ArticleFrontiers in human neuroscience2017
Functions of Learning Rate in Adaptive Reward Learning.
Article in Frontiers in human neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Effects of combined virtual reality assisted gait training and proprioceptive neuromuscular facilitation on motor function in children with spastic cerebral palsy: a randomized controlled trial.Acta neurologica Belgica · 2026Article
- Beyond Needling: Integrating a Bayesian Brain Model into Acupuncture Treatment.Brain sciences · 2025Review
- Sensation seeking and risk adjustment: the role of reward sensitivity in dynamic risky decisions.Frontiers in behavioral neuroscience · 2025Article
- Neural correlates of model-based behavior in internet gaming disorder and alcohol use disorder.Journal of behavioral addictions · 2024Article
- A New Explanation for the Frog-in-the-Pan Phenomenon Based on the Cognitive-Evolutionary Model of Surprise.Behavioral sciences (Basel, Switzerland) · 2022Article
- Relative salience signaling within a thalamo-orbitofrontal circuit governs learning rate.Current biology : CB · 2021Article
- Additively Combining Utilities and Beliefs: Research Gaps and Algorithmic Developments.Frontiers in neuroscience · 2021Article
- A dichoptic feedback-based oculomotor training method to manipulate interocular alignment.Scientific reports · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As a crucial cognitive function, learning applies prediction error (the discrepancy between the prediction from learning and the world state) to adjust predictions of the future. How much prediction error affects this adjustment also depends on the learning rate. Our understanding to the learning rate is still limited, in terms of (1) how it is modulated by other factors, and (2) the specific mechanisms of how learning rate interacts with prediction error to update learning. We applied computational modeling and functional magnetic resonance imaging to investigate these issues. We found that, when human participants performed a reward learning task, reward magnitude modulated learning rate. Modulation strength further predicted the difference in behavior following high vs. low reward across subjects. Imaging results further showed that this modulation was reflected in brain regions where the reward feedback is also encoded, such as the medial prefrontal cortex (MFC), precuneus, and posterior cingulate cortex. Furthermore, for the first time, we observed that the integration of the learning rate and the reward prediction error was represented in MFC activity. These findings extend our understanding of adaptive learning by demonstrating how it functions in a chain reaction of prediction updating.
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Registered trials
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.