Human gloss perception reproduced by tiny neural networks

Journal article

A key goal of visual neuroscience is to explain how our brains infer object properties like colour, curvature, or gloss. Here, we used machine learning to identify computations underlying human gloss judgments—traditionally considered a challenging inference. We rendered thousands of objects with varied shapes using a Ward reflectance model across lighting and viewpoints, then obtained gloss ratings for each image. Observers’ judgments were consistent with one another, yet systematically deviated from reality. We compared these ratings with neural networks trained either to estimate physical reflectance (“ground-truth networks”) or to reproduce human judgments (“human-like networks”). While estimating physical reflectance required deep networks, shallow networks accurately replicated human judgments. Remarkably, even a single-filter network could predict human judgments better than the best ground-truth network and generalized to known gloss illusions. These results suggest gloss perception relies on simple general-purpose computations, and demonstrate the power of interpretable ‘tiny‘ networks in understanding cognition.

Authors: Morimoto, T; Akbarinia, A; Storrs, KR; Smithson, HE

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Springer Nature
• Journal: Nature Human Behaviour
• Publication date: 2026-05-12
• Acceptance date: 2026-03-13
• DOI: 10.1038/s41562-026-02445-0
• EISSN: 2397-3374
• Language: English
• Keywords: human vision; material perception; tiny neural networks; online experiment