Discussion 1Prevalence rates for ADHD in children and adolescents have increased exponentially in the recent two decades. In parallel, prescriptions for stimulant medications have become commonplace.
A Developmental Perspective on Executive Function
John R. Best and Patricia H. Miller
Department of Psychology, University of Georgia.
Abstract This review paper examines theoretical and methodological issues in the construction of a
developmental perspective on executive function (EF) in childhood and adolescence. Unlike most
reviews of EF, which focus on preschoolers, this review focuses on studies that include large age
ranges. It outlines the development of the foundational components of EF—inhibition, working
memory, and shifting. Cognitive and neurophysiological assessments show that although EF
emerges during the first few years of life, it continues to strengthen significantly throughout
childhood and adolescence. The components vary somewhat in their developmental trajectories.
The paper relates the findings to longstanding issues of development (e.g., developmental
sequences, trajectories, and processes) and suggests research needed for constructing a
developmental framework encompassing early childhood through adolescence.
Keywords executive function; development; childhood; adolescence
Broadly defined, executive functions (EF) encompass those cognitive processes that underlie
goal-directed behavior and are orchestrated by activity within the prefrontal cortex (PFC)
(e.g., Shimamura, 2000; Olson & Luciana, 2008). Children’s EF has been of great interest to
developmental psychologists in recent years. However, this research has three limitations
that pose difficulties for constructing a truly developmental account of EF. First, most
research on the development of EF has examined narrow age ranges, for example, ages 2 to
5 (Isquith, Gioia, & Espy, 2004). Second and relatedly, most has focused on preschoolers
(e.g., Carlson, 2005; Garon, Bryson, & Smith, 2008), perhaps because rapid improvements
occur during the preschool and early school years on EF tasks (e.g., Carlson & Moses, 2001;
Zelazo, Müller, Frye, & Marcovitch, 2003). However, performance on other, more complex
tasks does not mature until adolescence or even early adulthood (e.g., Anderson, 2002;
Davidson, Amso, Anderson, & Diamond, 2006; Conklin, Luciana, Hooper, & Yarger, 2007;
Luciana, Conklin, Hooper, & Yarger, 2005; Romine & Reynolds, 2005). Moreover, the
rudiments of EF emerge before early childhood, likely within the first year of life (e.g.,
Diamond, 1990a,b). Third, we have little information about the processes by which children
move from one level to another, especially processes operating after age 5.
Consequently, despite the large literature on EF in children, we have no truly developmental
account of EF across childhood and adolescence. The purpose of this paper is to begin to
construct such an account, which distinguishes it from previously published reviews of EF
(but see Best, Miller, & Jones, 2009). We focus on the few studies that include a large age
range in an attempt to outline the broad developmental trajectories of EF and look at the
development of EF within the framework of developmental theoretical issues.
Correspondence concerning this article should be addressed to Patricia H. Miller, Department of Psychology, University of Georgi\
a,
Athens, GA 30602. [email protected]. NIH Public Access
Author Manuscript
Child Dev . Author manuscript; available in PMC 2011 November 1.
Published in final edited form as:
Child Dev . 2010 ; 81(6): 1641±1660. doi:10.1111/j.1467-8624.2010.01499.x.
NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript After a brief presentation of theoretical and methodological challenges to a developmental
account of EF, the main part of the review examines changes in three components of EF
across multiple ages. Then we address developmental trajectories, sequences of the
components, and mechanisms of development, and suggest future research to examine basic
issues of development.
Theoretical and Methodological Challenges Beyond the limitations of narrow age ranges and few studies examining developmental
sequences and mechanisms, it is very difficult for other reasons to extract a general
trajectory of EF development from the literature. A main challenge is the lack of agreement
concerning whether EF is a unitary construct or a set of independent components (e.g.,
Barkley, Edwards, Laneri, Fletcher, & Metevia, 2001; Brocki & Bohlin, 2004; Isquith et al.,
2004; Miyake et al., 2000). One prominent theoretical framework integrates these opposing
perspectives by suggesting that the EF construct consists of interrelated, but distinct,
components—described as the “unity and diversity of EF” by Miyake et al. (2000). In their
seminal study with young adults, Miyake et al. used confirmatory factor analysis (CFA) to
test this framework. The CFA extracted three correlated latent variables from several
commonly used EF tasks. These latent variables represented three EF components—
inhibition, working memory (WM), and shifting—that contributed differentially to
performance on complex EF tasks (Miyake et al., 2000; though Miyake recently—2009—
has questioned whether inhibition can be considered a distinct component). Thus, although
bound by some common underlying processes, in young adults EFs are distinguishable and
are employed differentially based on the task at hand.
Some research with children has investigated the EF construct and has found at least partial
support for an integrative framework. Hughes (1998) sought to expand a previous finding
that EF consists of dissociable components in older children (Welsh, Pennington, &
Groisser, 1991). She extracted three distinct factors—attentional flexibility, inhibitory-
control, and working memory—from preschoolers’ performance on several EF tasks,
suggesting that EF components are differentiated even at a young age. Both Hughes and
Welsh et al. emphasize the independence of these factors, leaving little discussion of
whether these factors may be interrelated. Senn, Espy, and Kaufmann (2004), also with
preschoolers, used path analysis, which forms each latent variable by drawing on only one
task rather than multiple tasks (which makes it more susceptible to extraneous influences
such as test order and task reliability that may affect relations among the measures).
Although performance on the WM and inhibition tasks was correlated and predicted
complex task performance, shifting performance was unrelated to the other measures. This
provided evidence that the EF components are dissociable in early childhood but also that
those components are interrelated to some degree.
Confirmatory factor analysis with older children seems to provide stronger support for
Miyake’s “unity and diversity view.” First, Lehto et al. (2003) found that Miyake’s three-
factor model provided the best fit of data from children ages 8 to 13. Second, Huizinga,
Dolan, and van der Molen (2006) employed CFA in a more developmental fashion by
comparing the models of 7-, 11-, 15-, and 21-year-olds. They found partial support for the
Miyake model as only the WM and shifting measures loaded onto latent variables, whereas
the inhibition measures did not load onto a common latent variable (see also Miyake, 2009
for similar results with adults). Importantly, this model was consistent across the age groups,
suggesting the stability of the EF construct across middle childhood, adolescence, and early
adulthood. Together, these studies provide considerable evidence that Miyake’s integrative
model of interrelated, yet dissociable, EF components may be a suitable theoretical
framework from which to examine EF development. However, these studies also suggest Best and Miller Page 2
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NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript that the degree of unity and independence of the three components may change
developmentally. This more complete picture of development and more nuanced version of
the Miyake et al. model would be missed by focusing on only adults or a narrow age range
on children, during the preschool years.
When a sample does include older school-age children and adolescents, methodological
challenges can arise. First, to avoid ceiling effects researchers often use complex EF tasks
that likely tap into multiple executive functions, a problem of task impurity (Miyake et al.,
2000). For complex tasks like the Wisconsin Card Sorting Test (WCST), the Tower of
London (TOL), or Tower of Hanoi (TOH), task completion likely requires the coordination
of multiple processes (e.g., Asato, Sweeney, & Luna, 2006; Huizinga et al., 2006; Miyake et
al., 2000). However, for simplicity researchers often classify tasks by a single cognitive
construct. For example, the WCST and its child-appropriate version (DCCS) have been
described as inhibition tasks by some and shifting tasks by others (Garon et al., 2008); the
TOL and TOH have been described as either inhibition, WM, or planning tasks in various
publications (e.g., Berg & Byrd, 2002; Huizinga et al., 2006; Welsh, Satterlee-Cartmell, &
Stine, 1999).
Second, and very much relatedly, the tasks used across an age range often are not uniform.
Tasks too difficult for the younger participants sometimes are only administered to the older
ones, which makes comparisons across age groups difficult (e.g., Klenberg, Korkman, &
Lahti-Nuuttila, 2001). Or very different tasks are used to assess a particular dimension for
preschoolers and older children (e.g., Hughes, 1998; Welsh et al., 1991).
