developmental cognitive neuroscience: origins, issues, and prospects

Developmental Review 27 (2007) 428–441

www.elsevier.com/locate/dr

Developmental cognitive neuroscience:Origins, issues, and prospects

Bruce F. Pennington *, Kelly A. Snyder, Ralph J. Roberts Jr.

University of Denver, Psychology Department, 2155 Race St., Denver, CO 80208, USA

Received 8 May 2007; revised 6 June 2007Available online 14 August 2007

Abstract

This commentary explains how the field of developmental cognitive neuroscience (DCN) holdsthe promise of a much wider interdisciplinary integration across sciences concerned with develop-ment: psychology, molecular genetics, neurobiology, and evolutionary developmental biology. Firstwe present a brief history of DCN, including the key theoretical issues it addresses; then we commenton how the four articles in this special section exemplify a DCN approach and raise important the-oretical and methodological issues for the field of DCN; and we close by considering the future ofDCN, especially the key role developmental psychologists can play as DCN becomes increasinglyinterdisciplinary.� 2007 Elsevier Inc. All rights reserved.

Keywords: Developmental cognitive neuroscience; Temperament; ADHD; Eye movements; Declarative memory

A reader of this special section might well ask what is developmental cognitive neuro-science (DCN), how the four articles exemplify this interdiscipline, and what this interdis-cipline has to contribute to developmental science more generally. In this commentary, wewill address these questions. We will begin this commentary by addressing the first andthird questions, then consider the four articles in this special section, and conclude withour take on where the field of DCN is going.

0273-2297/$ - see front matter � 2007 Elsevier Inc. All rights reserved.

doi:10.1016/j.dr.2007.06.003

* Corresponding author. Fax: +303 871 3982.E-mail address: [email protected] (B.F. Pennington).

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B.F. Pennington et al. / Developmental Review 27 (2007) 428–441 429

What is developmental cognitive neuroscience?

In the mid-1980s, the field of cognitive neuroscience was born, spurred in equal parts byadvances in neuroimaging technology and by growing dissatisfaction among cognitive sci-entists about the ability of behavioral methods alone to resolve fundamental theoreticalissues, which is the well-known identifiability problem described by Anderson (1978).The particular unresolved issue Anderson discussed concerned whether mental imageswere perceptual or linguistic, and he argued that behavioral experiments alone might beincapable of identifying the nature of mental representations underlying mental imagery.But he did hold out hope that the investigation of brain mechanisms might somedayresolve the issue, which indeed did happen when neuroimaging studies of mental imagingfound that it activated neural substrates for perceptual and not linguistic processing (e.g.,Kosslyn et al., 1993). So a neuroscience approach can help solve the indentifiability prob-lem by providing additional constraints that theoretical models must meet.

Although there was already a rich subfield of cognitive neuropsychology which usedcognitive methods to study patients with acquired brain lesions (e.g., Shallice, 1988) andwhich had produced some surprising and fundamental insights about normal memoryand language, lesion studies by themselves had inherent limitations and the vast majorityof cognitive psychologists did not use this method. It was only with the advent of struc-tural and functional neuroimaging technologies like CT, PET, and MRI, which greatlyenhanced the ability to examine the neural correlates of cognition in typical adults, thatmost cognitive psychologists became interested in the brain and the field of cognitive neu-roscience rapidly emerged.

The beginnings of the field of developmental cognitive neuroscience can be traced toLenneberg’s seminal book, Biological foundations of language (1967). Considerableresearch in the 1970 and 1980s followed mainly using dichotic listening to test whetherhemispheric specialization for language was invariant across development. In the mid1980s, one of us co-edited a special section in Child Development titled ‘‘DevelopmentalPsychology and the Neurosciences: Building a Bridge’’ (Crnic & Pennington, 1987). Thisspecial section contained Greenough, Black, and Wallace’s (1987) now classic article onexperience-dependent and experience-expectant synaptogenesis, as well as a review byPatricia Goldman-Rakic (1987) on her seminal work on the development and functionsof the prefrontal cortex.

