Beyond Bouma's window: How to explain global aspects of crowding?
Fig 6
Texture Synthesis and Texture Tiling Model.
a. A texture (right) synthesized from the input on the left using the Portilla & Simoncelli [29] summary statistics. The output resembles crowding. Pooling- and substitution-like effects occur. b. In the TTM, instead of applying the summary statistics process to the whole image at once, only local patches of the image are processed, yielding a local summary statistics model. The local patches are thought to reflect V2 receptive fields. c. Whole-field summary statistics. From left to right: stimuli and Portilla & Simoncelli textures for the vernier, 1-square and 7-square conditions. The vernier offset is easy to determine from the texture in the vernier alone condition, and slightly harder in the crowded condition (a right-offset is discernable in the middle top of the display). Across all data, the model consistently produces crowding, but no uncrowding, as exemplified in the right condition in which no offset is present at all. d. Texture Tiling model. The left column shows three synthesized examples from the 1-square condition. On the right is the 7-flanking squares case. The model cannot produce uncrowding: since the stimulus on the right is less crowded than the stimulus on the left in the human data, the direction of the vernier should be easier to make out on the right than on the left. However, this is not the case.