Beyond Bouma's window: How to explain global aspects of crowding?
Fig 14
Results for all models (columns). In black, the left panel displays all crowding stimuli and the right panel displays all uncrowding stimuli (i.e., better performance when extra elements are added to the crowded condition) as observed in human data (rows). Superscript numbers indicate which publication the results are taken from (1: Sayim, Westheimer & Herzog [17]; 2: Manassi et al. [11]; 3: Manassi, Sayim & Herzog [19]; 4: Manassi et al. [15]). Red indicates that the model predicts crowding, green indicates uncrowding and gray indicates that we did not run the model on the stimulus. A perfect model would have only red in the left half of the table and only green in the right half. Only the LAMINART is capable of producing uncrowding consistently. Fourier and the Wilson-Cowan network produce uncrowding, but suffer from overfitting (see discussion). For these two models, we provide the results for the best parameters. For example, the Wilson and Cowan with different parameters can explain the lines category but then it cannot explain the squares and irregular1 categories.