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Aims and objectives/purpose/research questions We characterized the impact of several bilingualism-related factors on the executive control of Spanish-Catalan bilinguals. Design/methodology/approach Participants self-reported information regarding their age of acquisition, second language proficiency and frequency of natural language switching, and performed non-linguistic tasks tapping into specific executive control subcomponents, including inhibition, switching and updating. Data and analysis Data were analyzed by means of a structural equation model (SEM) approach. Findings/conclusions Results revealed that the frequency of natural language switching positively modulated the executive control performance of Spanish-Catalan bilinguals, while neither age of acquisition nor second language proficiency had an effect. Moreover, we found that the impact of natural language switching exerted general-processing influences, affecting all subcomponents of executive control. Findings are discussed in relation to context-specific effects on the cognitive system of a particular bilingual population. Originality The current study applied an SEM approach to provide new evidence on the previously ambiguous relation between bilingualism-related factors and executive control. Significance/implications Our findings suggest that the frequency of natural language switching does globally influence the executive control of Spanish-Catalan bilinguals.
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International Journal of Bilingualism
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DOI: 10.1177/1367006920902525
Latent variable evidence
on the interplay between
language switching frequency
and executive control in
Spanish-Catalan bilinguals
Victor A. Sanchez-Azanza ,
Raúl López-Penadés, Eva Aguilar-Mediavilla
and Daniel Adrover-Roig
Department of Applied Pedagogy and Educational Psychology, Universitat de les Illes Balears, Spain
Aims and objectives/purpose/research questions: We characterized the impact of several
bilingualism-related factors on the executive control of Spanish-Catalan bilinguals.
Design/methodology/approach: Participants self-reported information regarding their age
of acquisition, second language proficiency and frequency of natural language switching, and
performed non-linguistic tasks tapping into specific executive control subcomponents, including
inhibition, switching and updating.
Data and analysis: Data were analyzed by means of a structural equation model (SEM)
Findings/conclusions: Results revealed that the frequency of natural language switching
positively modulated the executive control performance of Spanish-Catalan bilinguals, while
neither age of acquisition nor second language proficiency had an effect. Moreover, we found
that the impact of natural language switching exerted general-processing influences, affecting
all subcomponents of executive control. Findings are discussed in relation to context-specific
effects on the cognitive system of a particular bilingual population.
Originality: The current study applied an SEM approach to provide new evidence on the
previously ambiguous relation between bilingualism-related factors and executive control.
Significance/implications: Our findings suggest that the frequency of natural language switching
does globally influence the executive control of Spanish-Catalan bilinguals.
Executive control, structural equation modeling, bilingualism, language switching frequency,
inhibition, switching, updating
Corresponding author:
Victor A. Sanchez-Azanza, Department of Applied Pedagogy and Educational Psychology, Universitat de les Illes Balears,
Edifici Guillem Cifre de Colonya, Cra. de Valldemossa km 7.5, Palma, Illes Balears 07122, Spain.
902525IJB0010.1177/1367006920902525International Journal of BilingualismSanchez-Azanza et al.
Original Article
2 International Journal of Bilingualism 00(0)
Executive control (EC) is a domain-general group of superior cognitive processes that are required
in the face of changing situations to adjust goal-driven behaviors (Diamond, 2013; Miyake &
Friedman, 2012). Typically, the study of EC mechanisms has focused on three subcomponents:
the ability to resist interference of competing information (i.e., inhibition), shifting between tasks
or mental sets of information (i.e., switching), and maintaining relevant information in working
memory, replacing this information when it is no longer relevant (i.e., updating; Miyake et al.,
2000; Miyake & Friedman, 2012). Since bilinguals have to control and adapt repeatedly their
languages to varying contextual demands on a daily basis, bilingualism is thought to enhance EC
(for a review, see Bialystok, 2017). Theoretically, these benefits are assumed to be a consequence
of the lifelong experience bilinguals have in managing two competing languages, which are
simultaneously active and lead to cross-language interactions (Kroll et al., 2014), even when the
language not in use is not explicitly presented in a given situation (Wu & Thierry, 2010). In order
to resolve the conflict between the relevant and non-relevant language in a bilingual conversation,
bilinguals have to deliberately inhibit the non-target language by recruiting both language and
executive control resources (Abutalebi & Green, 2008; Borragan et al., 2018). Moreover, bilin-
guals also have to update the contextual linguistic cues in a conversation and switch between
languages to adjust the language they consider appropriate to speak according to their interlocu-
tors (Abutalebi et al., 2012). Furthermore, both the linguistic and the general processing mecha-
nisms (i.e., EC) seem to show a partial functional overlap in several brain regions (Abutalebi &
Green, 2008; Declerck et al., 2017). In this vein, it has been proposed that the core neuroanatomi-
cal substrates of the EC subcomponents lie mainly within the prefrontal cortex and extend towards
other regions, such as midline, subcortical and parietal brain areas (Friedman & Miyake, 2017;
Niendam et al., 2012). For example, the inferior frontal gyrus belonging to the frontal lobe has
been neuroanatomically related to both inhibition and switching (Brass et al., 2005), but also to
language control (Coderre et al., 2016; de Bruin et al., 2014). In addition, the supramarginal gyrus
in the parietal lobe has been shown to be engaged in both the switching subcomponent of EC
(Lemire-Rodger et al., 2019) and language switching (Abutalebi & Green, 2008; Reverberi et al.,
2018). Moreover, the caudate nucleus, a subcortical region, seems to contribute to the executive
inhibitory and switching performance (Heyder et al., 2004), while participating in language
switching as well (Hervais-Adelman et al., 2015). Finally, the anterior cingulate cortex seems to
be involved in tasks requiring EC in the form of inhibition and updating (Bomyea et al., 2018), as
well as in bilingual’s language control (Coderre et al., 2016; Seo et al., 2018). For a comprehen-
sive review regarding the overlap of EC and language control brain regions, see Wu et al. (2019).
Hence, it has been proposed that this linguistic-specific practice exerted by bilinguals when man-
aging their two languages transfers to non-verbal general-domain abilities through neuroplasticity
(Bialystok et al., 2012; Kroll & Chiarello, 2016; Mechelli et al., 2004).
Bilingualism, however, is not a categorical variable but an amalgam of dynamically related
dimensions that interact to shape every bilingual’s individual linguistic experience (Luk &
Bialystok, 2013). Thus, efforts have been made in the last years to explore how distinct aspects of
bilingualism might impact the EC abilities in this heterogeneous group (Bialystok, 2016). Among
others, research on the characterization of bilinguals has especially focused on bilingual linguistic
background factors (such as both age of second language acquisition and second language profi-
ciency; Luk et al., 2011; Tse & Altarriba, 2014) and usage factors (where the frequency of language
switching stands out; Soveri et al., 2011).
Regarding the bilingual linguistic background factors, the age of second language acquisition
(AoA) has been suggested to influence EC, in such a way that the earlier the second language is
Sanchez-Azanza et al. 3
acquired, the more pronounced would be the effects of bilingualism on general-domain abilities
(Garbin et al., 2011; Kapa & Colombo, 2013; Luk et al., 2011; Soveri et al., 2011; Tao et al.,
2011; Yow & Li, 2015). The rationale behind this relation lies in the amount of practice early
bilinguals have in exercising language control, which is assumed to be larger than in the case of
late bilinguals (Luk et al., 2011). Complementarily and closely related, the influence of the sec-
ond language proficiency (PL2) on EC has also drawn a considerable amount of attention on this
topic. It has been hypothesized that bilinguals who are more proficient in their second language
should find the inhibition of the non-target language more effortful, thus showing more EC ben-
efits as a consequence of this exercise (Blom et al., 2014). In this vein, several studies have
shown that bilinguals with higher PL2 evidence enhanced EC, as compared to participants with
a lower PL2. Specifically, these associations have been found in abilities such as lexical fluency
(related to EC in verbal production; Friesen et al., 2015; Luo et al., 2010), inhibition (Bialystok
& Feng, 2009; Fernandez et al., 2013; Iluz-Cohen & Armon-Lotem, 2013; Sabourin & Vinerte,
2015; Tse & Altarriba, 2012, 2014; Zied et al., 2004), switching (Green & Abutalebi, 2013; Iluz-
Cohen & Armon-Lotem, 2013; Tse & Arriba, 2015) and updating (Blom et al., 2014; Tse &
Altarriba, 2014).
Concerning bilingual language usage, several authors have proposed that a crucial (if not the
most important) factor for developing more robust influences of bilingualism on EC is the lan-
guage switching frequency (LSF) (Prior & Gollan, 2011; Verreyt et al., 2016). Although the
underlying mechanisms of this link are still unclear (Paap et al., 2017), it has been suggested
that the effect of language switching may be due to the functional (Prior & Gollan, 2011) and
neuroanatomical (De Baene et al., 2015) overlap with the non-verbal task switching. Moreover,
the adaptive control hypothesis poses that bilinguals’ training gains on EC might depend on the
interactional context in which bilinguals are immersed (Green & Abutalebi, 2013). Hence, it is
expected that those bilinguals who are used to switch languages more often in similar contexts
(i.e., dual-language context) experience larger effects on language switching and EC, since
demands to their language control are greater than those experienced by single-language con-
text bilinguals (Costa et al., 2009; Green & Abutalebi, 2013; Hartanto & Yang, 2016). In this
vein, several studies have found associations between switching languages more frequently and
better execution in different measures of non-verbal switching paradigms (Barbu et al., 2018;
Becker et al., 2016; de Bruin et al., 2015; Hartanto & Yang, 2016; Prior & Gollan, 2011; Soveri
et al., 2011). Furthermore, Verreyt et al. (2016) studied the effect of LSF on inhibitory control
abilities, showing that bilinguals who alternated between languages more often were more
likely to evidence smaller congruency effects in tasks tapping into interference suppression
(when PL2 was controlled for in the analyses). Similarly, Woumans et al. (2015) found a rela-
tion between more fluent switching and smaller Simon effects in terms of response times (RT).
Therefore, and in accordance with Costa et al. (2009), these results revealed that the practice
accrued by those bilinguals who switch languages more often seems to transfer to domain-
general mechanisms.
