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RESEARCH ARTICLE
Genetic bases of language control in bilinguals: Evidence from
an EEG study
Dongxue Liu
1,2,3
| Zehui Xing
1,2
| Junjun Huang
1,2
| John W. Schwieter
4,5
|
Huanhuan Liu
1,2
1
Research Center of Brain and Cognitive
Neuroscience, Liaoning Normal University,
Dalian, China
2
Key Laboratory of Brain and Cognitive
Neuroscience, Liaoning Province, Dalian, China
3
Beijing Key Laboratory of Applied
Experimental Psychology, Faculty of
Psychology, Beijing Normal University, Beijing,
China
4
Language Acquisition, Multilingualism, and
Cognition Laboratory / Bilingualism Matters @
Laurier, Wilfrid Laurier University, Waterloo,
Canada
5
Department of Linguistics and Languages,
McMaster University, Hamilton, Canada
Correspondence
Huanhuan Liu, Research Center of Brain and
Cognitive Neuroscience, Liaoning Normal
University, Dalian, 116029, China.
Email: abcde69503@126.com
Abstract
Previous studies have debated whether the ability for bilinguals to mentally control
their languages is a consequence of their experiences switching between languages
or whether it is a specific, yet highly-adaptive, cognitive ability. The current study
investigates how variations in the language-related gene FOXP2 and executive
function-related genes COMT, BDNF, and Kibra/WWC1 affect bilingual language
control during two phases of speech production, namely the language schema phase
(i.e., the selection of one language or another) and lexical response phase
(i.e., utterance of the target). Chinese–English bilinguals (N=119) participated in a
picture-naming task involving cued language switches. Statistical analyses showed
that both genes significantly influenced language control on neural coding and behav-
ioral performance. Specifically, FOXP2 rs1456031 showed a wide-ranging effect on
language control, including RTs, F(2, 113) =4.00, FDR p=.036, and neural coding
across three-time phases (N2a: F(2, 113) =4.96, FDR p=.014; N2b: F(2, 113)
=4.30, FDR p=.028, LPC: F(2, 113) =2.82, FDR p=.060), while the COMT
rs4818 (ts >2.69, FDR ps < .05), BDNF rs6265 (Fs >5.31, FDR ps < .05), and Kibra/
WWC1 rs17070145 (ts>3.29, FDR ps < .05) polymorphisms influenced two-time
phases (N2a and N2b). Time-resolved correlation analyses revealed that the relation-
ship between neural coding and cognitive performance is modulated by genetic varia-
tions in all four genes. In all, these findings suggest that bilingual language control is
shaped by an individual's experience switching between languages and their inherent
genome.
KEYWORDS
bilingualism, electroencephalogram, executive function, genes, language control
1|INTRODUCTION
Language is part of our genetic makeup that allows us to interact in
sophisticated ways and in a variety of contexts. For individuals who
speak more than one language, we are often intrigued by the fact that
these individuals appear to effortlessly juggle between their languages
as necessitated by their listeners and environmental cues (Abutalebi &
Green, 2007; Liu et al., 2021). It seems that bilinguals have the unique
ability to temporarily “ignore”one language while using another to
the extent that they rarely suffer unwanted intrusions from the
Received: 19 October 2022 Revised: 10 March 2023 Accepted: 21 March 2023
DOI: 10.1002/hbm.26301
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any
medium, provided the original work is properly cited and is not used for commercial purposes.
© 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.
3624 Hum Brain Mapp. 2023;44:3624–3643.
wileyonlinelibrary.com/journal/hbm
irrelevant language. This ability is generally thought to be supported
by the mental process known as language control. Based on the adap-
tive control hypothesis (ACH; Green & Abutalebi, 2013), language
control among bilinguals is an adaptative mechanism that meets situa-
tional and communicative needs by recruiting executive functions,
such as inhibition, attentional control, updating, conflict monitoring,
and working memory (Abutalebi & Green, 2007; Bialystok et al., 2005;
Calvo & Bialystok, 2014; Coderre et al., 2016; Declerck et al., 2017;
Kovács & Mehler, 2009; Kwon et al., 2021; Liu, Schwieter, Liu,
et al., 2022; Prior & Gollan, 2013; Verreyt et al., 2016). However,
accumulating evidence challenges the belief that language control and
executive functions share underlying mechanisms and instead, argues
for the specificity of language control (Ant
on et al., 2014; Calabria
et al., 2012; Calabria et al., 2015; Cattaneo et al., 2020; Declerck
et al., 2015; Duñabeitia et al., 2014; Liu, Dunlap, et al., 2016; Paap
et al., 2014). These inconsistencies may be partially due to specific
genes, identified in some bilinguals, that influence the relationship
between language control and executive function (Liu, Schwieter, Liu,
et al., 2022). Accordingly, the present study aims to explore whether
language control among bilinguals depends on language-related genes
or whether it inseparably relates to the particular genes that are
essential for executive function.
1.1 |Language control during two phases of
language switching
Switching between languages causes a processing delay, typically called
a switch cost. These switch costs are measurable indicators of language
control (Blanco-Elorrieta et al., 2018;Costa&Santesteban,2004;
Declerck & Koch, 2022; Declerck & Philipp, 2015; Liu et al., 2021;
Schwieter & Sunderman, 2008;Zhuetal.,2022) which represent tran-
sient, trial-to-trial control processes and engagement of additional cog-
nitive resources (Christoffels et al., 2007; Jackson et al., 2001;Linck
et al., 2012;Liuetal.,2021; Liu, Liang, et al., 2016; Martin et al., 2013;
Misra et al., 2012; Verhoef et al., 2009; Zhu et al., 2022). The picture-
naming task with cued language switches is one of the most common
measures of language control in bilinguals. The cues serve to indicate in
which language (i.e., the language schema phase involved in a switching
task) the participant is to name the picture (i.e., the lexical response
phase). In the first phase, switching between language schemas (e.g., a
first language, L1, or a second language, L2) requires multiple compo-
nents of executive functions, such as inhibiting the nontarget language
and updating the new language schema. This increased demand for
cognitive resources on switch trials has a significantly larger effect than
on non-switch trials (Christoffels et al., 2007; Martin et al., 2013;
Verhoef et al., 2009;Zhengetal.,2020). These findings have been evi-
denced by an event-related potential (ERP) component, the N2a, which
is believed to be an indicator of attentional control during language
schema selection (Liu et al., 2014; Liu et al., 2018; Misra et al., 2012;
Verhoef et al., 2010).
The second phase involves lexical processing, which can be
reflected by the late positive component (LPC) (Jackson et al., 2001;
Martin et al., 2013; Roelofs, 2003). This ERP component is associated
with the retrieval of the correct lexical item in the intended language
and reflects the reconfiguration of stimulus–response mappings
(Jackson et al., 2001; Liotti et al., 2000; Martin et al., 2013). Some
research has revealed that language control may be implicated during
the lexical response phase in which an N2 effect occurred after pic-
ture onset (N2b), suggesting that selective inhibition was engaged to
reduce competition during lexical selection and/or phonological
encoding (Cheng et al., 2010; Piai et al., 2014; Roelofs, 2003; Shao
et al., 2014). Other research, however, has suggested that the lexical
response phase involves cognitive processes related to lexical proces-
sing (Green, 1998; Linck et al., 2012; Liu et al., 2014,2018; Misra
et al., 2012), such as conceptual identification, lemma retrieval, and
word-form encoding. Whether executive functions also play an impor-
tant role in both the language schema and lexical response phases is
an ongoing question (Cheng et al., 2010; Jackson et al., 2001; Martin
et al., 2013; Roelofs, 2003; Verhoef et al., 2010). If language control is
cultivated by mental switching exercises, this control would solely
emerge in the language schema phase; otherwise, it means that lan-
guage control benefits from integration with executive function. In
the present study, we examine how language control is affected by
specific genes related to executive functions and language-related
genes during the two aforementioned phases of picture naming.
