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Bilingualism: Language and
Cognition
cambridge.org/bil
Research Article
Cite this article: Liu, S., Huang, J., Schwieter,
J.W., & Liu, H. (2023). The role of
morphological configuration in language
control during bilingual production and
comprehension. Bilingualism: Language and
Cognition,26, 1067–1078. https://doi.org/
10.1017/S1366728923000330
Received: 13 May 2022
Revised: 14 March 2023
Accepted: 3 April 2023
First published online: 24 May 2023
Keywords:
Language control; morphological
configuration; sequential process; bilingual;
ERP; language production; language
comprehension
Corresponding author:
Huanhuan Liu; Email: abcde69503@126.com
© The Author(s), 2023. Published by
Cambridge University Press
The role of morphological configuration in
language control during bilingual production
and comprehension
Shuang Liu1,2, Junjun Huang1,2, John W. Schwieter3,4 and Huanhuan Liu1,2
1
Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, 116029 Dalian, China;
2
Key
Laboratory of Brain and Cognitive Neuroscience, Liaoning Province, 116029 Dalian, China;
3
Language Acquisition,
Multilingualism, and Cognition Laboratory / Bilingualism Matters @ Wilfrid Laurier University, Waterloo, Canada
and
4
Department of Linguistics and Languages, McMaster University, Hamilton, Canada
Abstract
When bilinguals switch between their two languages, they often alternate between words
whose formation rules in one language are different from the other (e.g., a noun-verb com-
pound in one language may be a verb-noun compound in another language). In this study,
we analyze behavioral performance and electrophysiological activity to examine the effects
of morphological configuration on language control during production and comprehension.
Chinese–English bilinguals completed a joint naming-listening task involving cued language
switching. The findings showed differential effects of morphological configuration on lan-
guage production and comprehension. In production, morphological configuration was pro-
cessed sequentially, suggesting that bilingual production may be a combination of sequential
processing and inhibition of morphological levels and language interference. In comprehension,
however, bottom-up control processes appear to mask the influence of sequential processing on
language switching. Together, these findings underscore differential functionalities of language
control in speaking and listening.
1. Introduction
Humans use language in ways that meet the needs of various communicative situations in
which they find themselves. Economic and trade internationalization and the global dissem-
ination of science, technology, and culture necessitate that bilingual individuals toggle between
different languages, demonstrating the common practice of language switching. But as is well
known, not all languages are structurally the same. Among their many differences are the word
formation rules to which they systematically adhere. These inter-linguistic morphological
properties (i.e., within the word) may inevitably interfere with language comprehension and
production. Morphemes are meaningful units that make up words. They are the basic building
blocks that can be as small as a single sound, as the /-s/ in the word “cats,”or longer as in cases
of unbound morphemes like “coffee”and “elephant.”The rule that verbs in the present tense
can be marked as third person singular by adding the morpheme /-s/, with the exception of
irregular verbs, is an example of the many word formation rules that are used to construct
words and to express morphological changes (Göpel & Richter, 2016). When bilinguals
speak in either of their languages, they apply word formation rules as appropriate.
However, there are times when the rules in the two languages do not align. For instance,
the morpheme that generates the present tense of the third personal singular in English is
unlikely to work the same in other languages, as can clearly be seen when comparing the
English plural /-s/ to one of Italian’s plural markers, /-i/, as in ragazzi ‘boys’). Moreover, in
one language, compound words (e.g., “bottle-opener”), which consist of two or more words
of the same or different category (i.e., “bottle-opener”is a noun-verb), may not have com-
pound translations consisting of the same categories (e.g., apri bottiglia and abrebotellas ‘open-
bottles’in Italian and Spanish, respectively, are verb-noun combinations). What role, if any,
does the incongruency in word formation processes between two languages play in bilingual
language switching? In the next section, we will provide further background to this question.
1.1 Morphological configuration and language control
To our knowledge, only a few behavioral studies have examined the role of morphological con-
figuration in language control (Contreras-Saavedra et al., 2020,2021). In the study by
Contreras-Saavedra et al. (2020), German–English–Spanish trilinguals participated in a digit-
naming task with cued language switches. The authors specifically focused on two-digit num-
ber words that, depending on the language, had either inverted rules, non-inverted rules, or
both. In German, there is only an inverted composition rule (e.g., “sixteen”), while in
https://doi.org/10.1017/S1366728923000330 Published online by Cambridge University Press
Spanish, there is only a non-inverted composition rule (e.g.,
“twenty-three”), and in English, there are both. The analyses
included trial sequences with English in trial n (and either
German, English, or Spanish in the preceding trial n-1) as
English was the only language that included both non-inverted
and inverted composition rule trials. The findings revealed larger
switch costs in morphological configuration-repetition trials than
in morphological configuration-switch trials. Similarly, in the
study by Contreras-Saavedra et al. (2021), these findings were
generalized to language comprehension using non-numerical
words. Although these findings suggest that language switching
is affected by morphological configuration, these configurations
were only analyses on the second language (L2), English. As
such, it is not clear as to whether there are specific processing
modes arising from cross-language switching of morphological
configurations that are independent of language.
Models of bilingual word processing suggest that compound
word processing is different from distributed networks models
and serial processing of meaning to pronunciation. Distributed
models explain the rapid and parallel use of semantic and morpho-
logical features during speech planning (Miozzo et al., 2015;
Strijkers et al., 2010,2017). However, different morphological con-
figuration between languages may lead bilinguals to adopt sequen-
tial processing (Caramazza et al., 1988; Levelt et al., 1999;Lietal.,
2017; Taft & Forster, 1975; Uygun & Gürel, 2017). Sequential pro-
cessing models hold that compounds are not stored and accessed as
whole units, but rather, are able to be decomposed and separately
accessed (Libben et al., 1999). Using highly sensitive time-
resolution EEG technology, the present study aims to explore
how morphological configuration affects control mechanisms
involved in bilingual production and comprehension by simulating
simple dialogues in a language switching task.
1.2 Language switching and ERP evidence
The Bilingual Interactive-Activation Model from a developmental
perspective (BIA-d; Grainger et al., 2010) argues that both bilin-
gual production and comprehension require activation of the tar-
get language node and inhibition of the non-target language.
