The Emergence of Cognitive Control
Abilities in Childhood
Nina S. Hsu and Susanne M. Jaeggi
Abstract Cognitive control, otherwise known as executive function, refers to our
ability to flexibly adjust or regulate habitual actions or behaviors. As a cluster of
separable components, it depends heavily on the prefrontal cortex, one of the last
brain regions to reach adult maturity. Cognitive control processes are thought to be
among the key factors for scholastic success, and thus, underdeveloped cognitive
control abilities might contribute to an achievement gap. In this chapter, we first
discuss the prolonged maturation of the prefrontal cortex that leads to delayed
emergence of cognitive control abilities in children. We briefly describe some of
the functional effects of prolonged maturation of the prefrontal cortex. We then
discuss how experience and environmental factors such as education and socioeconomic status may affect the development of cognitive control abilities, before
turning to cognitive training interventions as a promising avenue for reducing this
cognitive ‘‘gap’’ in both healthy children and those with developmental disabilities. Taken together, our hope is that by understanding the interaction of brain
development, environmental factors, and the promise of cognitive interventions in
children, this knowledge can help to both guide educational achievement and
inform educational policy.
Keywords Executive function
Socioeconomic status
Cognitive intervention Plasticity
N. S. Hsu (&) S. M. Jaeggi
Department of Psychology, Center for Advanced Study of Language,
7005 52nd Avenue, College Park, MD 20742, USA
e-mail: [email protected]
N. S. Hsu S. M. Jaeggi
Program in Neuroscience and Cognitive Science (NACS),
University of Maryland, College Park, USA
N. S. Hsu
Center for Advanced Study of Language, University of Maryland,
College Park, USA
Curr Topics Behav Neurosci
DOI: 10.1007/7854_2013_241
Ó Springer-Verlag Berlin Heidelberg 2013
Hypofrontality
N. S. Hsu and S. M. Jaeggi
Contents
1 Introduction..............................................................................................................................
2 The Developing Brain .............................................................................................................
3 Functional Consequences of an Immature Prefrontal Cortex................................................
4 Environmental Factors in Cognitive Control Development ..................................................
5 Cognitive Interventions from a Developmental Perspective .................................................
6 Open Questions and Suggestions for Future Research..........................................................
References......................................................................................................................................
1 Introduction
A bicyclist in Maryland has the freedom to legally occupy the center of a full street
lane. This freedom confers a number of advantages, but most importantly, it’s the
safest place for a cyclist to ride. That is, when bicyclists travel as far to the right as
practicable, they risk being missed as drivers pull out of side streets and driveways.
A Maryland bicyclist transported to New York, then, encounters some immediate
problems. In New York, the law forbids bicyclists from occupying a full street
lane, relegating them to shoulders and sidewalks. Our brave Maryland bicyclist
must quickly shift cycling behavior in order to abide by New York law and avoid a
costly ticket (or worse).
The scenario that we have described above is a real-world illustration of cognitive control—also known as executive function (EF)—and it refers to our ability
to flexibly adjust or regulate habitual actions or behaviors. An essential component
to higher cognition, this ability allows us to successfully navigate our surroundings, overriding habitual behaviors and routines when current task goals or
demands require otherwise. The prefrontal cortex (PFC) has been associated with
cognitive control function by acting to guide the selection of task-relevant actions
during information processing (Miller and Cohen 2001; Shimamura 2000). Several
features of the PFC—including its distinctive anatomy, wide range of inputs and
outputs to other cortical regions, and its neuromodulatory regulation of other brain
systems—contribute to a role for the PFC in directing and regulating behavior
(Miller and Cohen 2001).
Cognitive control is not a single process but rather, refers to a cluster of separable components that collectively work to guide goal-directed behavior (Botvinick et al. 2001; Miller and Cohen 2001; Norman and Shallice 1986). Such
components can include self-regulation and self-awareness, task-switching,
updating, and response inhibition (Barkley 2001; Friedman and Miyake 2004;
Miyake 2000). It is thought that these components can operate over a wide variety
of domains including selective attention, working memory (WM), and language
processing (Badre and Wagner 2007; Novick et al. 2005; Smith and Jonides 1999;
Thompson-Schill et al. 2005). As illustrated in Fig. 1, neuroimaging and
The Emergence of Cognitive Control Abilities in Childhood
Fig. 1 A wide network of
brain regions is recruited
during cognitive control
processes. The panels above
show a view of the left
hemisphere laterally (top) and
medially (bottom). Regions
include dorsolateral
prefrontal cortex (brown),
ventrolateral prefrontal cortex
(orange), and anterior
cingulate cortex (purple).
Other regions include
posterior parietal cortex
(pink), premotor cortex
(periwinkle), and pre-SMA,
the anterior portion of the
supplementary motor area
(teal)
neuropsychological studies have demonstrated that cognitive control processes
recruit regions of prefrontal cortex (PFC), including dorsolateral and ventrolateral
prefrontal cortices (e.g., Jonides et al. 2008; Novick et al. 2009; Thompson-Schill
et al. 1997), as well as regions of anterior cingulate cortex (e.g., Miller and Cohen
2001, Botvinick et al. 1999).
Recent evidence has challenged the notion that cognitive control and its
components remain fixed once we reach adulthood (e.g., Gray and Thompson
2004); rather, these cognitive processes may in fact be malleable and amenable to
interventions. In order to better understand the mechanisms underlying these
processes, it is important to first understand the development of these processes
during childhood. Because these control processes are thought to be key factors for
scholastic success, underdeveloped cognitive control abilities might lead to an
achievement gap (e.g., Gathercole et al. 2004; Pickering 2006).
In this chapter, we will outline the current literature on the role that cognitive
control (or lack thereof) plays from a developmental perspective. First, we will
briefly summarize the literature on the neuroanatomical development of the brain
in childhood. Then, we will discuss (1) the effects of an immature prefrontal cortex
on behavior, (2) how environmental factors can influence cognitive control during
N. S. Hsu and S. M. Jaeggi
development, and (3) how targeted cognitive interventions may serve to demonstrate the malleability of cognitive control, having longitudinal implications for
educational achievement. We then conclude by highlighting outstanding issues for
the field to address.
