Active Inference · Paper · 2026

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

CSCIS vol 2857, Springer

Catalog Row111
Citation KeyFriedman2026DynamicAttentionalAgentsFocused111
Paper FolderAvailable
DOI10.1007/978-3-032-16955-6_11

Overview

Extracted from the local paper documentation when available.

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

Active Inferencefocused attention meditationthoughtseedsFree Energy Principlehierarchical modelingexpert-novice differencesDMNpredictive processingNeuronal Packet Hypothesiscontemplative neuroscience

Use Notes

Concise findings and methods pulled from README/SKILL documentation.

Findings / Concepts
  • A three-level hierarchical framework for focused attention meditation grounded in the Free Energy Principle and Neuronal Packet Hypothesis.
  • Formalization of thoughtseeds as transient, agent-like entities with Markov blankets competing for Global Workspace access via meta-cognitive precision weighting.
  • Biologically plausible simulations reproducing expert–novice differences: 49% lower free energy during breath focus, suppressed DMN activity (0.18 vs. 0.31), and faster distraction recovery in experts.
  • Testable predictions for meditation skill development; pathway to integrated AIF formalisms for contemplative neuroscience and computational psychiatry.
Methods / Techniques
  • Three-level hierarchical model: superordinate neuronal ensembles → thoughtseed agents (Markov blankets) → attentional networks (DMN, VAN, DAN, FPN).
  • Bidirectional message passing: bottom-up attentional state formation and top-down precision-weighted constraint.
  • Simulation across biologically plausible parameter ranges: precision weighting, complexity penalties, learning rates.
  • Comparison to neuroimaging findings on expert meditators.

Citation

Plain-text citation for quick reuse.

Friedman, Daniel Ari. 2026. Dynamic Attentional Agents in Focused Attention Meditation: Hierarchical Computational Modeling of Expert-Novice Differences. CSCIS vol 2857, Springer.

Primary source Documentation BibTeX