The Emergence of Cognitive ControlAbilities in Childhood

Nina S. Hsu and Susanne M. Jaeggi

Abstract Cognitive control, otherwise known as executive function, refers to ourability to flexibly adjust or regulate habitual actions or behaviors. As a cluster ofseparable components, it depends heavily on the prefrontal cortex, one of the lastbrain regions to reach adult maturity. Cognitive control processes are thought to beamong the key factors for scholastic success, and thus, underdeveloped cognitivecontrol abilities might contribute to an achievement gap. In this chapter, we firstdiscuss the prolonged maturation of the prefrontal cortex that leads to delayedemergence of cognitive control abilities in children. We briefly describe some ofthe functional effects of prolonged maturation of the prefrontal cortex. We thendiscuss how experience and environmental factors such as education and socio-economic status may affect the development of cognitive control abilities, beforeturning to cognitive training interventions as a promising avenue for reducing thiscognitive ‘‘gap’’ in both healthy children and those with developmental disabili-ties. Taken together, our hope is that by understanding the interaction of braindevelopment, environmental factors, and the promise of cognitive interventions inchildren, this knowledge can help to both guide educational achievement andinform educational policy.

Keywords Executive function � Socioeconomic status � Hypofrontality �Cognitive intervention � Plasticity

N. S. Hsu (&) � S. M. JaeggiDepartment of Psychology, Center for Advanced Study of Language,7005 52nd Avenue, College Park, MD 20742, USAe-mail: ninahsu@umd.edu

N. S. Hsu � S. M. JaeggiProgram in Neuroscience and Cognitive Science (NACS),University of Maryland, College Park, USA

N. S. HsuCenter for Advanced Study of Language, University of Maryland,College Park, USA

Curr Topics Behav NeurosciDOI: 10.1007/7854_2013_241� Springer-Verlag Berlin Heidelberg 2013

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 streetlane. This freedom confers a number of advantages, but most importantly, it’s thesafest place for a cyclist to ride. That is, when bicyclists travel as far to the right aspracticable, they risk being missed as drivers pull out of side streets and driveways.A Maryland bicyclist transported to New York, then, encounters some immediateproblems. In New York, the law forbids bicyclists from occupying a full streetlane, relegating them to shoulders and sidewalks. Our brave Maryland bicyclistmust quickly shift cycling behavior in order to abide by New York law and avoid acostly ticket (or worse).

The scenario that we have described above is a real-world illustration of cog-nitive control—also known as executive function (EF)—and it refers to our abilityto flexibly adjust or regulate habitual actions or behaviors. An essential componentto higher cognition, this ability allows us to successfully navigate our surround-ings, overriding habitual behaviors and routines when current task goals ordemands require otherwise. The prefrontal cortex (PFC) has been associated withcognitive control function by acting to guide the selection of task-relevant actionsduring information processing (Miller and Cohen 2001; Shimamura 2000). Severalfeatures of the PFC—including its distinctive anatomy, wide range of inputs andoutputs to other cortical regions, and its neuromodulatory regulation of other brainsystems—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 sep-arable components that collectively work to guide goal-directed behavior (Bot-vinick et al. 2001; Miller and Cohen 2001; Norman and Shallice 1986). Suchcomponents 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 varietyof domains including selective attention, working memory (WM), and languageprocessing (Badre and Wagner 2007; Novick et al. 2005; Smith and Jonides 1999;Thompson-Schill et al. 2005). As illustrated in Fig. 1, neuroimaging and

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neuropsychological studies have demonstrated that cognitive control processesrecruit regions of prefrontal cortex (PFC), including dorsolateral and ventrolateralprefrontal cortices (e.g., Jonides et al. 2008; Novick et al. 2009; Thompson-Schillet al. 1997), as well as regions of anterior cingulate cortex (e.g., Miller and Cohen2001, Botvinick et al. 1999).

Recent evidence has challenged the notion that cognitive control and itscomponents remain fixed once we reach adulthood (e.g., Gray and Thompson2004); rather, these cognitive processes may in fact be malleable and amenable tointerventions. In order to better understand the mechanisms underlying theseprocesses, it is important to first understand the development of these processesduring childhood. Because these control processes are thought to be key factors forscholastic success, underdeveloped cognitive control abilities might lead to anachievement gap (e.g., Gathercole et al. 2004; Pickering 2006).

