Computer modeling of adaptive depression.
Computer modeling, based on connectionist and artificial intelligence planning and learning programming techniques, was used to test the hypothesis that mild, delimited, and adaptive depression may be a specific example of a more general type of mechanism by which intelligent systems adapt to dynami...
| Publicado en: | Behavioral Science Vol. 40; pp. 314 - 331 |
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| Formato: | Artículo |
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John Wiley & Sons, Inc.
October 1995
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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=510377798&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 510377798 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00057940 BHV jtl: Behavioral Science issn: 00057940 maglogo: N pubinfo: dt: October 1995 vid: 40 pid: 62151 pub: John Wiley & Sons, Inc. artinfo: ui: 510377798 10.1002/bs.3830400404 ppf: 314 ppct: 17 formats: fmt: @attributes: type: T tig: atl: Computer modeling of adaptive depression. aug: au: Webster, Charles su: Computer simulation Adaptability (Personality) Mental depression sug: subj: Computer simulation Adaptability (Personality) Mental depression ab: Computer modeling, based on connectionist and artificial intelligence planning and learning programming techniques, was used to test the hypothesis that mild, delimited, and adaptive depression may be a specific example of a more general type of mechanism by which intelligent systems adapt to dynamic, uncertain, and dangerous environments. The computer modeling supported the hypothesis by generating both adaptive behavior and analogs for ten phenomena related to depression—global, stable, and internal failure explanation; a cognitive loop of failure rumination; decreased motivation, self-esteem, and self-efficacy; and increased realism, negative generalization, and cognitive change. It is concluded that a better comprehension of normal and adaptive depression could result in a better comprehension of clinical depression. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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