CONCEPT
Predictive Processing
Clark's framework in which the brain is fundamentally a prediction machine — generating top-down expectations about sensory input and learning from the errors when predictions miss.
Predictive processing is Andy Clark's theory of biological cognition, developed most fully in Surfing Uncertainty (2015) and The Experience Machine (2023). Its central claim is that the brain does not passively receive sensory information and then figure out what to do with it. Instead, the brain constantly generates top-down predictions about what sensory signals should look like, compares those predictions to actual incoming signals, and learns from the discrepancies — the prediction errors — that result. Perception is not interpretation. It is the brain's best prediction, updated by error when expectations fail. The framework turns out to be eerily relevant to AI, not because Clark designed it that way but because biological brains and large language models appear to share a deep computational principle.
In The You On AI Field Guide
The framework draws on a tradition going back to Hermann von Helmholtz in the nineteenth century, who first proposed that perception is unconscious inference. Twentieth-century computational neuroscientists — Karl Friston most notably — developed the mathematical