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.

翻译：层级预测处理通过精度加权推理解释适应性行为。显式信念修正通常无法对应产生应激反应性或自主神经调节的变化。这种不对称性表明，该框架对一种治理层级约束的表述尚不充分——这种约束涉及在不确定性条件下，哪些身份层级假设能够调控自主神经与行为控制。我们提出权威层级先验（ALPs）作为元结构约束，用以定义身份层级假设中具有调控适配性的子集（H_authority ⊆ H）。ALPs既非附加的表征状态，亦非关于精度的超先验；它们约束哪些假设可被允许进行调控控制。精度在受允许条件下决定影响程度，而ALPs则决定允许性本身。这解释了为何显式信念更新能改变表征性信念，而自主神经威胁反应仍保持稳定。一种计算形式化方法将策略优化限制于经授权假设生成的策略，产生关于应激反应动力学、恢复时间常数、补偿性控制投入及行为持续性的可检验预测。从神经生物学角度，ALPs通过前额叶分布式仲裁与控制网络实现。该提案与变分主动推理相容，未引入额外推理算子，而是形式化了确定性身份-调控映射所需的边界条件。该模型生成可证伪预测：治理转移应导致应激反应曲线、恢复动力学、补偿性认知努力及行为改变持久性的可测量变化。ALPs作为一种结构假设被提出，需通过计算建模与纵向应激诱导范式进行评估。