A Rational Analysis of Rule-Based Concept Learning

Autor/inn/en	Goodman, Noah D.; Tenenbaum, Joshua B.; Feldman, Jacob; Griffiths, Thomas L.
Titel	A Rational Analysis of Rule-Based Concept Learning
Quelle	In: Cognitive Science, 32 (2008) 1, S.108-154 (47 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0364-0213
Schlagwörter	Mathematics Education; Concept Formation; Models; Prediction; Learning; Learning Processes; Cognitive Psychology; Grammar; Mathematical Logic; Mathematical Formulas; Experiments; Bayesian Statistics + Suchen Sie Ihr Suchwort? Mathematische Bildung; Concept learning; Begriffsbildung; Analogiemodell; Vorhersage; Lernen; Learning process; Lernprozess; Kognitive Psychologie; Grammatik; Mathematical logics; Mathematische Logik; Mathematische Formel; Erprobung
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. (Contains 7 tables, 14 figures and 10 notes.) (Author).
Anmerkungen	Lawrence Erlbaum. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site: http://www.tandf.co.uk/journals/default.html
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10