1.2 |Genetic contributions to language control
and executive control
Bilinguals' language control ability may be inherently constrained by
language-related genes. Several studies have found that human lan-
guage abilities are influenced by genetic variations, particularly in the
gene forkhead box protein P2 (FOXP2) (Chabout et al., 2016; Crespi
et al., 2017; Fisher & Scharff, 2009; Mozzi et al., 2017). The FOXP2
gene is critically involved in the development of the neural systems
that mediate human speech and language acquisition (Fisher &
Scharff, 2009; Liégeois et al., 2003). Many studies on language impair-
ment have suggested that FOXP2 single nucleotide polymorphisms
(SNPs) are linked to dyspraxia (i.e., the reduced ability to accurately
sequence speech sounds), impaired expressive and receptive linguistic
abilities, reduced lateralization in brain speech-related areas (Chabout
et al., 2016; Liégeois et al., 2003; Reuter et al., 2017; Vernes
et al., 2006), as well as abnormalities in speech and language proces-
sing (Badcock, 2010; Ford et al., 2014;
ˇ
Spaniel et al., 2011). In a study
by Pinel et al. (2012), the researchers examined the brain activation of
94 healthy individuals during a sentence reading task and found that
FOXP2 rs6980093 polymorphism selectively modulated brain activity
in the left frontal cortex, showing that AA homozygotes elicited stron-
ger brain activity than GG homozygotes. Furthermore, GG homozy-
gotes could exhibit an advantage in a non-native speech learning task,
showing shifting faster to procedural learning strategies
(Chandrasekaran et al., 2015). In addition, Padovani et al. (2010)
reported that rs1456031 polymorphism had an effect on verbal flu-
ency and phonological fluency in patients with frontotemporal lobar
LIU ET AL.3625
degeneration, specifically among the patients with homozygous domi-
nant (TT) genotype (Padovani et al., 2010). Inconsistently, Crespi et al.
(2017) found a significant effect of FOXP2 rs1456031 variation on
inner speech rather than speech fluency.
Alongside the growing body of work on genetic factors of
humans' capacity for language, several studies have identified the
dopaminergic system as a possible contributing factor to L2 learning
and language control (Hernandez et al., 2015; Mamiya et al., 2016;
Sugiura et al., 2017; Vaughn et al., 2016; Vaughn & Hernandez, 2018).
Specifically, dopamine-related genes have been shown to play a criti-
cal role in executive function (Barnes et al., 2011; Barnett et al., 2008;
Chen et al., 2004; Sannino et al., 2015; Witte & Flöel, 2012), suggest-
ing that language control may be inseparable from the support of
dopamine-related genes. Previous studies have suggested that dopa-
mine (DA) levels have a significant influence on working memory, set-
shifting, updating, and cognitive flexibility and stability (Barnes
et al., 2011; Klanker et al., 2013; Logue & Gould, 2014; Robbins &
Arnsten, 2009; Zhang et al., 2015). For instance, the enzyme catechol-
O-methyltransferase (COMT) is widely represented in the human
brain and accounts for 60% of dopamine degradation in the prefrontal
cortex, making its involvement in prefrontal-guided executive func-
tions and second language learning of great interest to researchers.
Several studies have found that a functional SNP (rs4680) for COMT
influences executive functioning, working memory, fluid intelligence,
and attentional control (Barnett et al., 2008; Barnett, Heron,
et al., 2007; Barnett, Jones, et al., 2007; Egan et al., 2001; Flint &
Munafò, 2007). Moreover, a recent study using functional near-
infrared spectroscopy showed that COMT rs4680 polymorphism
exerted a significant effect on language performance and processing
(Sugiura et al., 2017). Other studies have demonstrated that A-allele
(Met) carriers with higher DA availability performed better in tasks
requiring stable performance, while G-allele (Val) carriers performed
better in tasks requiring flexible performance (Mier et al., 2010; Nolan
et al., 2004). Another functional polymorphism, the COMT rs4818,
has been reported to have differential effects on executive functions:
GG homozygotes with higher DA availability had better decision-
making performance but worse planning ability than the C allele vari-
ant (Roussos et al., 2008).
There are also genetic underpinnings of human memory, a cogni-
tive system crucial to language processing (Archibald, 2017;
Baddeley, 2003,2022; Daelemans & Van den Bosch, 2005; Declerck
et al., 2013; Desmond & Fiez, 1998; Linck et al., 2014). Brain-derived
neurotrophic factor (BDNF) has been shown to regulate the structure
and function of neurons involved in memory formation, and exten-
sively implicated the long-term potentiation (i.e., a form of synaptic
plasticity that underlies long-term memory storage) in the hippocam-
pus (Bekinschtein et al., 2007,2008). Studies have demonstrated that
BDNF rs6265, an SNP, plays a crucial role in short-term plasticity and
learning, in which A allele is associated with diminished memory func-
tion (Egan et al., 2003; Hariri et al., 2003). In an EEG study using a Go-
Nogo task by Beste et al. (2010), the researchers reported that the
BDNF polymorphism (rs6265) can selectively modulate response inhi-
bition, with a larger Nogo-N2 effect in A allele carriers. These findings
indicate that BDNF may affect human executive functions too. More-
over, a new gene—KIBRA (also referred to as WWC1 for WW-and-
C2-domain containing-protein-1) has been shown to influence mem-
ory performance and synaptic plasticity (Almeida et al., 2008; Zhang
et al., 2014). Genetic variations in KIBRA rs17070145 have been
associated with episodic memory and the activation of the hippocam-
pus during memory retrieval (Almeida et al., 2008; Papassotiropoulos
et al., 2006). In these studies, the CC genotype revealed significantly
worse immediate and delayed recalls scores than T-allele carriers.
1.3 |Present study
By examining the effect of genes related to executive functions
(BDNF, COMT, and Kibra/WWC1) and a language-related gene
(FOXP2) on language control during the two processing phases involv-
ing language schema and lexical responses, the present study sheds
new light on the nature of language control. In our experiment, we will
test the following possibilities:
•If language control is a language-specific ability, then the language-
related gene (FOXP2) is the only gene that has an impact on lan-
guage control.
•If language control is the consequence of ongoing experience with
language switching in daily life, the executive function genes
(BDNF, COMT, and Kibra/WWC1) should significantly interact
with switch costs of language schema phase (i.e., N2a), but not the
language processing involved in lexical response phase (N2b
and LPC).
•If language control is a function of its integration with executive
function, then both the executive function genes and language-
related gene (FOXP2) should have an impact on two phases of lan-
guage control (N2a, N2b, and LPC).
According to the ACH (Green & Abutalebi, 2013) and previous
empirical evidence (Abutalebi & Green, 2007; Branzi et al., 2019; Guo
et al., 2011; Wu et al., 2019), we hypothesize that language control is
actually developed by cognitive control and that corollary, executive
function genes will show an effect on processing at both the language
schema level and lexical selection level.
2|METHOD
2.1 |Participants
One hundred and nineteen Chinese (L1)–English (L2) bilinguals
(91 females, 28 males, mean age: 22.47, range: 19–30) were recruited
in the study. These individuals reported that they had begun learning
English at an average age of 9.09 years (SD =1.87 years, range =6–
13 years). All participants were right-handed with normal or
corrected-to-normal vision and reported no psychological, cognitive,
or motor impairments. The study was approved by the Ethics
3626 LIU ET AL.
Committee of Research Center of Brain and Cognitive Neuroscience
at Liaoning Normal University and all participants provided their
informed consent before beginning the experiment. When genotyping
for the two FOXP2 polymorphisms and two COMT polymorphisms
(discussed in Section 2.2), two of the participants failed to be geno-
typed, thus leaving 117 participants who were successfully genotyped
for these four polymorphisms. Moreover, there was one participant
who was not genotyped for Kibra/WWC1 polymorphism, resulting in
118 participants.
