用过程模型解释眼球运动

Dario D. Salvucci
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引用次数: 2

摘要

虽然眼球运动提供了大量关于人类如何与计算机互动的信息,但对眼球运动数据的分析可能极其繁琐和耗时。本文概述了一种追踪眼球运动的自动化方法,即基于底层过程模型解释眼球运动协议。所提出的跟踪方法利用隐马尔可夫模型等技术将观察到的眼动协议与过程模型的预测联系起来。这些方法已经成功地应用于方程求解领域,并将推广到其他任务领域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Interpreting eye movements with process models
Though eye movements provide a wealth of information about how humans interact with computers, the analysis of eye movement data can be extremely tedious and timeconsuming. This paper outlines an automated approach to tracing eye movements, that is, interpreting eye movement protocols based on an underlying process model. The proposed tracing methods utilize techniques such as hidden Markov models to relate observed eye movement protocols to the predictions of the process model. These methods have been applied successfully in the domain of equation solving and will be extended to several other task domains.
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