(A) Illustration of dynamic updating of predicted outcomes based on response information. Pre-response the agent aims to hit the bullseye and selects the action he believes achieves this goal. Post-response the agent realizes that he made a mistake and predicts to miss the target entirely, being reasonably confident in his prediction. In line with his prediction and thus unsurprisingly the darts hits the floor. (B) Illustration of key concepts. Left: The feedback received is plotted against the prediction. Performance and prediction can vary in their accuracy independently. Perfect performance (zero deviation from the target, dark blue line) can occur for accurate or inaccurate predictions and any performance, including errors, can be predicted perfectly (predicted error is identical to performance, orange line). When predictions and feedback diverge, outcomes (feedback) can be better (closer to the target, area highlighted with coarse light red shading) or worse (farther from the target, area highlighted with coarse light blue shading) than predicted. The more they diverge the less precise the predictions are. Right: The precision of the prediction is plotted against confidence in that prediction. If confidence closely tracks the precision of the predictions, that is if agents know when their predictions are probably right and when they’re not, confidence calibration is high (green). If confidence is independent of the precision of the predictions, then confidence calibration is low. (C) Illustration of theoretical hypotheses. Left: We expect the correspondence between predictions and Feedback to be stronger when confidence is high and to be weaker when confidence is low. Right: We expect that agents with better confidence calibration learn better. (D) Trial schema. Participants learned to produce a time interval by pressing a button following a tone with their left index finger. Following each response, they indicated on a visual analog scale in sequence the estimate of their accuracy (anchors: ‘much too short’ = ‘viel zu kurz’ to ‘much too long’ = ‘viel zu lang’) and their confidence in that estimate (anchors: ‘not certain’ = ‘nicht sicher’ to ‘fully certain’ = ‘völlig sicher’) by moving an arrow slider. Finally, feedback was provided on a visual analog scale for 150 ms. The current error was displayed as a red square on the feedback scale relative to the target interval indicated by a tick mark at the center (Target, t) with undershoots shown to the left of the center and overshoots to the right, and scaled relative to the feedback anchors of -/+1 s (Scale, s; cf. E). Participants are told neither Target nor Scale and instead need to learn them based on the feedback. (E) Bayesian Learner with Performance Monitoring. The learner selects an intended response (i) based on the current estimate of the Target. The Intended Response and independent Response Noise produce the Executed Response (r). The Efference Copy (c) of this response varies in its precision as a function of Efference Copy Noise. It is used to generate a Prediction as the deviation from the estimate of Target scaled by the estimate of Scale. The Efference Copy Noise is estimated and expressed as Confidence (co), approximating the precision of the Prediction. Learners vary in their Confidence Calibration (cc), that is, the precision of their predictions, and higher Confidence Calibration (arrows: green >yellow > magenta) leads to more reliable translation from Efference Copy precision to Confidence. Feedback is provided according to the Executed Response and depends on the Target and Scale, which are unknown to the learner. Target and Scale are inferred based on Feedback (f), Response Noise, Prediction, and Confidence. Variables that are observable to the learner are displayed in solid boxes, whereas variables that are only partially observable are displayed in dashed boxes. (F) Target and scale error (absolute deviation of the current estimates from the true values) for the Bayesian learner with Performance monitoring (green, optimal calibration), a Feedback-only Bayesian Learner (solid black), and a Bayesian Learner with Outcome Prediction (dashed black).