Humanities Lab, Lund University, Lund, Sweden.
Behav Res Methods. 2010 Feb;42(1):188-204. doi: 10.3758/BRM.42.1.188.
Event detection is used to classify recorded gaze points into periods of fixation, saccade, smooth pursuit, blink, and noise. Although there is an overall consensus that current algorithms for event detection have serious flaws and that a de facto standard for event detection does not exist, surprisingly little work has been done to remedy this problem. We suggest a new velocity-based algorithm that takes several of the previously known limitations into account. Most important, the new algorithm identifies so-called glissades, a wobbling movement at the end of many saccades, as a separate class of eye movements. Part of the solution involves designing an adaptive velocity threshold that makes the event detection less sensitive to variations in noise level and the algorithm settings-free for the user. We demonstrate the performance of the new algorithm on eye movements recorded during reading and scene perception and compare it with two of the most commonly used algorithms today. Results show that, unlike the currently used algorithms, fixations, saccades, and glissades are robustly identified by the new algorithm. Using this algorithm, we found that glissades occur in about half of the saccades, during both reading and scene perception, and that they have an average duration close to 24 msec. Due to the high prevalence and long durations of glissades, we argue that researchers must actively choose whether to assign the glissades to saccades or fixations; the choice affects dependent variables such as fixation and saccade duration significantly. Current algorithms do not offer this choice, and their assignments of each glissade are largely arbitrary.
事件检测用于将记录的注视点分类为注视、扫视、平滑追踪、眨眼和噪声等时段。尽管人们普遍认为当前的事件检测算法存在严重缺陷，而且实际上并不存在事件检测的标准，但令人惊讶的是，很少有工作致力于解决这个问题。我们提出了一种新的基于速度的算法，该算法考虑了几个先前已知的限制。最重要的是，新算法将所谓的滑行（glissades）识别为一种单独的眼动类别，这是许多扫视结束时的一种晃动运动。该解决方案的一部分涉及设计一个自适应速度阈值，使事件检测对噪声水平的变化不那么敏感，并且对用户来说算法设置是免费的。我们在阅读和场景感知过程中记录的眼动数据上展示了新算法的性能，并将其与当今最常用的两种算法进行了比较。结果表明，与当前使用的算法不同，新算法可以稳健地识别注视、扫视和滑行。使用该算法，我们发现滑行在阅读和场景感知过程中的大约一半扫视中发生，其平均持续时间接近 24 毫秒。由于滑行的高发生率和长持续时间，我们认为研究人员必须主动选择将滑行分配给扫视还是注视；这种选择会显著影响注视和扫视持续时间等依赖变量。当前的算法没有提供这种选择，而且它们对每个滑行的分配很大程度上是任意的。
