People rely on social information to inform their beliefs. We ask whether and to what degree the perceived prevalence of a belief influences belief adoption. We present the results of two experiments that show how increases in a person’s estimated prevalence of a belief led to increased endorsement of said belief. Belief endorsement rose when impressions of the belief’s prevalence were increased and when initial beliefs were uncertain, as predicted by a Bayesian cue integration framework. Thus, people weigh social information rationally. An implication of these results is that social engagement metrics that prompt inflated prevalence estimates in users risk increasing the believability and adoption of viral misinformation posts.

Prior research has yielded mixed findings on whether learners’ certainty reflects veridical probabilities from observed evidence. We compared predictions from an idealized model of learning to humans’ subjective reports of certainty during a Boolean concept-learning task in order to examine subjective certainty over the course of abstract, logical concept learning. Our analysis evaluated theoretically motivated potential predictors of certainty to determine how well each predicted participants’ subjective reports of certainty. Regression analyses that controlled for individual differences demonstrated that despite learning curves tracking the ideal learning models, reported certainty was best explained by performance rather than measures derived from a learning model. In particular, participants’ confidence was driven primarily by how well they observed themselves doing, not by idealized statistical inferences made from the data they observed.