Abstract
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.