brain
sciences
Systematic Review

Cultural Differences in Inhibitory Control: An ALE
Meta-Analysis
Gioele Gavazzi 1 , Chiara Noferini 1,2 , Viola Benedetti 1,† , Maria Cotugno 1,† , Fabio Giovannelli 1 ,Roberto Caldara 3 , Mario Mascalchi 4 and Maria Pia Viggiano 1, *
1

2

3

4

*
†

Citation: Gavazzi, G.; Noferini, C.;
Benedetti, V.; Cotugno, M.;
Giovannelli, F.; Caldara, R.;
Mascalchi, M.; Viggiano, M.P.
Cultural Differences in Inhibitory

Department of Neuroscience, Psychology, Drug Research and Child’s Health (NEUROFARBA), University of
Florence, 50135 Florence, Italy; gioele.gavazzi@unifi.it (G.G.); noferini@lens.unifi.it (C.N.);
viola.benedetti@unifi.it (V.B.); maria.cotugno1@stud.unifi.it (M.C.); fabio.giovannelli@unifi.it (F.G.)
European Laboratory for Non-Linear Spectroscopy, University of Florence, Sesto Fiorentino,
50019 Florence, Italy
Eye and Brain Mapping Laboratory (iBMLab), Department of Psychology, University of Fribourg,
1700 Fribourg, Switzerland; roberto.caldara@unifr.ch“Mario Serio” Department of Experimental and Clinical Biomedical Sciences, University of Florence,
50135 Florence, Italy; mario.mascalchi@unifi.it
Correspondence: mariapia.viggiano@unifi.it
These authors contributed equally to this work.

Abstract: Culture greatly influences our attitudes, beliefs, and behaviors, affecting how we communicate and make decisions. There is an ongoing debate regarding the belief that people from Easterncultures possess greater self-control abilities when compared to people from Western cultures. In thisstudy, we conducted a meta-analysis using the Activation Likelihood Estimation (ALE) algorithm tocompare 30 studies (719 subjects, 373 foci) that used fMRI to investigate the performance in Go–Nogoand Stop Signal Tasks of participants from Western and/or Eastern countries. Our meta-analysis
found differences between the networks activated in Eastern and Western culture participants. The
right prefrontal cortex showed distinct patterns, with the Inferior Frontal gyrus more active in the
Eastern group and the middle and superior frontal gyri more active in the Western group. Our findings suggest that Eastern culture subjects have a higher tendency to activate brain regions involvedin proactive inhibitory control, while Western culture subjects rely more on reactive inhibitory brain
regions during cognitive control tasks. This implies that proactive inhibition may play a crucial rolein promoting the collective and interdependent behavior typical of Eastern cultures, while reactive
inhibition may be more important for efficient cognitive control in subjects of Western cultures thatprioritize individualism and independence.

Control: An ALE Meta-Analysis.
Brain Sci. 2023, 13, 907. https://doi.org/10.3390/brainsci13060907

Keywords: culture; cultural differences; Western culture; Eastern culture; inhibitory control; rightprefrontal cortex; meta-analysis

Academic Editor: Luigi De Gennaro
Received: 25 April 2023
Revised: 31 May 2023
Accepted: 2 June 2023
Published: 3 June 2023

Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms andconditions of the Creative Commons
Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

1. Introduction
Culture is a significant factor in shaping our attitudes, beliefs, and behaviors, impacting everything from our communication styles to our decision-making processes andperceptions of the world. While it is evident that individuals from different cultures exhibit
inhomogeneous and sometimes peculiar behaviors and cognitive processes, the underlying
neural mechanisms behind these differences remain largely unknown. Although studies
have explored cultural differences in various psychological processes, it is still unclear
whether these differences are governed by the same or different neural networks. However,research using functional magnetic resonance imaging (fMRI) has identified multiple neuralcircuits that are involved in different psychological processes [1,2].
It is commonly believed that individuals from Eastern cultures have a greater capacity for inhibitory control than those from Western cultures, and this idea is a subject ofBrain Sci. 2023, 13, 907. https://doi.org/10.3390/brainsci13060907

https://www.mdpi.com/journal/brainsci

Brain Sci. 2023, 13, 907

2 of 14

ongoing debate. Research in cultural psychology indicates that there are differences ininhibitory control between individualistic, as are typically considered Western cultures,and collectivistic, as are typically considered Eastern cultures [3–7]. This is likely to be
associated with the Eastern philosophy, which conveys the message of the importanceof self-control and inner discipline that underlies a collectivist type of culture, where thegroup and society are very important.
The basic idea is that what is good for society is also good for the individual, and abalance between individual and society is achieved by modulating self-control throughinhibitory abilities [8–10]. From an early age, Eastern education systems adopt strict
parenting and parenting practices that are believed to stimulate and strengthen inhibitory
control [11–13]. Indeed, in studies conducted during early childhood, children aged
three to four years old from East Asian nations often demonstrate greater proficiency inmeasures of inhibitory control, including the Stroop task, when compared to their Americancounterparts [6,14]. Although it is likely that the difference between people of Westernand Eastern cultures may be related to the extent and type of brain regions activated for
self-control, when one compares fMRI studies in subjects belonging to the two types ofculture, there is a lack of consistency in the neural areas responsible for the respective
inhibitory control [15–19].
This inconsistency can be attributed to various factors and differences, including thechosen paradigms. In fact, while inhibitory control has been mainly assessed using the
Go–Nogo and Stop Signal Tasks interchangeably, these two tasks seem to tackle differently
inhibitory components [20–30].
There are other relevant factors worth taking into consideration, such as the participants’ ages [31] or education levels [32]. Notably, to control for education, it may be usefulto also control for cultural proficiency. Inadequate cultural knowledge and/or adaptation
have been linked to decreased performance in tasks that require inhibitory control, such as
the Wisconsin Card Sorting Test [33] and the Stroop test [34,35]. Conducting a meta-analysis
on the data obtained from previous studies could better clarify the implications of the various circuits in the modulation of cognitive control as a function of the two cultures. As far
as we know, no meta-analysis has been carried out to investigate the potential variations inneural correlates associated with inhibitory control in Western and Eastern cultures. Thus,
the aim of this study is to investigate the influence of Western and Eastern cultures on theneurobiological underpinnings of inhibitory control while controlling for variables that
may affect inhibition, such as task design [20], aging [31], and education [32].
To this end, we conducted a meta-analysis and compared 30 studies that used fMRI
to investigate the Go–Nogo and Stop Signal Tasks in Western and/or Eastern participants
using the Activation Likelihood Estimation (ALE) algorithm. Since our primary goal was
to explore the brain mechanisms underlying inhibitory control in these cultural contexts,
rather than focusing on the differences in behavioral outcomes, we took steps to minimize
confounding effects by exclusively analyzing volumes derived from trials where inhibition
was successfully achieved and by carefully matching the two groups based on the task
design.
2. Materials and Methods
2.1. Search Strategy and Selection Criteria
We conducted a systematic and comprehensive literature search to select relevant fMRI
studies published up to April 2022 using the databases PubMed (https://pubmed.ncbi.nlm.
nih.gov/—accessed on 8 April 2022) and Web of Science (https://webofknowledge.com—
accessed on 8 April 2022). The selected keywords were combined using the Boolean
operators AND and OR. The PubMed search input was: (“Stop-Signal Task” OR “StopSignal Task” OR “Go-NoGo” OR “Go NoGo” OR “GoNoGo” OR “Go/NoGo” OR “Go NoGo”) AND fMRI. The Web of Science search input was: TS = ((“Stop-Signal Task” OR “StopSignal Task” OR “Go-NoGo” OR “Go NoGo” OR “GoNoGo” OR “Go/NoGo” OR “GoNo-Go”) AND fMRI). Additional studies were searched from the references of all identified

