Discrete-Event Systems for Modelling Decision-Making in Human Motor Control

Abstract

Artificial intelligence, control theory and neuroscience have a long history of interplay. An example is human motor control: optimal feedback control describes low-level motor functions and reinforcement learning explains high-level decision-making, but where the two meet is not as well understood. Here I formulate the human motor decision-making problem, describe how discrete-event systems could model it and lay out future research paths to fill in this gap in the literature.