Literaturnachweis - Detailanzeige
| Autor/inn/en | Little, Daniel R.; Lewandowsky, Stephan |
|---|---|
| Titel | Better Learning with More Error: Probabilistic Feedback Increases Sensitivity to Correlated Cues in Categorization |
| Quelle | In: Journal of Experimental Psychology: Learning, Memory, and Cognition, 35 (2009) 4, S.1041-1061 (21 Seiten)
PDF als Volltext |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Zeitschriftenaufsatz |
| ISSN | 0278-7393 |
| DOI | 10.1037/a0015902 |
| Schlagwörter | Feedback (Response); Cues; Attention; Classification; Correlation; Models; Task Analysis; Learning Processes; College Students; Computation; Experimental Psychology; Indiana |
| Abstract | Despite the fact that categories are often composed of correlated features, the evidence that people detect and use these correlations during intentional category learning has been overwhelmingly negative to date. Nonetheless, on other categorization tasks, such as feature prediction, people show evidence of correlational sensitivity. A conventional explanation holds that category learning tasks promote rule use, which discards the correlated-feature information, whereas other types of category learning tasks promote exemplar storage, which preserves correlated-feature information. Contrary to that common belief, the authors report 2 experiments that demonstrate that using probabilistic feedback in an intentional categorization task leads to sensitivity to correlations among nondiagnostic cues. Deterministic feedback eliminates correlational sensitivity by focusing attention on relevant cues. Computational modeling reveals that exemplar storage coupled with selective attention is necessary to explain this effect. (Contains 6 tables, 14 figures and 5 footnotes.) (As Provided). |
| Anmerkungen | American Psychological Association. Journals Department, 750 First Street NE, Washington, DC 20002-4242. Tel: 800-374-2721; Tel: 202-336-5510; Fax: 202-336-5502; e-mail: order@apa.org; Web site: http://www.apa.org/publications |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2017/4/10 |
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