RESUMEN
Luminance contrast is one of the key factors in the visibility of objects in the world around us. Previous work has shown that the perceived depth from binocular disparity depends profoundly on the luminance contrast of the image. This dependence cannot be explained by existing disparity models, such as the well-established disparity energy model, because they predict no effect of luminance contrast on depth perception. Here, we develop a model for disparity processing that incorporates contrast normalization of the neural response into the disparity energy model to account for the contrast dependence of perceived depth from disparity. Our model contains an array of disparity channels, each with a different disparity selectivity. The binocular images are first processed by the left- and right-eye receptive fields of each channel. The outputs of the two receptive fields are combined linearly as the excitatory disparity sensitivity and then fed into a nonlinear contrast gain control mechanism. The perceived depth is determined by the weighted average of all the disparity channels that respond to the binocular images. This model provides the first analytic account of how luminance contrast affects perceived depth from disparity.
Asunto(s)
Percepción de Profundidad , Visión Binocular , Humanos , Disparidad VisualRESUMEN
The division of labor between the dorsal and ventral visual pathways has been well studied, but not often with direct comparison at the single-neuron resolution with matched stimuli. Here we directly compared how single neurons in MT and V4, mid-tier areas of the two pathways, process binocular disparity, a powerful cue for 3D perception and actions. We found that MT neurons transmitted disparity signals more quickly and robustly, whereas V4 or its upstream neurons transformed the signals into sophisticated representations more prominently. Therefore, signaling speed and robustness were traded for transformation between the dorsal and ventral pathways. The key factor in this tradeoff was disparity-tuning shape: V4 neurons had more even-symmetric tuning than MT neurons. Moreover, the tuning symmetry predicted the degree of signal transformation across neurons similarly within each area, implying a general role of tuning symmetry in the stereoscopic processing by the two pathways.
Asunto(s)
Macaca mulatta/fisiología , Lóbulo Temporal/fisiología , Disparidad Visual/fisiología , Vías Visuales/fisiología , Animales , Femenino , MasculinoRESUMEN
A great challenge of systems neuroscience is to understand the computations that underlie perceptual constancies, the ability to represent behaviorally relevant stimulus properties as constant even when irrelevant stimulus properties vary. As signals proceed through the visual system, neural states become more selective for properties of the environment, and more invariant to irrelevant features of the retinal images. Here, we describe a method for determining the computations that perform these transformations optimally, and apply it to the specific computational task of estimating a powerful depth cue: binocular disparity. We simultaneously determine the optimal receptive field population for encoding natural stereo images of locally planar surfaces and the optimal nonlinear units for decoding the population responses into estimates of disparity. The optimal processing predicts well-established properties of neurons in cortex. Estimation performance parallels important aspects of human performance. Thus, by analyzing the photoreceptor responses to natural images, we provide a normative account of the neurophysiology and psychophysics of absolute disparity processing. Critically, the optimal processing rules are not arbitrarily chosen to match the properties of neurophysiological processing, nor are they fit to match behavioral performance. Rather, they are dictated by the task-relevant statistical properties of complex natural stimuli. Our approach reveals how selective invariant tuning-especially for properties not trivially available in the retinal images-could be implemented in neural systems to maximize performance in particular tasks.