Priors for natural image statistics inform confidence in perceptual decisions

Abstract

Decision confidence plays a critical role in humans' ability to make adaptive decisions in a noisy perceptual world. Despite its importance, there is currently little consensus about the computations underlying confidence judgements in perceptual decisions. One leading theory suggests that confidence is computed following the rules of Bayesian inference. Accordingly, the goal of the current study was to investigate a fundamental assumption of Bayesian models: the use of prior knowledge in subjective confidence. Rather than requiring participants to internalise the parameters of an arbitrary prior distribution, we capitalised on the existing probability distributions of features in natural scenes, which are known to play a critical role in guiding perception. Participants reported the subjective upright of naturalistic image target patches, and then reported their confidence in their orientation responses. We used computational modelling to relate the statistics of the targets to participants' responses, confirming that participants used the prior probability distribution of features in natural scenes to judge subjective upright. Critically, our results reveal that participants also used natural image priors to inform their confidence judgements. Our findings provide important evidence supporting a Bayesian characterisation of confidence and highlight the influence of environmental priors on confidence.