It’s Not Always Black and White: How Color Enhances L1 and L2 Idiom Processing
Bibliographic record
Abstract
Idioms are non-compositional expressions whose meanings transcend the literal interpretation of their components (e.g., “break the ice”). They highlight the psycholinguistic tension between direct retrieval and compositional semantic analysis. Past research suggests L1 readers rely more on direct retrieval and idiom familiarity, while L2 readers depend more on word-by-word compositional processing. Supporting this, studies show that disrupting an idiom’s canonical form impacts L1 readers more than L2 readers. This study explored the reverse effect by strengthening an idiom’s canonical form through font color. L1 and L2 readers read English sentences containing idiomatic/literal phrases, presented in colored/standard font, and judged whether the phrases made sense. Accuracy and reaction times were recorded. In L1 readers, idiom superiority (i.e., better performance for idioms than literal phrases) was driven by familiarity, with color coding enhancing this effect for more familiar idioms. In L2 readers, idiom superiority was influenced by both familiarity and decomposability, with color coding amplifying both effects. These findings suggest that L1 readers primarily rely on direct retrieval, whereas L2 readers utilize both direct retrieval and compositional processing, with color coding aiding idiomatic processing for both groups.
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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 | 0.004 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".