However, the control processes for production and comprehen-
sion are distinct. In production, the target language is proactively
activated according to situational/communicative demands, and
the non-target language is suppressed using top-down control
(Declerck & Philipp, 2018; Peeters et al., 2014). In contrast, in lan-
guage comprehension, the target language is passively activated
and non-target language words are suppressed using bottom-up
control (Declerck & Philipp, 2018; Declerck et al., 2019).
Numerous event-related potential (ERP) studies on language
switching in production have used the N2 effect and the late posi-
tive component (LPC) as indicators of language control. The N2
effect has been associated with inhibition of cross-language
schema or language tags. The LPC reflects target language
lemma selection (Martin et al., 2013) and the release of a previ-
ously suppressed lemma (Jackson et al., 2001). For example, Liu
et al. (2020) reported that when trilinguals where cued to switch
away from a language and into one of their other languages of
their choosing (switch-away trials), there were more negative N2
amplitudes and smaller LPC activity compared to staying in the
same language (repeat trials) or when cued to switch into a spe-
cific language (switch-to trials). Kang et al. (2020) investigated the
predictive effect of cognitive control on language control and
found that switch trials induced a stronger N2 effect than repeat
trials and smaller flanker effect. Timmer et al. (2019) examined
inhibitory control in different contexts and found that L2 switch
trials induced greater LPCs than L2 non-switch trials. Similarly,
Liu et al. (2016,2018) found that switching into the L2 elicited
a larger LPC effect than switching into the first language (L1).
This switching effect quantified by LPC indicates that bilinguals
alternate between their two languages by suppressing the interfer-
ence of non-target lemmas. Moreover, the LPC is associated with
context updating, target selection, and allocation of attention
resources (Donchin, 1981; Polich, 2007) and its amplitude may
be regulated by the semantic nature of language stimuli. For
instance, words containing the same semantic category trigger
greater LPC amplitude than words with different semantic cat-
egories (Sanquist et al., 1980).
In addition to the typical N2 and LPC effects, early positive P2
activity is often examined during overt picture naming. Costa et al.
(2009) revealed a strong positive correlation between response
times (RTs) and mean amplitudes of P2 peaks. These correlations
support the view that the P2 component is sensitive to the competi-
tive nature of lexical selection. In a study by Branzi et al. (2014),
the researchers offered evidence for the P2 effect as an index of
difficulty in lexical access. Highly-proficient bilinguals named pic-
tures in their L1, in their L2, and then in their L1 again, or in
the opposite order (i.e., L2, L1, L2). The results showed that L1
recovery induced a behavioral cost and an enhanced P2 effect
which had an after-effect on the N2 component (reduced negativ-
ity). However, L2 recovery neither exhibited significant costs nor
enhanced P2 effects. The authors argued that the P2 effect reflects
language-specific selection mechanisms that are applied during the
early stage of lexical access. Given that morphological configuration
might be implicated in early word retrieval, the present study
includes analyses on P2 amplitude, along with N2 and LPC, as
indicators in assessing the impact of morphological configuration
on language control during production and comprehension.
1.3 The present study
It is unclear whether it is morphological configuration independ-
ent of language that influences bilingual production and compre-
hension of simple words in a language switching context. In the
present study, we focus on the configuration of compound
words whose combinations can be of various types: noun-noun,
noun-verb, adjective-noun, adjective-adjective, etc. English and
Chinese are additional examples of languages that can have differ-
ent morphological formation rules in creating compound words.
Accordingly, we specifically examine compounds in Chinese and
English that are created by the same concepts (i.e., translation
equivalents) but either have an incongruent morphological con-
figuration (e.g., “handshake”is a noun-verb compound, while
its translation “握手”(‘shake hand’) is a verb-noun compound)
or a congruent configuration (e.g., “sunrise”and its translation
日出(‘day come-out’) are both noun-verb compounds). Given
that the congruency of morphological configuration between lan-
guages may implicate sequential processing (Caramazza et al.,
1988; Levelt et al., 1999; Taft & Forster, 1975), we use the congru-
ent Chinese–English morphological configuration as a baseline to
compare it with incongruent morphological configuration during
a language switching task. In our experiment, bilingual speakers
(Participant A) named pictures in Chinese and English according
to language cues, while bilingual listeners (Participant B) heard
these utterances and then judged whether they included certain
sounds. In this dyad scenario, a closer approximation to dialogue
1068 Shuang Liu et al.
https://doi.org/10.1017/S1366728923000330 Published online by Cambridge University Press
can be achieved while carefully examining the control mechan-
isms involved in both production and comprehension.
We hypothesize that morphological configuration will exert a
significant impact on the lexical access stage during language
switching as reflected by behavioral performance and relevant
ERP components. We expect this to occur because bilinguals
may detect incongruent morphological configurations in early
processing stages and make processing adjustments to implement
language switching. Moreover, if bilingual production and com-
prehension share a similar control mechanism, we should expect
to observe effects of morphological configuration on production
and comprehension on shared ERP components. If morpho-
logical configuration involves parallel processing, the influence
of morphological configuration on language switching should
be relatively small. Therefore, this study will reveal the role of lan-
guage control in production and comprehension from the per-
spective of cross-linguistic morphological configuration.
2. Method
2.1 Participants
The calculated sample size was 28 using G.power 3.1.9.7 (Faul
et al., 2007) according to the following settings: F-tests >
ANOVA: Repeated measures, within factors, Effect size f = .25,
αerror probability = .05, correlation among repeat measures
= .5, Power (1-βerror probability) = .8, Number of groups = 1,
Number of measurements = 3, and nonsphericity correct ∈=1.
To avoid the reduction of effect size due to invalid subject data,
thirty-seven dyads of unbalanced Chinese–English bilinguals
studying at Liaoning Normal University participated in this
study. The participants were paired arbitrarily. The participants
were native Chinese speakers and had learned English in a class-
room setting since primary school. All participants were right-
handed with normal or corrected-to-normal vision and had no
history of neurological, psychiatric, or major somatic disorders.
Five dyads were excluded from the study because of excessive
EEG data artifacts during the preprocessing stage. Thus, the
final sample included 32 dyads (N= 48 females, 16 males; M
age
= 22 years, SD
age
= 3 year). The research protocol was approved
by the Research Center of Brain and Cognitive Neuroscience at
Liaoning Normal University and all participants provided their
written informed consent prior to participating in the study.
