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Cited by 978

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Hot Cognition or Cool Consideration? Testing the Effects of Motivated Reasoning on Political Decision Making

Redlawsk, David P.·2002·The Journal of Politics

Abstract

Researchers attempting to understand how citizens process political information have advanced motivated reasoning to explain the joint role of affect and cognition. The prominence of affect suggests that all social information processing is affectively charged and prone to biases. This article makes use of a unique data set collected using a dynamic information board experiment to test important effects of motivated reasoning. In particular, affective biases should cause citizens to take longer processing information incongruent with their existing affect and such biases should also direct search for new information about candidates. Somewhat perversely, motivated reasoners may actually increase their support of a positively evaluated candidate upon learning new negatively evaluated information. Findings are reported that support all of these expectations. Additional analysis shows that these affective biases may easily lead to lower quality decision making, leading to a direct challenge to the notion of voters as rational Bayesian updaters.

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Metadata

Authors
Redlawsk, D. P.
Venue
The Journal of Politics
Published
2002-11
Volume
64
Issue
4
Pages
1021-1044
ISSN
  • 0022-3816 (ISSN-L)
  • 1468-2508
OpenAlex
W2148685192
Cited by
978
References
6
FWCI
14.10
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Hot Cognition or Cool Consideration? Testing the Effects of Motivated Reasoning on Political Decision Making. https://app.cochat.ai/papers/10.1111/1468-2508.00161
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Redlawsk, D. P. (2002). Hot Cognition or Cool Consideration? Testing the Effects of Motivated Reasoning on Political Decision Making. The Journal of Politics. https://doi.org/10.1111/1468-2508.00161

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.