Reading minds in motion: Mouse tracking reveals transposed-character effects in Chinese compound word recognition
Bibliographic record
Abstract
Abstract This study investigated the development of character transposition effects during Chinese compound word recognition via computer mouse movements instead of the conventional key presses. Empirical evidence to reveal the impacts of vocabulary knowledge, grade level, and whole word frequency on Chinese transposed-character effect is lacking. In the present study, we measured the transposed-character effect in two groups of Taiwanese children (second and fourth graders) in a mouse-tracking lexical-decision task including nonwords derived from real words by transposing two characters (e.g., “習學” from “學習” [learning]) and control nonwords in which two characters are replaced (e.g., “以修”). Our results indicate that participants showed longer mouse movement times and larger spatial attraction in recognizing transposed-character nonwords than in replaced-character nonwords, suggesting that the dominant role of whole-word representation in processing Chinese compound words. Our results also further demonstrate that how the degree of character transposition was affected by vocabulary knowledge, grade level, and word frequency.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".