No abstract

Despite unprecedented access to information, partisans increasingly disagree about basic facts that are backed by data. We examine the underpinnings of this phenomenon using drift diffusion modeling (DDM). Partisans (N=148) completed a sequential sampling task where they evaluated the honesty of Democrat or Republican politicians during a debate based on fact-check scores. We found that partisans required less and weaker evidence to correctly categorize the ingroup as more honest, and were more accurate on trials when the ingroup candidate was more honest, compared to the outgroup. DDM revealed that biases arise from both a bias in the starting point and rate of evidence accumulation. Moreover, individual differences in cognitive reasoning moderated task performance for the most devoted partisans and maintained divergent associations with the DDM parameters. These findings suggest that partisans may reach biased conclusions via different pathways depending on their cognitive reasoning styles.