Infants tailor their attention to maximize learning.

Infants' remarkable learning abilities allow them to rapidly acquire many complex skills. It has been suggested that infants achieve this learning by optimally allocating their attention to relevant stimuli in the environment, but the underlying mechanisms remain poorly understood. Here, we modeled infants' looking behavior during a learning task through an ideal learner that quantified the informational structure of environmental stimuli. We show that saccadic latencies, looking time, and time spent engaged with a stimulus sequence are explained by the properties of the learning environments, including the level of surprise of the stimulus, overall predictability of the environment, and progress in learning the environmental structure. These findings reveal the factors that shape infants' advanced learning, emphasizing their predisposition to seek out stimuli that maximize learning.

The structural and functional brain networks that support human social networks.

Social skills rely on a specific set of cognitive processes, raising the possibility that individual differences in social networks are related to differences in specific brain structural and functional networks. Here, we tested this hypothesis with multimodality neuroimaging. With diffusion MRI (DMRI), we showed that differences in structural integrity of particular white matter (WM) tracts, including cingulum bundle, extreme capsule and arcuate fasciculus were associated with an individual's social network size (SNS). A voxel-based morphology analysis demonstrated correlations between gray matter (GM) volume and SNS in limbic and temporal lobe regions. These structural changes co-occured with functional network differences. As a function of SNS, dorsomedial and dorsolateral prefrontal cortex showed altered resting-state functional connectivity with the default mode network (DMN). Finally, we integrated these three complementary methods, interrogating the relationship between social GM clusters and specific WM and resting-state networks (RSNs). Probabilistic tractography seeded in these GM nodes utilized the SNS-related WM pathways. Further, the spatial and functional overlap between the social GM clusters and the DMN was significantly closer than other control RSNs. These integrative analyses provide convergent evidence of the role of specific circuits in SNS, likely supporting the adaptive behavior necessary for success in extensive social environments.

Classification and treatment of antisocial individuals: From behavior to biocognition.

Antisocial behavior is a heterogeneous construct that can be divided into subtypes, such as antisocial personality and psychopathy. The adverse consequences of antisocial behavior produce great burden for the perpetrators, victims, family members, and for society at-large. The pervasiveness of antisocial behavior highlights the importance of precisely characterizing subtypes of antisocial individuals and identifying specific factors that are etiologically related to such behaviors to inform the development of targeted treatments. The goals of the current review are (1) to briefly summarize research on the operationalization and assessment of antisocial personality and psychopathy; (2) to provide an overview of several existing treatments with the potential to influence antisocial personality and psychopathy; and (3) to present an approach that integrates and uses biological and cognitive measures as starting points to more precisely characterize and treat these individuals. A focus on integrating factors at multiple levels of analysis can uncover person-specific characteristics and highlight potential targets for treatment to alleviate the burden caused by antisocial behavior.

Task-free MRI predicts individual differences in brain activity during task performance.

When asked to perform the same task, different individuals exhibit markedly different patterns of brain activity. This variability is often attributed to volatile factors, such as task strategy or compliance. We propose that individual differences in brain responses are, to a large degree, inherent to the brain and can be predicted from task-independent measurements collected at rest. Using a large set of task conditions, spanning several behavioral domains, we train a simple model that relates task-independent measurements to task activity and evaluate the model by predicting task activation maps for unseen subjects using magnetic resonance imaging. Our model can accurately predict individual differences in brain activity and highlights a coupling between brain connectivity and function that can be captured at the level of individual subjects.

Neuroscience: a more dynamic view of the social brain.

Information relevant for social interactions is thought to be processed in specific neural circuits. Recent studies shed new light on how that social information is encoded and processed by different brain areas.

Social network size affects neural circuits in macaques.

It has been suggested that variation in brain structure correlates with the sizes of individuals' social networks. Whether variation in social network size causes variation in brain structure, however, is unknown. To address this question, we neuroimaged 23 monkeys that had been living in social groups set to different sizes. Subject comparison revealed that living in larger groups caused increases in gray matter in mid-superior temporal sulcus and rostral prefrontal cortex and increased coupling of activity in frontal and temporal cortex. Social network size, therefore, contributes to changes both in brain structure and function. The changes have potential implications for an animal's success in a social context; gray matter differences in similar areas were also correlated with each animal's dominance within its social network.

Distinct roles of three frontal cortical areas in reward-guided behavior.

Functional magnetic resonance imaging was used to measure activity in three frontal cortical areas, the lateral orbitofrontal cortex (lOFC), medial orbitofrontal cortex (mOFC)/ventromedial frontal cortex (vmPFC), and anterior cingulate cortex (ACC), when expectations about type of reward, and not just reward presence or absence, could be learned. Two groups of human subjects learned 12 stimulus-response pairings. In one group (Consistent), correct performances of a given pairing were always reinforced with a specific reward outcome, whereas in the other group (Inconsistent), correct performances were reinforced with randomly selected rewards. The mOFC/vmPFC and lOFC were not distinguished by simple differences in relative preference for positive and negative outcomes. Instead lOFC activity reflected updating of reward-related associations specific to reward type; lOFC was active whenever informative outcomes allowed updating of reward-related associations, regardless of whether the outcomes were positive or negative, and the effects were greater when consistent stimulus-outcome and response-outcome mappings were present. A psychophysiological interaction analysis demonstrated changed coupling between lOFC and brain areas for visual object representation, such as perirhinal cortex, and reward-guided learning, such as the amygdala, ventral striatum, and habenula/mediodorsal thalamus. In contrast, mOFC/vmPFC activity reflected expected values of outcomes and occurrence of positive outcomes, regardless of consistency of outcome mappings. The third frontal cortical region, the ACC, reflected the use of reward type information to guide response selection. ACC activity reflected the probability of selecting the correct response, was greater when consistent outcome mappings were present, and was related to individual differences in propensity to select the correct response.