Shared Mechanisms for Confidence Judgements and Error Detection in Human Decision Making

Abstract

People give accurate evaluations of their own choices and decisions: they are often aware of their mistakes without needing feedback, and report levels of confidence in their choices that correlate with objective performance. These metacognitive judgements guide current and future behaviour, helping people to avoid making the same mistake twice and to evaluate whether they have enough information on which to base a reliable choice. Here we review progress in characterising the neural and mechanistic basis of these related aspects of metacognition—confidence judgements and error monitoring—and identify several points of convergence between methods and theories in the two fields. This convergence promises to resolve key debates in the separate literatures, to identify productive new lines of enquiry, but also to highlight shared limitations in the two research fields. In particular, future theories of choice and metacognitive evaluation may need to look beyond simple, discrete decisions to model the structure and fluidity of real-world decisions and actions that are embedded in the broader context of evolving behavioural goals.