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

Tadahiro Taniguchi

REVIEW 3 major objections 5 minor 74 references

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

T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The paper proposes that the meaning of music is the change a musical passage induces in a listener's inferred emotional state, modeled as collective predictive coding over interoceptive signals.

desk verdict A clear, well-scoped speculative perspective whose central music-emotion analogy is asserted rather than shown; the paper's own admission of musical firstness breaks the proposed PGM mapping. read the letter →

arxiv 2501.15721 v1 pith:MD7FSLR6 submitted 2025-01-27 cs.HC cs.MM

Tadahiro Taniguchi This is my paper · ORCID

classification cs.HCcs.MM

keywords symbolemergencesystemsprobabilisticgenerativemodelscollectivepredictivecodinginteroceptiveinferencemeaningofmusiclanguageandparallelismautomaticcomposition

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Languages and music both look like linear sequences of discrete sounds, and the paper argues this surface similarity runs deeper: both can be modeled as symbol systems that emerge when agents predict their own sensory streams. The paper's central hypothesis is that symbol emergence in a society is collective predictive coding, and that the same machinery explains how language and music acquire meaning. For music, the claim is that the meaning of a piece is the change it induces in a listener's inferred internal state, where emotions are themselves predictions about interoceptive (visceral) signals. By replacing external sensorimotor grounding with internal, bodily grounding, the author proposes a direct parallel between language and music as emergent social symbol systems. A sympathetic reader would care because this offers a computational, testable route from emotion research to music semantics.

What carries the argument

The load-bearing mechanism is collective predictive coding realized as decentralized Bayesian inference in a probabilistic generative model. In the PGM for symbol emergence, two agents share a latent word $w$ while each maintains private internal representations $z_A$ and $z_B$ generated from their own observations, and communication is an approximate posterior sampling scheme, the Metropolis-Hastings naming game. The extension to music replaces the observation set $\{o_m\}$ with interoceptive signals and the internal representation with an emotional state, so that the same head-to-head graphical model describes how musical symbols arise and stabilize. This carries the analogy: what makes a sign meaningful is not a fixed dyadic relation to an object but its role in predicting an agent's own future signals.

What would settle it

A decisive test would compare listeners' reported or physiological emotional responses to a musical passage while their interoceptive priors are manipulated, for example by making their heartbeats audible or by inducing a reliable visceral state. If musical meaning can be fully predicted from acoustic structure alone, without any measurable dependence on interoceptive state inference, the central claim fails; conversely, if the same acoustic signal gives different inferred emotional meanings solely because of different bodily priors, the claim is supported.

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Extended reading notes

Core claim

On the paper's own terms, the discovery is a mapping: the equations that describe a robot learning words from multimodal sensorimotor data, $w, \{o_m\} \sim p(w,\{o_m\}|z)$ and $y \sim p(y|w)$, are identical in form to equations for music generation in which $z$ is an emotional state. The author then replaces the perceptual internal representation of symbol emergence systems with an emotional internal representation, and replaces interaction with the external world via sensorimotor signals with interaction with the internal environment via interoception. The resulting proposal is that 'the meaning of music' is a change in the mental state that the listener undergoes, i.e., inference or state updating of internal representations under emotional predictive coding. Music, on this view, is a socially emerged symbol system grounded not in exteroceptive facts but in interoceptive predictions, with individual agents' emotional states coordinated through semiotic communication just as perceptual states are coordinated in language.

Load-bearing premise

The argument rests on the assumption that music, unlike language, does not explicitly represent events in the external world, so its meaning can be grounded in interoceptive rather than exteroceptive signals; if music primarily represents external events or is primarily grounded in body movement rather than visceral feeling, the proposed parallel loses its basis.

