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Abstract

statistical regularities in sensory signals and thus acquire knowledge about the outside world (Barlow, 1997). In vision, several probabilistic models of local natural image regularities have been proposed which intriguingly replicate neural response properties (AttickRedlich 1992, BellSejnowski 1997, SchwartzSimoncelli 2001, KarklinLewicki 2009). To evaluate how such models relate to functional vision, we previously measured their perceptual relevance using a discrimination task pitting model image patches against true natural image patches (Gerhard, Wichmann, Bethge, 2011). Observers were remarkably sensitive to the regularities of grayscale patches, even for patches as small as 3x3 pixels. Performance relied greatly on how well the models captured luminance features like contrast fluctuation. Here we focus on how well the models capture local contour information in natural images. In a two-alternative forced choice task, observers viewed two tightly-tiled textures of binary image patches, one comprised of natural image samples, the other of model patches. The task was to select the natural image samples. We measured discrimination performance at patch sizes from 3x3 to 8x8 pixels for 8 models spanning the range from low likelihood to one among the current best in terms of likelihood. We compared human performance to an ideal observer with perfect knowledge of the natural distribution for patch sizes at which we could empirically estimate the distribution and tested potential texture cues with a classification analysis. While human performance suggested suboptimal strategies were used to discriminate contour statistics relative to grayscale statistics, observers were well above chance with binary 4x4 pixel patches and larger, meaning that neuronally-inspired models do not yet capture enough of the contour regularities in natural images that functional human vision can detect, even in very small natural image patches.