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Cited by 81Memory and Neural MechanismsNeural and Behavioral Psychology StudiesBayesian Modeling and Causal Inference

Explaining compound generalization in associative and causal learning through rational principles of dimensional generalization

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Soto, Fabian A., Gershman, Samuel J., Niv, Yael·2014·Psychological Review
DOI 10.1037/a0037018PMC PMC4165620
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Abstract

How do we apply learning from one situation to a similar, but not identical, situation? The principles governing the extent to which animals and humans generalize what they have learned about certain stimuli to novel compounds containing those stimuli vary depending on a number of factors. Perhaps the best studied among these factors is the type of stimuli used to generate compounds. One prominent hypothesis is that different generalization principles apply depending on whether the stimuli in a compound are similar or dissimilar to each other. However, the results of many experiments cannot be explained by this hypothesis. Here, we propose a rational Bayesian theory of compound generalization that uses the notion of consequential regions, first developed in the context of rational theories of multidimensional generalization, to explain the effects of stimulus factors on compound generalization. The model explains a large number of results from the compound generalization literature, including the influence of stimulus modality and spatial contiguity on the summation effect, the lack of influence of stimulus factors on summation with a recovered inhibitor, the effect of spatial position of stimuli on the blocking effect, the asymmetrical generalization decrement in overshadowing and external inhibition, and the conditions leading to a reliable external inhibition effect. By integrating rational theories of compound and dimensional generalization, our model provides the first comprehensive computational account of the effects of stimulus factors on compound generalization, including spatial and temporal contiguity between components, which have posed long-standing problems for rational theories of associative and causal learning.

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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 [45]

  • 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 [45]

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Research works printed but not linked: 6

  1. Ashby FG Soto FA Busemeyer JR Townsend JT Wang ZJ Eidels A Multidimensional signal detection theory. Oxford handbook of computational and mathematical psychology Oxford University Press in press
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  3. Courville AC A latent cause theory of classical conditioning. Unpublished doctoral dissertation 2006 Carnegie Mellon University
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Authors
Soto, F. A., Gershman, S. J., & Niv, Y.
Affiliations
  • Massachusetts Institute of Technology (US)
  • Princeton University (US)
ORCID
Venue
Psychological Review
Publisher
American Psychological Association (APA)
Published
2014
Volume
121
Issue
3
Pages
526-558
ISSN
  • 0033-295X (ISSN-L)
  • 1939-1471
DOI
10.1037/a0037018
PMC
PMC4165620
OpenAlex
W2096288475
Cited by
81
References
136
Citations by year
2026: 2 · 2025: 6 · 2024: 3 · 2023: 4 · 2022: 4 · 2021: 10 · 2020: 4 · 2019: 14 · 2018: 14 · 2017: 7 · 2016: 6 · 2015: 7
FWCI
3.72
Type
journal article
Language
en
Open access
Locations
  • Psychological Review (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • Sponsor name not included
  • Sage Center
  • National Science Foundation
  • National Institute of Mental Health (R01MH098861)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
  • Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
Keywords
associative learning, causal learning, stimulus factors, spatial contiguity, temporal contiguity, overshadowing, external inhibition, blocking, stimulus modality
MeSH terms
Association Learning, Bayes Theorem, Generalization, Psychological, Humans, Models, Psychological

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