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Article de revue:
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ID no. (ISBN etc.): 0278-7393
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Clé de citation BibTeX: Erickson2005
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Erickson, J. E., Chin-Parker, S., & Ross, B. H. (2005). Inference and classification learning of abstract coherent categories. Journal of experimental psychology: Learning, Memory, and Cognition, 31(1), p. p86–99.
Ajoutée par: Sterenn Audo 2008-01-08 12:45:51 Dernièrement modifiée par: Sterenn Audo 2008-01-08 12:47:30
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Catégories: Catégorisation, COEFF, Full text
Descripteurs: abstract coherent categories, Abstraction, category learning, Classification (Cognitive Process), classification learning, coherence, Concept Formation, exemplar familiarity, feature similarity, INFERENCE, inference learning, Learning, within-category information
Auteurs: Chin-Parker, Erickson, Ross
Collection: Journal of experimental psychology: Learning, Memory, and Cognition
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Nombre de vues: 382
Popularité: 34.7%
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Résumé
Category learning research has primarily focused on how people learn to classify items using simple observable features. However, classification is only 1 way to learn categories. In addition, many concepts have an underlying coherence that explains the featural similarity among exemplars, such as abstract coherent concepts whose instances differ greatly on their observable features. In 3 experiments, the authors investigated how abstract coherent categories are acquired through 2 common means of category learning, classification and inference. Because inference promotes more focus on within-category information than does classification, they hypothesized that inference learning would lead to a better understanding of the underlying coherence of abstract coherent categories. All 3 experiments support this prediction. (PsycINFO Database Record (c) 2007 APA, all rights reserved)(from the journal abstract)
Ajoutée par: Sterenn Audo
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Ajoutée par: Sterenn Audo
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