比较多种模型预测图可视化中视线轨迹的准确性。
A Comparative Study of Scanpath Models in Graph-Based Visualization
- 用40人眼动实验数据对比DeepGaze等3种模型生成的视线路径。
- 发现问题复杂度和节点数量显著影响模型预测准确率。
- 适合关注可视化界面优化的研究者与设计人员参考。
信息可视化（InfoVis）系统通过视觉呈现提升数据理解。理解视觉注意力分配对优化界面设计至关重要。然而，收集眼动（ET）数据面临成本、隐私和可扩展性挑战。计算模型可替代预测注视模式，推动InfoVis研究发展。本研究开展眼动实验，40名参与者在数字取证背景下分析图结构并回答不同复杂度的问题。将人类视线轨迹与DeepGaze、UMSS、Gazeformer等模型生成的合成轨迹进行对比。评估这些模型的准确性，并探究问题复杂度与节点数量对性能的影响。本研究推动了视觉分析中预测建模的发展，为提升InfoVis系统的设计与有效性提供洞见。
原文摘要 · Abstract (English)
Information Visualization (InfoVis) systems utilize visual representations to enhance data interpretation. Understanding how visual attention is allocated is essential for optimizing interface design. However, collecting Eye-tracking (ET) data presents challenges related to cost, privacy, and scalability. Computational models provide alternatives for predicting gaze patterns, thereby advancing InfoVis research. In our study, we conducted an ET experiment with 40 participants who analyzed graphs while responding to questions of varying complexity within the context of digital forensics. We compared human scanpaths with synthetic ones generated by models such as DeepGaze, UMSS, and Gazeformer. Our research evaluates the accuracy of these models and examines how question complexity and number of nodes influence performance. This work contributes to the development of predictive modeling in visual analytics, offering insights that can enhance the design and effectiveness of InfoVis systems.
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