Whole-brain modelling demonstrates that jhana states are associated with dynamics approaching criticality using neurophenomenological methods (Vohryzek et al. 2025)

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• Experienced meditators in jhana/dhyana states exhibit a shift toward a metastable near-critical regime characterised by increased neural signal diversity, reduced chaoticity and enhanced perturbational sensitivity (Mago et al. 2025)finding0.826
  Empirical convergence with the paper's criticality prediction for post-dual agents
• Buddhist phenomenology's careful attention to dependent origination makes tanha-like building blocks particularly likely to have simple, elegant neural implementations.claim0.791
  Argues that Buddhist analysis primes us to find clean neural mechanisms.
• Our central claim is deliberately limited. We do not claim that these models have conscious felt experience, nor that a numeric self-report gives direct access to anything like human phenomenology.quote0.775
  Explicit scope delimitation that situates the paper's claims within interpretability rather than consciousness science
• Connectionist models of cognition and learning identify conditions where collective intelligence can arise bottom-up, using only distributed learning mechanisms without system-level or global feedback.claim0.772
  Central claim about the power of connectionism.
• In response to disequilibrium either within an agent's own cognitive organization or between the agent's cognitive state and other internal dynamics, a person's current frame of mind can undergo a phase change and recalibrate to rectify the disequilibrium.concept0.770
• If sub-neural chemical dynamics are crucial for cognition, then neural models ignoring them will fail to capture key cognitive phenomena.hypothesis0.768
  Sloman's implicit hypothesis behind his critique of synaptic weight-only models.
• The ability to represent relationships (e.g. correlations or associations) among variables, rather than a system of independent variables, is essential for any connectionist model of cognition.claim0.762
  Necessary condition for connectionist cognition.
• H2: The conditions necessary for the induction of deep models, familiar in connectionist models of learning and cognition, are predictive of the conditions necessary for an ETI to occur.hypothesis0.760
  Second hypothesis linking learning theory directly to evolutionary transitions