Trial reportProceedings of the National Academy of Sciences of the United States of America2017
Brain networks for confidence weighting and hierarchical inference during probabilistic learning.
Trial report in Proceedings of the National Academy of Sciences of the United States of America, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
52 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Separate neural representations of prediction error valence and surprise: Evidence from an fMRI meta-analysis.Human brain mapping · 2018Pooled it
- Perceiving the uncertain: predictive coding and individual differences in the dynamics of multistable perception.Cognitive processing · 2026Review
- Metabolic interactions in the brain: the crucial roles of neurons, astrocytes, and microglia in health and disease.Frontiers in neuroscience · 2026Review
- A non-monotonic code for event probability in the human brain.Nature communications · 2025Article
- The Somatosensory Mismatch Negativity.The European journal of neuroscience · 2025Review
- An Information-Theoretic Framework for Understanding Learning and Choice Under Uncertainty.Entropy (Basel, Switzerland) · 2025Article
- Neural variability in the default mode network compresses with increasing belief precision during Bayesian inference.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Data-driven equation discovery reveals nonlinear reinforcement learning in humans.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Neural mechanisms of confidence propagation in hierarchical partially observable decision-making.iScience · 2025Article
- Direct brain recordings reveal implicit encoding of structure in random auditory streams.Scientific reports · 2025Article
- Brain network dynamics predict moments of surprise across contexts.Nature human behaviour · 2025Article
- Model-Based Approaches to Investigating Mismatch Responses in Schizophrenia.Clinical EEG and neuroscience · 2025Review
- Initial Expectations and Confidence Affect the Formation of Novel Self-Beliefs and Their Revision.Open mind : discoveries in cognitive science · 2025Article
- Information-Theoretic Measures of Metacognition: Bounds and Relation to Group Performance.Open mind : discoveries in cognitive science · 2025Article
- Predictive learning shapes the representational geometry of the human brain.Nature communications · 2024Article
- Statistical learning shapes pain perception and prediction independently of external cues.eLife · 2024Article
- Belief inference for hierarchical hidden states in spatial navigation.Communications biology · 2024Article
- Article
- Two Determinants of Dynamic Adaptive Learning for Magnitudes and Probabilities.Open mind : discoveries in cognitive science · 2024Article
- Confidence of probabilistic predictions modulates the cortical response to pain.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Learning is difficult when the world fluctuates randomly and ceaselessly. Classical learning algorithms, such as the delta rule with constant learning rate, are not optimal. Mathematically, the optimal learning rule requires weighting prior knowledge and incoming evidence according to their respective reliabilities. This "confidence weighting" implies the maintenance of an accurate estimate of the reliability of what has been learned. Here, using fMRI and an ideal-observer analysis, we demonstrate that the brain's learning algorithm relies on confidence weighting. While in the fMRI scanner, human adults attempted to learn the transition probabilities underlying an auditory or visual sequence, and reported their confidence in those estimates. They knew that these transition probabilities could change simultaneously at unpredicted moments, and therefore that the learning problem was inherently hierarchical. Subjective confidence reports tightly followed the predictions derived from the ideal observer. In particular, subjects managed to attach distinct levels of confidence to each learned transition probability, as required by Bayes-optimal inference. Distinct brain areas tracked the likelihood of new observations given current predictions, and the confidence in those predictions. Both signals were combined in the right inferior frontal gyrus, where they operated in agreement with the confidence-weighting model. This brain region also presented signatures of a hierarchical process that disentangles distinct sources of uncertainty. Together, our results provide evidence that the sense of confidence is an essential ingredient of probabilistic learning in the human brain, and that the right inferior frontal gyrus hosts a confidence-based statistical learning algorithm for auditory and visual sequences.
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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.