Editorial extensions

If this is right

If the meaning of music is an update of emotional internal representations, then automatic composition can be reframed as sampling from a posterior distribution over emotional states, extending existing language-model approaches to note sequences.
A musical symbol system, like a language, is not fixed in a score or a composer's intention; it is continuously re-emerged through social coordination of listeners' interoceptive predictions.
The same PGMs used for unsupervised phoneme and word discovery, double articulation analysis, and multimodal object and place concept formation should be transferable to modeling musical structure and its emotional grounding.
Emotion can be brought into computational music research as a latent variable with a well-defined generative semantics rather than a categorical label.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

This suggests a testable asymmetry: musical signs can act directly on visceral sensing, so the sign itself participates in its own grounding, whereas arbitrary linguistic signs must acquire grounding through learned exteroceptive association.
A natural extension is to expressive prosody and performance gesture, which also carry emotional content through interoceptive predictions and could be modeled with the same head-to-head PGM.
If musical meaning is collectively inferred emotional state, then cultural differences in musical taste become differences in shared priors over interoceptive signals, which could be studied by comparing predictive models fitted to listeners from different traditions.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Colleague,

The short version: this is a speculative perspective piece, not a result paper, and the author knows it. The only genuinely new content is the mapping in Sec. 4.2 and Fig. 2: replace perceptual internal representations with emotional ones, replace sensorimotor interaction with the external world by interoceptive interaction with the body, and then read symbol emergence in music as collective predictive coding of emotions. Everything else is a competent review of the author's and others' work on probabilistic generative models for language, object/place concepts, and the Metropolis-Hastings naming game. The review part is accurate and useful, and the author is honest that the music hypothesis is a hypothesis, explicitly not backed by evidence in this paper.

Where the paper is soft:

First, the formal parallelism in Eqs. (8) and (9) is real but thin. Those equations say music and language are both sequences generated from a latent cause; that is true of almost any sequential generative model. The content of the parallelism only comes from the mapping to interoceptive emotions, and that mapping is asserted rather than derived.

Second, Eq. (10) contains an obvious typo: p(zA|{oA_m}) appears twice and p(zB|{oB_m}) is missing. Since this equation is the centerpiece of the claimed correspondence, that is not cosmetic. Section 4.2 also has a garbled sentence about 'the meaning itself is observable,' which I assume should be 'not observable.'

Third, and most important, the stress-test concern is right. Section 5 concedes that music has 'many symbolic aspects of firstness': the sign itself can directly affect visceral sensations, e.g., heartbeat-synchronized sounds. In the symbol emergence PGM, w is a shared latent variable explaining each agent's observations through zA and zB, conditionally independent of observations given z. If a musical sign acts directly on interoceptive observations, the generative model should be o_m ~ p(o_m | z, w), not o_m ~ p(o_m | z). That breaks the head-to-head structure the paper relies on. The paper does not address this, and it undermines the claim that music is a socially emerged symbol system in the same sense as language.

Still, the paper is clearly written, well referenced, and honestly scoped. It is a position piece, and as a position piece it deserves engagement. I would send it to peer review if the venue takes perspectives; I would not let it through as an established account. The author should fix Eq. (10), clarify the 'observable' sentence, and either add a concrete minimal model with testable predictions or explicitly restrict the claim to the thirdness aspects of music.

Who gets value: researchers working on symbol emergence, grounded language, predictive coding, and music semantics. Useful for discussion, not as a result.

Recommendation: referee it, with the expectation of major revision.

Referee Report

3 major / 5 minor

Summary. The paper proposes a speculative parallelism between music and language from the viewpoint of symbol emergence systems modeled with probabilistic generative models (PGMs). It first reviews the author's prior work on symbol emergence in robotics, where a multi-agent symbol system is modeled as decentralized Bayesian inference and described as 'collective predictive coding.' It then extends this framework to music by reinterpreting the latent variables: perceptual internal representations become emotional internal representations, and sensorimotor interaction with the external world becomes interoceptive interaction with the internal environment. The paper hypothesizes that 'the meaning of music' is the change in the listener's mental state, i.e., the inference of emotional internal representations, and that music is a socially emerged symbol system grounded in interoceptive predictions. The paper explicitly states that it does not provide further details or evidence for the correspondence.

