Biology subjects

Warren, W. H.

Publications and source records attributed to Warren, W. H..

3 recordsLinked to original sources

bioRxiv2025

Visual Influence Networks in Walking Crowds

Collective motion in human crowds has been understood as a self-organizing phenomenon that is generated from local visual interactions between neighboring pedestrians. To analyze these interactions, we introduce an approach that estimates local influences in observational data on moving human crowds and represents them as spatially-embedded dynamic networks (visual influence networks). We analyzed data from a human "swarm" experiment (N = 10, 16, 20) in which participants were instructed to walk about the tracking area while staying together as a group. We reconstructed the network every 0.5 seconds using Time-Dependent Delayed Correlation (TDDC). Using novel network measures of local and global leadership (direct influence and branching influence), we find that both measures strongly depend on an individuals spatial position within the group, yielding similar but distinctive leadership gradients from the front to the back. There was also a strong linear relationship between individual influence and front-back position in the crowd. The results reveal that influence is concentrated in specific positions in a crowd, a fact that could be exploited by individuals seeking to lead collective crowd motion.

animal behavior and cognition↗

bioRxiv2022

The neighborhood of interaction in human crowds is neither metric nor topological, but visual

Global patterns of collective motion in bird flocks, fish schools, and human crowds are thought to emerge from local interactions within a neighborhood of interaction, the zone in which an individual is influenced by their neighbors. Both topological and metric neighborhoods have been reported in birds, but this question has not been addressed in humans. With a topological neighborhood, an individual is influenced by a fixed number of nearest neighbors, regardless of their physical distance; whereas with a metric neighborhood, an individual is influenced by all neighbors within a fixed radius. We test these hypotheses experimentally with participants walking in real and virtual crowds, by manipulating the crowds density. Our results rule out a strictly topological neighborhood, are approximated by a metric neighborhood, but are best explained by a visual neighborhood with aspects of both. This finding has practical implications for modeling crowd behavior and understanding crowd disasters.

animal behavior and cognition↗

bioRxiv2021

The visual coupling between neighbors explains 'flocking' in human crowds

Patterns of collective motion or flocking in birds, fish schools, and human crowds are believed to emerge from local interactions between individuals. Most models of collective motion attribute these interactions to hypothetical rules or forces, often inspired by physical systems, and described from an overhead view. We develop a visual model of human flocking from an embedded view, based on optical variables that actually govern pedestrian interactions. Specifically, people control their walking speed and direction by canceling the average optical expansion and angular velocity of their neighbors, weighted by visual occlusion. We test the model by simulating data from experiments with virtual crowds and real human swarms. The visual model outperforms our previous overhead model and explains basic properties of physics-inspired models: repulsion forces reduce to canceling optical expansion, attraction forces to canceling optical contraction, and alignment to canceling the combination of expansion/contraction and angular velocity. Critically, the neighborhood of interaction follows from Euclids Law of perspective and the geometry of occlusion. We conclude that the local interactions underlying human flocking are a natural consequence of the laws of optics. Similar principles may apply to collective motion in other species.

animal behavior and cognition↗