So, the field of developmental cognitive neuroscience was emerging by the mid 1980s,although its name came somewhat later. I first encountered this term in a grant that LizBates had written, seeking funding for her pioneering studies of language and cognitivedevelopment in children with early unilateral lesions. By adding the adjective ‘‘develop-mental’’ to the term ‘‘cognitive neuroscience,’’ Liz and other pioneers in this field who usedthis term, like Mark Johnson (Johnson, 1997, 2005) and Chuck Nelson (Nelson & Luci-ana, 2001), were doing more than saying we ought to study brain-behavior relations inchildren as well as adults. Instead, this addition signaled a bold theoretical claim, that cog-nitive neuroscience would be fundamentally incomplete without an understanding of howbrain-behavior relations develop. In other words, we cannot understand how the maturebrain functions without understanding how it develops. This claim rested in part on dra-matic advances in developmental neurobiology made by Hubel and Wiesel (1963); Hubel,Wiesel, and Stryker (1977), Greenough et al. (1987), Shatz (1992) and others. Theseadvances made it clear that plasticity was an intrinsic and necessary property of normal

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brain development, that instead of being ‘‘hardwired’’ at birth, neural circuits (and themental structures they mediate) emerge as a result of interactions among neurons, whoseactivity is initially endogenous and then increasingly responsive to environmental stimula-tion. So mental structures are a product of probabilistic epigenesis (Gottlieb, 1992) or neu-ral constructivism (Quartz & Sejnowski, 1997). Piaget’s emergentist theory about theontogeny of a child’s concepts and mental operations could be potentially grounded inthe materialist details of interactions in neural networks. So, the cognitive architectureof a ‘‘typical’’ adult is the product of a developmental process, just as is cognitive devolu-tion in aging, and we cannot fully understand that cognitive architecture without under-standing how it developed (and keeps developing, because plasticity also characterizesthe adult brain).

Another important scientific breakthrough contributed to this perspective, namely thedevelopment of connectionist or neural network models (O’Reilly & Munakata, 2000;Rumelhart, McClelland, & PDP, 1986). These networks modeled the emergence of mentalstructures from the interactions of artificial neurons exposed to a particular learning his-tory, and became an extremely powerful tool for studying typical and atypical develop-ment (e.g., models of normal and abnormal reading, such as those presented in Harm& Seidenberg, 1999).

The fact that a given individual’s cognitive architecture is a product of their own devel-opmental and learning history contains an important corollary: the study of individual dif-ferences will provide important insights about what is constrained and what can vary inbrain and behavior development. Atypical development provides an important test ofthe universality of developmental processes and sequences. For instance, Helen Neville’spioneering studies of language localization in the congenitally deaf provided a dramaticexample of normal brain plasticity: a visual language, American Sign Language, wasmapped onto left hemisphere auditory cortex. So, differences in experience will changebrain development and the localization of functions. We now have many more examplesof this phenomenon, from musicians, blind readers of Braille, and others (e.g., Galaburda& Pascual-Leone, 2003). These examples make one wonder how many individuals actuallyhave typical development or whether typical development is more of an average acrossdiverse developmental trajectories.

But individual differences also arise from genetic differences and the interaction of genesand environment. So, another important component of developmental cognitive neurosci-ence is behavioral and molecular genetics. We are beginning to understand how the typicalchemistry and wiring of the brain is influenced by genes, how genetic variations alter thischemistry and wiring, and how these genetic variations interact with environmental factorsto alter developmental trajectories (Rutter, 2006).

The incorporation of molecular genetics into the field of developmental cognitive neu-roscience provides a key link to the fields of developmental neurobiology (e.g., Sanes, Reh,& Harris, 2006) and evolutionary developmental biology—shortened as ‘‘evo devo’’—(Carroll, 2005). Both fields are concerned with essentially the same fundamental questionas developmental psychology, which is how do new forms emerge from simpler ones?Developmental neurobiology is concerned with how the form of the nervous systememerges in ontogeny and evo devo is concerned with how new forms emerge in evolution.Evo devo has provided the profound insight that the widely different forms found acrossanimals are not produced by species-specific genetic architectures but by variations in thetiming and expression of an ancient and generic set of ‘‘tool kit’’ genes. Species differences


are mainly due to developmental differences in the sequence and timing of expression ofthese tool kit genes. In other words, evolution works by ‘‘tweaking’’ development, and thistweaking leads to an incredible variety of life forms. Evo devo makes it clear why there isconsiderable genetic homology across species, and why animal models can be so valuablefor understanding human developmental neurobiology.

So, the seemingly innocuous addition of the word ‘‘developmental’’ to the new interdis-cipline of cognitive neuroscience has profound implications. This addition has the poten-tial to integrate our field with virtually all of biology and points toward a trulydevelopmental science that will likely find common answers to the question of how newforms arise across various levels of analysis.

