Neural systems underlying reinforcement learning

Summary

Date: 
September 19, 2017 - 12:00pm
Location: 
Northwest Building, Room 243
About the Speaker
Name: 
Bruno Averbeck
Speaker Affiliation: 
NIH

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. Standard models of RL assume that dopamine neurons code reward prediction errors. These RPEs are then communicated to the basal ganglia, specifically the striatum, because of its substantial dopamine innervation. Thus, the striatal neurons come to represent the values of choices. In contrast to the standard model, we have recently shown that the amygdala has a larger role in RL than the ventral striatum (VS). In addition, the role of the VS depends strongly on the reward environment. When rewards are predictable, the VS has almost no role in learning whereas when rewards are less predictable the VS plays a larger role. This data outlines a more specific role for the VS in RL than is attributed to it by current models. Given that the VS has been implicated in depression, particularly adolescent depression, this delineation of the contribution of the VS to normal behavior may help inform hypotheses about the mechanisms and circuitry underlying depression.