Action video game players resist oculomotor capture, but only when told to do so
Bibliographic record
Abstract
A sizable body of work has accumulated over the past decade highlighting the relationship between action video game experience and benefits in cognitive task performance. Research has been largely consistent in demonstrating that action video game players (AVGPs) outperform non-video game players (NVGPs) especially in tasks that involve selective attention. We, along with others, have previously demonstrated that AVGPs are better able to resist the interfering influence of task-irrelevant distractors and have argued for a top-down mechanism to account for these attentional effects. However, it is unclear whether AVGPs will always demonstrate reduced interference from distraction or whether this effect only occurs when they are explicitly instructed to avoid distraction. To address this question, we ran two experiments, collecting eye movements and manual responses from AVGP and NVGP in a traditional oculomotor capture paradigm. Participants searched for a colour-singleton target, while a task-irrelevant abrupt onset appeared on 50% of trials. In Experiment 1, where participants were not informed of the appearance of the abrupt onset, AVGPs failed to demonstrate reduced capture relative to NVGPs. In Experiment 2, participants were informed that an abrupt onset would appear but that it was task-irrelevant and to be ignored. Results indicate that when told to ignore a task-irrelevent distractor, AVGPs demonstrate reduced capture by an abrupt onset relative to NVGPs. These findings not only provide further evidence that the attentional differences observed between AVGPs and NVGPs on tasks of selective attention are subserved by a difference in top-down control but that it is also specific to given task demands. In addition, these findings lend further support for the notion that the capture of attention is susceptible to top-down influence. Meeting abstract presented at VSS 2012
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".