Keeping these issues in mind, we utilize Miyake’s “unity and diversity” theoretical
framework to focus on the “foundational” EFs—inhibition, information updating and
monitoring (WM), and shifting (Hughes, 1998; Huizinga et al., 2006; Lehto et al., 2003;
Miyake et al., 2000) —in part because several frequently used cognitive tasks ostensibly tap
into each dimension (Miyake et al., 2000). In using this framework to address EF
development from early childhood through adolescence, we keep in mind current
developmental theories of EF. In general, most of these theories depict EF development as
involving an increasing ability to resolve conflict. They differ in whether this conflict is
between rules that eventually become hierarchically organized (Zelazo et al., 2003), latent
and active representations (e.g., habits vs. attention/working memory, Munakata, 2001), or
the current representation versus prepotent mental sets or behaviors (Diamond, 2006). Most
also emphasize the role of changes in underlying neural networks. In particular, Posner and
Rothbart (2007; see also Garon et al., 2008) propose that the development of the anterior
attention system plays the major role in the resolution of conflict by regulating other brain
networks. Posited developmental change is both qualitative (e.g., changing from simpler to
more complex rule systems, Zelazo) and quantitative (e.g., strengthening active
representations so that they override latent representations, Munakata).
We focus on those studies that most clearly address developmental issues—those that
examine both preschoolers and school-age children, or school-age children and adolescents,
address the order of acquisition of different aspects of EF, or examine possible
developmental processes. This approach permits us to detect developmental trajectories,
sequences, and processes. Given a recent extensive review (Garon et al., 2008) of the large
literature on the preschool age, only representative studies of this age are included.
Converging evidence and multiple levels of analysis are provided by studies using
neuroscience techniques (e.g., fMRI, ERP) that assess the neural response underlying EF. It
has been known for years that patients with PFC damage can have EF deficits, yet normal
IQ (e.g., Stuss & Benson, 1984). More recent thinking about this is that the PFC coordinates Best and Miller Page 3
Child Dev. Author manuscript; available in PMC 2011 November 1.
NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript posterior cortical and subcortical brain activity via excitatory and inhibitory pathways
(Casey, Amso, & Davidson, 2006; Shimamura, 2000). Moreover, PFC activity holds
relevant information in WM (e.g., “the cracker is hidden under the left cup”) and prevents
distracting information from entering WM (Goldman-Rakic, 1987; Olson & Luciana, 2008;
Shimamura, 2000).
We also know from structural imaging studies (e.g., using MRI) that PFC development, like
brain development more generally, consists of both progressive (e.g., myelination, neuron
proliferation, synaptogenesis,) and regressive changes (e.g., cell death, synaptic pruning)
(Casey et al., 2006; O’Hare & Sowell, 2008). The PFC matures later in adolescence as
evidenced by further loss of gray matter (Gogtay et al., 2004; O’Hare & Sowell, 2008),
unlike many other brain regions that mature earlier (e.g., regions involved in attention,
motor and sensory processing, and speech and language development). During this time,
progressive and regressive changes (largely myelination and synaptic pruning, respectively)
occur concomitantly and are driven in part by the child’s experiences—the result being
“efficient networks of neuronal connections” (O’Hare & Sowell, 2008, p. 24).
Developmental neuroscience studies can enrich our understanding of EF development by
determining how the neural correlates of behavior change over time. Changes in neural
correlates (i.e., the neural response underlying task execution), in turn, can be interpreted in
light of the known structural development of the brain and of the PFC in particular.
Alternatively, changes in brain structure can be correlated with changes in task performance
to determine the relevance of structural changes to EF maturation. In either case, we must
remember that both progressive and regressive structural changes may influence how the
neural response changes over time.
Foundational Executive Functions
Inhibition Inhibition is considered foundational for EF (e.g., Miyake et al, 2000); however, most
inhibition tasks are not pure measures of inhibition (Simpson & Riggs, 2005) nor do they tap
into a single inhibitory process (Nigg, 2000). Garon et al. (2008) distinguished simple from
complex response inhibition tasks based on whether WM also is needed. Simple response
inhibition requires a minimal amount of WM, making it one of the purest forms of inhibition
(Cragg & Nation, 2008). It shows its rudiments during infancy (see Garon et al., 2008), as
when a child can delay eating a treat. Complex response inhibition also requires substantial
WM by requiring that an arbitrary rule be held in mind and/or by requiring that the child
inhibit one response (prepotent or not) and produce an alternative response. The Day/Night
task assesses complex response inhibition by requiring the child to inhibit a prepotent verbal
response (i.e., saying “day” upon viewing a picture of a sun) and activate an alternative
verbal response (i.e., saying “night” upon viewing a sun) (Gerstadt, Hong, & Diamond,
1994). Similarly, Carlson and Moses (2001), using factor analysis, distinguished delay tasks,
which require withholding a propotent response, from conflict tasks, which require the child
to make a response that conflicts with a prepotent response. Thus, the Day/Night task and
Luria’s hand game are considered conflict tasks (as well as complex response inhibition
tasks) because they require the child to respond in a way conflicting with the natural
response (i.e., associating a picture of the sun with night time and making a fist when shown
fingers, respectively). Finally, Nigg (2000) distinguished several forms of inhibition that
cover cognitive, behavioral, and emotional regulation.
Age differences— Garon et al. (2008) described rapid improvements in early childhood
on a variety of complex response inhibition tasks (i.e., conflict tasks), such as the Day/Night
task and Luria’s hand game (see also Carlson & Moses, 2001; Hughes, 1998; Lehto & Best and Miller Page 4
Child Dev. Author manuscript; available in PMC 2011 November 1.
NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript Uusitalo, 2006; Sabbagh, Xu, Carlson, Moses, & Lee, 2006). Despite their apparent
similarities, different conflict tasks show different ages of mastery, perhaps indicating
different cognitive demands. For Luria’s hand game, which requires children to make a fist
when shown a finger and vice versa, the most improvement typically occurs between age 3
and 4 (Hughes, 1998); however, for the Day/Night task, 3- and 4-year-olds find it equally
difficult (Carlson, 2005) and improvements may continue into middle childhood (Gerstadt et
al., 1994). Furthermore, preschool children perform better on Luria’s tapping task than the
Day/Night task (Diamond & Taylor, 1996). Like Luria’s hand game, the tapping task
requires the inhibition and activation of hand motor responses, whereas the Day/Night task
requires the inhibition and activation of verbal responses. In addition to different response
modalities, Diamond and Taylor argue that the two tasks differ in the degree of response
prepotency: There is a stronger tendency to say “Day” when shown a sun than to mimic the
motor movement of another person. However, evidence of a mirror neuron system that
facilitates the imitation of hand gestures (e.g., Iacoboni & Dapretto, 2006) calls into question
whether inhibiting the mimicking of hand movements is necessarily easier. In any case,
preschoolers have some ability, though still immature and sensitive to task demands, to
override a naturally prepotent response in favor of an alternative.
The Dimensional Change Card Sort (DCCS) is another complex response inhibition task
used frequently with preschool children. Rather than requiring the inhibition of a naturally
prepotent response, the DCCS creates a prepotent response during the pre-switch phase that
must later be inhibited. The child is shown a deck of cards that vary on two dimensions—
shape (e.g., rabbit versus battleship) and color (e.g., red versus blue). During the pre-switch
phase, the child must sort the cards according to one dimension (e.g., color; “If it’s red, it
goes here; if it’s blue, it goes here”). In the post-switch phase, the child is asked to sort the
cards by the other dimension (e.g., shape). Similar to other conflict tasks, reductions in
perseveration occur from age 3 through age 4 (Carlson, 2005; Zelazo et al., 2003).
Manipulations of the standard DCCS provide insight into the developmental sequence of
inhibition (e.g., Zelazo, 2006; Zelazo et al., 2003). In a No Conflict DCCS, the inhibitory
demands are minimized but the WM demands are maintained by presenting four non-
overlapping rules by which to sort the cards (e.g., sort by 2 colors at pre-switch and by 2
shapes at post-switch). Here, 3- and 4-year-olds perform equally well, suggesting that the
WM demands by themselves are not the cause of difficulty for the younger children in the
standard DCCS (Zelazo et al., 2003). In an Advanced DCCS, a third sorting dimension is
added; if there is a star on the card, the child should sort by color, but if there is not a star,
the child should sort by shape. Five-and six-year-olds find this task difficult, showing a less
than 50% chance of passing (Carlson, 2005). Thus, as predicted by Zelazo’s cognitive
complexity and control (CCC) theory (Zelazo et al., 2003), the complexity of a task, defined
in terms of the hierarchical structure of the child’s rule system, is critical to task
performance. The complexity of the child’s rule system increases as the child integrates and
embeds seemingly incompatible rules based on color and shape: “If sorting by color, the
blue one goes here; if sorting by shape, the rabbit goes here.” An inability to integrate the
rule systems causes perseverative errors; that is, the child will continue to sort the cards
based on the initial dimension—color or shape. The Advanced DCCS requires the
integration of another rule based on the presence of a star, whereas the No Conflict version
does not require the integration of any rules—supporting the notion that the integration of
rules is critical for inhibition development.
