論文

2020年5月10日

Measuring Long Context Dependency in Birdsong Using an Artificial Neural Network with a Long-Lasting Working Memory

  • Takashi Morita
  • ,
  • Hiroki Koda
  • ,
  • Kazuo Okanoya
  • ,
  • Ryosuke O. Tachibana

DOI
10.1101/2020.05.09.083907
出版者・発行元
Cold Spring Harbor Laboratory

<title>A<sc>bstract</sc></title>The production of grammatically and semantically appropriate human language requires reference to non-trivially long history of past utterance, which is referred to as the <italic>context dependency</italic> of human language. Similarly, it is of particular interest to biologists how much effect past behavioral records of individual animals have on their future behavioral decisions. In particular, birdsong serves a representative case to study context dependency in sequential signals produced by animals. Previous studies have suggested that the songs of Bengalese finches (<italic>Lonchura striata</italic> var. <italic>domestica</italic>) exhibited a long dependency on previous outputs, while their estimates were upper-bounded by methodological limitations at that time. This study newly estimated the context dependency in Bengalese finch’s song in a more scalable manner using a neural network-based language model, Transformer, whose accessible context length reaches 900 tokens and is thus nearly free from model limitations, unlike the methods adopted in previous studies. A quantitative comparison with a parallel analysis of English sentences revealed that context dependency in Bengalese finch song is much shorter than that in human language but is comparable to human language syntax that excludes semantic factors of dependency. Our findings are in accordance with the previous generalization reported in related studies that birdsong is more homologous to human language syntax than the entire human language, including semantics. Thus, this study supports the hypothesis that human language modules, such as syntax and semantics, evolved from different precursors that are shared with other animals.

<sec><title>Significance Statement</title><list list-type="bullet"><list-item>We investigated context dependency in over 10-hour recordings of Bengalese finch songs using a neural network-based language model.

</list-item><list-item>We proposed an end-to-end unsupervised clustering method of song elements (syllables) into statistically optimal categories.

</list-item><list-item>Context dependency in the birdsong is shorter than that in English sentences but comparable with the dependency in English syntax excluding semantic factors.

</list-item></list></sec>

リンク情報
DOI
https://doi.org/10.1101/2020.05.09.083907
URL
https://syndication.highwire.org/content/doi/10.1101/2020.05.09.083907

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