Significance. If the hypothesis were substantiated, it would provide a unifying computational framework for music semantics, linking music to emotion, predictive coding, and social symbol emergence, and it would open a concrete research program, such as extending PGM-based language models to music with emotional latent variables. The paper is honest about its speculative status and clearly lays out the key analogies in Figure 2. Its strengths are the explicit computational framing and the connection to a well-developed line of PGM-based symbol emergence research, including machine-checked or reproducible models in prior work. However, as a proposal it does not yet provide a derivation, simulation, or empirical test, and the formal part currently contains an error in Eq. (10) and an unresolved tension with the paper's own admission of music's 'firstness.' The paper's value is as a position statement that may provoke discussion, but its central claim is not yet adequately supported.

major comments (3)

[Section 4.2, Eq. (10)] Equation (10) as written is formally incorrect: it duplicates p(zA|{oAm}) and omits p(zB|{oBm}). The correct factorization should involve p(w|zA, zB) p(zA|{oAm}) p(zB|{oBm}), or an equivalent symmetric form. This is not a harmless typo, because the equation is presented as the mathematical description of the symbol emergence system, and the reader cannot verify the claimed decentralized Bayesian inference without the correct terms.
[Section 5 (and Section 4.2)] The paper concedes that music has 'many symbolic aspects of firstness' and that musical signs can directly affect visceral senses. In the language model, the shared sign w is conditionally independent of observations o given the internal states z, which is what allows two agents to align their internal representations through a common external world. For music, the sign may directly influence interoceptive observations, yielding o_m ~ p(o_m | z, w) rather than o_m ~ p(o_m | z). This changes the generative structure of Eq. (10) and undermines the proposed mapping. The paper does not explain how a symbol system can still emerge when the sign is partly non-arbitrary and causally coupled to the private observations it is supposed to signify. The author should either extend the model to include firstness and demonstrate that symbol emergence can still occur, or argue explicitly why firstness does not preclude the conventional alignment of musical signs.
[Section 2.2, Eqs. (8)-(9)] The statement that Eqs. (8) and (9) are 'identical' to Eqs. (1) and (2) is formally true but semantically superficial. In the language case, the latent z is presented as a cause of the utterance and is grounded in external objects and sensorimotor information; in the music case, z is an emotional state inferred from interoceptive signals. The formal similarity of two sequence-generating models is not by itself evidence of parallelism between the underlying cognitive mechanisms; it is exactly the proposed hypothesis that these latent variables play analogous roles. Please clarify that this is an analogy in generative form, not a derivation, and discuss what additional assumptions are needed to make the analogy substantive.

minor comments (5)

[Figure 2 caption] The right panel of Figure 2 uses 'introspective signals' while the text consistently uses 'interoceptive signals'; please unify the terminology.
[Section 5] The phrase 'the author pointed out' appears in the first-person narrative of a formal paper; please rephrase to avoid a personal voice.
[References] References [41] and [42] are the same Okanoya 2007 paper; remove the duplicate and renumber accordingly.
[Section 2.1, Eq. (5)] Equation (5) writes inference as z ~ p(z|{om}); this notation could be confused with sampling from the generative model. Consider using posterior notation such as z* = argmax z p(z|{om}) or a more explicit inference formulation.
[General] The paper would benefit from a short discussion of testable predictions of the hypothesis, for example, whether a multi-agent PGM with interoceptive observations can actually produce shared musical symbols in simulation.

Circularity Check

2 steps flagged · score 4.0 of 10

Music-as-symbol-emergence conclusion is largely guaranteed by defining musical meaning as internal-state inference and relabeling the language PGM.

self definitional [Section 4.2, paragraph after Fig. 2 (p. 11)]
"if we view the ”meaning of music” as a change in the mental state that the listener undergoes, or inference (or state updating) of internal representations, similar to the ”meaning of language,” and especially if we view it as an emotional impression (i.e., being moved, or its effect on the emotions), then through the discussion of predictive coding, we can connect it to the discussion of symbol emergence systems"

The claimed connection is guaranteed by the definition chosen. Musical meaning is stipulated to be inference or state updating of internal representations, which is exactly the operation performed by predictive coding and by the symbol-emergence PGM. Under that stipulation, the link to symbol emergence systems follows by construction rather than from independent evidence about music; the subsequent hypothesis inherits this definitional reduction.
renaming known result [Section 2.2, Eqs. (8)-(9); Section 4.2, Fig. 2 (right)]
"The generative process of music, including composition and performance, it can be described as follows: w = w1:S ∼ p(w|z), (8) y = y1:T ∼ p(y|w) (9) ... Interestingly, these equations are identical to those in (1) and (2), respectively. This correspondence apparently displays one parallelism between music and language. ... The sign of music corresponds to the sign of language. The ’perceptual’ internal representation system that supports the interpretation of language corresponds to the ’emotional’ internal representation system in music."

The music side is not derived from musical data or analysis; it is constructed by writing music in the same generative form as language and then relabeling z as the emotional state and sensorimotor observations as interoceptive ones. Figure 2 (right) is a relabeled version of the language/symbol-emergence diagram. The parallelism is therefore built into the notation rather than demonstrated, and the later hypothesis about music inherits that constructed equivalence.

full rationale

This is an explicitly hypothetical perspective paper: it calls the music claim a possible hypothesis and states that it does not provide further details or evidence. The central reduction is definitional: once musical meaning is stipulated as internal-state inference and the music generative process is written with the same equations as language with z renamed emotional state, the connection to collective predictive coding follows by construction. This is a real circularity, but it is limited because no fitted parameters or empirical predictions are made. The computational core comes from prior published models, including the author's own Metropolis-Hastings naming game work, but those are simulation-based results rather than the target music claim, so the self-citations are not by themselves the circular step. The paper also candidly concedes that music has many symbolic aspects of firstness, i.e., that signs can directly affect visceral senses; this undermines the arbitrary-sign assumption of the symbol emergence model, but that is a coherence gap rather than a circular step. Overall, the music hypothesis is an analogical proposal whose conclusion is largely guaranteed by its own definitions, but it is honestly offered as a hypothesis, so the circularity score is moderate.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

No numerical free parameters, fitted values, or invented physical entities are introduced. The central hypothesis rests on conceptual premises: the interoceptive-predictive-coding theory of emotion, the collective-predictive-coding view of symbol emergence, and an assumed contrast between linguistic and musical reference. These premises are imported from the literature, including several works by the author, and are not independently validated here.

assumptions (4)

domain assumption Emotion is based on predictive coding of interoceptive signals.
Invoked in Section 4.2 and Figure 2 (right) via citations [48,8] to ground the proposed emotional internal representation system; not independently tested in this paper.
ad hoc to paper Symbol emergence in a society is collective predictive coding and can be modeled as decentralized Bayesian inference.
Stated as a hypothesis in Section 3.2, building on the author's prior work [69]; it serves as the foundation for mapping music into symbol emergence systems.
ad hoc to paper The generative process of music can be written identically to language as w ~ p(w|z) and y ~ p(y|w), with z an emotional state.
Equations (8) and (9) mirror Equations (1) and (2) in Section 2.2; this equation-level identity is asserted, not derived, and is the main structural evidence for the parallelism.
domain assumption Music does not have a function explicitly representing events in the external world, unlike human language.
Stated in Section 4.2 as an assumption; if false, the mapping of music onto emotional internal representations loses its anchor.

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Cite this review

Pith. "Pith review of On Parallelism in Music and Language: A Perspective from Symbol Emergence Systems based on Probabilistic Generative Models." pith.science (2026). https://pith.science/paper/MD7FSLR6

@misc{pith2026250115721,
  author       = {Pith},
  title        = {Pith review of: On Parallelism in Music and Language: A Perspective from Symbol Emergence Systems based on Probabilistic Generative Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MD7FSLR6}},
  note         = {Machine review of arXiv:2501.15721}
}

read the original 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.

Figures

Figures reproduced from arXiv: 2501.15721 by the authors.

**Figure 1.** an overview of a symbol emergence system [62,67] communication. Agents form internal representations based on their interactions with their environment. In the terminology of Piaget’s genetic epistemology, this corresponds to the formation of a schema [22,66]. Barsalou’s perceptual symbol system corresponds to the formation of symbols [9]. Based on the terminology of modern artificial intelligence, this can be calle… view at source ↗

**Figure 2.** (Left) Diagram of the process symbol emergence based on collective predictive coding. Each agent forms internal representations reflecting their perceptual state, and they use and form language through semiotic communications. The complete process is regarded as collective predictive coding [69,27,25]. (Center) A PGM for symbol emergence systems corresponding to Inter-DM, Inter-MDM, and InterGMM+VAE [27,25,69]. M… view at source ↗

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