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Bilingualism: Language and
Cognition
cambridge.org/bil
Research Article
Cite this article: Liu, S., Huang, J., Xing, Z.,
Schwieter, J.W., & Liu, H. (2024). Neural
correlates of compound head position in
language control: Evidence from simultaneous
production and comprehension. Bilingualism:
Language and Cognition,1–13. https://doi.org/
10.1017/S1366728923000883
Received: 23 January 2023
Revised: 17 November 2023
Accepted: 18 November 2023
Keywords:
Bilingual language control; compound words;
head position; ERPs; language production;
language comprehension
Corresponding author:
Huanhuan Liu; Email: abcde69503@126.com
© The Author(s), 2024. Published by
Cambridge University Press
Neural correlates of compound head position
in language control: Evidence from
simultaneous production and comprehension
Shuang Liua,b,c, Junjun Huanga, b, Zehui Xinga,b, John W. Schwieterd,e
and Huanhuan Liua,b
a
Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Liaoning Province, 116029
Dalian, China;
b
Key Laboratory of Brain and Cognitive Neuroscience, Liaoning Province, 116029 Dalian, China;
c
Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental
Psychology Education, Faculty of Psychology, Beijing Normal University, 100875 Beijing, China;
d
Language
Acquisition, Multilingualism, and Cognition Laboratory / Bilingualism Matters @ Wilfrid Laurier University,
Waterloo, Canada and
e
Department of Linguistics and Languages, McMaster University, Hamilton, Canada
Abstract
Compound words consist of two or more words which combine to form a single word or
phrase that acts as one. In English, the head of compound words is usually, but not always,
the right-most root (e.g., “paycheck”is a noun because the head, “check,”is a noun). The cur-
rent study explores the effects of head position on language control by examining language
switching performance through electroencephalography (EEG). Twenty-one pairs of
Chinese (L1)–English (L2) bilinguals performed cued language switching in a simultaneous
production and comprehension task. The results showed that bilinguals recognized the
head position earlier both in production and comprehension. However, the language control
of the head position during production occurred in the middle stage (N2), but in the late stage
(LPC) during comprehension. These findings indicate that the head position in compound
words exerts differential influences on language control.
1. Introduction
Bilinguals often find themselves switching between their two languages depending on their
communicative situations. Studies investigating the cognitive nuances of this ability often
have employed the language switching paradigm in which participants use one of two or
more languages according to a pre-determined cue (e.g., a colour association) (Abutalebi,
2008; Abutalebi & Green, 2007; Blanco-Elorrieta et al., 2018; Blanco-Elorrieta & Pylkkänen,
2016; Declerck et al., 2012,2017; Linck et al., 2012; H. Liu et al., 2019; L. Liu et al., 2022;
Schwieter & Sunderman, 2008). This body of research demonstrates that language switching
is complicated by the need to understand and produce the target language in the context of
interference from the non-target language. To overcome such cross-language interference, lan-
guage control mechanisms are deployed (Green, 1998).
Language control can be indexed by a LANGUAGE SWITCHING COST, which is calculated as the
difference between performance (in speed and accuracy) on language switching trials and
non-switching (repeat) trials. A higher switching cost indicates a greater difference between
switching trials and repeat trials, and that non-target language interference during language
switching is not well controlled. In the past two decades, research has focused on how bilin-
guals produce a language while preventing interference from other languages, as well as the
phases of language switching. In addition to cross-language interference, other language fea-
tures may play a role in language switching. Compound words, and, in particular, their head
position, present one variable that merits investigation. Most languages use compound words
as a morphological operation to create new lexical items (Pollatsek et al., 2011). In compounds,
at least two words are combined to form a single lexical item or phrase. One of these words
acts as a functional head and the other word as a modifier (Bisetto & Scalise, 2005). For
example, in both English and Chinese, there are head-final compounds consisting of verb +
noun (e.g., “playground”,“操场”), and head-initial compounds formed by a noun + verb
(e.g., “sunrise”,“日出”). It is possible that bilingual performance may be influenced by the
compound head position in language switching. Below we discuss in more detail this possibility
and our motivation to examine it.
1.1 Background
An interesting question arises regarding how the privileged status of the head constituent
influences language control during bilingual communication. Some results have indicated
https://doi.org/10.1017/S1366728923000883 Published online by Cambridge University Press
that head position has an important effect on processing com-
pound words. Semenza et al. (2011) explored the reading of com-
pound words among Italian speakers with left-sided neglect
dyslexia to assess the influence of headedness. The left neglector
omits or substitutes the first component more often than the
second component, and the second component largely retains
its integrity. The participants were asked to read each word
aloud. The results revealed fewer neglect errors made on head-
initial compounds compared to head-final compounds. In a
study by Jarema et al. (2010), English–French aphasics conducted
a reading, repetition, and translation task with isolated compound
words. The results showed that there were fewer errors on heads
than modifiers in French head-initial compounds. However, for
English head-final compounds, errors were similarly distributed
across the head and the modifier.
A recent behavioral study by Contreras-Saavedra et al. (2021)
explored the impact of head position on language switching. In
the study, German–English–Spanish trilinguals participated in a
cued picture-naming task with compound words. German has
only head-final (noun + verb) compounds, Spanish has only
head-initial (verb + noun) compounds, and English has both.
The data only analyzed two types of English head position trials,
with language switching being from German or Spanish to
English, and language non-switching being limited to English.
The results showed an interaction between head position switch-
ing and language switching –that is, when the target was a
head-final word, larger switch costs emerged on head position-
repetition trials (head-final to head-final) than on head
position-switch trials (head-initial to head-final). This finding
indicates that language control is influenced by head position.