How do these articles exemplify DCN ?

While these four articles cover a range of methods and topics, they all illustrate that aconsideration of how functions are mediated by the brain can provide useful constraintsand unexpected connections in trying to understand behavioral development. When onefirst enters the field of psychology, one is struck by the vast number of constructs that havebeen proposed and studied, especially compared to fields like modern physics or biology.As a science matures, some constructs, like ether or phlogiston, turn out to be superfluousbecause the field has gained a better understanding of basic mechanisms. What is excitingis that we are now witnessing a conceptual integration across domains of psychology longconsidered to be distinct, such as the study of temperament and the study of executivefunctions.

We will now consider each of these four articles individually. The article by Hendersonand Wachs on temperament describes well the evolving integration of cognitive neurosci-ence approaches and traditional developmental psychology, and illustrates how each haschanged the other. At first blush, it seems surprising that the cognitive neuroscience ofattention had much to offer students of infant temperament or vice versa. But the historiccollaboration between Mike Posner, a cognitive neuroscientist, and Mary Rothbart, atemperament researcher, changed all that. As Henderson and Wachs explain in detail,the essence of what we call temperament is a manifestation of an emotion–cognition inter-action. Children vary in bottom-up, initial emotional reactivity to various stimuli, but theyalso vary in their capacity for self-regulation. What emerges in their behavior and getsrated as temperament is an interactive product of reactivity and self-regulation. Andself-regulation is mediated by the prefrontal cortex and is similar to constructs like effort-ful control and executive functions. So, instead of being a fixed, genetically innate propen-sity, temperament is turning out to be an emergent property of interactions within theperson and between the person and a given context. While genetic variations play a rolein individual differences in temperament, they do so by entering into interactions withthese other factors.

Coming to the review by Durston and Konrad on ADHD after reading Henderson andWachs’ review on temperament, it is hard not to make connections between the two fields,even though the two fields hardly reference each other at all. ADHD is by definition a dis-order of self-regulation, and prefrontally-mediated executive processes are implicated inADHD. So, the two fields certainly have the potential to contribute to each other, espe-cially since each field has complementary strengths. As is the case for many psychopathol-ogies, work on ADHD has been pursued extensively at several levels of biological analysis,


but ADHD researchers have focused less on a theoretical model of the normal develop-ment of self-regulation. In contrast, research on temperament has been much morefocused on such a model, but temperament has not been as thoroughly researched acrosslevels of biological analysis. So, a closer integration of the two fields could be veryproductive.

As Durston and Konrad make clear in their review, research on ADHD is a neurosci-ence success story. We are closer to an integrated neuroscience understanding of ADHDthan we are for many other psychopathologies, such as autism, bipolar disorder, or majordepression. The frontal–striatal model of ADHD has converging support from moleculargenetic, neuroimaging, neuropsychological, and treatment studies. Put simply, this modelposits that genetic variations (like the DAT1 and DRD4 risk alleles), sometimes in concertwith bioenvironmental risk factors (like maternal smoking), affect the development ofdopaminergic pathways in the frontal–striatal system, leading to inhibitory deficits (suchas on the Stopping task), that are reversed by treatment with dopamine agonists (like Rit-alin). This model certainly has face validity, but it must be remembered that we have notactually proven the causal links across levels of analysis. The key question that Durstonand Konrad pursue concerns how we can take the next step to actually test links acrossthese levels of analysis. In their exciting work, they have pursued several strategies to testsuch links, including combining assessment of risk alleles with neuroimaging, and relatinggenotype to treatment response. For instance, they have shown associations between geno-type and both brain structure and function. Specifically, they found that the DRD4 riskallele was associated with a reduction in prefrontal gray matter volume and that theDAT1 risk allele was associated with a reduction in caudate volume. Although theseresults need to be replicated in other samples, they definitely show the promise of this kindof integrative work.

While the frontal–striatal model of ADHD has received considerable support, Durstonand Konrad make it clear that it may be too simple. The cerebellum also appears to be animportant brain structure in ADHD, stimulant medication also affects norepinephrine lev-els, and the neuropsychological problem in ADHD may be broader than a deficit in exec-utive inhibition. Moreover, the current dopamine dysregulation theory of ADHDpresented by Durston and Konrad presents us with some puzzles. If ADHD is character-ized by a hypodopamanergic state in prefrontal cortex and a hyperdopaminergic state inthe striatum, where animal models show it leads to hyperactivity, and if the main bindingsite of dopamine agonists is in the striatum, where it increases extracellular dopamine, thenwhy does stimulant medication reduce the symptom of hyperactivity? If the binding sitewere in the prefrontal cortex, the theory would make more sense, because increasing pre-frontal dopamine would increase top–down executive control and reduce hyperactivityand other ADHD symptoms. So, it seems that the neuropharmacological level of analysisdoes not fit the neuropsychological level of analysis.

