Pooling of continuous features provides a unifying account of crowding

Visual crowding refers to phenomena in which the perception of a peripheral target is strongly affected by nearby flankers. Observers often report seeing the stimuli as "jumbled up," or otherwise confuse the target with the flankers. Theories of visual crowding contend over which aspect of the stimulus gets confused in peripheral vision. Attempts to test these theories have led to seemingly conflicting results, with some experiments suggesting that the mechanism underlying crowding operates on unbound features like color or orientation (Parkes, Lund, Angelucci, Solomon, & Morgan, 2001), while others suggest it "jumbles up" more complex features, or even objects like letters (Korte, 1923). Many of these theories operate on discrete features of the display items, such as the orientation of each line or the identity of each item. By contrast, here we examine the predictions of the Texture Tiling Model, which operates on continuous feature measurements (Balas, Nakano, & Rosenholtz, 2009). We show that the main effects of three studies from the crowding literature are consistent with the predictions of Texture Tiling Model. This suggests that many of the stimulus-specific curiosities surrounding crowding are the inherent result of the informativeness of a rich set of image statistics for the particular tasks.