用主动推理框架解释神经反馈训练机制，揭示效果波动原因。
An Active Inference perspective on Neurofeedback Training
- 用主动推理建模神经反馈闭环，统一解释感知、动作与学习
- 发现反馈噪声和先验信念显著影响训练效果，完美反馈也不保证成功
- 适合想理解神经反馈原理或设计个性化训练的研究者
神经反馈训练（NFT）旨在通过实时反馈教会个体自我调节脑活动，但其效果高度可变且机制不明，阻碍了验证。为此，我们提出了一个形式化的计算模型，将NFT闭环置于主动推理（Active Inference）这一贝叶斯框架下进行建模。该框架同时刻画感知、行动与学习过程，使我们能模拟智能体在不同设计选择（如反馈质量、生物标志物有效性）和个体因素（如先验信念）下的交互行为。仿真结果显示，训练效果对反馈噪声或偏差敏感，且受先验信念影响显著（凸显引导说明的重要性），但即使反馈完美也无法确保高性能。该方法为评估和预测NFT变异性、解释实证数据以及开发个性化训练方案提供了工具。
原文摘要 · Abstract (English)
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.
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