Episodic memory demands modulate novel metaphor use during event narration
Bibliographic record
Abstract
Metaphor is an important part of everyday thought and language. Although we are often not aware of metaphor in everyday speech, on occasion, a particularly creative or novel use of metaphor will make us pay attention. It has been hypothesized that one of the driving cognitive factors behind the use of novel metaphor is a need to describe a new reality (as opposed to a preexisting reality) that would otherwise be difficult to convey using conventionalized metaphor. To this extent, novel metaphor use in everyday language may be more associated with episodic memory demands in contrast to conventional metaphor that is associated with semantic memory. To test this idea we analyzed novel metaphor use in the Hippocorpus --- a corpus of more than 5000 recalled and imagined stories about memorable life events in the first person perspective. In this dataset, recalled events have been shown to rely on episodic memory to a greater extent than descriptions of imagined events (i.e., narrating an event as if it happened to you but not describing an event that actually happened to you), which largely draw on semantic memory. We hypothesized that novel metaphor use during event narration should be modulated by the extent to which language users are able to draw on primary experience to describe events. We found that novel metaphor counts in recalled events were significantly higher than imagined events. Importantly, we found that factors that influence the extent to which language users are able to draw on primary experience during event narration (i.e., openness to experience, similarity to one's own experience, and how memorable or important an event was) modulated novel metaphor use in different ways in imagined compared to recalled events. The work paves the way for using large scale corpora to analyze underlying cognitive processes that modulate metaphorical language use.
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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 | 0.013 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".