In this chapter, we will outline the current literature on the role that cognitivecontrol (or lack thereof) plays from a developmental perspective. First, we willbriefly summarize the literature on the neuroanatomical development of the brainin childhood. Then, we will discuss (1) the effects of an immature prefrontal cortexon behavior, (2) how environmental factors can influence cognitive control during

Fig. 1 A wide network ofbrain regions is recruitedduring cognitive controlprocesses. The panels aboveshow a view of the lefthemisphere laterally (top) andmedially (bottom). Regionsinclude dorsolateralprefrontal cortex (brown),ventrolateral prefrontal cortex(orange), and anteriorcingulate cortex (purple).Other regions includeposterior parietal cortex(pink), premotor cortex(periwinkle), and pre-SMA,the anterior portion of thesupplementary motor area(teal)

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development, and (3) how targeted cognitive interventions may serve to demon-strate the malleability of cognitive control, having longitudinal implications foreducational achievement. We then conclude by highlighting outstanding issues forthe field to address.

2 The Developing Brain

Primates, including humans, are born with immature brains, and it has been wellestablished 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 proliferationand changes in synaptic density. During this period, the increase in synapticconnections accompanies dendritic and axonal growth (i.e., fibers for communi-cation that extend from neurons) and myelination (i.e., insulation, thus boostingsignal transmission) of the subcortical white matter (Huttenlocher and Dabholkar1997). Synaptogenesis is then followed by pruning, a synapse elimination processthat lasts well into the third decade of life (Huttenlocher and Dabholkar 1997;Petanjek et al. 2011). Critically, rather than occurring concurrently throughout thewhole brain as they do in rhesus monkeys (e.g., Rakic et al. 1986), these processesdynamically occur at differing rates throughout the brain in humans (Huttenlocherand Dabholkar 1997). Brain regions subserving sensory functions, such as visionand hearing, develop first, followed by development of temporal and parietalcortices, regions responsible for sensory associations. Higher order cognitionareas, such as prefrontal and lateral temporal cortices, which serve to integrateinformation from primary sensorimotor cortices and modulate other cognitiveprocesses, appear to mature last (Casey et al. 2005b; Petanjek et al. 2011).

Boosted by the development of noninvasive neuroimaging technologies in thelast two decades, researchers have learned a great deal about the anatomical andfunctional networks of the developing brain. Early work with positron emissiontomography (PET) imaging demonstrated that human PFC metabolizes glucose ata slower rate, relative to occipital, temporal, and parietal cortices (Chugani andPhelps 1986). This result suggests that early development prioritizes basic humansurvival 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 andlateral temporal regions (Gogtay et al. 2004). Moreover, there appears to be evi-dence for a ‘‘fine-tuning’’ of cortical structures as activation shifts from diffuse tofocal recruitment as children develop (Brown et al. 2005). Taken together,structural and functional evidence suggests that prefrontal regions associated withintegration and goal-directed behaviors mature after regions responsible for pri-mary sensory functions (Casey et al. 2005a), with both progressive and regressiveprocesses (rather than simple linear patterns of change) underlying changes incognitive abilities (Amso and Casey 2006; Brown et al. 2005).

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3 Functional Consequences of an ImmaturePrefrontal Cortex

There are behavioral and functional consequences of these anatomical develop-ments in children, and most prominently, children exhibit deficits in cognitivecontrol which has been attributed to the immaturity of the cortical networkssubserving those control systems. For example, in laboratory tasks, children aremore susceptible to interference from competing information and actions ascompared 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 whichthey indicate whether an occluded object is in the same location as previouslyobserved (e.g., Diamond 1985). In adults, cognitive control is classically measuredwith the Stroop task (Stroop 1935), in which participants must inhibit readinghabits in order to indicate the word’s ink color (e.g., ‘‘red’’ written in blue ink). Inchildren, 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 moonand 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 andGraham 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 (Beveridgeet al. 2002). In addition to these cognitive factors, self-regulative processes (e.g.,achievement-related behavior, personal strivings, and regulating shared goals inclose relationships) are important to childhood development (Blair and Diamond2008; Hofmann et al. 2012). Individual differences in emotional self-regulationcan account for academic achievement beyond what can be explained by generalintelligence (Blair and Razza 2007). The development of self-regulation in chil-dren may be mediated by an interaction of top-down executive control and bottom-up influences of emotion and stress reactivity (Ursache et al. 2012), playing a rolein successful social and emotional competence (Blair 2002). Taken together, thisbrief summary shows that as a result of prolonged maturation of prefrontal cortex(relative to other parts of the brain which reach adult maturity sooner), childrenexhibit impaired behavioral and cognitive control for years (Thompson-Schillet al. 2009).

