Bosshard Alexandra B, Leroux Maël, Lester Nicholas A, Bickel Balthasar, Stoll Sabine, Townsend Simon W
Department of Comparative Language Science, University of Zurich, Affolternstrasse 56, 8050 Zurich, Switzerland.
Center for the Interdisciplinary Study of Language Evolution (ISLE), University of Zurich, Zurich, Switzerland.
Behav Ecol Sociobiol. 2022;76(9):122. doi: 10.1007/s00265-022-03224-3. Epub 2022 Aug 22.
Emerging data in a range of non-human animal species have highlighted a latent ability to combine certain pre-existing calls together into larger structures. Currently, however, the quantification of context-specific call combinations has received less attention. This is problematic because animal calls can co-occur with one another simply through chance alone. One common approach applied in language sciences to identify recurrent word combinations is collocation analysis. Through comparing the co-occurrence of two words with how each word combines with other words within a corpus, collocation analysis can highlight above chance, two-word combinations. Here, we demonstrate how this approach can also be applied to non-human animal signal sequences by implementing it on artificially generated data sets of call combinations. We argue collocation analysis represents a promising tool for identifying non-random, communicatively relevant call combinations and, more generally, signal sequences, in animals.
Assessing the propensity for animals to combine calls provides important comparative insights into the complexity of animal vocal systems and the selective pressures such systems have been exposed to. Currently, however, the objective quantification of context-specific call combinations has received less attention. Here we introduce an approach commonly applied in corpus linguistics, namely collocation analysis, and show how this method can be put to use for identifying call combinations more systematically. Through implementing the same objective method, so-called call-ocations, we hope researchers will be able to make more meaningful comparisons regarding animal signal sequencing abilities both within and across systems.
The online version contains supplementary material available at 10.1007/s00265-022-03224-3.
一系列非人类动物物种的新数据凸显了将某些预先存在的叫声组合成更大结构的潜在能力。然而，目前特定情境下叫声组合的量化受到的关注较少。这存在问题，因为动物叫声可能仅仅是偶然同时出现。语言科学中用于识别反复出现的单词组合的一种常见方法是搭配分析。通过将两个单词的共现情况与语料库中每个单词与其他单词的组合方式进行比较，搭配分析可以突出高于偶然水平的双词组合。在这里，我们通过在人工生成的叫声组合数据集上实施搭配分析，展示了这种方法也可以应用于非人类动物信号序列。我们认为搭配分析是一种很有前景的工具，可用于识别动物中非随机的、具有交流相关性的叫声组合，更广泛地说，还可用于识别信号序列。
评估动物组合叫声的倾向为了解动物发声系统的复杂性以及此类系统所面临的选择压力提供了重要的比较性见解。然而，目前特定情境下叫声组合的客观量化受到的关注较少。在这里，我们介绍一种语料库语言学中常用的方法，即搭配分析，并展示如何将此方法用于更系统地识别叫声组合。通过实施相同的客观方法，即所谓的叫声搭配，我们希望研究人员能够在系统内部和跨系统之间就动物信号排序能力进行更有意义的比较。
在线版本包含可在10.1007/s00265-022-03224-3获取的补充材料。
