Patrik Polatsek, Manuela Waldner, Ivan Viola, Peter Kapec, Wanda Benesova
Abstract. 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 usersâ€™ visual attention in visualizations during exploratory analysis, there is little evidence of 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 usersâ€™ path of attention when solving three low-level analytical tasks using 30 different charts from the MASSVIS database. We also compared our task-based eye-tracking data to the data from the original memorability experiment by Borkin et al.. We found that solving a task leads to more consistent viewing patterns compared to exploratory visual analysis. However, bottom-up saliency of visualization has negligible influence on usersâ€™ 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 targetâ€™s bottom-up saliency. Therefore, we conclude that bottom-up saliency models tailored toward 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.
TASKVIS dataset contains eye-tracking data from this task-based visual analysis experiment.
Please cite this paper if you use the dataset:
Polatsek, P., Waldner, M., Viola, I., Kapec, P., & Benesova, W. (2018)
Exploring Visual Attention and Saliency Modeling for Task-Based Visual Analysis
Computers & Graphics, 72, 26-38