2 The Developing Brain
Primates, including humans, are born with immature brains, and it has been well
established that brain maturation develops throughout childhood and adolescence
(e.g., Gogtay et al. 2004; Sowell et al. 2003). Throughout post-natal development,
the neocortex matures through an initial, rapid growth process of cell proliferation
and changes in synaptic density. During this period, the increase in synaptic
connections accompanies dendritic and axonal growth (i.e., fibers for communication that extend from neurons) and myelination (i.e., insulation, thus boosting
signal transmission) of the subcortical white matter (Huttenlocher and Dabholkar
1997). Synaptogenesis is then followed by pruning, a synapse elimination process
that lasts well into the third decade of life (Huttenlocher and Dabholkar 1997;
Petanjek et al. 2011). Critically, rather than occurring concurrently throughout the
whole brain as they do in rhesus monkeys (e.g., Rakic et al. 1986), these processes
dynamically occur at differing rates throughout the brain in humans (Huttenlocher
and Dabholkar 1997). Brain regions subserving sensory functions, such as vision
and hearing, develop first, followed by development of temporal and parietal
cortices, regions responsible for sensory associations. Higher order cognition
areas, such as prefrontal and lateral temporal cortices, which serve to integrate
information from primary sensorimotor cortices and modulate other cognitive
processes, appear to mature last (Casey et al. 2005b; Petanjek et al. 2011).
Boosted by the development of noninvasive neuroimaging technologies in the
last two decades, researchers have learned a great deal about the anatomical and
functional networks of the developing brain. Early work with positron emission
tomography (PET) imaging demonstrated that human PFC metabolizes glucose at
a slower rate, relative to occipital, temporal, and parietal cortices (Chugani and
Phelps 1986). This result suggests that early development prioritizes basic human
survival functions rather than higher order cognitive processes. More recently,
structural imaging studies have demonstrated that over the course of development,
cortical gray matter loss (i.e., a signature of cortical maturation after puberty)
occurs earliest in primary sensorimotor areas and last in dorsolateral prefrontal and
lateral temporal regions (Gogtay et al. 2004). Moreover, there appears to be evidence for a ‘‘fine-tuning’’ of cortical structures as activation shifts from diffuse to
focal recruitment as children develop (Brown et al. 2005). Taken together,
structural and functional evidence suggests that prefrontal regions associated with
integration and goal-directed behaviors mature after regions responsible for primary sensory functions (Casey et al. 2005a), with both progressive and regressive
processes (rather than simple linear patterns of change) underlying changes in
cognitive abilities (Amso and Casey 2006; Brown et al. 2005).
The Emergence of Cognitive Control Abilities in Childhood
3 Functional Consequences of an Immature
Prefrontal Cortex
There are behavioral and functional consequences of these anatomical developments in children, and most prominently, children exhibit deficits in cognitive
control which has been attributed to the immaturity of the cortical networks
subserving those control systems. For example, in laboratory tasks, children are
more susceptible to interference from competing information and actions as
compared to adults (Casey et al. 2002; Morton and Munakata 2002). Concretely,
infants fail at delayed-response paradigms such as the ‘‘A-not-B’’ task, in which
they indicate whether an occluded object is in the same location as previously
observed (e.g., Diamond 1985). In adults, cognitive control is classically measured
with the Stroop task (Stroop 1935), in which participants must inhibit reading
habits in order to indicate the word’s ink color (e.g., ‘‘red’’ written in blue ink). In
children, cognitive control can be assessed with Stroop-like tasks, including a day/
night task (in which subjects say ‘‘day’’ when they see a black card with a moon
and stars, and ‘‘night’’ when they see a white card with a bright sun), or the red/
blue dog task, in which subjects say ‘‘red’’ when they see a blue dog and ‘‘blue’’
when they see a red dog (Beveridge et al. 2002; Gerstadt et al. 1994; Nilsen and
Graham 2009). In particular, 6-year olds make more errors on this task (e.g.,
calling the dog by its color rather than by its name) than 8-year olds (Beveridge
et al. 2002). In addition to these cognitive factors, self-regulative processes (e.g.,
achievement-related behavior, personal strivings, and regulating shared goals in
close relationships) are important to childhood development (Blair and Diamond
2008; Hofmann et al. 2012). Individual differences in emotional self-regulation
can account for academic achievement beyond what can be explained by general
intelligence (Blair and Razza 2007). The development of self-regulation in children may be mediated by an interaction of top-down executive control and bottomup influences of emotion and stress reactivity (Ursache et al. 2012), playing a role
in successful social and emotional competence (Blair 2002). Taken together, this
brief summary shows that as a result of prolonged maturation of prefrontal cortex
(relative to other parts of the brain which reach adult maturity sooner), children
exhibit impaired behavioral and cognitive control for years (Thompson-Schill
et al. 2009).
How do the neurobiological signatures of cognitive control differ between
children and adults? In one seminal study by Casey and colleagues (Casey et al.
1997), children and adults both performed a go/no-go task while undergoing
functional magnetic resonance imaging (fMRI). In the go/no-go task, participants
are instructed to either respond (on go trials) or withhold a response (on no-go
trials). The authors found that activity in the anterior cingulate cortex and ventral
prefrontal regions correlated with error rate (i.e., false alarms, or responding when
told to withhold responding). Notably, exhibiting more false alarms than adults,
children activated prefrontal cortex (specifically, dorsolateral prefrontal cortex, or
DLPFC) more diffusely than adults during the task, but DLPFC activation did not
N. S. Hsu and S. M. Jaeggi
correlate with task performance. These results suggest that maturation of the
prefrontal cortex involves reduction of diffuse activity in the form of both
strengthened relevant connections, as well as attenuated irrelevant connections
(Casey et al. 1997; Hare and Casey 2005).
Using a flanker task to test interference resolution and response inhibition
(Eriksen and Eriksen 1974), Bunge and colleagues (Bunge et al. 2002) found that
immature cognitive control abilities were associated with the inability of children
to recruit ventrolateral prefrontal cortex (VLPFC) regions in a similar manner to
adults, that is—children failed to activate a region in right VLPFC that was
recruited for multiple types of cognitive control by adults. Other fMRI studies
have found that subcortical systems—specifically, the striatum—may also play an
important role in overriding inappropriate responses (Booth et al. 2003; Durston
et al. 2002). Specifically, lower activity in the caudate nucleus parallels poorer
performance on a go/no-go task in children and adolescents (Durston et al. 2003;
Rubia et al. 1999).
In addition to spatial differences in the anatomical substrates subserving cognitive control in children and adults, some research suggests age-related differences in temporal dynamics as well. Wendelken and colleagues (Wendelken et al.
