Cross-linguistic influence in the bilingual lexicon: Evidence for ubiquitous facilitation and context-dependent interference effects on lexical processing
Bibliographic record
Abstract
Abstract For bilinguals, lexical access in one language may affect, or be affected by, activation of words in another language. Research to date suggests seemingly contradictory effects of such cross-linguistic influence (CLI): in some cases CLI facilitates lexical access while in others it is a hindrance. Here we provide a comprehensive review of CLI effects drawn from multiple disciplines and paradigms. We describe the contexts within which CLI gives rise to facilitation and interference and suggest that these two general effects arise from separate mechanisms that are not mutually exclusive. Moreover, we argue that facilitation is ubiquitous, occurring in virtually all instances of CLI, while interference is not always present and depends on levels of cross-language lexical competition. We discuss three critical factors – language context, direction, and modality of CLI – which appear to modulate facilitation and interference. Overall, we hope to provide a general framework for investigating CLI in future research.
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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 | 0.013 |
| 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".