Without delving too deeply into the different types of learning processes that occur, both in development and beyond, there are undoubtedly some types of learning that are highly similar in both. For instance, learning by trial and error is one common and lifelong way that organisms, from rodents to primates, master their environment. Given their limited speaking and language comprehension abilities, infants constantly learn through trial and error; as such, they are problem-solvers who are constantly faced with a problem and the challenge of solving it. A common dilemma a young infant encounters is how to balance and sit upright. After repeated collapses and attempts at a solution, the infant eventually learns to use an arm in a so-called “tripod stance” to support him/herself. In adults, the neural activation that accompanies learning by trial and error, particularly through unexpected outcomes, is referred to as the “prediction error signal” [e.g. Hollerman and Schultz, 1998; Schultz et al., 1997], thought to be mediated primarily through the neurotransmitter dopamine. Briefly, there is an increased dopamine firing rate in nonhuman primates and increased activation in dopamine-rich region in humans when the organism receives an unexpected event [Fiorillo et al., 2003]. Eventually, the dopamine signal decreases as the organism learns to expect the event [Fiorillo et al., 2003; 2008]. These findings have implicated dopamine as a learning signal. While methodological constraints have precluded examination of the neural basis of prediction error learning in the young infant, I would argue that the dopaminergic neural mechanisms are the same. That is, dopamine neurons respond to expected and unexpected events similarly in the infant as they do in the adult. However, I would also assert that, because the system in which dopamine is acting is very different across development, that prediction error cannot be exactly the same in the infant as it is in the adult. While prediction error learning is a form of environmental adaptation across development, neural plasticity that arises from it differs. In the young infant, this plasticity will influence the basic architecture of the neural system (i.e. how the brain is going to be organized) while in the adult, this plasticity is modifying the existing architecture of the brain (i.e. reorganizing and modifying but not laying the groundwork). This dichotomy is analogous to building a house, where building a brand new house represents developmental plasticity and a house remodel represents plasticity in the mature system. The tools and mechanisms are identical, but the environment in which the change is occurring is vastly different.
The utility of “noise”
One of the greatest confounds in developmental work is the significant difference in ability or performance between children and adults. As compared with adults, who can and do implicitly draw on domain-general neural resources, the child's ability to perform any given cognitive operation is inefficient at best, as they require additional effortful, explicit, and implicit requirements to perform complex cognitive demands (e.g. response inhibition) as well as adults. For example, numerous studies have shown that even when children perform a given task as well as, or without any differences in observable behavior, as adults, they recruit distinct neural strategies. Tamm et al. [ 2002] compared the changing performance of children, adolescents, and young adults on the Go/No-Go task, a measure of inhibitory control. Despite an overall reduction in reaction time with age, younger subjects showed the same level of accuracy as adults. However, the fMRI data collected alongside the performance measures revealed that younger children demonstrated a greater level of activity within left superior and middle frontal gyri than did older children and that, conversely, older participants demonstrated an increased focal activation in the left inferior frontal gyrus relative to their younger counterparts. In a separate study of cognitive control, effective interference suppression in children was associated with prefrontal activation in the opposite hemisphere as adults while effective response inhibition was associated with activation in posterior, but not prefrontal, regions activated by adults [Bunge et al., 2002]. The authors also reported that children failed to activate a region in right ventrolateral prefrontal cortex that was recruited for overall cognitive control by adults. Similarly, a more recent study showed that children recruit distinct activation profiles from adults also differ temporally (i.e. show different time-courses of activation) across relational reasoning tasks [Crone et al., 2009]. Together, these studies provide evidence for the alternative neural strategies that immature systems often engage to support more mature behavioral demands. Despite the apparent nuisance that such extreme behavioral and neural variability introduces into the study of development, dynamic systems theory celebrates this variability [Smith and Thelan, 2003]. This noise allows investigators to examine developmental trajectories of change over the short timescales of problem-solving (i.e. because of intersubject individual differences) and/or over a longer developmental span (i.e. as when comparing children with adults).
