Predictive coding

The general framework Seth imports into the interoceptive domain. Originating with von Helmholtz’s “unconscious inference” and reaching modern prominence via Friston’s free-energy principle and the Bayesian-brain hypothesis (Clark, Hohwy, Friston). See anil-seth.

Core mechanics (as summarized in Seth 2013)

• Perception is not bottom-up feature accumulation; content is specified by top-down predictions from hierarchical generative models of the causes of sensory signals.
• The brain continuously minimizes prediction error (discrepancy between inputs and model-based predictions) by either (a) updating the model or (b) acting on the world (active-inference).
• Prediction errors carry precisions (inverse variances). Precision-weighting — plausibly via post-synaptic gain modulation, and interpreted as attention — sets the relative influence of prediction errors vs prior expectations. Low precision on error signals = high confidence in priors.
• Hierarchical (“empirical”) Bayes: posteriors at one level become priors for the level below, so priors are induced from the data stream itself.

Why it matters here

Predictive coding had been developed almost entirely for exteroception (vision, audition) and action. Seth’s contribution is to note that “one of the most relevant features of the world for an organism is the organism itself” and extend the machinery inward, yielding interoceptive-inference and a predictive account of embodied-selfhood. Precision-weighting becomes the lever explaining how the same visceral arousal can be attended-to (revising feelings) or attenuated (enslaving autonomic reflexes).