Learning through the ages: How the brain adapts to the social world across development

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Cognitive Development

Learning through the ages: How the brain adapts to the social world across development Eric E. Nelson The Research Institute of Nationwide Children’s Hospital and Department of Pediatrics at the Ohio State University, United States

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Article history: Received 2 November 2016 Received in revised form 15 February 2017 Accepted 16 February 2017 Available online xxx Keywords: Plasticity Sensitive periods Social cognition Social affect

a b s t r a c t In a number of ways development can be understood as a specialized form of learning. As new neuronal circuits are maturing they are molded by environmental experiences much in the way that experience molds synaptic strength in traditional learning models. Furthermore, many of the same mechanisms that underlie synaptic changes in learning also mediate changes that occur across development. This is true at both the molecular level in remodeling of dendritic spines and at the systems level via emotional amplification of associations. A key difference between the two however is that while learning can occur within virtually any circuit at any time, developmental plasticity is much more restrictive in both domain and time window. In this manuscript I review some of the mechanisms associated with developmental neuroplasticity and then provide specific examples from the development of social cognition. © 2017 Published by Elsevier Inc.

1. Introduction The maturation of the nervous system is extremely protracted relative to other physiological systems. Earliest stages of brain development begin soon after conception and, in most species, regulated changes in growth and refinement of brain circuits continue into early adulthood (Stiles, 2008). In humans this process begins about two weeks after conception, and is not complete until well into the third decade of life (Gogtay et al., 2004; Huttenlocher, 1979; Petanjek et al., 2011; Sowell et al., 2003). In this paper, I will argue that an important aspect of this prolonged maturation process is to incorporate neuroplasticity and adaptation into the framework of brain development. I will compare some of the mechanisms that are shared between brain maturation and learning, and will elaborate on some of the specialized features of learning that occurs in immature brain circuits; a concept I am referring to as developmental learning. I will then briefly provide some concrete examples of developmental learning principles from the domain of social development, in which the prolonged process of maturation is particularly apparent in both brain and behavioral responses. By developmental learning I am referring to the active guidance and modulation of regulated growth by experience. Developmental learning differs from traditional learning in that it can only occur when new synapses and circuits are undergoing growth and maturation. Although developmental learning often employs the same mechanisms as traditional learning, developmental learning refers to learning which is directly moderated by the maturation process. Learning which impacts circuits that are more (or less) receptive to modification as a function of developmental state is developmental learning. In many ways developmental learning reflects the concept of sensitive or critical periods in development (Hensch & Fagiolini, 2005; Knudsen, 2004; Lewis & Maurer, 2005). However, critical periods tend to be applied in a very restrictive manner to

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Please cite this article in press as: Nelson, E.E. Learning through the ages: How the brain adapts to the social world across development. Cognitive Development (2017), http://dx.doi.org/10.1016/j.cogdev.2017.02.013

