This episode unpacks the Bayesian brain and predictive processing: how the brain builds hierarchical generative models, minimizes prediction error and free energy, and uses precision (neuromodulators like dopamine, acetylcholine, norepinephrine) to weight surprises. It opens with the pilot "black hole" illusion and other real-world examples to show perception as active prediction.
Hosts and expert commentary explore active inference versus classic reinforcement learning, how dysregulated prediction can produce hallucinations and delusions, empirical and pharmacological evidence, major critiques of the framework, and a practical sensory prediction journaling exercise you can try.
Key takeaways include the roles of hierarchical priors, prediction-error signaling, precision-weighting, implications for mental health (ketamine, psychedelics), and how predictive ideas are shaping generative AI and control systems—offering a unifying lens for biological and artificial intelligence.

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