Dr. Richard Aslin

Biography

For over two decades, Richard Aslin has directed research studies on implicit learning, referred to as statistical learning. His initial focus was on word segmentation in fluent speech and has since broadened to include musical tones, phonetic categories, visual sequences, and motor responses. His work aims to reveal the computational constraints that enable statistical learning to be feasible amidst the complexity of inputs. Initially, his computational models concentrated on bi-gram statistics and conditional probabilities, but recent research has expanded to encompass Bayesian ideal learning models. Aslin's empirical studies explore order effects in learning various structures to understand how statistical patterns trigger the formation of categories within artificial grammars. He also investigates spoken word recognition in infants, toddlers, and adults using eye-tracking and EEG methods. His ongoing research examines how infants and toddlers recognize the meaning of speech signals, including previously known words, mispronounced words, and words that are preceded by disfluency. In recent years, his studies have shifted towards understanding brain function in both adults and infants through fMRI, fNIRS, and EEG techniques, revealing the neural correlates of behavioral measures such as looking time. Aslin is particularly interested in how optical signals change over time to understand habituation and aspects of statistical learning using multivariate methods for pattern classification and decoding accuracy across visual and auditory stimuli.

Research Interests