Beyond Bouma's window: How to explain global aspects of crowding?
Fig 9
a. The input is sampled at each grid position by neurons tuned to 12 orientations, mimicking V1 simple cells. b. The connectivity pattern between cells depends on their relative position and orientation as shown here. Solid lines indicate excitation and dashed lines indicate inhibition. As shown, each neuron excites aligned neurons and inhibits non-aligned neurons. Each neuron has the same connectivity pattern, suitably rotated and translated. c. Output images for the square category. Each small oriented bar shows the maximally active orientation at this grid position. d. Results for the squares category. The dashed red bar shows the vernier threshold, which is matched for humans and the model. As shown, uncrowding does not occur in the model, because performance is worse for the 7 squares than the 1 square stimulus. Note: the model outputs a cross-correlation quantifying how similar the model output is to the model output in the vernier alone condition (so the higher this cross-correlation, the better the performance). To make comparison with the human threshold easier, we applied the same procedure as we did for the epitomes, i.e., we applied the following monotonic transformation to the output: “threshold-like output” = 1/”raw output”. Then we scaled the result to be in the same range as the human results. This monotonic re-scaling does not change the conclusions–the phenomenon of uncrowding cannot be altered.