Age Effects in L2 Grammar Processing as Revealed by ERPs and How (Not) to Study Them

Abstract

In this study we investigate the effect of age of acquisition (AoA) on grammatical processing in second language learners as measured by event-related brain potentials (ERPs). We compare a traditional analysis involving the calculation of averages across a certain time window of the ERP waveform, analyzed with categorical groups (early vs. late), with a generalized additive modeling analysis, which allows us to take into account the full range of variability in both AoA and time. Sixty-six Slavic advanced learners of German listened to German sentences with correct and incorrect use of non-finite verbs and grammatical gender agreement. We show that the ERP signal depends on the AoA of the learner, as well as on the regularity of the structure under investigation. For gender agreement, a gradual change in processing strategies can be shown that varies by AoA, with younger learners showing a P600 and older learners showing a posterior negativity. For verb agreement, all learners show a P600 effect, irrespective of AoA. Based on their behavioral responses in an offline grammaticality judgment task, we argue that the late learners resort to computationally less efficient processing strategies when confronted with (lexically determined) syntactic constructions different from the L1. In addition, this study highlights the insights the explicit focus on the time course of the ERP signal in our analysis framework can offer compared to the traditional analysis.

Presaccadic EEG activity predicts visual saliency in free‐viewing contour integration

“…For the GAMM analysis of the right occipital region, the proportion variance explained is 5.6% and 5.7% for the contour‐absent and present conditions, respectively. These values are comparable with the values reported in studies that analyze single‐epoch EEG data (Dambacher, Kliegl, Hofmann, & Jacobs, ; Meulman et al, ; Payne, Lee, & Federmeier, ; Tremblay & Newman, ). The explained variance reported in EEG studies, which averages over multiple trials to obtain an ERP signal, is typically higher (although many EEG studies do not even report the variance explained or any other indication of effect size).…”

“…To model the nonlinear relationships between the predictors and EEG amplitude, thin plate regression splines have been proposed as a computationally efficient solution (Meulman et al, 2015;Wood, 2003), which were used in our model for the smooth and interaction terms. GAMM included a smooth term for Time, representing the partial effect of time on EEG amplitude.…”

“…Beyond the general suitability of mixed-effects models for EEG analysis, the GAMM approach has several additional merits (Meulman, Wieling, Sprenger, Stowe, & Schmid, 2015;Vossen, Van Breukelen, Hermens, Van Os, & Lousberg, 2011). First, EEG signals are analyzed on a single-trial basis, taking trial-by-trial variance within participants into account.…”

The influence of gradient foreign accentedness and listener experience on word recognition

“…GAMM has been previously applied successfully to model a variety of linguistic data. In particular, it has been used for investigating dialectal variation (Wieling, Montemagni, Nerbonne, & Baayen, 2014;Wieling, Nerbonne, & Baayen, 2011), event-related potentials (Kryuchkova, Tucker, Wurm, & Baayen, 2012;Meulman, Wieling, Sprenger, Stowe, & Schmid, 2015;Tremblay & Newman, 2015), prosodic prominence (Arnold, Wagner, & Baayen, 2013), reaction times (Baayen, 2010b(Baayen, , 2010c, and visual world eye-tracking (van Rij, Hollebrandse, & Hendriks, 2016). Outside of linguistics, it has been used extensively in the field of ecology (see Zuur, Ieno, Walker, Saveliev, & Smith, 2009).…”

An Event Related Field Study of Rapid Grammatical Plasticity in Adult Second-Language Learners

“…Although ultimate attainment is still debated, recent studies have focused on comparison of native speakers vs. non-native speakers that were familiar with the language for a long time prior to the study (Meulman et al, 2015; Díaz et al, 2016; Hanna et al, 2016; Johnson et al, 2016; Sung et al, 2016). However, few of them used a technique that allowed for source localization (Hanna et al, 2016).…”