2012) administered a task manipulating rule type, rule switching, and stimulus
incongruency to both children and adults. That is, in a task that asked subjects to
indicate color or direction of a stimulus on any given trial, consecutive trials could
switch the given rule (i.e., a switch from indicating color to indicating direction).
Both participant groups activated a brain network related to cognitive control,
including posterior parietal cortex, left dorsolateral prefrontal cortex (DLPFC),
pre-motor cortex (PMC), and the anterior portion of the supplementary motor area
(pre-SMA); see Fig. 1. However, the temporal dynamics demonstrated that
DLPFC activation in children reflected the previous rule type (regardless of the
current rule), suggesting ‘‘sluggishness’’ in executive function shifting abilities.
Whether a similar pattern might emerge in other cognitive control regions requires
further study, but behavioral data suggest the persistence of a similar ‘‘carry-over’’
effect observed in children but not adults (Crone et al. 2006a).
Other noninvasive imaging technologies, including diffusor tensor imaging
(DTI), can map structural connectivity across brain regions. In one study, Liston
and colleagues (Liston et al. 2006) found that the degree of connectivity between
prefrontal cortex and striatum (both linked to cognitive control task performance
as described earlier) correlated positively with age. However, in comparing the
frontostriatal tract to a second fiber tract (i.e., the corticospinal fiber, which also
correlated with age), only frontostriatal connectivity predicted go/no-go task
performance. This result indicates that maturation of frontostriatal connectivity (as
indexed by DTI measures) contributes to the development of cognitive control
abilities. Taken together, these findings suggest that a combination of structural
and functional information gained from noninvasive brain technology can inform
us about the developing brain and how it affects cognitive control systems.
As outlined above, there are clearly negative consequences to the prolonged
development and maturation of the prefrontal cortex in children. We have
The Emergence of Cognitive Control Abilities in Childhood
described a number of studies showing that across a variety of cognitive control
paradigms, children exhibit poorer performance relative to adults. Conversely,
there might be an evolutionary advantage to the late development of prefrontal
cortices and the cognitive control network. What might be the nature of this
advantage? There may be positive consequences to this lack of cognitive control
during childhood (Chrysikou et al. 2011; Thompson-Schill et al. 2009), wherein an
immature frontal cortex (sometimes referred to as hypofrontality) might confer
adaptive benefits. Thus, there may exist a trade-off between a system tuned to
optimal performance (i.e., rule-based), versus a system built for learning (i.e., datadriven). A testable prediction for this proposal is that children should outperform
their older counterparts on some cognitive tasks, and this is indeed the case, most
prominently in the domain of language acquisition and language learning (e.g.,
Gleitman et al. 1984). For example, in noun pluralization, Ramscar and Yarlett
(2007) demonstrated that children are easily able to master irregular plurals (e.g.,
mouse ? mice) rather than adopting a pluralization dictated by the more frequent
convention (e.g., mouse ? mouses). This result points to the idea that children
maximize probabilistic input, which in this case, optimizes performance. In contrast, adults tend to rely on a probabilistic approach by monitoring the probabilities
of alternative patterns (Ramscar and Yarlett 2007). This age-related difference in
learning (observed in domains such as language learning) may be promoted by a
lack of cognitive flexibility driven by an underdeveloped prefrontal cortex.
On the other hand, the hypofrontality as observed in children may have other
consequences in the domain of language comprehension. In particular, consider
the incremental nature of language processing—that is, the notion that we interpret
language as it unfolds in real time, rather than awaiting the end of the sentence. As
such, readers (and listeners) commit to an initial interpretation and anticipate
arriving information. One consequence of this incremental processing is temporary
ambiguity in parsing decisions—in cases where an initial interpretation turns out to
be wrong, subjects must revise their initial interpretations in order to arrive at the
correct meaning of the sentence. This process triggers cognitive control abilities,
as subjects detect a need to recover from misanalysis (Novick et al. 2005). 5-year
olds, relative to 8-year olds and healthy adults, demonstrate a failure to revise in
these initial parsing decisions (e.g., Trueswell et al. 1999), paralleling performance
observed in patients with left PFC damage (Novick et al. 2009).
As a final example, consider the case of functional fixedness, a bias in which
participants are hindered in reaching a solution to a problem because of their
knowledge of an object’s conventional use. In a classic laboratory problem solving
task, participants must indicate that a box can be used to reach an object high on
the shelf (i.e., a height booster), rather than (conventionally) used as an object for
containing things. German and Defeyter (2000) found that when the conventional
use of the box was initially demonstrated, older children (7-years old) took longer
to arrive at the solution compared to when the conventional use was not demonstrated. Notably, younger children (5-years old) had comparable performance,
regardless of whether the object’s conventional use was demonstrated or not. This
‘‘immunity’’ to functional fixedness at an early age suggests that older children
N. S. Hsu and S. M. Jaeggi
were more likely to apply a ‘‘rule’’ to the box’s use, resulting in longer solution
times (or failing to arrive at the correct solution at all). There are other cases when
a lack or reduction of cognitive control permits cognitive flexibility, particularly in
the domain of creativity. That is, a lowered state of cognitive control might allow
adults to reduce the filtering properties of the PFC (e.g., Shimamura 2000), thus
promoting a more data-driven rather than a rule-based approach to the cognitive
task at hand. Chrysikou and Thompson-Schill (2011) did just this using an fMRI
paradigm. For each object in a set, young adults generated common uses (e.g.,
shoe as foot apparel) or uncommon-but-plausible uses (e.g., shoe as hammer) for
the object. Common object use generation reliably activated prefrontal cortex,
whereas uncommon object use reliably activated regions of occipito-temporal
cortex. Further, perceptually based responses (e.g., using a chair for firewood,
which does not require prior knowledge of a chair’s function) predicted activation
in posterior cortex, suggesting that a diminished state of cognitive control may, in
fact, confer benefits to certain creativity tasks.
In sum, these results demonstrate that an immature prefrontal cortex bears
negative consequences on some cognitive domains, but that it can also provide
benefits toward other cognitive domains. In particular, hypofrontality may have
positive consequences for tasks in which data-driven learning, rather the rulebased performance, drives optimal performance (Chrysikou et al. 2011; Thompson-Schill et al. 2009). Considering this trade-off, the argument for accelerating
maturation of cognitive control networks that are mediated by regions of prefrontal
cortices may need to be tempered with the evolutionary advantages that immature
frontal lobes may confer.
