LINGUIST List 33.2691
Sun Sep 04 2022
Diss: Neurolinguistics: David Abugaber: ''Disentangling Neural Indices of Implicit vs. Explicit Morphosyntax Processing in an Artificial Language''
Editor for this issue: Sarah Goldfinch <sgoldfinchlinguistlist.org>
Date: 09-Aug-2022
From: David Abugaber <davidabugaber
gmail.com>
Subject: Disentangling Neural Indices of Implicit vs. Explicit Morphosyntax Processing in an Artificial Language
E-mail this message to a friend Institution: University of Illinois at Chicago
Program: MA and PhD in Hispanic Linguistics
Dissertation Status: Completed
Degree Date: 2022
Author: David Abugaber
Dissertation Title: Disentangling Neural Indices of Implicit vs. Explicit Morphosyntax Processing in an Artificial Language
Linguistic Field(s): Neurolinguistics
Dissertation Director:
Kara Morgan-Short
Laura Batterink
Kimberly Potowski
Phillip Hamrick
Jennifer Cabrelli
Dissertation Abstract:
Learning new languages is a complex task involving both explicit and implicit processes (i.e., that do/do not involve awareness). Understanding how these processes interact is essential to a full account of second language (L2) learning, but accounts vary as to whether explicit processes help, hinder, or have no effect on acquisition of implicit processing routines. Studies using an artificial language paradigm suggest that participants can learn L2 morphosyntactic regularities that they are unaware of (Leung & Williams, 2011, 2012), and one electroencephalography (EEG) study (Batterink et al., 2014) reported distinct event-related potentials (ERPs) in participants with vs. without rule awareness. However, the univariate nature of ERPs makes it impossible to determine whether/to what extent implicit processing occurred in rule-aware learners at a neural level. Our study addresses this via multivariate pattern analysis (MVPA) by training a decoder to detect neural indices of grammar processing at times in the experiment after behavioral measures indicated learning but before participants became rule-aware, and subsequently testing this decoder after participants became rule-aware. We conduct two additional analyses on the interplay between implicit/explicit processing, asking whether MVPA-based indices of semantic prediction vary between implicit/explicit learning, and whether the timing of grammar processing at the neural level is correlated (and thus closely coupled) with response times (RTs).
Following Batterink et al., 52 participants performed a word-classification task that covertly tests for grammar learning by comparing responses to words that follow vs. violate an underlying pattern. Rule-awareness was assessed via systematic debriefing halfway through, at which point the rule was revealed and a final block of trials was performed. Slower RTs and lower accuracies for rule-violating trials indicated learning even in rule-unaware participants. However, we did not replicate Batterink et al.’s ERP findings, as we only found a negative ERP in unaware participants and no significant ERP in aware participants. This may be due to natural interindividual variability in ERPs during grammar processing (Tanner, 2019). Furthermore, our MVPA decoding did not show above-chance trial classification accuracy, providing no evidence for co-occurrence of implicit processing during awareness. We also found no MVPA evidence for semantic prediction at the neural level in either aware or unaware learners. However, for both of these results, follow-up analyses suggested limited MVPA decoding sensitivity on our data in the first place, even when using alternate analysis parameters. Our ERP-to-RT correlation analyses showed evidence of time locking between neural indices of grammar processing and behavioral responses, suggesting a link between the two. Overall, the results show strong behavioral effects but limited EEG effects. This, along with several post hoc analyses, casts doubt on the extent to which learning in this paradigm is linguistic vs. non-linguistic. To the extent that learning was linguistic, our results favor weak/no interface models in that unaware and aware participants showed similar behavioral performance and there was no MVPA evidence for implicit processing during awareness. However, further inspection of behavioral and debriefing data suggests possible downsides to awareness. More broadly, this study demonstrates how alternate analysis methods may inform future research on the implicitness/explicitness of L2 grammar learning.
Page Updated: 04-Sep-2022