Bibliographic record
Abstract
We all have experienced the positive effects of laughter. It induces a deep state of relaxation and a sense of well-being. It is also an important form of non-verbal communication that allows others to know we agree that something is funny. In this way, laughter strengthens social bonds because when we laugh we lower our guard and do not perceive the other person as a potential threat. Some have proposed that the positive emotions associated with laughter help us learn new things from others and promote cooperation. But what are the mechanisms behind all of this? How does laughter make us feel good? Robin Dunbar from the University of Oxford and his team of collaborators proposed that endorphins might be responsible for many of the beneficial effects of laughter. Endorphins are internally produced opioids that have an important role in social bonding in primates, as well as having an analgesic effect. Dunbar and his colleagues proposed that the physical action of laughing induces the release of these endorphins, just as any form of physical exercise does, causing the positive feelings we are all familiar with.Because of the analgesic effect of endorphins, it is common practice for scientists to use pain thresholds to assess individual endorphin levels. Using this technique, Dunbar and his colleagues performed a series of experiments in which they evaluated the effect of laughter on endorphin release. During some of the experiments, volunteers were tested in groups whereas other experiments were performed on individuals. The participants were shown either funny videos, such as ‘America's Funniest Home Videos’ or other comedy shows, or videos with neutral emotional content, such as a documentary. To rule out any effects that positive feelings alone might have on their endorphin levels, the scientists also showed a group of participants non-humorous ‘feel-good’ videos of beautiful scenery. The researchers recorded the participants' laughter throughout the experiments and tested each participant's pain tolerance before and after they had watched the videos. They did this either by touching a frozen wine cooler sleeve to a participant's skin and measuring the time at which they could not tolerate it anymore or, in a separate set of experiments, by inflating a pressure cuff around the participant's arm until they could no longer stand the pain (ouch!).Not surprisingly, the people who watched the comedy videos spent much more time laughing than those who saw the documentaries or the videos of nice scenery. Furthermore, those who watched the funny videos in a group laughed much more than those who watched the same videos alone. More interestingly, the participants increased their pain tolerance in a laughter dose-dependent fashion: the more they laughed, the more their pain threshold increased.The team proposes that the physical exertion of sustained laughter triggers the release of endorphins, in a way similar to other types of exercise. Because humans, in contrast to other laughing apes, are capable of sustaining laughter for several minutes, the opioid effects of a good chuckle might be particularly enhanced in our species, increasing not only our pain thresholds but also strengthening social bonds and promoting collaboration and altruistic behaviour. So it seems that laughter really is the best medicine after all!
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.052 | 0.026 |
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".