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 for integrated AIF formalisms and implementation, on top of rules-based statistical learning. Simulations reveal how across ranges of biologically plausible parameters—precision weighting (0.5 vs. 0.4), complexity penalties (0.2 vs. 0.4), learning rates (0.02 vs. 0.01)— patterns associated with expert-novice differences arise from the “in silico” model. Experts with higher precision weighting and higher learning rate, notably achieve 49% lower free energy during breath focus, suppressed DMN activity (0.18 vs. 0.31), and faster distraction recovery, consistent with neuroimaging findings. Bidirectional message passing enables bottom-up formation of attentional states and top-down constraint of dynamics, offering a mechanistic account of expertise as optimized precision allocation. This framework provides testable predictions for meditation skill development, with future extensions planned to enhance AIF rigor and computational phenomenology for applications in contemplative neuroscience, computational psychiatry, and cognitive training.