Predictive and Causal Coding

Predictive coding is a neural learning framework in which hierarchical layers continually generate predictions and update themselves through local prediction-error dynamics. Within this picture, learning is not treated as a single monolithic end-to-end adjustment, but as an iterative inferential process in which latent states and parameters are refined through structured exchanges of top-down prediction and bottom-up error.

Predictive and causal coding work with information-geometric principles and commutator relationships to shape how learning propagates through the system: local influence estimates, mixed-curvature structure, and small-commutator dynamics help determine where updates should go, where they should not go, and how modular competence can be preserved under continual adaptation.

Causal coding extends this framework by introducing interventional influence into the learning process, so that updates are directed preferentially toward the modules that are actually causally implicated in a given context, while clarity and pruning pressures suppress redundant or merely correlational pathways. The result is a more modular learning dynamic intended to reduce interference, bound catastrophic forgetting, and support continual adaptation under distribution shift.

Recent formulations develop this further through a two-level architecture in which Bayesian routing governs which columns or modules should be active, reused, or forked, while predictive-coding microstructures within those modules are kept internally coherent through pruning, inhibition, and shell-based consolidation. In this form, predictive and causal coding are not merely neural techniques but a broader strategy for scalable neural-symbolic integration, offering a principled account of how sub-symbolic learning dynamics, modular reuse, transfer, and emergent higher-level structure may be coordinated across the Hyperon stack.