Executive Function: Implications 

for Education

Authors

Philip David Zelazo

Clancy B. Blair

Michael T. Willoughby

National Center for Education Research (NCER)

Meredith Larson (Project Officer)

Erin Higgins

National Center for Special Education Research (NCSER)

Amy Sussman

NCER 2017-2000

U.S. DEPARTMENT OF EDUCATION
This paper was prepared for the National Center for Education Research, Institute of Education 

Sciences under Contract ED-IES-12-D-0015. Meredith Larson was the project officer.

Disclaimer

The Institute of Education Sciences at the U.S. Department of Education contracted with Westat to 

develop a paper on executive function. The views expressed in this report are those of the authors, 

and they do not necessarily represent the opinions and positions of the Institute of Education 

Sciences or the U.S. Department of Education.

U.S. Department of Education 

John B. King, Jr, Secretary 

Institute of Education Sciences 

Ruth Neild, Deputy Director for Policy and Research, Delegated Duties of the Director

National Center for Education Research

Thomas W. Brock, Commissioner 

National Center for Special Education Research 

Joan McLaughlin, Commissioner 

This report is in the public domain. Authorization to reproduce it in whole or in part is granted. 

Although permission to reprint this publication is not necessary, the citation should be: 

Zelazo, P.D., Blair, C.B., and Willoughby, M.T. (2016). Executive Function: Implications for 

Education (NCER 2017-2000) Washington, DC: National Center for Education Research, Institute 

of Education Sciences, U.S. Department of Education. This report is available on the Institute 

website at http://ies.ed.gov/.
Alternate Formats

Upon request, this report is available in alternate formats such as Braille, large print, audiotape, or 

computer diskette. For more information, please contact the Department’s Alternate Format Center 

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Disclosure of Potential Conflict of Interest

Westat Inc. is the prime contractor for the NCER Analysis and Research Management Support 

project, under which this paper was developed. Tamara Nimkoff was the project manager. Philip 

Zelazo, author on this paper, is entitled to royalties from the sale of the Minnesota Executive 

Function Scale (MEFS) by Reflection Sciences, LLC, along with Stephanie Carlson and the 

University of Minnesota. The authors of this paper are not aware of any conflicts of interest. 

Authors’ Note

All authors contributed equally, and author order was determined arbitrarily. While all authors 

contributed to all chapters, each author was primarily responsible for drafting different chapters or 

sections within chapters: Zelazo (chapter 1, sections 1.1-1.4 and 1.6; chapter 2), Blair (chapter 1, 

section 1.5; chapters 5-6), and Willoughby (chapters 3-4). The authors drafted chapter 7 jointly. 
Contents

1. Introduction.................................................................................................................................................... 1

1.1 Definition of Executive Function: What It Is and What It Is Not ................................................. 2

1.2 Executive Function in Relation to Other Constructs ...................................................................... 3

1.3 A Levels of Analysis Approach to Executive Function: Neural, Cognitive, and Behavioral ..... 5

1.4 “Hot” Versus “Cool” Executive Function ........................................................................................ 8

1.5. Neural Basis for Executive Function................................................................................................10

1.5.1 PFC Connectivity.........................................................................................................................12

1.5.2 The Role of Neurotransmitters in PFC Function ...................................................................13

1.5.3 PFC and Motivation ....................................................................................................................13

1.5.4 PFC Development .......................................................................................................................14

1.5.5 Neural Plasticity: Experiential Influences on Brain Development.......................................15

1.5.6 Sensitive Periods in Brain Development..................................................................................16

1.6 Summary................................................................................................................................................17

2. The Role That Executive Function Plays in Learning and Adaptation ...............................................19

2.1 Executive Function and Learning in School .....................................................................................20

2.2 Difficulties With Executive Function Are a Prominent Feature of Many Disabilities and 

Disorders that Interfere With Learning ...................................................................................................25

2.3 Summary................................................................................................................................................27

3. Assessment of Executive Function ...........................................................................................................28

3.1 Historical Context for Assessing Executive Function in Children ................................................28

3.2 Approaches to Assessment ..................................................................................................................29

3.3 Assessment Challenges.........................................................................................................................33

3.3.1 Measurement Impurity .................................................................................................................33

3.3.2 Ecological Validity.........................................................................................................................35

3.3.3 Poor Convergence between Questionnaire and Performance-Based Tasks........................36
3.4 Decision Rules for Assessing Executive Functions in Educational Settings ..............................37

3.4.1 Academic Impairments.................................................................................................................38

3.4.2 Differentiated Instruction ............................................................................................................38

3.4.3. Intervention and Individual Progress Monitoring ...................................................................39

3.5 Summary.................................................................................................................................................40

4. Developmental Change in Executive Function ......................................................................................42

4.1 Changes in the Structure of EF .........................................................................................................42

4.2 Changes in the Level or Efficiency of EF Skills..............................................................................45

4.2.1 Evidence from Cross-Sectional Studies.....................................................................................45

4.2.2 Evidence from Longitudinal Studies..........................................................................................47

4.2.3 Integrating Evidence Across Cross-Sectional and Longitudinal Studies and Implications 

for Educators ...........................................................................................................................................48

4.3 Summary................................................................................................................................................50

5. Individual Differences in Executive Function Development ...............................................................52

5.1 Temperament and Executive Function............................................................................................52

5.2 Individual Differences in Executive Function Associated With Cognitive Ability, Sex, and 

Genes ............................................................................................................................................................53

5.3 Individual Differences in Executive Function Associated With Experience .............................55

5.4 Summary................................................................................................................................................57

6. Malleability of Executive Function ...........................................................................................................58

6.1 Educational Practices Designed to Promote Executive Function As a Means to Promote 

Academic Abilities.......................................................................................................................................58

6.2 Direct Approaches to the Improvement of Executive Function Without a Focus on 

Academic Abilities: Working Memory Training .....................................................................................62

6.2.1 Near Transfer of Working Memory Training ..........................................................................63

6.2.2 Far Transfer of Working Memory Training.............................................................................63
6.2.3 Working Memory Training With Children With Attention Deficit Hyperactivity Disorder

...................................................................................................................................................................64

6.2.4 Training of Inhibitory Control and Cognitive Flexibility.......................................................66

6.2.5 Reflection Training ......................................................................................................................66

6.3 Indirect Approaches to the Improvement of Executive Function Without a Focus on 

Academic Abilities.......................................................................................................................................67

6.3.1 Aerobic Exercise as a Means to Improve Executive Function .............................................68

6.3.2 Contemplative Practice as a Means to Improve Executive Function ..................................68

6.3.3 Action Videogames as a Means to Improve Executive Function.........................................70

6.4 Summary................................................................................................................................................70

7. Overall Summary, Directions for Future Research, and Implications for Educational Policy and 

Practice ..............................................................................................................................................................72

7.1 EF and Learning ...................................................................................................................................74

7.2 Topics for Future Research.................................................................................................................75

7.2.1. Transfer..........................................................................................................................................75

7.2.2 EF and Individualized Approaches to Education ...................................................................76

7.2.3 Developing the Research Base on the Reciprocal Relation between EF and Learning ....77

7.2.4 The Role of Stress in EF and Learning.....................................................................................77

7.2.5 Resolving Discrepancies Between Questionnaire and Performance-Based Assessments..78

7.3 Implications for Policy and Practice ..................................................................................................79

References.........................................................................................................................................................82

Glossary.......................................................................................................................................................... 136
1 

1. Introduction 

Executive function (EF) skills are the attention-regulation skills1 that make it possible to sustain 

attention, keep goals and information in mind, refrain from responding immediately, resist 

distraction, tolerate frustration, consider the consequences of different behaviors, reflect on past 

experiences, and plan for the future. In the past two decades, EF skills have become a major focus 

of research in psychology, neuroscience, and education, and increasingly both teachers and parents 

are aware that these skills provide an important foundation for learning in school settings. Indeed, 

EF is central to school readiness and early school achievement (Blair 2002; Blair and Raver 2015). 

Research has found that EF measured in childhood predicts a wide range of important outcomes, 

including readiness for school (e.g., McClelland et al. 2007) and the successful transition to 

kindergarten (e.g., Blair and Razza 2007); school performance and social competence in adolescence 

(e.g., Mischel, Shoda, and Rodriguez 1989); better physical health; higher socioeconomic status 

(SES); and fewer drug-related problems and criminal convictions in adulthood (Moffitt et al. 2011). 

This paper provides a selective overview of recent research on EF and explores the implications of 

this research for educational research and practice. The current widespread interest in EF and its 

development during childhood is based in large part on the following: 

(1) Childhood EF skills provide an important foundation for learning and adaptation across a 

wide range of contexts, including in school. 

(2) Difficulties with EF are associated with learning challenges and a greater likelihood of 

behavior problems, and they are a prominent feature of many emotional and behavioral 

disorders (EBDs); neurodevelopmental disorders (e.g., attention deficit hyperactivity 

disorder (ADHD); autism spectrum disorders (ASD); and specific learning disabilities that 

interfere with children’s education. 

(3) EF skills are malleable, meaning they can change and are influenced by both positive and 

negative experiences. For example, stress, poverty, and disadvantage are associated with 

worse EF skills. However, supportive caregiving, high quality early education, and even 

 

1 This and other similarly technical terms are hyperlinked upon their first use in the paper to a definition in the glossary 

found at the end of this document.
2 

practice can help improve EF skills. A key topic here is the extent to which schooling in 

childhood shapes the development of EF as well as the extent to which EF is important for 

doing well in school. 

The paper is written to highlight what the authors take to be key findings about EF, focused on the 

implications of EF research as it relates to educational research and practice, with a particular 

relevance to early education. It is not a systematic review of all literature related to the field of EF, 

nor is it intended to be a What Works Clearinghouse review. The primary audience for this paper is 

researchers and advanced students (e.g., graduate students) in the fields of education, developmental 

psychology, educational neuroscience, and public policy. 

In the remainder of this chapter we provide a definition of EF, describe how it relates to other 

constructs, and describe the neural basis for EF. In chapter 2, we outline some of the reasons 

research on EF is relevant to education, and these reasons are explored more fully in subsequent 

chapters. In chapter 3, we address the assessment of EF, and underscore the importance of 

selecting measures that are optimized for their intended use. In chapter 4, we consider 

developmental changes in both the organization and efficiency of EF skills, as well as associated 

strategy use. Chapter 5 focuses on individual and group differences in EF, including relations 

between EF and SES, gender, language, culture, parenting, genes, and sleep, among other variables. 

In chapter 6, we review evidence from interventions designed to promote EF skill development. 

Finally, chapter 7 summarizes the main findings of this review, outlines future directions for 

research, and addresses the implications of research on EF for education policy and practice. 

1.1 Definition of Executive Function: What It Is and What It Is Not 

Historically, EF has been an ill-defined construct, often including broad and diverse processes 

relevant to many forms of self-regulation, from sustained attention to planning (Zelazo et al. 1997). 

Over the past two decades, however, research on EF and its development during childhood has 

increased and led to a sharper, more focused definition. Researchers generally, but not uniformly, 

characterize EF as a specific set of attention-regulation skills involved in conscious goal-directed 

problem solving. These skills include cognitive flexibility, working memory, and inhibitory control

(e.g., Blair and Diamond 2008; Carlson, Zelazo, and Faja 2013; Diamond 2013; Garon, Bryson, and 
3 

Smith 2008; Hughes 2011; Jacques and Marcovitch 2010; Meuwissen and Zelazo 2014). Cognitive 

flexibility involves thinking about something in multiple ways—for example, considering someone 

else’s perspective on a situation or solving a mathematics problem in multiple ways. Working memory

involves both keeping information in mind and, usually, manipulating it in some way, such as in 

passage comprehension when a reader must integrate several pieces of information or ideas into a 

coherent whole. Inhibitory control is the process of deliberately suppressing attention (and subsequent 

responding) to something, such as ignoring a distraction, stopping an impulsive utterance, or 

overcoming a highly learned response. 

These three skills depend on increasingly well understood neural circuits involving regions in the 

prefrontal cortex (PFC) and other areas of the brain, and hence may be described as neurocognitive

skills. These skills are ways of actively and intentionally controlling attention in order to accomplish 

a goal, such as keeping a question in mind. In fact, attention can be intentionally shifted, maintained 

over time, and applied selectively, and so EF is typically measured behaviorally as the three skills of 

cognitive flexibility, working memory, and inhibitory control (Miyake et al. 2000). Like other skills, 

EF skills are acquired largely as a function of experience, or practice: the repeated engagement and 

use of EF skills in problem solving strengthens these skills, increases the efficiency of the 

corresponding neural circuitry, and increases the likelihood that the skills will be activated in the 

future (see Zelazo and Lee 2010, for a review). 

1.2 Executive Function in Relation to Other Constructs 

As shown in Figure 1, EF skills are neurocognitive skills that are required to engage in the goaldirected control of thought, action, and emotion. This goal-directed control in turn is needed for 

various kinds of behavior, including persistence, being able to focus on several things at once, shifting 

easily between tasks, and reflective learning. These behaviors imply the exercise of EF skills but are 

not the same as those skills. The behaviors are more commonly displayed by individuals with 

particular temperamental or personality characteristics, such as being high in effortful control (e.g., Rothbart 

2011), high in conscientiousness and openness to experience (e.g., Shiner and DeYoung 2013), and high in 

self-discipline (e.g., Duckworth and Seligman 2005). 
4 

Figure 1. Semantic map of executive function and related terms 

EF can be further differentiated from a number of related, and sometimes partially overlapping 

constructs, and doing so helps sharpen our characterization of EF skills. For example, EF skills are 

related to, but different from, what we normally mean by “intelligence,” particularly when defined as 

crystallized intelligence, which is based on facts and knowledge such as vocabulary or knowing the 

times tables (Blair 2006; Kane and Engle 2002). In general, EF has less to do with possessing 

intellectual knowledge than it does with being able to reason—to use knowledge purposefully and 

put it into practice. EF skills are the attentional skills that allow for the adaptive use of one’s 

knowledge in the service of one’s goals, for example keeping specific knowledge in mind, or using it 

to make inferences. These attentional skills allow students not only to learn more effectively, but 

also to take the content knowledge they have learned in the classroom and to apply it on an exam, or 

in their daily lives. As such, EF overlaps considerably with reasoning ability or what is referred to as 

fluid intelligence (Ackerman, Beier, and Boyle 2005; Kane, Hambrick, and Conway 2005). By some 

accounts, EF is essentially identical with fluid intelligence (Kyllonen and Christal 1990). The reason 

for this is that EF is essential for making predictions, identifying patterns, and drawing logical 

conclusions. Dissociations between crystallized knowledge-based aspects of mental ability (e.g., 

vocabulary, general information) and EF have been shown in several different types of studies, 
5 

including neuropsychological studies of patients with damage to specific areas of PFC that impairs

fluid intelligence but not crystallized intelligence (Duncan, Burgess, and Emslie 1995; Waltz et al. 

1999), psychometric studies investigating the structure of cognitive abilities (Ardila et al. 2000), and 

studies examining academic ability in which predictions from measures of EF and measures of 

general knowledge are distinct (Espy et al. 2004). 

EF skills also overlap with the broader constructs of self-regulation, social and emotional learning 

(SEL), and noncognitive skills. As discussed further below, self-regulation is a broad term that refers to 

the full range of ways in which human beings adjust their behavior, including not only intentional 

self-regulation, which depends on effortful EF skills, but also involuntary processes, like shivering to 

increase body temperature (e.g., Blair 2014). EF skills are involved only when self-regulation is 

conscious and deliberate, i.e., where individuals purposefully modify their behavior in order to 

achieve a goal.2

 The term SEL refers to the process of acquiring social and emotional competence, 

including emotional self-awareness and a wide range of self-regulatory and pro-social strategies, 

sometimes including EF skills.3 The construct of noncognitive skills—poorly named because many 

of these skills, including EF skills, are in fact cognitive—is especially broad (see Heckman, Stixrud, 

and Urzua 2006). It refers to whatever skills are not considered academic (e.g., math, reading) or IQ 

but are nonetheless important for academic success, such as persistence, determination, and SEL. 

1.3 A Levels of Analysis Approach to Executive Function: Neural, 

Cognitive, and Behavioral 

Research on EF involves multiple levels of analysis (e.g., brain, cognition, behavior), and research in 

developmental cognitive neuroscience explicitly examines relations among levels. Figure 2 depicts 

the relations among three levels of analysis, from neural processes (e.g., neural pathways) to the 

neurocognitive skills that these processes support (e.g., ways of using attention) to the behavioral 

consequences of attentional control (e.g., goal-directed problem solving). This figure, which is based 

on one theoretical model, the Iterative Reprocessing model (e.g., Cunningham and Zelazo 2007; 

Zelazo 2015), illustrates how EF skills modulate attention and consequently control behavior in 

 

2 For a brief video overview of executive function, see: http://developingchild.harvard.edu/resources/inbrief-executivefunction-skills-for-life-and-learning/. 3 See http://www.casel.org/. 
6 

corresponding ways, allowing behavior to be more adaptive, planful, and focused when necessary. 

According to this model, the goal-directed control of attention is typically verbally mediated and 

involves the formulation and maintenance in working memory of explicit rules. The development of 

EF is made possible, in part, by increases in the efficiency of reflection, which refers to children’s 

ability to notice challenges, pause, consider their options, and put things into context prior to 

responding. Having reflected on their situation, children are then in a position to exercise their EF 

skills (i.e., cognitive flexibility, working memory, and inhibitory control), often using self-directed 

speech as they do so. When children respond to situations reactively, without much reflection upon 

what they are doing, they are more likely to show classic EF failures, such as assuming they know 

what to do, and treating a new situation as if it were an old, familiar one. The model describes both 

the cognitive and the neural processes associated with reflection and how these lead to specific EF 

skills and to consciously controlled behavior. 

Figure 2. Example of levels of analysis of theoretical characterizations of executive 

function, from neural processes to behavior

Other theoretical models of EF (e.g., Buss and Spencer 2014; Munakata, Snyder, and Chatham 2012, 

2013) vary in what is proposed at one or more of these levels of analysis, but increasingly there is 

consensus about the relation between EF skills and behavior. 

A converging theoretical approach to EF that also spans levels of analysis is Blair’s psychobiological 

model of the broader construct of self-regulation (Blair 2014; Blair and Ursache 2011). This model is 

based in what is known as dual systems or dual process theory (Shiffrin and Schneider 1977). It 
7 

addresses how the ability to reflect on information and intentionally direct behavior through “higher 

level” EF—in an intentional, goal-directed, top-down way—interacts with and depends to some 

extent on “lower level,” more automatic, bottom-up responses to stimulation associated with 

emotion and the physiological response to stress. The model also addresses variation in EF 

associated with genes that are related to sensitivity to emotional stimulation and the physiological 

response to stress and that affect neural activity in PFC (see figure 3 and Sections 1.5 and 5.2). In the 

psychobiological model, EF is only one component of self-regulation and is facilitated or undercut 

by emotion and stress. For example, it is difficult to put EF skills into practice when we are stressed 

out or bored and lethargic. However, just as negative emotion, boredom, and stress interfere with 

EF, positive emotion and a small amount of stress facilitate EF. Here, the psychobiological model 

proposes that moderate challenge (eliciting a small amount of stress) increases engagement and 

learning. 

Figure 3. Psychobiological model of self-regulation in which EF is one component of a 

hierarchy of processes relevant to self-regulation

Another example of a bottom-up influence on EF that is relevant to education is the daily 

fluctuation of physiological arousal, measured using a variety of biological and behavioral indices 

(e.g., core body temperature and sleep-wake cycles). There are reliable developmental and individual 

differences in these circadian (daily) rhythms of arousal, and individual differences in circadian timing 

may be reliably and validly estimated in both adults and children using self-report measures. Such 

measures ask participants about when during the day they prefer to engage in various intellectual and 
8 

physical activities (optimal vs. non-optimal times of day). EF performance varies considerably as a 

function of circadian rhythms of arousal. During the transition to adolescence, most children show a 

phase shift in these daily rhythms, such that arousal peaks later in the day. Children between the ages 

of 11 and 14 years, tested in the morning or afternoon, have been shown to perform better on 

measures of EF when tested at their optimal (versus non-optimal) times of day, even when 

controlling for sleep duration on the previous night (Hahn et al. 2012; see also Goldstein et al. 

2007). That is, most older children, for whom arousal is higher later in the day, showed better EF 

when tested in the afternoon, whereas most younger children, for whom arousal is higher earlier in 

the day, showed better EF when tested in the morning. The developmental change in rhythms 

during the transition to adolescence may provide insight into individual differences in educational 

achievement and behavioral problems, and it may further inform discussions regarding school start 

times (e.g., Carskadon et al. 1998).

1.4 “Hot” Versus “Cool” Executive Function 

The interactions between EF and “lower level” influences such as emotion and stress are relevant to 

the distinction made between more “hot” EF and more “cool” EF (Zelazo and Müller 2002; see 

Peterson and Welsh 2014, for a review). There is now considerable evidence from human and 

nonhuman animal research, including brain lesion studies, neuroimaging research, and behavioral 

research that the top-down, neurocognitive processes (i.e., EF skills) needed in the presence vs. 

absence of high levels of emotion differ somewhat. Hot and cool EF, which typically work together 

in solving real-world problems, are both forms of deliberate, effortful, top-down, self-regulatory 

processing that depend on prefrontal brain regions; however, they vary in the extent to which they 

require managing motivation and emotion, including the goal-directed management of basic 

approach and avoidance motivations, and different specific neural circuits are involved. Hot EF 

involves the processes that operate in motivationally and emotionally significant situations, while 

cool EF involves the processes that operate in more affectively neutral contexts (Zelazo and Carlson 

2012). 

