Exploring the efficiency of users' visual analytics strategies based on sequence analysis of eye movement recordings
Exploring the efficiency of users' visual analytics strategies based on sequence analysis of eye movement recordings
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DOI：
10.1080/13658816.2010.511718
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发表时间：
2010-01-01
影响因子：
5.7
通讯作者：
Lacayo, Martin
中科院分区：
文献类型：
--
作者：
Coeltekin, A.;Fabrikant, S. I.;Lacayo, Martin
可视化分析通常基于这样的直觉：复杂和多元数据库的高度交互和动态描述增强了人类的推理和决策能力，因为它们促进了诸如模式识别、关联和分析推理等认知任务（Thomas 和 Cook 2005）。但我们如何知道可视化分析是否真的有效？本文提供了一种基于序列相似性分析的结合理论和数据驱动方法的通用评估方法。该方法系统地研究用户在使用高度交互界面时的视觉交互策略。我们特别询问用户的效率（即速度）是否可以通过特定的显示交互事件序列来表征，以及是否可以通过研究用户策略来改进动态显示的（交互）设计。我们使用在具有动态视觉分析显示的受控人体实验期间收集的眼球运动记录的非常大、细粒度的时空数据集来展示我们的方法。通过这种基于经验证据的方法论，我们希望有助于更深入地理解人们如何通过高度交互式的可视化工具和复杂的显示进行推理和决策。
Visual analytics is often based on the intuition that highly interactive and dynamic depictions of complex and multivariate databases amplify human capabilities for inference and decision-making, as they facilitate cognitive tasks such as pattern recognition, association, and analytical reasoning (Thomas and Cook 2005). But how do we know whether visual analytics really works? This article offers a generic evaluation approach combining theory- and data-driven methods based on sequence similarity analysis. The approach systematically studies users' visual interaction strategies when using highly interactive interfaces. We specifically ask whether the efficiency (i.e., speed) of users can be characterized by specific display interaction event sequences, and whether studying user strategies could be employed to improve the (interaction) design of the dynamic displays. We showcase our approach using a very large, fine-grained spatiotemporal dataset of eye movement recordings collected during a controlled human subject experiment with dynamic visual analytics displays. With this methodological approach based on empirical evidence, we hope to contribute to a deeper understanding of how people make inferences and decisions with highly interactive visualization tools and complex displays.