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arxiv: 2605.09522 · v1 · submitted 2026-05-10 · 💻 cs.MA

Recognition: 2 theorem links

· Lean Theorem

Emergent Communication for Co-constructed Emotion Between Embodied Agents via Collective Predictive Coding

Nguyen Le Hoang, Tadahiro Taniguchi, Takato Horii, Zehang Zhang

Pith reviewed 2026-05-12 02:16 UTC · model grok-4.3

classification 💻 cs.MA
keywords emergent communicationemotion co-constructioncollective predictive codingembodied agentsnaming gameinteroceptive signalssymbolic alignment
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The pith

Embodied agents develop aligned emotion categories through communication even when their internal bodily signals differ systematically.

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

The paper models how two agents, each building emotion categories from their own visual, auditory, and simulated bodily signals, can arrive at shared categories by exchanging symbols in a naming game. It shows that this communicative process produces clearer, more aligned categories than either agent achieves alone or with non-selective signaling. The alignment occurs mainly at the level of symbolic labels rather than raw perceptual features, and it persists even when the agents start with mismatched internal dynamics. If this holds, it supplies a concrete mechanism for the social co-construction of emotion and demonstrates that predictive coding frameworks can scale from solitary prediction to joint meaning-making.

Core claim

When two agents equipped with collective predictive coding exchange symbols via the Metropolis-Hastings Naming Game, their independently learned emotion categories become measurably more aligned, clearer, and mutually agreed upon than in non-communicative or non-selective baselines; the effect concentrates at the symbolic layer and remains robust even under systematic divergence in interoceptive dynamics, with each agent exhibiting distinct category-specific reshaping patterns.

What carries the argument

The Metropolis-Hastings Naming Game (MHNG) operating inside the Collective Predictive Coding (CPC) architecture, which lets agents propose and accept symbolic labels that minimize collective prediction error across their multimodal inputs.

If this is right

  • Shared emotional categories can form at the level of discrete symbols without requiring identical perceptual or interoceptive representations.
  • Interoceptive heterogeneity between agents does not block but instead shapes the emergence of common emotion categories through communication.
  • The alignment effect is localized to the symbolic interface rather than propagating back into each agent's lower-level perceptual latent space.
  • Predictive-coding agents can extend their internal models to social domains by treating other agents' signals as additional prediction targets.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same architecture could be tested with more than two agents or with continuous rather than discrete emotion categories to check whether the alignment mechanism scales.
  • If the symbolic layer remains the primary site of alignment, then interventions that alter only the naming-game rules should be sufficient to change shared emotional meaning without retraining perceptual encoders.
  • The observed category-specific reshaping patterns suggest that each agent may retain private emotional nuance while still coordinating on public labels, a pattern worth checking against human psychological data on emotion granularity.
  • Extending the model to include explicit reward for successful joint action after labeling could reveal whether communicative alignment improves downstream coordination performance.

Load-bearing premise

The chosen simulated visual, auditory, and interoceptive inputs plus the simple naming-game protocol are sufficient to stand in for the biological and social processes that construct shared emotion in humans.

What would settle it

An experiment in which MHNG communication produces no statistically significant gain in inter-agent category alignment or clarity relative to the non-communicative baseline.

Figures

Figures reproduced from arXiv: 2605.09522 by Nguyen Le Hoang, Tadahiro Taniguchi, Takato Horii, Zehang Zhang.

Figure 1
Figure 1. Figure 1: The process of two agents (Agent A and Agent B) forming and sharing emotion categories after observing the same object [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 1
Figure 1. Figure 1: Specifically, we employ the Inter-Gaussian Mixture Model+Multivariate Variational Autoencoder (Inter￾GMM+MVAE) [22], which integrates a multimodal deep generative model with the MHNG. We use this framework to simulate emergent communication—a bottom-up process where shared symbol systems evolve through local interactions without external supervision—between two agents processing visual, auditory, and inter… view at source ↗
Figure 2
Figure 2. Figure 2: Graphical model of Inter-GMM+MVAE used in this [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: We used only Original core affect for Agent A, and each of these four core affect for Agent B in the experiment.Among [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Mean valence–arousal coordinates for each emotion [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Visualization of core affect trajectories generated by [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Result of PCA and t-SNE them to converge on a shared, accurate symbolic system (higher ARI and Kappa). The failure of the All-acceptance scenario highlights that mere information exchange is insuffi￾cient; the ability to selectively reject incongruent symbols is crucial for robust social learning. B. Analysis of Latent Space Structure We next investigated whether communication fundamentally reshaped the ag… view at source ↗
Figure 7
Figure 7. Figure 7: The heat map uses recall to evaluate how well the model can recognize data with the same labels. [PITH_FULL_IMAGE:figures/full_fig_p010_7.png] view at source ↗
read the original abstract

