combining computational modeling and neuroimaging to examine multiple category learning systems in the brain结合计算建模和神经影像学检查在大脑中多个类别学习系统.pdfVIP

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combining computational modeling and neuroimaging to examine multiple category learning systems in the brain结合计算建模和神经影像学检查在大脑中多个类别学习系统.pdf

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combining computational modeling and neuroimaging to examine multiple category learning systems in the brain结合计算建模和神经影像学检查在大脑中多个类别学习系统

Brain Sci. 2012, 2, 176-202; doi:10.3390/brainsci2020176 OPEN ACCESS brain sciences ISSN 2076-3425 /journal/brainsci/ Article Combining Computational Modeling and Neuroimaging to Examine Multiple Category Learning Systems in the Brain 1 2, Emi M. Nomura and Paul J. Reber * 1 Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94720, USA; E-Mail: eminomura@ 2 Department of Psychology, Northwestern University, Evanston, IL 60208, USA * Author to whom correspondence should be addressed; E-Mail: preber@; Tel.: +1-847-467-1624; Fax: +1-847-491-7859. Received: 1 March 2012; in revised form: 30 March 2012 / Accepted: 18 April 2012 / Published: 23 April 2012 Abstract: Considerable evidence has argued in favor of multiple neural systems supporting human category learning, one based on conscious rule inference and one based on implicit information integration. However, there have been few attempts to study potential system interactions during category learning. The PINNACLE (Parallel Interactive Neural Networks Active in Category Learning) model incorporates multiple categorization systems that compete to provide categorization judgments about visual stimuli. Incorporating competing systems requires inclusion of cognitive mech