The DOI matches its Crossref record.
Intuitive Physics
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Constructing Scientific Knowledge in the Classroom
Driver, R., Asoko, H., Leach, J., Scott, P., & Mortimer, E. (1994) · Educational Researcher · doi:10.3102/0013189x023007005 · Cited by 2,016
The Essential Child: Origins of Essentialism in Everyday Thought
Gelman, S. A. (2003) · doi:10.1093/acprof:oso/9780195154061.001.0001 · Cited by 1,247
Spelke, E. S., & Kinzler, K. D. (2007) · Developmental Science · doi:10.1111/j.1467-7687.2007.00569.x · Cited by 1,089
Explainable machine learning for scientific insights and discoveries
Roscher, R., Bohn, B., Duarte, M. F., & Garcke, J. (2020) · IEEE Access · doi:10.1109/access.2020.2976199 · Cited by 1,040
“Intuitive physics are everyday-observed rules of nature that help us to predict the outcome of events even with a relatively untrained human perception [38].”
Gauch Jr, H. G. (2002) · doi:10.1017/cbo9780511815034 · Cited by 385
Inductive biases for deep learning of higher-level cognition
Goyal, A., & Bengio, Y. (2022) · Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rspa.2021.0068 · Cited by 300
How Robust Are Probabilistic Models of Higher-Level Cognition?
Marcus, G. F., & Davis, E. (2013) · Psychological Science · doi:10.1177/0956797613495418 · Cited by 195
Principles of evolutionary educational psychology
Geary, D. C. (2002) · Learning and Individual Differences · doi:10.1016/s1041-6080(02)00046-8 · Cited by 163
Saying the wrong thing: improving learning with multimedia by including misconceptions
Muller, D. A., Bewes, J., Sharma, M. D., & Reimann, P. (2008) · Journal of Computer Assisted Learning · doi:10.1111/j.1365-2729.2007.00248.x · Cited by 145
Evolution and Children’s Cognitive and Academic Development
Geary, D. C., & Berch, D. B. (2016) · Evolutionary Psychology · doi:10.1007/978-3-319-29986-0_9 · Cited by 101
Using Descriptive Drawings as a Conceptual Change Strategy in Elementary Science
Edens, K. M., & Potter, E. (2003) · School Science and Mathematics · doi:10.1111/j.1949-8594.2003.tb18230.x · Cited by 76
Valanides, N., & Angeli, C. (2008) · Journal of Research on Technology in Education · doi:10.1080/15391523.2008.10782510 · Cited by 40
The limits and potentials of deep learning for robotics
Sünderhauf, N., Brock, O., Scheirer, W., Hadsell, R., Fox, D., Leitner, J., Upcroft, B., Abbeel, P., Burgard, W., Milford, M., et al. (2018) · The International Journal of Robotics Research · doi:10.1177/0278364918770733 · Cited by 38
Dennett's realisation theory of the relation between folk and scientific psychology
Cussins, A. (1988) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00058659 · Cited by 4
Ivanova, A. A., Sathe, A., Lipkin, B., Kumar, U., Radkani, S., Clark, T. H., Kauf, C., Hu, J., Pramod, R. T., Grand, G., et al. (2024) · arXiv · doi:10.48550/arxiv.2405.09605 · Cited by 2
“…of knowledge domains that have been shown to recruit dedicated cognitive and/or neural machinery in humans, such as knowledge of intuitive physics (McCloskey, 1983; Battaglia et al., 2013), knowledge of physical and spatial relations (Hafri and Firestone, 2021), intuitive number sense (Dehaene,…”
Metadata
- Authors
- McCloskey, M.
- Venue
- Scientific American
- Published
- 1983-04
- Volume
- 248
- Issue
- 4
- Pages
- 122-130
- ISSN
- 0036-8733 (ISSN-L)
- OpenAlex
- W4247466550
- Cited by
- 583
- References
- 0
- FWCI
- 58.71
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
