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. To better understand these mechanisms, we addressed the extent to which confidence is informed by a naturalistic prior distribution. Contrary to previous research, we did not require participants to internalise parameters of an arbitrary prior distribution. We instead used a novel psychophysical paradigm leveraging probability distributions of low-level image features in natural scenes, which are well-known to influence perception. Participants reported the subjective upright of naturalistic image patches, targets, and then reported their confidence in their orientation responses. We used computational modelling to relate the statistics of the low-level features in the targets to the average distribution of these features across many naturalistic images, a prior. Our results showed that participants’ perceptual and importantly, their confidence judgments aligned with an internalised prior for image statistics. Overall, our study highlights the importance of naturalistic task designs that capitalise on existing, long-term priors to further understand the computational basis of confidence.