A neurally plausible parallel distributed processing model of event-related potential word reading data |
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Authors: | Laszlo Sarah Plaut David C |
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Affiliation: | a Department of Psychology, State University of New York, Binghamton, NY, United States b Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, United States c Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA, United States |
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Abstract: | 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. |
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Keywords: | Computational modeling Parallel Distributed Processing Event-Related Potentials N400 Visual word recognition |
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