Affect, Not Ideology: The Psychological Mechanisms of Partisan Information Processing

Abstract

How do individuals process political information? What psychological mechanisms drive partisan bias? In this paper, we evaluate the extent to which partisan bias is driven by affect or ideology in a three-pronged approach informed by both psychological theories and recent advances in methodology. First, we use a novel survey experiment designed to disentangle the competing mechanisms of motivated bias and affect transfer. Second, we leverage multidimensional scaling methods for latent variable estimation for both partisan affect and ideology. Third, we employ a principled machine learning method, causal forest, to detect and estimate heterogeneous treatment effects. Contrary to previous literature, we find that affect is the sole moderator of partisan cueing processes, and only for out-party cues, suggesting that affect transfer dominates partisans' bias. These findings not only contribute to the literature on political behavior, but underscore the importance of careful measurement and robust subgroup analysis.