AI 中文总结
研究专注注意力冥想，提出双过程计算现象学模型，有三层嵌套架构，经变分期望最大化训练，模拟结果再现相关行为，搭建起现象学与神经生理学测量间的联系。
AI 中文摘要
冥想专业技能涉及持续注意力、从分心状态快速恢复以及大规模脑网络的协调动态。我们提出了一种专注注意力冥想的计算现象学，它遍历四种吸引子状态：呼吸专注、走神、元意识和重新定向注意力。在双过程主动推理公式中，该模型实现了三层嵌套马尔可夫毯架构。通过跨专家和新手表型的变分期望最大化进行训练。模拟结果再现了与实证观察和沉思神经科学研究结果一致的行为，在第一人称现象学和客观神经生理学测量之间提供了一个易于处理的联系。
英文摘要
Meditative expertise involves sustained attention, rapid recovery from distraction, and coordinated dynamics of large-scale brain networks. We present a computational phenomenology of focused-attention meditation traversing four attractor states: breath focus, mind-wandering, meta-awareness, and redirect attention. Within a dual-process active inference formulation, the model implements a three-layer nested Markov-blanket architecture: (L1) a high-dimensional physiological neuronal substrate modeled as a stochastic multivariate Ornstein--Uhlenbeck process over attentional Yeo networks; (L2) a low-dimensional generative model (System 1) that encodes latent mental content as thoughtseeds and evaluates autonomic action tendencies; and (L3) an agentic metacognitive monitor (System 2) that implements a Global Neuronal Workspace (GNW) capacity bottleneck to selectively gate these tendencies. In L3, meta-awareness functions as the GNW ignition signal, derived from policy-prior divergence and dynamically gated by direct competition between orchestrator and distractor thoughtseeds. Policy selection actively minimizes expected free energy, and L2 actions furnish descending predictions over network activity to close the enactive perception--action cycle. Training uses variational Expectation-Maximization (EM) across expert and novice phenotypes. Simulations reproduce behavior consistent with empirical observations and findings in contemplative neuroscience, providing a tractable link between first-person phenomenology and objective neurophysiological measures.
Comments29 pages including Supplementary section. 10 figures
