Idiom meaning selection following a prior context: eye movement evidence of L1 direct retrieval and L2 compositional assembly
Bibliographic record
Abstract
Past work has suggested that L1 readers retrieve idioms (i.e., spill the tea) directly vs. matched literal controls (drink the tea) following unbiased contexts, whereas L2 readers process idioms more compositionally. However, it is unclear whether this occurs when a figuratively or literally biased context precedes idioms. We tested this in an eye-tracking study in which 40 English-L1 and 35 English-L2 adults read English sentences containing idioms having figurative, literal, or control prior contexts. Linear mixed-effects models revealed that L1 readers processed idioms faster after a literal preamble; however, at the disambiguation region, they processed idioms’ figurative interpretations more quickly as familiarity increased, suggesting a L1 reliance on direct retrieval. In contrast, L2 readers processed idioms’ figurative interpretations faster as verb decomposability increased, suggesting an L2 reliance on compositional assembly. Collectively, these results suggest that meaning selection occurs in a hybrid fashion when idioms follow a biased context.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".