The bilingual experience may place special cognitive demands on speakers and has been argued to lead to improvements in domain-general executive abilities, like cognitive control and working memory. Such improvements have been argued for based on both behavioral and brain imaging evidence. However, the empirical landscape is complex and ridden with controversy. Here we attempt to shed light on this question through an fMRI investigation of relatively large, relatively homogeneous, and carefully matched samples of early balanced bilinguals (n = 55) and monolinguals (n = 54), using robust, previously validated individual-level markers of neural activity in the domain-general multiple demand (MD) network, which supports executive functions. We find that the bilinguals, compared to the monolinguals, show significantly stronger neural responses to an executive (spatial working memory) task, and a larger difference between a harder and an easier condition of the task, across the MD network. These stronger neural responses are accompanied by better behavioral performance on the working memory task. We further show that the bilingual-vs.-monolingual difference in neural responses is not ubiquitous across the brain as no group difference in magnitude is observed in primary visual areas, which also respond to the task. Although the neural group difference in the MD network appears robust, it remains difficult to causally link it to bilingual experience specifically.

Bilingualism is a growing phenomenon across the globe (e.g., Bacon-Shone & Bolton, 1998; Hoffmann, 2000; Ryan, 2013). How does the ability to speak and understand two or more languages affect our cognitive and neural architecture? Bilingualism necessarily affects linguistic knowledge representations, where instead of a 1:1 mapping between linguistic forms and meanings, a 2:1 mapping exists. Bilingualism also affects the retrieval of linguistic representations in the course of both comprehension and production, given that words and constructions in multiple languages may get activated (e.g., Kaushanskaya & Marian, 2007; Kroll et al., 2014; Thierry & Wu, 2007). More controversially, bilingualism has been argued to affect cognitive abilities beyond language. The claim that has received the most attention in the literature concerns executive abilities. The general reasoning is that switching between languages requires domain-general cognitive control—a core executive ability—and, over time, strengthens this ability (e.g., Abutalebi & Green, 2007; Bialystok, 2017; Declerck & Philipp, 2015; Kroll et al., 2015).

A number of behavioral and brain imaging studies have claimed to provide evidence in support of this bilingual advantage in executive functions (e.g., Bialystok, 1999; Bialystok et al., 2005; Grundy & Timmer, 2017; Kapa & Colombo, 2013), and some have even argued that this advantage may have protective benefits in aging and neurodegenerative disorders (e.g., Abutalebi & Green, 2016; Alladi et al., 2013; Antoniou & Wright, 2017; Craik et al., 2010; Guzmán-Vélez et al., 2016; Kroll & Dussias, 2017). However, a growing number of investigations have now challenged these claims, failing to observe a behavioral advantage on executive function tasks (e.g., Antón et al., 2014; Duñabeitia et al., 2014; Lehtonen et al., 2018; Paap & Greenberg, 2013), including in massive samples of thousands of participants (Nichols et al., 2020). Without a robust behavioral manifestation, neural differences between bilinguals and monolinguals may be difficult to interpret. But whether such neural differences exist is also not yet clear.

Although a number of studies have reported differences in activation between bilinguals and monolinguals, different studies have used different paradigms and have reported effects in diverse brain regions (see Luk et al., 2012, for a meta-analysis, and Tao et al., 2021, for a review). In particular, neural differences have been reported in the left and right inferior and middle frontal gyri (e.g., Gold et al., 2013; Mohades et al., 2014; Rodríguez-Pujadas et al., 2013; Teubner-Rhodes et al., 2019), left and right anterior cingulate cortex (e.g., Abutalebi et al., 2013; Gold et al., 2013; Mohades et al., 2014; Waldie et al., 2009), left posterior cingulate cortex (e.g., Mohades et al., 2014), left superior temporal gyrus (e.g., Mohades et al., 2014), and left and right caudate (e.g., Abutalebi et al., 2013; Mohades et al., 2014). Further, in studies where similar brain structures have been implicated, the direction of the effect sometimes differs: For example, Abutalebi et al. (2012) reported lower activations in bilinguals in the anterior cingulate cortex and interpreted this effect as more efficient recruitment, but Mohades et al. (2014) reported stronger activation in bilinguals. More generally, to the best of our knowledge, no direct replications of any reported effect have been carried out (even within the same research group), and publication bias may be “hiding” investigations that have failed to observe a difference (e.g., de Bruin et al., 2015).

Why have we not arrived at a clear and consistent answer about whether bilinguals have superior executive function abilities? One general source of complexity that likely affects both behavioral and brain imaging studies has to do with the nature of the population in question. Bilingualism is a heterogeneous phenomenon (e.g., Luk & Bialystok, 2013; Zirnstein et al., 2019): Bilinguals differ in how early and by what means they acquire their languages, the relative proficiencies and proportions of daily use for each language, and whether they live in a primarily monolingual vs. bilingual environment. The latter factor, in particular, was recently hypothesized to importantly affect executive functions in bilinguals: Perhaps only bilinguals living in primarily monolingual environments and thus having to switch between languages based on environmental constraints would exhibit a bilingual executive advantage (Blanco-Elorrieta & Pylkkänen, 2018). Efforts are ongoing to better characterize the variability in the bilingual population and to relate this variability to brain structure and function (e.g., de Bruin, 2019; Del Maschio & Abutalebi, 2019; Deluca et al., 2019; Gallo et al., 2021; Sulpizio et al., 2020; Zirnstein et al., 2019). Whether or not differences among the samples of bilingual populations used in prior studies can explain the inconsistencies of observing vs. not observing a bilingual executive advantage remains to be determined (García-Pentón et al., 2016).

In terms of prior neural studies reporting a bilingual executive advantage, a number of methodological limitations have plausibly contributed to the complex empirical landscape that has emerged, and to the difficulty of interpreting and evaluating the robustness of the reported effects. Before highlighting some of these issues, let us consider what would constitute neural evidence for a bilingual executive advantage. Where would we expect to find the effect? Given the nature of the claim, we would expect to observe a difference between bilinguals and monolinguals in a brain region or regions that have been linked to executive functions. The prime candidate is the bilateral frontoparietal domain-general multiple demand (MD) network (Assem, Blank, et al., 2020; Duncan, 2010, 2013; Duncan et al., 2020). Activity in this network has been reported for diverse demanding cognitive tasks, with stronger responses to more demanding conditions (e.g., Duncan & Owen, 2000; Fedorenko et al., 2013; Hugdahl et al., 2015; Shashidhara et al., 2020) and linked to cognitive constructs like attention, working memory, cognitive control, and fluid intelligence. In the behavioral literature, different aspects of executive abilities have been argued to be at least partially dissociable (e.g., Miyake et al., 2000). However, how these alleged dissociations may be implemented in the brain remains debated. Given strong interregional correlations in neural activity among the MD regions (e.g., Assem, Blank, et al., 2020; Assem, Glasser, et al., 2020; Blank et al., 2014; Braga et al., 2020; Mineroff et al., 2018; Paunov et al., 2019; Power et al., 2011; Yeo et al., 2011), we here consider the MD network to be a functionally integrated system and executive functions to be a host of interrelated abilities.

What about the direction of the effect? Should we expect the MD network to be more active or less active in individuals with superior executive abilities? Prior work has compellingly established that stronger MD responses are associated with better behavioral performance both within and across individuals (e.g., Assem, Glasser, et al., 2020; Basten et al., 2013; Burgess et al., 2011; Choi et al., 2008; Cole et al., 2012; Gray et al., 2003; Lee et al., 2006; Tschentscher & Mitchell, 2017). So, if bilinguals were better at (some aspect of) executive functions, we would expect to observe stronger activation—relative to a matched group of monolinguals—within the domain-general MD network for a task targeting executive functions. This neural difference should further be accompanied by better performance in the form of higher accuracies and/or faster reaction times.

To motivate the current study, let us now highlight several issues that have plagued prior brain imaging studies of executive functions in bilinguals (for reviews, see Costa & Sebastián-Gallés, 2014; Pliatsikas & Luk, 2016; Tao et al., 2021). First, most past studies have relied on “reverse inference” reasoning (Fedorenko, 2021; Poldrack, 2006, 2011)—from anatomy to function—to interpret the observed effects. For example, many studies have reported effects somewhere in the left frontal cortex (e.g., Gold et al., 2013; Mohades et al., 2014; Rodríguez-Pujadas et al., 2013; see Luk et al., 2012 for a meta-analysis) and argued that these effects reflect differences in executive functions given that many executive function tasks activate frontal areas. However, this reasoning is not valid: Left frontal cortex is structurally and functionally heterogeneous and contains subsets of at least two distinct brain networks (e.g., Fedorenko et al., 2012; see Fedorenko & Blank, 2020, for a review). One of these is the network of interest—the MD network, but the other is the language-selective network (e.g., Braga et al., 2020; Fedorenko et al., 2011; Fedorenko & Thompson-Schill, 2014), which does not support executive functions. Given the well-documented interindividual variability in the precise locations of the MD and language areas (e.g., Fedorenko et al., 2011, 2013; Shashidhara et al., 2020), an anatomical location cannot be used to interpret an effect as arising within the MD network vs. the language network.