Schlaggar et al. have elegantly demonstrated how variability can be used to gather insight into developmental versus performance-related neural activity. Using a single-word processing task, they compared neural activity in a performance-matched subgroup of children and adults taken from a larger sample [Schlaggar et al., 2002]. That is, the children and adults in the matched group did not differ in behavioral performance, making it possible to determine whether any functional activation differences were due to developmental stage or performance. They found distinct patterns of activation that were age-related, performance-related, or independent from either. As such, their data shed new light on age-related regions (regions that were more/less active in children regardless of performance) that most likely reflect effects of brain development. In a follow-up study [Brown et al., 2005], the same group reported a more thorough examination of progressive and regressive neural changes across development. Using lexical association tasks, the authors identified increases and decreases in different brain regions that varied by age, performance ability, or neither. Seventy-five percent of the regions identified as showing age-related changes (i.e. independent of performance) showed decreases in activity over age. These regions were most prominently located in medial frontal and anterior cingulate cortex, right frontal cortex medial parietal, and posterior cingulate cortex. The remaining 25% of regions that showed increases in brain activity with age, were primarily later-stage processing regions, including lateral and medial dorsal frontal cortex and left parietal cortex. This strategy, of taking advantage of developmental and behavioral variability post hoc, is precisely the approach that needs to be adopted to disentangle neural mechanisms of development- and learning-related plasticity.
The approach by Schlaggar et al. described above can easily be modified to examine training-related plasticity. By substituting a training component for the “performance” group (i.e. the group who had naturally occurring variability in performance ability), one could separate neural activation changes related to age, training, or neither. For instance, a group of individuals ranging in age from childhood to adulthood could be trained on a motor task, such as juggling, that all were naive to. Participants would be scanned before and after training. Post hoc, participants would be divided into groups based on their level of juggling skill. In this manner, neural regions would be divided in those that are age-related and training-related, thereby allowing insight into neural regions more susceptible to experience and those with greater developmental constraints.
Longitudinal training studies across development
Despite the inherent challenges, the only way to identify the root of neural plasticity as either developmental or experiential is to conduct a longitudinal training study. There is no question that the incredibly challenging, logistically difficult, and expensive nature of this type of work is what has precluded the field from embarking more vigorously on this type of research. However, a few recent studies have proven its feasibility. For instance, Durston et al. [ 2006] used a combined longitudinal and cross-sectional study to examine shifts in cortical activity during a response inhibition task. The longitudinal findings, relative to the cross-sectional data, showed attenuated activity in dorsolateral prefrontal cortical areas with age. In parallel, there was increased focal activation in ventral prefrontal cortex that was related to improvements in task performance [Durston et al., 2006]. A more recent study by Hyde et al. [ 2009] implemented a training component. Their study builds on previous studies in adult musicians and matched nonmuscians that have revealed structural and functional differences in brain regions relevant to music production [Bermudez and Zatorre, 2005; Elbert et al., 1995; Gaser and Schlaug, 2003; Pantev et al., 1998; Zatorre et al., 2007]. The authors were motivated by the question begged of this type of research: Do musicians (or others who show skill-related neural plasticity) do so because of biological predispositions to music or because of intensive music training? Hyde et al. compared structural changes in relation to behavioral changes in young children who received 15 months of instrumental musical training relative to a group of children who did not. The children who received private keyboard lessons showed greater behavioral improvements on music discrimination and related tasks than the nontrained children; neither group showed differences between baseline and testing on nonmusical tasks. In addition, the musically trained children showed greater structural changes in right precentral gyrus, corpus callosum, and the primary auditory region [Hyde et al., 2009], consistent with findings in adults [Zatorre et al., 2007 for review]. Their data provide new evidence for training-induced structural brain plasticity in early childhood. Using structural MRI, Schlaug et al. [ 2005, 2009] also identified structural differences in the corpus callosum in young musicians. Based on total weekly practice time, they divided a sample of 5- to 7-year-old children into three groups: high-practicing, low-practicing, and controls. There were no differences in corpus callosum size at baseline, but differences emerged after approximately 29 months, with the greatest increased change in the high-practicing group of children [Schlaug et al., 2009]. Further, total weekly music exposure predicted degree of change in the corpus callosum as well as improvement on a nonmusic related motor-sequencing task.
