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Cited by 36Decision-Making and Behavioral EconomicsBayesian Modeling and Causal InferenceStatistics Education and Methodologies

A description–experience gap in statistical intuitions: Of smart babies, risk-savvy chimps, intuitive statisticians, and stupid grown-ups

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Schulze, Christin, Hertwig, Ralph·2021·Cognition
DOI 10.1016/j.cognition.2020.104580
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Abstract

Comparison of different lines of research on statistical intuitions and probabilistic reasoning reveals several puzzling contradictions. Whereas babies seem to be intuitive statisticians, surprisingly capable of statistical learning and inference, adults' statistical inferences have been found to be inconsistent with the rules of probability theory and statistics. Whereas researchers in the 1960s concluded that people's probability updating is "conservatively" proportional to normative predictions, probability updating research in the 1970s suggested that people are incapable of following Bayes's rule. And whereas animals appear to be strikingly risk savvy, humans often seem "irrational" when dealing with probabilistic information. Drawing on research on the description-experience gap in risky choice, we integrate and systematize these findings from disparate fields of inquiry that have, to date, operated largely in parallel. Our synthesis shows that a key factor in understanding inconsistencies in statistical intuitions research is whether probabilistic inferences are based on symbolic, abstract descriptions or on the direct experience of statistical information. We delineate this view from other conceptual accounts, consider potential mechanisms by which attributes of first-hand experience can facilitate appropriate statistical inference, and identify conditions under which they improve or impair probabilistic reasoning. To capture the full scope of human statistical intuition, we conclude, research on probabilistic reasoning across the lifespan, across species, and across research traditions must bear in mind that experience and symbolic description of the world may engage systematically distinct cognitive processes.

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  • Many experiments showing biases in system 1 thinking, such as base-rate fallacies, could be partly attributed to experiment design.

    “For example, many experiments showing biases system 1 thinking, such as base-rate fallacies, could be partly attributed to the experiment design [35]”

    From Explainable AI is Dead, Long Live Explainable AI!: Hypothesis-driven Decision Support using Evaluative AI (2023) · cites this paper as [35]

  • There is now sufficient research questioning whether the system 1 vs system 2 distinction is so clear.

    “Second, there is now sufficient research questioning whether the system 1 vs system 2 distinction is so clear [25,35].”

    From Explainable AI is Dead, Long Live Explainable AI!: Hypothesis-driven Decision Support using Evaluative AI (2023) · cites this paper as [35]

  • Differences might be expected given claims that probabilistic and statistical reasoning is more difficult when probabilities are described rather than experienced (the description-experience gap).

    “Differences might be expected given claims that probabilistic and statistical reasoning is more difficult when probabilities are described rather than experienced (i.e., the description-experience gap; e.g., Schulze and Hertwig, 2021).”

    From Probability matching and statistical naïveté (2025) · cites this paper as Schulze and Hertwig, 2021

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Research works printed but not linked: 4

  1. Bryant, P., & Nunes, T. (2012). Children's understanding of probability: A literature review. Nuffield Foundation.
  2. Hogarth, R. M. (2001). Educating intuition. The University of Chicago Press.
  3. Newell, B. R., & Schulze, C. (2017). Probability matching. In R. F. Pohl (Ed.), Cognitive illusions: Intriguing phenomena in thinking, judgment, and memory (2nd ed., pp. 62-78). Routledge.
  4. In P. Sedlmeier & T. Betsch (Eds.), Etc. frequency processing and cognition (pp. 21-36). Oxford University Press.

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Authors
Schulze, C., & Hertwig, R.
Affiliations
Max Planck Institute for Human Development (DE)
Corresponding author
Schulze, Christin
ORCID
Venue
Cognition
Publisher
Elsevier BV
Published
2021-05
Volume
210
Pages
104580
ISSN
0010-0277 (ISSN-L)
DOI
10.1016/j.cognition.2020.104580
OpenAlex
W3128498561
Cited by
36
References
209
Citations by year
2026: 4 · 2025: 5 · 2024: 6 · 2023: 13 · 2022: 5 · 2021: 2 · 2017: 1
FWCI
4.35
Type
journal article
Language
en
Open access
Licence
Locations
  • Cognition (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
Topics
  • Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
  • Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
  • Physical Sciences › Mathematics › Statistics and Probability › Statistics Education and Methodologies
Keywords
probabilistic reasoning, description-experience gap, statistical learning, statistical inference, probability updating, decision making under risk, chimpanzees, infants, cognitive development, symbolic representation, experiential learning
MeSH terms
Adult, Animals, Bayes Theorem, Pan troglodytes, Cognition, Humans, Infant, Problem Solving, Intuition

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