PDF(Pubmed)
Abstract：
Background: Automated eye tracking data correction algorithms such as Dynamic-Time Warp always made a trade-off between the ability to handle regressions (jumps back) and distortions (fixation drift). At the same time, eye movement in code reading is characterized by non-linearity and regressions. Objective: In this paper, we present a family of hybrid algorithms that aim to handle both regressions and distortions with high accuracy. Method: Through simulations with synthetic data, we replicate known eye movement phenomena to assess our algorithms against Warp algorithm as a baseline. Furthermore, we utilize two real datasets to evaluate the algorithms in correcting data from reading source code and see if the proposed algorithms generalize to correcting data from reading natural language text. Results: Our results demonstrate that most proposed algorithms match or outperform baseline Warp in correcting both synthetic and real data. Also, we show the prevalence of regressions in reading source code. Conclusion: Our results highlight our hybrid algorithms as an improvement to Dynamic-Time Warp in handling regressions.
摘要：
背景:诸如动态时间扭曲的自动眼睛跟踪数据校正算法总是在处理回归(跳回)和失真(注视漂移)的能力之间进行权衡。同时,代码读取中的眼动具有非线性和回归的特征。目的:在本文中,我们提出了一系列混合算法，旨在高精度地处理回归和失真。方法：通过合成数据模拟，我们复制已知的眼动现象，以评估我们的算法与Warp算法作为基线。此外，我们利用两个真实的数据集来评估从阅读源代码中纠正数据的算法，并查看所提出的算法是否可以推广到从阅读自然语言文本中纠正数据。结果：我们的结果表明，大多数提出的算法在校正合成和真实数据方面都匹配或优于基线Warp。此外，我们显示了在阅读源代码时回归的普遍性。结论：我们的结果强调了我们的混合算法在处理回归时对动态时间扭曲的改进。
