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Cited by 70Bayesian Modeling and Causal InferenceDecision-Making and Behavioral EconomicsCognitive Science and Education Research

A theory of learning to infer.

Open access
Dasgupta, Ishita, Schulz, Eric, Tenenbaum, Joshua B., Gershman, Samuel J.·2020·Psychological Review
DOI 10.1037/rev0000178

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Abstract

Bayesian theories of cognition assume that people can integrate probabilities rationally. However, several empirical findings contradict this proposition: human probabilistic inferences are prone to systematic deviations from optimality. Puzzlingly, these deviations sometimes go in opposite directions. Whereas some studies suggest that people underreact to prior probabilities ( base rate neglect ), other studies find that people underreact to the likelihood of the data ( conservatism ). We argue that these deviations arise because the human brain does not rely solely on a general-purpose mechanism for approximating Bayesian inference that is invariant across queries. Instead, the brain is equipped with a recognition model that maps queries to probability distributions. The parameters of this recognition model are optimized to get the output as close as possible, on average, to the true posterior. Because of our limited computational resources, the recognition model will allocate its resources so as to be more accurate for high probability queries than for low probability queries. By adapting to the query distribution, the recognition model learns to infer. We show that this theory can explain why and when people underreact to the data or the prior, and a new experiment demonstrates that these two forms of underreaction can be systematically controlled by manipulating the query distribution. The theory also explains a range of related phenomena: memory effects, belief bias, an…

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  • Meta-learned models reproduce human biases in probabilistic reasoning.

    “They, amongst others, reproduce human biases in probabilistic reasoning (Dasgupta, Schulz, Tenenbaum, & Gershman, 2020)”

    From Meta-learned models of cognition (2024) · cites this paper as Dasgupta, Schulz, Tenenbaum, & Gershman, 2020

  • Meta-learned models can capture a wide range of empirically observed phenomena that could not be explained otherwise.

    “It has been shown that meta-learned models can capture a wide range of empirically observed phenomena that could not be explained otherwise.”

    From Meta-learned models of cognition (2024) · cites this paper as Dasgupta, Schulz, Tenenbaum, & Gershman, 2020

  • Meta-learning has been previously used to discover algorithms with a limited computational budget that show human-like cognitive biases.

    “Meta-learning has been previously used to discover algorithms with a limited computational budget that show human-like cognitive biases as we have already alluded to earlier.”

    From Meta-learned models of cognition (2024) · cites this paper as Dasgupta et al. (2020)

  • Dasgupta et al. (2020) found that their model, when restricted to a single hidden unit, captured many biases in human reasoning, including conservatism bias and base rate neglect.

    “They found that their model - when restricted to just a single hidden unit - captured many biases in human reasoning, including a conservatism bias and base rate neglect.”

    From Meta-learned models of cognition (2024) · cites this paper as Dasgupta et al. (2020)

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Authors
Dasgupta, I., Schulz, E., Tenenbaum, J. B., & Gershman, S. J.
Affiliations
  • Harvard University (US)
  • Massachusetts Institute of Technology (US)
ORCID
Venue
Psychological Review
Publisher
American Psychological Association (APA)
Published
2020-04
Volume
127
Issue
3
Pages
412-441
ISSN
  • 0033-295X (ISSN-L)
  • 1939-1471
DOI
10.1037/rev0000178
OpenAlex
W3013065462
Cited by
70
References
165
Citations by year
2026: 5 · 2025: 9 · 2024: 14 · 2023: 19 · 2022: 9 · 2021: 5 · 2020: 8 · 2019: 1
FWCI
4.03
Type
journal article
Language
en
Open access
Licence
Locations
  • Psychological Review (publishedVersion, other-oa)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed (publishedVersion)
  • DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
  • MPG.PuRe (Max Planck Society) (submittedVersion)
Funders
  • Center for Brains, Minds, and Machines (CCF-1231216)
  • Microsoft Research
  • Office of Naval Research (N00014-17-1-2984)
  • Alfred P. Sloan Foundation
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
  • Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Cognitive Science and Education Research
Keywords
Bayesian inference, recognition model, approximate inference, query distribution, belief bias, memory effects, probabilistic reasoning, response variability, computational models of learning
MeSH terms
Heuristics, Bayes Theorem, Cognition, Humans, Learning, Memory, Models, Theoretical, Probability Theory, Problem Solving, Bias

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