title: A rational analysis of rule-based concept learning.
abstract: This article proposes a new model of human concept learning that provides a rational analysis of learning feature-based concepts. This model is built upon Bayesian inference for a grammatically structured hypothesis space-a concept language of logical rules. This article compares the model predictions to human generalization judgments in several well-known category learning experiments, and finds good agreement for both average and individual participant generalizations. This article further investigates judgments for a broad set of 7-feature concepts-a more natural setting in several ways-and again finds that the model explains human performance.
authors.affiliation: Department of Brain and Cognitive Sciences, Massachusetts Institute of TechnologyDepartment of Psychology, Rutgers UniversityDepartment of Psychology, University of California, Berkeley.
authors.forename: Noah D
authors.initials: ND
authors.lastname: Goodman
authors.forename: Joshua B
authors.initials: JB
authors.lastname: Tenenbaum
authors.forename: Jacob
authors.initials: J
authors.lastname: Feldman
authors.forename: Thomas L
authors.initials: TL
authors.lastname: Griffiths