Structured statistical models of inductive reasoning
Open accessAbstract
Everyday inductive inferences are often guided by rich background knowledge. Formal models of induction should aim to incorporate this knowledge and should explain how different kinds of knowledge lead to the distinctive patterns of reasoning found in different inductive contexts. This article presents a Bayesian framework that attempts to meet both goals and describes [corrected] 4 applications of the framework: a taxonomic model, a spatial model, a threshold model, and a causal model. Each model makes probabilistic inferences about the extensions of novel properties, but the priors for the 4 models are defined over different kinds of structures that capture different relationships between the categories in a domain. The framework therefore shows how statistical inference can operate over structured background knowledge, and the authors argue that this interaction between structure and statistics is critical for explaining the power and flexibility of human reasoning.
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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 [8]
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- Authors
- Kemp, C., & Tenenbaum, J. B.
- Affiliations
- Carnegie Mellon University (US)
- Massachusetts Institute of Technology (US)
- Venue
- Psychological Review
- Published
- 2009
- Volume
- 116
- Issue
- 1
- Pages
- 20-58
- ISSN
- 0033-295X (ISSN-L)
- 1939-1471
- OpenAlex
- W1994114516
- Cited by
- 259
- References
- 103
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 19 · 2023: 12 · 2022: 11 · 2021: 7 · 2020: 14 · 2019: 17 · 2018: 15 · 2017: 11 · 2016: 13 · 2015: 18 · 2014: 20 · 2013: 13 · 2012: 24
- FWCI
- 29.15
- Type
- journal article
- Language
- en
- Update notices
- correction (2009-04-06)
- Open access
- green
- figshare.com
- Licence
- cc-by
- Locations
- Psychological Review (publishedVersion)
- PubMed (publishedVersion)
- KiltHub Repository (submittedVersion)
- Figshare (submittedVersion, cc-by)
- KiltHub Repository
- Funders
- US Air Force Office of Scientific Research
- William Asbjornsen Albert Memorial Fellowship
- Paul E. Newton Chair
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Categorization, perception, and language
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Keywords
- inductive reasoning, prior knowledge, structured priors, taxonomic model, spatial modeling, threshold model, causal models, probabilistic inference, category structure, human reasoning
- MeSH terms
- Association Learning, Bayes Theorem, Concept Formation, Decision Making, Humans, Judgment, Problem Solving, Psychological Theory, Mental Recall, Models, Statistical, Neural Networks, Computer, 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.
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