Predicting Webpage Aesthetics with Heatmap Entropy
This provides a quantitative metric for evaluating webpage aesthetics based on eye-tracking data, which could aid in user interface design, though it is incremental as it builds on existing eye-tracking methods.
The study tackled the challenge of quantifying the relationship between eye movements and aesthetic judgments of web pages by introducing heatmap entropy (VAE) and its improved version (rVAE) to analyze eye-tracking data, finding that rVAE correlates significantly with perceived aesthetics (r=-0.65) and achieves about 85% accuracy in distinguishing good- from bad-looking pages.
Today, eye trackers are extensively used in user interface evaluations. However, it's still hard to analyze and interpret eye tracking data from the aesthetic point of view. To find quantitative links between eye movements and aesthetic experience, we tracked 30 observers' initial landings for 40 web pages (each displayed for 3 seconds). The web pages were also rated based on the observers' subjective aesthetic judgments. Shannon entropy was introduced to analyze the eye-tracking data. The result shows that the heatmap entropy (visual attention entropy, VAE) is highly correlated with the observers' aesthetic judgements of the web pages. Its improved version, relative VAE (rVAE), has a more significant correlation with the perceived aesthetics. (r=-0.65, F= 26.84, P$<$0.0001). This single metric alone can distinguish between good- and bad-looking pages with an approximate 85\% accuracy. Further investigation reveals that the performance of both VAE and rVAE became stable after 1 second. The curves indicate that their performances could be better, if the tracking time was extended beyond 3 seconds.