How do the neurobiological signatures of cognitive control differ betweenchildren 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 undergoingfunctional magnetic resonance imaging (fMRI). In the go/no-go task, participantsare instructed to either respond (on go trials) or withhold a response (on no-gotrials). The authors found that activity in the anterior cingulate cortex and ventralprefrontal regions correlated with error rate (i.e., false alarms, or responding whentold to withhold responding). Notably, exhibiting more false alarms than adults,children activated prefrontal cortex (specifically, dorsolateral prefrontal cortex, orDLPFC) more diffusely than adults during the task, but DLPFC activation did not

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correlate with task performance. These results suggest that maturation of theprefrontal cortex involves reduction of diffuse activity in the form of bothstrengthened 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 thatimmature cognitive control abilities were associated with the inability of childrento recruit ventrolateral prefrontal cortex (VLPFC) regions in a similar manner toadults, that is—children failed to activate a region in right VLPFC that wasrecruited for multiple types of cognitive control by adults. Other fMRI studieshave found that subcortical systems—specifically, the striatum—may also play animportant role in overriding inappropriate responses (Booth et al. 2003; Durstonet al. 2002). Specifically, lower activity in the caudate nucleus parallels poorerperformance 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 cog-nitive control in children and adults, some research suggests age-related differ-ences in temporal dynamics as well. Wendelken and colleagues (Wendelken et al.2012) administered a task manipulating rule type, rule switching, and stimulusincongruency to both children and adults. That is, in a task that asked subjects toindicate color or direction of a stimulus on any given trial, consecutive trials couldswitch 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 thatDLPFC activation in children reflected the previous rule type (regardless of thecurrent rule), suggesting ‘‘sluggishness’’ in executive function shifting abilities.Whether a similar pattern might emerge in other cognitive control regions requiresfurther 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, Listonand colleagues (Liston et al. 2006) found that the degree of connectivity betweenprefrontal cortex and striatum (both linked to cognitive control task performanceas described earlier) correlated positively with age. However, in comparing thefrontostriatal tract to a second fiber tract (i.e., the corticospinal fiber, which alsocorrelated with age), only frontostriatal connectivity predicted go/no-go taskperformance. This result indicates that maturation of frontostriatal connectivity (asindexed by DTI measures) contributes to the development of cognitive controlabilities. Taken together, these findings suggest that a combination of structuraland functional information gained from noninvasive brain technology can informus about the developing brain and how it affects cognitive control systems.

As outlined above, there are clearly negative consequences to the prolongeddevelopment and maturation of the prefrontal cortex in children. We have

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described a number of studies showing that across a variety of cognitive controlparadigms, children exhibit poorer performance relative to adults. Conversely,there might be an evolutionary advantage to the late development of prefrontalcortices and the cognitive control network. What might be the nature of thisadvantage? There may be positive consequences to this lack of cognitive controlduring childhood (Chrysikou et al. 2011; Thompson-Schill et al. 2009), wherein animmature frontal cortex (sometimes referred to as hypofrontality) might conferadaptive benefits. Thus, there may exist a trade-off between a system tuned tooptimal performance (i.e., rule-based), versus a system built for learning (i.e., data-driven). A testable prediction for this proposal is that children should outperformtheir older counterparts on some cognitive tasks, and this is indeed the case, mostprominently 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 frequentconvention (e.g., mouse ? mouses). This result points to the idea that childrenmaximize probabilistic input, which in this case, optimizes performance. In con-trast, adults tend to rely on a probabilistic approach by monitoring the probabilitiesof alternative patterns (Ramscar and Yarlett 2007). This age-related difference inlearning (observed in domains such as language learning) may be promoted by alack of cognitive flexibility driven by an underdeveloped prefrontal cortex.

On the other hand, the hypofrontality as observed in children may have otherconsequences in the domain of language comprehension. In particular, considerthe incremental nature of language processing—that is, the notion that we interpretlanguage as it unfolds in real time, rather than awaiting the end of the sentence. Assuch, readers (and listeners) commit to an initial interpretation and anticipatearriving information. One consequence of this incremental processing is temporaryambiguity in parsing decisions—in cases where an initial interpretation turns out tobe wrong, subjects must revise their initial interpretations in order to arrive at thecorrect meaning of the sentence. This process triggers cognitive control abilities,as subjects detect a need to recover from misanalysis (Novick et al. 2005). 5-yearolds, relative to 8-year olds and healthy adults, demonstrate a failure to revise inthese initial parsing decisions (e.g., Trueswell et al. 1999), paralleling performanceobserved in patients with left PFC damage (Novick et al. 2009).

