Interactive memory systems in the human brain

R. A. Poldrack (), J. Clark, E. J. Paré-Blagoev, D. Shohamy, J. Creso Moyano, C. Myers and M. A. Gluck
Additional contact information
R. A. Poldrack: Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, and Harvard Medical School
J. Clark: Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, and Harvard Medical School
E. J. Paré-Blagoev: Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, and Harvard Medical School
D. Shohamy: Center for Molecular and Behavioural Neuroscience, Rutgers University
J. Creso Moyano: Center for Molecular and Behavioural Neuroscience, Rutgers University
C. Myers: Rutgers University
M. A. Gluck: Center for Molecular and Behavioural Neuroscience, Rutgers University

Nature, 2001, vol. 414, issue 6863, 546-550

Abstract: Abstract Learning and memory in humans rely upon several memory systems, which appear to have dissociable brain substrates1,2. A fundamental question concerns whether, and how, these memory systems interact. Here we show using functional magnetic resonance imaging (FMRI) that these memory systems may compete with each other during classification learning in humans. The medial temporal lobe and basal ganglia were differently engaged across subjects during classification learning depending upon whether the task emphasized declarative or nondeclarative memory, even when the to-be-learned material and the level of performance did not differ. Consistent with competition between memory systems suggested by animal studies3,4 and neuroimaging5, activity in these regions was negatively correlated across individuals. Further examination of classification learning using event-related FMRI showed rapid modulation of activity in these regions at the beginning of learning, suggesting that subjects relied upon the medial temporal lobe early in learning. However, this dependence rapidly declined with training, as predicted by previous computational models of associative learning6,7,8.

Date: 2001
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DOI: 10.1038/35107080

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