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Stochastic Optimal Control models represent the state-of-the-art in modelinggoal-directed human movements. The linear-quadratic sensorimotor (LQS) modelbased on signal-dependent noise processes in state and output equation is thecurrent main representative. With our newly introduced Inverse StochasticOptimal Control algorithm building upon two bi-level optimizations, we canidentify its unknown model parameters, namely cost function matrices andscaling parameters of the noise processes, for the first time. In this paper,we use this algorithm to identify the parameters of a deterministiclinear-quadratic, a linear-quadratic Gaussian and a LQS model from humanmeasurement data to compare the models' capability in describing goal-directedhuman movements. Human steering behavior in a simplified driving task shown toposses similar features as point-ot-point human hand reaching movements servesas our example movement. The results show that the identified LQS modeloutperforms the others with statistical significance. Particularly, the averagehuman steering behavior is modeled significantly better by the LQS model. Thisvalidates the positive impact of signal-dependent noise processes on modelinghuman average behavior.

Validation of Stochastic Optimal Control Models for Goal-Directed Human Movements on the Example of Human Driving Behavior