Social-spatial dependencies for learning visual navigation
For researchers studying collective behavior in artificial and biological systems, this work challenges the individual-centric approach and emphasizes bottom-up analysis of social navigation.
The paper studies how neural network agents learn navigation strategies in social contexts, finding that social information quality drives phase transitions between individual, following, and collision-avoidance behaviors, with nonstationary environments causing behavioral hybridization.
Navigation for social organisms rarely is a fully independent activity. Group structure and dynamics, as well as embodied interactions, critically influence useful behavior. Individual neural network controlled agents are trained to navigate in different social contexts, where social dependence and behavioral strategy learned is determined by relative task performance and spatial effect. Increasing high quality social information drives phase transitions from individual to following navigational strategy, and to collision avoidance in response to a crowded foraging patch. Predictable, nonstationary environmental dynamics drive behavioral hybridization between individual and social navigation, far and near the patch. Our findings challenge the approach of only inspecting individual behavior for social organisms and highlight the importance of taking a bottom-up approach in understanding how organisms behave.