Neurofeedback through the lens of reinforcement learning

• Verfasst von: Lubianiker, Nitzan [VerfasserIn]
• Verfasst von: Paret, Christian [VerfasserIn]
• Verfasst von: Dayan, Peter [VerfasserIn]
• Verfasst von: Hendler, Talma [VerfasserIn]
• Titel: Neurofeedback through the lens of reinforcement learning
• Titelzusatz: opinion
• Verf.angabe: Nitzan Lubianiker, Christian Paret, Peter Dayan, and Talma Hendler
• E-Jahr: 2022
• Jahr: August 2022
• Umfang: 15 S.
• Illustrationen: Illustrationen
• Fussnoten: Online verfügbar: 10 May 2022, Artikelversion: 19 July 2022 ; Gesehen am 24.10.2023
• Titel Quelle: Enthalten in: Trends in neurosciences
• Ort Quelle: Amsterdam [u.a.] : Elsevier Science, 1978
• Jahr Quelle: 2022
• Band/Heft Quelle: 45(2022), 8 vom: Aug., Seite 579-593
• ISSN Quelle: 1878-108X
• DOI: doi:10.1016/j.tins.2022.03.008
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: BCI
• Sach-SW: biofeedback
• Sach-SW: computational psychology
• Sach-SW: imagery
• Sach-SW: metacognition
• Sach-SW: neuromodulation
• K10plus-PPN: 1867427257

Abstract

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.