Findings of further improvement in inhibition after age 5 are mixed. In a rare study
examining a wide age range, Klenberg et al. (2001) found improvement from age 3 to 6 on
the Statue task (maintaining a body position while the experimenter attempts to distract the
child) and the Knock and Tap game (e.g., tap when the experimenter knocks and vice versa) Best and Miller Page 5
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NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript but no further significant improvement through age 12. However, these tasks may have been
too easy for the older children—perhaps because of the low prepotency of the response to be
inhibited. A similar problem of finding appropriate tasks when using a wide age range is that
the cognitive battery used in this study, the NEPSY, contains subtests not suitable for the
youngest children. Thus, the 3- and 4-year-olds only completed 3 of the possible 12 subtests,
making comparisons across the entire age range difficult. Still, in support of the conclusion
that inhibition stabilizes by the early school years, Lehto et al. (2003) found no significant
changes in inhibition between ages 8 and 13 on the Tower of London (TOL) and Matching
Familiar Figures tasks (though the inhibitory aspects of these tasks are not clearly identified,
and see Steinberg, Albert, Cauffman, Banich, Graham, & Woolard, 2008, for continued
improvements in an impulsivity measure of TOL performance through adolescence and
early adulthood).
Other studies find further development after age 8. Interestingly, many of these studies have
utilized computerized tasks such as the Go/no-go task or the continuous performance task
(CPT), both of which require a response to certain stimuli and inhibition of response to other
stimuli (Brocki & Bohlin, 2004; Casey et al., 1997; Cragg & Nation, 2008; Johnstone et al.,
2007; Jonkman, 2006; Jonkman, Lansbergen, & Stauder, 2003; Lamm, Zelazo, & Lewis,
2006). In the Go/no-go computer task, the child must respond (by pressing a designated
keyboard button) only to “go” stimuli (e.g., all letters except X) and inhibit response to the
“no-go” stimulus (e.g., the letter X). The CPT adds a cue prior to go and no-go stimuli. For
example, the child may be asked to respond to the letter X, but only when preceded by the
letter A. Thus, responding to a no-go stimulus is considered a failure of inhibition
(“commission errors”). Unlike conflict tasks, these tasks do not require the execution of an
alternative response.
In one of these studies (Brocki & Bohlin, 2004), significant improvements occurred from
age 7 to 9 to 11 on behavioral measures that loaded onto a factor that the authors labeled a
“disinhibition” factor (CPT disinhibition, CPT impulsivity, CPT inattentive impulsivity, and
Go/no-go commissions). Likewise, both Jonkman et al. (2003) and Casey et al. (1997) found
significant decreases in commission errors on these tasks between age 9 and young
adulthood (see also Klimkeit, Mattingley, Sheppard, Farrow, and Bradshaw, 2004; Jonkman,
2006). Cragg and Nation (2008) used a modified Go/no-go task in which children had to
depress a home key prior to pressing a target key. This modification allowed for partial
commission errors (depressing of home key but not pressing target key in response to the
no-go stimulus) in addition to traditional commission errors. In comparing 5–7 year olds to
9–11 year olds, only the partial commission measure was sensitive to developmental change,
revealing that older children were able to inhibit a motor plan (releasing the home key and
pressing the target key) at an earlier stage of execution than younger children. Johnstone et
al. (2007) also found that the traditional no-go commission error was insensitive to change
from ages 7 to 12. In contrast, they did find that performance on a Stop-signal task, during
which the child must inhibit a currently activated response, was sensitive to change across
this age span. Thus, these two studies suggest that the stage of execution is a factor in
inhibition difficulty: Terminating an already executed response appears to be more difficult
than inhibiting a response that has yet to be executed or is in an earlier stage of execution.
In a computerized anti-saccade task (Fischer, Biscaldi, & Gezeck, 1997; Munoz, Broughton,
Goldring, & Armstrong, 1998), children fixate on a central target. The target is turned off
and a peripheral cue is turned on. Children are told not to look at the target, but instead to
look to the opposite side. Thus, any initial glance towards the cue is an inhibitory failure.
Dramatic improvement in both reaction time and accuracy during the grade-school years is
followed by slower improvement during early adolescence. Similarly, Williams, Ponesse,
Schachar, Logan, and Tannock (1999) found improvement up through age 12 on a “stop- Best and Miller Page 6
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NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript signal reaction time” task, involving inhibition of a response (key press) to stimuli when a
tone sounds. Moreover, Huizinga et al. (2006) found continued improvement in both
reaction time and accuracy measures on the Stop-signal task and Eriksen Flankers task until
age 15 and on a Stroop-like task (inhibiting saying a color word in order to state its
conflicting font color) until age 21. Finally, adults, more than adolescents, appeared aware
of making an inhibition error as they momentarily slowed their response for the next trial in
order to prevent further error (Hogan, Vargha-Khadem, Kirkham, & Baldeweg, 2005),
which suggests the contributions of metacognitive development even after adolescence.
The fact that several studies have found improved performance beyond early childhood on
ostensibly simple response inhibition tasks (e.g., anti-saccade task)—or at least on tasks
(e.g., Go/No-go task) simpler than conflict tasks—challenges the notion that performance on
these tasks matures early on. For example, the computerized Go/No-go task should be easier
than Luria’s hand game because it requires only response inhibition of a prepotent response
rather than response inhibition followed by the execution of an alternative response. One
explanation for this involves how task performance is measured. Computerized tasks contain
multiple trials and measure reaction time very precisely (to the millisecond)—thus,
increasing sensitivity to subtle changes. Alternatively, since many of these studies with older
children have utilized computer-based tasks, perhaps some of the age-related improvements
are related to computer-specific abilities (e.g., more efficient use of keyboard or mouse).
In summary, the first leap in attaining inhibition appears in the preschool years. By age 4
children show signs of successful performance on both simple (i.e., pure response inhibition)
and complex inhibition tasks (i.e., response inhibition plus alternative response). By the
same age children can operate on a bi-dimensional card by one dimension and then inhibit
that to use the second dimension (i.e., successfully perform DCCS). Inhibition continues to
improve, particularly from age 5 to 8 (Romine & Reynolds, 2005) and particularly for tasks
that combine inhibition and WM (Carlson, 2005; Gerstadt et al., 1994), but also at later ages,
especially on computerized tasks. Unlike the early improvements, these are unlikely to be
fundamental changes in cognition (e.g., like preschoolers’ acquisition of the rule-formation
ability needed to perform the DCCS). Instead, refinements seem to involve quantitative
improvements in accuracy, perhaps due to an increasing efficiency to override prepotent
responses. Thus, it may be that inhibition tasks have varying sensitivities, with some being
sensitive to the conceptual gains in early childhood and others being sensitive to the
refinements in strength of the relevant cognitive skills or the generality of application in
later childhood and even adolescence. These sensitivities seem to be determined by a
number of factors including how performance is measured, the response modality, the
strength of the response bias, the stage of response execution, and the degree of
simultaneous WM demands imposed by the task.
Evidence from neuroscience— One method to examine the neural response underlying
response inhibition is to measure the brain’s electrical activity via EEG. Measuring during
infancy, early childhood, and middle childhood, one longitudinal EEG study (Bell, Wolfe, &
Adkins, 2007) reported qualitative change in brain activity underlying complex response
inhibition—measured by working memory/inhibitory control tasks. At 8 months of age,
correct performance on the A-not-B task was associated with increased global cortical
activity, whereas at age 4½, Day/Night performance was associated only with increased
medial-frontal activity. By age 8 this activity became even more focused in the right frontal
scalp regions during completion of the WCST. This shift from global to localized activity
during task completion may signal the growing efficiency of the brain and the growing
functionality of the PFC for complex response inhibition. Best and Miller Page 7
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NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript EEG studies with older children indicate continued localization of brain activity from
middle childhood to adulthood. Looking closely at frontal activity, Jonkman (2006) found a
linear increase in no-go P3 amplitude (a positive wave 300 – 500 ms after stimulus
presentation) across development: Children ages 6–7 showed no P3 activity, children ages
9–10 showed limited P3 activity in frontal-central electrodes, and young adults showed
broad P3 activity across frontal and frontal-central electrodes (see also Jonkman el al.,
2003). P3 activity has been interpreted to index the allocation of attentional resources during
stimulus engagement, with greater activity representing greater resource allocation (Polich,
1987). In contrast, the no-go N2 response (a negative wave 150 – 400 ms after stimulus
presentation) decreased across the same age span, with the largest decrease in amplitude
occurring between ages 6 and 10. The N2 response is thought to indicate conflict
monitoring, needed when the prepotent response conflicts with the task-required response.
