Modelling local and global explanations for shape aftereffects with naturalistic novel stimuli

Abstract

A widely-used psychophysical tool for inferring visual mechanisms is adaptation. Perceptual distortions known as aftereffects arise following extended visual exposure to a stimulus, including complex patterns and shapes. Some researchers have argued that shape aftereffects reveal adaptation of mechanisms sensitive to global shape properties, while others propose they can be explained by localized adaptation to simpler properties such as tilt. Here, we investigate methods to tease these hypotheses apart. Most previous works used simpler and/or familiar forms (e.g., radial frequency patterns, geometric shapes, faces). Here we use complex but naturalistic shapes synthesized by Generative Adversarial Networks (GANs) trained on >25,000 animal silhouettes. Drawing samples from the GAN’s latent space allows us to synthesize novel 2D shapes that transition smoothly between one another, and are complex yet systematically related. Observers adapted to individual shapes from this generative shape space, then judged the appearance of nearby shapes in a two-alternative forced-choice task. Their responses demonstrated robust and systematic perceptual distortions of shape using such stimuli. Indeed, a given shape could predictably be made to look like specific other shapes depending on which adaptor stimulus was used. To tease apart the relative role of local vs. global adaptation, we simulated the effects of two variants of tilt and positional aftereffects: the local model assumes aftereffects exaggerate differences between test and adaptor within localized image regions, while the global model assumes such distortions exaggerate differences between ‘corresponding’ parts of shapes, after completing high-level inferences regarding part-correspondence. Initial findings show that positional adaptation has a larger contribution than tilt adaptation to novel-shape aftereffects. We then show how to use the generative shape networks to synthesize tailored shape sets that can tease apart the predictions of local vs global models of adaptation. Our findings provide new methods for probing and modeling adaptation to complex visual stimuli.