Work

Statistical Learning

Year: 2012

Type: article

Abstract: Statistical learning is a rapid and robust mechanism that enables adults and infants to extract patterns of stimulation embedded in both language and visual domains. Importantly, statistical learning operates implicitly, without instruction, through mere exposure to a set of input stimuli. However, much of what learners must acquire about a structured domain consists of principles or rules that can be applied to novel inputs. Although it has been claimed that statistical learning and rule learning are separate mechanisms, here we review evidence and provide a unifying perspective that argues for a single mechanism of statistical learning that accounts for both the learning of the input stimuli and the generalization to novel instances. The balance between instance-learning and generalization is based on two factors: the strength of perceptual biases that highlight structural regularities, and the consistency of unique versus overlapping contexts in the input.

Source: Current Directions in Psychological Science

DOI: 10.1177/0963721412436806

Authors: Richard N. Aslin, Elissa L. Newport

Institution: University of Rochester

Language: English

Topic: Language Development and Disorders

Subfield: Developmental and Educational Psychology

Field: Psychology

Domain: Social Sciences

Open Access status: green

Funders: National Institute on Deafness and Other Communication Disorders, Eunice Kennedy Shriver National Institute of Child Health and Human Development

Awards: Structure and Acquisition of Signed and Spoken Languages, Statistical approaches to linguistic pattern learning