Computational Mechanisms for Perceptual Stability using Disparity and Motion Parallax.

Walking and other forms of self-motion create global motion patterns across our eyes. With the resulting stream of visual signals, how do we perceive ourselves as moving through a stable world? Although the neural mechanisms are largely unknown, human studies (Warren and Rushton, 2009) provide strong evidence that the visual system is capable of parsing the global motion into two components: one due to self-motion and the other due to independently moving objects. In the present study, we use computational modeling to investigate potential neural mechanisms for stabilizing visual perception during self-motion that build on neurophysiology of the middle temporal (MT) and medial superior temporal (MST) areas. One such mechanism leverages direction, speed, and disparity tuning of cells in dorsal MST (MSTd) to estimate the combined motion parallax and disparity signals attributed to the observer's self-motion. Feedback from the most active MSTd cell subpopulations suppresses motion signals in MT that locally match the preference of the MSTd cell in both parallax and disparity. This mechanism combined with local surround inhibition in MT allows the model to estimate self-motion while maintaining a sparse motion representation that is compatible with perceptual stability. A key consequence is that after signals compatible with the observer's self-motion are suppressed, the direction of independently moving objects is represented in a world-relative rather than observer-relative reference frame. Our analysis explicates how temporal dynamics and joint motion parallax-disparity tuning resolve the world-relative motion of moving objects and establish perceptual stability. Together, these mechanisms capture findings on the perception of object motion during self-motion.