An adaptive Kalman filter model for stable perception of visual space unaffected by eye movement

A number of visual psychological-physical (PP) models, i.e., the saccadic suppression model, exist for explaining the phenomenon of visually stable perception in visual space regardless of eye movement (EM). However, most models ignore the visual system noise that is added between the retina and the high visual information processing system. This paper introduces a new visual model based on an adaptive Kalman filter (AKF) model, derived from an optimum control theory, which can optimally estimate and predict near-original visual information from a visual information signal disturbed by noise. The AKF model has three main mechanisms that eliminate some of the deficiencies appearing in conventional PP models: filter gain adapts in response to texture similarity, optimal estimation/prediction, and a smoothing effect from step-wise motion of EM. Based on visual experiments, three autocorrelation functions concerned with EM acceleration when viewing eleven texture images are clustered. Numerical simulations show that AKF can cover several of the visual phenomena which traditionally thought to be caused by different mechanisms.