ArticleNature communications2021
Deep neural network models reveal interplay of peripheral coding and stimulus statistics in pitch perception.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Incorporating softmax in psychophysical detection models for normal and electric hearing.MethodsX · 2026Article
- Optimized feature gains explain and predict successes and failures of human selective listening.Nature human behaviour · 2026Article
- From sound to source: Human and model recognition of environmental sounds.bioRxiv : the preprint server for biology · 2026Article
- Humans can use positive and negative spectrotemporal correlations to detect rising and falling pitch.Nature human behaviour · 2026Article
- Task-optimized models of sensory uncertainty reproduce human confidence judgments.bioRxiv : the preprint server for biology · 2025Article
- Dissociating the neural codes for multiple pitch perception in humans.bioRxiv : the preprint server for biology · 2025Article
- A deep learning framework for understanding cochlear implants.bioRxiv : the preprint server for biology · 2025Article
- Individual differences elucidate the perceptual benefits associated with robust temporal fine-structure processing.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Learning to hear again with alternating cochlear frequency allocations.Scientific reports · 2025Article
- Models optimized for real-world tasks reveal the task-dependent necessity of precise temporal coding in hearing.Nature communications · 2024Article
- Models optimized for real-world tasks reveal the task-dependent necessity of precise temporal coding in hearing.bioRxiv : the preprint server for biology · 2024Article
- Individual Differences Elucidate the Perceptual Benefits Associated with Robust Temporal Fine-Structure Processing.bioRxiv : the preprint server for biology · 2024Article
- A maturational frequency discrimination deficit may explain developmental language disorder.Psychological review · 2024Article
- Many but not all deep neural network audio models capture brain responses and exhibit correspondence between model stages and brain regions.PLoS biology · 2023Article
- Model metamers reveal divergent invariances between biological and artificial neural networks.Nature neuroscience · 2023Article
- The neuroconnectionist research programme.Nature reviews. Neuroscience · 2023Review
- Hearing as adaptive cascaded envelope interpolation.Communications biology · 2023Article
- Human-Like Modulation Sensitivity Emerging through Optimization to Natural Sound Recognition.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Sensitivity to Frequency Modulation is Limited Centrally.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Encoding of speech in convolutional layers and the brain stem based on language experience.Scientific reports · 2023Article
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3 authors.
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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.
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