Move faster, think later: Women who play action video games have quicker visually-guided responses with later onset visuomotor-related brain activity
Bibliographic record
Abstract
A history of action video game (AVG) playing is associated with improvements in several visuospatial and attention-related skills and these improvements may be transferable to unrelated tasks. These facts make video games a potential medium for skill-training and rehabilitation. However, examinations of the neural correlates underlying these observations are almost non-existent in the visuomotor system. Further, the vast majority of studies on the effects of a history of AVG play have been done using almost exclusively male participants. Therefore, to begin to fill these gaps in the literature, we present findings from two experiments. In the first, we use functional MRI to examine brain activity in experienced, female AVG players during visually-guided reaching. In the second, we examine the kinematics of visually-guided reaching in this population. Imaging data demonstrate that relative to women who do not play, AVG players have less motor-related preparatory activity in the cuneus, middle occipital gyrus, and cerebellum. This decrease is correlated with estimates of time spent playing. Further, these correlations are strongest during the performance of a visuomotor mapping that spatially dissociates eye and arm movements. However, further examinations of the full time-course of visuomotor-related activity in the AVG players revealed that the decreased activity during motor preparation likely results from a later onset of activity in AVG players, which occurs closer to beginning motor execution relative to the non-playing group. Further, the data presented here suggest that this later onset of preparatory activity represents greater neural efficiency that is associated with faster visually-guided responses.
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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) | 0.011 | 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".