A common problem in eye-tracking research is vertical drift—the progressive displacement of fixation registrations on the vertical axis that results from a gradual loss of eye-tracker calibration over time. This is particularly problematic in experiments that involve the reading of multiline passages, where it is critical that fixations on one line are not erroneously recorded on an adjacent line. Correction is often performed manually by the researcher, but this process is tedious, time-consuming, and prone to error and inconsistency. Various methods have previously been proposed for the automated, post hoc correction of vertical drift in reading data, but these methods vary greatly, not just in terms of the algorithmic principles on which they are based, but also in terms of their availability, documentation, implementation languages, and so forth. Furthermore, these methods have largely been developed in isolation with little attempt to systematically evaluate them, meaning that drift correction techniques are moving forward blindly. We document ten major algorithms, including two that are novel to this paper, and evaluate them using both simulated and natural eye-tracking data. Our results suggest that a method based on dynamic time warping offers great promise, but we also find that some algorithms are better suited than others to particular types of drift phenomena and reading behavior, allowing us to offer evidence-based advice on algorithm selection.

眼动追踪研究中的一个常见问题是垂直漂移——注视配准在垂直轴上的逐渐位移，这是由于随着时间的推移逐渐失去眼动追踪校准而导致的。这在涉及阅读多行段落的实验中尤其成问题，其中至关重要的是，一行上的注视不会错误地记录在相邻行上。校正通常由研究人员手动执行，但此过程繁琐、耗时，并且容易出错和不一致。之前已经提出了各种方法来自动、事后校正读取数据中的垂直漂移，但这些方法差异很大，不仅在它们所基于的算法原理方面，而且在它们的可用性、文档、实现语言，等等。此外，这些方法在很大程度上是孤立地开发的，几乎没有尝试系统地评估它们，这意味着漂移校正技术正在盲目地向前发展。我们记录了 10 种主​​要算法，包括本文中的两种新算法，并使用模拟和自然眼动追踪数据对其进行评估。我们的结果表明，基于动态时间扭曲的方法具有很大的前景，但我们也发现某些算法比其他算法更适合特定类型的漂移现象和阅读行为，从而使我们能够就算法选择提供基于证据的建议。包括两个对本文来说是新颖的，并使用模拟和自然的眼动追踪数据对其进行评估。我们的结果表明，基于动态时间扭曲的方法具有很大的前景，但我们也发现某些算法比其他算法更适合特定类型的漂移现象和阅读行为，从而使我们能够就算法选择提供基于证据的建议。包括两个对本文来说是新颖的，并使用模拟和自然的眼动追踪数据对其进行评估。我们的结果表明，基于动态时间扭曲的方法具有很大的前景，但我们也发现某些算法比其他算法更适合特定类型的漂移现象和阅读行为，从而使我们能够就算法选择提供基于证据的建议。