Publication | Closed Access
Hot Cognition or Cool Consideration? Testing the Effects of Motivated Reasoning on Political Decision Making
975
Citations
36
References
2002
Year
Political DecisionBehavioral Decision MakingRational Bayesian UpdatersPublic OpinionIndividual Decision MakingPolitical BehaviorSmart VotingSocial SciencesPsychologyExperimental Decision MakingBiasManagementSocial ReasoningCognitive Bias MitigationUnconscious BiasDecision TheoryPolitical CognitionElection ForecastingBehavioral SciencesCognitive ScienceReasoning About ActionPersuasionAffective BiasesBias DetectionCool ConsiderationMotivated ReasoningHot CognitionDecision SciencePolitical Science
Motivated reasoning shows that affectively charged political information processing biases citizens to delay incongruent information and may even increase support for a liked candidate when faced with negative data. The study uses a dynamic information board experiment to test key effects of motivated reasoning. The authors employ a dynamic information board experiment to observe how motivated reasoning shapes information search and decision timing. The experiment confirms that affective biases delay processing of incongruent information, can boost support for liked candidates when negative data is encountered, and result in lower quality decisions, undermining the rational Bayesian voter model.
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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