Yovel Yossi, Melcon Mariana Laura, Franz Matthias O, Denzinger Annette, Schnitzler Hans-Ulrich
Animal Physiology, Institute for Neurobiology, University of Tuebingen, Tuebingen, Germany.
PLoS Comput Biol. 2009 Jun;5(6):e1000400. doi: 10.1371/journal.pcbi.1000400. Epub 2009 Jun 5.
Echolocating bats use the echoes from their echolocation calls to perceive their surroundings. The ability to use these continuously emitted calls, whose main function is not communication, for recognition of individual conspecifics might facilitate many of the social behaviours observed in bats. Several studies of individual-specific information in echolocation calls found some evidence for its existence but did not quantify or explain it. We used a direct paradigm to show that greater mouse-eared bats (Myotis myotis) can easily discriminate between individuals based on their echolocation calls and that they can generalize their knowledge to discriminate new individuals that they were not trained to recognize. We conclude that, despite their high variability, broadband bat-echolocation calls contain individual-specific information that is sufficient for recognition. An analysis of the call spectra showed that formant-related features are suitable cues for individual recognition. As a model for the bat's decision strategy, we trained nonlinear statistical classifiers to reproduce the behaviour of the bats, namely to repeat correct and incorrect decisions of the bats. The comparison of the bats with the model strongly implies that the bats are using a prototype classification approach: they learn the average call characteristics of individuals and use them as a reference for classification.
使用回声定位的蝙蝠利用其回声定位叫声的回声来感知周围环境。利用这些主要功能并非交流的持续发出的叫声来识别同种个体，这一能力可能有助于解释在蝙蝠身上观察到的许多社会行为。几项关于回声定位叫声中个体特异性信息的研究发现了其存在的一些证据，但并未进行量化或解释。我们采用了一种直接的范式来表明，大鼠耳蝠（Myotis myotis）能够基于回声定位叫声轻松区分个体，并且能够将其知识进行推广，以区分未经训练识别的新个体。我们得出结论，尽管宽带蝙蝠回声定位叫声具有高度变异性，但其中包含足以用于识别的个体特异性信息。对叫声频谱的分析表明，与共振峰相关的特征是个体识别的合适线索。作为蝙蝠决策策略的模型，我们训练了非线性统计分类器来重现蝙蝠的行为，即重复蝙蝠的正确和错误决策。将蝙蝠与模型进行比较强烈表明，蝙蝠采用的是原型分类方法：它们学习个体的平均叫声特征，并将其用作分类的参考。
