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Neurofeedback through the lens of reinforcement learning: opinion

Despite decades of experimental and clinical practice, the neuropsychological mechanisms underlying neurofeedback (NF) training remain obscure. NF is a unique form of reinforcement learning (RL) task, during which participants are provided with rewarding feedback regarding desired changes in neural...

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Hauptverfasser: Lubianiker, Nitzan (VerfasserIn) , Paret, Christian (VerfasserIn) , Dayan, Peter (VerfasserIn) , Hendler, Talma (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: August 2022
In: Trends in neurosciences
Year: 2022, Jahrgang: 45, Heft: 8, Pages: 579-593
ISSN:1878-108X
DOI:10.1016/j.tins.2022.03.008
Online-Zugang:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.tins.2022.03.008
Verlag, lizenzpflichtig, Volltext: https://www.sciencedirect.com/science/article/pii/S0166223622000595
Volltext
Verfasserangaben:Nitzan Lubianiker, Christian Paret, Peter Dayan, and Talma Hendler

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520 |a Despite decades of experimental and clinical practice, the neuropsychological mechanisms underlying neurofeedback (NF) training remain obscure. NF is a unique form of reinforcement learning (RL) task, during which participants are provided with rewarding feedback regarding desired changes in neural patterns. However, key RL considerations - including choices during practice, prediction errors, credit-assignment problems, or the exploration-exploitation tradeoff - have infrequently been considered in the context of NF. We offer an RL-based framework for NF, describing different internal states, actions, and rewards in common NF protocols, thus fashioning new proposals for characterizing, predicting, and hastening the course of learning. In this way we hope to advance current understanding of neural regulation via NF, and ultimately to promote its effectiveness, personalization, and clinical utility. 
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