Fauxvea: Crowdsourcing Gaze Location Estimates for Visualization Analysis Tasks.

Area of Science:

• Human-Computer Interaction
• Data Visualization
• Crowdsourcing

Background:

• Understanding user gaze patterns is crucial for evaluating information visualizations and user behavior.
• Traditional eye-tracking studies are limited by specialized hardware requirements and the need for local participants.

Purpose of the Study:

• To design and evaluate a crowdsourcing-based method (Fauxvea) for estimating gaze locations during static visualization analysis.
• To provide a cost-effective and accessible alternative to hardware-based eye-tracking for visualization research.

Main Methods:

• Developed Fauxvea, a web-based crowdsourcing method utilizing cursor interactions to estimate gaze fixations without eye-tracking hardware.
• Conducted experiments on Amazon Mechanical Turk (MTurk) to evaluate Fauxvea against traditional eye-tracking data.
• Assessed performance across three common information visualization types and replicated findings from a prior tree layout study.

Main Results:

• Gaze fixation estimates from Fauxvea demonstrated qualitative and quantitative similarity to eye-tracking data for comparable visualization tasks.
• The method proved effective for analyzing common information visualizations and replicating established eye-tracking study findings.

Conclusions:

• Crowdsourcing visual analysis tasks with static information visualizations using Fauxvea is a viable alternative to traditional eye-tracking.
• This approach democratizes gaze analysis in visualization research and design by removing hardware constraints.