Characterizing the role of impulsivity in costly, reactive aggression using a novel paradigm
Bibliographic record
Abstract
Lack of self-control has long been theorized to predict an individual’s likelihood to engage in antisocial behaviors. However, existing definitions of self-control encompass multiple psychological constructs and lab-based measures of aggression have not allowed for the examination of aggression upon provocation where self-control is needed most. We introduce two versions of a novel paradigm, the Retaliate or Carry-on: Reactive AGgression Experiment (RC-RAGE) to fill this methodological gap. Using large online samples of US adults (N = 354 and N = 366), we test to what extent dispositional impulsivity, self-control, aggression, and state anger contribute to aggression upon provocation when there is a financial cost involved. Results showed that costly retaliation on this task was related to trait aggression and being in an angry emotional state, but not related to social desirability. Importantly, we show that the tendency to act impulsively is a better predictor of costly retaliation than other forms of self-control, such as the ability to delay gratification, resist temptation, or plan ahead. As a browser-based task, the RC-RAGE provides a tool for the future investigation of reactive aggression in a variety of experimental settings.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".