Video game playing enhances practical attentional skills
Bibliographic record
Abstract
Recent evidence has shown that habitual video game playing enhances a subset of visual-attentional skills. In this study, we investigated whether prolonged experience playing action video games can improve performance on tasks related to lifeguarding, a job where allocation of attention in the visual field can have life and death consequences. To examine this question, Video Game Players (VGPs) and Non-Video Game Players (NVGPs) were tested on two computerized lifesaving tasks and one conventional measure of attentional performance. To emulate lifeguarding performance, one task was a modified Useful Field of View / Multiple Object Tracking hybrid where we measured participants' ability to detect schematic ‘non-swimmers’, at either 10°, 20°, or 30°; from fixation, amongst a large group of ‘distractor’ swimmers. A second task used a Change Detection (CD) paradigm requiring participants to detect the absence of a swimmer in a naturalistic scene. Findings revealed that VGPs showed greater ‘non-swimmer’ detection accuracy at larger eccentricities from fixation; however no reliable group difference in CD performance was found. In addition, basic attentional performance was measured by having participants perform a Temporal Order Judgment task, using a step-function to calculate the amount of time the uncued target needed to appear before the cued target in order for both target items to be perceived as arriving simultaneously. VGPs were found to be more sensitive to the peripheral cue, thereby reliably lengthening the time the uncued target needed to appear before the cued target as compared to NVGPs. Overall, these findings show that playing action video games may improve performance on detecting certain events in the periphery (e.g., motion, onset, etc.), but do not necessarily facilitate the detection of changes presented within the foveal window.
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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) | 0.004 | 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".