The Influence of Explicit Markers on Slow Cortical Potentials During Figurative Language Processing
Bibliographic record
Abstract
Recent event related brain potential (ERP) results show that figurative interpretations of proverbial phrases (e.g., lightning never strikes the same place twice) elicit sustained slow cortical potentials that are more negative over the front of the head than for literal interpretations of the same phrases (Ferretti, Schwint, & Katz, under review).We extend this research by examining the influence of explicit markers placed before the proverbs, such as literally speaking and figuratively speaking, and by contrasting two literal conditions in which there either is overlap or no overlap between content words in the proverbs and the preceding contexts.The results show that slow cortical potentials were most negative for proverbs interpreted in figurative contexts, and most positive for literal contexts that contained overlapping words.Moreover, markers directed readers toward the contextually appropriate interpretation of the proverbs earlier than found in previous research.These findings have direct relevance for theoretical explanations of figurative language processing.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".