How vision governs the collective behaviour of dense cycling pelotons

Abstract

In densely packed groups demonstrating collective behaviour, such as bird flocks, fish schools or packs of bicycle racers (cycling pelotons), information propagates over a network, with individuals sensing and reacting to stimuli over relatively short space and time scales. What remains elusive is a robust, mechanistic understanding of how sensory system properties affect interactions, information propagation and emergent behaviour. Here, we show through direct observation how the spatio-temporal limits of the human visual sensory system govern local interactions and set the network structure in large, dense collections of cyclists. We found that cyclists align in patterns within a ± 30° arc corresponding to the human near-peripheral visual field, in order to safely accommodate motion perturbations. Furthermore, the group structure changes near the end of the race, suggesting a narrowing of the used field of vision. This change is consistent with established theory in psychology linking increased physical exertion to the decreased field of perception. Our results show how vision, modulated by arousal-dependent neurological effects, sets the local arrangement of cyclists, the mechanisms of interaction and the implicit communication across the group. We furthermore describe information propagation phenomena with an analogous elastic solid mechanics model. We anticipate our mechanistic description will enable a more detailed understanding of the interaction principles for collective behaviour in a variety of animals.

Friction-enhanced lifetime of bundled quantum vortices

“…This remarkable collective effect -the reduction of friction via hydrodynamic cooperation -is similar to what has been observed in systems of active particles such as swimming bacteria [1][2][3], fungal spores [4], racing cyclists [5,6] and particle pairs trapped in an optical vortex [7,8], in which self-organized structures emerge from energysaving mechanisms [33]. The system that we have investigated, superfluid helium, is however richer: whereas in fact in the cited active matter systems the agents, besides modifying the common background fluid, may interact with each others directly only via short-range twobody collisions, in our case the vortex lines also experi-ence a significant collective long-range interaction which for instance induces them to rotate around each other (leapfrogging).…”

“…A second example is the synchronized ejection of fungal spores which, by collectively creating an airflow, reduce the drag, thus increasing their spatial dispersion [4]. A third example is road racing cyclists cooperatively organized in the peloton, who benefit from an overall greatly reduced aerodynamic drag [5,6] compared to cyclists riding alone. A fourth (nonbiological) example is the increase of the speed of pairs of particles trapped inside an optical vortex due to the hydrodynamical interaction [7,8].…”

Physics of road cycling and the three jerseys problem

“…We have only discussed individual phases. Collective effects, such as the ones at play in peloton, induce a very stimulating physics which has just started to be considered (Blocken et al 2018b;Belden et al 2019). (ii) Team strategy is known to have a major effect in the final ranking of a grand tour.…”

Scale-Free Dynamics in Animal Groups and Brain Networks

“…Many biological systems were found to fit into this latter category specifically when considering systems of self-driven particles to model movements of ants (Millonas, 1992 ; Rauch et al, 1995 ), fish schools (Huth and Wissel, 1992 ) and bird flocks resulting in the seminal model by Vicsek et al ( 1995 ) for flocking in biological systems based on local interactions impacted by noise. Since then, variations of the Vicsek model (Grégoire and Chaté, 2004 ; Chate et al, 2008 ) as well as other models that utilize attraction and distance rules (Couzin et al, 2002 ; Romanczuk et al, 2009 ) have been combined with experimental observations to capture population dynamics of many species such as locust swarms (Huepe et al, 2011 ), ants (Gelblum et al, 2016 ), fish schools (Tunstrøm et al, 2013 ), migrating white storks (Nagy et al, 2018 ), and cycling pelotons (Belden et al, 2019 ) with a major goal to understand the emergence of collective behavior from the mechanistic interactions between individuals [for a review, see e.g., Wang and Lu ( 2019 )].…”