用 HCEye 转移焦点：探索视觉高亮与认知负荷对用户注意力和显著性预测的动态影响
Shifting Focus with HCEye: Exploring the Dynamics of Visual Highlighting and Cognitive Load on User Attention and Saliency Prediction
AI总结：
本文通过 HCEye 眼动实验研究视觉高亮与认知负荷对用户注意力的联合影响，发现动态高亮在认知负荷下仍能吸引注意力，并证明考虑认知负荷可提升显著性模型性能。
摘要：
视觉高亮可以引导用户在复杂界面中的注意力。然而，其在注意力容量有限情况下的有效性尚未得到充分探索。本文考察了视觉高亮（永久性和动态性）与双任务诱发的认知负荷对注视行为的联合影响。我们利用来自 27 名参与者观看 150 个独特网页的眼动数据进行分析，结果表明：尽管参与者关注 UI 元素的能力随着认知负荷的增加而下降，但动态适应（即高亮）仍然能够吸引注意力。这些因素的存在显著改变了人们关注的内容，从而改变了什么是显著的。相应地，我们表明，当考虑到不同的认知负荷时，最先进的显著性模型会提高其性能。我们的实证见解以及我们公开可用的数据集，增进了我们对不同认知（和感知）负荷下 UI 中注意过程的理解，并为能够预测多任务处理时用户注意力的新模型打开了大门。
英文摘要：
Visual highlighting can guide user attention in complex interfaces. However, its effectiveness under limited attentional capacities is underexplored. This paper examines the joint impact of visual highlighting (permanent and dynamic) and dual-task-induced cognitive load on gaze behaviour. Our analysis, using eye-movement data from 27 participants viewing 150 unique webpages reveals that while participants' ability to attend to UI elements decreases with increasing cognitive load, dynamic adaptations (i.e., highlighting) remain attention-grabbing. The presence of these factors significantly alters what people attend to and thus what is salient. Accordingly, we show that state-of-the-art saliency models increase their performance when accounting for different cognitive loads. Our empirical insights, along with our openly available dataset, enhance our understanding of attentional processes in UIs under varying cognitive (and perceptual) loads and open the door for new models that can predict user attention while multitasking.
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。
