AI 中文总结
该研究提出关联自由能原理的互信息依赖非线性阈值响应模型，通过模拟验证信息-响应耦合可生成阈值依赖的非线性响应，为生物响应机制提供新视角。
AI 中文摘要
生物系统不仅能从感官输入中推断外部世界的状态，还会根据已获取的信息调整自身响应的表达。本文在不改变自由能原理（Free Energy Principle, FEP）和主动推理的标准推断与策略评估框架的前提下，提出了一种最小动力学模型，该模型将通过推断形成的外部状态与内部表征之间的互信息，与一种独立于策略选择的响应表达变量关联起来。在该模型中，已建立的信息被作为调节响应表达的状态信号。模型引入了分段非线性项，仅当互信息超过信息阈值时，响应才会被激活。在两状态马尔可夫环境中进行的蒙特卡洛模拟显示，提高观测精度会增加外部状态与内部表征之间的互信息，进而提升响应激活水平；而在无信息-响应耦合的对照条件下，响应始终维持在基线水平。当改变观测精度、信息-响应耦合系数、信息阈值以及响应与感官采样关联的闭环系数时，响应激活的基本模式保持不变。这些结果表明，将FEP一致推断形成的信息与独立的响应表达动力学耦合，可生成依赖于信息阈值的非线性响应。
英文摘要
Biological systems not only infer states of the external world from sensory input but also vary the expression of their responses according to the information they have acquired. Here, without altering the standard inferential and policy-evaluation schemes of the Free Energy Principle (FEP) and active inference, we propose a minimal dynamics that links the mutual information formed between an external state and an internal representation through inference to a response-expression variable distinct from policy selection. In this formulation, established information is positioned as a state signal that modulates response expression. The model introduces a piecewise nonlinear term in which response activation is driven only when mutual information exceeds an information threshold. Monte Carlo simulations using a two-state Markov environment showed that increasing observation accuracy increased the mutual information between the external state and the internal representation and, in turn, increased response activation. By contrast, in a control condition without information-response coupling, the response remained at its baseline level. The basic pattern of response activation was preserved when observation accuracy, the information-response coupling coefficient, the information threshold, and the closed-loop coefficient linking response to sensory sampling were varied. These results show that coupling information formed by FEP-consistent inference to an independent response-expression dynamics can generate a nonlinear response that depends on an information threshold.
