What's in a game: Video game visual-spatial demand location exhibits a double dissociation with reading speed
Bibliographic record
Abstract
This research sought to clarify the nature of the relationship between video game experience, attention, and reading. Previous studies have suggested playing action video games can improve reading ability in children with dyslexia. Other research has linked video game experience with visual-spatial attention, and visual-spatial attention with reading. We hypothesized that the visual-spatial demands of video games may drive relationships with reading through attentional processing. In this experiment we used a hybrid attention/reading task to explore the relationship between video game visual-spatial demands, reading and attention. We also developed novel visual-spatial demand measures using participants' top five played video games for an individual-specific measure of visual demands. Peripheral visual demands in video games were associated with faster reading times, while central visual demands were associated with slower reading times for both phonetic decoding and lexical reading. In addition, video game experience in terms of hours spent playing video games each week interacted with the cueing effect size in the lexical reading condition, with experienced video game players exhibiting a larger cueing effect than participants with less video game experience. These results suggest that exposure to peripheral visual spatial demands in video games may be related to both lexical and sublexical reading processes in hybrid attentional reading tasks such as ours with skilled adult readers, which has implications not only for models of how ventral and dorsal stream reading and visual-spatial attention are integrated, but also for the development of dyslexia diagnostics and remediation.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".