A cross-linguistic study of spatial parameters of eye-movement control during reading.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.012 | 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 it