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.

Movement and Perceptual Strategies to Intercept Virtual Sound Sources

To intercept a moving object, one needs to be in the right place at the right time. In order to do this, it is necessary to pick up and use perceptual information that specifies the time to arrival of an object at an interception point. In the present study, we examined the ability to intercept a laterally moving virtual sound object by controlling the displacement of a sliding handle and tested whether and how the interaural time difference (ITD) could be the main source of perceptual information for successfully intercepting the virtual object. The results revealed that in order to accomplish the task, one might need to vary the duration of the movement, control the hand velocity and time to reach the peak velocity (speed coupling), while the adjustment of movement initiation did not facilitate performance. Furthermore, the overall performance was more successful when subjects employed a time-to-contact (tau) coupling strategy. This result shows that prospective information is available in sound for guiding goal-directed actions.

Expert players accurately detect an opponent's movement intentions through sound alone

Sounds offer a rich source of information about events taking place in our physical and social environment. However, outside the domains of speech and music, little is known about whether humans can recognize and act upon the intentions of another agent’s actions detected through auditory information alone. In this study we assessed whether intention can be inferred from the sound an action makes, and in turn, whether this information can be used to prospectively guide movement. In two experiments experienced and novice basketball players had to virtually intercept an attacker by listening to audio recordings of that player’s movements. In the first experiment participants had to move a slider, while in the second one their body, to block the perceived passage of the attacker as they would in a real basketball game. Combinations of deceptive and non-deceptive movements were used to see if novice and/or experienced listeners could perceive the attacker’s intentions through sound alone. We showed that basketball players were able to more accurately predict final running direction compared to non-players, particularly in the second experiment when the interceptive action was more basketball specific. We suggest that athletes present better action anticipation by being able to pick up and use the relevant kinematic features of deceptive movement from event-related sounds alone. This result suggests that action intention can be perceived through the sound a movement makes and that the ability to determine another person’s action intention from the information conveyed through sound is honed through practice.