IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0024516.html
   My bibliography  Save this article

Complex Sequencing Rules of Birdsong Can be Explained by Simple Hidden Markov Processes

Author

Listed:
  • Kentaro Katahira
  • Kenta Suzuki
  • Kazuo Okanoya
  • Masato Okada

Abstract

Complex sequencing rules observed in birdsongs provide an opportunity to investigate the neural mechanism for generating complex sequential behaviors. To relate the findings from studying birdsongs to other sequential behaviors such as human speech and musical performance, it is crucial to characterize the statistical properties of the sequencing rules in birdsongs. However, the properties of the sequencing rules in birdsongs have not yet been fully addressed. In this study, we investigate the statistical properties of the complex birdsong of the Bengalese finch (Lonchura striata var. domestica). Based on manual-annotated syllable labeles, we first show that there are significant higher-order context dependencies in Bengalese finch songs, that is, which syllable appears next depends on more than one previous syllable. We then analyze acoustic features of the song and show that higher-order context dependencies can be explained using first-order hidden state transition dynamics with redundant hidden states. This model corresponds to hidden Markov models (HMMs), well known statistical models with a large range of application for time series modeling. The song annotation with these models with first-order hidden state dynamics agreed well with manual annotation, the score was comparable to that of a second-order HMM, and surpassed the zeroth-order model (the Gaussian mixture model; GMM), which does not use context information. Our results imply that the hierarchical representation with hidden state dynamics may underlie the neural implementation for generating complex behavioral sequences with higher-order dependencies.

Suggested Citation

  • Kentaro Katahira & Kenta Suzuki & Kazuo Okanoya & Masato Okada, 2011. "Complex Sequencing Rules of Birdsong Can be Explained by Simple Hidden Markov Processes," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-9, September.
  • Handle: RePEc:plo:pone00:0024516
    DOI: 10.1371/journal.pone.0024516
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0024516
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0024516&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0024516?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tim Sainburg & Marvin Thielk & Timothy Q Gentner, 2020. "Finding, visualizing, and quantifying latent structure across diverse animal vocal repertoires," PLOS Computational Biology, Public Library of Science, vol. 16(10), pages 1-48, October.
    2. Joshua M Mueller & Primoz Ravbar & Julie H Simpson & Jean M Carlson, 2019. "Drosophila melanogaster grooming possesses syntax with distinct rules at different temporal scales," PLOS Computational Biology, Public Library of Science, vol. 15(6), pages 1-25, June.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0024516. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.