4 Environmental Factors in Cognitive Control
Development
Recent work posits an important influence of environmental factors, including
socioeconomic status (SES), on the developing brain. SES, a composite measure,
considers economic and non-economic factors, including material wealth, social
prestige, and education. Educational advocates have long discussed the negative
implications of low SES backgrounds on cognition, and ultimately, on academic
achievement. Moreover, SES correlates with life stress and neighborhood quality,
and previous work has shown that low SES children suffer from poorer health,
impaired psychological well-being, and impaired development throughout the
lifespan. These consequences point to a role for SES in shaping candidate neural
pathways by which SES might compromise academic achievement or increase the
risk of mental illness (see Hackman et al. 2010 or Hackman and Farah 2009 for
review). Because neurobiological systems may mediate these SES-cognition gradients, we focus here on work demonstrating a link between SES and assessments
of cognitive control, executive function, and language. For example, Noble and
colleagues (Noble et al. 2005) found that in kindergarteners of differing SES
The Emergence of Cognitive Control Abilities in Childhood
backgrounds, low SES children performed worse than middle SES children on
language (mediated by the left perisylvian regions) and EF (mediated by the
prefrontal cortex) measures. The groups did not differ on the other cognitive
measures, and the authors point to the delayed maturation of the brain regions
mediating these abilities as being more susceptible to environmental factors (like
SES). A subsequent study of individual differences using a population of firstgraders demonstrated that SES explained 30 % of the variance in language performance (Noble et al. 2007). SES also explained 6 % of the variance in both
cognitive control and working memory performance (despite the small value, this
value was statistically significant in both cases).
While the role of SES has been examined in sociological and epidemiological
contexts, research is just now beginning to shed light on its impact on neurobiological mechanisms. A network of regions mediates reading processes, including
left hemisphere middle temporal and inferior frontal gyri, and left-posterior super
temporal sulci (Turkeltaub et al. 2003). SES can modulate activity in these regions
during reading. Specifically, there is a negative correlation between the brainbehavioral relationship and SES levels in these regions, with an amplified impact
of SES at lower SES levels (Noble et al. 2006b). Indeed, there appears to be a
systematic effect of SES on reading skills (even after controlling for other factors),
suggesting a multiplicative effect of reading ability and SES that exaggerates
poorer performance at low SES levels (Noble et al. 2006a). Further, EEG measures
indicate that SES modulates measures of attention, with low SES children showing
an attenuated response to novel stimuli relative to high SES children (Kishiyama
et al. 2009), and a reduced ability to inhibit distractors (D’Angiulli et al. 2008;
Stevens et al. 2009). In 5-year-old children, SES predicts hemispheric asymmetry
of the inferior frontal gyrus (even after controlling for scores on a standardized set
of language and cognition tests), with left lateralization associated with higher SES
(Raizada et al. 2008). In line with this result, Sheridan and colleagues (Sheridan
et al. 2012) found that right medial frontal gyrus activity was inversely related to
accuracy in acquiring a novel stimulus–response association (e.g., through a
dimensional change card sorting task). Moreover, Raizada and colleagues (Raizada et al. 2008) also found that SES trended toward predicting both white and gray
matter volume in these regions, suggesting anatomical consequences to SES
variation.
In sum, these results suggest that SES variation results in altered prefrontal
anatomy, with negative consequences on various cognitive domains, particularly
those subserving language and executive function. On the other hand, the susceptibility of prefrontal cortices to experiential and environmental factors suggests
that these cortical networks might be malleable. Indeed, there is evidence from
naturalistic experiments showing that schooling can promote cognitive control
beyond natural development. For example, Burrage et al. (2008) have demonstrated that schooling had a significant impact on executive function in a group of
prekindergarten and kindergarten children when compared to children at the same
age which did not attend school. In light of these findings, there exists the potential
for targeted educational interventions to enrich the neural architecture of the child
N. S. Hsu and S. M. Jaeggi
in order to give rise to enhanced cognitive function. In particular, these interventions might be especially beneficial for children who are facing learning difficulties or who come from low SES backgrounds. Thus, although factors such as
low SES might result in an educational achievement gap, timely educational
interventions may be able to minimize or even close this gap. We turn now to the
research that has explored this possibility.
5 Cognitive Interventions from a Developmental
Perspective
Conventionally, cognitive training is defined as a means of improving cognitive
functioning through practice and/or instruction (Jolles and Crone 2012). A rapidly
emerging field concerns the impact of cognitive training (and in particular, WM
training) on transfer measures of cognitive control, EF abilities, and general fluid
intelligence in adults (e.g., Jaeggi et al. 2008; see Morrison and Chein 2011 or
Hussey and Novick 2012 for recent reviews on the topic). Our focus here is on
cognitive training studies from a developmental perspective. Cognitive training
studies in children can take many forms (e.g., mindfulness training, and core or
supplemental curricula—see Diamond and Lee 2011), but in this chapter, we focus
on cognitive interventions in the form of computerized games that specifically
target WM and EF processes. For example, an early study used an adaptive (i.e.,
adjusting for difficulty as the participant’s performance improved) and intense (i.e.,
repeated several times a week for at least five weeks) intervention in a child population with attention deficit hyperactivity disorder (ADHD), characterized by
inattention, impulsivity, and hyperactivity (Barkley 1997) and linked to impaired
function of the frontal lobes (for review, see Castellanos and Proal 2012). In
addition to improving on the trained WM task, participants significantly improved
on an untrained WM task, as well as on Raven’s Progressive Matrices (RPM), a
non-verbal complex reasoning task (Klingberg et al. 2002). A subsequent study
demonstrated that across measures of WM (items successfully remembered in spanboard and digit span), EF (Stroop task completion time), and complex reasoning
(correct RPM items), ADHD children that trained on computerized WM tasks
outperformed those children completing a control training program (Klingberg
et al. 2005). Some of these effects persisted three months after the end of training,
suggesting that long-term changes are possible with short, intense training periods.
Since then, other studies have used such intervention paradigms in children in
order to improve EF and WM abilities through training. These studies have used
paradigms targeting attention (Rueda et al. 2005), WM (Holmes et al. 2009; Jaeggi
et al. 2011; Loosli et al. 2012; Thorell et al. 2009), inhibition (Thorell et al. 2009),
and reasoning (Bergman Nutley et al. 2011; Mackey et al. 2011). Some of these
studies have examined intervention paradigms in the context of ameliorating
developmental disabilities, including ADHD (Hoekzema et al. 2010); developmental dyscalculia (Kucian et al. 2011), reading abilities in at-risk youth (Yamada
The Emergence of Cognitive Control Abilities in Childhood
et al. 2011), and dyslexia (Temple et al. 2003). Such interventions generally
demonstrate improvement on executive functions and cognitive control—that is,
those skills that are critical for scholastic achievement (e.g., Diamond and Lee
2011). Additionally, these interventions may interact with environmental factors,
such as SES. In one study, low-SES children trained on either reasoning (e.g.,
‘‘Rush Hour’’) or speed (e.g., ‘‘Blink’’) processing using a battery of commercially
available games. Post-training results suggested that both processes are separately
modifiable with improvements seen in a special population that may need the
intervention the most (Mackey et al. 2011).
