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

Marcela Palejova

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.

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

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.
Paper Structure (20 sections, 1 theorem, 4 equations, 1 figure, 1 table)

This paper contains 20 sections, 1 theorem, 4 equations, 1 figure, 1 table.

Table of Contents

  1. Background and the Identified Gap
  2. Predictive Processing and Identity-Level Regulation
  3. Governance-Level Under-Specification
  4. Definition of Authority-Level Priors
  5. Computational Placement
  6. ALPs and Precision-Weighting Architecture
  7. ALPs and Policy Space Constraints
  8. ALPs and Prefrontal Arbitration Networks
  9. Functional Neurobiological Expression
  10. Dynamical Implications and Parsimony
  11. Explaining Durable vs. Non-Durable Change
  12. Insight Without Stability: The Representational-Regulatory Divide
  13. Potential Clinical Implications and Misclassification Risk
  14. Repetition Failure Under Stress: Neuromodulation vs. Governance
  15. Optimization Plateau: Residual Regulatory Constraint
  16. ...and 5 more sections

Key Result

Proposition 1

Precision determines influence within the regulatory-admissible hypothesis set, whereas Authority-Level Priors (ALPs) determine which hypotheses constitute that set.

Figures (1)

  • Figure 1: Regulatory admissibility architecture. Authority-Level Priors constrain the subset of identity hypotheses permitted to generate policies that interface with autonomic and behavioural regulation. Precision weighting operates only within this authorised subset. This distinction formalises why representational belief updating can occur without corresponding autonomic stabilisation when an adaptive hypothesis remains outside $\mathcal{H}_{\text{auth}}$.

Theorems & Definitions (1)

  • Proposition 1