Turning to the neuropsychological level of analysis, we encounter other puzzles for thefrontal–striatal model of ADHD. For instance, Nigg and colleagues (2005) found in alarge meta-analysis of executive function measures in ADHD that up to half of individualsin ADHD samples do not have an executive deficit. There are other theories of ADHDthat focus more on aspects of motivation, such as the state regulation theory proposedby Sergeant (2005) and the delay aversion theory proposed by Sonuga-Barke (2005). Itseems likely that we will need multiple cognitive deficit models of ADHD (e.g., Penning-ton, 2006) or perhaps a better theory of the development of self-regulation, such as is being


pursued by temperament researchers, which might provide better measures of the neuro-psychological deficit in ADHD.

The article by Karatekin presents a comprehensive review of studies that have used eyemovements to study development and developmental disorders over the past 20 years. Theuse of eye movements in cognitive neuroscience has grown tremendously, partly becausethe technology involved in recording and analyzing eye movements has become moreavailable and much easier to implement, and partly because the neural systems involvedin planning and executing eye movements are relatively well-understood because of thegreat deal of non-human animal work on the subject. In addition, eye movement behaviorcan offer a unique window into the real-time dynamics of perception, attention, and cog-nition, and can be examined across the lifespan. The challenge ahead, however, is to notallow the method and its increasingly easy availability to drive research programs, butinstead use the methods, where relevant, to address broader theoretical questions. Boththe neuroimaging and molecular genetics fields are disciplines that can be prone to asimilar problem of method-driven research.

Some of the literature reviewed in the article focuses on the development of eye move-ments to understand the oculomotor system per se, and says less about how eye move-ments are involved in complex real-world tasks. In such studies, the tasks are typicallydiscrete trial-by-trial affairs that require a subject to look at a flashed target or to visuallytrack a moving target. Karatekin reviews a variety of such studies that find relatively sub-tle developmental differences in the parameters of these eye movements, such as in theiraccuracy and timing. Depending on which aspects of eye movement control are examined,researchers hypothesize which relevant brain regions and associated developmentalchanges might account for such differences. Slower developing structures or connectivitypatterns are hypothesized to account for later emerging adult-like behavioral profiles(e.g., Salman et al., 2006). These brain-behavior associations are typically inferred, butthe use of neuroimaging technologies will increasingly allow for more direct tests of suchrelations. And as such studies become more prevalent, we expect, as in other areas, thatthe precise nature of these relations will be more complex than anticipated, which will leadto more complicated hypotheses about how neural processing and development relate todevelopmental changes in behavior.

Perhaps the most exciting work employing eye movements from a cognitive neurosci-ence perspective, however, is when eye movements are examined in more complex settingsto examine higher-level processes of visual attention, inhibitory processes, sustained atten-tion, visual search, working memory, reading, social processes, and the like. In such casesthe timing and patterning of eye movements in relation to static and changing visual stim-uli can provide a window into the relevant real-time processes involved in performance.Developmental or clinical differences are most often not due to an inability to carry outthe required eye movements per se, but reflect the development of higher-level control sys-tems involved in attention, memory, and executive control (eye movement abnormalitiesfound in schizophrenia may be a notable exception where there appear to be eye-move-ment specific problems). A good example is the antisaccade task that is used to studyhigher-level cognition via eye movements. In the most straightforward variant of the task,a subject is asked to look in the opposite direction of a cue that flashes a few visual degreesleft or right of a center fixation point. As simple as it is to understand what one is to do,performing the task consistently correct is remarkably difficult, and even healthy adultsmake errors 25% of the time. The difficulty with the task is because one is required to


go against a pre-existing tendency to look at the flash. Not looking at the flash, and insteadlooking away, is presumed to require significant top–down intervention, and early workwith patient populations implicated prefrontal cortex functioning as critical for success.Many further research programs have confirmed and further elaborated the workings ofthe relevant cognitive-neural systems. Thus, the use of eye movements in this work, asin other domains as well, nicely illustrates how eye movements can provide a convenientway to examine high-level cognitive/attentional processes.

