Acting impulsively when “upset”: Examining associations among negative urgency, undifferentiated negative affect, and impulsivity using momentary and experimental methods
Bibliographic record
Abstract
INTRODUCTION: Negative urgency is a personality pathway toward impulsive behavior that increases risk for transdiagnostic psychopathology. Limited research supports the core tenant of urgency theory, that is, that individuals with high trait negative urgency act more impulsive when experiencing increased negative emotion. We hypothesized that it may not be negative emotion intensity, but difficulty in differentiating among negative emotions, that prompts impulsive behavior among individuals with elevated negative urgency. METHODS: We tested this hypothesis in 200 undergraduates using both ecological momentary assessment (measured momentary undifferentiated negative affect and impulsivity) and experimental methods (manipulated emotion differentiation and measured behavioral impulsivity). RESULTS: Momentary undifferentiated negative affect predicted impulsivity in the specific domains of work/school and exercise, but interactions between momentary undifferentiated negative affect and negative urgency were not supported. Manipulated emotion differentiation did not impact behavioral impulsivity regardless of negative urgency scores. CONCLUSION: Inconsistent with theory, the impulsive behavior of individuals with negative urgency may not be conditional on elevated or undifferentiated negative affect.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".