IEEE Trans Vis Comput Graph. 2018 Jan;24(1):563-573. doi: 10.1109/TVCG.2017.2743939. Epub 2017 Aug 29.
Evaluating the effectiveness of data visualizations is a challenging undertaking and often relies on one-off studies that test a visualization in the context of one specific task. Researchers across the fields of data science, visualization, and human-computer interaction are calling for foundational tools and principles that could be applied to assessing the effectiveness of data visualizations in a more rapid and generalizable manner. One possibility for such a tool is a model of visual saliency for data visualizations. Visual saliency models are typically based on the properties of the human visual cortex and predict which areas of a scene have visual features (e.g. color, luminance, edges) that are likely to draw a viewer's attention. While these models can accurately predict where viewers will look in a natural scene, they typically do not perform well for abstract data visualizations. In this paper, we discuss the reasons for the poor performance of existing saliency models when applied to data visualizations. We introduce the Data Visualization Saliency (DVS) model, a saliency model tailored to address some of these weaknesses, and we test the performance of the DVS model and existing saliency models by comparing the saliency maps produced by the models to eye tracking data obtained from human viewers. Finally, we describe how modified saliency models could be used as general tools for assessing the effectiveness of visualizations, including the strengths and weaknesses of this approach.
评估数据可视化的有效性是一项具有挑战性的任务，通常依赖于一次性的研究，这些研究在特定任务的背景下测试可视化。数据科学、可视化和人机交互领域的研究人员正在呼吁采用基础工具和原则，以便更快速和更具普遍性地评估数据可视化的有效性。这样的工具之一可能是数据可视化的视觉显著性模型。视觉显著性模型通常基于人类视觉皮层的特性，预测场景中哪些区域具有视觉特征（例如颜色、亮度、边缘），可能会吸引观察者的注意力。虽然这些模型可以准确预测观察者在自然场景中会看哪里，但它们通常不适用于抽象的数据可视化。在本文中，我们讨论了现有显著性模型在应用于数据可视化时表现不佳的原因。我们引入了数据可视化显著性（DVS）模型，这是一种专门针对解决这些弱点的显著性模型，并通过将模型生成的显著性图与从人类观察者获得的眼动追踪数据进行比较，测试了 DVS 模型和现有显著性模型的性能。最后，我们描述了如何使用修改后的显著性模型作为评估可视化效果的通用工具，包括这种方法的优缺点。