The decrease in the N2 response may indicate that compensatory mechanisms are present in
younger children and that the task induces less conflict with increasing age (Jonkman,
2006).
Similar findings come from Lamm et al. (2006) who reported decreases in frontal N2
amplitudes from age 7 to 17 on no-go trials, which they attributed to increasing neural
efficiency that may result from the regressive neural changes described above (e.g., synaptic
pruning). As evidence that frontal N2 activity reflects conflict monitoring and inhibition,
performance on tasks requiring conflict monitoring and inhibition (the Stroop task and Iowa
Gambling Task) predicted decreases in frontal N2 amplitudes beyond that predicted by age.
As further support, Johnstone et al. (2007) observed stronger frontal N2 activity in response
to no-go stimuli than go stimuli. However, they found no change in the ability to inhibit no-
go responses across the ages of 7 to 12. Moreover, what age-related changes in brain activity
the authors did observe were found in non-frontal brain regions—including decreases in
central and parietal N2 amplitude—suggestive more of refinements in stimulus processing
than in the inhibition response per se. All together, these EEG studies provide evidence that
inhibition development is paralleled both by increases and decreases in neural activity,
perhaps indicative of progressive and regressive neural change.
Researchers also have used neuroimaging assessments, such as functional magnetic
resonance imaging (fMRI), to document the increasing efficiency of the neural response
underlying response inhibition. On the Go/no-go task, Casey et al. (1997) observed no
difference in the location of activation in the PFC between children ( M
age = 9.92) and young
adults but did measure greater volume of activation in children during the no-go condition,
particularly within the dorsal and lateral PFC. Moreover, a longitudinal imaging study
(Durston et al., 2006) reported that activation within the PFC only increased from age 9 to
11 for specific prefrontal regions (ventral PFC) correlated with task performance. In
contrast, activity within prefrontal regions uncorrelated with task performance (dorsolateral
PFC) decreased with age. Similar to the explanation offered by Lamm et al. (2006), Casey et
al. and Durston et al. suggested that the greater activation in children may correspond to
inefficiency in the inhibition mechanism and that neural development is characterized by
increased localization of activity to brain regions directly linked to the behavioral response
and decreases in activity in supplementary brain regions.
One structural imaging technique, Diffusion Tensor Imaging (DTI), measures the
myelination of axons. Myelinated axons transmit signals more efficiently and rapidly than
unmyelinated axons. Liston et al. (2006) measured myelination in participants aged 7 to 31,
who also performed a Go/No-go task and found that myelination of projections from the
PFC to the striatum increased with age and correlated with no-go task performance.
Importantly, myelination of corticospinal axons also increased with age but did not correlate
with task performance. Thus, the increasing myelination of frontostriatal connections, in Best and Miller Page 8
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NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript particular, across childhood and adolescence seems to underlie the maturation of response
inhibition.
A synthesis of these neuroscience studies does not suggest a one-to-one correspondence
between changes in brain activity and changes in task performance. Instead, task
performance often changes subtly, if at all (e.g., Johnstone et al., 2007), whereas the pattern
of neural activity may change dramatically. It seems that school-age children can
successfully complete response inhibition tasks (with concurrent WM requirements or not),
but in doing so, enlist a more global pattern of activation than they will later on. With
development comes localized and efficient activation in specific PFC regions (e.g., ventral
PFC) pertinent to task completion. These dramatic changes in neural activity may translate
into only subtle improvements in response inhibition, such as greater efficiency and less
effort.
Developmental issues— Several developmental issues about inhibition need to be
examined further. First, by what process does a child move from one level (e.g., simple
inhibition of a response) to another level (e.g., the development of a more complex rule that
controls two responses)? Does the former contribute to the development of the latter? Would
examining the transition period yield clues to the qualitative and quantitative aspects of this
change? Second, what is the form of the developmental trajectory from age 3 to
adolescence? Is development linear? This question is difficult to answer because of the
difficulty of finding appropriate tasks for a large age span. The dramatic improvement from
age 3 to 5 appears to be followed by less dramatic change from 5 to 8 and even less change
after age 8 (though brain maturation continues). However, the tasks used with younger
children likely have different inhibitory requirements than those used with older children. In
a meta-analysis of EF studies from age 5 to adulthood, Romine and Reynolds (2005) found
the greatest advancements in inhibition of prepotent responses from age 5 to 8. A meta-
analysis that includes younger children is greatly needed to clarify the rate of change from
the preschool to grade school years.
Relevant to this apparent developmental trajectory is the problem of task impurity.
Inhibition appears not to be a uniform construct. For example, interference control, cognitive
inhibition and motor inhibition may be distinct processes tapped by different tasks and
develop at different rates across childhood (Nigg, 2000). Furthermore, classic tasks of
inhibition utilize cognitive components in addition to inhibition. As already mentioned,
many tasks also have significant WM requirements. For another example, performance on
the Stroop task may depend not only on the ability to inhibit reading a color word in order to
read its actual color, but also on the child’s level of reading automaticity (Leon-Carrion,
García-Orza, & Pérez-Santamaría, 2004). As a result, performance, measured in terms of
reading errors, does not improve in a linear fashion, but rather forms a quadratic relation:
There is an initial increase in reading errors from age 6 to age 10, followed by a dramatic
decrease in errors through age 17. This suggests that as word reading becomes more and
more automatic from age 6 to 10, inhibition of that process in order to say the color becomes
more difficult, which negatively affects reading accuracy. Afterwards, the inhibition
mechanism needed may be mature enough to compensate for this reading automaticity.
Thus, this developmental trajectory may have as much to do with developing reading
automaticity as with developing inhibition. A similar careful analysis of other inhibition
tasks might reveal similar complexities regarding the question “What develops?”
Adding further complexity, social factors likely influence inhibition performance. For
example, there is evidence for a developmental regression in self-regulation in early
adolescence (Anderson, 2002), particularly as evidenced by increased risky behavior during
this time (Steinberg, 2007). One perspective suggests that the interaction of a mature Best and Miller Page 9
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NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript socioemotional network (involving mainly limbic structures) and an immature EF-related
control network within a context of increased peer pressure leads to adolescents’ increased
risk-taking (Steinberg, 2007). This perspective suggests that outward regressions may occur
not so much because there are actual regressions in inhibition per se but because exposure to
emotionally-laden and risky situations increases at this point in development when inhibition
is still immature.
Summary— Regarding the first component in the Miyake et al. model, cognitive,
behavioral, and brain assessments generally show rapid early improvements in inhibition
followed by slower improvements through adolescence, along with greater brain localization
throughout childhood and adolescence. Mechanisms of development may include brain
maturation, increased ability to handle task complexity, increased ability to use rules, and
emerging metacognition.
Working Memory Like inhibition, WM research has been complicated by various definitions of the construct,
as well as (and perhaps resulting in) the use of differing assessment tasks. In general, WM
involves the ability to maintain and manipulate information over brief periods of time
without reliance on external aids or cues (Alloway, Gathercole, & Pickering, 2006;
Goldman-Rakic, 1987; Huizinga et al., 2006). Neuroimaging research suggests that WM
tasks vary by how much the task elicits PFC activity; consequently, researchers suggest that
WM tasks vary in the degree to which they require “executive control” (Luciana et al.,
2005). That is, more complicated WM tasks that require the maintenance and manipulation
of information in order to direct behavior toward future goals (e.g., backward digit span,
delayed-response tasks, self-ordered searches) ostensibly rely on more executive
involvement (and consequently more PFC activity; D’Esposito & Postle, 1999) than simpler
WM tasks that require only the maintenance of information (e.g., forward digit span).
Accordingly, the rate and form of WM development will hinge upon the degree to which a
task requires executive processes.
Gathercole, Pickering, Ambridge, and Wearing (2004) invoked the classic WM model of
Baddeley and Hitch (1974) to distinguish between executive WM tasks and online storage
tasks. According to Gathercole et al., executive WM requires that either the verbal storage
system (i.e., the phonological loop) or the visuo-spatial storage system (i.e., visuo-spatial
sketchpad) work in concert with a coordinating central executive. Simple WM tasks require
little input from the central executive but rely solely on either the phonological loop (e.g.,
forward digit recall) or visuo-spatial sketchpad (e.g., visual pattern recall). Complex tasks
(e.g., backwards digit recall), on the other hand, may require that multiple WM tasks be
performed concurrently, and therefore the central executive must coordinate those processes.