To assess L1 and L2 proficiency levels of the participants, we
adopted the Oxford quick placement test (OPT; Geranpayeh, 2003;
Liu, Schwieter, Liu, et al., 2022; Liu, Schwieter, Wang, et al., 2022),
and asked individuals to rate their language abilities on a six-point
scale in which “1”indicated no knowledge and “6”indicated perfect
knowledge (Liu et al., 2021). A one-way ANOVA was next conducted
for each polymorphism and revealed no significant differences
between genotypes in age, age of L2 acquisition, self-ratings of lan-
guage abilities, and OPT scores (see Table 1). Paired-sample t-tests
revealed that the participants' L1 was significantly stronger than
their L2 in listening (t=16.11, p< .001), speaking (t=19.24,
p< .001), reading (t=17.37, p< .001), and writing (t=11.97,
p< .001). These self-ratings and OPT scores are similar to intermedi-
ate Chinese–Englishbilingualstestedinpriorresearch(Liu
et al., 2021;Liu,Liang,etal.,2016; Liu, Schwieter, Wang,
et al., 2022), implying that the individuals have unequal proficiency
between their two languages.
2.2 |DNAs extraction and genotyping
An overview of the experimental procedures and analyses is illus-
trated in Figure 1. In the first step, the genomic DNAs were extracted
from peripheral blood leukocytes of all participants, which were col-
lected with anticoagulant ethylene diamine tetraacetic acid tubes. All
interested polymorphisms (FOXP2: rs6980093, rs1456031; COMT:
rs4680, rs4818; BDNF: rs6265, rs2049046; Kibra/WWC1:
rs17070145) were genotyped using MassARRAY flight mass spec-
trometry. SNPs were sorted according to the dbSNP database. The
gene fragments containing each SNP were amplified by PCR. Follow-
ing this, for each SNP, the dNTP generated was phosphorylated by
alkaline phosphatase reaction to form ddNTP. The UEP was used for
a single base extension reaction in the ddNTP system to form a single
base extension product complementary to the SNP genotype to be
detected, and the results were subjected to resin purification and chip
sampling. Finally, the samples were analyzed by a MALDI-TOF mass
spectrometer. Based on the principle that the flight time of ions gen-
erated by radiation ionization of matrix molecules in a vacuum envi-
ronment is directly proportional to mass, genotyping was obtained.
TABLE 1 One-way ANOVA results of L1 and L2 proficiency.
Age
L1 L2
AoA OPTListen Speak Read Write Listen Speak Read Write
FOXP2 rs6980093
F.01 .04 .98 .92 1.14 .08 .90 1.12 1.67 1.05 .24
p.986 .964 .379 .404 .325 .922 .409 .329 .192 .355 .800
FOXP2 rs1456031
F.91 .55 .48 .34 1.31 .69 .62 2.24 .14 .41 .32
p.407 .579 .617 .714 .275 .504 .542 .111 .867 .663 .725
COMT rs4680
t.69 .02 .34 1.26 .46 .001 .06 .72 .02 2.75 1.95
p.408 .885 .560 .264 .499 .974 .814 .397 .886 .100 .166
COMT rs4818
t2.90 1.56 .51 1.14 .53 .90 .45 .14 .34 .20 3.61
p.091 .215 .478 .289 .468 .344 .502 .709 .562 .657 .060
BDNF rs6265
F.47 2.05 .46 1.31 .93 .43 .41 .64 .87 1.44 .51
p.627 .134 .631 .275 .398 .649 .666 .529 .422 .241 .603
BDNF rs2049046
F1.15 .25 .67 1.18 .82 .47 .04 1.82 .91 .25 .50
p.319 .777 .516 .311 .444 .627 .965 .184 .041 .776 .611
Kibra/WWC1 rs17070145
t.71 .26 .26 .99 .66 .18 .18 .02 .02 .09 <.001
p.402 .615 .612 .332 .418 .669 .674 .895 .878 .769 .984
LIU ET AL.3627
Allele frequencies and genotyping distributions of interested gene
polymorphisms (FOXP2: rs6980093, rs1456031; COMT: rs4680,
rs4818; BDNF: rs6265, rs2049046; Kibra-WWC1: rs17070145) were
consistent with Hard–Weinberg expectations (see Table 2). As AA
homozygotes for COMT rs4680, GG homozygotes for COMT rs4818,
and CC homozygotes for KIBRA/WWC1 rs17070145 had expectedly
FIGURE 1 Overview of the procedure and analyses. Experimental procedures contain genotyping for all polymorphisms (step 1),
experimental design and time course of the language switching task (step 2), and statistical analyses conducted in the present study (step 3).
TABLE 2 Hardy–Weinberg
equilibrium test results.
Polymorphisms Actual counts (frequency) Expected counts (frequency) χ
2
p
FOXP2 rs6980093 (N=117) 2.37 .124
GG 19 (.16) 23.11 (.20)
GA 66 (.56) 57.78 (.49)
AA 32 (.27) 36.11 (.31)
FOXP2 rs1456031 (N=117) .08 .782
TT 30 (.26) 29.25 (.25)
TC 57 (.49) 58.5 (.50)
CC 30 (.25) 29.25 (.25)
COMT rs4680 (N=117) .93 .334
GG 56 (.48) 58.17 (.50)
GA 53 (.45) 48.65 (.42)
AA 8 (.07) 1.17 (.08)
COMT rs4818 (N=117) 2.86 .091
CC 58 (.49) 54.02 (.46)
CG 43 (.37) 5.96 (.44)
GG 16 (.14) 12.02 (.10)
BDNF rs6265 (N=119) .52 .469
CC 33 (.28) 34.96 (.29)
CT 63 (.53) 59.08 (.50)
TT 23 (.19) 24.96 (.21)
BDNF rs2049046 (N=119) 1.28 .258
AA 21 (.17) 24.05 (.20)
AT 65 (.55) 58.89 (.50)
TT 33 (.28) 36.05 (.30)
Kibra/WWC1 rs17070145 (N=118) 1.54 .215
CC 4 (.03) 6.41 (.05)
CT 47 (.40) 42.18 (.36)
TT 67 (.57) 69.41 (.59)
3628 LIU ET AL.
low frequencies, we combined them with their polymorphic heterozy-
gotes into one group. The final groups of each polymorphism were as
follows: FOXP2 rs6980093 contained GG homozygotes, GA hetero-
zygotes and AA homozygotes; FOXP2 rs1456031 contained TT
homozygotes, TC heterozygotes and CC homozygotes; COMT rs4680
was divided into GG homozygotes and A carriers; COMT rs4818
included CC homozygotes and G carriers; BDNF rs6265 was divided
into CC homozygotes, CT heterozygotes and TT homozygotes; BDNF
rs2049046 was genotyped into AA homozygotes, AT heterozygotes
and TT homozygotes; Kibra/WWC1 rs17070145 was divided into TT
homozygotes and C carriers.
2.3 |Language switching task
In the second step, we administered a picture naming task with cued
language switches, in which participants were asked to name simple
line drawings as accurately and quickly as possible in their L1 or L2
according to a color cue. We selected 24 black-and-white drawings
(15 cm 15 cm) from a standardized picture inventory (Snodgrass &
Vanderwart, 1980;Zhang&Yang,2003), whose Chinese names
were two-characters in length and whose English equivalents were
one- or two-syllable words containing 3–6 letters. To ensure that
the Chinese and English names were familiar to the participants, we
recruited a group of 35 age-matched participants from the same
population, but who did not participate in the formal experiment,
and asked them to judge their familiarity with the Chinese and
English names of the drawings. Paired-sample t-tests revealed no
significant differences between their familiarity with words in the
two languages (L1: M=4.85 ± .08, L2: M=4.84 ± .12, t
(23) =.69, p=.76).
Each individually-presented drawing represented conditions that
were either switch trials in which the response language is different
from the immediately preceding trial or non-switch trials where the
response language is the same as in the previous trial. The experiment
contained a total of 5 blocks, each of which included 98 trials. Each
block contained 2 warm-up trials followed by 48 non-switch trials
(24 L1–L1 and 24 L2–L2) and 48 switch trials (24 L1–L2 and 24 L2–
L1) which were randomly distributed throughout the block. The
response language (L1 or L2) was also randomized throughout
the task.
