Humans can meaningfully express their confidence about uncertain events. Normatively, these beliefs should correspond to Bayesian probabilities. However, it is unclear whether the normative theory provides an accurate description of the human sense of confidence, partly because the self-report measures used in most studies hinder quantitative comparison with normative predictions. To measure confidence objectively, we developed a dual-decision task in which the correctness of a first decision determines the correct answer of a second decision, thus mimicking real-life situations in which confidence guides future choices. While participants were able to use confidence to improve performance, they fell short of the ideal Bayesian strategy. Instead, behaviour was better explained by a model with a few discrete confidence levels. These findings question the descriptive validity of normative accounts, and suggest that confidence judgments might be based on point estimates of the relevant variables, rather than on their full probability distributions.

人类可以有意义地表达他们对不确定事件的信心。通常，这些信念应该对应于贝叶斯概率。然而，尚不清楚规范理论是否提供了人类自信感的准确描述，部分原因是大多数研究中使用的自我报告措施阻碍了与规范预测的定量比较。为了客观地衡量信心，我们开发了一个双重决策任务，其中第一个决策的正确性决定了第二个决策的正确答案，从而模拟了信心指导未来选择的现实情况。虽然参与者能够利用信心来提高表现，但他们没有达到理想的贝叶斯策略。相反，行为可以通过具有几个离散置信水平的模型更好地解释。