Brain Sci. 2023, 13, 907

3 of 14publications. Eligibility was determined via a two-step procedure performed by four of theauthors (GG, CN, VB, and MC). First, the titles and abstracts of all identified articles werescreened. In the second step, the full texts of studies, according to predefined eligibility
criteria, were independently examined, and agreement was reached after discussion. Our
study was conducted following the preferred reporting items for systematic reviews andmeta-analyses (PRISMA) guidelines [36].
The studies were considered eligible if they met the following inclusion criteria:(1) whole-brain analysis performed on fMRI data (we excluded studies conducted using
positron emission tomography and fMRI in which only results from ROI analysis were reported); (2) availability of coordinates of activation foci clearly provided either in Montreal
Neurological Institute (MNI) or Talairach reference space; (3) studies conducted on healthy
adults. Exclusion criteria: studies conducted on children or the elderly; studies using
stimuli with emotional content, reward, or other inhibitory tasks; studies with pharmacological treatment or training; non-standard neuroimaging data analysis or procedures. The
selection of these strict criteria allowed us to select homogeneous studies in order to obtain
more robust measures [37]. From the pool of potentially eligible studies, we performed
further selection in order to identify studies for the comparison (quantitative analysis)
between studies on Western vs. Eastern culture matched for the type of experimental
paradigm (SST and GoNogo) and participant characteristics (age and education level). See
Supplementary Materials (Table S1) for details on culture studies included in the Eastern orWestern groups of studies.
2.2. Activation Likelihood Estimation (ALE)
Data were analyzed using the Activation Likelihood Estimation (ALE) meta-analysis
algorithm implemented in GingerALE 3.0.2 software (www.brainmap.org/ale). ALE is acoordinate-based meta-analysis method that uses peak coordinates from functional studies
as inputs, and has been described well in previous methodological papers [38–41]. The
ALE algorithm assesses the convergence of activation foci from different neuroimaging
studies, modeled as probability distributions, against null distributions of random spatial
associations, while controlling for sample size. To minimize within-experiment effects, thenon-additive algorithm described in Turkeltaub et al. [39] was applied. Inference was madeat the cluster level, as this provides the best balance between sensitivity and specificity [41].
The cluster-forming threshold was set to p < 0.005, and the size of the resulting suprathreshold clusters was compared to a null distribution determined by 2000 permutations of
the data (with a threshold of p < 0.05). The selected studies are outlined in Supplementary
Materials (Table S1).
The first two meta-analyses were performed with the activation foci generated by
the Go–Nogo and the SST, and were divided into two (Eastern culture and Western culture) groups of studies. To further examine the shared and differential neural substrates
during the inhibition task, the two groups of studies (Eastern culture vs. Western culture)
were analyzed. A conjunction analysis was performed to assess the potential overlap
among the brain networks from the Western and Eastern culture groups, and a pairwise
subtraction analysis was used to identify the differences in activation between these two
networks [41]. The analysis was conducted with a statistical threshold of uncorrectedp < 0.01 and 10,000 permutations, and a cluster-size threshold of 200 mm3 .
The neuroanatomical coordinates reported in Talairach space [42] were transformed
to MNI space for all analyses. Whole-brain maps of the thresholded ALE images werevisualized in Mango V.4.0.1 (http://rii.uthscsa.edu/mango/), an anatomical image overlay
program, superimposed onto a standardized anatomical template.
3. Results
3.1. Results of the Study Search
A PRISMA flow chart of the article selection process is illustrated in Figure 1. Our
search yielded 597 potentially eligible studies. After evaluation of the full text of theseBrain Sci. 2023, 13, x FOR PEER REVIEW

4 of 14

3. Results
Brain Sci. 2023, 13, 907

3.1. Results of the Study Search

4 of 14A PRISMA flow chart of the article selection process is illustrated in Figure 1. Our
search yielded 597 potentially eligible studies. After evaluation of the full text of thesearticles, 73 studies were potentially eligible. This pool of studies included 14 Eastern and
59 Western studies. In fact, we considered 15 studies from Eastern cultures, because one
study (Supplementary(Supplementary Materials,
Materials, Table
Table S1) presented data with both SST and GoNogo
tasks. In order
order toto performperformaabalanced
balancedcomparison,comparison,a asubset
subsetofof1515
Western
studiesmatchWesternstudiesmatching
ing
the Eastern
group
for typeof experimental
designand GoNogo)and participant
the Eastern
group
for typeof experimental
design(SST (SSTand GoNogo)and participantcharacteristics (age (ageand educationlevel) level)
was extracted.
Therefore,
30 studies(15 from(15Easterncharacteristics
and education
was extracted.
Therefore,
30 studies
fromculture countries
and 15 fromcountries)
from 2003 to
20222003
weretoincludedinEasternculture countries
andWestern
15 fromculture
Westernculture countries)
from2022 were
our quantitative
From
these studies,
cumulativeof 719 healthy
includedin our analyses.
quantitative
analyses.
From athesestudies,number
a cumulative
numbersubjectsof 719and 373 fociwereandobtained.
Theweremainobtained.
characteristicsof thecharacteristics
studies includedin theanalysishealthysubjects
373 foci
The mainof thestudies
inare reported
the Supplementary(Table S1). Materials (Table S1).
cludedin theinanalysis
are reported Materials
in the Supplementary

Figure 1. PRISMA flow chart.
Figure 1. PRISMA flow chart.

3.2. ALE Results
3.2. ALE
The Results
ALE meta-analysis of the group of studies from Eastern countries (Figure 2; Table 1)

The ALEmeta-analysis
the(7408
groupof3 studies
from cerebral
Eastern countries(Figure2; Taidentified
the largest
cluster ofsizemm) in the righthemisphere
extending3) in the right cerebral hemisphere exble
1)
identified
thelargestcluster
size
(7408
mm
from the striatum, the claustrum, the caudate, the insula, and the inferior frontal gyrus andtendingfromthe portions
striatum,ofthe
the caudate,the insula,and
andglobus
the inferior
frontalcomprisingsmall
theclaustrum,
right precentral
gyrus, putamen,
pallidus.
The3 ) included
gyrus
comprisingsmall
portionstheof theright
precentral
gyrus,
putamen,and globus
secondandcluster(2232 mmright
striatumand theclaustrum,
followedby a3 ) located3 ) in thepallidus.
The mmsecondclusterin(2232
mm3middle) included
the gyrus,
right striatum
themmclaustrum,
cluster (1752
the right
frontala clusterand(1640
3)3) locatedfollowedby a cluster(1752
rightpart,
middlea cluster(1640
bilateral cingulate
gyrus
andmm
extendingto in
its theanterior
and,frontal
finally,gyrus,
a cluster(1376 mm3) right
in thefrontalgyrus.gyrus and extending to its anterior part, and, finally, ammin themiddle
bilateralcingulate3
cluster (1376 mm ) in the right middle frontal gyrus.

