Abstract
Collaboration and creativity are consistently among the top-ranked values across societies, industries, and educational organizations. What makes collaboration possible is social norms. Group-based norms have played a key role in the evolution and maintenance of human ability to work and create together. We are not born collaborative-beings; it is the ability for social cognition and normativity that allows us to collaborate with others. Despite social norms ubiquity and pervasiveness—and being one of the most invoked concepts in social science—it remains unclear what are the underlying mechanisms to the extent to be one of the big unsolved problems in the field. To contribute to close this gap, the authors take an enactive-ecological approach, in which social norms are dynamic and context-dependent socio-material affordances for collaborative activity. Social norms offer the agent possibilities for collaborative action with others in the form of pragmatic social cues. The novelty of this research is the application of quantitative methods using computational models and computer vision to collect and analyze data on the pragmatic social cues of social norms in creative collaboration. Researchers will benefit from those methods by having fast and reliable data collection and analysis at a high level of granularity. In the present study, we analyzed the interpersonal synchrony of physiological signals and facial expressions between participants, together with the participant’s perceived team cohesion. Despite the small size of the experiment, we could find correlations between signals and patterns that provide confidence in the feasibility of the methods employed. We conclude that the methods employed can be a powerful tool to collect and analyze data from larger groups and, therefore, shed some light on the—still not fully understood—underlying mechanisms of social normativity. The findings from the preliminary study are by no means conclusive, but serve as a proof of concept of the applicability of body signals and facial expressions to study social norms.
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Santuber, J., Owoyele, B., Mukherjee, R., Ghosh, S.K., Edelman, J.A. (2020). Using Body Signals and Facial Expressions to Study the Norms that Drive Creative Collaboration. In: Przegalinska, A., Grippa, F., Gloor, P. (eds) Digital Transformation of Collaboration. COINs 2019. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-48993-9_2
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