Brain dynamics for confidence-weighted learning - Inserm - Institut national de la santé et de la recherche médicale Access content directly
Journal Articles PLoS Computational Biology Year : 2020

Brain dynamics for confidence-weighted learning


Learning in a changing, uncertain environment is a difficult problem. A popular solution is to predict future observations and then use surprising outcomes to update those predictions. However, humans also have a sense of confidence that characterizes the precision of their predictions. Bayesian models use a confidence-weighting principle to regulate learning: for a given surprise, the update is smaller when the confidence about the prediction was higher. Prior behavioral evidence indicates that human learning adheres to this confidence-weight-ing principle. Here, we explored the human brain dynamics sub-tending the confidence-weighting of learning using magneto-encephalography (MEG). During our volatile probability learning task, subjects' confidence reports conformed with Bayesian inference. MEG revealed several stimulus-evoked brain responses whose amplitude reflected surprise, and some of them were further shaped by confidence: surprise amplified the stimulus-evoked response whereas confidence dampened it. Confidence about predictions also modulated several aspects of the brain state: pupil-linked arousal and beta-range (15-30 Hz) oscillations. The brain state in turn modulated specific stimulus-evoked surprise responses following the confidence-weighting principle. Our results thus indicate that there exist, in the human brain, signals reflecting surprise that are dampened by confidence in a way that is appropriate for learning according to Bayesian inference. They also suggest a mechanism for confidence-weighted learning: confidence about predictions would modulate intrinsic properties of the brain state to amplify or dampen surprise responses evoked by discrepant observations.
Fichier principal
Vignette du fichier
journal.pcbi.1007935.pdf (2.19 Mo) Télécharger le fichier
Origin : Publication funded by an institution

Dates and versions

inserm-02964482 , version 1 (12-10-2020)



Florent Meyniel. Brain dynamics for confidence-weighted learning. PLoS Computational Biology, 2020, 16 (6), pp.e1007935. ⟨10.1371/journal.pcbi.1007935⟩. ⟨inserm-02964482⟩
34 View
39 Download



Gmail Facebook Twitter LinkedIn More