A characterization of the neural representation of confidence during probabilistic learning - Bounmy, Tiffany; Eger, Evelyn; Meyniel, Florent (NeuroImage (Orlando, Fla.), 2023)
•Human learning includes a Bayesian (statistically ideal) representation of confidence.•We used high-field (7T) MRI and surface-based analysis for precise brain mapping.•The identified representation of confidence is specific, invariant and functional.•Confidence is neurally distinct from surprise and unpredictability. Learning in a stochastic and changing environment is a difficult task. Models of learning typically postulate that observations that deviate from the learned predictions are surprising and used to update those predictions. Bayesian accounts further posit the existence of a confidence-weighting mechanism: learning should be modulated by the confidence level that accompanies those predictions. However, the neural bases of this confidence are much less known than the ones of surprise. Here, we used a dynamic probability learning task and high-field MRI to identify putative cortical regions involved in the representation of confidence about predictions during human learning. We devised a stringent test based on the conjunction of four criteria. We localized several regions in parietal and frontal cortices whose activity is sensitive to the confidence of an ideal observer, specifically so with respect to potential confounds (surprise and predictability), and in a way that is invariant to which item is predicted. We also tested for functionality in two ways. First, we localized regions whose activity patterns at the subject level showed an effect of both confidence and surprise in qualitative agreement with the confidence-weighting principle. Second, we found neural representations of ideal confidence that also accounted for subjective confidence. Taken together, those results identify a set of cortical regions potentially implicated in the confidence-weighting of learning.