Core knowledge
Abstract
Human cognition is founded, in part, on four systems for representing objects, actions, number, and space. It may be based, as well, on a fifth system for representing social partners. Each system has deep roots in human phylogeny and ontogeny, and it guides and shapes the mental lives of adults. Converging research on human infants, non‐human primates, children and adults in diverse cultures can aid both understanding of these systems and attempts to overcome their limits.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
All ARC-AGI-3 environments are strictly limited to Core Knowledge priors and seek to avoid similarities with existing games.
“To ensure the benchmark remains a test of innate reasoning rather than acquired knowledge, all environments are strictly limited to Core Knowledge priors (20), and seek to avoid similarities with existing games.”
From ARC-AGI-3: A New Challenge for Frontier Agentic Intelligence (2026) · cites this paper as [20]
Cites
A meta-analytic test of intergroup contact theory.
Pettigrew, T. F., & Tropp, L. R. (2006) · Journal of personality and social psychology · doi:10.1037/0022-3514.90.5.751 · Cited by 8,964 · see citing works
Cortical mechanisms of human imitation
Iacoboni, M., Woods, R. P., Brass, M., Bekkering, H., Mazziotta, J. C., & Rizzolatti, G. (1999) · Science · doi:10.1126/science.286.5449.2526 · Cited by 2,683 · see citing works
Origins of domain specificity: The evolution of functional organization
Cosmides, L., & Tooby, J. (1994) · Mapping the Mind · doi:10.1017/cbo9780511752902.005 · Cited by 849 · see citing works
The Development of Implicit Attitudes: Evidence of Race Evaluations From Ages 6 and 10 and Adulthood
Baron, A. S., & Banaji, M. R. (2006) · Psychological Science · doi:10.1111/j.1467-9280.2005.01664.x · Cited by 791 · see citing works
Motor and cognitive functions of the ventral premotor cortex
Rizzolatti, G., Fogassi, L., & Gallese, V. (2002) · Current Opinion in Neurobiology · doi:10.1016/s0959-4388(02)00308-2 · Cited by 592 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Building machines that learn and think like people
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017) · Behavioral and Brain Sciences · doi:10.1017/s0140525x16001837 · Cited by 2,550 · see citing works
Neuroscience-Inspired Artificial Intelligence
Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017) · Neuron · doi:10.1016/j.neuron.2017.06.011 · Cited by 1,557 · see citing works
Interactive Specialization: A domain-general framework for human functional brain development?
Johnson, M. H. (2011) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2010.07.003 · Cited by 722 · see citing works
Reinforcement learning, fast and slow
Botvinick, M., Ritter, S., Wang, J. X., Kurth-Nelson, Z., Blundell, C., & Hassabis, D. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.02.006 · Cited by 667 · see citing works
Observing the unexpected enhances infants’ learning and exploration
Stahl, A. E., & Feigenson, L. (2015) · Science · doi:10.1126/science.aaa3799 · Cited by 617 · see citing works
Atzil, S., Gao, W., Fradkin, I., & Barrett, L. F. (2018) · Nature Human Behaviour · doi:10.1038/s41562-018-0384-6 · Cited by 415 · see citing works
Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks
Hasson, U., Nastase, S. A., & Goldstein, A. (2020) · Neuron · doi:10.1016/j.neuron.2019.12.002 · Cited by 387 · see citing works
The debate over understanding in AI’s large language models
Mitchell, M., & Krakauer, D. C. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2215907120 · Cited by 340 · see citing works
Social cognition and the evolution of language: constructing cognitive phylogenies
Fitch, W. T., Huber, L., & Bugnyar, T. (2010) · Neuron · doi:10.1016/j.neuron.2010.03.011 · Cited by 264 · see citing works
Psychological reasoning in infancy
Baillargeon, R., Scott, R. M., & Bian, L. (2016) · Annual Review of Psychology · doi:10.1146/annurev-psych-010213-115033 · Cited by 263 · see citing works
Using neuroscience to develop artificial intelligence
Ullman, S. (2019) · Science · doi:10.1126/science.aau6595 · Cited by 231 · see citing works
Spontaneous Theory of Mind and Its Absence in Autism Spectrum Disorders
Senju, A. (2012) · The Neuroscientist · doi:10.1177/1073858410397208 · Cited by 219 · see citing works
Meta-learning in natural and artificial intelligence
Wang, J. X. (2021) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2021.01.002 · Cited by 171 · see citing works