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specific early life experiences. My conception of developmental learning is a more expansive application of sensitive period concept. Developmental learning applies whenever regulated changes in brain circuits occur in development. By this definition developmental learning can occur well beyond early life and can extend even into what is traditionally considered early adulthood (Gogtay et al., 2004; Petanjek et al., 2011). It should be noted that this paper is aimed at integrating developmental findings from a variety of disciplines (including both animal and human based studies) to generate theoretical concepts of neural maturation. I have tried to indicate clearly which models I am referring to in the text but in order to focus on general principles, they have not been segregated. 2. Developmental learning as an intermediate form of adaptation A primary function of the nervous system is to optimally match behavioral and physiological systems with the environment in which the individual is embedded. Adaptations can occur via genetic modifications across generations, or via changes brought about by learning within the individual. I will argue that, developmental learning, as a specialized form of traditional learning, is a third category of adaptation that, in terms of stability, flexibility, and cost, lies in between these two other forms of adaptation. Some environmental features are relatively stable, like gravity and climate. However, there are also many aspects of the environment that change dramatically across generations, seasons, or days. Examples of fluctuating environmental features include sources of food, potential predators, and social structure. Adaptations brought about by intergenerational shifts in the genome provide optimal fit between the organism and the more constant features of the environment. However, such shifts do not provide adequate flexibility for more rapidly changing features like food sources or social structure. If a primary source of food disappears within a generation, for example, inherited tendencies to seek out that food-source would lead to starvation. On the other hand, traditional learning provides a much more flexible and rapid means for the organism to adapt to the environment, because adaptations are individually customized and tailored to specific circumstances. There are, however, costs associated with learning. Frequent, dramatic changes to the wiring of the nervous system can be energetically expensive and sluggish. In addition, using a trial and error based strategy can be deadly if the wrong food source is chosen or social affiliations are directed at the wrong individual. In developmental learning, emerging brain circuits are highly susceptible to environmental experiences during their formation, but are much more stable thereafter. This generates a specified period of time when environmental context can mold adaptations but it does so within certain parameters. In the classic example of the critical period for visual system development, for example, visual circuits are refined by experience rather than being created de novo. In addition, the refinements are restricted to a particular period of time in development which restricts the cost and effort dedicated to specific structural development. As such, developmental learning is an intermediate form of adaptation – in terms of flexibility, stability, and cost. There are several important differences between traditional and developmental learning, even though the mechanisms of circuit change are similar. First, developmental learning is more rapid but also more labile than traditional learning. The ultimate outcome of developmental learning occurs on a much slower time scale. Secondly, developmental learning occurs within relatively narrow constraints, because the overall architecture is determined in the genome and experience can only make relatively minor changes to its implementation. Finally, developmental learning proceeds along pre-determined timelines and sequences, as the maturation of the brain unfolds over time. Developmental learning tends to be less flexible than traditional learning because it is more sequential and hierarchical (eg. you must walk before you run). On the other hand, because developmental learning depends on actively maturing brain circuits, it is also more robust and resistant to extinction than traditional learning. To use the classical example of visual system development as an example again, once ocular dominance columns are established they are generally very resistant to change. 3. Mechanistic similarities between developmental plasticity and learning For many years brain development was thought to be orthogonal to environmental experience (Meaney, 2010). However, it is now widely recognized that maturation of brain circuits cannot be dissociated from adaptations to the local environment, and indeed brain development can only be understood in the context of an interaction between nature and nurture (Meaney, 2010). From this perspective, environmental influences are a constant factor on brain maturation throughout brain development, and brain development should be construed as a form of learning. Indeed, from a mechanistic standpoint many of the same factors that govern change at the synaptic and circuit levels involve establishing and stabilizing contacts between cells. Cellular research on models of learning and development indicate similar mechanisms are employed in both circumstances. In addition, the recent surge of interest in epigenetic modulation of genetic expression has indicated another area in which common mechanisms seem to be employed in learning and maturation related processes. In this section I will review several models that reveal common mechanisms for brain change in development and learning. Long-term potentiation, or LTP, is a model of activity-dependent synaptic plasticity that has served as a model of learning for many years. LTP critically depends on the synaptic contacts between cells mediated by small dendritic protrusions called spines (Kasai, Fukuda, Watanabe, Hayashi-Takagi, & Noguchi, 2010). Across the lifespan, spines, and synapses more generally, ebb and flow, thereby generating intermittent contact between cells (Caroni, Donato, & Muller, 2012). When coactivity between cells occurs, transient spines strengthen and stabilize, and new more lasting neural associations are created. Please cite this article in press as: Nelson, E.E. Learning through the ages: How the brain adapts to the social world across development. Cognitive Development (2017), http://dx.doi.org/10.1016/j.cogdev.2017.02.013

