Structure learning via Bayesian model reduction has a clear biological interpretation in terms of synaptic decay and switching off certain synaptic connections, reminiscent of REM sleep.

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• Sleep implements Bayesian model reduction: non-REM sleep performs synaptic regression (complexity reduction) and REM sleep re-evaluates posteriors under the new reduced model.claim0.850
  Neurobiological interpretation linking sleep stages to distinct computational roles in model optimization
• Sleep (Bayesian model reduction) should improve performance on rule-learning tasks, with the prevalence of insight-dependent performance changes roughly doubling after nocturnal sleep.hypothesis0.817
  Prediction consistent with Wagner et al. (2004) finding; extended to the active inference account of sleep
• Bayesian model reduction after 12 trials correctly removes off-diagonal (redundant) parameters from the likelihood array, recovering the true contingency structure.finding0.803
  Validation that BMR correctly identifies and prunes wrong connections in the likelihood mapping
• Bayesian model reduction formalises post-hoc hypothesis testing to simplify the generative model.claim0.795
  Definition of Bayesian model reduction, Section 9.1.
• When a model discovers that its outputs produce effects, it accelerates learning through in-context learning, analogous to lucid dreaming.claim0.776
  Describes scaffolding method and the model's meta-learning loop.
• 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.claim0.771
  Central claim about the power of connectionism.
• The huge chemical complexity within each synapse suggests that neural models of cognition that refer only to changing weights of synaptic connections and ignore sub-neural chemistry are probably ignoring some of the most important explanatory mechanisms in brains.claim0.770
  Sloman's critique of mainstream neural network theories.
• Buddhist awakening can be formalised as Bayesian model reduction of the separation prior sigma, yielding a post-dual agent with unconstrained QRF deploymentsclaim0.766
  Central theoretical contribution of the paper unifying contemplative path with active inference framework