Early Birds Can Fly: Awakening the Literal Meaning of Conventional Metaphors Further Downstream
Bibliographic record
Abstract
Conventional metaphors such as early bird are interpreted rather fast and efficiently. This is so because they might be stored as lexicalized, non-compositional expressions. In a previous study, employing a maze task, we showed that, after reading metaphors (John is an early bird so he can …), participants took longer and were less accurate in selecting the appropriate word (attend) when it was paired with a literally-related distractor (fly) rather than an unrelated one (cry). This suggests that the literal meaning of conventional metaphors is awakened or made available immediately after their metaphorical interpretation. But does the literal meaning remain available further downstream during sentence comprehension? In two experiments also employing a maze task, we examined whether the awakening effect can be obtained when there is a medium (6 to 8 words) and a large (11 to 13 words) distance between the metaphor and lexical choice. Results indicated that the metaphor awakening effect persists but decreases as word distance increases. An analysis of our data based on a GPT model showed that our maze effects could not be attributed to target predictability. Overall, our results suggest that the literal meaning of a metaphor is accessed and remains available for about three seconds, fading as the sentence unfolds over time. The results support a model of metaphor comprehension that postulates the availability of both literal and metaphoric content in the course of sentence 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 | 0.007 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".