标题： 基于主动推理视角的神经反馈训练 显示英文标题

标题： An Active Inference perspective on Neurofeedback Training

摘要： 神经反馈训练（NFT）旨在通过实时反馈教授大脑活动的自我调节，但其结果高度不稳定且机制理解不足，阻碍了其验证。 为了解决这些问题，我们提出了一个NFT闭环的正式计算模型。 使用主动推理，一种建模感知、行动和学习的贝叶斯框架，我们模拟与NFT环境互动的代理。 这使我们能够测试设计选择（如反馈质量、生物标志物有效性）和受试者因素（如先验信念）对训练的影响。 仿真表明，训练效果对反馈噪声或偏差以及先验信念敏感（强调指导性指示的重要性），但也揭示了完美的反馈不足以保证高性能。 这种方法提供了一种评估和预测NFT变异性的工具，解释实证数据，并可能开发个性化训练协议。 显示英文摘要

摘要： 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.