Reading is fundamentally similar across disparate writing systems: A systematic characterization of how words and characters influence eye movements in Chinese reading.
Bibliographic record
Abstract
While much previous work on reading in languages with alphabetic scripts has suggested that reading is word-based, reading in Chinese has been argued to be less reliant on words. This is primarily because in the Chinese writing system words are not spatially segmented, and characters are themselves complex visual objects. Here, we present a systematic characterization of the effects of a wide range of word and character properties on eye movements in Chinese reading, using a set of mixed-effects regression models. The results reveal a rich pattern of effects of the properties of the current, previous, and next words on a range of reading measures, which is strikingly similar to the pattern of effects of word properties reported in spaced alphabetic languages. This finding provides evidence that reading shares a word-based core and may be fundamentally similar across languages with highly dissimilar scripts. We show that these findings are robust to the inclusion of character properties in the regression models and are equally reliable when dependent measures are defined in terms of characters rather than words, providing strong evidence that word properties have effects in Chinese reading above and beyond characters. This systematic characterization of the effects of word and character properties in Chinese advances our knowledge of the processes underlying reading and informs the future development of models of reading. More generally, however, this work suggests that differences in script may not alter the fundamental nature of 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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".