单状态本体模型中的上下文性：信息论上的障碍
Contextual Information Allocation in Shared-State Cognitive Models: An Information-Theoretic Bound
AI总结：
本文研究了单状态本体模型中上下文性的信息论限制，指出在共享状态重用的情况下，上下文信息需由条件互信息下界约束，揭示了子系统经典表示的局限性。
摘要：
上下文性是量子理论的核心特征，传统上被理解为无法用非上下文本体模型复现量子测量统计。本文研究了经典本体描述，其中固定子系统层次本体状态空间在多个干预中被重复使用。主要结果是一个信息论障碍：当经典单状态模型使用辅助上下文寄存器复现操作统计时，所需的上下文信息由干预C与结果O在子系统本体状态λ条件下的条件互信息I(C;O|λ)下界约束。该数学不等式本身简单，但其解释意义是结构性的：在共享状态重用下，上下文区分无需完全内化于子系统本体状态本身。我们提供了构造性示例，阐明该问题应被视为子系统层次经典表示的限制，而非物理现实的二元论。我们进一步讨论了该视角与本体模型及量子基础中的上下文性关系。
英文摘要：
Context-sensitive behavior can be modeled by enriching an internal state, by allowing a response rule to access context directly, or by preserving a shared state while introducing an auxiliary criterion or control variable. This paper isolates an information-theoretic constraint on the third architecture. Let $C$ denote context, $S$ a candidate internal or latent state, $O$ an observable response, and $M$ an auxiliary variable such that $O\perp C\mid(S,M)$. Then \[ I(C;O\mid S)\le I(C;M\mid S)\le H(M\mid S). \] Once a shared state has been specified, residual context dependence in behavior therefore lower-bounds both the context information and the conditional entropy that an auxiliary context-mediating mechanism must carry. The bound is representation-relative rather than a measure of state-space size or a universal contextuality measure. A worked recognition-memory example shows how the quantity can be computed for payoff-induced criterion shifts and compared across alternative representational allocations. Ontological contextuality and quantum probability are treated as a separate specialization rather than as prerequisites for the cognitive interpretation. More broadly, the framework provides a basis for analyzing context-memory-control trade-offs in cognitive models and artificial agents that must maintain coherent behavior across changing contexts under bounded internal representations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
