Neurofeedback through the lens of reinforcement learning

Nitzan Lubianiker*, Christian Paret, Peter Dayan, Talma Hendler

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review


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.

Original languageEnglish
Pages (from-to)579-593
Number of pages15
JournalTrends in Neurosciences
Issue number8
StatePublished - Aug 2022


  • BCI
  • biofeedback
  • computational psychology
  • imagery
  • metacognition
  • neuromodulation


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