Work in the antisaccade literature also illustrates how various research programs canlead to a convergent but complex picture of a set of cognitive-neural-developmental pro-cesses. Researchers have examined the planning and execution of antisaccades (as well asreflexive error saccades) using a large variety of experimental manipulations, subject pop-ulations, and with a variety of methodologies (fRMI, ERPs, lesions, animal studies). Thiswork implicates a complex network involving the dorsolateral prefrontal cortex, frontaleye fields, superior colliculus, and other structures and interactive, time-sensitive processesinvolved in preparation, attentional vigilance, working memory, saccadic programming,and motor execution. The antisaccade developmental work in typical and atypical popu-lations reviewed by Karatekin benefits greatly from the wealth of previous work andbuilds on this knowledge base by organizing questions and methods in an effective man-ner. The field as a whole, and eye movement research in particular, will benefit greatlywhen we can obtain this sort of convergence across methods and sample populations.Relatedly, as Karatekin notes, there are few studies across the field at this point that com-bine neuroimaging and eye movements, but the numbers of such studies are growing, andsuch work holds great promise for contributing to such convergences.

Karatekin concludes her article with a number of important concerns about currentwork that utilizes eye movements. She notes that eye movements are highly context-depen-dent and that results can vary with even subtle variations in stimuli, task instructions, andtask ordering. In addition, interpreting what particular behaviors, or developmental differ-ences, reflect can be very tricky. Different aged children can fail or pass for different rea-sons. High sensitivity to task differences and the inherent ambiguity in determining whatbehavior reflects are problems in behavioral science in general, and as just discussed, canbe mitigated over time by the presence of converging methodologies. But another contrib-utor to these problems may reside in the nature of the eye movement tasks themselves.Most of these tasks involve discrete responses in discrete trials with highly simplified stim-uli in otherwise ‘‘empty’’ environments. And in these stripped-down contexts, we typicallyexamine differences on the order of tens of milliseconds. Such design decisions allow forexperimental control, but may result in extreme sensitivity to even slight task changes inthe discrete responses we measure. But in the ‘‘real world’’ we make close to 200,000eye movements each day in visually cluttered environments in the service of navigatingthrough our world and for obtaining visual information to inform the planning and exe-cution of ongoing and upcoming action. In this context, eye movements are fundamentallyembedded into a larger, fully integrated action–perception system. While much of what welearn in simplified settings will undoubtedly be important for understanding fundamentaleye movements mechanisms, such findings will be severely limited when trying to under-stand eye movements processes when viewed as part of a larger interconnected system thatis continually in motion. Karatekin wonders about this problem as well, and notes that afew studies that have examined eye movements in more naturalistic settings have obtainedfindings that are sometimes not consistent with what has been seen in more constrained lab


tasks. Until recently, studying eye movements in more life-like settings has been close toimpossible, but technological advances now make the measurement of eye movementsin such contexts much more feasible (e.g., Richardson, Dale, & Kirkham, 2007). Workis only now beginning that takes advantage of these new technologies, and there is greatpromise in this work as well as in the convergence with other methodologies to studybrain, behavior and development. The utilization of eye movement recording can be apowerful tool in this regard as long as researchers use these methods to pursue importanttheoretical questions and don’t allow the methods themselves to drive the questions.

Richmond and Nelson (this issue) take a cognitive neuroscience approach to under-standing the development of declarative memory. They begin with an analysis of whichtasks tap declarative memory, and then describe what is known about the developmentof infants’ performance on these tasks. In doing so, they are able to abstract the funda-mental processes of memory (i.e., encoding, storage, and retrieval) from the specific tasksthemselves and relate developmental change in these memory processes with what isknown about the development of brain systems thought to support these processes. Thepaper exemplifies DCN in that it relates changes in memory development to changes inbrain development, providing valuable insights into one important aspect of cognitivedevelopment: maturation of the underlying neurobiology.

The paper by Richmond and Nelson represents an important first chapter in the storyof memory development. As the authors note, however, speculation about the relationbetween changes in memory development and changes in brain development is only astarting point. Studies that directly examine these relations in young infants, as well asimprovements in the tools that make this possible, are important next steps. We agree thatdirect empirical data and better neuroimaging tools will add to our understanding ofbrain-behavior relations in development. We propose, however, that conceptual and the-oretical issues limit our current understanding of brain-behavior relations in developmentas much as (or perhaps more so) than data and tools. For example, what is the relationbetween a task and a memory system? Is there a one-to-one mapping, or can a given tasktap different kinds of memory under different circumstances? What are the limitations ofthe methods currently available for examining brain-behavior relations, and the implica-tions of these limitations for our understanding of behavioral development? To illustratethese issues in the context of current research and theory, we use as an example the visual-paired comparison (VPC) paradigm.