Nevertheless, recent reports regarding the relation between bilingualism and EC are hetero-
geneous and mixed (Sanchez-Azanza et al., 2017), contributing to the debate on the key factors
involving bilingualism-related effects on EC. For instance, there are also several studies show-
ing findings that are in conflict or nuance the literature presented in the two previous para-
graphs regarding the AoA (Vega-Mendoza et al., 2015), PL2 (Pelham & Abrams, 2014) and
LSF (Paap et al., 2017) factors. One of the possible reasons for this mixture of results might be
the divergent sample characteristics, in particular regarding the bilingual groups (Luk &
Bialystok, 2013; Woumans & Duyck, 2015). Thus, some authors have claimed for a better char-
acterization of bilingual samples in order to correctly differentiate between distinct outcomes
4 International Journal of Bilingualism 00(0)
and to accommodate discrepancies according to the extant divergence of results between studies
(Takahesu Tabori et al., 2018).
In this study, we aimed at characterizing the influences of several bilingualism-related factors’
on the EC in a large sample of Spanish-Catalan bilinguals. This objective was addressed by using
a structural equation model (SEM) approach. SEM is a statistical technique that combines factor
analysis and multiple regression allowing to examine and test the hypothesized simultaneous
covariation among latent variables (Morrison et al., 2017; Schreiber et al., 2006). In other words,
SEM enables researchers to inspect whether the interrelations among objectively measured varia-
bles, clustered in latent variables by their shared variance, behave like in a given specific theoreti-
cal model. Therefore, we proposed two competing models accounting for the influences of AoA,
PL2, and LSF on the EC of bilinguals, based on the previously commented studies (see Figure 1).
In particular, in a first model (Figure 1a) we evaluated if all the above-mentioned factors do con-
currently influence EC in this particular sample of bilinguals. In this vein, note that AoA and PL2
are both assumed to impact the functional and structural neuroplasticity of the bilingual’s brain
regions independently (Nichols & Joanisse, 2016). Furthermore, AoA seems to modulate several
factors related to the degree at which individuals achieve PL2 and the magnitude of the neuroplas-
ticity processes involved in its development (Birdsong, 2018). Additionally, it has been proposed
that LSF might be partially affected by the individual’s attained PL2 (Rodriguez-Fornells et al.,
2012). Hence, to provide an exhaustive theoretical framework, all the aforementioned relations
were introduced in the first model (Figure 1a). However, due to the sociolinguistic context of
Spanish-Catalan bilinguals, where both languages are extensively and interchangeably used, their
talkers are recurrently involved in bilingual conversations in which natural language switching is
frequent. In this vein, there is evidence that the bilingual linguistic background factors (i.e., AoA
and PL2) do not seem to have a significant effect on EC in bilingual samples who switch languages
frequently in dual-language contexts (Prior & Gollan, 2011; Verreyt et al., 2016), such as the one
Spanish-Catalan bilinguals are immersed in. Hence, we tested with the second model (Figure 1b)
whether LSF was the only latent variable positively influencing the EC. Therefore, and in relation
to the first and main aim of this study, it was hypothesized that the second model would better
characterize the influence of bilingualism on EC since the sample of the present study is immersed
in a dual-language context. Secondarily, we aimed at exploring whether the indirect influence of
the bilingualism-related factors had an impact on all subcomponents of EC (i.e., domain-general
executive processing involving inhibition, switching, and updating), or whether it was circum-
scribed to a particular subcomponent (e.g., inhibition or switching).
Figure 1. Graphical representation of the proposed theoretical models displaying the influence of the
bilingualism-related factors on the executive control of Spanish-Catalan bilinguals. The first model (a)
involves the effect of the bilingual linguistic background factors: age of acquisition (AoA) and second
language proficiency (PL2), along with the language switching frequency; while the second model (b)
comprises only the effect of the language switching frequency on executive control.
Sanchez-Azanza et al. 5
The sample of this study comprised 184 participants (146 females), all of them were Spanish-
Catalan bilingual university students recruited at the University of the Balearic Islands. Participants
were between 19 and 45 years old (M = 22.1 ± 5 years), and had between one and six years of uni-
versity education (M = 2.3 ± 1 years).
Moreover, all participants considered themselves as bilinguals, and several of them referred to
Catalan as their first language (L1; 62.5%) and Spanish as their L2; while the rest of the sample
indicated Spanish to be their L1 (37.5%) and Catalan their L2. Participants learned their second
language early in life (M = 3 ± 2 years) and were very proficient in both languages (ML1 = 11.2 ± 1;
ML2 = 9.8 ± 2; in a 12-graded scale), even though their languages were not balanced in terms of
self-reported proficiency, t(183) = –3.42, p = .001.
All participants answered two questionnaires and performed three computerized tasks. First, all
participants received on-line forms of self-reported questionnaires, having to answer questions
regarding demographic (i.e., age, gender and years of university studies) and linguistic background
information. Once all participants had answered the self-reported instruments, the computerized
session took place. The computerized session lasted about 60 minutes, in which three tasks were
applied: the flanker task, the feature-switching task, and the block-tapping task, all of them included
in the Psychology Experiment Building Language Test Battery (PEBL; Mueller & Piper, 2014).
The distance between the participant and the computer screen was approximately 60 cm.
All participants gave written informed consent before testing, had a normal or corrected-to-
normal vision and did not report to have a history of mental or neurological illness. At study com-
pletion, all participants received extra credit points in a psychology subject for their participation.
This study was conducted in accordance with the recommendations of the Committee on Research
Ethics of the Balearic Islands University.
Materials and measures
Bilingual linguistic background. Participants received a questionnaire in which they were asked about
linguistic background questions, including whether they considered themselves as bilinguals, the
age of acquisition of their L2 (AoA), which was their mother tongue (L1) and their second lan-
guage (L2), and proficiency in their two languages.
Overall scores for the proficiency on L2 (PL2) and L1 were calculated separately as the addition
of three items: oral and written expression, and oral comprehension proficiencies. Hence, overall
scores could range from 0 to 12, given that specific scores were obtained by means of five-point
Likert scales quantifying the competence in each of these language skills (0 = very poor, 1 = poor,
2 = intermediate, 3 = good, 4 = very good).
Regarding bilingual linguistic background information, no latent variable was specified. Two
bilingual linguistic background measures were used (only in Model 1, see the Model Specification
section for more details): AoA and PL2. While larger values of AoA reflected an older age acquir-
ing the L2, larger values on PL2 reflected better proficiency in this language.
Language switching frequency. In order to assess LS habits, participants answered the Spanish ver-
sion of the Bilingual Switching Questionnaire (BSWQ; Rodriguez-Fornells et al., 2012). This
6 International Journal of Bilingualism 00(0)
instrument provides scores on four scales involving three items each: tendencies to switch from L2
to L1 (named hereafter L1S), tendencies to switch from L1 to L2 (L2S), the tendency to switch
languages in specific situations or contexts (CS), and the tendency to switch languages unintend-
edly (US). Answers to each item of the BSWQ were given in a five-point Likert scale quantifying
the self-reported frequency of the behavior described on each question (1 = never, 2 = rarely,
3 = occasionally, 4 = frequently, 5 = always).
To specify the LSF latent variable, the four scales’ overall scores were used: L1S, L2S, CS, and
US. Higher values of each scale reflected more frequent language switching according to their
respective conditions (e.g., a high L2S score implied that a participant switched languages from L1
to L2 very often).
Inhibition. A modified version of the original flanker task (Eriksen & Eriksen, 1974) was used to
explore interference suppression of irrelevant information. Participants were instructed to indicate
the direction of the central arrow (target) of an array of five horizontally aligned stimuli (Figure 2a)
by pressing the appropriate keyboard button (with the left or the right index finger). The target was
always an arrow, while the four flanking distractors could be either arrows or horizontal lines.
According to the nature of the distractors, three conditions were presented: congruent (e.g.,
<<<<<), incongruent (e.g., >><>>) or neutral (e.g., –<–). The task comprised 120 rand-
omized trials, and the number of trials per condition was kept equivalent (40 trials each). Prior to
the actual test, participants performed 12 practice trials that were not analyzed. The trial sequence
started with a fixation cross displayed for 500 milliseconds (ms), then the target was presented in
the middle of a black screen until a response was given or up to a maximum of 800 ms. The inter-
trial-interval was 1000 ms. Response accuracy and RTs were recorded and averaged for each exper-
imental condition and participant.
To specify the Inhibition latent variable of EC, three different flanker effects were used: propor-
tional flanker costs for incongruent versus congruent trials and for incongruent versus neutral tri-
als, and the inverse efficiency score of the flanker effect. We used these RT corrected measures in
order to avoid shared variance that could be attributable to the speed of processing between these
measures and the ones used for the Switching latent variable (all of them RT-dependent).
Proportional flanker costs were computed as follows for incongruent–congruent proportional
flanker costs and incongruent–neutral proportional flanker costs, respectively: [(RTincongruent
RTcongruent)/ RTincongruent]2 and [(RTincongruent – RTneutral )/ RTincongruent]2 (de Bruin & Della Sala, 2018).
Moreover, the inverse efficiency score was computed by dividing the mean RT of correct trials for
both congruent and incongruent trials by the overall proportion of corrects (Pcor), separately for
each condition. Then, we subtracted the congruent inverse efficiency score to the incongruent one
to achieve the FIES measure, that is, flanker costs in terms of inverse efficiency scores: (RTincongruent
Figure 2. Graphical representation of the tasks administered, including (a) the Flanker task, (b) the
Feature-switching task and (c) the Block-tapping task. See text for more details regarding the specifics of
each task. “ITI” refers to the inter-trial interval, “RR” to response required and “ms” to milliseconds.
Sanchez-Azanza et al. 7
/(Pcor) – (RTcongruent /(Pcor) (Bruyer & Brysbaert, 2011). Note that we reversed these score’s values
(ρs original Flanker scores, reversed Flanker scores = –1) in order to provide a unified scale among EC latent
variables, in which larger values revealed a better performance in EC. Thus, larger incongruent–
congruent proportional flanker costs, incongruent–neutral proportional flanker costs and incongru-
ent–congruent flanker inverse efficiency score values (i.e., lesser interference or cost, reversed)
reflected better performance.