Moreover, their study demonstrated larger switch costs in
head-final compound trials than in head-initial compound trials.
The Inhibitory Control Model (ICM; Green, 1998) argues that
switching between languages requires inhibition to suppress
cross-language interference. The dominant language causes
more interference for the non-dominant language than vice
versa, and thus requires more inhibition. When switching back
to the dominant language, overcoming this relatively large inhib-
ition leads to larger switch costs. Contreras-Saavedra et al. (2021)
applied the logic of the ICM to study morpheme dominance
structures, believing that the presence of larger switch costs in a
morphological structure is considered an indicator of dominant
morphological structures. The authors found that larger switch
costs are observed for head-final compounds than for head-initial
compounds, indicating that head-final compounds appear to be
more dominant than head-initial compounds. Both Jarema
et al. (2010) and Contreras-Saavedra et al. (2021) have explored
the role of compound head position in bilingual/trilingual lan-
guage processing. Jarema et al. (2010) examined one unique
head position in each language, while Contreras-Saavedra et al.’s
study (2021) analyzed compounds with two head positions.
Cross-language interferences could not be ruled out based on
their findings. When switching to English, the head-final com-
pound is the dominant structure, but to our knowledge, there
has been no research exploring whether there is a head position
preference when switching to Chinese.
At the same time, previous studies on head position have
mostly focused on behavioural techniques and explicit measures,
and thus, cannot reveal the specific phases at which head position
affects language control. Furthermore, these effects may also be
influenced by the processing method of compound words.
There are several studies focusing on whether a compound: is
represented holistically (i.e., the compound is represented as
one entity within the mental lexicon) (Aitchison, 2012; Miozzo
et al., 2015; Osgood & Hoosain, 1974; Silva & Clahsen, 2008;
Strijkers et al., 2010,2017; Zyzik & Azevedo, 2009); is decom-
posed (i.e., two compositions of compounds are represented sep-
arately in the mental lexicon) (Li et al., 2017; Taft & Forster, 1975,
1976; Uygun & Gürel, 2017); or is a hybrid combination of both
(Pinker, 2015; Pinker & Ullman, 2002; Sandra, 1990,2020; Zhou
& Marslen-Wilson, 1995). Thus, in the present study, we use EEG
technology to examine two languages, Chinese and English,
which have head-initial and head-final compounds, respectively,
to explore preferences for head position and the role that it
plays in bilingual language control.
The Bilingual Interactive Activation Model from a Developmental
perspective (BIA-d; Grainger et al., 2010) argues that both bilingual
production and comprehension require activation of the target
language node and inhibition of non-target language lexical
representations. However, the control pathways between produc-
tion and comprehension are distinct. In production, endogenous
control operates via top-down activation to ensure that only lex-
ical representations in the target language are selected for output,
while inhibiting non-target word representation. In comprehen-
sion, exogenous control arises via automatic bottom-up activation
of language nodes through lexical representations, and the subse-
quent inhibition of non-target lexical representations.
These hypotheses have been supported by evidence from sep-
arate production and comprehension electrophysiological studies.
First, language switching tasks at the level of both production and
comprehension have yielded N2 effects. It has been found that
inhibition may help to resolve cross-language schema interference
(e.g., target language selection) as reflected by the finding that
switch trials trigger more pronounced N2 effects than non-switch
trials (Jackson et al., 2001; Zheng et al., 2020). Second, language
switching studies examining production and comprehension
have revealed Late Positive Component (LPC) effects. The LPC
has been posited to reflect inhibition of non-target lexical repre-
sentations, as shown by the finding that switch trials evoke larger
LPC amplitudes than non-switch trials (Jackson et al., 2001; Jiao
et al., 2020,2022; H. Liu et al., 2016). Simultaneous bilingual pro-
duction and comprehension involves inhibitory control, also
indexed by an LPC. For example, the study by H. Liu et al.
(2018) asked pairs of bilinguals to collaboratively complete a
cued picture-naming task. The cue phase guides selection of the
target language task schema, and the picture naming phase
involves the selection of the target lexicon. The researchers
found that, for both production and comprehension, switching
from the L1 to the L2 elicited larger LPC amplitudes than the
reverse. These results indicate that inhibition occurred at the lexical
selection phase. Moreover, LPC modulation is associated with more
strategic/explicit and controlled aspects of semantic retrieval, inte-
gration, and revision, with larger LPC amplitudes potentially
being triggered by an increase in semantic control (Amsel & Cree,
2013; Fang & Perfetti, 2017; Kounios et al., 2009;Rohautetal.,
2015). Third, the P2 component is also sensitive to the difficulty
of lexical access as demonstrated by a strong positive correlation
between naming latencies and mean P2 amplitudes (Branzi et al.,
2014; Costa et al., 2009;Strijkersetal.,2011).
1.2 Present study
The current study investigates the influence of head position on
language control in bilingual communication using EEG
2 Shuang Liu et al.
https://doi.org/10.1017/S1366728923000883 Published online by Cambridge University Press
hyperscanning technology. Hyperscanning is a technique that
permits measurement of the neural activity of interacting dyads.