In the experiment, each trial started with a red or blue square as
a cue for 250 ms on the center of the screen followed by a blank
screen for 500 ms. A line drawing then appeared until participants
named it into a microphone or until 2000 ms passed. A blank screen
was presented for 1000 ms before the next trial started. Reaction
times (RTs) were recorded by a PSTSR-BOX connected to a micro-
phone and accuracy of responses was recorded manually by a
research assistant. Unfortunately, some responses were too low to
be recorded by the microphone and accordingly, we excluded these
trials from data analyses (0.83%). Before the formal experiment, par-
ticipants completed 12 practice trials to ensure that they fully under-
stood the task.
2.4 |Data recording and analyses
Electrophysiological data were recorded by 64-channel caps with
Ag/AgCl impedance-optimized active electrodes (ANT Neuro). Stan-
dard electrodes sites were placed according to the extended 10–20
positioning system and impedances were kept below 5 kΩ. The con-
tinuous EEG signal was recorded with a 1000 Hz sampling rate, a low
cut-off filter of .01 Hz, and a high cut-off filter of 100 Hz online. All
electrode sites were referenced online to the CPz and re-referenced
offline to the average of the left and right mastoids. Using EEGLAB
(Brunner et al., 2013; Delorme & Makeig, 2004) for preprocessing, the
electroencephalographic activity was down-sampled to 500 Hz and
refiltered with a high-pass filter of .1 Hz and a lowpass filter of 30 Hz.
Ocular artifact reduction was performed through independent compo-
nent analysis rejection (Makeig et al., 1996). The continuous record-
ings were cut into epochs ranging from 200 to 1500 ms relative to
the cue onset. A 200 ms prestimulus (i.e., language cue) period was
used as a baseline. Signals exceeding ±80 μV in any given epoch were
automatically discarded.
Statistical analyses on the behavioral data (i.e., RTs and accuracy)
were performed in R (Version 4.0.3; R Core Team, 2019), using the
“lme4”package (Bates et al., 2014). The following trials were excluded
from the analyses: warm-up trials (2.04%), null trials (0.83%), and trials
with a response time faster than 100 ms (1.03%). Data of the naming
RT beyond M± 3SD per participant were also excluded (0.69%). Accu-
racy was analyzed through a generalized linear mixed model. A linear
mixed-effect model was conducted on RTs of correct responses. To
satisfy the assumption of normally-distributed residuals for linear
models, the RT values were power-transformed using the Box–Cox
method (Box & Cox, 1964; Osborne, 2010; Zhu et al., 2022), which
determined the optimal transformation power to be λ=.14. Fixed
effects in the models included “language”(L1/L2), “trial type”(switch/
non-switch), and “genotypes”(for all polymorphisms). Participants and
items were added as a random effect and sex was added as a covari-
ate. Because genotypes for BDNF rs6265, BDNF rs2049046, FOXP2
rs6980093, and FOXP2 rs1456031 involved three levels, we per-
formed a three-way ANOVA to examine any main effects and interac-
tions by comparing the estimated marginal means using the
“emmeans”package (Lenth et al., 2020). Moreover, we conducted
pairwise comparisons within each gene to assess whether language
switch costs (switch vs. non-switch) were sensitive to genetic variants.
All effects were considered statistically significant at p< .05.
The electrophysiological data analyses focused on the N2a (200–
350 ms) effect of the language schema phase, N2b (1000–1200 ms),
and the LPC (1200–1500 ms) during the lexical response phase. Previ-
ous studies have demonstrated that the N2 effect typically reflects
conflict detection and monitoring, inhibition, task updating, and
heightened demands on cognitive control to resolve the competition
between language schemas (Cavanagh et al., 2009; Huster
et al., 2013; Kirmizi-Alsan et al., 2006; Liu, Liang, et al., 2016). How-
ever, many studies have reported an N2 effect 200–400 ms after pic-
ture onset, suggesting that selective inhibition is engaged to reduce
competition during lexical selection and/or phonological encoding
LIU ET AL.3629
(Cheng et al., 2010; Piai et al., 2014; Roelofs, 2003; Shao et al., 2014).
The LPC has been linked to language control during lexical access, the
disinhibition between languages, and the reactivation of previously
suppressed lexical items (Liu et al., 2014; Liu, Schwieter, Wang,
et al., 2022; Rodriguez-Fornells et al., 2006). We spatially pre-defined
anterior (sensors: F5, F3, F1, Fz, F2, F4, F6, FC5, FC3, FC1, FCz, FC2,
FC4, FC6) and posterior (sensors: CP5, CP3, CP1, CPz, CP2, CP4,
CP6, P5, P3, P1, Pz, P2, P4, P6) ROIs, then extracted the single-trial
amplitude of these sites for the linear mixed-effect models. Fixed
effects in the models included “language”(L1/L2), “trial type”(switch/
non-switch), and “genotypes”(for all polymorphisms). Participants
were added as a random effect, while sex was added as a covariate.
We again performed a three-way ANOVA followed by pairwise com-
parisons. The false-positive rate was controlled using false discovery
rate (FDR) correction.
To test relationships between neural coding and cognitive perfor-
mance, we conducted a time-resolved correlation analysis. To do this,
we calculated L1 and L2 switch costs using participants' neural activi-
ties at each time point of the trials and tested whether they correlated
with their RT switch costs. Specifically, a vector of 119 neural switch
costs at a single time point was correlated with a vector of 119 behav-
ioral switch costs. The same correlation analysis was then performed
on the 200–1500 ms window. The false-positive rate for this
approach was controlled using FDR correction.
3|RESULTS
3.1 |The effect of genetic variations in the
language-related gene FOXP2 on language control
Analyses on RT data revealed a significant interaction between trial
type and FOXP2 rs1456031, F(2, 113) =4.00, FDR p=.036. Follow-
up tests for rs1456031 revealed trial type effects for all genotypes
(Zs >7.15, FDR ps < .001), showing that switch trials
(CC homozygotes: M=835 ± 248 ms; TT homozygotes: M=891
± 257 ms; TC heterozygotes: M=832 ± 233 ms) elicited slower
responses than non-switch trials (CC homozygotes: M=810
± 239 ms; TT homozygotes: M=852 ± 244 ms; TC heterozygotes:
M=799 ± 219 ms), whereas the differences between the three
genotypes did not reach significance for either switch trials or repeat
trials. In the accuracy data, we also found a marginally significant
interaction of language trial type rs1456031, F(2, 113) =3.35,
FDR p=.07. Follow-up tests revealed that only TT homozygotes eli-
cited a significant interaction of language trial type, b=.58,
SE =.18, z=3.13, FDR p=.005, showing a switch cost in both L1,
b=.91, SE =.13, z=6.91, FDR p=.002, and L2, b=.33,
SE =.13, Z=2.53, FDR p=.011. The behavioral findings for RTs
and accuracy can be seen in Figure 2(a), (b), respectively.
At the neural level, to investigate whether switch costs signifi-
cantly interact with FOXP2 polymorphisms, we conducted similar ana-
lyses on the electrophysiological data and found that FOXP2
rs1456031 played a predominant role in both the language schema
phase (i.e., N2a) and lexical response phase (i.e., N2b and LPC) (see
Figure 2(b)), while rs6980093 limitedly showed a significant effect
during lexical response phase (i.e., LPC) (see Figure 2(c)). Specifically,
analyses on the N2a effect revealed that the interaction of
language trial type varied markedly across genotypes for FOXP2
rs1456031, F(2, 113) =4.96, FDR p=.014. Follow-up tests detected
an interaction of language trial type in TT homozygotes, b=.50,
SE =.22, t=2.29, FDR p=.033, and CC homozygotes, b=.65,
SE =.22, t=2.96, FDR p=.009, but not in TC heterozygotes,
b=.20, SE =.10, t=1.96, FDR p=.053. L2 switch costs reached
significance for both TT homozygotes, b=.45, SE =.15, t=2.92,
FDR p=.012, and CC genotypes, b=.48, SE =.16, t=3.10,
FDR p=.004, whereas no difference was found in the L1 for neither
genotype, TT: b=.05, SE =.15, t=.33, FDR p=.745; CC: b=.20,
SE =.10, t=1.96, FDR p=.363.
