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From stereogram to surface: how the brain sees the world in depth.

When we look at a scene, how do we consciously see surfaces infused with lightness and color at the correct depths? Random-Dot Stereograms (RDS) probe how binocular disparity between the two eyes can generate such conscious surface percepts. Dense RDS do so despite the fact that they include multiple false binocular matches. Sparse stereograms do so even across large contrast-free regions with no binocular matches. Stereograms that define occluding and occluded surfaces lead to surface percepts wherein partially occluded textured surfaces are completed behind occluding textured surfaces at a spatial scale much larger than that of the texture elements themselves. Earlier models suggest how the brain detects binocular disparity, but not how RDS generate conscious percepts of 3D surfaces. This article proposes a neural network model that predicts how the layered circuits of visual cortex generate these 3D surface percepts using interactions between visual boundary and surface representations that obey complementary computational rules. The model clarifies how interactions between layers 4, 3B and 2/3A in V1 and V2 contribute to stereopsis, and proposes how 3D perceptual grouping laws in V2 interact with 3D surface filling-in operations in V1, V2 and V4 to generate 3D surface percepts in which figures are separated from their backgrounds. The model explanations of 3D surface percepts raised by various RDS are demonstrated by computer simulations. The model hereby unifies the explanation of data about stereopsis and data about 3D figure-ground separation and completion of partially occluded object surfaces. It shows how these model mechanisms convert the complementary rules for boundary and surface formation into consistent visual percepts of 3D surfaces.