Switching. A computerized feature-switching task (Anderson et al., 2012) was used in order to
assess the ability to flexibly switch between tasks or mental sets. Participants were instructed to
match an object in accordance with a target feature that changed in every trial. On each of the 108
trials, 10 objects were presented on a black screen (Figure 2b). Each object was characterized as a
function of three features: shape (circle, plus, ellipse, square, and star), color (blue, green, orange,
red, and yellow), and the letter appearing inside (from A to Z); yet objects matched a single another
object in only one feature. At the beginning of each trial, a target feature was displayed at the top
of the screen, and participants had to search for the object matching that feature. Once participants
had correctly matched the object, a different target feature was specified. The task was divided into
nine blocks of 12 trials each. Object configuration was different for every block. For the first three
blocks, participants alternated between two of the three features (two-predictable features condi-
tion). For the next three blocks, participants switched between all three features in a consistent
order that differed for each block (three-predictable features condition). For the last three blocks,
target features were alternated randomly after each correct response, so that the next target feature
could not be anticipated prior to making the response (three-unpredictable features condition). The
number of errors and RT for each trial were recorded and averaged for each of the three experimen-
tal conditions.
To specify the Switching latent variable of EC, direct RT measures to the correct responses for
each condition of the feature-switching task were used: two-predictable features, three-predictable
features, and three-unpredictable features conditions. Note that we reversed these score’s values
(ρs original Feature-switching scores, reversed Feature-switching scores = –1) in order to provide a unified scale among the
EC latent variables, in which larger values revealed a better performance in EC. Thus, larger
two-predictable features, three-predictable features, and three-unpredictable features RT values
(i.e., the reversed values of the more rapid RTs while shifting) reflected better performance.
Updating. A computerized version of the Corsi’s (1973) block-tapping task (Croschere et al., 2012)
was used to assess the visuospatial updating of information. Participants were instructed to remem-
ber visual patterns of increasing length. On each trial, nine blue squares were presented on a black
screen (Figure 2c). Squares lit up (changed from blue to yellow; 1000 ms duration) one at a time in
a sequence. After the sequence was completed, participants were instructed to click on the squares
in the same order they lit up (forward). The task started with a sequence of two squares length. If
participants were able to correctly respond to two sequences of the same length, the following
sequence increased its length in one lit square. The task finished when the participant failed to cor-
rectly remember two consecutive sequences of the same length. Prior to the actual test, participants
performed three practice trials that were not analyzed. The length of the last correctly remembered
sequence and the number of correct trials were recorded.
To specify the Updating latent variable of EC, the block-tapping total score was modeled as a
single-indicator factor (Keith, 2014). The block-tapping total score was calculated as the product
of the length of the last correctly remembered sequence and the number of correct trials, and it was
computed for each participant. Note that this score was not reversed. Thus, larger block-tapping
total score values reflected better performance.
8 International Journal of Bilingualism 00(0)
Model specification and data analyses
Model specification. In order to address the relations among the target latent variables, we followed
an SEM approach. We proposed two theory-driven models differing in whether bilingual linguistic
background measures affected or not the frequency of either the LSF or EC. In both models LSF
influenced EC.
In particular, Model 1 (see Figure 1a) assumed that one (or both) of the bilingual linguistic
background factors (i.e., AoA and PL2) had an effect on EC. In this model, AoA is assumed to
influence PL2 (thus, they did not form a latent variable) and they both were assumed to influence,
or not, EC. Moreover, PL2 was expected to influence both LSF and EC, and LSF was hypothesized
to influence EC. On the other hand, Model 2 (see Figure 1b) involved only the relation between
LSF and EC. A summary of descriptive statistics for every measure used in the two models, along
with their respective categories, can be found in Table 1.
Data analyses. Since preliminary variable analyses revealed that none of the observed measures
were normally distributed (see Table 1), we opted to use the asymptotically distribution-free
(ADF; Mooijaart, 1985) procedure to estimate all models. Confirmatory factor analysis (CFA)
was first used to examine model fit for both LSF and EC latent variables. Standardized and
unstandardized path coefficients and significance values were obtained for the two hypothesized
Table 1. Descriptive statistics and normality tests for the bilingual linguistic background, language
switching and executive control measures (N = 184). All measures are in their original scale (none of the
Inhibition or Switching scores is reversed in this table).
Measures Descriptives Normality test
Mean SD Min Max Skewness K-S p
Bilingual linguistic background
AoA 3.033 2.293 1 10 1.19 0.207 <.0001
PL2 9.832 1.692 6 12 –0.44 0.141 <.0001
Language switching frequency (LSF)
L1S 5.951 1.844 3 10 0.09 0.128 <.0001
L2S 8.391 2.019 4 13 –0.07 0.131 <.0001
CS 6.419 2.126 3 13 0.43 0.127 <.0001
US 5.913 2.179 3 11 0.31 0.12 <.0001
FCIC 0.007 0.008 0 0.04 3.47 0.183 <.0001
FCIN 0.017 0.013 0 0.07 0.82 0.116 <.0001
FIES 57.393 49.607 –78.70 393.51 2.03 0.114 <.0001
FS2P 2241.921 347.968 1507.94 3710.42 0.92 0.116 <.0001
FS3P 2182.355 332.066 1412.61 3323.67 0.72 0.083 .004
FS3U 2207.777 309.564 1541.75 3276.69 0.59 0.075 .013
BTTS 59.168 20.812 0 106 0.57 0.18 <.0001
Note: AoA: age of L2 acquisition; BTTS: block-tapping total score; CS: contextual switch; FCIC: incongruent–congruent
proportional flanker costs; FCIN: incongruent–neutral proportional flanker costs; FIES: incongruent–congruent flanker
inverse efficiency score; FS2P: feature-switching two-predictable features RT; FS3P: feature-switching three-predictable
features RT; FS3U: feature-switching three-unpredictable features RT; K-S: Kolmogorov-Smirnov statistic; L1S: switch
from L2 to L1; L2S: switch from L1 to L2; PL2: L2 proficiency; US: unintended switch.
Sanchez-Azanza et al. 9
structural models. The SEM was specified to test direct and indirect associations among meas-
ures involving bilingual linguistic background (only in Model 1), LSF, and EC. Thus, 1000 ADF
bootstrapping samples were used to generate 95% bias-corrected confidence interval (CI) of
specific indirect effects, since this CI has shown to be the most accurate test of mediation in
SEM simulation studies (Cheung & Lau, 2007). Moreover, no imputation method was used
because there were no missing values.
In relation to the different types of effects mentioned before, direct effects reveal the unmedi-
ated influence of a variable on another given a proposed theory-driven model (MacKinnon, 2008).
On the other hand, indirect effects quantify the specific amount of the total influence on a variable
that is attributable to the preceding factor once the effect of the mediating variable is partialled out
(Preacher & Hayes, 2008). For example, EC mediates the relation between LSF and Inhibition in
Figure 1b. Hence, the indirect effect of LSF on Inhibition estimates the specific impact of LSF on
Inhibition controlling for the influence of EC on the latter. This procedure is conducted for every
variable that is mediated in a given model (following the example, in both Switching and Updating
as well), and is calculated using the parameters estimated on all paths connecting the relevant vari-
ables of the model for each indirect effect. In summary, the main goal of the present study (charac-
terizing the influence of bilingualism on EC) was focused on estimating direct effects on EC. The
secondary aim of the work was centered on the estimation of the indirect effects between the
bilingualism-related factors and the specific subcomponents of EC, assuming that the EC general
ability mediates that relation.
Regarding model fitting, that is, indices that reflect how well does a particular model fit the
data, chi-square (χ2) and the chi-square/degrees of freedom ratio (χ2/df), Comparative fit index
(CFI), Tucker-Lewis index (TLI), root mean square error of approximation (RMSEA), and Akaike
information criterion (AIC) were calculated. A model was considered to have a good fit when the
following indices were above or below their specified cut-off criteria (Schermelleh-Engel et al.,
2003; Schreiber et al., 2006). When χ2 was non–significant at the .05 level (i.e., p > .05) and when
χ2/df was less than two (values closer to 1 indicate better fit because of model parsimony). When
the RMSEA was below .05, including values smaller than .05 for the lower bound and smaller than
.08 for the upper bound of the confidence interval. Moreover, the p-value for the test of the close
fit (pclose) of the RMSEA was accepted as a good fit when its values were between .1 and 1. When
both CFI and TLI revealed values equal or above .95 (note that the latter can show values above 1).
Finally, a model was considered to have a better fit when its AIC value was smaller, as compared
to the AIC value of another model.
SPSS v22 statistical software was used for descriptive and correlational analyses. SPSS AMOS
v21 software was used for CFA and SEM estimation.
Correlations among all the measures used in this work are presented in Table 2. Prior to assessing
the hypothesized relations among variables in the two theoretical models, we examined by means
of two CFAs the LSF and EC latent variables. Regarding the LSF latent variable, inspection of fit
indices showed an overall adequate fit (with the exception of the TLI, for which the value fell
moderately below its cut-off criterion), χ2 (2 dfs) = 3.9, p = .145, CFI = .97, TLI = .89. Moreover, all
scales showed significant loadings on the LSF factor (ps < .022), ranging from .3 to .73. With
respect to the EC latent variable, fit indices inspection did also reveal a good fit, χ2 (12 dfs) = 7.1,
p = .852, CFI = 1, TLI = 1.08. Furthermore, all measures showed significant loadings on their
respective factors (ps < .001), ranging from .46 to .95. Thus, we used these latent variables in all
subsequent analyses.
