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Revealing the hidden networks of interaction in mobile animal groups allows prediction of complex behavioral contagion

机译:揭示移动动物群体中隐藏的相互作用网络可以预测复杂的行为传染

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摘要

Coordination among social animals requires rapid and efficient transfer of information among individuals, which may depend crucially on the underlying structure of the communication network. Establishing the decision-making circuits and networks that give rise to individual behavior has been a central goal of neuroscience. However, the analogous problem of determining the structure of the communication network among organisms that gives rise to coordinated collective behavior, such as is exhibited by schooling fish and flocking birds, has remained almost entirely neglected. Here, we study collective evasion maneuvers, manifested through rapid waves, or cascades, of behavioral change (a ubiquitous behavior among taxa) in schooling fish (Notemigonus crysoleucas). We automatically track the positions and body postures, calculate visual fields of all individuals in schools of ∼150 fish, and determine the functional mapping between socially generated sensory input and motor response during collective evasion. We find that individuals use simple, robust measures to assess behavioral changes in neighbors, and that the resulting networks by which behavior propagates throughout groups are complex, being weighted, directed, and heterogeneous. By studying these interaction networks, we reveal the (complex, fractional) nature of social contagion and establish that individuals with relatively few, but strongly connected, neighbors are both most socially influential and most susceptible to social influence. Furthermore, we demonstrate that we can predict complex cascades of behavioral change at their moment of initiation, before they actually occur. Consequently, despite the intrinsic stochasticity of individual behavior, establishing the hidden communication networks in large self-organized groups facilitates a quantitative understanding of behavioral contagion.
机译:社会动物之间的协调需要在个人之间快速有效地传递信息,这可能严重取决于通信网络的基础结构。建立引起个体行为的决策电路和网络一直是神经科学的中心目标。然而,类似的问题是确定引起协调一致的集体行为的生物之间的通信网络的结构,例如养鱼和成群的禽类所表现出的问题,几乎完全被忽略了。在这里,我们研究了集体逃避行为,这种行为通过对受养鱼(Notemigonus crysoleucas)的行为改变(在分类群中普遍存在的行为)进行快速波动或级联来体现。我们会自动跟踪位置和身体姿势,计算大约150条鱼群中所有个体的视野,并确定集体逃避过程中社交产生的感觉输入与运动反应之间的功能映射。我们发现,个人使用简单,可靠的方法来评估邻居的行为变化,并且行为传播遍及整个群体的结果网络是复杂的,被加权的,有针对性的和异质的。通过研究这些互动网络,我们揭示了社会传染的(复杂的,部分的)性质,并确定具有相对较少但紧密联系的邻居的个体在社会上最具影响力,并且最容易受到社会影响。此外,我们证明了我们可以在行为改变的实际发生之前预测它们的复杂级联。因此,尽管个体行为具有内在的随机性,但在大型自组织群体中建立隐藏的通信网络有助于定量了解行为传染。

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