Notice bibliographique
Résumé
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!
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».