用模拟优化提升阅读系统，让文字呈现自动适应读者认知能力。
Simulation-based Optimization for Augmented Reading
- 将阅读优化建模为认知资源分配的仿真问题，模拟人脑处理过程。
- 提出离线探索与在线个性化双路径，无需依赖人工反复测试。
- 适合需要自适应界面的教育、出版和无障碍阅读场景。
增强阅读系统旨在通过调整文本呈现方式来提升理解力与任务表现，但现有方法多依赖启发式规则、黑箱数据模型或反复的人类参与设计循环。本文将增强阅读问题建模为基于资源理性人类阅读模型的仿真优化问题。该模型构建了模拟读者，可分配有限的认知资源（如注意力、记忆、时间）以响应任务需求，从而系统评估文本用户界面。我们提出了两种互补的优化流程：一种是利用模拟读者离线探索设计选项；另一种是基于实时交互数据在线个性化阅读界面。这一框架使增强阅读设计具备自适应性、可解释性和可扩展性，且不完全依赖人工测试。
原文摘要 · Abstract (English)
Augmented reading systems aim to adapt text presentation to improve comprehension and task performance, yet existing approaches rely heavily on heuristics, opaque data-driven models, or repeated human involvement in the design loop. We propose framing augmented reading as a simulation-based optimization problem grounded in resource-rational models of human reading. These models instantiate a simulated reader that allocates limited cognitive resources, such as attention, memory, and time under task demands, enabling systematic evaluation of text user interfaces. We introduce two complementary optimization pipelines: an offline approach that explores design alternatives using simulated readers, and an online approach that personalizes reading interfaces in real time using ongoing interaction data. Together, this perspective enables adaptive, explainable, and scalable augmented reading design without relying solely on human testing.
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