An Active Inference perspective on Neurofeedback Training

TL;DR

This paper introduces a computational model based on Active Inference to analyze and predict the variability and effectiveness of Neurofeedback Training, highlighting the influence of feedback quality and prior beliefs.

Contribution

It presents a novel Bayesian framework simulation of NFT, enabling assessment of design choices and individual factors affecting training outcomes.

Findings

Training effectiveness is affected by feedback noise and bias.

Prior beliefs significantly influence training success.

Perfect feedback alone does not ensure high performance.

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.