Parametric identification of stochastic dynamic model of human visuomotor tracking control

We conducted an experiment on a visuomotor tracking task using human participants and compared it with numerical simulations on a stochastic dynamic model of the same task. Our numerical model comprises additive and multiplicative white Gaussian noises and a state feedback term. The parameters of the numerical model were identified using particle swarm optimization. To examine the stochastic behavior of the tracking task, we experimentally estimated the probability density functions (PDFs) of the state variables. Three of the four experimentally obtained PDFs show good agreement with those numerically obtained by the proposed model.