How vision governs the collective behaviour of dense cycling pelotons. Issue 156 (26th July 2019)

• Record Type: Journal Article
• Authors: Belden, J.; Mansoor, M. M.; Hellum, A.; Rahman, S. R.; Meyer, A.; Pease, C.; Pacheco, J.; Koziol, S.; Truscott, T. T.
• Is Part Of: Journal of the Royal Society interface. Volume 16:Issue 156(2019)
• Publication Date: 2019-07-26
• Subjects: collective behaviour -- cycling pelotons -- visual sensory system -- visual field -- bicycling; Physical sciences -- Research -- Periodicals; Life sciences -- Research -- Periodicals; Interdisciplinary research -- Periodicals; 570.5
• DOI: 10.1098/rsif.2019.0197
• Languages: English
• ISSNs: 1742-5689

Abstract

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.