A feedforward mechanism for human-like contour integration
Fig 6
Fine-tuning a purely feedforward network reveals a capacity for visual uncrowding.
(A) A schematic of the uncrowding phenomenon. Identifying the offset of a vernier target is easy when presented in isolation (baseline), becomes difficult when surrounded by a single flanker (crowding), and becomes easier again as more identical flankers are added to the configuration (uncrowding). (B) The out-of-distribution training and testing paradigm. Models were trained on non-overlapping stimuli, where the vernier and flanker configurations appeared in the same image but were spatially separate and tested on overlapping (crowded) stimuli, where the vernier was centered within the flanker configuration. (C) Performance of a VGG19 architecture with a frozen, pretrained backbone. While the model can identify the vernier in isolation, its accuracy drops to chance level for all crowded conditions, regardless of the number of flankers. The model fails to exhibit uncrowding, consistent with prior reports on the limits of pretrained feedforward architectures. (D) Performance of a VGG19 network with a fine-tuned backbone. Accuracy is high for the isolated vernier, drops with a single flanker, and then systematically increases as more flankers are added. The results shown are from the model with the clearest emergent uncrowding.