Inverse Optimal Control Adapted to the Noise Characteristics of the Human Sensorimotor System
Abstract
Computational level explanations based on optimal feedback control with signaldependent noise have been able to account for a vast array of phenomena in human sensorimotor behavior. However, commonly a cost function needs to be assumed for a task and the optimality of human behavior is evaluated by comparing observed and predicted trajectories. Here, we introduce inverse optimal control with signaldependent noise, which allows inferring the cost function from observed behavior. To do so, we formalize the problem as a partially observable Markov decision process and distinguish between the agent's and the experimenter's inference problems. Specifically, we derive a probabilistic formulation of the evolution of states and belief states and an approximation to the propagation equation in the linearquadratic Gaussian problem with signaldependent noise. We extend the model to the case of partial observability of state variables from the point of view of the experimenter. We show the feasibility of the approach through validation on synthetic data and application to experimental data. Our approach enables recovering the costs and benefits implicit in human sequential sensorimotor behavior, thereby reconciling normative and descriptive approaches in a computational framework.
 Publication:

arXiv eprints
 Pub Date:
 October 2021
 arXiv:
 arXiv:2110.11130
 Bibcode:
 2021arXiv211011130S
 Keywords:

 Computer Science  Machine Learning;
 Electrical Engineering and Systems Science  Systems and Control;
 Quantitative Biology  Neurons and Cognition;
 Statistics  Machine Learning
 EPrint:
 24 pages, 11 figures, to be published at NeurIPS 2021