As a final example, consider the case of functional fixedness, a bias in whichparticipants are hindered in reaching a solution to a problem because of theirknowledge of an object’s conventional use. In a classic laboratory problem solvingtask, participants must indicate that a box can be used to reach an object high onthe shelf (i.e., a height booster), rather than (conventionally) used as an object forcontaining things. German and Defeyter (2000) found that when the conventionaluse of the box was initially demonstrated, older children (7-years old) took longerto arrive at the solution compared to when the conventional use was not demon-strated. 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

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were more likely to apply a ‘‘rule’’ to the box’s use, resulting in longer solutiontimes (or failing to arrive at the correct solution at all). There are other cases whena lack or reduction of cognitive control permits cognitive flexibility, particularly inthe domain of creativity. That is, a lowered state of cognitive control might allowadults to reduce the filtering properties of the PFC (e.g., Shimamura 2000), thuspromoting a more data-driven rather than a rule-based approach to the cognitivetask at hand. Chrysikou and Thompson-Schill (2011) did just this using an fMRIparadigm. 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) forthe object. Common object use generation reliably activated prefrontal cortex,whereas uncommon object use reliably activated regions of occipito-temporalcortex. Further, perceptually based responses (e.g., using a chair for firewood,which does not require prior knowledge of a chair’s function) predicted activationin posterior cortex, suggesting that a diminished state of cognitive control may, infact, confer benefits to certain creativity tasks.

In sum, these results demonstrate that an immature prefrontal cortex bearsnegative consequences on some cognitive domains, but that it can also providebenefits toward other cognitive domains. In particular, hypofrontality may havepositive consequences for tasks in which data-driven learning, rather the rule-based performance, drives optimal performance (Chrysikou et al. 2011; Thomp-son-Schill et al. 2009). Considering this trade-off, the argument for acceleratingmaturation of cognitive control networks that are mediated by regions of prefrontalcortices may need to be tempered with the evolutionary advantages that immaturefrontal lobes may confer.

4 Environmental Factors in Cognitive ControlDevelopment

Recent work posits an important influence of environmental factors, includingsocioeconomic status (SES), on the developing brain. SES, a composite measure,considers economic and non-economic factors, including material wealth, socialprestige, and education. Educational advocates have long discussed the negativeimplications of low SES backgrounds on cognition, and ultimately, on academicachievement. 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 thelifespan. These consequences point to a role for SES in shaping candidate neuralpathways by which SES might compromise academic achievement or increase therisk of mental illness (see Hackman et al. 2010 or Hackman and Farah 2009 forreview). Because neurobiological systems may mediate these SES-cognition gra-dients, we focus here on work demonstrating a link between SES and assessmentsof cognitive control, executive function, and language. For example, Noble andcolleagues (Noble et al. 2005) found that in kindergarteners of differing SES

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backgrounds, low SES children performed worse than middle SES children onlanguage (mediated by the left perisylvian regions) and EF (mediated by theprefrontal cortex) measures. The groups did not differ on the other cognitivemeasures, and the authors point to the delayed maturation of the brain regionsmediating these abilities as being more susceptible to environmental factors (likeSES). A subsequent study of individual differences using a population of first-graders demonstrated that SES explained 30 % of the variance in language per-formance (Noble et al. 2007). SES also explained 6 % of the variance in bothcognitive control and working memory performance (despite the small value, thisvalue was statistically significant in both cases).

