AI 中文总结
本文为具身智能系统提出动机情感心智架构的数学模型，通过动机学习与再入回路形式化，整合感知、记忆和情感控制，以维持内稳态并优化行动选择。
AI 中文摘要
本文提出了一个用于具身智能系统的动机情感心智认知架构的数学模型。此类系统通过一种基于其内部动机的广义强化学习形式（称为动机学习，ML）来学习维持其内稳态。本文的主要贡献是对整合前馈处理、侧向交互和反馈通路的再入回路，以及支配适应性系统响应的表征选择机制进行了严格的形式化。该模型规定了持续的外感受和内感受信号、身体动机背景和记忆痕迹如何被绑定到称为“相似体”（semblions）的联想记忆结构中，这些结构竞争进一步处理和自上而下重建的访问权。该形式化涵盖了次级感知、表征竞争、好奇心、程序性缺口以及旨在限制稳态失调的行动选择。在此框架内，动机学习被定制用于其动力学由需求、情感和当前调节状态塑造的具身系统。与标准强化学习模型不同，所提出的方法纳入了需求阈值、目标生成和目标转移、身体状态、资源约束和行动不确定性，从而在调节压力下提供了对反应选择的更充分解释。全局情感作为中央控制信号，调节学习率、表征效价以及探索与利用之间的平衡。本文提出的模型是迈向更严格形式化认知现象的一步，并可能为受生物过程启发的人工智能系统中的进一步理论分析、计算机模拟和实现提供基础。
英文摘要
This article presents a mathematical model of the Motivated Emotional Mind cognitive architecture developed for embodied intelligent systems. Such a system learns to maintain its homeostasis through a generalized form of reinforcement learning based on its internal motivations, termed motivated learning (ML). The principal contribution of this article is a rigorous formalization of the re-entrant loop integrating feedforward processing, lateral interactions, and feedback pathways, together with the representational selection mechanisms that govern adaptive system responses. The model specifies how ongoing exteroceptive and interoceptive signals, bodily-motivational context, and memory traces are bound into associative memory structures termed semblions, which compete for access to further processing and top-down reconstruction. The formalization encompasses secondary perception, representational competition, curiosity, procedural gaps, and action selection directed toward limiting allostatic violations. Within this framework, motivated learning is tailored to embodied systems whose dynamics are shaped by needs, affect, and the current regulatory state. Unlike standard reinforcement-learning models, the proposed approach incorporates need thresholds, goal generation and shifting goals, bodily state, resource constraints, and action uncertainty, thereby providing a more adequate account of response selection under regulatory pressure. Global affect functions as a central control signal, modulating the learning rate, representational valence, and the balance between exploration and exploitation. The model presented here is a step toward a more rigorous formalization of cognitive phenomena and may provide a basis for further theoretical analysis, computer simulation, and implementation in artificial-intelligence systems inspired by biological processes.