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In traditional learning contexts, the strengthening of specific synaptic contacts via enlarging and stabilizing dendritic spines is correlated with emergence of new memories (Caroni et al., 2012; Chen et al., 2015; Holtmaat & Svoboda, 2009; Kasai et al., 2010). In memories that persist for long periods of time, the spines become physically enlarged, and the neurochemical constituents of the synaptic contact change thereby stabilizing the new connection (Caroni et al., 2012; Holtmaat & Svoboda, 2009; Kasai et al., 2010). At a molecular level, the dynamics of spine formation and elimination is greatly influenced by the overall balance between excitatory and inhibitory activation within and between cells mediated by excitatory glutamate and inhibitory GABA neurotransmission (Caroni et al., 2012). Long term maintenance of stable synaptic contacts is likely to involve enlarging spine connections via protein synthesis which is mediated by a host of other factors (Caroni et al., 2012). This same model has also been applied to basic synaptic changes in the brain across development (Takesian & Hensch, 2013). However, two important differences have been observed. First, relative to the mature brain, the formation and elimination of spines is a very active process in young animals. Dendritic spines demonstrate a pattern of frequent spontaneous activation and retraction (Grutzendler, Kasthuri, & Gan, 2002), that is markedly increased relative to mature models (Clark, Collins, Sanford, & Phillips, 2013; Grutzendler et al., 2002; Trachtenberg et al., 2002). However this process may vary by brain region, and some regions may demonstrate both heightened plasticity and stabilization into early adulthood (Johnson et al., 2016). These findings suggest that, from a traditional learning perspective, the juvenile and adolescentbrain is in a period of relative instability but also possesses a heightened level of plasticity. The second major difference that has emerged in models of synaptic plasticity in development is that an additional modulatory mechanism has been identified. In a series of studies targeting the molecular mechanisms governing the opening and closing of specific sensitive periods during neurodevelopment, Hensch and colleagues have demonstrated that regulated shifts in the excitatory/inhibitory balance of specific circuits occurs in a temporally and regionally specific manner in development. Importantly, these shifts correspond to regions that are in sensitive periods of organization (eg. the visual cortex during early postnatal life). Region specific maturation of a particular cell type (parvalbumin inhibitory cells) mark the beginning or opening of sensitive periods and shift the balance of local networks to be more sensitive to incoming information. This is then followed by the maturation of large scale inhibitory mechanisms, or the appearance of “molecular brakes” (Takesian & Hensch, 2013; Werker & Hensch, 2015). This work indicates that in what has been identified at a behavioral level as critical or sensitive periods of development (eg. visual and language development), the brain simply co-opts the existing mechanisms of learning and shifts the gain on circuit modulation to enhance and then inhibit learning. The onset of specific sensitive periods is thought to be controlled primarily by intrinsic factors related to maturational sequence. However, it can also be modulated by the presence of specific stimuli in the environment. For example animals reared in a dark environment will have a delayed onset of the visual sensitive period, though the visual sensitive period will eventually occur even in the absence of any visual stimulation. While most of the research on critical periods has been focused on organization of primary sensory cortex in early postnatal life, there has been some suggestion that adolescence and puberty may be a second important period of heightened plasticity (Blakemore & Mills, 2014; Crone & Dahl, 2012; Piekarski et al., 2017), Piekarski and colleagues have recently argued that the associative neocortex may undergo a period of hyper-plasticity during adolescence in much the same manner that primary sensory cortices do during early postnatal life. They have argued that the impact of sex steroids in the brain may modulate the pavalbumin inhibitory networks toward hyper-plasticity in some regions and hypo-plasticity in others (Piekarski et al., 2017). Thus, when considering development from the perspective of learning mechanisms, the evidence indicates both a pattern of nonspecific hyper-plasticity across the neocortex mediated by rapidly cycling LTP-like mechanisms and windows of more focal hyper-plasticity within specific circuits which occur at functionally specific sensitive periods and is governed by intrinsically and extrinsically controlled brake mechanisms. In addition to molecular models of LTP discussed above, two other models of brain circuit modulation have been identified as important mediators of both maturational and learning related changes in the nervous system. The first is axonal myelination. Myelin, which is the insulating sheath that coats axons, serves two important functions in this context. First, myelin increases the speed and efficiency of communication between neurons. Thicker coats of myelin along cells which form a circuit will increase the speed and efficiency of inter cellular communication. Secondly, myelin has been shown to reduce the formation of spontaneous spines between neurons, and thereby serves as an additional means of stabilizing synaptic connections already in place (Takesian & Hensch, 2013). Recent neuroimaging studies of neurodevelopment have demonstrated that the overall levels of white matter in the brain continue to increase into the early 20 s (Paus, Pesaresi, & French, 2014), and may be specifically impacted by puberty (Menzies, Goddings, Whitaker, Blakemore, & Viner, 2015; Peper et al., 2013). Thus, in a manner that is complementary to the reduced frequency of spontaneous spine formation across development, the overall increase in myelin serves to reduce the opportunity to form new associations, but also serves to stabilize inter-neuronal connections once they are established. Like other brain changes associated with hyper-plasticity and stability in development, myelin accrual is not a gradual and uniform process across the brain but rather the maturational timing varies regionally (Paus, 2010; Wierenga et al., 2016). In late developing areas like the prefrontal and parietal association cortex, myelin maturation occurs at a slower pace than in earlier maturing regions like primary sensory areas (Paus, 2010; Wierenga et al., 2016). This pattern indicates that the plasticity and stability of circuit functions will vary across the brain as a function of development. While most of the findings on shifting patterns of myelin have focused on development and mature function of interneuronal communication, recently there have also been a number of studies suggesting that myelin may also be an important factor in plasticity (Fields, 2015; Wang & Young, 2014). A number of studies have demonstrated that neural activity induces Please cite this article in press as: Nelson, E.E. Learning through the ages: How the brain adapts to the social world across development. Cognitive Development (2017), http://dx.doi.org/10.1016/j.cogdev.2017.02.013

