Attention, similarity, and the identification–categorization relationship.

A unified quantitative approach to modeling Ss' identification and categorization of multidimensional perceptual stimuli is proposed and tested. Two Ss identified and categorized the same set of perceptually confusable stimuli varying on separable dimensions. The identification data were modeled using R. N. Shepard's (see record 1959-05134-001) multidimensional scaling-choice framework, which was then extended to model the Ss' categorization performance. The categorization model, which generalizes the context theory of classification developed by D. L. Medin and M. M. Schaffer (see record 1979-12633-001), assumes that Ss store category exemplars in memory. Classification decisions are based on the similarity of stimuli to the stored exemplars. It is assumed that the same multidimensional perceptual representation underlies performance in both the identification and categorization paradigms. However, because of the influence of selective attention, similarity relationships change systematically across the 2 paradigms. Findings provide some support for the hypothesis that Ss distribute attention among component dimensions so as to optimize categorization performance and that Ss may have augmented their category representations with inferred exemplars. Results demonstrate that excellent predictions of categorization performance can be made given knowledge of performance in an identification paradigm. (51 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)