Abstract
Observer translation produces differential image motion between objects that are located at different distances from the observer's point of fixation [motion parallax (MP)]. However, MP can be ambiguous with respect to depth sign (near vs far), and this ambiguity can be resolved by combining retinal image motion with signals regarding eye movement relative to the scene. We have previously demonstrated that both extra-retinal and visual signals related to smooth eye movements can modulate the responses of neurons in area MT of macaque monkeys, and that these modulations generate neural selectivity for depth sign. However, the neural mechanisms that govern this selectivity have remained unclear. In this study, we analyze responses of MT neurons as a function of both retinal velocity and direction of eye movement, and we show that smooth eye movements modulate MT responses in a systematic, temporally precise, and directionally specific manner to generate depth-sign selectivity. We demonstrate that depth-sign selectivity is primarily generated by multiplicative modulations of the response gain of MT neurons. Through simulations, we further demonstrate that depth can be estimated reasonably well by a linear decoding of a population of MT neurons with response gains that depend on eye velocity. Together, our findings provide the first mechanistic description of how visual cortical neurons signal depth from MP.SIGNIFICANCE STATEMENT Motion parallax is a monocular cue to depth that commonly arises during observer translation. To compute from motion parallax whether an object appears nearer or farther than the point of fixation requires combining retinal image motion with signals related to eye rotation, but the neurobiological mechanisms have remained unclear. This study provides the first mechanistic account of how this interaction takes place in the responses of cortical neurons. Specifically, we show that smooth eye movements modulate the gain of responses of neurons in area MT in a directionally specific manner to generate selectivity for depth sign from motion parallax. We also show, through simulations, that depth could be estimated from a population of such gain-modulated neurons.