Spoken second language words activate native language orthographic information in late second language learners
Bibliographic record
Abstract
ABSTRACT This study investigated the time course of activation of orthographic information in spoken word recognition with two visual world eye-tracking experiments in a task where second language (L2) spoken word forms had to be matched with their printed referents. Participants (n= 64) were native Finnish learners of L2 French ranging from beginners to highly proficient. In Experiment 1, L2 targets (e.g., /sidʀ/) were presented with either orthographically overlapping onset competitors (e.g., /sɛ̃tʀ/) or phonologically overlapping onset competitors ( /sikl/). In Experiment 2, L2 targets (e.g., /pom/) were associated with competitors in Finnish, L1 of the participants, in conditions symmetric to Experiment 1 ( /pauhu/ vs. /pom:i/). In the within-language experiment (Experiment 1), the difference in target identification between the experimental conditions was not significant. In the between-language experiment (Experiment 2), orthographic information impacted the mapping more in lower proficiency learners, and this effect was observed 600 ms after the target word onset. The influence of proficiency on the matching was nonlinear: proficiency impacted the mapping significantly more in the lower half of the proficiency scale in both experiments. These results are discussed in terms of coactivation of orthographic and phonological information in L2 spoken word recognition.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".