Material categorization from natural texture images proceeds quickly and accurately, supporting a number of visual and motor behaviors. In real-world settings, mechanisms for material categorization must function effectively based on the input from foveal vision, where image representation is high fidelity, and the input from peripheral vision, which is comparatively impoverished. What features support successful material categorization in the visual periphery, given the known reductions in acuity, contrast sensitivity, and other lossy transforms that reduce the fidelity of image representations? In general, the visual features that support material categorization remain largely unknown, but recent work suggests that observers’ abilities in a number of tasks that depend on peripheral vision can be accounted for by assuming that the visual system has access to only summary statistics (texture-like descriptors) of image structure. We therefore hypothesized that a model of peripheral vision based on the Portilla-Simoncelli texture synthesis algorithm might account for material categorization abilities in the visual periphery. Using natural texture images and synthetic images made from these stimuli, we compared performance across material categories to determine whether observer performance with natural inputs could be predicted by their performance with synthetic images that reflect the constraints of a texture code.

Texture synthesis models have become a popular tool for studying the representations supporting texture processing in human vision. In particular, the summary statistics implemented in the Portilla-Simoncelli (PS) model support high-quality synthesis of natural textures, account for performance in crowding and search tasks, and may account for the response properties of V2 neurons. We chose to investigate whether or not these summary statistics are also sufficient to support texture discrimination in a task that required illumination invariance. Our observers performed a match-to-sample task using natural textures photographed with either diffuse overhead lighting or lighting from the side. Following a briefly presented sample texture, participants identified which of two test images depicted the same texture. In the illumination change condition, illumination differed between the sample and the matching test image. In the no change condition, sample textures and matching test images were identical. Critically, we generated synthetic versions of these images using the P-S model and also tested participants with these. If the statistics in the P-S model are sufficient for invariant texture perception, performance with synthetic images should not differ from performance in the original task. Instead, we found a significant cost of applying texture synthesis in both lighting conditions. We also observed this effect when power-spectra were matched across images (Experiment 2) and when sample and test images were drawn from unique locations in the parent textures to minimize the contribution of image-based processing (Experiment 3). Invariant texture processing thus depends upon measurements not implemented in the P-S algorithm.

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Disruptions of natural texture appearance are known to negatively impact performance in texture discrimination tasks, for example, such that contrast-negated textures, synthetic textures, and textures depicting abstract art are processed less efficiently than natural textures. Presently, we examined how visual ERP responses (the P1 and the N1 in particular) were affected by violations of natural texture appearance. We presented participants with images depicting either natural textures or synthetic textures made from the original stimuli. Both stimulus types were additionally rendered either in positive or negative contrast. These appearance manipulations (negation and texture synthesis) preserve a range of low-level features, but also disrupt higher-order aspects of texture appearance. We recorded continuous EEG while participants completed a same/different image discrimination task using these images and measured both the P1 and N1 components over occipital recording sites. While the P1 exhibited no sensitivity to either contrast polarity or real/synthetic appearance, the N1 was sensitive to both deviations from natural appearance. Polarity reversal and synthetic appearance affected the N1 latency differently, however, suggesting a differential impact on processing. Our results suggest that stages of visual processing indexed by the P1 and N1 are sensitive to high-order statistical regularities in natural textures and also suggest that distinct violations of natural appearance impact neural responses differently.

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