Figure 7.

Using higher-order correlations to predict perceptual sensitivity. (a) Cross-scale, cross-position, and cross-orientation correlations are computed by taking products of localized V1-like filter responses. Each circle represents an image location. Filters at each location are tuned to orientation and frequency, and compute either linear or energy responses (see panel b). (b) Linear filters are sensitive to phase, akin to V1 simple cells; energy filters compute the square root of the sum of squared responses of two phase-shifted filters (in quadrature pair) and are thus insensitive to phase, akin to V1 complex cells (Adelson & Bergen, 1985). For both filter types, products (as in panel a) are averaged across spatial locations to yield correlations. (c) We used multiple linear regression to predict perceptual sensitivity to naturalistic textures based on higher-order correlations and other image statistics used in texture synthesis. Each data point corresponds to a texture family; black dots indicate all texture families used in physiological experiments (from Figs. 2e, 5de, 6de). Black dashed line is the line of equality. (d) Wedges indicate the fractional R² assigned to each group of texture synthesis parameters from the regression analysis. See Portilla & Simoncelli (2001) and Balas (2008) for example images demonstrating the role of some of these parameters in texture synthesis.