10 International Journal of Bilingualism 00(0)
To address the hypothesis of the present work, both Model 1 and Model 2 were estimated. As
can be seen in Table 3, Model 1 showed a bad fit to the data according to the cut-off criteria. In
particular, χ2 was significant at the .05 level, RMSEA was above the .06 value, and both CFI and
TLI showed values below .95. Even though this model did not fit well, we report here the specific
relations between bilingual linguistic background variables and both LSF and EC for informative
purposes. AoA revealed a negative effect on PL2, β (standardized direct effect) = –.12, p = .022, and
PL2 showed a negative influence on LSF, β = –.38, p < .0001. These results might suggest that
participants who learned their L2 later were less proficient. Beyond, less proficient bilinguals
would be more prone to switch frequently between languages. Neither AoA nor PL2 had an effect
on EC (ps > .56). Nevertheless, we emphasize again that no conclusions should be drawn regard-
ing Model 1 because of its lack of fit. Furthermore, to ascertain whether Model 1 was suitably
selected, we also estimated two alternative and more parsimonious nested models. We specified a
nested model constraining to zero the path between AoA and EC, in which both LSF and PL2 (but
not AoA) influence EC; and another constraining to zero the path between PL2 and EC, in which
both LSF and AoA (but not PL2) influence EC. These models were similar in every other way to
the default nesting model (Model 1), except for the constraints mentioned, and showed no fit to the
data. As compared to the original Model 1, they revealed a similar fit (χ2sdiff < 0.351, dfsdiff = 1,
psdiff > .554), even though they were more parsimonious models. These outcomes confirm that
Model 1 was properly selected, and support the results reported here.
On the other hand, Model 2 did show a good fit to the data (see Table 3): its fit indices values
were appropriately above or below their respective cut-offs. Structural equation results for the
hypothesized Model 2 can be seen in Figure 3. Both the LSF and the EC latent variables are
depicted as ellipses, and the arrows linking latent variables among them, as well as the observable
measures, showed the direction and magnitude of direct standardized effects, which were all sig-
nificant (ps < .013).
Table 2. Spearman correlation matrix for the bilingual linguistic background, language switching and
executive control measures (N = 184).
AoA 1
PL2 –.274 1
L1S –.174 .08 1
L2S .177 –.337 .211 1
CS –.155 .05 .485 .187 1
US .08 –.190 .191 .199 .223 1
FCIC –.11 .11 .12 –.07 .11 .02 1
FCIN –.11 .184 .08 –.146 .07 –.04 .669 1
FIES –.11 .09 .01 –.09 .02 .01 .606 .437 1
FS2P .04 –.05 –.150 .04 –.172 –.07 –.177 –.13 –.197 1
FS3P .07 –.14 –.198 .06 –.10 –.07 –.225 –.199 –.232 .618 1
FS3U .01 –.13 –.14 .05 –.13 .00 –.299 –.200 –.212 .609 .680 1
BTTS –.05 .11 –.13 –.10 –.10 –.11 –.12 –.08 –.06 .167 .189 .159 1
Note: Bold cases indicate significant correlations (p < .05). AoA: age of L2 acquisition; BTTS: block-tapping total score;
CS: contextual switch; FCIC: incongruent–congruent proportional flanker costs; FCIN: incongruent–neutral proportion-
al flanker costs; FIES: incongruent–congruent flanker inverse efficiency score; FS2P: feature-switching two-predictable
features RT; FS3P: feature-switching three-predictable features RT; FS3U: feature-switching three-unpredictable features
RT; L1S: switch from L2 to L1; L2S: switch from L1 to L2; PL2: L2 proficiency; US: unintended switch.
Sanchez-Azanza et al. 11
Our primary interest regarding this model was in the direct influence of LSF on EC. In this
regard, Model 2 results showed that LSF had a positive significant effect on the EC latent variable,
β = .44, p = .013, R2 = .19. Besides, regarding the standardized coefficients of EC on its subcompo-
nents, all effects were significant. In particular, both the Switching subcomponent, β = .66, p = .006,
R2 = .44, and the Updating subcomponent, β = .87, p = .004, R2 = .76, did positively load on the EC
second-order latent variable. Surprisingly, the Inhibition subcomponent showed the opposite pat-
tern (see Figure 3), β = –.41, p = .003, R2 = .17, showing a negative relation with EC. Note that it
was hypothesized that all subcomponents should have shown a positive relation with EC since we
did unify the scale of every observed measure (i.e., larger values in all observed measures should
reveal a better performance in EC, as stated in the Methods section). However, this was not the case
with the Inhibition subcomponent. Close inspection of Table 2 revealed that all Inhibition-related
measures (incongruent–congruent proportional flanker costs, FCIC in the table; incongruent–neu-
tral proportional flanker costs, FCIN; and incongruent–congruent flanker inverse efficiency score,
FIES) showed a negative relation with both Switching-related measures (feature-switching two-
predictable features RT, FS2P; feature-switching three-predictable features RT, FS3P; and feature-
switching three-unpredictable features RT, FS3U) and the Updating measure (block-tapping total
score, BTTS). Moreover, the EC latent variable’s CFA did also show that this pattern of negative
Table 3. Fit indices for the two models estimated.
Statistic pdf χ2/df Statistic ± 90%CI pclose
Model 1 (with BLB) 114.6 <.0001 60 1.91 .071 .051 – .09 .045 .847 0.802 176.63
Model 2 (without BLB) 36.99 .649 41 0.902 0 0 – .043 .979 1 1.018 86.99
Note: AIC: Akaike information criterion; BLB: bilingual linguistic background; CFI: Comparative fit index; CI: confidence
interval; df: degrees of freedom; pclose: p of close fit; TLI: Tucker-Lewis index; χ2: chi-square.
Figure 3. Structural equation results of Model 2 (without the bilingual linguistic background variables), in
which the language switching frequency (LSF) positively influences the executive control (EC) of bilingual
individuals. Ellipses indicate latent variables and rectangles show observable measures. Numbers on arrows
represent the standardized direct effects (β), all of them significant at the .05 level. Note. L1S: switch from
L2 to L1; L2S: switch from L1 to L2; CS: contextual switch; US: unintended switch; FCIC: incongruent–
congruent proportional flanker costs; FCIN: incongruent–neutral proportional flanker costs; FIES:
incongruent–congruent flanker inverse efficiency score; FS2P: feature-switching two-predictable features
RT; FS3P: feature-switching three-predictable features RT; FS3U: feature-switching three-unpredictable
features RT; BTTS: block-tapping total score.
12 International Journal of Bilingualism 00(0)
correlations was replicated at the latent level, corroborating that this result was not an artifact or a
mistake when estimating the model.
Furthermore, we also aimed at identifying the specific effects of LSF (since this is the only
model that fitted the data) on the different EC subcomponents (i.e., Inhibition, Switching, and
Updating), an aspect that was evaluated by means of the indirect effects. Standardized indirect
effect coefficients (βi) of LSF on the EC subcomponents were all significant (ps < .032), indicat-
ing that LSF had an indirect effect on every specific subcomponent through the mediating EC
latent variable. In particular, LSF showed a negative indirect effect on Inhibition, βi = –.18, a posi-
tive indirect effect on Switching, βi =.29, and a positive indirect effect on the Updating subcom-
ponent, βi = .38.
Bilinguals are a heterogeneous group with diverse environmental influences that might play differ-
ent roles in the development of their cognitive processes. Thus, it seems likely that distinct contexts
(e.g., single versus dual-language contexts) lead to varied bilingual experiences and ways to cog-
nitively accommodate the practice exercised according to the particular dimensions of each context
(Green & Abutalebi, 2013). Hence, the present study sought to characterize by means of an SEM
approach the impact of several bilingualism-related factors previously described in the literature,
age of second language acquisition (AoA), second language proficiency (PL2), and language
switching frequency (LSF), on the executive control (EC) ability of Spanish-Catalan bilinguals
who are immersed in a dual-language bilingual context. Our results showed that the model that
included the bilingual linguistic background factors, such as AoA and PL2, along with the LSF
(i.e., Model 1), did not adequately fit the data. In contrast, Model 2, which was specified so that
LSF was the only factor hypothesized to influence EC, revealed a good model fit. Hence, in accord-
ance with our hypothesis and in line with previous literature (Costa et al., 2009; Hartanto & Yang,
2016; Prior & Gollan, 2011; Verreyt et al., 2016), the main finding of this study is that the only
bilingualism-related factor that seems to influence the EC of Spanish-Catalan bilinguals is the LSF,
regardless of AoA and PL2. However, this pattern of results is only likely when bilinguals have
achieved a high degree of PL2 (Barbu et al., 2018; Prior & Gollan, 2011), as it is the case of our
sample, which was composed by high-proficient bilinguals immersed in a dual-language context
in which the two languages are widely used and the communication language may change accord-
ing to the addressee. Furthermore, this finding partially endorses the hypothesis that bilingualism
in dual-language contexts is assumed to increase the demands on the EC (Green & Abutalebi,
2013; Henrard & Van Daele, 2017) because the EC adaptation appears to be driven to some extent
by the LSF in this specific interactional setting, as evidenced by our results.
Although this first main conclusion might be appealing to shed some light onto the varied
outcomes found in the research field investigating the relation between bilingualism and EC, it
might also lead to some misconceptions. Thus, we believe it is noteworthy to recommend caution
when integrating this outcome into the theoretical background and to comment on some specific
issues regarding our results. The bilingualism-related factor of LSF did only explain 19% of the
EC variance in the accepted model (Model 2), even when considering a sample of participants
immersed in the interactional context assumed to be the most demanding (Green & Abutalebi,
2013). Albeit this seems to be a considerable quantity of explained variance, it remains unclear
which other factors (bilingualism-related or not) might be influencing the EC of these individuals.
Regarding the bilingualism-related factors, there might be a plethora of covariates involved, such
as the similarity of languages (Oschwald et al., 2018) or the degree of bilingualism associated to
the linguistic context (e.g., bilingual mass media exposure; Costa et al., 2009). Besides, even
Sanchez-Azanza et al. 13
though neither AoA nor PL2 seem to influence the EC of Spanish-Catalan bilinguals, presumably
because of the scarce variability of these factors in this sample, it does not imply these aspects
might not exert a positive influence in different bilingual populations (e.g., Yow & Li, 2015). In
this vein, it is noteworthy to underline that the present conclusions are to be ascribable mainly to
bilinguals who acquired both languages very early, show a high level of proficiency in their sec-
ond language, and whose language usage develops and takes place in dual-language contexts, as
it is the case with Spanish-Catalan bilinguals. However, for other bilingual populations, the
impact of the bilingualism-related factors on EC might be different. This is why it is relevant to
provide a more exhaustive model that is capable of accounting for these influences (Model 1) and
can be used as a framework to compare particular bilingual populations in future research. The
fact that we have revealed with Model 2 which of the factors seems to be the most important in
the specific case of the Spanish-Catalan bilinguals does not imply that the outcomes of Model 1
are completely meaningless. In practice, these results are very informative because they can be
compared with those of other bilingual populations as long as the methodology used to test them
resembles the one employed in the present study.
