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Computational level explanations based on optimal feedback control withsignal-dependent noise have been able to account for a vast array of phenomenain human sensorimotor behavior. However, commonly a cost function needs to beassumed for a task and the optimality of human behavior is evaluated bycomparing observed and predicted trajectories. Here, we introduce inverseoptimal control with signal-dependent noise, which allows inferring the costfunction from observed behavior. To do so, we formalize the problem as apartially observable Markov decision process and distinguish between theagent's and the experimenter's inference problems. Specifically, we derive aprobabilistic formulation of the evolution of states and belief states and anapproximation to the propagation equation in the linear-quadratic Gaussianproblem with signal-dependent noise. We extend the model to the case of partialobservability of state variables from the point of view of the experimenter. Weshow the feasibility of the approach through validation on synthetic data andapplication to experimental data. Our approach enables recovering the costs andbenefits implicit in human sequential sensorimotor behavior, therebyreconciling normative and descriptive approaches in a computational framework.

Inverse Optimal Control Adapted to the Noise Characteristics of the Human Sensorimotor System