Brain Sci.
Sci. 2023,
2023, 13,
13, 907x FOR PEER REVIEW
Brain

55 of
of 14

Figure 2.
2. ALE meta-analysis map for the Western
Western andand Eastern
Eastern groups
groups ofof ourour data
data selection.
selection. The
algorithm converged
convergedfor
forthetheWestern
Westerngroup
groupyellow-red)precentral
gyrus(l-PCG),andalgorithm(in(inyellow-red)on onleftleftprecentral
gyrus(l-PCG),and waswas bilateralon medial
frontal(mFG),
right superior/middle(r-S/MFG),
andbilateralon medial
frontalgyrusgyrus(mFG),
right superior/middle
frontalfrontal
gyrus gyrus(r-S/MFG),
and right
rightleft
andleft insula
The algorithm
converged
for Easterngroup(in white-blue)onandinsula(r-INS,(r-INS,l-INS). l-INS).
The algorithm
converged
for Easterngroup (inwhite-blue)
on bilateralbilateral anterior cingulate cortex (ACC), right inferior frontal gyrus (r-IFG), left striatum nucleus
anterior cingulate cortex (ACC), right inferior frontal gyrus (r-IFG), left striatum nucleus extended
extended to the insula (l-INS), right inferior frontal gyrus (r-IFG) and right superior/middle frontal
to the insula (l-INS), right inferior frontal gyrus (r-IFG) and right superior/middle frontal gyrus
gyrus (r-S/MFG). The algorithm converged for the conjunction analysis of the two groups (in(r-S/MFG).
The
algorithm
for thegyrus
conjunctionof the parietal
two groups(in (r-IPL),
white-green)
white-green)bilaterally
onconverged
medial frontal(mFG), analysis
right inferior
lobulerightbilaterallyonmedialfrontalgyrus(mFG),
rightinferior
parietallobule(r-IPL),rightsuperior
parietal
superior parietal lobule (r-SPL), right superior/middle frontal gyrus (r-S/MFG), bilateral anterior
lobule(r-SPL),superior/middle
frontal gyrus
gyrus (r-IFG),(r-S/MFG),
anterior
cortexcingulate
cortexright(ACC),
right inferior frontal
and bilateralright (r-INS)and cingulate
left striatum
ex(ACC),frontal gyrus(r-IFG),and right(r-INS) andleft striatum
to the insula
tended rightto theinferior
insula (l-INS)—p< 0.05cluster-level
corrected
inferenceusing pextended< 0.005 uncorrectedatvoxel-level<as0.05
thecluster-levelcluster-forming
threshold.
(l-INS)—pcorrected
inference using p < 0.005 uncorrected at voxel-level as thecluster-forming threshold.
Table 1. Results of ALE meta-analysis. Foci are reported as MNI coordinates. BA = Broadman Area.
Table
1. Results of ALE
meta-analysis.
reportedas MNI coordinates. BA = Broadman Area.
Eastern Group of Studies: ALE
Meta-Analysis
Computed
fromFoci
OurareStudy
Selection
Clusterx y z
ALEpLabel (Nearest Gray Matter within 5 mm)
Eastern Group of Studies: ALE Meta-Analysis Computed from Our Study Selection−932 16 −12 0.02397523 8.957319 × 10 Right Cerebrum. Sub-lobar. Claustrum. Gray Matter
Clusterxyz
ALEpLabel (Nearest Gray Matter within 5 mm)
16 16 −8 0.016835388 3.894887 × 10−6 Right Cerebrum. Sub-lobar. Caudate. Gray Matter. Caudate Head
−
9
32
16
−12
0.02397523
Right Cerebrum. Sub-lobar. Claustrum. Gray Matter
8.957319 × 10
38 16 −2 0.016307237 5.931126 × 10−6 Right Cerebrum. Sub-lobar. Claustrum. Gray Matter
Right Cerebrum. Sub-lobar. Caudate. Gray Matter.
−80.016835388
× 10−6 Frontal Lobe. Precentral Gyrus. Gray Matter. BA 44
501618 216 0.014818636
2.2523935 × 10−5 3.894887
Right Cerebrum.
Caudate Head
−6
38
16
−2
0.016307237
Right Cerebrum.
Sub-lobar.
Claustrum.
MatterRight
Cerebrum.
FrontalLobe. Inferior
FrontalGyrus.Gray
Gray
Matter
5.931126
×10
1
56 16 14 0.01302748 1.0037824 × 10−4
Right
Cerebrum.
FrontalLobe.
Precentral
Gyrus.
Gray
BA
44
−
5
50
18
2
0.014818636
2.2523935 × 10
Matter. BA 44
Right Cerebrum. Sub-lobar. Lentiform Nucleus. Gray Matter. Puta1
Right Cerebrum. Frontal Lobe. Inferior Frontal Gyrus.
2256 6 −6160.0101995105
6.4743846 × 10−41.0037824 × 10−4140.01302748
men
Gray Matter BA 44
Right
Cerebrum.
Sub-lobar.
Lentiform
Gray
Right
Cerebrum.
Sub-lobar.
Lentiform
Nucleus.
Gray Nucleus.
Matter. Medial−60.0101995105
1622 2 −26 0.009658105
8.880825 × 10−4 6.4743846 × 10−4
Matter.
PutamenGlobus Pallidus
Right Cerebrum. Sub-lobar. Lentiform Nucleus. Gray−2
0.009658105−301618 −42 0.018294685
1.2900151 × 10−6 8.880825
Left Cerebrum.
× 10−4 Sub-lobar. Claustrum. Gray Matter
Matter. Medial Globus Pallidus
2
Left
Cerebrum.
Sub-lobar.
Lentiform Nucleus. Gray Matter. Puta−26 10 0 0.01170183 2.4654568 × 10−4menBrain Sci. 2023, 13, 907

6 of 14

Table 1. Cont.
Eastern Group of Studies: ALE Meta-Analysis Computed from Our Study Selection
Cluster
2

x

y

z

ALE

p

Label (Nearest Gray Matter within 5 mm)
Left Cerebrum. Sub-lobar. Claustrum. Gray Matter
Left Cerebrum. Sub-lobar. Lentiform Nucleus. Gray
Matter. Putamen

−30

18

−4

0.018294685

1.2900151 × 10−6

−26

10

0

0.01170183

2.4654568 × 10−4

48

10

28

0.016780104

4.072827 × 10−6

48

16

32

0.01458163

2.7073333 × 10−5

44

4

44

0.009647945

8.9332176 × 10−4

44

10

42

0.009213654

0.001171954

−6

32

22

0.017859418

1.7803774 × 10−6

6

34

24

0.01445027

3.053667 × 10−5

−8

30

32

0.009196116

0.0011785898

36

40

20

0.021027796

1.342605 × 10−7

3

4

5

Right Cerebrum. Frontal Lobe. Inferior Frontal Gyrus.
Gray Matter. BA 9
Right Cerebrum. Frontal Lobe. Middle Frontal Gyrus.
Gray Matter. BA 9
Right Cerebrum. Frontal Lobe. Middle Frontal Gyrus.
Gray Matter. BA 6
Right Cerebrum. Frontal Lobe. Middle Frontal Gyrus.
Gray Matter. BA 6
Left Cerebrum. Limbic Lobe. Anterior Cingulate. Gray
Matter. BA 32
Right Cerebrum. Limbic Lobe. Cingulate Gyrus. Gray
Matter. BA 32
Left Cerebrum. Frontal Lobe. Cingulate Gyrus. Gray
Matter. BA 32
Right Cerebrum. Frontal Lobe. Middle Frontal Gyrus.
Gray Matter. BA 9

Western Group of Studies: ALE Meta-Analysis Computed from Our Study Selection
Cluster
1
2

3

4

5

x
32

y
20

z

−8

ALE

p

Label (Nearest Gray Matter within 5 mm)

0.036751416

1.1696465 × 10−12

Right Cerebrum. Sub-lobar. Claustrum. Gray Matter
Left Cerebrum. Sub-lobar. Claustrum. Gray Matter
Left Cerebrum. Sub-lobar. Insula. Gray Matter. BA 13
Left Cerebrum. Sub-lobar. Insula. Gray Matter. BA 13

−30
−44
−28

20
20
30

−8
0
0

0.021623248
0.010548196
0.009875406

3.6322265 × 10−7

32

46

16

0.01598811

2.3766925 × 10−5

32

42

24

0.01520087

4.282069 × 10−5

28

50

30

0.014807648

5.7067977 × 10−5

4

16

50

0.018120997

4.6963864 × 10−6

Right Cerebrum. Frontal Lobe. Superior Frontal Gyrus.
Gray Matter. BA 6

−44

−10

52

0.013735037

1.20725206 × 10−4

−34

−10

50

0.013622271

1.3100117 × 10−4

−44

−2

54

0.010301465

0.0010217617

Left Cerebrum. Frontal Lobe. Precentral Gyrus. Gray
Matter. BA 4
Left Cerebrum. Frontal Lobe. Precentral Gyrus. Gray
Matter. BA 4
Left Cerebrum. Frontal Lobe. Precentral Gyrus. Gray
Matter. BA 6

8.794995 × 10−40.0013448054

Right Cerebrum. Frontal Lobe. Middle Frontal Gyrus.
Gray Matter. BA 10
Right Cerebrum. Frontal Lobe. Middle Frontal Gyrus.
Gray Matter. BA 9
Right Cerebrum. Frontal Lobe. Superior Frontal Gyrus.
Gray Matter. BA 9Regarding the Western group of studies (Figure 2, Table 1), the ALE algorithm converged in a first cluster (4358 mm3 ) in the right cerebral hemisphere, including the striatum,the claustrum, and the right insula and extending to a small part of the right inferior
frontal gyrus, followed by an almost symmetric cluster (2152 mm3 ) in the left hemisphere,
including the insula and claustrum. The third cluster (2120 mm3 ) was located in the right
middle and superior frontal gyrus, whereas the fourth cluster (1768 mm3 ) was centered in
the medial prefrontal cortex (pre-SMA). Finally, we observed a last cluster (1496 mm3 ) in
the left pre-central gyrus.
As expected, the conjunction analysis between the Western and Eastern groups revealed almost all the above-listed areas (Figure 2, Table S2).