Secondarily, we were also interested in exploring whether the effect of the bilingualism-related
factors, as mediated by a general executive processing ability, was restricted to any specific sub-
component of the EC, or whether the influences were domain-general. Our results support the
latter notion: LSF indirectly influenced all EC subcomponents (as mediated by a general execu-
tive processing mechanism). This outcome is in line with the available evidence showing modula-
tions on inhibition, switching and updating as a function of LSF (Costa et al., 2009; Prior &
Gollan, 2011; Verreyt et al., 2016), and with the expected increase in general cognitive demands
elicited by the dual-language context (Green & Abutalebi, 2013). Moreover, each subcomponent
was distinctly affected by this bilingualism-related factor. In particular, the Updating dimension
was the most benefited by the LSF, presumably because of the constant need to monitor for lin-
guistic cues in order to efficiently react in a bilingual conversation (Bialystok, 2017; Costa et al.,
2009). Consequently, our results seem to converge with the hypothesis suggesting that the general
improvement of EC stems mainly from a general updating ability that modulates the cascade of
cognitive control processes required to adequately manage two languages simultaneously accord-
ing to the enhanced demands of a bilingual environment (Green & Abutalebi, 2013; Hilchey &
Klein, 2011). Furthermore, our results revealed that the Switching subcomponent was also indi-
rectly influenced by the LSF. In other words, it seems that bilinguals who tend to alternate between
languages more often when engaging in natural conversations in which interlocutors may be
addressed in different languages are more efficient in managing their set-shifting abilities.
However, some studies have not been able to capture the effect of LSF on the task-switching abil-
ity when comparing bilingual groups differing in how often individuals switch languages. For
example, both Paap et al. (2017) and Yim and Bialystok (2012) failed to find an association
between their LSF measures and nonlinguistic task-switching costs. In contrast, yet similarly to
Barbu et al. (2018), we found that those participants who switched languages more often were
faster (i.e., more efficient) when switching in a non-verbal switching task. Even though these four
studies differ in several characteristics (e.g., participants’ context, LSF measurement methods),
one crucial aspect might explain the inconsistencies between results: the task-switching paradigm
used. While Paap et al. (2017) and Yim and Bialystok (2012) administered non-verbal switching
tasks involving switching and non-switching trials (thus being able to calculate task-switching
costs), both our task-switching paradigm and that of Barbu et al. (2018) did not allow this possi-
bility, since all trials required to set-shift. Hence, it seems that the key characteristic to find an
association between LSF and non-verbal switching abilities might be related to the expected fre-
quency of task-switching in a specific setting (Dreisbach & Haider, 2006; Mayr et al., 2013).
14 International Journal of Bilingualism 00(0)
Thereby, the effects of LSF could only be captured when the switching abilities are tested in a
high-frequency set-shifting setting that might resemble the linguistic context these bilinguals are
immersed in.
The LSF did also indirectly affect the Inhibition subcomponent, revealing that those bilinguals
more prone to switch languages frequently were less efficient at suppressing irrelevant informa-
tion. In this vein, the direction of this influence was unexpected and counterintuitive according to
previous literature suggesting that bilinguals show better inhibition abilities given the lifelong
practice resolving the continuous competition elicited by the language not in use (Kroll et al.,
2014), an effect that is assumed to be magnified in Spanish-Catalan bilinguals because of their
interactional context (Green & Abutalebi, 2013). However, our results showed the opposite pattern;
not only did we not find an advantage in inhibition, but our data revealed a disadvantage in this
regard. A plausible explanation for this finding might be accounted for by the stability-flexibility
dilemma (Dreisbach & Fröber, 2019; Goschke, 2000, 2013). In brief, the executive cognitive
system has a limited amount of resources that can be allocated in order to achieve an effective
adaptation in an environment in constant change. Within this context, two opposing strategies
compete to adjust the goal-directed behavior: stability, in which cognitive processes that maintain
the current task representation avoiding irrelevant intrusions (i.e., inhibition); and flexibility, in
which cognitive processes allow to shift efficiently to another task representation (i.e., switching).
Moreover, the balance between these strategies regarding the way control is exerted appears to be
dependent of both the individuals and the context, and it can be biased or directed towards stability
or flexibility, thus triggering a trade-off. Additionally, it has been suggested that societal practice
(prompted by the particular features of a given environment, such as a dual-language context)
might contribute to the long-term development and persistence of a bias or style favoring either
stability or flexibility (Gruber & Goschke, 2004; Hommel, 2015; Hommel & Colzato, 2017), a
process argued to be neurally modulated by the dopamine system (Cools & D’Esposito, 2011). In
this vein, we speculate that our results might constitute evidence of an inhibition-switching trade-
off mediated by the particular Spanish-Catalan bilingual interactional context. Therefore, we sug-
gest that the lifelong adaptation to a high-frequency of language switching might have biased the
executive control towards a switching-prone style. This would, in turn, facilitate set-shifting
reconfiguration processes with an associated cost of lowered inhibition ability (Mayr et al., 2013;
Musslick et al., 2018).
To summarize, we found that the LSF was the only factor affecting EC, while neither AoA nor
PL2 modulated the cognitive ability of Spanish-Catalan bilinguals. Moreover, this influence was
both general and specific, that is, it did show an indirect effect in the three subcomponents of the
EC tested: Inhibition, Switching, and Updating. In this respect, while the effect of the LSF on the
Switching and Updating dimensions of EC was the expected, according to previous studies,
the influence on the Inhibition subcomponent was inconsistent regarding previous evidence and
theory. Our proposal to account for this finding was that the diminished inhibition subcomponent
was a result of both a verbal and non-verbal set-shifting specialization associated with the interac-
tional context Spanish-Catalan bilinguals are immersed in. In particular, and following our results,
it seems that the sociolinguistic context of the present sample of participants modulates their EC in
such a manner that their updating and switching abilities are enhanced, while their inhibition is
lessened because of the limited cognitive resources that can be efficiently allocated. However, even
though these findings require further replication, the present study provides the first evidence on
the direct influence of LSF on EC using an SEM approach. Further studies might extend and com-
plement the present results in order to better understand how bilingualism-related factors modulate
the cognitive characteristics of specific bilingual populations.
Sanchez-Azanza et al. 15
Declaration of conflicting interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publi-
cation of this article.
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publica-
tion of this article: This study received the support from the Spanish government (MINECO/AEI) and the
ERDF, UE (European Regional Development Fund): grants EDU2013-45174-P and EDU2017-85909-P; and
a predoctoral fellowship (BES-2014-069063) for the first author. The funders had no role in study design, data
collection and analysis, decision to publish, or preparation of the manuscript.
Victor A. Sanchez-Azanza
Eva Aguilar-Mediavilla
Abutalebi, J., Della Rosa, P. A., Green, D. W., Hernandez, M., Scifo, P., Keim, R., Cappa, S. F., & Costa,
A. (2012). Bilingualism tunes the anterior cingulate cortex for conflict monitoring. Cerebral Cortex, 22,
Abutalebi, J., & Green, D. W. (2008). Control mechanisms in bilingual language production: Neural evidence
from language switching studies. Language and Cognitive Processes, 23, 557–582.
Anderson, K., Deane, K., Lindley, D., Loucks, B., & Veach, E. (2012). The effects of time of day and practice
on cognitive abilities: The PEBL Tower of London, Trail-making, and Switcher tasks. PEBL Technical
Report Series #2012-04, 1–5.
Barbu, C., Orban, S., Gillet, S., & Poncelet, M. (2018). The impact of language switching frequency
on attentional and executive functioning in proficient bilingual adults. Psychologica Belgica, 58,
Becker, M., Schubert, T., Strobach, T., Gallinat, J., & Kühn, S. (2016). Simultaneous interpreters vs. pro-
fessional multilingual controls: Group differences in cognitive control as well as brain structure and
function. NeuroImage, 134, 250–260.
Bialystok, E. (2016). The signal and the noise. Linguistic Approaches to Bilingualism, 6, 517–534.
Bialystok, E. (2017). The bilingual adaptation: How minds accommodate experience. Psychological Bulletin,
143, 233–262.
Bialystok, E., Craik, F. I. M., & Luk, G. (2012). Bilingualism: Consequences for mind and brain. Trends in
Cognitive Sciences, 16, 240–250.
Bialystok, E., & Feng, X. (2009). Language proficiency and executive control in proactive interference:
Evidence from monolingual and bilingual children and adults. Brain and Language, 109, 93–100.
Birdsong, D. (2018). Plasticity, variability and age in second language acquisition and bilingualism. Frontiers
in Psychology, 9, 81.
Blom, E., Küntay, A. C., Messer, M., Verhagen, J., & Leseman, P. (2014). The benefits of being bilingual:
Working memory in bilingual Turkish-Dutch children. Journal of Experimental Child Psychology, 128,
Bomyea, J., Taylor, C. T., Spadoni, A. D., & Simmons, A. N. (2018). Neural mechanisms of interference
control in working memory capacity. Human Brain Mapping, 39, 772–782.
Borragan, M., Martin, C. D., de Bruin, A., & Duñabeitia, J. A. (2018). Exploring different types of inhibition
during bilingual language production. Frontiers in Psychology, 9, 2256.
Brass, M., Derrfuss, J., Forstmann, B., & von Cramon, DY. (2005). The role of the inferior frontal junction
area in cognitive control. Trends in Cognitive Sciences, 9, 314–316.
16 International Journal of Bilingualism 00(0)
Bruyer, R., & Brysbaert, M. (2011). Combining speed and accuracy in cognitive psychology: Is the inverse
efficiency score (IES) a better dependent variable than the mean reaction time (RT) and the percentage
of errors (PE)? Psychologica Belgica, 51, 5.
Cheung, G. W., & Lau, R. S. (2007). Testing mediation and suppression effects of latent variables.
Organizational Research Methods, 11, 296–325.
Coderre, E. L., Smith, J. F., Van Heuven, W. J. B., & Horwitz, B. (2016). The functional overlap of executive
control and language processing in bilinguals. Bilingualism: Language and Cognition, 19, 471–488.
