ArticleProceedings of the National Academy of Sciences of the United States of America2018
Chance, long tails, and inference in a non-Gaussian, Bayesian theory of vocal learning in songbirds.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 1 citations in OpenAlex.
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- Generative models of birdsong learning link circadian fluctuations in song variability to changes in performance.PLoS computational biology · 2023Article
- Unsupervised Bayesian Ising Approximation for decoding neural activity and other biological dictionaries.eLife · 2022Article
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- Generating synthetic aging trajectories with a weighted network model using cross-sectional data.Scientific reports · 2020Article
- Neural Substrates of Drosophila Larval Anemotaxis.Current biology : CB · 2019Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Traditional theories of sensorimotor learning posit that animals use sensory error signals to find the optimal motor command in the face of Gaussian sensory and motor noise. However, most such theories cannot explain common behavioral observations, for example, that smaller sensory errors are more readily corrected than larger errors and large abrupt (but not gradually introduced) errors lead to weak learning. Here, we propose a theory of sensorimotor learning that explains these observations. The theory posits that the animal controls an entire probability distribution of motor commands rather than trying to produce a single optimal command and that learning arises via Bayesian inference when new sensory information becomes available. We test this theory using data from a songbird, the Bengalese finch, that is adapting the pitch (fundamental frequency) of its song following perturbations of auditory feedback using miniature headphones. We observe the distribution of the sung pitches to have long, non-Gaussian tails, which, within our theory, explains the observed dynamics of learning. Further, the theory makes surprising predictions about the dynamics of the shape of the pitch distribution, which we confirm experimentally.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.