Authority-Level Priors: An Under-Specified Constraint in Hierarchical Predictive Processing

TL;DR

This paper introduces Authority-Level Priors (ALPs), a novel constraint in hierarchical predictive processing that governs which hypotheses regulate autonomic and behavioral responses under uncertainty, explaining observed asymmetries in belief updating.

Contribution

It proposes ALPs as a new meta-structural constraint that restricts hypothesis admissibility for regulation, integrating neurobiological and computational perspectives without adding representational complexity.

Findings

ALPs constrain policy selection to admissible hypotheses.

Model predicts measurable effects on stress reactivity and recovery.

Neurobiological implementation involves prefrontal control networks.

Abstract

Hierarchical predictive processing explains adaptive behaviour through precision-weighted inference. Explicit belief revision often fails to produce corresponding changes in stress reactivity or autonomic regulation. This asymmetry suggests the framework leaves under-specified a governance-level constraint concerning which identity-level hypotheses regulate autonomic and behavioural control under uncertainty. We introduce Authority-Level Priors (ALPs) as meta-structural constraints defining a regulatory-admissible subset (Hauth, a subset of H) of identity-level hypotheses. ALPs are not additional representational states nor hyperpriors over precision; they constrain which hypotheses are admissible for regulatory control. Precision determines influence conditional on admissibility; ALPs determine admissibility itself. This explains why explicit belief updating modifies representational beliefs while autonomic threat responses remain stable. A computational formalisation restricts policy optimisation to policies generated by authorised hypotheses, yielding testable predictions concerning stress-reactivity dynamics, recovery time constants, compensatory control engagement, and behavioural persistence. Neurobiologically, ALPs manifest through distributed prefrontal arbitration and control networks. The proposal is compatible with variational active inference and introduces no additional inferential operators, instead formalising a boundary condition required for determinate identity-regulation mapping. The model generates falsifiable predictions: governance shifts should produce measurable changes in stress-reactivity curves, recovery dynamics, compensatory cognitive effort, and behavioural change durability. ALPs are advanced as an architectural hypothesis to be evaluated through computational modelling and longitudinal stress-induction paradigms.