Most laboratory-based, behavioral measures of EF skills assess cool EF, and do so using abstract, 

decontextualized tasks that lack a significant affective or motivational component. For example, in 

the Dimensional Change Card Sort (DCCS) task (Zelazo 2006), participants are required to match a 
9 

stimulus flexibly, such as sorting a red rabbit first with a blue rabbit (by shape) and then with a red 

car (by color). This task involves using arbitrary rules for responding to arbitrary stimuli (see section 

6.3.4 for a more detailed description). In contrast, hot EF refers to those aspects of EF that are 

needed in situations that are motivationally significant—about which people really care because they 

stand to win or lose something. Hot EF is typically assessed in tasks that require the flexible reappraisal 

of whether to approach or avoid a salient stimulus. A classic example is delay of gratification as measured by 

the “marshmallow test” (Mischel, Shoda, and Rodriguez 1989). To the extent that it requires 

effortfully avoiding a more salient immediate reward and instead approaching a less salient delayed 

reward, delay of gratification involves hot EF. Another example is the Children’s Gambling Task 

(Kerr and Zelazo 2004), a simplified version of the Iowa Gambling Task (Bechara et al. 1994; Noël, 

Brevers, and Bechara 2013), in which the options that at first appear advantageous (higher rewards) 

are revealed gradually to be disadvantageous (higher rewards but even higher losses), and vice versa. 

Hot EF is also involved in deliberate emotion regulation, as when we down-regulate (i.e., attempt to 

reduce) anxiety, sadness, or anger, or deliberately up-regulate motivation to accomplish a particular 

goal (Zelazo and Cunningham 2007). 

The distinction between hot and cool EF can be observed in children’s behavior by 3 years of age 

(e.g., Brock et al. 2009; Carlson, Davis, and Leach 2005; Hongwanishkul et al. 2005; Kim et al. 2013; 

Prencipe and Zelazo 2005; Prencipe et al. 2011; Willoughby et al. 2011), and there is some evidence 

that this distinction emerges even earlier in childhood (e.g., Bernier, Carlson and Whipple 2010).

Whereas poor hot EF in preschoolers is associated with inattentive-overactive problem behaviors, 

cool EF is associated with academic outcomes, including math and reading (e.g., Brock et al. 2009; 

Kim et al. 2013; Willoughby et al. 2011). Deficits in hot EF (measured using the Iowa Gambling 

Task) also distinguish children with antisocial behavior disorders (oppositional defiant disorder or 

conduct disorder) from those with ADHD in adolescence (Hobson, Scott, and Rubia 2011), and hot 

and cool EF may be differentially implicated in different forms of ADHD (e.g., Castellanos et al. 

2006). Further developmental research on hot vs. cool aspects of EF is needed to refine our 

conceptualization of this important distinction and explore its implications for learning and 

classroom behavior, including emotional and behavioral disorders. 
10

1.5. Neural Basis for Executive Function 

Understanding the brain systems involved in EF provides insight into the many experiential (e.g., 

stress) and other (e.g., genetic, physiological) influences on EF and EF development. It also informs 

efforts to improve EF skills by providing (1) a clearer understanding of the mechanisms whereby EF 

skills change, (2) various neurophysiological influences on EF skill, and (3) another level of analysis 

at which to measure the effects of interventions. More generally, research on the neurobiology of 

EF suggests that it might be productive to examine physiological as well as behavioral indicators of 

children’s responsiveness to and engagement with specific types of learning activities and teaching 

approaches. 

Both hot and cool aspects of EF depend on neural circuits involving PFC. From the study of 

neurological patients sustaining damage to this area of the brain (e.g., Waltz et al. 1999) to the 

findings of the latest functional magnetic resonance imaging (fMRI) research (e.g., D’Esposito and 

Postle 2015), associations of EF skills with well-defined areas of PFC are among the most robust 

examples of relations between brain and behavior in all of psychology (see Fuster 2015). 

Neuroanatomically, the EF skills of cognitive flexibility, working memory, and inhibitory control are 

associated with distinct and partially overlapping networks involving regions of PFC. These include 

dorsal (on top) and lateral (on the side) areas of PFC; ventral (underneath) and medial (toward the 

middle) areas of PFC; and a medial brain structure adjacent to PFC referred to as the anterior 

cingulate cortex, or ACC for short. Whereas cool EF relies more on dorsal and lateral parts of PFC, 

hot EF relies more on ventral and medial parts. Regions of PFC have been shown to be active in 

perhaps hundreds of functional neuroimaging studies with adults when they are completing tasks 

involving holding information in mind, inhibiting one response in favor of another, and shifting 

attention from one task set (or approach to a problem) to another (see figures 4 and 5).
11

Figure 4. Lateral view of select brain regions relevant to executive function

Figure 5. Medial view of select brain regions relevant to executive function
12

1.5.1 PFC Connectivity 

PFC is highly connected with and coordinates activity in many areas throughout the brain. As such, 

PFC has been likened to the conductor of an orchestra or an air traffic controller (Center on the 

Developing Child at Harvard University 2011). Connections of PFC with other brain areas occur 

primarily through white matter tracts,

4

 allowing for very rapid goal-directed, “top down” regulation 

of activity in multiple brain areas, such as those associated with language, memory, attention, and 

motor responses. PFC is also highly interconnected with subcortical structures such as the basal 

ganglia, which are important for learning patterns and routines, and the amygdala, which is 

important for emotion and the stress response. The interconnection of PFC with subcortical brain 

areas related to emotion regulation and the stress response is a key feature of the neurobiology of 

EF relevant to understanding the development of EF and its relation to educational outcomes. 

The coordinating role for PFC in the brain distinguishes EF from other cognitive functions and 

from crystallized intelligence. This feature of PFC is illustrated by the fact that when neurologists 

began systematically studying large numbers of patients with damage to PFC, they were at first 

puzzled by exactly what this brain region does. When scientists administered tests of knowledge or 

basic information processing to these patients—that is, nonexecutive tasks such as measures of 

vocabulary or long-term memory—the patients did surprisingly well. It was only when patients were 

asked to coordinate multiple pieces of information; to plan; to reason, even very simply; or to inhibit 

seemingly inconsequential automatic responses that deficits were apparent. Neuropsychologists 

described these difficulties as “goal neglect” (reviewed in Duncan, Burgess, and Emslie 1995), 

indicating that the individual fails to hold an overarching goal in mind when coordinating two or 

more even relatively simple relations among distinct pieces of information (Waltz et al. 1999). This 

set of findings indicates the general distinction between EF and knowledge-based or crystallized 

abilities described in Section 1.2.

 

4 White matter refers to neurons that have developed a fatty, myelin sheath, an experience-dependent process that 

occurs over the course of development and which greatly speeds communication between neurons. 
13

1.5.2 The Role of Neurotransmitters in PFC Function 

Research with nonhuman primates has taught us a great deal about the neuroanatomy and neural 

basis of EF (e.g., Wilson et al. 2010). This research has shown that activity in individual neurons in 

PFC is sensitive to variation in levels of neurotransmitters present in the synapse, most specifically 

the catecholamines, dopamine, and norepinephrine. In humans, levels of dopamine and 

norepinephrine vary as a function of many things, one of which is the amount of stress a person is 

experiencing (see Cools and D’Esposito 2011 for review). When levels of these neurotransmitters 

increase moderately from baseline, indicating that the individual is attentive and engaged and 

experiencing what might be considered moderate arousal or stress, there is more activity in neurons 

in regions of PFC that play key roles in networks underlying working memory. This activity 

maintains information relevant to the task at hand and suppresses extraneous and competing 

information (Arnsten 2009). Importantly, research relating catecholamine levels to neural activity in 

PFC has shown that the form of the relation between levels of catecholamines and working memory 

ability is an inverted U-shape curve. That is, activity in neurons that underlie EF is high when levels 

of these neurotransmitters increase moderately from baseline. However, as levels of these 

neurotransmitters rise beyond an intermediate level, reflecting higher levels of stress or arousal, they 

have an opposite effect; they shut down neural activity in PFC. This research provides evidence of 

the neuroscientific basis for the well-known Yerkes-Dodson inverted U-shape curve relating stress

to performance (Yerkes and Dodson 1908). 

1.5.3 PFC and Motivation 

The inverted U-shape curve relating neural activity in PFC to EF highlights the interactive or 

recursive (feed forward-feedback) “top down, bottom up” nature of this neural system. As noted 

above, some of the neurotransmitters that stimulate activity in PFC, dopamine and norepinephrine, 

originate in areas of the brain (limbic system and brainstem) that influence attention, emotional

responses to stimulation, and the physiological response to stress. Experience can rapidly activate 

neural activity in these areas, leading to the release of dopamine and norepinephrine, as well as the 

glucocorticoid hormone cortisol. These chemicals increase neural activity in PFC. PFC then feeds 

back on the limbic and brainstem areas, so as to maintain stimulation in what can be considered an 

optimal or effective range depending on the context (Benes 2001; Gunnar and Quevedo 2007). 
14

When stimulation is in a moderate range and controllable, such as well-structured experiences in the 

classroom, neural activity in this brain network is balanced and EF is readily engaged. When 

stimulation is uncontrollable and overwhelming, however, as might occur in a highly chaotic and 

disorganized classroom, activity in the system is weighted toward the subcortical structures as 

activity in PFC is reduced, and activity in brain areas associated with more reactive and automatic 

responses to stimulation increases (Arnsten 2009). 

1.5.4 PFC Development 

Although most research on EF and the brain has been conducted with adults, the burgeoning field 

of developmental cognitive neuroscience has shown that many findings extend to children (e.g., 

Bunge et al. 2002; Luna et al. 2013). Certain brain imaging modalities, including near infrared 

spectroscopy (NIRS) and electroencephalography (EEG), provide evidence of relations between changes in 

activity in PFC and changes in child EF (e.g., Espinet et al. 2013; Moriguchi and Hiraki 2009; Rueda 

et al. 2005). Studies of structural connectivity and white matter tracts using MRI with sleeping 

infants also indicate relations of early brain connectivity to later EF (Short et al. 2013). In addition, 

evidence of relations between changes in levels of stress hormones and EF in children (Blair, 

Granger, and Razza 2005) is similar to that seen in adults (Lupien et al. 2009). Studies including 

children, however, provide unique information about the developmental relation between brain 

activity and EF. For example, in neuroimaging studies with children, EF-related brain activity is

generally more diffuse, meaning more spread out and less isolated to the specific pathways seen in 

adults (Durston et al. 2006; Eslinger et al. 2009). With increasing age and experience, there is a shift 

from more diffuse to more focal activity (albeit with more long-term connections; Fair et al. 2009), 

which is thought to indicate an increase in the efficiency as well as capacity of the brain as it 

processes information (Rypma and Prabhakaran 2009), likely due to the development of white 

matter and synaptic pruning (Klingberg 2006; Sheridan et al. 2014). There is also evidence that brain 

development is hierarchical, meaning that cortical areas associated with basic processes (e.g., sensory 

and motor) develop first, and cortical areas associated with more complex processes, such as EF, 

develop later—literally growing on top and around the primary cortical areas. One influential 

approach to understanding adolescent development emphasizes the imbalance during adolescence 

between a relatively well-developed emotional reactivity system and a relatively immature prefrontal 
15

cortical system associated with EF skills (e.g., Casey et al. 2008; Crone and Dahl 2012; Ernst 2014; 

Steinberg 2010).

1.5.5 Neural Plasticity: Experiential Influences on Brain Development 

Brain development occurs in large part as a function of experience, and the neural circuitry that 

supports EF is highly plastic, or modifiable, during development. Research is revealing the ways in 

which experience shapes the neural circuitry underlying EF, including experience in educational 

environments. As discussed more fully in chapter 6, behavioral interventions targeting EF have 

demonstrated the potential to help children at risk for difficulty in school. To some extent, however, 

PFC never stops developing as both EF skill and connectivity in PFC peak in early to midadulthood and then exhibit gradual decline across the lifespan (Zelazo et al. 2013, 2014; see Schaie 

1994 for a discussion of adult cognitive development more generally). The protracted course of PFC 

development and the development of EF allows for an ongoing influence of experience on EF 

skills. 

Given a general principle of brain development, namely, that cells that fire together wire together

(also referred to as use- or experience-dependent plasticity), experience more or less shapes the 

neural connections that underlie EF (Posner and Rothbart 2007). The repeated engagement and use 

of EF skills in problem solving should strengthen those skills, increase the efficiency of the 

corresponding neural circuitry, and increase the likelihood that the skills will be activated in the 

future (see Zelazo and Lee 2010, for a review). This experience-dependent shaping of brain 

networks is well established (Greenough, Black, and Wallace 1987) and is evident in many cognitive 

abilities and behaviors, as seen in studies of musicians, jugglers, and taxi drivers (e.g., Bengtsson et 

al. 2005; Elbert et al. 1995; Maguire et al. 2000). A central research question concerns the ways in 

which diverse types of educational experiences influence the neural connections that underlie EF. 

The protracted malleability of PFC is a double-edged sword. Although EF can be improved by 

training, for example, it can also be diminished by adverse experience. For example, children 

growing up in poverty typically perform worse on EF tasks than their more affluent peers (e.g., 

Noble, Norman, and Farah 2005). Lower EF is also associated with higher levels of stress in 

childhood, likely because stress impairs EF, and impaired EF in turn leads to more stress (Evans and 
16

Schamberg 2009). There is also evidence, however, that good EF skills can protect against risks 

associated with poverty and adversity, including the risk of academic failure (Masten et al. 2012). 

A number of studies, primarily in animal models, demonstrate that high levels of stress impair brain 

development and the development of EF (Holmes and Wellman 2009). An example of this 

phenomenon with an acute stressor in humans is found in a study with students preparing for 

medical board exams. Stress impaired students’ EF performance and reduced their functional 

activity in PFC relative to a low-stress control group. One month later, after a period of reduced 

stress, however, EF ability and brain activity returned to normal (Liston et al. 2009). The mechanism 

whereby stress leads to changes in the brain and changes in EF is through the toxic effects of 

persistently elevated stress hormone levels on neurons in several brain regions (Holmes and 

Wellman 2009). Longitudinal developmental studies have demonstrated that chronic stress is 

associated with elevated stress hormone levels and reduced EF (Blair et al. 2011; Evans and 

Schamberg 2009; Hostinar, Sullivan, and Gunnar 2014). A growing evidence base indicates that with 

improvements in conditions, both levels of stress hormones and EF will improve (Fisher et al. 

2006).

1.5.6 Sensitive Periods in Brain Development 

The human brain is inherently plastic, continually adapting to its environment, but there are periods 

of relatively high plasticity (so-called “sensitive periods”) when particular parts of the brain and their 

corresponding functions are especially susceptible to environmental influences. These periods 

typically correspond to times of rapid growth in those regions and functions, when the relevant 

neural regions are adapting especially rapidly to structure inherent in the environment (Huttenlocher 

2002). Because EF skills undergo a particularly rapid transformation during early childhood, from 

about 2 to 6 years of age (see chapter 3), the preschool period may be a window of opportunity for 

the cultivation of these skills via well-timed, targeted scaffolding and support. 

Although the preschool years may be an especially sensitive period for EF, there is also considerable 

reorganization of prefrontal systems during the transition to adolescence, when gray matter volume 

in PFC reaches a peak (Giedd et al. 1999). Prefrontal cortical plasticity is not limited to the 

preschool period, and training studies have demonstrated the malleability of the neural circuitry that 
17

underlies working memory and other aspects of EF in both children and adults (e.g., Klingberg et al. 

2005; Olesen, Westerberg, and Klingberg 2003; see Karbach and Unger 2014, for review). Evidence 

supporting the generalizability of gains in EF skills to academic achievement and other positive life 

outcomes, however, is mixed at best (Melby-Lervag, Redick, and Hulme 2016). 

Such research has implications for educational practice in subject areas that make high demands on 

EF, such as math, reading comprehension, and any other subjects that require deliberate reasoning 

(such as science learning; e.g., Gropen, Hoisington, and Ehrlich 2011). Training in academic subjects 

that makes high demands on reasoning, including the formation of hypotheses and the subsequent 

revision of those hypotheses based on evidence, would be expected to increase EF, just as EF is 

important for engagement and achievement with this material. Longitudinal studies looking at the 

development of academic ability and EF over time provide evidence consistent with the suggestion 

that academic learning improves EF and vice versa (Fuhs et al. 2014; Welsh et al. 2010). 

1.6 Summary 

EF can be understood as a set of three neurocognitive skills—cognitive flexibility, inhibitory control, 

and working memory—that together allow for the conscious control of attention and behavior in 

order to achieve a goal. These EF skills can be distinguished from relatively stable individual 

differences in the tendency to display these skills (e.g., effortful control, conscientiousness). 

Contemporary theoretical models often address EF at multiple levels of analysis, from behavior to 

brain, and they emphasize bidirectional interactions between deliberate, intentional top-down EF 

and bottom-up automatic processing of information, such as that associated with emotion and the 

stress response. 

A distinction can also be made between more cool EF, which involves processes that operate in 

affectively neutral contexts, and more hot EF, which is involved in emotionally significant situations 

and involves the goal-directed, flexible reappraisal of whether to approach or avoid a salient 

stimulus. Difficulty with hot EF is associated with social and emotional behavior problems, while 

difficulty with cool EF is associated with poor academic performance. 
18

Research on the brain networks involved in EF provides insight into the nature and development of 

EF. For example, there are reasons to believe that the preschool years may be a period of relatively 

high plasticity in PFC. Although PFC is connected with many regions throughout the brain, the 

interconnectivity of PFC with brain areas associated with emotion and stress is particularly relevant

from an educational standpoint. High levels of stress, particularly early in development, for example,

influence brain development in ways that can limit EF. However, PFC and EF are highly malleable 

and continue to develop across the lifespan. Consequently, high-quality educational experiences as 

well as direct and indirect training of EF can positively influence brain development and the 

development of EF. 
19

2. The Role That Executive Function Plays in Learning 

and Adaptation 

In this chapter, we provide a brief overview of the evidence that executive function (EF) provides a 

foundation for learning and achievement—evidence that is addressed more fully in subsequent 

chapters. Generally, EF has both direct and indirect roles in classroom learning. EF skills directly make 

it possible for students to sit still, pay attention, remember and follow rules, and flexibly adopt new 

perspectives. Indeed, some researchers assert that skills such as cognitive flexibility, working 

memory, and inhibitory control contribute to more fully engaged, active, and reflective forms of 

learning, which are just what is needed in classrooms (Lyons and Zelazo 2011; Marcovitch et al. 

2008; Zimmerman 2008). Children who arrive at school with well-practiced EF skills may learn 

more easily, and this may initiate a positive cascade of indirect effects, such as liking school and 

being motivated to work hard. More research on these indirect effects is needed, but children with 

good EF skills who learn more easily may be more likely to enjoy school, feel optimistic about their 

own learning potential (e.g., adopt a growth mindset; Dweck 2006), and get along with teachers and 

peers. In contrast, poor EF skills may interfere with children’s own (and others’) learning and may 

lead to behavior problems, suspension, expulsion, or being held back (U.S. Department of 

Education 2014).

A number of sources provide evidence about the roles of EF in learning and adaptation. These 

sources include individual differences research that links EF to school readiness and academic 

achievement in kindergarten and the early school grades (e.g., Alloway et al. 2005; Blair and Razza 

2007; Bull, Espy, and Wiebe 2008). In these studies, the relation of EF to academic achievement is 

seen over and above effects of intelligence and prior knowledge. Moreover, longitudinal studies have 

shown that measures of EF and closely related constructs, such as self-control, predict important 

outcomes, including Scholastic Aptitude Test (SAT) scores in adolescence (Mischel, Shoda, and 

Peake 1988); the likelihood of graduating from college by age 25 (McClelland et al. 2013); and 

physical health, substance dependence, socioeconomic status (SES), and the lifetime likelihood of a 

criminal conviction by age 32 (Moffit et al. 2011). EF predicts these outcomes even after controlling 

for SES and IQ. 
20

2.1 Executive Function and Learning in School 

The links between EF and academic functioning in early childhood are robust (see Allan et al. 2014, 

for a meta-analysis showing a mean effect size of r =.27 across studies, indicating a moderate and 

statistically significant association). EF assessed in early childhood (either directly or indirectly via 

related measures, such as teacher ratings of effortful control) has been found to predict school 

readiness for both math and reading (Espy et al. 2004; McClelland et al. 2007; Morrison, Ponitz, and 

McClelland 2010; St. Clair-Thompson and Gathercole 2006); overall school achievement (e.g., Bull 

and Scerif 2001; Bull, Espy, and Wiebe 2008; Clark, Pritchard, and Woodward 2010; Mazzocco and 

Kover 2007); grades (Duckworth and Seligman 2005); high school completion (Vitaro et al. 2005), 

and even college graduation (McClelland et al. 2013). In many cases, EF predicted outcomes better 

than IQ. Kindergarteners with poorer EF skills and poorer social competence had more difficulty in 

reading and math, with growth curves indicating that this gap in performance widened until second 

grade and then persisted from third through sixth grade (McClelland, Acock, and Morrison 2006). 

There is also evidence that children with better EF skills actually learn more (i.e., retain more 

information) from a given amount of instruction and practice than do their peers with worse EF 

skills (Benson et al. 2013). For example, children with better EF skills showed a larger gain in math 

achievement between kindergarten and first grade, especially on applied problems (Hassinger-Das et 

al. 2014), and children with better EF skills were more likely to learn new concepts when trained in 

biology (Zaitchik, Iqbal, and Carey 2014). 

