33.2691, Diss: Neurolinguistics: David Abugaber: ''Disentangling Neural Indices of Implicit vs. Explicit Morphosyntax Processing in an Artificial Language''

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LINGUIST List: Vol-33-2691. Sun Sep 04 2022. ISSN: 1069 - 4875.

Subject: 33.2691, Diss: Neurolinguistics: David Abugaber: ''Disentangling Neural Indices of Implicit vs. Explicit Morphosyntax Processing in an Artificial Language''

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Date: Sun, 04 Sep 2022 19:44:59
From: David Abugaber [davidabugaber at gmail.com]
Subject: Disentangling Neural Indices of Implicit vs. Explicit Morphosyntax Processing in an Artificial Language

 
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(s):
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.




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