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Attention, similarity, and the identification-Categorization Relationship (1986)
| Citations: | 663 - 28 self |
BibTeX
@MISC{Nosofsky86attention,similarity,,
author = {Robert M. Nosofsky},
title = {Attention, similarity, and the identification-Categorization Relationship },
year = {1986}
}
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Abstract
A unified quantitative approach to modeling subjects ' identification and categorization of multidimensional perceptual stimuli is proposed and tested. Two subjects identified and categorized the same set of perceptually confusable stimuli varying on separable dimensions. The identification data were modeled using Sbepard's (1957) multidimensional scaling-choice framework. This framework was then extended to model the subjects ' categorization performance. The categorization model, which generalizes the context theory of classification developed by Medin and Schaffer (1978), assumes that subjects 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 two paradigms. Some support was gained for the hypothesis that subjects distribute attention among component dimensions so as to optimize categorization performance. Evidence was also obtained that subjects may have augmented their category representations with inferred exemplars. Implications of the results for theories of multidimensional scaling and categorization are discussed.
Keyphrases
identification-categorization relationship categorization performance selective attention identification data multidimensional perceptual representation underlies performance similarity relationship multidimensional perceptual stimulus categorization paradigm stored exemplar classification decision category exemplar unified quantitative approach separable dimension distribute attention component dimension context theory category representation confusable stimulus multidimensional scaling inferred exemplar multidimensional scaling-choice framework categorization model