As Richmond and Nelson note, the current approach to understanding the develop-ment of declarative memory is largely paradigm-driven. The critical first step is determin-ing whether a given task can be considered a measure of declarative memory. Once a taskhas been classified in this way, age-related changes in infants’ performance on the task canbe correlated with developmental change in underlying neurobiology. Thus, the hypothe-sized relation between the task and a memory system is fundamental to the ensuing casefor developmental change. This raises the question of how to conceptualize the relationbetween a task and a memory system? Is there a one-to-one mapping between an infantmemory task and a memory system, or is the relation more complex?

Take, for example, the hypothesis that the visual-paired comparison (VPC) is a measureof declarative memory. This hypothesis is viable, in part, because human adults and exper-imental animals with damage to the medial–temporal lobe system are impaired in tasksanalogous to the VPC (Bachevalier, Brickson, & Hagger, 1993; Buffalo et al., 1999; Hag-gar, Brickson, & Bachevalier, 1985; Manns, Stark, & Squire, 2000; McKee & Squire, 1993;


Pascalis & Bachevalier, 1999; Pascalis, Hunkin, Holdstock, Isaac, & Mayes, 2004; Neman-ic, Alvarado, & Bachevalier, 2004; Zola et al., 2000). As Richmond and Nelson point out,however, the relation between performance and medial–temporal lobe damage is delay-dependent. Importantly, damage to the medial–temporal lobe (i.e., the hippocampus,parahippocampal gyrus, and perirhinal cortex) impairs performance when there is a delaybetween encoding and test, but not when subjects are tested immediately after encoding(e.g., Nemanic et al., 2004). What are we to make, then, of infants’ performance in theVPC when they are tested immediately after encoding? All available evidence indicatesthat immediate performance does not depend on the medial–temporal lobe, but mayinstead depend on the functioning of visual perceptual areas that are not specificallyrelated to any particular type of memory (e.g., area TE; Buffalo et al., 1999; Haggaret al., 1985). This pattern indicates a more complex relation between the VPC and a mem-ory system than a simple one-to-one mapping implies.

In addition, the methods used to examine the relation between a task and a memorysystem impose important constraints on conclusions about the nature of that relation.In the case of the VPC, for example, all of the data cited by Richmond and Nelson comefrom studies of adults or experimental animals with permanent damage to particular brainstructures (i.e., lesion studies). An important limitation of lesion studies for investigatingthe neurobiology of memory development is that it cannot be determined whether thelesion impairs encoding, storage or retrieval. Yet, the neural structures that support encod-ing vs. retrieval in the VPC have important implications for the type of memory reflectedin infant novelty preferences. For instance, words activated during an implicit memorytask (e.g., word-stem completion) may have been initially encoded via the medial–tempo-ral lobe system at some earlier point in time, yet successful retrieval during the implicittask can occur in the absence of conscious awareness and does not require the medial–tem-poral lobe. This is because implicit memory involves the re-activation of previously storedrepresentations (located in the neocortex), regardless of how the representations were ini-tially encoded. Similarly, it is possible for the medial–temporal lobe system to play a rolein encoding during the familiarization phase of the VPC, yet not support the retrieval ofinformation that produces differences in looking behavior at test. Consistent with this lineof reasoning, recent research indicates that the medial–temporal lobe system supportsencoding but not retrieval in the VPC (Wan et al., 2004). In this study, researchers injectedbenzodiazepine, a drug that temporarily inhibits neural activity in medial–temporal lobestructures, into the perirhinal cortex of rodents prior to either the acquisition or retrievalphase of the VPC. Injections prior to acquisition abolished novelty preferences at test,whereas injections prior to retrieval but after encoding had no effect on performance.These findings suggest that the medial–temporal lobe system plays a role in the long-termstorage of object representations during the study phase of the VPC, but is not necessaryfor successful retrieval at test. Thus, by imposing temporary rather than permanent lesionsto different brain structures, the relation between task performance and memory systembecomes clearer.

