Abstract
Initial stages of visual processing are well characterized in terms of band-limited oriented receptive filters. However, brain mechanisms underlying the integration of their outputs are much less understood. In the domain of texture perception, two types of mechanisms have been suggested: (A) first-order statistics and (B) autocorrelation function. In texture perception, considering local symmetry as a statistical property, we can employ the order parameter used in physics to analyze transitions between order and disorder. When the thermodynamic temperature (T) decreases monotonically, the order parameter changes monotonically from zero for disordered systems to one for symmetric systems. Recently, we have synthesized images corresponding to different T's and showed that human observers are sensitive to phase transition. Their sensitivity function is well approximated by an observer based on the order parameter. Here, we investigated the neural correlates of order-disorder perception using functional imaging combined with a phase-encoded paradigm. We hypothesized that BOLD response would depend monotonically on T if first-order statistics are involved. Conversely, the BOLD response would be larger for images around phase transition than for symmetric and disordered images if autocorrelation is involved, since correlations of all lengths are present only in these images. We presented the stimuli in 4 consecutive 16 s blocks: 1) disordered images, 2) images with continuous change of order parameter from disordered to symmetric, 3) symmetric images, 4) images with continuous change of the order parameter from symmetric to disordered. We found that the BOLD response in early visual areas as well as in lateral occipital complex (LOC) was highest for images close to the phase transition, thus supporting the autocorrelation hypothesis and rejecting first-order statistics as an underlying mechanism. These results may partially account for the weak activation of the LOC to both highly ordered and highly disordered textures compared to object shapes.
Meeting abstract presented at VSS 2018