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white matter growth within specific circuits by increasing proliferation of oligodendrocytes which are the source of myelin in the brain (Wang & Young, 2014). Moreover, in a mouse model of motor skill learning, pharmacological blockade of myelin proliferation was found to adversely impact behavioral acquisition of a novel skill (McKenzie et al., 2014) suggesting a direct causal role between myelin and memory. Localized myelination increases as a function of learning have been demonstrated in both mature and immature organisms (Fields, 2015; McKenzie et al., 2014; Sampaio-Baptista et al., 2013; Wang & Young, 2014). In human studies localized levels of myelin correlate with proficiency of specific skills across development (Nagy, Westerberg, & Klingberg, 2004), and targeted training has been shown to result in increases of white matter within specific regions (Gebauer et al., 2012). Thus, as with the literature on synaptic plasticity, studies on white matter indicate that a similar process mediates developmental maturation and traditional learning. In development, these mechanisms are both more active and demonstrate a localized pattern that correlates with emerging function prior to full maturational development. Also like synaptic plasticity, these processes are generally more active in development than in maturity. Traditional learning may have co-opted mechanisms related to circuit maturation to use for more traditional types of learning in maturity. A final example of the mechanistic similarity between traditional learning and developmental change can be found in epigenetics. Epigenetics involves regulating the ability of specific genes to be expressed by altering the chromatin and histone structure on which the DNA sits. This structural modification makes genes more or less accessible to transcription factors (Champagne et al., 2008; Meaney, 2010), which then modulates gene transcription and ultimately genetic expression. Epigenetic factors were first identified as a means of modifying gene expression in development, and were originally thought to be function primarily to differentiate tissue types in early development (Meaney, 2010; Rasmussen, 2014). However, intensive research on epigenetics of the last several decades has now revealed a number of ways in which gene expression can be modulated. Several of these mechanisms are activated across the life-span and can be flexibly engaged or disengaged (Jarome & Lubin, 2014). Studies of the molecular basis of memory formation have found that gene transcription is a key factor in turning a labile memory trace into a stable and long term memory (Bailey, Bartsch, & Kandel, 1996; Schafe & LeDoux, 2000); a process known as memory consolidation. Gene transcription resulting in protein synthesis is a necessary step in making structural changes to the synapse to enable long lasting alterations in circuit function (Bailey et al., 1996). Epigenetic factors such as modifiable histone acetylation, DNA methylation and phosphorylation are now thought to play an important role in regulating this process which ultimately supports initial memory consolidation (Chwang, O’Riordan, Levenson, & Sweatt, 2006; Jarome & Lubin, 2014; Levenson et al., 2004; Sweatt, 2016; Tognini, Napoli, & Pizzorusso, 2015), and perhaps as importantly, in memory reconsolidation after retrieval (Jarome & Lubin, 2014). Thus, as with the dynamics of dendritic interaction and axonal myelination, epigenetic modification of gene expression is another adaptation mechanism that is shared between both traditional forms of neuronal maturation and learning. The existence of common mechanisms of change further blur the lines between development and traditional learning. The inevitable interaction between these basic mechanisms may amplify learning in unique ways during specified periods of development and thereby generate contexts of hyper-susceptibility to modification that may define periods of developmental learning. 4. Development and insensitivity to change When considering development in the framework of learning, it is also important to keep in mind that in addition to the many ways in which the brain is hyper-plastic as it develops there are also some ways in which the brain demonstrates developmental rigidity or a diminished ability to make particular associations in development. There are many examples of developmental resistance to plasticitythat are based on immaturity of function (van Duijvenvoorde, Zanolie, Rombouts, Raijmakers, & Crone, 2008). Some of these can be attributed to “wiring” insufficiencies where full information cannot be incorporated because of a lack of physical integration (Fair et al., 2008; Fair et al., 2009), and others may result from mechanisms which actively inhibit formations because it would be detrimental to other developmental needs. Several instances of the former can be found the recent developmental cognitive neuroscience literature. For example, studies of aversive conditioning have demonstrated that relative to adults adolescents demonstrate a potentiated response to fear associated stimuli in the amygdala, but they are compromised in ability to verbally label fear stimuli, and have a diminished ability to extinguish fear memories once they are acquired (Lau et al., 2011; Pattwell et al., 2012). The developmental lag in adolescence is thought to be a function of weak connectivity between amygdala and prefrontal circuits. This phenomenon has recently been noted in cellular studies of rodents as well (Johnson et al., 2016). Similar deficiencies in judgments and behavioral control have also been extensively reported in the reward literature for adolescents relative to adults (Piekarski et al., 2017; Shulman et al., 2016). In addition there is some suggestion that children and adolescents weight rewarding and punishing outcomes differently in reinforcement based learning. In younger individuals, negative feedback was more impactful than positive feedback in guiding subsequent behavioral choices, and maturation of the impact of reward was correlated with the strengthening of the connection between the ventral striatum and the medial prefrontal cortex (van den Bos, Cohen, Kahnt, & Crone, 2012). In addition, a number of recent studies have demonstrated a remarkable shift in large scale patterns of co-activation across the brain in resting state neuroimaging. These studies investigate patterns of co-activity, and the formation of networks across the brain, when the subject is lying at rest and not engaged in any particular task. Using graph theory to analyze network organization these studies have demonstrated a shift from networks that are primarily organized along anatomical Please cite this article in press as: Nelson, E.E. Learning through the ages: How the brain adapts to the social world across development. Cognitive Development (2017), http://dx.doi.org/10.1016/j.cogdev.2017.02.013

