Adolescents are notorious for engaging in reward-seeking behaviors, a tendency attributed to heightened activity in the brain's reward systems during adolescence. It has been suggested that reward sensitivity in adolescence might be adaptive, but evidence of an adaptive role has been scarce. Using a probabilistic reinforcement learning task combined with reinforcement learning models and fMRI, we found that adolescents showed better reinforcement learning and a stronger link between reinforcement learning and episodic memory for rewarding outcomes. This behavioral benefit was related to heightened prediction error-related BOLD activity in the hippocampus and to stronger functional connectivity between the hippocampus and the striatum at the time of reinforcement. These findings reveal an important role for the hippocampus in reinforcement learning in adolescence and suggest that reward sensitivity in adolescence is related to adaptive differences in how adolescents learn from experience.

Adolescents take more risks when peers monitor their behavior. However, it is largely unknown how different types of peer influence affect adolescent decision-making. In this study, we investigate how information about previous choices of peers differentially influences decision-making in adolescence and young adulthood. Participants (N = 99, age range 12-22) completed an economic choice task in which choice options were systematically varied on levels of risk and ambiguity. On each trial, participants selected between a safer choice (low variability in outcome) and a riskier choice (high variability in outcome). Participants made choices in three conditions: a solo condition in which they made choices with no additional information, a social condition in which they saw choices of supposed peers, and a computer condition in which they saw choices of a computer. Results showed that participants' choices conform to the choices made by the peers, but not a computer. Furthermore, when peers chose the safe option, late adolescents were especially likely to make a safe choice. Conversely, when the peer made a risky choice, late adolescents were least likely to follow choices made by the peer. We did not find evidence for differential influence of social information on decisions depending on their level of risk and ambiguity. These results show that information about previous decisions of peers are a powerful modifier for behavior and that the effect of peers on adolescents' decisions is less ubiquitous and more specific than previously assumed.

An important aspect of adaptive learning is the ability to flexibly use past experiences to guide new decisions. When facing a new decision, some people automatically leverage previously learned associations, while others do not. This variability in transfer of learning across individuals has been demonstrated repeatedly and has important implications for understanding adaptive behavior, yet the source of these individual differences remains poorly understood. In particular, it is unknown why such variability in transfer emerges even among homogeneous groups of young healthy participants who do not vary on other learning-related measures. Here we hypothesized that individual differences in the transfer of learning could be related to relatively stable differences in intrinsic brain connectivity, which could constrain how individuals learn. To test this, we obtained a behavioral measure of memory-based transfer outside of the scanner and on a separate day acquired resting-state functional MRI images in 42 participants. We then analyzed connectivity across independent component analysis-derived brain networks during rest, and tested whether intrinsic connectivity in learning-related networks was associated with transfer. We found that individual differences in transfer were related to intrinsic connectivity between the hippocampus and the ventromedial prefrontal cortex, and between these regions and large-scale functional brain networks. Together, the findings demonstrate a novel role for intrinsic brain dynamics in flexible learning-guided behavior, both within a set of functionally specific regions known to be important for learning, as well as between these regions and the default and frontoparietal networks, which are thought to serve more general cognitive functions.

