Work

A Rational Analysis of Rule‐Based Concept Learning

Year: 2008

Type: article

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.

Source: Cognitive Science

DOI: 10.1080/03640210701802071

Authors: Noah D. Goodman, Joshua B. Tenenbaum, Jacob Feldman, Thomas L. Griffiths

Institutions: Rutgers, The State University of New Jersey, Massachusetts Institute of Technology, University of California, Berkeley

Language: English

Topic: Categorization, perception, and language

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

Open Access status: bronze

Funders: National Science Foundation, James S. McDonnell Foundation, Air Force Office of Scientific Research

Award: EITM: Minimization of complexity in human concept learning