Bidirectional cross-linguistic influence with different-script languages: Evidence from eye tracking
Bibliographic record
Abstract
Abstract This study compared patterns of nonselective cross-language activation in L1 and L2 visual word recognition with different-script bilinguals. The aim was to determine (1) whether lexical processing is nonselective in the L1 (as in L2), and (2) if the same cross-linguistic factors affected processing similarly in each language. To examine the time course of activation, eye movements were tracked during lexical decision. Thirty-two Japanese–English bilinguals responded to 250 target words in Japanese and in English. The same participants and items (i.e., cognate translation equivalents) were used to directly compare L1 and L2 processing. Response latencies as well as eye movements representing early and late processing were analyzed using mixed-effects regression modeling. Similar cross-linguistic effects, namely cognate word frequency, phonological similarity, and semantic similarity, were found in both languages. These factors affected processing to different degrees in each language, however. While cognate frequency was significant as early as the first fixation, effects of cross-linguistic phonological and semantic similarity arose later in time. Increased phonological similarity slowed responses in L2 but speeded them in L1, while greater semantic overlap was facilitatory in both languages. Results are discussed from the perspective of the BIA+ model of visual word recognition.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.001 | 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".