Behavioral Differences in Aggressive Children Linked with Neural Mechanisms of Emotion Regulation
Bibliographic record
Abstract
Children with aggressive behavior problems may have difficulties regulating negative emotions, resulting in harmful patterns of interpersonal behavior at home and in the schoolyard. Ventral and dorsal regions of the prefrontal cortex (PFC) have been associated with response inhibition and self-control-key components of emotion regulation. Our research program aims to explore differences among aggressive and normal children in the activation of these cortical regions during emotional episodes, to the extent possible using electrophysiological techniques, to identify diagnostic subtypes, gain insights into their interpersonal difficulties, and help develop effective treatment strategies. This report reviews several recent studies investigating individual and developmental differences in cortical mechanisms of emotion regulation, corresponding with different patterns of interpersonal behavior. Our methods include event-related potentials (ERPs) and cortical source modeling, using dense-array electroencephalography (EEG) technology, as well as videotaped observations of parent-child interactions, with both normal and aggressive children. By relating patterns of brain activation to observed behavioral differences, we find (i) a steady decrease in cortical activation subserving self-regulation across childhood and adolescence, (ii) different cortical activation patterns as well as behavioral constellations distinguishing subtypes of aggressive children, and (iii) robust correlations between the activation of cortical mediators of emotion regulation and flexibility in parent-child emotional communication in children referred for aggressive behavior problems. These findings point toward models of developmental psychopathology based on the interplay among biological, psychological, and social factors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".