Patterns of Attention: How Data Visualizations Are Read.

Data visualizations are used to communicate information to people in a wide variety of contexts, but few tools are available to help visualization designers evaluate the effectiveness of their designs. Visual saliency maps that predict which regions of an image are likely to draw the viewer's attention could be a useful evaluation tool, but existing models of visual saliency often make poor predictions for abstract data visualizations. These models do not take into account the importance of features like text in visualizations, which may lead to inaccurate saliency maps. In this paper we use data from two eye tracking experiments to investigate attention to text in data visualizations. The data sets were collected under two different task conditions: a memory task and a free viewing task. Across both tasks, the text elements in the visualizations consistently drew attention, especially during early stages of viewing. These findings highlight the need to incorporate additional features into saliency models that will be applied to visualizations.