Habitual action video game playing is associated with caudate nucleus-dependent navigational strategies
Bibliographic record
Abstract
The habitual playing of video games is associated with increased grey matter and activity in the striatum. Studies in humans and rodents have shown an inverse relationship between grey matter in the striatum and hippocampus. We investigated whether action video game playing is also associated with increased use of response learning strategies during navigation, known to be dependent on the caudate nucleus of the striatum, when presented in a dual solution task. We tested 26 action video game players (actionVGPs) and 33 non-action video game players (nonVGPs) on the 4-on-8 virtual maze and a visual attention event-related potential (ERP) task, which elicits a robust N-2-posterior-controlateral (N2pc) component. We found that actionVGPs had a significantly higher likelihood of using a response learning strategy (80.76%) compared to nonVGPs (42.42%). Consistent with previous evidence, actionVGPs and nonVGPs differed in the way they deployed visual attention to central and peripheral targets as observed in the elicited N2pc component during an ERP visual attention task. Increased use of the response strategy in actionVGPs is consistent with previously observed increases in striatal volume in video game players (VGPs). Using response strategies is associated with decreased grey matter in the hippocampus. Previous studies have shown that decreased volume in the hippocampus precedes the onset of many neurological and psychiatric disorders. If actionVGPs have lower grey matter in the hippocampus, as response learners normally do, then these individuals could be at increased risk of developing neurological and psychiatric disorders during their lifetime.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".