An evaluation of exemplar-based models of generalization was provided for illdefined categories in a category abstraction paradigm. Subjects initially classified 35 high-level distortions into three 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 one of five levels of similarity to a particular training (old) stimulus. In both experiments, increases in category size and oldnew 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 was 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.

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