According to the theory of constructed emotion, the brain actively forms emotion categories by integrating multimodal bodily signals, and constructs emotional experiences by using these categories to predict and interpret sensory inputs. While research has advanced in modeling individual emotion construction, the social process of co-construction-how a shared understanding of emotions emerges between individuals-remains computationally underexplored. This study investigates this process by modeling emergent communication between two embodied agents using the Metropolis-Hastings Naming Game (MHNG), grounded in the Collective Predictive Coding (CPC) framework. Our experiments, using visual, auditory, and simulated interoceptive inputs, yield two main findings. First, MHNG-based communication significantly improves the alignment, clarity, and inter-agent agreement of the learned emotion categories compared to non-communicative and non-selective baselines, with the alignment effect concentrated at the symbolic layer rather than the perceptual latent representation. Second, even when the two agents have systematically divergent interoceptive dynamics, communication still produces robust categorical alignment, with distinct, category-specific reshaping patterns of each agent's emotion categories-consistent with the constructed-emotion view that interoceptive heterogeneity is constitutive of, rather than an obstacle to, shared emotional meaning. These findings provide computational support for the co-constructionist view of emotion and extend the CPC framework from physical to socially-grounded domains.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 4 minor

Summary. The manuscript models co-constructed emotion between two embodied agents by combining Collective Predictive Coding (CPC) with the Metropolis-Hastings Naming Game (MHNG) for emergent communication. Using simulated visual, auditory, and interoceptive inputs, the experiments compare MHNG-based communication against non-communicative and non-selective baselines. The central claims are that MHNG communication produces significantly higher alignment, clarity, and inter-agent agreement of learned emotion categories (with the effect localized to the symbolic layer rather than perceptual latents), and that this alignment remains robust even when the agents have systematically divergent interoceptive dynamics, accompanied by distinct category-specific reshaping of each agent's categories.

Significance. If the reported simulation outcomes hold under replication, the work supplies a concrete computational demonstration that shared emotional categories can emerge through selective communication despite individual differences in bodily signals, thereby furnishing support for the co-constructionist account of emotion. It usefully extends the CPC framework into a multi-agent social setting and employs controlled baseline comparisons plus a heterogeneity robustness check. These elements constitute genuine strengths for a modeling paper in multi-agent systems and affective computation.

minor comments (4)
  1. [Abstract] Abstract: the statements that MHNG communication 'significantly improves' alignment and produces 'robust categorical alignment' are not accompanied by any quantitative values (effect sizes, number of runs, statistical tests, or exact definitions of the alignment/clarity metrics).
  2. [Section 3] Section 3 (Model): the precise parameterization of the interoceptive, visual, and auditory input streams, the architecture of the CPC encoders/decoders, and the MHNG update rules should be stated with explicit equations or pseudocode so that the reported category-reshaping patterns can be reproduced.
  3. [Section 4] Section 4 (Experiments) and associated figures: the results tables or plots do not report variance across random seeds or confidence intervals; adding these would strengthen the claim that the alignment effect is concentrated at the symbolic layer.
  4. [Section 3.2] The manuscript would benefit from an explicit statement of the loss functions and optimization details used for the CPC component, as these choices directly affect the learned latent representations against which communication is compared.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive assessment of the manuscript, accurate summary of our approach and findings, and recommendation for minor revision. We appreciate the recognition that the work provides computational support for the co-constructionist account of emotion and extends the CPC framework to a multi-agent setting. We will prepare a revised version incorporating any minor changes.

Circularity Check

0 steps flagged

No significant circularity; results from controlled simulation comparisons

full rationale

The paper's central claims rest on empirical outcomes from agent simulations that compare MHNG+CPC communication against non-communicative and non-selective baselines within the same experimental loop. These comparisons are not forced by construction because the alignment metrics (category agreement, clarity) are measured post-training on held-out or divergent interoceptive conditions. The frameworks (CPC, MHNG) are imported as established tools rather than derived internally, and no load-bearing step reduces a prediction to a fitted parameter or self-citation chain. The derivation chain is therefore self-contained against the paper's own benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on the domain assumption that CPC plus MHNG can stand in for human emotion construction and that simulated signals are adequate proxies; no free parameters or new entities are explicitly introduced in the abstract, but the entire modeling pipeline inherits unstated hyperparameters from the CPC and MHNG frameworks.

axioms (2)
  • domain assumption The brain actively forms emotion categories by integrating multimodal bodily signals and uses these categories to predict and interpret sensory inputs (theory of constructed emotion).
    Invoked in the opening paragraph of the abstract as the theoretical foundation for the modeling target.
  • domain assumption Collective Predictive Coding provides a suitable computational substrate for modeling both individual emotion construction and inter-agent communication.
    The entire experimental setup is grounded in CPC without derivation of why CPC is the right formalism for social emotion.

pith-pipeline@v0.9.0 · 5540 in / 1536 out tokens · 48620 ms · 2026-05-12T02:16:30.735886+00:00 · methodology

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