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.

翻译：理解图形用户界面(GUI)中的视觉显著性有助于揭示人们如何感知GUI设计以及哪些元素吸引其注意力。一个常被忽视的问题是，计算模型依赖于一系列难以明确确定的设计参数。我们系统性地分析了不同设计参数如何通过当前最优计算模型(DeepGaze++)影响扫描路径评估指标。我们重点关注三个设计参数：输入图像尺寸、返回抑制衰减和掩蔽半径。研究表明，即使这些设计参数的微小变化，也会对DTW或Eyenalysis等标准评估指标产生显著影响。这些效应同样存在于其他扫描路径模型(如UMSS和ScanGAN)以及其他数据集(如MASSVIS)中。综合来看，我们的结果凸显了设计决策对预测用户在GUI上注视行为的重要性。