Factors that Influence the Processing of Noun-Noun Metaphors
Bibliographic record
Abstract
We analyzed the processing of noun-noun metaphors (e.g., velvet lips), which have been relatively understudied, compared to other types of figurative expressions, such as X is Y metaphors (e.g., Her lips are velvet) and similes (e.g., Her lips are like velvet). Experiment 1 revealed that noun-noun metaphors are semantically comparable to X is Y metaphors and similes, in the sense that the figurative meaning stays the same across these three different formats (e.g., participants agree to similar degrees that Lips are velvet, Lips are like velvetand velvet lips all mean that lips are soft). Experiment 2 showed that noun-noun metaphors behave similarly to compound words: In the same way that compound words with semantically opaque heads (e.g., jailbird) are processed slower than compounds with transparent heads (e.g., strawberry), noun-noun phrases with metaphorical heads (e.g., relationship patch) are processed slower than noun-noun phrases with literal heads and metaphorical modifiers (e.g., bandaid solution). Experiment 3 determined that noun-noun metaphors behave similarly to X is Y metaphors: In the same way that X is Y metaphors require the inhibition of irrelevant features (e.g., Some barrels are wooden interferes with the interpretation of Some stomachs are barrels because the former activates irrelevant features of barrel that later need to be suppressed), noun-noun metaphors also involve inhibition (e.g., jean patch interferes with the interpretation of relationship patch because the former activates certain features of patch, such as being made of cloth, that are irrelevant for the proper comprehension of the noun-noun metaphor).
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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 | 0.017 |
| 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".