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Deep neural network models reveal interplay of peripheral coding and stimulus statistics in pitch perception

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Listed:
  • Mark R. Saddler

    (MIT
    MIT
    MIT)

  • Ray Gonzalez

    (MIT
    MIT
    MIT)

  • Josh H. McDermott

    (MIT
    MIT
    MIT
    Harvard University)

Abstract

Perception is thought to be shaped by the environments for which organisms are optimized. These influences are difficult to test in biological organisms but may be revealed by machine perceptual systems optimized under different conditions. We investigated environmental and physiological influences on pitch perception, whose properties are commonly linked to peripheral neural coding limits. We first trained artificial neural networks to estimate fundamental frequency from biologically faithful cochlear representations of natural sounds. The best-performing networks replicated many characteristics of human pitch judgments. To probe the origins of these characteristics, we then optimized networks given altered cochleae or sound statistics. Human-like behavior emerged only when cochleae had high temporal fidelity and when models were optimized for naturalistic sounds. The results suggest pitch perception is critically shaped by the constraints of natural environments in addition to those of the cochlea, illustrating the use of artificial neural networks to reveal underpinnings of behavior.

Suggested Citation

  • Mark R. Saddler & Ray Gonzalez & Josh H. McDermott, 2021. "Deep neural network models reveal interplay of peripheral coding and stimulus statistics in pitch perception," Nature Communications, Nature, vol. 12(1), pages 1-25, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-27366-6
    DOI: 10.1038/s41467-021-27366-6
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    References listed on IDEAS

    as
    1. Christophe Micheyl & Paul R Schrater & Andrew J Oxenham, 2013. "Auditory Frequency and Intensity Discrimination Explained Using a Cortical Population Rate Code," PLOS Computational Biology, Public Library of Science, vol. 9(11), pages 1-7, November.
    2. Oded Barzelay & Miriam Furst & Omri Barak, 2017. "A New Approach to Model Pitch Perception Using Sparse Coding," PLOS Computational Biology, Public Library of Science, vol. 13(1), pages 1-36, January.
    3. Malinda J. McPherson & Josh H. McDermott, 2018. "Diversity in pitch perception revealed by task dependence," Nature Human Behaviour, Nature, vol. 2(1), pages 52-66, January.
    4. Daniel Bendor & Xiaoqin Wang, 2005. "The neuronal representation of pitch in primate auditory cortex," Nature, Nature, vol. 436(7054), pages 1161-1165, August.
    5. Johannes Mehrer & Courtney J. Spoerer & Nikolaus Kriegeskorte & Tim C. Kietzmann, 2020. "Individual differences among deep neural network models," Nature Communications, Nature, vol. 11(1), pages 1-12, December.
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