Visual-vestibular estimation of the body's curvilinear motion through the world: A computational model.

Motion along curved paths (curvilinear self-motion) introduces a rotation component to the radial expanding patterns of visual motion generated in the eyes of moving animals with forward-facing eyes. The resultant image motion (vector flow field) is no longer purely radial, and it is difficult to infer the heading direction from such combined translation-plus-rotation flow fields. The eye need not rotate relative to the head or body during curvilinear self-motion, and so there is an absence of efference signals directing and indicating the rotation. Yet the eye's rotation relative to the world needs to be measured accurately and its effect removed from the combined translation-rotation image motion in order for successful navigation to occur. I demonstrate that to be able to account for human heading-estimation performance, the precision of the eye-in-world rotation velocity signal needs to be at least 0.2°/s. I show that an accurate estimate of the eye's curvilinear motion path through the world can be achieved by combining relatively imprecise vestibular estimates of the rotation rate and direction with visual image-motion velocities distributed across the retina. Combined visual-vestibular signals produce greater accuracy than each on its own. The model can account for a wide range of existing human heading- and curvilinear-estimation psychophysical data.