Fauxvea: Crowdsourcing Gaze Location Estimates for Visualization Analysis Tasks

Abstract

This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract-We present the design and evaluation of a method for estimating gazes 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 created 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. Permanent repository link

MouseView.js: Reliable and valid attention tracking in web-based experiments using a cursor-directed aperture

“…We have replicated earlier work (Gomez et al, 2017;Jiang et al, 2015) by showing that gaze 23/29 and mouse exploration heatmaps are qualitatively similar.…”

“…Plenty of studies have established that the cursor-locked apertures produces exploration behaviour that resembles gaze behaviour during free viewing (Blackwell et al, 2000;Gomez et al, 2017;Jiang et al, 2015). The purpose of the current validation study was to extend previous validation efforts into a preferential looking paradigm with pairs of affective stimuli.…”

“…In addition, ratings had opposite effects between conditions (likely driven by higher avoidance for disgust, and no change or higher approach with higher pleasantness ratings), and there was also an interaction effect of condition and presentation number (likely driven by an increased tendency of avoidance of disgusting stimuli at increasing presentations). (Gomez et al, 2017;Jiang et al, 2015). Here, we aimed to replicate these findings by comparing traditional saliency maps, but also by comparing scanpaths between eye-tracking and MouseView.js experiments.…”

“…More recent work has taken both the bubble (Deng et al, 2013;Kim et al, 2017Kim et al, , 2015 and the moving aperture approaches (Gomez et al, 2017;Jiang et al, 2015) to the internet, usually with a view to investigate specific types of stimuli (e.g. data visualisations) or to accrue large datasets for the development of visual saliency models.…”

Pipelines Bent, Pipelines Broken: Interdisciplinary Self-Reflection on the Impact of COVID-19 on Current and Future Research (Position Paper)

“…Similarly, evaluation setups using eye trackers [7,50] might get completely removed from the equation due to hygiene requirements. Work that seeks to simulate eye movements without the need of special hardware, such as BubbleView [23] and Fauxvea [16] might become more relevant. These approaches aim to blur the screen with the exception of a small circular area, which the user can shift to reveal the screen content or where the authors aim to predict saliency maps of user attention.…”