For the N2b effect, there was a significant interaction between
FOXP2 rs1456031, language, and trial type, F(2, 113) =4.30, FDR
p=.028. Separate tests revealed that only TC heterozygotes showed
a significant interaction between language and trial type, b=1.01,
SE =.21, t=4.80, FDR p=.0003. No significant interactions were
observed in TT, b=.05, SE =.30, t=.18, FDR p=.854, nor CC
homozygotes, b=.34, SE =.30, t=1.14, FDR p=.379. Follow-
up tests for TC genotype confirmed that L1 switch costs were signifi-
cant, b=.87, SE =.20, t=4.39, FDR p=.002, but not L2 switch
costs, b=.15, SE =.20, t=.75, FDR p=.351.
The analyses on the LPC effect found a marginally significant
interaction for rs1456031, F(2, 113) =2.82, FDR p=.060, and a sig-
nificant three-way interaction for rs6980093, F(2, 113) =3.62, FDR
p=.036. Separate t-tests for FOXP2 rs1456031 found that only the
TC genotype revealed a reversed L1 switch cost, b=.64, SE =.16,
t=4.05, FDR p=.002, while there was no significant contrast in
L2 switch costs, b=.01, SE =.16, t=.07, FDR p=.942. As for
FOXP2 rs6980093, follow-up tests showed a significant interaction
between language and trial type in GA heterozygotes, b=.55,
SE =.20, t=2.70, FDR p=.018, and GG homozygotes, b=.99,
SE =.43, t=2.31, FDR p=.032, but not in AA homozygotes,
b=.23, SE =.30, t=.77, FDR p=.441. Planned pairwise compari-
sons revealed a reversed L1 switch cost only for GA heterozygotes,
b=.66, SE =.14, t=4.59, FDR p=.002, with a stronger LPC
effect in non-switch trials than in switch trials, but no significant dif-
ference between switch and non-switch in the L2, b=.11,
SE =.14, t=.78, FDR p=.437. No significant switch costs were
present in GG homozygotes (L1: b=.56, SE =.30, t=1.85, FDR
p=.128; L2: b=.43, SE =.30, t=1.42, FDR p=.157).
3.2 |The effect of genetic variations in executive-
function-related genes on language control
3.2.1 | COMT polymorphisms
Analyses on RTs and accuracy in the behavioral data revealed no sig-
nificant effects. A similar linear mixed-effect model conducted on the
3630 LIU ET AL.
electrophysiological data revealed that the COMT rs4680 polymor-
phism exhibited a significant effect on the language schema phase
(N2a) (Figure 3(a)), while the COMT rs4818 polymorphism had a wider
influence on language processing as evidenced by significant effects
on the N2a and N2b components and the LPC (Figure 3(b)). Moreover,
during the language schema phase, we found that the interaction of
FIGURE 2 Electrophysiological results for FOXP2 rs1456031 and rs6980093 polymorphisms. (a) Behavioral switch costs in RT data (left) and
accuracy results for FOXP2 1456031. (b) Waveforms and mean neural switch costs of ERP components for FOXP2 rs1456031. (c) Waveforms
and mean neural switch cost of ERP components for FOXP2 rs6980093. Gray shading represents early (200–300 ms), middle (1000–1200 ms),
and late (1200–1500 ms) time windows. Numbers in red represent mean differences (switch trials–non-switch trials) for that condition. *p< .05.
LIU ET AL.3631
language trial type varied markedly across genotypes for COMT
rs4680, b=.61, SE =.22, t=2.84, FDR p=.007, and COMT
rs4818, b=.58, SE =.22, t=2.69, FDR p=.007. Follow-up tests for
COMT rs4680 showed that GG homozygotes elicited robust L2
switch costs, b=.38, SE =.11, t=3.52, FDR p=.002, but not
L1 switch costs, b=.17, SE =.11, t=1.54, FDR p=.125. No signifi-
cant three-way interaction was present in A allele carriers, b=.07,
SE =.16, t=.44, FDR p=.662. Follow-up tests for the COMT
rs4818 polymorphism showed a significant language trial type
effect in G allele carriers, b=.56, SE =.16, t=3.60, FDR p=.002,
but not in CC homozygotes, b=.02, SE =.16, t=.16, FDR
p=.872. Planned pairwise comparisons for G allele carriers revealed
a significant N2a switch cost in the L2, b=.38, SE =.11, t=3.45,
FDR p=.004, but not in the L1, b=.18, SE =.11, t=1.64,
FDR p=.135.
The influence of COMT rs4818 on the N2b time phase showed a
two-way interaction between language and trial type in CC homozy-
gotes, b=.99, SE =.21, t=4.66, FDR p=.002, but not in G
allele carriers, b=.30, SE =.21, t=1.46, FDR p=.144. Planned
pairwise comparisons revealed that switch trials had a stronger N2b
effect than non-switch trials in the L1, b=.74, SE =.15, t=4.87,
FDR p=.002, but not in the L2, b=.26, SE =.15, t=1.71,
FDR p=.116.
Analyses on the LPC for rs4818 revealed showed a significant
interaction between language and trial type for CC homozygotes,
b=.81, SE =.22, t=3.64, FDR p=.002, but not for G allele car-
riers, b=.02, SE =.22, t=.08, FDR p=.938. Reversed switch
costs were detected in the L1, b=.73, SE =.16, t=4.64, FDR
p=.002, but not in the L2, b=.08, SE =.16, t=.50, FDR p=.616.
3.2.2 | BDNF polymorphisms
Analyses on behavioral data revealed no significant effects. The elec-
trophysiological analyses found that for BDNF rs6265, there was a
robust effect on N2a, F(2, 115) =7.32, FDR p=.003, and N2b, F
(2, 115) =5.31, FDR p=.015 (Figure 4(a)), whereas for BDNF
rs2049046, there was only a significant effect on N2a, F(2, 115)
=3.56, FDR p=.028 (Figure 4(b)). In the language schema phase,
follow-up tests for BDNF rs6265 showed an interaction between
FIGURE 3 Waveforms and mean switch costs of ERP components for COMT rs4680 (a) and rs4818 (b). Gray shading represents early (200–
300 ms), middle (1000–1200 ms), and the late (1200–1500 ms) time windows. Numbers in red represent mean differences (switch–non-switch
trials) for that condition. * p< .05.
3632 LIU ET AL.
language and trial in the TT genotype, b=.58, SE =.26, t=2.20,
FDR p=.042, and in TC heterozygotes, b=.51, SE =.15, t=3.54,
FDR p=.001, but not in CC homozygotes, b=.38, SE =.22,
t=1.78, FDR p=.08. Specifically, for TT homozygotes, L1 switch
costs were significant, b=.68, SE =.19, t=3.65, FDR p=.001,
but L2 switch costs were not, b=.11, SE =.18, t=.58, FDR
p=.561. For TC heterozygotes, planned pairwise comparisons
showed reversed switch costs for the L1, b=.25, SE =.10, t=2.39,
FDR p=.022. This reversed switch cost effect was not detected in
the L2, b=.27, SE =.10, t=2.61, FDR p=.018. For BDNF
rs2049046, there was a significant language trial type interaction in
AA homozygotes, b=.89, SE =.27, t=3.35, FDR p=.002. The
FIGURE 4 Waveforms and mean switch costs of ERP components for BDNF rs6265 (a), BDNF rs2049046 (b), and Kibra/WWC1
rs17070145 (c). Gray shading represents early (200–300 ms), middle (1000–1200 ms), and the late (1200–1500 ms) time windows. Numbers in
red represent mean differences (switch trials–non-switch trials) for that condition. .* p< .05.
LIU ET AL.3633
planned pairwise comparisons showed significant switch costs in the
L2, b=.88, SE =.19, t=4.71, FDR p=.004, but not in the L1
(switch: M=.60 ± 8.37 μV; non-switch: M=.60 ± 8.36 μV),
b=.01, SE =.18, t=.05, FDR p=.964. No such effects were signifi-
cant in TT homozygotes, b=.12, SE =.22, t=.53, FDR p=.597, nor
in AT genotypes, b=.15, SE =.14, t=1.01, FDR p=.467.
