Emotion Regulation: Is it More Taxing for Adolescents than for Adults?
Bibliographic record
Abstract
The capacity to regulate emotions appears to improve with age across adolescence; but adolescence is also a time of heightened emotional intensity (Fjell et al. 2012; Larson & Ham, 1993; Shulman et al., 2016; Steinberg 2008). The coexistence of immature emotion regulation and heightened emotional intensity during adolescents complicates attempts to index improvement in emotion regulation capacity during this period. We used a novel approach to quantify emotion regulation, where participants performed a cognitively taxing working memory task (the N-Back) before and after a manipulation designed to elicit strong negative feelings (anger or embarrassment). Performance on the N-Back should be impaired by strong emotion (Baumeister et al., 2007; Richards, 2004), and failure to improve in performance from time 1 to time 2 on the N-back following emotion elicitation served as our index of difficulties in emotion regulation. To account for emotional intensity, we assessed emotional state before and after the experimental manipulation. We could therefore test whether any age differences in practice-related improvement on the N-back task were robust to adjustment for emotional intensity (the change in the relevant emotion pre- to post-manipulation). A total of 184 participants between the ages of 14 and 17 (Mage = 15.7, SD = 1.1), and 22 and 30 (Mage = 25.7, SD = 2.3) took part in the study. Overall, emotion elicitation interfered with performance on the N-back task in both age groups. As predicted, adolescents’ performance was more impaired than adults’ when the emotion elicited was embarrassment but not anger. This suggests that adolescents’ emotion regulation capacity is weaker than adults’ for at least some emotions. Importantly, this age difference was not attributable to differences in emotional intensity.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".