In order to accurately describe the specific relations of EF to school readiness, achievement, and 

learning, it is important to consider EF as one aspect of self-regulation. As noted in chapter 1, selfregulation is a broader construct that includes not only effortful EF skills but also involuntary, 

automatic processes of self-regulation (e.g., Blair 2014). For example, in contrast to the meta-analysis 

by Allan et al. (2014), referred to above, a separate meta-analysis by Jacob and Parkinson (2015) used 

a broader definition of EF with a broader age range. While Allen and colleagues (2014) focused only 

on the inhibitory control dimension of EF in children between 2.5 and 7 years of age, Jacob and 

Parkinson (2015) included all three aspects of EF as well as measures of sustained, focused attention 

(e.g., a continuous performance task involving pressing a button as fast as possible only when a 

target picture appears onscreen ) and motor response inhibition (e.g., the walk-a-line task involving 

walking a six foot line as slowly as possible) in children ages 2 to 18 years. Across 67 studies and 
21

across EF skills, this meta-analysis found EF to be positively related to reading achievement (average 

r = .30) and math (r = .31), for both concurrent and predictive correlations. Correlations were 

highest for attention shifting (i.e., cognitive flexibility), with an average r = .42 for reading and 

average r = .34 for math. Correlations were lowest for response inhibition, with an average r = .25 

for reading and average r = .31 for math. As reviewed more comprehensively in chapter 6, there is 

some evidence from controlled experiments that a focus on improving EF skills can lead to 

improved reading and math outcomes (e.g., Raver et al. 2011). In addition, there is evidence that 

improving math and language skills can lead to improved EF outcomes (#

Weiland and Yoshikawa 

2013).5 

Issues of construct differentiation are highlighted in research on EF and self-regulation. EF is 

occasionally used as an umbrella term encompassing all aspects of self-regulation. It is clear, 

however, that aspects of self-regulation other than EF per se—such as being compliant and 

cooperative, being prosocial and considerate of others, and having a growth mindset (i.e., believing 

that one can improve one’s own cognitive skills)—are also important for school readiness and 

school achievement (Blair 2002; Dweck 2006; Yeager and Walton 2011). These other aspects of selfregulation have been shown in a number of studies to uniquely influence early progress in school 

(Bierman et al. 2008; Duncan et al. 2007; McClelland et al. 2007). 

Nonetheless, numerous studies have found EF to be a unique predictor of academic learning and 

achievement in kindergarten and during the school-age years (Fuchs et al. 2003, 2006; Siegler and

Pyke 2013; Vukovic et al. 2014). Although this is clearly established for mathematics (e.g., Bull and 

Lee 2014; Monette, Bigras, and Guay 2011), it is also found for reading (e.g., Kieffer, Vukovic, and 

Berry 2013; St. Clair-Thompson and Gathercole 2006) and for science (Nayfield, Fuccillo, and 

Greenfield 2013).

The role of EF in mathematics learning has been particularly well studied, perhaps because of the 

obvious need for working memory and cognitive flexibility when solving math problems. 

Longitudinal studies have typically attempted to isolate the added value of EF skills to math learning 

 

5 Citations with an # reflect publications reporting findings from experimental studies that were examined by the What 

Works Clearinghouse (http://ies.ed.gov/ncee/wwc/) and that, as of May 2016, did not met What Works 

Clearinghouse’s group design standards. 
22

by controlling for baseline math skills (e.g., Bull et al. 2011). Studies of EF and math learning have 

also controlled for relevant cognitive covariates such as verbal ability and general processing speed 

(although both of these are positively correlated with EF skills), as well as child and family 

background characteristics that might be associated with both higher levels of EF and higher levels 

of achievement (e.g., Blair et al. 2014). In some studies (Fuhs et. al 2014; Welsh et al. 2010), 

relations between EF and math from pre-kindergarten (pre-K) to kindergarten have been shown to 

be reciprocal. That is, initial pre-K EF skills predicted change in math over the pre-K year 

(controlling for initial pre-K math skills), and initial pre-K math skills predicted change in EF over 

the pre-K year (controlling for initial pre-K EF skills). To the extent that math involves EF skills, 

and given that both math and EF skills improve with practice, this type of reciprocal ratcheting-up 

of both skills is to be expected. 

Teacher-report measures of attention, in which stable individual differences in EF are perhaps 

implied but not measured directly, are robustly associated with multiple aspects of math

competence, including word problem solving, algorithmic computation, and arithmetic (Fuchs et al. 

2006). These correlations are obtained over and above associations with other cognitive 

characteristics such as language, processing speed, and conceptual ability. Notably, teacher report 

measures of attention problems have been shown to differentiate students with math disability from 

students with reading disability (Cirino et al. 2007). 

The prominent role of EF in math learning is further highlighted by research on the phenomenon of 

math anxiety. In keeping with the inverted U-shape relation between stress and EF, math anxiety 

refers to a heightened negative emotional arousal in response to math challenges that interferes with 

and overwhelms EF (Ashcraft and Krause 2007; Beilock and Carr 2005). Math anxiety, therefore, 

illustrates the bidirectional relation between stress and EF. When the experience of emotion and 

stress is high, EF is compromised (as described in section 1.5 above and section 5.2 below). 

Compromised EF then interferes with math competence, further increasing anxiety and leading to 

even larger deficits in EF and in math. This back and forth, or reciprocal relation between EF and 

ability, in this instance math ability, is central to the relation between EF and educational 

achievement generally and suggests that EF is maximized when task difficulty and, hence, stress is in 

a moderate range. In addition, the EF requirements of math learning may be greater in the context 

of math anxiety and may include a greater need for hot EF to down-regulate negative emotion. 
23

Although a good deal of recent research on EF and academic achievement has focused on math, the 

relation of EF to reading ability is also well established. This is particularly the case for working 

memory (e.g., Alloway and Alloway 2010; Daneman and Carpenter 1980; Karbach, Strobach, and 

Schubert 2014; Loosli et al. 2012). In part, associations between EF and reading are attributable to 

strong associations between EF and language development (Daneman and Merikle 1996; Gathercole 

and Baddeley 1989). Specific effects of EF on reading are seen most consistently for reading 

comprehension and fluency, however, rather than for more basic, knowledge-based aspects of 

reading, such as knowledge of letters and words (Cutting et al. 2009; Fuchs et al. 2015; Kieffer, 

Vukovic, and Berry 2013; Sesma et al. 2009). By kindergarten, knowledge of letters and words often 

represents acquired, crystallized aspects of mental ability rather than fluid, reasoning-based aspects of 

mental ability associated with EF. As such, by the school age years, some aspects of reading ability 

are primarily accounted for by measures of vocabulary and speed of information processing rather 

than measures of EF (reviewed in Blair, Protzko, and Ursache 2011; Evans et al. 2002). There is 

some evidence, however, that EF is important when children are first acquiring knowledge of letters 

and words. For example, analysis of a large middle-income sample assessed longitudinally indicated 

that EF skills were associated with letter and word knowledge during pre-K but not kindergarten

(Fuhs et al. 2014). Relations between EF and oral language comprehension, however, were present 

both in pre-K and kindergarten and were reciprocal. There are also relations between EF and 

phonemic awareness (Blair and Razza 2007; Welsh et al. 2010). Children demonstrating phonemic 

awareness recognize smaller units of meaning and sound (e.g., smaller words embedded within larger 

words, such as the word tooth in toothbrush). This requires cognitive flexibility, such as seeing the word 

toothbrush in two distinct ways, both as a complete word and as composed of two smaller words. 

Similarly, learning to spell in English requires working memory to hold multiple representations of 

letter-sound correspondence in mind. It also requires inhibitory control to inhibit one representation 

over the other, such as when learning the letters C and K, and learning letter combinations such as 

“kn” and “ph.” These are only two of many examples of the EF demand of early literacy acquisition. 

The association of EF with academic learning more generally is also seen in research on self-regulated 

learning (Zimmerman 2008). Self-regulated learning refers to an active form of learning, in which the 

learner is metacognitively, motivationally, and behaviorally engaged in the learning process. 

Although the specific relation of EF to self-regulated learning remains to be explored, EF is surely a 
24

key contributor to self-regulated learning, with its emphasis on goal setting, self-monitoring, and 

strategy use. Experimental research on self-regulated learning strategies, therefore, provides some 

indirect causal evidence linking EF (or EF as applied to self-regulated learning) to academic 

achievement. A meta-analysis of the effectiveness of self-regulated learning strategies revealed 

relatively large effects on math, with an average effect size of 1.0 standard deviation (SD), compared 

to an average effect size of .44 SD for reading and writing (Dignath, Buettner, and Langfeldt 2008). 

For example, in one experiment (Fuchs et al. 2003), third grade students were randomly assigned to 

one of three conditions. Children who received activities designed to promote goal-setting and selfassessment in math learning, as well as assistance with the transfer of problem solutions (all of which 

are heavily dependent on EF skills), outperformed children assigned to control or transfer-trainingonly conditions. It will be of considerable interest in the future to examine the roles of EF in selfregulated learning, the efficacy of self-regulated learning interventions, and the transfer of what is 

learned. 

Research focusing on EF for classroom learning and academic function shows that EF skills may 

play a key role in the achievement gap between children from lower vs. higher SES backgrounds. It is 

now well established that lower SES is associated with lower EF skills, even when controlling for

general cognitive ability (e.g., Farah et al. 2006; Farah et al. 2008; Masten et al. 2012; Mezzacappa 

2004; Noble, Norman, and Farah 2005; Obradović 2010). Lower EF skills, in turn, place children 

from lower SES families at a further disadvantage in a classroom context, potentially setting up a 

cascade of events leading to negative self-perceptions and undermining motivation. There is also 

evidence, however, that having strong EF skills serves as a protective factor against the academic risks 

associated with extreme poverty (Masten et al. 2012; Obradović 2010). Masten and colleagues found 

that children who are (or have been) homeless and highly mobile are, on average, much more likely 

than other children to perform poorly in school but that this did not apply to children with good EF 

skills. These children tended to succeed in school despite being homeless, showing evidence of 

resilience. Two studies have shown that EF partially accounts for the association between SES and 

early learning in school, and these studies also suggest that EF accounts for more of the variance 

related to SES than do measures of IQ or language ability (Fitzpatrick, McKinnon, and Blair 2014; 

Nesbitt, Baker-Ward, and Willoughby 2013). Further research is needed to understand why lower 

SES is associated with lower EF, including which aspects of lower SES environments (e.g., nutrition, 

parenting, unpredictability) contribute to lower EF and why. 
25

As will be reviewed more fully in chapter 6, recent studies using randomized controlled designs 

suggest that improving EF through classroom-based activities improves academic achievement. For 

example, in a study with 5-year-olds in foster care, a short-term, classroom-based intervention 

focusing on self-regulation (e.g., how to focus attention, sit still, and wait for one’s turn) and school 

readiness led to improved inhibitory control and to higher literacy scores (Pears et al. 2013). In 

another study, kindergarteners receiving a commercially-available curriculum named Tools of the 

Mind6 showed gains in EF skills that were associated with gains in achievement (e.g., reading) that 

carried over into first grade (#Blair and Raver 2014). Finally, research on the impact of a selfregulation intervention that took place with 4-year-olds in Head Start classrooms over the course of 

8 weeks (two 20–30 minutes playgroups per week) found that, compared to children who did not 

receive the intervention, trained children showed better EF skills, even controlling for baseline selfregulation, child age, and English language learner (ELL) status (Schmitt et al. 2015). Furthermore, 

ELL students receiving the self-regulation intervention showed higher math scores at the end of the 

year than ELL students who did not. 

2.2 Difficulties With Executive Function Are a Prominent Feature of 

Many Disabilities and Disorders that Interfere With Learning 

Given the important role that EF plays in learning and the robust association between EF and 

academic achievement, it is perhaps not surprising that difficulties with EF, particularly in working 

memory, are associated with learning difficulties and specific learning disabilities (Geary 2011; 

McLean and Hitch 1999; Swanson, Howard, and Saez 2006; Toll et al. 2011). Difficulties with EF 

are also associated with autism spectrum disorder (ASD) (e.g., O’Hearn et al. 2008), conduct 

disorder (e.g., Séguin 2004; Séguin and Zelazo 2005), obsessive-compulsive disorder (e.g., Pietrefesa 

and Evans 2007), and attention deficit hyperactivity disorder (ADHD) (e.g., Castellanos et al. 2006; 

Willcutt et al. 2005). Different aspects of EF may be implicated in each disorder, but there are also

reasons to believe that the same EF difficulties may result from many different forms of atypical 

cognitive development. 

 

6 The Tools of the Mind program blends teacher-led scaffolding of a comprehensive curriculum of early literacy, 

mathematics, and science activities with child-directed activities and structured sociodramatic play, as well as the use of 

specific tactics to support EF and learning in young children (Bodrova and Leong 2007). 
26

EF difficulty may then be expected to interfere with learning, both directly through the ability to 

process complex information and indirectly through behaviors that are related to learning. Several 

studies have found EF is also implicated in specific learning disabilities such as reading disability 

(Alloway et al. 2009; Booth, Boyle, and Kelly 2010). Working memory, in particular, has been found 

to be related to specific language impairment (Archibald and Gathercole 2006; Briscoe and Rankin 

2009). 

Difficulties with different aspects of EF may be implicated in different forms of a single disorder. 

Because of the multiple influences on and pathways that interact with EF, researchers are working to 

identify the heterogeneity in disorders characterized by EF difficulty. They are also exploring the 

heterogeneity of psychological functioning in typically developing populations so that they can better 

understand the etiology, diagnosis, and treatment of disorders (e.g., Fair et al. 2012). For example, 

although models of ADHD have focused on EF deficits as a core feature of the disorder (e.g., 

Barkley 1997; Nigg 2001), there is evidence that ADHD is a heterogeneous disorder, with different 

subtypes, and that several pathways can lead to ADHD (Castellanos and Tannock 2002; Nigg et al. 

2005; Willcutt et al. 2005), including an increased sensitivity to rewards, particularly in adolescence 

(Nigg and Casey 2005). Castellanos and colleagues (2006) have proposed that while inattention 

symptoms are due to cool EF deficits, hyperactivity/impulsivity symptoms depend on more 

difficulties with hot EF. Empirical studies suggest that measures of hot EF (e.g., delay aversion, or 

the motivation to escape or avoid delay) and measures of cool EF (e.g., the Stop-Signal task which 

measures inhibitory control) are independent predictors of ADHD status (e.g., Solanto et al. 2001). 

Further, even after controlling for working memory, hot EF is associated with symptoms of 

hyperactivity/impulsivity but not with symptoms of inattention (Toplak, Jain, and Tannock 2005; 

but see Geurts, van der Oord, and Crone 2006). 

It is currently unclear to what extent difficulties with EF contribute to the broad category of 

emotional and behavioral disorders (EBDs) (Individuals with Disabilities Education Act (IDEA) 

Regulations, Sect. 300.8 c 4). However, several studies have found EF to be associated with conduct 

problems in preschool-aged children (e.g., Hughes et al. 2000; Speltz et al. 1999). Others have found 

EF to relate to parent reports of social competence and social skills in elementary school (Hughes 

and Ensor 2011; Riggs, Blair, and Greenberg 2003). This is an area for future research, and it will be 
27

of interest to examine the role of hot vs. cool EF in EBDs, as well as the role of bottom-up 

influences such as stress and arousal. 

2.3 Summary 

The evidence briefly summarized in this chapter highlights only a few of the reasons research on EF 

is relevant to learning and to education more generally. First, EF skills predict both math and 

reading comprehension in kindergarten and during the elementary grades. There is also evidence 

that children with better EF skills learn more from a given amount of instruction, although other 

aspects of self-regulation, such as having a growth mindset, are also important. EF difficulty may be 

caused by many forms of atypical cognitive development. Difficulties with EF are prominent in 

numerous disorders with childhood onset, moreover, including specific learning disabilities and 

ADHD, although there is increasing awareness of the heterogeneity of these disorders and 

associated EF deficits. However, good EF skills have been found to serve as a protective factor 

against the risk of low academic achievement in disadvantaged children. Research has shown clearly 

that EF skills can be improved by relatively brief interventions that provide children with 

opportunities to practice their developing EF skills at increasing levels of challenge. The preschool 

years may be an especially sensitive period for the acquisition of EF skills, but the transition to 

adolescence is another promising target for interventions. Ongoing research is directed at finding 

optimal strategies for cultivating EF skills at different ages and for tailoring these strategies to 

individual children’s needs. 

 
28

3. Assessment of Executive Function 

This chapter considers issues related to the assessment of EF in children. We begin with a brief 

account of historical changes in the assessment of EF. Next, exemplar measures are introduced. 

Finally, key challenges in the assessment of EF are summarized and decision rules for the selection 

of measures are proposed. A unifying idea of this chapter is that there are variety of different 

reasons EF might be assessed, each of which benefits from specific measurement approaches 

(questionnaires, performance-based tasks) and the selection of instruments or tasks that have 

specific psychometric properties. 

3.1 Historical Context for Assessing Executive Function in Children 

For most of the 20th century, neuropsychological assessments of so-called frontal lobe functions, 

which were the predecessors of current conceptualizations of EF, were limited to adults. This was 

due to an early belief that frontal lobe development in childhood was limited (Golden 1981). 

However, improvements in our understanding of normative brain development established that the 

frontal lobes undergo a prolonged period of development that spans infancy through early 

adulthood. This shift in understanding was pivotal in initiating modern research on EF in children 

(reivewed by Hughes 2011; Teeter et al. 2009). 

In the 1980s and 1990s, a number of studies demonstrated age-graded changes in performance on 

“frontal lobe” tasks that were evident for children as young as 5 years old into early adulthood 

(Becker, Isaac, and Hynd 1987; Levin et al. 1991; Passler, Isaac, and Hynd 1985; Welsh and

Pennington 1988; Welsh, Pennington, and Groisser 1991). This work was complemented by efforts 

to develop performance-based assessments of EF that were appropriate for preschool-aged children 

(Archibald and Kerns 1999; Espy 1997; Espy et al. 1999; Zelazo, Frye, and Rapus 1996). By the end 

of the 20th century, vague notions of “frontal lobe functions” were replaced by more well-defined 

notions of specific EF skills, and the construct of EF was increasingly understood to be a life-course 

construct in which developmental changes were evident from early childhood into old age 

(Anderson, Sleeper, et al. 2008; Anderson, Jacobs, and Anderson 2008; Zelazo, Craik, and Booth 

2004). 
29

In addition to expanding ages in which EF was assessed, there has been a broadening of the 

intended uses of EF assessments. Like the adult studies that preceded them, many of the early 

studies of EF in children focused on discrete clinical populations or events, including prematurity

(Luciana et al. 1999); prenatal drug exposure (Espy, Kaufmann, and Glisky 1999); early brain disease 

(Taylor et al. 1996); suspected or known genetic, clinical, or neurological disorders (Diamond, 

Prevor, Callendar, and Druin 1997; Fletcher et al. 1996; Hughes, Russell, and Robbins 1994; Temple, 

Carney, and Mullarkey 1996); or specific learning disabilities often with comorbid attention deficit 

hyperactivity disorder (ADHD) (Denckla, 1996; Pennington, Groisser, and Welsh 1993; Swanson 

and Alexander 1997; Swanson and Berninger 1995). Although EF assessments continue to be used 

with children who have known clinical conditions or who have been defined by singular risk events 

(e.g., preterm birth; Mulder et al. 2009), EF has increasingly transitioned from a construct primarily 

relevant in clinical populations to a key aspect of cognitive development and a source of individual 

differences in self-regulation that contributes to social and academic success (Blair 2002; Heckman, 

Stixrud, and Urzua 2006; Moffitt et al. 2011), thus making it a developmental and individual 

differences variable. This expanded characterization of EF has resulted in both increases in the use 

of questionnaires to assess EF and in the use of performance-based assessments in ways that diverge 

from their original uses. 

3.2 Approaches to Assessment 

The shift from conceptualizing EF as an indirect proxy of presumed neurological dysfunction 

involving the frontal lobes to a core aspect of cognitive development and an individual differences 

variable has raised important questions about how EF is best assessed. Performance-based tasks

have long been the dominant approach for measuring EF, and they continue to be considered the 

gold standard method of assessment. A comprehensive summary of performance-based EF tasks is 

beyond the scope of this chapter (but see Carlson 2005; Chan, et al. 2008). Instead, we describe 

exemplar measures in order to provide a shared understanding of how EF skills are often assessed. 

Exemplar measures were chosen based on their wide use and are not intended to convey any 

endorsement. 
30

The National Institutes of Health (NIH) Toolbox for the Assessment of Neurological and 

Behavioral Function includes free, validated, reliable, norm-referenced measures of EF that can used 

with children and adults ages 2.5-85 years.7 These brief (~5 minute) tablet-based measures are 

designed so that they can be administered repeatedly to the same individuals, making them suitable 

for clinical trials and large-scale epidemiological and longitudinal studies. Three measures of EF are 

available. The Dimensional Change Card Sort (Zelazo 2006) requires children to match a series of 

bivalent test pictures (e.g., yellow balls and blue trucks) to target pictures, first according to one 

dimension (e.g., color) and subsequently to a second dimension (e.g., shape). The ability to 

successfully switch the sorting rules is an indicator of cognitive flexibility. In the Flanker task 

(Eriksen and Eriksen 1974; Rueda et al. 2005), individuals are asked to identify the direction of a 

centrally presented stimulus while inhibiting attention to stimuli (fish for ages 3-7 or arrows for ages 

8-85) that are flanking it. Sometimes the central stimulus is pointed in the same direction as the 

“flankers” (congruent) and sometimes in the opposite direction (incongruent). Responses to the 

incongruent items are an indicator of inhibitory control. In the Toolbox List Sorting Working Memory 

Test, pictures of different foods and animals are displayed with accompanying audio recording and 

written text (e.g., “elephant”). Individuals are instructed to repeat the items back in size order from 

smallest to largest, first within a single dimension (either animals or foods, called 1-List) and then on 

two dimensions (foods, then animals, called 2-List). The proportion of correctly recalled items is an 

indicator of working memory. The NIH Toolbox also includes brief measures of processing speed 

(90 seconds) and vocabulary (~ 4 minutes), which can be useful for examining EF in relation to 

other cognitive processes. 

The CANTAB is a commercially available (through Cambridge Cognition) computerized assessment 

of a broad set of cognitive processes, of which EF skills are only a subset.8 The EF battery consists 

of seven tasks that are appropriate for use in childhood through adulthood (age 4-90 years of age). 

