Identifying influential neighbors in animal flocking

Jiang, Li; Giuggioli, Luca; Perna, Andréa; Escobedo, Ramón; Lecheval, Valentin; Sire, Clément; Han, Zhangang; Théraulaz, Guy

doi:10.1371/journal.pcbi.1005822

Cited by 71 publications

(75 citation statements)

References 42 publications

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“…The linear propagation of information in all group sizes suggests that there is no amplification of the individual tendency to perform a U-turn: the time between two successive individuals performing U-turns does not decrease with the number of fish that already performed a U-turn. This suggests that individuals only pay attention to a small number of neighbours at a given time as was shown in [18].…”

Section: Spatiotemporal Dynamics Of Collective U-turnmentioning

confidence: 86%

“…show that the interval between successive turns of individuals during a collective U-turn decreases with swimming speed, although distance between individuals may also play a role [19]. However, the mean time interval between successive individual U-turns is almost constant and independent of the group size, once time has been rescaled by the group velocity.…”

Section: /11mentioning

confidence: 93%

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Domino-like propagation of collective U-turns in fish schools

Lecheval

Jiang

Tichit

et al. 2017

Preprint

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Moving animal groups such as schools of fish or flocks of birds often undergo sudden collective changes of their travelling direction as a consequence of stochastic fluctuations in heading of the individuals. However, the mechanisms by which these behavioural fluctuations arise at the individual level and propagate within a group are still unclear. In the present study, we combine an experimental and theoretical approach to investigate spontaneous collective U-turns in groups of rummy-nose tetra (Hemigrammus rhodostomus) swimming in a ring-shaped tank. U-turns imply that fish switch their heading between the clockwise and anticlockwise direction. We reconstruct trajectories of individuals moving alone and in groups of different sizes. We show that the group decreases its swimming speed before a collective U-turn. This is in agreement with previous theoretical predictions showing that speed decrease facilitates an amplification of fluctuations in heading in the group, which can trigger U-turns. These collective U-turns are mostly initiated by individuals at the front of the group. Once an individual has initiated a U-turn, the new direction propagates through the group from front to back without amplification or dampening, resembling the dynamics of falling dominoes. The mean time between collective U-turns sharply increases as the size of the group increases. We develop an Ising spin model integrating anisotropic and asymmetrical interactions between fish and their tendency to follow the majority of their neighbours nonlinearly (social conformity). The model quantitatively reproduces key features of the dynamics and the frequency of collective U-turns observed in experiments.

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Section: Spatiotemporal Dynamics Of Collective U-turnmentioning

confidence: 86%

Section: /11mentioning

confidence: 93%

Domino-like propagation of collective U-turns in fish schools

Lecheval

Jiang

Tichit

et al. 2017

Preprint

Self Cite

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“…Despite the fact that the design of the experiments involved a constrained environment for the fish, i.e., a circular corridor, in order to obtain a clear collective response, these results can be extended in further studies in more complex environments, involving societies composed of more agents, in order to study in more detail how information propagates between the agents such as shown in Jiang et al (2017), how the robots can accurately modulate the behaviors of the fish, and, possibly, how the robots can automatically adapt to these behaviors.…”

Section: Discussionmentioning

confidence: 99%

“…1). This is a common setup to study the collective behavior of fish (Abaid and Porfiri 2010;Jiang et al 2017) and offers a binary choice for the fish, as they can either move in a clockwise direction (CW) or a counterclockwise (CCW), which will be used to evaluate the effect of the robots on the collective decisions of the fish. The dimensions of the corridor were as follows: an external diameter of 58 cm, an internal diameter of 38 cm, and a corridor's width of 10 cm.…”

Section: Arenamentioning

confidence: 99%

Closed-loop interactions between a shoal of zebrafish and a group of robotic fish in a circular corridor

et al. 2018

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Collective behavior based on self-organization has been observed in populations of animals from insects to vertebrates. These findings have motivated engineers to investigate approaches to control autonomous multi-robot systems able to reproduce collective animal behaviors, and even to collectively interact with groups of animals. In this article, we show collective decision making by a group of autonomous robots and a group of zebrafish, leading to a shared decision about swimming direction. The robots can also modulate the collective decision-making process in biased and non-biased experimental setups. These results demonstrate the possibility of creating mixed societies of vertebrates and robots in order to study or control animal behavior.

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“…Such social structures may in fact be common in nature, where visual occlusion [30,31] or other mechanisms generate clustering in animal groups [22]. Furthermore, existing empirical evidence suggests that, for many social animal species, individuals pay attention to their closest 1-7 nearest neighbors [17,18,27,32], resulting in relatively sparse social networks.…”

Section: Spatial Clustering and Sparseness Improves The Filtering Of mentioning

confidence: 99%

The wisdom of stalemates: consensus and clustering as filtering mechanisms for improving collective accuracy

Winklmayr

Kao

Bak-Coleman

et al. 2020

Preprint

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Groups of organisms, from bacteria to fish schools to human societies, depend on their ability to make accurate decisions in an uncertain world. Most models of collective decision-making assume that groups reach a consensus during a decision-making bout, often through simple majority rule. In many natural and sociological systems, however, groups may fail to reach consensus, resulting in stalemates. Here, we build on opinion dynamics and collective wisdom models to examine how stalemates may affect the wisdom of crowds. For simple environments, where individuals have access to independent sources of information, we find that stalemates improve collective accuracy by selectively filtering out incorrect decisions. In complex environments, where individuals have access to both shared and independent information, this effect is even more pronounced, restoring the wisdom of crowds in regions of parameter space where large groups perform poorly when making decisions using majority rule. We identify network properties that tune the system between consensus and accuracy, providing mechanisms by which animals, or evolution, could dynamically adjust the collective decision-making process in response to the reward structure of the possible outcomes. Overall, these results highlight the adaptive potential of stalemale filtering for improving the decision-making abilities of group-living animals.

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Identifying influential neighbors in animal flocking

Cited by 71 publications

References 42 publications

Domino-like propagation of collective U-turns in fish schools

Domino-like propagation of collective U-turns in fish schools

Closed-loop interactions between a shoal of zebrafish and a group of robotic fish in a circular corridor

The wisdom of stalemates: consensus and clustering as filtering mechanisms for improving collective accuracy

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