Brain Sci. 2023, 13, 907

7 of 14

The contrast analysis (Figure 3, Table 2) between Western and Eastern groups ofstudies showed, in the former, one cluster (1152 mm3 ) centered in the left pre-central gyrus,
followed by a cluster (624 mm3 ) located in the right middle frontal gyrus, with one cluster
Brain Sci. 2023, 13, x FOR PEER REVIEW
of 14mm3 ) in
(352 mm3 ) included in the right insula. Finally, we observed one last cluster7(256the right superior frontal gyrus.

Figure 3.
betweenWestern
and andEasterngroups.
The scalein redFigure3. Contrast
Contrastanalysis
analysis
betweenWestern
Easterngroups.
Thebarscale
barrepresents
in red represents
z-values
from
1
to
2.5
revealedbytheWestern>
Easterncontrast
analysis:left
pre-central
gyrus gyrus (lz-values from 1 to 2.5 revealed by the Western > Eastern contrast analysis: left pre-central(l-PCG), right middle frontal gyrus (r-MFG), right insula (r-INS), right superior frontal gyrus
PCG),
right middle frontal gyrus (r-MFG), right insula (r-INS), right superior frontal gyrus (r-S/MFG).
(r-S/MFG). The scale bar in blue represents z-values from 1 to 2.5 revealed by the Eastern > Western
The
scale
bar in blue
z-values
1 toand
2.5therevealed
the Eastern> Western
contrast analysis:right represents
middle frontal
gyrus from(r-MFG)
inferiorby
frontalgyrus (r-IFG),
right contrast
lentiform nucleus(r-LN).frontal gyrus (r-MFG) and the inferior frontal gyrus (r-IFG), right lentiform
analysis:right middle
nucleus (r-LN).
Table 2. Results of ALE contrast analysis between Western and Eastern studies. MNI coordinates.

BA = Broadman
Table
2. ResultsArea.
of ALE contrast analysis between Western and Eastern studies. MNI coordinates.
BA=
BroadmanArea.
Eastern–Western Contrast: ALE Meta-Analysis Computed from Our Study Selection

Cluster x
y z ALEp
Label (Nearest Gray Matter within 5 mm)
Eastern–Western Contrast: ALE Meta-Analysis Computed from Our Study Selection
52 16 32 0.0179 2.0991917 Right Cerebrum. Frontal Lobe. Middle Frontal Gyrus. Gray Matter. BA 9
Cluster x44 14y 32 0.0186
z
ALE
p Cerebrum. Frontal Lobe.
Label(Nearest
Gray Gray
Matter
within
5 mm)
2.0835624
Right
Precentral
Gyrus.
Matter.
BA
950 616 30 0.0195
2.0641868
Cerebrum.
Lobe.Frontal
Inferior
FrontalGyrus.
GrayGyrus.
Matter.
BA 6Matter. BA 952320.0179 Right
2.0991917
RightFrontal
Cerebrum.
Lobe.
MiddleFrontal
Gray
1
44
32
0.0186 Right
2.0835624
RightFrontal
Cerebrum.
Lobe.Gyrus.
Precentral
46 614 30 0.0197
2.0599847
Cerebrum.
Lobe.Frontal
Precentral
GrayGyrus.
Matter.Gray
BA 6Matter. BA 9
50 12.76 28 0.0294
30
0.0195 Right
2.0641868
RightFrontal
Cerebrum.
Lobe.
Inferior
Frontal
50.7
1.8896858
Cerebrum.
Lobe.Frontal
Inferior
FrontalGyrus.
GrayGyrus.
Matter.Gray
BA 9Matter. BA 6
1
46
6
30
0.01972.0599847
Right
Cerebrum.
FrontalLobe.
Precentral
Gyrus.
Gray
Matter.
44 14 28 0.0304 1.8749471 Right Cerebrum. Frontal Lobe. Inferior Frontal Gyrus. Gray Matter. BA 9 BA 6
50.7
12.7
28
0.0294 1.8896858 Right Cerebrum. Frontal Lobe. Inferior Frontal Gyrus. Gray Matter. BA 92
36 14 −18 0.0149 2.1727386 Right Cerebrum. Frontal Lobe. Inferior Frontal Gyrus. Gray Matter. BA 13441428
0.0304 1.8749471 Right Cerebrum. Frontal Lobe. Inferior Frontal Gyrus. Gray Matter. BA 9
−2.4 33.4 24.8 0.0386 1.7671685 Left Cerebrum. Limbic Lobe. Cingulate Gyrus. Gray Matter. BA 32
3−2 31.7 19.3 0.0444 1.701762 Left Cerebrum. Limbic Lobe. Anterior Cingulate. Gray Matter. BA 24
4
14 14 −12 0.0206 2.0415115 Right Cerebrum. Sub-lobar. Caudate. Gray Matter. Caudate Head

Brain Sci. 2023, 13, 907

8 of 14

Table 2. Cont.
Eastern–Western Contrast: ALE Meta-Analysis Computed from Our Study Selection
Cluster

x

y

z

ALE

p

Label (Nearest Gray Matter within 5 mm)

2

36

14

−18

0.01492.1727386

Right Cerebrum. Frontal Lobe. Inferior Frontal Gyrus. Gray Matter. BA 13

3

−2.4
−2

33.4
31.7

24.8
19.3

0.03860.04441.7671685
1.701762

Left Cerebrum. Limbic Lobe. Cingulate Gyrus. Gray Matter. BA 32
Left Cerebrum. Limbic Lobe. Anterior Cingulate. Gray Matter. BA 24

4

14
20

14
8

−12
−8

0.0206
0.03692.0415115
1.7878513

Right Cerebrum. Sub-lobar. Caudate. Gray Matter. Caudate Head
Right Cerebrum. Sub-lobar. Lentiform Nucleus. Gray Matter. Putamen

Western–Eastern Contrast: ALE Meta-Analysis Computed from Our Study Selection
Cluster

x

y

z

ALE

p

1

−41.7
−45
−40
−46.9
−36
−46
−32

−9.5
0
−14
−7.7
−12
−14
−12

53.5
54
50
53.9
48
50
52

0.00560.00590.01160.01180.02450.02530.03762.536396
2.5180695
2.270125
2.2635794
1.9685917
1.9548566
1.7792426

Left Cerebrum. Frontal Lobe. Precentral Gyrus. Gray Matter. BA 4
Left Cerebrum. Frontal Lobe. Precentral Gyrus. Gray Matter. BA 6
Left Cerebrum. Frontal Lobe. Precentral Gyrus. Gray Matter. BA 4
Left Cerebrum. Frontal Lobe. Precentral Gyrus. Gray Matter. BA 4
Left Cerebrum. Frontal Lobe. Precentral Gyrus. Gray Matter. BA 4
Left Cerebrum. Parietal Lobe. Postcentral Gyrus. Gray Matter. BA 3
Left Cerebrum. Frontal Lobe. Precentral Gyrus. Gray Matter. BA 4

2

38
37
35.3

50
58
54

−2
1
−1

0.01380.02310.02542.2029254
1.9935615
1.9531654

Right Cerebrum. Frontal Lobe. Middle Frontal Gyrus. Gray Matter. BA 10
Right Cerebrum. Frontal Lobe. Middle Frontal Gyrus. Gray Matter. BA 10
Right Cerebrum. Frontal Lobe. Middle Frontal Gyrus. Gray Matter. BA 10

