  We study the problem of optimal oculomotor control during the execution of
visual search tasks. We introduce a computational model of human eye movements,
which takes into account various constraints of the human visual and oculomotor
systems. In the model, the choice of the subsequent fixation location is posed
as a problem of stochastic optimal control, which relies on reinforcement
learning methods. We show that if biological constraints are taken into
account, the trajectories simulated under learned policy share both basic
statistical properties and scaling behaviour with human eye movements. We
validated our model simulations with human psychophysical eye-tracking
experiments