While the role of SES has been examined in sociological and epidemiologicalcontexts, research is just now beginning to shed light on its impact on neurobio-logical mechanisms. A network of regions mediates reading processes, includingleft hemisphere middle temporal and inferior frontal gyri, and left-posterior supertemporal sulci (Turkeltaub et al. 2003). SES can modulate activity in these regionsduring reading. Specifically, there is a negative correlation between the brain-behavioral relationship and SES levels in these regions, with an amplified impactof SES at lower SES levels (Noble et al. 2006b). Indeed, there appears to be asystematic effect of SES on reading skills (even after controlling for other factors),suggesting a multiplicative effect of reading ability and SES that exaggeratespoorer performance at low SES levels (Noble et al. 2006a). Further, EEG measuresindicate that SES modulates measures of attention, with low SES children showingan attenuated response to novel stimuli relative to high SES children (Kishiyamaet 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 asymmetryof the inferior frontal gyrus (even after controlling for scores on a standardized setof language and cognition tests), with left lateralization associated with higher SES(Raizada et al. 2008). In line with this result, Sheridan and colleagues (Sheridanet al. 2012) found that right medial frontal gyrus activity was inversely related toaccuracy in acquiring a novel stimulus–response association (e.g., through adimensional change card sorting task). Moreover, Raizada and colleagues (Raiz-ada et al. 2008) also found that SES trended toward predicting both white and graymatter volume in these regions, suggesting anatomical consequences to SESvariation.

In sum, these results suggest that SES variation results in altered prefrontalanatomy, with negative consequences on various cognitive domains, particularlythose subserving language and executive function. On the other hand, the sus-ceptibility of prefrontal cortices to experiential and environmental factors suggeststhat these cortical networks might be malleable. Indeed, there is evidence fromnaturalistic experiments showing that schooling can promote cognitive controlbeyond natural development. For example, Burrage et al. (2008) have demon-strated that schooling had a significant impact on executive function in a group ofprekindergarten and kindergarten children when compared to children at the sameage which did not attend school. In light of these findings, there exists the potentialfor targeted educational interventions to enrich the neural architecture of the child

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in order to give rise to enhanced cognitive function. In particular, these inter-ventions might be especially beneficial for children who are facing learning dif-ficulties or who come from low SES backgrounds. Thus, although factors such aslow SES might result in an educational achievement gap, timely educationalinterventions may be able to minimize or even close this gap. We turn now to theresearch that has explored this possibility.

5 Cognitive Interventions from a DevelopmentalPerspective

Conventionally, cognitive training is defined as a means of improving cognitivefunctioning through practice and/or instruction (Jolles and Crone 2012). A rapidlyemerging field concerns the impact of cognitive training (and in particular, WMtraining) on transfer measures of cognitive control, EF abilities, and general fluidintelligence in adults (e.g., Jaeggi et al. 2008; see Morrison and Chein 2011 orHussey and Novick 2012 for recent reviews on the topic). Our focus here is oncognitive training studies from a developmental perspective. Cognitive trainingstudies in children can take many forms (e.g., mindfulness training, and core orsupplemental curricula—see Diamond and Lee 2011), but in this chapter, we focuson cognitive interventions in the form of computerized games that specificallytarget 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 pop-ulation with attention deficit hyperactivity disorder (ADHD), characterized byinattention, impulsivity, and hyperactivity (Barkley 1997) and linked to impairedfunction of the frontal lobes (for review, see Castellanos and Proal 2012). Inaddition to improving on the trained WM task, participants significantly improvedon an untrained WM task, as well as on Raven’s Progressive Matrices (RPM), anon-verbal complex reasoning task (Klingberg et al. 2002). A subsequent studydemonstrated that across measures of WM (items successfully remembered in span-board and digit span), EF (Stroop task completion time), and complex reasoning(correct RPM items), ADHD children that trained on computerized WM tasksoutperformed those children completing a control training program (Klingberget 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 inorder to improve EF and WM abilities through training. These studies have usedparadigms targeting attention (Rueda et al. 2005), WM (Holmes et al. 2009; Jaeggiet 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 thesestudies have examined intervention paradigms in the context of amelioratingdevelopmental disabilities, including ADHD (Hoekzema et al. 2010); develop-mental dyscalculia (Kucian et al. 2011), reading abilities in at-risk youth (Yamada

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et al. 2011), and dyslexia (Temple et al. 2003). Such interventions generallydemonstrate improvement on executive functions and cognitive control—that is,those skills that are critical for scholastic achievement (e.g., Diamond and Lee2011). 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 commerciallyavailable games. Post-training results suggested that both processes are separatelymodifiable with improvements seen in a special population that may need theintervention the most (Mackey et al. 2011).

