Dynamic Attentional Agents in Focused Attention Meditation: Hierarchical Computational Modeling of Expert-Novice Differences

We develop a three-level hierarchical framework to model the attentional dynamics of focused attention (FA) meditation, laying a foundation for advanced active inference (AIF) implementations. Grounded in the Free Energy Principle and Neuronal Packet Hypothesis, we conceptualize meditation as a predictive processing system where “thoughtseeds”—transient, agent-like entities forming Markov blankets—minimize variational free energy via bidirectional coupling with attentional networks (DMN, VAN, DAN, FPN). Simulations across biologically plausible parameters reproduce expert–novice differences: 49% lower free energy during breath focus, suppressed DMN activity (0.18 vs. 0.31), and faster distraction recovery.

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