Discrete confidence levels revealed by sequential decisions

Do human confidence judgments follow Bayesian principles? Using a task in which confidence is not reported on a scale but used to inform decisions, Lisi et al. find that behaviour is better explained by discrete confidence levels than Bayesian probability. 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.

Automated summary

The study found that human confidence judgments tend to follow discrete confidence levels rather than Bayesian probabilities. While humans can express confidence about uncertain events, they do not fully adhere to the ideal Bayesian strategy. By developing a dual-decision task, researchers suggest that confidence judgments may be based on point estimates of relevant variables.