Kamo Masashi, Ghirlanda Stefano, Enquist Magnus
Department of Biology, Graduate School of Sciences, Kyushu University, Higashi-ku, Hakozaki 6-10-1, Fukuoka, Japan.
Proc Biol Sci. 2002 Sep 7;269(1502):1765-71. doi: 10.1098/rspb.2002.2081.
Organisms can learn by individual experience to recognize relevant stimuli in the environment or they can genetically inherit this ability from their parents. Here, we ask how these two modes of acquisition affect signal evolution, focusing in particular on the exaggeration and cost of signals. We argue first, that faster learning by individual receivers cannot be a driving force for the evolution of exaggerated and costly signals unless signal senders are related or the same receiver and sender meet repeatedly. We argue instead that biases in receivers' recognition mechanisms can promote the evolution of costly exaggeration in signals. We provide support for this hypothesis by simulating coevolution between senders and receivers, using artificial neural networks as a model of receivers' recognition mechanisms. We analyse the joint effects of receiver biases, signal cost and mode of acquisition, investigating the circumstances under which learned recognition gives rise to more exaggerated signals than inherited recognition. We conclude the paper by discussing the relevance of our results to a number of biological scenarios.
生物体可以通过个体经验学习来识别环境中的相关刺激,或者从父母那里遗传获得这种能力。在此,我们探讨这两种获取模式如何影响信号进化,尤其关注信号的夸张程度和成本。我们首先认为,除非信号发送者存在亲缘关系或者同一接收者和发送者反复相遇,否则个体接收者更快的学习速度不可能成为夸张且成本高昂的信号进化的驱动力。相反,我们认为接收者识别机制中的偏差能够促进信号中成本高昂的夸张现象的进化。我们通过模拟发送者和接收者之间的共同进化来支持这一假设,使用人工神经网络作为接收者识别机制的模型。我们分析了接收者偏差、信号成本和获取模式的联合效应,研究在何种情况下习得的识别会比遗传的识别产生更夸张的信号。我们在论文结尾讨论了我们的结果与一些生物学情景的相关性。