Emotion and Political Judgment: Expectancy Violation and Affective Intelligence

Christopher D. Johnston, Howard Lavine, Benjamin Woodson

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

Abstract

What factors prompt citizens to switch from a partisan judgment strategy, one in which they reflexively side with the in-group in policy and electoral contests, to a more thoughtful one, in which they pause to consider additional information? Previous work suggests that variation in political reasoning is triggered by the experience of anxiety. In this research, we examine a broader consideration: whether the overall pattern of experienced emotions confirms or violates one’s partisan expectations. Using both cross-sectional and panel data from the American National Election Studies, we examine how the emotions of anxiety, anger, and enthusiasm influence the manner in which voters appraise presidential candidates and update their opinions on salient policy issues. In line with an expectancy violation framework, the results consistently indicate that expectancy-violating emotions (e.g., experiencing enthusiasm toward the other party’s candidate) heighten deliberative reasoning and suppress partisan cue-taking, and that expectancy-confirming emotions (e.g., experiencing anxiety toward the other party’s candidate) have the reverse set of effects. We discuss the implications of our findings for American politics and for theories of political information processing and judgment.

Original languageEnglish (US)
Pages (from-to)474-492
Number of pages19
JournalPolitical Research Quarterly
Volume68
Issue number3
DOIs
StatePublished - Sep 13 2015

Bibliographical note

Publisher Copyright:
© 2015, © 2015 University of Utah.

Keywords

  • U.S. politics
  • electoral choice
  • partisanship
  • political judgment
  • political psychology
  • public opinion

Fingerprint

Dive into the research topics of 'Emotion and Political Judgment: Expectancy Violation and Affective Intelligence'. Together they form a unique fingerprint.

Cite this