YEARS

2004-2006

AUTHORS

Shawn W. Ell

TITLE

Neural basis of category learning

ABSTRACT

DESCRIPTION (provided by applicant): The proposed research investigates the neural bases of category learning in rule-based (RB) and information-integration (II) categorization tasks, and the extent to which learning in these tasks is based upon a procedural learning mechanism. The studies test two broad predictions of a biologically-plausible, multiple systems model of category learning (COVIS; Ashby et al., 1998). In the COVIS framework, category learning is hypothesized to be a competition between separate explicit and implicit systems. The implicit system is procedural learning-based and assumed to mediate learning in II tasks. In contrast, the explicit system is a logical-reasoning system that is assumed to mediate learning in RB tasks. Learning in the explicit system is assumed to rely primarily upon frontal cortical structures and the head of the caudate nucleus whereas the implicit system depends primarily upon the tail of the caudate nucleus and high-level motor structures. The first two experiments test the hypothesis that learning in the implicit system is procedural learning-based and strongly tied to motor systems whereas learning in the explicit system is RB and more abstract. The remaining four experiments use neuropsychological and neuroimaging techniques to test the hypothesis that the neural systems mediating performance in RB and II tasks are dissociable. The experiments also entail a novel neuropsychological direction in category learning research in investigating the role of the cerebellum in these tasks. This research is important because it uses a converging operations approach to provide insight into the learning algorithms and neural mechanisms involved in RB and II category learning tasks.

FUNDED PUBLICATIONS

  • Focal putamen lesions impair learning in rule-based, but not information-integration categorization tasks.
  • Rule-based categorization deficits in focal basal ganglia lesion and Parkinson's disease patients.
  • Cerebellar pathology does not impair performance on identification or categorization tasks.
  • Criterial noise effects on rule-based category learning: the impact of delayed feedback.
  • Prefrontal contributions to rule-based and information-integration category learning.
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    22 TRIPLES      17 PREDICATES      23 URIs      9 LITERALS

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    1 grants:d28ed890a175ba7c7904c38d507b770a sg:abstract DESCRIPTION (provided by applicant): The proposed research investigates the neural bases of category learning in rule-based (RB) and information-integration (II) categorization tasks, and the extent to which learning in these tasks is based upon a procedural learning mechanism. The studies test two broad predictions of a biologically-plausible, multiple systems model of category learning (COVIS; Ashby et al., 1998). In the COVIS framework, category learning is hypothesized to be a competition between separate explicit and implicit systems. The implicit system is procedural learning-based and assumed to mediate learning in II tasks. In contrast, the explicit system is a logical-reasoning system that is assumed to mediate learning in RB tasks. Learning in the explicit system is assumed to rely primarily upon frontal cortical structures and the head of the caudate nucleus whereas the implicit system depends primarily upon the tail of the caudate nucleus and high-level motor structures. The first two experiments test the hypothesis that learning in the implicit system is procedural learning-based and strongly tied to motor systems whereas learning in the explicit system is RB and more abstract. The remaining four experiments use neuropsychological and neuroimaging techniques to test the hypothesis that the neural systems mediating performance in RB and II tasks are dissociable. The experiments also entail a novel neuropsychological direction in category learning research in investigating the role of the cerebellum in these tasks. This research is important because it uses a converging operations approach to provide insight into the learning algorithms and neural mechanisms involved in RB and II category learning tasks.
    2 sg:endYear 2006
    3 sg:fundingAmount 126874.0
    4 sg:fundingCurrency USD
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    17 sg:scigraphId d28ed890a175ba7c7904c38d507b770a
    18 sg:startYear 2004
    19 sg:title Neural basis of category learning
    20 sg:webpage http://projectreporter.nih.gov/project_info_description.cfm?aid=7026970
    21 rdf:type sg:Grant
    22 rdfs:label Grant: Neural basis of category learning
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