A person-centered examination of emotion dysregulation, sensitivity to threat, and impulsivity among children and adolescents: An ERP study
Bibliographic record
Abstract
OBJECTIVES: Adolescence often is characterized by the onset of social anxiety and risk taking; yet, not all youth are anxious and/or risk takers. There are several factors that may help differentiate youth with anxiety (e.g., threat sensitivity and emotion dysregulation) and youth who take risks (e.g., impulsivity and emotion dysregulation). We conducted a latent class analysis to identify groups of youth who differ in these processes, and then investigated group differences on the error-related negativity, an ERP that has been differentially associated with threat sensitivity and impulsivity. METHOD: = 11, range = 8-15 years) completed a survey assessing their emotion dysregulation, sensitivity to threat, and impulsivity. A subsample (N = 424) also completed a go/no-go task while EEG was recorded. RESULTS AND CONCLUSIONS: Four groups were identified with differential levels of emotion dysregulation, sensitivity to threat, and impulsivity. Adolescents had greater odds than children of being in the High_Dysregulation/ThreatSensitivity or ModerateDysregulation/HighImpulsivity Groups in comparison to two other groups with lower scores. The High_Dysregulation/ThreatSensitivity Group had the largest ERN, while the ModerateDysregulation/HighImpulsivity Group had the smallest ERN. The ERN may be a potential biomarker to help distinguish between different profiles of adolescents who may be at risk for either anxiety or risk taking.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".