Laughter Induction Techniques Suitable for Generating Motion Capture Data of Laughter Associated Body Movements

Laughter is a frequently occurring social signal and an important part of human non-verbal communication. However it is often overlooked as a serious topic of scientific study. While the lack of research in this area is mostly due to laughter���s non-serious nature, it is also a particularly difficult social signal to produce on demand in a convincing manner; thus making it a difficult topic for study in laboratory settings. In this paper we provide some techniques and guidance for inducing both hilarious laughter and conversational laughter. These techniques were devised with the goal of capturing mo- tion information related to laughter while the person laughing was either standing or seated. Comments on the value of each of the techniques and general guidance as to the importance of atmosphere, environment and social setting are provided.

Gender Differences in the Perceptions of Genuine and Simulated Laughter and Amused Facial Expressions

This article addresses gender differences in laughter and smiling from an evolutionary perspective. Laughter and smiling can be responses to successful display behavior or signals of affiliation amongst conversational partners—differing social and evolutionary agendas mean there are different motivations when interpreting these signals. Two experiments assess perceptions of genuine
and simulated male and female laughter and amusement social signals. Results show male simulation can always be distinguished. Female simulation is more complicated as males seem to distinguish cues of simulation yet judge simulated signals to be genuine. Females judge other female’s genuine signals to have higher levels of simulation. Results highlight the importance of laughter and smiling in human interactions, use of dynamic stimuli, and using multiple methodologies to assess perception.

Laughter Type Recognition from Whole Body Motion

Despite the importance of laughter in social interactions it remains little studied in affective computing. Respiratory, auditory, and facial laughter signals have been investigated but laughter-related body movements have received almost no attention. The aim of this study is twofold: first an investigation into observers' perception of laughter states (hilarious, social, awkward, fake, and non-laughter) based on body movements alone, through their categorization of avatars animated with natural and acted motion capture data. Significant differences in torso and limb movements were found between animations perceived as containing laughter and those perceived as nonlaughter. Hilarious laughter also differed from social laughter in the amount of bending of the spine, the amount of shoulder rotation and the amount of hand movement. The body movement features indicative of laughter differed between sitting and standing avatar postures. Based on the positive findings in this perceptual study, the second aim is to investigate the possibility of automatically predicting the distributions of observer's ratings for the laughter states. The findings show that the automated laughter recognition rates approach human rating levels, with the Random Forest method yielding the best performance.

Perception and Automatic Recognition of Laughter from Whole-Body Motion: Continuous and Categorical Perspectives

Despite its importance in social interactions, laughter remains little studied in affective computing. Intelligent virtual agents are often blind to users’ laughter and unable to produce convincing laughter themselves. Respiratory, auditory, and facial laughter signals have been investigated but laughter-related body movements have received less attention. The aim of this study is threefold. First, to probe human laughter perception by analyzing patterns of categorisations of natural laughter animated on a minimal avatar. Results reveal that a low dimensional space can describe perception of laughter “types”. Second, to investigate observers’ perception of laughter (hilarious, social, awkward, fake, and non-laughter) based on animated avatars generated from natural and acted motion-capture data. Significant differences in torso and limb movements are found between animations perceived as laughter and those perceived as non-laughter. Hilarious laughter also differs from social laughter. Different body movement features were indicative of laughter in sitting and standing avatar postures. Third, to investigate automatic recognition of laughter to the same level of certainty as observers’ perceptions. Results show recognition rates of the Random Forest model approach human rating levels. Classification comparisons and feature importance analyses indicate an improvement in recognition of social laughter when localized features and nonlinear models are used.

The Relationship Between Laughter Intensity and Perceived Humour

There is a long history of recognizing a continuum in social signals of positive affect, with the continuum ranging from mild amusement signals to strong laughter. However there has been little systematic effort to assess what this continuum might mean. We present data that shows that incorporating intensity measures of laughter into laughter research is an important component and that there is a strong relationship between laughter intensity and humour. This may be intuitively obvious but the strength of the relationship suggests that intensity measures should be included in all laughter research.

Human Perception of Laughter from Context-free Whole Body Motion Dynamic Stimuli

Laughter is a ubiquitous social signal in human interactions yet it remains understudied from a scientific point of view. The need to understand laughter and its role in human interactions has become more pressing as the ability to create conversational agents capable of interacting with humans has come closer to a reality. This paper reports on three aspects of the human perception of laughter when context has been removed and only the body information from the laughter episode remains. We report on ability to categorise the laugh type and the sex of the laugher; the relationship between personality factors with laughter categorisation and perception; and finally the importance of intensity in the perception and categorisation of laughter.

The Belfast Storytelling Database: A spontaneous social interaction database with laughter focused annotation

To support the endeavor of creating intelligent interfaces between computers and humans the use of training materials based on realistic human-human interactions has been recognized as a crucial task. One of the effects of the creation of these databases is an increased realization of the importance of often overlooked social signals and behaviours in organizing and orchestrating our interactions. Laughter is one of these key social signals; its importance in maintaining the smooth flow of human interaction has only recently become apparent in the embodied conversational agent domain. In turn, these realizations require training data that focus on these key social signals. This paper presents a database that is well annotated and theoretically constructed with respect to understanding laughter as it is used within human social interaction. Its construction, motivation, annotation and availability are presented in detail in this paper.

Laughter Research: A Review of the ILHAIRE Project

Laughter is everywhere. So much so that we often do not even notice it. First, laughter has a strong connection with humour. Most of us seek out laughter and people who make us laugh, and it is what we do when we gather together as groups relaxing and having a good time. But laughter also plays an important role in making sure we interact with each other smoothly. It provides social bonding signals that allow our conversations to flow seamlessly between topics; to help us repair conversations that are breaking down; and to end our conversations on a positive note.

Laughter and Humour as Conversational Mind-Reading Displays

Laughter and humor are pervasive phenomena in conversa- tional interactions. This paper argues that they function as displays of mind-reading abilities in social interactions–as suggested by the Analogi- cal Peacock Hypothesis (APH). In this view, they are both social bonding signals and can elevate one’s social status. The relational combination of concepts in humor is addressed. However, it is in the inclusion of context and receiver knowledge, required by the APH view, that it contributes the most to existing theories. Taboo and offensive humor are addressed in terms of costly signaling, and implications for human computer inter- action and some possible routes to solutions are suggested.

Laugh-aware virtual agent and its impact on user amusement

In this paper we present a complete interactive system en- abled to detect human laughs and respond appropriately, by integrating the information of the human behavior and the context. Furthermore, the impact of our autonomous laughter-aware agent on the humor experience of the user and interaction between user and agent is evaluated by sub- jective and objective means. Preliminary results show that the laughter-aware agent increases the humor experience (i.e., felt amusement of the user and the funniness rating of the film clip), and creates the notion of a shared social experience, indicating that the agent is useful to elicit posi- tive humor-related affect and emotional contagion.