In the lexical response phase (N2b effect), follow-up tests for
BDNF rs6265 confirmed an effect of language trial type in TT
homozygotes, b=1.43, SE =.35, t=4.15, FDR p=.003, and CC
homozygotes, b=.77, SE =.29, t=2.66, FDR p=.012, but not
in TC genotypes, b=.22, SE =.20, t=1.09, FDR p=.274. Spe-
cifically, for TT homozygotes, there was a significant main effect of
trial type in L1, b=1.07, SE =.25, t=4.33, FDR p=.002, which
indicated that switch trials evoked a stronger N2b than non-switch tri-
als. No such difference was reliable in the L2, b=.37, SE =.24,
t=1.51, FDR p=.131. The L1 switch cost was also significant for
CC genotypes, b=.69, SE =.20, t=3.37, FDR p=.003, while no
such effect was found in the L2, b=.08, SE =.20, t=.40, FDR
p=.691. No significant effects were found in the LPC analyses.
3.2.3 | Kibra/WWC1 polymorphism
Kibra/WWC1 rs17070145 also exhibited a significant effect on N2a,
b=.72, SE =.22, t=3.29, FDR p=.002, and a marginally signifi-
cant effect on N2b, b=.57, SE =.29, t=1.97, FDR p=.072 (see
Figure 4(c)). Follow-up tests on N2a confirmed that only TT genotype,
not C allele carriers, present a significant interaction between lan-
guage and trial type (TT: b=.57, SE =.15, t=3.89, FDR p=.002; C
carriers: b=.14, SE =.17, t=.83, FDR p=.406). Specifically, TT
homozygotes elicited both a reversed switch cost of L1, b=.20,
SE =.10, t=1.96, FDR p=.066, while a typical switch cost of L2,
b=.37, SE =.10, t=3.55, FDR p=.002. On the other hand,
follow-up tests on N2b detected the interaction between language
and trial type was significantly present in C allele carriers, b=.92,
SE =.23, t=4.03, FDR p=.002, but marginally significant in TT
homozygotes, b=.36, SE =.20, t=1.82, FDR p=.068. Planned
pairwise comparisons showed similar patterns for two genotypes,
which showed only a typical L1 switch cost in TT, b=.41, SE =.14,
t=3.00, FDR p=.006, and C allele carriers, b=.69, SE =.16,
t=4.23, FDR p=.002. No significant effects were found in the
LPC analyses.
3.3 |The differential role of genes in bilinguals'
brain–behavior correlation
Having separately established the robust impact of genetic variants
on language control, we next examined whether the relationship
between neural coding and behavioral performance varied across all
SNPs. We took participants' L1 and L2 neural switch costs (i.e., the
difference between switch trials and non-switch trials) at each time
point and correlated the values with their respective RT switch costs.
These analyses revealed distinct patterns among different genotypes
during the time windows of interest (i.e., 200–1500 ms following cue
onset).
3.3.1 | FOXP2 polymorphisms
Correlating neural switch costs with behavioral differences separately
on L1 or L2 confirmed that FOXP2 rs6980093 polymorphism trig-
gered distinct correlation patterns among the three genotypes (see
Figure 5(a)). The analysis on AA homozygotes detected a marginally
positive brain–behavior relationship in the L1 (time window: 452–
504 ms, mean rho =.36, FDR p=.057), while there was a significant
negative correlation in the L2 (first-time window: 200–250 ms, mean
rho =.46, FDR p=.03; second-time window: 608–666 ms, mean
rho =.43, FDR p=.031; third-time window: 716–1030 ms, mean
rho =.45, FDR p=.030; fourth-time window: 1140–1162 ms,
mean rho =.37, FDR p=.043; fifth time window: 1188–1286 ms,
mean rho =.41, FDR p=.034). Contrarily, GA heterozygotes
showed that L1 neural switch costs negatively correlated with L1
behavioral switch cost (first-time window: 690–770 ms, mean
rho =.31, FDR p=.027; second-time window: 1188–1226 ms,
mean rho =.27, FDR p=.038; third-time window: 1254–1326 ms,
mean rho =.34, FDR p=.025; fourth-time window: 1366–
1454 ms, mean rho =.29, FDR p=.031), but a positive association
was detected in the L2 (first-time window: 320–560 ms, mean
rho =.31, FDR p=.033; second-time window: 720–750 ms, mean
rho =.27, FDR p=.040; third-time window: 774–836 ms, mean
rho =.29, FDR p=.036; fourth-time window: 990–1068 ms, mean
rho =.27, FDR p=.039).
Similarly, the analysis on FOXP2 rs1456031 showed that a nega-
tive L1 brain–behavior correlation was significant in TT homozygotes
(see Figure 5(b); first-time window: 1250–1336 ms, mean rho =.42,
FDR p=.037; second-time window: 1442–1468 ms, mean
rho =.40, FDR p=.038) and TC heterozygotes (Figure 5(b); first-
time window: 1158–1214 ms, mean rho =.31, FDR p=.032;
second-time window: 1280–1330 ms, mean rho =.32, FDR
p=.032), but positively present in CC homozygotes (Figure 5(b);
first-time window: 368–440 ms, mean rho =.42, FDR p=.033;
second-time window: 476–696 ms, mean rho =.52, FDR p=.019;
third-time window: 954–1046 ms, mean rho =.50, FDR p=.017;
fourth-time window: 1096–1158 ms, mean rho =.40, FDR p=.038;
fifth-time window: 1172–1284 ms, mean rho =.46, FDR p=.022).
Moreover, the significant positive association in the L2 was only pre-
sent in TC heterozygotes (Figure 5(b); first-time window: 368–
426 ms, mean rho =.31, FDR p=.037; second-time window: 460–
484 ms, mean rho =.29, FDR p=.038).
3.3.2 | COMT polymorphisms
Two polymorphisms of COMT showed similar patterns in L2 switch
costs (see Figure 5(c), (d)). We detected a positive neural-brain switch
3634 LIU ET AL.
FIGURE 5 Time-resolved correlations between neural and behavioral switch costs for FOXP2 and COMT polymorphisms. (a) FOXP2
rs6980093. (b) FOXP2 rs1156031. (c) COMT rs4680. (d) COMT rs4818. Solid lines represent the genotypes, dashed lines in corresponding colors
depict 95% confidence bounds for spearman correlation coefficient, and brackets represent significant time windows surviving from FDR
correction.
LIU ET AL.3635
cost association in the GG homozygotes for rs4680 (Figure 5(c); first-
time window: 360–418 ms, mean rho =.33, FDR p=.041; second-
time window: 478–518 ms, mean rho =.28, FDR p=.047; third-time
window: 878–904 ms, mean rho =.28, FDR p=.047) and G allele
carriers for rs4818 (Figure 5(d); time window: 322–490 ms, mean
rho =.34, FDR p=.023). Contrarily, L1 neural switch costs were neg-
atively correlated with cognitive performance in GG homozygotes for
COMT rs4680 (Figure 5(c); first-time window: 718–740 ms, mean
FIGURE 6 Time-resolved correlations between neural and behavioral switch costs for BDNF and Kibra/WWC1 polymorphisms. (a) BDNF
rs6265. (b) BDNF rs2049046. (c) Kibra/WWC1 rs17070145. Solid lines represent the genotypes, dashed lines in corresponding colors depict 95%
confidence bounds for spearman correlation coefficient, and brackets represent significant time windows surviving from FDR correction.
3636 LIU ET AL.
rho =.29, FDR p=.044; second-time window: 1302–1338 ms,
mean rho =.30, FDR p=.044; third-time window: 1362–1396 ms,
mean rho =.30, FDR p=.044).
