The features that control discrimination of an isodipole texture pair.

Visual features such as edges and corners are carried by high-order statistics. Previous analysis of discrimination of isodipole textures, which isolate specific high-order statistics, demonstrates visual sensitivity to these statistics but stops short of analyzing the underlying computations. Here we use a new texture centroid paradigm to probe these computations. We focus on two canonical isodipole textures, the even and odd textures: any 2 × 2 block of even (odd) texture contains an even (odd) number of black (and white) checks. Each stimulus comprised a spatially random array of black-and-white texture-disks (background = mean gray) that varied in their fourth-order statistics. In the Even (Odd) condition, disks varied along the continuum between random coinflip texture and pure (highly structured) even (odd) target texture. The task was to mouse-click the centroid of the disk array, weighting each disk location by the target structure level of the disk-texture (ranging from 0 for coinflip to 1 for even or odd). For each of block-sizes S=2×2, 2 × 3, 2 × 4 and 3 × 3, a linear model was used to estimate the weight exerted on the subject's responses by the differently patterned blocks of size S. Only the results with 2 × 4 and 3 × 3 blocks were consistent with the data. In the Even condition, homogeneous blocks exerted the most weight; in the odd condition, block-pattern symmetry was important. These findings show that visual mechanisms sensitive to four-point correlations do not compute evenness or oddness per se, but rather are activated selectively by features whose frequency varies across isodipole textures.