The Relationship Between Cognitive Resource Consumption During Gameplay and Postgame Aggressive Behaviors: Between-Subjects Experiment
Bibliographic record
Abstract
Background: The question of how video games can shape aggressive behaviors has been a focus for many researchers. Previous research has focused on how violent video game content leads to postgame aggressive behaviors. However, video games not only convey violence or prosocial content to players but also require cognitive effort from individuals. Since human cognitive resources are limited, consuming more cognitive resources in a game leads to less cognitive resources to suppress aggressive impulses. Therefore, the depletion of cognitive resources from playing video games may also lead to changes in postgame aggressive behaviors. Objective: This study aimed to examine the relationship between cognitive resources consumed in video games and postgame aggressive behaviors. Methods: A total of 60 participants (age: mean 20.22; range 18-24 y) were randomly assigned to either the high-load group or the low-load group. Participants from both groups played a video game centered around college life. In the low-load group, participants followed the gameplay instructions to complete it. In the high-load group, participants were given an extra digital memory task to complete while playing the game. Participants in both groups played the video game for about 25 minutes. A maze selection task was then conducted to measure the participants' helping and hurting behaviors. Results: The independent samples 2-tailed t tests showed that the high-load group had significantly higher hurting scores (mean 3.13, SD 2.47) than the low-load group (mean 1.90, SD 2.12; t58=-2.07, P=.04; Cohen d=-0.535), whereas helping behaviors were not significantly affected (t58=1.52, P=.13; Cohen d=0.393). Conclusions: As more cognitive resources are consumed in a video game, more hurting behaviors are exhibited after the game. This finding proposes an alternative route by which video games impact aggressive behaviors, adding to previous theories and raising concerns about the popularity of cognitive training games.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.006 | 0.001 |
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".