Data-Driven Modeling of Group Entitativity in Virtual Environments
This work addresses the challenge of understanding group dynamics in virtual environments for applications in psychology and simulation, though it appears incremental as it builds on existing psychological findings with a new computational method.
The researchers tackled the problem of modeling and predicting the socio-emotional impact of cohesive groups on observers by developing a data-driven algorithm that classifies group entitativity based on motion characteristics, and they validated it through a VR user study, showing that high-entitativity groups induce more negative emotions than low-entitativity ones.
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.