Chance, long tails, and inference: a non-Gaussian, Bayesian theory of vocal learning in songbirds

TL;DR

This paper introduces a Bayesian, non-Gaussian theory of sensorimotor learning in songbirds, explaining complex behaviors and distribution dynamics observed during vocal pitch adaptation.

Contribution

It proposes a novel theory that models learning as distribution inference rather than single optimal commands, supported by experimental validation in songbirds.

Findings

Distribution of sung pitches has long, non-Gaussian tails.

The theory predicts and explains the dynamics of pitch distribution shape.

Experimental data confirms the theory's predictions about learning dynamics.

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 that large abrupt (but not gradually introduced) errors lead to weak learning. Here we propose a new theory of sensorimotor learning that explains these observations. The theory posits that the animal learns an entire probability distribution of motor commands rather than trying to arrive at 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.