Kavli Affiliate: George A. Alvarez
| Authors: Srijani Saha, Talia Konkle and George A. Alvarez
| Summary:
The human visual system transforms patterns of light into rich perceptual experiences, where what we see is a construction that goes beyond simple measurement. Lightness illusions—where identical parts of an image can appear dramatically different depending on context—provide a window into these processes. Here we leverage a deep learning framework to investigate the constructive processes that give rise to lightness illusions, introducing the core computational goal of edge-based image reconstruction. Specifically, we demonstrate that autoencoder models trained to reconstruct natural images based only on an edge-based image representation naturally recapitulate a wide range of lightness illusions, which were previously assumed to require distinct mechanisms, inference over lighting sources, and explicit three-dimensional scene representation. These results offer a simpler, unified account of diverse lightness phenomena as emerging naturally from surface filling-in mechanisms, and broadly provide a framework for understanding the computational principles that underlie our perception of the visual world.