3

38
32
26

24
20
16

−2
−2
−2

0.01030.01960.02512.3152363
2.0620813
1.9582559

Right Cerebrum. Sub-lobar. Insula. Gray Matter. BA 13
Right Cerebrum. Sub-lobar. Claustrum. Gray Matter
Right Cerebrum. Sub-lobar. Lentiform Nucleus. Gray Matter. Putamen

4

24

48

30

0.0261

1.9414806

Right Cerebrum. Frontal Lobe. Superior Frontal Gyrus. Gray Matter. BA 9

Label (Nearest Gray Matter within 5 mm)

The opposite contrast (Eastern versus Western) showed a first cluster (632 mm3 ) in theright middle frontal gyrus, the inferior frontal gyrus and the pre-central gyrus, followed
by a cluster (312 mm3 ) in the right inferior frontal gyrus and a cluster (296 mm3 ) in theanterior cingulate gyrus. Finally, we observed one last cluster (280 mm3 ) in the striatum.
4. Discussion
The present study aimed to explore how belonging to either Western or Easterncultures may result in the distinct functional recruitment of brain areas involved in cognitive
control. To accomplish this, we gathered data from studies investigating cognitive control
in Western and Eastern population samples, utilizing fMRI to measure performance in the
Stop Signal Task (SST) and Go/Nogo tasks.
Our meta-analyses revealed that most of the clusters identified via ALE in both Westernand Eastern culture groups were located in the same brain regions. This was evident not
only in the separate analyses of each group of studies, but also in the conjunction analysis,
which allowed us to identify common areas of activation across both populations. The
regions observed in the present meta-analysis align with those identified in previous
meta-analyses of fMRI studies on the Go–Nogo and SST tasks [29,43,44]. Specifically,
the pre-supplementary motor area (pre-SMA), right prefrontal cortices (r-IFG, r-MFG, rSFG), anterior cingulate cortex (ACC), insula, claustrum, and thalamus were consistently
observed across these studies.
Notably, upon conducting a contrast analysis on the two groups of studies (Eastern
vs. Western culture countries), we observed differences between the underlying networks
revealed by the ALE. A discernible pattern of significant clusters was distributed over
the right prefrontal cortex. In particular, the contrast between Eastern and Western study
groups revealed activation in the right inferior frontal gyrus, while the opposite contrast(Western vs. Eastern study groups) showed distinct clusters in the right middle and superior
frontal gyri. These results indicate pronounced recruitment of the middle and superior

Brain Sci. 2023, 13, 907

9 of 14

regions of the right prefrontal cortex in the Western group, whereas the lower regions(i.e., inferior prefrontal cortex) are more prominently activated in the Eastern group. Thisfinding is particularly noteworthy as the prefrontal cortex is widely recognized as a region
responsible for the inhibitory control of cognitive processes [45–48], and there appears to
be a dissociation between the right inferior frontal gyrus and the middle/superior frontal
gyri depending on the inhibitory phase. To be more precise, the right inferior frontal gyrus(r-IFG) plays a significant role in the attentional aspects of cognitive control and is involved
in all stages of inhibition. However, its primary function, according to recent studies, lies in
the proactive phase of inhibition [29,49,50]. That is, a top-down form of inhibitory control
that governs the processes preceding event occurrence, thereby improving the accuracy
and efficiency of motor responses [20,51]. In contrast, the right superior and middle frontal
gyri (r-S/MFG) are pivotal brain regions for reactive inhibition, which is a bottom-up formof inhibitory control. Reactive inhibition is believed to act as a “cut-trigger,” halting an
already initiated motor response [29,49,50,52].
The results herein highlight the greater propensity of Eastern populations to activate
brain regions that mediate proactive inhibitory control, while those from Western cultures
may rely more on reactive inhibitory brain regions to achieve accurate inhibition duringcognitive control tasks. This is an intriguing result as it may suggest that proactive inhibition, as reflected in brain activations, may play a crucial role in fostering the collective
and interdependent behavior that is commonly observed in people of Eastern cultures. On
the other hand, reactive inhibition, as a cognitive control mechanism, may be predominant in achieving efficient cognitive control for cultures that prioritize individualism andindependence, which are commonly observed in Western cultures [3–7].
However, from an alternative perspective, we can speculate that the difference inprioritizing reactive control might arise from the Western inclination for swift action,
influenced by their fast-paced lifestyles, while the Eastern mindset might tend to favorcontemplation and a more meditative approach to life.
Another difference observed in the contrast between the Eastern and Western groupsof studies is the anterior cingulate cortex. This result corroborates our interpretation; infact, the anterior cingulate cortex is not just a brain region responsible for detecting andresolving conflict [53–55], but it is also at the basis of the proactive mechanism, as recently
pointed out in different studies [48,50]. Indeed, being able to better manage and resolve
conflicts is undoubtedly a necessary skill for adapting to more collective cultures such as
those in the Eastern countries. Hence, it is not surprising that this brain region has been
consistently identified as neuro-anatomically and functionally [56] different in individuals
from Eastern cultures when compared to their Western counterparts.
Finally, we observed greater activation of the striatum in the group of Eastern culture
countries and higher activation of the left precentral cortex in the group of Western culture
countries. The former finding aligns with the possibility that individuals from Easterncultures may possess stronger inhibitory control, although this assertion lacks conclusive
evidence. Conversely, the latter finding suggests that individuals from Western cultures
may exhibit a more impulsive response during the Go–Nogo and SST tasks. Indeed,
Gavazzi et al. [48] reported a positive correlation between impulsivity during the proactive
phase of a Go–Nogo task and increased BOLD signals in the left precentral gyrus. This is a
result that is consistent with many studies reporting that the left hemisphere is crucial in
the link between impulsivity and inhibitory control at the functional and morphometricneuroimaging levels [57]. Additionally, there is evidence showing that the left hemispheremay be dominant, or is at least involved, in some aspects of inhibitory control [58,59].
Despite the existence of several pieces of evidence linking inhibition and impulsivity,
an ongoing debate remains regarding the possibility [60] that impulsivity may not bedirectly related to inhibitory control. Therefore, future studies are needed to assess ourinterpretation of the precentral gyrus.
Taking into account the examination of these findings in the broader ongoing discussion about the complex interplay between culture and the brain, and their reciprocalBrain Sci. 2023, 13, 907

