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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.

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