Less Direct, More Analytical: Eye-Movement Measures of L2 Idiom Reading
Bibliographic record
Abstract
Idioms (e.g., break the ice, spill the beans) are ubiquitous multiword units that are often semantically non-compositional. Psycholinguistic data suggests that L1 readers process idioms in a hybrid fashion, with early comprehension facilitated by direct retrieval, and later comprehension inhibited by factors promoting compositional parsing (e.g., semantic decomposability). In two eye-tracking experiments, we investigated the role of direct retrieval and compositional analysis when idioms are read naturally in sentences in an L2. Thus, French–English bilingual adults with French as their L1 were tested using English sentences. For idioms in canonical form, Experiment 1 showed that prospective verb-related decomposability and retrospective noun-related decomposability guided L2 readers towards bottom-up figurative meaning access over different time courses. Direct retrieval played a lesser role, and was mediated by the availability of a congruent “cognate” idiom in the readers’ L1. Next, Experiment 2 included idioms where direct retrieval was disrupted by a phrase-final language switch into French (e.g., break the glace, spill the fèves). Switched idioms were read comparably to switched literal phrases at early stages, but were penalized at later stages. These results collectively suggest that L2 idiom processing is mostly compositional, with direct retrieval playing a lesser role in figurative meaning comprehension.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".