An Investigation of Idiom Processing Advantage using Translated Familiar Idioms
Bibliographic record
Abstract
The facilitatory effect shown in native speakers processing idiomatic phrases compared to matched novel phrases may be explained by a dual route model. This postulates that all phrases are processed literally at first, and if a phrase was recognized as familiar during processing, it would then be processed by a faster retrieval-route; if the phrase was not perceived as familiar, it would continue to be processed literally by the slower computation-route. The goals of the current project were to test the dual route model and to decipher the underlying mechanism in retrieval-route activation. English idioms and translated Chinese idioms were presented to both native English speakers and Chinese-English bilinguals in random order. Participants listened to the idiom up until the last word (e.g., “draw a snake and add”), then saw either the idiom ending (e.g., “feet”) or the matched control ending (e.g., “hair”); to which they made lexical decision and reaction times were recorded. We examined the priming effect for idioms compared to controls across the two language groups. Results showed that the two groups processed idioms of different origins differently. Native English speakers’ faster responses to English idioms than controls supported a dual route model; however, both native English and bilingual speakers’ faster responses to Chinese idioms than controls called for a less straightforward interpretation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".