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Research Topics

The section on Learning and Decision making studies the neural circuitry that underlies reinforcement learning. Reinforcement learning (RL) is the behavioral process of learning to make advantageous choices. While some preferences are innate, many are learned over time. How do we learn what we like and what we want to avoid? The lab uses a combination of experiments in in-vivo model systems, human participants including patients and computational modeling. We examine several facets of the learning problem including learning from gains vs. losses, learning to select rewarding actions vs. learning to select rewarding objects, and the explore-exploit trade-off. The explore-exploit trade-off describes a fundamental problem in learning. Should you try every restaurant when visiting a new city, or explore a small set of them and then return to your favorite several times?

Most work on RL focuses on dopamine and its effects in the striatum.  However, we have found that a broader set of areas is involved.  In early work we established a role for the amygdala.  Our work now focuses on studying how the broader limbic network, including orbitofrontal cortex, the amygdala, ventral striatum, pallidum, and mediodorsal thalamus, mediate RL.  These areas are part of a network of strongly interconnected areas and form the ventral part of the cortical-basal ganglia-thalamocortical system.  Modern experimental and computational methods are allowing us to examine the way in which this network gives rise to RL.  


Dr. Averbeck obtained a B.S. in Electrical Engineering from the University of Minnesota in 1994, worked in industry for three years, then returned to Minnesota to complete a doctorate in neuroscience under Dr. Apostolos Georgopoulos. Awarded a Ph.D. 2001, with a dissertation on Neural Mechanisms of Copying Geometrical Shapes . For his postdoctoral studies, Dr. Averbeck joined the laboratory Dr. Daeyeol Lee at the University of Rochester, where he studied neural mechanisms underlying sequential learning, coding of vocalizations, and population coding. In 2006, as a Senior Lecturer at University College London, he began using neuroimaging of human study participants to investigate the role of frontal-striatal circuits in learning. Dr. Averbeck joined the NIMH Intramural Research Program as a Principal Investigator in 2009. A tenured member of the faculty since 2016, he is Chief of the Section on Learning and Decision Making.

Selected Publications

Averbeck BB (2022). Pruning recurrent neural networks replicates adolescent changes in working memory and reinforcement learning. Proc Natl Acad Sci U S A 119, e2121331119. [Pubmed Link ]

Tang H, Costa VD, Bartolo R, Averbeck BB (2022). Differential coding of goals and actions in ventral and dorsal corticostriatal circuits during goal-directed behavior. Cell Rep 38, 110198. [Pubmed Link ]

Averbeck BB, Murray EA (2020). Hypothalamic Interactions with Large-Scale Neural Circuits Underlying Reinforcement Learning and Motivated Behavior. Trends Neurosci 43, 681-694. [Pubmed Link ]

Bartolo R, Averbeck BB (2020). Prefrontal Cortex Predicts State Switches during Reversal Learning. Neuron 106, 1044-1054.e4. [Pubmed Link ]

Costa VD, Mitz AR, Averbeck BB (2019). Subcortical Substrates of Explore-Exploit Decisions in Primates. Neuron 103, 533-545.e5. [Pubmed Link ]

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