This tripartite model was supported by a CFA of children ages 6 to15 on a battery of verbal
WM, visuo-spatial WM, and executive WM tasks, which suggests the differentiation of the
WM subsystems by early grade-school.
Age differences— A variety of tasks document improvement in WM during the preschool
years (see Garon et al., 2008). Gathercole et al. (2004) reported that by age 6 the executive
component of WM is sufficiently developed to be used during complex tasks that require
coordination of WM subcomponents. In addition, the same researchers found that simple
and complex WM tasks had similar developmental trajectories—a linear increase from age 4
to 14 and a leveling off between ages 14 and 15 across nearly all tasks examined. Luciana et
al. (2005), drawing on a battery of nonverbal tasks, found that the developmental course of
WM depends on the complexity and, thus, the executive demands, of the task, with less
demanding tasks being mastered earlier in development. Their battery of nonverbal tasks Best and Miller Page 10
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NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript ranged from a nonverbal face recognition task (lowest executive demand) to a spatial self-
ordered search (highest executive demand). The former simply required the child to
maintain a facial representation over a delay in order to discriminate a previously viewed
face from a novel one, and performance was unchanged between age 9 and 20. The latter
task required the child to search varying locations on a computer screen for hidden tokens,
to remember locations where a token was found, and to strategically explore other locations,
and performance continued to improve until age 16.
Extending the work of Luciana et al. (2005), Conklin et al. (2007) used a battery of verbal
and spatial WM tasks across the ages of 9 – 17. Across age, children tended to perform
better on the spatial versus verbal WM tasks, even though the tasks were thought to tap
similar cognitive processes (e.g., strategic self-organization). Still, the developmental
trajectories were similar for verbal and visuo-spatial WM tasks, and instead, differed based
on task complexity. In agreement with the conclusions of Luciana et al., Conklin et al.
suggest that the age of mastery depends more on the degree of processing (with more
complex tasks requiring a greater degree of processing) rather than the content to be
processed (e.g., verbal versus visual-spatial material).
One problem, though, with this use of different tasks to manipulate the executive demands
on WM is that it is difficult to ensure that non-EF processes are equivalent across tasks and
that they do not influence age differences in performance. Luciana and Nelson (1998)
addressed this important issue by employing only a self-ordered search task, which varied
over trials in the executive demands, based on the number of locations (2 to 8) the child may
search for tokens. By increasing the number of search locations, greater demands are placed
on the executive components of WM to search strategically and to avoid previously searched
locations by continually updating WM. Similar to the findings of Luciana et al. (2005), for
the least demanding condition, performance was equivalent among children ages 4 to 8,
adolescents, and young adults. However, as the number of search locations increased, age
differences emerged. For three locations, performance maturity was reached at age 6, for 4
locations, maturity was not reached until adolescence, and for 6 and 8 locations,
improvements continued until adulthood. Thus, the development of executive WM occurs
gradually with continued refinement through adolescence, especially for tasks that require
the maintenance and manipulation of multiple items.
Evidence from neuroscience— In accord with the behavioral results, fMRI evidence
points to a protracted developmental course leading to localized activity within the PFC
during WM functioning. Kwon, Reiss, and Menon (2002) reported a quantitative linear
increase in activity within a fronto-parietal network, including ventral and dorsal regions of
the PFC, from ages 7 to 22 while performing a visuo-spatial WM task ( n-back task). The
authors note that the increased activity within right-lateralized dorsal PFC likely subserves
the maturation of visuo-spatial attention and executive processes, whereas the increased
activity within a left-lateralized fronto-parietal network subserves the maturation of a
phonological rehearsal system. Increases in this neural activity were related more to age than
to task performance (accuracy and RT). Thus, with a dramatic and prolonged increase in
specialization of the WM neural circuitry through childhood and adolescence comes only
limited overt task improvement, especially during adolescence.
Scherf, Sweeney, and Luna (2006) reported both qualitative changes (location of activation)
and quantitative changes (amount of activation) in the neural response underlying
visuospatial working memory from childhood ( M
age = 11.2) through adulthood (
M
age =
29.5). During childhood, activation occurred in qualitatively different premotor regions and
also in the lateral cerebellum, which was absent in later development. Also during
childhood, there was quantitatively greater activity in ventromedial regions, including the Best and Miller Page 11
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NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript thalamus and basal ganglia. Adolescence (M
age = 15.7) brought a shift in activity to frontal
regions, including the right dorsolateral PFC. Finally, from adolescence to adulthood
activity became more localized and lateralized as left dorsolateral PFC activation increased
and right dorsolateral PFC activation decreased. Moreover, activity increased substantially
in the as anterior cingulated (described as qualitative change by the authors). The end result
of these changes (quantitative and qualitative, progressive and regressive) from childhood to
adulthood is a functionally-specialized visuospatial WM neural network (see Klingberg,
Forssberg, and Westerberg, 2002, for similar changes from age 9 to18 on a visuo-spatial
WM task).
Increased WM also correlates with structural neural indices, such as the maturation of white
matter (i.e., the myelination of neuronal axons), measured by DTI. Nagy, Westerberg, and
Klingberg (2004) reported that the development of visuo-spatial WM from age 8 to 18
correlated with myelination in regions primarily of the frontal lobe close to the parietal lobe
(superior and inferior left frontal lobe), whereas the development of reading ability
correlated with myelination in the left temporal lobe. Thus, during late childhood and
adolescence, the maturation of specific cognitive functions is linked to the maturation of
specific neural circuits, rather than to global brain maturation.
Developmental issues— One developmental issue concerns the developmental
relationship between WM and inhibition. It seems that many inhibitory tasks, particularly
complex ones, also place demands on WM (Bell et al., 2007; Garon et al., 2008; Simpson &
Riggs, 2005), and the combination of the two within a single task poses significant difficulty
for young children (e.g., Carlson, 2005). For example, in the Day/Night task the child must
maintain the rule in WM. However, research also suggests their independence. If the
inhibitory component is eliminated on this task, the difference between 3.5- and 5-year-olds
nearly disappears (Simpson & Riggs, 2005), which suggests that it is inhibition, not WM,
that causes the age difference. (It is also possible that eliminating the WM component also
would reduce the age differences, which would suggest that the interaction of the two
components is important). Similar outcomes were found with a Stroop-like task, a CPT task,
and a start/stop task (Beveridge, Jarrold, & Pettit, 2002), as well as the DCCS (Zelazo et al.,
2003). Thus, relevant to the Miyake et al. (2000) model, it appears that WM and inhibition
are largely separate constructs (for a counterargument, see Bell et al., 2007, and Davidson et
al., 2006). That said, many tasks described as either WM or inhibition tasks likely place
demands on both types of processes (e.g., the Day/Night task), and therefore, it is difficult to
obtain a pure measure of one or the other.
Summary— Performance on complex WM tasks (i.e., those tasks requiring a greater degree
of processing such as the maintenance and manipulation of information) improves at least
through adolescence. Like the development of the neural circuitry subserving response
inhibition, the development of the WM circuitry involves progressive and regressive
changes, resulting in a localized pattern of activity within a fronto-parietal network,
including the DL-PFC. Unlike the trajectory of inhibition development that shows large
improvements during the preschool years followed by more modest, linear improvements
through adolescence, most of the evidence suggests that the trajectory of WM development
is linear from preschool through adolescence.
Shifting The third core EF is the ability to shift between mental states, rule sets, or tasks (Miyake et
al., 2000). There appears to be substantial need for inhibition and WM processes for
shifting. Reminiscent of the cognitive processes associated with inhibition tasks (e.g.,
DCCS), Miyake et al. suggest that shifting may “involve the ability to perform a new Best and Miller Page 12
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NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript operation in the face of proactive interference or negative priming” (p. 56). It would seem,
then, that the ability to inhibit previously activated mental sets would be important for
successful shifting and that perseverative errors (i.e., continued responding based on the
previous mental set) would indicate shifting failures (Anderson, 2002). The typical
distinction between tasks deemed “inhibition tasks” and those deemed “shifting tasks” is
that the latter typically rely on switching between two or more mental sets—with each set
possibly containing several task rules—rather than the inhibition of a single response
(Crone, Somsen, Zanolie, & Van der Molen, 2006). Moreover, in tasks of inhibition, the
rules are usually explicitly expressed rather than implied through either negative or positive
feedback. Thus, the DCCS with its explicit indication of set change is often categorized as
an inhibition task (but see Garon et al., 2008, who classify the DCCS as a shifting task).