Second, to the best of our knowledge, all prior work has relied on comparisons of group-level activation maps. In such analyses, individual maps in each group are aligned in the common brain space, and voxel-wise functional correspondence is assumed to hold across participants, and the group-level maps for bilinguals and monolinguals are then compared. Such analyses suffer from limited sensitivity and functional resolution (Nieto-Castañón & Fedorenko, 2012) due to interindividual differences in the precise locations of the functional regions (see Shashidhara et al., 2020, for evidence of such variability for the MD network in particular). In cases of between-group comparisons, this variability can lead to misleading, and even altogether opposite, patterns of results. For example, imagine that the functional topography is less variable in the monolingual population, leading to better alignment at the group level. In this scenario, even if at the individual level, every bilingual individual shows stronger effects than every monolingual individual, the group-level comparison will show a more pronounced effect in the monolingual group, which is the opposite of the true effect.

Third, most prior neuroimaging studies of bilinguals have relied on small, and sometimes heterogeneous, samples, which can lead to spurious effects driven by a small number of outliers (e.g., Assem, Glasser, et al., 2020).

Finally, in order to ensure that an observed effect in the MD network is not due to a group-level difference in variables that would affect responses across the brain, such as brain vascularization (e.g., Erdogan et al., 2016; He et al., 2010; Poldrack, 2011), motion (e.g., Hajnal et al., 1994; Power et al., 2015), vigilance levels (e.g., Wong et al., 2013), or arousal (e.g., Chang et al., 2016; Schölvinck et al., 2010), it is important to demonstrate that any group difference observed between bilinguals and monolinguals in the MD system is not present in some control brain region, as supported by a region by group interaction (e.g., Nieuwenhuis et al., 2011). To the best of our knowledge, none of the past studies have included such control regions.

In an effort to bring clarity to the ongoing debate about whether or not bilingual individuals have superior executive abilities, we carried out an fMRI investigation where we (i) localized the network of interest (the MD network) in each individual participant using a well-established paradigm (a spatial working memory task) that has been previously shown to activate the same areas as other diverse executive-function tasks (e.g., Fedorenko et al., 2013; Shashidhara et al., 2020) and to robustly isolate the MD network from the language network (Blank et al., 2014; Fedorenko et al., 2012, 2013; Ivanova et al., 2020; Mineroff et al., 2018); (ii) examined individual-level neural markers (magnitudes of response to the target task, estimated using data independent from the data used to localize the regions of interest) that have been shown to be stable within individuals over time and to correlate with behavioral performance (Assem, Glasser, et al., 2020); (iii) included a control set of regions—primary visual areas—to evaluate the spatial specificity of the effect; and (iv) examined a relatively large (n = 55) and relatively homogeneous set of bilinguals (early balanced bilinguals who live in an English-speaking country—the United States; see Figure 2a for details), matched carefully to a similarly sized group of monolinguals (see Table 1 for details).

Table 1.

Summary of the variables for which the two groups were matched.

GroupAge mean (SD)% Female% Right-handed
Bilingual 25.47 (4.87) 43.6% 81.8%
Monolingual 25.42 (5.81) 48.1% 87.0%
GroupAge mean (SD)% Female% Right-handed
Bilingual 25.47 (4.87) 43.6% 81.8%
Monolingual 25.42 (5.81) 48.1% 87.0%

### Participants

The study included 109 participants: 55 bilinguals and 54 monolinguals. Participant selection proceeded as follows. First, 87 bilingual–monolingual pairs of participants were identified among the 800+ participants in the Fedorenko Lab’s database, the majority of whom had completed the task of interest (the spatial working memory task). These pairs were selected so as to be similar in age and have the same gender and handedness. Next, 11 participants were removed (6 bilingual, 5 monolingual) because they had completed only one run of the task (two runs are necessary to estimate the response magnitudes in individually defined functional regions of interest (fROIs); see below for details); and 14 additional participants were removed (1 bilingual, 13 monolingual) due to data quality issues. These exclusions left 149 participants (80 bilingual and 69 monolingual). Finally, following feedback from the reviewers, 40 additional participants were removed (25 bilingual, 15 monolingual) in order to ensure that (i) all bilingual participants learned their second language before the age of 6 and reported a proficiency score of 4 or 5 on a scale from 1 to 5 (see below for details), and that (ii) all monolingual participants that reported having studied any foreign language in school did so after the age of 10 and reported a proficiency score of 1 or 2. (See Supporting Information 1, which can be found at https://doi.org/10.1162/nol_a_00058.) These exclusions left 109 participants (55 bilingual, 54 monolingual). In the final set, 32 of the original 87 pairs remained, with the other 45 participants not being pairwise matched. However, the two groups remained well-matched on age (p = 0.86), gender (p = 0.92), and handedness (p = 0.98; see Table 1).

Participants in the bilingual group were native speakers of diverse languages (see Table SI-1 for detailed language profiles of all participants) and reported speaking two (n = 17), three (n = 24), or four or more (n = 14) languages. Crucially, as noted above, all participants acquired their second language at an early age (mean = 2.14 years, SE = 0.30), and on a scale from 1 (no knowledge) to 5 (native-like proficiency), they self-reported speaking their second language with high proficiency (mean = 4.91, SE = 0.03; Figure 2a). The majority (n = 44) listed English as their second language or as one of two languages acquired simultaneously from birth, while the rest (n = 11) listed a different language as their second language (Table SI-1) and English as their third language. Participants in the monolingual group were native English speakers; the majority did not report having studied a second language (n = 35), and the rest (n = 19) reported learning a second language at school and relatively late in life (mean = 13.84 years, SE = 0.62) and self-reported a low proficiency level (mean = 1.8, SE = 0.04) (Figure 2a and Table SI-1).

Participants had normal or corrected-to-normal vision. All participants gave informed consent as required by the Committee on the Use of Humans as Experimental Subjects (COUHES; https://couhes.mit.edu/) and were paid for their participation.

### Experimental Design

Every participant completed a spatial working memory task as part of a 2-hr fMRI scanning session for one of the projects in the Fedorenko Lab. This task is routinely used in the lab as a localizer for the domain-general MD system (Assem, Blank, et al., 2020; Duncan, 2010, 2013; Duncan et al., 2020; Fedorenko et al., 2013). In this task, participants are presented with a 3 × 4 grid, and on each trial, they see a sequence of locations flash up within the grid. In the Easy condition, locations appear one at a time for a total of four locations, and in the Hard condition, locations appear two at a time for a total of eight locations. After the sequence, participants are presented with two grids showing two different sets of locations and have to indicate which set of locations they had just seen. The grid with the incorrect set of locations has one or two incorrect locations. Participants are given feedback on whether they chose correctly in the form of a green checkmark or a red “X.” Each trial lasts 8 s (see Figure 1 for details of the timing), and trials are grouped into blocks of four. Each run consists of twelve 32-s-long experimental blocks (six per condition) and four 16-s-long fixation blocks for a total run duration of 448 s (7 min 28 s). All participants completed two runs (for a total task duration of ∼15 min), with condition order counterbalanced across runs.

Figure 1.

Sample trials of the Easy and Hard conditions of the spatial working memory task.

Figure 1.

Sample trials of the Easy and Hard conditions of the spatial working memory task.

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### fMRI Data Acquisition

A whole-body 3 Tesla Siemens Trio scanner with a 32-channel head coil was used to collect both structural and functional data. The structural images were collected with 1 mm isotropic voxels (TR = 2,530 ms, TE = 3.48 ms) in 179 sagittal slices. An echo-planar imaging sequence (flip angle: 90°, GRAPPA with 2 times acceleration factor) was used for the acquisition of functional BOLD signal. The acquisition parameters were as follows: 31 4-mm thick near-axial slices, in an interleaved order with a 10% distance factor; 2.1 mm × 2.1 mm in-plane resolution; field of view of 200 mm in the phase encoding anterior to posterior (A > P) direction; matrix size of 96 × 96; TR of 2,000 ms; and TE of 30 ms. The gradient positioning based on participant’s motion was adjusted using prospective acquisition correction. In order to allow for the magnetization to become steady state, the first 10 s of each run were discarded.

### fMRI Data Preprocessing and First-Level Analysis

fMRI data were analyzed using SPM12 (Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, UK; https://www.fil.ion.ucl.ac.uk/spm/software/spm12/) and custom MATLAB (https://www.mathworks.com/) scripts. SPM was used for preprocessing and first-level data modeling. Each participant’s data were motion corrected and then normalized into a common brain space (the Montreal Neurological Institute (MNI) template) and resampled into 2-mm isotropic voxels. The data were then smoothed with a 4-mm Gaussian filter and high-pass filtered (at 128 s). To model the spatial working memory task, a standard mass univariate analysis was performed whereby a general linear model estimated the effect size of each condition in each experimental run. These effects were each modeled with a boxcar function (representing entire blocks) convolved with the canonical hemodynamic response function. The model also included first-order temporal derivatives of these effects, as well as nuisance regressors representing entire experimental runs, offline-estimated motion parameters, and time points classified as outliers during the preprocessing (i.e., scans where the scan-to-scan differences in global BOLD signal are above 5 standard deviations, or where the scan-to-scan motion is above 0.9 mm).

