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Published July 2011 | public
Journal Article

Differentiable contributions of human amygdalar subregions in the computations underlying reward and avoidance learning


To understand how the human amygdala contributes to associative learning, it is necessary to differentiate the contributions of its subregions. However, major limitations in the techniques used for the acquisition and analysis of functional magnetic resonance imaging (fMRI) data have hitherto precluded segregation of function with the amygdala in humans. Here, we used high-resolution fMRI in combination with a region-of-interest-based normalization method to differentiate functionally the contributions of distinct subregions within the human amygdala during two different types of instrumental conditioning: reward and avoidance learning. Through the application of a computational-model-based analysis, we found evidence for a dissociation between the contributions of the basolateral and centromedial complexes in the representation of specific computational signals during learning, with the basolateral complex contributing more to reward learning, and the centromedial complex more to avoidance learning. These results provide unique insights into the computations being implemented within fine-grained amygdala circuits in the human brain.

Additional Information

© 2011 The Authors. European Journal of Neuroscience © 2011 Federation of European Neuroscience Societies and Blackwell Publishing Ltd. Received 10 March 2011, accepted 21 March 2011. Article first published online: 3 May 2011. We thank Christian Kerskens and Mimi Liljeholm for helpful discussions. This work was funded by Science Foundation Ireland grant 08/IN.1/B1844 to J.O.D.

Additional details

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October 23, 2023