Year: 2025
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Institutions: Institut national de recherche en sciences et technologies du numérique, Inserm, Centre de Recherche en Neurosciences de Lyon, Centre Inria de l'université de Bordeaux, Laboratoire Bordelais de Recherche en Informatique, Calcul, Cognition et Neurophysiologie, POTIOC: Nouvelles Interactions Multimodales pour une Expérience Utilisateur Stimulante
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W6892044518
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