Fauxvea: Crowdsourcing Gaze Location Estimates for Visualization Analysis Tasks
Fauxvea: Crowdsourcing Gaze Location Estimates for Visualization Analysis Tasks
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DOI：
10.1109/tvcg.2016.2532331
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发表时间：
2017-02-01
影响因子：
5.2
通讯作者：
Laidlaw, David H.
中科院分区：
文献类型：
--
作者：
Gomez, Steven R.;Jianu, Radu;Laidlaw, David H.
我们提出的设计和评估的方法，估计凝视位置的静态可视化分析过程中使用众包。了解注视模式有助于评估可视化和用户行为，但传统的眼动跟踪研究需要专门的硬件和本地用户。为了避免这些限制，我们开发了一种名为Fauxvea的方法，该方法在Web上众包可视化任务，并通过光标交互来估计注视点，而无需眼动跟踪硬件。我们进行了实验，以评估我们的方法与眼动跟踪数据相比的凝视估计。首先，我们使用Amazon Mechanical Turk（MTurk）评估了三种常见类型的信息可视化和基本可视化任务的众包估计。在另一项研究中，我们使用MTurk上的方法复制了之前关于树布局的眼动跟踪研究的结果。从这些实验的结果表明，固定估计使用Fauxvea是定性和定量相似的眼跟踪相同的刺激任务对。这些研究结果表明，众包视觉分析任务与静态信息可视化可能是一个可行的替代传统的眼动跟踪研究的可视化研究和设计。
We present the design and evaluation of a method for estimating gaze locations during the analysis of static visualizations using crowdsourcing. Understanding gaze patterns is helpful for evaluating visualizations and user behaviors, but traditional eye-tracking studies require specialized hardware and local users. To avoid these constraints, we developed a method called Fauxvea, which crowdsources visualization tasks on the Web and estimates gaze fixations through cursor interactions without eye-tracking hardware. We ran experiments to evaluate how gaze estimates from our method compare with eye-tracking data. First, we evaluated crowdsourced estimates for three common types of information visualizations and basic visualization tasks using Amazon Mechanical Turk (MTurk). In another, we reproduced findings from a previous eye-tracking study on tree layouts using our method on MTurk. Results from these experiments show that fixation estimates using Fauxvea are qualitatively and quantitatively similar to eye tracking on the same stimulus-task pairs. These findings suggest that crowdsourcing visual analysis tasks with static information visualizations could be a viable alternative to traditional eye-tracking studies for visualization research and design.