TurkEyes: A Web-Based Toolbox for Crowdsourcing Attention Data

ACM Conference on Human Factors in Computing Systems (CHI)

Publication date: April 25, 2020

Anelise Newman, Barry McNamara, Camilo Fosco, Yun Bin Zhang, Pat Sukhum, Matthew Tancik, Nam Wook Kim, Zoya Bylinskii

Eye movements provide insight into what parts of an image a viewer finds most salient, interesting, or relevant to the task at hand. Unfortunately, eye tracking data, a commonly-used proxy for attention, is cumbersome to collect. Here we explore an alternative: a comprehensive web-based toolbox for crowdsourcing visual attention. We draw from four main classes of attention-capturing methodologies in the literature. ZoomMaps is a novel zoom-based interface that captures viewing on a mobile phone. CodeCharts is a self-reporting methodology that records points of interest at precise viewing durations. ImportAnnots is an annotation tool for selecting important image regions, and cursor-based BubbleView lets viewers click to deblur a small area. We compare these methodologies using a common analysis framework in order to develop appropriate use cases for each interface. This toolbox and our analyses provide a blueprint for how to gather attention data at scale without an eye tracker.

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Research Area:  Human Computer Interaction