Learning nonlinearly separable categories by inference and classification.

Full Abstract

Previous research suggests that learning categories by classifying new instances highlights information that is useful for discriminating between categories. In contrast, learning categories by making predictive inferences focuses learners on an abstract summary of each category (e.g., the prototype). To test this characterization of classification and inference learning further, the authors evaluated the two learning procedures with nonlinearly separable categories. In contrast to previous research involving cohesive, linearly separable categories, the authors found that it is more difficult to learn nonlinearly separable categories by making inferences about features than it is to learn them by classifying instances. This finding reflects that the prototype of a nonlinearly separable category does not provide a good summary of the category members. The results from this study suggest that having a cohesive category structure is more important for inference than it is for classification.

Author information

Author/s: Yamauchi, Takashi (T); Love, Bradley C (BC); Markman, Arthur B (AB);

Affiliation: Department of Psychology, Texas A&M University, College Station 77843, USA. tya(-atsign-)psyc.tamu.edu

Journal and publication information

Publication Type: Comparative Study; Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.

Journal: Journal of experimental psychology. Learning, memory, and cognition (J Exp Psychol Learn Mem Cogn), published in United States. (Language: eng)

Reference: 2002-May; vol 28 (issue 3) : pp 585-93

Dates: Created 2002/05/20; Completed 2002/12/09; Revised 2006/11/15;

PMID: 12018510, status: MEDLINE (last retrieved date: 2/18/2009)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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