Though the results of the studies described above are promising, there is much
variability in terms of training length, frequency, and assessment type across the
various designs of intervention studies in children. Thus, in order to best understand the mechanisms underlying training and transfer of EF and WM abilities,
further work is necessary (Buschkuehl et al. 2012; Jaeggi et al. 2011, 2012; Shah
et al. 2012). The underlying neural mechanisms are beginning to be addressed in
adults (e.g., Dahlin et al. 2008), but only a handful of studies have looked at the
neural correlates underlying cognitive interventions in children. As a result, the
neural mechanisms that lead to the observed neural changes and plasticity are still
unclear. That is, some studies report decreased changes in activation following
training (Haier et al. 2009; Kucian et al. 2011; Qin et al. 2004), whereas others
report increases in activation following training (Hoekzema et al. 2010; Jolles
et al. 2012; Shaywitz et al. 2004; Stevens et al. 2008; Temple et al. 2003). At least
one study has examined structural changes in the form of cortical thickness differences after training (Haier et al. 2009), though the authors note that there was no
overlap between regions showing functional changes and those showing structural
changes, suggesting a complex relationship that warrants further study.
These mixed findings point to an array of results that seek to demonstrate how
the brain changes after cognitive training. Decreased activation after training
suggests increased processing efficiency or performance of the task within capacity
limits (cf. Nyberg et al. 2009), whereas increased activation might reflect additional recruitment of neural resources. A combination of these two patterns could
represent a re-distribution of neural activity after training, whereas activation in
different brain regions (i.e., brain regions not active pre-intervention) would reflect
qualitative changes in brain activity that could represent a change in strategy
(Buschkuehl et al. 2012; Jolles and Crone 2012). In general, future cognitive
training studies that can incorporate a neuroimaging component are necessary in
order to better understand the neural mechanisms (i.e., understanding the how)
underlying training and transfer. Researchers also have to keep in mind that age
might moderate the outcomes of intervention studies. For example, although
variable training (i.e., training in which there are novel task demands in each
training session) can boost transfer effects in adults, this variability can also hinder
those same desired effects in children (Karbach and Kray 2009). Thus, researchers
have to keep in mind that just because cognitive training paradigm shows promise
in adults, that does not mean that the same will be true for children—the child
brain is not necessarily a ‘‘miniature version’’ of the adult brain. It is possible that
N. S. Hsu and S. M. Jaeggi
while training in adults may modify existing neural architecture, training in young
children may influence the construction of that architecture (Galvan 2010), suggesting qualitative and quantitative differences across children and adults (Jolles
and Crone 2012). For example, during the delay period of a WM task (i.e., when
no stimulus is present, but participants manipulate the to-be-remembered information), children (8 to 12-year olds) failed to recruit right DLPFC and bilateral
superior parietal cortex, whereas young adults did activate this region (Crone et al.
2006b). Thus, it may be possible to boost recruitment of this region during WM
manipulation with a cognitive training paradigm, particularly since intensive
cognitive training in younger individuals may actually lead to more widespread
transfer effects, possibly due to the unspecialized state of functional neural networks at a younger age (Wass et al. 2012).
What makes an intervention effective, and how can previous research inform
the optimized development of future intervention studies, particularly in children?
Diamond and Lee (2011) offer some suggestions, including promoting motivation,
repeated practice, challenging tasks that engage children, the inclusion of aerobic
exercise, and other elements of the intervention that provide children with a sense
of joy and acceptance.
6 Open Questions and Suggestions for Future Research
Several open questions remain as we work to more fully understand cognitive
development as an interplay amongst (a) anatomical and functional changes of the
brain throughout childhood and adolescence, (b) environmental factors, and (c)
targeted interventions that may boost cognitive control processes (cf. Fig. 2).
Given the progressive and regressive development of the child and adolescent
brain, cognitive intervention studies that combine behavioral and neuroimaging
measures may provide a better understanding the cognitive and neural mechanisms
underlying cognitive control in the context of other factors.
Moreover, there is likely an important role of individual differences in terms of
cognitive control and the extent of its susceptibility to intervention. Previous
research has demonstrated training-related individual differences (e.g., Jaeggi et al.
2011; Rueda et al. 2005), and we have also reviewed a role for environmental
factors (i.e., SES, which will also vary by individual) in cognitive control, and
finally, developmental disabilities may also determine training outcome. In considering and designing intervention studies, then, we suggest that researchers give
serious thought to the role that these environmental factors and individual differences may play in the effects of training and transfer, as well as their underlying
mechanisms.
Finally, in spite of the interventions that seek to reduce or even close an
achievement gap from those who exhibit poorer EF skills than others, there may
also exist a fine balance between hastening frontal lobe development through
cognitive interventions in order to improve cognitive control and EF, and allowing
The Emergence of Cognitive Control Abilities in Childhood
Fig. 2 Cognitive control processes are mediated by a number of interacting factors, including
typical (and atypical) brain development, environmental factors, and behavior
an immature prefrontal cortex to confer cognitive benefits (Thompson-Schill et al.
2009). Nonetheless, reducing the achievement gap, particularly for those children
at an economic disadvantage or with developmental disabilities, is of critical
interest for our educational system. We will need a better developed and characterized understanding of the interaction of prefrontal maturation, learning, and
cognitive control in order to guide research and inform educational policy.
