Reward-based option competition in human dorsal stream and transition from stochastic exploration to exploitation in continuous space.

ABSTRACT: Primates exploring and exploiting a continuous sensorimotor space rely on dynamic maps in the dorsal stream. Two complementary perspectives exist on how these maps encode rewards. Reinforcement learning models integrate rewards incrementally over time, efficiently resolving the exploration/exploitation dilemma. Working memory buffer models explain rapid plasticity of parietal maps but lack a plausible exploration/exploitation policy. The reinforcement learning model presented here unifies both accounts, enabling rapid, information-compressing map updates and efficient transition from exploration to exploitation. As predicted by our model, activity in human frontoparietal dorsal stream regions, but not in MT+, tracks the number of competing options, as preferred options are selectively main

SUBMITTER: Hallquist MN 

PROVIDER: S-EPMC10889364 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature