Capturing the multi-determined nature of idiom processing using ERPs.
Bibliographic record
Abstract
The multi-determined model (Titone & Libben, The Mental Lexicon, 2014, 9, 473) suggests that processing of idioms depends on multiple linguistic factors (e.g., familiarity, literal plausibility, decomposability). According to this model, these sources of information modulate the comprehension of idioms at different time courses. In the current study, we investigated whether these linguistics factors modulate the neurophysiological underpinnings associated with processing of different types of idioms. Adult native speakers of English read sentences that contained idioms with high and low level of familiarity and literal plausibility while their electroencephalography (EEG) was recorded. Event-related potentials data showed that idioms with low level of familiarity elicited larger negativity starting from 300 ms post stimulus onset and lasted for about 200 ms. A similar negativity, but which started later (at around 400 ms post stimulus onset) was also observed for idioms with a low level of literal plausibility. These results are consistent with the multi-determined model of idiom processing indicating the role of these linguistics factors over different time courses. Finally, the observed negativity for low familiar and low literally plausible idioms was greater over the right hemisphere. Accordingly, the possible role of the right hemisphere in processing idioms will be discussed. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".