Shifting tasks also place demands on WM by requiring the maintenance and updating of that
mental set based on feedback.
Age differences— The ability to shift improves with age (Anderson, 2002; Cepeda,
Kramer, & Gonzales de Sather, 2001; Crone, 2007; Crone et al., 2006; Garon et al., 2008;
Somsen, 2007). Preschoolers, ages 3 to 4, can successfully shift between two simple
response sets in which the rules are placed in a story context (Hughes, 1998) or when
demands on inhibition are reduced (Rennie, Bull, & Diamond, 2004). For example, in a
simplified version of the WCST (Hughes, 1998), preschoolers can determine what a teddy-
bear’s favorite shape is, based on feedback, and then after a set shift they can decide what a
second teddy-bear’s favorite color is, based on differing feedback.
As previously mentioned, Senn et al. (2004) reported that whereas inhibition and WM were
interrelated and predicted complex task performance (i.e., TOH), shifting was unrelated to
inhibition, WM, or TOH performance in preschoolers. Accordingly, the authors suggest that
shifting may not be differentiated from WM and inhibition—and therefore, is less developed
—at this age. This is sensible given that inhibition and WM processes seem to be
prerequisite processes for successful shifting. As Garon et al. (2008) noted, before children
can successfully shift between response sets, they must be able to maintain a response set in
WM and then be able to inhibit the activation of a response set in order to activate an
alternative one.
More complex tasks show further development in older children and adolescents. Luciana
and Nelson (1998) utilized a set-shifting task that progressed through nine stages of
increasing difficulty and complexity (the intradimensional/extradimensional self-shifting
task from the CANTAB). This is a fruitful strategy as it shows exactly how complex a set
children can handle at each age, thus permitting comparisons across a wide age range. This
task required children to respond correctly, based on previous feedback, to either lines or
shapes presented on a computer. Children had to attend to feedback, infer the correct rule at
that moment, and respond accordingly. At set points, reinforcement switched such that the
correct response (e.g., shapes) switched to the opposite of what was previously correct (e.g.,
lines). The main improvement occurred from age 5 to 6, at stage 7, in which the rule did not
switch (i.e., between lines and shapes), but the examples of lines and shapes did change. For
successful completion of this stage, children needed to utilize feedback from previous stages
to shift their response to new examples of either lines or shapes. With increasing age up
through young adulthood, there was a steady increase in the proportion of subjects who
completed all nine stages of the task, indicating that shifting ability continues to improve
over many years.
Huizinga et al. (2006) investigated set-shifting on three computerized tasks in which a cue
signaled to which dimension the child should respond. Sporadically, the cue would switch,
indicating a set shift. “Shift cost” was the difference in response time between shift trials Best and Miller Page 13
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NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript and non-shift trials. The shift cost for the 7- and 11-year-olds was significantly greater than
for the 15-year-olds, who did not differ from the young adults ( M
age = 20.8). Thus, shifting
reached adult-like levels around age 15. Similarly, Davidson et al. (2006) found
improvement from age 4 through adolescence. Interestingly, they found different
developmental trajectories for the switch cost for accuracy versus reaction time. Whereas the
shift cost to accuracy diminished through early adolescence, the switch cost to reaction time
increased until adulthood. This speed-accuracy trade-off indicates that with increasing age,
participants were more likely to slow down their responses on shift trials to ensure that they
were responding accurately. Thus, improved metacognition—knowing that slowing helps
performance and being able to detect when it is advantageous to do so—may be one
mechanism of developing accurate set-shifting. This design, examining developmental
curves on two or more measures, is a model for how future research could tease apart
aspects of an EF component and tell a more nuanced developmental story.
Evidence from neuroscience— In further support of the role of metacognition,
especially monitoring and changing one’s own performance, Crone et al. (2006) measured
heart rate changes during a task shifting paradigm, in which the child “opened” doors on a
computer screen in order to help a computerized donkey find its way home. Heart rate
slowing following negative feedback (indicated by a negative sign after opening an incorrect
door) would indicate a realization of an error and the evaluation of the current set rules. For
ages 8 to 18, heart rate slowed to a similar degree following the unexpected feedback that
occurs immediately after a task shift. The difference between the age groups occurred only
in errors that continued after receiving feedback for a task shift. Following such an error, the
8- to 10-year-olds did not show heart rate slowing as much as the 12- to 14-year-olds or the
16- to 18-year-olds. Thus, although younger children could detect a task shift as well as
older ones, they did not detect performance errors after the shift as well. Somsen (2007)
reported that on a computerized WCST an increase in attention to feedback about errors
predicted performance in adolescents, but not younger children, which again supports the
role of metacognition, particularly being able to use feedback to change one’s behavior.
fMRI analyses implicate neural activity within multiple regions of the PFC and elsewhere as
shifting develops. Rubia and her colleagues (Rubia et al., 2006) reported increased
activation in inferior frontal, parietal, and anterior cingulate regions, but decreased DL-PFC
activation across adolescence during shifting. They proposed that the increased activity in
the anterior cingulate cortex (ACC) reflects the maturation of conflict monitoring processes,
whereas the increased DL-PFC activation in younger participants reflects compensatory
neural activity—quite similar to the explanation offered by Casey et al. (1997) regarding
Go/no-go performance.
Also focusing on conflict monitoring, Crone (2007) suggested that other regions may
underlie the growing ability to monitor and change one’s performance. During development,
adult levels of processing feedback about performance on a shift task are reached first for
the medial PFC (important for violations of processing expectations), and then for the left
dorsal PFC (important for hypothesis testing and seeing the need for adjustment of
behavior). The first development occurs between ages 8–10 and adolescence and the second
between adolescence and adulthood. Thus, because cognitive shifting requires the child to
switch between multiple response sets based on feedback, neural networks involving the
ACC and regions of the PFC that are responsible for monitoring and detecting conflict (e.g.,
performing a response and receiving negative feedback) seem to be critical to successful
shifting.
Developmental issues— A useful approach for pinpointing exactly what changes with
age on a shifting task is to break such a task down into component processes and examine Best and Miller Page 14
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NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript neural correlates of age changes in these processes. A recent interesting study (Morton,
Bosma, & Ansair, 2009) examined separately two processes in the DCCS task: 1) rule
switching (e.g., switching from color- or shape-based sorting) and 2) detection and
resolution of conflict when stimuli can legitimately be sorted in two ways (color or shape).
Rule switching showed fMRI activity in the lateral PFC and posterior parietal cortex in both
children and adults. Most interesting, however, was an interaction of rule switching and age
in these regions. Rule switching modulated activity in the left posterior parietal cortex and
right middle frontal gyrus in adults but not children. Thus, the networks involved in
switching presented a qualitative developmental change from childhood to adulthood.
Although the age-related results were less clear for conflict processing, they showed that
conflict processing and rule switching are separable processes that, when considered
separately, can give a more fine-grained analysis of age-related neural and behavioral
changes on the DCCS task.
Age-related improvements in shifting also occur through the development of processes other
than shifting per se. For example, the ability to generalize a rule set to a novel set of stimuli
facilitates shifting performance (Luciana & Nelson, 1998). The abilities to maintain the new
rule set and to detect performance errors after a successful shift also are important (Crone et
al., 2006). Finally, the development of metacognitive strategies, such as slowing down
responses to preserve a high level of accuracy, enhances accurate shifting (Crone et al.,
2006; Davidson et al., 2006).
Summary— The ability to successfully shift between task sets follows a protracted
development through adolescence. It appears that preschool-aged children can handle shifts
between simple task sets and later can handle unexpected shifts between increasingly
complex task sets. Both behavioral and physiological measures indicate that during
adolescence, monitoring of one’s errors is evident, and by middle adolescence, task
switching on these complex shift paradigms typically reaches adult-like levels. Because of
greater need for multiple cognitive processes, mature shifting likely involves a network of
activity in many PFC regions.
Conclusions about Development and Directions for Future Research
Developmental Trajectories It is clear that previous reviews of EF in children, mainly focused on preschoolers, leave out
much of the story of the development of EF and limit the search for sequences and
mechanisms of development. There is substantial further development in all components
after age 5, and even through adolescence (see Best et al., 2009). Including broader age
ranges provides information about developmental trajectories. Results are inconsistent, but
inhibition appears to show particularly striking improvement during the preschool years and
less change later on. WM shows more gradual linear improvement throughout development,
as does shifting. These different trajectories provide support for the Miyake et al. (2000)
position that the three components are somewhat diverse. However, the differing trajectories
extend the Miyake et al. model by suggesting that the degree of unity or diversity of EF
varies from age to age.
