  Stochastic Optimal Control models represent the state-of-the-art in modeling
goal-directed human movements. The linear-quadratic sensorimotor (LQS) model
based on signal-dependent noise processes in state and output equation is the
current main representative. With our newly introduced Inverse Stochastic
Optimal Control algorithm building upon two bi-level optimizations, we can
identify its unknown model parameters, namely cost function matrices and
scaling parameters of the noise processes, for the first time. In this paper,
we use this algorithm to identify the parameters of a deterministic
linear-quadratic, a linear-quadratic Gaussian and a LQS model from human
measurement data to compare the models' capability in describing goal-directed
human movements. Human steering behavior in a simplified driving task shown to
posses similar features as point-ot-point human hand reaching movements serves
as our example movement. The results show that the identified LQS model
outperforms the others with statistical significance. Particularly, the average
human steering behavior is modeled significantly better by the LQS model. This
validates the positive impact of signal-dependent noise processes on modeling
human average behavior.
