Beyond Bouma's window: How to explain global aspects of crowding?
Fig 11
a. Stimuli consisted of either verniers, verniers surrounded by a single square or verniers with seven squares. The stimuli had varying sizes, vernier offsets and positions. Alexnet’s architecture and a classifier are shown on the right (there was a classifier at each layer). The boxes correspond to the input (leftmost box) and activated neuron layers (see [49] for the detailed architecture of Alexnet). We trained softmax classifiers on all ReLU layers following the convolution layers and the last fully connected layer to detect vernier orientation from the layer’s activity. b. Accuracy of softmax classifiers trained to detect vernier orientation from different layers in the deep neural network Alexnet. Across all layers, the offsets in crowded stimuli (1 square flanker) are always better detected than offsets in uncrowded stimuli (7 square flankers). This runs contrary to human performance. NB. This model only produces percent correct, there is no output image.