Bayesian models of eye movement selection with retinotopic maps.

Among the various possible criteria guiding eye movement selection, we investigate the role of position uncertainty in the peripheral visual field. In particular, we suggest that, in everyday life situations of object tracking, eye movement selection probably includes a principle of reduction of uncertainty. To evaluate this hypothesis, we confront the movement predictions of computational models with human results from a psychophysical task. This task is a freely moving eye version of the multiple object tracking task, where the eye movements may be used to compensate for low peripheral resolution. We design several Bayesian models of eye movement selection with increasing complexity, whose layered structures are inspired by the neurobiology of the brain areas implied in this process. Finally, we compare the relative performances of these models with regard to the prediction of the recorded human movements, and show the advantage of taking explicitly into account uncertainty for the prediction of eye movements.