Cools, R., & D’Esposito, M. (2011). Inverted-U–Shaped dopamine actions on human working memory and
cognitive control. Biological Psychiatry, 69, e113–e125.
Corsi, P. M. (1973). Human memory and the medial temporal region of the brain. (Dissertation Abstracts
International). McGill University, Canada.
Costa, A., Hernández, M., Costa-Faidella, J., & Sebastián-Gallés, N. (2009). On the bilingual advantage in
conflict processing: Now you see it, now you don’t. Cognition, 113, 135–149.
Croschere, J., Dupey, L., Hilliard, M., Koehn, H., & Mayra, K. (2012). The effects of time of day and prac-
tice on cognitive abilities: Forward and backward Corsi block test and digit span. PEBL Technical
Report Series #2012-03.
De Baene, W., Duyck, W., Brass, M., & Carreiras, M. (2015). Brain circuit for cognitive control is shared by
task and language switching. Journal of Cognitive Neuroscience, 27, 1752–1765.
de Bruin, A., Bak, T. H., & Della Sala, S. (2015). Examining the effects of active versus inactive bilingualism
on executive control in a carefully matched non-immigrant sample. Journal of Memory and Language,
85, 15–26.
de Bruin, A., & Della Sala, S. (2018). Effects of age on inhibitory control are affected by task-specific fea-
tures. Quarterly Journal of Experimental Psychology, 71, 1219–1233.
de Bruin, A., Roelofs, A., Dijkstra, T., & FitzPatrick, I. (2014). Domain-general inhibition areas of the brain
are involved in language switching: fMRI evidence from trilingual speakers. NeuroImage, 90, 348–359.
Declerck, M., Grainger, J., Koch, I., & Philipp, A. M. (2017). Is language control just a form of executive con-
trol? Evidence for overlapping processes in language switching and task switching. Journal of Memory
and Language, 95, 138–145.
Diamond, A. (2013). Executive Functions. Annual Review of Psychology, 64, 135–168.
Dreisbach, G., & Fröber, K. (2019). On how to be flexible (or not): Modulation of the stability-flexibility
balance. Current Directions in Psychological Science, 28, 3–9.
Dreisbach, G., & Haider, H. (2006). Preparatory adjustment of cognitive control in the task switching para-
digm. Psychonomic Bulletin & Review, 13, 334–338.
Eriksen, B. A., & Eriksen, C. W. (1974). Effects of noise letters upon the identification of a target letter in a
nonsearch task. Perception & Psychophysics, 16, 143–149.
Fernandez, M., Tartar, J. L., Padron, D., & Acosta, J. (2013). Neurophysiological marker of inhibition distin-
guishes language groups on a non-linguistic executive function test. Brain and Cognition, 83, 330–336.
Friedman, N. P., & Miyake, A. (2017). Unity and diversity of executive functions: Individual differences as a
window on cognitive structure. Cortex, 86, 186–204.
Friesen, D. C., Luo, L., Luk, G., & Bialystok, E. (2015). Proficiency and control in verbal fluency perfor-
mance across the lifespan for monolinguals and bilinguals. Language, Cognition and Neuroscience, 30,
Garbin, G., Costa, A., Sanjuan, A., Forn, C., Rodriguez-Pujadas, A., Ventura, N., Belloch, V., Hernandez,
M., & Ávila, C. (2011). Neural bases of language switching in high and early proficient bilinguals. Brain
and Language, 119, 129–135.
Goschke, T. (2000). Intentional reconfiguration and involuntary persistence in task set switching. In
S. Monsell & J. Driver (Eds.), Control of cognitive processes: Attention and performance XVIII
(pp. 331–355). Cambridge, MA: MIT Press.
Goschke, T. (2013). Volition in action: Intentions, control dilemmas, and the dynamic regulation of cognitive
control. In W. Prinz, M. Beisert, & A. Herwig (Eds.), Action science (pp. 408–434). Cambridge, MA:
MIT Press.
Sanchez-Azanza et al. 17
Green, D. W., & Abutalebi, J. (2013). Language control in bilinguals: The adaptive control hypothesis.
Journal of Cognitive Psychology, 25, 515–530.
Gruber, O., & Goschke, T. (2004). Executive control emerging from dynamic interactions between brain sys-
tems mediating language, working memory and attentional processes. Acta Psychologica, 115, 105–121.
Hartanto, A., & Yang, H. (2016). Disparate bilingual experiences modulate task-switching advantages: A
diffusion-model analysis of the effects of interactional context on switch costs. Cognition, 150, 10–19.
Henrard, S., & Van Daele, A. (2017). Different bilingual experiences might modulate executive tasks
advantages: Comparative analysis between monolinguals, translators, and interpreters. Frontiers in
Psychology, 8, 1870.
Hervais-Adelman, A., Moser-Mercer, B., Michel, C. M., & Golestani, N. (2015). fMRI of simultaneous inter-
pretation reveals the neural basis of extreme language control. Cerebral Cortex, 25, 4727–4739.
Heyder, K., Suchan, B., & Daum, I. (2004). Cortico-subcortical contributions to executive control. Acta
Psychologica, 115, 271–289.
Hilchey, M. D., & Klein, R. M. (2011). Are there bilingual advantages on nonlinguistic interference tasks?
Implications for the plasticity of executive control processes. Psychonomic Bulletin and Review, 18,
Hommel, B. (2015). Between persistence and flexibility: The Yin and Yang of action control. In A. J. Elliot
(Ed.), Advances in motivation science (pp. 33–67). New York: Elsevier.
Hommel, B., & Colzato, L. S. (2017). The social transmission of metacontrol policies: Mechanisms underly-
ing the interpersonal transfer of persistence and flexibility. Neuroscience & Biobehavioral Reviews, 81,
Iluz-Cohen, P., & Armon-Lotem, S. (2013). Language proficiency and executive control in bilingual children.
Bilingualism: Language and Cognition, 16, 884–899.
Kapa, L. L., & Colombo, J. (2013). Attentional control in early and later bilingual children. Cognitive
Development, 28, 233–246.
Keith, T. Z. (2014). Multiple regression and beyond (2nd ed.). New York: Routledge.
Kroll, J. F., Bobb, S. C., & Hoshino, N. (2014). Two languages in mind: Bilingualism as a tool to investigate
language, cognition, and the brain. Current Directions in Psychological Science, 23, 159–163.
Kroll, J. F., & Chiarello, C. (2016). Language experience and the brain: Variability, neuroplasticity, and bilin-
gualism. Language, Cognition and Neuroscience, 31, 345–348.
Lemire-Rodger, S., Lam, J., Viviano, J. D., Stevens, W. D., Spreng, R. N., & Turner, G. R. (2019). Inhibit,
switch, and update: A within-subject fMRI investigation of executive control. Neuropsychologia, 132,
Luk, G., & Bialystok, E. (2013). Bilingualism is not a categorical variable: Interaction between language
proficiency and usage. Journal of Cognitive Psychology, 25, 605–621.
Luk, G., De Sa, E., & Bialystok, E. (2011). Is there a relation between onset age of bilingualism and enhance-
ment of cognitive control? Bilingualism: Language and Cognition, 14, 588–595.
Luo, L., Luk, G., & Bialystok, E. (2010). Effect of language proficiency and executive control on verbal flu-
ency performance in bilinguals. Cognition, 114, 29–41.
MacKinnon, D. (2008). Introduction to statistical mediation analysis. New York: Routledge.
Mayr, U., Kuhns, D., & Rieter, M. (2013). Eye movements reveal dynamics of task control. Journal of
Experimental Psychology: General, 142, 489–509.
Mechelli, A., Crinion, J. T., Noppeney, U., O’Doherty, J., Ashburner, J., Frackowiak, R. S., & Price, C. J.
(2004). Structural plasticity in the bilingual brain. Nature, 431, 757.
Miyake, A., & Friedman, N. P. (2012). The nature and organization of individual differences in executive
functions: Four general conclusions. Current Directions in Psychological Science, 21, 8–14.
Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., Howerter, A., & Wager, T. D. (2000). The unity
and diversity of executive functions and their contributions to complex “Frontal Lobe” tasks: A latent
variable analysis. Cognitive Psychology, 41, 49–100.
Mooijaart, A. B. (1985). Factor analysis for non-normal variables. Psychometrika, 50, 323–342.
Morrison, T. G., Morrison, M. A., & McCutcheon, J. M. (2017). Best practice recommendations for using
structural equation modelling in psychological research. Psychology, 8, 1326–1341.
18 International Journal of Bilingualism 00(0)
Mueller, S. T., & Piper, B. J. (2014). The Psychology Experiment Building Language (PEBL) and PEBL Test
Battery. Journal of Neuroscience Methods, 222, 250–259.
Musslick, S., Shenhav, A., Jang, S. J., Shvartsman, M., & Cohen, J. D. (2018). Constraints associ-
ated with cognitive control and the stability-flexibility dilemma. Proceedings of the 40th Annual
Meeting of the Cognitive Science Society (2018). Madison, WI.
Nichols, E. S., & Joanisse, M. F. (2016). Functional activity and white matter microstructure reveal the inde-
pendent effects of age of acquisition and proficiency on second-language learning. NeuroImage, 143,
Niendam, T. A., Laird, A. R., Ray, K. L., Dean, Y. M., Glahn, D. C., & Carter, C. S. (2012). Meta-analytic evi-
dence for a superordinate cognitive control network subserving diverse executive functions. Cognitive,
Affective, & Behavioral Neuroscience, 12, 241–268.
Oschwald, J., Schättin, A., von Bastian, C. C., & Souza, A. S. (2018). Bidialectalism and bilingualism:
Exploring the role of language similarity as a link between linguistic ability and executive control.
Frontiers in Psychology, 9, 1997.
Paap, K. R., Myuz, H. A., Anders, R. T., Bockelman, M. F., Mikulinsky, R., & Sawi, O. M. (2017). No
compelling evidence for a bilingual advantage in switching or that frequent language switching reduces
switch cost. Journal of Cognitive Psychology, 29, 89–112.
Pelham, S. D., & Abrams, L. (2014). Cognitive advantages and disadvantages in early and late bilinguals.
