Learning Perceptual Rules from a Fragmented Visual World

Visual perception organizes incomplete evidence into coherent contours and shapes. Gestalt principles describe regularities in this organization, but how might such expectations be acquired from visual experience? We investigate whether masked image reconstruction supports learned structural priors that guide predictions beyond local interpolation. Using controlled stimuli, we find that masked autoencoders (MAEs) trained on natural images exhibit contour continuation, symmetry-dependent reconstruction, and boundary completion in Kanizsa configurations. Changing distant elements alters the predicted content even when the visible neighborhood of the missing region remains fixed, showing that completion depends on broader spatial relationships. We then test whether visual experience changes the balance between local and global completion by continually pretraining the same initial model on natural images or matched texforms that disrupt global organization while retaining selected texture and coarse-form information. Texform adaptation weakens boundary completion and shifts reconstruction toward local continuation when local contour geometry conflicts with global symmetry, including a reversal of the average global preference for cross stimuli. These findings support an account of perceptual organization in which regularities learned from visual experience constrain predictions about missing content. The organizing tendencies expressed in reconstruction are sensitive both to the surrounding configuration and to the structural statistics encountered during learning.