In our study, we recorded EEG activity from pairs of Chinese–
English bilinguals as they simultaneously completed a production
and comprehension task in which one individual participated in a
cued picture-naming task (production) and the other individual
completed a cued head judgment task (comprehension). Our
hypotheses are that behavioral performance (i.e., reaction times
(RTs) and accuracy) are sensitive to different preferences for
head position. For example, participants may have a preference
for head-final compounds in English, as reflected by faster and
more accurate performance on head-final trials than on head-
initial trials. The preference for head position in Chinese is cur-
rently unknown, so it may be the same or different from
English. Future research may be able to reflect head position pref-
erence in Chinese by analyzing RTs and accuracy. Moreover, we
expect that the ERP components will show that head position
has an impact on language control in production and comprehen-
sion –for example, there may be switching costs on N2 or LPC in
head-final trials. This said, the results may also indicate that
head-final compounds are the dominant structure. However,
due to the different control methods of the two modalities (i.e.,
production is top-down; comprehension is bottom-up –see
BIA-d Model of Grainger et al., 2010), the role of head position
in language control may vary. For instance, production occurs
in early (P2) and/or mid stages (N2), while comprehension occurs
in later stages (LPC). Finally, we assume that compound words
are processed sequentially or in a hybrid way that combines
decomposed forms and whole forms. This permits us to measure
the impact of head position on language control.
2. Method
2.1 Participants
The calculated sample size was 16 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
(medium effect size), αerror probability = .05, correlation
among repeat measures = .5, Power (1-βerror probability) = .8,
Number of groups = 1, Number of measurements = 8, and non-
sphericity correct ∈= 1. To avoid the reduction of effect size
due to invalid subject data, 27 dyads of unbalanced Chinese
(L1)–English (L2) bilinguals were recruited to participate in the
study and were paired arbitrarily. All participants were right-
handed with normal or corrected-to-normal vision and had no
history of neurological, psychiatric, or major somatic disorders.
Six dyads were excluded from the study because of excessive
EEG data artifacts during the preprocessing stage. Thus, the
final sample included 21 dyads (18 pairs of females, M
age
=22
years, SD
age
= 2 years, 3 pairs of males, M
age
= 26 years, SD
age
=
2 years). 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 taking part in the study.
Table S1 shows characteristics of the participants’objective
and subjective language proficiency. The objective proficiency
level of English was tested by the Oxford Quick Placement Test
(QPT) (Syndicate, 2001). The average scores among the partici-
pants in the present study was 34, indicating a lower intermediate
L2 proficiency (see Table S2 for English proficiency criteria of the
QPT). The participants also completed a subjective questionnaire
in which they provided self-ratings of their 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”. Paired sample t-tests showed
that the proficiency ratings were significantly higher for the L1
compared to the L2 in listening, speaking, reading, and writing.
These ratings and QPT scores both indicate that the participants
are Chinese–English unbalanced bilinguals with intermediate L2
proficiency. Both proficiency measures were completed before
administering the joint production-comprehension task.
2.2 Materials
The stimuli consisted of 28 white-and-black line drawings repre-
senting compound words (see Table S3 and Figure S1). Among
them, half were head-initial (i.e., noun-verb) combinations like
“sunrise”and the other half was head-final (i.e., verb-noun) com-
binations like “playground.”An additional eight compound
words (4 head-initial and 4 head-final) were used in practice
trials. A separate group of participants (N= 23) who did not
take part in the formal experiment, but who were from the
same research population, rated their familiarity with the experi-
mental words. Their ratings were based on a 9-point scale on
which “1”meant “least familiar”and “9”meant “most familiar.”
Table S4 shows the means and standard deviations of the famil-
iarity ratings for the experimental words. A two-factor within-
subject ANOVA was performed on the familiarity ratings with
language (L1, L2) × head position (head-initial, head-final ) as fac-
tors. There was no main effect of language (L1: M= 8.03 ± .40, L2:
M= 8.01 ± .47), F(1,13) = .24, p= .633, η
2
= .02, or of head pos-
ition (head-initial: M= 7.98 ± .47, head-final: M= 8.06 ± .39), F
(1,13) = .19, p= .670, η
2
= .01. Moreover, the interaction between
language and head position was not significant, F(1,13) = .004,
p= .953, η
2
< .001, suggesting that there were no differences in
familiarity of head position across the two languages.
To ensure that there were no differences in the semantic trans-
parency of the compound word stimuli, participants, the same as
N= 23 participants in the previous paragraph, were asked to rate
the semantic overlap of the heads (e.g., between “sun”and “sun-
rise”), modifiers (between “rise”and “sunrise”), and whole com-
pounds (“sun + rise”and sunrise) on a 9-point scale (“1”for low
semantic overlap and “9”for very high semantic overlap). A two-
factor within-subject ANOVA was performed on these ratings
with language (L1, L2) and compound word components, separ-
ately for (a) the modifier, (b) the head, (c) the whole-word com-
pound in two head position. There was no main effect of language
(head: F(1,13) = .39, p= .542, η
2
= .03; modifier: F(1,13) = .89,
p= .364, η
2
= .06; whole-word compound: F(1,13) = .07, p= .790,
η
2
= .01), or of head position (head: F(1,13) = 1.11, p= .311,
η
2
= .08; modifier: F(1,13) = .02, p= .897, η
2
= .001; whole-word
compound: F(1,13) = .51, p= .489, η
2
= .04). Moreover, the inter-
action between language and head position was not significant
(head: F(1,13) = .08, p= .777, η
2
= .01; modifier: F(1,13) = 1.05,
p= .325, η
2
= .07; whole-word compound: F(1,13) = .07, p= .792,
η
2
= .01). These analyses suggest that there was no difference in
the transparency of heads, modifiers, or compounds correspond-
ing to the two head conditions across the two languages.
To ensure that the participants were not confused by the
classification of English words we used as stimuli, we asked
another sample of participants (N= 20) from the same research
population to evaluate the materials, including the parts
of speech of the components and the compounds’heads.
Bilingualism: Language and Cognition 3
https://doi.org/10.1017/S1366728923000883 Published online by Cambridge University Press
The results showed that all participants believed that the head-
initial compounds used in the experiment were NV structures,
while the head final compounds were VN structures. The
percentage of participants who believed that the compounds
met the classification criteria in each condition was: 98.21%
L1-head-final, 97.86% L2-head-final, 98.21% L1-head-initial,
98.21% L2-head-initial.