Supplementary Materials Table 1 shows the participants’object-
ive and subjective language proficiency characteristics. The object-
ive proficiency level of English was tested by the Oxford Quick
Placement Test [QPT] (Syndicate, 2001). The QPT is scored out
of 60 points and is a valid placement test published by Oxford
University Press (see Supplementary Materials Table 2 and
Table 3). The average scores among the participants in the present
study was 33 points, indicating a lower intermediate L2 proficiency.
The participants also completed a questionnaire in which they
provided subjective self-ratings of their own L1 and L2 abilities in
listening, speaking, reading, and writing. The ratings were based
on a seven-point scale in which “7”indicated “perfect knowledge”
and “1”indicated “no knowledge.”Both the QPT and the language
questionnaire were completed before the formal experiment.
2.2 Materials
Sixteen compound words (see Supplementary Materials Table 4)
in Chinese and their sixteen compound word translations were
selected and presented as stimuli in white-and-black line drawings
(see Supplementary Materials Figure 1). Half of these stimuli had
congruent morphological configurations in Chinese and English
and the other half had incongruent morphological configurations
between the two languages. We define congruent morphological
configuration as compound words which are constructed using
the same concepts that belong to the same lexical category (e.g.,
the noun-verb compound 日出(‘sun come-out’) and the noun-
verb compound “sunrise”). We refer to incongruent morpho-
logical configuration as compound words, also constructed
using the same concepts in the two languages, but the order of
Figure 1. Procedure of an Example Trial from the Joint Naming (Participant A) and Listening (Participant B) Task
Bilingualism: Language and Cognition 1069
https://doi.org/10.1017/S1366728923000330 Published online by Cambridge University Press
the lexical categories of the morphemes is incongruent between
the two languages (e.g., the verb-noun compound 握手(‘shake
hand’) and the noun-verb compound “handshake”). An add-
itional eight compound words (4 congruent and 4 incongruent)
were used in a practice experiment.
A separate group of participants (N = 20) who did not take
part in the formal experiment, but who were from the same
research population, rated their familiarity with the experimental
words. The familiarity ratings were based on a 9-point scale on
which “1”meant “least familiar”and “9”meant “most familiar.”
A two-factor within-subject ANOVA was performed on the famil-
iarity ratings with language (L1, L2) × morphological configur-
ation (congruent, incongruent) as factors. There was no main
effect of language (L1: M= 8.31 ± .18, L2: M= 8.29 ± .19, F(1,7)
= .80, p= .402, η
2
= .10) or of morphological configuration (con-
gruent: M= 8.28 ± .20, incongruent: M= 8.32 ± .16, F(1,7) = .19,
p= .68, η
2
= .03). Moreover, the interaction between language
and morphological configuration was not significant, F(1,7) =
2.33, p= .170, η
2
= .25, suggesting that there were no differences
in familiarity of morphological configuration between the
languages.
2.3 Design and procedure
The study is a language (L1, L2) × switching (non-switch,
switch) × morphological configuration (congruent, incongruent)
within-subject design and was administered using E-Prime 2.0
Software. To create a simple interactive response for each dyad,
we asked participants to perform a joint naming-listening task
in which one participant (Participant A) named pictures while
another (Participant B) listened and made decisions about
whether these utterances contained certain sounds. Each dyad
wore an EEG cap and sat in the same room to perform the
task. An opaque foam board (1.5 m × 1.1 m) separated
Participants A and B and divided the computer screen into two
equal parts.
Prior to the formal experiment, participants familiarized
themselves with each picture which appeared on a computer
screen along with their L1 and L2 names. The experimenter
ensured that the participants knew the name of each picture by
asking them to name each one aloud. Following this, the partici-
pants started a practice experiment including 64 trials. The pro-
cedure was the same as that of the formal experiment. During
the experiment, Participant A named pictures into a microphone
in the L1 or L2 based on a color cue (e.g., pictures in red boxes
were named in the L1 and pictures in blue boxes were named
in the L2). The language-color association was counterbalanced
across dyads.
Participant B performed a sound decision after hearing each
word uttered by Participant A, such that they judged whether
the utterances included an [ou] sound if in the L1, or [ai]/[æ]/
[e] sounds if in the L2. Although the specific syllables of the
two languages are different, they both reflect sound judgments.
These judgments were uttered into a microphone as “是/否”
(‘yes/no’) in the L1 and “yes/no”in the L2. The comprehension
portion of the task did not require language non-specific categor-
ization (i.e., animacy judgment). Because our word materials were
not suitable for semantic judgment, sound judgments were used
to highlight morphological configuration. The rationale for asking
Participant B to provide oral responses was to eliminate potential
effects caused by different response modalities (oral response vs.
key response) and to elicit an interactive response that could be
heard by Participant A. The response language of Participant B
follows that of Participant A, and Participant B performed a lan-
guage repeat or switch trial as determined by Participant B’spre-
vious trial.
The experiment consisted of 4 experimental blocks with
80 trials per block. Two of the blocks included L1 and L2 com-
pound words with congruent morphological configuration and
two blocks included L1 and L2 compounds with incongruent
configuration. Each condition (L1-congruent, L1-incongruent,
L2-congruent, L2-incongruent) appeared in 20 trials per block.
The presentation order of the four blocks was Congruent-
Congruent-Incongruent-Incongruent for half of the dyads and
Incongruent-Incongruent-Congruent-Congruent for the other
half. Figure 1 illustrates an example of the procedure for a single
trial. Each trial started with a 250-ms-presentation of a red or blue
square visible only to Participant A and a white square visible
only to Participant B. After a blank screen of 500 ms, a target pic-
ture appeared for Participant A and a triangle within a circle
appeared for Participant B. The geographic shape was meaning-
less and was included only to draw Participant B’s attention to
visual information and to eliminate differences in input modality
between the two participants. Upon seeing the target picture,
Participant A overtly named it into a microphone in the L1 or
L2 according to the predetermined color cue. The picture disap-
peared when Participant A responded or after 2000 ms. Then
Participant B made a sound judgment based on what Participant
A had just uttered. The screen disappeared when Participant B
responded or after 3200 ms. Finally, a blank screen randomly
appeared between 1500–2200 ms before the next trial began.
