AI 中文总结
该研究通过两项实验，采用LBA框架建模用户决策，发现分散注意力会放大与期望一致的可视化设计的优势，凸显了该场景下此类设计的重要性。
AI 中文摘要
已有研究表明，当可视化解读是用户的唯一任务时，可视化设计会影响可解释性。然而在现实场景中，用户往往在执行并发任务时接触可视化内容，比如同时监控警报或回复消息。这种分散注意力可能会改变用户解读可视化的方式，进而提升与用户期望一致的设计的重要性。我们通过两项实验验证了这一可能性，对比了单任务与双任务条件下的可视化解读情况。具体而言，我们考察了在用户执行并发任务时，颜色、空间位置与语义概念之间已确立的推断映射，在无限制查看时间（实验1）和有限查看时间（实验2）下对解读的影响。结果显示，分散注意力会放大与期望一致的设计和违背期望的设计之间的性能差距，影响响应时间、解读准确率以及在时间约束下做出判断的能力。为解释这些结果，我们采用线性弹道累加器（Linear Ballistic Accumulator，LBA）框架对用户的决策过程进行建模。我们的研究结果强调，在分散注意力的场景下，使可视化设计与用户期望保持一致的重要性有所提升，并引入了一种面向过程的建模方法，用于理解期望与多任务如何影响可视化解读。
英文摘要
Studies have shown that visualization design affects interpretability when visualization interpretation is the user's sole task. However, in real-world settings, users often engage with visualizations while performing concurrent tasks, such as when users simultaneously monitor alerts or respond to messages. Such divided attention may alter how users interpret visualizations, potentially increasing the importance of designs that align with viewer expectations. We investigated this possibility through two experiments comparing visualization interpretation under single-task and dual-task conditions. Specifically, we examined how well-established inferred mappings between color, spatial position, and semantic concepts affect interpretation when users perform a concurrent task, both with unlimited viewing time (Exp. 1) and under limited viewing time (Exp. 2). Our results show that divided attention amplifies the performance gap between expectation-aligned and expectation-violating designs, affecting response time, interpretation accuracy, and the ability to produce a judgment under time constraints. To explain these results, we model the user's decision-making process using a Linear Ballistic Accumulator (LBA) framework. Our findings highlight the increased importance of aligning visualization designs with viewer expectations under divided attention and introduce a process-oriented modeling approach to understanding how expectation and multitasking shape visualization interpretation.
Comments33 pages, 22 figures (11 pages, 5 figures for the main text; 22 pages, 17 figures for the supplementary material), to be published in IEEE Transactions on Visualization and Computer Graphics
