Exploring a latent cause theory of classical conditioning
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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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When prediction errors are large, the present hidden cause is treated as obsolete, and another cause-new or previously stored-must be selected.
“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]
Latent cause inference theory proposes that individuals store many statistical models of the world and determine which unseen cause best explains what they currently sense.
“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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- 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
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