Chance, long tails, and inference in a non-Gaussian, Bayesian theory of vocal learning in songbirds.
Proc Natl Acad Sci U S A
; 115(36): E8538-E8546, 2018 09 04.
Article
en En
| MEDLINE
| ID: mdl-30127024
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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Vocalización Animal
/
Pájaros Cantores
/
Aprendizaje
/
Modelos Biológicos
Tipo de estudio:
Prognostic_studies
Límite:
Animals
Idioma:
En
Revista:
Proc Natl Acad Sci U S A
Año:
2018
Tipo del documento:
Article