The evidence suggests both quantitative and qualitative EF development. Much of the
change appears to be quantitative and gradual, though the change may be more rapid in the
early years. In many relevant brain regions, activity decreases with age, perhaps reflecting
the growing efficiency of the neural response. Some change appears to be qualitative,
suggesting changes in brain organization as the site of brain activity shifts during
development (e.g., Scherf et al., 2006). In other regions, activity seems to increase—for
example, in the ventral PFC during response inhibition and in the DL-PFC during WM. As Best and Miller Page 15
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NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript Olson and Luciana (2008) note, the discrepant behavioral trajectories along with this
segregation of activity tentatively suggest that different regions of the PFC support different
EFs. Thus, regional differences in the course of neural development may be responsible for
different developmental trajectories of response inhibition, WM, and shifting. The
emergence of metacognition may also bring qualitative change when children learn to use
feedback about errors to change their approach to the task.
It is important to note that despite evidence for the functional differentiation of the PFC,
there also seems to be activation of common regions during the completion of distinct EF
tasks. This may in part be due to EF task impurity—notably the fact that it is difficult to
tease apart working memory and inhibitory processes (Roberts & Pennington, 1996) and that
shifting likely builds on working memory and inhibitory processes (Garon et al., 2008).
Additionally, it may indicate the common processes that underlie the various EF
components, that is, the “unity” aspect of the “unity and diversity” of EF (Miyake et al.,
2000). Exactly how EF is instantiated in the brain, including to what degree the PFC is
functionally differentiated, continues to be a debated issue (Olson & Luciana, 2008).
Several task-related factors also influence the observed developmental trajectory of a latent
EF component. One is the degree of task complexity, with better success expected on
simpler tasks. Task complexity can be manipulated in several ways, but notably by
increasing the degree of response prepotency in inhibition tasks (Diamond & Taylor, 1996)
or by increasing the degree of “working” with information in WM tasks (Luciana et al.,
2005). Thus, a simple response inhibition task should be easier than the Stroop task, and a
WM task that requires online storage should be easier than one that requires information
manipulation. A second is how performance is measured (e.g., partial versus full
commission error on the Go/no-go task; Cragg & Nation, 2008). A third is the response
modality (e.g., bodily versus verbal response; Diamond & Taylor, 1996). Thus, researchers
need to consider carefully how these task factors may shape the developmental trajectory of
constructs ostensibly measured by such tasks. Moreover, the influence of these task factors
suggests caution in drawing strong conclusions about the development of EF.
Mechanisms of Development Research needs to move beyond a focus on description—the ages at which the EF
components emerge, show rapid development, and reach maturity—and address
mechanisms of development. How do children move from early to later levels of
competence within an EF component, for example, inhibition? Does the early phase of
development of one component facilitate the development of other components? The
developmental relationships between components of EF could be examined by research
designs that include assessments of several components of EF together in the same study, at
different ages. The comparison of trajectories of several EF components can suggest
mechanisms of development. For example, the early rapid improvement in inhibition may
contribute to the later developments of shifting and planning.
Promising designs— Several designs used by studies reviewed here seem particularly
promising for examining mechanisms of development within or between components: 1)
Use meta-analyses (Romine & Reynolds, 2005) to examine effects of moderating variables
at different ages by including a larger age range. 2) Compare the developmental trajectories
for two or more aspects of performance (e.g., speed and accuracy, Davidson et al., 2006,
Baker, Segalowitz, & Ferlisi, 2001; number of moves, amount of planning time prior to
making the first move, and the proportion of perfect solutions on the TOL task, Huizinga et
al., 2006) for clues as to whether one aspect influences another. 3) Look for correlations
between a measure of neural activity and EF performance (e.g., Morton et al., 2009). Brain Best and Miller Page 16
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NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript maturation could permit, and thus serve as a developmental mechanism for, more advanced
EF behavior, or the latter behaviors could cause changes in neural networks.
Another promising, yet rarely used, approach is to focus on the transition phase from one
developmental level to the next. An example is the change from the simple rule necessary
for inhibition on simple conflict tasks to the more complex rules necessary for inhibition on
the DCCS task. The microgenetic method is particularly useful in this regard. In this
method, children have multiple trials on the same task or similar tasks, typically in several
sessions over several weeks, and trial-to-trial changes are examined. Using this method,
McNamara, DeLucca, and Berg (2007) tracked detailed change in the type of strategy used
with increased experience on the TOL task. Potentially a microgenetic design could show
that rapid change in one component is subsequently followed by rapid change in another
component, suggesting a possible causal relation.
Microgenetic studies could be particularly useful for examining what happens at the point of
shifting on shift tasks, given that several studies have observed changes that suggest
metacognitive processes at work on the trials after the shift (e.g., Crone, 2007; Crone et al.,
2006). In a task similar to shift tasks (De Marie-Dreblow & Miller, 1988) when young
children have to shift from one category (e.g., animals) to another category (household
items) on a selective memory task, their previously effective strategy of attending to only
relevant items transferred successfully to the new category but temporarily became
ineffective at facilitating recall of the relevant items in this newly relevant category. Such
fine-grained assessments of microgenetic changes in behavior would be particularly
powerful if combined with neuroimaging assessments that track changes in brain activity at
the point of behavior change.
Training studies also are useful for examining possible mechanisms of development. These
can look at short term change by a) training one EF component and then assessing any
immediate changes in other components, or b) providing particular experiences, such as
metacognitive instruction, and observing any facilitation of EF.
Training studies also can examine mechanisms over longer periods of time. The most
powerful assessments of developmental mechanisms would track changes in both cognitive
performance and brain organization, thus providing a multi-level assessment of influences.
Such studies are rare. One such line of investigation showed both improved EFs and change
in fMRI patterns of brain activity associated with EF after a 3-month after-school high-
intensity exercise program for ages 7–11 (Davis et al., 2007, in press). In comparison to a
control group, the exercise group had increased bilateral prefrontal cortex activity and
reduced bilateral posterior parietal cortex activity during an inhibition task. Another study
(Rueda, Rothbart, McCandliss, Saccomanno, & Posner, 2005) reported evidence that one
week of training on a computerized program centered on executive attention led to a more
mature EEG response to an inhibition task (modified flanker task) and to small, though often
insignificant, behavioral improvements on the inhibition task and on a generalized
intelligence test.
Finally, a powerful approach to identifying developmental mechanisms underlying changes
in EF would be to study whether the cognitive, biological, and social correlates of EF
change from one age to another (see Best et al., 2009 for a discussion of the social correlates
of EF). This is particularly important because different processes may contribute to the
development of EF at different ages.
Developmental sequences— Another good starting point to search for possible
mechanisms of development is to examine developmental sequences underlying the Best and Miller Page 17
Child Dev. Author manuscript; available in PMC 2011 November 1.
NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript emergence of each EF component (for a description of types of developmental sequences,
see Flavell, 1972). Observed developmental sequences can suggest how early cognitive
skills might be related to later ones. For example, does the development of a more advanced
inhibitory ability supplement earlier inhibitory ability (i.e., an “addition” sequence), for
example, by strengthening it, adding the ability to select an alternative response to the ability
to inhibit the prepotent response, or adding metacognitive skills? Or does a more advanced,
integrated inhibition skill replace earlier forms of inhibition, as suggested by the evidence of
brain reorganization (e.g., the loss of compensatory activation with development) correlated
with more advanced inhibition? In this qualitative change, the old brain organization
associated with simple inhibition may no longer exist. The microgenetic design mentioned
above could address supplementing versus replacing by showing whether Skill A continues
to be used even after Skill B emerges.
A similar analysis of sequences and possible mechanisms could also address how the
various EF components are related developmentally. That is, some EF components may
facilitate the development of other EF components. For example, since WM and inhibition
seem to develop ahead of switching (Davidson et al., 2006), perhaps a certain level of WM
and inhibition has to be developed before children can use them towards the development of
shifting behaviors (Garon et al., 2008). Two EF components may even be mutually
facilitative, as each bootstraps the development of the other in a back-and-forth fashion.
Garon et al. (2008) suggested that the emergence of the three components in the first three
years of life may be followed by an integrative period in which they become coordinated. In
short, a greater emphasis on detecting possible developmental sequences will allow for a
clearer and more detailed understanding of the developmental trajectories of EF components
as well as the developmental relations between those components. Finally, attention to
sequences can clarify some of the differences among theories of EF development in how
conflicting representations are resolved. For example, in Zelazo’s CCC theory, two rules are
integrated to produce a new overarching rule system. In contrast, in Munakata’s theory,
latent representations are maintained, even after active representations strengthen and
override latent ones.
