Predictive Maps in the Brain
Explore a comprehensive theory of reinforcement learning that bridges the gap between "model-based" and "model-free" approaches in this 53-minute lecture by Sam Gershman from Harvard University. Delve into the concept of "predictive maps" in the brain and their role in efficiently computing values. Discover how this theory explains various aspects of hippocampal spatial representation and how its eigendecomposition reveals latent structures resembling entorhinal grid cells. Examine evidence from a novel revaluation task demonstrating how humans utilize predictive maps in reinforcement learning tasks. Investigate the role of dopamine error signals in learning these predictive maps. Cover topics such as cognitive maps, path integration, sequential decision problems, successor representation, grid cells, and hierarchical reinforcement learning. Gain insights into the relationship between grid cells and place cells, as well as evidence for population coding in the brain.