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. 2023 Mar 8;4(1):178-197.
doi: 10.1162/nol_a_00093. eCollection 2023.

Spatiotemporal Dynamics of Activation in Motor and Language Areas Suggest a Compensatory Role of the Motor Cortex in Second Language Processing

Affiliations

Spatiotemporal Dynamics of Activation in Motor and Language Areas Suggest a Compensatory Role of the Motor Cortex in Second Language Processing

Lili Tian et al. Neurobiol Lang (Camb). .

Abstract

The involvement of the motor cortex in language understanding has been intensively discussed in the framework of embodied cognition. Although some studies have provided evidence for the involvement of the motor cortex in different receptive language tasks, the role that it plays in language perception and understanding is still unclear. In the present study, we explored the degree of involvement of language and motor areas in a visually presented sentence comprehension task, modulated by language proficiency (L1: native language, L2: second language) and linguistic abstractness (literal, metaphorical, and abstract). Magnetoencephalography data were recorded from 26 late Chinese learners of English. A cluster-based permutation F test was performed on the amplitude of the source waveform for each motor and language region of interest (ROI). Results showed a significant effect of language proficiency in both language and motor ROIs, manifested as overall greater involvement of language ROIs (short insular gyri and planum polare of the superior temporal gyrus) in the L1 than the L2 during 300-500 ms, and overall greater involvement of motor ROI (central sulcus) in the L2 than the L1 during 600-800 ms. We interpreted the over-recruitment of the motor area in the L2 as a higher demand for cognitive resources to compensate for the inadequate engagement of the language network. In general, our results indicate a compensatory role of the motor cortex in L2 understanding.

Keywords: abstractness; language proficiency; magnetoencephalography; motor cortex involvement; native language; second language.

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Conflict of interest statement

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

Figures

<b>Figure 1.</b>
Figure 1.
Schematic view of the experimental procedure.
<b>Figure 2.</b>
Figure 2.
Grand-averaged results at sensor and source level. (A) Grand-averaged sensor waveform (204 gradiometers) across conditions in the L1 and L2 experiments; the light-shaded areas indicate the time window applied to the region-of-interest-based statistical analysis. (B) Grand-averaged source activation quantified as mean dynamic statistical parametric mapping (dSPM) value over time points corresponding to each peak: ±40 ms duration prior and after the relatively transient peaks (peak 1 and peak 2), and ±100 ms prior and after the relatively sustainable peaks (peak 3 and peak 4). The intensity of the color in the cortical activation map indicates the degree of dSPM value. L1: native language (Chinese); L2: second language (English); lit: literal; met: metaphorical; abs: abstract; L: left hemisphere; R: right hemisphere.
<b>Figure 3.</b>
Figure 3.
Grand-averaged source time courses for the literal, metaphorical, and abstract conditions in the L1 and L2 experiments in the indicated ROIs (language ROIs: short insular gyri, planum polare of the superior temporal gyrus, and superior temporal sulcus; motor ROIs: inferior part of the precentral sulcus and central sulcus). The parcellation of each ROI is shown in the inflated brain surface with a lateral view. For a better view, the planum polare of the superior temporal gyrus is also shown with a rostral view.
<b>Figure 4.</b>
Figure 4.
Results of permutation F test on the language and motor ROIs. (Left) Significant clusters at source space (clusters exceeding the randomization distribution under H0 hypothesis). The intensity of the color in the cortical map indicates the duration of the time window of clusters. (Right) Source time courses (represented as dSPM value) extracted from significant spatiotemporal clusters; light orange shading area shows time window of the significant cluster. dSPM: dynamic statistical parametric mapping.

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