ArticleNature human behaviour2022
Intuitive physics learning in a deep-learning model inspired by developmental psychology.
Article in Nature human behaviour, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Perceptual and conceptual novelty independently guide infant looking behaviour: a systematic review and meta-analysis.Nature human behaviour · 2024Pooled it
- Material fictions: Comparing physically based renderings and generative AI images through material perception.Journal of vision · 2026Article
- Bottom-up and generative computations uniquely explain neural responses across the social brain.bioRxiv : the preprint server for biology · 2026Article
- Computational basis of hierarchical and counterfactual information processing.Nature human behaviour · 2025Article
- Integrating physics and topology in neural networks for learning rigid body dynamics.Nature communications · 2025Article
- Computational models reveal that intuitive physics underlies visual processing of soft objects.Nature communications · 2025Article
- The Animal-AI Environment: A virtual laboratory for comparative cognition and artificial intelligence research.Behavior research methods · 2025Article
- Article
- Mutual human-robot understanding for a robot-enhanced society: the crucial development of shared embodied cognition.Frontiers in artificial intelligence · 2025Review
- The role of action concepts in physical reasoning: insights from late childhood.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
- Article
- Cognitive modelling of concepts in the mental lexicon with multilayer networks: Insights, advancements, and future challenges.Psychonomic bulletin & review · 2024Review
- Perception of 3D shape integrates intuitive physics and analysis-by-synthesis.Nature human behaviour · 2024Article
- Object geometry serves humans' intuitive physics of stability.Scientific reports · 2024Article
- Electrophysiology Reveals That Intuitive Physics Guides Visual Tracking and Working Memory.Open mind : discoveries in cognitive science · 2024Article
- 3 = 1 + 2: how the divide conquered de novo protein structure prediction and what is next?National science review · 2023Article
- Grounding Intuitive Physics in Perceptual Experience.Journal of Intelligence · 2023Review
- Machine learning for cross-scale microscopy of viruses.Cell reports methods · 2023Review
- Large Language Models and the Reverse Turing Test.Neural computation · 2023Article
- A rubric for human-like agents and NeuroAI.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Review
Corrections and comments
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- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
'Intuitive physics' enables our pragmatic engagement with the physical world and forms a key component of 'common sense' aspects of thought. Current artificial intelligence systems pale in their understanding of intuitive physics, in comparison to even very young children. Here we address this gap between humans and machines by drawing on the field of developmental psychology. First, we introduce and open-source a machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology. Second, we build a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children. We demonstrate that our model can learn a diverse set of physical concepts, which depends critically on object-level representations, consistent with findings from developmental psychology. We consider the implications of these results both for AI and for research on human cognition.
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Registered trials
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