Abstract
AbstractMuch 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 athttps://osf.io/exbmk/.
Highlights
Studying the timecourse of comprehension is a central goal in bilingual processing research, which has been significantly fostered by the use of time-sensitive methods such as self-paced reading, eye-tracking, and event-related potentials
To encourage the use of divergence point analyses, we provide a practical introduction using a L1-L2 visual world eye-tracking dataset
The critical contribution of the bootstrapping method is that it precisely quantifies the delay in the L2 speakers, while allowing a direct between-group comparison of divergence points and estimating their uncertainty
Summary
Studying the timecourse of comprehension is a central goal in bilingual processing research, which has been significantly fostered by the use of time-sensitive methods such as self-paced reading, eye-tracking, and event-related potentials. Despite the rich data generated by current methods, our inferences about L1-L2 temporal asymmetries are often limited by using methods demonstrating that differences in native vs non-native processing affect different sentence regions (in self-paced reading), different temporal windows (in event-related potentials), or different reading measures (in eye-tracking). This article summarizes several techniques for achieving this goal Such information is relevant to testing a variety of L2 accounts. We note that divergence point analyses differ from another set of techniques which examine timeseries data by modeling the shape (i.e., functional form) of change across time (e.g., Mirman, 2017; Porretta, Kyröläinen, van Rij & Järvikivi, 2018).
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