dc.contributor.author	Burch, Michael	en_US
dc.contributor.author	Blascheck, Tanja	en_US
dc.contributor.author	Kurzhals, Kuno	en_US
dc.contributor.author	Pflüger, Hermann	en_US
dc.contributor.author	Raschke, Michael	en_US
dc.contributor.author	Weiskopf, Daniel	en_US
dc.contributor.author	Pfeiffer, Thies
dc.contributor.editor	M. Zwicker and C. Soler	en_US
dc.date.accessioned	2015-04-15T13:58:27Z
dc.date.available	2015-04-15T13:58:27Z
dc.date.issued	2015	en_US
dc.description.abstract	Eye tracking has become a widely used method to analyze user behavior in marketing, neuroscience, human-computer interaction, and visualization research. Apart from measuring completion times and recording accuracy rates of correctly given answers during the performance of visual tasks in classical controlled user experiments, eye tracking-based evaluations provide additional information on how visual attention is distributed and changing for a presented stimulus. Due to the wide field of applications of eye tracking and various kinds of research questions, different approaches have been developed to analyze eye tracking data such as statistical algorithms (either descriptive or inferential), string editing algorithms, visualization-related techniques, and visual analytics techniques. Regardless of whether statistical or visual methods are used for eye tracking data analysis, a large amount of data generated during eye tracking experiments has to be handled.	en_US
dc.description.sectionheaders	Track 2	en_US
dc.description.seriesinformation	EG 2015 - Tutorials	en_US
dc.identifier.doi	10.2312/egt.20151044	en_US
dc.identifier.pages	t2	en_US
dc.identifier.uri	https://doi.org/10.2312/egt.20151044	en_US
dc.publisher	The Eurographics Association	en_US
dc.title	Eye Tracking Visualization	en_US

Eye Tracking Visualization

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