Vocalization categorization behavior explained by a feature-based auditory categorization model

Abstract

Vocal animals produce multiple categories of calls with high between- and within-subject variability, over which listeners must generalize to accomplish call categorization. The behavioral strategies and neural mechanisms that support this ability to generalize are largely unexplored. We previously proposed a theoretical model that accomplished call categorization by detecting features of intermediate complexity that best contrasted each call category from all other categories. We further demonstrated that some neural responses in the primary auditory cortex were consistent with such a model. Here, we asked whether a feature-based model could predict call categorization behavior. We trained both the model and guinea pigs on call categorization tasks using natural calls. We then tested categorization by the model and guinea pigs using temporally and spectrally altered calls. Both the model and guinea pigs were surprisingly resilient to temporal manipulations, but sensitive to moderate frequency shifts. Critically, the model predicted about 50% of the variance in guinea pig behavior. By adopting different model training strategies and examining features that contributed to solving specific tasks, we could gain insight into possible strategies used by animals to categorize calls. Our results validate a model that uses the detection of intermediate-complexity contrastive features to accomplish call categorization.

Data availability

All data generated or analyzed during this study are included in the manuscript and supporting file; Source Data files have been provided for Figures 3 - 12.

Article and author information

Author details

  1. Manaswini Kar

    Center for Neuroscience, University of Pittsburgh, Pittsburgh, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Marianny Pernia

    Department of Neurobiology, University of Pittsburgh, Pittsburgh, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9889-3577
  3. Kayla Williams

    Department of Neurobiology, University of Pittsburgh, Pittsburgh, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Satyabrata Parida

    Department of Neurobiology, University of Pittsburgh, Pittsburgh, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2896-2522
  5. Nathan Alan Schneider

    Center for Neuroscience, University of Pittsburgh, Pittsburgh, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9145-5427
  6. Madelyn McAndrew

    Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Isha Kumbam

    Department of Neurobiology, University of Pittsburgh, Pittsburgh, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Srivatsun Sadagopan

    Center for Neuroscience, University of Pittsburgh, Pittsburgh, United States
    For correspondence
    vatsun@pitt.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1116-8728

Funding

National Institutes of Health (R01DC017141)

  • Srivatsun Sadagopan

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Dan FM Goodman, Imperial College London, United Kingdom

Ethics

Animal experimentation: All experimental procedures conformed to the NIH Guide for the Care and Use of Laboratory Animals and were approved by the institutional animal care and use committee of the University of Pittsburgh (protocol number 21069431).

Version history

  1. Received: March 1, 2022
  2. Preprint posted: March 10, 2022 (view preprint)
  3. Accepted: October 12, 2022
  4. Accepted Manuscript published: October 13, 2022 (version 1)
  5. Version of Record published: November 3, 2022 (version 2)

Copyright

© 2022, Kar et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Manaswini Kar
  2. Marianny Pernia
  3. Kayla Williams
  4. Satyabrata Parida
  5. Nathan Alan Schneider
  6. Madelyn McAndrew
  7. Isha Kumbam
  8. Srivatsun Sadagopan
(2022)
Vocalization categorization behavior explained by a feature-based auditory categorization model
eLife 11:e78278.
https://doi.org/10.7554/eLife.78278

Share this article

https://doi.org/10.7554/eLife.78278

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