Abstract
An evaluation of exemplar-based models of generalization was provided for ill-defined categories in a category abstraction paradigm. 72 undergraduates initially classified 35 high-level distortions into 3 categories, defined by 5, 10, and 20 different patterns, followed by a transfer test administered immediately and after 1 wk. The transfer patterns included old, new, prototype, and unrelated exemplars of which the new patterns were at 1 of 5 levels of similarity to a particular training (old) stimulus. In both experiments, increases in category size and old–new similarity facilitated transfer performance. However, the effectiveness of old–new similarity was strongly attenuated by increases in category size and delay of the transfer test. It is concluded that examplar-based generalization may be effective only under conditions of minimal category experience and immediacy of test; with continued category experience, performance on the prototype determines classification accuracy. (22 ref)Preview Only. This article cannot be rented because we do not currently have permission from the publisher.
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