High-level aftereffects reveal the role of statistical features in visual shape encoding.

Visual shape perception is central to many everyday tasks, from object recognition to grasping and handling tools. 1,2,3,4,5,6,7,8,9,10 Yet how shape is encoded in the visual system remains poorly understood. Here, we probed shape representations using visual aftereffects—perceptual distortions that occur following extended exposure to a stimulus. 11,12,13,14,15,16,17 Such effects are thought to be caused by adaptation in neural populations that encode both simple, low-level stimulus characteristics 17,18,19,20 and more abstract, high-level object features. 21,22,23 To tease these two contributions apart, we used machine-learning methods to synthesize novel shapes in a multidimensional shape space, derived from a large database of natural shapes. 24 Stimuli were carefully selected such that low-level and high-level adaptation models made distinct predictions about the shapes that observers would perceive following adaptation. We found that adaptation along vector trajectories in the high-level shape space predicted shape aftereffects better than simple low-level processes. Our findings reveal the central role of high-level statistical features in the visual representation of shape. The findings also hint that human vision is attuned to the distribution of shapes experienced in the natural environment. • Prolonged viewing of shapes makes subsequent stimuli appear systematically distorted • Models of low-level visual processes cannot predict these aftereffects • A high-level model derived from statistical shape features can Morgenstern et al. probe shape representations in the human visual system by comparing perceptual aftereffects with computational models. A high-level model, based on the statistical features of natural shapes, outperforms low-level models, suggesting that human vision is attuned to the distribution of shapes in the natural environment. [ABSTRACT FROM AUTHOR]