AM-DCT: A Visual Attention Modeling Data Capturing Tool for Investigating Users' Interface Monitoring Behavior

S. Feuerstack, Bertram Wortelen
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引用次数: 3

Abstract

Methods to get insights about users' monitoring behavior either depend on the expertise of Human Factor experts to model and predict stereotypic monitoring behavior or on performing eye tracking studies in simulated environments, which require subjects to be physically present and usually to be tested successively. AM-DCT is a tool that can be applied by domain experts without expertise in human factors and with limited training in parallel sessions to learn about a population's monitoring behavior. In an experiment 20 car drivers used the AM-DCT independently after watching a 15 minutes video tutorial. 19 subjects were able to model their monitoring behavior for a car overtaking scenario in 36 minutes on average. The identification of areas of interest for areas with clearly defined borders was very consistent among subjects. For those without clear borders an aggregated model of all participants seems surprisingly accurate to represent the real monitoring area.
AM-DCT:用于调查用户界面监控行为的视觉注意力建模数据捕获工具
了解用户监控行为的方法,要么依靠人因专家的专业知识来模拟和预测刻板的监控行为,要么依靠在模拟环境中进行眼动追踪研究,这需要受试者亲自在场,通常是连续进行测试。AM-DCT是一种工具,可以由没有人为因素专业知识和在并行会议上接受有限培训的领域专家应用,以了解人群的监测行为。在一项实验中,20名司机在观看了15分钟的视频教程后,独立使用了AM-DCT。19名受试者能够在平均36分钟内模拟出他们对汽车超车场景的监控行为。确定具有明确边界的地区的兴趣领域在各主题之间是非常一致的。对于那些没有明确边界的人来说,所有参与者的汇总模型似乎非常准确地代表了真正的监测区域。
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