Computational origins of shape perception
Fig 4
Controlled-rearing experiments on generic fitting models.
(a) In the ‘dense exploration’ condition, the agent freely moves around the virtual controlled-rearing chamber, densely sampling the visual experiences available in the chamber with head movements. (b) In the ‘shuffled images’ condition, we randomized the order of the simulated training images to test the importance of temporal continuity on the development of shape-based vision. (c) Untrained generic fitting models had color-based representational spaces, as shown in the RDMs (top) and color/shape scores (bottom). (d) Generic fitting models trained in the dense exploration condition developed robust shape perception. The one exception was the smallest (1H) model, which failed to develop shape perception. (e) Generic fitting models trained in the shuffled images condition also failed to develop shape perception, highlighting the importance of temporal continuity in shape learning. For all RDMs, the images used to make the RDMs were the same as those used in Fig 1. Error bars denote standard error for each model across the color cells and shape cells shown in Fig 1b, c.