2.4 Behavioral data and analyses
Behavioral data were obtained from and analyzed on naming and
listening RTs and accuracy performance. We excluded from the
data analyses incorrect responses (e.g., wrong target word, disflu-
ent responses, no responses, or self-corrected responses), the first
two trials of each block, and responses that were < 200 ms or
beyond M±3SD. The excluded data totaled 6.31% of the naming
data and 12.29% of the listening task. We used R software (version
3.6) (lme4 and lmerTest package, Bates et al., 2014; Kuznetsova
et al., 2017) to perform a linear mixed model for RTs and general-
ized linear mixed model for accuracy. We used language (L1, L2),
switching (non-switch, switch), and morphological configuration
(congruent, incongruent) as fixed effects and participants were
added as random effects. Apart from the fixed effects, the models
included participants and items as random effects (random inter-
cepts and slopes). When the models did not converge, we
removed the slope that explained the least variance until they con-
verged. Results from the best-fitting model justified by the data
are reported. We used Akaike information criteria, an indicator
for the optimal model, to determine retention or omission factors.
We started with a model of language switching using log naming
latencies as the dependent variable and language, switching, and
morphological configuration as fixed effects. The best-fitting
model structure included random intercepts for participants. All
fixed effect factors were two-level categorical predictors and
were coded as −0.5 and 0.5. For language, L1 was coded as
−0.5 and L2 as 0.5; for switching, non-switches were coded as
−0.5 and switches as 0.5; for morphological configuration, the
congruent condition was coded as −0.5 and the incongruent con-
dition as 0.5. All models converged and the reported pvalues were
corrected with Bonferroni correction.
1070 Shuang Liu et al.
https://doi.org/10.1017/S1366728923000330 Published online by Cambridge University Press
2.5 Electrophysiological data and analyses
Electrophysiological data were recorded using a set of 64 electro-
des placed according to the extended 10–20 positioning system.
The signal was recorded from eemagine (ANT Neuro) at a rate
of 500 Hz in reference to CPz electrode. The electrodes M1 and
M2 were separately placed on the left and right mastoids.
Impedances were kept below 5 kΩ. Offline processing was refer-
enced to the average of M1 and M2. Electroencephalographic
activity was filtered online within a bandpass between .1 and
100 Hz and refiltered offline with a highpass filter of .01Hz and
a lowpass filter of 30Hz. The signals recorded by the peripheral
electrodes were poor and were thus removed so that subsequent
data analyses would not be affected by these electrodes. Finally,
40 electrodes were left after removing the peripheral electrode
with more artifacts (FPz, FP1, FP2, AF3, AF4, AF7, AF8, F7, F8,
FT7, FT8, T7, T8, TP7, TP8, P7, P8, PO7, PO8, Oz, O1, O2) (Liu
et al., 2016,2018,2020,2021). Ocular artifact reduction was per-
formed through Independent Component Analysis using EEGLAB
(Makeig et al., 1995). The mean number of independent compo-
nents rejected was 1.55 ± 1.07 per participant. In both tasks, continu-
ous recordings were analyzed in picture-locked −100 to 1000 ms
epochs. Correspondingly, the epochs were referenced to a 100 ms
pre-stimulus baseline. Signals exceeding ± 90 mV in any given
epoch were automatically discarded. The mean (and SD) number
of accepted epochs per condition across participants are shown in
Supplementary Materials Table 5. All preprocesses were performed
by EEGLAB (Brunner et al., 2013; Delorme & Makeig, 2004).
ERP components were defined based on grand means and
analyzed in time windows that are typically used in picture nam-
ing: locked P2 (170–220 ms), N2 (240–290 ms), LPC (450–
600 ms) (Branzi et al., 2014; Liu et al., 2016; Misra et al., 2012),
and in sound judgments in listening tasks: locked LPC (760–
950 ms) (Davis & Jerger, 2014). Spatially, we pre-defined frontal-
parietal (sensors: F3, F1, Fz, F2, F4, FC3, FC1, FCz, FC2, FC4, C3,
C1, Cz, C2, C4) regions of interest. Topographical analyses were
based on mean amplitudes measured over 40 electrodes distribu-
ted over the entire scalp.
In the preprocessing stage, the time series of each dyad were
aligned, and the number of trials retained between each condition
was the same for each dyad. For each time window, we conducted
a generalized linear mixed model using language, switching, and
morphological configuration as fixed effects and participants as
the random effect. We conducted simple effects follow-up ana-
lyses when main fixed effects or interaction reached significance
at p< .05. In the results below, we report findings from follow-up
analyses that are most relevant to our objectives. To see the results
of other analyses testing all possible directions, consult in
Supplementary Materials Table 6–10.
3. Results
3.1 Behavioral results
3.1.1 Reaction times: Naming
The results of the language (L1, L2) × switching (non-switch,
switch) × morphological configuration (congruent, incongruent)
mixed-effects models showed significant main fixed effects of
the three variables. For language, there were faster RTs in the
L1 (M= 865 ms ± 250) compared to the L2 (M= 999 ms ± 265),
faster RTs in non-switch trials (M= 922 ms ± 261) than switch trials
(M= 942 ms ± 271), and faster RTs for congruent morphological
configuration (M= 908 ms ± 255) compared to incongruent
morphological configuration (M= 955 ms ± 274 > congruent) (see
Supplementary Materials Table 11 for full statistics).
There was a significant interaction between language and
switching. Follow-up analyses revealed that in the L1, there
were faster RTs in non-switch trials (M= 850 ± 238 ms) com-
pared to switch trials (M= 880 ± 260 ms), b=−.03, SE = .007,
z=−4.69, p< .001, while in the L2, this difference was not signifi-
cant (switch: M= 1005 ± 267 ms; non-switch: M= 993 ± 263 ms,
b=−.01, SE = .007, z=−1.91, p= .06). There was also a signifi-
cant interaction between language and morphological configur-
ation. Further analyses showed that while the difference between
incongruent and congruent trials was significant in both the L1
(incongruent: M= 879 ± 259 ms > congruent: M= 851 ± 239 ms,
b=−.03, SE = .006, z=−4.20, p< .001) and L2 (incongruent:
M= 1032 ± 269 ms > congruent: M= 966 ± 258 ms, b=−.07,
SE = .007, z=−9.77, p< .001), the congruency effect was larger in
the L2 (M=−66 ms ± 79) compared to the L1 (M=−27 ms ± 75),
t=2.25, p= .032.
