Motivated Reasoning in an Explore‐Exploit Task

Abstract The current research investigates how prior preferences affect causal learning. Participants were tasked with repeatedly choosing policies (e.g., increase vs. decrease border security funding) in order to maximize the economic output of an imaginary country and inferred influence on economy. task was challenging ambiguous, allowing participants interpret relations between economy multiple ways. In three studies, we found evidence motivated reasoning despite financial incentives for accuracy. For example, who believed that funding should be increased more likely conclude increasing actually caused a better task. Study 2, hypothesized having neutral preferring neither nor decreased spending security) would lead accurate assessments overall, compared strong initial preference; however, did not find such effect. 3, tested whether providing possible functional forms policy takes some time work or initially has negative but eventually positive influence) smaller little this This advances field learning by studying role preferences, doing so, integrates fields using novel explore‐exploit