Working Memory Capacity and Categorization: Individual Differences and Modeling.
Working memory is crucial for many higher-level cognitive functions, ranging from mental arithmetic to reasoning and problem solving. Likewise, the ability to learn and categorize novel concepts forms an indispensable part of human cognition. However, very little is known about the relationship betw...
| Publicado en: | Journal of Experimental Psychology. Learning, Memory & Cognition Vol. 37; no. 3; pp. 720 - 739 |
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| Formato: | Artículo |
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American Psychological Association
May 2011
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=508202986&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 508202986 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 02787393 EXL jtl: Journal of Experimental Psychology. Learning, Memory & Cognition issn: 02787393 maglogo: N pubinfo: dt: May 2011 vid: 37 iid: 3 pid: 34 pub: American Psychological Association artinfo: ui: 508202986 10.1037/a0022639 ppf: 720 ppct: 19 formats: tig: atl: Working Memory Capacity and Categorization: Individual Differences and Modeling. aug: au: Lewandowsky, Stephan su: Categorization (Psychology) Mathematical models Short-term memory sug: subj: Categorization (Psychology) Mathematical models Short-term memory ab: Working memory is crucial for many higher-level cognitive functions, ranging from mental arithmetic to reasoning and problem solving. Likewise, the ability to learn and categorize novel concepts forms an indispensable part of human cognition. However, very little is known about the relationship between working memory and categorization, and modeling in category learning has thus far been largely uninformed by knowledge about people's memory processes. This article reports a large study (N = 113) that related people's working memory capacity (WMC) to their category-learning performance using the 6 problem types of Shepard, Hovland, and Jenkins (1961). Structural equation modeling revealed a strong relationship between WMC and category learning, with a single latent variable accommodating performance on all 6 problems. A model of categorization (the Attention Learning COVEring map, ALCOVE; Kruschke, 1992) was fit to the individual data and a single latent variable was sufficient to capture the variation among associative learning parameters across all problems. The data and modeling suggest that working memory mediates category learning across abroad range of tasks. Reprinted by permission of the publisher. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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