A three-way interaction of language, switching, and morpho-
logical configuration reached significance (see Figure 2a).
Follow-up analyses for this three-way interaction were split by
language. In the L1, we found a significant main fixed effect of
switching in which non-switch trials (850 ± 238 ms) were signifi-
cantly faster than switch trials (M= 880 ± 260 ms), b= .03, SE
= .007, t= 4.75, p< .001, and a significant effect of morphological
configuration such that congruent trials (M= 851 ± 239 ms) were
faster than incongruent trials (M= 879 ± 259 ms), b= .03,
SE = .007, t= 4.18, p< .001. In the L2, there was a significant
effect of morphological configuration in which congruent trials
(M= 966 ± 258 ms) were faster than incongruent trials
(M= 1032 ± 269 ms), b= .07, SE = .006, t= 10.15, p< .001. In
addition, in the L2, the interaction between switching and mor-
phological configuration was significant, demonstrating that in
congruent morphological configurations, non-switch trials
(947 ± 245 ms) were faster than switch trials (M= 985 ±
269 ms), b=−.04, SE = .009, z=−3.92, p< .001, but in incongru-
ent morphological configurations, a significant switch cost
effect did not emerge (switch: M= 1025 ± 264 ms; non-switch:
M= 1039 ± 273 ms, b= .01, SE = .009, z= 1.23, p= .217).
3.1.2 Reaction times: Listening
Theresultsofthelanguage(L1,L2)×switching(non-switch,switch)×
morphological configuration (congruent, incongruent)mixed-effects
models on listening RTs showed significant main fixed effects
of the three variables. For language, there were faster RTs in the
L1 (M= 1452 ms ± 483) compared to the L2 (M= 1617 ms ±
483), faster RTs for non-switch trials (M= 1479 ms ± 482) com-
pared to the switch trials (M= 1587 ms ± 497), and faster RTs for
congruent morphological configuration (M= 1498 ms ± 482) com-
pared to incongruent morphological configuration (M= 1568 ms
± 480) (see Supplementary Materials Table 11 for full statistics).
There was a significant interaction between language and
switching (see Figure 2b). Follow-up analyses revealed that
while non-switch trials were faster than switch trials in both the
L1 (non-switch trials: M= 1390 ± 446 ms; switch trials: M=
1513 ± 480 ms; b=−.13, SE = .008, z=−16.30, p< .001) and L2
(non-switch trials: M= 1571 ± 458 ms; switch trials: M= 1665 ±
503 ms, b=−.10, SE = .008, z=−12.85, p< .001), this difference
was larger in the L1 (M= 116 ms ± 91) compared to the L2
(M= 94 ms ± 70). There was also a significant interaction between
switching and morphological configuration. Further analyses
showed that although the difference between non-switch and
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switch trials was significant for both congruent (switch: M= 1558
± 501 ms > non-switch: M= 1438 ± 455 ms, b=−.08, SE = .008,
z=−10.09, p< .001) and incongruent morphological configur-
ation (switch: M= 1656 ± 481 ms > non-switch: M= 1580 ±
483 ms, b=−.05, SE = .007, z=−6.83, p< .001), this difference
was larger for congruent trials (M= 117 ms ± 81) than for incon-
gruent trials (M= 90 ms ± 76).
3.1.3 Accuracy: Naming
A similar mixed-effects model was conducted on the accuracy
rates of the naming data. We found a main fixed effect of
switching, such that non-switch trials were more accurate
(M= .98 ± .13) than switch trials (M= .97 ± .16) (see
Supplementary Materials Table 12 for full statistics). There were
no other significant effects or interactions in the naming data.
3.1.4 Reaction times: Listening
For listening accuracy, the main fixed effects of language, switch-
ing, and morphological configuration were significant, such that
responses in the L1 (M= .95 ± .21) were more accurate than in
the L2 (M= .92 ± .27), non-switch trials (M= .95 ± .21) were
more accurate than switch trials (M= .92 ± .27), and congruent
Figure 2. RTs (a) of Naming Split by Switch ×
Morphological Configuration in the L2 and RTs
(b) and Accuracy (c) of Listening Split by
Switch × Morphological Configuration.
Notes: White circles indicate mean values; white
lines indicate medians. Box plots indicate 75%
and 25% quartiles; black dots represent data dis-
tribution.
*** p< .001.
1072 Shuang Liu et al.
https://doi.org/10.1017/S1366728923000330 Published online by Cambridge University Press
morphological configuration (M= .95 ± .22) was more accurate
than incongruent morphological configuration (M= .93 ± .26)
(see Supplementary Materials Table 12 for full statistics). There
was also an interaction between language and morphological con-
figuration, demonstrating higher accuracy of congruent (M= .94
± .23) compared to incongruent morphological configuration
(M= .90 ± .30) in the L2, b= .69, SE = .102, z= 6.80, p< .001,
but no different morphological configuration in the L1 (congru-
ent: M= .95 ± .22, incongruent: M= .95 ± .21, b=−.04,
SE = .121, z=−.31, p= .760). The interaction between switching
and morphological configuration was also significant (see
Figure 2c). Further analyses revealed that although accuracy was
higher in non-switch trials compared to switch trials for both
the congruent (non-switch: M= .97 ± .18 > switch: M= .93 ± .26,
b= .83, SE = .119, z=6.99,p< .001) and incongruent morphological
configuration (non-switch: M= .94 ± .24, switch: M= .92 ± .28,
b= .39, SE = .103, z=3.79, p< .001), this effect was larger for con-
gruent trials (Congruent-Switch cost: M=.03±.04>Incongruent-
Switch cost: M= .01 ± .05).
Table 1 summarizes the results of the significant interactions
in the RTs and accuracy analyses from the naming and listening
data.
3.2 Electrophysiological results
The results of the ERP analyses in the naming and listening task
can be seen in Supplementary Materials Table 13 and the signifi-
cant interactions can be found in Table 2.