3.3.3 | BDNF polymorphisms
For BDNF rs6265, L1 neural switch costs showed a significant corre-
lation with L1 behavioral switch costs only in TT homozygotes
(Figure 6(a); first-time window: 1112–1200 ms, mean rho =.44,
FDR p=.049; second-time window: 1238–1322 ms, mean
rho =.49, FDR p=.048), while L2 switch costs showed a positive
brain–behavior relationship only in CC homozygotes (Figure 6(a); first-
time window: 268–324 ms, mean rho =.43, FDR p=.034; second-
time window: 1124–1146 ms, mean rho =.37, FDR p=.047).
BDNF rs2049046 induced opposite correlation patterns in L1
switch costs, which showed that the brain–behavior relationship was
significantly positive in AA homozygotes (Figure 6b; time window:
948–1056 ms, mean rho =.55, FDR p=.022), but negatively present
in TT homozygotes (Figure 6(b); first-time window: 736–756 ms,
mean rho =.40, FDR p=.033; second-time window: 982–
1076 ms, mean rho =.41, FDR p=.032; third-time window: 1106–
1364 ms, mean rho =.45, FDR p=.030; fourth-time window:
1372–1486 ms, mean rho =.40, FDR p=.034). Moreover, the AA
genotype also revealed a positive correlation between neural and
behavioral switch costs in the L2 (Figure 6(b); time window: 1120–
1192 ms, mean rho =.51, FDR p=.029).
3.3.4 | Kibra/WWC1 polymorphism
Analyses on Kibra-WWC1 rs17070145 revealed a negative brain–
behavior relationship in the L1 among C allele carriers (Figure 6(c);
time window: 738–758 ms, mean rho =.29, FDR p=.048), and an
L2 positive correlation in TT homozygotes (Figure 6(c); time window:
364–386 ms, mean rho =.26, FDR p=.047).
3.4 |Summary of results
Statistical analyses for FOXP2 rs1456031 revealed a significant
effect of rs1456031 polymorphism on behavioral switch costs in RT
and accuracy and neural switch costs in N2a, N2b, and LPC. For
COMT rs4818, we found that rs4818 significantly interacted with
neural switch costs in N2a, N2b, and LPC, while BDNF rs6265 and
Kibra/WWC1 rs17070145 showed influences on neural switch costs
in N2a and N2b. Polymorphisms for COMT rs4680 and BDNF
rs2049046 showed a limited effect on processing of language
schema phase (N2a). Brain–behavior relationship analyses found that
FOXP2 and BDNF polymorphisms broadly modulated the predict-
ability of neural switch costs on behavioral switch costs in two
phases, whereas polymorphisms for COMT and Kibra/WWC1 nar-
rowly affected such predictability. The results from pairwise
comparisons on behavioral and electrophysiological data are shown
in Tables 3and 4, respectively.
4|DISCUSSION
This study examined the interaction between language control and
genetic variations and whether these variations modulated the rela-
tionship between neural coding and cognitive performance. We con-
ducted a picture-naming task with cued language switches and tested
the effects of a language-related gene (i.e., FOXP2) and executive
function-related genes (i.e., COMT, BDNF, and Kibra/WWC1 poly-
morphisms), and found that: (1) FOXP2 rs1456031 showed a wide-
ranging effect on language control, including behavioral performance
(RTs and accuracy) and neural coding across three-time phases (N2a,
N2b, and LPC); (2) COMT rs4818, BDNF rs6265, and Kibra/WWC1
rs17070145 polymorphisms significantly influenced two-time phases
(N2a and N2b); (3) further brain–behavior relationship analyses indi-
cated that language-related gene and executive function-related genes
can modulate the predictability of neural switch costs on behavioral
switch costs in two phases of language control. Taken together, these
findings demonstrate that language control in bilinguals is not simply
an adaptive function of executive control, but rather its essence is the
integration of language-switching experience and inherent genomes.
4.1 |Endogenous language ability influences
language control
An individual's capacity of acquiring speech and language must derive,
at least in part, from their genome. A substantial body of research has
shown that disruptions or structural variants (i.e., chromosome trans-
location or inversion) of FOXP2 can cause complications with speech
motor programming, which in turn, affects production, sequencing,
timing, and stress (Crespi et al., 2017; Fisher & Scharff, 2009; Liégeois
et al., 2003; Marcus & Fisher, 2003; Morgan et al., 2017). Our study
further demonstrated that FOXP2 variations influenced language con-
trol from the language schema phase until the utterance of the
response. For rs1456031 polymorphism, TT homozygotes and TC het-
erozygotes displayed significant L2 switch costs during the language
schema phase, suggesting that these carriers recruit additional execu-
tive functions to inhibit a more dominant language (i.e., the L1). How-
ever, during the lexical response phase, only TC heterozygotes
displayed significant L1 switch costs during the N2b time phase and
reversed L1 switch costs during the LPC phase. This finding suggests
that selective inhibition engaged in lexical selection facilitates subse-
quent lexical retrieval. We found no modulatory effect of FOXP2
rs1456031 polymorphism on RTs, as TT, TC, and CC exhibited signifi-
cant switch costs in both the L1 and L2. However, with respect to
accuracy, TT homozygotes displayed L1 and L2 switch costs, while TC
and CC did not. Neuroimaging studies have reported that rs6980093
can modulate speech category learning and development of language
networks during reading tasks (Chandrasekaran et al., 2015; Pinel
LIU ET AL.3637
et al., 2012). Specifically, the A allele, compared to GG, has been asso-
ciated with greater activation of the left inferior frontal gyrus during
reading (Pinel et al., 2012), while the GG genotype has displayed
higher accuracy in a non-native learning task relative to AA due to a
more efficient switch to a reflective, procedural-based learning system
that involves the executive corticostriatal loop (Chandrasekaran
et al., 2015). Our study, however, found that only GA heterozygotes
elicited reversed L1 switch costs during the LPC, indicating a higher
cognitive demand on non-switch trials.
Furthermore, we found that FOXP2 polymorphisms widely modu-
lated the brain–behavior relationship of neural and behavioral switch
costs. The AA genotype for rs6980093 showed a positive relationship
with the L1 in the language schema phase but a negative relationship
with the L2 in both the language schema and lexical response phases.
The GA genotype showed a negative relationship with the L1 in the
later language schema phase (690–750 ms) and lexical response phase
and a positive relationship with the L2 during both phases. Contrarily,
the CC genotype for rs1456031 displayed a positive relationship with
the L1 in the language schema and lexical response phases, and with
the L2 in the language schema phase, while the TT genotype showed
a negative relationship with the L1 in the lexical response phase. The
TC genotype revealed a negative relationship with the L1 during the
lexical response phase and a positive relationship with the L2 during
the language schema phase. Together, these findings suggest that
FOXP2 polymorphisms extensively modulate the predictability of neu-
ral coding and cognitive performance and that this predictive relation-
ship is exhibited in different phases of language control processing.
These findings underscore the coding preferences of language control
processing for each genetic variation.
4.2 |Executive function-related genes influence
language control
The findings confirmed that executive function-related genes play a
critical role not only in language schema phase but also in the lexical
response phase. For instance, COMT rs4818 significantly interacted
with three ERP components (N2a, N2b, and LPC). Specifically, G allele
carriers exhibited L2 switch costs in the language schema phase,
whereas the CC genotype had significant L1 switch costs in the lexical
response phase. Contrastively, BDNF rs6265 and Kibra/WWC1
rs17070145 significantly interacted with the N2a and N2b compo-
nents. The TT genotype for BDNF rs6265 revealed significant L1
switch costs for both N2a and N2b, while TC displayed reversed L1
switch costs and typical L2 switch costs. The CC genotype showed L1
switch costs during the time phase related to N2b. In addition, we
TABLE 3 Summary of the pairwise
comparisons for the behavioral data.
Gene RT ACC
FOXP2 rs1456031 TT L1: switch > non-switch
L2: switch > non-switch
L1: switch > non-switch
L2: switch > non-switch
TC L1: switch > non-switch
L2: switch > non-switch
-
CC L1: switch > non-switch
L2: switch > non-switch
-
TABLE 4 Summary of the pairwise comparisons for the electrophysiological data.
