Paper Info
Title
Fauxvea: Crowdsourcing Gaze Location Estimates for Visualization Analysis Tasks.
Abstract
We present the design and evaluation of a method for estimating gaze locations during the analysis of static visualizations using crowdsourcing. Understanding gaze patterns is helpful for evaluating visualizations and user behaviors, but traditional eye-tracking studies require specialized hardware and local users. To avoid these constraints, we developed a method called Fauxvea, which crowdsources visualization tasks on the Web and estimates gaze fixations through cursor interactions without eye-tracking hardware. We ran experiments to evaluate how gaze estimates from our method compare with eye-tracking data. First, we evaluated crowdsourced estimates for three common types of information visualizations and basic visualization tasks using Amazon Mechanical Turk (MTurk). In another, we reproduced findings from a previous eye-tracking study on tree layouts using our method on MTurk. Results from these experiments show that fixation estimates using Fauxvea are qualitatively and quantitatively similar to eye tracking on the same stimulus-task pairs. These findings suggest that crowdsourcing visual analysis tasks with static information visualizations could be a viable alternative to traditional eye-tracking studies for visualization research and design.
Year
DOI
Venue
2017
10.1109/TVCG.2016.2532331
IEEE Trans. Vis. Comput. Graph.
Keywords
Field
DocType
Data visualization,Visualization,Crowdsourcing,Gaze tracking,Presses,Hardware,Layout
Computer vision,Data visualization,Fixation (psychology),Information visualization,Gaze,Crowdsourcing,Visualization,Computer science,Human–computer interaction,Eye tracking,Artificial intelligence,Cursor (user interface)
Journal
Volume
Issue
ISSN
23
2
1077-2626
Citations 
PageRank 
References 
3
0.37
15
Authors
5
Name
Order
Citations
PageRank
Steven R. Gomez1516.66
Radu Jianu21269.90
Ryan P. Cabeen3476.05
Hua Guo4564.86
David H. Laidlaw51781234.58