Bruno Averbeck, Ph.D.

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.