On Parallelism in Music and Language: A Perspective from Symbol Emergence Systems based on Probabilistic Generative Models

TL;DR

This paper explores the structural parallels between music and language through probabilistic generative models, proposing a framework for symbol emergence in robotics and hypothesizing about the emergence of musical meaning.

Contribution

It introduces a novel probabilistic generative modeling approach for symbol emergence in robotics, linking emotion, predictive coding, and the emergence of musical meaning.

Findings

Development of PGMs for language and symbol systems

Modeling of multi-agent symbol emergence as collective predictive coding

Hypothesis on the emergence of musical meaning from predictive processes

Abstract

Music and language are structurally similar. Such structural similarity is often explained by generative processes. This paper describes the recent development of probabilistic generative models (PGMs) for language learning and symbol emergence in robotics. Symbol emergence in robotics aims to develop a robot that can adapt to real-world environments and human linguistic communications and acquire language from sensorimotor information alone (i.e., in an unsupervised manner). This is regarded as a constructive approach to symbol emergence systems. To this end, a series of PGMs have been developed, including those for simultaneous phoneme and word discovery, lexical acquisition, object and spatial concept formation, and the emergence of a symbol system. By extending the models, a symbol emergence system comprising a multi-agent system in which a symbol system emerges is revealed to be modeled using PGMs. In this model, symbol emergence can be regarded as collective predictive coding. This paper expands on this idea by combining the theory that ''emotion is based on the predictive coding of interoceptive signals'' and ''symbol emergence systems,'' and describes the possible hypothesis of the emergence of meaning in music.