The Dynamics of Learning and Attention in Infants and Adults

Dynamics of Learning and Attention in Infants and Adults: Richard Aslin, PhD (Univ of Minnesota), is William R. Kenan Professor of Brain & Cognitive Sciences and Center for Visual Science, and Director of the Rochester Center for Brain Imaging. His research has been directed at exploring and understanding implicit learning mechanisms, which are typically referred to as "statistical learning". Although initially studied in the task of word segmentation from fluent speech, statistical learning has been extended to other domains, such as musical tones, phonetic categories, sequences of visual shapes, sequences of motor responses, and combinations of objects (or object parts) in complex visual scenes. An important goal of these studies is to reveal the computational constraints that enable statistical learning to be tractable given the complexity of the input and the infinite number of statistical computations that are possible over any set of inputs. Initial computational models of statistical learning focused on bigram statistics and conditional probabilities, but more recent work has broadened to include Bayesian ideal learning models. Empirical studies of statistical learning have also evolved to explore order effects in learning multiple structures and to understand how statistical patterns trigger the formation of categories.