Effects of visual span on Chinese reading performance in normal peripheral vision
Bibliographic record
Abstract
The current study examined the relationship among temporal processing speed, spatial visual span and Chinese character reading speed in normal central and peripheral vision. Maximum reading speed (MRS) and critical print size (CPS) of 26 native Chinese readers (13 young and 13 older adults) were determined at three visual field locations: central vision, 10o left and 10o below fixation using the rapid serial visual presentation (RSVP) task. Temporal processing speed was measured using trigrams of randomly selected Chinese characters presented at a range of exposure durations, while spatial visual span was measured using trigrams presented at different spatial positions. We found that faster MRS was significantly associated with faster temporal processing speed and larger spatial visual span at the central and inferior visual field locations, but not at the left of the fixation location. As expected, reading and visual span metrics were better in central vision compared to both non-central presentation locations. In addition, reading, temporal processing, and spatial visual span metrics were better in the younger than older subjects for central and inferior vision, but not for the left of fixation condition. Our results for central and inferior presentation locations support the hypothesis that temporal processing speed and spatial visual span were associated with Chinese character reading speed. Surprisingly, no association was observed for the 10o left of the fixation presentation location, suggesting that the factors affecting reading speed differ for inferior and lateral mid-peripheral viewing locations.
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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 | 0.001 | 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) | 0.002 | 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".