Visual social information use in collective foraging.

Area of Science:

• Ecology
• Behavioral Ecology
• Computational Biology

Background:

• Collective dynamics arise from individual decisions, but the link to real-world outcomes is unclear.
• Understanding information use (personal vs. social) is crucial for collective behavior.
• Agent-based models offer a way to explore these complex interactions.

Purpose of the Study:

• To mechanistically link individual decision-making to collective foraging outcomes.
• To investigate the trade-off between using personal and social information.
• To explore the impact of environmental and physical constraints on collective performance.

Main Methods:

• Developed a spatially-explicit, agent-based model simulating individual evidence accumulation.
• Integrated personal (internal) and social (visual) cues with particle-based movement.
• Simulated collective foraging in both idealized and constrained environments.

Main Results:

• The model reproduced established findings under idealized conditions, explaining bottom-up emergence.
• Optimal collective performance depended on environmental structure: strong social influence in clustered, individualistic search in uniform environments.
• Physical and perceptual constraints significantly altered collective dynamics, sometimes preventing maladaptive herding.

Conclusions:

• Reveals mechanisms connecting individual cognition to collective foraging in humans and animals.
• Demonstrates how environmental context and constraints modulate information use strategies.
• Provides a framework for designing decentralized robotic systems for collective tasks.