基于模拟的增强阅读优化
Simulation-based Optimization for Augmented Reading
AI总结：
本文提出基于模拟的优化方法，通过资源理性模型实现可解释且可扩展的增强阅读设计，结合离线和在线优化流程提升文本用户界面的适应性和个性化能力。
摘要：
增强阅读系统旨在通过适应文本呈现来提高理解力和任务表现，但现有方法依赖于大量启发式方法、不透明的数据驱动模型或反复的人类参与设计循环。我们提出将增强阅读框架为基于模拟的优化问题，其基础是人类阅读的资源理性模型。这些模型实例化了一个模拟读者，该读者在任务需求下分配有限的认知资源，如注意力、记忆和时间，从而系统地评估文本用户界面。我们引入了两种互补的优化流程：一种是离线方法，利用模拟读者探索设计替代方案；另一种是在线方法，利用持续的交互数据实时个性化阅读界面。共同来看，这种视角使增强阅读设计能够适应、可解释和可扩展，而无需仅依赖人类测试。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
