ArticleNature human behaviour2024
Perception of 3D shape integrates intuitive physics and analysis-by-synthesis.
Article in Nature human behaviour, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- Multiarea processing in body patches of the primate inferotemporal cortex implements inverse graphics.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Computational models reveal that intuitive physics underlies visual processing of soft objects.Nature communications · 2025Article
- The brain computes dynamic facial movements for emotion categorization using a third pathway.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Human shape perception spontaneously discovers the biological origin of novel, but natural, stimuli.Journal of the Royal Society, Interface · 2025Article
- How does the primate brain combine generative and discriminative computations in vision?ArXiv · 2024Article
- Tangled Physics: Knots Strain Intuitive Physical Reasoning.Open mind : discoveries in cognitive science · 2024Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
Abstract
Many surface cues support three-dimensional shape perception, but humans can sometimes still see shape when these features are missing-such as when an object is covered with a draped cloth. Here we propose a framework for three-dimensional shape perception that explains perception in both typical and atypical cases as analysis-by-synthesis, or inference in a generative model of image formation. The model integrates intuitive physics to explain how shape can be inferred from the deformations it causes to other objects, as in cloth draping. Behavioural and computational studies comparing this account with several alternatives show that it best matches human observers (total n = 174) in both accuracy and response times, and is the only model that correlates significantly with human performance on difficult discriminations. We suggest that bottom-up deep neural network models are not fully adequate accounts of human shape perception, and point to how machine vision systems might achieve more human-like robustness.
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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.