Cross-linguistic comparison in reading sentences of uniform length: Visual–perceptual demands override readers’ experience
Bibliographic record
Abstract
Accurate saccadic targeting is critical for efficient reading and is driven by the sensory input under the eye-gaze. Yet whether a reader’s experience with the distributional properties of their written language also influences saccadic targeting is an open debate. This study of Russian sentence reading follows Cutter et al.’s (2017) study in English and presents readers with sentences consisting of words of the same length. We hypothesised that if the readers’ experience matters as per discrete control account , Russian readers would produce longer saccades and farther landing positions than the ones produced by English readers. On the contrary, if the saccadic targeting is primarily driven by the immediate perceptual demands that override readers’ experience as per the dynamic adjustment account , the saccades of Russian and English readers would be of the same length, resulting in similar landing positions. The results in both Cutter et al. and the present study provided evidence for the latter account: Russian readers showed rapid and accurate adjustment of saccade lengths and landing positions to the highly constrained input. Crucially, the saccade lengths and landing positions did not differ between English and Russian readers even in the cross-linguistically length-matched stimuli.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".