References
Amso D, Casey BJ (2006) Beyond what develops when neuroimaging may inform how cognition
changes with development. Curr Dir Psychol Sci 15:24–29
Badre D, Wagner AD (2007) Left ventrolateral prefrontal cortex and the cognitive control of
memory. Neuropsychologia 45:2883–2901. doi:10.1016/j.neuropsychologia.2007.06.015
Barkley RA (1997) Behavioral inhibition, sustained attention, and executive functions:
constructing a unifying theory of ADHD. Psychol Bull 121:65–94
Barkley RA (2001) The executive functions and self-regulation: an evolutionary neuropsychological perspective. Neuropsychol Rev 11:1–29
Bergman Nutley S, Söderqvist S, Bryde S et al (2011) Gains in fluid intelligence after training
non-verbal reasoning in 4-year-old children: a controlled, randomized study. Dev Sci
14:591–601. doi:10.1111/j.1467-7687.2010.01022.x
Beveridge M, Jarrold C, Pettit E (2002) An experimental approach to executive fingerprinting in
young children. Infant Child Dev 11:107–123. doi:10.1002/icd.300
Blair C (2002) School readiness: integrating cognition and emotion in a neurobiological
conceptualization of children’s functioning at school entry. Am Psychol 57:111–127
Blair C, Diamond A (2008) Biological processes in prevention and intervention: the promotion of
self-regulation as a means of preventing school failure. Dev Psychopathol 20:899–911
N. S. Hsu and S. M. Jaeggi
Blair C, Razza RP (2007) Relating effortful control, executive function, and false belief
understanding to emerging math and literacy ability in kindergarten. Child Dev 78:647–663
Booth JR, Burman DD, Meyer JR et al (2003) Neural development of selective attention and
response inhibition. NeuroImage 20:737–751. doi:10.1016/S1053-8119(03)00404-X
Botvinick M, Nystrom L, Fissell K et al. (1999) Conflict monitoring versus selection-for-action in
anterior cingulate cortex. Nature 402:179–181
Botvinick MM, Braver TS, Barch DM et al (2001) Conflict monitoring and cognitive control.
Psychol Rev 108:624–652
Brown TT, Lugar HM, Coalson RS et al (2005) Developmental changes in human cerebral
functional organization for word generation. Cereb Cortex 15:275–290. doi:10.1093/cercor/
bhh129
Bunge SA, Dudukovic NM, Thomason ME et al (2002) Immature frontal lobe contributions to
cognitive control in children: evidence from fMRI. Neuron 33:301–311
Burrage MS, Ponitz CC, McCready EA et al (2008) Age- and schooling-related effects on
executive functions in young children: a natural experiment. Child Neuropsychol 14:510–524.
doi:10.1080/09297040701756917
Buschkuehl M, Jaeggi SM, Jonides J (2012) Neuronal effects following working memory
training. Dev Cogn Neurosci 2(Suppl 1):S167–179. doi:10.1016/j.dcn.2011.10.001
Casey B, Galvan A, Hare TA (2005a) Changes in cerebral functional organization during
cognitive development. Curr Opin Neurobiol 15:239–244. doi:10.1016/j.conb.2005.03.012
Casey B, Tottenham N, Liston C, Durston S (2005b) Imaging the developing brain: what have we
learned about cognitive development? Trends Cogn Sci 9:104–110. doi:10.1016/j.tics.
2005.01.011
Casey BJ, Tottenham N, Fossella J (2002) Clinical, imaging, lesion, and genetic approaches
toward a model of cognitive control. Dev Psychobiol 40:237–254. doi:10.1002/dev.10030
Casey BJ, Trainor RJ, Orendi JL et al (1997) A developmental functional MRI study of prefrontal
activation during performance of a go-no-go task. J Cogn Neurosci 9:835–847
Castellanos FX, Proal E (2012) Large-scale brain systems in ADHD: beyond the prefrontal–
striatal model. Trends Cogn Sci 16:17–26. doi:10.1016/j.tics.2011.11.007
Chrysikou EG, Novick JM, Trueswell JC, Thompson-Schill SL (2011) The other side of
cognitive control: can a lack of cognitive control benefit language and cognition? Top Cogn
Sci 3:253–256. doi:10.1111/j.1756-8765.2011.01137.x
Chrysikou EG, Thompson-Schill SL (2011) Dissociable brain states linked to common and
creative object use. Hum Brain Mapp 32:665–675. doi:10.1002/hbm.21056
Chugani HT, Phelps ME (1986) Maturational changes in cerebral function in infants determined
by 18 FDG positron emission tomography. Science 231:840–843
Crone EA, Bunge SA, Van Der Molen MW, Ridderinkhof KR (2006a) Switching between tasks
and responses: a developmental study. Dev Sci 9:278–287
Crone EA, Wendelken C, Donohue S et al (2006b) Neurocognitive development of the ability to
manipulate information in working memory. Proc Natl Acad Sci 103:9315–9320
D’Angiulli A, Herdman A, Stapells D, Hertzman C (2008) Children’s event-related potentials of
auditory selective attention vary with their socioeconomic status. Neuropsychology
22:293–300
Dahlin E, Neely AS, Larsson A et al (2008) Transfer of learning after updating training mediated
by the striatum. Science 320:1510–1512
Diamond A (1985) Development of the ability to use recall to guide action, as indicated by
infants’ performance on AB. Child Dev 56:868–883
Diamond A, Lee K (2011) Interventions shown to aid executive function development in children
4 to 12 years old. Science 333:959–964. doi:10.1126/science.1204529
Durston S, Thomas KM, Yang Y et al (2002) A neural basis for the development of inhibitory
control. Dev Sci 5:F9–F16
Durston S, Tottenham NT, Thomas KM et al (2003) Differential patterns of striatal activation in
young children with and withot ADHD. Biol Psychiatry 53:871–878
The Emergence of Cognitive Control Abilities in Childhood
Eriksen BA, Eriksen CW (1974) Effects of noise letters upon identification of a target letter in a
non-search task. Percept Psychophys 16:143–149
Friedman NP, Miyake A (2004) The relations among inhibition and interference control
functions: a latent-variable analysis. J Exp Psychol Gen 133:101–135. doi:10.1037/00963445.133.1.101
Galvan A (2010) Neural plasticity of development and learning. Hum Brain Mapp 31:879–890
Gathercole SE, Pickering SJ, Knight C, Stegmann Z (2004) Working memory skills and
educational attainment: evidence from national curriculum assessments at 7 and 14 years of
age. Appl Cogn Psychol 18:1–16. doi:10.1002/acp.934
German TP, Defeyter MA (2000) Immunity to functional fixedness in young children. Psychon
Bull Rev 7:707–712
Gerstadt CL, Hong YJ, Diamond A (1994) The relationship between cognition and action:
performance of children 3 1/2-7 years old on a stroop-like day-night test. Cognition
53:129–153
Gleitman L, Newport E, Gleitman H (1984) The current status of the Motherese hypothesis.
