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. 2017 Sep 13;37(37):9022-9036.
doi: 10.1523/JNEUROSCI.0553-17.2017. Epub 2017 Aug 16.

Bilingual Language Switching in the Laboratory versus in the Wild: The Spatiotemporal Dynamics of Adaptive Language Control

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Bilingual Language Switching in the Laboratory versus in the Wild: The Spatiotemporal Dynamics of Adaptive Language Control

Esti Blanco-Elorrieta et al. J Neurosci. .

Abstract

For a bilingual human, every utterance requires a choice about which language to use. This choice is commonly regarded as part of general executive control, engaging prefrontal and anterior cingulate cortices similarly to many types of effortful task switching. However, although language control within artificial switching paradigms has been heavily studied, the neurobiology of natural switching within socially cued situations has not been characterized. Additionally, although theoretical models address how language control mechanisms adapt to the distinct demands of different interactional contexts, these predictions have not been empirically tested. We used MEG (RRID: NIFINV:nlx_inv_090918) to investigate language switching in multiple contexts ranging from completely artificial to the comprehension of a fully natural bilingual conversation recorded "in the wild." Our results showed less anterior cingulate and prefrontal cortex involvement for more natural switching. In production, voluntary switching did not engage the prefrontal cortex or elicit behavioral switch costs. In comprehension, while laboratory switches recruited executive control areas, fully natural switching within a conversation only engaged auditory cortices. Multivariate pattern analyses revealed that, in production, interlocutor identity was represented in a sustained fashion throughout the different stages of language planning until speech onset. In comprehension, however, a biphasic pattern was observed: interlocutor identity was first represented at the presentation of the interlocutor and then again at the presentation of the auditory word. In all, our findings underscore the importance of ecologically valid experimental paradigms and offer the first neurophysiological characterization of language control in a range of situations simulating real life to various degrees.SIGNIFICANCE STATEMENT Bilingualism is an inherently social phenomenon, interactional context fully determining language choice. This research addresses the neural mechanisms underlying multilingual individuals' ability to successfully adapt to varying conversational contexts both while speaking and listening. Our results showed that interactional context critically determines language control networks' engagement: switching under external constraints heavily recruited prefrontal control regions, whereas natural, voluntary switching did not. These findings challenge conclusions derived from artificial switching paradigms, which suggested that language switching is intrinsically effortful. Further, our results predict that the so-called bilingual advantage should be limited to individuals who need to control their languages according to external cues and thus would not occur by virtue of an experience in which switching is fully free.

Keywords: adaptive cognitive control; bilingualism; language control; language switching; magnetoencephalography; prefrontal cortex.

