Striatal and Hippocampal Entropy and Recognition Signals in Category Learning: Simultaneous Processes Revealed by Model-Based fMRI.
Category learning is a complex phenomenon that engages multiple cognitive proce.sses, many of which occur simultaneously and unfold dynamically over time. For example, as people encounter objects in the world, they simultaneously engage processes to determine their fit with current knowledge structu...
| Published in: | Journal of Experimental Psychology. Learning, Memory & Cognition Vol. 38; no. 4; pp. 821 - 840 |
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| Main Authors: | , , |
| Format: | Article |
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American Psychological Association
Jul2012
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=77491319&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 77491319 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 02787393 EXL jtl: Journal of Experimental Psychology. Learning, Memory & Cognition issn: 02787393 maglogo: N pubinfo: dt: Jul2012 vid: 38 iid: 4 pid: 34 pub: American Psychological Association artinfo: ui: 77491319 10.1037/a0027865 ppf: 821 ppct: 19 formats: tig: atl: Striatal and Hippocampal Entropy and Recognition Signals in Category Learning: Simultaneous Processes Revealed by Model-Based fMRI. aug: au: Davis, Tyler Love, Bradley C. Preston, Alison R. affil: Imaging Research Center, University of Texas, Austin Department of Psychology, University of Texas, Austin Department of Cognitive, Perceptual, and Brain Sciences, University College London, London, United Kingdom Department of Psychology, Center for Learning and Memory, and Institute of Neuroscience, University of Texas, Austin su: Cognitive learning Hippocampus (Brain) Magnetic resonance imaging of the brain Pattern perception Corpus striatum Mathematical models sug: subj: Cognitive learning Hippocampus (Brain) Magnetic resonance imaging of the brain Pattern perception Corpus striatum Mathematical models keyword: category learning entropy medial temporal lobe model-based imaging recognition category learning entropy medial temporal lobe model-based imaging recognition ab: Category learning is a complex phenomenon that engages multiple cognitive proce.sses, many of which occur simultaneously and unfold dynamically over time. For example, as people encounter objects in the world, they simultaneously engage processes to determine their fit with current knowledge structures, gather new information about the objects, and adjust their representations to support behavior in future encounters. Many techniques that are available to understand the neural basis of category learning assume that the multiple processes that subserve it can be neatly separated between different trials of an experiment. Model-based functional magnetic resonance imaging offers a promising tool to separate multiple, simultaneously occurring processes and bring the analysis of neuroimaging data more in line with category learning's dynamic and multifaceted nature. We use model-based imaging to explore the neural basis of recognition and entropy signals in the medial temporal lobe and striatum that are engaged while participants learn to categorize novel stimuli. Consistent with theories suggesting a role for the anterior hippocampus and ventral striatum in motivated learning in response to uncertainty, we find that activation in both regions correlates with a model-ba.sed measure of entropy. Simultaneously, separate subregions of the hippocampus and striatum exhibit activation correlated with a model-based recognition strength measure. Our results suggest that model-based analyses are exceptionally useful for extracting information about cognitive processes from neuroimaging data. Models provide a basis for identifying the multiple neural processes that contribute to behavior, and neuroimaging data can provide a powerful test bed for constraining and testing model predictions. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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