10 of 14influence [61], it is plausible to speculate that environmental factors significantly contributeto the impact of culture on inhibitory neuroprocesses. Notably, the cultural differentiationof the brain appears to be influenced more by the cultural aspects of the environment inwhich individuals reside, rather than solely by their race or original culture [62]. However,
it is important to note that the involvement of genetic or gene–environment effects cannot
be completely discounted. In fact, genetic expression appears to be implicated in cultural
aspects, such as the acquisition of cultural norms [63].
Overall, the present findings suggest differential activation of the inhibitory network
in Eastern-collectivist and Western-individualist cultures. Our meta-analysis corroborates
the findings of the few studies conducted to compare inhibitory processes using a crosscultural design [62,64,65]. Namely, differential activations of brain regions embedded in theinhibitory network (i.e., the dACC in relation to prediction error [64] and the IFG [62,65])
have been reported. Therefore, previous neuroimaging data may support our hypothesis ofhigher engagement of the proactivity-related brain regions [29] during inhibitory tasks incollectivist cultures compared to individualist cultures. Interestingly, Telzer et al. [65] founda greater decline in inhibitory behavioral performance over time in Western participants
than in Eastern participants, an effect paralleled by increasing activation of the IFG among
the collectivist group, while this neural activation stayed low in the individualist group.
Most evidence of cultural differentiation in inhibitory behavioral performance comesfrom childhood studies, with children from collectivist cultures outperforming those fromindividualist ones in inhibition efficiency [6,14,66–70]. Regarding adults, if neural results
always point out a cultural differentiation, this is not always the case for behavioral data.
Namely, enhanced inhibitory performance for collectivism vs. individualism have beenfound in some studies [65,71], but not in others [62,64,72]. Different task designs or groupcompositions might represent confounding variables to be taken into careful consideration
while interpreting contrasting evidence from these behavioral findings. A dissociation
between neuroimaging and behavioral data does not come as a surprise, as behavioral
measures, especially when embedded in neuroimaging designs, might lack sensibility. In
fact, cultural differentiation in neurocognitive functions might occur even in the absence of
purely behavioral evidence thanks to the extra fine-grained level of analysis [62,73,74].
Some limitations should be acknowledged. First, to reach adequate statistical power,
we were forced to use broad inclusion criteria for studies examining subjects from Westernand Eastern culture countries. In addition, our current methodology did not enable us toeliminate certain potential confounding variables that require further examination in futurestudies. Specifically, two key factors that necessitate exploration in subsequent research
are the lack of measures allowing for the exploration of brain–behavior relationships, andthe requirement for a quantitative assessment of the cultural aspects pertaining to theparticipants included in the studies. Additionally, due to missing data, we were unable toretrieve education information from all of the studies. Finally, it is important to recognizethat our meta-analysis did not reveal some commonly observed subcortical brain areas,such as the subthalamic nucleus [75,76]. This limitation has been commonly observed in
fMRI studies, as demonstrated by Isherwood et al. [44].
Future studies should aim to utilize larger and more diverse samples, control for
more confounding variables, and incorporate culturally sensitive experimental designs to
elucidate the mechanisms underlying these cultural differences in inhibitory control andthe impact of cultural context on the development and manifestation of these processes.
Moreover, understanding how these cultural differences in inhibitory control influence
behavior and cognition can have practical implications, such as in educational and clinicalsettings. For instance, in the case of clinical settings, this knowledge may help to develop
customized interventions that align with the cultural context of patients, thereby increasing the likelihood of positive outcomes, enhancing therapeutic alliances, and improving
treatment adherence [77,78]. Furthermore, it can aid in identifying at-risk individuals andimplementing early interventions to prevent mental health problems.

Brain Sci. 2023, 13, 907

11 of 14

By better understanding the neural mechanisms associated with cultural differences,
we can gain insights into how culture influences cognition and behavior, and ultimately develop more effective interventions to address cultural disparities in health and well-being.
Supplementary Materials: The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/brainsci13060907/s1, Table S1: Characteristics of studies includedin the quantitative analysis; Table S2: Eastern–Western and Western-Eastern contrast analyses: ALE
meta-analysis computed from our study selection.
Author Contributions: Conceptualization, G.G., F.G. and M.P.V.; methodology, G.G., C.N., V.B. and
M.C.; software, G.G., C.N., V.B. and M.C.; formal analysis, G.G. and C.N.; data curation, G.G., C.N.,
V.B. and M.C.; writing—original draft preparation, G.G. and C.N.; writing—review and editing, F.G.,
R.C., M.M. and M.P.V.; supervision, F.G., M.M. and M.P.V. All authors have read and agreed to the
published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable. This meta-analysis uses data from publishedpapers.
Informed Consent Statement: Not applicable.
Data Availability Statement: Data are available from the corresponding author upon request.
Acknowledgments: We thank Davide Mariucci for his support in the database management.
Conflicts of Interest: The authors declare no conflict of interest.

References
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.

16.

Kennedy, D.P.; Adolphs, R. The social brain in psychiatric and neurological disorders. Trends Cogn. Sci. 2012, 16, 559–572.
[CrossRef] [PubMed]
Stanley, D.A.; Adolphs, R. Toward a neural basis for social behavior. Neuron 2013, 80, 816–826. [CrossRef] [PubMed]
Markus, H.R.; Kitayama, S. Culture and the self: Implications for cognition, emotion, and motivation. Psychol. Rev. 1991, 98,
224–253. [CrossRef]
Nisbett, R.E.; Peng, K.; Choi, I.; Norenzayan, A. Culture and systems of thought: Holistic versus analytic cognition. Psychol. Rev.
2001, 108, 291–310. [CrossRef]
Matsumoto, D.; Yoo, S.H.; Hirayama, S.; Petrova, G. Development and Validation of a Measure of Display Rule Knowledge: The
Display Rule Assessment Inventory. Emotion 2005, 5, 23–40. [CrossRef]
Oh, S.; Lewis, C. Korean Preschoolers’ Advanced Inhibitory Control and Its Relation to Other Executive Skills and Mental State
Understanding. Child Dev. 2008, 79, 80–99. [CrossRef]
Na, J.; Grossmann, I.; Varnum, M.E.W.; Kitayama, S.; Gonzalez, R.; Nisbett, R.E. Cultural differences are not always reducible toindividual differences. Proc. Natl. Acad. Sci. USA 2010, 107, 6192–6197. [CrossRef]
Fujita, K. On Conceptualizing Self-Control as More than the Effortful Inhibition of Impulses. Pers. Soc. Psychol. Rev. 2011, 15,352–366. [CrossRef]
Li, J.-B.; Vazsonyi, A.T.; Dou, K. Is Individualism-Collectivism Associated with Self-Control? Evidence from Chinese and U.S.
Samples. PLoS ONE 2018, 13, e0208541. [CrossRef]
Zhao, X.; Wente, A.; Flecha, M.F.; Galvan, D.S.; Gopnik, A.; Kushnir, T. Culture Moderates the Relationship between Self-Control
Ability and Free Will Beliefs in Childhood. Cognition 2021, 210, 104609. [CrossRef]
Wang, J.; Mao, S. Culture and the Kindergarten Curriculum in the People’s Republic of China. Early Child Dev. Care 1996, 123,
143–156. [CrossRef]
Chao, R.; Tseng, V. Parenting of Asians. In Handbook of Parenting: Vol. 4: Social Conditions and Applied Parenting, 2nd ed.; Bornstein,M.H., Ed.; Lawrence Erlbaum Associates Publishers: Mahwah, NJ, USA, 2002; pp. 59–93.
Kwon, Y.-I. Western influences in Korean preschool education. Int. Educ. J. 2002, 3, 153–164.
Sabbagh, M.A.; Xu, F.; Carlson, S.M.; Moses, L.J.; Lee, K. The Development of Executive Functioning and Theory of Mind: A
Comparison of Chinese and U.S. Preschoolers. Psychol. Sci. 2006, 17, 74–81. [CrossRef]
Watanabe, J.; Sugiura, M.; Sato, K.; Sato, Y.; Maeda, Y.; Matsue, Y.; Fukuda, H.; Kawashima, R. The human prefrontal andparietal association cortices are involved in NO-GO performances: An event-related fMRI study. Neuroimage 2002, 17, 1207–1216.
[CrossRef]
Wager, T.D.; Sylvester, C.Y.C.; Lacey, S.C.; Nee, D.E.; Franklin, M.; Jonides, J. Common and unique components of response
inhibition revealed by fMRI. Neuroimage 2005, 27, 323–340. [CrossRef]

Brain Sci. 2023, 13, 907

17.

18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.

37.
38.
39.
40.

41.
42.
43.
44.