2.3 Design and procedure
The study is a language (L1, L2) × switching (non-switch,
switch) × head position (head-initial, head-final) within-subject
design and was administered using E-Prime 2.0. To create a sim-
ple 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) lis-
tened and subsequently uttered the head of the compound that
was heard. 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.
Before the experiment, the participants were familiarized with
the experimental pictures and their corresponding names in the
two languages. After doing so, they participated in a practice
task of 32 trials followed by 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. After hearing each word, Participant B performed
a head judgement by naming the head of the compound in the
same language into a microphone, regardless of the head position.
The rationale for asking Participant B to provide oral responses
was twofold: 1) to prevent the impact of different response modal-
ities (oral response vs. button response) on the results, ensuring
that the results of production and comprehension tasks could
be compared; and 2) to elicit an interactive response that could
be heard by Participant A.
There were 6 experimental blocks with 58 trials per block.
Each block included 2 warm-up trials and 7 trials of each:
L1-head modifier non-switch/switch, L1-modifier head non-
switch/switch, L2-head modifier non-switch/switch, and
L2-modifier head non-switch/switch. We formed six blocks that
included pseudo-randomized non-switch and switch trials. The
trials were shown in the form of pictures and were arranged
based on their switching type, such that the same switching
type did not appear continuously for more than two trials, and
that the same picture did not appear more than once within
five consecutive trials. All participants completed the same
order of blocks and trials within blocks. To test potential practice
effects, we divided the behavioral data into two parts and con-
ducted separate language (L1, L2) × switching (non-switch,
switch) × head position (head-initial, head-final) mixed-effects
models for both production and comprehension (see Appendix
S1: Testing practice effects).
Figure 1a 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 to both Participants A and B. After a blank
screen of 500 ms, a target picture appeared. Upon seeing the tar-
get picture, Participant A uttered the name of the picture into a
microphone in the L1 or the L2 according to the predetermined
color cue. The picture disappeared when Participant A responded
or after 2000 ms. Participant B then heard a beep at which time
they named the head of the compound uttered by Participant
A. The screen disappeared when Participant B responded or
after 2000 ms. A blank screen appeared for a random amount
of time (between 1000-1500 ms) before the next trial began.
2.4 Behavioral data analyses
Behavioral data analyses were performed on RTs and accuracy for
both naming and listening. In the RT analyses, we excluded incor-
rect responses (e.g., wrong target word, disfluent responses, no
responses, or self-corrected responses); the first two trials of
each block; and responses that were < 200 ms or beyond M±3
SD (within participants). The excluded data totaled 6.04% of
the naming data and 7.67% of the listening task. We used R soft-
ware (version 3.6) (lme4 and lmerTest package, Bates et al., 2014;
Kuznetsova et al., 2017) to perform a linear mixed model for RTs
and a generalized linear mixed model for accuracy. In order to
make the data more in line with linear association, we performed
log transformation on the RT data. We used language (L1, L2),
switching (non-switch, switch), and head position (head-initial,
head-final) as fixed effects. Apart from the fixed effects, the
models included participants and items as random effects
(random intercepts and slopes). When the models did not con-
verge, we removed the slope that explained the least variance
until they converged. We used the Akaike information criteria
to determine the optimal model in which smaller values generally
reflect more likely models (Symonds & Moussalli, 2011; Tremblay
& Newman, 2015). We started with the most complex model and
gradually reduced its complexity until it converged to the Akaike
values that were smallest. The best-fitting model was: logRT ∼
data$ language* data$ switching* data$ head + (1|participant).
We conducted follow-up analyses when an interaction reached
significance at p< .05.
2.5 Electrophysiological data and analyses
Electrophysiological data were recorded from each dyad using
two sets of 64 electrodes 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 referenced 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 high-
pass filter of .01Hz and a lowpass filter of 30Hz. Finally, 40
electrodes were left after removing the peripheral electrodes
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).
Ocular artifact reduction was performed through an independent
component analysis using EEGLAB (Makeig et al., 1995). The
mean number of independent components rejected was 1.00 ± .73
per participant. 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 (i.e., the data analysis
included only successful simultaneous production and compre-
hension trials –for example, if either party deleted the trial due
to artifacts or other issues, the trial of the other participant was
also deleted accordingly). In production and comprehension
tasks, continuous recordings were analyzed in cue-locked −100
to 700 ms epochs and naming/listening-locked −100 to 800 ms
4 Shuang Liu et al.
https://doi.org/10.1017/S1366728923000883 Published online by Cambridge University Press
epochs.Correspondingly,the epochs werereferencedto a 100 mspre-
stimulus baseline .Signals exceeding ±90 μV in anygiven epoch were
discarded . As mentioned previously , six dyads were excluded from
the initial sample of participants , given that the data processing
resulted in the conser vation of less than 20 trials per cond ition . A
three -way ANOVA was performed on number of trials for each
condition per participa nt with language (L1 ,L2 )× switching ( non-
switch ,switch ) ×head position (head -initial ,head -final) as factors .
