Developmental Change in the Neurophysiological Correlates of Self-Regulation in High- and Low-Emotion Conditions
Bibliographic record
Abstract
One of the most important tasks of childhood is learning to self-regulate in the presence of negative emotions. Until recently, almost no research has examined the neurophysiological correlates of emotional self-regulation as it develops over childhood and adolescence. We were interested in plotting a fine-grained developmental profile of the neural underpinnings of self-regulation, in the context of negative emotion, for 7- to 14-year-old children. We predicted that children would recruit less cortical activation with age in the service of self-regulation, reflecting increased neural efficiency with development. We also predicted that children would recruit more cortical activation with increased negative emotion, possibly reflecting greater demand on cortical resources. We administered a Go No-Go task with an emotion induction block and we measured the amplitude of the N2, an event related potential associated with inhibitory control, as it varied with block and with age. Furthermore, we estimated activation for a ventral prefrontal region of interest (ROI; suggestive of orbital frontal, ventromedial prefrontal, or rostral anterior cingulate activation) and a dorsomedial prefrontal ROI (suggestive of dorsal anterior cingulate activation) frequently modeled as cortical generators underlying the N2. Results revealed a marginal decrease in mediofrontal scalp activation, but a more pronounced decrease in activation of the ventromedial prefrontal ROI, with age. There were no age-related changes in dorsomedial prefrontal ROI activation. Lastly, as predicted, we found increased ventral prefrontal ROI activation during the negative emotion induction, possibly reflecting greater recruitment of frontocortical resources underlying emotion regulation, but developmental change in this activation was no different than for the other conditions. Thus, both self-regulation in general and emotion regulation in particular recruited less cortical activation with age, suggesting more efficient cortical mechanisms of response inhibition.
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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 | 0.001 |
| 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".