Journal of Experimental Psychology: Learning Memory and Cognition, 40, 313–325.
Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing
indirect effects in multiple mediator models. Behavior Research Methods, 40, 879–891.
Prior, A., & Gollan, T. H. (2011). Good language-switchers are good task-switchers: Evidence from Spanish-
English and Mandarin-English bilinguals. Journal of the International Neuropsychological Society, 17,
Reverberi, C., Kuhlen, A. K., Seyed-Allaei, S., Greulich, R. S., Costa, A., Abutalebi, J., & Haynes, J.-D.
(2018). The neural basis of free language choice in bilingual speakers: Disentangling language choice
and language execution. NeuroImage, 177, 108–116.
Rodriguez-Fornells, A., Krämer, U. M., Lorenzo-Seva, U., Festman, J., & Münte, T. F. (2012). Self-assessment
of individual differences in language switching. Frontiers in Psychology, 2, 388.
Sabourin, L., & Vinerte, S. (2015). The bilingual advantage in the Stroop task: Simultaneous vs. early bilin-
guals. Bilingualism: Language and Cognition, 18, 350–355.
Sanchez-Azanza, V. A., López-Penadés, R., Buil-Legaz, L., Aguilar-Mediavilla, E., & Adrover-Roig, D.
(2017). Is bilingualism losing its advantage? A bibliometric approach. PLoS ONE, 12, e0176151.
Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation mod-
els: Tests of significance and descriptive Goodness-of-Fit measures. MPR-Online, 8, 23–74.
Schreiber, J. B., Nora, A., Stage, F. K., Barlow, E. A., & King, J. (2006). Reporting structural equation
modeling and confirmatory factor analysis results: A review. The Journal of Educational Research, 99,
Seo, R., Stocco, A., & Prat, C. S. (2018). The bilingual language network: Differential involvement of anterior
cingulate, basal ganglia and prefrontal cortex in preparation, monitoring, and execution. NeuroImage,
174, 44–56.
Soveri, A., Rodriguez-Fornells, A., & Laine, M. (2011). Is there a relationship between language switch-
ing and executive functions in bilingualism? Introducing a withingroup analysis approach. Frontiers in
Psychology, 2, 183.
Takahesu Tabori, A. A., Mech, E. N., & Atagi, N. (2018). Exploiting language variation to better understand
the cognitive consequences of bilingualism. Frontiers in Psychology, 9, 1686.
Tao, L., Marzecová, A., Taft, M., Asanowicz, D., & Wodniecka, Z. (2011). The efficiency of attentional
networks in early and late bilinguals: The role of age of acquisition. Frontiers in Psychology, 2, 123.
Tse, C. S., & Altarriba, J. (2012). The effects of first- and second-language proficiency on conflict resolution
and goal maintenance in bilinguals: Evidence from reaction time distributional analyses in a Stroop task.
Bilingualism: Language and Cognition, 15, 663–676.
Sanchez-Azanza et al. 19
Tse, C. S., & Altarriba, J. (2014). The relationship between language proficiency and attentional control in
Cantonese-English bilingual children: Evidence from Simon, Simon switching, and working memory
tasks. Frontiers in Psychology, 5, 954.
Tse, C. S., & Altarriba, J. (2015). Local and global task switching costs in bilinguals who vary in second
language proficiency. The American Journal of Psychology, 128, 89–106.
Vega-Mendoza, M., West, H., Sorace, A., & Bak, T. H. (2015). The impact of late, non-balanced bilingualism
on cognitive performance. Cognition, 137, 40–46.
Verreyt, N., Woumans, E., Vandelanotte, D., Szmalec, A., & Duyck, W. (2016). The influence of lan-
guage-switching experience on the bilingual executive control advantage. Bilingualism: Language and
Cognition, 19, 181–190.
Woumans, E., Ceuleers, E., Van Der Linden, L., Szmalec, A., & Duyck, W. (2015). Verbal and nonverbal
cognitive control in bilinguals and interpreters. Journal of Experimental Psychology: Learning Memory
and Cognition, 41, 1579–1586.
Woumans, E., & Duyck, W. (2015). The bilingual advantage debate: Moving toward different methods for
verifying its existence. Cortex, 73, 356–357.
Wu, J., Yang, J., Chen, M., Li, S., Zhang, Z., Kang, C., Ding, G., & Guo, T. (2019). Brain network
reconfiguration for language and domain-general cognitive control in bilinguals. NeuroImage, 199,
Wu, Y. J., & Thierry, G. (2010). Investigating bilingual processing: The neglected role of language processing
contexts. Frontiers in Psychology, 1, 178.
Yim, O., & Bialystok, E. (2012). Degree of conversational code-switching enhances verbal task switching in
Cantonese–English bilinguals. Bilingualism: Language and Cognition, 15, 873–883.
Yow, W. Q., & Li, X. (2015). Balanced bilingualism and early age of second language acquisition as the
underlying mechanisms of a bilingual executive control advantage: Why variations in bilingual experi-
ences matter. Frontiers in Psychology, 6, 164.
Zied, K. M., Phillipe, A., Karine, P., Valerie, H. T., Ghislaine, A., Arnaud, R., & Didier, L. G. (2004).
Bilingualism and adult differences in inhibitory mechanisms: Evidence from a bilingual stroop task.
Brain and Cognition, 54, 254–256.
Author biographies
Victor A. Sanchez-Azanza is a PhD candidate at the Universitat de les Illes Balears (Spain), and Assistant
lecturer in the same institution. His research explores the relation between bilingualism and executive
Raúl López-Penadés is an associate lecturer at the Universitat de les Illes Balears (Spain). His research focuses
on bilingualism, executive control, and emotional reactivity and regulation.
Eva Aguilar-Mediavilla is a senior lecturer at the Universitat de les Illes Balears (Spain). Her research focuses
on typical and atypical language development, language evaluation, bilingualism, school adaptation, and
phonological acquisition.
Daniel Adrover-Roig is a senior lecturer at the Universitat de les Illes Balears (Spain). His research focuses
on the mutual relationships between language development, bilingualism, and scholar adaptation.
... Intra-sentential code-switching is predicted to engage less EFs because none of the involved languages are suppressed. This hypothesis is supported by Sanchez-Azanza et al. (2020), who found language switching to have positive effects on EFs amongst Spanish-Catalan bilinguals who function in a dual language context. Although the Sanchez-Azanza et al. (2020) study provides insights into the relationship between dual context language switching and EFs, it did not investigate the relative impact of inter-and intra-sentential code-switching on EFs. ...
... This hypothesis is supported by Sanchez-Azanza et al. (2020), who found language switching to have positive effects on EFs amongst Spanish-Catalan bilinguals who function in a dual language context. Although the Sanchez-Azanza et al. (2020) study provides insights into the relationship between dual context language switching and EFs, it did not investigate the relative impact of inter-and intra-sentential code-switching on EFs. When comparing the effects of intrasentential and inter-sentential effects on EFs, Hartanto and Yang (2020) found both intra-and inter-sentential code-switching to modulate EFs. ...
... Moving forward, it would be informative to examine this interaction with L1-monolingual and L2monolingual blocks as well as a mixed block with "intersentential" switches inducing a dual control mode in the sense of the ACH. In this study, we did not have a condition inducing a dual language mode, so no conclusions can be drawn about the predictions of the ACH regarding dual language contexts and no direct comparison can be made to studies that investigated dual language contexts (Sanchez-Azanza et al., 2020). ...
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Bilingualism may modulate executive functions (EFs), but the mechanisms underlying this phenomenon are poorly understood. In this study, we investigated two potential sources of variability in bilinguals' EF performance: (1) interactional contexts and code-switching, and (2) dominance profiles. Previous research on code-switching often relied on self-reports of regular code-switching habits. In this study, we investigated the effects of experimentally induced language modes (single language versus code-switching modes) on bilinguals' EF performance. Crucially, in the bilingual conditions, we differentiated between different types of intra-sentential code-switching (Insertion, Alternation, and Dense code-switching). Moreover, we investigated the interaction of the effects of temporary language modes with bilinguals' sociolinguistic code-switching habits. All our participants were L1-dominant German-English bilinguals (N = 29) immersed in an L2 context. We assessed the effects of dominance by correlating individual bilinguals' L1-dominance with their EF performance. In addition, we investigated whether language modes activate different EF patterns in bilinguals, as opposed to monolinguals, i.e., individuals who have no additional language to suppress. Based on models of bilingual language processing, we predicted our bilinguals to display the best EF performance in L2 single language contexts, as these require them to activate inhibitory schemata to suppress their dominant L1. Indeed, bilinguals performed better in the single language than in the code-switching conditions. The results also suggested that bilinguals activated more inhibitory control compared to monolinguals, supporting the notion that bilingual processing involves inhibition. The task conditions inducing different code-switching modes differed only in terms of the predictors explaining EF performance in the regression. We observed negative correlations between the frequency of engaging in a given type of code-switching and performance in language modes inducing non-corresponding control modes. The results suggested that Dense code-switching draws upon proactive control modes that differ from the reactive control involved in Alternation. Importantly, bilinguals' dominance profiles played a crucial role in explaining EF performance. The more balanced individuals in our overall L1-dominant sample displayed better EF performance in the bilingual conditions, suggesting that more balanced bilingualism trains the control modes involved in code-switching. This highlights the importance of assessing bilinguals' sociolinguistic profiles in bilingualism research.
... Among these contexts, language switching in a dual-language context requires more inhibitory control than in a singlelanguage or dense code-switching language context. Bilinguals perform better on inhibitory control tasks because of their experience of frequently switching languages in a bilingual environment (e.g., Sanchez-Azanza et al., 2020 ). ...
... Many studies have supported the positive relationship between language switching frequency and inhibitory control in bilinguals. For example, Sanchez-Azanza et al. (2020) found that the frequency of language switching in daily life was positively associated with executive control performance in Spanish-Catalan bilinguals (19-44 years old). Liu et al. (2019) found that language switching training may enhance bilinguals' inhibitory control; however, some recent empirical studies suggested that the association between language switching frequency in daily life and inhibitory control does not always exist. ...
