Exploring visual attention and saliency modeling for task-based visual analysis

Information

• Publication Type: Journal Paper (without talk)
• Workgroup(s)/Project(s):
  • Visualization Group
  • Visual Information Foraging on the Desktop
• Date: February 2018
• DOI: https://doi.org/10.1016/j.cag.2018.01.010
• Journal: Computers & Graphics
• Number: 2
• Open Access: no
• Keywords: Information visualization, Eye-tracking experiment, Saliency, Visual attention, Low-level analytical tasks

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 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.

Additional Files and Images

Weblinks

• https://www.sciencedirect.com/science/article/pii/S0097849318300104
• DOI: https://doi.org/10.1016/j.cag.2018.01.010

BibTeX

@article{polatsek-2018-stv,
  title =      "Exploring visual attention and saliency modeling for
               task-based visual analysis",
  author =     "Patrik Polatsek and Manuela Waldner and Ivan Viola and Peter
               Kapec and Wanda Benesova",
  year =       "2018",
  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 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.",
  month =      feb,
  doi =        "https://doi.org/10.1016/j.cag.2018.01.010",
  journal =    "Computers & Graphics",
  number =     "2",
  keywords =   "Information visualization, Eye-tracking experiment,
               Saliency, Visual attention, Low-level analytical tasks",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2018/polatsek-2018-stv/",
}