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The collected gaze fixation data are then visualized and analyzed via gaze plots and heat maps.
We then collected both questionnaire and user visual fixation data for analysis.
This has been accomplished by considering fixation data, annotated image regions, and stimulus patterns inspired by psychophysics.
Eye fixation data indicated that the compositional animation led to superior mental models because it particularly fostered relational processing.
Therefore in this study we propose methods for collecting gaze fixation data for a three-dimensional model of a given product and visualizing corresponding gaze plots and heat maps also in three dimensions.
However, given the fixation data the video scenarios were apparently similarly approached by both groups.
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The analysis of eye-fixation data (Kozhevnikov, Motes, & Hegarty, 2007), revealed that low-spatial ability participants spent a greater amount of time studying the overall shape of graphs compared to studying the graph axes, whereas high-spatial ability participants spent more time studying the axes than the overall shape of the graph.
We consider two eye-fixation data sets.
The first is the standard eye-fixation data set collected by Bruce and Tsotsos [ 4].
Hence, it is important to consider a sufficient amount of subjects in order to learn consistent patterns in eye-fixation data and to construct a stable ground truth.
To correlate the human data with the output of the saliency models, we transform the eye-fixation data to fixation- distance maps (see Fig. 5).
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