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Exploring a latent cause theory of classical conditioning
Open accessAbstract
We frame behavior in classical conditioning experiments as the product of normative statistical inference. According to this theory, animals learn an internal model of their environment from experience. The basic building blocks of this internal model are latent causes-explanatory constructs inferred by the animal that partition observations into coherent clusters. Generalization of conditioned responding from one cue to another arises from the animal's inference that the cues were generated by the same latent cause. Through a wide range of simulations, we demonstrate where the theory succeeds and where it fails as a general account of classical conditioning.
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“Large prediction errors signal that the current latent cause is no longer relevant (i.e., the situation has changed) and a new or stored latent cause should be inferred.”
From Does mental context drift or shift? (2017) · cites this paper as [46]
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“According to a recent theory of latent cause inference, people build up a library of statistical models of their environment, and then infer which is the hidden, or “latent,” cause of their current sensory observations [43–46].”
From Does mental context drift or shift? (2017) · cites this paper as [46]
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Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003) · doi:10.1201/9780429258480 · Cited by 27,276
Advances in neural information processing systems 7
Cios, K. J., & Shields, M. E. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)90018-7 · Cited by 22,093
Advances in Neural Information Processing Systems 19
(2007) · The MIT Press eBooks · doi:10.52202/085713 · Cited by 16,043
Advances in Neural Information Processing Systems 14
(2002) · The MIT Press eBooks · doi:10.7551/mitpress/1120.001.0001 · Cited by 9,187
Rescorla, R. A. (1972) · Medical Entomology and Zoology · Cited by 7,268
Cognitive maps in rats and men
Tolman, E. C. (1948) · Psychological Review · doi:10.1037/h0061626 · Cited by 6,641
Marr, D. (1982) · Cumulative Index of Computer Aided Architectural Design · Cited by 5,733
Barlow, H. B. (1983) · Journal of Mathematical Psychology · doi:10.1016/0022-2496(83)90030-5 · Cited by 5,506
Classical conditioning II: Current research and theory.
Black, A. H., & Prokasy, W. F. (1972) · Appleton-Century-Crofts eBooks · Cited by 4,449
Pearce, J. M., & Hall, G. (1980) · Psychological Review · doi:10.1037/0033-295x.87.6.532 · Cited by 3,023
Research works printed but not linked: 1
- Pavlov, I. P. (1927). Conditioned reflexes (G. V. Anrep, Trans). London, UK: Oxford University Press.
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Metadata
- Authors
- Gershman, S. J., & Niv, Y.
- Affiliations
- Princeton University (US)
- Corresponding author
- Gershman, Samuel J.
- Venue
- Learning & Behavior
- Published
- 2012-08-29
- Volume
- 40
- Issue
- 3
- Pages
- 255-268
- ISSN
- 1543-4494 (ISSN-L)
- 1543-4508
- OpenAlex
- W2155479528
- Cited by
- 149
- References
- 74
- Citations by year
- 2026: 9 · 2025: 10 · 2024: 8 · 2023: 11 · 2022: 15 · 2021: 15 · 2020: 14 · 2019: 10 · 2018: 9 · 2017: 11 · 2016: 11 · 2015: 11 · 2014: 9 · 2013: 6
- FWCI
- 4.26
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Learning & Behavior (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Social Sciences › Psychology › Developmental and Educational Psychology › Child and Animal Learning Development
- Keywords
- classical conditioning, animal learning, Bayesian inference
- MeSH terms
- Animals, Bayes Theorem, Computer Simulation, Conditioning, Classical, Models, Psychological, Psychological Theory
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 ›
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