Recent work in XAI for eye tracking data has evaluated the suitability of
feature attribution methods to explain the output of deep neural sequence
models for the task of oculomotric biometric identification. These methods
provide saliency maps to highlight important input features of a specific eye
gaze sequence. However, to date, its localization analysis has been lacking a
quantitative approach across entire datasets. In this work, we employ
established gaze event detection algorithms for fixations and saccades and
quantitatively evaluate the impact of these events by determining their concept
influence. Input features that belong to saccades are shown to be substantially
more important than features that belong to fixations. By dissecting saccade
events into sub-events, we are able to show that gaze samples that are close to
the saccadic peak velocity are most influential. We further investigate the
effect of event properties like saccadic amplitude or fixational dispersion on
the resulting concept influence.

本研究旨在通过使用现有的凝视事件检测算法定量评估比较眼球运动中其不同成分的影响，从而展示 Saccades 事件对于数据集中输入特征的显著重要性， 并通过使用 Saccades 的子事件进一步研究样本的速度对于概念的影响。