3.2.1 Naming
A language (L1, L2) × switching (non-switch, switch) × morpho-
logical configuration (congruent, incongruent) mixed-effects
model on locked ERP components showed a significant main
fixed effect of morphological configuration on P2 (incongruent:
M= 4.27 ± 8.95 μV > congruent: M= 3.56 ± 8.92 μV) and on
LPC (incongruent: M= 4.39 ± 12.81 μV > congruent: M= 3.62
± 13.55 μV), but a reversed congruency effect on N2 (congruent:
M=−.93 ± 10.38 μV > incongruent: M= .83 ± 10.22 μV) (see
Supplementary Materials Table 13). The main fixed effect of lan-
guage only occurred on N2 (L1: M= -.19 ± 10.31 μV > L2:
M= .07 ± 10.38 μV) and the main fixed effect of switching only
occurred on LPC as reflected by a reversed switch cost effect
(non-switch: M= 4.25 ± 13.33 μV > switch: M= 3.75 ± 13.06 μV).
There was a significant interaction between language and
switching on N2 (see Figure 3-a1) which revealed switch costs
in the L2 (switch: M=−.33 ± 10.69 μV > non-switch: M= .47 ±
10.04 μV, b= .92, SE = .276, z= 3.31, p< .001), but not in the L1
(switch: M=−.04 ± 10.17 μV, non-switch: M=−.34 ± 10.44 μV,
b=−.23, SE = .271, z=−.85, p= .393). Furthermore, the inter-
action between switching and morphological configuration also
reached significance on N2 (see Figure 3-a2) reflected by switch
costs in incongruent morphological configuration (switch:
M= .47 ± 10.28 μV > non-switch: M= 1.18 ± 10.16 μV, b= .83,
SE = .275, z= 3.03, p= .003), but not in congruent morphological
configuration (switch: M=−.82 ± 10.53 μV, non-switch: M=
−1.04 ± 10.23 μV, b= -.15, SE = .272, z=−.55, p= .586).
More importantly, the three-way interaction of language,
switching, and morphological configuration was significant on
P2 (see Figure 3-a3). Follow-up analyses for this three-way inter-
action were split by language. In both the L1 and L2, there was a
significant main fixed effect of morphological configuration as
seen in the typical congruency effect (L1-incongruent: M= 4.31
± 9.05 μV > L1-congruent: M= 3.45 ± 9.00 μV, b= .82, SE
= .260, t= 3.14, p= .002; L2-incongruent: M= 4.23 μV ± 8.84 >
L2-congruent: M= 3.68 ± 8.85 μV, b= .55, SE = .256, t= 2.14,
p= .033). This effect was significantly larger in the L2 (M=
−.8.5 ± 4.46) compared to the L1 (M=−.38 ± 1.09), t= 9.39,
p< .001. However, the P2 effect showed a significant interaction
between switching and morphological configuration only in the
L2 as demonstrated by reversed switch costs in incongruent
morphological configuration (non-switch: M= 4.65 ± 8.44 μV>
switch: M= 3.80 ± 9.21 μV, b=−1.18, SE = .512, z=−2.30,
p= .022), but not in congruent morphological configuration
(non-switch: M= 3.46 ± 8.96 μV, switch: M= 3.90 ± 8.74 μV,
b=−.33, SE = .520, z=−.635, p= .526).
3.2.2 Listening
A similar mixed-effects model was used to analyze the listening
data on locked ERP components. We found a main fixed effect
Table 1. Significant Interactions in RTs and Accuracy for Naming and Listening
Task Significant Difference
Response Times
Language × Switching Naming L1: Switch > Non-switch
Listening L1: Switch > Non-switch
L2: Switch > Non-switch
Language × Morp Naming L1: Incongruent > Congruent
L2: Incongruent > Congruent
Switching × Morp Listening Congruent: Switch >
Non-switch
Incongruent: Switch >
Non-switch
Language ×
Switching × Morp
Naming L2-Congruent: Switch >
Non-switch
Accuracy
Language × Morp Listening L2: Congruent > Incongruent
Switching × Morp Listening Congruent: Non-switch >
Switch
Incongruent: Non-switch >
Switch
Table 2. Significant Interactions on P2, N2, and LPC Components for Naming
and Listening
Components Significant Difference
Naming
Language ×
Switching
N2 L2: Switch > Non-switch
Switching × Morp N2 Incongruent: Switch >
Non-switch
Language ×
Switching × Morp
P2 L2-Incongruent:
Non-switch > Switch
Listening
Language ×
Switching
LPC L1: Non-switch > Switch
Language × Morp LPC L2: Congruent >
Incongruent
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Figure 3. Mean Waveforms Time-Locked to the Onset of Naming (a1-a3) and Listening (b1-b2) and Topographic Distributions of Mean Amplitude for Significant
Interactions.
Notes: Panels a1-a3 represent naming data; panels b1-b2 show listening data. (a1) Switching × Morp in the L2 during the 180–220 ms time frame (P2); (a2)
Language × Switching and (a3) Switching × Morp during the 240–290 ms time frame (N2); (b1) Language × Switching and (b2) Language × Morp during the 640–
850 ms time frame (LPC). Double asterisks that appear in the dotted boxes indicate a significant difference between the colored variables listed in the legend
(e.g., the two asterisks ** in panel a1 indicate a significant difference between L2-incongruent non-switch trials and L2-incongruent switch trials). The bar graphs
display mean voltages for P2, N2, and LPC in the corresponding conditions averaged across sites. Error bars show the standard error of means.
1074 Shuang Liu et al.
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of language on LPC such that the L1 elicited greater amplitude
than the L2 (L1: M= 1.29 ± 6.90 μV > L2: M= .05 ± 6.44 μV),
and a main fixed effect of switching in which non-switch trials
(M= .91 ± 6.79 μV) elicited greater amplitude than switch trials
(M= .44 ± 6.60 μV) (see Supplementary Materials Table 13).
The two-way interaction between language and switching on
LPC (see Figure 3-b1) indicated reversed switch costs in the L1
(non-switch: M= 1.65 ± 6.90 μV > switch: M= .92 ± 6.88 μV,
b= .75, SE = .186, z= 4.03, p< .001), but not in the L2 (non-
switch: M=−.15 ± 6.59 μV, switch: M=−.06 ± 6.27 μV,
b=−.21, SE = .189, z= 1.11 p= .265). There was also a significant
two-way interaction between language and morphological config-
uration on LPC (see Figure 3-b2), demonstrating a reversed con-
gruency effect in the L2 (congruent: M= .26 ± 6.61 μV>
incongruent: M=−.17 ± 6.25 μV, b= .45, SE = .189, z= 2.37,
p= .018), but not in the L1 (congruent: M= 1.21 ± 6.89 μV,
incongruent: M= 1.36 ± 6.90 μV, b=−.12, SE = .186, z=−.67,
p= .506).
