Active Suppression in Video-Game Players: An ERP Study
Bibliographic record
Abstract
Several aspects of visual attention are thought to be affected by time spent playing action-oriented video-games. One such aspect is attentional capture, which occurs in visual search when attention is involuntarily deployed to a task-irrelevant item in a search array. One recent study has found the strength of capture to be less for video-game players (VGP), and suggested that, once attention had been captured, VGPs were faster at redeploying attention to the relevant target. An alternative explanation is that both VGPs and typical individuals may not be captured by the irrelevant distractor, and may instead be actively suppressing it. This might suggest that VGPs are better able to suppress salient but irrelevant stimuli. This hypothesis was tested in an experiment using the Event Related Potential (ERP) components known as the N2Pc, which is thought to index the spatial deployment of attention, and the Pd, which is thought to index active suppression. ERPs were recorded from VGPs and typical individuals in a visual search experiment featuring task-irrelevant distractors. Additionally, subjects were evaluated using a measure of visual short term working memory capacity (“K”), as previous research has suggested that individuals with high K also show more active suppression. In keeping with the extant literature, VGPs performed significantly better in the K task. Additionally, both VGPs and typical subjects did not show an N2Pc to the salient distractor, but rather a Pd, suggesting that both groups were suppressing, rather than attending to it. Furthermore, VGPs showed a significantly reduced latency of the Pd component, which would indicate an improved ability to actively suppress distracting items. These electrophysiological results add to the body of literature on VGPs and provide evidence for the mechanisms underlying previously observed behavioral differences. Meeting abstract presented at VSS 2015
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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 | 0.002 |
| 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 | 0.001 | 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".