TurkEyes: A Web-Based Toolbox for Crowdsourcing Attention Data

CHI '20: CHI Conference on Human Factors in Computing Systems Honolulu HI USA April, 2020(2020)

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摘要
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 tool-box and our analyses provide a blueprint for how to gather attention data at scale without an eye tracker.
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关键词
Eye tracking, attention, crowdsourcing, interaction techniques
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