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dc.contributor.authorBurch, Michaelen_US
dc.contributor.authorBlascheck, Tanjaen_US
dc.contributor.authorKurzhals, Kunoen_US
dc.contributor.authorPflüger, Hermannen_US
dc.contributor.authorRaschke, Michaelen_US
dc.contributor.authorWeiskopf, Danielen_US
dc.contributor.authorPfeiffer, Thies
dc.contributor.editorM. Zwicker and C. Soleren_US
dc.date.accessioned2015-04-15T13:58:27Z
dc.date.available2015-04-15T13:58:27Z
dc.date.issued2015en_US
dc.identifier.urihttp://dx.doi.org/10.2312/egt.20151044en_US
dc.description.abstractEye 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.publisherThe Eurographics Associationen_US
dc.titleEye Tracking Visualizationen_US
dc.description.seriesinformationEG 2015 - Tutorialsen_US
dc.description.sectionheadersTrack 2en_US
dc.identifier.doi10.2312/egt.20151044en_US
dc.identifier.pagest2en_US


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