Parallel Interactive Processing as a Way to Understand Complex Information Processing: The Conjunction Fallacy and Other Examples.
Parallel interactive processing (PIP) represents an approach in which specific context generates interactive relationships between general attributes. This article summarizes previous research that demonstrates how such relationships influence inference making in categorization. This is followed by...
| Publicado en: | American Journal of Psychology Vol. 130; no. 2; pp. 201 - 223 |
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| Formato: | Artículo |
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University of Illinois Press
Summer2017
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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=123067457&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 123067457 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00029556 AJY jtl: American Journal of Psychology issn: 00029556 maglogo: N pubinfo: dt: Summer2017 vid: 130 iid: 2 pid: 416 pub: University of Illinois Press artinfo: ui: 123067457 10.5406/amerjpsyc.130.2.0201 ppf: 201 ppct: 22 formats: tig: atl: Parallel Interactive Processing as a Way to Understand Complex Information Processing: The Conjunction Fallacy and Other Examples. aug: au: NAHINSKY, IRWIN D. affil: University of Louisville su: Information processing Cognition Cognitive analysis Naturalistic fallacy Probability theory sug: subj: Information processing Cognition Cognitive analysis Naturalistic fallacy Probability theory keyword: abstract information processing conjunction fallacy probability judgment abstract information processing conjunction fallacy probability judgment ab: Parallel interactive processing (PIP) represents an approach in which specific context generates interactive relationships between general attributes. This article summarizes previous research that demonstrates how such relationships influence inference making in categorization. This is followed by evidence that the approach can be extended to other areas of cognition, including probability judgments. PIP was successful in fitting data that revealed the prevalence of the conjunction fallacy as well as other probability estimation data. PIP provided better fits overall than the signed summation model and the configural weighted average model. The quantum probability model provided good fits for the conjunction fallacy data but not for other probability judgments. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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