Embodied Decision Making: System interactions in movement execution and action selection

Two well-established literatures have provided elegant computational models of sensorimotor learning and decision making, with relative little connection between the two.  I will discuss ways in which we can bring these two worlds together.   In the first part of the talk, I will discuss work that has brought into focus the relevance of multiple learning mechanisms for sensorimotor learning, examining how strategic processes interact with a cerebellar error-based learning system in sensorimotor adaptation.  The results here highlight a distinction between error signals that improve action execution or action selection.   In the second part, I will turn to learning in decision making tasks, asking how competence in motor execution might be incorporated in models of reinforcement learning.   Our work here addresses the question of how an agent determines if the absence of reward reflects a property of the environment or an error in motor execution.  We suggest that sensorimotor errors provide a “gating” signal to regulate reinforcement learning, providing a simple solution to this fundamental credit assignment problem.