Beyond Bouma's window: How to explain global aspects of crowding?
Fig 12
Hierarchical Sparse Selection model.
a. The model posits that receptive fields along the visual hierarchy are large and dense. This allows for “lossless” transmission of information through the visual system. For instance, the offset of the vernier in this illustration is not corrupted by pooling thanks to the density of the receptive fields (blue and red circles). Crowding occurs because, when we try to access information, only a few sparse receptive fields are used for readout (red circles). Hence, crowding occurs at readout because of sparse sampling of receptive fields. This sparse readout can occur at any stage of visual processing, from low-level features (shown here) to faces. b. Uncrowding does not occur in the Hierarchical Sparse Selection model because performance is worse for the model on the 7 squares than the 1 square condition, contrary to human performance. NB. This model only produces a scalar output, there is no output image.