The Parallel Episodic Processing (PEP) model 2.0: A single computational model of stimulus-response binding, contingency learning, power curves, and mixing costs.
The current paper presents an extension of the Parallel Episodic Processing model. The model is developed for simulating behaviour in performance (i.e., speeded response time) tasks and learns to anticipate both how and when to respond based on retrieval of memories of previous trials. With one fixe...
| Publicado en: | Cognitive Psychology Vol. 91; pp. 82 - 109 |
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| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Academic Press Inc.
Dec2016
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| Materias: | |
| 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=119582967&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 119582967 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00100285 COP jtl: Cognitive Psychology issn: 00100285 maglogo: N pubinfo: dt: Dec2016 vid: 91 pid: 735 pub: Academic Press Inc. artinfo: ui: 119582967 10.1016/j.cogpsych.2016.10.004 ppf: 82 ppct: 27 formats: tig: atl: The Parallel Episodic Processing (PEP) model 2.0: A single computational model of stimulus-response binding, contingency learning, power curves, and mixing costs. aug: au: Schmidt, James R. De Houwer, Jan Rothermund, Klaus affil: Department of Experimental Clinical and Health Psychology, Ghent University, Belgium Department of Psychology, Friedrich-Schiller-Universität Jena, Germany su: Human behavior Facilitated learning Learning curve Cognitive structures Psychology of learning sug: subj: Human behavior Facilitated learning Learning curve Cognitive structures Psychology of learning keyword: Binding Computational modelling Contingency learning Episodic memory Mixing costs Practice Binding Computational modelling Contingency learning Episodic memory Mixing costs Practice ab: The current paper presents an extension of the Parallel Episodic Processing model. The model is developed for simulating behaviour in performance (i.e., speeded response time) tasks and learns to anticipate both how and when to respond based on retrieval of memories of previous trials. With one fixed parameter set, the model is shown to successfully simulate a wide range of different findings. These include: practice curves in the Stroop paradigm, contingency learning effects, learning acquisition curves, stimulus-response binding effects, mixing costs, and various findings from the attentional control domain. The results demonstrate several important points. First, the same retrieval mechanism parsimoniously explains stimulus-response binding, contingency learning, and practice effects. Second, as performance improves with practice, any effects will shrink with it. Third, a model of simple learning processes is sufficient to explain phenomena that are typically (but perhaps incorrectly) interpreted in terms of higher-order control processes. More generally, we argue that computational models with a fixed parameter set and wider breadth should be preferred over those that are restricted to a narrow set of phenomena. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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