Effects of central vs. peripheral attentional-oculomotor exercise on lexical processing
Bibliographic record
Abstract
Past research from our lab has suggested visual demands in video games serve to exercise attentional-oculomotor (A-O) processing in a manner beneficial to reading. However, testing the effect of video games on reading typically requires long timeframes (e.g., multiweek training or years of accumulated video game experience). The current study manipulated within-experiment peripheral and central demands to evaluate the effects of A-O exercise on task performance. Our study included two tasks: an orthographic lexical decision task (OLDT), designed to optimise orthographic lexical processing, and a novel graphic-based health bar decision task (HBDT). In Experiment 1, the stimuli were presented centrally in one block and peripherally in another block to manipulate A-O exercise. We observed greater improvements in the peripheral-first than the central-first group, particularly for the OLDT. In Experiments 2 and 3, we focused on the OLDT, with the HBDT serving as the A-O exercise task, and observed improvements in both centrally and peripherally trained participants. We additionally observed, through analyses of word and bigram frequency, a double dissociation, whereby increased target word frequency was associated with faster target reaction times and improved error rates, whereas increased foil bigram frequency was associated with slower foil reaction times and worse error rates. Taken together, the experiments demonstrate a mechanism beyond simple task learning that drives reading improvements, and A-O exercise, even if movements are small, appears to play a role in the improvements observed. We suggest future research should further develop this paradigm and examine its utility for reading remediation in dyslexia.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".