Global measures of syntactic and lexical complexity are not strong predictors of eye-movement patterns in sentence and passage reading
Bibliographic record
Abstract
The link between the cognitive effort of word processing and the eye-movement patterns elicited by that word is well established in psycholinguistic research using eye-tracking. Yet less evidence or consensus exists regarding whether the same link exists between linguistic complexity measures of a sentence or passage and eye movements registered at the sentence or passage level. This article focuses on "global" measures of syntactic and lexical complexity, i.e., the measures that characterise the structure of the sentence or passage rather than aggregate lexical properties of individual words. We selected several commonly used global complexity measures and tested their predictive power against sentence- and passage-level eye movements in samples of text reading from 13 languages represented in the Multilingual Eye Movement Corpus (MECO). While some syntactic or lexical complexity measures elicited statistically significant effects, they were negligibly small and not of practical relevance for predicting the processing effort either in individual languages or across languages. These findings suggest that the "eye-mind" link known to be valid at the word level may not scale up to larger linguistic units.
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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 | 0.000 | 0.004 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".