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Figures

Figure 1.
Figure 1.
A, Different experimental conditions varying from less natural to more natural contexts. B, Trial design for production and comprehension laboratory tasks as well as for the natural conversation. In production, participants were asked to name the drawing as quickly and as accurately as possible in the language that matched the cue they had just seen. In comprehension, participants had to judge via button press whether an auditorily presented word and a subsequently presented picture matched. In the natural conversation, participants listened to snippets of a real conversation between two Arabic-English bilinguals.
Figure 2.
Figure 2.
Context effects in production (A) and comprehension (B). The analysis of the MEG activity time-locked to the presentation of the cue revealed reliable clusters in the posterior part of the ACC bilaterally. In both sections, the freeSurfer average brains represent the spatial distribution of the reliable cluster (every source that was part of the cluster at some point in time is color-coded with the F statistic summed over the duration of the cluster). On the waveform plots, we show the time course of activity for the sources in the cluster. Shaded regions represent that the difference in activity between the tested conditions was significant at p = 0.05 (corrected). Significance was determined using a nonparametric permutation test (Maris and Oostenveld, 2007) performed from 0 to 300 ms after the presentation of the cue (10,000 permutations). Right, Bar graph represents the average activity per condition for the sources and time points that constitute the cluster. Pairwise significance is indicated.
Figure 3.
Figure 3.
Production trial design and results. The analysis of the MEG activity time-locked to the stimulus picture revealed two temporally distinct significant interaction clusters in the left ACC and dlPFC. The freeSurfer average brains on the left-hand side represent the spatial distribution of the reliable cluster (every source that was part of the cluster at some point in time is color-coded with the sum F or t statistic). On the waveform plots, we show the time course of activity for the sources in the cluster, where 0 is the presentation of the stimulus. Shaded regions represent that the difference in activity between the tested conditions was significant at p = 0.05 (corrected). Significance was determined using a nonparametric permutation test (Maris and Oostenveld, 2007) performed from 100 to 300 ms (10,000 permutations). Right, Bar graph represents the average activity per condition for the sources and time points that constitute the cluster. *Pairwise significance.
Figure 4.
Figure 4.
Mean reaction times (A) and error rates (B) as a function of the conversational context and performed switching condition within production tasks. The number at the bottom of each bar indicates the average value for that condition. Error bars indicate SEM.
Figure 5.
Figure 5.
Comprehension trial design and results: A, Laboratory contexts. B, Natural conversation. The analysis of the MEG activity time-locked to the beginning of the auditory word revealed a significant cluster of activity in the left ACC and dlPFC, reflecting a main effect of Switch. Left, The freeSurfer average brains represent the spatial distribution of the reliable cluster (every source that was part of the cluster at some point in time is color-coded with the sum F or t statistic). On the waveform plots, we show the time course of activity for the sources in the cluster, where 0 is the beginning of the auditory stimulus. A, Shaded region represents that the difference in activity between the tested conditions was significant at p = 0.05 (corrected). B, Vertical lines indicate the analyzed window. Significance was determined using a nonparametric permutation test (Maris and Oostenveld, 2007) performed from 100 to 300 ms (10,000 permutations). Right, Bar graphs represent the average activity per condition for the sources and time points that constitute the cluster. *Pairwise significance.
Figure 6.
Figure 6.
Regression analysis on the MEG data acquired during the presentation of the Natural conversation. A, Waveform amplitude for an example stimulus. B, Regressors in the model. C, Examples of the fifth predictor, discourse boundary. D, E, Clusters of activity in the left and right auditory cortices, respectively, for the bilingual speakers. F, G, Same analyses for the monolingual speakers. The freeSurfer average brains illustrate the spatial distribution of the reliable clusters (every source that was part of the cluster at some point in time is color-coded with the sum β values). G, Colored area represents the extent of the analyzed area. On the waveform plots, we show the time course of activity for the sources in the cluster, as indicated by the regression coefficients at each time point, where 0 is the beginning of the first word of the language switch. Shaded regions represent that the regression equation was significant at p = 0.05 (corrected). Significance was determined using a nonparametric permutation test (Maris and Oostenveld, 2007) performed from 100 to 500 ms (10,000 permutations). Right, Bar graphs represent the average activity per condition for the sources and time points that constitute the cluster. The analysis of the MEG activity was time-locked to the beginning of the first word of the code switch.
Figure 7.
Figure 7.
MVPA of context for Production (A) and Comprehension (B) using Generalization across time (King et al., 2014). A, B, Left panels, Classifier accuracy trained and tested at every time point. Right panels, Classifier accuracy for the diagonal of the matrix (i.e., when the classifier was trained and tested on the same time point). Shading along the decoding accuracy represents 95% CIs. In each panel, a full brain shows the source-localization of the pattern weights at the peak of the classification accuracy.
Figure 8.
Figure 8.
Summary of results for production (a) and comprehension (b) showing an early effect of Context, time locked to the presentation of the cue, but no effect of switching until the word to be produced or comprehended was revealed. In each panel, a standard free surfer average brain illustrates the extent of each cluster. Vertical lines on each side of the brain indicate the duration of the effect in the horizontal time line. Underneath the average brains, a horizontal line indicates context decoding accuracy at each millisecond: the shading indicates 95% CIs.

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