Visual confidence accurately tracks increasing internal noise with eccentricity in peripheral vision.

Sensory representations are inherently noisy, and monitoring this noise is essential for effective decision-making. This metacognitive ability of evaluating the quality of one's perceptual decision is referred to as perceptual confidence. However, whether perceptual confidence accurately tracks internal noise remains unresolved. Peripheral vision provides a natural testing ground for this question, yet previous studies report mixed results complicated by different definitions and measurements of confidence. Here, we used a normative Bayesian framework with incentivized confidence measurements to address these discrepancies. We tested the Bayesian-confidence hypothesis that confidence is derived from the posterior probability distribution of the feature being judged, given noisy sensory measurements. We tested two perceptual tasks while varying stimulus eccentricity: spatial localization and orientation estimation. We measured confidence by post-decision wagering, by which participants set a symmetrical range around the perceptual estimates. Participants earned higher reward for narrower confidence ranges but received zero reward if the range did not enclose the target. We estimated sensory noise from the perceptual responses to predict confidence, assuming that sensory noise linearly increases with eccentricity. We then compared a normative Bayesian model with three alternative models that challenged different assumptions. Across both tasks, the Bayesian ideal-observer model best predicted confidence. These results suggest that humans can accurately monitor the increased internal noise in peripheral vision and use this information to make optimal confidence judgments.