J Child Lang 11:43–79
Gogtay N, Giedd JN, Lusk L et al (2004) Dynamic mapping of human cortical development
during childhood through early adulthood. Proc Natl Acad Sci USA 101:8174–8179
Gray JR, Thompson PM (2004) Neurobiology of intelligence: science and ethics. Nat Rev
Neurosci 5:471–482. doi:10.1038/nrn1405
Hackman DA, Farah MJ (2009) Socioeconomic status and the developing brain. Trends Cogn Sci
13:65–73. doi:10.1016/j.tics.2008.11.003
Hackman DA, Farah MJ, Meaney MJ (2010) Socioeconomic status and the brain: mechanistic
insights from human and animal research. Nat Rev Neurosci 11:651–659
Haier RJ, Karama S, Leyba L, Jung RE (2009) MRI assessment of cortical thickness and
functional activity changes in adolescent girls following three months of practice on a visualspatial task. Bmc Res Notes 2:174. doi:10.1186/1756-0500-2-174
Hare TA, Casey BJ (2005) The neurobiology and development of cognitive and affective control.
Cogn Brain Behav 9:273–286
Hoekzema E, Carmona S, Tremols V et al (2010) Enhanced neural activity in frontal and
cerebellar circuits after cognitive training in children with attention-deficit/hyperactivity
disorder. Hum Brain Mapp 31:1942–1950. doi:10.1002/hbm.20988
Hofmann W, Schmeichel BJ, Baddeley AD (2012) Executive functions and self-regulation.
Trends Cogn Sci 16:174–180. doi:10.1016/j.tics.2012.01.006
Holmes J, Gathercole SE, Dunning DL (2009) Adaptive training leads to sustained enhancement
of poor working memory in children. Dev Sci 12:F9–F15. doi:10.1111/j.1467-7687.2009.
00848.x
Hussey EK, Novick JM (2012) The benefits of executive control training and the implications for
language processing. Front Psychol 3:158. doi: 10.3389/fpsyg.2012.00158
Huttenlocher PR, Dabholkar AS (1997) Regional differences in synaptogenesis in human cerebral
cortex. J Comp Neurol 387:167–178
Jaeggi SM, Buschkuehl M, Jonides J, Perrig WJ (2008) Improving fluid intelligence with training
on working memory. Proc Natl Acad Sci 105:6829
Jaeggi SM, Buschkuehl M, Jonides J, Shah P (2012) Cogmed and working memory training:
current challenges and the search for underlying mechanisms. J Appl Res Mem Cogn
1:211–213. doi:10.1016/j.jarmac.2012.06.003
Jaeggi SM, Buschkuehl M, Jonides J, Shah P (2011) Short- and long-term benefits of cognitive
training. Proc Natl Acad Sci USA 108:10081–10086. doi:10.1073/pnas.1103228108
Jolles DD, van Buchem MA, Rombouts SARB, Crone EA (2012) Practice effects in the
developing brain: a pilot study. Dev Cogn Neurosci 2:S180–S191. doi:10.1016/
j.dcn.2011.09.001
Jolles DD, Crone EA (2012) Training the developing brain: a neurocognitive perspective. Front
Hum Neurosci. doi:10.3389/fnhum.2012.00076
N. S. Hsu and S. M. Jaeggi
Jonides J, Lewis RL, Nee DE et al. (2008) The mind and brain of short-term memory. Annu Rev
Psychol 59:193–224. doi: 10.1146/annurev.psych.59.103006.093615
Karbach J, Kray J (2009) How useful is executive control training? Age differences in near and
far transfer of task-switching training. Dev Sci 12:978–990. doi:10.1111/j.1467-7687.
2009.00846.x
Kishiyama MM, Boyce WT, Jimenez AM et al (2009) Socioeconomic disparities affect prefrontal
function in children. J Cogn Neurosci 21:1106–1115
Klingberg T, Fernell E, Olesen PJ et al (2005) Computerized training of working memory in
children with ADHD—a randomized, controlled trial. J Am Acad Child Adolesc Psychiatry
44:177–186
Klingberg T, Forssberg H, Westerberg H (2002) Training of working memory in children with
ADHD. J Clin Exp Neuropsychol 24:781–791
Kucian K, Grond U, Rotzer S et al (2011) Mental number line training in children with
developmental dyscalculia. NeuroImage 57:782–795. doi:10.1016/j.neuroimage.2011.01.070
Liston C, Watts R, Tottenham N et al (2006) Frontostriatal microstructure modulates efficient
recruitment of cognitive control. Cereb Cortex 16:553–560. doi:10.1093/cercor/bhj003
Loosli SV, Buschkuehl M, Perrig WJ, Jaeggi SM (2012) Working memory training improves
reading processes in typically developing children. Child Neuropsychol 18:62–78.
doi:10.1080/09297049.2011.575772
Mackey AP, Hill SS, Stone SI, Bunge SA (2011) Differential effects of reasoning and speed
training in children. Dev Sci 14:582–590. doi:10.1111/j.1467-7687.2010.01005.x
Miller EK, Cohen JD (2001) An integrative theory of prefrontal cortex function. Annu Rev
Neurosci 24:167–202
Miyake A (2000) The unity and diversity of executive functions and their contributions to
complex ‘‘frontal lobe’’ tasks: a latent variable analysis. Cognit Psychol 41:49–100.
doi:10.1006/cogp.1999.0734
Morrison AB, Chein JM (2011) Does working memory training work? The promise and
challenges of enhancing cognition by training working memory. Psychon Bull Rev 18:46–60
Morton JB, Munakata Y (2002) Active versus latent representations: a neural network model of
perseveration, dissociation, and decalage. Dev Psychobiol 40:255–265. doi:10.1002/
dev.10033
Nilsen E, Graham S (2009) The relations between children’s communicative perspective-taking
and executive functioning. Cognit Psychol 58:220–249. doi:10.1016/j.cogpsych.2008.07.002
Noble KG, Farah MJ, McCandliss BD (2006a) Socioeconomic background modulates cognition–
achievement relationships in reading. Cogn Dev 21:349–368. doi:10.1016/j.cogdev.
2006.01.007
Noble KG, Norman MF, Farah MJ (2005) Neurocognitive correlates of socioeconomic status in
kindergarten children. Dev Sci 8:74–87
Noble KG, Wolmetz ME, Ochs LG et al (2006b) Brain–behavior relationships in reading
acquisition are modulated by socioeconomic factors. Dev Sci 9:642–654
Noble KG, McCandliss BD, Farah MJ (2007) Socioeconomic gradients predict individual
differences in neurocognitive abilities. Dev Sci 10:464–480. doi: 10.1111/j.1467-7687.2007.