This study explored the predictive relations between executive function and second language vocabulary. Data on receptive and expressive vocabulary in Mandarin and on working memory, inhibitory control, and cognitive flexibility were collected in two waves within a year from 186 Uyghur-Mandarin bilingual preschoolers in China. The results indicated that the predictive relations between executive function and second language vocabulary differed between receptive and expressive vocabulary and were mainly found in inhibitory control and cognitive flexibility. Specifically, inhibitory control and cognitive flexibility in Wave 1 significantly positively predicted Mandarin receptive vocabulary but not expressive vocabulary in Wave 2, whereas Mandarin expressive but not receptive vocabulary in Wave 1 significantly positively directly predicted inhibitory control and cognitive flexibility in Wave 2. Predictive relations between working memory and Mandarin receptive and expressive vocabulary were insignificant. These findings contribute significantly to understanding second language learning, especially Mandarin learning among Uyghur preschoolers in China.
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Multilinguals have to control their languages constantly to produce accurate verbal output. They have to inhibit possible lexical competitors not only from the target language, but also from non-target languages. Bilinguals’ training in inhibiting incongruent or irrelevant information has been used to endorse the so-called bilingual advantage in executive functions, assuming a transfer effect from language inhibition to domain-general inhibitory skills. Recent studies have suggested that language control may rely on language-specific inhibitory control mechanisms. In the present study, unbalanced highly proficient bilinguals completed a rapid naming multi-inhibitory task in two languages. The task assessed three types of inhibitory processes: inhibition of the non-target language, inhibition of lexical competitors, and inhibition of erroneous auditory feedback. The results showed an interaction between lexical competition and erroneous auditory feedback, but no interactions with the inhibition of the non-target language. The results suggested that different subcomponents of language inhibition are involved during bilingual language production.
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The notion of bilingual advantages in executive functions (EF) is based on the assumption that the demands posed by cross-language interference serve as EF training. These training effects should be more pronounced the more cross-language interference bilinguals have to overcome when managing their two languages. In the present study, we investigated the proposed link between linguistic and EF performance using the similarity between the two languages spoken since childhood as a proxy for different levels of cross-language interference. We assessed the effect of linearly increasing language dissimilarity on linguistic and EF performance in multiple tasks in four groups of young adults (aged 18–33): German monolinguals (n = 24), bidialectals (n = 25; German and Swiss German dialect), bilinguals speaking two languages of the same Indo-European ancestry (n = 24; e.g., German-English), or bilinguals speaking two languages of different ancestry (n = 24; e.g., German-Turkish). Bayesian linear-mixed effects modeling revealed substantial evidence for a linear effect of language similarity on linguistic accuracy, with better performance for participants with more similar languages and monolinguals. However, we did not obtain evidence for the presence of a similarity effect on EF performance. Furthermore, language experience did not modulate EF performance, even when testing the effect of continuous indicators of bilingualism (e.g., age of acquisition, proficiency, daily foreign language usage). These findings question the theoretical assumption that life-long experience in managing cross-language interference serves as EF training.
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Within the past decade, there has been an explosion of research investigating the cognitive consequences of bilingualism. However, a controversy has arisen specifically involving research claiming a “bilingual advantage” in executive function. In this brief review, we re-examine the nature of the “bilingual advantage” and suggest three themes for future research. First, there must be a theoretical account of how specific variation in language experience impacts aspects of executive function and domain general cognition. Second, efforts toward adequately characterizing the participants tested will be critical to interpreting results. Finally, designing studies that employ converging analytical approaches and sensitive methodologies will be important to advance our knowledge of the dynamics between bilingual language experience and cognition.
Conference Paper
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One of the most compelling characteristics of controlled processing is our limitation to exercise it. Theories of control allocation account for such limitations by assuming a cost of control that constrains how much cognitive control is allocated to a task. However, this leaves open the question of why such a cost would exist in the first place. Here, we use neural network simulations to test the hypothesis that constraints on cog-nitive control may reflect an optimal solution to the stability-flexibility dilemma: allocating more control to a task results in greater activation of its neural representation but also in greater persistence of this activity upon switching to a new task, yielding switch costs. We demonstrate that constraints on control impair performance of any given task but reduce performance costs associated with task switches. Critically, we show that optimal control constraints are higher in environments with a higher probability of task switches.
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Bilingual advantages in executive functions are well documented (see Bialystok, 2009; Dong & Li, 2015, for a review), but the specific aspects of bilingualism that underlie these advantages are unclear. The few studies conducted up until now on this subject (e.g., Hartanto & Yang, 2016; Prior & Gollan, 2011; Verreyt, Woumans, Vandelanotte, Szmalec, & Duyck, 2016) have suggested that the frequency of language switching may partially mediate this advantage. We further investigate the impact of oral language-switching frequency on the development of alerting, response inhibition and cognitive flexibility skills in proficient bilinguals. Two groups of proficient bilingual adults (21 low-frequency language switchers and 21 high-frequency language switchers), matched for age, gender, second-language proficiency and socio-cultural status, participated in the study. Tasks assessing alerting, response inhibition and cognitive flexibility were administered. Our results revealed that high-frequency language switchers responded more quickly in the task assessing cognitive flexibility. No group effect was found on the tasks assessing alerting and response inhibition. These results suggest that language-switching frequency is likely an underlying factor in the enhanced cognitive flexibility of proficient bilinguals.
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Much of what is known about the outcome of second language acquisition and bilingualism can be summarized in terms of inter-individual variability, plasticity and age. The present review looks at variability and plasticity with respect to their underlying sources, and at age as a modulating factor in variability and plasticity. In this context we consider critical period effects vs. bilingualism effects, early and late bilingualism, nativelike and non-nativelike L2 attainment, cognitive aging, individual differences in learning, and linguistic dominance in bilingualism. Non-uniformity is an inherent characteristic of both early and late bilingualism. This review shows how plasticity and age connect with biological and experiential sources of variability, and underscores the value of research that reveals and explains variability. In these ways the review suggests how plasticity, variability and age conspire to frame fundamental research issues in L2 acquisition and bilingualism, and provides points of reference for discussion of the present Frontiers in Psychology Research Topic.
An influential model of executive control suggests that it comprises three dissociable processes: working memory, inhibition, and task switching. Multiple studies have investigated how these processes are individually implemented in the human brain. However, few have directly investigated this question using a common task architecture and a within-subject design. Here, healthy adult humans (N = 22) performed a novel executive control task during fMRI scanning. The paradigm independently manipulated working memory updating, inhibition, and task switching demands, while keeping all other task features constant. Direct contrasts of each executive task with a closely matched control condition revealed a differentiated pattern of recruitment across control tasks: working memory was associated with activity in dorsolateral prefrontal, lateral parietal and insular cortices bilaterally; Inhibition engaged right lateral and superior medial prefrontal cortex, inferior parietal lobules bilaterally, right middle and inferior temporal cortex, and ventral visual processing regions; Task switching was associated with bilateral activity in medial prefrontal cortex, posterior cingulate cortex and precuneus, as well as left inferior parietal lobule, lateral temporal cortex and right thalamus. A conjunction of all executive versus control task activations revealed common areas of activation overlapping regions of canonical frontoparietal control and dorsal attention networks. Further, multivariate analyses suggest that working memory may be a putative common factor supporting executive functioning. Taken together, these results are consistent with a hybrid model of executive control in the human brain.
For bilinguals, language control is needed for selecting the target language during language production. Numerous studies have examined the neural correlates of language control and shown a close relationship between language control and domain-general cognitive control. However, it remains unknown how these brain regions coordinate with each other when bilinguals exert cognitive control over linguistic and nonlinguistic representations. We addressed this gap using an extended unified structural equation modeling (euSEM) approach. Sixty-five Chinese-English bilinguals performed language switching and nonverbal switching tasks during functional magnetic resonance imaging (fMRI) scanning. The results showed that language control was served by a cooperative brain network, including the frontal lobe, the parietal cortex, subcortical areas, and the cerebellum. More importantly, we found that language control recruited more subcortical areas and connections from frontal to subcortical areas compared with domain-general cognitive control, demonstrating a reconfigurable brain network. In addition, the reconfiguration efficiency of the brain network was mainly determined by general cognitive ability but was also mediated by second language (L2) proficiency. These findings provide the first data-driven connectivity model that specifies the brain network for language control in bilinguals and also shed light on the relationship between language control and domain-general cognitive control.
Goal-directed behavior in a constantly changing environment requires a dynamic balance between two antagonistic modes of control: On the one hand, goals need to be maintained and shielded from distraction (stability), and on the other hand, goals need to be relaxed and flexibly updated whenever significant changes occur (flexibility). A dysregulation of this stability-flexibility balance can result in overly rigid or overly distractible behavior, and it is therefore important to understand how this balance is regulated in a context-sensitive, adaptive manner. In the present article, we review recent evidence on how positive affect, reward prospect, and task context modulate the stability-flexibility balance. Two distinct underlying cognitive mechanisms will be discussed: Flexibility may result either from lowering the updating threshold in working memory or from keeping multiple tasks active in working memory. Critically, these two mechanisms allow different (testable) predictions: Whereas lowering the updating threshold should ease the access of new information in working memory and thereby increase flexibility in general, concurrent task activation should only increase flexibility between the respective tasks.
For everyday communication, bilingual speakers need to face the complex task of rapidly choosing the most appropriate language given the context, maintaining this choice over the current communicative act, and shielding lexical selection from competing alternatives from non-target languages. Yet, speech production of bilinguals is typically flawless and fluent. Most of the studies available to date constrain speakers' language choice by cueing the target language and conflate language choice with language use. This left largely unexplored the neural mechanisms underlying free language choice, i.e., the voluntary situation of choosing the language to speak. In this study, we used fMRI and Multivariate Pattern Analysis to identify brain regions encoding the target language when bilinguals are free to choose in which language to name pictures. We found that the medial prefrontal cortex encoded the chosen language prior to speaking. By contrast, during language use, language control recruited a wider brain network including the left inferior frontal lobe, the basal ganglia, and the angular and inferior parietal gyrus bilaterally. None of these regions were involved in language choice. We argue that the control processes involved in language choice are different from those involved in language use. Furthermore, our findings confirm that the medial prefrontal cortex is a domain-general region critical for free choice and that bilingual language choice relies on domain general processes.