Complex learned behaviors must involve the integrated action of distributed brain circuits. While the contributions of individual regions to learning have been extensively investigated, understanding how distributed brain networks orchestrate their activity over the course of learning remains elusive. To address this gap, we used fMRI combined with tools from dynamic network neuroscience to obtain time--resolved descriptions of network coordination during reinforcement learning. We found that learning to associate visual cues with reward involves dynamic changes in network coupling between the striatum and distributed brain regions, including visual, orbitofrontal, and ventromedial prefrontal cortex. Moreover, we found that flexibility in striatal network dynamics correlates with participants' learning rate and inverse temperature, two parameters derived from reinforcement learning models. Finally, we found that not all forms of learning relate to this circuit: episodic memory, measured in the same participants at the same time, was related to dynamic connectivity in distinct brain networks. These results suggest that dynamic changes in striatal--centered networks provide a mechanism for information integration during reinforcement learning. Significance Statement Learning from the outcomes of actions ----referred to as reinforcement learning ----is an essential part of life. The roles of individual brain regions in reinforcement learning have been well characterized in terms of the updating of values for actions or sensory stimuli. Missing from this account, however, is a description of the manner in which different brain areas interact during learning to integrate sensory and value information. Here we characterize flexible striatal--cortical network dynamics that relate to reinforcement learning behavior.. CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/094383 doi: bioRxiv preprint first posted online Dec. 15, 2016; 1 Introduction Learning from reinforcement is central to adaptive behavior and requires continuous and dynamic integration of sensory, motor, and reward information over time. Major progress has been made in understanding how individual brain regions support reinforcement learning. However, remarkably little is known about how these brain regions interact during learning, how their interactions change over time, and how these dynamic circuit--level changes relate to successful learning. In a typical reinforcement learning task, participants use reinforcement over hundreds of trials to associate cues or actions with their most probable outcome (e.g. (1--4)). Computationally, this is captured by so--called "model--free" reinforcement learning algorithms, a class of models that provide a quantitative and mechanistic framework for describing behavior (1, 5, 6). These models have also been successful in accounting for neuronal signals underlying learning b...

No abstract

Adults have little difficulty perceiving objects as complete despite occlusion, but newborn infants perceive moving, partly occluded objects solely in terms of visible surfaces. The developmental mechanisms leading to perceptual completion have never been adequately explained. Here, we examine the potential contributions of oculomotor behavior and motion sensitivity to perceptual completion performance in individual infants. Young infants were presented with a center-occluded rod, moving back and forth against a textured background, to assess perceptual completion. Infants also participated in tasks to assess oculomotor scanning patterns and motion direction discrimination. Individual differences in perceptual completion performance were strongly correlated with scanning patterns, but were unrelated to motion direction discrimination. We present a new model of development of perceptual completion that posits a critical role for targeted visual scanning, an earlydeveloping oculomotor action system.

Complex learned behaviors must involve the integrated action of distributed brain circuits. While the contributions of individual regions to learning have been extensively investigated, much less is known about how distributed brain networks orchestrate their activity over the course of learning. To address this gap, we used fMRI combined with tools from dynamic network neuroscience to obtain time-resolved descriptions of network coordination during reinforcement learning in humans. We found that learning to associate visual cues with reward involves dynamic changes in network coupling between the striatum and distributed brain regions, including visual, orbitofrontal, and ventromedial prefrontal cortex (n=22, 13 females). Moreover, we found that this flexibility in striatal network coupling correlates with participants' learning rate and inverse temperature, two parameters derived from reinforcement learning models. Finally, we found that episodic learning, measured separately in the same participants at the same time, was related to dynamic connectivity in distinct brain networks. These results suggest that dynamic changes in striatal-centered networks provide a mechanism for information integration during reinforcement learning.Learning from the outcomes of actions - referred to as - is an essential part of life. The roles of individual brain regions in reinforcement learning have been well characterized in terms of updating values for actions or cues. Missing from this account, however, is an understanding of how different brain areas interact during learning to integrate sensory and value information. Here we characterize flexible striatal-cortical network dynamics that relate to reinforcement learning behavior.

We examined the role of visual exploration strategies in infants’ discrimination between facial emotion expressions. Twenty-eight 6- to 11-month olds were habituated to alternating models posing the same expression (happy N = 14/fearful N = 14) as eye gaze data were collected with a corneal reflection eye tracker. Gaze behavior analyses indicated that duration of gaze to the eyes and mouth was similar, consistent with what would be expected based on area subtended by those regions, and negatively correlated. This pattern did not differ as a function of age, sex, or habituation condition. There were no posthabituation performance differences as a function of age group (6- to 8-month- versus 9- to 11-month olds). Only infants habituated to happy faces showed longer looking at the novel emotion (fear) when the model was held constant from habituation to test. We found no reliable correlation between this performance and proportion of gaze directed at any one facial region. Consistent with previous work, the group habituated to fear faces showed no reliable posthabituation novelty preference. Individual differences in gaze behavior shed light on this finding. Greater proportion of gaze directed at the eyes correlated positively with preference for the novel emotion (happy). These data suggest that, as in other object classes, visual exploration strategies are an important agent of change in infants’ capacity to learn about emotion expressions.