人类感知、认知和决策动力学的模块化状态空间模型
A modular state-space model of human perception, cognition, and decision dynamics
AI总结：
研究针对人类行为模型存在的问题，提出模块化状态空间模型，通过耦合数学映射表示相关过程，建立多种特性条件，经数值分析和案例研究验证其有效性，为人类中心环境下的相关控制提供白箱动态结构。
AI中文摘要：
以人类为中心的自适应系统需要心理上可解释且数学上可分析的行为模型。许多现有预测器要么是黑箱输入输出映射，要么对潜在内部动态的访问有限。本文通过将行为建模为感知 - 认知 - 决策管道来解决这一差距。我们提出了一个模块化状态空间模型，其中注意力选择、预测推理、认知状态演变、意图形成和动作选择由耦合数学映射表示。该模型通过潜在内部状态将感官输入与可观察行为联系起来，同时保持与神经认知机制的可解释连接。我们建立了有界性、利普希茨正则性、正向不变性、恒定输入下感知推理的收缩性以及认知状态动态的输入到状态稳定性的充分条件。数值敏感性分析表明，该模型在感知跟踪、认知放大、意图表达和动作决定性方面产生了可解释的变化。我们进一步展示了一个闭环康复案例研究，其中滚动时域控制器使用该模型根据部分反馈调整运动难度。在这个概念验证设置中，基于模型的控制器维持了模拟任务参与，并实现了比目标跟踪和随机基线更低的实际累积成本。总体而言，该框架为以人类为中心的环境中的估计、验证和基于模型的控制提供了一个白箱动态结构。
英文摘要：
Human-centered adaptive systems require behavioral models that are both psychologically interpretable and mathematically analyzable. Many existing predictors either operate as black-box input-output mappings or provide limited access to latent internal dynamics. This paper addresses this gap by modeling behavior as a perception-cognition-decision pipeline. We propose a modular state-space model in which attentional selection, predictive inference, cognitive-state evolution, intention formation, and action selection are represented by coupled mathematical mappings. The model links sensory inputs to observable behavior through latent internal states while retaining interpretable connections to neuro-cognitive mechanisms. We establish sufficient conditions for boundedness, Lipschitz regularity, forward invariance, contraction of perceptual inference under constant input, and input-to-state stability of the cognitive state dynamics. Numerical sensitivity analyses show that the model yields interpretable changes in perceptual tracking, cognitive amplification, intention expression, and action decisiveness. We further demonstrate a closed-loop rehabilitation case study in which a receding-horizon controller uses the model to adapt movement difficulty from partial feedback. In this proof-of-concept setting, the model-based controller sustains simulated task participation and achieves lower realized cumulative cost than target-following and random baselines. Overall, the framework provides a white-box dynamical structure for estimation, validation, and model-based control in human-centered settings.
