Low-level contrast statistics are diagnostic of invariance of natural textures - Groen, Iris I. A.; Ghebreab, Sennay; Lamme, Victor A. F.; Scholte, H. S. (Frontiers in Computational Neuroscience, 2012)
Texture may provide important clues for real world object and scene perception. For these clues to be reliable, they should ideally be invariant to common viewing variations such as changes in illumination and orientation. In a large image database of natural materials, we found textures with low-level contrast statistics that varied substantially under viewing variations, as well as textures that remained relatively constant. This led us to ask whether textures with constant contrast statistics give rise to more invariant representations compared to other textures. To test this, we selected natural texture images with either high (HV) or low (LV) variance in statistics and presented these to human observers. In two distinct behavioral categorization paradigms, participants more often judged HV textures as 'different' compared to LV textures, showing that textures with constant contrast statistics are perceived as being more invariant. In a separate EEG experiment, evoked responses to single texture images (single-image ERPs) were collected. The results show that differences in contrast statistics predicted both early and late differences in ERP amplitude between individual images. Importantly, ERP differences between images of HV textures were mainly driven by illumination angle, which was not the case for LV images: there, differences were completely driven by texture membership, as predicted by contrast statistics. These converging neural and behavioral results imply that some natural textures are surprisingly invariant and that low-level contrast statistics predict the extent of this invariance.