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P. Polatsek, M. Waldner, I. Viola et al.:
"Exploring visual attention and saliency modeling for task-based visual analysis";
Computers & Graphics (invited), 72 (2018), 26 - 38.

Memory, visual attention and perception play a critical role in the design of visualizations. The way users observe a visualization is affected by salient stimuli in a scene as well as by domain knowledge, interest, and the task. While recent saliency models manage to predict the users´ visual attention in visualizations during exploratory analysis, there is little evidence how much influence bottom-up saliency has on task-based visual analysis. Therefore, we performed an eye-tracking study with 47 users to determine the users´ path of attention when solving three low-level analytical tasks using 30 different charts from the MASSVIS database [1]. We also compared our task-based eye tracking data to the data from the original memorability experiment by Borkin et al. [2]. We found that solving a task leads to more consistent viewing patterns compared to exploratory visual analysis. However, bottom-up saliency of a visualization has negligible influence on users´ fixations and task efficiency when performing a low-level analytical task. Also, the efficiency of visual search for an extreme target data point is barely influenced by the target´s bottom-up saliency. Therefore, we conclude that bottom-up saliency models tailored towards information visualization are not suitable for predicting visual attention when performing task-based visual analysis. We discuss potential reasons and suggest extensions to visual attention models to better account for task-based visual analysis.

Information visualization, Eye-tracking experiment, Saliency, Visual attention, Low-level analytical tasks

http://dx.doi.org/10.1016/j.cag.2018.01.010

https://publik.tuwien.ac.at/files/publik_270387.pdf