The effect of video game competition and violence on aggressive behavior: Which characteristic has the greatest influence?
Bibliographic record
Abstract
Objective: This study is the first to our knowledge to isolate the effect of video game violence and competitiveness on aggressive behavior. Method: In Pilot Study 1, a violent and nonviolent video game were matched on competitiveness, difficulty, and pace of action, and the effect of each game on aggressive behavior was then compared using an unambiguous measure of aggressive behavior (i.e., the Hot Sauce Paradigm) in Experiment 1. In Pilot Study 2, competitiveness was isolated by matching games on difficulty and pace of action, and systematically controlling for violence. The effect of video game competition on aggressive behavior was then examined in Experiment 2. Results: We found that video game violence was not sufficient to elevate aggressive behavior compared with a nonviolent video game, and that more competitive games produced greater levels of aggressive behavior, irrespective of the amount of violence in the games. Conclusion: It appears that competition, not violence, may be the video game characteristic that has the greatest influence on aggressive behavior. Future research is needed to explore the mechanisms through which video game competitiveness influences aggressive behavior, as well as whether this relation holds in the long-term.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".