{{htmlmetatags>
metatag-keywords=(Soins, Communication, NonVerbale, Prosodie, Mensonge)
metatag-og:title=(Listeners' perceptions of the certainty and honesty of a speaker are associated with a common prosodic signature. Goupil L, et al, Nat Commun 2021.)
metatag-description=(Listeners' perceptions of the certainty and honesty of a speaker are associated with a common prosodic signature. Goupil L, et al, Nat Commun 2021.)
metatag-og:type=article
metatag-article:published_time=2021-02-11
metatag-article:modified_time=2021-03-07
}}
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{{pmid>addtt:33558510|Listeners' perceptions of the certainty and honesty of a speaker are associated with a common prosodic signature.}}
{{pmid>addhash_fr:33558510|Soins, NonVerbale, Prosodie, Mensonge}}
====== Listeners' perceptions of the certainty and honesty of a speaker are associated with a common prosodic signature. Goupil L, et al, Nat Commun 2021. ======
{{tag>Soins Communication }}
{{tag>NonVerbale, Prosodie, Mensonge}}
* **Proposé le :** 11/02/2021 17:07:05
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{{tag>Proposé_à_relecture 2021-02 Non_attribué Non_finalisé}}
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===== Résumé et points clés =====
The success of human cooperation crucially depends on mechanisms enabling individuals to detect unreliability in their conspecifics. Yet, how such epistemic vigilance is achieved from naturalistic sensory inputs remains unclear. Here we show that listeners' perceptions of the certainty and honesty of other speakers from their speech are based on a common prosodic signature. Using a data-driven method, we separately decode the prosodic features driving listeners' perceptions of a speaker's certainty and honesty across pitch, duration and loudness. We find that these two kinds of judgments rely on a common prosodic signature that is perceived independently from individuals' conceptual knowledge and native language. Finally, we show that listeners extract this prosodic signature automatically, and that this impacts the way they memorize spoken words. These findings shed light on a unique auditory adaptation that enables human listeners to quickly detect and react to unreliability during linguistic interactions.
===== Références de l'article =====
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===== Références =====
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