4. Discussion
To investigate the influence of morphological configuration on
language switching, pairs of bilinguals performed a joint naming
and listening task. The behavioral performance and electrophysio-
logical activity revealed several significant effects and interactions.
First, the effect of morphological configuration on language
switching during production occurs in early (P2) and mid-stages
(N2), but morphological configuration has a limited impact on
language switching during comprehension. Second, control
mechanisms underlying language production might suppress
interference of morphological configuration via sequential pro-
cessing, while bottom-up control in comprehension may mask
the effect of morphological configuration in language switching
contexts. Below we elaborate further on these differential effects
and discuss the role of inhibition and language organization in
bilingual language control.
4.1 Influence of morphological configuration on language
switching during production
The P2 component has been argued to reflect effort involved in
lexical retrieval (Branzi et al., 2014; Costa et al., 2009). Our results
showed that in production, the P2 effect, localized in the frontal
central region, revealed a reversed switch cost in the L2 for incon-
gruent morphological configurations. This finding suggests that
bilinguals can distinguish morphological configuration across lan-
guages as early as 180 ms after stimulus onset. Models of sequen-
tial processing hold that compound words are not represented as
whole words, but rather as separate morphemes that can be pro-
cessing and accessed independently (Caramazza et al., 1988;
Levelt et al., 1999; Taft & Forster, 1975). To produce an incongru-
ent morphological compound in the weaker L2, more cognitive
resources are recruited. Consequently, such effort reduces differ-
ences between non-switch and switch trials, resulting in fairly
symmetrical switch costs. Given that the P2 component may
reflect detection of morphological configuration across languages,
it is plausible that it emerged in incongruent, but not in congruent
morphological configurations. This result is consistent with a previ-
ous study on compound word processing by Uygun and Gürel
(2017) in which the researchers conducted a masked priming task
to explore English noun-noun compound processing by L1-
Turkish-speaking learners of English (advanced and intermediate-
level learners) and by L1 speakers of English. The results showed
that both constituents (i.e., the first and second words that make
up a compound) acted as primes for the English speakers and
advanced-level learners, while in intermediate learners, only the
first constituent promoted lexical access. These findings suggest
that compound words are decomposed and affected by proficiency,
such that the higher the proficiency, the more obvious the decom-
position. In addition, some researchers have explored morphological
decomposition effects by manipulating the transparency of mor-
phemes. Semantic transparency refers to the extent to which the
semantics of compound words are predicted by their combined
meanings (Badecker, 2001;Lorenzetal.,2021). Research shows
that morphemes are more likely to be decomposed and integrated
when processing transparent compound words (Isel et al., 2003;
MacGregor et al., 2012;MacGregor&Shtyrov,2013). It appears
that compound words are not stored and accessed as whole units,
but instead, can be decomposed and accessed separately. This said,
other factors such as L2 proficiency and the relative transparency
of compound words may affect these processes.
In contrast, in L2 congruent morphological configuration, our
results showed typical switch costs, which is consistent with pre-
vious findings that switch trials elicit slower and less accurate
responses compared to non-switch trials. (Costa & Santesteban,
2004; Declerck et al., 2015; Finkbeiner et al., 2006; Kang et al.,
2020; Liu et al., 2016,2018; Ma et al., 2016; Meuter & Allport,
1999; Rogers & Monsell, 1995; Schwieter & Sunderman, 2008).
These switch costs reflect cross-language interference when
switching from one language to the other (see the Inhibitory
Control Model [ICM], Green, 1998). Moreover, these results
align with previous results reported by Contreras-Saavedra et al.
(2020,2021), who found that switch costs occurred with
composition-rule repetitions, but not with composition-rule
switches. The researchers argued that this finding reflects an
interaction between morpheme morphology and language
schema. The results of the three-way interaction can distinguish
language factors, underscoring the interaction between morpho-
logical configuration and language control. At the same time
the particularly noteworthy things are that they manipulate con-
gruency on a trial-by-trial basis, but the consistency effect also
appears in our block design, which indicates the robustness of
the influence of morphological configuration on language switch-
ing. However, the behavioral results demonstrated that there were
asymmetric switch costs in the L1 but not in the L2. Interestingly,
as mentioned above, with the addition of morphological configur-
ation, switch costs appear in L2 congruent morphological config-
uration trials. This may be because the sequential processing of
morphological configuration makes it more difficult for weaker
languages to process compound words, while processing com-
pound words in an L2 may reflect automatic parallel processing.
Another issue to consider is that we did not find a reversed lan-
guage dominance effect (sometimes also called L1 slowing) in
which proactive L1 inhibition led to less interference in the L2
under mixed language conditions (Declerck & Koch, 2022;
Gade et al., 2021a,2021b). This may be because unbalanced
Chinese–English bilinguals process L1 compounds faster, which
is inconsistent with the results of language switching found in
studies using simple (i.e., non-compound) words.
In the mid-time course of naming pictures, the N2 effect
revealed typical switch costs in the L2, but not in L1, during
the language selection stage. According to the ICM (Green,
1998), this reflects the fact that switching into a weaker L2 impli-
cates the need to suppress the stronger L1. In addition, the N2
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component has been associated with inhibitory control during
production tasks involving language switching (Jackson et al.,
2001; Jiao et al., 2020; Kang et al., 2020; Liu et al., 2016; Martin
et al., 2013; Verhoef et al., 2009,2010). Our results reinforced
this cognitive function of the N2 effect which revealed stronger
interference suppression of the non-target language in switch
trials compared to non-switch trials. Crucially, the N2 effect
showed typical switch costs in incongruent but not congruent
morphological configurations. This again might indicate that
due to sequential processing of morphological configuration,
competition from cross-language incongruent morphological
configuration may influence language switching. In addition,
the adaptive control model (Green & Abutalebi, 2013) proposes
that dual language contexts require more resources including
goal maintenance and inhibitory control. The P2 component
may reflect the process of goal maintenance, which may be at
the level of the language task schema itself, or at the level of spe-
cific lexical or syntactic competitors. Goal maintenance requires
processes that suppress interference through inhibitory control,
an effect that can be observed in the N2 component.