12 of 14

Ko, C.-H.; Hsieh, T.-J.; Chen, C.-Y.; Yen, C.-F.; Chen, C.-S.; Yen, J.-Y.; Wang, P.-W.; Liu, G.-C. Altered brain activation during
response inhibition and error processing in subjects with Internet gaming disorder: A functional magnetic imaging study. Eur.
Arch. Psychiatry Clin. Neurosci. 2014, 264, 661–672. [CrossRef]
Köhler, S.; Schumann, A.; de la Cruz, F.; Wagner, G.; Bär, K.-J. Towards response success prediction: An integrative approach
using high-resolution fMRI and autonomic indices. Neuropsychologia 2018, 119, 182–190. [CrossRef]
Zhao, R.; Zhang, X.; Fei, N.; Zhu, Y.; Sun, J.; Liu, P.; Yang, X.; Qin, W. Decreased cortical and subcortical response to inhibition
control after sleep deprivation. Brain Imaging Behav. 2019, 13, 638–650. [CrossRef]
Benedetti, V.; Gavazzi, G.; Giovannelli, F.; Bravi, R.; Giganti, F.; Minciacchi, D.; Mascalchi, M.; Cincotta, M.; Viggiano, M.P. Mouse
Tracking to Explore Motor Inhibition Processes in Go/No-Go and Stop Signal Tasks. Brain Sci. 2020, 10, 464. [CrossRef]
Raud, L.; Westerhausen, R.; Dooley, N.; Huster, R.J. Differences in Unity: The Go/No-Go and Stop Signal Tasks Rely on Different
Mechanisms. Neuroimage 2020, 210, 116582. [CrossRef]
Aron, A.R. From Reactive to Proactive and Selective Control: Developing a Richer Model for Stopping Inappropriate Responses.
Biol. Psychiatry 2011, 69, e55–e68. [CrossRef] [PubMed]
Criaud, M.; Wardak, C.; Ben Hamed, S.; Ballanger, B.; Boulinguez, P. Proactive Inhibitory Control of Response as the Default Stateof Executive Control. Front. Psychol. 2012, 3, 59. [CrossRef] [PubMed]
Singh, J.P.; Kar, B.R. Effect of Language Proficiency on Proactive Occulo-Motor Control among Bilinguals. PLoS ONE 2018, 13,e0207904. [CrossRef] [PubMed]
Cunillera, T.; Fuentemilla, L.; Brignani, D.; Cucurell, D.; Miniussi, C. A Simultaneous Modulation of Reactive and Proactive
Inhibition Processes by Anodal TDCS on the Right Inferior Frontal Cortex. PLoS ONE 2014, 9, e113537. [CrossRef]
Li, C.-S.R.; Yan, P.; Sinha, R.; Lee, T.-W. Subcortical Processes of Motor Response Inhibition during a Stop Signal Task. Neuroimage
2008, 41, 1352–1363. [CrossRef]
Picazio, S.; Ponzo, V.; Caltagirone, C.; Brusa, L.; Koch, G. Dysfunctional Inhibitory Control in Parkinson’s Disease Patients with
Levodopa-Induced Dyskinesias. J. Neurol. 2018, 265, 2088–2096. [CrossRef]
Wang, C.-H.; Chang, C.-C.; Liang, Y.-M.; Shih, C.-M.; Chiu, W.-S.; Tseng, P.; Hung, D.L.; Tzeng, O.J.L.; Muggleton, N.G.; Juan,
C.-H. Open vs. Closed Skill Sports and the Modulation of Inhibitory Control. PLoS ONE 2013, 8, e55773. [CrossRef]
Gavazzi, G.; Giovannelli, F.; Currò, T.; Mascalchi, M.; Viggiano, M.P. Contiguity of proactive and reactive inhibitory brain areas:
A cognitive model based on ALE meta-analyses. Brain Imaging Behav. 2021, 15, 2199–2214. [CrossRef]
Guo, Y.; Schmitz, T.W.; Mur, M.; Ferreira, C.S.; Anderson, M.C. A Supramodal Role of the Basal Ganglia in Memory and Motor
Inhibition: Meta-Analytic Evidence. Neuropsychologia 2018, 108, 117–134. [CrossRef]
Knox, P.C.; Wolohan, F.D.A. Cultural Diversity and Saccade Similarities: Culture Does Not Explain Saccade Latency Differences
between Chinese and Caucasian Participants. PLoS ONE 2014, 9, e94424. [CrossRef]
Cabello, R.; Gutiérrez-Cobo, M.J.; Fernández-Berrocal, P. Parental Education and Aggressive Behavior in Children: A ModeratedMediation Model for Inhibitory Control and Gender. Front. Psychol. 2017, 8, 1181. [CrossRef]
Grant, D.A.; Berg, E. A behavioral analysis of degree of reinforcement and ease of shifting to new responses in a Weigl-typecard-sorting problem. J. Exp. Psychol. 1948, 38, 404–411. [CrossRef]
Coffey, D.; Marmol, L.; Schock, L.; Adams, W. The influence of acculturation on the Wisconsin Card Sorting Test by Mexican
Americans. Arch. Clin. Neuropsychol. 2005, 20, 795–803. [CrossRef]
Razani, J.; Burciaga, J.; Madore, M.; Wong, J. Effects of acculturation on tests of attention and information processing in an
ethnically diverse group. Arch. Clin. Neuropsychol. 2007, 22, 333–341. [CrossRef]
Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.;
Brennan, S.E.; et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ 2021, 372, n71.
[CrossRef]
Borenstein, M.; Hedges, L.V.; Higgins, J.P.T.; Rothstein, H.R. Introduction to Meta-Analysis; John Wiley & Sons, Ltd.: Hoboken, NJ,
USA, 2009. [CrossRef]
Turkeltaub, P.E.; Eden, G.F.; Jones, K.M.; Zeffiro, T.A. Meta-Analysis of the Functional Neuroanatomy of Single-Word Reading:Method and Validation. Neuroimage 2002, 16, 765–780. [CrossRef]
Turkeltaub, P.E.; Eickhoff, S.B.; Laird, A.R.; Fox, M.; Wiener, M.; Fox, P. Minimizing within-experiment and within-group effects
in activation likelihood estimation meta-analyses. Hum. Brain Mapp. 2012, 33, 1–13. [CrossRef]
Laird, A.R.; Fox, P.M.; Price, C.J.; Glahn, D.C.; Uecker, A.M.; Lancaster, J.L.; Turkeltaub, P.E.; Kochunov, P.; Fox, P.T. ALE
meta-analysis: Controlling the false discovery rate and performing statistical contrasts. Hum. Brain Mapp. 2005, 25, 155–164.
[CrossRef]
Eickhoff, S.B.; Bzdok, D.; Laird, A.R.; Kurth, F.; Fox, P.T. Activation likelihood estimation meta-analysis revisited. Neuroimage
2012, 59, 2349–2361. [CrossRef]
Talairach, J.; Tournoux, P.; Rayport, M. Co-Planar Stereotaxic Atlas of the Human Brain: 3-Dimensional Proportional System: An
Approach to Cerebral Imaging (Reprint); Stuttgart: Thieme, Germany, 1997.
Simmonds, D.J.; Pekar, J.J.; Mostofsky, S.H. Meta-analysis of Go/No-go tasks demonstrating that fMRI activation associated withresponse inhibition is task-dependent. Neuropsychologia 2008, 46, 224–232. [CrossRef]
Isherwood, S.J.S.; Keuken, M.C.; Bazin, P.L.; Forstmann, B.U. Cortical and subcortical contributions to interference resolution andinhibition—An fMRI ALE meta-analysis. Neurosci. Biobehav. Rev. 2021, 129, 245–260. [CrossRef] [PubMed]

Brain Sci. 2023, 13, 907

45.
46.

47.

48.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61.
62.
63.
64.
65.
66.
67.

68.
69.
70.
71.
72.

