Lund University Humanities Laboratory and Department of Psychology, Lund University, Lund, Sweden.
Šiauliai University, Šiauliai, Lithuania.
Behav Res Methods. 2020 Dec;52(6):2515-2534. doi: 10.3758/s13428-020-01400-9.
The magnitude of variation in the gaze position signals recorded by an eye tracker, also known as its precision, is an important aspect of an eye tracker's data quality. However, data quality of eye-tracking signals is still poorly understood. In this paper, we therefore investigate the following: (1) How do the various available measures characterizing eye-tracking data during fixation relate to each other? (2) How are they influenced by signal type? (3) What type of noise should be used to augment eye-tracking data when evaluating eye-movement analysis methods? To support our analysis, this paper presents new measures to characterize signal type and signal magnitude based on RMS-S2S and STD, two established measures of precision. Simulations are performed to investigate how each of these measures depends on the number of gaze position samples over which they are calculated, and to reveal how RMS-S2S and STD relate to each other and to measures characterizing the temporal spectrum composition of the recorded gaze position signal. Further empirical investigations were performed using gaze position data recorded with five eye trackers from human and artificial eyes. We found that although the examined eye trackers produce gaze position signals with different characteristics, the relations between precision measures derived from simulations are borne out by the data. We furthermore conclude that data with a range of signal type values should be used to assess the robustness of eye-movement analysis methods. We present a method for generating artificial eye-tracker noise of any signal type and magnitude.
眼动追踪器记录的注视位置信号的变化幅度（也称为精度）大小是眼动追踪器数据质量的一个重要方面。然而，眼动追踪信号的数据质量仍未得到很好的理解。因此，在本文中，我们研究了以下内容：（1）在注视期间，表征眼动追踪数据的各种可用测量值彼此之间如何相关？（2）它们如何受到信号类型的影响？（3）在评估眼动分析方法时，应该使用哪种类型的噪声来增强眼动追踪数据？为了支持我们的分析，本文提出了新的措施，基于 RMS-S2S 和 STD 这两个已确立的精度测量标准，来分别对信号类型和信号幅度进行特征描述。通过模拟来研究这些措施中的每一个是如何依赖于计算它们的注视位置样本数量的，以及揭示 RMS-S2S 和 STD 彼此之间以及与描述记录的注视位置信号的时间谱组成的测量值之间的关系。进一步通过使用来自人类和人工眼睛的五个眼动追踪器记录的注视位置数据进行了实证研究。我们发现，尽管所研究的眼动追踪器产生的注视位置信号具有不同的特征，但模拟得出的精度测量值之间的关系与数据相符。我们进一步得出结论，应该使用具有不同信号类型值的数据集来评估眼动分析方法的稳健性。我们提出了一种用于生成任何信号类型和幅度的人工眼动追踪器噪声的方法。
