A cross-linguistic study of spatial parameters of eye-movement control during reading.
Bibliographic record
Abstract
Current theories of oculomotor control in reading differ in their accounts of saccadic targeting. Some argue that targets for saccades are solely selected on the basis of the rapidly changing sensory input, whereas others additionally allow for the reader's experiential biases to modulate saccade lengths. We investigated this debate using cross-linguistic data on text reading in 12 alphabetic languages from the Multilingual Eye-Movement Corpus (MECO) database. These languages vary widely in their word length distributions, suggesting that expected word lengths and corresponding biases toward optimal saccade lengths may also vary across readers of these languages. Regression analyses confirmed that readers of languages with longer words (e.g., Finnish) rather than shorter words (e.g., Hebrew) landed further into the word, even when sensory aspects relevant for saccade planning (e.g., word lengths) were controlled for. In the prevalent saccade type, a one-letter difference in mean word length between languages came with one-quarter-letter of a difference in initial landing position and saccade length, and a decrease in 1.5% in refixation probability. Interpreted in the Bayesian framework, the findings highlight the relevance of global language-wide settings for accounts of spatial oculomotor control and lead to testable predictions for further cross-linguistic research. (PsycInfo Database Record (c) 2022 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 | 0.010 |
| 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 | 0.001 |
| 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".