Login / Signup

The political (a)symmetry of metacognitive insight into detecting misinformation.

Michael GeersHelen FischerStephan LewandowskyStefan M Herzog
Published in: Journal of experimental psychology. General (2024)
Political misinformation poses a major threat to democracies worldwide, often inciting intense disputes between opposing political groups. Despite its central role for informed electorates and political decision making, little is known about how aware people are of whether they are right or wrong when distinguishing accurate political information from falsehood. Here, we investigate people's metacognitive insight into their own ability to detect political misinformation. We use data from a unique longitudinal study spanning 12 waves over 6 months that surveyed a representative U.S. sample (N = 1,191) on the most widely circulating political (mis)information online. Harnessing signal detection theory methods to model metacognition, we found that people from both the political left and the political right were aware of how well they distinguished accurate political information from falsehood across all news. However, this metacognitive insight was considerably lower for Republicans and conservatives-than for Democrats and liberals-when the information in question challenged their ideological commitments. That is, given their level of knowledge, Republicans' and conservatives' confidence was less likely to reflect the correctness of their truth judgments for true and false political statements that were at odds with their political views. These results reveal the intricate and systematic ways in which political preferences are linked to the accuracy with which people assess their own truth discernment. More broadly, by identifying a specific political asymmetry-for discordant relative to concordant news-our findings highlight the role of metacognition in perpetuating and exacerbating ideological divides. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
Keyphrases
  • social media
  • decision making
  • healthcare
  • health information
  • emergency department
  • high resolution
  • machine learning
  • single cell