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dc.contributor.authorAmis, Gregory P.en_US
dc.contributor.authorCarpenter, Gail A.en_US
dc.contributor.authorErsoy, Bilginen_US
dc.contributor.authorGrossberg, Stephenen_US
dc.date.accessioned2011-11-14T18:17:09Z
dc.date.available2011-11-14T18:17:09Z
dc.date.issued2009-03en_US
dc.identifier.urihttps://hdl.handle.net/2144/1966
dc.description.abstractDo humans and animals learn exemplars or prototypes when they categorize objects and events in the world? How are different degrees of abstraction realized through learning by neurons in inferotemporal and prefrontal cortex? How do top-down expectations influence the course of learning? Thirty related human cognitive experiments (the 5-4 category structure) have been used to test competing views in the prototype-exemplar debate. In these experiments, during the test phase, subjects unlearn in a characteristic way items that they had learned to categorize perfectly in the training phase. Many cognitive models do not describe how an individual learns or forgets such categories through time. Adaptive Resonance Theory (ART) neural models provide such a description, and also clarify both psychological and neurobiological data. Matching of bottom-up signals with learned top-down expectations plays a key role in ART model learning. Here, an ART model is used to learn incrementally in response to 5-4 category structure stimuli. Simulation results agree with experimental data, achieving perfect categorization in training and a good match to the pattern of errors exhibited by human subjects in the testing phase. These results show how the model learns both prototypes and certain exemplars in the training phase. ART prototypes are, however, unlike the ones posited in the traditional prototype-exemplar debate. Rather, they are critical patterns of features to which a subject learns to pay attention based on past predictive success and the order in which exemplars are experienced. Perturbations of old memories by newly arriving test items generate a performance curve that closely matches the performance pattern of human subjects. The model also clarifies exemplar-based accounts of data concerning amnesia.en_US
dc.description.sponsorshipDefense Advanced Projects Research Agency SyNaPSE program (Hewlett-Packard Company, DARPA HR0011-09-3-0001; HRL Laboratories LLC #801881-BS under HR0011-09-C-0011); Science of Learning Centers program of the National Science Foundation (NSF SBE-0354378)en_US
dc.language.isoen_USen_US
dc.publisherBoston University Center for Adaptive Systems and Department of Cognitive and Neural Systemsen_US
dc.relation.ispartofseriesBU CAS/CNS Technical Reports;CAS/CNS-TR-2009-002en_US
dc.rightsCopyright 2009 Boston University. Permission to copy without fee all or part of this material is granted provided that: 1. The copies are not made or distributed for direct commercial advantage; 2. the report title, author, document number, and release date appear, and notice is given that copying is by permission of BOSTON UNIVERSITY TRUSTEES. To copy otherwise, or to republish, requires a fee and / or special permission.en_US
dc.subjectCategorizationen_US
dc.subjectClassificationen_US
dc.subjectPattern recognitionen_US
dc.subjectExemplaren_US
dc.subjectPrototypeen_US
dc.subjectSupervised learningen_US
dc.subjectAdaptive Resonance Theory (ART)en_US
dc.subjectARTen_US
dc.subjectExpectationen_US
dc.subjectAttentionen_US
dc.titleCortical Learning of Recognition Categories: A Resolution of the Exemplar Vs. Prototype Debateen_US
dc.typeTechnical Reporten_US
dc.rights.holderBoston University Trusteesen_US


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