Reading proficiency predicts spatial eye-movement control in the first and second language.
Bibliographic record
Abstract
Research on first language (L1) reading has long since established the link between the proficiency of the reader and their efficiency in oculomotor control. More proficient readers make longer saccades and land closer to the word's center, which is a word's optimal viewing position, and make fewer refixations. Eye-tracking studies of second language (L2) reading have so far provided little evidence in this regard. This study analyzes spatial oculomotor measures in the Multilingual Eye-movement Corpus, which contains data on English text reading and its component skills from 543 participants representing 12 different L1s. Our analyses establish a strong role of proficiency in English, both for L1 and L2 readers of English. While most effects replicated ones observed in L1 reading, we also found that more proficient readers of English were less accurate in targeting optimal viewing positions. We link this finding to Fitts' law of motor control for aimed movements. This article discusses the theoretical implications of the novel findings for reading research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.007 | 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".