Exemplar-based accounts of relations between classification, recognition, and typicality.

Abstract

Previously published sets of classification and old-new recognition memory data are reanalyzed within the framework of an exemplar-based generalization model. The key assumption in the model is that, whereas classification decisions are based on the similarity of a probe to exemplars of a target category relative to exemplars of contrast categories, recognition decisions are based on overall summed similarity of a probe to all exemplars. The summed-similarity decision rule is shown to be consistent with a wide variety of recognition memory data obtained in classification learning situations and may provide a unified approach to understanding relations between categorization and recognition. Recently, there has been an upsurge of interest among categorization researchers in exploring relations between classification learning and old-new recognition memory. This interest has been fueled by the exemplar view of category representation, which holds that people base classification decisions on similarity comparisons with stored exemplars (Hintzman, 1986b; Medin & Schaffer, 1978; Nosofsky, 1986). Recognition data provide a source of converging evidence bearing on the nature of people's category representations. Presumably, if individual exemplars are being stored in memory, the fact ought to be revealed by postacquisition recognition tests. Indeed, a number of researchers have taken exemplar models to task on grounds of certain dissociations between classification learning and recognition memory, or patterns of recognition data deemed to be inconsistent with the predictions of exemplar-only memory models. In virtually all cases, however, there has been a failure to specify and test an explicit