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 (Default Mode Network [DMN], Ventral Attention Network [VAN], Dorsal Attention Network [DAN], Frontoparietal Network [FPN]). Thoughtseeds, emerging from superordinate neuronal ensembles, compete for Global Workspace access, modulated by meta-cognitive precision weighting to stabilize attention. This model advances the Thoughtseeds Framework toward a computational phenomenology of Vipassana, setting the stage f