Although the human amygdala and striatum have both been implicated in associative learning, only the striatum's contribution has been consistently computationally characterized. Using a reversal learning task, we found that amygdala blood oxygen level–dependent activity tracked associability as estimated by a computational model, and dissociated it from the striatal representation of reinforcement prediction error. These results extend the computational learning approach from striatum to amygdala, demonstrating their complementary roles in aversive learning.
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We thank P. Glimcher, R. Rutledge and E. DeWitt for discussions and comments. This research was supported by a McKnight Foundation Scholar Award, Human Frontiers Science Program grant RGP0036/2009-C, US National Institutes of Health (NIH) grant MH087882 (part of the CRCNS program, to N.D.D.), a James S. McDonnell Foundation grant and NIH grant MH080756 to E.A.P., and NIH grants DA015718 and AG027097 to G.S. This work was also supported by a Seaver Foundation grant to the Center for Brain Imaging.
The authors declare no competing financial interests.
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Li, J., Schiller, D., Schoenbaum, G. et al. Differential roles of human striatum and amygdala in associative learning. Nat Neurosci 14, 1250–1252 (2011) doi:10.1038/nn.2904
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