Correlation between LIFG and Autonomic Activation during Stressful Tasks： A Functional Near-infrared Spectroscopy （fNIRS） Study
Bibliographic record
Abstract
It remains unclear whether language tasks in one’s first(L1) or second(L2) language can cause stress responses and whether frontal, autonomic and behavioral responses to stressful tasks are correlated. In this study, we studied 22 Chinese subjects whose L2 was English and measured the cerebral blood oxygenation in their frontal lobe by using functional near-infrared spectroscopy(fNIRS) as participants engaged in a mental arithmetic task(MAT) and verbal fluency tasks(VFTs) in L1(Chinese) and L2(English). To examine the activated cortical areas, we estimated the channel location based on Montreal Neurological Institute(MNI) standard brain space by using a probabilistic estimation method. We evaluated heart rate(HR) changes to analyze autonomic nervous system(ANS) functioning. We found that the MAT and VFTs induced greater increases in HR than did the control(Ctrl) task. Furthermore, subjects developed greater increases in HR in the MAT and VFTL2 than they did in the VFTL1. Compared with the Ctrl task, the MAT and both VFTL1 and VFTL2 produced robust and widespread bilateral activation of the frontal cortex. Interestingly, partial correlation analysis indicated that the activity in the left inferior frontal gyrus(LIFG) [Brodmann’s area(BA) 47] was consistently correlated with the increases in HR across the three tasks(MAT, VFTL2, and VFTL1), after controlling for the performance data. The present results suggested that a VFT in L2 may be more stressful than in L1. The LIFG may affect the activation of the sympathetic system induced by stressful tasks, including MATs and VFTs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".