Given the inherent limitations of different methods, and the lesion method in particular,it is important to provide converging evidence for the relation between infant memorytasks and memory systems. Neuroimaging methods, for example, are better able to distin-guish neural activity related to encoding and neural activity related to retrieval. Althoughneuroimaging methods are not currently feasible for use with infants participating invisual paradigms, researchers have used neuroimaging techniques to examine the neural


basis of VPC performance in experimental animals. For instance, using immediate earlygene (IEG) imaging techniques, researchers have examined neural activity in rodents dur-ing tasks requiring the animal to discriminate between familiar and novel objects (i.e., itemmemory, similar to the VPC) or re-arrangements of familiar objects (i.e., relational mem-ory; for review see Aggleton & Brown, 2005). Neural activity related to item memory wasobserved in occipital visual processing areas, area TE, and perirhinal cortex, but not thehippocampus (e.g., Wan, Aggleton, & Brown, 1999; Zhu, Brown, McCabe, & Aggleton,1995). Specifically, familiar compared to novel objects elicit a decrease in neural activityin perirhinal cortex and area TE, consistent with electrophysiological evidence in bothhumans and monkeys that neurons in perirhinal cortex and adjacent visual associationcortex (area TE) respond less to visual stimuli that were previously encountered, a phe-nomenon known as repetition suppression (for review see Desimone, 1996). In contrast,neural activity related to memory for spatial relations has been observed in the hippocam-pus, but not area TE and perirhinal cortex (e.g., Jenkins, Amin, Pearce, Brown, & Aggle-ton, 2004; Wan et al., 1999). These findings suggest that repetition suppression inperirhinal cortex and adjacent visual association areas provides a neural basis for discrim-inating between new and previously encountered stimuli (i.e., novelty preferences) duringthe test phase of the VPC.

Taken together, data from lesion studies and neuroimaging studies suggest that themedial–temporal lobe system, and the hippocampus in particular, may support the encod-ing of visual information into long-term memory, but does not participate in the retrievalof information that produces differences in looking behavior at test (for review see Snyder& Torrence, in press). Based on these findings, Snyder (2007) proposed that infants’ per-formance in the VPC reflects a stimulus-driven bias toward novelty in visual attention,mediated by a reduction of neural responses in the occipital–temporal visual processingpathway with stimulus repetition (i.e., repetition suppression), rather than declarativememory for the familiar stimulus. In theory, the reduction in neural activation to arepeated stimulus results in a smaller neural signal for familiar (i.e., repeated) comparedto novel stimuli, causing the novel stimulus to exert more control over visual attention(see Desimone & Duncan, 1995, for review of the biased competition theory of visualattention). In paired visual comparison trials, novelty preferences may reflect a greater ten-dency for attention to be allocated to the novel stimulus as a result of repetition suppres-sion to the familiar stimulus. Desimone and Duncan relate this bias to repetition primingeffects that can occur independently of declarative memory for the familiar stimulus.

The future of DCN: Taking development seriously

What are our models of human functional brain development? Richmond and Nelsonrelate changes in memory development to the maturation of brain systems thought tounderlie memory processes, and conceptualize brain development as ‘‘driving’’ develop-mental changes in memory. Age-related changes in retention and retrieval are attributedto the continued maturation of the hippocampus and the dentate gyrus, whereas age-related changes in encoding are attributed to increased myelination. This reflects a fairlystrict maturational view of development, in which there is a one-to-one mapping betweenbrain structure and function (e.g., hippocampus fi declarative memory), and changes inbehavioral development are attributed to the maturation of a structure (e.g., changes inretention are attributed to maturation of the dentate). As a consequence, brain regions


are viewed as supporting the same function across development, and the emergence of newcognitive functions is attributed to the maturation of brain structures having more pro-tracted maturational timetables. This type of model is reminiscent of early preformationistviews, in which development was conceptualized as a simple process of growth.

