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Cited by 128Decision-Making and Behavioral EconomicsElectoral Systems and Political ParticipationSocial and Intergroup Psychology

The DOI matches its Crossref record; 71 of the 71 references it cites were found in a registry or an index.

Bayesian or biased? Analytic thinking and political belief updating

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Tappin, Ben M., Pennycook, Gordon, Rand, David G.·2020·Cognition
DOI 10.1016/j.cognition.2020.104375
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Abstract

A surprising finding from U.S. opinion surveys is that political disagreements tend to be greatest among the most cognitively sophisticated opposing partisans. Recent experiments suggest a hypothesis that could explain this pattern: cognitive sophistication magnifies politically biased processing of new information. However, the designs of these experiments tend to contain several limitations that complicate their support for this hypothesis. In particular, they tend to (i) focus on people's worldviews and political identities, at the expense of their other, more specific prior beliefs, (ii) lack direct comparison with a politically unbiased benchmark, and (iii) focus on people's judgments of new information, rather than on their posterior beliefs following exposure to the information. We report two studies designed to address these limitations. In our design, U.S. subjects received noisy but informative signals about the truth or falsity of partisan political questions, and we measured their prior and posterior beliefs, and cognitive sophistication, operationalized as analytic thinking inferred via performance on the Cognitive Reflection Test. We compared subjects' posterior beliefs to an unbiased Bayesian benchmark. We found little evidence that analytic thinking magnified politically biased deviations from the benchmark. In contrast, we found consistent evidence that greater analytic thinking was associated with posterior beliefs closer to the benchmark. Together, these results are inconsistent with the hypothesis that cognitive sophistication magnifies politically biased processing. We discuss differences between our design and prior work that can inform future tests of this hypothesis.

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  1. Leeper, T. J., Arnold, J., & Arel-Bundock, V. (2018). Marginal Effects for Model Objects (margins). https://cran.r-project.org/web/packages/margins/margins.pdf. · DOI
  2. Pew Research Center. (2019, January 24). Public's 2019 Priorities: Economy, Health Care, Education and Security All Near Top of List | Pew Research Center. https://www.people-press.org/2019/01/24/publics-2019-priorities-economy-health-care-education-and-security-all-near-top-of-list/.

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Metadata

Authors
Tappin, B. M., Pennycook, G., & Rand, D. G.
Affiliations
  • Massachusetts Institute of Technology (US)
  • University of Regina (CA)
Corresponding author
Tappin, Ben M.
ORCID
Venue
Cognition
Publisher
Elsevier BV
Published
2020-11
Volume
204
Pages
104375
ISSN
0010-0277 (ISSN-L)
DOI
10.1016/j.cognition.2020.104375
OpenAlex
W3034853018
Cited by
127
References
70
Citations by year
2026: 10 · 2025: 23 · 2024: 32 · 2023: 20 · 2022: 25 · 2021: 13 · 2020: 4
FWCI
11.57
Type
journal article
Language
en
Open access
Licence
Locations
  • Cognition (publishedVersion)
  • PubMed (publishedVersion)
  • DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-nd)
  • OSF Preprints (OSF Preprints) (submittedVersion, cc-by)
  • OSF Preprints (OSF Preprints) (submittedVersion)
Funders
  • John Templeton Foundation (61061)
  • Economic and Social Research Council (ES/J500148/1)
Topics
  • Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
  • Social Sciences › Political Science and International Relations › Electoral Systems and Political Participation
  • Social Sciences › Sociology and Political Science › Social and Intergroup Psychology
Keywords
analytical thinking, cognitive sophistication, Bayesian updating, cognitive reflection, prior beliefs, posterior beliefs, noisy signals, political bias, partisanship
MeSH terms
Bayes Theorem, Humans, Judgment, Neuropsychological Tests, Thinking

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