Beyond Bouma's window: How to explain global aspects of crowding?
Fig 5
a. Illustration of the epitome model. An image (left) is compressed into an epitome (center), a summary of local features. The image on the right is reconstructed from the epitome. b. As an example for the classic texture evaluation, we show the stimulus and reconstructed image for the 1- and 7-square conditions. Human vernier offset thresholds are better for the 1-square than the 7-square condition. The model does not produce uncrowding because vernier offset direction in the output is not easier to make out in the 7-square than in the 1-square case (according to the authors’ judgment). c. Example for our performance measure. Human and model thresholds (see main text for how model threshold was computed) for vernier alone, single square and 7 squares conditions. The 7-square threshold is higher than the 1- square threshold, in contrast with human performance. Note: the model outputs a number quantifying how different the left and right vernier offset versions of the input are (so the higher this difference, the better the performance). To make comparison with the human threshold easier, 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 cannot change the conclusions because monotonic outputs are mapped on monotonic performance and the same is true for U-shaped functions (see methods).