Large-Scale Patterns in a Minimal Cognitive Flocking Model: Incidental Leaders, Nematic Patterns, and Aggregates

Area of Science:

• Physics
• Complex Systems
• Collective Motion

Background:

• Collective motion is prevalent in nature, from biological swarms to synthetic active matter.
• Existing models often rely on velocity alignment or complex interactions.
• Understanding minimal models is key to deciphering emergent behaviors.

Purpose of the Study:

• To investigate a novel minimal cognitive flocking model based solely on visual information.
• To explore the self-organization dynamics of active particles with position-based interactions within a vision cone.
• To identify the range of emergent patterns and their underlying mechanisms.

Main Methods:

• Development of a minimal cognitive flocking model with active particles.
• Simulation of particle dynamics incorporating a position-based attractive force within a vision cone (VC).
• Analysis using nonlinear field equations alongside agent-based simulations.

Main Results:

• Demonstration of complex, large-scale self-organized patterns emerging from the VC's asymmetry.
• Observation of aggregates, milling patterns, and leader-driven polar-file formations.
• Formation of macroscopic nematic structures exhibiting long-ranged nematic order.

Conclusions:

• The model represents a new class of active systems distinct from velocity-alignment models.
• Position-based interactions within a vision cone are sufficient to generate diverse collective behaviors.
• Findings are crucial for modeling and interpreting collective motion in both living and nonliving systems.