Reading proficiency predicts spatial eye-movement control in the first and second language
Bibliographic record
Abstract
Research on eye movement control during first language (L1) reading has long since established that (i) words are read most efficiently when the first saccade into the word lands near its center, (ii) words are refixated more often when landing positions deviate from the center of the word, and (iii) relatively proficient readers' saccades land closer to this center position. Eye-tracking studies of second language (L2) reading tend to compare participant groups based on their language background (L1 vs L2) rather than L2 proficiency. As of yet, there has been no comparison of these approaches. This study reports a comparative analysis of the Multilingual Eye-movement COrpus (MECO), which contains data on English text reading and its component skills from 543 participants representing 12 different L1s. Our analyses of the distributions of initial landing positions and refixation probabilities establish that the gradient measure of proficiency in English (as L1 or L2) has a greater explanatory power than categorical contrasts between language backgrounds. We also found that English proficiency has a gradient effect on efficiency of saccadic targeting: more proficient readers landed their initial saccades closer to the word's center. However, more proficient readers of English were also less accurate in their saccadic targeting, showing greater dispersion of initial landing positions. We link this puzzling finding to the observation that landing in a suboptimal position comes with a much higher processing cost (refixation probability) for less proficient readers. This paper discusses theoretical and methodological implications of the novel findings for reading research.
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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 | 0.001 | 0.007 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".