42– 43rule use, 39, 40stress, 40, 48task performance inconsistencies, 39team sports, young children's

difficulties, 44thoughtful reflection, 39

Stahl, L., 145tStanovich, K. E., 164– 165Stockings of Cambridge, 30t, 142

autism spectrum disorders, 142Stop Signal Task, 32tStress, 40, 48Stroop effect, 159

Stroop test, 90, 159autism spectrum disorders, 141issues, 25

Success, guidelines, 41, 42– 43Swank, P. R., 73– 74Swanson, J., 139Symons, D. K., 64Synaptic pruning, 11, 16Synaptogenesis, 11

Tager- Flusberg, H., 139Tap inhibitory control, 79Tapping Task, 79, 80Task performance. See also specific tasks

inconsistencies, 39Tasks of Executive Control (TEC), 32tTasks of Variables of Attention

(T.O.V.A.), 32tTeacher– child relationships, 3, 80– 82. See

also Parent– child relationshipsTeacher Interview Probe (TIP), 168Team sports, young children's

difficulties, 44Theory- of- mind reasoning, 3, 54– 65

autism spectrum disorders, 138, 139– 140

definition, 54, 55development, 55– 57example, 54, 55experience, 61false- belief tasks, 55– 57infants, 55preschool transitions, 55, 56 (See also

Transition to school)social interactions, promoting

successful, 62– 65Theory- of- mind reasoning, executive

functioning, 54, 57– 62in acquisition of theory of mind, 59– 62in expression of theory of mind, 58– 59response conflict skill, 57, 60– 61in social interactions, 3, 80– 82

Thoughtful reflection, 39Tools of the Mind, 63, 97– 99Toplak, M. E., 24– 25, 163Tower of Hanoi, 22, 30t

autism spectrum disorders, 142, 147

184 Index

184

Tower of London, 23– 24, 30tautism spectrum disorders, 140, 142

Tower Subtest (NEPSI- II subtest), 30tTower Test (D- KEFS subtest), 30tTranscranial magnetic stimulation (TMS), 11Transition to school, 3– 4, 88– 101

adjustment difficulties, 88child– teacher interactions, 88classroom environment, 88confirmatory factor analysis, 90– 91Continuous Performance Task, 94– 95executive functioning, definition, 89– 91fixed effects analysis, 100Montessori schools, 99PATHS, 99– 100preschool, theory- of- mind

reasoning, 55, 56REDI program, Head Start, 100Tools of the Mind, 63, 97– 99working memory, 92– 93

Transition to school, academic skill emergence and school achievement, 91– 100

concurrent relations, 92– 94executive functioning,

causation, 96– 100predictive relations, 94– 96

Translanguaging, 127Tuckey, M., 75

Unexpected contents false- belief tasks, 55, 56

Unitary view, 22University of Montreal study, 74– 77University of Quebec in Troise- Rivieres

study, 79– 80

Verbal exchange, 77Verbal fluency tests, 159Vestberg, T., 49, 50Vineland Adaptive Behavior Scales

(VABS), autism spectrum disorders, 141

Vineland Adaptive Behavior Scales (VABS)- II, autism spectrum disorders, 141

Virtual Errands Task (VET), autism spectrum disorders, 148– 149

Vitiello, V. E., 95– 96Voegler- Lee, M. E., 100Vygotsky, L. S., 72, 97

Wagner, A. E., 141Waters, E., 75Weikum, W. M., 125Welsh, J. A., 94Werner, H., 45, 46Werner, Heinz, 40White, A., 64White matter development

prefrontal cortex, 13– 14protracted course, 13, 16

White matter microstructure composition, 12developmental maturation, 12– 13developmental maturation,

on executive functioning performance, 13

frontostriatal tracts, 13Whorf, B. L., 120– 121Wide Range Assessment of Memory and

Learning, 32tWillcutt, E. G., 160, 161Williams, L., 138Willoughby, M. T., 93, 100Wilson, R. C., 98– 99Wisconsin Card Sorting Test

(WCST), 30tADHD, 159autism spectrum disorders, 142

Wooden, John, 41Working memory, 23

academic achievement, early, 92– 93academic achievement, longitudinal

studies, 94– 95autism spectrum disorders, 136autonomy support, 75confirmatory factor analysis, 90– 91letter skills, early, 93– 94math skills, early, 93– 94measures, 26, 31t– 32t

Index 185

185

plurilingualism, 119proficiency, skill transfer from, 97

Working memory + inhibition counting skills,5- 6 yr olds, 93math skills, early, 93

Working Memory Rating Scale, 32t

Yang, S., 122Yerys, B. E., 145tYeung, S. S., 144t

Zelazo, P. E., 22Zidane, Zenadine, 46

186

187

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Executive functions in children's everyday lives : a handbook for professionals in applied