Therewasno main effect of language, switching, or head position ( p>
.05 ). Moreover , there was no sig - nificant interaction ( p> .05 ),
suggesting that therewere no differ- ences innumber of trials for each
condition per participant . The mean ( and SD) number of accepted
epochs per cond ition across participants are shown in Table S5 .All
preprocesses were per - formed 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 for Participant
A in cue-locked naming epochs: N2 (240-320 ms), LPC
(320-460 ms), in picture-locked naming epochs: P2
(140-170 ms) (Branzi et al., 2014; Misra et al., 2012; Strijkers
et al., 2011), N2 (200-350 ms) (Branzi et al., 2014; Jackson
et al., 2001; Jiao et al., 2022; H. Liu et al., 2016,2018; Misra
et al., 2012), LPC (350-600 ms) (Jackson et al., 2001; Jiao et al.,
2020,2022; H. Liu et al., 2016,2018), and for Participant B in cue-
locked listening epochs: N2 (250-350 ms), LPC (350-430 ms), in
judgement-locked listening epochs: P2 (120-180 ms) (Branzi
et al., 2014; H. Liu et al., 2018; Misra et al., 2012; Strijkers
et al., 2011), N2 (220-420 ms), LPC (520-750 ms) (Davis &
Jerger, 2014; H. Liu et al., 2018). Figure 1b shows a listing of
the stages of EEG analyses of Participants A and B. In order to
better display the differences between regions, the drawing of
topographic maps was based on 40 electrodes. Spatially, we pre-
defined frontal-central regions of interest (Jackson et al., 2001;
Jiao et al., 2022; H. Liu et al., 2016,2018) (sensors: F3, F1, Fz,
F2, F4, FC3, FC1, FCz, FC2, FC4, C3, C1, Cz, C2, C4). The ana-
lysis of mean amplitude is based on pre-defined frontal-central
regions of interest. The structure of the linear mixed model for
ERP data was specified in the same way as was done for RT
data. For each time window, we conducted a generalized linear
mixed model using language (L1, L2), switching (non-switch,
switch), and head position (head-initial, head-final) as fixed
effects and participants as the random effect, as well as the average
amplitude of each condition within the pre-defined 15 electrodes
as the dependent variable (normal Probability-Probability plot of
EEG data under each time window in Figure S2). We conducted
follow-up analyses when an interaction reached significance at
p< .05.
Figure 1. Procedure of an Example Trial from the Picture Naming (Participant A) and Head judgement (Participant B) Task (a); and the EEG analysis stage (b).
Bilingualism: Language and Cognition 5
https://doi.org/10.1017/S1366728923000883 Published online by Cambridge University Press
3. Results
3.1 Behavioral results
3.1.1 Reaction times in naming
The results of the language (L1, L2) × switching (non-switch,
switch) × head position (head-initial, head-final) mixed-effects
model showed significant main fixed effects of language and
switching (see Table S6 for full statistics). There were faster RTs in
the L1 (M= 874 ms ± 229) compared to the L2 (M= 959 ms ± 240),
and faster RTs in non-switch trials (M=901ms±228)thaninswitch
trials (M= 932 ms ± 248). There was also a significant interaction
between language and head position (see Figure 2a). Further ana-
lyses showed faster RTs in head-final trials compared to head-initial
in the L2 (head-initial: M= 971 ± 246 ms > head-final: M= 947 ±
234 ms), b= .02, SE = .008, z= 2.98, p= .003, but not in the L1
(head-final: M= 876 ± 233 ms; head-initial: M= 872 ± 226 ms),
b= -.004, SE = .008, z= -.51, p= .609.
3.1.2 Reaction times in listening
The results of the language (L1, L2) × switching (non-switch,
switch) × head position (head-initial, head-final) mixed-effects
model on listening RTs showed significant main fixed effects of
the three variables (see Table S6 for full statistics). There were
faster RTs in the L1 (M= 812 ms ± 257) compared to the L2
(M=961 ms±265), faster RTs for non-switch trials (M=881ms±
270) compared to the switch trials (M= 891 ms ± 273), and faster
RTs for head-final trials (M= 879 ms ± 264) compared to head-
initial trials (M= 893 ms ± 279). There was also a significant inter-
action between language and head position (see Figure 2b). Further
analyses showed faster RTs in head-final trials (M=944±264ms)
compared to head-initial trials (M=978±265ms) in the L2,
b=.04, SE =.008, z=4.63, p< .001, but not in the L1 (head-final:
M= 814 ± 248 ms; head-initial: M= 809 ± 267 ms), b=−.01,
SE =.008, z=−1.58, p=.114.
3.1.3 Accuracy in naming
A similar mixed-effects model was conducted on the accuracy
rates of the naming data. The results revealed a main fixed effect
of head position, such that head-final trials (M= .985 ± .12) were
more accurate than head-initial trials (M= .977 ± .15), b= .44,
SE = .179, z= 2.46, p= .014. There was no other significant effect
or interaction identified by the analyses.
3.1.4 Accuracy in listening
A similar mixed-effects model was conducted on the accuracy
rates of the listening data. The results showed a main fixed effect
of head position, such that head-final trials (M= .985 ± .12) were
more accurate than head-initial trials (M= .974 ± .16), b= .58,
SE = .174, z= 3.36, p< .001. There was no other significant effect
or interaction found in the analyses.
3.2 Electrophysiological results
3.2.1 Cued-locked naming phase
A language (L1, L2) × switching (non-switch, switch) × head
position (head-initial, head-final) mixed-effects model on
cued-locked naming phase showed a significant main fixed effect
of switching in the N2 and LPC time-windows. Switch trials
generated larger N2 effects (larger negative amplitudes) than
non-switch trials (switch: M=−6.28 ± 10.85 μV, non-switch:
M=−5.62 ± 10.70 μV), b=−.66, SE = .248, t=−2.67, p= .008,
and non-switch trials triggered larger LPC amplitudes than switch
trials (non-switch: M=−3.44 ± 10.83 μV > switch: M=−4.34 ±
10.81 μV), b=−.91, SE = .248, t=−3.67, p< .001. There was a sig-
nificant interaction between language and switching in the N2
time-window (see Figure 3a), showing that in the L1, switch trials
generated larger N2 effects than non-switch trials (switch:
M=−6.60 ± 10.91 μV, non-switch: M=−5.22 ± 10.68 μV),
b= 1.36, SE = .351, z= 3.87, p< .001. This effect did not emerge
in the L2 (switch: M=−5.97 ± 10.79 μV, non-switch: M=−6.03 ±
10.71 μV), b=−.3, SE = .351, z=−.09, p= .927. There was also a
significant interaction between language and switching in the LPC
time-window as reflected by a reversed switch cost effect in the
L1. Non-switch trials in the L1 generated larger LPC effects
than switch trials (non-switch: M=−2.97 ± 10.60 μV > switch:
M=−4.70 ± 10.79 μV), b= 1.73, SE = .350, z=4.93, p< .001.
