A neurally plausible parallel distributed processing model of event-related potential word reading data.

The Parallel Distributed Processing (PDP) framework has significant potential for producing models of cognitive tasks that approximate how the brain performs the same tasks. To date, however, there has been relatively little contact between PDP modeling and data from cognitive neuroscience. In an at...

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Publicado en:Brain & Language Vol. 120; no. 3; pp. 271 - 282
Autores principales: Laszlo S, Plaut DC, Laszlo, Sarah, Plaut, David C
Formato: Journal Article
Publicado: Academic Press Inc. Mar2012
Acceso en línea:Ver este registro en EBSCOhost
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      pub: Academic Press Inc.
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        atl: A neurally plausible parallel distributed processing model of event-related potential word reading data.
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        au:
          Laszlo S
          Plaut DC
          Laszlo, Sarah
          Plaut, David C
        affil: Department of Psychology, State University of New York, Binghamton, NY, United States
      sug:
        subj:
          Cognition Physiology
          Evoked Potentials Physiology
          Models, Biological
          Visual Perception Physiology
          Reading
          Computer Simulation
          Evoked Potentials, Visual Physiology
          Parietal Lobe Physiology
          Semantics
          Neural Pathways Physiology
      ab: The Parallel Distributed Processing (PDP) framework has significant potential for producing models of cognitive tasks that approximate how the brain performs the same tasks. To date, however, there has been relatively little contact between PDP modeling and data from cognitive neuroscience. In an attempt to advance the relationship between explicit, computational models and physiological data collected during the performance of cognitive tasks, we developed a PDP model of visual word recognition which simulates key results from the ERP reading literature, while simultaneously being able to successfully perform lexical decision-a benchmark task for reading models. Simulations reveal that the model's success depends on the implementation of several neurally plausible features in its architecture which are sufficiently domain-general to be relevant to cognitive modeling more generally.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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