Abstract
The success of live comedy depends on a performer's ability to “work” an audience. Ethnographic studies suggest that this involves the co-ordinated use of subtle social signals such as body orientation, gesture, gaze by both performers and audience members. Robots provide a unique opportunity to test the effects of these signals experimentally. Using a life-size humanoid robot, programmed to perform a stand-up comedy routine, we manipulated the robot's patterns of gesture and gaze and examined their effects on the real-time responses of a live audience. The strength and type of responses were captured using SHORE™computer vision analytics. The results highlight the complex, reciprocal social dynamics of performer and audience behavior. People respond more positively when the robot looks at them, negatively when it looks away and performative gestures also contribute to different patterns of audience response. This demonstrates how the responses of individual audience members depend on the specific interaction they're having with the performer. This work provides insights into how to design more effective, more socially engaging forms of robot interaction that can be used in a variety of service contexts.
Highlights
Not everyone knows how to tell a joke
We use Generalized Linear Mixed Model (GLMM) analyses to model the combined random effects, categorical and interval fixed effects and repeated measures involved in the audience responses measured in this study
Punchlines To test if audience members respond selectively to the jokes, their facial displays of “Happiness” were averaged over three “Response
Summary
Not everyone knows how to tell a joke. A good joke depends as much on the quality of the delivery as it does on the quality of the material. Intonation, posture, gaze, gesture, expression, and timing all contribute to successful comic delivery. Effective delivery is not just a matter of what the speaker does, it depends on the reciprocal dynamics of the speaker–listener interaction. The fluency of speakers’ performance in conversation depends on the moment-tomoment responsiveness of their audience and, in turn, on the speakers’ ability to concurrently accommodate and adjust to these responses while they are speaking (Goodwin, 1979; Bavelas et al, 2000). If addressees appear to be bored or distracted, speakers become disfluent. An appropriately timed smile or raised eyebrow by an addressee provides useful feedback that speakers can use to adapt their message
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