The CANTAB EF tasks correspond to well-known tasks in the literature. For example, the One 

Touch Stockings of Cambridge test, which measures spatial planning and working memory, is based on 

the Tower of Hanoi test. Participants are shown two displays of three colored balls. Participants are

instructed to reconfigure one set to resemble a display set, while following a set of rules about 

 

7 See http://www.healthmeasures.net/explore-measurement-systems/nih-toolbox. 8 See http://www.cambridgecognition.com/. 
31

permitted moves. Items differ with respect to the number of moves that are required to replicate the 

display set. The Spatial Working Memory task, which measures working memory, is a self-ordered task 

that involves locating a blue token that is hidden under one of a number of colored boxes. 

Participants have to recall which boxes have been previously touched in an effort to locate the 

token. Items become more difficult as the number of colored boxes that require searching is 

increased. The Stop-Signal test, which measures inhibitory control, closely approximately Logan’s task 

by the same name (Verbruggen, Logan, and Stevens 2008). Participants are instructed to touch either 

a left or right button according to the direction that an arrow is pointing unless that arrow is 

preceded by an audible tone. Items become more difficult as the time interval between the 

presentation of the arrow and the audible sound vary. Two unique feature of the CANTAB battery 

are (1) the reliance on touch-screen technology and (2) its extensive use in research studies (see 

maintained bibliography at www.cambridgecognition.com). 

The Delis-Kaplan Executive Function System (D-KEFS; Delis, Kaplan, and Kramer 2001) is 

another commercially available, norm-referenced battery of nine verbal and nonverbal EF tasks that 

are appropriate for use in childhood through adulthood (age 8-89 years). The tasks include both 

adaptations of existing EF tasks, as well as newly developed tasks. For example, whereas the ColorWord Interference task closely approximates the classic Stroop task, which is a well-established measure 

of inhibitory control, the Word Context test is a new task that measures deductive reasoning, 

hypothesis testing, and flexibility in thinking. Notably, the D-KEFS tasks focus less on the three 

core domains of EF that we have emphasized throughout (i.e., inhibitory control, working memory, 

and cognitive flexibility) and more on measures fluency and fluid intelligence. This represents a more 

expansive characterization of the construct of EF than comparison batteries, and assesses behaviors 

that depend on more specific EF skills.

In addition to dedicated EF task batteries, the Wechsler Preschool and Primary Scale of Intelligence 

Fourth Edition (WPPSI-IV; Wechsler 2012), the Wechsler Intelligence Scale for Children Fifth 

Edition (WISC-V; Wechsler 2014), and the Stanford-Binet Intelligence Scale, Fifth Edition (SB5; 

Roid 2003) all have multiple subtests that are combined to yield working memory and fluid 

intelligence composite scores. Whereas working memory is a core domain of EF, fluid intelligence is 

a broader but closely related construct that involves reasoning or novel problem-solving efforts that 

make use of EF skills. School psychologists routinely make use of the WISC-V and SB5 in the 

context of psychoeducational assessments; hence, the working memory and fluid intelligence subtest 
32

are frequently used in educational contexts. For example, school psychologists often consider 

children’s differential performance on nonverbal versus verbal working memory and fluid 

intelligence tasks in order to make educational recommendations (e.g., children who have poor 

verbal, but not nonverbal, working memory may benefit from the use of visual reminders, preprinted notes, or other written materials that serve as accommodations). 

Over the last decade, there has been a burgeoning interest in developing questionnaire measures of 

EF for use with children, adolescents, and adults (Buchanan et al 2010; Geurten et al. 2016; Gioia 

2000; Naglieri and Goldstein 2004-2015; Nilsen et al. 2016; Shaw, Oei, and Sawang 2015; Thorell 

and Nyberg 2008; Vallat-Azouvi, Pradat-Diehl, and Azouvi 2012). Whereas performance-based tasks 

are understood to measure a child’s cognitive skills directly in a standardized way, EF questionnaires 

have been described as measuring the behavioral enactment of EF skills in “everyday” contexts 

(Gioia et al 2000). Questionnaires that are used with children are typically, though not exclusively, 

completed by parents and teachers (older children and adolescents may provide self-reports). 

Questionnaires are presumed to benefit from parent and teachers’ opportunities to observe 

children’s behaviors in multiple contexts and are understood to index behaviors over longer spans of 

time than performance-based tasks are able to assess. EF questionnaires are also appealing because 

they often require less effort to administer relative to performance-based measures.

The family of Behavioral Rating Inventory of Executive Functions (BRIEF) scales are the most 

widely used and researched questionnaire measures of EF (Gioia, Espy, and Isquith 2003; Gioia et 

al. 2002; Isquith et al. 2005; Rabin et al. 2006; Roth et al. 2013). The scales now include preschool, 

child, and adult versions. The childhood version of the BRIEF, which is commercially available 

through PAR, includes 86 items that are combined to form eight individual (inhibit, shift, emotional 

control, initiate, working memory, plan/organize, organization of materials, and monitor) and two 

higher-order (behavioral regulation and metacognition) scales. Notably, these scales are all normreferenced which facilitates clinical inferences. Interested readers are referred to two recent narrative 

reviews that described the ways in which the BRIEF scales have been used to facilitate clinical 

assessment, as well as to inform treatment planning and evaluation (Isquith, Roth, and Gioia 2013; 

Isquith et al. 2014). 

The Childhood Executive Functioning Inventory (CHEXI) is another example of a questionnaire 

measure of EF that can be used with children (Thorell et al. 2010; Thorell and Nyberg, 2008). 
33

Although not as widely used or researched as the BRIEF, the CHEXI is relatively short (26 items), 

has been translated into multiple languages (i.e., English, Swedish, French, Spanish, Galician and 

Persian), and most importantly is freely available for wide scale use (see www.chexi.se). Although the 

CHEXI was designed to measure four dimensions of EF (inhibition, regulation, working memory 

and planning), empirically two factors (inhibition/regulation and working memory/planning) 

provide a more parsimonious representations of the items (Catale, Meulemans, and Thorell 2013). 

Exemplar items for working memory include “has difficulty remembering length instructions” and 

“when asked to do several things, s/he only remembers the first or the last.” Exemplar items for the 

inhibition scale include “has difficulty holding back his/her activity despite being told to do so” and 

“has a tendency to do things without first thinking about what could happen.” 

3.3 Assessment Challenges 

3.3.1 Measurement Impurity 

One of the most frequently cited challenges of assessing EF is what is known as the measurement 

impurity problem (Miyake, Emerson, and Friedman 2000). Measurement impurity refers to the idea 

that there are multiple processes that contribute to individual differences in performance on any EF 

task. Measurement impurity refers to at least three interrelated ideas. 

First, many EF tasks require a combination of so-called executive and non-executive cognitive 

processes in order to be completed. For example, in the flanker task, which is featured in the NIH 

Toolbox as a measure of inhibitory control (Bauer and Zelazo 2014), children respond to stimuli 

that include a central arrow that points to either the left or the right and that is flanked by arrows 

that point in either the same (congruent) or opposite (incongruent) direction as the central arrow. 

Children are instructed to touch the button to which the central arrow points. Task performance is 

based on both the accuracy and speed of children’s responses. Children’s scores on the flanker task 

(and others like it) likely reflect a combination of inhibitory control and processing speed (i.e., 

executive and non-executive processes). To the extent that the resulting scores conflate these two 

sources of variation, however, they become “impure” measures of inhibitory control. 

Second, many EF tasks require multiple executive processes in order to be completed. For example, 

the hearts and flowers (formerly dots) task has been used to demonstrate developmental differences 
34

in both the structure and level of proficiency of EF skills across time, as well as the dependence of 

EF skills on genetic and neurochemical factors (Davidson et al. 2006; Diamond et al. 2004; Shing et 

al. 2010). In this task, individuals see pictures of heart or flowers, one at a time, on either the left or 

right side of the screen. When pictures of hearts appear, individuals are to press a designated key on 

the keyboard that is on the same side as the stimulus (e.g., heart on left side requires pressing the left 

key). When pictures of flowers appear, individuals are instructed to press a designated key on the 

keyboard that is on the opposite side as the stimulus (e.g., flower on right side requires pressing the 

left key). The task involves three conditions—hearts only, flowers only, and mixed hearts and flower 

stimuli. Task developers have emphasized that while all three conditions make demands on working 

memory, the flowers only condition makes unique demands on inhibitory control, while the mixed 

condition makes unique demands on cognitive flexibility (Brocki and Tillman 2014; Diamond and 

Wright 2014). This is consistent with the suggestion that there are no “pure” measures of any

specific EF skill. 

Third, virtually all performance-based EF tasks require some degree of attention, engagement, and 

willingness on the part of the child to participate. For example, children’s observable behaviors 

during the completion of EF tasks is correlated with their performance on those tasks (Bassett et al. 

2012). Moreover, even modest disruptions in children’s sleep are associated with poorer 

performance on EF tasks (Molfese et al. 2013; Vartanian et al. 2014). Individual differences in 

children’s behavior, as well as idiosyncratic factors including mood or fatigue, contribute to their 

performance on EF assessments and serve as additional sources of measurement impurity. 

Thus far, researchers have relied on the use of latent variable models as their primary strategy for 

addressing concerns about measurement impurity. Although a detailed account of these approaches 

and their rationale is beyond the scope of this chapter, the basic idea is to administer to multiple EF 

tasks to children and to use latent variable models to isolate that variation in each task that is shared 

with other EF tasks (see e.g., Huizinga, Dolan, and van der Molen 2006; Miyake et al. 2000; Wiebe, 

Espy, and Charak 2008). More recent efforts have begun fitting models that further partition the 

variation that is shared across EF tasks into that which represents a combination of general (e.g., 

processes that are shared across inhibitory control, working memory, and cognitive flexibility tasks) 

and domain specific (i.e., variation that is specific to inhibitory control, working memory, or 

cognitive flexibility tasks) EF skills (Miyake and Friedman 2012; Schmiedek, Lövdén, and 

Lindenberger 2014). It is worth noting that all of these approaches implicitly assume that the 
35

variation that is shared across a set of EF tasks is indicative of “true” EF competence, whereas the 

variation that is unique or specific to each task represents multiple sources of variation that jointly 

contribute to measurement impurity. It is not yet clear whether these are reasonable assumptions 

and consideration of alternative statistical approaches for attending to measurement impurity 

remains an active area of research (see Willoughby et al. 2014). It is noteworthy that issues related to 

measurement impurity have only been considered with respect to performance-based assessments of 

EF. Although questionnaire based assessments of EF likely suffer from similar problems (i.e., scores 

reflect individual differences in more than EF abilities), this has not been an area of much empirical 

study to date. 

3.3.2 Ecological Validity 

Despite the long history and widespread use of performance-based assessments of EF, researchers 

have frequently raised concerns about their ecological validity (Anderson 2002; Barkley and Murphy 

2010, 2011; Burgess et al. 1998; Chaytor, Schmitter-Edgecombe, and Burr 2006; Kenworthy et al. 

2008; Sbordone 2001; Sbordone and Purisch 1996). The essential concern is that performance-based 

measures (i.e., highly structured EF tasks, completed during a single testing session in a quiet and 

emotionally neutral setting) do not adequately characterize the actual manifestation of EF skills in 

“everyday” life. For example, many of the school-based situations in which children presumably 

engage their EF skills (e.g., competitive interactions with peers at recess or high-stakes test-taking) 

are characterized by strong emotional or motivational influences. It has been suggested that 

children’s performance on traditional “cool” performance-based measures may bear little 

resemblance to their EF performance in “hot” contexts (see the hot vs cool distinction, chapter 1, 

section 1.4). Although questionnaire-based assessments of EF have frequently been described as 

measuring EF behaviors in “everyday contexts” (e.g., Isquith, Gioia, and Espy 2004), their 

consistently weak associations with performance-based assessments have raised questions about 

what they actually measure (see section 3.3.3, below). 

Ten years ago, Burgess et al. (2006) highlighted the limitations of using traditional performancebased assessments of EF for clinical purposes and emphasized the value of developing a new class 

of EF assessments that better approximated real world contexts. The Multiple Errands Task (MET) 

was highlighted as an exemplar measure. The MET required individuals to complete a hypothetical 

set of shopping tasks following rules that were consistent with real-world context (Shallice and 
36

Burgess 1991). Over the last two decades, the MET has been iteratively developed, including being 

migrated to virtual reality environments, and has demonstrated acceptable reliability, as well as 

concurrent and predictive validity (Alderman et al. 2003; Cuberos-Urbano et al. 2013; Dawson et al. 

2009; Maeir, Krauss, and Katz 2011; Morrison et al. 2013; Raspelli et al. 2010). More recent tasks 

that were inspired by the MET include the Virtual Errands Task (Law, Logie, and Pearson 2006; 

Logie, Trawley, and Law 2011; McGeorge et al. 2001), the Virtual Coffee Task (Allain et al. 2014), the 

Virtual Library Task (Renison et al. 2012), and the Kitchen Task (Berg, Edwards, and King 2012; 

Rocke et al. 2008). Notably, the Kitchen Task was the only one of these functionally-based tasks that 

was specifically developed for use with children. All of these efforts reflect a broader interest in 

understanding how technological developments might modernize the assessment of EF in ways that 

have clear benefits for clinical and educational practice (Lalonde et al. 2013; Parsons et al. 2015). An 

important direction for future research involves clarifying whether and how the conceptualization of 

EF skills might change through the use of functionally-based assessments. For example, the 

children’s Kitchen task was described as evaluating “the level of cognitive assistance that children 

require to complete a challenging functional activity” (Rocke et al. 2008). It is not yet clear to what 

extent tasks of this sort necessarily measure EF versus correlated processes (i.e., functionally-based 

assessments may still suffer from measurement impurity problems). 

3.3.3 Poor Convergence between Questionnaire and Performance-Based 

Tasks 

The widespread interest in using questionnaires to assess EF was described above. However, the 

validity of EF questionnaires is predicated on a strong assumption—the items on questionnaires are 

assumed to represent behavioral manifestations of children’s underlying cognitive (EF) skills. 

Currently, there is limited empirical support for this assumption. In a review of 20 studies that 

included child and adult samples, Toplak and colleagues reported a median correlation of r = .19 

between questionnaire and performance-based assessments of EF (Toplak, West, and Stanovich

2013). Moreover, EF questionnaires are often more strongly associated with ratings of other aspects 

of children’s behaviors than they are with performance-based measures of EF (e.g., Catale, 

Meulemans, and Thorell 2013; Mahone et al. 2002; Mahone and Hoffman 2007), raising the 

possibility that ratings reflect relatively non-specific evaluations of children, such as how likeable 

they are. This would be broadly consistent with evidence that adult self-reports of their own EF
37

problems are more strongly correlated with aspects of their personality than performance-based 

indicators of EF skill (Buchanan 2016). 

Despite their poor correspondence with performance-based tasks, questionnaires continue to be 

advocated as part of a comprehensive assessment of EF because they provide information that 

complements performance-based measures and that can be useful for treatment planning and 

evaluation (Isquith, Roth, and Gioia 2013; Isquith et al. 2014). However, at the level of individual 

children, questionnaire and performance-based assessments of EF often result in discrepant results 

that are difficult to reconcile (Silver 2014). At the present time, it seems best to conclude that 

questionnaires and performance-based measures of EF measure different phenomena. Although 

these methods of assessment may yield complementary data, they certainly do not yield 

interchangeable data. EF questionnaires index parental and teacher impressions of children’s 

behaviors in every day contexts. It is not clear whether these behaviors necessarily represent 

manifestations of underlying cognitive skills (behaviors are multiply determined). In contrast, 

performance-based EF tasks index a child’s EF skills under optimal testing conditions and may not 

be indicative of a child’s typical use of those skills in every day contexts.

3.4 Decision Rules for Assessing Executive Functions in Educational 

Settings 

In this final section, we introduce three instances in which educators might be interested in the 

assessment of EF in children. In each instance, we highlight the motivating question, consider which 

method of assessment may be most appropriate, and emphasize psychometric issues that should 

inform the selection of specific instruments. We emphasize that the selection of specific measures of 

EF should be informed by the broader objectives of the assessment. 
38

3.4.1 Academic Impairments 

There are a variety of ways in which EF may contribute to children’s academic impairments. Some 

children’s academic difficulties may result from behaviors that are considered “executive” in nature, 

including poor planning and time management (Langberg, Dvorsky, and Evans 2013). Other 

students may have a specific impairment in working memory that may undermine their ability to 

comprehend or apply specific strategies involved in academic instruction (Gathercole and Alloway 

2008). Still other children may experience subject-matter (e.g., math-related) or general performance

anxiety that undermines their use of specific EF skills and, thereby, impedes their academic progress 

(Ashcraft and Krause 2007). 

If the primary presenting problem involves a child’s poor planning or organizational skills in general, 

questionnaires may be the preferred approach. In contrast, if the primary presenting problem is 

impairments in working memory or other specific EF skills, performance-based assessments may be 

the preferred approach. In both cases, instruments that have norm-referenced scores should be 

prioritized. Norm-referenced scores facilitate inferences about how a child’s behavior or cognitive 

function compares to other children of similar ages. In addition, the selection of specific instruments 

should prioritize those that have excellent precision of measurement (i.e., test information) in the 

low ability range. That is, measures that were demonstrated to maximally distinguish between 

children of low versus average executive function ability should be prioritized. Although it has been 

relatively rare for EF assessments to be evaluated from this perspective (i.e., delineating differences 

in measurement prediction as a function of underlying ability), this is the type of information that 

end-users require for purposes of measure selection (Willoughby, Wirth and Blair 2011). 

3.4.2 Differentiated Instruction 

Differentiated instruction involves matching instruction to a particular child’s needs or level of 

understanding. It may also include modifying the process of instruction or other aspects of the 

learning environment. Students who receive differentiated instruction show greater rates of 

academic improvement (e.g., Connor et al. 2011; Connor et al. 2010). 

Although not yet routinely done, differentiated instruction might benefit from considering individual 

differences in EF skills and/or other cognitive processes. In terms of behavioral indicators of EF, 

children who are characterized by poor regulation (e.g., easily bored, difficulty persisting in things 
39

that are not interesting) may benefit from the receipt of more frequent but shortened periods of 

instruction or instruction that involves manipulatives. Alternatively, children who are characterized 

by strong overall EF skills may benefit from more independent learning opportunities or more 

deductive instructional input. 

The key psychometric issue that informs measure selection for differentiated instruction again 

involves measurement precision. However, in this case, the best measures would have excellent 

measurement precision along the full spectrum of children’s ability. This differs from the original 

intent of many measures, which were designed to identify impairment in function. Given the focus 

on differentiating among all children in a classroom (e.g., in the case where different groups receive 

different types of instruction), norm referenced scoring is less important for assessments that are 

used to facilitate differentiated instruction. 

3.4.3. Intervention and Individual Progress Monitoring 

As reviewed in chapter 2, there is mounting evidence that children’s earlier EF skills predict their 

future academic performance, above and beyond earlier levels of academic performance (e.g., 

McClelland et al. 2014; Ursache, Blair, and Raver 2012). This suggests that schools may benefit from 

systematic efforts to improve children’s EF skills. Doing so requires regular assessments of EF that 

can be used to evaluate intervention effects or monitor student progress. Given that most of the 

evidence linking earlier EF to later academics comes from studies that have used performance-based 

assessments, we focus our attention on this assessment method. 

Prepost designs are routinely used to evaluate intervention effects (this is true for both open trials 

and randomized controlled trials). In prepost designs, children are assessed immediately prior to and 

following the delivery of an intervention. Prepost designs (and repeated measures designs more 

generally) make unique demands of EF assessments. First, in order for any observed changes in EF 

scores to be interpretable, the assessment measure must exhibit longitudinal measurement 

invariance. This ensures that any differences in scores across time are attributable to true changes in 

ability and not an artifact of changing measurement. Second, the test-retest reliability of the measure 

has a direct impact on the extent to which any observed score changes can be attributable to the 

intervention versus situational variability or measurement error. In situations where individual EF 

tasks exhibit poor to modest test-retest reliability, educators might consider aggregating children’s 
40

performance across a battery of tasks in order to obtain a composite score that exhibits improved 

test-retest reliability (Willoughby, Blair, and Family Life Project 2015). Estimates of test-retest 

reliability are also useful for constructing indices of minimally detectable change (MDC). MDC 

values can be used by school personnel as a benchmark that defines the minimal amount of change 

in EF scores that are not attributable to measurement error. This becomes a minimum amount of 

change that can be interpreted as meaningful improvement. Third, program evaluation efforts 

benefit from the selection of assessment tools that have strong precision of measurement across the 

full range of child ability. This differs from many performance-based measures of EF in early 

childhood, which typically only measure a relatively narrow range of ability and often demonstrate 

floor and ceiling effects (Carlson 2005). It is imperative that newer measures that do not suffer from 

these limitations be used for program evaluation efforts. 

While prepost designs are commonly used for the evaluation of small-group and classroom-based 

interventions, more frequent assessments may be used when evaluating individualized programs. 

Although progress monitoring tools (including curriculum based assessments) are widely used to 

evaluate children’s ongoing improvements in core academic areas (Hintze, Christ, and Methe 2006), 

they have not yet been developed for EF. This is an important direction for future research. 

3.5 Summary 

Over the last 30 years, substantial changes have occurred in the assessment of EF. For example, 

while EF assessments were initially exclusively conducted with adults, they are now routinely 

conducted with children; while EF assessments were initially exclusively used in clinical contexts as a 

cognitive proxy of potential neurological dysfunction involving the frontal lobes, more recently they 

have been used with typically developing children as an index of healthy neurocognitive 

development and as a predictor of social and academic success. The expanded conceptualization of 

EF has resulted in a proliferation of performance-based assessments of EF, as well as the 

development of questionnaire-based assessments. 

