Eye movement behaviour during reading of Japanese sentences: Effects of word length and visual complexity
Bibliographic record
Abstract
Two experiments are presented that examine how the visual characteristics of Japanese words influence eye movement behaviour during reading. In Experiment 1, reading behaviour was compared for words comprising either one or two kanji characters. The one-character words were significantly less likely to be fixated on first-pass, and had significantly longer overall reading times, than the two-character words. In Experiment 2, reading behaviour was compared for two-kanji character words, for which the first character was either visually simple or visually complex (determined by the number of strokes). Visual complexity significantly influenced total word reading times and the probability of the individual visually simple/complex characters being fixated on first pass. Additional analyses showed no preferred viewing position for two-kanji character words. Overall, the study provides experimental evidence of an influence of specific visual characteristics of Japanese words on eye movement behaviour during reading, as shown by both fixation probabilities and reading times. The findings must be explained by processing at (or beyond) a visual level impacting on eye movement behavior during reading of Japanese text.
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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 | 0.000 | 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) | 0.001 | 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".