Publications

Cortical processing of complex visual information, like form, texture, and object identity, is thought to be hierarchical. Naturalistic textures are a useful tool for probing these hierarchical representations because they are well characterized by a hierarchical statistical model (Portilla & Simoncelli 2000). According to the model, image pairs will appear to be composed of the same texture if they match with respect to a small collection of image statistics. These statistics are based on a multi-scale, oriented image representation, and include both marginal statistics (e.g., energy in each band) and correlations (between neighboring filter responses in space, orientation and scale). Using this model, one can synthesize a large number of different naturalistic images that are texturally equivalent (with respect to the model’s statistics). We tested the hypothesis that the set of texture samples from a given natural texture category produce a reliable, unique and distributed pattern of fMRI responses across visual cortical areas.

We measured fMRI responses in multiple retinotopic areas while subjects viewed texture samples drawn from 6 different texture categories. Categories were based on texture images selected from the Brodatz database. These images were preprocessed to ensure that their power spectra, averaged over orientation, were equal. For each category, many samples were generated using the Portilla/Simoncelli model. On each trial (inter-stimulus interval: 3.5-6.5 s), we showed a 1 s burst at 5 Hz of random texture samples drawn from the same category, mitigating any response specific to a particular texture sample. Texture category was randomized across trials.  Stimuli were presented within an annulus (radii: 0.5 and 9 deg). To divert and control attention, subjects performed a difficult letter-identification task at fixation.

We used a multivariate pattern-classification analysis with leave-one-run-out cross validation to test whether distributed patterns of fMRI responses reliably differed between the texture categories. Classification was significantly above chance in multiple visual areas, including primary visual cortex (V1), and areas V2 and V4. Classification performance was more robust across texture categories in V2 and V4 than in V1. Also, classification performance in V1 was highest for those texture categories with larger orientation differences, but this was not the case for V2 and V4. We conclude that responses in striate and extra-striate cortex reflect the statistical properties that distinguish natural texture categories, and that higher-tier areas extract increasingly complex statistics.