These effects did not emerge in the L2 (non-switch: M=−3.91 ±
11.03 μV, switch: M=−3.98 ± 10.83 μV), b= .09, SE = .350,
z=.260, p=.795.
3.2.2 Picture-locked naming phase
As shown in Table 1, a language (L1, L2) × switching (non-switch,
switch) × head position (head-initial, head-final) mixed-effects
model on picture-locked naming phase showed a significant
main fixed effect of head position in the three time windows
examined. The effects on P2 showed that larger amplitudes
were generated by head-initial trials (M= 5.90 ± 9.79 μV) com-
pared to head-final trials (M= 5.11 ± 9.72 μV). This pattern was
also found for LPC: head-initial trials (M= 4.44 ± 12.34 μV)
produced larger amplitudes than head-final trials (M=3.17±
12.17 μV). However, there was a reversed effect on N2 in
which head-final trials (M=−.99 ± 10.44 μV) generated larger
N2 amplitudes than head-initial trials (M=.88±10.44 μV).
Figure 2. RTs of Naming for (a) Language × Head Position and (b) of Listening for
Language × Head Position.
Notes. White circles indicate mean values, white lines indicate medians, and black
dots represent data distribution. Box plots indicate 75% and 25% quartiles.
*** p< .001.
6 Shuang Liu et al.
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The main fixed effect of switching only occurred in the P2 time-
window, showing that non-switch trials (M= 5.75 ± 9.68 μV)
triggered larger amplitudes than switch trials (M= 5.27 ± 9.85 μV).
The main fixed effect of language only occurred in the LPC time-
window, as reflected by a reversed language effect such that the
L2 (M= 4.46 ± 12.48 μV) provoked larger amplitudes than the
L1 (M= 3.13 ± 11.91 μV). Additionally, there was a significant inter-
action between switching and head position on N2 (see Figure 3b),
showing that in head-final trials, switch trials generated larger N2
amplitudes than non-switch trials (switch: M=−1.34 ± 10.67 μV,
non-switch: M=−.64 ± 10.19 μV), b= .70, SE = .334, z= 2.09,
p= .036, but not in head-initial trials (switch: M=1.05±10.56 μV,
non-switch: M= .71 ± 10.33 μV), b= -.29, SE = .334, z=−.88,
p= .381.
3.2.3 Cued-locked listening phase
A language (L1, L2) × switching (non-switch, switch) × head pos-
ition (head-initial, head-final) mixed-effects model on the
Figure 3. Mean Waveforms Time-Locked to the Onset of Cue-Naming (a), Picture Naming (b), and Judgement Listening (c) and Topographic Distributions of Mean
Amplitude for Significant Interactions.
Notes. Panel (a) represents naming-cue data, panel (b) shows picture naming data, and panel (c) show judgement listening data. (a) Language × Switching during
the 240-320 ms time (N2) and Language × Switching during the 320-460 ms time (LPC); (b) Swit ching × Head during the 250-350 ms time (N2); (c) Switching × Head
in the L1 during the 520-750 ms time (LPC). The light shaded part indicates the range of standard error for the condition. 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 (a) indicate a significant difference
between L1 switch trials and L1 non-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. Topographic distributions of mean amplitude for two significantly different conditions in each time window
over 40 electrodes.
Bilingualism: Language and Cognition 7
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Table 1. Linear mixed model results of the Naming Task (picture-locked phase, Participant A). The three time-windows (P2, N2, LPC) are reported separately.
Picture Naming Task
P2 N2 LPC
bSE
Contrast
bSE
Contrast
bSE
Contrast
tp tp tp
Fixed effects
Intercept 5.63 1.055 5.34 < .001*** −.11 1.251 −.09 .927 3.78 1.391 2.55 .019*
Language .11 .230 .49 .625 .46 .236 1.95 .052 1.27 .231 4.65 < .001***
Switching −.48 .230 −2.09 .037* −.20 .236 −.86 .389 −.14 .231 −.52 .601
Head −.71 .230 −3.11 .002** −2.02 .236 −8.57 < .001*** −1.35 .231 −4.96 < .001***
Language × Switching .28 .459 .60 .548 −.38 .472 −.81 .416 −.52 .461 −.95 .341
Language × Head −.82 .459 −1.78 .076 −.37 .472 −.79 .432 −.64 .461 −1.18 .239
Switching × Head −.618 .459 −1.35 .178 −.99 .472 −2.10 .036* −.44 .461 −.81 .416
Language × Switching × Head −1.18 .917 −1.29 .196 .30 .944 .32 .752 −.37 .922 −.34 .734
Random effects
Participants 23.06 4.803 32.55 5.706 45.61 6.754
Notes. model = lmer(Amplitude∼data$language*data$switching*data$Head+(1|participant)). Bold words mean significant results.
*p< .05, ** p< .01, *** p< .001.
