A Single Mechanism Can Account for Human Perception of Depth in Mixed Correlation Random Dot Stereograms

In order to extract retinal disparity from a visual scene, the brain must match corresponding points in the left and right retinae. This computationally demanding task is known as the stereo correspondence problem. The initial stage of the solution to the correspondence problem is generally thought to consist of a correlation-based computation. However, recent work by Doi et al suggests that human observers can see depth in a class of stimuli where the mean binocular correlation is 0 (half-matched random dot stereograms). Half-matched random dot stereograms are made up of an equal number of correlated and anticorrelated dots, and the binocular energy model—a well-known model of V1 binocular complex cells—fails to signal disparity here. This has led to the proposition that a second, match-based computation must be extracting disparity in these stimuli. Here we show that a straightforward modification to the binocular energy model—adding a point output nonlinearity—is by itself sufficient to produce cells that are disparity-tuned to half-matched random dot stereograms. We then show that a simple decision model using this single mechanism can reproduce psychometric functions generated by human observers, including reduced performance to large disparities and rapidly updating dot patterns. The model makes predictions about how performance should change with dot size in half-matched stereograms and temporal alternation in correlation, which we test in human observers. We conclude that a single correlation-based computation, based directly on already-known properties of V1 neurons, can account for the literature on mixed correlation random dot stereograms.Author Summary: Relating neural activity to perception is one of the most challenging tasks in neuroscience. Stereopsis—the ability of many animals to see in stereoscopic 3D—is a particularly tractable problem because the computational and geometric challenges faced by the brain are very well understood. In essence, the brain has to work out which elements in the left eye’s image correspond to which in the right image. This process is believed to begin in primary visual cortex (V1). It has long been believed that neurons in V1 achieve this by computing the correlation between small patches of each eye’s image. However, recent psychophysical experiments have reported depth perception in stimuli for which this correlation is zero, suggesting that another mechanism might be responsible for matching the left and right images in this case. In this article, we show how a simple modification to model neurons that compute correlation can account for depth perception in these stimuli. Our model cells mimic the response properties of real cells in the primate brain, and importantly, we show that a perceptual decision model that uses these cells as its basic elements can capture the performance of human observers on a series of visual tasks. That is, our computer model of a brain area, based on experimental data about real neurons and using only a single type of depth computation, successfully explains and predicts human depth judgments in novel stimuli. This reconciles the properties of human depth perception with the properties of neurons in V1, bringing us closer to understanding how neuronal activity causes perception.