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  Neurofeedback through the lens of reinforcement learning

Lubianiker, N., Paret, C., Dayan, P., & Hendler, T. (2022). Neurofeedback through the lens of reinforcement learning. Trends in Neurosciences, 45(8), 579-593. doi:10.1016/j.tins.2022.03.008.

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Lubianiker, N, Author
Paret, C, Author
Dayan, P1, Author           
Hendler, T, Author
Affiliations:
1Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_3017468              

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 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.

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 Dates: 2022-052022-08
 Publication Status: Published in print
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 Identifiers: DOI: 10.1016/j.tins.2022.03.008
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Title: Trends in Neurosciences
  Other : Trends Neurosci.
Source Genre: Journal
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Publ. Info: Oxford : Elsevier Current Trends
Pages: - Volume / Issue: 45 (8) Sequence Number: - Start / End Page: 579 - 593 Identifier: ISSN: 0166-2236
CoNE: https://pure.mpg.de/cone/journals/resource/954927741850