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Zebra finches identify individuals using vocal signatures unique to each call type

Author

Listed:
  • Julie E. Elie

    (UC Berkeley)

  • Frédéric E. Theunissen

    (UC Berkeley
    UC Berkeley)

Abstract

Individual recognition is critical in social animal communication, but it has not been demonstrated for a complete vocal repertoire. Deciphering the nature of individual signatures across call types is necessary to understand how animals solve the problem of combining, in the same signal, information about identity and behavioral state. We show that distinct signatures differentiate zebra finch individuals for each call type. The distinctiveness of these signatures varies: contact calls bear strong individual signatures while calls used during aggressive encounters are less individualized. We propose that the costly solution of using multiple signatures evolved because of the limitations of the passive filtering properties of the birds’ vocal organ for generating sufficiently individualized features. Thus, individual recognition requires the memorization of multiple signatures for the entire repertoire of conspecifics of interests. We show that zebra finches excel at these tasks.

Suggested Citation

  • Julie E. Elie & Frédéric E. Theunissen, 2018. "Zebra finches identify individuals using vocal signatures unique to each call type," Nature Communications, Nature, vol. 9(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-06394-9
    DOI: 10.1038/s41467-018-06394-9
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    Cited by:

    1. A Ivanenko & P Watkins & M A J van Gerven & K Hammerschmidt & B Englitz, 2020. "Classifying sex and strain from mouse ultrasonic vocalizations using deep learning," PLOS Computational Biology, Public Library of Science, vol. 16(6), pages 1-27, June.
    2. Julie E Elie & Frédéric E Theunissen, 2019. "Invariant neural responses for sensory categories revealed by the time-varying information for communication calls," PLOS Computational Biology, Public Library of Science, vol. 15(9), pages 1-43, September.
    3. Margot C Bjoring & C Daniel Meliza, 2019. "A low-threshold potassium current enhances sparseness and reliability in a model of avian auditory cortex," PLOS Computational Biology, Public Library of Science, vol. 15(1), pages 1-20, January.
    4. 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.

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