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proximity to ones arranged along functional networks (Fair et al., 2008, 2009). As with the studies above, focusing on task based circuit maturation, these studies indicate that organization of mature functional circuits at rest also evolves across development. Finally, in addition to the findings highlighted above that point to developmental inflexibility due to a lack of circuit integration, there have been some studies demonstrating a failure to acquire specific associations that may result from an active mechanism. These active mechanisms appear to have evolved to protect the developing organism from acquiring associations which would be specifically detrimental in a particular developmental niche. For example, prior to weaning, rodents demonstrate an inability to develop aversions to tastes, places, and caretakers who are associated with events that are clearly aversive (Gubernick & Alberts, 1984; Sullivan & Holman, 2009; Sullivan, Moriceau, Roth, & Shionoua, 2010). At a functional level this is protective because infants have few options regarding ingesta, nest site, or caretakers prior to weaning. Some of the mechanisms of this inhibition have been identified (Sullivan et al., 2010), but from the perspective of developmental learning these appear to be examples of sensitive periods where the brain demonstrates a specific hypoplasticity not as a result of immaturity related integration but rather the result of specific protective mechanisms.

5. The role of emotion Findings of structural changes across development, such as transient emergence of inhibitory interneurons, regional changes in myelination, and synaptic pruning, have highlighted cellular mechanisms for localized periods of hyper-plasticity across development (Paus, 2010; Petanjek et al., 2011; Takesian & Hensch, 2013). As highlighted in several places below, hormones also appear to serve as an important mechanism for developmental plasticity (Crone & Dahl, 2012; Piekarski et al., 2017). Another important psychological feature of development that may also make important contributions to developmental plasticity is emotion (Nelson, Jarcho, & Guyer, 2016; Nelson, Lau, & Jarcho, 2014). One of the many functions served by emotion is to act as a filter for the barrage of stimuli that continuously impact the nervous system (Pessoa, 2011). Subcortical structures such as the amygdala and striatum that play a central role in affective responding, also serve to tag stimuli that are salient to the individual, and by activating widespread networks in the brain, act to direct attention, amplify sensory processing, and enhance the formation of memory, via a number of different mechanisms (Gallagher & Chiba, 1996; Okon-Singer, Hendler, Pessoa, & Shackman, 2015; Pessoa, 2011; Schultz, 2016; Vuilleumier & Pourtois, 2007). Importantly, the environmental features that elicit emotion, change across development. In the social world, for example, the affective systems of infants and toddlers are tuned to the presence and interaction with the primary caretaker (Kuhl, 2010; Zhang et al., 2012), whereas in adolescence affective reactivity is more tightly coupled to presence and interaction with peers (Larson & Richards, 1998; Larson & Richards, 1991; Steinberg & Morris, 2001). These emotional patterns across development have also been observed in brain response tendencies (Chein, Albert, O’Brien, Uckert, & Steinberg, 2011; Guyer, McClure-Tone, Shiffrin, Pine, & Nelson, 2009; Jones et al., 2014; Moor, Leijenhorst, Rombouts, Crone, & Van der Molen, 2010; Tottenham, Shapiro, Telzer, & Humphreys, 2012). Thus, shifting targets of emotion may be another means by which biological maturation incorporates environmentally specific experiences into brain development (Nelson et al., 2016; Nelson et al., 2014).

6. Examples from the social world Although the best studied examples of hyper-plasticity in development relate to organization of sensory cortex (Levelt & Hubener, 2012; Lewis & Maurer, 2005; Lomber, Meredith, & Kral, 2016; Sharma, Nash, & Dorman, 2009), other functional systems in the brain have also been found to have periods of heightened plasticity during distinct periods of development One psychological domain where distinct periods of hyper plasticity have been identified is social cognition, or the cognitive and affective processes related to engaging with conspecifics. Social cognition is also a domain where development is particularly protracted and proceeds in several distinct phases (Nelson et al., 2016, 2014). Many of the aspects of social cognition make it an ideal domain for developmental learning. Factors such as social group structure, relevant individuals, and normative social behavior can vary distinctly both across individuals and across generations. However, within a specific individual these factors are likely to be relatively stable. Your mother will always be your mother but your mother is very different from my mother. Likewise peers; mating partners; and social group norms will often vary markedly between individuals but remain relatively stable within an individual. Thus, in the domain of social cognition, developmental learning would enable adaptation which is maximally crafted to the individual but provide stability in brain response tendencies that matches the general stability of social factors in the environment. The protracted development of brain regions that support these functions enables them to emerge in sequence as the social context itself unfolds (Konner, 2010). Several examples of developmental learning are evident in social cognition. The most well-studied of these include filial imprinting, face processing, and language acquisition which all occur during early postnatal life. However, distinct periods of learning sensitivity have also been identified at several other points in social development which may also constitute times of heightened plasticity and developmental learning (Blakemore & Mills, 2014).

