Attention-Aware Visualization: Tracking and Responding to User Perception Over Time
Arvind Srinivasan, Johannes Ellemose, Peter Butcher, Panagiotis D. Ritsos, Niklas Elmqvist · 2024 · 9 citationsRead the paper
We propose the notion of attention-aware visualizations (AAVs) that track the user's perception of a visual representation over time and feed this information back to the visualization. Such context awareness is particularly useful for ubiquitous and immersive analytics where knowing which embedded visualizations the user is looking at can be used to make visualizations react appropriately to the user's attention: for example, by highlighting data the user has not yet seen. We can separate the approach into three components: (1) measuring the user's gaze on a visualization and its parts; (2) tracking the user's attention over time; and (3) reactively modifying the visual representation based on the current attention metric. In this paper, we present two separate implementations of AAV: a 2D data-agnostic method for web-based visualizations that can use an embodied eyetracker to capture the user's gaze, and a 3D data-aware one that uses the stencil buffer to track the visibility of each individual mark in a visualization. Both methods provide similar mechanisms for accumulating attention over time and changing the appearance of marks in response. We also present results from a qualitative evaluation studying visual feedback and triggering mechanisms for capturing and revisualizing attention.
What the full text shows
Working prototype- The 3D visualization with explicit triggering achieved an 'excellent' usability score. — 86 SUS score (3D data-aware implementation)
- Average system usability across all participants was slightly above average. — 70 SUS score (SD = 15) (Combined 2D and 3D implementations)
- Participants in 3D environments expressed a clear preference for explicit triggering over always-on heatmaps, which were described as distracting. (3D data-aware implementation)
- The 2D data-agnostic implementation showed no clear preference among participants for specific revisualization or triggering techniques. (2D data-agnostic implementation)
- Setup
- 12 participants (students in computer science/engineering) using a Pupil Labs Neon eyetracker with a 34-inch monitor (2D) and a Meta Quest Pro HMD in video passthrough mode (3D).
- How
- The researchers developed two AAV implementations: a 2D data-agnostic web-based method using an embodied eyetracker and a 3D data-aware method using a stencil buffer. They conducted a qualitative evaluation with human participants performing data analysis tasks to assess triggering mechanisms and visual feedback.
- Limitations
- Potential for reinforcing behavior where revisualization skews subsequent user attention patterns. · Eye-tracking hardware is not yet commonplace in typical computing setups, limiting adoption. · Qualitative focus and lack of a baseline comparison restrict definitive conclusions on efficacy. · Observed inaccuracy in the Pupil Labs Neon eyetracker (measuring slightly above or below actual gaze).
Summarized by Gemma 4 from the open-access full text · 8 Oct 2026. Read it