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.

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• Do human participants demonstrate the same insight dynamics predicted by active inference in the rule-learning paradigm (currently under investigation with eye tracking and crowd-sourced reaction times)?question0.831
  Empirical gap explicitly acknowledged; experiments reportedly in progress at time of writing
• 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.finding0.803
  Group-level simulation result showing generalizability of BMR benefit across agents
• In the absence of prior preferences, Active Inference null model and Bayesian RL maintain exploration with average scores of 44.00 and 39.94 respectively, whereas Q-learning does not explore.finding0.794
  Table 2 first row; reward shaping section.
• Sleep (Bayesian model reduction) should improve performance on rule-learning tasks, with the prevalence of insight-dependent performance changes roughly doubling after nocturnal sleep.hypothesis0.792
  Prediction consistent with Wagner et al. (2004) finding; extended to the active inference account of sleep
• Under reward shaping (G=100, H=-100, F=0), Active Inference scored 99.52, Bayesian RL 99.77, Q-learning 95.56, with nearly identical behavior between belief-based agents.finding0.778
  Table 2, row 3, showing equivalence when prior preferences match rewards.
• 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).finding0.776
  Quantitative threshold used for accepting reduced models; linked to Bayes factor of ~20
• Bayesian model reduction after 12 trials correctly removes off-diagonal (redundant) parameters from the likelihood array, recovering the true contingency structure.finding0.775
  Validation that BMR correctly identifies and prunes wrong connections in the likelihood mapping
• If loss keeps going down on the test set, in the limit the model must be learning to interpret and predict all patterns represented in language, including common-sense reasoning, goal-directed optimization, and deployment of the sum of recorded human knowledge.hypothesis0.771
  Extrapolation of scaling predictive models to AGI.