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Cited by 210Bayesian Modeling and Causal InferenceLogic, Reasoning, and KnowledgePhilosophy and History of Science

Causality in thought

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Sloman, Steven A., Lagnado, David·2015·Annual Review of Psychology
DOI 10.1146/annurev-psych-010814-015135
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Abstract

Causal knowledge plays a crucial role in human thought, but the nature of causal representation and inference remains a puzzle. Can human causal inference be captured by relations of probabilistic dependency, or does it draw on richer forms of representation? This article explores this question by reviewing research in reasoning, decision making, various forms of judgment, and attribution. We endorse causal Bayesian networks as the best normative framework and as a productive guide to theory building. However, it is incomplete as an account of causal thinking. On the basis of a range of experimental work, we identify three hallmarks of causal reasoning—the role of mechanism, narrative, and mental simulation—all of which go beyond mere probabilistic knowledge. We propose that the hallmarks are closely related. Mental simulations are representations over time of mechanisms. When multiple actors are involved, these simulations are aggregated into narratives.

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  • There are further intuitive rules that humans adhere to.

    “Clearly, there are further intuitive rules that humans adhere to; consider, for example, causalrelevance rules, according to which only features that are causally relevant to the unobserved property should affect the decision (Sloman and Lagnado 2015;Ye et al. 2024).”

    From Explainable AI Isn't Enough! Rethinking Algorithmic Contestability (2026) · cites this paper as Sloman and Lagnado 2015

    In “On Mechanism” (page unavailable), the review says causal knowledge helps people infer unobserved features and reports that people use causal structure when some features are unknown. It does not establish the stricter rule that only features causally relevant to the unobserved property should affect a decision. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

  • An evidence-based cognitive model for human-AI interaction would allow designing the most effective human-AI collaboration protocols.

    “First and foremost, establishing an evidence-based cognitive model for human-AI interaction is imperative, as this would allow for designing the most effective human-AI collaboration protocols [68 , 97 , 98] .”

    From Enhancing human-AI collaboration: The case of colonoscopy (2024) · cites this paper as [98]

    The Introduction discusses models of human causal reasoning, and the Discussion considers limits of causal Bayes nets. Neither addresses human–AI interaction or whether an evidence-based cognitive model would enable more effective collaboration protocols. Searches for human–AI collaboration and cognitive models for its design found no relevant passage in our copy; page numbers were unavailable. (LLM, conf=0.75)

    Experimental — accuracy not yet measured

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Authors
Sloman, S. A., & Lagnado, D.
Affiliations
  • Brown University (US)
  • University College London (GB)
ORCID
Venue
Annual Review of Psychology
Publisher
Annual Reviews
Published
2015-01-03
Volume
66
Issue
1
Pages
223-247
ISSN
  • 0066-4308 (ISSN-L)
  • 1545-2085
DOI
10.1146/annurev-psych-010814-015135
OpenAlex
W2098627973
Cited by
210
References
13
Citations by year
2026: 18 · 2025: 25 · 2024: 30 · 2023: 20 · 2022: 26 · 2021: 10 · 2020: 20 · 2019: 6 · 2018: 20 · 2017: 14 · 2016: 14 · 2015: 6 · 2014: 1
FWCI
1.18
Type
journal article
Language
en
Open access
Locations
  • Annual Review of Psychology (publishedVersion)
  • PubMed (publishedVersion)
  • UCL Discovery (University College London) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
  • Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
  • Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy and History of Science
Keywords
causal reasoning, causal inference, causal representation, causal Bayesian networks, probabilistic reasoning, causal mechanisms, narrative, mental simulation, decision-making, judgment, attribution
MeSH terms
Bayes Theorem, Decision Making, Humans, Judgment, Logic, Thinking

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