Flute birds and creamy skies: The metaphor interference effect in modifier–noun phrases.
Bibliographic record
Abstract
People take longer to determine that metaphoric sentences (e.g., some birds are flutes) are literally false compared to anomalous sentences (e.g., some birds are pickles). This metaphor interference effect (MIE) shows that metaphorical interpretations are automatically computed even in contexts and tasks that only require literal interpretations. Although a well-replicated finding, the MIE has only been investigated in sentence stimuli in which the metaphoric composition is explicitly stated (such that birds are asserted to be flutes). This raises questions about the generalizability of the MIE because (a) A is B metaphors are rare in discourse and (b) other metaphor variants, such as flute bird, are unspecified in their metaphoric composition (i.e., do not specifically assert which concept, if any, is metaphorical). In this experiment, we investigated whether metaphoric modifier-noun phrases such as flute bird and creamy sky produce a MIE. In addition, we explored if word-level semantic variables (semantic neighborhood density and concreteness) play a role in the MIE. We asked participants to determine if modifier-noun phrases refer to things that literally exist or not. We found a MIE in which metaphoric phrases (e.g., flute bird, creamy sky) took longer to judge as literally false relative to scrambled counterparts (e.g., flute sky, creamy bird). Moreover, we found that word-level semantic variables affect the magnitude of the MIE only for adjective-noun phrases. Therefore, metaphoric meaning can be automatically extracted from metaphoric compounds, suggesting that the MIE is more robust than previously demonstrated. (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 | 0.002 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".