Cognate translation priming with Chinese–Japanese bilinguals: No effect of interlingual phonological similarity.
Bibliographic record
Abstract
Previous masked translation priming studies, especially those with different-script bilinguals, have shown that cognates provide more priming than noncognates, a difference attributed to cognates' phonological similarity. In our experiments employing a word naming task, we examined this issue for Chinese-Japanese bilinguals in a slightly different way, using same-script cognates as primes and targets. In Experiment 1, significant cognate priming effects were observed. The sizes of the priming effects were, however, statistically not different for phonologically similar (e.g., /xin4lai4/-/shiNrai/) and dissimilar cognate pairs (e.g., /bao3zheng4/- /hoshoR/), suggesting no impact of phonological similarity. In Experiment 2, using exclusively Chinese stimuli, we demonstrated a significant homophone priming effect using two-character logographic primes and targets, indicating that phonological priming is possible for two-character Chinese targets. However, priming only emerged for pairs that had the same tone pattern (e.g., /shou3wei4/-/shou3wei4/), suggesting that a match in lexical tone is crucial for observing phonologically based priming in that situation. Therefore, Experiment 3 involved phonologically similar Chinese-Japanese cognate pairs in which the similarity of their suprasegmental phonological features (i.e., lexical tone and pitch-accent information) was varied. Priming effects were statistically not different for tone/accent similar pairs (e.g., /guan1xin1/-/kaNsiN/) and dissimilar pairs (e.g., /man3zu2/-/maNzoku/). Our results indicate that phonological facilitation is not involved in producing cognate priming effects for Chinese-Japanese bilinguals. Possible explanations, based on underlying representations of logographic cognates, are discussed. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.001 |
| 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 | 0.001 |
| 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".