8 Shuang Liu et al.
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cued-locked listening phase showed a significant main fixed effect
of switching in the LPC time-window such that non-switch trials
(M= .63 ± 9.58 μV) triggered larger amplitudes than switch trials
(M= .08 ± 9.55 μV), b= -.55, SE = .233, t=−2.35, p= .019.
3.2.4 Judgement-locked listening phase
As shown in Table 2, a similar mixed-effects model was used to
analyze the listening data from the judgement-locked listening
phase. We found a main fixed effect of language in the P2 time-
window such that the L2 (M= 2.16 ± 8.67 μV) elicited greater
amplitude than the L1 (M= 1.60 ± 8.72 μV). But on the LPC,
the L1 (M= .44 ± 10.12 μV) elicited greater amplitude than the
L2 on LPC (M=−.42 ± 9.53 μV). For the N2 time window, a
main fixed effect of switching revealed greater amplitude in
non-switch trials (M=−2.26 ± 8.60 μV) compared to switch trials
(M=−1.77 ± 8.47 μV), and a main fixed effect of head position
revealed greater amplitude in head-final trials (M=−2.36 ±
8.48 μV) compared to head-initial trials (M=−1.66 ± 8.58 μV).
A three-way interaction of language, switching, and head position
reached significance in the LPC time-window. Follow-up analyses
for this three-way interaction were split by language. In the L1, we
found a significant interaction between switching and head
position on LPC (see Figure 3c), showing switch costs in
head-initial trials (switch: M= 1.15 ± 10.18 μV > non-switch:
M= .04 ± 9.91 μV), b=−1.14, SE = .480, z=−2.38, p= .017, but
not in head-final trials (switch: M= .01 ± 9.90 μV, non-switch:
M= .55 ± 10.44 μV), b= .55, SE = .475, z= 1.16, p= .248. There
was no significant main effect or interaction in the L2.
4. Discussion
Using a simultaneous production and comprehension task, this
study explored the role of compound words’head position in
bilingual language control. Our results show that Chinese–
English bilinguals have faster processing and higher accuracy
for head-final compounds than head-initial compounds when
switching into the L2, but are more sensitive to the head-initial
position when switching into the L1. Moreover, in production,
head-initial compounds elicit greater amplitude than head-final
compounds on P2 and LPC and cause opposite contrasts
(head-final > head-initial) for the N2 effect. In comprehension,
head-final compounds elicit greater amplitudes than head-initial
compounds on N2, and switch costs emerge in the L1 for head-
final position on LPC. These results indicate that head position
exerts an influence on language control during bilingual commu-
nication; and, specifically, due to the influence of different modal-
ities, processing head position in production and comprehension
is distinct and exerts differential influences on language control.
4.1 Head position preference in an L1 and L2
The findings showed that L2 English head-final compounds are
processed faster than head-initial compounds, both in production
and in comprehension. This is consistent with previous findings
that in English, in which a compound’s head is usually the right-
most element, there is a preference for head-final structures
(Lieber & Baayen, 1993; Williams, 1981). For L1 Chinese, there
was no head position preference found in the behavioral results
of production and comprehension. Overall, for bilinguals who
are native speakers of Chinese, head-final compounds compared
to head-initial compounds elicit faster responses in production,
and more accurate responses in both production and
comprehension. These results show that there appears to be no
preference for head-final compounds in L1 and L2, so the
responses of head-final compounds are consequentially faster
and more accurate. However, on the LPC effect in comprehen-
sion, switch costs were found for L1 head-initial compounds.
This indicates that the head position may have a potential impact
on the L1. Following the logic of the ICM (Green, 1998), condi-
tions that cause greater switch costs are taken as indicators of lar-
ger inhibition towards a dominant condition. In the present study,
larger switch costs for L1 head-initial compounds on the LPC
effect in comprehension suggest that head-initial compounds
are more dominant than head-final compounds and require
more inhibition when switching to the L1.
4.2 The effect of head position on language control is
influenced by switching modality
At the cue-locked naming phase in the L1, switch costs emerged
in the N2 time window. According to the ICM (Green, 1998), this
reflects the fact that during production of a weaker language (e.g.,
L2), there is more effort needed to suppress interference from a
stronger language (e.g., L1). Subsequently, when switching to
the L1, more resources are required to resolve residual L1 suppres-
sion. Thus, our finding indicates that interference resolution
mainly occurrs at the language cue phase, where language schema
selection occurs. At the picture-locked naming phase, head-initial
compounds elicit greater amplitude than head-final compounds
on P2 and LPC and cause opposite contrasts (head-final > head-
initial) in the N2 effect. These results show that bilinguals can dis-
tinguish the head position at 140 ms after the stimulus onset and
that head position processing finishes around 600 ms after onset.
More importantly, we observed switch costs for head-final com-
pounds on N2. Greater N2 amplitude has been associated with
greater language control (Jackson et al., 2001; Jiao et al., 2022;
Verhoef et al., 2010; Zheng et al., 2020). Our results show that
in the head-final compounds, interference suppression of switch
trials is greater than that of non-switch trials. In brief, due to top-
down control in production, language schema selection appears to
be completed at the cue phase. At the naming phase, the process-
ing of the head position is always continuous, and inhibitory con-
trol of head position occurs in mid-stages (N2).
However, at the cue-locked listening phase, we found no inter-
action between language and switching, although at the
judgement-locked listening phase, the L2 elicited greater P2
amplitudes than the L1, and head-final compounds elicited
greater N2 amplitudes than head-initial compounds. These find-
ings indicate that language is first processed, followed by head
position based on the temporal course of the components. The
LPC effect showed stronger amplitudes in the L1 compared to
the L2, with L1 switch costs for head-initial compounds.
