A Bayesian model for estimating observer translation and rotation from optic flow and extra-retinal input

Abstract

We present a Bayesian ideal observer model that estimates observer translation and rotation from optic flow and an extra-retinal eye movement signal. The model assumes a rigid environment and noise in velocity measurements, and that eye movement provides a probabilistic cue for rotation. The model can simulate human heading perception across a range of conditions, including: translation with simulated vs. actual eye rotations, environments with various depth structures, and the presence of independently moving objects.