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2010
Conference Paper
Titel
Reduce working memory load for visual classification tasks through gaze-based interaction
Abstract
The rate of working memory errors as an influence on the performance of visual classification at computer screens, e. g. in image exploitation, is expected to get reduced by use of gaze tracking instead of conventional pointing techniques. We compared two gaze-based techniques with the usage of a computer mouse: pure visual fixation (PFix) and visual fixation with confirmation (FixC). A prediction of memory errors while performing visual classification has been carried out using the "Human Processor Modeling Language" with the result that the least errors are expected for PFix followed by FixC and MOUSE. This prediction has been confirmed by empirical validation.