Large-scales patterns in a minimal cognitive flocking model: incidental leaders, nematic patterns, and aggregates

TL;DR

This paper introduces a minimal cognitive flocking model where particles navigate using visual information, leading to complex patterns like aggregates, milling, and nematic order, highlighting a new class of active systems.

Contribution

The study demonstrates that position-based active particles with visual interaction can produce diverse large-scale patterns, distinct from traditional velocity-alignment models.

Findings

Emergence of aggregates and milling patterns.

Formation of moving, locally polar files with effective leaders.

Development of macroscopic nematic order.

Abstract

We study a minimal cognitive flocking model, which assumes that the moving entities navigate using exclusively the available instantaneous visual information. The model consists of active particles, with no memory, that interact by a short-ranged, position-based, attractive force that acts inside a vision cone (VC) and lack velocity-velocity alignment. We show that this active system can exhibit -- due to the VC that breaks Newton's third law -- various complex, large-scale, self-organized patterns. Depending on parameter values, we observe the emergence of aggregates or milling-like patterns, the formation of moving -- locally polar -- files with particles at the front of these structures acting as effective leaders, and the self-organization of particles into macroscopic nematic structures leading to long-ranged nematic order. Combining simulations and non-linear field equations, we show that position-based active models, as the one analyzed here, represent a new class of active systems fundamentally different from other active systems, including velocity-alignment-based flocking systems. The reported results are of prime importance in the study, interpretation, and modeling of collective motion patterns in living and non-living active systems.