Doubly Bayesian Analysis of Confidence in Perceptual Decision-Making

• Published in: PLoS computational biology Vol. 11; no. 10; p. e1004519
• Main Authors: Aitchison, Laurence, Bang, Dan, Bahrami, Bahador, Latham, Peter E.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.10.2015; Public Library of Science (PLoS)
• Subjects: Analysis; Bayes Theorem; Bayesian analysis; Bayesian statistical decision theory; Calibration; Charitable foundations; Choice Behavior; Computer Simulation; Confidence; Datasets; Decision making; Decision Support Techniques; Heuristic; Heuristics; Humans; Metacognition; Models, Statistical; Probability; Visual Perception; United Kingdom
• DOI: 10.1371/journal.pcbi.1004519
• ISSN: 1553-7358; 1553-734X

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.