Bottom-up control enables us to observe the interference from
the non-target language that occurs on head-initial trials in the
LPC. Overall, inhibitory control of head position emerges in the
middle stage (N2) during production, and in a later stage (LPC)
in comprehension. These results show that head position in com-
pound words exerts differential influences on language control
under different modalities.
4.3 Storage and processing of compound words
Compound word processing has been of great interest in the
exploration of word composition and decomposition (Günther
Bilingualism: Language and Cognition 9
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Table 2. Linear mixed model results of the Listening Task (judgement-locked phase, Participant B). The three time-windows (P2, N2, LPC) are reported separately.
Judgement Listening Task
P2 N2 LPC
bSE
Contrast
bSE
Contrast
bSE
Contrast
t p tp tp
Fixed effects
Intercept 1.86 .322 5.76 < .001*** −2.03 .369 −5.49 < .001*** .02 .644 .04 .971
Language .57 .214 2.64 .001* .36 .209 1.73 .084 .88 .234 −3.77 < .001***
Switching .36 .214 1.67 .095 .47 .209 2.24 .024* .27 .234 1.14 .256
Head −.03 .214 −.16 .873 −.69 .209 −3.29 < .001*** −.41 .234 −1.76 .079
Language × Switching −.10 .428 −.24 .808 .10 .417 −.24 .810 −.03 .468 −.07 .943
Language × Head −.44 .428 −1.04 .299 −.03 .417 −.08 .933 −.22 .468 −.48 .632
Switching× Head −.12 .428 −.29 .773 .22 .417 −.52 .604 −.67 .468 −1.44 .150
Language × Switching × Head −.55 .856 .64 .524 −.23 .835 .27 .787 2.03 .937 2.16 .031*
Random effects
Participants 1.94 1.392 32.55 5.706 45.61 6.754
Notes. model = lmer(Amplitude∼data$language*data$switching*data$Head+(1|participant)). Bold words mean significant results.
*p< .05, ** p< .01, *** p< .001.
10 Shuang Liu et al.
https://doi.org/10.1017/S1366728923000883 Published online by Cambridge University Press
& Marelli, 2021; Juhasz, 2018; Leminen et al., 2019). Previous
studies on compound word processing have focused on storage
and retrieval processes. In general, there are three points of
view. Sequential models argue that morphemes are independently
stored in the mental lexicon and processed in sequence (Li et al.,
2017; Taft & Forster, 1975,1976; Uygun & Gürel, 2017).
Distributed models assume that morphemes are stored in the
form of whole words and are processed in parallel (Aitchison,
2012; Miozzo et al., 2015; Osgood & Hoosain, 1974; Silva &
Clahsen, 2008; Strijkers et al., 2010,2017; Zyzik & Azevedo,
2009). Hybrid models hold that decomposed (morpheme-based)
forms and whole forms of compounds are equally stored and
retrieved in the mental lexicon (Pinker, 2015; Pinker & Ullman,
2002; Sandra, 1990,2020; Zhou & Marslen-Wilson, 1995).
Moreover, Libben et al. (2020) argue that compound words are
both greater than the sum of their parts and greater than their
overall division.
The present study used a simultaneous production and com-
prehension task consisting of cued picture naming and head judg-
ments. Although the combination of the two tasks allows
participants to pay attention to the composition of a compound,
it has a disadvantage in that participants are more likely to notice
the composition of compound, leading to sequential processing
rather than parallel processing. However, our findings do not sup-
port parallel processing of form and meaning of compounds. The
behavioral and EEG analyses showed that head position plays dif-
ferential roles in each language –that is, L2 (English) prefers
head-final structures, while L1 (Chinese) prefers head-initial struc-
tures. The language-specific head position preference should not
emerge if the participants process whole words simultaneously.
Thus, the present findings suggest that compound words are pro-
cessed in accordance with distributed models or hybrid models.
5. Conclusion
This study examined the influence of compound head position on
bilingual language control during simultaneous production and
comprehension. Behavioural and electrophysiological results
showed that language switching is sensitive to head position pre-
ferences: the head-final structure is more dominant when switch-
ing to English (L2), while the head-initial structure tends to be
more dominant when switching to Chinese (L1). In addition,
the effect of head position on language control is influenced by
switching modality such that bilinguals process head position
earlier in production than in comprehension.
Supplementary Material. For supplementary material accompanying this
paper, visit https://doi.org/10.1017/S1366728923000883
Supplementary Materials
Table S1. Mean (± SD) Characteristics of Participants’Language Background
Table S2. English Proficiency Classification Criteria of QPT Scores
Table S3. Head Position Conditions of the Stimuli
Figure S1. Stimulus Pictures in the Joint Production-Comprehension Task.
Table S4. Characteristics of the Stimuli (Mean ± SD).
Figure S2. Normal Probability-Probability Plot of EEG Data under Each Time
Window.
Table S5. Mean Number of Trials for Each Condition per Participant after
Independent Component Analyses.
Table S6. Model Parameters for the Best-Fitting Generalized Linear Mixed
Model of RTs for Naming and Listening.
Appendix S1. Testing Practice Effects
Acknowledgments. We have no known conflict of interest to disclose.
This research was supported by Grants from the National Natural Science
Foundation of China (32371089), Youth Project of Liaoning Provincial
Department of Education (LJKQZ2021089), Liaoning Social Science
Planning Fund of China (L20AYY001), Dalian Science and Technology Star
Fund of China (2020RQ055), Liaoning Province Economic and Social
Development Cooperation Project (2024lslybhzkt-17), and Liaoning
Educational Science Planning Project (JG21DB306).
Availability of data and materials. The datasets generated and analyzed in
this study are available in the OSF repository: Liu, H. (2022, December 14).
“Head position and language control.”Retrieved from https://accounts.osf.io/
login (osf.io/pxb8f/).
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