Modeling visual saliency in graphical user interfaces (GUIs) allows to understand how people perceive GUI designs and what elements attract their attention. One aspect that is often overlooked is the fact that computational models depend on a series of design parameters that are not straightforward to decide. We systematically analyze how different design parameters affect scanpath evaluation metrics using a state-of-the-art computational model (DeepGaze++). We particularly focus on three design parameters: input image size, inhibition-of-return decay, and masking radius. We show that even small variations of these design parameters have a noticeable impact on standard evaluation metrics such as DTW or Eyenalysis. These effects also occur in other scanpath models, such as UMSS and ScanGAN, and in other datasets such as MASSVIS. Taken together, our results put forward the impact of design decisions for predicting users' viewing behavior on GUIs.

中文翻译：

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扫描路径建模中设计决策的影响

对图形用户界面 (GUI) 中的视觉显着性进行建模可以了解人们如何看待 GUI 设计以及哪些元素吸引了他们的注意力。经常被忽视的一个事实是，计算模型取决于一系列不易确定的设计参数。我们使用最先进的计算模型 (DeepGaze++) 系统地分析不同的设计参数如何影响扫描路径评估指标。我们特别关注三个设计参数：输入图像大小、返回衰减抑制和掩蔽半径。我们表明，即使这些设计参数的微小变化也会对 DTW 或 Eyenaanalysis 等标准评估指标产生显着影响。这些影响也出现在其他扫描路径模型（例如 UMSS 和 ScanGAN）以及其他数据集（例如 MASSVIS）中。总而言之，我们的结果提出了设计决策对预测用户在 GUI 上的观看行为的影响。