Pun processing from a psycholinguistic perspective: Introducing the Model of Psycholinguistic Hemispheric Incongruity Laughter (M.PHIL)
Bibliographic record
Abstract
Ambiguity processing was examined using a stimulus set consisting of homograph puns in which semantic salience, as measured by semantic co-occurrence, was manipulated. Two lexical decision tasks using puns as primes for ambiguous targets revealed that high co-occurrence meanings were processed faster than low co-occurrence meanings. A divided visual field protocol revealed involvement of both hemispheres, but with the pattern of priming from the right visual field more similar to that of the centrally presented condition than the left visual field pattern. In contrast to the lexical decision data that favoured high co-occurrence targets, data from a forced-choice relatedness task showed an advantage for the low co-occurrence associates. Results from this series of experiments are consistent with Bryden's [(1982). Laterality: Functional asymmetry in the intact brain. New York, NY: Academic Press] proposal that there are several different laterality effects when processing language and emotionally valent stimuli. The results are used to frame a working model of pun processing based on the Graded Salience Hypothesis [Giora, R. (1997). Understanding figurative and literal language: The graded salience hypothesis. Cognitive Linguistics, 8(3), 183-206].
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".