TurkEyes: A Web-Based Toolbox for Crowdsourcing Attention Data

被引:9
|
作者
Newman, Anelise [1 ]
McNamara, Barry [1 ]
Fosco, Camilo [1 ]
Zhang, Yun Bin [2 ]
Sukhum, Pat [2 ]
Tancik, Matthew [3 ]
Kim, Nam Wook [4 ]
Bylinskii, Zoya [5 ]
机构
[1] MIT, 77 Massachusetts Ave, Cambridge, MA 02139 USA
[2] Harvard, Cambridge, MA USA
[3] Univ Calif Berkeley, Berkeley, CA 94720 USA
[4] Boston Coll, Newton, MA USA
[5] Adobe Res, San Jose, CA USA
关键词
Eye tracking; attention; crowdsourcing; interaction techniques; MEMORABILITY; FIXATION;
D O I
10.1145/3313831.3376799
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
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.
引用
收藏
页数:13
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