An examination of L2-L1 noncognate translation priming in the lexical decision task: insights from distributional and frequency-based analyses
Bibliographic record
Abstract
The main fact that is currently known about the nature of masked L2-L1 noncognate translation priming effects in the lexical decision task is simply that those effects are significant in some studies but not in others. In an effort to better understand these effects, we examined the data pattern for very proficient Japanese–English bilinguals using RT distributional analyses. We also examined the impacts of prime and target frequency on the priming effect. Significant priming was present even on the fastest trials, becoming larger on slower trials. Nonetheless, priming effects were generally constant across prime and target frequency with the only exception being when very high frequency L2 primes were used. In that situation, priming and target frequency were negatively related, a result that essentially produced the observed pattern of increasing priming on slower trials. Implications of these results and potential reasons for the presence/absence of L2-L1 priming effects are discussed.
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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 | 0.000 | 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) | 0.000 | 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".