Datadriven Modeling of Group Entitativity in Virtual Environments


We present a data-driven algorithm to model and predict the socio-emotional impact of groups on observers. Psychological research finds that highly entitative i.e. cohesive and uniform groups induce threat and unease in observers. Our algorithm models realistic trajectory-level behaviors to classify and map the motion-based entitativity of crowds. This mapping is based on a statistical scheme that dynamically learns pedestrian behavior and computes the resultant entitativity induced emotion through group motion characteristics. We also present a novel interactive multi-agent simulation algorithm to model entitative groups and conduct a VR user study to validate the socio-emotional predictive power of our algorithm. We further show that model-generated high-entitativity groups do induce more negative emotions than low-entitative groups.


Datadriven Modeling of Group Entitativity in Virtual Environments, VRST 2018.
Aniket Bera, Tanmay Randhavane, Emily Kubin, Husam Shaik, Kurt Gray, and Dinesh Manocha

  title={Data-driven modeling of group entitativity in virtual environments},
  author={Bera, Aniket and Randhavane, Tanmay and Kubin, Emily and Shaik, Husam and Gray, Kurt and Manocha, Dinesh},
  booktitle={Proceedings of the 24th ACM Symposium on Virtual Reality Software and Technology},