Word length and frequency effects on text reading are highly similar in 12 alphabetic languages
Bibliographic record
Abstract
One of the most robust findings in research on eye-movement control in reading is that shorter and more frequent words are recognized faster and skipped more often than longer and less frequent words. These benchmark effects of word length and frequency are reported in all languages studied to date and inform computational models of eye-movements in reading. This paper asks whether each of these effects is similar in magnitude across languages. We analyzed 12 typologically diverse alphabetic languages from the Multilingual Eye-Movement Corpus (MECO). The languages varied substantially in their word length and frequency distributions as a function of orthographic conventions and the morpho-syntactic type. Despite this variability, the effects of word length and frequency on fixation durations and skipping rate were highly comparable in size between the languages. This finding suggests a high degree of cross-linguistic universality in the readers' behavioral response to visual and linguistic complexity (indexed by word length) and the amount of familiarity with the word (indexed by word frequency). It also suggests feasibility of, and provides empirical data for, generalizable cross-linguistic computational models of eye-movement control in reading.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.005 | 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".