Simulation as an engine of physical scene understanding
Open accessAbstract
In a glance, we can perceive whether a stack of dishes will topple, a branch will support a child's weight, a grocery bag is poorly packed and liable to tear or crush its contents, or a tool is firmly attached to a table or free to be lifted. Such rapid physical inferences are central to how people interact with the world and with each other, yet their computational underpinnings are poorly understood. We propose a model based on an "intuitive physics engine," a cognitive mechanism similar to computer engines that simulate rich physics in video games and graphics, but that uses approximate, probabilistic simulations to make robust and fast inferences in complex natural scenes where crucial information is unobserved. This single model fits data from five distinct psychophysical tasks, captures several illusions and biases, and explains core aspects of human mental models and common-sense reasoning that are instrumental to how humans understand their everyday world.
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Generative programs have been proposed in the context of scene understanding.
“Generative programs have also been proposed in the context of scene understanding (Battaglia et al., 2013).”
From Toward an integration of deep learning and neuroscience (2016) · cites this paper as Battaglia et al., 2013
Bayesian models of cognition have primarily flourished as explanations of the complex dependency of behavior on the environment in domains including intuitive physics, causal learning, perception, motor control, and language.
“Bayesian models of cognition have primarily flourished as explanations of the complex dependency of behavior on the environment in domains including intuitive physics, causal learning, perception, motor control, and language [1-10]”
From Bayesian Brains without Probabilities (2016) · cites this paper as [1]
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- Authors
- Battaglia, P. W., Hamrick, J. B., & Tenenbaum, J. B.
- Affiliations
- Massachusetts Institute of Technology (US)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2013-10-21
- Volume
- 110
- Issue
- 45
- Pages
- 18327-18332
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC3831455
- OpenAlex
- W2059100041
- Cited by
- 681
- References
- 34
- Citations by year
- 2026: 62 · 2025: 56 · 2024: 56 · 2023: 49 · 2022: 49 · 2021: 78 · 2020: 73 · 2019: 66 · 2018: 64 · 2017: 36 · 2016: 47 · 2015: 29 · 2014: 13 · 2013: 2 · 2012: 1
- FWCI
- 16.75
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Funders
- National Eye Institute (F32EY019228)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Social Sciences › Psychology › Developmental and Educational Psychology › Child and Animal Learning Development
- Keywords
- intuitive physics, stochastic simulation, commonsense reasoning, mental models, psychophysics, natural scene perception, computational model of cognition
- MeSH terms
- Bayes Theorem, Cognition, Humans, Imagination, Judgment, Models, Psychological
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