Despite their shared intent, questionnaires and performance-based assessments of EF measure 

different phenomena. While performance-based tasks measure optimal cognitive ability in highly 

structured testing environments, questionnaires measure behavior in everyday contexts. It is not yet 

clear that the behaviors that are measured by EF questionnaires are functionally related to 
41

underlying EF skills. In light of their differences, the decision about whether to use questionnaire or 

performance-based assessments should be based on the objectives of the assessment (i.e., whether 

the presenting problem or area of interest is believed to be primarily behavioral or cognitive in 

nature). In light of the expanded number of uses of EF assessments, it is also important for testdevelopers and end-users to consider the specific psychometric properties that are most appropriate 

for the intended use. Many traditional performance-based measures of EF were designed to detect 

EF impairments in the low ability level. These tasks are not optimal for differentiating among 

children across a full range of ability or for monitoring developmental changes in EF skills. 
42

4. Developmental Change in Executive Function 

This chapter summarizes developmental changes in executive function (EF) from early childhood 

through adolescence, with an emphasis on pre-kindergarten (pre-K)–12th grade (for recent reviews 

of EF development that include infancy and the toddler years, see Carlson et al. 2013; Müller and 

Kerns 2015). We consider developmental changes in the dimensionality (structure) of EF and in the 

level or efficiency of EF. A unifying idea is that, although EF undergoes rapid developmental change 

from pre-K–12th grades, this change likely reflects a combination of quantitative improvements in 

skill and efficiency as well as qualitative shifts in the ways that EF tasks are solved and/or the ways 

that EF skills are used. 

4.1 Changes in the Structure of EF 

We have defined EF narrowly as consisting of cognitive flexibility, working memory, and inhibitory 

control. These three neurocognitive skills have been understood to reflect partially dissociable 

constructs that rely on overlapping but distinct neural circuitry and that collectively contribute to 

goal-directed behaviors. Researchers have relied heavily on factor analytic techniques to inform 

questions about whether cognitive flexibility, working memory, and inhibitory control represent 

distinct cognitive processes. 

We begin by briefly reviewing three high impact studies that have helped to frame current thinking 

about the factor structure (i.e., dimensionality) of EF. In a seminal study by Miyake and colleagues

(2000), 137 undergraduate students completed 9 performance-based EF tasks, which were intended 

to represent 3 distinct aspects of EF—shifting (cognitive flexibility), updating (working memory), 

and inhibition (inhibitory control)—for a total of 3 tasks per construct. A three-factor confirmatory 

factor analysis (CFA) model provided excellent fit to the observed data, which provided empirical 

evidence in support of the hypothesis that shifting, updating, and inhibitory control represented 

correlated but separate cognitive processes. Lehto and colleagues (2003) subsequently administered 

eight different performance-based tasks to 108 children who were age 8 to 13 years. A three-factor 

CFA that resembled that used by Miyake and colleagues (2000) again provided excellent fit to the 

data. The authors interpreted this as evidence that the structure of EF in middle childhood was 

comparable to that in adulthood. Finally, Wiebe and colleagues (2008) administered 10 performance-
43

based tasks to 243 children who were on average 3.9 years old. A series of CFA models indicated 

that a one-factor model provided excellent fit to the data; two- and three-factor models did not 

provide an improvement in model fit. The results of this study gave rise to the idea that EF may be 

better construed as an undifferentiated set of cognitive skills in early childhood, which subsequently 

differentiate during the transition to middle childhood. Although this conclusion differed from 

conventional wisdom at the time, the results of three additional studies, which used a similar analytic 

approach, that were published soon after Wiebe et al. (2008) came to the same conclusion (Hughes 

et al. 2010; Wiebe et al. 2011; Willoughby, Blair et al. 2010). Collectively, these studies suggested that 

the cognitive processes implicated in the completion of performance-based EF tasks might undergo 

developmental reorganization during the transition from early childhood into middle childhood and 

adolescence. 

The studies that were used to infer developmental changes in the structure of EF skills were limited 

by the use of cross-sectional designs (which could not inform questions about changes in the 

structure of EF at the level of individual children) and the use of different assessment tools for 

participants of differing ages. A study by Shing and colleagues was important because it included a 

common set of assessments that were administered to a sample of children who varied in age (Shing 

et al. 2010). Shing and colleagues reported that inhibitory control and working memory were not 

separable factors among children who were 4 to 7 years or 7 to 9.5 years old but were separable 

among children who were 9.5 to 14.5 years old. Notably, the correlations between inhibitory control 

and working memory (memory maintenance) factors were reported to be .98, .81, and .32 for the 

three age groups, respectively. These results provided evidence in support of a differentiation 

hypothesis. A later analysis of the NIH Toolbox Cognition Battery9

 by Mungas and colleagues 

(2013) came to similar conclusions regarding the differentiation of cognitive functions for children 

and adolescents who ranged from age 3 to 15 years. These studies provided additional evidence in 

support of the suggestion that the structure of EF skills may shift during the transition from early to 

middle childhood. These empirical results were also in alignment with models of neural 

development that emphasize experience-dependent neural specialization (e.g., Johnson 2011). 

 

9 See http://www.healthmeasures.net/explore-measurement-systems/nih-toolbox/intro-to-nih-toolbox/cognition. 
44

Despite the consistency in results across these early studies and their conceptual appeal (changes in 

the structure of EF skills conforming to models of neural development), a number of more recent 

studies have reported contradictory results. For example, at least five recent studies that involved 

preschool-aged children concluded that EF was best characterized by two-factor models in which 

inhibitory control and working memory were distinguished (Gandolfi et al. 2014; Lerner and 

Lonigan 2014; Miller et al. 2012; Schoemaker et al. 2012; Usai et al. 2014). Conversely, at least two 

other recent studies that involved elementary school-aged children concluded that EF was best 

characterized by a single undifferentiated factor (Brydges et al. 2012; Xu et al. 2013). Finally, in one 

of the few studies that used a prospective longitudinal design, Lee and colleagues reported that while 

a two-factor model, which distinguished working memory (updating) from inhibitory control and 

cognitive flexibility, provided the best fit to data for children 5- to 13-years old, a three-factor model, 

which distinguished working memory, inhibitory control, and cognitive flexibility, provided the best 

fit to the data among 15-year-olds (Lee, Bull, and Ho 2013). 

These results are difficult to reconcile because individual studies differed along a number of 

important dimensions including sample characteristics (ascertainment method, sample size, clinical 

condition, and age distribution); research designs (cross sectional, longitudinal); and measurement

characteristics (number of indicators per factor, score distributions, correlations among task scores). 

The preponderance of studies has lent support to the idea that cognitive flexibility, working 

memory, and inhibitory control represent separable (but correlated) dimensions of cognitive 

function in school aged children, adolescents, and adults. What is less clear is whether EF is best 

conceptualized as an undifferentiated set of skills in early childhood and, if so, when it begins to 

“fractionate” into dissociable dimensions. 

Practically speaking, questions about developmental changes in the structure of EF are likely of 

more interest to researchers than to educators. If the structure of EF changes over time, this 

complicates the ability to make clear inferences about developmental changes in the efficiency 

(improvements) in EF skills (i.e., the interpretation of changes in scores is ambiguous, as it may 

reflect either changes in ability level or in the structure of tasks). If cognitive flexibility, working 

memory, and inhibitory control are not differentiated in early childhood, this may have implications 

for intervention development efforts. For example, rather than focusing on the improvement of 

specific aspects of EF (e.g., computerized training programs that specifically target improvements in 

working memory), interventions may be better focused on improving broader aspects of EF (e.g., 
45

professional development activities that encourage teachers to use scaffolded instructional 

strategies). 

4.2 Changes in the Level or Efficiency of EF Skills 

As noted earlier, the prefrontal cortex (PFC) exhibits a protracted period of change across the first 

two decades of life, which is correlated with improvements in EF skills (Giedd et al. 1999; Miller and 

Cohen 2001; Ordaz et al. 2013; Sheridan, et al. 2014; Zhong et al. 2014). Two general approaches 

have been used to investigate quantitative developmental changes in EF skills across childhood and 

adolescence. The first, most common approach has involved the use of cross-sectional designs in 

which a standard set of EF tasks is administered to participants who were recruited into specific age 

groups. Subsequent between-group (i.e., inter-individual) comparisons provided an indirect means of 

understanding developmental changes in EF skills across the range of ages considered. The second, 

less common approach has involved the use of prospective longitudinal designs in which a standard 

set of EF task(s) is repeatedly administered to the sample participants across multiple assessments. 

These data facilitate tests of individual differences in developmental change (i.e., inter-individual 

differences in intra-individual change) and provide a more compelling characterization of changes in 

EF skills across time. 

4.2.1 Evidence from Cross-Sectional Studies 

An initial set of cross-sectional studies focused on young children who were generally between 2 and 

5 years old. These studies typically recruited children in relatively narrow age bands (e.g., 3.0-3.5

years, 3.5-4.0 years) and would have them all complete the same task. Of interest was the age at 

which children, on average, transitioned from being unable to being able to successfully complete 

the task (e.g., Gerardi 2000; Gerstadt, Hong, and Diamond 1994; Jones, Rothbart, and Posner 2003; 

Kloo et al. 2008). These studies did not emphasize individual differences in EF. Instead, they 

demonstrated that age-graded changes in EF were evident across early childhood. 

Studies that have investigated developmental changes in EF skills in early childhood have typically 

characterized children’s task performance with respect to the accuracy of their responses. In 

contrast, studies that have investigated developmental changes in EF among school-aged and 

adolescent-aged participants have often relied on tasks that include accuracy and reaction time 
46

metrics. The use of reaction time has facilitated the use of the same tasks over wider ranges of time. 

A recurring conclusion across many of these studies was that EF skills typically exhibit continued 

improvement throughout middle childhood and into adolescence (e.g., Becker et al. 1987; Davidson 

et al. 2006; Gur et al. 2012; Huizinga, Dolan, and van der Molen 2006; Levin et al., 1991; Luciana 

and Nelson 2002; Luna, et al. 2004; Magar, Phillips, and Hosie 2010; Rebok et al. 1996; Williams, et 

al. 1999; Zelazo et al. 2013). 

An example of the data that result from these sorts of studies is shown in figure 6, which depicts 

mean level changes in performance as indicated by youth who ranged from 3- to 15-years-old for 

two EF tasks that are part of the NIH Toolbox Cognition Battery. One of the notable features of 

the NIH Toolbox Cognition Battery is that it provides scores that combine information about 

participants’ item accuracy and reaction time. This combined scoring approach permits age-based 

comparisons that span early childhood (minimum age 3 years) through adulthood. 

Relatively few studies have formally tested differences in the functional form (i.e., the shape) of 

change in EF skills across time (see Luna et al. 2004 for an exception). However, doing so has the 

potential to reveal developmental periods in which improvements in EF skills are either more or less 

pronounced. For example, the NIH Toolbox Cognition Battery data that are depicted in figure 6

suggest that improvements in EF skills may be more likely to occur in early childhood and 

adolescence than in middle childhood. 
47

Figure 6. Pediatric data from a cross-sectional validation study on two measures of 

executive function

Note: Normalized scaled scores from (a) the NIH Toolbox Dimensional Change Card Sort (measure of cognitive 

flexibility) and (b) the NIH Toolbox Flanker (measure of inhibitory control). The best fitting polynomial model (cubic, 

R2 = .76) indicates two periods of relatively rapid growth (preschool and early adolescence). Error bars are +/- 2 

standard errors.

Source: Adapted with permission from Zelazo et al. (2013). II. NIH Toolbox Cognition Battery (CB): Measuring 

Executive Function and Attention. Monographs of the Society for Research in Child Development, 78(4). doi: 

10.1111/Mono.12039. 

 

4.2.2 Evidence from Longitudinal Studies

A small number of studies have examined correlations of children’s performance across two 

assessments. For example, a recent study demonstrated that EF measures that were collected in 

middle childhood (age 8) were significantly correlated with the same measures that were

administered in early adolescence (age 12), with the resulting implication that the rank-order stability 

of EF was preserved across an important developmental transition period (Harms et al. 2014). Other 

researchers have adopted a similar approach and established the continuity of EF skills (or deficits in 

EF) during the transitions from childhood to adolescence and adolescence into early adulthood 

(Biederman et al. 2008; Biederman et al. 2007; Boelema et al. 2014; Rebok et al. 1996). Studies that 

investigate the short-term stability of EF skills implicitly characterize EF as an individual differences 

variable and demonstrate that individual differences in EF ability are preserved across time. This 

provides a counterpoint to the historically older practice of identifying the age at which an ability 

“came online.” However, a limitation of short-term correlation studies is that they inform questions 
48

only about the rank order stability of EF skills across time. Even studies that report strong 

associations are uninformative with respect to the amount of actual change that occurred across time 

(this is further compounded when different measures are used at different ages). 

Prospective longitudinal designs in which participants are measured at three or more times using the 

same measure(s) provide the most compelling approach to characterizing developmental changes in 

EF across time. To date, relatively few studies have used prospective longitudinal designs to 

characterize developmental changes in EF, and most of these studies have focused exclusively on 

relatively short intervals of time across the early childhood period (Clark et al. 2013; Hughes et al. 

2010; Wiebe, Sheffield, and Espy 2012; Willoughby et al. 2012). These studies have provided 

consistent evidence for linear improvements in EF between ages 3 to 6 years, with some evidence 

that changes may be more pronounced for 3 to 4 versus 4 to 6 years of age. 

As has been noted elsewhere (Best, Miller, and Jones 2009), a disproportional amount of research on 

EF has focused on the early childhood period, despite the fact that much EF development occurs 

throughout middle childhood and adolescence. We are aware of only one study that used a 

prospective longitudinal design to evaluate changes in EF across adolescence. In the context of a 

large Dutch cohort study of preadolescents, Boelema and colleagues (2014) reported developmental 

changes in multiple aspects of EF over an approximately 8-year period (i.e., performance was 

measured on assessments that occurred when study participants were approximately 11 and 19 years 

of age). Although they were able to identify tasks for which developmental changes were least 

pronounced (i.e., inhibition) and most pronounced (i.e., working memory, focused attention), they 

were unable to address questions about the functional forms of change given only two repeated 

assessments. 

4.2.3 Integrating Evidence Across Cross-Sectional and Longitudinal Studies 

and Implications for Educators 

A number of authors have attempted to integrate the results of individual studies to provide an 

aggregate characterization of developmental improvements in EF skills from early childhood into 

adulthood. For example, Garon and colleagues proposed that a specific sequence of changes in basic 

attention, response inhibition, working memory, and shifting occurred across the first 5 years of life 

(Garon, Bryson, and Smith 2008; Garon, Smith, and Bryson 2014). Their proposal emphasized a 
49

long held view that more rudimentary EF skills, which lay the foundation for the development of 

more complex skills, emerge early in life. Romine and Reynolds (2005) used a meta-analytic 

approach to quantitatively characterize the degree of age-related improvements in EF tasks from 5 

to 22 years of age. Medium- to large-sized differences were evident from age 5 to 22 years, with 

some evidence that changes were more pronounced between 5 to 11 than 11 to 22 years (the 

aggregate Cohen d effect sizes were 1.2, 0.9, 0.5, .03, and 0.6 for participants in the 5- to 8- , 8- to 

11-, 11- to 14-, 14- to 17-, and 17- to 22-year-old age groups, respectively). Finally, adopting a more 

qualitative approach, De Luca and Leventer (2008) proposed a set of age-typical trajectories of 

multiple aspects of EF from 4 to 72 years of age. Their summary also emphasized the differential 

time course of specific EF skills with some reaching “mature function” as early as middle childhood 

(cognitive flexibility), others in adolescence (inhibitory control and affective decision making), and 

others in early adulthood (goal-setting and problem solving). 

Over the last 30 years, we have accumulated substantial and irrefutable evidence that children and 

adolescents exhibit nearly continuous improvements in their EF skills prior to and throughout their 

educational careers. Given evidence that EF skills uniquely contribute to learning-related outcomes 

and academic achievement, an important question for educators is how best to facilitate the 

progression of EF skills in individual children, as well as how to best structure school experiences in 

ways that leverage these ongoing improvements in EF skills. For example, it may be valuable to 

consider ways in which the existing academic curricula could be modified to explicitly promote EFrelated improvements. Similarly, efforts to tailor instructional approaches to a child’s individual need 

have the potential to improve their EF skills (e.g., Connor et al. 2010).

More generally, the existence of developmental periods during which normative improvements in 

EF skills are more typical or more rapid (e.g., during early childhood and adolescence), may have 

implications for intervention. One line of reasoning suggests that developmental periods in which 

change is most evident represent a particularly appropriate time for EF-related interventions (e.g., 

the neural substrates that support the development of EF may be especially malleable during these 

time periods). An alternative line of reasoning suggests that interventions should occur during those 

periods in which normative changes are less evident. This may recast these periods of less 

pronounced change as opportunities to facilitate “catch up” among children who did not make 

earlier expected EF gains. 
50

Despite the consistency in evidence that EF skills continue to improve from early childhood 

through adolescence, what is less clear is how best to interpret these improvements. Although the 

tendency has been to characterize quantitative improvements in EF performance as reflecting 

increased efficiencies in the neural networks that support EF skills, these improvements likely also 

reflect qualitative changes in the strategies that children use to solve EF tasks. For example, 

Chevalier and colleagues have published a series of studies in which they manipulated traditional EF 

tasks in ways that revealed shifts in children’s strategy use from reactive to proactive control 

(Chevalier, Huber et al. 2013; Chevalier, Wiebe et al. 2011). Reactive control strategies involve the 

engagement of EF on an “as-needed” basis, whereas proactive control strategies involve the 

engagement of EF on the basis of their anticipated need. Chevalier and colleagues’ research suggests 

that an initial shift from reactive to proactive control occurs somewhere between the ages of 5 and 

10 years. 

Consideration of stage-like transitions in the use of EF can have implications for educators. For 

example, the transition from using EF skills on an ‘as needed’, reactive basis towards using them for 

an anticipated need may indicate a period of time in which children begin to benefit from efforts to 

improve their meta-cognitive skills. Moreover, the period of time in which children shift from using 

EF skills to solve externally to internally initiated goals may correspond to the transition in which 

children become more self-regulated learners. Consideration of stage-like transitions in the use or 

function of EF skills may signify periods in which specific interventions (e.g., metacognitive reading 

strategies) are differentially efficacious. Of course, there will be individual differences in the rates at 

which these stage-like transitions occur. 

4.3 Summary 

Developmental changes in EF can be understood in terms of changes in the structure or 

organization of EF skills as well as changes in the level or efficiency of EF skills. In terms of their 

structure, there is consistent evidence that cognitive flexibility, working memory, and inhibitory 

control represent dissociable but correlated processes. What is unclear is how early in development 

these processes are distinct. 

With respect to level or efficiency, EF skills undergo large and continuous improvements from early 

childhood through adolescence. However, much of what is known is derived from studies that used 
51

cross-sectional designs and relied on single tasks (not constructs) as outcomes. Future studies will 

benefit from a greater reliance on prospective longitudinal designs and the use of assessment tools 

that are appropriate for a wide age range to provide stronger tests of these questions. A recurring 

idea is that EF develops in a hierarchical manner, such that the more complex forms of behavior 

that depend on EF skills (e.g., planning, self-monitoring) and are characteristic of adolescence are 

preceded by, and depend upon, more rudimentary skills that develop in early childhood. There are 

some indications that EF skills may develop more rapidly during some developmental periods (i.e., 

early childhood and adolescence) than others (i.e., middle childhood, early adulthood). This raises 

questions about whether interventions are differentially effective depending on when in 

development they are delivered, which is an important area for future research. Most studies that 

have focused on the development of EF skills have characterized these changes as relatively smooth 

and continuous improvements across a period of time. Although useful, this characterization risks 

obscuring more stage-like shifts in the ways in which EF tasks are solved, as well as the ways in 

which EF skills facilitate goal directed behaviors. Attending to both quantitative and qualitative 

aspects of change in EF may inform thinking about the design and evaluation of interventions. 
52

5. Individual Differences in Executive Function 

Development 

Because executive function (EF) and associated neural circuitry develop gradually, individuals can 

differ substantially in the rate at which these cognitive skills grow and change throughout childhood. 

Individual differences in EF are associated with temperament, with the presence or absence of other 

cognitive abilities that are related to EF, and to a small extent with genetic background. Individual 

differences in EF, however, are primarily attributable to aspects of experience in childhood, 

especially the socioeconomic conditions in which children are reared and differences in parental and 

out-of-home care that children receive, as well as differences in cultural beliefs and practices in 

families and communities. The purpose of this chapter is to review some of the evidence on 

individual differences in EF and consider implications of this research for understanding variation in 

EF, particularly in early childhood as children are approaching school age and getting ready to enter 

kindergarten. 

5.1 Temperament and Executive Function 

EF can be assessed directly in late infancy using what is known as a delayed-response paradigm, such 

as the A-not-B task, in which the infant must keep the location of a hidden object in mind and 

respond flexibly (Marcovitch and Zelazo 2009; see Müller and Kerns 2015, for review). However,

tasks explicitly designed to assess complex EF skills often rely on verbal instructions and cannot be 

administered until age 2 or 3 years (Carlson 2005; see Gandolfi et al. 2014 and Mulder et al. 2014 for

comprehensive discussion of the age at which conventional measures of EF can be administered). 

Research on temperament, however, provides a useful framework for describing the early 

development of individual differences in self-regulation and EF-like behaviors. Research on 

temperament has shown that the construct is best described by three general dimensions of behavior 

(Derryberry and Rothbart 1988; Rothbart and Ahadi 1994): namely, activity level and approach, 

referred to as surgency/extraversion; fear and withdrawal, referred to as negative affectivity; and the

emerging ability to control the focus of attention and inhibit automatic responses to stimulation, 

referred to as effortful control (EC). In research on temperament, EC is often treated as analogous to 
53

EF. As noted earlier, however, EC is differentiated from EF in that it refers to a relatively stable 

temperamental predisposition to use EF to control attention, emotion, and approach/withdrawal 

tendencies. EF, in contrast, specifically refers to a set of neurocognitive information processing skills 

that are active in response to specific situational cues and goals (Liew 2012; Sulik et al. in 2015; 

Zhou et al. 2012). 

