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Rational variability in children's causal inferences: the sampling hypothesis
Published version of a preprint
Abstract
We present a proposal-"The Sampling Hypothesis"-suggesting that the variability in young children's responses may be part of a rational strategy for inductive inference. In particular, we argue that young learners may be randomly sampling from the set of possible hypotheses that explain the observed data, producing different hypotheses with frequencies that reflect their subjective probability. We test the Sampling Hypothesis with four experiments on 4- and 5-year-olds. In these experiments, children saw a distribution of colored blocks and an event involving one of these blocks. In the first experiment, one block fell randomly and invisibly into a machine, and children made multiple guesses about the color of the block, either immediately or after a 1-week delay. The distribution of guesses was consistent with the distribution of block colors, and the dependence between guesses decreased as a function of the time between guesses. In Experiments 2 and 3 the probability of different colors was systematically varied by condition. Preschoolers' guesses tracked the probabilities of the colors, as should be the case if they are sampling from the set of possible explanatory hypotheses. Experiment 4 used a more complicated two-step process to randomly select a block and found that the distribution of children's guesses matched the probabilities resulting from this process rather than the overall frequency of different colors. This suggests that the children's probability matching reflects sophisticated probabilistic inferences and is not merely the result of a naïve tabulation of frequencies. Taken together the four experiments provide support for the Sampling Hypothesis, and the idea that there may be a rational explanation for the variability of children's responses in domains like causal inference.
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“Bayesian samplers can match behavioral stochasticity in tasks such as perception, categorization, causal reasoning, and decision making [20,21,23,24].”
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Metadata
- Authors
- Denison, S., Bonawitz, E., Gopnik, A., & Griffiths, T. L.
- Affiliations
- University of Waterloo (CA)
- University of California, Berkeley (US)
- Corresponding author
- Denison, Stephanie
- Venue
- Cognition
- Publisher
- Elsevier BV
- Published
- 2013-02
- Volume
- 126
- Issue
- 2
- Pages
- 285-300
- ISSN
- 0010-0277 (ISSN-L)
- OpenAlex
- W2120212072
- Cited by
- 126
- References
- 61
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 15 · 2023: 9 · 2022: 8 · 2021: 6 · 2020: 11 · 2019: 9 · 2018: 15 · 2017: 16 · 2016: 7 · 2015: 12 · 2014: 7 · 2013: 2
- FWCI
- 13.46
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Cognition (publishedVersion)
- PubMed (publishedVersion)
- Rutgers University Community Repository (Rutgers University) (submittedVersion)
- UWA Profiles and Research Repository (UWA) (submittedVersion)
- 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 › Experimental and Cognitive Psychology › Categorization, perception, and language
- Keywords
- causal inference, inductive reasoning, probability matching, preschool children, probabilistic reasoning, response variability, random sampling, subjective probability
- MeSH terms
- Child Development, Child, Preschool, Cognition, Concept Formation, Female, Humans, Male, Probability Learning, Problem Solving
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