Data-Driven Modeling of Group Entitativity in Virtual Environments

TL;DR

This paper introduces a data-driven algorithm that models and predicts the socio-emotional impact of group entitativity in virtual environments, validated through a VR user study showing its effectiveness.

Contribution

It presents a novel algorithm for modeling group entitativity based on motion behaviors and a new multi-agent simulation method validated by user studies.

Findings

High-entitativity groups induce more negative emotions.

The algorithm accurately predicts socio-emotional responses.

Simulation effectively models realistic group behaviors.

Abstract

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.