The value of confidence: Confidence prediction errors drive value-based learning in the absence of external feedback
Fig 5
Latent variables and posterior predictive fits of model ConfUnspec.
All time courses represent averages across blocks and subjects, split according to the duration of phase 1 (line styles) and the four CS value levels within a block (colors). (A) Expected values indicate current beliefs about the value of each stimulus. (B) Posterior predictive fit for model performance: expected proportion correct responses based on choice probabilities. (C) Posterior predictive fit for model confidence. Model confidence is computed based on the choice probability for the chosen CS (normalized to the range 0–1). Black lines indicate averages across value levels. (D) Confidence slopes of (C) in phase 2 in dependence of the CS value level. (E) Expected confidence corresponds to an integration of past confidence experiences using a Rescorla-Wagner-type learning rule. (F) Confidence prediction errors indicate the deviation of a momentary confidence experience from expected confidence. (G) Absolute confidence prediction error.