Predictive coding yields a brain-like code for shape from motion
Year: 2026
Type: preprint
Abstract: Interacting with the world relies on knowing its three-dimensional structure, yet vision receives a stream of two-dimensional images. Recovering 3D geometry from dynamic input is therefore foundational to vision, but lacks a computational account. Here we identify one, using artificial neural networks to predict human behavior and neuronal responses from the two major visual pathways in macaques. Using videos of camouflaged objects whose shape is revealed through motion, we found both pathways carry information for motion-defined surfaces, while neurons in parietal cortex most closely track what humans perceive. Only predictive coding video models—trained to fill in missing content in natural videos—reproduced both neuronal responses and human behavior. Predictive coding thus emerges as a principle that yields a brain-like geometric world model of the physical world.
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7161563882