Our daily visual experiences are inevitably linked to recognizing the rich variety of textures. However, how the brain encodes and differentiates a plethora of natural textures remains poorly understood. Here, we show that many neurons in macaque V4 selectively encode sparse combinations of higher-order image statistics to represent natural textures. We systematically explored neural selectivity in a high-dimensional texture space by combining texture synthesis and efficient-sampling techniques. This yielded parameterized models for individual texture-selective neurons. The models provided parsimonious but powerful predictors for each neuron's preferred textures using a sparse combination of image statistics. As a whole population, the neuronal tuning was distributed in a way suitable for categorizing textures and quantitatively predicts human ability to discriminate textures. Together, we suggest that the collective representation of visual image statistics in V4 plays a key role in organizing the natural texture perception.texture perception | material perception | visual area V4 | single-cell recording | image analysis I n the visual world, objects are characterized in part by their shapes, but also by their textures (1). The wide variety of textures we experience enables us to segment objects from backgrounds, perceive object properties, and recognize materials. It is well established that the representation of complex shapes and contours is gradually built up along the ventral visual pathway (2-5). On the other hand, how textural information is processed in the cortex is largely unknown, although some recent studies have examined the representation of natural textures and surface properties in the macaque ventral visual areas (6-11). Because, unlike contours, textures cannot be described based on combinations of edge fragments, we need to consider different underlying cortical processing.In contrast to the limited knowledge available from physiology, computational descriptions of textures have been extensively developed in the fields of psychophysics (12-16) and computer vision (17)(18)(19). In one such description, Portilla and Simoncelli (20) proposed that textures could be represented using an ensemble of summary statistics, including features derived from the luminance histogram and the amplitudes of the outputs of Gabor-like filters, as well as higher-order statistics such as the correlations across the filter outputs (see Fig. 3 for details; hereafter, we call this collection of statistics "PS statistics," using the authors' initials). Portilla and Simoncelli successfully generated new textures that were visually indistinguishable from the originals solely by making their PS statistics identical. Their algorithm is particularly inspiring because PS statistics use filters and computations that share biological properties. It was recently shown, for example, that a version of their synthesis algorithm can generate perceptually indistinguishable visual images (visual metamers) (21) and that naturalisti...

Complex shape and texture representations are known to be constructed from V1 along the ventral visual pathway through areas V2 and V4, but the underlying mechanism remains elusive. Recent study suggests that, for processing of textures, a collection of higher-order image statistics computed by combining V1-like filter responses serves as possible representations of textures both in V2 and V4. Here, to gain a clue for how these image statistics are processed in the extrastriate visual areas, we compared neuronal responses to textures in V2 and V4 of macaque monkeys. For individual neurons, we adaptively explored their preferred textures from among thousands of naturalistic textures and fitted the obtained responses using a combination of V1-like filter responses and higher-order statistics. We found that, while the selectivity for image statistics was largely comparable between V2 and V4, V4 showed slightly stronger sensitivity to the higher-order statistics than V2. Consistent with that finding, V4 responses were reduced to a greater extent than V2 responses when the monkeys were shown spectrally matched noise images that lacked higher-order statistics. We therefore suggest that there is a gradual development in representation of higher-order features along the ventral visual hierarchy.

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