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

Pierre Orhan¹, Yves Boubenec¹, Jean-Rémi King^1,2

¹Laboratoire des Systèmes Perceptifs, Département d'études Cognitives, École Normale Supérieure, PSL University, CNRS, Paris, France.

Plos Computational Biology

|September 5, 2025

Summary

This summary is machine-generated.

Deep learning models show that exposure to natural sounds alone can enable the spontaneous detection of complex algebraic structures in auditory processing. This ability is enhanced by music and environmental sounds, but not speech.

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Area of Science:

Cognitive Science
Computational Neuroscience
Artificial Intelligence

Background:

Humans spontaneously detect algebraic structures, crucial for language and music.
Debate exists between innate mechanisms versus experience-based learning for this ability.
Experimental testing of these theories is challenging.

Purpose of the Study:

To evaluate factors driving spontaneous algebraic structure detection in auditory processing using deep learning.
To model the impact of different sound stimuli (natural, environmental, speech, music) on this ability.
To provide an operational framework for understanding innate versus acquired principles.

Main Methods:

Utilized self-supervised learning to train deep learning models.
Trained models with varying amounts of natural, environmental, speech, and music sounds.
Exposed trained models to standard experimental paradigms for algebraic structure processing.

Main Results:

Models spontaneously detected sequences, chunks, and complex structures, mirroring human abilities.
Detection ability decreased with increasing structure complexity.
Experience with natural sounds alone fostered structure detection; music accelerated it more than environmental sounds.
Pretraining solely on speech did not yield this ability.

Conclusions:

Spontaneous algebraic structure detection can emerge from experience with auditory input.
Environmental and cultural sounds significantly influence the development of this cognitive capacity.
Deep learning models offer a viable framework for dissecting cognitive abilities like auditory structure processing.