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.

中文翻译：

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用热图熵预测网页美感

今天，眼动仪广泛用于用户界面评估。然而，从美学的角度分析和解释眼动追踪数据仍然很困难。为了找到眼球运动和审美体验之间的定量联系，我们跟踪了 30 位观察者对 40 个网页（每个显示 3 秒）的初始登陆。网页还根据观察者的主观审美判断进行评分。引入香农熵来分析眼动追踪数据。结果表明，热图熵（visual attention entropy，VAE）与观察者对网页的审美判断高度相关。其改进版本，相对 VAE (rVAE)，与感知美感有更显着的相关性。（r=-0.65，F=26.84，P$<$0.0001）。仅此一个指标就可以以大约 85% 的准确率区分好看和不好看的页面。进一步调查表明，VAE 和 rVAE 的性能在 1 秒后变得稳定。曲线表明，如果将跟踪时间延长到 3 秒以上，它们的性能会更好。