Ep.01. The Brain That Predicts: Catching Hierarchical Predictive Coding in the Act

Your brain is not listening to this. It already guessed what you were going to hear — before you heard it — and it's just checking whether it was right. That's predictive coding theory. And in Episode 1 of The Latent State, we dig into the paper that tried to catch this process in action — in real primate brains, in real time, with 128 electrodes screwed directly into a monkey's skull. Because science. The paper is Chao et al. (2018) from Neuron — one of the most mechanistically detailed studies of hierarchical predictive coding ever published. We cover the neural architecture, the gamma and alpha/beta frequency dissociation, the data-driven decomposition method, and — because this is The Latent State — exactly where the evidence is strong, where it's overstated, and where two monkeys are just two monkeys. Welcome to the show. Paper: Chao et al. (2018). Large-Scale Cortical Networks for Hierarchical Prediction and Prediction Error in the Primate Brain. Neuron, 100, 1252–1266. Key concepts covered: Predictive coding, local-global paradigm, ECoG, PARAFAC tensor decomposition, gamma and alpha/beta oscillations, Granger causality, prediction error hierarchies, free energy principle Further reading: ​Rao & Ballard (1999), Nature Neuroscience — the computational foundation ​Clark (2013), Behavioral and Brain Sciences — the broadest theoretical synthesis ​Friston (2010), Nature Reviews Neuroscience — the free energy formulation ​Bekinschtein et al. (2009), PNAS — the original local-global paradigm