ReviewCurrent opinion in behavioral sciences2019
Learning to see stuff.
Review in Current opinion in behavioral sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
28 citing papers in PubMed.
- Speed constant material perception via touch relies on natural material statistics.Perception · 2026Article
- Gloss discrimination: Toward an image-based perceptual model.Journal of vision · 2025Article
- Orientation fields predict human perception of 3D shape from shading.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- A simple optical flow model explains why certain object viewpoints are special.Proceedings. Biological sciences · 2024Article
- The attentive reconstruction of objects facilitates robust object recognition.PLoS computational biology · 2024Article
- Head rotation and the perception of eyelid height and contour.BMJ open ophthalmology · 2024Article
- materialmodifier: An R package of photo editing effects for material perception research.Behavior research methods · 2024Article
- The Architecture of Object-Based Attention.Psychonomic bulletin & review · 2023Review
- A role for cortical interneurons as adversarial discriminators.PLoS computational biology · 2023Article
- Material category of visual objects computed from specular image structure.Nature human behaviour · 2023Article
- Unsupervised learning reveals interpretable latent representations for translucency perception.PLoS computational biology · 2023Article
- The case against probabilistic inference: a new deterministic theory of 3D visual processing.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Review
- New Approaches to 3D Vision.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Article
- The effects of distractors on brightness perception based on a spiking network.Scientific reports · 2023Article
- The representation of shape and texture in category-selective regions of ventral-temporal cortex.The European journal of neuroscience · 2022Article
- A neural network model of the effect of prior experience with regularities on subsequent category learning.Cognition · 2022Article
- Distinguishing mirror from glass: A "big data" approach to material perception.Journal of vision · 2022Article
- Article
- Unsupervised learning predicts human perception and misperception of gloss.Nature human behaviour · 2021Article
- Translucency perception: A review.Journal of vision · 2021Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Materials with complex appearances, like textiles and foodstuffs, pose challenges for conventional theories of vision. But recent advances in unsupervised deep learning provide a framework for explaining how we learn to see them. We suggest that perception does not involve estimating physical quantities like reflectance or lighting. Instead, representations emerge from learning to encode and predict the visual input as efficiently and accurately as possible. Neural networks can be trained to compress natural images or to predict frames in movies without 'ground truth' data about the outside world. Yet, to succeed, such systems may automatically discover how to disentangle distal causal factors. Such 'statistical appearance models' potentially provide a coherent explanation of both failures and successes in perception.
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Registered trials
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.