Deep neural network models reveal interplay of peripheral coding and stimulus statistics in pitch perception
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s41467-021-27366-6.pdf
Description
Published version
Size
5.25 MB
Format
Adobe PDF
Checksum (MD5)
ea9aba6682f1163a5449babb353bba85
Author(s)
Saddler, Mark R
Gonzalez, Ray
McDermott, Josh H
Date Issued
2021
Journal
Nature Communications
Publisher
Springer Science and Business Media LLC
Citation
Saddler, Mark R, Gonzalez, Ray and McDermott, Josh H. 2021. "Deep neural network models reveal interplay of peripheral coding and stimulus statistics in pitch perception." Nature Communications, 12 (1).
Version
Final published version
Abstract
AbstractPerception 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.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/S41467-021-27366-6
