Detecting affiliation in colaughter across 24 societies - PubMed (original) (raw)

. 2016 Apr 26;113(17):4682-7.

doi: 10.1073/pnas.1524993113. Epub 2016 Apr 11.

Daniel M T Fessler 2, Riccardo Fusaroli 3, Edward Clint 2, Lene Aarøe 4, Coren L Apicella 5, Michael Bang Petersen 4, Shaneikiah T Bickham 6, Alexander Bolyanatz 7, Brenda Chavez 8, Delphine De Smet 9, Cinthya Díaz 8, Jana Fančovičová 10, Michal Fux 11, Paulina Giraldo-Perez 12, Anning Hu 13, Shanmukh V Kamble 14, Tatsuya Kameda 15, Norman P Li 16, Francesca R Luberti 2, Pavol Prokop 17, Katinka Quintelier 18, Brooke A Scelza 2, Hyun Jung Shin 19, Montserrat Soler 20, Stefan Stieger 21, Wataru Toyokawa 22, Ellis A van den Hende 23, Hugo Viciana-Asensio 24, Saliha Elif Yildizhan 25, Jose C Yong 16, Tessa Yuditha 26, Yi Zhou 13

Affiliations

Detecting affiliation in colaughter across 24 societies

Gregory A Bryant et al. Proc Natl Acad Sci U S A. 2016.

Erratum in

Abstract

Laughter is a nonverbal vocal expression that often communicates positive affect and cooperative intent in humans. Temporally coincident laughter occurring within groups is a potentially rich cue of affiliation to overhearers. We examined listeners' judgments of affiliation based on brief, decontextualized instances of colaughter between either established friends or recently acquainted strangers. In a sample of 966 participants from 24 societies, people reliably distinguished friends from strangers with an accuracy of 53-67%. Acoustic analyses of the individual laughter segments revealed that, across cultures, listeners' judgments were consistently predicted by voicing dynamics, suggesting perceptual sensitivity to emotionally triggered spontaneous production. Colaughter affords rapid and accurate appraisals of affiliation that transcend cultural and linguistic boundaries, and may constitute a universal means of signaling cooperative relationships.

Keywords: cooperation; cross-cultural; laughter; signaling; vocalization.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.

Fig. 1.

Map of the 24 study site locations.

Fig. 2.

Fig. 2.

Rates of correct judgments (hits) in each study site broken down by experimental condition (friends or strangers), and dyad type (male–male, male–female, female–female). Chance performance represented by 0.50. For example, the bottom right graph of the United States results shows that female–female friendship dyads were correctly identified 95% of the time, but female–female stranger dyads were identified less than 50% of the time. Male–male and mixed-sex friendship dyads were identified at higher rates than male–male and mixed-sex stranger dyads. This contrasts with Korea, for example, where male–male and mixed-sex friendship dyads were identified at lower rates than male–male and mixed-sex stranger dyads. In every society, female–female friendship dyads were identified at higher rates than all of the other categories.

Fig. 3.

Fig. 3.

Acoustic-based model predictions of friend ratio (defined as the overall likelihood of each single laugh being part of a colaugh segment produced between individuals identified by participants as being friends) (on the_x_ axis) with the actual values (on the _y_axis) (95% CI).

Fig. 4.

Fig. 4.

Six sample waveforms and narrowband FFT spectrograms (35-ms Gaussian analysis window, 44.1-kHz sampling rate, 0- to 5-kHz frequency range, 100- to 600-Hz_F_0 range) of colaughter from each experimental condition (friends and strangers), and dyad type (male–male, male–female, female–female). For each colaugh recording, the_Top_ and Middle show the waveforms from each of the constituent laughs, and the spectrogram collapses across channels. Blue lines represent _F_0 contours. The recordings depicted here exemplify stimuli that were accurately identified by participants. Averaging across all 24 societies, the accuracy (hit rate) for the depicted recordings were: female–female friendship, 85%; mixed-sex friendship, 75%; male–male friendship, 78%; female–female strangers, 67%; mixed-sex strangers, 82%; male–male strangers, 73%.

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References

    1. Dale R, Fusaroli R, Duran N, Richardson D. The self-organization of human interaction. Psychol Learn Motiv. 2013;59:43–95.
    1. Provine RR. Laughter: A Scientific Investigation. Penguin; New York: 2000.
    1. Gervais M, Wilson DS. The evolution and functions of laughter and humor: A synthetic approach. Q Rev Biol. 2005;80(4):395–430. - PubMed
    1. Scott SK, Lavan N, Chen S, McGettigan C. The social life of laughter. Trends Cogn Sci. 2014;18(12):618–620. - PMC - PubMed
    1. Sauter DA, Eisner F, Ekman P, Scott SK. Cross-cultural recognition of basic emotions through nonverbal emotional vocalizations. Proc Natl Acad Sci USA. 2010;107(6):2408–2412. - PMC - PubMed

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