Mathematical Models of Visual Category Learning Enhance fMRI Data Analysis

Emi M Nomura, W Todd Maddox, Paul J Reber

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Models of categorization instantiate specific hypotheses about psychological processes controlling behavior. Neuroimaging provides a tool to visualize the neural correlates of these same processes in human subjects. By combining techniques, we can begin to make the connection between behavior and neural activity. Here we collected fMRI data using a category learning paradigm where subjects were encouraged toward a particular strategy by the underlying category structure. The application of mathematical models of category learning to this behavioral data enabled the organization of fMRI data according to the actual strategy employed.
Original languageEnglish (US)
Title of host publicationThe 29th Annual Conference of the Cognitive Science Society
EditorsD S McNamara, J G Trafton
PublisherCognitive Science Society
Pages539-544
Number of pages6
ISBN (Print)978-0-9768318-3-9
StatePublished - 2007

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    Nomura, E. M., Maddox, W. T., & Reber, P. J. (2007). Mathematical Models of Visual Category Learning Enhance fMRI Data Analysis. In D. S. McNamara, & J. G. Trafton (Eds.), The 29th Annual Conference of the Cognitive Science Society (pp. 539-544). Cognitive Science Society.