Johnson (1997, 2005), on the other hand, has proposed an alternative model, Interac-tive Specialization (IS), that entails a systems perspective of functional brain development.In Johnson’s view, functional brain development involves interactions between brain areassuch that the function of a specific area is determined by its pattern of connectivity withother areas. In contrast to the maturational perspective, IS views cognitive development asan emergent property of interactions between different brain regions, emphasizing changesin inter-regional connectivity rather than intra-regional connectivity (i.e., connectionsbetween structures rather than maturation of individual structures). As a result, changesin the patterns of connectivity between brain structures can change cognitive functions.For example, declarative memory is known to involve interactions between brain regionsinvolved in memory storage and retrieval (e.g., hippocampus), attention (e.g., parietal lobeand cingulate cortex), and executive control (e.g., the prefrontal cortex) in adults. Givenchanges in the anatomical and functional maturity of the medial–temporal lobe duringdevelopment, as well as changes in connections between the medial–temporal lobe andcortical structures, it is reasonable to ask whether the hippocampus can really be thoughtto support ‘‘declarative memory’’ early in development? If the connections change, mightthe circuits (and hence processes) underlying task performance change as well? Thus, theway in which we conceive of functional brain development (maturational or IS) has impor-tant implications for our understanding of cognitive development.

As the field of developmental science increasingly adapts neuroscience methods and col-laborates with neuroscience and genetic researchers, it is important to not forget what our

field brings to these endeavors. To put it bluntly, scientists in these other fields need ourexpertise as much as we need theirs. Imaging or genetic studies of a behavioral phenotypedepend crucially on the understanding of the behavioral tasks involved and the investiga-tors’ sophistication about developmental theory. It is not hard to think of examples ofmultilevel research where the significance of a behavioral task was oversimplified or rei-fied. The Morris water maze is not simply a marker for hippocampal function, nor isthe Wisconsin Card Sorting Test a simple marker for prefrontal function. So multilevelresearch and interdisciplinary collaborations depend crucially on sophisticated cognitiveand behavioral analyses. Just as we are not trained in the nuances of genetics, geneticistsare not trained in the nuances of interpreting behavioral data.

Somewhat earlier in the field of DCN, it was hoped that ‘‘marker’’ tasks would simplifythe task of studying brain-behavior relations in children or immature animals. If a giventask was sensitive to dysfunction of a given brain structure in adults, then that task or asuitable variant could be used as a marker for function or dysfunction of that brain struc-ture in children. But there are three fallacies in this logic. One is that we have alreadysolved the localization problem in adults and that the solution is simple, i.e. there is astraightforward mapping between behavioral tasks and brain structures. Neuroimagingand other results have helped us discard that fallacy, although it still persists (see VanOrden, Pennington, & Stone, 2001 for a discussion). The mapping between a task, evena task component, and brain structures is complex. The second fallacy is that brain behav-ior relations are invariant across development. We now know this is not the case for lan-guage, vision, attention, and likely memory. The third fallacy is that the causal arrow is


unidirectionnal, from brain to behavior, instead of bidirectional (see Oliver, Johnson, Kar-miloff-Smith, & Pennnington, 2000).

A newer incarnation of marker tasks is found in recent research on psychiatric geneticsin the concept of endophenotypes. The hope is that by finding simpler behavioral pheno-types that are associated and coheritable with a complex behavioral disorder, we will makeit easier to find some of the genes that contribute to the complex behavioral disorder.Although this strategy has shown some promise, we must be careful to not oversimplifythe mapping between genes and behavior. Just as there are not genes for schizophrenia,there are not genes for endophenotypes of schizophrenia. Instead, there are genetic vari-ants that increase the risk for the development of schizophrenia, and it is possible (butnot yet proven) that a smaller set of these genes increases the risk for the developmentof an endophenotype of schizophrenia. Especially when the endophenotypes are behav-ioral, they face some of the same complexities faced by marker tasks. For instance, deficitson certain eye movement tasks, like smooth pursuit, are established endophenotypes inschizophrenia, but the mapping of smooth pursuit on to the brain and especially ontothe genome is very complex. While it is conceivable that a single risk allele might disrupta singer receptor type and this might lead to a deficit in smooth pursuit, there will be manyother ways to disrupt smooth pursuit, as well as protective factors that lead some individ-uals with the risk allele to have normal smooth pursuit.

In summary, the mapping between brain and behavior is complex, not unidirectional,and changes with development. The same is even more true for the mapping between genesand behavior. Although behavior does not change DNA sequences, it does affect geneexpression. Most developmental scientists understand the complexities in localizing behav-iors or deficits. Their sophistication in analyzing behavior and interpreting relations withother levels of biological analysis will be crucial as our capacity for finding genes and brainstructures that influence complex behaviors increases.

Acknowledgments

Bruce F. Pennington was supported by NIH Grants HD-27802 and HD-049027. KellyA. Snyder was supported by NIH Grant HD-049366.

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developmental cognitive neuroscience: origins, issues, and prospects

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