Causal Learning With Local Computations.
The authors proposed and tested a psychological theory of causal structure learning based on local computations. Local computations simplify complex learning problems via cues available on individual trials to update a single causal structure hypothesis. Structural inferences from local computations...
| Publicado en: | Journal of Experimental Psychology. Learning, Memory & Cognition Vol. 35; no. 3; pp. 678 - 694 |
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| Autores principales: | , |
| Formato: | Artículo |
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American Psychological Association
May 2009
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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=508061787&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 508061787 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 2009 vid: 35 iid: 3 pid: 34 pub: American Psychological Association artinfo: ui: 508061787 10.1037/a0014928 ppf: 678 ppct: 16 formats: tig: atl: Causal Learning With Local Computations. aug: au: Fernbach, Philip M. Sloman, Steven A. su: Prompts (Psychology) Causation (Philosophy) Psychology of learning sug: subj: Prompts (Psychology) Causation (Philosophy) Psychology of learning ab: The authors proposed and tested a psychological theory of causal structure learning based on local computations. Local computations simplify complex learning problems via cues available on individual trials to update a single causal structure hypothesis. Structural inferences from local computations make minimal demands on memory, require relatively small amounts of data, and need not respect normative prescriptions as inferences that are principled locally may violate those principles when combined. Over a series of 3 experiments, the authors found (a) systematic inferences from small amounts of data; (b) systematic inference of extraneous causal links; (c) influence of data presentation order on inferences; and (d) error reduction through pretraining. Without pretraining, a model based on local computations fitted data better than a Bayesian structural inference model. The data suggest that local computations serve as a heuristic for learning causal structure. 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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