GazeAppraise advances the state of the art of gaze pattern analysis using methods that simultaneously analyze spatial and temporal characteristics of gaze patterns. GazeAppraise enables novel research in visual perception and cognition; for example, using shape features as distinguishing elements to assess individual differences in visual search strategy. Given a set of point-to-point gaze sequences, hereafter referred to as scanpaths, the method constructs multiple descriptive features for each scanpath. Once the scanpath features have been calculated, they are used to form a multidimensional vector representing each scanpath and cluster analysis is performed on the set of vectors from all scanpaths. An additional benefit of this method is the identification of causal or correlated characteristics of the stimuli, subjects, and visual task through statistical analysis of descriptive metadata distributions within and across clusters.
Eyetracking data analysis typically relies on statistical algorithms that examine the distribution and frequency of defined gaze events, such as saccades, fixations, and glissades (directed movement across a display or a scene). Instead of treating gaze events as discrete elements in a visual workflow, GazeAppraise uses computational geometric methods to treat a set of gaze points as a holistic spatiotemporal path across a display space. GazeAppraise enables novel research in visual perception and cognition; for example, using shape features as distinguishing elements to assess individual differences in visual search strategy.
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
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Software Package Details
Title: GazeAppraise v. 0.1
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