Time-dependent effects of decomposability, familiarity and literal plausibility on idiom meaning activation
Bibliographic record
Abstract
We address a core question about idioms relevant to formulaic language generally: are the figurative meanings of idioms directly retrieved or compositionally built? An understanding of this question has been previously obscured by the fact that idioms vary in ways that can affect processing, and also because experimental tasks, which differ across studies, probe different kinds of comprehension processes. We thus investigate how linguistic differences among idioms in semantic decomposability, familiarity, and literal plausibility modulate figurative meaning activation using cross-modal semantic priming, which is ideal for tracking activation of a particular target meaning over time. Across two experiments, we obtained two key findings. First, a comparison of different prime-target delay conditions suggests that figurative meaning activation steadily accrues as the idiom unfolds to 1000 ms later. Second, different linguistic attributes of idioms modulate figurative activation at different time points: increased literal plausibility interferes with idiom priming prior to the offset of the phrase, increased familiarity facilitates idiom priming at phrase offset, and increased semantic decomposability (surprisingly) interferes with idiom priming 1000 ms following phrase offset. These results contradict strong decompositional models of idiom processing and rather suggest that multiple linguistic factors jointly constrain figurative meaning retrieval in a time-dependent fashion.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".