Capturing the evolution of a transformative cognitive capacity: the case of vocal learning in dogs

Modifying the vocal repertoire using an external auditory model is a prerequisite for speech production. But how full-fledged vocal learning evolved in humans, uniquely among primates, is still unclear.

Integrating recent evidence from ethology and genetics, I hypothesize that selection for tameness, alloparental behavior and cooperativity in concert were the key driving processes for the emergence of human vocal learning and speech.

I propose that dogs constitute an ideal model to test this hypothesis as they underwent a strong selection for tameness and cooperativity with, and often alloparented by humans; at the same time, their vocal behavior markedly changed compared to their closest relatives, the wolves.

Dogs have control over their vocalizations, suggesting the presence of some primordial forms of vocal learning abilities.

Combining bioacoustics, ethology, genetics, and brain imaging, K9VocLearn thus sets out to reveal the mechanisms underlying the emergence of vocal learning capacities in dogs.

Specifically, I test how genetic similarity with wolves, particular gene variations of breeds, extreme domestication-syndrome phenotype, and individual cooperativity affect abilities and neural processes involved in vocal learning.

WP1 utilizes citizen science, bioacoustics and machine learning approaches to develop a comprehensive catalogue of dogs’ vocal repertoire.

WP2 seeks evidence of vocal usage and vocal production learning, including vocal imitative abilities in dogs, while also testing neural and vocal control mechanisms involved.

WP3 tests dogs’ vocal coordination abilities in con- and heterospecific interactions both on the behavioral and the neural level.

In early intervention experiments, WP4 longitudinally examines vocal ontogeny and the role of the acoustic environment in vocal flexibility.

Breaking ground on capturing the emergence of vocal learning in a cooperative domestic species, K9VocLearn will provide new insights into human language evolution.