Third Interdisciplinary Workshop on Laughter and other Non-Verbal Vocalisations in Speech (original) (raw)

Investigating facial features of four types of laughter in historic illustrations

Several conceptually different types of laughter were proposed in the historic literature, but only four types were represented in visual and verbal illustrations by four or more historic illustrators (joyful, intense, schadenfreude laughter, grinning). Study 1 examined the encoding of facial features in 18 illustrations by the Facial Action Coding System and study 2 investigated the decoding by laypeople. Illustrations of laughter involving a Duchenne Display (DD) were perceived as joyful irrespective of their initial classification. In intense laughter, the intensity of the zygomatic major muscle predicted the perception of intensity, but not the proposed changes in the upper face. In fact, "frowning" seemed to be antagonistic to the perception of joy. Schadenfreude and grinning did not have high recognition rates. Going along with the idea that schadenfreude is either a blend of a positive and negative emotion, or solely joy with attempts of masking it, it may entail additional features beyond the DD. Grinning was best represented by low intensity laughter, narrowed eye aperture and mouth prolonging actions. So far, only the DD could be reliably morphologically differentiated and recognized, supporting Darwin's proposal of joyful laughter being the laughter prototype.

Laughter exaggerates happy and sad faces depending on visual context

Psychonomic Bulletin & …, 2012

Laughter is an auditory stimulus that powerfully conveys positive emotion. We investigated how laughter influenced the visual perception of facial expressions. We presented a sound clip of laughter simultaneously with a happy, a neutral, or a sad schematic face. The emotional face was briefly presented either alone or among a crowd of neutral faces. We used a matching method to determine how laughter influenced the perceived intensity of the happy, neutral, and sad expressions. For a single face, laughter increased the perceived intensity of a happy expression. Surprisingly, for a crowd of faces, laughter produced an opposite effect, increasing the perceived intensity of a sad expression in a crowd. A follow-up experiment revealed that this contrast effect may have occurred because laughter made the neutral distractor faces appear slightly happy, thereby making the deviant sad expression stand out in contrast. A control experiment ruled out semantic mediation of the laughter effects. Our demonstration of the strong context dependence of laughter effects on facial expression perception encourages a reexamination of the previously demonstrated effects of prosody, speech content, and mood on face perception, as they may be similarly context dependent.

Quantitative Laughter Detection, Measurement, and Classification—A Critical Survey

IEEE Reviews in Biomedical Engineering, 2016

The study of human nonverbal social behaviors has taken a more quantitative and computational approach in recent years due to the development of smart interfaces and virtual agents or robots able to interact socially. One of the most interesting nonverbal social behaviors, producing a characteristic vocal signal, is laughing. Laughter is produced in several different situations: in response to external physical, cognitive, or emotional stimuli; to negotiate social interactions; and also, pathologically, as a consequence of neural damage. For this reason, laughter has attracted researchers from many disciplines. A consequence of this multidisciplinarity is the absence of a holistic vision of this complex behavior: the methods of analysis and classification of laughter, as well as the terminology used, are heterogeneous; the findings sometimes contradictory and poorly documented. This survey aims at collecting and presenting objective measurement methods and results from a variety of different studies in different fields, to contribute to build a unified model and taxonomy of laughter. This could be successfully used for advances in several fields, from artificial intelligence and human-robot interaction to medicine and psychiatry.

Socio-Cultural Dimensions of Laughter and Smile as Ways of Nonverbal Communication

Language and Literature - European Landmarks of Identity, 2019

Smile and laughter are universal means of transmitting nonverbal messages. Smile is a very complex expression capable to transmit a wide range of information, denoting a wide range of emotions and feelings, such as pleasure, joy, happiness, satisfaction, promise, sociability, amusement, but also cynicism, embarrassment, emotional pain, slyness, shame and sarcasm. However, the interpretation of the meaning of the smile also varies from one culture to another, or even from one subculture to another. Instead, laughter seems to have more universal human connotations and interpretations, because it is the expression of some basic universal human emotions, such as joy, cheerfulness, pleasure, happiness. Laughter is generally the expression of good mood, but, more than that, it can also cause good mood. That is why laughter is also used as part of some therapies. However, the vocal component of laughter, considered part of paraverbal communication, may have specific cultural determinations. For example, when people accentuate by laughing a certain vowel, they want to express, at least in European culture, different mental states and behavioural intentions, which are decipherable only when the codes of communication and their meanings are known. Significations and interpretations of laughter and smile are thus functions of the socio-cultural context. The present paper deals with these aspects, highlighting the social and cultural dimensions of these two suggestive components of nonverbal communication.

Spatio-Temporal Properties of Amused, Embarrassed, and Pained Smiles

Journal of Nonverbal Behavior

Smiles are universal but nuanced facial expressions that are most frequently used in face-to-face communications, typically indicating amusement but sometimes conveying negative emotions such as embarrassment and pain. Although previous studies have suggested that spatial and temporal properties could differ among these various types of smiles, no study has thoroughly analyzed these properties. This study aimed to clarify the spatiotemporal properties of smiles conveying amusement, embarrassment, and pain using a spontaneous facial behavior database. The results regarding spatial patterns revealed that pained smiles showed less eye constriction and more overall facial tension than amused smiles; no spatial differences were identified between embarrassed and amused smiles. Regarding temporal properties, embarrassed and pained smiles remained in a state of higher facial tension than amused smiles. Moreover, embarrassed smiles showed a more gradual change from tension states to the smi...

Differentiation of Emotions in Laughter at the Behavioral Level

Emotion, 2009

Although laughter is important in human social interaction, its role as a communicative signal is poorly understood. Because laughter is expressed in various emotional contexts, the question arises as to whether different emotions are communicated. In the present study, participants had to appraise 4 types of laughter sounds (joy, tickling, taunting, schadenfreude) either by classifying them according to the underlying emotion or by rating them according to different emotional dimensions. The authors found that emotions in laughter (a) can be classified into different emotional categories, and (b) can have distinctive profiles on W. Wundt's (1905) emotional dimensions. This shows that laughter is a multifaceted social behavior that can adopt various emotional connotations. The findings support the postulated function of laughter in establishing group structure, whereby laughter is used either to include or to exclude individuals from group coherence.

Investigating form and content of emotional and non-emotional laughing

Cerebral Cortex

As cold actions (i.e. actions devoid of an emotional content), also emotions are expressed with different vitality forms. For example, when an individual experiences a positive emotion, such as laughing as expression of happiness, this emotion can be conveyed to others by different intensities of face expressions and body postures. In the present study, we investigated whether the observation of emotions, expressed with different vitality forms, activates the same neural structures as those involved in cold action vitality forms processing. To this purpose, we carried out a functional magnetic resonance imaging study in which participants were tested in 2 conditions: emotional and non-emotional laughing both conveying different vitality forms. There are 3 main results. First, the observation of emotional and non-emotional laughing conveying different vitality forms activates the insula. Second, the observation of emotional laughing activates a series of subcortical structures known ...

The Informational Patterns of Laughter

Entropy, 2003

Laughter is one of the most characteristic-and enigmatic-communicational traits of human individuals. Its analysis has to take into account a variety of emotional, social, cognitive, and communicational factors densely interconnected. In this article we study laughter just as an auditive signal (as a 'neutral' information carrier), and we compare its structure with the regular traits of linguistic signals. In the experimental records of human laughter that we have performed, the most noticeable trait is the disorder content of frequencies. In comparison with the sonograms of vowels, the information content of which appears as a characteristic, regular function of the first vibration modes of the dynamic system formed, for each vowel, by the vocal cords and the accompanying resonance of the vocalization apparatus, the sonograms of laughter are highly irregular. In the episodes of laughter, a highly random content in frequencies appears, reason why it cannot be considered as a genuine codification of patterned information like in linguistic signals. In order to numerically gauge the disorder content of laughter frequencies, we have performed several "entropy" measures of the spectra-trying to unambiguously identify spontaneous laughter from "faked", articulated laughter. Interestingly, Shannon's entropy (the most natural candidate) performs rather poorly.