In a world where ideas flow freely across multiple platforms, people must often rely on others' advice and opinions without an objective standard to judge whether this information is accurate. The present study explores the hypothesis that an individual's internal decision confidence can be used as a signal to learn the accuracy of others' advice, even in the absence of feedback. According to this "agreement-in-confidence" hypothesis, people can learn about an advisor's accuracy across multiple interactions according to whether the advice offered agrees with their own initial opinions, weighted by the confidence with which these initial opinions are held. We test this hypothesis using a judge-advisor system paradigm to precisely manipulate the profiles of virtual advisors in a perceptual decision-making task. We find that when advisors' and participants' judgments are independent, people can correctly learn advisors' features, like their accuracy and calibration, whether or not objective feedback is available. However, when their judgments (and thus errors) are correlated-as is the case in many real social contexts-predictable distortions in trust can be observed between feedback and feedback-free scenarios. Using agent-based simulations, we explore implications of these individual-level heuristics for network-level patterns of trust and belief formation. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
J Exp Psychol Gen