### MD fROI Definition and Response Estimation

For each participant, fROIs were defined using the Group-constrained subject-specific (GSS) approach (Fedorenko et al., 2010). In this approach, a set of masks (or parcels) delineating brain areas, within which most individuals in prior studies had shown activity for the localizer contrast, are combined with each individual participant’s activation map for the same contrast. Here, for comparability with Fedorenko et al. (2013) and Assem, Glasser, et al. (2020), we used a set of eighteen anatomical parcels; these covered the bilateral frontal and parietal brain regions that have long been implicated in executive functions (e.g., Cabeza & Nyberg, 2000; Duncan, 2010, 2013) and included the middle frontal gyrus (MFG; 4,863 voxels LH, 5,104 voxels RH), the orbital part of the middle frontal gyrus (MFGorb; 888 voxels LH, 1,015 voxels RH), the inferior frontal gyrus (IFG; 1,038 voxels LH, 1,399 voxels RH), the precentral gyrus (PrecG; 3,528 voxels LH, 3,381 voxels RH), the supplementary motor area (SMA; 2,147 voxels LH, 2,371 voxels RH), the anterior cingulate cortex (ACC; 1,615 voxels LH, 1,958 voxels RH), the superior parietal cortex (ParSup; 2,065 voxels LH, 2,222 voxels RH), the inferior parietal cortex (ParInf; 2,447 voxels LH, 1,345 voxels RH), and the insula (1,858 voxels LH, 1,770 voxels RH). The masks are available for download from: evlab.mit.edu/funcloc/.

For each individual participant, MD fROIs were defined by selecting 10% of voxels within each parcel that were most responsive to the Hard > Easy spatial working memory contrast, as defined by their t values. To estimate the responses of these fROIs to the Easy and Hard conditions, an across-runs cross-validation procedure was used (Nieto-Castañón & Fedorenko, 2012): first, run 1 of the localizer was used to define the fROIs, and run 2 to estimate the responses (in percent BOLD signal change) to the localizer condition, ensuring independence (Kriegeskorte et al., 2009); second, run 2 was used to define the fROIs, and run 1 to estimate the responses; finally, the extracted magnitudes were averaged across the two runs to derive a single response magnitude per condition (hard and easy spatial working memory) per fROI per participant.

### Control Regions

To test whether the group difference in response to the spatial working memory task that may be observed within the MD network is present across the brain, we selected a set of control brain regions. In particular, we used three bilateral anatomical parcels (from Tzourio-Mazoyer et al., 2002) that cover primary visual areas. The rationale for selecting visual areas was that they should show strong responses to the spatial working memory task given its visual nature, so the comparison with the MD fROIs was fair. For this analysis, the responses to the Easy and Hard conditions of the task were estimated across all voxels in each parcel in each participant, and then averaged across the voxels in each parcel to obtain a single estimate per condition per parcel per participant.

### Statistical Analyses

The data were analyzed with linear mixed-effect models using the lme4 package in R (https://cran.r-project.org/web/packages/lme4/index.html); p-value approximation was performed with the lmerTest package, while effect sizes were calculated with the rstatix package (Bates et al., 2015; Kuznetsova et al., 2017). The following linear mixed-effect regression models were fit in order to address three critical research questions (all the analysis scripts and the data tables are available at OSF [https://osf.io/b6xjy/]):
• (a)

Does the MD network respond differentially in bilinguals and monolinguals during an executive (spatial working memory) task?

The BOLD response was predicted by a model that included two fixed effects: condition (Hard (relative to fixation), Easy (relative to fixation), and Hard > Easy) and group (bilingual and monolingual). ROIs (n = 18) and participants (n = 109) were modeled as random effects with random intercepts. ROIs were included as a random effect instead of a fixed effect because, as discussed in the Introduction, the regions in the MD network have been previously reported to be strongly functionally integrated, as evidenced by a high degree of synchronization during naturalistic cognition (Assem, Blank, et al., 2020; Blank et al., 2014; Braga et al., 2020; Paunov et al., 2019) and strong interregional correlations in effect sizes (Assem, Glasser, et al., 2020; Mineroff et al., 2018). However, for completeness, in Supporting Information 2, we report models estimated for each ROI separately.
$EffectSize∼Condition+Group+Group*Condition+(1|ROI)+(1|Participant)$
• (b)

Do bilinguals perform better than monolinguals behaviorally on the spatial working memory task?

The accuracy and reaction times on the spatial working memory task were predicted by two separate models that included a fixed effect for group (bilingual and monolingual). Participants (n = 65; 28 bilingual, 37 monolingual) were included as random effects with random intercepts. (Note that the behavioral data for the remaining 44 participants (27 bilingual, 17 monolingual) were not collected due to experimenter error or equipment malfunction, or were lost/overwritten.)
$AccuracyorRT∼Group+(1|Participant)$
• (c)

Do the control (primary visual) areas respond differentially in bilinguals and monolinguals during the spatial working memory task, and do the MD network and the primary visual areas differ in their responses?

First, the BOLD response was predicted by a model that included two fixed effects: condition (Hard (relative to fixation), Easy (relative to fixation), and Hard > Easy) and group (bilingual and monolingual). ROIs (n = 18) and participants (n = 109) were modeled as random effects with random intercepts.
$EffectSize∼Condition+Group+Group*Condition+(1|ROI)+(1|Participant)$
Next, to explicitly test whether the MD network and the primary visual areas differ in their responses between the two groups (e.g., Nieuwenhuis et al., 2011), the BOLD response was predicted by a model that included four fixed effects: condition (Hard (relative to fixation), Easy (relative to fixation), and Hard > Easy), group (bilingual and monolingual), network (MD and Visual), and critically, a group by network interaction. ROIs (n = 18) and participants (n = 109) were modeled as random effects with random intercepts.
$EffectSize∼Condition+Group+Network+Group*Network+(1|ROI)+(1|Participant)$

As expected, and in line with previous research (Assem, Glasser, et al., 2020; Fedorenko et al., 2013), the MD network showed a highly robust Hard > Easy effect across participants (b = 0.89, SE = 0.19; p < 0.001), and in each group separately (bilinguals: b = 0.96, SE = 0.19; p < 0.001; monolinguals: b = 0.82, SE = 0.19; p < 0.001). The critical results were as follows.

• (1)

The MD network responded more strongly in bilinguals than in monolinguals during an executive (spatial working memory) task.

A significant effect of group was observed: The MD fROIs responded more strongly in the bilingual compared to the monolingual participants during both the Hard condition (bilingual: mean = 2.62, SE = 0.02; monolingual: mean = 2.16, SE = 0.05; p < 0.01; Figure 2b) and the Easy condition (bilingual: mean = 1.66, SE = 0.04; monolingual: mean = 1.34, SE = 0.04; p < 0.01). Further, the Hard > Easy effect was larger in the bilinguals (mean = 0.96, SE = 0.02) than in the monolinguals (mean = 0.82, SE = 0.02; p < 0.001).
• (2)

Bilinguals performed better than monolinguals behaviorally on the spatial working memory task.

The bilinguals’ accuracies were higher (mean = 84.8%, SE = 1.64) than the monolinguals’ (mean = 79.3%, SE = 1.94; p = 0.03). Moreover, bilingual participants were numerically faster (mean = 1.33 s, SE = 0.04) than monolingual participants (mean = 1.42 s, SE = 0.04; p = 0.16). Both effects were small (Cohen’s d = 0.48 and −0.31, respectively), so we may not have had sufficient power to detect an effect in the reaction time data.
• (3)

The primary visual areas responded similarly in bilinguals and monolinguals during the spatial working memory task, and the MD network and the primary visual areas differed in their responses.

Similar to the MD network, the primary visual areas showed a robust Hard > Easy effect across participants (b = 0.33, SE = 0.08; p < 0.001), and in each group separately (bilinguals: b = 0.32, SE = 0.09; p < 0.01; monolinguals: b = 0.33, SE = 0.09; p < 0.01). This is to be expected given that the Hard condition contains more visual information (two squares, compared to one square, for each trial component; see Figure 1). Critically, the primary visual areas of the bilingual participants responded similarly to those of the monolingual participants during the Hard condition (bilingual: mean = 1.41, SE = 0.09; monolingual: mean = 1.50, SE = 0.11; p = 0.78) and the Easy condition (bilingual: mean = 1.10, SE = 0.08; monolingual: mean = 1.17, SE = 0.10; p = 0.35). Further, the size of the Hard > Easy contrast was similar between the groups (bilingual: mean = 0.32, SE = 0.03; monolingual: mean = 0.33, SE = 0.04; p = 0.32). Moreover, a significant group by network interaction obtained (b = 0.36, SE = 0.04; p < 0.001), such that the bilingual vs. monolingual difference in the size of the Hard > Easy effect was reliably larger in the MD network compared to the primary visual areas.

Figure 2.

(A) The language background of bilingual and monolingual participants: usage of language (in %), age of first exposure, and self-rated proficiency scores from 1 (no knowledge) to 5 (native-like proficiency) for speaking, listening, writing, and reading are reported. (B) Activation (in % BOLD signal change) across the MD system during the Hard and Easy conditions of the spatial working memory task. (C) Accuracy and reaction times for the spatial working memory task. (D) Activation (in % BOLD signal change) across the primary visual areas during the Hard and Easy conditions of the spatial working memory task.

Figure 2.

(A) The language background of bilingual and monolingual participants: usage of language (in %), age of first exposure, and self-rated proficiency scores from 1 (no knowledge) to 5 (native-like proficiency) for speaking, listening, writing, and reading are reported. (B) Activation (in % BOLD signal change) across the MD system during the Hard and Easy conditions of the spatial working memory task. (C) Accuracy and reaction times for the spatial working memory task. (D) Activation (in % BOLD signal change) across the primary visual areas during the Hard and Easy conditions of the spatial working memory task.

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To address the question of whether bilingual individuals exhibit superior executive abilities—an area of investigation characterized by a complex and controversial empirical landscape—we carried out an fMRI investigation that had several unique design features, relative to the past studies, that gave it a stronger chance to obtain a clearer answer, as elaborated in the Introduction and below. We found stronger responses to the spatial working memory task, and a larger difference between a harder and an easier condition, in the bilingual participants compared to the monolingual participants in the MD network, but not in the control (primary visual) areas. This neural difference was accompanied by numerically better behavioral performance. In the remainder of the discussion, we highlight a few implications of these results and some questions they raise, and talk about the limitations of the current investigation.

### The Nature of the Superior Executive Abilities in Bilingual Individuals

We report effects that are reliable and substantial in size such that the bilinguals’ MD network was overall more active during an executive (spatial working memory) task than the MD network in age-, gender-, and handedness-matched monolingual participants, and the difference between the harder and the easier conditions of the task was larger in bilinguals than in monolinguals. What do these effects reflect? Historically, behavioral and neural differences in executive abilities between bilinguals and monolinguals have been attributed to differences in their linguistic experiences: in particular, to the bilinguals’ need to switch between languages, and consequent improvements in their general cognitive control abilities. However, attributing these effects specifically to the differences between the two groups in their linguistic experience is difficult. (This interpretive challenge is also present in all prior studies, even if they have not explicitly acknowledged it.) In particular, bilingual individuals (or at least the type of bilinguals examined here: most individuals are living in a country where their second language is the dominant language, the majority of whom came to the United States to pursue educational and/or professional goals) may be more motivated (Baker, 1988) and/or have higher grit (e.g. Credé et al., 2017). The relationship between such factors and executive abilities remains controversial (e.g., Ebbesen, 2020; Pessoa, 2009; Taylor et al., 2004; Uddin, 2021); whereas some studies control for education and immigration status (Alladi et al., 2013), at present, it is not possible to rule out the potential contribution of such factors to the effects observed here.

General fluid intelligence is also worth a mention: We only had IQ scores on a small subset of our participants, so we could not match the groups for IQ. However, it is actually unclear whether matching on IQ makes sense in evaluating individual differences in executive abilities given the intimate link between fluid intelligence and executive functions (e.g., Assem, Blank, et al., 2020; Duncan, 2010, 2013; Duncan et al., 2020; Gläscher et al., 2010; Woolgar et al., 2010). Indeed, damage to the MD network has been shown to lead to deficits in executive functions as well as to loss of fluid intelligence abilities (see Duncan, 2020, for an extensive discussion), and stronger responses in the MD network have been associated with both better performance on executive tasks and higher IQ scores (e.g., Assem, Glasser, et al., 2020; Basten et al., 2013; Burgess et al., 2011; Choi et al., 2008; Cole et al., 2012; Gray et al., 2003; Lee et al., 2006; Tschentscher & Mitchell, 2017).

These interpretive challenges call for further studies across diverse bilingual populations. If these effects hold across different kinds of bilinguals, that would help rule out potential explanations in terms of motivation/grit, or establish that superior executive abilities characterize only some bilingual/multilingual populations (e.g., Blanco-Elorrieta & Pylkkänen, 2018). For example, it is worth noting that using the same paradigm as the one used here, Jouravlev et al. (2021) found no difference in the neural responses in the MD network in a set of 17 polyglots and hyperpolyglots, most of whom acquired their non-native languages post critical period, as compared to a matched set of monolingual controls. However, in Jouravlev et al.’s study, polyglots and monolinguals were matched for IQ, which as noted above, may not be the right approach when probing for individual differences in executive functions.

To conclusively link superior executive abilities to linguistic experience, longitudinal developmental studies will be critical. In particular, tracking executive abilities in a population of young monolingual children some of whom proceed to acquire a second language (e.g., through a language immersion program) and some of whom do not would be extremely valuable. Of course, longitudinal studies are notoriously challenging, and full experimental control over which subset of children become bilingual may be hard or impossible to achieve.

### Methodological Considerations in Future Studies of Bilingualism

Several unique features of the current study may have enabled us to detect a clear and robust effect, and we hope some of these practices will become more widely adopted in the field of bilingualism research. Perhaps most importantly, we identified the network of interest (the MD network) functionally in each individual participant using a robust MD localizer paradigm. There are three key advantages to this approach. First, functional localization has long been established to vastly improve sensitivity (i.e., the ability to detect an effect; e.g., Brett et al., 2002; Fedorenko et al., 2010; Nieto-Castañón & Fedorenko, 2012; Saxe et al., 2006). This issue is especially pertinent when examining high-level cognitive processes. Such processes are supported by the association cortex, where functional areas (i) are not predictable from macroanatomy (e.g., Frost & Goebel, 2012; Tahmasebi et al., 2012; Vázquez-Rodríguez et al., 2019), and (ii) vary substantially across individuals in their precise locations in a common brain space (e.g., Fedorenko et al., 2010, 2013; Shashidhara et al., 2020). An inevitable consequence is that many effects may be robustly present in each individual participant but would be missed in a standard group analysis, which relies on voxelwise alignment across individuals (note that the use of larger samples does not help with this problem). The use of this low-power analytic approach may explain why prior studies have reported effects in only a subset of the MD network. (Incidentally, arguments that only regions where an effect emerged in a traditional group analysis, but not other regions, show the effect of interest are fallacious for the reasons above. In particular, region A but not region B may emerge in a group analysis because region A is better aligned with anatomic landmarks; see, e.g., Blank et al., 2016, for discussion.) The use of this approach may also obscure between-population differences.

Second, functional localization confers a substantial interpretive advantage, removing the need for precarious reverse inference (e.g., Fedorenko, 2021; Poldrack, 2006). In particular, by functionally identifying a network that has been robustly linked to executive functions across diverse tasks (e.g., Fedorenko et al., 2013; Hugdahl et al., 2015; Shashidhara et al., 2020) the observed effects can be straightforwardly interpreted as reflecting differences in executive functions. Because the cortex is highly functionally heterogeneous, and distinct areas often lay adjacent to one another within the same macroanatomic area, interpreting effects functionally based on coarse macroanatomy is not justified. For example, effects within the left IFG are sometimes interpreted as reflecting the engagement of executive resources (e.g., Garbin et al., 2010), and other times as reflecting the engagement of linguistic resources (e.g., Rodríguez-Pujadas et al., 2013). Such flexibility in interpretation is clearly undesirable. Functional localization helps to unambiguously identify the MD vs. the language-selective portions of the left IFG (Fedorenko & Blank, 2020). The same holds for other areas of the association cortex, most of which are highly heterogeneous, containing numerous distinct areas in close proximity to one another.

And third, the use of the same functional localizer paradigms across individuals, studies, and labs enables the establishment of a cumulative research enterprise—the cornerstone of robust and replicable science. This general approach has been de rigueur in other fields, like vision (e.g., Kanwisher et al., 1997) from the earliest days of brain imaging research, and more recently, social cognition (e.g., Saxe & Kanwisher, 2003) and language (Fedorenko et al., 2010). Adopting this approach in the study of executive functions in bilingualism is likely to lead to greater clarity and consensus because of the greater ease of comparing and replicating findings across studies.

Another important feature of our study, which was not present in any prior study, is the use of neural markers that have been previously established (a) to be stable within individuals, (b) to vary across individuals, and (c) to relate to behavioral performance (Assem, Glasser, et al., 2020). This is critically important: A study that does not find a difference between bilinguals and monolinguals is impossible to interpret if the relevant neural marker has not been shown to have these properties.

Finally, when arguing for a neural difference between two groups in a particular brain region or network, it is critical to establish the spatial selectivity of the effect. In particular, some effects may be ubiquitously present across the brain and result from nonspecific differences, for example, in the degree of vascularization or arousal. To rule out such effects, we examined a control set of brain areas that respond to the task but are not part of the MD network (primary visual areas). Such control areas have typically been absent from past studies and would be valuable to include in future work.

#### Limitations of scope

Although our study had several methodological advantages over much prior work, it remains a single study probing a particular population of bilinguals: balanced early bilinguals currently residing in the United States. The observation of superior executive abilities in this particular bilingual population is consistent with, but does not directly evaluate, the hypothesis laid out in Blanco-Elorrieta and Pylkkänen (2018). It would help move the field forward if future studies (a) focused on relatively homogeneous groups of bilinguals (e.g., Costa & Santesteban, 2004; Rossi et al., 2017), and/or (b) provided a detailed characterization of their language background and use patterns (de Bruin, 2019).

### Conclusion

In conclusion, we report the first investigation of executive abilities in early bilinguals and matched monolinguals using the kind of robust individual-subject functional localization analytic approach that is likely to yield more interpretable and more easily replicable results than those obtained in past work. We hope that the field of bilingualism research adopts at least some aspects of the approach advocated here, so as to lead to a more robust and cumulative research enterprise.

We would like to dedicate this paper to the memory of Albert Costa, who we both knew well and loved as a mentor and a friend. Saima will always be grateful that Albert let her spend her senior year in his lab despite not even being from the same university; his support, mentorship, and guidance helped her not stray away from academia when things got tough. And Ev will forever remember the weekly Friday night partying with Albert and the rest of the “crew” in The Cellar and The People’s Republik during her undergrad years in the Caramazza Lab in the late 1990s and early 2000s.

We would like to acknowledge the Athinoula A. Martinos Imaging Center at the McGovern Institute for Brain Research at MIT, and its support team (Steve Shannon and Atsushi Takahashi). We thank former and current EvLab members for their help with fMRI data collection (especially Dima Ayyash and Olessia Jouravlev). We also thank Rachel Ryskin and Ted Gibson for helpful discussions. Saima Malik-Moraleda was supported by la Caixa Fellowship LCF/BQ/AA17/11610043. Evelina Fedorenko was supported by the R00 award HD057522, R01 awards DC016607 and DC016950 from NIH, and funds from the Brain and Cognitive Sciences department and the McGovern Institute for Brain Research.

Saima Malik-Moraleda, “la Caixa” Foundation (https://dx.doi.org/10.13039/100010434), Award ID: LCF/BQ/AA17/11610043. Evelina Fedorenko, National Institutes of Health (https://dx.doi.org/10.13039/100000002), Award ID: HD057522. Evelina Fedorenko, National Institutes of Health (https://dx.doi.org/10.13039/100000002), Award ID: DC016607. Evelina Fedorenko, National Institutes of Health (https://dx.doi.org/10.13039/100000002), Award ID: DC016950.

• Executive processing:

A set of cognitive operations required for goal-directed behavior, including working memory, inhibitory control, and selection, among others.

•
• Multiple demand network:

A bilateral brain network of frontal and parietal areas that has been implicated in executive processes and linked to fluid intelligence.

•
• Group-constrained subject-specific (GSS) approach:

An fMRI approach that enables algorithmic definition of fROIs in individual participants (see Fedorenko et al., 2010).

Abutalebi
,
J.
,
Della Rosa
,
P. A.
,
Ding
,
G.
,
Weekes
,
B.
,
Costa
,
A.
, &
Green
,
D. W.
(
2013
).
Language proficiency modulates the engagement of cognitive control areas in multilinguals
.
Cortex
,
49
(
3
),
905
911
. ,
[PubMed]
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
(
9
),
2076
2086
. ,
[PubMed]
Abutalebi
,
J.
, &
Green
,
D. [W.]
(
2007
).
Bilingual language production: The neurocognition of language representation and control
.
Journal of Neurolinguistics
,
20
(
3
),
242
275
.
Abutalebi
,
J.
, &
Green
,
D. W.
(
2016
).
Neuroimaging of language control in bilinguals: Neural adaptation and reserve
.
Bilingualism: Language and Cognition
,
19
(
4
),
689
698
.
,
S.
,
Bak
,
T. H.
,
Duggirala
,
V.
,
Surampudi
,
B.
,
Shailaja
,
M.
,
Shukla
,
A. K.
,
Chaudhuri
,
J. R.
, &
Kaul
,
S.
(
2013
).
Bilingualism delays age at onset of dementia, independent of education and immigration status
.
Neurology
,
81
(
22
),
1938
1944
. ,
[PubMed]
Antón
,
E.
,
Duñabeitia
,
J. A.
,
Estévez
,
A.
,
Hernández
,
J. A.
,
Castillo
,
A.
,
Fuentes
,
L. J.
,
Davidson
,
D. J.
, &
Carreiras
,
M.
(
2014
).
Is there a bilingual advantage in the ANT task? Evidence from children
.
Frontiers in Psychology
,
5
,
Article 398
. ,
[PubMed]
Antoniou
,
M.
, &
Wright
,
S. M.
(
2017
).
Uncovering the mechanisms responsible for why language learning may promote healthy cognitive aging
.
Frontiers in Psychology
,
8
,
Article 2217
. ,
[PubMed]
Assem
,
M.
,
Blank
,
I. A.
,
Mineroff
,
Z. A.
,
,
A.
, &
Fedorenko
,
E.
(
2020
).
Activity in the fronto-parietal multiple-demand network robustly associated with individual differences in working memory and fluid intelligence
.
Cortex
,
131
,
1
16
. ,
[PubMed]
Assem
,
M.
,
Glasser
,
M. F.
,
Van Essen
,
D. C.
, &
Duncan
,
J.
(
2020
).
A domain-general cognitive core defined in multimodally parcellated human cortex
.
Cerebral Cortex
,
30
(
8
),
4361
4380
. ,
[PubMed]
Bacon-Shone
,
J.
, &
Bolton
,
K.
(
1998
).
Charting multilingualism: Language censuses and language surveys in Hong Kong
. In
M. C.
Pennington
(Ed.),
Language in Hong Kong at century’s end
(pp.
43
90
).
Hong Kong University Press
.
Baker
,
C.
(
1988
).
Key issues in bilingualism and bilingual education
.
Multilingual Matters
.
Basten
,
U.
,
Stelzel
,
C.
, &
Fiebach
,
C. J.
(
2013
).
Intelligence is differentially related to neural effort in the task-positive and the task-negative brain network
.
Intelligence
,
41
(
5
),
517
528
.
Bates
,
D.
,
Mächler
,
M.
,
Bolker
,
B. M.
, &
Walker
,
S. C.
(
2015
).
Fitting linear mixed-effects models using lme4
.
Journal of Statistical Software
,
67
(
1
),
1
48
.
Bialystok
,
E.
(
1999
).
Cognitive complexity and attentional control in the bilingual mind
.
Child Development
,
70
(
3
),
636
644
.
Bialystok
,
E.
(
2017
).
The bilingual adaptation: How minds accommodate experience
.
Psychological Bulletin
,
143
(
3
),
233
262
. ,
[PubMed]
Bialystok
,
E.
,
Martin
,
M. M.
, &
Viswanathan
,
M.
(
2005
).
Bilingualism across the lifespan: The rise and fall of inhibitory control
.
International Journal of Bilingualism
,
9
(
1
),
103
119
.
Blanco-Elorrieta
,
E.
, &
Pylkkänen
,
L.
(
2018
).
Ecological validity in bilingualism research and the bilingual advantage
.
Trends in Cognitive Sciences
,
22
(
12
),
1117
1126
. ,
[PubMed]
Blank
,
I.
,
Balewski
,
Z.
,
Mahowald
,
K.
, &
Fedorenko
,
E.
(
2016
).
Syntactic processing is distributed across the language system
.
NeuroImage
,
127
,
307
323
. ,
[PubMed]
Blank
,
I.
,
Kanwisher
,
N.
, &
Fedorenko
,
E.
(
2014
).
A functional dissociation between language and multiple-demand systems revealed in patterns of BOLD signal fluctuations
.
Journal of Neurophysiology
,
112
(
5
),
1105
1118
. ,
[PubMed]
Braga
,
R. M.
,
DiNicola
,
L. M.
,
Becker
,
H. C.
, &
Buckner
,
R. L.
(
2020
).
Situating the left-lateralized language network in the broader organization of multiple specialized large-scale distributed networks
.
Journal of Neurophysiology
,
124
(
5
),
1415
1448
. ,
[PubMed]
Brett
,
M.
,
Anton
,
J.-L.
,
Valabregue
,
R.
, &
Poline
,
J.-B.
(
2002, June 2–6
).
Region of interest analysis using an SPM toolbox
[Paper presentation]
.
The 8th International Conference on Functional Mapping of the Human Brain
,
Sendai, Japan
.
Burgess
,
G. C.
,
Gray
,
J. R.
,
Conway
,
A. R. A.
, &
Braver
,
T. S.
(
2011
).
Neural mechanisms of interference control underlie the relationship between fluid intelligence and working memory span
.
Journal of Experimental Psychology: General
,
140
(
4
),
674
692
. ,
[PubMed]
Cabeza
,
R.
, &
Nyberg
,
L.
(
2000
).
Imaging cognition II: An empirical review of 275 PET and fMRI studies
.
Journal of Cognitive Neuroscience
,
12
(
1
),
1
47
. ,
[PubMed]
Chang
,
C.
,
Leopold
,
D.
,
Schölvinck
,
M.
,
Mandelkow
,
H.
,
Picchioni
,
D.
,
Liu
,
X.
,
Ye
,
F.
,
Turchi
,
J.
, &
Duyn
,
J.
(
2016
).
Tracking brain arousal fluctuations with fMRI
.
Proceedings of the National Academy of Sciences
,
113
(
16
),
4518
4523
. ,
[PubMed]
Choi
,
Y. Y.
,
Shamosh
,
N. A.
,
Cho
,
S. H.
,
DeYoung
,
C. G.
,
Lee
,
M. J.
,
Lee
,
J.-M.
,
Kim
,
S. I.
,
Cho
,
Z.-H.
,
Kim
,
K.
,
Gray
,
J. R.
, &
Lee
,
K. H.
(
2008
).
Multiple bases of human intelligence revealed by cortical thickness and neural activation
.
Journal of Neuroscience
,
28
(
41
),
10323
10329
. ,
[PubMed]
Cole
,
M. W.
,
Yarkoni
,
T.
,
Repovš
,
G.
,
Anticevic
,
A.
, &
Braver
,
T. S.
(
2012
).
Global connectivity of prefrontal cortex predicts cognitive control and intelligence
.
Journal of Neuroscience
,
32
(
26
),
8988
8999
. ,
[PubMed]
Costa
,
A.
, &
Santesteban
,
M.
(
2004
).
Lexical access in bilingual speech production: Evidence from language switching in highly proficient bilinguals and L2 learners
.
Journal of Memory and Language
,
50
(
4
),
491
511
.
Costa
,
A.
, &
Sebastián-Gallés
,
N.
(
2014
).
How does the bilingual experience sculpt the brain?
Nature Reviews Neuroscience
,
15
(
5
),
336
345
. ,
[PubMed]
Craik
,
F. I. M.
,
Bialystok
,
E.
, &
Freedman
,
M.
(
2010
).
Delaying the onset of Alzheimer disease: Bilingualism as a form of cognitive reserve
.
Neurology
,
75
(
19
),
1726
1729
. ,
[PubMed]
Credé
,
M.
,
Tynan
,
M. C.
, &
Harms
,
P. D.
(
2017
).
.
Journal of Personality and Social Psychology
,
113
(
3
),
492
511
. ,
[PubMed]
de Bruin
,
A.
(
2019
).
Not all bilinguals are the same: A call for more detailed assessments and descriptions of bilingual experiences
.
Behavioral Sciences
,
9
(
3
),
Article 33
. ,
[PubMed]
de Bruin
,
A.
,
Treccani
,
B.
, &
Della Sala
,
S.
(
2015
).
Cognitive advantage in bilingualism: An example of publication bias?
Psychological Science
,
26
(
1
),
99
107
. ,
[PubMed]
Declerck
,
M.
, &
Philipp
,
A. M.
(
2015
).
A review of control processes and their locus in language switching
.
Psychonomic Bulletin and Review
,
22
(
6
),
1630
1645
. ,
[PubMed]
Del Maschio
,
N.
, &
Abutalebi
,
J.
(
2019
).
Language organization in the bilingual and multilingual brain
. In
J. W.
Schwieter
&
M.
(Eds.),
The handbook of the neuroscience of multilingualism
(pp.
199
213
).
John Wiley & Sons Ltd
.
DeLuca
,
V.
,
Rothman
,
J.
,
Bialystok
,
E.
, &
Pliatsikas
,
C.
(
2019
).
Redefining bilingualism as a spectrum of experiences that differentially affects brain structure and function
.
Proceedings of the National Academy of Sciences
,
116
(
15
),
7565
7574
. ,
[PubMed]
Duñabeitia
,
J. A.
,
Hernández
,
J. A.
,
Antón
,
E.
,
Macizo
,
P.
,
Estévez
,
A.
,
Fuentes
,
L. J.
, &
Carreiras
,
M.
(
2014
).
The inhibitory advantage in bilingual children revisited: Myth or reality?
Experimental Psychology
,
61
,
234
251
. ,
[PubMed]
Duncan
,
J.
(
2010
).
The multiple-demand (MD) system of the primate brain: Mental programs for intelligent behaviour
.
Trends in Cognitive Sciences
,
14
(
4
),
172
179
. ,
[PubMed]
Duncan
,
J.
(
2013
).
The structure of cognition: Attentional episodes in mind and brain
.
Neuron
,
80
(
1
),
35
50
. ,
[PubMed]
Duncan
,
J.
(
2020
).
How intelligence happens
.
Yale University Press
.
Duncan
,
J.
,
Assem
,
M.
, &
Shashidhara
,
S.
(
2020
).
Integrated intelligence from distributed brain activity
.
Trends in Cognitive Sciences
,
24
(
10
),
838
852
. ,
[PubMed]
Duncan
,
J.
, &
Owen
,
A.
(
2000
).
Common regions of the human frontal lobe recruited by diverse cognitive demands
.
Trends in Cognitive Sciences
,
23
(
10
),
475
483
.
Ebbesen
,
C. L.
(
2020
).
Flawed estimates of cognitive ability in Clark et al. Psychological Science, 2020
.
PsyArXiv
.
Erdogan
,
S.
,
Tong
,
Y.
,
Hocke
,
L.
,
Lindsey
,
K.
, &
Frederick
,
B d.
(
2016
).
Correcting for blood arrival time in global mean regression enhances functional connectivity analysis of resting state fMRI-BOLD signals
.
Frontiers in Human Neuroscience
,
10
,
Article 311
. ,
[PubMed]
Fedorenko
,
E.
(
2021
).
The early origins and the growing popularity of the individual-subject analytic approach in human neuroscience
.
Current Opinion in Behavioral Sciences
,
40
,
105
112
.
Fedorenko
,
E.
,
Behr
,
M. K.
, &
Kanwisher
,
N.
(
2011
).
Functional specificity for high-level linguistic processing in the human brain
.
Proceedings of the National Academy of Sciences
,
108
(
39
),
16428
16433
. ,
[PubMed]
Fedorenko
,
E.
, &
Blank
,
I. A.
(
2020
).
Broca’s area is not a natural kind
.
Trends in Cognitive Sciences
,
24
(
4
),
270
284
. ,
[PubMed]
Fedorenko
,
E.
,
Duncan
,
J.
, &
Kanwisher
,
N.
(
2012
).
Language-selective and domain-general regions lie side by side within Broca’s area
.
Current Biology
,
22
(
21
),
2059
2062
. ,
[PubMed]
Fedorenko
,
E.
,
Duncan
,
J.
, &
Kanwisher
,
N.
(
2013
).
Broad domain generality in focal regions of frontal and parietal cortex
.
Proceedings of the National Academy of Sciences
,
110
(
41
),
16616
16621
. ,
[PubMed]
Fedorenko
,
E.
,
Hsieh
,
P.-J.
,
Nieto-Castañón
,
A.
,
Whitfield-Gabrieli
,
S.
, &
Kanwisher
,
N.
(
2010
).
New method for fMRI investigations of language: Defining ROIs functionally in individual subjects
.
Journal of Neurophysiology
,
104
(
2
),
1177
1194
. ,
[PubMed]
Fedorenko
,
E.
, &
Thompson-Schill
,
S. L.
(
2014
).
Reworking the language network
.
Trends in Cognitive Sciences
,
18
(
3
),
120
126
. ,
[PubMed]
Frost
,
M.
, &
Goebel
,
R.
(
2012
).
Measuring structural–functional correspondence: Spatial variability of specialised brain regions after macro-anatomical alignment
.
NeuroImage
,
59
(
2
),
1369
1381
. ,
[PubMed]
Gallo
,
F.
,
Novitskiy
,
N.
,
Myachykov
,
A.
, &
Shtyrov
,
Y.
(
2021
).
Individual differences in bilingual experience modulate executive control network and performance: Behavioral and structural neuroimaging evidence
.
Bilingualism: Language and Cognition
,
24
(
2
),
293
304
.
Garbin
,
G.
,
Sanjuan
,
A.
,
Forn
,
C.
,
Bustamante
,
J. C.
,
,
A.
,
Belloch
,
V.
,
Hernandez
,
M.
,
Costa
,
A.
, &
Ávila
,
C.
(
2010
).
Bridging language and attention: Brain basis of the impact of bilingualism on cognitive control
.
NeuroImage
,
53
(
4
),
1272
1278
. ,
[PubMed]
García-Pentón
,
L.
,
Fernández García
,
Y.
,
Costello
,
B.
,
Duñabeitia
,
J. A.
, &
Carreiras
,
M.
(
2016
).
The neuroanatomy of bilingualism: How to turn a hazy view into the full picture
.
Language, Cognition and Neuroscience
,
31
(
3
),
303
327
.
Gläscher
,
J.
,
Rudrauf
,
D.
,
Colom
,
R.
,
Paul
,
L. K.
,
Tranel
,
D.
,
Damasio
,
H.
, &
,
R.
(
2010
).
Distributed neural system for general intelligence revealed by lesion mapping
.
Proceedings of the National Academy of Sciences
,
107
(
10
),
4705
4709
. ,
[PubMed]
Gold
,
B. T.
,
Kim
,
C.
,
Johnson
,
N. F.
,
Kryscio
,
R. J.
, &
Smith
,
C. D.
(
2013
).
Lifelong bilingualism maintains neural efficiency for cognitive control in aging
.
Journal of Neuroscience
,
33
(
2
),
387
396
. ,
[PubMed]
Gray
,
J. R.
,
Chabris
,
C. F.
, &
Braver
,
T. S.
(
2003
).
Neural mechanisms of general fluid intelligence
.
Nature Neuroscience
,
6
(
3
),
316
322
. ,
[PubMed]
Grundy
,
J. G.
, &
Timmer
,
K.
(
2017
).
Bilingualism and working memory capacity: A comprehensive meta-analysis
.
Second Language Research
,
33
(
3
),
325
340
.
Guzmán-Vélez
,
E.
,
Warren
,
D. E.
,
Feinstein
,
J. S.
,
Bruss
,
J.
, &
Tranel
,
D.
(
2016
).
Dissociable contributions of amygdala and hippocampus to emotion and memory in patients with Alzheimer’s disease
.
Hippocampus
,
26
(
6
),
727
738
. ,
[PubMed]
Hajnal
,
J.
,
Myers
,
R.
,
Oatridge
,
A.
,
Schwieso
,
J.
,
Young
,
I.
, &
Bydder
,
G.
(
1994
).
Artifacts due to stimulus correlated motion in functional imaging of the brain
.
Magnetic Resonance in Medicine
,
31
(
3
),
283
291
. ,
[PubMed]
He
,
H.
,
Shin
,
D.
, &
Liu
,
T. T.
(
2010
).
Resting state BOLD fluctuations in large draining veins are highly correlated with the global mean signal
. In
Proceedings of the 18th annual meeting of the ISMRM
(p.
3488
).
ISMRM
.
Hoffmann
,
C.
(
2000
).
The spread of English and the growth of multilingualism with English in Europe
. In
J.
Cenoz
&
U.
Jessner
(Eds.),
English in Europe: The acquisition of a third language
(pp.
1
21
).
Multilingual Matters
.
Hugdahl
,
K.
,
Raichle
,
M. E.
,
Mitra
,
A.
, &
Specht
,
K.
(
2015
).
On the existence of a generalized non-specific task-dependent network
.
Frontiers in Human Neuroscience
,
9
,
Article 430
. ,
[PubMed]
Ivanova
,
A. A.
,
Srikant
,
S.
,
Sueoka
,
Y.
,
Kean
,
H. H.
,
Dhamala
,
R.
,
O’Reilly
,
U. M.
,
Bers
,
M. U.
, &
Fedorenko
,
E.
(
2020
).
Comprehension of computer code relies primarily on domain-general executive brain regions
.
ELife
,
9
,
Article 58906
. ,
[PubMed]
Jouravlev
,
O.
,
Mineroff
,
Z.
,
Blank
,
I.
, &
Fedorenko
,
E.
(
2021
).
The small and efficient language network of polyglots and hyper-polyglots
.
Cerebral Cortex
,
31
(
1
),
62
76
. ,
[PubMed]
Kanwisher
,
N.
,
McDermott
,
J.
, &
Chun
,
M. M.
(
1997
).
The fusiform face area: A module in human extrastriate cortex specialized for face perception
.
Journal of Neuroscience
,
17
(
11
),
4302
4311
. ,
[PubMed]
Kapa
,
L. L.
, &
Colombo
,
J.
(
2013
).
Attentional control in early and later bilingual children
.
Cognitive Development
,
28
(
3
),
233
246
. ,
[PubMed]
Kaushanskaya
,
M.
, &
Marian
,
V.
(
2007
).
Bilingual language processing and interference in bilinguals: Evidence from eye tracking and picture naming
.
Language Learning
,
57
(
1
),
119
163
.
Kriegeskorte
,
N.
,
Simmons
,
W. K.
,
Bellgowan
,
P. S. F.
, &
Baker
,
C. I.
(
2009
).
Circular analysis in systems neuroscience: The dangers of double dipping
.
Nature Neuroscience
,
12
,
535
540
. ,
[PubMed]
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
(
3
),
159
163
. ,
[PubMed]
Kroll
,
J. F.
, &
Dussias
,
P. E.
(
2017
).
The benefits of multilingualism to the personal and professional development of residents of the US
.
Foreign Language Annals
,
50
(
2
),
248
259
. ,
[PubMed]
Kroll
,
J. F.
,
Dussias
,
P. E.
,
Bice
,
K.
, &
Perrotti
,
L.
(
2015
).
Bilingualism, mind, and brain
.
Annual Review of Linguistics
,
1
,
377
394
. ,
[PubMed]
Kuznetsova
,
A.
,
Brockhoff
,
P. B.
, &
Christensen
,
R. H.
(
2017
).
lmerTest package: Tests in linear mixed effects models
.
Journal of Statistical Software
,
82
(
13
),
1
26
.
Lee
,
K. H.
,
Choi
,
Y. Y.
,
Gray
,
J. R.
,
Cho
,
S. H.
,
Chae
,
J. H.
,
Lee
,
S.
, &
Kim
,
K.
(
2006
).
Neural correlates of superior intelligence: Stronger recruitment of posterior parietal cortex
.
NeuroImage
,
29
(
2
),
578
586
. ,
[PubMed]
Lehtonen
,
M.
,
Soveri
,
A.
,
Laine
,
A.
,
Järvenpää
,
J.
,
de Bruin
,
A.
, &
Antfolk
,
J.
(
2018
).
Is bilingualism associated with enhanced executive functioning in adults? A meta-analytic review
.
Psychological Bulletin
,
144
(
4
),
394
425
. ,
[PubMed]
Luk
,
G.
, &
Bialystok
,
E.
(
2013
).
Bilingualism is not a categorical variable: Interaction between language proficiency and usage
.
Journal of Cognitive Psychology
,
25
(
5
),
605
621
. ,
[PubMed]
Luk
,
G.
,
Green
,
D. W.
,
Abutalebi
,
J.
, &
,
C.
(
2012
).
Cognitive control for language switching in bilinguals: A quantitative meta-analysis of functional neuroimaging studies
.
Language and Cognitive Processes
,
27
(
10
),
1479
1488
. ,
[PubMed]
Mineroff
,
Z.
,
Blank
,
I. A.
,
Mahowald
,
K.
, &
Fedorenko
,
E.
(
2018
).
A robust dissociation among the language, multiple demand, and default mode networks: Evidence from inter-region correlations in effect size
.
Neuropsychologia
,
119
,
501
511
. ,
[PubMed]
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
(
1
),
49
100
. ,
[PubMed]
,
S. G.
,
Struys
,
E.
,
Van Schuerbeek
,
P.
,
Baeken
,
C.
,
Van De Craen
,
P.
, &
Luypaert
,
R.
(
2014
).
Age of second language acquisition affects nonverbal conflict processing in children: An fMRI study
.
Brain and Behavior
,
4
(
5
),
626
642
. ,
[PubMed]
Nichols
,
E. S.
,
Wild
,
C. J.
,
Stojanoski
,
B.
,
Battista
,
M. E.
, &
Owen
,
A. M.
(
2020
).
Bilingualism affords no general cognitive advantages: A population study of executive function in 11,000 people
.
Psychological Science
,
31
(
5
),
548
567
. ,
[PubMed]
Nieto-Castañón
,
A.
, &
Fedorenko
,
E.
(
2012
).
Subject-specific functional localizers increase sensitivity and functional resolution of multi-subject analyses
.
NeuroImage
,
63
(
3
),
1646
1669
. ,
[PubMed]
Nieuwenhuis
,
S.
,
Forstmann
,
B. U.
, &
Wagenmakers
,
E. J.
(
2011
).
Erroneous analyses of interactions in neuroscience: A problem of significance
.
Nature Neuroscience
,
14
(
9
),
1105
1107
. ,
[PubMed]
Paap
,
K. R.
, &
Greenberg
,
Z. I.
(
2013
).
There is no coherent evidence for a bilingual advantage in executive processing
.
Cognitive Psychology
,
66
(
2
),
232
258
. ,
[PubMed]
Paunov
,
A. M.
,
Blank
,
I. A.
, &
Fedorenko
,
E.
(
2019
).
Functionally distinct language and theory of mind networks are synchronized at rest and during language comprehension
.
Journal of Neurophysiology
,
121
(
4
),
1244
1265
. ,
[PubMed]
Pessoa
,
L.
(
2009
).
How do emotion and motivation direct executive control?
Trends in Cognitive Sciences
,
13
(
4
),
160
166
. ,
[PubMed]
Pliatsikas
,
C.
, &
Luk
,
G.
(
2016
).
Executive control in bilinguals: A concise review on fMRI studies
.
Bilingualism: Language and Cognition
,
19
(
4
),
699
705
.
Poldrack
,
R. A.
(
2006
).
Can cognitive processes be inferred from neuroimaging data?
Trends in Cognitive Sciences
,
10
(
2
),
59
63
. ,
[PubMed]
Poldrack
,
R. A.
(
2011
).
Inferring mental states from neuroimaging data: From reverse inference to large-scale decoding
.
Neuron
,
72
(
5
),
692
697
. ,
[PubMed]
Power
,
J. D.
,
Cohen
,
A. L.
,
Nelson
,
S. M.
,
Wig
,
G. S.
,
Barnes
,
K. A.
,
Church
,
J. A.
,
Vogel
,
A. C.
,
Laumann
,
T. O.
,
Miezin
,
F. M.
,
Schlaggar
,
B. L.
, &
Petersen
,
S. E.
(
2011
).
Functional network organization of the human brain
.
Neuron
,
72
(
4
),
665
678
. ,
[PubMed]
Power
,
J. D.
,
Schlaggar
,
B.
, &
Petersen
,
S.
(
2015
).
Recent progress and outstanding issues in motion correction in resting state fMRI
.
NeuroImage
,
107
,
536
551
. ,
[PubMed]
,
A.
,
Sanjuán
,
A.
,
Ventura-Campos
,
N.
,
Román
,
P.
,
Martin
,
C.
,
Barceló
,
F.
,
Costa
,
A.
, &
Ávila
,
C.
(
2013
).
Bilinguals use language-control brain areas more than monolinguals to perform non-linguistic switching tasks
.
PLOS ONE
,
8
(
9
),
Article e73028
. ,
[PubMed]
Rossi
,
E.
,
Diaz
,
M.
,
Kroll
,
J. F.
, &
Dussias
,
P. E.
(
2017
).
Late bilinguals are sensitive to unique aspects of second language processing: Evidence from clitic pronouns word-order
.
Frontiers in Psychology
,
8
,
Article 342
. ,
[PubMed]
Ryan
,
C.
(
2013
).
Language use in the United States: 2011
. https://www.census.gov/library/publications/2013/acs/acs-22.html
Saxe
,
R.
, &
Kanwisher
,
N.
(
2003
).
People thinking about thinking people: The role of the temporo-parietal junction in “theory of mind.”
NeuroImage
,
19
(
4
),
1835
1842
. ,
[PubMed]
Saxe
,
R.
,
Moran
,
J. M.
,
Scholz
,
J.
, &
Gabrieli
,
J.
(
2006
).
Overlapping and non-overlapping brain regions for theory of mind and self reflection in individual subjects
.
Social Cognitive and Affective Neuroscience
,
1
(
3
),
229
234
. ,
[PubMed]
Schölvinck
,
M.
,
Maier
,
A.
,
Ye
,
F.
,
Duyn
,
J.
, &
Leopold
,
D.
(
2010
).
Neural basis of global resting-state fMRI activity
.
Proceedings of the National Academy of Sciences
,
107
(
22
),
10238
10243
. ,
[PubMed]
Shashidhara
,
S.
,
Spronkers
,
F. S.
, &
Erez
,
Y.
(
2020
).
Individual-subject functional localization increases univariate activation but not multivariate pattern discriminability in the “multiple-demand” frontoparietal network
.
Journal of Cognitive Neuroscience
,
32
(
7
),
1348
1368
. ,
[PubMed]
Sulpizio
,
S.
,
Del Maschio
,
N.
,
Del Mauro
,
G.
,
Fedeli
,
G.
, &
Abutalebi
,
J.
(
2020
).
Bilingualism as a gradient measure modulates functional connectivity of language and control networks
.
NeuroImage
,
205
,
Article 116306
. ,
[PubMed]
Tahmasebi
,
A. M.
,
Davis
,
M. H.
,
Wild
,
C. J.
,
Rodd
,
J. M.
,
Hakyemez
,
H.
,
Abolmaesumi
,
P.
, &
Johnsrude
,
I. S.
(
2012
).
Is the link between anatomical structure and function equally strong at all cognitive levels of processing?
Cerebral Cortex
,
22
(
7
),
1593
1603
. ,
[PubMed]
Tao
,
L.
,
Wang
,
G.
,
Zhu
,
M.
, &
Cai
,
Q.
(
2021
).
Bilingualism and domain-general cognitive functions from a neural perspective: A systematic review
.
Neuroscience and Biobehavioral Reviews
,
125
,
264
295
. ,
[PubMed]
Taylor
,
S. F.
,
Welsh
,
R. C.
,
Wager
,
T. D.
,
Phan
,
K. L.
,
Fitzgerald
,
K. D.
, &
Gehring
,
W. J.
(
2004
).
A functional neuroimaging study of motivation and executive function
.
NeuroImage
,
21
(
3
),
1045
1054
. ,
[PubMed]
Teubner-Rhodes
,
S.
,
Bolger
,
D. J.
, &
Novick
,
J. M.
(
2019
).
Conflict monitoring and detection in the bilingual brain
.
Bilingualism: Language and Cognition
,
22
(
2
),
228
252
.
Thierry
,
G.
, &
Wu
,
Y. J.
(
2007
).
Brain potentials reveal unconscious translation during foreign-language comprehension
.
Proceedings of the National Academy of Sciences
,
104
(
30
),
12530
12535
. ,
[PubMed]
Tschentscher
,
N.
, &
Mitchell
,
D.
(
2017
).
Fluid intelligence predicts novel rule implementation in a distributed frontoparietal control network
.
Journal of Neuroscience
,
37
(
18
),
4841
4847
. ,
[PubMed]
Tzourio-Mazoyer
,
N.
,
Landeau
,
B.
,
Papathanassiou
,
D.
,
Crivello
,
F.
,
Etard
,
O.
,
Delcroix
,
N.
, &
Joliot
,
M.
(
2002
).
Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain
.
NeuroImage
,
15
(
1
),
273
289
. ,
[PubMed]
Uddin
,
L. Q.
(
2021
).
Cognitive and behavioural flexibility: Neural mechanisms and clinical considerations
.
Nature Reviews Neuroscience
,
22
(
3
),
167
179
. ,
[PubMed]
Vázquez-Rodríguez
,
B.
,
Suárez
,
L. E.
,
Markello
,
R. D.
,
Shafiei
,
G.
,
Paquola
,
C.
,
Hagmann
,
P.
,
van den Heuvel
,
M. P.
,
Bernhardt
,
B. C.
,
Spreng
,
R. N.
, &
Misic
,
B.
(
2019
).
Gradients of structure–function tethering across neocortex
.
Proceedings of the National Academy of Sciences
,
116
(
42
),
21219
21227
. ,
[PubMed]
Waldie
,
K. E.
,
,
G.
,
Miliivojevic
,
B.
, &
Kirk
,
I. J.
(
2009
).
Neural activity during Stroop colour-word task performance in late proficient bilinguals: A functional magnetic resonance imaging study
.
Psychology & Neuroscience
,
2
(
2
),
125
136
.
Wong
,
C.
,
Olafsson
,
V.
,
Tal
,
O.
, &
Liu
,
T.
(
2013
).
The amplitude of the resting-state fMRI global signal is related to EEG vigilance measures
.
NeuroImage
,
83
,
989
990
. ,
[PubMed]
Woolgar
,
A.
,
Parr
,
A.
,
Cusack
,
R.
,
Thompson
,
R.
,
Nimmo-Smith
,
I.
,
Torralva
,
T.
,
Roca
,
M.
,
Antoun
,
N.
,
Manes
,
F.
, &
Duncan
,
J.
(
2010
).
Fluid intelligence loss linked to restricted regions of damage within frontal and parietal cortex
.
Proceedings of the National Academy of Sciences
,
107
(
33
),
14899
14902
. ,
[PubMed]
Yeo
,
B. T. T.
,
Krienen
,
F. M.
,
Sepulcre
,
J.
,
Sabuncu
,
M. R.
,
Lashkari
,
D.
,
,
M.
,
Roffman
,
J. L.
,
Smoller
,
J. W.
,
Zöllei
,
L.
,
Polimeni
,
J. R.
,
Fisch
,
B.
,
Liu
,
H.
, &
Buckner
,
R. L.
(
2011
).
The organization of the human cerebral cortex estimated by intrinsic functional connectivity
.
Journal of Neurophysiology
,
106
(
3
),
1125
1165
. ,
[PubMed]
Zirnstein
,
M.
,
Bice
,
K.
, &
Kroll
,
J. F.
(
2019
).
Variation in language experience shapes the consequences of bilingualism
. In
I. A.
Sekerina
,
L.
, &
V.
Valian
(Eds.),
Bilingualism, executive function, and beyond: Questions and insights
(
Studies in Bilingualism 57
, pp.
35
47
).
John Benjamins Publishing
. https://benjamins.com/catalog/sibil.57.03zir.

## Author notes

Competing Interests: The authors have declared that no competing interests exist.

Handling Editor: Karen Emmorey

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