Learning about causes from people: observational causal learning in 24-month-old infants
Open accessAbstract
How do infants and young children learn about the causal structure of the world around them? In 4 experiments we investigate whether young children initially give special weight to the outcomes of goal-directed interventions they see others perform and use this to distinguish correlations from genuine causal relations--observational causal learning. In a new 2-choice procedure, 2- to 4-year-old children saw 2 identical objects (potential causes). Activation of 1 but not the other triggered a spatially remote effect. Children systematically intervened on the causal object and predictively looked to the effect. Results fell to chance when the cause and effect were temporally reversed, so that the events were merely associated but not causally related. The youngest children (24- to 36-month-olds) were more likely to make causal inferences when covariations were the outcome of human interventions than when they were not. Observational causal learning may be a fundamental learning mechanism that enables infants to abstract the causal structure of the world.
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- Authors
- Meltzoff, A. N., Waismeyer, A., & Gopnik, A.
- Affiliations
- University of Washington (US)
- University of California, Berkeley (US)
- ORCID
- Meltzoff, Andrew N.
- Venue
- Developmental Psychology
- Published
- 2012
- Volume
- 48
- Issue
- 5
- Pages
- 1215-1228
- ISSN
- 0012-1649 (ISSN-L)
- 1939-0599
- PMC
- PMC3649070
- OpenAlex
- W2155678792
- Cited by
- 107
- References
- 58
- Citations by year
- 2026: 7 · 2025: 4 · 2024: 17 · 2023: 5 · 2022: 6 · 2021: 8 · 2020: 8 · 2019: 5 · 2018: 7 · 2017: 6 · 2016: 5 · 2015: 9 · 2014: 9 · 2013: 7 · 2012: 4
- FWCI
- 18.68
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Developmental Psychology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- James S. McDonnell Foundation
- National Institutes of Health (HD-22514)
- National Science Foundation (OMA-0835854)
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Child and Animal Learning Development
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Social Sciences › Psychology › Developmental and Educational Psychology › Language Development and Disorders
- Keywords
- causal inference, infants, young children, correlation versus causation, time reversal, causal structure, social learning, covariation
- MeSH terms
- Association Learning, Child Development, Child, Preschool, Choice Behavior, Concept Formation, Female, Humans, Male, Photic Stimulation, Causality, Observation
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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.
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