The statistics of natural shapes predict high-level aftereffects in human vision
Morgenstern, Y.; Storrs, K. R.; Schmidt, F.; Hartmann, F.; Tiedemann, H.; Wagemans, J.; Fleming, R.
Show abstract
Shape perception is essential for numerous everyday behaviors from object recognition to grasping and handling objects. Yet how the brain encodes shape remains poorly understood. Here, we probed shape representations using visual aftereffects--perceptual distortions that occur following extended exposure to a stimulus--to resolve a long-standing debate about shape encoding. We implemented contrasting low-level and high-level computational models of neural adaptation, which made precise and distinct predictions about the illusory shape distortions the observers experience following adaptation. Directly pitting the predictions of the two models against one another revealed that the perceptual distortions are driven by high-level shape attributes derived from the statistics of natural shapes. Our findings suggest that the diverse shape attributes thought to underlie shape encoding (e.g., curvature distributions, skeletons, aspect ratio) are the result of a visual system that learns to encode natural shape geometries based on observing many objects.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- One shot generalization in humans revealed through a drawing task 96%
- A Bayesian and efficient observer model explains concurrent attractive and repulsive history biases in visual perception 95%
- Unsupervised changes in core object recognition behavior are predicted by unsupervised neural plasticity in inferior temporal cortex 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.