The Politically Motivated Reasoning Paradigm, Part 1: What Politically Motivated Reasoning Is and How to Measure It
Abstract
Recent research identifiespolitically motivated reasoningas the source of persistent public conflict over policy‐relevant facts. This essay, the first in a two‐part set, presents a basic conceptual model—thePolitically Motivated Reasoning Paradigm—and an experimental setup—thePMRP design—geared to distinguishing the influence of PMRP from a truth‐seeking Bayesian process of information processing and from recurring biases understood to be inimical to the same. It also discusses alternative schemes for operationalizing “motivating” group predispositions and the characteristics of valid study samples for examining this phenomenon.
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People may process information in a manner consistent with their partisan identities, prior beliefs, and motivations (motivated cognition).
“People may process information in a manner that is consistent with their partisan identities, prior beliefs, and motivations, a process known as motivated cognition (34–37).”
From Out-group animosity drives engagement on social media (2021) · cites this paper as [34]
The negative influence/backfire effect is elusive, with limited relevance for politics outside the laboratory; while we may be less influenced by political opponents, we are unlikely to be negatively influenced by them.
“This has brought researchers to conclude that negative influence effect is elusive, with limited relevance for politics outside the laboratory (88, 90, 93–95): while we may be less influenced by our political opponents, we are unlikely to be negatively influenced by them.”
From How digital media drive affective polarization through partisan sorting (2022) · cites this paper as [88]
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- Authors
- Kahan, D. M.
- Affiliations
- Yale University (US)
- Corresponding author
- Kahan, Dan M.
- ORCID
- Kahan, Dan M.
- Venue
- Emerging Trends in the Social and Behavioral Sciences
- Publisher
- Wiley
- Published
- 2016-11-29
- Pages
- 1-16
- OpenAlex
- W2564709365
- Cited by
- 341
- References
- 39
- Citations by year
- 2026: 19 · 2025: 23 · 2024: 31 · 2023: 59 · 2022: 60 · 2021: 46 · 2020: 36 · 2019: 37 · 2018: 20 · 2017: 8 · 2016: 2
- Type
- other
- Language
- en
- Open access
- closed
- Locations
- Emerging Trends in the Social and Behavioral Sciences (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Social and Intergroup Psychology
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Social Sciences › Political Science and International Relations › Electoral Systems and Political Participation
- Keywords
- cognitive biases
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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