A Task-Based View on the Visual Analysis of Eye-Tracking Data

被引:22
|
作者
Kurzhals, Kuno [1 ]
Burch, Michael [1 ]
Blascheck, Tanja [2 ]
Andrienko, Gennady [3 ,4 ]
Andrienko, Natalia [3 ]
Weiskopf, Daniel [1 ]
机构
[1] Univ Stuttgart, Allmandring 19, D-70569 Stuttgart, Germany
[2] Univ Stuttgart, Univ Str 38, D-70569 Stuttgart, Germany
[3] Fraunhofer Inst IAIS, Schloss Birlinghoven, D-53757 St Augustin, Germany
[4] City Univ London, London EC1V 0HB, England
关键词
ANALYTICS; VISUALIZATION; MOVEMENTS;
D O I
10.1007/978-3-319-47024-5_1
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The visual analysis of eye movement data has become an emerging field of research leading to many new visualization techniques in recent years. These techniques provide insight beyond what is facilitated by traditional attention maps and gaze plots, providing important means to support statistical analysis and hypothesis building. There is no single "all-in-one" visualization to solve all possible analysis tasks. In fact, the appropriate choice of a visualization technique depends on the type of data and analysis task. We provide a taxonomy of analysis tasks that is derived from literature research of visualization techniques and embedded in our pipeline model of eye-tracking visualization. Our task taxonomy is linked to references to representative visualization techniques and, therefore, it is a basis for choosing appropriate methods of visual analysis. We also elaborate on how far statistical analysis with eye-tracking metrics can be enriched by suitable visualization and visual analytics techniques to improve the extraction of knowledge during the analysis process.
引用
收藏
页码:3 / 22
页数:20
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