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Measuring context dependency in birdsong using artificial neural networks
bioRxiv - Animal Behavior and Cognition Pub Date : 2021-11-25 , DOI: 10.1101/2020.05.09.083907
Takashi Morita , Hiroki Koda , Kazuo Okanoya , Ryosuke O. Tachibana

Context dependency is a key feature in sequential structures of human language, which requires reference between words far apart in the produced sequence. Assessing how long the past context has an effect on the current status provides crucial information to understand the mechanism for complex sequential behaviors. Birdsongs serve as a representative model for studying the context dependency in sequential signals produced by non-human animals, while previous reports were upper-bounded by methodological limitations. Here, we newly estimated the context dependency in birdsongs in a more scalable way using a modern neural-network-based language model whose accessible context length is sufficiently long. The detected context dependency was beyond the order of traditional Markovian models of birdsong, but was consistent with previous experimental investigations. We also studied the relation between the assumed/auto-detected vocabulary size of birdsong (i.e., fine-vs. coarse-grained syllable classifications) and the context dependency. It turned out that the larger vocabulary (or the more fine-grained classification) is assumed, the shorter context dependency is detected.

中文翻译:

使用人工神经网络测量鸟鸣中的上下文依赖性

上下文依赖是人类语言序列结构中的一个关键特征,它需要在生成的序列中相距很远的单词之间进行引用。评估过去的上下文对当前状态的影响时间为理解复杂顺序行为的机制提供了关键信息。Birdsongs 作为研究非人类动物产生的连续信号中的上下文依赖性的代表性模型,而以前的报告受到方法学限制的上限。在这里,我们使用基于现代神经网络的语言模型,其可访问的上下文长度足够长,以更具可扩展性的方式新估计了鸟鸣中的上下文依赖性。检测到的上下文依赖性超出了鸟鸣的传统马尔可夫模型的顺序,但与之前的实验研究一致。我们还研究了假设/自动检测到的鸟鸣词汇大小(即细粒度与粗粒度音节分类)与上下文依赖性之间的关系。事实证明,假设词汇量越大(或更细粒度的分类),则检测到的上下文依赖性越短。
更新日期:2021-11-28
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