Doubly Bayesian Analysis of Confidence in Perceptual Decision-Making

Humans stand out from other animals in that they are able to explicitly report on the reliability of their internal operations. This ability, which is known as metacognition, is typically studied by asking people to report their confidence in the correctness of some decision. However, the computations underlying confidence reports remain unclear. In this paper, we present a fully Bayesian method for directly comparing models of confidence. Using a visual two-interval forced-choice task, we tested whether confidence reports reflect heuristic computations (e.g. the magnitude of sensory data) or Bayes optimal ones (i.e. how likely a decision is to be correct given the sensory data). In a standard design in which subjects were first asked to make a decision, and only then gave their confidence, subjects were mostly Bayes optimal. In contrast, in a less-commonly used design in which subjects indicated their confidence and decision simultaneously, they were roughly equally likely to use the Bayes optimal strategy or to use a heuristic but suboptimal strategy. Our results suggest that, while people’s confidence reports can reflect Bayes optimal computations, even a small unusual twist or additional element of complexity can prevent optimality.Author Summary: Confidence plays a key role in group interactions: when people express an opinion, they almost always communicate—either implicitly or explicitly—their confidence, and the degree of confidence has a strong effect on listeners. Understanding both how confidence is generated and how it is interpreted are therefore critical for understanding group interactions. Here we ask: how do people generate their confidence? A priori, they could use a heuristic strategy (e.g. their confidence could scale more or less with the magnitude of the sensory data) or what we take to be an optimal strategy (i.e. their confidence is a function of the probability that their opinion is correct). We found, using Bayesian model selection, that confidence reports reflect probability correct, at least in more standard experimental designs. If this result extends to other domains, it would provide a relatively simple interpretation of confidence, and thus greatly extend our understanding of group interactions.