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Article de revue:
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ID no. (ISBN etc.): 0096-3445
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Clé de citation BibTeX: Rouder2004
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Rouder, J. N., & Ratcliff, R. (2004). Comparing categorization models. Journal of Experimental Psychology: General, 133(1), p. p63–82.
Ajoutée par: Sterenn Audo 2008-01-08 16:53:46 Dernièrement modifiée par: Sterenn Audo 2008-01-08 16:55:12
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Catégories: Catégorisation, Full text
Descripteurs: assignment probabilities, categorization behavior, categorization models, Classification (Cognitive Process), Contextual Associations, DECISION making, exemplar-based models, general recognition theory, generalized context model, Matching to Sample, Recognition (Learning), rule-based prediction, stimulus classification, Stimulus Parameters, Stochastic Modeling
Auteurs: Ratcliff, Rouder
Collection: Journal of Experimental Psychology: General
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Nombre de vues: 371
Popularité: 33.67%
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Résumé
Four experiments are presented that competitively test rule- and exemplar-based models of human categorization behavior. Participants classified stimuli that varied on a unidimensional axis into 2 categories. The stimuli did not consistently belong to a category; instead, they were probabilistically assigned. By manipulating these assignment probabilities, it was possible to produce stimuli for which exemplar- and rule-based explanations made qualitatively different predictions. F. G. Ashby and J. T. Townsend's (1986) rule-based general recognition theory provided a better account of the data than R. M. Nosofsky's (1986) exemplar-based generalized context model in conditions in which the to-be-classified stimuli were relatively confusable. However, generalized context model provided a better account when the stimuli were relatively few and distinct. These findings are consistent with multiple process accounts of categorization and demonstrate that stimulus confusion is a determining factor as 10 which process mediates categorization. (PsycINFO Database Record (c) 2006 APA, all rights reserved)(from the journal abstract)
Ajoutée par: Sterenn Audo
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Idées
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Ajoutée par: Sterenn Audo
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