Though the results of the studies described above are promising, there is muchvariability in terms of training length, frequency, and assessment type across thevarious designs of intervention studies in children. Thus, in order to best under-stand the mechanisms underlying training and transfer of EF and WM abilities,further work is necessary (Buschkuehl et al. 2012; Jaeggi et al. 2011, 2012; Shahet al. 2012). The underlying neural mechanisms are beginning to be addressed inadults (e.g., Dahlin et al. 2008), but only a handful of studies have looked at theneural correlates underlying cognitive interventions in children. As a result, theneural mechanisms that lead to the observed neural changes and plasticity are stillunclear. That is, some studies report decreased changes in activation followingtraining (Haier et al. 2009; Kucian et al. 2011; Qin et al. 2004), whereas othersreport increases in activation following training (Hoekzema et al. 2010; Jolleset al. 2012; Shaywitz et al. 2004; Stevens et al. 2008; Temple et al. 2003). At leastone study has examined structural changes in the form of cortical thickness dif-ferences after training (Haier et al. 2009), though the authors note that there was nooverlap between regions showing functional changes and those showing structuralchanges, suggesting a complex relationship that warrants further study.

These mixed findings point to an array of results that seek to demonstrate howthe brain changes after cognitive training. Decreased activation after trainingsuggests increased processing efficiency or performance of the task within capacitylimits (cf. Nyberg et al. 2009), whereas increased activation might reflect addi-tional recruitment of neural resources. A combination of these two patterns couldrepresent a re-distribution of neural activity after training, whereas activation indifferent brain regions (i.e., brain regions not active pre-intervention) would reflectqualitative changes in brain activity that could represent a change in strategy(Buschkuehl et al. 2012; Jolles and Crone 2012). In general, future cognitivetraining studies that can incorporate a neuroimaging component are necessary inorder to better understand the neural mechanisms (i.e., understanding the how)underlying training and transfer. Researchers also have to keep in mind that agemight moderate the outcomes of intervention studies. For example, althoughvariable training (i.e., training in which there are novel task demands in eachtraining session) can boost transfer effects in adults, this variability can also hinderthose same desired effects in children (Karbach and Kray 2009). Thus, researchershave to keep in mind that just because cognitive training paradigm shows promisein adults, that does not mean that the same will be true for children—the childbrain is not necessarily a ‘‘miniature version’’ of the adult brain. It is possible that

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while training in adults may modify existing neural architecture, training in youngchildren may influence the construction of that architecture (Galvan 2010), sug-gesting qualitative and quantitative differences across children and adults (Jollesand Crone 2012). For example, during the delay period of a WM task (i.e., whenno stimulus is present, but participants manipulate the to-be-remembered infor-mation), children (8 to 12-year olds) failed to recruit right DLPFC and bilateralsuperior 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 WMmanipulation with a cognitive training paradigm, particularly since intensivecognitive training in younger individuals may actually lead to more widespreadtransfer effects, possibly due to the unspecialized state of functional neural net-works at a younger age (Wass et al. 2012).

What makes an intervention effective, and how can previous research informthe 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 aerobicexercise, and other elements of the intervention that provide children with a senseof joy and acceptance.

6 Open Questions and Suggestions for Future Research

Several open questions remain as we work to more fully understand cognitivedevelopment as an interplay amongst (a) anatomical and functional changes of thebrain 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 adolescentbrain, cognitive intervention studies that combine behavioral and neuroimagingmeasures may provide a better understanding the cognitive and neural mechanismsunderlying cognitive control in the context of other factors.

Moreover, there is likely an important role of individual differences in terms ofcognitive control and the extent of its susceptibility to intervention. Previousresearch has demonstrated training-related individual differences (e.g., Jaeggi et al.2011; Rueda et al. 2005), and we have also reviewed a role for environmentalfactors (i.e., SES, which will also vary by individual) in cognitive control, andfinally, developmental disabilities may also determine training outcome. In con-sidering and designing intervention studies, then, we suggest that researchers giveserious thought to the role that these environmental factors and individual dif-ferences may play in the effects of training and transfer, as well as their underlyingmechanisms.

Finally, in spite of the interventions that seek to reduce or even close anachievement gap from those who exhibit poorer EF skills than others, there mayalso exist a fine balance between hastening frontal lobe development throughcognitive interventions in order to improve cognitive control and EF, and allowing

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an immature prefrontal cortex to confer cognitive benefits (Thompson-Schill et al.2009). Nonetheless, reducing the achievement gap, particularly for those childrenat an economic disadvantage or with developmental disabilities, is of criticalinterest for our educational system. We will need a better developed and char-acterized understanding of the interaction of prefrontal maturation, learning, andcognitive control in order to guide research and inform educational policy.

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Fig. 2 Cognitive control processes are mediated by a number of interacting factors, includingtypical (and atypical) brain development, environmental factors, and behavior

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