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6.1. Filial imprinting Filial imprinting is the process first identified in a number of avian species whereby a newly hatched chick orients to the first moving object it encounters (typically the hen) and begins following it as it moves through the environment. Gradually through associative learning mechanisms, the chick then learns other more abstract aspects and develops a preference for the entire individual (Bolhuis & Honey, 1998; Horn, 2004). Similar mechanisms have been identified in some mammals including rodents where olfactory based imprinting occurs (Sullivan et al., 2010). This process also occurs in the mothers of some mammalian species immediately after birth when an imprint is formed for its offspring (Poindron, Levy, & Keller, 2007) and in some monogamous species after sexual experience (Lee, Macbeth, Pagani, & Young, 2009). In all three of these examples a window of hyper-plasticity for specific environmental stimuli is opened for a short period of time. Exposure to the specific sensory experience during this temporal window results in long term changes in both brain and behavior. Although the parameters that govern human behavior following these events are less rigid than revealed in most animal models, similar mechanisms of neural plasticity following distinct developmental events also likely occur in humans as well (Di Giorgio et al., 2016; Fleming, O’Day, & Kraemer, 1999; Young & Wang, 2004). 6.2. Face processing In both humans and nonhuman primates there are several regions within the occipital lobe and temporal cortex that respond selectively to faces. These include the occipital face area, the fusiform face area, the superior temporal sulcus and several patches in the temporal lobe (Atkinson & Adolphs, 2011; Haxby, Hoffman, & Gobbini, 2000; Kanwisher & Yovel, 2006; Tsao, Moeller, & Freiwald, 2008). Both behavioral and neural based studies suggest that these areas undergo distinct periods of experience dependent tuning during development. At a behavioral level, both monkeys and humans demonstrate equal – though low level – abilities to discriminate among different members of both species during early periods of development, but across time and with selective exposure to one species, discrimination capabilities increase for the exposed species and diminish for the non-exposed species (Pascalis, de Haan, & Nelson, 2002; Pascalis et al., 2005; Sugita, 2008). This suggests experience dependent tuning of perceptual abilities for exposed faces, likely via synapse-level remodeling of face selective regions in occipital and temporal cortices (Scott, Pascalis, & Nelson, 2007). Neuroimaging studies have demonstrated a remarkably prolonged process of maturation within the primary and extended face processing systems. While the basic patterns of category selectivity is evident within the fusiform face area (by 6 or 7 years of age in humans), the volume of the FFA that responds selectively to faces continues to increase through young adulthood (Golarai, Liberman, & Grill-Spector, 2015; Golarai, Liberman, Yoon, & Grill-Spector, 2010; Haist, Adamo, Han Wazny, Lee, & Stiles, 2013). Development is also associated with a more flexible, engagement of face regions with other functional systems that modulate face activity (Cohen Kadosh, Heathcote, & Lau, 2014; Cohen Kadosh, Johnson, Henson, Dick, & Blakemore, 2013; Haist et al., 2013). The developmental changes of this region appear to result from maturation factors that arise at least in part from direct experience-dependent processes, that continue to mold FFA responses through at least adolescence (Golarai et al., 2015). 6.3. Language acquisition During the first year of life human infants acquire the ability to detect subtleties, such as particular vowel inflections, that are unique to their native language (Cheour et al., 1998; Kuhl, 2010). During this time infants also lose the ability to make distinctions of some subtle differences that are not present in their native tongue (Kuhl, 2010; Werker & Hensch, 2015). These developmental changes are evident at both the behavioral and neural levels, and similar to development of sensory systems, in that they are both experience dependent and occur only if exposure occurs during a particular postnatal window (Kuhl, 2010; Werker & Hensch, 2015). Interestingly however, unlike development of primary sensory systems, language acquisition may require that exposure to the linguistic exemplars occur within an explicitly social context (Kuhl, 2010). Stimuli presented on a computer are less effective than stimuli presented by a human. Furthermore, recent conceptualizations suggest that language development likely involves multiple different sensitive periods in which the brain is uniquely susceptible to different aspects of speech at different periods of development (Kuhl, 2010; Werker & Hensch, 2015). A similar, multi-stage profile, has also emerged for development of the visual system in humans (Lewis & Maurer, 2005). 6.4. Play behavior Soon after weaning ends virtually all mammals enter a period that is characterized by high levels of social play with conspecifics of the same age range (Cooke & Shukla, 2011; Siviy & Panksepp, 2011). In spite of being energetically costly and risky, social play during the juvenile period is a motivated and highly rewarding behavior (Panksepp, Siviy, & Normansell, 1984; Trezza, Campolongo, & Vanderschuren, 2011; Varlinskaya & Spear, 2008). There is also some evidence that play may be under homeostatic control with brief periods of deprivation resulting in compensatory rebounds (Panksepp et al., 1984; Varlinskaya & Spear, 2008). However, this compensatory effect is only apparent during a specific juvenile window of development (Varlinskaya & Spear, 2008). The fact that play is conserved, motivated, and possibly under homeostatic control, indicates it is likely serving some useful function in guiding maturation of both brain and behavior (Cooke & Shukla, Please cite this article in press as: Nelson, E.E. Learning through the ages: How the brain adapts to the social world across development. Cognitive Development (2017), http://dx.doi.org/10.1016/j.cogdev.2017.02.013

