Are figurative interpretations of idioms directly retrieved or compositionally built? Evidence from eye movement measures of first and second language reading
Bibliographic record
Abstract
Idioms are part of a general class of multiword expressions that convey a figurative interpretation that is not fully determined through an on-demand compositional (syntactic and semantic) analysis of their component words (e.g., kick the bucket, save your skin). Idioms are simultaneously amenable to direct retrieval from memory, and to an on-demand compositional analysis, yet it is unclear which processes lead to figurative interpretations of idioms during comprehension. Idioms also pose challenges for second language learners, who may rely less on direct retrieval than first language users due to reduced L2 experience. In this study, first and second language users (L1 and L2, respectively) read sentences containing idioms followed by figurative- or literal-biased disambiguating regions. The results for L1 readers showed that increased familiarity but not decomposability facilitated comprehension of figurative interpretations. The results for L2 readers showed that cross-language overlap and decomposability facilitated comprehension of figurative interpretations. Thus, consistent with hybrid or multidetermined idiom processing models (Titone & Connine, 1999; Libben & Titone, 2008), first language idiom interpretations arise more from direct retrieval (indexed by familiarity) than compositional analysis. Second language idiom interpretations, however, arise from both.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".