Visual perceptual learning through practice or training can significantly improve performance on visual tasks. Originally seen as a manifestation of plasticity in the primary visual cortex, perceptual learning is more readily understood as improvements in the function of brain networks that integrate processes, including sensory representations, decision, attention, and reward, and balance plasticity with system stability. This review considers the primary phenomena of perceptual learning, theories of perceptual learning, and perceptual learning's effect on signal and noise in visual processing and decision. Models, especially computational models, play a key role in behavioral and physiological investigations of the mechanisms of perceptual learning and for understanding, predicting, and optimizing human perceptual processes, learning, and performance. Performance improvements resulting from reweighting or readout of sensory inputs to decision provide a strong theoretical framework for interpreting perceptual learning and transfer that may prove useful in optimizing learning in real-world applications.

The goal of this study was to investigate the neural correlates of affective decision making, as measured by the Iowa Gambling Task (IGT), which are associated with adolescent binge drinking. Fourteen adolescent binge drinkers (16-18 years of age) and 14 age-matched adolescents who had never consumed alcohol--never drinkers--were recruited from local high schools in Chengdu, China. Questionnaires were used to assess academic performance, drinking experience, and urgency. Brain regions activated by the IGT performance were identified with functional magnetic resonance imaging. Results showed that, compared to never drinkers, binge drinkers performed worse on the IGT and showed higher activity in the subcomponents of the decision-making neural circuitry implicated in the execution of emotional and incentive-related behaviors, namely, the left amygdala and insula bilaterally. Moreover, measures of the severity of drinking problems in real life, as well as high urgency scores, were associated with increased activity within the insula, combined with decreased activity within the orbitofrontal cortex. These results suggest that hyperreactivity of a neural system implicated in the execution of emotional and incentive-related behaviors can be associated with socially undesirable behaviors, such as binge drinking, among adolescents. These findings have social implications because they potentially reveal underlying neural mechanisms for making poor decisions, which may increase an individual's risk and vulnerability for alcoholism.

Experimentation is at the core of research in the behavioral and neural sciences, yet observations can be expensive and time-consuming to acquire (e.g., MRI scans, responses from infant participants). A major interest of researchers is designing experiments that lead to maximal accumulation of information about the phenomenon under study with the fewest possible number of observations. In addressing this challenge, statisticians have developed adaptive design optimization methods. This letter introduces a hierarchical Bayes extension of adaptive design optimization that provides a judicious way to exploit two complementary schemes of inference (with past and future data) to achieve even greater accuracy and efficiency in information gain. We demonstrate the method in a simulation experiment in the field of visual perception.