Brain dynamics for confidence-weighted learning

• Published in: PLoS computational biology Vol. 16; no. 6; p. e1007935
• Main Author: Meyniel, Florent
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 02.06.2020; PLOS; Public Library of Science (PLoS)
• Subjects: Algorithms; Amplification; Arousal; Bayesian analysis; Biology and Life Sciences; Brain; Brain research; Cognitive Sciences; Cognitive tasks; Computer and Information Sciences; Confidence; EEG; Estimates; Evoked potentials; Evoked response (psychophysiology); Expected values; Learning; Life Sciences; Machine learning; Mathematical models; Neurons and Cognition; Observations; Oscillations; Physical Sciences; Physiological aspects; Predictions; Probabilistic inference; Probability; Probability learning; Research and Analysis Methods; Social Sciences; Statistical inference; Weighting; France
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1007935

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-weighting 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.