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 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".