00600.x
Norman W, Shallice T (1986) Attention to action: willed and automatic control of behavior.
Conscious. Self Regul. Adv. Res. Theory, 4th ed. Plenum, New York, pp 1–18
Novick JM, Kan IP, Trueswell JC, Thompson-Schill SL (2009) A case for conflict across multiple
domains: memory and language impairments following damage to ventrolateral prefrontal
cortex. Cogn Neuropsychol 26:527–567. doi:10.1080/02643290903519367
Novick JM, Trueswell JC, Thompson-Schill SL (2005) Cognitive control and parsing:
reexamining the role of Broca’s area in sentence comprehension. Cogn Affect Behav
Neurosci 5:263–281
Nyberg L, Dahlin E, Stigsdotter N, Backman l (2009) Neural correlates of variable working
memory load across adult age and skill: dissociative patterns within the fronto-parietal
network. Scandanavian J Psychol 50:41–46
The Emergence of Cognitive Control Abilities in Childhood
Petanjek Z, Judas M, Simic G et al (2011) Extraordinary neoteny of synaptic spines in the human
prefrontal cortex. Proc Natl Acad Sci 108:13281–13286. doi:10.1073/pnas.1105108108
Pickering SJ (2006) Working memory and education. Academic Press
Qin Y, Carter CS, Silk EM et al (2004) The change of the brain activation patterns as children
learn algebra equation solving. Proc Natl Acad Sci USA 101:5686–5691
Raizada RDS, Richards TL, Meltzoff A, Kuhl PK (2008) Socioeconomic status predicts
hemispheric specialisation of the left inferior frontal gyrus in young children. NeuroImage
40:1392–1401. doi:10.1016/j.neuroimage.2008.01.021
Rakic P, Bourgeois J-P, Eckenhoff MF et al (1986) Concurrent overproduction of synapses in
diverse regions of the primate cerebral cortex. Science 232:232–235
Ramscar M, Yarlett D (2007) Linguistic self-correction in the absence of feedback: a new
approach to the logical problem of language acquisition. Cogn Sci 31:927–960
Rubia K, Overmeyer S, Taylor E et al (1999) Hypofrontality in attention deficit hyperactivity
disorder during higher-order motor control: a study with functional MRI. Am J Psychiatry
156:891–896
Rueda MR, Rothbart MK, McCandliss BD et al (2005) Training, maturation, and genetic
influences on the development of executive attention. Proc Natl Acad Sci USA
102:14931–14936
Shah P, Buschkuehl M, Jaeggi SM, Jonides J (2012) Cognitive training for ADHD: The
importance of individual differences. J Appl Res Mem Cogn 1:204–205. doi:10.1016/
j.jarmac.2012.06.003
Shaywitz BA, Shaywitz SE, Blachman BA et al (2004) Development of left occipitotemporal
systems for skilled reading in children after a phonologically- based intervention. Biol
Psychiatry 55:926–933. doi:10.1016/j.biopsych.2003.12.019
Sheridan MA, Sarsour K, Jutte D et al (2012) The impact of social disparity on prefrontal
function in childhood. PLoS ONE 7:e35744. doi:10.1371/journal.pone.0035744
Shimamura AP (2000) The role of the prefrontal cortex in dynamic filtering. Psychobiology
28:207–218
Smith E, Jonides J (1999) Storage and executive processes in the frontal lobes. Science
283:1657–1661
Sowell ER, Peterson BS, Thompson PM et al (2003) Mapping cortical change across the human
life span. Nat Neurosci 6:309–315
Stevens C, Fanning J, Coch D et al (2008) Neural mechanisms of selective auditory attention are
enhanced by computerized training: Electrophysiological evidence from language-impaired
and typically developing children. Brain Res 1205:55–69. doi:10.1016/j.brainres.2007.10.108
Stevens C, Lauinger B, Neville H (2009) Differences in the neural mechanisms of selective
attention in children from different socioeconomic backgrounds. Dev Sci 12:634–646
Stroop JR (1935) Studies of interference in serial verbal reactions. J Exp Psychol 18:643–662
Temple E, Deutsch GK, Poldrack RA et al (2003) Neural deficits in children with dyslexia
ameliorated by behavioral remediation: evidence from functional MRI. Proc Natl Acad Sci
100:2860–2865
Thompson-Schill SL, Bedny M, Goldberg RF (2005) The frontal lobes and the regulation of
mental activity. Curr Opin Neurobiol 15:219–224. doi:10.1016/j.conb.2005.03.006
Thompson-Schill SL, D’Esposito M, Aguirre GK, Farah MJ (1997) Role of left inferior prefrontal
cortex in retrieval of semantic knowledge: a reevaluation. Proc Natl Acad Sci USA
94:14792–14797
Thompson-Schill SL, Ramscar M, Chrysikou EG (2009) Cognition without control when a little
frontal lobe goes a long way. Curr Dir Psychol Sci 18:259–263
Thorell LB, Lindqvist S, Bergman Nutley S et al. (2009) Training and transfer effects of
executive functions in preschool children. Dev Sci 12:106–113 doi:10.1111/j.14677687.2008.00745.x
Trueswell JC, Sekerina I, Hill NM, Logrip ML (1999) The kindergarten-path effect: studying online sentence processing in young children. Cognition 73:89–134
N. S. Hsu and S. M. Jaeggi
Turkeltaub PE, Gareau L, Flowers DL et al (2003) Development of neural mechanisms for
reading. Nat Neurosci 6:767–773
Ursache A, Blair C, Raver CC (2012) The promotion of self-regulation as a means of enhancing
school readiness and early achievement in children at risk for school failure. Child Dev
Perspect 6:122–128. doi:10.1111/j.1750-8606.2011.00209.x
Wass SV, Scerif G, Johnson MH (2012) Training attentional control and working memory—Is
younger, better? Dev Rev 32:360–387. doi:10.1016/j.dr.2012.07.001
Wendelken C, Munakata Y, Baym C et al (2012) Flexible rule use: common neural substrates in
children and adults. Dev Cogn Neurosci 2:329–339. doi:10.1016/j.dcn.2012.02.001
Yamada Y, Stevens C, Dow M et al (2011) Emergence of the neural network for reading in fiveyear-old beginning readers of different levels of pre-literacy abilities: an fMRI study.
NeuroImage 57:704–713. doi:10.1016/j.neuroimage.2010.10.057