Safron, A. (2020) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2020.00030 · Cited by 133 · see citing works
Abstraction and analogy‐making in artificial intelligence
Mitchell, M. (2021) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.14619 · Cited by 125 · see citing works
Roots of a social brain: Developmental models of emerging animacy-detection mechanisms
Rosa Salva, O., Mayer, U., & Vallortigara, G. (2015) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2014.12.015 · Cited by 123 · see citing works
Intuitive physics learning in a deep-learning model inspired by developmental psychology
Piloto, L. S., Weinstein, A., Battaglia, P., & Botvinick, M. (2022) · Nature Human Behaviour · doi:10.1038/s41562-022-01394-8 · Cited by 118 · see citing works
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 · see citing works
Hattan, C., Alexander, P. A., & Lupo, S. M. (2024) · Review of Educational Research · doi:10.3102/00346543221148478 · Cited by 90 · see citing works
Large Language Models Fail on Trivial Alterations to Theory-of-Mind Tasks
Ullman, T. (2023) · arXiv · doi:10.48550/arxiv.2302.08399 · Cited by 81 · see citing works
Preaching to the converted? From constructivism to neuroconstructivism
Karmiloff-Smith, A. (2009) · Child Development Perspectives · doi:10.1111/j.1750-8606.2009.00086.x · Cited by 76 · see citing works
From simple innate biases to complex visual concepts
Ullman, S., Harari, D., & Dorfman, N. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1207690109 · Cited by 77 · see citing works
The Language Network Is Recruited but Not Required for Nonverbal Event Semantics
Ivanova, A. A., Mineroff, Z., Zimmerer, V., Kanwisher, N., Varley, R., & Fedorenko, E. (2021) · Neurobiology of Language · doi:10.1162/nol_a_00030 · Cited by 65 · see citing works
The hippocampus and exploration: dynamically evolving behavior and neural representations
Johnson, A., Varberg, Z., Benhardus, J., Maahs, A., & Schrater, P. (2012) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2012.00216 · Cited by 61 · see citing works
Perception of object persistence: the origins of object permanence in infancy
Bremner, J. G., Slater, A. M., & Johnson, S. P. (2015) · Child Development Perspectives · doi:10.1111/cdep.12098 · Cited by 55 · see citing works
Unit Testing for Concepts in Neural Networks
Lovering, C., & Pavlick, E. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00514 · Cited by 12 · see citing works
ARC-AGI-3: A New Challenge for Frontier Agentic Intelligence
Foundation, A. P. (2026) · arXiv · doi:10.48550/arxiv.2603.24621 · Cited by 10 · see citing works
Intuitive physics understanding emerges from self-supervised pretraining on natural videos
Garrido, Q., Ballas, N., Assran, M., Bardes, A., Najman, L., Rabbat, M., Dupoux, E., & LeCun, Y. (2025) · arXiv · doi:10.48550/arxiv.2502.11831 · Cited by 3 · see citing works
The ARC of Progress towards AGI: A Living Survey of Abstraction and Reasoning
Vahdati, S., Aioanei, A., Suresh, H., & Lehmann, J. (2026) · arXiv · doi:10.48550/arxiv.2603.13372
- Authors
- Spelke, E. S., & Kinzler, K. D.
- Affiliations
- Harvard University (US)
- Corresponding author
- Spelke, Elizabeth S.
- Venue
- Developmental Science
- Publisher
- Wiley
- Published
- 2006-12-20
- Volume
- 10
- Issue
- 1
- Pages
- 89-96
- ISSN
- 1363-755X (ISSN-L)
- 1467-7687
- OpenAlex
- W4251009746
- Cited by
- 1,089
- References
- 5
- Citations by year
- 2026: 95 · 2025: 62 · 2024: 62 · 2023: 90 · 2022: 86 · 2021: 82 · 2020: 93 · 2019: 86 · 2018: 59 · 2017: 58 · 2016: 46 · 2015: 38 · 2014: 36 · 2013: 38 · 2012: 30
- FWCI
- 3.64
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Developmental Science (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- Goddard Space Flight Center
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (HD23103)
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Child and Animal Learning Development
- Physical Sciences › Mathematics › Statistics and Probability › Cognitive and developmental aspects of mathematical skills
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Categorization, perception, and language
- Keywords
- core knowledge, object representation, action representation, number representation, spatial representation, social cognition, infant cognition, non-human primates, cross-cultural cognition, cognitive development
- MeSH terms
- Animals, Cognition, Concept Formation, Humans, Models, Psychological, Knowledge
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
