A comparison of rule-based versus exemplar-based categorization using the ACT-R architecture

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dc.contributor.authorRutledge-Taylor, Matthew
dc.contributor.authorLebiere, Christian
dc.contributor.authorThomson, Robert
dc.contributor.authorStaszewski, James
dc.contributor.authorAnderson, John
dc.date.accessioned2024-10-11T19:40:32Z
dc.date.available2024-10-11T19:40:32Z
dc.date.issued2012
dc.description.abstractA rule-based approach to categorization is compared with an exemplar-based approach. Both models were developed using the ACT-R architecture. Both approaches yield similar accuracy and are relatively impervious to varying model parameters. Implications for the nature of implicit and explicit knowledge and learning are discussed.
dc.description.sponsorshipIARPA Carnegie Mellon University EECS BS&L Army Cyber Institute
dc.identifier.citationRutledge-Taylor, M., C. Lebiere, R. Thomson, J. Staszewski, and J. R. Anderson. "A comparison of rule-based versus exemplar-based categorization using the ACT-R architecture." In Proceedings of the 21st Conference on Behavior Representation in Modeling and Simulation, BRIMS Society: Amelia Island, FL. 2012.
dc.identifier.urihttps://hdl.handle.net/20.500.14216/1608
dc.publisherBRiMS Annual Conference
dc.subjectcognitive modeling
dc.subjectcategorization
dc.subjectlearning
dc.titleA comparison of rule-based versus exemplar-based categorization using the ACT-R architecture
dc.typeConference presentations, papers, posters
local.USMAemailrobert.thomson@westpoint.edu
local.peerReviewedYes

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