Gaze shifts as dynamical random sampling

We discuss how gaze behavior of an observer can be simulated as a Monte Carlo sampling of a distribution obtained from the saliency map of the observed image. To such end we propose the Levy Hybrid Monte Carlo algorithm, a dynamic Monte Carlo method in which the walk on the distribution landscape is modelled through Levy flights. Some preliminary results are presented comparing with data gathered by eye-tracking human observers involved in an emotion recognition task from facial expression displays.