To Follow or Not to Follow

How do living organisms learn to navigate when surrounded by a moving, unpredictable group? The Collective Information Processing Group (Pawel Romanczuk) investigated how individual agents learn to balance spatial memory with social cues. Using a computational model, they trained neural network-controlled agents to navigate toward a hidden goal. This happened in a simulated environment alongside other untrained agents acting as experienced guides or wandering randomly. The study revealed that when high-quality social information is available (i.e. multiple experienced agents moving directly to the target), trained agents undergo a behavioral phase shift. Rather than navigating solely via environmental landmarks or purely following others, agents often adopt a hybrid strategy. They use spatial landmarks in open areas and switch to social cues near crowded targets, while also developing looping trajectories to dodge collisions in high-density areas. These findings challenge traditional models that study navigation as an isolated, individual task, highlighting how spatial dynamics and group structure naturally shape collective intelligence. You can learn more about visual navigation in their article. 

Abstract

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.