Circuit mechanisms of arbitration between distinct reinforcement learning systems
PROJECT SUMMARY Animals can exhibit goal-directed behaviors in novel environments, despite limited experience with them. How does the brain make and use inferences about the underlying statistics and generative structure of environments to guide behavior? The field of reinforcement learning refers to this capacity as “model-based” reasoning, meaning that it relies on an internal model of the structure of the world. Critically, this internal model can be used to flexibly estimate the best actions by mental simulation or planning, without direct experience.