Phonology, homophony, and eyes-closed rest in Mandarin novel word learning: An eye-tracking study in adult native and non-native speakers
Bibliographic record
Abstract
Abstract This study used the visual world paradigm to investigate novel word learning in adults from different language backgrounds and the effects of phonology, homophony, and rest on the outcome. We created Mandarin novel words varied by types of phonological contrasts and homophone status. During the experiment, native (n = 34) and non-native speakers (English; n = 30) learned pairs of novel words and were tested twice with a 15-minute break in between, which was spent either resting or gaming. In the post-break test of novel word recognition, an interaction appeared between language backgrounds, phonology, and homophony: non-native speakers performed less accurately than native speakers only on non-homophones learned in pairs with tone contrasts. Eye movement data indicated that non-native speakers’ processing of tones may be more effortful than their processing of segments while learning homophones, as demonstrated by the time course. Interestingly, no significant effects of rest were observed across language groups; yet after gaming, native speakers achieved higher accuracy than non-native speakers. Overall, this study suggests that Mandarin novel word learning can be affected by participants’ language backgrounds and phonological and homophonous features of words. However, the role of short periods of rest in novel word learning requires further investigation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".