An Active Inference perspective on Neurofeedback Training
This work addresses the validation and improvement of neurofeedback training for clinical or research applications, but it is incremental as it applies an existing framework to a specific domain.
The paper tackled the problem of variable outcomes and unclear mechanisms in neurofeedback training by proposing a computational model using Active Inference to simulate the training loop, showing that effectiveness is sensitive to feedback quality and prior beliefs, with perfect feedback not guaranteeing high performance.
Neurofeedback training (NFT) aims to teach self-regulation of brain activity through real-time feedback, but suffers from highly variable outcomes and poorly understood mechanisms, hampering its validation. To address these issues, we propose a formal computational model of the NFT closed loop. Using Active Inference, a Bayesian framework modelling perception, action, and learning, we simulate agents interacting with an NFT environment. This enables us to test the impact of design choices (e.g., feedback quality, biomarker validity) and subject factors (e.g., prior beliefs) on training. Simulations show that training effectiveness is sensitive to feedback noise or bias, and to prior beliefs (highlighting the importance of guiding instructions), but also reveal that perfect feedback is insufficient to guarantee high performance. This approach provides a tool for assessing and predicting NFT variability, interpret empirical data, and potentially develop personalized training protocols.