Computational origins of shape perception
Fig 10
(a) Realistic artificial retinas convert RGB images into retinal-formatted images, akin to the transformations performed by biological retinas. For this experiment, we used fovea sizes of 15 and 30, 99% of cones in fovea and 1% of cones in periphery, 1% of rods in fovea and 99% of rods in periphery, larger receptive fields in the periphery versus fovea, visual crowding in the periphery, and a dynamic fovea that moved to salient regions across successive images. (b) We tested the untrained and trained models across the three stimuli sets. (c) Untrained models were color-based, whereas trained models (d-e) developed forms of shape perception. Error bars denote standard error for each model across the color cells and shape cells shown in Fig 1b, c.