用理性决策框架模拟人眼注意力分配，更真实且可解释。
A Resource-Rational Principle for Modeling Visual Attention Control
- 将视觉任务建模为受限条件下的最优控制问题
- 在阅读与边走边读场景中复现经典注意力现象
- 适合关注人机交互设计与认知建模的研究者
理解人类如何分配视觉注意力是人机交互（HCI）的核心问题。现有计算模型多为描述性、任务特定或难以解释。本文提出一种资源理性、基于仿真的注意力建模框架，将阅读与多任务等视觉任务形式化为部分可观测马尔可夫决策过程（POMDP），使注视与注意力切换等眼动行为源于理性适应，而非手动编码或纯数据驱动。模型在传统文本阅读及智能眼镜边走边读等仿真环境中实现，能重现经典实验效应，解释理解力与安全性之间的权衡，并在时间压力与界面变化下生成新预测。该工作提供统一的视觉注意力计算框架，为理论驱动、资源高效的HCI设计提供新工具。
原文摘要 · Abstract (English)
Understanding how people allocate visual attention is central to Human-Computer Interaction (HCI), yet existing computational models of attention are often either descriptive, task-specific, or difficult to interpret. My dissertation develops a resource-rational, simulation-based framework for modeling visual attention as a sequential decision-making process under perceptual, memory, and time constraints. I formalize visual tasks, such as reading and multitasking, as bounded-optimal control problems using Partially Observable Markov Decision Processes, enabling eye-movement behaviors such as fixation and attention switching to emerge from rational adaptation rather than being hand-coded or purely data-driven. These models are instantiated in simulation environments spanning traditional text reading and reading-while-walking with smart glasses, where they reproduce classic empirical effects, explain observed trade-offs between comprehension and safety, and generate novel predictions under time pressure and interface variation. Collectively, this work contributes a unified computational account of visual attention, offering new tools for theory-driven and resource-efficient HCI design.
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