The temporal and spatial brain dynamics of automatic emotion regulation in children
Bibliographic record
Abstract
Mechanisms for automatic emotion regulation (AER) are essential during childhood as they offset the impact of unwanted or negative emotional responses without drawing on limited attentional resources. Despite the importance of AER in improving the efficiency and flexibility of self-regulation, few research studies have investigated the underlying neurophysiological mechanisms. To fill this gap, we used magnetoencephalography (MEG) to investigate AER-related brain processes in 25 children (∼10 years old) who performed a go/no–go task that included an incidental exposure to faces containing socio-emotional cues. Whole brain results revealed that the inhibition of angry faces (compared with happy faces) was associated with a stronger recruitment of several brain regions from 100 to 425 ms. These activations involved the right angular and occipital gyri from 100 to175 ms, the right orbito-frontal gyrus (OFG) from 250 to 325 ms (pcorr < 0.05), and finally, the left anterior temporal lobe (ATL) from 325 to 425 ms. Our results suggest a specific involvement of these regions in the automatic regulation of negative emotional stimuli in children. In the future, this knowledge may help understand developmental conditions where inhibition impairments are exacerbated by an emotional context.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".