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2011; Pellegrini, Dupuis, & Smith, 2007). Indeed, animals deprived of opportunities to play during the juvenile period tend to show low levels of social behavior in adulthood (Hol, Van den Berg, Van Ree, & Spruijt, 1999). The social interactions that do occur in play deprived animals tend to be awkward and inflexible (Cooke & Shukla, 2011; Himmler et al., 2014; van den Berg et al., 1999). Although it has been demonstrated that play induces coordinated activity among several brain regions in juvenile animals including striatum, amygdala, thalamus and prefrontal cortex (Siviy & Panksepp, 2011; van Kerkhof, Damsteegt, Trezza, Voorn, & Vanderschuren, 2013; van Kerkhof, Trezza et al., 2013), the exact role that play serves in guiding development of these circuits is not clear. Interestingly, however, there is some relatively recent evidence suggesting that play may induce modifications to the development of dendrites within the medial prefrontal cortex which make them more malleable to a variety of experiences (both social and non- social) subsequently encountered in adult life (Himmler, Pellis, & Kolb, 2013). 6.5. Peer integration & romantic behavior In humans, as the developmental phase marked by intense play begins to wane, a new social phase emerges which is marked by strong desire for integration with peer groups (Nelson et al., 2016; Nelson, Leibenluft, McClure, & Pine, 2005). During this period, emotions, cognitions, and behaviors all shift from relatively non-specific (eg. not individually targeted) play, toward peers and peer group acceptance. This tends to happen during early adolescence soon after the process of puberty has begun (Blakemore, 2012; Blakemore & Mills, 2014; Crone & Dahl, 2012; Larson, Richards, Moneta, Holmbeck, & Duckett, 1996). While the neural mechanisms responsible for this dramatic shift in behavior are only beginning to be explored, some preliminary findings suggest that at least some of the changes in adolescent social behavior may be mediated by effects of steroid hormones on brain function (Forbes & Dahl, 2010; Goddings, Burnett Heyes, Bird, Viner, & Blakemore, 2012; Goddings et al., 2014). It is interesting to note that the end of the period of active play in rodent models is also marked by the emergence of puberty (Cooke & Shukla, 2011). The emergence of sexual and romantic behaviors, which follows and appears to be directly related to the social re-orientation toward peer groups (Connolly, Furman, & Konarski, 2000), almost certainly relates to changes in brain function that are brought about by pubertal hormones (Forbes & Dahl, 2010; Suleiman, Galvan, Harden, & Dahl, 2016). In humans, there is little evidence at present pointing toward specific behaviors or brain functions that are funneled by environmental experience in a manner similar to language or face processing. However, this is a relatively recent focus of investigation and it seems likely that some focal regions of hyper-plasticity will be found in development of brain during puberty, and critical periods of behavioral organization will emerge (Blakemore & Mills, 2014; Steinberg, 2005). Some animal studies have revealed striking examples of behaviors which undergo critical organizational periods during puberty. In rodents, a number of different social behaviors including both mating and non-mating related social interactions are specifically organized by the presence of gonadal hormones at puberty (Sisk, 2016). These behavioral changes are also associated with alterations in brain function (Juraska, Sisk, & DonCarlos, 2013). Manipulation of hormonal levels only during a specific window of development impacts organization of these neural and behavioral measures. They do not occur if juveniles are exposed to pubertal levels of gonadal hormones, nor can adult treatment restore normalcy in animals that are castrated prior to puberty (De Lorme & Sisk, 2013; Sisk, 2016). Importantly, the organizational effects of gonadal hormones at puberty appears related to the effects gonadal hormones have on engaging with and learning from conspecifics during this period of time (De Lorme & Sisk, 2013; Sisk, 2016). This pattern does suggest that some specific social behaviors may undergo a period of hyper-plasticity at puberty in a manner analogous to face processing or language acquisition, though the details remain unclear. A similar profile has emerged in studies of parental behavior after birth − particularly in rodents. In rats, exposure to infant stimuli produces dramatically different results in postpartum relative to nulliparous female rats (Fleming et al., 1999). Much of the difference appears to come about by changes in brain structure brought about by hormonal context of pregnancy. However hormonal manipulations tend to have diminished impact on the behavior of female rats who have already gained experience with infants (Fleming et al., 1999). Here, as with the behavioral priming evident in puberty, the initial hormonal context appears to facilitate social learning, and once that learning has occurred the role of hormones in guiding behavioral responses is diminished. Human based studies have found some similarities in effects of hormones on both maternal (Fleming et al., 1993; Fleming, Steiner, & Corter, 1997) and paternal (Atzil, Hendler, Zagoory-Sharon, Winetraub, & Feldman, 2012; Fleming, Corter, Stallings, & Steiner, 2002)behaviors although the effects are much less dramatic in human than animal based models. Nonetheless, the relatively scant literature does indicate that gonadal hormones are likely to impact human social behavior in important ways. The effects of such hormonal modulation in guiding learning during puberty may be particularly important in shaping behavior and social behavior in particular throughout the lifespan. 7. Conclusions & future directions It is clear that brain development must take both cellular maturation and the environmental context into account (Meaney, 2010). The role of experience in shaping the organization of the nervous system across development was first clearly demonstrated by the work of Hubel and Wiesel in the 1960s who identified a so called critical period in which visual experience was required for the brain to develop the normative architectural organization (Kiorpes, 2015). Since then multiple different critical (or more accurately sensitive) periods have been identified for a number of different brain functions (Blakemore Please cite this article in press as: Nelson, E.E. Learning through the ages: How the brain adapts to the social world across development. Cognitive Development (2017), http://dx.doi.org/10.1016/j.cogdev.2017.02.013