Although clearly differentiated, EF and EC are united in their relation to what can be considered a 

central element of both, in particular the ability to control attention and behavior in the face of 

interfering or competing information, referred to as executive attention (Posner and Rothbart 2000). 

Executive attention is distinct from more automatic alerting and orienting responses to stimulation 

(Petersen and Posner 2012). Executive attention is the aspect of attention that is sensitive to 

conflicting information, such as when something unexpected occurs, or when something that is 

expected does not occur. Executive attention is understood to rapidly signal the need for increased 

intentional control of behavior through EF. As such, executive attention can be considered an 

indicator of the tendency to engage EF as well as an indicator of the temperamental predisposition 

to EC. 

5.2 Individual Differences in Executive Function Associated With 

Cognitive Ability, Sex, and Genes 

Individual differences in EF are also associated with aspects of cognition such as theory of mind 

(Carlson, Mandell, and Williams 2004) and language comprehension (Just and Carpenter 1992). In 

research on theory of mind, as well as social development more generally, developmental 

psychologists have examined whether advances in EF precede the ability to take the perspective of 

another person or whether increases in social perspective taking precede and set the stage for 

advances in EF. At least two longitudinal studies (Carlson et al. 2004; Hughes and Ensor 2007) have 

addressed this question with children between the ages of 2 and 4. Both found evidence that EF 

predicts later social perspective taking but not vice versa. Associations between EF, particularly 

working memory, and language comprehension, are well documented, primarily in adults (Daneman 

and Merikle 1996) as well as in early childhood. In two longitudinal studies, oral language 

comprehension was reciprocally related to EF (Fuhs and Day 2011; Fuhs et al. 2014). Associations 

between language development and EF are also seen in research on bilingualism. In several studies, 
54

children growing up in homes in which they are exposed to two languages have been shown to have 

better EF, particularly the inhibitory control dimension (e.g., Bialystok 2010; Bialystok and Martin

2004; Carlson and Meltzoff 2008). 

Research on sex differences in EF indicates few differences in direct assessments, but when 

differences are found, they are small and usually favor females (e.g., Bjorklund and Kipp 1996; 

Carlson and Moses 2001; Isquith, Gioia, and Espy 2004; Kalkut et al. 2009; Kochanska et al. 1996; 

Wiebe, Espy, and Charak 2008; Zelazo et al. 2013). 

Research on individual differences in EF associated with genes has focused most specifically on 

working memory and on genes associated with the activity of dopamine in the brain (Barnes et al. 

2011; Tunbridge, Harrison, and Weinberger 2006). As noted in chapter 1, dopamine is important for 

neural activity throughout the brain, including the prefrontal cortex (PFC), and there are numerous 

genes that influence how sensitive one is to the amount of dopamine in the brain (Frank and 

Fossella 2011). Examinations of genes related to the activity of dopamine and the catecholamine 

norepinephrine have tended to demonstrate that higher levels of these neurotransmitters (through 

less efficient breakdown or reuptake) are associated with increased neural activity in PFC in response 

to EF tasks (Mier, Kirsch, and Meyer-Lindenberg 2010). Genes associated with greater availability of 

these neurotransmitters are also associated with better EF skills—at least up to a point. Given that 

neural activity in PFC is sensitive to levels of dopamine and norepinephrine in an inverted U-shaped 

curve (described in detail in section 1.5), one should take into account context when examining 

genes affecting sensitivity to dopamine and norepinephrine (Cools and D’Esposito 2011; Dickinson 

and Elvevag 2009). Specifically, studies demonstrate that versions of genes associated with increased 

sensitivity to catecholamines are beneficial for EF when resting levels are low (indicating that the 

individual is in a relaxed, calm state) but detrimental to EF when resting levels are high (Mattay et al. 

2003; Qin et al. 2012). Furthermore, most of the research on genes and EF has been conducted with 

adults. Findings can likely be generalized to children, but this remains to be clearly demonstrated. 

The above example highlights a central point in the literature on genes and EF, i.e., the need to take 

into account context, particularly educational context, when examining relations of genes to EF. The 

theories of differential susceptibility (Belsky and Pluess 2009), biological embedding of experience 

(Boyce and Ellis 2005), and experiential canalization (Blair and Raver 2012) all clearly specify that 
55

the relations of specific genes to many aspects of behavior, including EF, are likely to vary by 

context (Blair 2010). Implications for education from this research are that children who are the 

most deleteriously affected by low quality classroom and educational experiences may be the ones 

who show the most benefit from and responsiveness to high-quality classroom environments and 

instruction. The phenomenon of differential susceptibility is well established (Belsky and Pluess 

2009). There have, however, been relatively few applications to educational topics. Those that are 

currently available relate to aspects of early care experience, namely parenting quality and childcare 

quality, to later educational outcomes. In one analysis (Pluess and Belsky 2010), negative 

emotionality as reported by the mother at child age 6 months—an indicator for genes associated 

with increased sensitivity to stress, among other things—interacted with the quality of parenting that 

the child received from birth through school entry (assessed at 6, 15, 24, 36, and 54 months). For 

children with high levels of negative emotionality and low-quality early parental care, math, reading, 

and vocabulary skills were much lower than they were in children with low negative emotionality but 

also receiving low-quality care. Among children receiving high-quality parental care, however, 

children with high negative emotionality were no different from children with low negative 

emotionality. And in fact, children with high negative emotionality in infancy receiving high-quality 

care had the lowest levels of behavior problems and conflict with teachers. 

Clear expectations for the relation of specific genes to EF are particularly important given that 

behavior genetic analyses using samples of monozygotic and dizygotic twins, in which genetic 

contributions to behavior are implied rather than measured directly, indicate that EF in school age 

children and young adults is highly heritable (Engelhardt et al. 2015; Friedman et al. 2008). 

Heritability is sometimes misinterpreted as meaning that a given characteristic or behavior is fixed 

and not influenced by the environment. However, a characteristic can be highly heritable and still be 

strongly influenced by experience (Tucker-Drob et al. 2011). 

5.3 Individual Differences in Executive Function Associated With 

Experience 

As with many aspects of children’s development, individual differences in EF are also associated 

with the quality of early caregiving (Bernier et al. 2012; Geoffroy et al. 2010). Early care and the 

initial relationship with the primary caregiver are essential for the development of basic skills to 
56

regulate attention and emotion that form the foundation for learning and school readiness. 

Combined, these skills provide the foundation for the development of more mature and more 

intentional forms of self-regulation such as EF that children require when starting school (Kopp 

1989). 

Parenting is a complex construct composed of distinct aspects, including support for autonomy, 

positive regard, animation, detachment, and intrusiveness, among other characteristics. These 

characteristics can be grouped into general positive and negative factors, and these general positive 

and negative aspects of parenting are related to variation in EF in early childhood (Blair et al. 2011) 

and adolescence (Evans et al. 2007). Studies have also examined specific aspects of parenting 

behavior and have shown that support for autonomy is associated with individual differences in EF 

in young children over and above more general dimensions of positivity (Bernier, Carlson, and 

Whipple 2010). 

The neurobiology of EF highlights the importance of early caregiving. Early caregiving has been 

shown in human and animal models to influence the development of the central and autonomic 

nervous systems and the stress response (Hostinar, Sullivan, and Gunnar 2013) in ways that are 

expected to directly influence the development of EF. This influence is understood to predispose 

the developing child either toward a tendency to engage EF or toward more reactive, less intentional 

responses to stimulation, such as a high level of emotional reactivity (Blair and Raver 2012). This 

hypothesized developmental process is highly relevant to the experience of children in poverty and 

may help to explain individual differences in EF and in school readiness and early educational 

outcomes associated with poverty status. The context of poverty is frequently characterized by 

household chaos, stress, and instability in parental care. High levels of psychosocial and physical 

disadvantage in the home are understood to increase stress to what can be considered “toxic” levels, 

meaning that physiological stress response systems adapt by maintaining high resting levels and, 

thereby, altering brain development and behavior in ways that are conducive to functioning in such 

high stress contexts, but at the expense of functioning in lower stress contexts. This process is 

relevant to both children and their caregivers. A growing number of studies with children and 

families in poverty have shown that levels of stress in the home are associated with lower levels of 

parenting quality and increases in resting stress response physiology in children (Evans 2003) and, 

through parenting and stress, with lower levels of EF in childhood (Blair et al. 2011; Evans and 
57

Schamberg 2009). Importantly, this understanding of the relation of stress to the development of EF 

does not indicate that a stressful beginning invariably leads to later problems with EF, nor does it 

indicate that stress is uniformly bad for development (Parker and Maestripieri 2011). 

Finally, given the role of early parenting and care in the development of self-regulation and EF, 

researchers have recently turned their attention to the study of EF in children’s caregivers, including 

teachers as well as parents. Although a relatively recent area of inquiry, this growing literature 

indicates that parental EF is relevant to the type of parenting that adults provide young children 

(Bridgett et al. 2015), which affects the development of EF in young children (e.g., Cuevas et al. 

2014; Deater-Deckard 2014). Further inquiry in this area of research will help to address questions 

about the mechanisms of the intergenerational transmission of EF as well as information on the 

malleability of EF. 

5.4 Summary 

Individual differences in EF are to some extent associated with characteristics of individuals, such as 

temperament, genes, and other cognitive skills. There is some evidence for the presence of EF skills 

in infancy, and the study of temperament in infancy provides some basis for understanding early 

influences on the development of EF. Numerous studies, primarily with adult populations, 

examining the EF of working memory, have found that genes associated with increased availability 

in the brain of the neurotransmitters dopamine and norepinephrine have small but beneficial effects 

on EF. Individual differences in EF, however, are primarily associated with the environment in 

which the child is situated. That is, the contexts in which children are growing and developing are 

larger influences on EF. Stressful early experience and low quality of early parental care, for example, 

can lead to reduced early learning opportunities and increases in stress hormones that interfere with 

EF development. In some instances, environmental influences and individual characteristics such as 

genes interact to influence EF development. One important direction for future research on EF and 

education is to consider the interaction between the individual characteristics of children, including 

genes, and specific aspects of the classroom and learning environment. 
58

6. Malleability of Executive Function 

There is strong interest in the malleability and, hence, trainability of executive function (EF) skills in 

children. Given associations of EF with a wide variety of developmental outcomes, particularly 

educational outcomes, and the protracted development of brain circuitry associated with EF, EF 

training might hold substantial educational benefit. The literature on EF provides a number of 

examples of effective approaches to increasing EF, including specific types of educational practices 

and both direct and indirect approaches to improving EF. Three illustrative examples indicate the 

range of approaches that have been examined. The first study, perhaps the first of its kind with 

young children, demonstrated in 4- and 6-year-olds (N=25 per group) that 5 days of practice on 

game-like tasks designed to exercise attention and EF was associated with improvements on a 

measure of fluid reasoning and with a more mature pattern of electroencephalography (EEG) 

activity in response to an EF task in the experimental relative to the control group (Rueda et al. 

2005). A later study (Mackey et al. 2011) used commercially available computerized and 

noncomputerized games and found that game use was associated with significant increases fluid 

reasoning and speed of processing in 7- and 9-year-old children (N=28) from low socioeconomic 

status (SES) homes. Finally, a third exemplary study utilized an approach involving parent training, 

focused on the development of selective attention (^Neville et al. 2013)10 in preschoolers from lowincome homes (N=141) and found that the program was effective in improving children’s neural 

response to a selective auditory attention task and also with increases in fluid reasoning relative to an 

active control condition. 

6.1 Educational Practices Designed to Promote Executive Function 

As a Means to Promote Academic Abilities 

Research on early educational practices designed to enhance child learning and development through 

a focus on EF has grown substantially over the past two decades. The efficacy of several programs–

primarily in pre-kindergarten (pre-K) but also in kindergarten and the elementary grades–has been 

 

10 Citations with an ^ reflect publications reporting findings from experimental studies that were examined by the What 

Works Clearinghouse (http://ies.ed.gov/ncee/wwc/) and that, as of May 2016, met What Works Clearinghouse’s 

evidence standards without reservations. 
59

demonstrated in evaluations using randomized controlled trials (RCTs) (see Diamond and Lee 2011 

for review). Some of these programs were designed as comprehensive curricula, while others were 

designed to supplement existing curricula and approaches. There are also examples of direct, 

targeted approaches to supplement existing practice in which children engage in game-like activities 

designed to exercise EF. 

The current focus on EF in innovative comprehensive early childhood education programs is best 

understood in relation to classic early intervention research such as the Abecedarian Project and 

Perry Preschool Project. Those projects focused on IQ as a primary outcome and mechanism of 

program effects on academic achievement, clearly demonstrating the capacity of early childhood 

education to support the school readiness and academic achievement of children in poverty. With 

the growth of research on EF and self-regulation as an aspect of school readiness and educational 

achievement, the focus in early education programs shifted to address these skills. As noted in 

chapter 3, relations between the development of EF and the development of language and math 

ability are recursive. As such, early education approaches that seek to promote children’s academic 

abilities while simultaneously focusing on EF are highly likely to foster school readiness and 

academic ability in the early elementary grades. 

An example of a comprehensive approach to the education of young children that focuses on early 

academic learning through the mechanism of EF is the Tools of the Mind program (Bodrova and 

Leong 2007). Tools of the Mind blends teacher-led scaffolding of a comprehensive curriculum of 

early literacy, mathematics, and science activities with child-directed activities and structured 

sociodramatic play. The program also includes the use of specific tactics to support EF and learning 

in young children. There have been several evaluations of Tools of the Mind, but the results are 

mixed. An early RCT evaluation with 147 children in subsidized pre-K programs found that Tools 

of the Mind improved EF (#

Diamond et al. 2007), with limited and specific effects on aspects of 

child language development and academic outcomes (*

Barnett et al. 2008).11 A subsequent evaluation 

of the preschool version of the program in a large-scale cluster RCT with a predominantly middleincome sample of 794 preschool children in 60 classrooms found no effects of Tools of the Mind 

 

11 Citations with an * reflect publications reporting findings from experimental studies that were examined by the What 

Works Clearinghouse (http://ies.ed.gov/ncee/wwc/) and that, as of May 2016, met What Works Clearinghouse’s group 

design standards with reservations.
60

on any aspect of children’s development, including EF and language, literacy, and math outcomes 

(Wilson and Farran 2012). A recent large sample cluster RCT evaluation of the kindergarten version 

of Tools of the Mind (729 children in 79 classrooms in 29 schools in 12 school districts) found that 

the program was effective in the sample overall in improving EF (d =.11) and academic outcomes, 

including math (d = .14) and reading (d = .07), as well as improving the ability to control attention in 

the context of emotional stimulation (d = .10) (Blair and Raver 2014). Notably, effects on EF (d = 

.50), attention control, (d = .80), reasoning ability (d = .51), and vocabulary (d = .42) were large in 

high-poverty schools, those in which 75 percent or more of the students are eligible for free- or 

reduced-price lunch. Finally, results from a cluster RCT of pre-K Tools of the Mind with children in 

subsidized care at two sites, most of whom were predominantly Spanish-speaking English language 

learners (ELLs), indicate no effects at one site and substantial effects (d = ~.50) at the other on 

measures of EF, mathematics, and early literacy ability administered in English and in Spanish for 

children who were ELLs (Blair et al. under review).

Additional evidence of the effect of comprehensive early education on children’s language, literacy, 

and mathematics learning as well as EF is also found in a large-scale evaluation (79 schools, 250 

classrooms, 2,108 children) of a comprehensive language and mathematics pre-K program in Boston 

(

#

Weiland and Yoshikawa 2013). Using a regression discontinuity design, the analysis demonstrated 

that the combination of the Opening the World of Learning (Schickedanz and Dickinson 2005) 

language and literacy curriculum and the Building Blocks (^Clements and Sarama 2007) mathematics 

curriculum resulted in substantial gains (ES=.44-.62) on math and literacy outcomes and small to 

moderate gains on EF and emotion regulation measures (ES=.11-.21). Similar to the RCT evaluation 

of the kindergarten version of Tools of the Mind, gains in EF were larger (ES=.33) and in this 

instance specific to children in high-poverty schools, as indicated by percent of students eligible for 

free- or reduced-price lunch. 

Examples of educational approaches that supplement existing practices include the Promoting 

Alternative Thinking Skills (PATHS; Greenberg et al. 1995) program and the Chicago School 

Readiness Project (CSRP; Raver et al. 2009). Both PATHS and CSRP focus specifically on children’s 

social and emotional development and reduction of behavior problems, with the expectation that 

improvements in behavior and emotion regulation will benefit EF. The CSRP program is specific to 

pre-K and includes an adaptation of the Incredible Years program (Webster-Stratton 1998) in
61

combination with providing mental health consultation and regular coaching and support to assist 

teachers in applying behavioral principles to reducing children’s behavior problems. The PATHS 

program can be used from pre-K through the elementary grades and is implemented by the 

classroom teacher to increase children’s knowledge of emotion and to provide children with 

strategies to regulate emotion and to solve problems that arise in social interactions with other 

children. 

Evaluations of PATHS on its own or in combination with an efficacious approach to teaching early 

reading (dialogic reading) in a cluster RCT with children in subsidized pre-K have shown that the 

program’s focus on emotion knowledge, emotion regulation, and social problem solving is effective 

in increasing aspects of children’s social-emotional readiness for school with moderate effect sizes 

(e.g., d =.25) (^

Bierman, Domitrovich et al. 2008). There was also some evidence of small-tomoderate effects of this combined program on the Dimensional Change Card Sort (DCCS) (d = .20) 

and an examiner rating of child attention in which EF is implied (d = .28) (Bierman, Nix, et al. 

2008). No effects were observed on the peg tapping task, backward span task, or walk-a-line task. 

There was also some evidence that measures in which EF is implied, such as the examiner rating of 

attention and the walk-a-line task, moderated program effects on print awareness and teacher and 

observer ratings of social-emotional readiness for school. 

The CSRP evaluation, also in subsidized pre-K, included high numbers of children with challenging 

behaviors at high risk for school difficulty. A cluster RCT with 609 preschool children in 35 Head 

Start classrooms found large effects on reductions in problem behavior (d = .53–.89) (Raver et al. 

2009). Additional analyses indicated that the CSRP program also improved children’s EF and 

academic skills, with moderate to large effects (d = .20–.63). Mediation analysis indicated that the 

program-related increases in EF accounted in part for program-related increases in academic 

outcomes at the end of the Head Start year, including vocabulary, letter knowledge, and mathematics 

(Raver et al. 2011). 

The moderate to large effects in efficacy trials seen for PATHS and the CSRP are in contrast to 

smaller effects seen in a large-scale (N = 2,200 students) cluster RCT evaluations of these programs 

in Head Start preschool programs (d = .1–.2; Morris et al. 2014). This demonstration project, known 

as Head Start CARES, also evaluated the play-based component of the Tools of the Mind preschool 
62

program as a classroom “add-on” and found no effects of this component on any aspects of 

children’s school readiness, including EF. These findings indicate that attempts to take successful 

programs to scale can produce some positive benefits for children but that these benefits may not be 

as large as those seen in smaller scale efficacy trials.

Two studies have evaluated the efficacy of EF training through specific sets of adult-led, game-like 

classroom activities with preschoolers. These activities include typical children’s games involving 

inhibitory control, cognitive flexibility, and working memory, similar to Simon Says and Red Light, 

Green Light. An initial evaluation of six activities with 65 preschool children delivered over 16 brief 

(20-30 minutes) playgroup sessions by a trained adult indicated no main effects of the activities on 

child EF (Tominey and McClelland 2011). A second evaluation of the activities with 276 children in 

14 subsidized pre-K classrooms (Schmitt et al. 2015) found that the activities were associated with 

small to moderate gains on measures of EF, the DCCS (d = .16) and the Head-Toes-KneesShoulders task (d = .32).. In this study, there were strong effects of the intervention on gains in math 

(d = .44) for the subset of children (N = 88) who were identified as ELL. 

6.2 Direct Approaches to the Improvement of Executive Function 

Without a Focus on Academic Abilities: Working Memory 

Training 

The most extensively researched direct training for enhancing EF focuses specifically on working 

memory. Much of the research on working memory training has been conducted with young adults 

but research with young children is steadily increasing. Generally speaking, working memory training 

involves repeated practice on increasingly challenging versions of a specific type of working memory 

task with the expectation that performance will improve on that task and similar tasks and that it will 

also transfer to other types of working memory tasks and to the real world behaviors with which 

performance on the tasks is associated, such as academic abilities. The research base is largest for the 

training of visual-spatial working memory, a type of working memory focused on the retention of 

information about the location of an increasingly large number of sequentially presented stimuli (see 

Klingberg 2010). Another commonly researched task, the n-back task, requires the individual to 

retain sequentially presented information and continually update that information (Jaeggi et al. 2008). 
63

6.2.1 Near Transfer of Working Memory Training 

Research on visual-spatial working memory training and n-back training indicates that repeated 

practice on these types of tasks (approximately 20 sessions of 30+ minutes) in an adaptive 

computerized videogame-type format (meaning that the difficulty level increases or decreases so that 

the task is always appropriately challenging to the participant) leads to immediate improvement on 

the task and to improvements on similar types of working memory tasks. This is “near transfer.” 

The conclusion of several meta-analyses of randomized controlled evaluations of various training 

programs is that training has moderate to large effects (d = > .6) on trained tasks and on near 

transfer tasks in children and adults (Melby-Lervag and Hulme 2013; Schwaighofer, Fischer, and 

Bühner 2015; Weicker, Villringer, and Thöne-Otto 2016). 

Working memory training with young adults is associated with changes in the brain. The specific 

nature of changes, however, follows no clear pattern. Some studies demonstrate an increase in 

neural activity in relevant brain areas following the training and others demonstrate a decrease 

(Buschkuehl, Jaeggi, and Jonides 2012). Other studies have also demonstrated functional and 

structural changes in the brain associated with working memory training. 

