Bayesian or biased? Analytic thinking and political belief updating
Open accessAbstract
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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Individuals act as Bayesian learners who incorporate new information into their evaluations of the political and social environment.
“and that individuals act as Bayesian learners who incorporate new information into their evaluations of the political and social environment25,26.”
From Factual knowledge can reduce attitude polarization (2025) · cites this paper as [26]
Few (if any) studies actually connect the dots between psychological speculations about partisan communication activating group-justifying motives or conservative communication activating system-justifying motives and the mechanisms that mediate message reception.
“However, few (if any) studies actually connect the dots (see, for example, critiques of existing research on motivated political reasoning103,300,301).”
From Cognitive–motivational mechanisms of political polarization in social-communicative contexts (2022) · cites this paper as [300]
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- Authors
- Tappin, B. M., Pennycook, G., & Rand, D. G.
- Affiliations
- Massachusetts Institute of Technology (US)
- University of Regina (CA)
- Corresponding author
- Tappin, Ben M.
- Venue
- Cognition
- Publisher
- Elsevier BV
- Published
- 2020-11
- Volume
- 204
- Pages
- 104375
- ISSN
- 0010-0277 (ISSN-L)
- 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
- green
- hdl.handle.net
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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