Two papers have attempted to extract developmental sequences. Anderson (2002) inferred
the following sequence of EF components from an integrative review: attentional control
(e.g., inhibition), information processing (e.g., processing speed), cognitive flexibility (e.g.,
switching), and goal setting. Romine and Reynolds (2005) inferred sequences from the age
at which performance leveled off, based on a meta-analysis of ages 5–22 and average effect
sizes of age-related change in performance. They found the following sequence: inhibition
of perseveration, set maintenance, design fluency, planning, and verbal fluency.
One design for detecting sequences, rarely used in this research area, is to give children
slightly different versions of the same task (to try to equate task demands, such as verbal
demands and content area), with each task assessing a different component of EF (see
Wellman & Liu, 2004, for the successful use of this design for assessing the acquisition
sequence for theory-of-mind tasks). If task demands, other than the EF component of
interest, are in fact equated, the mean performance on each version or the percent of children
who pass each version suggests the order in which the components of EF are acquired. Also,
a scalogram analysis could examine how many children pass all of the hypothesized easier
versions of the task before a hypothesized more difficult task. That is, if the hypothesized
ordering of component tasks from easiest to hardest is A, B, C, D, then the outcome of
interest is how many children passed A, B, and C, but not D; A and B but not C and D; and
A but not B, C, and D. Best and Miller Page 18
Child Dev. Author manuscript; available in PMC 2011 November 1.
NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript Carlson (2005) applied this method in a large sample aged 2 – 5 to determine the probability
of passing common EF tasks at each age. Although the ordering of specific tasks varied
from age to age, inhibition tasks routinely were passed earlier than WM tasks and the very
hardest tasks consistently involved both inhibition and WM demands (e.g., reverse
categorization at age 2; DCCS at age 3; backward digit span at ages 3 and 4; Advanced
DCCS at age 5 to 6).
Finally, longitudinal studies obviously are ideal to detect sequences, but few exist. Several
longitudinal studies have examined the TOH or TOL task, with a focus on developmental
sequences in strategy use (McNamara et al., 2007), and family, cognitive, school
achievement, and social adjustment correlates (Friedman et al., 2007; Jacobson & Pianta,
2007). On inhibitory tasks, one striking sequence identified is that performance on a delay of
gratification task at age 4 predicts, and thus may be a developmental precursor for,
performance on inhibitory tasks such as the go/no-go task at age 18 (Eigsti et al., 2006). In a
longitudinal study from ages 2 to 4 (Hughes & Ensor, 2007), EF (an aggregate of inhibition,
WM, and shifting tasks) improved with age and showed stable individual differences,
indicating the predictive ability of early EF for later EF.
This review suggests several influences on children’s level of performance on EF
assessments that should be considered when examining sequences. One complication in
identifying sequences is that estimates of performance, and thus developmental trajectories,
may vary, depending on what aspect of performance is scored and how it is scored (e.g.,
Baker et al., 2001; Huizinga et al., 2006). Moreover, the apparent developmental order of
two aspects (A then B) of EF actually could be due to the greater performance demands of
B. Reducing the demands of B would reverse the sequence. For this reason the design
suggested above, with different versions of the same task, ideally would use similar levels of
task difficulty. These measurement issues obviously become even more challenging when
assessing the sequence of a task requiring one EF component and a task requiring two
components.
Mechanisms identified— The studies reviewed here suggest several likely developmental
mechanisms (biological and environmental) of EF development for the focus of future
research. Consistent with most developmental EF theories, the assessment of brain activity
is important because it provides clues about how developing EFs become organized, as seen
in changes in neural networks and increased localization. Such research is well underway,
though this research focuses on inhibition and WM, and rarely shifting. One influential
developmental model (Posner & Rothbart, 2007) proposes that the development of the
anterior attention system—the executive attention network—during preschool is important
for regulating other brain networks.
Large neural and behavioral changes in the components during the preschool years are
followed by more gradual, fine-grained improvements later. However, establishing specific
links between brain changes and changes in behavior has proven to be more difficult. Also,
distinguishing individual performance differences in brain activity from maturational
differences continues to challenge neuroimaging researchers (Thomas & Tseng, 2008). One
promising approach is to include several brain and behavioral measures and examine which
ones change from one age to another and which do not. For example, LaVallee, Muenke,
Robertson, and Watamure (2007) compared EEG responses, reaction time, number of gaze
shifts, and accuracy on a modified Stroop task (e.g., see a boy, hear the word “girl”) in 3-
versus 4-year-olds. Another promising approach is one (Morton et al., 2009) in which age
differences in neural correlates of component processes (shifting versus processing of
conflict) were examined separately. Best and Miller Page 19
Child Dev. Author manuscript; available in PMC 2011 November 1.
NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript Two points should be made about brain-based changes as possible mechanisms. First, any
change in brain function could be either a cause (i.e., neural maturation provides a
mechanism of development) or an effect (i.e., EF behaviors lead to brain changes) or both.
Second, cognitive neuroscience work would contribute more to the existing behavioral
literature if it were tied to theoretical developmental issues such as quantitative versus
qualitative change, degree of generalization of new EF skills, and domain-specific versus
domain-general EF skills.
As for experiential-based mechanisms of EF development, several studies reviewed (e.g.,
Crone, 2007; Crone et al., 2006; Davidson et al., 2006; Hogan et al., 2005; Somsen, 2007)
suggest that metacognition may play an important role during the school-age years and
adolescence. Examples are an awareness of inhibition failures and the subsequent
adjustments in response to avoid future errors, as well as slowing one’s response in order to
ensure high accuracy on a switching task. Current developmental EF theories, because they
are based mainly on development in the first five years of life, are limited in this respect.
Post-preschool detection of success or failure of one’s current rule (Zelazo), latent
representation (Munakata), mental set or prepotent behavior (Diamond), or focus of
attention (Posner and Rothbart) may be important metacognitive developments that would
extend these theories based on preschoolers to older children. The relevant aspects of
metacognition may vary from one component to another. For example, it seems likely that
knowing to slow down is particularly important in theories that emphasize inhibition,
whereas detecting errors and considering alternative responses may be particularly important
in theories emphasizing shifting.
Several other suspected contributors to EF include practice (e.g., McNamara et al., 2007),
intense motor activity (Bell et al., 2007; Campbell, Eaton, & McKeen, 2002; Davis et al.,
2007), language (Bell et al., 2007, for preschoolers only; Kray, Eber, & Lindenberger, 2004;
Wolfe & Bell, 2004), bilingualism (Carlson & Meltzoff, 2008), maternal education and
parenting (Friedman et al., 2007), and theory of mind (Hughes & Ensor, 2007; Perner &
Lang, 2000)—understanding that mental states exist and affect behavior. Moreover, cultural
differences, such as the earlier acquisition of EF in Chinese than U. S. children (e.g.,
Sabbagh et al., 2006), suggest that cultural values, perhaps as expressed in practices at
school, may affect the development of EF.
Conclusions Unlike previous reviews focused on preschoolers’ EF, this review focused on EF across a
much larger age span. This perspective permitted an examination of EF in light of central
developmental issues such as the form of developmental trajectories, sequences of
acquisition within EF development, qualitative and quantitative change, and developmental
mechanisms at both behavioral and neural levels. Based on this framework of developmental
issues, the key components of needed future research include: a) use of a wide age range and
comparable tasks to reveal the form of developmental trajectories of each EF component, b)
examination of several EF components so that relations among components can be
examined, and c) assessment of possible mechanisms of development. Such designs would
move EF research from its current state—strong theoretical and empirical work on
preschoolers, scattered non-integrated work on older children, and emphasis on description
of age differences—to a truly developmental account. This account would provide a
developmental theoretical focus to cognitive neuroscience studies of children’s EF. In turn, a
more theoretically-based developmental cognitive neuroscience would provide greater
constraints on developmental issues and theories of EF than we have now. Best and Miller Page 20
Child Dev. Author manuscript; available in PMC 2011 November 1.
NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript AcknowledgmentsSupport for this work was provided in part by a grant from the National Institute of Diabetes and Digestive And
Kidney Diseases (#RO1 DK70922-01). We thank Lara Jones, Katherine Davis, Philip Tomporowski, and Jack
Naglieri for reading and offering comments on an earlier draft.
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