4.2 Influence of morphological configuration on language
switching during comprehension
The findings from the comprehension data showed more pro-
nounced LPC effects and a reversed congruency effect in the
L2. Previous evidence indicates that the LPC may reflect semantic
processing (Sanquist et al., 1980), and more positive LPCs for
words with many senses likely indicate an easier retrieval process
of word meaning (Huang & Lee, 2018). In a similar vein, with
respect to morphological configuration, when the two mor-
phemes in a compound word are congruent between the two lan-
guages, it will be easier to retrieve compared to incongruent ones.
Moreover, the behavioral results showed higher accuracy rates in
L2 congruent morphological configurations than incongruent
morphological configurations. This further suggests that incon-
gruent morphological configuration is processed sequentially
which appears to be different from congruent morphological con-
figurations. The results also showed reversed L1 switch costs on
the LPC, which might reflect easier lexical access in non-switch
trials relative to switch trials. We found switch costs in RTs for
both the L1 and L2. Furthermore, we observed switch costs in
RTs and accuracy in congruent and incongruent morphological
configurations, although this effect was not supported in the
ERP results.
Why does morphological configuration appear to be sensitive
to modality (i.e., production versus comprehension)? The answer
may lie in the distinct control pathways between comprehension
and production. According to the BIA-d (Grainger et al., 2010),
language control in bilingual production endogenously activates
corresponding language nodes of the target language and inhibits
nontarget language representations. Contrarily, comprehension
exhibits exogenous control driven by stimuli, which automatically
activates the target language node and inhibits nontarget language
representations. And the Bilingual Language Interaction Network
for Comprehension of Speech (BLINCS) model suggests that
cross-language activation during comprehension tasks results
from bottom-up, sub-lexical perceptual competition in phono-
logical input between the two languages (Shook & Marian,
2013). In our results, language control in production recruited
inhibitory control, as indicated by the N2 effect, while in compre-
hension, language control was less pronounced as implied by the
lack of significant ERP switch costs. Thus, we believe that the
sequential processing of morphological configuration does not
play a role in comprehension due to the bottom-up, automatic
activation of language as triggered by stimuli.
4.3. The roles of inhibition and language organization in
language control among bilinguals
Some researchers have suggested that language organization may
play an important role in language control. Blanco-Elorrieta and
Caramazza (2022) put forward a theory of bilingual language
organization, which holds that monolingual and bilingual lan-
guage systems operate under identical principles. The theory
assumes a common principle for selection of elements across all
linguistic levels (e.g., phonology, morphology, syntax, lexical,
and semantics). This selection mechanism is responsible for iden-
tifying the element with the highest level of activation, monitoring
it during retrieval, and filtering out information that does not
align with situational needs. Language switching brings the add-
itional challenge of (re)activating elements that have recently
been used, which can result in language switch costs.
In our study, when naming pictures in the L2, we found a sig-
nificant interaction between switching and morphological config-
uration which demonstrated a switch cost for congruent but not
for incongruent trials. It appears that our results cannot be
explained by a language organization account which holds that
semantic features with corresponding grammatical expressions
will receive activation directly from the conceptual level. While
morphosyntactic networks (and subnetworks within) are shared
across languages, purely intrinsic grammatical features will auto-
matically receive activation from the lexical level. From the initial
stages of language production, as shown by the P2 effect, bilin-
guals detected morphological configuration between the two
languages, which required quickly extracting the opposing mor-
phological configuration. Since the P2 component reflects more
effort devoted to continuously retrieving words with specific
grammatical features, the reversed switch cost in the L2 incongru-
ent morphological configuration on P2 indicates that bilinguals
fail to immediately retrieve the grammatical form of the target
word to use in production. These P2 results do not align with a
theory of language organization mentioned above, but rather
demonstrates evidence of parallel activation at each level and
that shared semantic features across languages can enable bilin-
guals to quickly identify morphological features across languages.
We found switch costs in the incongruent morphological con-
figuration on the N2 component, a classic index of inhibitory con-
trol (Jackson et al., 2001; Jiao et al., 2020; Kang et al., 2020; Liu
et al., 2016; Martin et al., 2013; Verhoef et al., 2009,2010).
Sequential processing of morphological configuration resulted in
suppression of cross-language interference. This finding implies
that in speech production, bilinguals may first detect the target
morphological configuration in which sequential processing of
morphological configuration on language switching recruits
inhibitory control to suppress the non-target language.
5. Conclusion
The results from our study demonstrate that morphological con-
figuration has differential effects on language production and
comprehension which may be due to nature of the use of language
control in the two domains. Particularly for speech production,
we found support for a combination of sequential processing
1076 Shuang Liu et al.
https://doi.org/10.1017/S1366728923000330 Published online by Cambridge University Press
and inhibition of morphological levels to control cross- language
interference. To our knowledge, this is the first study to offer
novel and important electrophysiological evidence demonstrating
that morphological configuration affects language control pro-
cesses in a language switching context. Overall, these findings
are useful in understanding the relationship between words in dif-
ferent languages and how bilinguals are able to smoothly switch
between their two language systems. The findings also underscore
the independent and interdependent nature of languages as sys-
tems of various modules.
Supplementary Material. The supplementary material for this article can
be found at https://doi.org/10.1017/S1366728923000330
Availability of Data and Materials. The datasets generated and analyzed in
this study are available in the OSF repository: Liu, H. (2022, April 29). “The
influence of morphological configuration on language switching.”Retrieved
from https://accounts.osf.io/login(osf.io/469cb).
Acknowledgements. This research was supported by Grants from Youth
Foundation of Social Science and Humanity, China Ministry of Education
(21YJC190009), Youth Project of Liaoning Provincial Department of
Education (LJKQZ2021089), Dalian Science and Technology Star Fund of
China (2020RQ055), Liaoning Social Science Planning Fund of China
(L20AYY001), Research Project on Economic and Social Development of
Liaoning Province (2023lslqnkt-054), and Liaoning Educational Science
Planning Project (JG21DB306).
Conflict of Interest. We have no known conflict of interest to disclose.
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