Department of Experimental Psychology, University of Oxford.
J Exp Psychol Gen. 2021 Mar;150(3):507-526. doi: 10.1037/xge0000960. Epub 2020 Oct 1.
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) 2021 APA, all rights reserved).
在一个思想可以在多个平台上自由流动的世界中，人们常常不得不依靠他人的建议和意见，而没有客观的标准来判断这些信息是否准确。本研究探讨了一个假设，即个体的内部决策信心可以作为一个信号，用于学习他人建议的准确性，即使没有反馈。根据这一“信心一致”假设，人们可以根据所提供的建议与自己最初意见的一致性，根据自己对这些最初意见的信心程度，来学习顾问在多次互动中的准确性。我们使用判断-顾问系统范式来检验这一假设，该范式可以在感知决策任务中精确地操纵虚拟顾问的特征。我们发现，当顾问和参与者的判断是独立的时候，无论是否有客观的反馈，人们都可以正确地学习顾问的特征，比如他们的准确性和校准。然而，当他们的判断（因此也是错误）相关时——这在许多现实的社会环境中是如此——可以观察到在有反馈和无反馈的情况下，信任之间存在可预测的扭曲。我们使用基于代理的模拟，探索了这些个体层面的启发式对信任和信念形成的网络层面模式的影响。
