Department of Physics, Sharif University of Technology, P.O. Box 11165-9161, Tehran, Iran.
Kavli Institute for Systems Neuroscience and Centre for Neural Computation, Norwegian University of Science and Technology (NTNU), Olav Kyrres gate 9, 7030, Trondheim, Norway.
Sci Rep. 2019 Mar 5;9(1):3428. doi: 10.1038/s41598-019-40141-4.
We introduce a model for collective information acquisition from the environment, in a biological population. In this model, individuals can make noisy observations of the environment, and communicate their observation by production and comprehension of signals. As the communication noise decreases, the model shows an order-disorder transition from a disordered phase in which no consensus about the environmental state exists to an ordered phase where the population forms a consensus about the environmental state. The ordered phase itself is composed of an informed consensus, in which the correct belief about the environment prevails, and an uninformed consensus phase, in which consensus on a random belief about the environmental state is formed. The probability of reaching informed consensus increases with increasing the observation probability. This phenomenology implies that a maximum noise level, and a minimum observation probability are necessary for informed consensus in a communicating population. Furthermore, we show that the fraction of observant individuals needed for the group to reach informed consensus decreases with increasing population size. This results from a shift in the uninformed-informed transition to smaller observation probabilities by increasing population size. Importantly, we also find that an amount of noise in signal production deteriorates the information flow and the inference capability, more than the same amount of noise in comprehension. This finding implies that there is higher selection pressure to reduce noise in production of signals compared to comprehension. Regarding this asymmetry, we propose an experimental design to separately measure comprehension and production noise in a given population and test the predicted asymmetry.
我们介绍了一种从生物群体的环境中进行集体信息采集的模型。在该模型中,个体可以对环境进行噪声观测,并通过信号的产生和理解来进行交流。随着通信噪声的降低,模型显示出从无序相到有序相的转变,在无序相中,没有关于环境状态的共识,而在有序相中,群体对环境状态形成了共识。有序相本身由知情共识组成,其中正确的环境信念占主导地位,以及非知情共识阶段,在该阶段中,对环境状态的随机信念形成共识。达到知情共识的概率随着观察概率的增加而增加。这种现象学意味着,在一个有交流的群体中,最大噪声水平和最小观察概率是知情共识所必需的。此外,我们表明,群体达到知情共识所需的观察个体的比例随着群体规模的增加而减少。这是由于通过增加群体规模,非知情-知情转变转移到较小的观察概率。重要的是,我们还发现,信号产生中的噪声量比理解中的噪声量更能降低信息流和推理能力。这一发现意味着,与理解相比,信号产生中降低噪声的选择压力更高。关于这种不对称性,我们提出了一个实验设计,以分别测量给定群体中的理解和产生噪声,并测试预测的不对称性。