A key challenge for any animal (or sampling technique) is to avoid wasting time by searching for resources (information) in places already found to be unprofitable. In biology, this challenge is particularly strong when the organism is a central place forager - returning to a nest between foraging bouts - because it is destined repeatedly to cover much the same ground. This problem will be particularly acute if many individuals forage from the same central place, as in social insects such as the ants. Foraging (sampling) performance may be greatly enhanced by coordinating movement trajectories such that each ant ('walker') visits separate parts of the surrounding (unknown) space. We find experimental evidence for an externalised spatial memory in Temnothorax albipennis ants: chemical markers (either pheromones or cues such as cuticular hydrocarbon footprints) that are used by nestmates to mark explored space. We show these markers could be used by the ants to scout the space surrounding their nest more efficiently through indirect coordination. We also develop a simple model of this marking behaviour that can be applied in the context of Markov chain Monte Carlo methods (Baddeley et al. 2019). This substantially enhances the performance of standard methods like the Metropolis-Hastings algorithm in sampling from sparse probability distributions (such as those confronted by the ants) with little additional computational cost. Our Bayesian framework for superorganismal behaviour motivates the evolution of exploratory mechanisms such as trail marking in terms of enhanced collective information processing.

中文翻译：

---

贝叶斯超生物：外在记忆促进分布式采样

任何动物（或采样技术）的主要挑战是，通过在已经发现无利可图的地方搜索资源（信息）来避免浪费时间。在生物学中，当有机体是觅食者的中心位置时-觅食回合之间的巢穴-由于它被注定要重复覆盖几乎相同的地面，因此这一挑战尤其强烈。如果许多人从同一中心地点觅食，例如在社会昆虫（如蚂蚁）中觅食，这一问题将尤为严重。通过协调运动轨迹可以使觅食（采样）性能大大提高，从而使每个蚂蚁（“行者”）都能访问周围（未知）空间的各个部分。我们发现实验性证据为Temnothorax albipennis中的外部空间记忆蚂蚁：巢友用来标记探索空间的化学标记（信息素或线索，例如表皮碳氢化合物足迹）。我们证明了这些标记可以被蚂蚁用来通过间接协调更有效地侦察其巢周围的空间。我们还开发了这种标记行为的简单模型，可以在马尔可夫链蒙特卡洛方法的背景下应用（Baddeley等人2019）。这极大地提高了诸如Metropolis-Hastings算法之类的标准方法从稀疏概率分布（例如蚂蚁所面对的概率分布）中进行采样的性能，而几乎没有额外的计算成本。我们关于超生物行为的贝叶斯框架从增强的集体信息处理角度出发，推动了探索机制（例如足迹标记）的发展。