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arXiv 2508.20522cs.HC

VisiTrail:一种用于注意力游戏眼动数据时间序列分析的认知可视化工具

VisiTrail: A Cognitive Visualization Tool for Time-Series Analysis of Eye Tracking Data from Attention Game

Abdul Rehman, Ilona Heldal, Jerry Chun-Wei Lin

AI总结:

本文提出VisiTrail认知可视化工具,通过时间序列分析、注视测量、对象点击序列跟踪和表现指标,揭示注意力游戏中眼动数据与任务表现的关系。

摘要:

眼动追踪(Eye Tracking,ET)有助于理解交互环境中的视觉注意和认知过程。在注意力任务中,区分相关目标对象与干扰物对于有效表现至关重要,然而驱动任务成功完成的潜在注视模式仍未得到充分理解。传统的注视分析缺乏对注意分配时间动态以及注视行为与任务表现之间关系的全面洞察。当应用于复杂的视觉搜索场景时,当前的注视分析方法面临若干局限,包括测量孤立、视觉稳定性、搜索效率以及这些场景中涉及的决策过程。本文提出了一种分析工具,该工具考虑来自任务表现的眼动数据时间序列以及注视测量指标(注视、眼跳和平滑追踪);揭示注意在任务表现过程中如何演变的时间模式分析;直接将视觉注意与用户操作关联起来的对象点击序列跟踪;以及量化准确性和效率的表现指标。该工具提供了全面的可视化技术,使刺激与注视连接的复杂模式变得可解释。

英文摘要:

Eye Tracking (ET) can help to understand visual attention and cognitive processes in interactive environments. In attention tasks, distinguishing between relevant target objects and distractors is crucial for effective performance, yet the underlying gaze patterns that drive successful task completion remain incompletely understood. Traditional gaze analyses lack comprehensive insights into the temporal dynamics of attention allocation and the relationship between gaze behavior and task performance. When applied to complex visual search scenarios, current gaze analysis methods face several limitations, including the isolation of measurements, visual stability, search efficiency, and the decision-making processes involved in these scenarios. This paper proposes an analysis tool that considers time series for eye tracking data from task performance and also gaze measures (fixations, saccades and smooth pursuit); temporal pattern analysis that reveals how attention evolves throughout task performance; object-click sequence tracking that directly links visual attention to user actions; and performance metrics that quantify both accuracy and efficiency. This tool provides comprehensive visualization techniques that make complex patterns of stimuli and gaze connections interpretable.

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。

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