The who and what of validation: an experimental examination of validation and invalidation of specific emotions and the moderating effect of emotion dysregulation
Bibliographic record
Abstract
BACKGROUND: Theory and research indicate that validation is associated with reductions in negative emotions, whereas invalidation is associated with escalation of negative emotions. However, it remains unclear whether these effects are consistent across emotions, and/or moderated by an individual's levels of emotion dysregulation. The present study experimentally examines the effects of validation and invalidation across emotions and as moderated by emotion dysregulation. METHODS: One hundred twenty-six participants completed a measure of emotion dysregulation, and then listened to a rejection-themed imagery script after which they reported the intensity of several emotions. Participants were then presented with either validating or invalidating feedback about their most intense self-reported emotion, depending on their counterbalancing order. They then repeated the procedure for the other condition. Self-reported negative emotions via continuous rating dial, heart rate (HR), and skin conductance level (SCL) were monitored throughout. RESULTS: Higher emotion dysregulation was associated with greater increases in self-reported positive emotion when shame or sadness was validated and lesser increases when fear was validated. There were no significant moderating effects of emotion dysregulation in response to invalidation for any emotion on any index. CONCLUSIONS: The effects of validation appear emotion specific and dependent on levels of emotion dysregulation. These findings may help inform more strategic use of validation in psychotherapeutic interventions.
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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 | 0.007 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".