ArticleNature human behaviour2021
Unsupervised learning predicts human perception and misperception of gloss.
Article in Nature human behaviour, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- Making models disagree to learn how brains compute.Nature reviews. Neuroscience · 2026Review
- Human gloss perception reproduced by tiny neural networks.Nature human behaviour · 2026Article
- Material discrimination relies on context-dependent active sensing strategies.Journal of vision · 2026Article
- Interaction of optical parameters in the perception of transparency/translucency and their neural representation in the visual cortex.Journal of vision · 2026Article
- A Taxonomy of Six Perceptual Cues Underlying Photorealism in 3D-Rendered Architectural Scenes: A Cue-Based Narrative Review.Journal of imaging · 2026Review
- Material fictions: Comparing physically based renderings and generative AI images through material perception.Journal of vision · 2026Article
- A deep convolutional neural network trained for lightness constancy is susceptible to lightness illusions.Journal of vision · 2026Article
- Deep neural networks trained for estimating reflectance and illumination achieve lightness constancy differently than human observers.Journal of vision · 2026Article
- Unsupervised learning-based optimization of college students' fundamental capacity evaluation method.Frontiers in physiology · 2026Article
- Human-like monocular depth biases in deep neural networks.PLoS computational biology · 2025Article
- Computational models reveal that intuitive physics underlies visual processing of soft objects.Nature communications · 2025Article
- Energy optimization induces predictive-coding properties in a multi-compartment spiking neural network model.PLoS computational biology · 2025Article
- Human shape perception spontaneously discovers the biological origin of novel, but natural, stimuli.Journal of the Royal Society, Interface · 2025Article
- Poisson Variational Autoencoder.ArXiv · 2024Article
- Parallel development of object recognition in newborn chicks and deep neural networks.PLoS computational biology · 2024Article
- Probing the link between vision and language in material perception using psychophysics and unsupervised learning.PLoS computational biology · 2024Article
- High Dynamic Range Image Reconstruction from Saturated Images of Metallic Objects.Journal of imaging · 2024Article
- Hierarchical VAEs provide a normative account of motion processing in the primate brain.bioRxiv : the preprint server for biology · 2023Article
- The Architecture of Object-Based Attention.Psychonomic bulletin & review · 2023Review
- Color and gloss constancy under diverse lighting environments.Journal of vision · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Reflectance, lighting and geometry combine in complex ways to create images. How do we disentangle these to perceive individual properties, such as surface glossiness? We suggest that brains disentangle properties by learning to model statistical structure in proximal images. To test this hypothesis, we trained unsupervised generative neural networks on renderings of glossy surfaces and compared their representations with human gloss judgements. The networks spontaneously cluster images according to distal properties such as reflectance and illumination, despite receiving no explicit information about these properties. Intriguingly, the resulting representations also predict the specific patterns of 'successes' and 'errors' in human perception. Linearly decoding specular reflectance from the model's internal code predicts human gloss perception better than ground truth, supervised networks or control models, and it predicts, on an image-by-image basis, illusions of gloss perception caused by interactions between material, shape and lighting. Unsupervised learning may underlie many perceptual dimensions in vision and beyond.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.