Breaking the link between provocation and aggression: The role of mitigating information
Bibliographic record
Abstract
In two experimental studies, we examine the extent to which strong or weak mitigating information after a provocation alters aggressive responding. In Study 1, we randomly assigned 215 (108 female) college-aged participants to a strong or weak provocation by having a research assistant talk to the participant about failing a task in a harsh or confused tone. This was followed by a second research assistant giving a strong or weak excuse to the participant regarding the first research assistant's behavior. Then, aggressive behavior was assessed using a researcher rating task. In Study 2, 63 (25 female) college-aged participants interacted with a confederate on the CRT. All participants were strongly provoked by receiving strong noise blasts. After five CRT trials, the confederate delivered weak or strong mitigating information to the participant regarding the noises blasts. The results indicated that: (i) strong provocations are more likely to increase aggression than weak provocations; (ii) strong mitigating information is more likely to decrease aggression than weak mitigating information; and (iii) the varying strength of mitigating information is important in situations involving weak, but not strong provocations: strong mitigating information is more likely than weak mitigating information reduce aggression when provocation is strong, but not when provocation is weak. We discuss the importance of mitigating information in decreasing aggressive behavior and the conditions in which mitigating information is especially likely to be effective. Aggr. Behav. 42:555-562, 2016. © 2016 Wiley Periodicals, Inc.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".