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      Towards an Exemplar Model of Face Processing: The Effects of Race and Distinctiveness

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      The Quarterly Journal of Experimental Psychology Section A

      Informa UK Limited

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          Most cited references 19

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          Understanding face recognition

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            Attention, similarity, and the identification-categorization relationship.

            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 Shepard'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.
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              Context theory of classification learning.

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                Author and article information

                Journal
                The Quarterly Journal of Experimental Psychology Section A
                The Quarterly Journal of Experimental Psychology Section A
                Informa UK Limited
                0272-4987
                1464-0740
                January 2018
                January 2018
                : 44
                : 4
                : 671-703
                Article
                10.1080/14640749208401305
                © 2018

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

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