Nearly all of 64 simulated agents attain 100% performance at around the 10th trial when allowed to perform abductive Bayesian model reduction after each trial.

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• In single-agent simulation of 32 trials, performance becomes perfect after trial 14 without Bayesian model reduction, with confidence increasing progressively.finding0.887
  Baseline learning curve for pure epistemic learning without structure learning
• Simulated agent achieves perfect performance after trial 12 following Bayesian model reduction (sleep), versus ~14 trials without BMR.finding0.855
  Key result showing BMR dramatically accelerates rule learning in simulation
• Bayesian model reduction after 12 trials correctly removes off-diagonal (redundant) parameters from the likelihood array, recovering the true contingency structure.finding0.815
  Validation that BMR correctly identifies and prunes wrong connections in the likelihood mapping
• 3 of 64 simulated agents exhibited superstitious (incorrect) abduction, leading to persistently poor performance, demonstrating a trade-off between ampliative benefit and susceptibility to false insight.finding0.804
  Demonstration of failure mode of abductive model reduction
• Human participants in the rule-learning paradigm should acquire insight after approximately 7–8 trials, fewer than the ~14 required by Bayes-optimal inference without model reduction, suggesting they perform Bayesian model selection.hypothesis0.803
  Based on informal audience experiments; implies people use prior knowledge about rule structure
• In simulations, positive evidence threshold for Bayesian model reduction corresponds to ΔF ≤ −3, equivalent to odds ratio of exp(−3) ≈ 0.05 (reduced model ~20 times more likely than full model).finding0.800
  Quantitative threshold used for accepting reduced models; linked to Bayes factor of ~20
• The results of abductive reasoning (reduced model priors) can be communicated to other agents as prior beliefs, provided all agents share the same model lexicon or hypothesis space.claim0.793
  Explanation of how knowledge (not just parameters) is shared between agents; links to pre-Cartesian consciousness
• A naive agent equipped with reduced priors from an experienced agent performs perfectly with maximum confidence from the first trial.finding0.789
  Demonstration that model-level priors (not parameter-level knowledge) suffice for immediate transfer