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Using Area Weighting and Confidence Intervals to Improve Quantitative Eye Tracking Data

eye tracking / featured / quantitative / user experience

For this blog post I wanted to address an issue user researchers often encounter when conducting an eye tracking study with different sized areas of interest (AOIs). Specifically, researchers often attempt to identify which AOIs are attracting the most attention.

For example, imagine the heat map in Figure 1 represents the results from 30 participants asked to identify how many followers this Twitter profile has. The image on the left shows five main AOIs: Profile, Trends, Feed and Suggestions.

Eye Tracking Fors Marsh Group

In general, the heat map tells a clear story: most people looked at the "Profile" AOI, which makes sense since that is where followers are listed. However, let's say we wanted to...

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