Much work has shown that differences in the timecourse of language processing are central to comparing native (L1) and non-native (L2) speakers. However, estimating the onset of experimental effects in timecourse data presents several statistical problems including multiple comparisons and autocorrelation. We compare several approaches to tackling these problems and illustrate them using an L1-L2 visual world eye-tracking dataset. We then present a bootstrapping procedure that allows not only estimation of an effect onset, but also of a temporal confidence interval around this divergence point. We describe how divergence points can be used to demonstrate timecourse differences between speaker groups or between experimental manipulations, two important issues in evaluating L2 processing accounts. We discuss possible extensions of the bootstrapping procedure, including determining divergence points for individual speakers and correlating them with individual factors like L2 exposure and proficiency. Data and an analysis tutorial are available at https://osf.io/exbmk/.

中文翻译：

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视觉世界数据的分歧点分析：在双语研究中的应用

许多工作表明，语言处理时间过程的差异是比较母语（L1）和非母语（L2）说话者的核心。然而，估计时间过程数据中实验效应的开始存在几个统计问题，包括多重比较和自相关。我们比较了几种解决这些问题的方法，并使用 L1-L2 视觉世界眼动追踪数据集来说明它们。然后，我们提出了一个引导程序，它不仅可以估计效应的开始，还可以估计这个分歧点周围的时间置信区间。我们描述了如何使用分歧点来证明说话者组之间或实验操作之间的时间过程差异，这是评估 L2 处理帐户的两个重要问题。我们讨论了引导程序的可能扩展，包括确定个别说话者的分歧点，并将它们与 L2 接触和熟练程度等个别因素相关联。数据和分析教程可在https://osf.io/exbmk/.