6.2.2 Far Transfer of Working Memory Training 

Although studies demonstrating near transfer indicate the efficacy of training, “far transfer,” in 

which the effects of working memory training can be seen in other contexts and in “real world” 

behaviors with which EF is associated is essential for any potential application of the science of 

direct training of EF to education. To date, however, findings of far transfer are much less definitive 

than for near transfer. Several studies have demonstrated that working memory training generalizes 

to measures of fluid intelligence, with an overall effect size of .25 (see Au et al. 2014, for a metaanalysis). At least three studies, however, have found no effect of working memory training on 

measures of fluid intelligence or on measures of working memory processes through which fluid 

intelligence would be enhanced (Chooi and Thompson 2012, Redick et al. 2012; Thompson et al.

2013). It is well established that working memory is important for performance on measures of fluid 

intelligence (Kyllonen and Christal 1990). It is also well established that fluid intelligence is an
64

excellent predictor of general intelligence, and that general intelligence is an excellent predictor of 

academic achievement (McGrew and Hessler 1995). 

Furthermore, only a limited but growing number of studies have examined transfer of working 

memory training to academic abilities in children. Perhaps the most definitive study to date included 

a population-based sample of 452 children with low working memory in first grade in Melbourne, 

Australia. Children were screened with working memory measures and those scoring in the bottom 

percentiles were randomly assigned to receive computerized visual-spatial working memory training 

delivered in 20-25 sessions over 5-7 weeks or to a control condition. Effects of the training on one 

measure of visual-spatial working memory were observed at 6 and 12 months following the training 

but not at 24 months. No effects were observed on any other measures of working memory or on 

academic assessments of math, reading, and spelling (Roberts et al. 2016). These results stand 

somewhat in contrast to those obtained in a number of similar studies with smaller samples of 

children (~N = 20 to 50 per group). Evaluations of visual-spatial working memory training, for 

example, have demonstrated far transfer to academic outcomes, some of which have been sustained 

over time (Holmes and Gathercole 2014; Kuhn and Holling 2014; Söderqvist and Nutley 2015; see 

Titz and Karbach 2014 for review). These relations suggest that working memory training holds 

potential promise, however, much remains to be learned about conditions under which such training 

is or is not effective. 

6.2.3 Working Memory Training With Children With Attention Deficit 

Hyperactivity Disorder 

A number of working memory training studies have been completed with children with 

developmental disorders, such as attention deficit hyperactivity disorder (ADHD). As with the 

working memory training literature generally, however, conclusions are mixed. One meta-analysis of 

12 published studies of a specific training program focused on visual-spatial working memory found 

that the program was associated with a moderate decrease in inattention difficulties as rated by 

parents and/or teachers (d = .47) which was reduced somewhat at an approximate 2-4 month 

follow-up (d = .33) (Spencer-Smith and Klingberg 2015). 
65

The findings of the Spencer-Smith and Klingberg meta-analysis, however, are called into question 

somewhat by two meta-analyses that have controlled for whether reporters on child behavior are 

“blinded,” meaning that they are unaware of the child’s participation in the treatment or control 

condition of the study (Cortese et al. 2015; Rapport et al. 2013). One of these (Cortese et al. 2015), 

analyzed 15 published reports on approximately six working memory and attention training 

programs with children with ADHD and found an effect on inattention symptoms identical to the 

analysis of Spencer-Smith and Klingberg (2015). (Notably, several of the published reports included 

in this analysis overlapped with those included in Spencer-Smith and Klingberg.) This effect was 

reduced, however, when reporters on inattention were considered as “probably blinded” (d = .33). 

This analysis also reported substantial effects of cognitive training on visual and verbal working 

memory of approximately (d = .52). There were no effects of cognitive training on hyperactivity 

symptoms and no effects on academic outcomes. The second of the two meta-analyses (Rapport et 

al. 2013), analyzed the findings of 25 published reports of approximately 12 cognitive training 

programs (again, several of which overlapped with those included in Spencer-Smith and Klingberg.) 

As with Cortese et al. (2015), this analysis found an effect of training on observer-rated inattention 

essentially identical to that of Spencer-Smith and Klingberg (2015). Here, however, restriction of the 

analysis to studies with blinded raters reduced the effect substantially, d = .12, ns. This analysis also 

reported substantial effects of working memory training specifically on objective measures of 

working memory, d = .63, but no effects of cognitive training on academic outcomes. 

Overall, the foregoing review highlights the need to address several methodological shortcomings of 

EF training studies—for children and adults—to strengthen inference about this potentially 

efficacious approach to improving EF and academic outcomes. These include problems with the 

limited range of working memory skills measured, the extent to which the types of working memory

training have focused on simple as opposed to complex tasks, and the need for various types of 

control groups—particularly active control conditions that address relevant alternative variables, 

such as social interaction and task engagement, which could reasonably be expected to account for 

any observed effects of training (Shipstead et al. 2014). In addition, few studies have been designed 

specifically to promote far transfer, for example by training EF skills in a wide range of contexts. 

Attention to a broader range of skills that would be improved by increases in EF could help to 

determine the extent to which direct training can be a productive approach to fostering educational 

outcomes in children. 
66

6.2.4 Training of Inhibitory Control and Cognitive Flexibility 

Although working memory training has dominated the scientific research on the malleability of EF, 

at least two studies have examined the direct training of inhibitory control. For example, one study 

with 4- to 6-year-old children (N=48) playing computerized inhibitory control games (e.g., involving 

go/no go types of activities) for 15 minutes per day for 5 weeks found little evidence of 

effectiveness (Thorrell et al. 2009), while a second (Berkman, Kahn, and Merchant 2014), which 

used a young adult sample (N=60), found improvement in an inhibitory control task (the StopSignal task) following 3 weeks of approximately 10 sessions. In addition, this study found that the 

improvement in inhibitory control was associated with increased activity in areas of PFC associated 

with inhibitory control following a cue signaling the need for control. No effects of the training on 

near or far transfer were examined. The one study to examine training of cognitive flexibility 

included children, young adults, and older adults (N=56 per group) who completed training using a 

task-switching procedure. Results confirmed effects of training on switching in all groups and also 

transfer to working memory and fluid intelligence (Karbach and Kray 2009). A similar study with an 

adolescent sample (N=80) found effects of training on task switching on indicators of cognitive 

flexibility with very limited evidence of transfer to other EF tasks. Additional studies examining 

training of inhibitory control and cognitive flexibility are clearly needed (Karbach and Unger 2014). 

6.2.5 Reflection Training 

One line of research on training EF has focused not only on providing practice with specific EF 

skills (such as cognitive flexibility) but also helping children learn to pause momentarily and reflect 

before responding. For example, in a randomized study by Espinet, Anderson, and Zelazo (2013), 

children were given the DCCS task, in which they are required to sort pictures, such as a red rabbit 

first with a blue rabbit (by shape) and then with a red car (by color). Children who failed the DCCS 

were given a new DCCS (with different shapes and colors) and taught during a brief, 15-minute 

session to pause before responding, reflect on the hierarchical nature of the task, and formulate 

higher order rules for responding flexibly. For example, children would receive rules such as, “In the 

color game, if it’s a red rabbit, then it goes here; but in the shape game, that same red rabbit goes 

there.” Children received either reflection training or one of two active control conditions: DCCS 
67

practice with no feedback at all, or DCCS practice with only minimal yes/no corrective feedback 

(i.e., without practice in reflection). Of these three groups, only children who received the reflection 

training showed significant improvements in performance on a subsequent administration of the 

DCCS. Reflection-training improvements on the DCCS were substantial, and greater than 

improvements seen for corrective feedback (d = 1.04) and mere practice (d = 1.18). Improvements 

were also evident on other tasks, including a measure of flexible perspective taking, showing 

transfer. These behavioral changes were accompanied by predictable changes in children’s brain 

activity that suggest more efficient detection of the need for reflection and EF. This research 

suggests that even very brief (15 minute) interventions targeting high-level skills like reflection and 

cognitive flexibility are effective, and the corresponding neural changes may reflect use-dependent 

myelination, dendritic thickening, and synaptic pruning (reduction of connections among neurons 

that are not used). The trained networks may become more efficient, so reflection and EF occur 

more automatically and more quickly, providing more time for thoughtful reflection prior to overt 

action or decision making. 

Moriguchi and colleagues (2015) also provided 3- to 5-year-old children with practice on the DCCS, 

but then had children teach the rules to a puppet. It has long been suggested that best way to really 

learn something is to teach it (e.g., Gartner et al. 1971), and teaching is arguably a form of reflection 

training insofar as it demands consideration and reconsideration of what is being taught. Compared 

to controls, trained children showed considerable improvement in performance on the DCCS along 

with increased brain activity (oxygenated hemoglobin) in the left lateral parts of PFC.

6.3 Indirect Approaches to the Improvement of Executive Function 

Without a Focus on Academic Abilities 

Several lines of research examine a variety of activities that are designed to improve EF indirectly, 

including aerobic exercise, contemplative practices, and videogame playing. Although less extensive 

in some instances than research on direct training, the research base on these approaches suggests 

that indirect approaches may be highly effective. 
68

6.3.1 Aerobic Exercise as a Means to Improve Executive Function 

A growing research literature on physical activity indicates a robust relation between aerobic exercise 

and EF in children as well as adults. Correlational and within subject designs indicate relations of 

measures of physical activity and physical fitness in children with behavioral and neurophysiological 

indicators of EF as well as with academic outcomes (Castelli et al. 2007; Hillman, Castelli, and Buck 

2005; Hillman et al. 2009). Controlled studies demonstrate effects of both chronic (over several 

weeks) and acute (15-20 mins) aerobic activity on multiple aspects of EF in children in the 

elementary grades (e.g., ^Hillman et al. 2014). There is also some evidence to suggest that aerobic 

exercise involving some form of cognitive engagement, as in videogames involving exercise (referred 

to as exergames), have a greater effect on EF than do less cognitively engaging forms of exercise 

(reviewed in Best 2010). Meta-analysis of 19 studies on the effects of acute exercise on EF indicates 

that the effect size is moderate (d = .52) and does not vary by age, with similar effects in children, 

adolescents, and young adults (Verburgh et al. 2013). Analysis of five studies examining chronic 

exercise on EF failed to find a significant overall effect (d = .14), a result that the authors attribute to 

the small number of studies available for the analysis. No studies, however, have examined the 

duration of gains associated with acute activity or the transfer of exercise-related gains in EF to 

academic achievement. 

6.3.2 Contemplative Practice as a Means to Improve Executive Function

Research on the effects of contemplative practices on EF includes a variety of practices that can 

generally be grouped as mindfulness (for reviews see Gallant 2016; Shapiro et al. 2014; Zelazo and 

Lyons 2012). Mindfulness involves sustained, focused attention on moment-to-moment experience. 

A number of studies, most with adults, indicate that repeated engagement in mindfulness practices 

(e.g., paying attention to one’s breathing and gently redirecting attention back to one’s breathing 

when the mind wanders) improves performance on measures of EF and emotion regulation (e.g., 

Baer 2003; Chambers, Yee Lo, and Allan 2007; Grossmann et al. 2004; Heeren, Van Broeck, and 

Philippot 2009; Tang et al. 2007; Zeidan et al. 2010; Zylowska et al. 2008). For example, in a 

randomized design, 7 weeks of mindfulness training lessened the interfering effect of negative 

stimuli on performance of a simple cognitive task. Specifically, adult participants saw emotionally 

charged pictures, such as a photograph of a bloody car accident, and then while the picture remained 
69

visible, heard a tone that was either high-pitched or low-pitched. They were told to ignore the 

pictures and categorize the tones as quickly and accurately as possible. Compared to an active 

control group trained in relaxation meditation, mindfulness participants were better able to 

disengage from the emotional pictures and categorize the tones. Mindfulness training also led to 

corresponding reductions in skin conductance responses to those stimuli (Ortner, Kilner, and 

Zelazo 2007). Similar results have been found after a shorter training period (10-15 minutes per day 

over 5-10 days) in research on integrative body-mind training (IBMT), designed to enhance 

physiological regulation (heart rate variability, skin conductance) through changes in activity in an 

area of the neural network that underlies EF, including the anterior cingulate cortex (ACC) (Tang et 

al. 2009). The ACC is a key structure in the interaction between limbic structures and PFC and is 

central to the coordination of physiological responses to stimulation (see section 1.5). IBMT also 

enhances white matter connectivity in the ACC (Tang et al. 2010). As an instance of the potential 

efficacy of contemplative practice on EF, IBMT is notable in that effects have emerged after very 

brief periods of training (10-15 minutes over 5 days).

Some U.S. and Canadian schools and preschools are now introducing contemplative practices 

adapted for use with children (Wisner, Jones, and Gwin 2010). Children’s lessons in these programs 

typically include activities designed to foster self-reflection (e.g., reflection on one’s breathing, 

sensations, emotions, or thoughts) and observation of one’s surroundings (e.g., awareness of others, 

awareness of stimuli in the environment). A growing body of research suggests that these practices 

are beneficial (e.g., Burke 2010; Biegel et al. 2009) for reducing anxiety in a range of populations, 

including adolescent outpatients with psychiatric disorders (Biegel et al. 2009); children in dense 

urban settings (Semple et al. 2010); and typically developing first-, second-, and third-graders 

(

#

Napoli, Krech, and Holley 2005). Mindfulness practice also appears to reduce depressive 

symptoms in children and adolescents (Biegel et al. 2009; Liehr and Diaz 2010), including 

rumination and intrusive thoughts (Mendelson et al. 2010). Reductions in anxiety and depressive 

symptomatology may be expected to improve EF performance, given the reciprocal interaction 

between bottom-up and top-down influences discussed previously. There is also preliminary direct 

evidence that mindfulness training may enhance EF in children, although we need more research to 

establish the effectiveness of these approaches. No studies to date have examined the duration of 

training-related gains or the transfer of gains to academic achievement. 
70

6.3.3 Action Videogames as a Means to Improve Executive Function 

Researchers have also been examining the effects of action videogame playing on EF. Using 

randomized designs, researchers have found action videogames (e.g., first-person shooter games) to 

affect multiple aspects of cognition, including aspects of attention, such as enhanced visual search 

skill and a faster attentional blink (i.e., the amount of time in milliseconds it takes an individual to 

register that a stimulus has been presented), as well as faster reaction time and improvements to EF 

(Bavelier et al. 2012; see also Granic et al. 2014). No studies appear to have examined the duration 

or transfer of cognitive gains associated with videogame playing. However, one randomized 

controlled experiment with children with dyslexia found that 12 hours of action videogame playing 

improved reading ability (phonological decoding of pseudo-words and word text reading) relative to 

a control group playing non-action games (Franceschini et al. 2013). Meta-analysis of videogame 

playing on multiple aspects of cognition found small to medium effect sizes on auditory and visual 

processing and motor skills but no evidence of effects on EF (Powers et al. 2013). 

6.4 Summary 

A number of programs have shown that high-quality early education can foster EF development. All 

of these programs have targeted children in preschool or kindergarten. Some have focused more 

directly on EF and self-regulation, while others have focused on the improvement of emotion 

regulation and the control of behavior, with evidence of effects on EF. Other, non-academic 

interventions may also help to improve EF. For example, aerobic exercise, contemplative practices 

such as mindfulness meditation, and videogame playing can also improve EF. A large body of 

research examining the direct trainability of EF through repeated practice on EF types of tasks

complements such indirect training of EF. This literature, much of which focuses on the training of 

working memory, shows an increase in working memory on the trained tasks and on tasks similar to 

the trained tasks. Evaluation of the extent to which direct training generalizes to other cognitive 

skills and academic achievement, has produced mixed results. Relatively few studies have examined 

the direct or indirect trainability of other aspects of EF, such as inhibitory control, though studies 

have found that teaching children to pause and reflect prior to responding improves cognitive 

flexibility. Overall, the literature on the malleability of EF suggests that training can lead to 

improvements in performance on the trained tasks, as well as corresponding changes in the brain. 
71

There is a pressing need, however, to definitively address the conditions under which such training 

might lead to the generalization of trained skills, including gains in academic achievement. Given the 

close link between EF and progress in school, the prospect that EF training can increase academic 

achievement is of strong interest. 
72

7. Overall Summary, Directions for Future Research, 

and Implications for Educational Policy and Practice 

As described in this paper, executive function (EF) skills are the set of neurocognitive, attentionregulation skills involved in conscious goal-directed problem solving. These skills include cognitive 

flexibility, working memory, and inhibitory control, and in “hotter” emotional contexts, they also 

include the flexible reappraisal of whether to approach or avoid stimuli. This characterization of EF 

differentiates it clearly from other related constructs, such as stable individual differences in 

personality or behavior (e.g., effortful control; grit) and the broader construct of self-regulation 

(which includes both top-down, EF-influenced conscious control and less deliberate forms of 

regulation). Research on EF and its development during childhood has underscored the importance 

of these skills for flexible adaptation in a wide range of contexts, and added considerably to our 

understanding of the construct, resulting in a sharper, more focused definition.12 

EF skills make it possible to pay attention, keep relevant information in mind, reflect on that 

information and consider it relative to past knowledge and future goals, inhibit old ways of 

responding, and flexibly consider new interpretations. These skills are obviously essential for 

learning and problem solving, and research on EF has established clearly that individual differences 

in EF are related to school readiness and academic achievement, both concurrently and 

prospectively, even after controlling for intelligence and prior knowledge. As reviewed, individual 

differences in EF measured in childhood not only predict academic outcomes, but also predict other 

important outcomes, including long-term physical and mental health. There is also evidence that 

children with better EF skills actually learn more (i.e., retain more information) from a given amount 

of instruction and practice. Together, this research suggests that EF skills, and the reflective 

processes that underlie them, allow children to learn more easily and effectively. 

 

12 This chapter summarizes the main findings of this review, outlines future directions for research, and addresses the 

implications of research on EF for education policy and practice. As such, specific citations upon which findings are 

based are not restated. For information on the specific research included in this review, please refer back to chapters 1 

through 6. 
73

EF skills are also important for managing emotional reactions and social behavior. Children with 

better EF skills are less likely to display disruptive behavior, and difficulties with EF are associated 

with learning difficulties and disabilities, as well as a wide range of other conditions, from attention 

deficit hyperactivity disorder (ADHD) to mathematics anxiety. Whereas difficulty with hot EF is 

more associated with social and emotional behavior problems (e.g., EBDs), difficulty with cool EF is 

more associated with poorer academic achievement.

Clearly, school readiness and learning are a function not only of EF skills, but also of prior 

experience and opportunities for learning as well as many other characteristics (e.g., temperament). 

EF and learning are subject to a wide range of promotive influences (e.g., autonomy supportive 

parenting) and inhibitory influences (e.g., prolonged, uncontrollable stress). Compared to national 

norms, children growing up in poverty often show a lag in the acquisition of EF skills, and it seems 

likely that poverty often leads to lower academic outcomes, at least in part, because of its effects on 

EF. There is also evidence that EF interacts reciprocally, over time, with bottom-up influences, 

such as arousal and stress. These bottom-up influences vary over the course of the day, and can 

result in both positive and negative cascades. For example, the strong association between childhood 

stress and lower levels of EF skill is likely reciprocal, in a downward spiral: higher levels of stress in 

childhood impair EF and EF development, and impaired EF in turn leads to more failures and more 

stress. There is also evidence, however, that good EF skills can protect against the risk of academic 

failure associated with poverty and adversity. Efforts targeting the improvement of EF skills in 

children from disadvantaged environments may lead to beneficial changes in many domains of 

activity, including social relationships and these may lead, in turn, to further improvement in EF 

skill.

Recent advances in our characterization of EF have co-occurred with major improvements in the 

tools available for assessing EF skills reliably and validly. There are now direct behavioral measures 

of EF with strong psychometric properties, allowing for the repeated assessment of EF across a 

wide range of ages. These national norm-referenced measures assess EF in ways that map clearly 

onto relevant neural systems and allow for objective progress monitoring of EF comparable to the 

progress monitoring of literacy and numeracy. 
74

Research using psychometrically sound, direct behavioral measures of EF has yielded clear evidence 

that EF skills improve markedly during the preschool period but continues to develop into early 

adulthood. These age-related improvements correspond to increases in the efficiency and 

effectiveness of neural circuits involving the prefrontal cortex (PFC) and related regions of the brain. 

Recent advances in understanding EF and influences on it have been greatly enriched by major 

advances in neuroscience. The neural correlates of the cognitive skills that characterize EF, 

particularly working memory, have been extensively studied. This research has yielded information 

about the neuroanatomy and brain chemistry that underlie EF and confirm the role of EF in 

coordinating activity throughout the brain. This research has also confirmed the relevance of 

emotional and motivational influences on EF. In addition, research in neuroscience has provided 

valuable insight into the extended time course of EF development and the malleability of these 

aspects of cognition across the lifespan. Periods of particularly rapid improvement in EF skill—the 

preschool years and the transition to adolescence—co-occur with rapid changes in neural structure 

and function, and suggest the possibility of sensitive periods during which EF skills may be 

especially malleable. Research shows that EF skills can be trained across the lifespan, but it is 

possible that earlier investments in EF skills (at younger ages) may yield larger EF gains. 

7.1 EF and Learning 

EF provides a valuable lens for looking at the challenges that learners may face in mastering specific 

academic content, such as those aspects of mathematics and reading that make substantial demands 

on EF. The distinction between EF and knowledge-based aspects of ability and learning is an 

important one for education. Both are vitally important, but effective approaches to fostering EF 

skills are particularly important in the early stages of development because these skills allow children 

to acquire content knowledge more easily. There are currently some good examples of programs and 

approaches that are effective in promoting EF skills, reviewed in chapter 6. A growing body of 

intervention studies has established that the acquisition of EF skills can be enhanced through 

repeated practice in the process of reflecting upon and using specific EF skills. This research 

suggests that it is important to keep children motivated to practice EF skills and to challenge those 

skills continually using a graduated series of exercises that vary in difficulty. Much more work is 

needed, however, to develop and refine promising approaches and to extend these programs to the 

later elementary and secondary grades. Although the acquisition of EF skills may be especially 
75

important in early childhood, these skills continue to be necessary for learning and adaptation across 

the lifespan. To date, programs focusing on EF in education have been developed primarily for 

early childhood. However, the principles on which these programs are based also apply to learning 

and achievement throughout the elementary and secondary grades. An important direction for 

future research is to develop programs to foster EF that are applicable throughout the school years. 