Gene
Language schema phase Lexical response phase
N2a N2b LPC
FOXP2 rs6980093 GA - - L1: non-switch > switch
FOXP2 rs1456031 TT/CC L2: switch > non-switch - -
TC L1: switch > non-switch L1: non-switch > switch
COMT rs4680 GG L2: switch > non-switch - -
COMT rs4818 CC - L1: switch > non-switch L1: non-switch > switch
G
+
L2: switch > non-switch - -
BDNF rs6265 TT L1: switch > non-switch L1: switch > non-switch -
TC L1: non-switch > switch
L2: switch > non-switch
--
CC - L1: switch > non-switch -
BDNF rs2049046 AA L2: switch > non-switch - -
Kibra/WWC1 rs17070145 TT L1: non-switch > switch
L2: switch > non-switch
L1: switch > non-switch -
C
+
- L1: switch > non-switch -
Note:G
+
represents G allele carriers for COMT rs4818; C+means the C allele carriers for Kibra/WWC1 rs17070145.
3638 LIU ET AL.
found that the Kibra/WWC1 polymorphism exhibited different
effects on the language schema and lexical response phases. The TT
homozygotes displayed reversed L1 switch costs and typical L2 switch
costs in the language schema phase, but only L1 switch costs in the
lexical response phase (N2b). The C allele carriers had significant L1
switch costs in the lexical response phase (N2b) but note in the lan-
guage schema phase. These findings demonstrate that bilinguals' lan-
guage control is not exclusively a consequence of switching
experiences. Among the three executive function genes of interest in
the present study, COMT rs4818 was the only polymorphism that
affected all three ERP components. Studies have reported that COMT
polymorphism (rs4680) is related to L2 learning, as evidenced by
changes in white matter tracts among G allele carriers, but not among
AA genotype (Mamiya et al., 2016). Sugiura et al. (2017) found that
six- to eight-year-old children carriers of A allele performed better on
language abilities than those with the GG genotype for COMT
rs4680. Our findings revealed that COMT rs4818 exerts a broader
influence across the time course of language control than COMT
rs4680, whose effects are limited to the language schema phase.
Moreover, the executive function-related genes also modulate
the relationship between neuroprocessing and cognitive performance
for language control during the language schema and lexical response
phases. The GG genotype for COMT rs4680 exhibited a negative L1
brain–behavior relationship but a positive relationship in two phases.
The G allele carriers for COMT rs4818 exhibited a negative brain–
behavior relationship with the L2 during the language schema phase.
The TT genotype for BDNF rs6265 showed a negative L1 relationship
in the lexical response phase, while CC homozygotes exhibited a posi-
tive L2 relationship in both language schema and lexical response
phases. The brain–behavior relationship varied by genetic variations
of Kibra/WWC1, in which C allele carriers showed a negative L1 rela-
tionship in the language schema phase, while the TT carriers showed
a positive L2 relationship in the language schema phase. These find-
ings suggest that polymorphisms for executive function-related genes
may modulate the relationship between neural coding and cognitive
performance.
4.3 |Language control is shaped by the integration
of language-switching experience and inherent
genomes
The configuration of language control and executive function shares
multiple subsets of cognitive skills, such as inhibitory control, conflict
monitoring, updating, and shifting (Abutalebi et al., 2012; Abutalebi
et al., 2013; Blanco-Elorrieta & Pylkkänen, 2016; Branzi et al., 2016;
Coderre et al., 2016; De Baene et al., 2015; De Bruin et al., 2014;
Emmorey et al., 2008; Wu et al., 2019). This claim is central to the
ACH (Green & Abutalebi, 2013) which argues that bilinguals' language
control is adaptive to situational and communicative needs. This view
is strongly supported by studies that have reported overlapping neural
substrates for both language and cognitive control (Abutalebi, 2008;
Blanco-Elorrieta & Pylkkänen, 2016; Branzi et al., 2016; Coderre
et al., 2016; Crinion et al., 2006; Hernandez, 2009; Hernandez
et al., 2001; Rodriguez-Fornells et al., 2002; Wang et al., 2007). Fur-
ther to these findings, the present study provides compelling evidence
that language control is the integration of an individual's switching
experience and their inherent genome. We found that, like the
language-related gene FOXP2, the executive function-related genes
COMT, BDNF, and Kibra/WWC1 exhibited robust effects in both the
language schema and lexical response phases, and modulated the
brain–behavior relationships on language control. These findings point
to a similar influence of the language-related gene and executive
function-related genes on language control processing. Moreover, our
analyses on the behavioral data revealed that the language-related
gene FOXP2 exhibited a broader impact on language control.
To our knowledge, our study is the first to explore the influence
of genetic bases of language ability and executive function on the pro-
cesses of how bilinguals control their two languages. Previous studies
have reported on how variations of the FOXP2 gene affect language
and language development (Chabout et al., 2016; Crespi et al., 2017;
Fisher & Scharff, 2009; Liégeois et al., 2003; Mozzi et al., 2017), on
how the COMT genes affect prefrontal-guided cognition (Barnett
et al., 2008; Chen et al., 2004; Klanker et al., 2013; Logue &
Gould, 2014; Sannino et al., 2015; Witte & Flöel, 2012; Zhang
et al., 2015), and how the BDNF and Kibra/WWC1 genes modulate
memory functioning (Almeida et al., 2008; Beste et al., 2010; Egan
et al., 2003; Hariri et al., 2003; Zhang et al., 2014). In our study, we
have shown that genetic variations of these genes differentially influ-
ence bilinguals' language control processing. Polymorphisms of
FOXP2 rs1456031, COMT rs4818, BDNF rs6265, and Kibra/WWC1
rs17070145 showed significant effects during language schema and
lexical response phases, whereas FOXP2 rs6980093, COMT rs4680,
and BDNF rs2049046 only affected the language schema phase.
Finally, the brain–behavior correlation analyses revealed differential
effects of each polymorphism on the predictability of neural coding
and cognitive performance. Specifically, we found that COMT rs4818
and Kibra/WWC1 rs17070145 narrowly modulated the relationship
between neural coding and cognitive performance in the language
schema phase, however, the other polymorphisms showed effects
during the language schema and lexical response phases. These find-
ings further suggest that these particular genetic variations affect pro-
cessing preferences and recruit more cognitive resources during
different phases of language production.
4.4 |Limitations
The present study demonstrated that bilinguals with different geno-
types flexibly adapt to external needs (i.e., the cues) in the dual-
language context. According to Green and Abutalebi (2013), there are
three language contexts that have different requirements for language
control: single-language context, dual-language context, and dense
code-switching context. The present study did not reveal that genes
affect the adaptation of language control on these different language
contexts. On the other hand, this study used EEG to examine genes'
LIU ET AL.3639
effects on electrical activity patterns. It is still unclear as to whether
language-related genes and executive function-related genes will elicit
similar effects on the cortical activation patterns or brain network of
bilingual language control.
5|CONCLUSION
This study uniquely sheds new light on the genetic basis of bilingual
language control. In the study, we found that the language-related
gene and executive function-related function genes exhibit robust
influences on bilingual language control during language schema and
lexical response phases. Our findings have also revealed the modula-
tory nature of brain–behavior relationships on language control. As
hypothesized, our findings suggest that language control among bilin-
guals is neither a language-specific ability nor merely an adaptive part
of executive functions, but rather a reflection of an individual's
switching experience and their inherent genome.
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), Liaoning Social Science Planning Fund of
China (L20AYY001), and Dalian Science and Technology Star Fund of
China (2020RQ055), and the Research Project on Economic and
Social Development of Liaoning Province (2023lslqnkt-054).
CONFLICT OF INTEREST STATEMENT
We have no known conflicts of interest to disclose.
DATA AVAILABILITY STATEMENT
The datasets generated and analyzed in this study are available in the
OSF repository: Liu, H. (2022, August 16). The genetic bases of lan-
guage control. Retrieved from osf.io/fm432.
ORCID
John W. Schwieter https://orcid.org/0000-0003-1798-3915
Huanhuan Liu https://orcid.org/0000-0002-9709-4757
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