Social Context Influences the Acoustic Properties of Laughter

Wood, Adrienne

doi:10.1007/s42761-020-00022-w

Cited by 15 publications

(16 citation statements)

References 24 publications

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“…One is the tension of the vocal fold adduction linked to physiological changes and vocal effort: Hypertension and hypotension can lead to irregular vocal fold vibration and nonlinear vocal phenomena (Anikin, 2020b; Fitch et al, 2002; Scherer, 1989). Furthermore, roughness, an overall measure of amplitude and frequency modulation, has previously been described for extremely intense vocalizations, such as screams (Arnal et al, 2015); as well as for vocalizations including unvoiced parts and a breathy voice quality (Wood, 2020), associated with lower emotional intensity (see Figures 3 and 4). A promising avenue for future work, for which the VIVAE materials are a suitable resource, is a more fine-grained analysis of nonlinear phenomena with regard to the perceived emotional intensity and the behavioral relevance of different types of ‘noise’ in such variably-intense vocalizations.…”

Section: Resultsmentioning

confidence: 87%

The variably intense vocalizations of affect and emotion (VIVAE) corpus prompts new perspective on nonspeech perception.

Holz

Larrouy-Maestri

Poeppel

2022

Emotion

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The human voice is a potent source of information to signal emotion. Nonspeech vocalizations (e.g., laughter, crying, moans, or screams), in particular, can elicit compelling affective experiences. Consensus exists that the emotional intensity of such expressions matters; however how intensity affects such signals, and their perception remains controversial and poorly understood. One reason is the lack of appropriate data sets. We have developed a comprehensive stimulus set of nonverbal vocalizations, the first corpus to represent emotion intensity from one extreme to the other, in order to resolve the empirically underdetermined basis of emotion intensity. The full set, comprising 1085 stimuli, features eleven speakers expressing 3 positive (achievement/triumph, sexual pleasure, surprise) and 3 negative (anger, fear, physical pain) affective states, each varying from low to peak emotion intensity. The smaller core set of 480 files represents a fully crossed subsample (6 emotions 3 4 intensities 3 10 speakers 3 2 items) selected based on judged authenticity. Perceptual validation and acoustic characterization of the stimuli are provided; the expressed emotional intensity, like expressed emotion, is reflected in listener evaluation and signal properties of nonverbal vocalizations. These carefully curated new materials can help disambiguate foundational questions on the communication of affect and emotion in the psychological and neural sciences and strengthen our theoretical understanding of this domain of emotional experience.

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Section: Resultsmentioning

confidence: 87%

The variably intense vocalizations of affect and emotion (VIVAE) corpus prompts new perspective on nonspeech perception.

Holz

Larrouy-Maestri

Poeppel

2022

Emotion

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“…At the same time, there is some leeway in the meaning of all human vocalizations. For example, laughs are typically associated with amusement and are therefore less ambiguous than screams and moans, but they can still express a range of social attitudes including malice, sarcasm and nervousness [ 24 , 41 , 42 ]. Thus, contextual cues are likely to enrich the meaning of most nonverbal displays, even those that are not inherently ambiguous, a hypothesis that now needs to be empirically tested.…”

Section: Discussionmentioning

confidence: 99%

Do nonlinear vocal phenomena signal negative valence or high emotion intensity?

2020

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Nonlinear vocal phenomena (NLPs) are commonly reported in animal calls and, increasingly, in human vocalizations. These perceptually harsh and chaotic voice features function to attract attention and convey urgency, but they may also signal aversive states. To test whether NLPs enhance the perception of negative affect or only signal high arousal, we added subharmonics, sidebands or deterministic chaos to 48 synthetic human nonverbal vocalizations of ambiguous valence: gasps of fright/surprise, moans of pain/pleasure, roars of frustration/achievement and screams of fear/delight. In playback experiments ( N = 900 listeners), we compared their perceived valence and emotion intensity in positive or negative contexts or in the absence of any contextual cues. Primarily, NLPs increased the perceived aversiveness of vocalizations regardless of context. To a smaller extent, they also increased the perceived emotion intensity, particularly when the context was negative or absent. However, NLPs also enhanced the perceived intensity of roars of achievement, indicating that their effects can generalize to positive emotions. In sum, a harsh voice with NLPs strongly tips the balance towards negative emotions when a vocalization is ambiguous, but with sufficiently informative contextual cues, NLPs may be re-evaluated as expressions of intense positive affect, underlining the importance of context in nonverbal communication.

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“…These findings are in line with research showing that nonverbal vocalizations in general are a potent tool to communicate the sender’s affective state, such as different positive emotions 10 . Wood 31 found evidence that differences in acoustic parameters are related to differences in the affective context of laughter, i.e., laughter emitted during the perception of videoclips evoking reward (humorous laughter), affiliation (humorous and tenderness laughter), and dominance (derisive and ridiculing laughter) showed differences in acoustic parameters. Similarly, Tanaka & Campbell 32 have shown that in spontaneous conversations polite-formal laughs differ acoustically from mirthful laughs.…”

Section: Introductionmentioning

confidence: 99%

Vocal Expression of Affective States in Spontaneous Laughter reveals the Bright and the Dark Side of Laughter

Szameitat

Wildgruber

2022

Sci Rep

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It has been shown that the acoustical signal of posed laughter can convey affective information to the listener. However, because posed and spontaneous laughter differ in a number of significant aspects, it is unclear whether affective communication generalises to spontaneous laughter. To answer this question, we created a stimulus set of 381 spontaneous laughter audio recordings, produced by 51 different speakers, resembling different types of laughter. In Experiment 1, 159 participants were presented with these audio recordings without any further information about the situational context of the speakers and asked to classify the laughter sounds. Results showed that joyful, tickling, and schadenfreude laughter could be classified significantly above chance level. In Experiment 2, 209 participants were presented with a subset of 121 laughter recordings correctly classified in Experiment 1 and asked to rate the laughter according to four emotional dimensions, i.e., arousal, dominance, sender’s valence, and receiver-directed valence. Results showed that laughter types differed significantly in their ratings on all dimensions. Joyful laughter and tickling laughter both showed a positive sender’s valence and receiver-directed valence, whereby tickling laughter had a particularly high arousal. Schadenfreude had a negative receiver-directed valence and a high dominance, thus providing empirical evidence for the existence of a dark side in spontaneous laughter. The present results suggest that with the evolution of human social communication laughter diversified from the former play signal of non-human primates to a much more fine-grained signal that can serve a multitude of social functions in order to regulate group structure and hierarchy.

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Social Context Influences the Acoustic Properties of Laughter

Cited by 15 publications

References 24 publications

The variably intense vocalizations of affect and emotion (VIVAE) corpus prompts new perspective on nonspeech perception.

The variably intense vocalizations of affect and emotion (VIVAE) corpus prompts new perspective on nonspeech perception.

Do nonlinear vocal phenomena signal negative valence or high emotion intensity?

Vocal Expression of Affective States in Spontaneous Laughter reveals the Bright and the Dark Side of Laughter

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