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& Mills, 2014; Himmler et al., 2013; Kiorpes, 2015; Kuhl, 2010; Lewis & Maurer, 2005; Pascalis et al., 2005; Sugita, 2008; Werker & Hensch, 2015; Wong, Chabot, Kok, & Lomber, 2013). During all these periods the brain can be characterized as being in a state of domain specific hyper-plasticity, in which specific features in the environment are particularly impactful on the organization of the brain and the long term regulation of behavior. The majority of developmental studies of sensitive periods have focused on the early postnatal period. With the concept of developmental learning put forth in this paper I have tried to make several points. First, sensitive periods are present whenever development is taking place which is now known to last at least into the early 20 s in humans. Any learning which impacts synapses or circuits prior to maturity will impact their ultimate organization. Therefore, sensitive periods are not confined to the postnatal period but are present throughout development. Second, while learning can occur in neural circuits after maturity has been reached, it is often more difficult to exert change after development has completed. In contrast, there are few if any ways in which development can occur in the absence of learning if learning is broadly defined to include change affected by the environment. Maturation is always taking place in some kind of environment, and that environment will exert an influence on maturation. It is interesting to note that many of the mechanisms which have been associated with brain maturation are also associated with learning and vice versa. In this paper I have reviewed many instances were development alters the gain on learning mechanisms to alter the modifiability of different brain circuits across development. Understanding how learning and maturation act synergisticallyand antagonistically across the entire spectrum of development will likely foster greater understanding of both learning and maturation. A second overarching point of this manuscript is that there are likely to be a number of different “biomarkers” for periods of learning sensitivity across development. These include genetic markers (Jarome & Lubin, 2014); cellular markers (Werker & Hensch, 2015); network markers (Fair et al., 2009; Paus, 2010); neurochemical markers (Forbes & Dahl, 2010); and even emotional markers (Nelson et al., 2014). Many of these markers will be both domain specific and brain region specific. Generating a landscape of when, where, and under what circumstances these markers appear and disappear will likely generate a better picture of the intersection between learning and maturation than we currently have. While only some of these markers (eg. myelin, emotion) are clearly accessible in human studies, future work that targets cross-species models, or those which aim to make technical advances to enable greater accessibility of markers will likely be most productive. Finally, in contrast to some domains (eg. sensory or motor skills) which mature more rapidly, social cognition is an area which may be particularly relevant for considerations of the intersection between traditional and developmental learning. Social cognition matures in sequential manner for a protracted period of time, with a number of different developmental phases readily apparent. Future studies which identify the means and mechanisms by which the human brain amplifies or attenuates plasticity in social development will likely be very impactful for our understanding of developmental science in general. 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