社会信息处理的离散模式预测群体中鱼类的个体行为
Discrete modes of social information processing predict individual behavior of fish in a group
摘要：
动物群体集体行为背后的个体计算和社会互动在动物行为学、行为学和理论上都具有重要意义。虽然复杂的个人行为已经成功地被解析为刻板行为模式的小字典，但集体行为的研究在很大程度上忽略了这些发现;相反，他们的重点是推断单一的，独立于模式的社会互动规则，再现宏观的，往往是定性的群体行为特征。在这里，我们把这两种方法结合起来，预测个人的成年斑马鱼在一组游泳模式。我们发现，鱼交替的主动模式，他们是敏感的同种的游泳模式，和被动模式，他们忽略它们。使用明确说明这两种模式的模型，我们预测个体鱼的行为具有很高的准确性，优于以前的方法，假设一个单一的连续计算的个人和简单的度量或拓扑加权的邻居的行为。在组水平上，主动和被动模式之间的切换是不相关的鱼，但相关的定向游泳行为仍然出现。我们用于研究复杂的、多模式的个人行为以及新兴的群体行为的定量方法可以很容易地扩展到其他行为模式及其神经相关物，以及其他物种。
英文摘要：
Individual computations and social interactions underlying collective behavior in groups of animals are of great ethological, behavioral, and theoretical interest. While complex individual behaviors have successfully been parsed into small dictionaries of stereotyped behavioral modes, studies of collective behavior largely ignored these findings; instead, their focus was on inferring single, mode-independent social interaction rules that reproduced macroscopic and often qualitative features of group behavior. Here we bring these two approaches together to predict individual swimming patterns of adult zebrafish in a group. We show that fish alternate between an active mode in which they are sensitive to the swimming patterns of conspecifics, and a passive mode where they ignore them. Using a model that accounts for these two modes explicitly, we predict behaviors of individual fish with high accuracy, outperforming previous approaches that assumed a single continuous computation by individuals and simple metric or topological weighing of neighbors behavior. At the group level, switching between active and passive modes is uncorrelated among fish, yet correlated directional swimming behavior still emerges. Our quantitative approach for studying complex, multi-modal individual behavior jointly with emergent group behavior is readily extensible to additional behavioral modes and their neural correlates, as well as to other species.
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