Acquisition of category-level information can be based on experience with category members (induced) as well as on direct presentation of prototypical values (given). To investigate the effects of these two types of information, a relational coding model of categorization was developed in which classification is based on a mixture of exemplar and prototype information. In two experiments, subjects learned about two ill-defined categories. Stimuli were geometric shapes varying along four binary-valued dimensions. For three groups of subjects, training consisted of (a) experience with exemplars only, (b) learning prototype values followed by exemplar experience, or (c) learning prototype values concurrently with exemplar experience. Following training, all subjects received classification tests on prototype values as well as on old and new exemplars. By varying the relative use of prototype and exemplar information, the mixture model accurately accounted for category judgements in all three groups. For subjects directly presented with prototype values, classification was based on a mixture of similarity to prototypes and to stored exemplars. In contrast, subjects who only received experience with exemplars appeared to base their category judgements solely on similarity to stored exemplars, even though they could accurately judge the prototype values. The two components of the mixture model are related to subjects' classification strategies and the nature of abstracted, category-level information.

Recent studies of concept formation using ill-defined categories suggest that abstraction of category-level information is more or less an automatic consequence of experience with exemplars. An experiment using ill-defined categories composed of photographs of women was designed to test this assumption. Experimental manipulations required subjects to learn either identification (exemplar level) and/ or classification (category level) responses to the photographs. Exemplar learning was generally found to proceed faster and with fewer errors than did category learning, and depending on the specific procedural manipulation, abstraction ranged from good to poor. Interactive-and independent-cue models were applied to the transfer data. The fits of the models indicated that when abstraction was good (as defined by transfer performance), the interactive-cue model best characterized the results; as degree of abstraction declined, the independent-cue model performed best. The results were discussed in terms of the relative contributions of categorylevel and exemplar-specific (idiosyncratic) information during classification learning.A considerable amount of recent research has been directed at the question of how people learn about and use ill-defined or "fuzzy" concepts and categories (see Mervis & Rosch, 1981; Smith & Medin, 1981, for reviews). A common theme running through virtually all these studies is the assumption, either implicit or explicit, that abstraction is more or less an automatic consequence of experience with exemplars of a category. It is well known in the literature on problem solving that transfer between two problems having the same underlying structure is often far from perfect (e.g., Reed, Ernst, & Banerji, 1974), and one might expect similar limitations in the learning of ill-defined concepts. Nonetheless, research on learning fuzzy concepts has focused on the what of abstraction, not the whether. Candidates for the what of abstraction include a prototype representing the central tendency of a category, simple or conjoint feature frequency, complex hypotheses or descriptions, best examples, or even the exemplars themselves (e.g.,

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