In addition, training interventions have been implemented in clinical populations and have similarly shown robust plasticity. In a group of children with ADHD, training significantly improved performance on a nontrained visuospatial working memory tasks. In addition, motor activity, as measured by the number of head movements during a computerized task, was significantly reduced in the treatment group [Klingberg et al., 2002]. In a separate study, strong improvement in attention was found after only 5 days of attention training in a group of 4- and 6-year-old children. This change was paralleled by changes in EEG patterns that resembled a more mature pattern of activation, such that training had specific effects on the scalp distribution of the ERPs that was similar to the influence of development [Rueda et al., 2005].
Several studies have provided strong support for the claim that children with reading disabilities can benefit significantly from intervention techniques; the impact of such interventions on neural plasticity has been assessed using fMRI [McCandliss and Noble, 2003]. In one study, dyslexic children received an intervention after an initial baseline scan showing the typical reduced activation of the left posterior superior temporal gyrus (STG) during a phonologically challenging task [Simos et al., 2002]. Following the 80-hour intervention, all dyslexic children showed significant increases in reading skill, as well as increased activation in the left posterior STG [Simos et al., 2002]. Similar changes in neural activation were reported in separate intervention training in children with other dyslexia [McCandliss et al., 2001; Temple, 2002]. In a recent report, Keller and Just showed that reading remediation induces changes in white matter of poor readers [Keller and Just, 2009], such that fractional anisotrophy (FA) was significantly increased following remedial instruction. This FA increase was correlated with improvement in phonological decoding ability, which demonstrates how behavioral intervention can influence neural plasticity.
A recent study was able to examine the effects of reading on structural neural change without the influence of development [Carreiras et al., 2009]. Structrual MRI scans were obtained from adult participants who had recently completed a literacy program in adulthood (before the program, they were illiterate) and matched illiterates who had not yet started the literacy program. Their findings suggest that learning to read strengthens the coupling between left and right angular gyri and between the left dorsal occipital gyrus and left supramarginal gyrus [Carreiras et al., 2009].
Collectively, these studies have suggested that plasticity within the immature brain shows similarities to plasticity in the adult system. First behavioral improvements related to intensive training or experience are associated with neural plasticity specific to the task at hand (e.g. increased activation in the STG following reading intervention). What this suggests is that experience-dependent mechanisms do not differ greatly across the lifespan. Second, neural regions previously associated with experience-expectant mechanisms, such as motor abilities and language, show a high degree of plasticity across development, suggesting that perhaps there is plasticity in processes that are initially precipitated by expectant interactions with the environment.
These studies have also led to more questions that will undoubtedly be addressed in the next generation of research on this topic. First, which neural systems show greater or less training-related plasticity earlier in development? For example, there is significantly greater plasticity in receiving and learning from language input during infancy than in any other point in life. As infants receive increasing exposure to their native language, neural systems sensitive to language lose plasticity, which is translated into more difficulty discriminating speech sounds of foreign languages and learning new languages [Doupe and Kuhl, 2008]. Are there other examples of such extreme behavioral and neural loss of plasticity across the lifespan, whereby learning itself imposes constraints on plasticity? Second, how do the timescales of neural plasticity change across development? That is, do observed behavioral and neural changes occur more or less quickly in the developing brain? Again, to borrow from the language literature, young children learn to discriminate foreign languages more quickly and more proficiently than adults [Snow, 1987]; does this accelerated timescale hold for all cognition? Last, which behaviors cannot be “sped up” by exposure earlier in development because of time-locked experience-expectant mechanisms? Certainly, pubertal constraints (as described in more detail in Blakemore et al. , this issue) will impose at least some limits on plasticity associated with this maturational change.