Active Inference · Paper · 2026

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

Prakash Chandra Kavi, Daniel Ari Friedman, Gustavo Patow

CSCIS vol 2857, Springer

Catalog Row111
Citation KeyFriedman2026DynamicAttentionalAgentsFocused111
Paper FolderAvailable
Platform availability
  • ⬜ Zenodo
  • ⬜ GitHub
  • ⬜ arXiv
  • ⬜ OSF
  • ⬜ HuggingFace
  • ⬜ Software Heritage
  • ⬜ PyPI
  • Full documentation

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...

Active Inferencefocused attention meditationthoughtseedsFree Energy Principlehierarchical modelingexpert-novice differencesDMNprecision weightingNeuronal Packet Hypothesiscontemplative neuroscience

Use Notes

Concise findings and methods pulled from README/SKILL documentation.

Findings / Concepts
  • Three-level hierarchical Active Inference framework for focused attention meditation: thoughtseed agents (Markov blankets) couple to DMN/VAN/DAN/FPN; simulations reproduce 49% lower free energy and DM
Methods / Techniques
  • Free energy minimization
  • Bayesian modeling and inference

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. DOI: 10.1007/978-3-032-16955-6_11. URL: https://doi.org/10.1007/978-3-032-16955-6_11.

Primary source Documentation BibTeX

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