Using Psychophysiological Measures to Examine the Temporal Profile of Verbal Humor Elicitation
Bibliographic record
Abstract
Despite its pervasiveness in popular culture, there remains much to be learned about the psychological and physiological processes that underlie our experience of humor. In the present study, we examined the temporal profile of verbal humor elicitation using psychophysiological measures of heart rate (HR) and facial electromyography (EMG). Consistent with recent prior research on cardiovascular changes to perceived humor, we found that HR acceleration was greater for jokes relative to non-jokes, and was positively related to the level of perceived humor elicited by these jokes. In addition, activity recorded from the zygomaticus major muscle that controls smiling was found to be greater for jokes relative to non-jokes. To link these physiological changes to the psychological processes that govern humor comprehension, we took the initial inflection point of the zygomatic EMG response as a marker for the onset of humor comprehension, and used this marker to probe the pattern of cardiovascular activity at this time-point. We estimated the onset of the humor response to occur during the initial HR deceleration phase, and found that jokes relative to non-jokes elicited a decreased HR response at this time-point. This result questions the previously forwarded notion that the psychological "moment of insight" that signals the start of the humor response is always associated with heightened cardiovascular activity. This discrepancy is discussed in relation to possible differences in the cognitive processes required to comprehend different forms of humor. At a broader level, our results also demonstrate the advantages of combining different psychophysiological measures to examine psychological phenomena, and illustrate how one such measure can constrain the interpretation of others.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".