A related point is the need for continued research on the measurement of EF skills and on the 

typical course of EF development in childhood. As reviewed in chapter 5, although there are 

increasingly well-established measures of EF appropriate for longitudinal use, ongoing psychometric 

and longitudinal developmental research is needed. A central goal of this research will be to develop 

measures that can be used in formative assessments of school-based programs designed to foster 

self-regulation. This is highly relevant to the development and refinement of programs for children 

with or at risk for specific learning disabilities and other developmental disorders that interfere with 

learning, including ADHD and EBDs. Assessment of EF prior to kindergarten and during the 

school years would provide for more rapid detection of potential EF difficulties, potentially leading 

to earlier and more effective intervention and remediation. In addition, however, information about 

developmental and individual differences in EF may support the tailoring of individual and 

classroom-based instruction, as when teachers scaffold children’s EF skills when introducing new 

concepts. 

7.2 Topics for Future Research 

Despite the enormous advances in our understanding of EF and its development, many important 

questions remain unanswered. In this section, we highlight just a few of the topics that require 

additional research. We conclude with a number of implications for educational policy and 

instructional practice.

 

7.2.1. Transfer 

Although there is considerable evidence that EF skills can be trained, and that the benefits of this 

training extend to tasks that are similar to the ones used during training (near transfer), there is less 

evidence that trained skills transfer to behavior on less similar non-trained tasks (far transfer). Few 
76

intervention studies have been designed specifically to promote far transfer, including far transfer of 

trained EF skills to academic achievement. Future research may explore the benefits of training EF 

skills in a wide range of contexts, including in the context of acquiring academic skills, and 

promoting transfer to specific contexts via the use of techniques such as fading of visual cues (e.g., 

Salomon and Perkins 1989). For example, future research may investigate how the likelihood of 

transfer varies as a function of the frequency and duration of training, and whether transfer to 

academic achievement is promoted by “embedding” EF training into curricular lessons on math, 

reading, and other academic areas.

7.2.2 EF and Individualized Approaches to Education 

Based on what is known about sensitive periods for other aspects of neural function, it seems likely 

that there are sensitive periods for acquiring EF skills, but this remains difficult to determine. Future 

research might usefully examine whether the potency of particular interventions varies as a function 

of age, considering, for example, whether particular EF interventions are especially effective during 

the preschool years and around the transition to adolescence, compared to the years in between. 

This research might also consider the development and deployment of interventions in light of 

developmental changes in the efficiency of EF skills and changes in strategy use. During periods of 

improving efficiency, interventions that focus on repeated practice may be optimal. During periods 

of improving strategy, interventions that focus on new approaches (e.g., reflection training) may be 

optimal. Finally, interventions might be strategically timed to provide a boost in EF skills prior to 

key transitions (e.g., pre-K intervention to improve transition to kindergarten; elementary 

intervention to improve transition to middle; high school intervention to improve transition to 

workforce/college).

A related set of questions concerns how best to facilitate EF development at different ages and for 

different children. Different interventions may be more or less effective for individuals with 

different levels of EF skill, where effectiveness is measured not only in terms of improvements in 

EF skills but also in terms of transfer to learning and classroom behavior. 
77

7.2.3 Developing the Research Base on the Reciprocal Relation between EF 

and Learning 

Although there is a strong association between EF and learning, much more information is needed. 

Much of the research base is correlational, and research using designs that allow stronger causal 

inferences are needed. As well, much of the research base on the relation between EF and learning 

in school has focused on school readiness and on the early elementary grades. Research examining 

the relation of EF to learning at all educational levels is needed. Much more information is needed 

on exactly how EF interacts with learning at different periods in development and in different 

contexts. 

Further information is also needed on the role that EF plays in supporting the transfer of learning—

in the generalization of what is learned to new contexts. A better understanding of how EF 

contributes to learning and generalization, and how difficulties with EF interfere with learning, may 

lead to refinements in curricula and instructional style. This is true at the level of the classroom, but 

may also support individualized education programs (IEPs) for students with disabilities and a more 

universal personalization of learning to meet individual children’s needs. Individual and 

developmental differences in EF may help inform selection of optimal instructional styles (e.g., more 

concrete, practice-based approaches vs. more abstract, reasoning-based approaches that place heavy 

demands on EF skills). 

As noted, EF training can also be embedded into traditional content-based instruction, and it will be 

important to determine to what extent specific curricular approaches and/or instructional styles may 

affect EF development itself, with potential synergistic effects over time (e.g., see Grammer, 

Coffman, and Ornstein 2013, for work on teaching memory-relevant language during science 

instruction). 

7.2.4 The Role of Stress in EF and Learning 

One of the key insights that research on EF has provided is that EF interacts with more bottom-up 

processes such as arousal, motivation, and stress. One way in which EF appears to be related to 

learning is through the management of stress, including stress that results from the complexity and 
78

uncertainty inherent in learning new material. Much more research is needed to address the specific 

ways in which stress is related to EF and to learning in the school context. Extant research suggests 

that a moderate amount of stress, psychologically and physiologically, facilitates EF and its 

application to novel information and contexts. This basic aspect of research on EF requires 

translation into educational applications. For example, can educational approaches be tailored in 

ways that optimize EF and learning for individual students? And can physiological indicators of a 

moderate response to stress be incorporated in the evaluation of novel, individualized approaches to 

teaching? There is strong interest in the potential applications of neuroscience to education. 

Research on stress, EF, and learning provides one important avenue for this application. 

7.2.5 Resolving Discrepancies Between Questionnaire and PerformanceBased Assessments 

Over the last decade, a number of questionnaires were introduced to obtain teacher and parent 

ratings of EF, in part to address concerns that performance-based assessments may not adequately 

characterize EF skills in “everyday” contexts. Although questionnaire-based assessments have 

proven to be useful for both intervention planning and evaluation, they measure individual 

differences in behavior that are largely unrelated to performance-based metrics. The poor 

correspondence between questionnaire and performance-based assessments contradicts 

conventional wisdom in psychological measurement, which has contended that the use of multiple 

methods and informants results in improved measurement of the construct of interest (Campbell 

and Fiske 1959). At a more practical level, educators and researchers, alike, have been provided with 

little guidance about how to resolve discrepancies that may arise between questionnaire and 

performance-based assessments of EF. 

Future research would benefit from a more systematic treatment of discrepancies between 

questionnaire and performance-based assessments of EF. As described in chapters 1 and 2, EF can 

be studied at multiple levels of analysis. The poor correspondence between questionnaire and 

performance-based assessments can be construed as evidence of the loose association between 

behavioral and cognitive levels of analysis, and/or as evidence of the subjectivity and bias inherent 

in parent- and teacher ratings. Future research should identify for whom or under what conditions 

questionnaire and performance-based assessments are in agreement. Future research might also 
79

consider a range of statistical approaches that make joint use of questionnaire and performancebased measures but that do not assume that they are interchangeable indicators of the same 

underlying construct (e.g., statistical interactions; person-based profiling methods). Finally, future 

research would benefit from using neuroimaging and neurophysiological measurement to help 

“ground” our understanding of what performance-based and questionnaire-based assessments of 

EF are actually measuring. That is, it will be important to determine the extent to which 

performance-based and questionnaire-based assessments of EF reflect variation not only in similar 

vs. different types of behavior but also in similar vs. different neural circuitry. There are a multitude 

of factors that potentially account for variance in children’s performance on EF assessments and

parent or teacher ratings of children's EF behaviors that are distinct from variance associated with 

the underlying neural processes that support EF. The failure of most assessments to explicitly 

address this “conceptual slippage” undermines the quality of current EF research and impedes 

progress in the application of this research for clinical and educational use. 

7.3 Implications for Policy and Practice 

Although there are numerous implications of research on EF for educational policy and practice we 

could consider, here we present three broad policy and practice implications. 

1. Much of the focus of current research on EF and education, and consequently of this 

review, is on the way in which EF contributes to academic learning. EF, and self-regulation 

more broadly, however, are meaningful educational outcomes in their own right. EF and selfregulation generally are important aspects of individual development. EF skills themselves can be a 

target of practice-based instruction in early childhood. This has the potential to help provide 

children with the foundational skills that they need to learn and adapt to school. It will be 

increasingly important to consider not only the ways in which improvements in EF may lead to 

improvements in academic ability, but also the extent to which improvements in EF can contribute 

to the growth of personal responsibility and social-emotional competence that are also highly 

relevant to the mission of elementary and secondary education. 

2. A continued research and policy focus is needed on the measurement of EF and on 

trajectories of EF development from early childhood through young adulthood. It is 
80

important to assess EF skill development so that an understanding of children’s EF skills can inform 

individual and classroom-based instruction. Much of the focus of current research on EF in the 

context of education has been on the early elementary grades. A concomitant focus on the middle 

and secondary grades, as well as post-secondary and adult education, is also needed. Continued work 

on the longitudinal measurement of EF is needed so that benchmarks will be available against which 

to gauge the relative effect of novel educational approaches. Also, given increasing interest in the 

measurement of EF as an educational outcome of interest on its own, there is a need for 

psychometrically strong longitudinal measures that can be readily administered in school settings. 

3. Knowledge of EF can be used to develop psychological and physiological measures of 

engagement in learning activities and hence provide information about effective teaching 

practices and approaches. Measures of EF and of electrophysiological activity in the brain can 

provide information to educators about levels of attentiveness and engagement in learning activities. 

Further, given relations between EF and motivation, measures of EF and brain electrophysiology 

could be used to evaluate approaches to support EF and to enhance motivation. In addition, EF 

varies as a function of physiological arousal and time of day, and this EF-arousal interaction itself 

varies as a function of development. School schedules could be arranged to enhance both EF and 

learning. For example, around the transition to adolescence, when most children undergo a phase 

shift in their circadian rhythms, it has been shown that children get more sleep and do better 

academically when school starts later. Topics such as math, which depend heavily on EF skills, 

might usefully be scheduled for later in the day, not first thing in the morning. Other considerations 

are the timing of lunch (post-prandial dip in arousal and EF) and the benefits of cardiovascular 

exercise for EF. Scheduling multiple daily bouts of moderate to vigorous physical activity might be 

an effective means of “priming” kids for learning (e.g., recess and PE could strategically occur prior 

to core classes). 

Overall, research suggests that EF provides a foundation for learning and adaptation in a wide range 

of circumstances, including school. This research indicates that the EF skills needed to pay attention, 

keep information in mind, think flexibly, and inhibit impulsive responding can be acquired in school 

settings, leading not only to improved EF but also to improved academic achievement. Regular, 

longitudinal assessment of children’s EF skill can inform teaching practices and approaches, 

including differentiated instruction. The extent to which EF difficulties contribute to learning 
81

difficulties, behavior problems, suspension, expulsion, or being held back, is currently unknown, but 

assessment of individual students’ EF skills as they develop may help educators understand obstacles 

to students’ learning that can be addressed more directly and effectively.

 
82

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Glossary 

A-Not-B Task: This task is a variant of the delayed-response paradigm in which the location of the 

object is not random, but rather follows a prescribed sequence. The object is hidden first at 

one location (Location A) for a number of trials and then at the other location (Location B). 

Retrieving the object when it is hidden at Location B requires the infant to keep the location 

in mind, respond flexibly, and inhibit any tendency to search at the previously correct 

Location A. 

Amygdala: An almond-shaped mass of gray matter in the front part of the temporal lobe of the 

cerebrum that is part of the limbic system and is involved in the processing and expression 

of emotions, especially anger and fear.

Anterior Cingulate Cortex: The frontal part of the cingulate cortex that resembles a "collar" 

surrounding the frontal part of the corpus callosum. It appears to play a role in a wide 

variety of autonomic nctions, such as regulating blood pressure and heart rate. It is also 

involved in rational cognitive functions, such as reward anticipation, decision-making, 

empathy, impulse control, and emotion. 

Attention-Regulation Skills: The ability to process stimuli, focus and sustain attention, and maintain 

engagement in accordance with social and cultural contexts.

Bottom-Up Processing: Processing sensory information as it is coming in. 

Child Ability: From the perspective of modern test theory, all children have an underlying latent 

ability that informs their performance on EF tasks, as well as their likelihood of exhibiting 

behaviors that are presumed to be indicative of EF. This ability level is latent because it is 

not directly measurement (only inferred based on children’s performance or rated 

behaviors). In the case of EF, the underlying latent ability that corresponds to a child’s 

performance on an EF task appears to be largely distinct from their latent ability that 

corresponds to EF-related behaviors.
137

Circadian Rhythms: A 24-hour cycle that tells our bodies when to sleep and regulates many other 

physiological processes. 

Cognitive Flexibility: The mental ability to switch between thinking about two different concepts, 

and to think about multiple concepts simultaneously. 

Cohen d Effect Size: The difference between two means divided by a standard deviation for the 

data. 

Confirmatory Factor Analysis (CFA): A special form of factor analysis, a statistical method used to 

describe variability among observed, correlated variables in terms of a potentially lower 

number of unobserved variables called factors. CFA is used to test whether measures of a 

construct are consistent with a researcher's understanding of the nature of that construct (or 

factor). The objective of confirmatory factor analysis is to test whether the data fit a 

hypothesized measurement model. This hypothesized model is based on theory and/or 

previous analytic research.

Crystallized Intelligence: The ability to use skills, knowledge, and experience. It does not equate to 

memory, but it does rely on accessing information from long-term memory.

Delayed-Response Paradigm: In delayed-response tasks, an attractive object is shown to an infant 

and then very conspicuously hidden at one of two or more locations (e.g., an infant watched 

as a toy is hidden under one of two cloths). A delay is imposed and then the infant is allowed 

to retrieve the object. Across trials, the hiding location of the object is determined randomly. 

Retrieving the object requires the infant to keep the location in mind, and across trials, it also 

requires that infants respond flexibly and inhibit any tendency to search at locations that 

were previously correct.

Dendritic Thickening: Extreme skeletal thickening along axes of colony branches. 

Developmental Cognitive Neuroscience: An interdisciplinary scientific field devoted to 

understanding psychological processes and their neurological bases in the developing 
138

organism. It examines how the mind changes as children grow up, interrelations between 

that and how the brain is changing, and environmental and biological influences on the 

developing mind and brain. 

Dopamine: An organic chemical that plays several important roles in the brain and body. It’s a

neurotransmitter that helps control the brain's reward and pleasure centers. It also helps 

regulate movement and emotional responses, and it enables us not only to see rewards, but 

to take action to move toward them. 

Educational Neuroscience: A scientific field that brings together researchers in cognitive

neuroscience, developmental cognitive neuroscience, educational psychology, educational 

technology, education theory and other related disciplines to explore the interactions 

between biological processes and education. Researchers in this area may link basic findings 

in cognitive neuroscience with educational technology to help in curriculum implementation 

for mathematics education and reading education. 

Executive Attention: The ability to effectively block outside distractions while focusing on a single 

object or task. 

Fluid Intelligence: The capacity to think logically and solve problems in novel situations, 

independent of acquired knowledge. 

fMRI: Functional magnetic resonance imaging or functional MRI (fMRI) is a functional 

neuroimaging procedure using MRI technology that measures brain activity by detecting 

changes associated with blood flow. This technique relies on the fact that cerebral blood 

flow and neuronal activation are coupled. 

Formative Assessments: Monitors student learning to provide ongoing feedback that can be used by 

instructors to improve their teaching and by students to improve their learning. More 

specifically, to help students identify their strengths and weaknesses and target areas that 

need work. 
139

Inhibitory Control: The capacity voluntarily to inhibit or regulate prepotent (i.e., strong or 

automatic) attentional or behavioral responses. 

Longitudinal Measurement Invariance: The individual items that comprise a test score are 

characterized by their difficulty level (e.g., how frequently they were endorsed) and their 

discrimination (the extent to which item responses help distinguish children in terms of their 

true ability). When a given measure is administered at multiple points in time, the properties 

of individual items (i.e., difficulty, discrimination) must be unchanged in order for the 

resulting scores to be interpretable. Longitudinal measurement invariance refers to a general 

analytic approach that is used to formally evaluate the consistency of item functioning across 

time. 

Mean Effect Size: A name given to a family of indices that measure the magnitude of a treatment 

effect. Unlike significance tests, these indices are independent of sample size. ES measures 

are the common currency of meta-analysis studies that summarize the findings from a 

specific area of research. One type of effect size, the standardized mean effect, expresses 

the mean difference between two groups in standard deviation units. 

Mediation Analysis: A hypothesized causal chain in which one variable affects a second variable 

that, in turn, affects a third variable. The intervening variable, M, is the mediator. It 

“mediates” the relationship. 

Minimally Detectable Change (MDC): A statistical estimate of the smallest amount of change that 

can be detected by a measure that corresponds to a noticeable change in ability.

Myelination: A term in anatomy that is defined as the process of forming a fatty white substance 

(myelin sheath) around a nerve to allow nerve impulses to move more quickly. 

Negative Affectivity: A personality variable that involves the experience of negative emotions and 

poor self-concept. 
140

Neurocognitive skills: Skills that involve cognitive functioning and associated structures and 

processes of the central nervous system. 

Neurotransmitter: A chemical that is released from a nerve cell which, by diffusing across the 

synapse or junction, transmits an impulse from a nerve cell to another nerve, muscle, organ, 

or other tissue. A neurotransmitter is a messenger of neurologic information from one cell 

to another. 

Norepinephrine (NE): A stress hormone that affects parts of the brain where attention and 

responding actions are controlled. Its release is lowest during sleep, rises during 

wakefulness, and reaches much higher levels during situations of stress or danger, in the socalled fight-or-flight response. 

Norm-Referenced Scores: All questionnaires and performance based tests have some process 

through which individual items or ratings are converted into summary scores. These raw 

scores can be used to communicate how well a child performed relative to similar children 

who have completed the same task (or who were rated on the same items). Norm-referenced 

scores represent the transformation of raw scores into a metric that facilitates between-child 

inferences (e.g., percentile or T scores). Norm-reference scores communicate information 

about a child’s standing on a given measure relative to some broader population of similar 

children (nationally representative samples are ideal). 

Polynomial Model: A tool for determining which input factors drive responses and in what 

direction. 

Prefontal Cortex - The gray matter of the anterior part of the frontal lobe that is highly developed in 

humans and plays a role in the regulation of complex cognitive, emotional, and behavioral 

functioning. Neuroanatomically, the executive function skills of cognitive flexibility, working 

memory, and inhibitory control are associated with distinct and partially overlapping 

networks involving regions of the prefrontal cortex (PFC).
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Randomized Controlled Trials (RCTs): (A type of scientific (often medical) experiment, where the 

people being studied are randomly allocated one or other of the different treatments under 

study. RCTs are often used to test the efficacy or effectiveness of various types of medical 

intervention and may provide information about adverse effects, such as drug reactions. 

Random assignment of intervention is done after subjects have been assessed for eligibility 

and recruited, but before the intervention to be studied begins. 

Reflection: To pause, consider the options, and put things into context prior to responding. 

Reflective Learning: Active and intentional learning that involves critical reflection upon the learning 

experience and what is learned. 

School Readiness: The skills, knowledge, and attitudes necessary for success in kindergarten.

Self-Regulated Learning Strategies: Strategies that include self-monitoring and goal setting to 

facilitate learning objectives. 

Surgency/Extraversion: Surgency is a trait aspect of emotional reactivity in which a person tends 

towards high levels of positive affect and engagement with their environment. Extraversion 

tends to be manifested in outgoing, talkative, and energetic behavior. 

Synaptic Pruning: The process by which extra neurons and synaptic connections are eliminated in 

order to increase the efficiency of neuronal transmissions. 

Temperament: Enduring biological predispositions to react to things in a particular way; the 

foundations of relatively stable personality traits. 

Test-Retest Reliability: A quantitative index of the stability or repeatability of score across a given 

interval of time. Test-retest reliability is of special concern for performance-based EF tasks 

because they represent an assessment of a child’s ability during a single testing occasion. In 

order for performance-based scores to be useful, it is assumed that they have some enduring 

quality to them (though still malleable). Test-retest reliability is less frequently a source of 
142

concerns for questionnaire measures, as they typically measure a broad set of behaviors over 

a longer period of time (weeks). 

Test Information: The utility of a test score depends, in part, on its associated standard error of 

measurement. Scores with larger standard errors are less precise (due to greater uncertainty 

about the accuracy of the estimated score) than are scores with smaller standard errors of 

measurement. In item response theory, test information curves are used to visually depict the 

relative precision of measurement of a score as a function of the child’s true ability (see 

above for definition). Test information curves are important because they demonstrate how 

the precision of measurement that is associated with an instrument depends on child ability. 

As such, some measures may be more appropriate for distinguishing between children at 

some ability levels (e.g., low) than others (e.g., high). Although test information curves have 

not been provided for most EF measures (questionnaires or performance-based tasks), at a 

minimum, they help to inform educators of the types of questions that they should ask when 

trying to select a measurement tool. 

Theory of Mind: The ability to attribute and differentiate mental states to oneself and others, that is, 

to perceive how others think and feel, and how that relates to oneself. 

Top-Down Processing: Perception that is driven by cognition. Top-down processing involves the 

recognition of patterns through the use of contextual information. 

Two-Factor Models: A form of confirmatory factor analysis that involves two continuous latent 

variables, or factors. 

Working Memory: The system responsible for the transient holding and processing of new and 

already-stored information, and is an important process for reasoning, comprehension, 

learning and memory updating.