A novel role for visual perspective cues in the neural computation of depth

Abstract

As we explore a scene, our eye movements add global patterns of motion to the retinal image, complicating visual motion produced by self-motion or moving objects. Conventionally, it has been assumed that extra-retinal signals, such as efference copy of smooth pursuit commands, are required to compensate for the visual consequences of eye rotations. We consider an alternative possibility: namely, that the visual system can infer eye rotations from global patterns of image motion. We visually simulated combinations of eye translation and rotation, including perspective distortions that change dynamically over time. We demonstrate that incorporating these “dynamic perspective” cues allows the visual system to generate selectivity for depth sign from motion parallax in macaque area MT, a computation that was previously thought to require extra-retinal signals regarding eye velocity. Our findings suggest novel neural mechanisms that analyze global patterns of visual motion to perform computations that require knowledge of eye rotations.