The detection of algebraic auditory structures emerges with self-supervised learning.

ABSTRACT: Humans can spontaneously detect complex algebraic structures. Historically, two opposing views explain this ability, at the root of language and music acquisition. Some argue for the existence of an innate and specific mechanism. Others argue that this ability emerges from experience: i.e. when generic learning principles continuously process sensory inputs. These two views, however, remain difficult to test experimentally. Here, we use deep learning models to evaluate the factors that lead to the spontaneous detection of algebraic structures in the auditory modality. Specifically, we use self-supervised learning to train multiple deep-learning models with a variable amount of either natural (environmental sounds) and/or cultural sounds (speech or music) to evaluate the impact of these sti

SUBMITTER: Orhan P 

PROVIDER: S-EPMC12431648 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature