Group differences in cognitive control as well as brain ...

NeuroImage 134 (2016) 250–260

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Simultaneous interpreters vs. professional multilingual controls: Group differences in cognitive control as well as brain structure and function Maxi Becker a,⁎, Torsten Schubert b, Tilo Strobach c, Jürgen Gallinat a, &, Simone Kühn a,d,⁎⁎ a

University Clinic Hamburg-Eppendorf, Clinic and Policlinic for Psychiatry and Psychotherapy, Martinistraße 52, 20246 Hamburg, Germany Humboldt University Berlin, Department of Psychology, RudowerChaussee 18, 12489 Berlin, Germany Medical School Hamburg, Department of Psychology, Am Kaiserkai 1, 20457 Hamburg, Germany d Max-Planck-Institute for Human Development, Center for Lifespan Psychology, Lentzeallee 94, 14195 Berlin, Germany b c

a r t i c l e

i n f o

Article history: Received 16 December 2015 Revised 7 March 2016 Accepted 31 March 2016 Available online 13 April 2016 Keywords: Simultaneous interpreting Cognitive control Resting state fMRI Voxel-based-morphometry Frontal pole BA 10

a b s t r a c t There is a vast amount of literature indicating that multiple language expertise leads to positive transfer effects onto other non-language cognitive domains possibly due to enhanced cognitive control. However, there is hardly any evidence about underlying mechanisms on how complex behavior like simultaneous interpreting benefits cognitive functioning in other non-language domains. Therefore, we investigated whether simultaneous interpreters (SIs) exhibit cognitive benefits in tasks measuring aspects of cognitive control compared to a professional multilingual control group. We furthermore investigated in how far potential cognitive benefits are related to brain structure (using voxel-based morphometry) and function (using regions-of-interest-based functional connectivity and graph-analytical measures on low-frequency BOLD signals in resting-state brain data). Concerning cognitive control, the results reveal that SIs exhibit less mixing costs in a task switching paradigm and a dual-task advantage compared to professional multilingual controls. In addition, SIs show more gray matter volume in the left frontal pole (BA 10) compared to controls. Graph theoretical analyses revealed that this region exhibits higher network values for global efficiency and degree and is functionally more strongly connected to the left inferior frontal gyrus and middle temporal gyrus in SIs compared to controls. Thus, the data provide evidence that SIs possess cognitive benefits in tasks measuring cognitive control. It is discussed in how far the central role of the left frontal pole and its stronger functional connectivity to the left inferior frontal gyrus represents a correlate of the neural mechanisms for the observed behavioral effects. © 2016 Published by Elsevier Inc.

1. Introduction Research has shown that reaching a certain level of expertise in one domain can lead to a transfer of the acquired skills to other domains that require similar abilities (Kimball and Holyoak, 2000). There are numerous studies indicating that bi- or multilingualism (the ability to understand, fluently speak and frequently use two or more languages, respectively) leads to cognitive benefits (Bialystok, 2001; Bialystok et al., 2006b,a; Mägiste, 1980; Poulin-Dubois et al., 2013). Especially cognitive abilities like mental flexibility, task switching, attentional and inhibitory control are assumed to be enhanced in bi- or multilinguals compared to monolinguals (Bialystok and DePape, 2009; Garbin et al., 2010; Prior and MacWhinney, 2010). Bialystok and colleagues explain this benefit with a stronger need to control attention due to the

⁎ Corresponding author. ⁎⁎ Correspondence to: S. Kühn, University Clinic Hamburg-Eppendorf, Clinic and Policlinic for Psychiatry and Psychotherapy, Martinistraße 52, 20246 Hamburg, Germany. E-mail addresses: [email protected] (M. Becker), [email protected] (S. Kühn).

http://dx.doi.org/10.1016/j.neuroimage.2016.03.079 1053-8119/© 2016 Published by Elsevier Inc.

demand to switch between languages, which in turn boosts the development of cognitive control processes (Bialystok et al., 2006b; Bialystok, 2007; Bialystok and Barac, 2012). Cognitive control is broadly regarded as the ability to coordinate thoughts and action in relation to internal goals (Koechlin et al., 2003). At the same time, multilingualism is not only associated with cognitive benefits but also with changes in brain structure (Mechelli et al., 2004; Moser-Mercer et al., 2000; Lehtonen et al., 2005; Bialystok et al., 2012). It has been suggested that the (left) prefrontal cortex (PFC) mediates cognitive control in bilinguals by being involved in the top-down selection of the relevant language and suppression of the irrelevant one (Abutalebi and Green, 2008; Rodriguez-Fornells et al., 2006). Furthermore, studies investigating structural brain differences between bi- and monolinguals also found differences in left prefrontal areas (inferior frontal gyrus) among others (Stein et al., 2012; Klein et al., 2014). Most of the existing research focuses on how multi-language expertise relates to brain differences and cognitive benefits (Bialystok et al., 2006b; Bialystok and DePape, 2009; Schweizer et al., 2012; Morales et al., 2013; Stein et al., 2012; Klein et al., 2014). However, only few researchers have investigated how complex behavior like simultaneous

M. Becker et al. / NeuroImage 134 (2016) 250–260

interpreting and thus an extreme form of language control is associated with aspects of cognitive control (like multitasking or task switching) and its neuronal underpinnings in the brain. Within the population of professional multilinguals, one can differentiate between simultaneous (SIs) and consecutive (CIs) interpreters and translators (TRs). However, the extent of language control and the resulting cognitive demands differ in important aspects (Christoffels and de Groot, 2009): Whereas translators do text-to-text translations, SIs and CIs both verbally rephrase an utterance from one language (source language) into another language (target language) but strongly differ in timing of input and output: CIs only start interpreting when the speaker stops or after an entire speech. In contrast, SIs speak and listen at the same time instead of alternating between both. In addition, SIs need to continuously switch between language perception (input) and production (output) and thereby continuously control their input and output streams. We assume that, compared to CIs and TRs, SIs require the highest degree of language control and consequently cognitive control for the processing information (see Prior and MacWhinney, 2010: 253). There is behavioral evidence that experience in SI does transfer to other non-linguistic domains leading to cognitive benefits in, for example, working memory (Christoffels et al., 2006; Köpke and Nespoulous, 2006; Signorelli et al., 2012) and some aspects of cognitive control such as updating (Morales et al., 2015) and cognitive flexibility (Yudes et al., 2011). In this regard, it needs to be mentioned that studies investigating cognitive and neuronal benefits in SIs usually use rather small and non-professional but multilingual control groups. However, in contrast, we regard TRs and CIs (N = 23) as a more comparable control group to SIs (N = 27) due to the fact that all individuals are professionals spending a major part of their working life using their foreign language expertise. Other aspects of cognitive control (according to Miyake et al. (2000)) that seem to be closely related to SI but have hardly been addressed in the literature are dual-tasking and task switching: There is behavioral evidence that bilinguals perform superiorly to monolinguals in a task switching paradigm (Prior and MacWhinney, 2010) and in a dual-task situation (Bialystok et al., 2006a). Within a task switching paradigm, participants have to respond to two potential tasks while constantly mapping the right response to the respective task which causes interference (for extensive discussion on the cognitive mechanism, see Philipp et al. (2008); Kiesel et al. (2010)). In the dual-task situation of the Psychological Refractory Period (PRP) type (e.g., Welford, 1952; Pashler, 1994) individuals have to respond to two choice reaction time tasks (task 1 and task 2) separated by a varying time interval; Stimulus Onset Asynchrony (SOA). The typical results pattern is that performance in task 2 deteriorates with decreasing SOA – this is what one refers to as dual task interference (see Pashler, 1994). In an earlier study, we found behavioral evidence for SIs to exhibit less dual task interference (Strobach et al., 2015, [of note, behavioral data from the dualtask paradigm of the same sample is being presented here again but put into context with evidence from brain data; all other presented data is original]). Evidence from brain imaging and lesion studies suggests that the prefrontal cortex is predominantly involved in language switching (De Baene et al., 2015; Quaresima et al., 2002) as well as aspects of cognitive control like task switching (DiGirolamo et al., 2001; Dove et al., 2000; Hernandez et al., 2001) and multitasking (Dux et al., 2009; Schubert and Szameitat, 2003; Takeuchi et al., 2014). In a lesion study, Dreher and colleagues showed that the extent of damage to the frontopolar cortex (FPC) predicts impairment in the management of multiple goals (Dreher et al., 2008). Takeuchi and colleagues investigated structural brain effects of a four-week-multitaskingtraining on young participants. They found increased gray matter volume also in the left FPC among two other prefrontal cortex regions (dorsolateral prefrontal cortex and the left inferior frontal junction) besides other regions following a multitasking training (Takeuchi et al., 2014).

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There is furthermore evidence from a PET study that the left lateral prefrontal cortex has also a pivotal role in simultaneous interpretation (Rinne et al., 2000). Accordingly, in a longitudinal study with students training to become professional SIs, researchers found evidence for increases in gray matter volume in SI students but not multilingual controls in left lateral prefrontal brain structures such as the middle frontal gyrus and pars orbitalis among other temporal and parietal areas (Hervais-Adelman et al., 2011). Thus, given the above-mentioned evidence for neuronal correlates of tasks measuring aspects of cognitive control and plastic structural changes in SI compared to a multilingual control group, we assume that it is most likely to find structural brain differences in left lateral prefrontal cortex areas. Apart from cognitive aspects, we were mostly interested in differences concerning brain structure because we assume that potential differences between both groups are a function of persistent changes in the brain. We therefore used voxel-based morphometry (VBM) and compared gray matter volumes between SIs and a professional multilingual control group. We were additionally interested in functional resting-state (rsfMRI) images, in order to a) explore resting-state functional connectivity between pivotal brain regions that might show structural differences which could also reflect plastic functional changes in the brain at rest. There is evidence that restingstate functional connectivity reflects structural connectivity and thus relates to the underlying anatomical circuitry (Greicius et al., 2009; Hagmann et al., 2008). We furthermore wanted to find b) convergent evidence for the relevance of these brain regions on a functional level performing graph theoretical analyses on these rsfMRI data using degree and global efficiency as dependent measures. These graph theoretical measures indicate how central a specific brain region is to the functional network compared to other brain regions (see Bullmore and Sporns, 2009). 2. Methods 2.1. Participants Fifty interpreters and translators (age [in years]: M = 41.46, SD = 10.02, range = 26–64; 13 males) were recruited via the BDÜ (Federal Association of Interpreters and Translators) mailing list or by direct invitation through the associations' webpage in the Berlin area. In the recruitment letter and personal emails, we informed the potential participants that we were equally interested in all three occupational groups (SIs, CIs and TIs) in order to prevent motivational differences and to keep expectation effects constant (Green et al., 2014). All participants were screened for normal or corrected-to-normal vision and hearing based on self-report. After complete description of the study, the participants' informed written consent was obtained and they received a financial compensation after the testing session. The ethics committee of the German Society of Psychology (DGPs) approved of the study. The participants completed the tasks in the same order, although due to tight scheduling of the MRI scanner some did the (f)MRI scanning first whereas others did the cognitive tasks and the questionnaires first. For the VBM analysis, a final sample of 45 participants was included. The remaining five participants could not be scanned due to MRI exclusion criteria (e.g., non-removable metal within body). For the functional resting-state analysis, a final sample of 44 participants was included, because one subject aborted the fMRI data acquisition. Furthermore, due to technical reasons, we obtained data from only 49 participants in the task switching paradigm and 47 participants in the dual-task paradigm. The classification of individuals into SIs or controls was accomplished via self-report, i.e., participants indicated whether they work or do not work as simultaneous interpreter. In order to assure that the group classification according to this single variable was sufficiently robust, participants were additionally classified via a hierarchical cluster analysis. To do this, we first identified the 12 most selective variables

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out of a pool of 22 variables differentiating best between SIs and the control group. These variables entail questions regarding for example their self-rated ability to interpret simultaneously, consecutively or to translate, the amount of monthly interpreting/ translating jobs or time spend with them (for details on the selected items, see Table 2). To select the most differentiating variables, we used a Principal Component Analysis (PCA) (R-Package: http://factominer.free.fr/) and selected all variables that significantly correlated with the first dimension (p b .0001). We only used the first dimension because all SI related variables (positively) correlated with this component. Second, we reran the PCA on the selected 12 items (leading to a closer line up of the individuals on the first dimension which correlated positively with variables concerning SI). Third, we performed a hierarchical cluster analysis (Lê et al., 2008) and predetermined a two-cluster solution. When comparing the data-driven cluster solution with the a priori group classification via the self-report on work as SI, it was a perfect match except for one individual. We therefore concluded that this single variable was sufficiently robust to differentiate between both groups and continued our analyses with this variable. As illustrated in Table 1, mean ages for both groups were M = 41.96 years (SD = 2.07; n = 27; 5 males) for the SIs group and M = 40.87 years (SD = 1.94; n = 23; 8 males) for the control group. The groups did neither differ significantly in age [t(48) = 0.38, p = .71] nor in gender [p = .21, Fisher's Exact Test]. However, since there is substantial within-group variance in terms of age and gender, we included both variables as covariates of no interest for all reported betweengroup analyses. In order to control for deficits in general speed processing and memory, we administered a timed paper and pencil digit-symbol substitution test and remember-paths test (Jäger et al., 1997). Both paper and pencil tests are subtests from the Berliner Intelligenzstruktur Test (BIS-4) a validated performance test measuring general intelligence (Jäger et al., 1997). No outliers were identified and both groups did not differ significantly in neither the digit-symbol substitution test [F(1,44) = .83, p = .367] nor in the remember-paths test [F(1,44) = .024, p = .878]. Furthermore, SIs generally scored slightly higher in their (self-rated) foreign language competence according to the Common European Framework of Reference for Language (CEFR) (especially in their second foreign language) (see Table 1). The CEFR is a six-step scale ranging from A1 (beginners) to C2 (highest level). We tested potential differences with a multivariate analysis of covariance (MANCOVA) with the group (SIs – yes/no) as between-participant factor and the (self-rated) expertise in their 3 foreign languages (controlling for age and gender). The main model was not significant [F(3,42) = 1.65, p = .192] and the

Table 2 Items used for the PCA and hierarchical cluster analysis. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.

Am I a simultaneous interpreter? (yes/no) How well am I able to interpret simultaneously? (scale: 0–4) Monthly salary as simultaneous interpreter How easy is interpreting simultaneously for me? (scale: 0–4) How well am I able to perform whispered interpreting? (scale: 0–4) Amount of simultaneous interpreting jobs per month How much percent of my working hours do I translate? How easy is translating for me? (scale: 0–4) How well am I able to translate? (scale: 0–4) Amount of translating jobs per month Amount of consecutive interpreting jobs per month Average amount of weekly hours spent with interpreting consecutively

univariate ANOVA revealed only a significant group effect for the second foreign language [F(1,42) = 4.77, p = .034, partial ŋ2 = .57] but not for the first or the third foreign language [Fs b 1.87, ps N .178]. We are not aware of any evidence that the second foreign language is supposed to lead to specific cognitive benefits exceeding the effects of the first (and the third) foreign language. Also most multilingual professionals usually translate/interpret between their first foreign language and their mother language. 2.2. Task switching paradigm The task switching paradigm consisted of two speeded response discrimination tasks: Participants were asked to discriminate as quickly and accurately as possible either between two colors (red or blue) or between two shapes (square or diamond). The amount of trials in both tasks was counterbalanced. For the red color or the square shape, the right button needed to be pressed with the right index finger on a button response box. For the blue color or the diamond shape, the left button needed to be pressed with the left index finger. This mapping of task and hand remained the same throughout the single-task and mixedtask blocks. Each experimental and practice trial began with a central fixation cross lasting for 200 ms or 1200 ms (see Fig. 1). Subsequently one of the two task cues appeared on the top center of the screen for 1000 ms. The cue for the form task was the written word “Form” (German: form) and the cue for the color task was the written word “Farbe” (German: color). The cue remained on the screen and the target appeared in the center of the screen. The targets were red or blue squares or diamonds with a size of 424 × 424 pixels. Both cue and target remained on the screen until a response (button press) was given or

Table 1 Biographical details of participants with and without experience in SI. Difference (t-test)

N of participants N females Age (in years, mean [SD]) BIS-4 digit symbol (mean, [SD]) BIS-4 remember paths (mean, [SD]) Mother tongue (N & language) 1st foreign language (N & language)

Age of acquisition of 1st foreign language (in year, mean [SD]) 1st foreign language expertise (CEFR) (mean, [SD]) 2nd foreign language expertise (CEFR) (mean, [SD]) 3rd foreign language expertise (CEFR) (mean, [SD]) N with structured education in interpreting/translation (e.g., university education) Duration of structured education in interpreting/translating (in years, mean [SD]) Working time spent on SI per week (in hours, mean [SD]) Duration of professional interpreting and/ or translating until time of testing (in years, mean [SD])

SIs

Controls

27 22 42.74 [11.76] 38.30 [7.90] 16.63 [3.96] German 17 Other 10 English 15 German 8 Other 4 11.56 [4.88] 6.12 [.59] 5.27 [1.00] 2.07 [.62] 23 4.32 [2.16] 9.61 [7.13] 13.33 [9.85]

23 15 42.59 [10.74] 35.51 [8.99] 16.22 [5.02] German 16 Other 7 English 13 German 3 Other 7 12.78 [7.62] 6.00 [.75] 4.55 [1.26] 1.96 [.93] 16 5.75 [1.57] 0 [0] 11.85 [10.35]

t

p

.38 1.16 .32

.705 .251 .747

−.69 .60 2.21 1.30

.495 .551 .032 .201

−2.25 6.45 .52

0.31 .000 .606

M. Becker et al. / NeuroImage 134 (2016) 250–260 Table 3 Mean reaction time in milliseconds (and error rate) of mixing and switching costs. Dependent variable Group Mixing costs RT (error rate) Switching costs RT (error rate)

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Table 5 Dual-Task Task 2: Mean and standard error of trials in single- and dual-task (in ms). 95% Confidence interval

95% Confidence interval Mean

Std. error

Lower bound

Upper bound

Controls 0 SIs 106.62a (0.35)

12.99 (0.27) 206.77 (0.77) 154.40 (0.32) 11.92(0.25) 130.64 (0.84) 82.60 (0.15)

Controls 78.77a (1.60) SIs 72.35a (1.03)

17.24(0.37)

44.03 (0.86) 113.52 (2.34)

15.82(0.34)

40.47 (0.35) 104.22 (1.71)

Group

Trials

Controls Single-task Dual-task SOA 50 Dual-task SOA 100 Dual-Task SOA 400 SIs Single-task Dual-task SOA 50 Dual-task SOA 100 Dual-task SOA 400

Mean

Std. error Lower bound Upper bound

683.31a 1510.03a 1430.72a 1171.76a 640.53a 1337.85a 1306.61a 973.90a

16.81 42.97 47.79 50.14 15.09 38.56 42.89 44.99

649.4 1423.4 1334.4 1070.7 610.1 1260.5 1220.1 883.2

717.2 1596.7 1527.1 1272.9 671.0 1415.6 1393.1 1064.6

a Covariates appearing in the model are evaluated at the following values: age = 41.83, gender = 1.75.

a Covariates appearing in the model are evaluated at the following values: gender = 1.74, age = 41.60.

until the maximum duration of two seconds was exceeded. The time interval between any button press (or after the time out) and the beginning of the next trial (indicated by the fixation cross) was 500 ms. There were two kinds of blocks: a single-task and a mixed-task block. In the single-task block, the task was always the same (either only form or only color discrimination). In the mixed-task block, the task could change and within this block one can differentiate between two kinds of trials – repeat and switch trials. A repeat trial is defined as a trial where the same task was presented one trial earlier (n-1). A switch trial is defined as a trial where the other task was presented one trial earlier (n-1). At the beginning of the experiment, there were three practice blocks (2 single-task blocks and 1 mixed-task block) consisting of 8 practice trials. The following experimental phase was divided into 6 different blocks (24 trials per block): 4 mixed-task blocks and 2 single-task blocks (1 color-task and 1 shape-task block). This division resulted in a counter balanced amount of single trials (in the single-task block) and repeat as well as switch trials (in the mixed-task blocks).

switching cost) repeated measures ANCOVA with group (SIs – yes/no) as a between-participant factor (controlling for age and gender).

2.3. Analysis We extracted RT measures and error rates (accuracy) as dependent measures. First, we computed a one-way analysis of covariance (ANCOVA) using performance in single-task blocks to see whether both groups differ in general RT and error rate (controlling for age [in years] and gender). Because the error rate was not normally distributed (skewed and multiple outliers but within two standard deviations), we additionally performed a Mann–Whitney U Test (using the residuals of the error rate in the single-task block – age and gender regressed out) as a more robust estimate of potential group differences. Second, we analyzed the data according to switching and mixing costs. Switching costs were defined as difference in average performance on switch trials compared to non-switch trials, within the mixed-task block (Rogers and Monsell, 1995). Mixing costs were defined as the contrast between repetition trials in mixed-task blocks and trials in the single-task block. Both tasks (color and shape discrimination) for task switching were collapsed to receive a sufficient amount of trials. RT (as well as accuracy) as dependent variable was analyzed using a two-way (mixing cost vs.

2.4. Dual-task paradigm The dual-task design consists of visual and auditory reaction time tasks. In order to assure that performance reflects differences in cognitive control processes and not just possible interferences between perceptual input processes, we selected tasks with different modalities (Wickens, 1989). In the auditory task, the participants were presented with three possible sine-wave tones (350, 900 and 1650 Hz) and had to identify the respective tone using either the index, middle or ring finger of their right hand. In the visual task, the participants were presented with a white triangle (on a black background) of three possible sizes (small, middle or big) and they were requested to identify the presented triangle size with their left index, middle or ring finger, respectively. The dual-task design consisted of single-task and dual-task blocks: In the single-task blocks, only one of the tasks (visual or auditory) was presented: The trials started with the presentation of three white dashes next to each other and they remained there until the end of the trial (see Fig. 2). An auditory stimulus (sine-wave tone) or a visual stimulus (white triangle) was presented 500 ms after onset of the white dashes. The duration of the stimulus presentation was 50 ms and the trial lasted until a response was given or for a time interval of 2500 ms (if no button was pressed). There were 27 visual and 27 auditory task trials for the single-task condition. The dual-task block was identical to the single-task block (also 54 trials) with the difference that the two tasks were presented together but with random selection of the stimuli from visual and auditory tasks. In the dual-task block, the auditory task was always the first followed by the visual task. The stimulus onset asynchrony (SOA) between the visual and auditory

Table 4 Mean reaction time in milliseconds (SE) and mean error rate (SE) for single-task blocks, non-switch and switch trials in mixed-task blocks, by language group. Mixed-task blocks Group Controls SIs

Single-task block RT Error rate RT Error rate

a

663.31 (23.56) 2.26a (.82) 635.39a (21.61) .316a (.75)

Non-switch a

843.90 (29.31) 2.49a (.81) 742.01a (26.89) .66a (.74)

Switch 922.67a (34.81) 4.10a (.74) 814.35a (31.93) 1.69a (.68)

a Covariates appearing in the model are evaluated at the following values: age = 41.83, gender = 1.75.

Fig. 1. Time flow of the task switching design.

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Fig. 2. A) Time flow of the dual-task design in the dual-task condition. B) Picture represents the stimulus and hand mapping.

task could vary between 50, 100 and 400 ms and the amount of trials for each SOA was the same. In the dual-task condition, participants were instructed to give priority to the first (auditory) task. The dual-task experiment was designed to start with two single-task blocks followed by three dual-task blocks.

sequence sensitive to blood oxygen level dependent (BOLD) contrast (TR = 2000 ms,TE = 30 ms, image matrix = 64 × 64, FOV = 216 mm, flip angle = 80°, voxel size = 3 mm × 3 mm × 3.6 mm, 36 axial slices). During resting-state measurements, the participants were instructed to relax and keep their eyes closed and a total of 150 volumes were acquired.

2.5. Analysis 2.7. Data analysis We used RT measures and error rates (accuracy) as dependent measures for single-task situations as well as for the three different SOAs in the second task within the dual-task situation. These measures were extracted only for the second task (visual task) because we were only interested in the dual-task interference of the second task due to the PRPeffect (for a more thorough description of the dual-task paradigm on this sample, see Strobach et al., 2015). However, to control between mere processing speed and task coordination processes (a measure for cognitive control), we first ran a repeated measures ANCOVA with the between-subjects factor group (SIs – yes/no) and the single-task (the visual task) as well as the three respective SOAs (50 ms, 100 ms, 400 ms) controlling for age and gender. In order to reach normality for accuracy, we directly calculated dual-task costs (accuracy for second task in dual situation vs. accuracy in single task situation) for all three SOAs for the second task and compared these dual-task costs in a repeated measures ANCOVA. Second, in order to later on correlate dualtask performance with gray matter volume, we calculated a single dual-task measure termed dual-task cost (RT). This is the performance difference between the average performance in the single-task (visual task) and the average performance in the second (visual) task of the dual-task when the SOA was 50 ms. Dual-task interference is expected to be the strongest in the second task for trials with the shortest SOA due to the largest amount of temporal overlap when processing two tasks (see Pashler and Johnston, 1989). Subsequently, we performed a univariate ANCOVA with group as between-subject factor and dualtask cost (RT). 2.6. Scanning procedure Brain images were collected with a 3 Tesla Magnetom Trio MRI scanner system (Siemens Medical Systems, Erlangen, Germany) using a 12channel radiofrequency head coil. The structural images were collected using a three-dimensional T1-weighted magnetization prepared gradient-echo sequence (MPRAGE) (repetition time (TR) = 2500 ms; echo time (TE) = 4.77 ms; TI = 1100 ms, acquisition matrix = 256 × 256 × 192, flip angle = 7°; FOV = 256 mm, voxel size = 1 mm × 1 mm × 1 mm). For the resting-state analysis, functional images were obtained using a T2⁎-weighted echo planar imaging (EPI)

2.7.1. Behavioral analysis For all tasks, we excluded trials with erroneous or omitted responses in the RT analyses. To assure (multivariate) normal distribution of the dependent variables, we excluded participants from the respective RT and accuracy analyses if they deviated more than ± 2 SD from the group mean. Thus, we excluded two participants (SI) from the analysis of task-switching. 2.7.2. Voxel-based morphometry analysis All anatomical data were processed with the VBM8 toolbox (http://www.neuro.uni-jena.de/vbm) with default parameters by Gaser and the SPM8 software package (http://www.fil.ion.ucl.ac. uk/spm/software/spm8). Preprocessing involved the following steps: affine registration to Montreal Neurological Institute (MNI) space (http://www.mni.mcgill.ca/) and tissue classification into gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) segmentations. Furthermore, GM population templates were generated from the entire image dataset using the diffeomorphic anatomical registration which is based on the exponentiated Lie algebra (DARTEL) technique (Ashburner, 2007). GM (and WM) segments were warped non-linearly to the DARTEL GM (and WM) template in MNI space. Subsequently, the segmented images were modulated by multiplying voxel values in the segmented images by the Jacobian determinants derived from the spatial normalization step. Finally, the structural images were smoothed with an 8-mm FWHM-kernel. For statistical analysis, between-group regional differences in GM volumes were assessed using the general linear model in SPM8. This was carried out by comparing voxelwise gray matter volume (of the whole brain) between SIs and the control group. Gender, age (in years) and total brain volume (measured with SPM8) were entered as covariates of no interest. The resulting maps were thresholded with p b 0.001 and the statistical extent threshold was corrected for multiple comparisons and combined with a nonstationary smoothness correction (Hayasaka and Nichols, 2004). To investigate a potential relationship between voxel-based brain volume and the dependent variables exceeding the influence of group membership (SIs – yes/no), we furthermore extracted a single brain


volume value for this region of interest (ROI) per subject using the MarsBaR toolbox (http://marsbar.sourceforge.net) and adjusted these values by the variables total brain volume, age and gender. We used these resulting residual values for two separate hierarchical regression analyses in order to predict variance in the dependent variables: mixing cost (RT) and dual-task cost (RT). In the first step of the model, we entered the control variables age and gender, in the second step we added group membership (SIs – yes/no) and in the third step we entered the residual values from the frontal pole ROI of the VBM analysis. 2.7.3. Graph theoretical and functional connectivity analysis with resting-state data To preprocess the functional resting-state images and subsequently perform statistical analysis (with the time series between ROIs), we used the CONN-fMRI functional connectivity toolbox, version 15.p (www.nitrc.org/projects/conn) (Whitfield-Gabrieli and NietoCastanon, 2012) based on SPM8 (www.fil.ion.ucl.ac.uk/spm/). 2.7.4. Preprocessing First, functional and structural images were spatially preprocessed (slice-time correction, motion realignment, coregistration, gray and white matter as well as cerebral fluid structural segmentation, normalization of functional and structural scans to Montreal Neurological Institute (MNI) space and smoothing with an 8-mm FWHM-Gaussian filter). The spatial preprocessing was performed using the default SPM8 default parameter choices. Second, the functional resting state data were further temporally preprocessed: Estimated subject motion parameters, the global BOLD signal and BOLD signals in WM and CSF areas were used as additional covariates to attenuate the impact of motion and physiological noise factors. The implemented aCompCor approach (Behzadi et al., 2007) was used to regress out five principal components from noise regions (WM, CSF) and six realignment parameters as well as their first temporal derivatives for each participant. The resulting residual time series were additionally band-pass filtered between 0.008 and 0.09 Hz to investigate low frequency correlations (Biswal et al., 1995, 2010; Fox et al., 2005). Empirical studies have shown that functional connectivity is generally greater at very low frequencies (Salvador et al., 2005; Zuo et al., 2010). A recent work by Wu and colleagues determined the most significant frequency ranges that were enhanced by T2* weightings with a finer spectral resolution (0.0017 Hz) in the default-mode, attention, visual and motor network (Wu et al., 2012). They found that common T2*specific frequency ranges were located between 0.008–0.023 Hz and 0.037–0.043 Hz in the brain at rest (Wu et al., 2012). Furthermore, similar frequency bands were reported for functional connectivity networks (Chang and Glover, 2010; Sasai et al., 2011). Using a time-frequency coherence analysis, Chang and Glover found fMRI fluctuations in the default-mode network to have positive coherence at 0.015 Hz (Chang and Glover, 2010). Given the evidence that hemodynamic fluctuations (using fMRI) are spectrally more specific than what is being represented by the default frequency band (0.01–0.1 Hz), we further decomposed the time-series of each ROI (.008–.09 Hz) into three equal-size non-overlapping segments (1st band: .008 N f1 b .0353 Hz, 2nd band: .0353 N f 2 b .0626 Hz, 3rd band: .0626 N f 3 b .09 Hz) (Kalcher et al., 2014; Wu et al., 2012). That is to say, the already prefiltered and denoised time series in every ROI were additionally band pass filtered to the above-named frequency ranges using rectangular windows on the frequency domain. Three bands were chosen as a tradeoff between a more sensitive analysis (to account for Wu's findings of the highest spectral power in the above-named lower frequency ranges) and not too many different and narrow frequency bands. Each frequency-band resulted in one condition.

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2.7.5. First-level analysis Functional connectivity measures were computed between each pair of seed areas (ROI-to-ROI analysis) using the standard atlas (132 ROIs) of the CONN toolbox as well as the frontal pole cluster that we extracted from the earlier VBM analysis. For each ROI pair, a zero-lagged bivariate-correlation was calculated. Furthermore, a Fisher transformation was applied to all calculated bivariate correlation measures. 2.7.6. Graph theoretical analysis (second-level) The subject-specific ROI-to-ROI connectivity matrix consisted of the cortical and subcortical ROIs from the FSL Harvard–Oxford Atlas minus the default left frontal pole ROI. The reason why we excluded the default left frontal pole ROI from the analysis is because our predefined left frontal pole cluster taken from the VBM analysis spatially overlaps with it and graph-measures are notoriously sensitive to the choice of nodes/ROIs. We set the adjacency matrix threshold at a range of possible correlation coefficients [K = 0.1:0.1:0.9] based on raw connectivity values to define an undirected graph representing the entire network. Our graph measure of interest was node degree and node global efficiency. Node degree is defined as the number of edges connected to the node. Global efficiency is defined as the average inverse shortest path distance from node n to all other nodes in the graph (WhitfieldGabrieli and Nieto-Castanon, 2012). Node global efficiency is specifically relevant because it represents a measure for the centrality of each ROI within the network (Whitfield-Gabrieli and Nieto-Castanon, 2012). For the statistical analysis, we used group as between-subjects factor while controlling for age and gender. Because we have three conditions (i.e. three frequency bands [f1, f2, f3]), we ran three separate analyses using the same contrast per condition. Because we assumed that network values for global efficiency and degree is higher for SIs compared to controls, we tested one-sided but set the analysis threshold to p b .05 (FDR corrected) to correct for multiple comparison between the network nodes/ROIs and the fact that we tested for three conditions. 2.7.7. Functional connectivity analysis (second-level) We wanted to investigate to which ROIs the frontal pole cluster is functionally more connected in SIs compared to controls. In order to do this, we entered the entire ROI-to-ROI connectivity matrix into a second-level general linear model per condition (i.e. per frequency band [f1, f2, f3]) using the same second-level contrast as for the graph analysis (group as between-subjects factor while controlling for age and gender). Given the above-mentioned evidence for left prefrontal brain areas being involved in multitasking or task and language switching (see Hernandez et al., 2001; Takeuchi et al., 2014), we specifically tested the hypothesis whether the left frontal pole cluster (taken from the VBM analysis) is functionally more connected to other left lateral prefrontal areas in SIs compared to controls. Because we had a concrete hypothesis with an a-priori study specific (left frontal pole cluster) ROI but three different frequency bands and four potential other left lateral prefrontal cortex ROIs (superior frontal gyrus, middle frontal gyrus, inferior frontal gyrus pars opercularis and pars triangularis), we used uncorrected but adjusted p-values dividing the p-value of .05 by 12. Therefore, we used a threshold of p b .0042. 3. Results 3.1. Task switching — Single-task block The one-way ANCOVA (RT in single-tasks) revealed no significant main or interaction effect [Fs b .754, ps N .390] except for a statistical trend for an age effect [F(1,44) = 3.64, p = .076, partial ŋ2 = .08]. Regarding accuracy, there was a trend [F(1,44) = 2.99, p = .091, partial ŋ2 = .064] towards more produced errors by the control group in the single-task block (all other results remaining non-significant). However, the Mann–Whitney U Test revealed no group differences in the single-task block [U = 272.0, p = .620].

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3.2. Task switching — Switching and mixing costs

3.5. Graph theoretical analysis

The two-way repeated measures ANCOVA (mixing costs vs. switching costs) revealed a significant interaction effect of group and cost [F(1,44) = 9.75, p = .003, partial ŋ2 = .181] and a significant main effect for group [F(1,44) = 4.12 , p = .048, partial ŋ2 = .086]. All other main and interaction effects were non significant [Fs b 3.04, ps N .089]. Given the significance of the interaction effect between group and cost, the univariate main effects of each cost were examined: A significant univariate main effect was observed for mixing costs [F(1,44) = 9.29, p = .000, partial ŋ2 = .302] but not for switching costs [F b .073, p N .78]. Error analysis in mixing or switching costs revealed no significant main effect for group, interaction or covariate [Fs b 1.10, ps N .301].

3.5.1. Condition 1 [frequency band 1] Graph theoretical properties of resting-state functional brain data were observed in the range of correlation coefficients between 0.1 and 0.9 (between all ROIs of the adjacency matrix except for the default left frontal pole ROI from the atlas). Global efficiency [β = .05, t(40) = 3.77, p-FDR = .035] and degree [β = 6.97, t(40) = 3.78, p-FDR = .034] was found to be significantly greater for SIs compared to controls in the VBM frontal pole cluster (see Fig. 6B). No other ROIs/nodes were significant at this threshold level, nor did the reverse contrast (controls N SIs) reveal any significant results. Condition 2 and 3 [frequency band 2 and 3] Graph analysis revealed no significant ROIs/nodes for the contrast SIsN controls for neither global efficiency nor for degree. Also the reverse contrast (controls N SIs) did not reveal any significant results.

3.3. Dual tasking The repeated measures ANCOVA for the second task indicated a significant main effect for trials [F(2.08,43) = 9.46, p b .001, partial ŋ2 = .180, Greenhouse-Geisser corrected] and an interaction effect for trials and group [F(2.08,43) = 4.48, p = .013, partial ŋ2 = .094, Greenhouse-Geisser corrected] (see Table 5, Fig. 4). A post hoc test revealed that dual-task costs (i.e., difference of dual-task RTs minus single-task RTs) in SIs in contrast to controls were decreased in trials with SOA = 50 ms [t(32.380) = 2.63, p = .009, Levene test adapted] and in trials with SOA = 400 ms [t(32,107) = 2.53, p = .017, Levene test adapted]; the between-group difference in dual-task costs in trials with SOA = 100 ms was not significant [t(45) = 1.60, p = .116]. The significant between-group differences in dual-task costs in trials with SOA = 50 ms and trials with SOA = 400 ms did not result from similar single-task RTs [t(45) = 1.63, p = .110], but from faster RTs in SIs in the dual-task trials of these SOA levels (SOA = 50 ms: [t(45) = 2.79, p = .008]; SOA = 400 ms: [t(31.142) = 2.71, p = .006, Levene test adapted]). Furthermore, there was a significant group main effect [F(1,43) = 7.54, p = .009, partial ŋ2 = .149], indicating that individuals with experience in SI (1064 ± 32 ms) were (on average) 134 ms faster compared to controls (1199 ± 36 ms). There was also a significant age covariate effect [F(1,43) = 6.76, p = .013, partial ŋ2 = .136]. All other main, interaction and covariate effects were non-significant [Fs b 1.89, ps N .15]. The univariate ANCOVA for dual-task cost (RT) indicated a significant main effect for group [F(1,43) = 7.59, p = .009, partial ŋ2 = .150] and age [F(1,43) = 4.83, p = .033, partial ŋ2 = .101]. This analysis parallels the results from the repeated measures ANCOVA for SOA 50 ms. It was reported here to ensure that this single measure of dual-task performance does exhibit a group effect and, thus, represents a suitable variable for later regression analyses with gray matter volume.

3.6. Functional connectivity analysis 3.6.1. Condition 1 [frequency band 1] In order to investigate possible group differences in functional connectivity between the frontal pole cluster and the left lateral prefrontal areas, we analyzed a connectivity matrix with all 132 ROIs (excluding the default left frontal pole). The analysis revealed a significant functional connectivity between the frontal pole cluster and the left inferior frontal gyrus (IFG) pars opercularis [t(40) = 3.20, p = .003] as well as pars triangularis [t(40) = 3.12, p = .003] in SIs compared to controls (see Fig. 6A). Furthermore, there was also greater functional connectivity between the frontal pole cluster and the left middle temporal gyrus (MTG) (posterior division) [t(40) = 3.36, p-uncorr = .002] in SIs compared to controls. 3.6.2. Condition 2 and 3 [frequency band 2 and 3] Connectivity analyses with frequency band 2 and 3 did not reveal any significant results [ps N .05]. The reverse contrast (controls N SIs) also did not lead to significant results [ps N .06]. 4. Discussion We provided evidence that SIs show cognitive benefits in two nonlanguage domains compared to a professional multilingual control group: Concerning task switching, SIs did not react significantly faster or produce less errors in blocks where the same tasks were presented compared to controls. It can, thus, be assumed, that both groups do not differ statistically in RT when performing this task. Furthermore,

3.4. Voxel-based morphometry When computing a whole-brain analysis on GM volume in search of brain regions that show more volume in persons with experience in SI, we found a single cluster (p b 0.001, multiple comparison corrected using expected cluster size: 73 voxels) in the left frontal pole region (Brodmann area 10, MNI coordinates: − 25.5, 54, − 4.5) (see Fig. 5). The reverse contrast (controls N SIs) revealed no significant results. The hierarchical regression analysis revealed that this frontal pole cluster does marginally significantly predict variance in dual-task costs (RT) [R2 change = .056, β = −.291, t = −1.66, p = .053 (one-tailed)] and in mixing costs (RT) [R2change = .051, β = −.236, t = − 1.50, p = .072 (one-tailed)] when investigating the amount unique variance explained by the frontal pole cluster in the cognitive task values additionally to group membership. Furthermore, when only regarding SIs, we found a negative partial correlation between mixing costs (RT) and the left frontal pole cluster [r(19) = −.377, p = .046, one-tailed] while controlling for age and gender.

Fig. 3. Switching and mixing costs (RT) compared between SIs and controls in the task switching paradigm. Note. Error bars represent standard errors. Covariates appearing in the model are evaluated at the following values: age = 41.83, gender = 1.75.


SIs have less mixing costs but not less switching costs (see Fig. 3, Table 3 for difference values and Table 4 for mean values). Mixing or switching costs in accuracy between both groups do not differ. Thus, advantages in task-switching speed processing in SIs are not a result of a speedaccuracy trade-off. Concerning dual tasking, individuals with experience in SI react generally faster over all trials (single- and dual-tasks) compared to controls (see Fig. 4). But the significant interaction effect also demonstrates that SIs react additionally faster in the dual-task condition when the SOA was 50 ms and 400 ms. This indicates a dual-task advantage in the second task for SIs compared to controls for these intervals. The lacking significant decrease in Task 2 from the SOA level of 50 ms to that level of 100 ms in SIs might be due to the very efficient switch between the bottleneck stage of Task 1 and this stage in Task 2 at the former SOA level while this switch is less efficient at the latter SOA level (Hartley and Little, 1999; Strobach et al., 2012). It is possible that SI-training related improvement of dual-task skills could specifically improve the efficiency of switching in bottleneck stages of early and late SOA levels. Furthermore, the positive age effect signifies that younger individuals have smaller RTs compared to older individuals independently of group membership. This age effect on RTs in dual-task situations has been well investigated (Verhaeghen et al., 2003). The main effect for trial also revealed the above-described effect that individuals tend to react slower to the same task if another task is simultaneously presented compared to single-tasks and reaction times increase with decreasing SOAs (Pashler, 1994). When only considering the dual-task costs for the shortest lag (dual-task interference and degree of difficulty are the highest), SIs also show a dual-task cost advantage (129 ± 47 ms) compared to controls. Furthermore, the age effect reflects the above-mentioned decline in RT with age (Verhaeghen et al., 2003). In terms of brain structure, SIs exhibit more GM volume in the left frontal pole (BA 10). We only found a trendwise negative association between RT costs in the cognitive tasks and GM volume in the left frontal pole cluster from the VBM analysis. However, there is a significant negative relationship between mixing costs (RT) and this left frontal pole cluster when only regarding the group of SIs. Graph theoretical analyses on rsfMRI data additionally confirmed the assumption that the left frontal pole is an important node in the functional network of SIs compared to the control group by being more functionally connected to other nodes and globally more integrated into the network. Finally, the left frontal pole is functionally more strongly connected to the left IFG and left MFG in SIs compared to

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Fig. 5. Differences in GM volume in (left) frontal pole between SIs and controls. Note. Error bars represent standard errors. Covariates appearing in the model are evaluated at the following values: Gender = 1.76, age = 40.96, total brain volume = 1423.00.

controls but no other areas. However, our results indicate only group differences in functional connectivity and graph theoretical network properties in the lower frequency range between .008 and .0353 Hz. An explanation for this could be due to the fact that most significant frequency ranges that are enhanced by T2* weightings (and believed to best represent the BOLD signal) are located in the lowest frequency ranges between .008–.023 Hz and .037–.043 Hz in the brain at rest (for further discussion see Wu et al., 2012; Chang and Glover, 2010; Sasai et al., 2011). In addition to our findings, there is evidence, that the (left) frontopolar cortex (FPC) and its stronger functional connectivity to the left IFG could serve as neural mechanism for the observed behavioral effects. 4.1. Cognitive tasks & BA 10

Fig. 4. Mean RTs in Task 2 of the dual-task paradigm for the single-task and dual-task (SOA: 50, 100, 400ms) condition in SIs and controls. Note. Error bars represent standard errors. Covariates appearing in the model are evaluated at the following values: gender = 1.74, age = 41.60.

The FPC has been consistently associated with multitasking (for a meta-analysis, see Gilbert et al., 2006). Braver and Bongiolatti suggest that the FPC is selectively engaged in monitoring and integrating subgoals during working memory tasks (Braver and Bongiolatti, 2002). Similarly, Ramnani and Owen argue that this region integrates the outcomes of two or more separate cognitive operations in the pursuit of a higher behavioral goal (Ramnani and Owen, 2004; Dreher et al., 2008). There is evidence from lesion studies demonstrating that damage to the FPC (BA 10) correlates with decreased performance in a dual-tasking condition and the reduced ability to manage multiple goals (Dreher et al., 2008). This is consistent with our findings, that the amount GM volume in the frontal pole cluster (BA 10) at least descriptively predicts variance in dual-task costs (p = .053), when group is controlled for. Furthermore, there is evidence from lesion studies in primates and fMRI studies in humans that the left FPC is involved in attention shifting and in the reallocation of executive resources in ill-defined task settings (Pollmann, 2016; Mansouri et al., 2015; Konishi et al., 2005). Pollmann and colleagues for example found increased left FPC activation when participants had to shift their attention to a different visual target

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Fig. 6. A) Functional connectivity analysis: The left frontal pole cluster (extracted from the VBM analysis) is functionally more connected to two left prefrontal areas and one temporal area in SIs compared to controls. B) Graph theoretical analysis: This figure represents all nodes whose t-values for global efficiency (and degree) survived the threshold p-FDR b.05 when comparing the networks nodes of both groups. The left frontal pole cluster (extracted from the VBM analysis) exhibits more global efficiency and degree in SIs compared to controls.

dimension (Pollmann, 2001). When interpreting, SIs also do have to switch attention more often between different language dimensions (comprehension in L1 vs. production in L2). This is consistent with our finding that, when only regarding SIs, there is a negative correlation between the left frontal pole cluster from the VBM analysis and mixing costs (RT). Thus, it is possible that the reduced mixing costs (RT) in SIs compared to controls could be due to a superiorly developed left FPC in SIs. Moreover, the FPC is known to be closely interconnected with related regions like supramodal prefrontal areas (i.e. the lateral prefrontal cortex) as well as the cingulate cortex and anterior temporal cortex but not with actual sensory areas (Ramnani and Owen, 2004). Due to these connectivity characteristics it has been argued that the FPC influences abstract information processing and integrates the outcomes of multiple cognitive operations (Koechlin et al., 2003; Ramnani and Owen, 2004; Petrides and Pandya, 2007). As already mentioned further above, Takeuchi and colleagues provided evidence for neuroplastic changes within this prefrontal interconnected brain network following a multitasking training (Takeuchi et al., 2014). Because our results indicate that the left IFG is relatively more functionally connected to the left FPC in SIs compared to controls, this enhanced coupling of lateral prefrontal areas could also reflect neuroplastic changes (although on a functional level) due to SI training and language related multitasking demands. Furthermore, it is plausible that the observed effects are predominately left hemispheric due to the fact that language organization is mainly left hemispheric (Vigneau et al., 2006). In addition, the left IFG has been consistently reported to be involved in language related tasks and task-switching (Hirshorn and Thompson-Schill, 2006; Poldrack et al., 1999; Derfuss et al., 2005). 4.2. Transfer effects One of our main research questions was whether individuals with experience in SI show cognitive benefits in a non-language domain that can be associated with their more extreme language control compared to professional multilingual controls. The underlying assumption is that SIs exhibit transfer effects, i.e. they acquired specific cognitive

subskills due to training in SI that allows them to perform more efficiently in the respective tasks. However, of course, the described results do not allow any causal interpretation (due to the cross-sectional design) whether SIs have always possessed these specific abilities required for performing the tasks adequately or whether SIs acquired these skills due to training and SI experience (Christoffels and de Groot, 2009). Only longitudinal designs allow such a causal interpretation and this kind of design exceeds the scope of the present study. However, there is longitudinal evidence indicating that SIs do acquire specific abilities due to training. A study by Bajo showed that training can improve performance at least on basic language skills (comprehension, categorization and lexical decision) compared to a control group (Bajo, 2000 in Christoffels and de Groot, 2009). Furthermore, there is evidence for plastic changes in brain structure due to SI training: Hervais-Adelman and colleagues found that the left lateral PFC (middle frontal gyrus and IFG, pars orbitalis) as well as the left middle temporal gyrus increased in volume in SI students compared to multilingual controls over the course of a 15-month training program (further regions involved the rostral anterior cingulate and the left supramarginal gyrus) (Hervais-Adelman et al., 2011). These results are consistent with our resting-state connectivity analyses; however we only find group differences for GM volume in the left FPC and not in the rest of all these brain areas. One reason for these different results could be the fact that we contrasted professional SIs with a professional multilingual control group (TRs and CIs). It can be assumed that both groups already are a lot more similar in terms of their daily professional activity, i.e. how often they make use of their language expertise, compared to trained simultaneous interpreters and non-professional multilingual individuals. Furthermore, participants from our sample have not just undergone professional training but are actively working in their respective occupational field for about twelve years on average (see Table 1). Thus, training-induced plastic changes within the abovenamed areas might not be as visible anymore in brain structure but rather reflect in brain function at rest (resting-state BOLD activity). To our knowledge, this has not been investigated so far, but it would be very informative to know in how far SI training induced volumetric increase of left hemispheric areas (especially temporal and prefrontal regions) involves an increase in functional connectivity between these


regions and is associated with enhanced performance in dual-tasks and task switching paradigms involving cognitive control. Especially the investigation of the latter relationship would be substantially informative to understand the relationship between the complex behavior of SIs, cognitive and language control. 5. Limitations One limitation of the present study is the heterogeneity of our sample. Apart from age and different number of males in both groups which we controlled for by integrating those variables as covariates of no interest into all analyses, the groups differ in terms of the foreign language people were proficient in and the educational level. The control group indicated to have studied longer (see Table 1). However, it would be unintuitive to assume that a longer university education negatively affects cognitive control. Finally, to our knowledge, there are no studies indicating that the foreign language (i.e. German, English or Arabic) someone is proficient in is associated with differences in cognitive performance. Thus, taken together, we have no reasons to believe that the heterogeneity of our sample systematically biased the reported results. However future studies are needed to replicate the present results with a more homogeneous sample. Conflict of interest The authors have no conflict of interest. Acknowledgments We want to thank Andre Hildebrandt and Susanne Konschak for their help with recruiting the interpreters, Sonali Beckmann, Nadine Taube and the MRI team at the Max-Planck-Institute for Human Development for their assistance in acquiring the brain data and Martyna Lochstet as well as Yuan-Hao Wu for acquiring the behavioral data. References Abutalebi, J., Green, D., 2008. Control mechanisms in bilingual language production: neural evidence from language switching studies. Lang. Cogn. Process. 23 (4), 557–582. Ashburner, J., 2007. A fast diffeomorphic image registration algorithm. NeuroImage 38, 95–113. Behzadi, Y., Restom, K., Liau, J., Liu, T., 2007. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. NeuroImage 37 (1), 90–101. Bialystok, E., 2001. Bilingualism in Development: Language, Literacy, and Cognition. Cambridge University Press, New York. Bialystok, E., 2007. Cognitive effects of bilingualism: how linguistic experience leads to cognitive change. Int. J. Biling. Educ. Biling. 10 (3), 210–223. Bialystok, E., Barac, R., 2012. Emerging bilingualism: dissociating advantages for metalinguistic awareness and cognitive control. Cognition 122 (1), 67–73. Bialystok, E., DePape, A., 2009. Musical expertise, bilingualism, and executive functioning. J. Exp. Psychol. Hum. Percept. Perform. 35 (2), 565. Bialystok, E., Craik, F., Ruocco, A., 2006a. Dual-modality monitoring in a classification task: the effects of bilingualism and ageing. Q. J. Exp. Psychol. 59 (11), 1968–1983. Bialystok, E., Craik, F., Ryan, J., 2006b. Cognitive control in a modified antisaccade task: effects of aging and bilingualism. J. Exp. Psychol. Learn. Mem. Cogn. 32 (6), 1341. Bialystok, E., Craik, F., Luk, G., 2012. Bilingualism: consequences for mind and brain. Trends Cogn. Sci. 16 (4), 240–250. Biswal, B., Mennes, M., Zuo, X., Gohel, S., Kelly, C., Smith, S., ... Windischberger, C., 2010. Toward discovery science of human brain function. Proc. Natl. Acad. Sci. 107 (10), 4734–4739. Biswal, B., Yetkin, F., Haughton, V., Hyde, J., 1995. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34 (4), 537–541. Braver, T., Bongiolatti, S., 2002. The role of frontopolar cortex in subgoal processing during working memory. NeuroImage 15 (3), 523–536. Bullmore, E., Sporns, O., 2009. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10 (3), 186–198. Chang, C., Glover, G.H., 2010. Time–frequency dynamics of resting-state brain connectivity measured with fMRI. NeuroImage 50 (1), 81–98. Christoffels, I., de Groot, A., 2009. Simultaneous Interpreting. In: Kroll, J., de Groot, A. (Eds.), Handbook of Bilingualism: Psycholinguistic Approaches. Oxford University Press, pp. 454–479. Christoffels, I., de Groot, A., Kroll, J., 2006. Memory and language skills in simultaneous interpreters: the role of expertise and language proficiency. J. Mem. Lang. 54 (3), 324–345.

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