13 of 14

Aron, A.R.; Fletcher, P.C.; Bullmore, E.T.; Sahakian, B.J.; Robbins, T.W. Stop-signal inhibition disrupted by damage to right inferior
frontal gyrus in humans. Nat. Neurosci. 2003, 6, 115–116. [CrossRef] [PubMed]
Gavazzi, G.; Lenge, M.; Bartolini, E.; Bianchi, A.; Agovi, H.; Mugnai, F.; Guerrini, R.; Giordano, F.; Viggiano, M.P.; Mascalchi, M.
Left inferior frontal cortex can compensate the inhibitory functions of right inferior frontal cortex and pre-supplementary motor
area. J. Neuropsychol. 2019, 13, 503–508. [CrossRef] [PubMed]
Gavazzi, G.; Orsolini, S.; Salvadori, E.; Bianchi, A.; Rossi, A.; Donnini, I.; Rinnoci, V.; Pescini, F.; Diciotti, S.; Viggiano, M.P.;
et al. Functional Magnetic Resonance Imaging of Inhibitory Control Reveals Decreased Blood Oxygen Level Dependent Effectin Cerebral Autosomal Dominant Arteriopathy With Subcortical Infarcts and Leukoencephalopathy. Stroke 2019, 50, 69–75.
[CrossRef]
Gavazzi, G.; Rossi, A.; Orsolini, S.; Diciotti, S.; Giovannelli, F.; Salvadori, E.; Pantoni, L.; Mascalchi, M.; Viggiano, M.P. Impulsivity
trait and proactive cognitive control: An FMRI study. Eur. J. Neurosci. 2019, 49, 1171–1179. [CrossRef]
Apšvalka, D.; Ferreira, C.S.; Schmitz, T.W.; Rowe, J.B.; Anderson, M.C. Dynamic targeting enables domain-general inhibitory
control over action and thought by the prefrontal cortex. Nat. Commun. 2022, 13, 274. [CrossRef]
Choo, Y.; Matzke, D.; Bowren, M.D.; Tranel, D.; Wessel, J.R. Right inferior frontal gyrus damage is associated with impaired
initiation of inhibitory control, but not its implementation. eLife 2022, 11, e79667. [CrossRef]
Braver, T.S. The variable nature of cognitive control: A dual mechanisms framework. Trends Cogn. Sci. 2012, 16, 106–113.
[CrossRef]
Meyer, H.C.; Bucci, D.J. Neural and behavioral mechanisms of proactive and reactive inhibition. Learn. Mem. 2016, 23, 504–514.
[CrossRef]
Botvinick, M.M.; Braver, T.S.; Barch, D.M.; Carter, C.S.; Cohen, J.D. Conflict monitoring and cognitive control. Psychol. Rev. 2001,
108, 624–652. [CrossRef]
Kerns, J.G.; Cohen, J.D.; MacDonald, A.W.; Cho, R.Y.; Stenger, V.A.; Carter, C.S. Anterior Cingulate Conflict Monitoring andAdjustments in Control. Science 2004, 303, 1023–1026. [CrossRef]
Bari, A.; Robbins, T.W. Inhibition and impulsivity: Behavioral and neural basis of response control. Prog. Neurobiol. 2013, 108,
44–79. [CrossRef]
Han, S.; Ma, Y. Cultural differences in human brain activity: A quantitative meta-analysis. Neuroimage 2014, 99, 293–300.
[CrossRef]
Pan, N.; Wang, S.; Zhao, Y.; Lai, H.; Qin, K.; Li, J.; Biswal, B.B.; Sweeney, J.A.; Gong, Q. Brain Gray Matter Structures Associatedwith Trait Impulsivity: A Systematic Review and Voxel-Based Meta-Analysis. Hum. Brain Mapp. 2021, 42, 2214–2235. [CrossRef]
Mancini, C.; Mirabella, G. Handedness Does Not Impact Inhibitory Control, but Movement Execution and Reactive Inhibition
Are More under a Left-Hemisphere Control. Symmetry 2021, 13, 1602. [CrossRef]
Swick, D.; Ashley, V.; Turken, A.U. Left Inferior Frontal Gyrus Is Critical for Response Inhibition. BMC Neurosci. 2008, 9, 102.
[CrossRef]
Mirabella, G. Inhibitory Control and Impulsive Responses in Neurodevelopmental Disorders. Dev. Med. Child Neurol. 2021, 63,
520–526. [CrossRef]
Malinowska, J.K. Cultural Neuroscience and the Category of Race: The Case of the Other-Race Effect. Synthese 2016, 193,
3865–3887. [CrossRef]
Pornpattananangkul, N.; Hariri, A.R.; Harada, T.; Mano, Y.; Komeda, H.; Parrish, T.B.; Sadato, N.; Iidaka, T.; Chiao, J.Y. Cultural
influences on neural basis of inhibitory control. Neuroimage 2016, 139, 114–126. [CrossRef]
Kitayama, S.; King, A.; Yoon, C.; Tompson, S.; Huff, S.; Liberzon, I. The Dopamine D4 Receptor Gene (DRD4) Moderates Cultural
Difference in Independent versus Interdependent Social Orientation. Psychol. Sci. 2014, 25, 1169–1177. [CrossRef]
Li, C.S.; Zhornitsky, S.; Ide, J.S. Cultural differences in anterior cingulate cortical response to prediction error. Cult. Brain 2019, 7,
67–79. [CrossRef]
Telzer, E.H.; Qu, Y.; Lin, L.C. Neural processes underlying cultural differences in cognitive persistence. Neuroimage 2017, 156,
224–231. [CrossRef] [PubMed]
Chen, X.; Hastings, P.D.; Rubin, K.H.; Chen, H.; Cen, G.; Stewart, S.L. Child-rearing attitudes and behavioral inhibition in Chineseand Canadian toddlers: A cross-cultural study. Dev. Psychol. 1998, 34, 677–686. [CrossRef] [PubMed]
Lan, X.; Legare, C.H.; Ponitz, C.C.; Li, S.; Morrison, F.J. Investigating the links between the subcomponents of executive function
and academic achievement: A cross-cultural analysis of Chinese and American preschoolers. J. Exp. Child Psychol. 2011, 108,
677–692. [CrossRef]
Ng, F.F.Y.; Tamis-LeMonda, C.; Yoshikawa, H.; Sze, I.N.L. Inhibitory control in preschool predicts early math skills in first grade:
Evidence from an ethnically diverse sample. Int. J. Behav. Dev. 2015, 39, 139–149. [CrossRef]
Tran, C.D.; Arredondo, M.M.; Yoshida, H. Early executive function: The influence of culture and bilingualism. Biling. Lang. Cogn.
2019, 22, 714–732. [CrossRef]
Doan, S.N.; Song, Q. Culture, emotion socialization and children’s inhibitory control. J. Cogn. Dev. 2022, 23, 591–607. [CrossRef]
Tae, J.; Nam, Y.E.; Lee, Y.; Weldon, R.B.; Sohn, M.H. Neutral but not in the middle: Cross-cultural comparisons of negative bias of“neutral” emotional stimuli. Cogn. Emot. 2020, 34, 1171–1182. [CrossRef]
Ellefson, M.R.; Ng, F.F.Y.; Wang, Q.; Hughes, C. Efficiency of executive function: A two-generation cross-cultural comparison ofsamples from Hong Kong and the United Kingdom. Psychol. Sci. 2017, 28, 555–566. [CrossRef]

Brain Sci. 2023, 13, 907

73.
74.
75.
76.
77.
78.

14 of 14

Chiao, J.Y.; Harada, T.; Komeda, H.; Li, Z.; Mano, Y.; Saito, D.; Parrish, T.B.; Sadato, N.; Iidaka, T. Dynamic cultural influences onneural representations of the self. J. Cogn. Neurosci. 2010, 22, 5155. [CrossRef]
Lahat, A.; Todd, R.; Mahy, C.E.; Lau, K.; Zelazo, P.D. Neurophysiological correlates of executive function: A comparison ofEuropean-Canadian and Chinese-Canadian 5-year-olds. Front. Hum. Neurosci. 2010, 3, 72. [CrossRef]
Mancini, C.; Modugno, N.; Santilli, M.; Pavone, L.; Grillea, G.; Morace, R.; Mirabella, G. Unilateral Stimulation of Subthalamic
Nucleus Does Not Affect Inhibitory Control. Front. Neurol. 2018, 9, 1149. [CrossRef]
Mirabella, G.; Iaconelli, S.; Romanelli, P.; Modugno, N.; Lena, F.; Manfredi, M.; Cantore, G. Deep Brain Stimulation of Subthalamic
Nuclei Affects Arm Response Inhibition in Parkinson’s Patients. Cereb. Cortex 2012, 22, 1124–1132. [CrossRef]
Hall, G.C. Psychotherapy Research with Ethnic Minorities: Empirical, Ethical, and Conceptual Issues. J. Consult. Clin. Psychol.
2001, 69, 502–510. [CrossRef]
Sue, S.; Zane, N.; Nagayama Hall, G.C.; Berger, L.K. The Case for Cultural Competency in Psychotherapeutic Interventions. Annu.
Rev. Psychol. 2009, 60, 525–548. [CrossRef]

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury topeople or property resulting from any ideas, methods, instructions or products referred to in the content.