Harsh is large: nonlinear vocal phenomena lower voice pitch and exaggerate body size.

acoustic communication body size nonlinear vocal phenomena pitch roughness voice

Journal

Proceedings. Biological sciences
ISSN: 1471-2954
Titre abrégé: Proc Biol Sci
Pays: England
ID NLM: 101245157

Informations de publication

Date de publication:
14 07 2021
Historique:
entrez: 7 7 2021
pubmed: 8 7 2021
medline: 4 8 2021
Statut: ppublish

Résumé

A lion's roar, a dog's bark, an angry yell in a pub brawl: what do these vocalizations have in common? They all sound harsh due to nonlinear vocal phenomena (NLP)-deviations from regular voice production, hypothesized to lower perceived voice pitch and thereby exaggerate the apparent body size of the vocalizer. To test this yet uncorroborated hypothesis, we synthesized human nonverbal vocalizations, such as roars, groans and screams, with and without NLP (amplitude modulation, subharmonics and chaos). We then measured their effects on nearly 700 listeners' perceptions of three psychoacoustic (pitch, timbre, roughness) and three ecological (body size, formidability, aggression) characteristics. In an explicit rating task, all NLP lowered perceived voice pitch, increased voice darkness and roughness, and caused vocalizers to sound larger, more formidable and more aggressive. Key results were replicated in an implicit associations test, suggesting that the 'harsh is large' bias will arise in ecologically relevant confrontational contexts that involve a rapid, and largely implicit, evaluation of the opponent's size. In sum, nonlinearities in human vocalizations can flexibly communicate both formidability and intention to attack, suggesting they are not a mere byproduct of loud vocalizing, but rather an informative acoustic signal well suited for intimidating potential opponents.

Identifiants

pubmed: 34229494
doi: 10.1098/rspb.2021.0872
pmc: PMC8261225
doi:

Banques de données

figshare
['10.6084/m9.figshare.c.5479636']

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

20210872

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Auteurs

Andrey Anikin (A)

Division of Cognitive Science, Lund University, 22100 Lund, Sweden.
Equipe de Neuro-Ethologie Sensorielle, CNRS and University of Saint Étienne, UMR 5293, 42023 St-Étienne, France.

Katarzyna Pisanski (K)

Equipe de Neuro-Ethologie Sensorielle, CNRS and University of Saint Étienne, UMR 5293, 42023 St-Étienne, France.
CNRS, French National Centre for Scientific Research, Laboratoire de Dynamique du Langage, University of Lyon 2, 69007 Lyon, France.

Mathilde Massenet (M)

Equipe de Neuro-Ethologie Sensorielle, CNRS and University of Saint Étienne, UMR 5293, 42023 St-Étienne, France.

David Reby (D)

Equipe de Neuro-Ethologie Sensorielle, CNRS and University of Saint Étienne, UMR 5293, 42023 St-Étienne, France.

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Classifications MeSH