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.

中文翻译：

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TurkEyes：用于众包注意力数据的基于 Web 的工具箱

眼球运动可以让观众深入了解图像的哪些部分最显着、最有趣或与手头的任务相关。不幸的是，眼动追踪数据是一种常用的注意力代理，收集起来很麻烦。在这里，我们探索了一个替代方案：一个基于网络的综合工具箱，用于众包视觉注意力。我们从文献中的四类主要注意力捕获方法中汲取了灵感。ZoomMaps 是一种新颖的“基于缩放”的界面，可以捕捉在手机上的查看情况。CodeCharts 是一种“自我报告”方法，可在精确的查看持续时间内记录兴趣点。ImportAnnots 是一个用于选择重要图像区域的“注释”工具，而“基于光标的”BubbleView 允许查看者单击以消除小区域的模糊。我们使用通用分析框架比较这些方法，以便为每个接口开发合适的用例。这个工具箱和我们的分析为如何在没有眼动仪的情况下大规模收集注意力数据提供了蓝图。