Cross-script L2-L1 noncognate translation priming in lexical decision depends on L2 proficiency: Evidence from Japanese–English bilinguals
Bibliographic record
Abstract
Previous research with unbalanced, different-script bilinguals has typically produced null L2-L1 noncognate masked translation priming effects in lexical decision tasks (LDT). Two novel models of the bilingual mental lexicon have emerged to account for these null results: the episodic L2 hypothesis and the Sense model. In contrast, the BIA+ model predicts significant priming whenever bilinguals are sufficiently proficient in L2. Using Japanese–English bilinguals, the role of L2 proficiency in L2-L1 noncognate translation priming in an LDT was examined. In Experiments 1 and 2, significant priming effects were observed for highly proficient bilinguals. In contrast, in Experiment 3, less-proficient bilinguals produced a null priming effect. This pattern demonstrates that L2-L1 priming effects do arise in an LDT and those effects are modulated by L2 proficiency, consistent with the BIA+ model's expectations. The pattern can be also explained by the episodic L2 hypothesis, provided that certain modifications are made to its assumptions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".