ArticlePLoS computational biology2020
Brain dynamics for confidence-weighted learning.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- How does explicit reliability guide choices? Effect of trustworthiness on choice and metacognition.PLoS computational biology · 2026Article
- Predictive metacognition: a neuro-computational framework for self-monitoring in large language models.Scientific reports · 2026Article
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- The Somatosensory Mismatch Negativity.The European journal of neuroscience · 2025Review
- Neural variability in the default mode network compresses with increasing belief precision during Bayesian inference.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Evolving choice hysteresis in reinforcement learning: Comparing the adaptive value of positivity bias and gradual perseveration.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Initial Expectations and Confidence Affect the Formation of Novel Self-Beliefs and Their Revision.Open mind : discoveries in cognitive science · 2025Article
- Model-Based Approaches to Investigating Mismatch Responses in Schizophrenia.Clinical EEG and neuroscience · 2025Review
- Erroneous predictive coding across brain hierarchies in a non-human primate model of autism spectrum disorder.Communications biology · 2024Article
- Fast adaptation to rule switching using neuronal surprise.PLoS computational biology · 2024Article
- Two Determinants of Dynamic Adaptive Learning for Magnitudes and Probabilities.Open mind : discoveries in cognitive science · 2024Article
- Studying the neural representations of uncertainty.Nature neuroscience · 2023Review
- Neural and computational underpinnings of biased confidence in human reinforcement learning.Nature communications · 2023Article
- Confidence of probabilistic predictions modulates the cortical response to pain.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Computational and neural mechanisms of statistical pain learning.Nature communications · 2022Article
- Active inference and the two-step task.Scientific reports · 2022Article
- A quantitative model reveals a frequency ordering of prediction and prediction-error signals in the human brain.Communications biology · 2022Article
- Neurocomputational Underpinnings of Expected Surprise.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022Article
- Article
- An implicit representation of stimulus ambiguity in pupil size.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
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1 author.
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No grant is acknowledged in the PubMed record.
Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.