Chance, long tails, and inference in a non-Gaussian, Bayesian theory of vocal learning in songbirds
Year: 2018
Type: article
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.
Authors: Baohua Zhou, David Hofmann, Itai Pinkoviezky, Samuel J. Sober, Ilya Nemenman
Institution: Emory University
Language: English
Subfield: Developmental Biology
Domain: Life Sciences
Open Access status: bronze
Awards: In search of simplicity: Coarse-graining cellular information processing networks, The neural basis of online error correction, G285290610, Vocal motor control and sensorimotor learning - behavior, muscles, and neurons, Neural mechanisms and behavioral consequences of non-Gaussian likelihoods in sensorimotor learning, G5801408398, G730559200, G9000016918, G9000028628
OpenAlex ID: W2737148451