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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".