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Cognitive Biases and the Strength of Political Arguments
Abstract
Competition in political debate is not always sufficient to neutralize the effects of political rhetoric on public opinion. Yet little is known about the factors that shape the persuasiveness of political arguments. In this article, I consider whether cognitive biases influence the perceived strength of political arguments, making some arguments more persuasive than others. Lessons from neurobiology and recent political psychology research on emotion lead to the expectation that individuals are more likely to be persuaded by political arguments that evoke loss aversion via a fearful response—even in the face of a counterargument. Evidence from two experiments corroborates this expectation. I consider the normative implications of these empirical findings and potential avenues for future research.
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Evaluating Online Labor Markets for Experimental Research: Amazon.com's Mechanical Turk
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Cited by
The Nature and Origins of Misperceptions: Understanding False and Unsupported Beliefs About Politics
Flynn, D. J., Nyhan, B., & Reifler, J. (2017) · Political Psychology · doi:10.1111/pops.12394 · Cited by 1,183
Debunking: A Meta-Analysis of the Psychological Efficacy of Messages Countering Misinformation
Chan, M.-P. S., Jones, C. R., Hall Jamieson, K., & Albarracín, D. (2017) · Psychological Science · doi:10.1177/0956797617714579 · Cited by 898
Huff, C., & Tingley, D. (2015) · Research & Politics · doi:10.1177/2053168015604648 · Cited by 903
How Conditioning on Posttreatment Variables Can Ruin Your Experiment and What to Do about It
Montgomery, J. M., Nyhan, B., & Torres, M. (2018) · American Journal of Political Science · doi:10.1111/ajps.12357 · Cited by 800
The demographic and political composition of Mechanical Turk samples
Levay, K. E., Freese, J., & Druckman, J. N. (2016) · Sage Open · doi:10.1177/2158244016636433 · Cited by 676
Metadata
- Authors
- Arceneaux, K.
- Venue
- American Journal of Political Science
- Publisher
- Wiley
- Published
- 2012-01-17
- Volume
- 56
- Issue
- 2
- Pages
- 271-285
- ISSN
- 0092-5853 (ISSN-L)
- 1540-5907
- OpenAlex
- W2154187722
- Cited by
- 244
- References
- 18
- FWCI
- 12.32
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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