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arxiv: 2605.07524 · v2 · submitted 2026-05-08 · 💻 cs.MA

Recognition: no theorem link

Dynamic Latent-Belief Synchrony through Collective Predictive Coding: A Computational Model of Parent--Infant Homeostatic Co-Regulation

Yushi Tsubamoto , Takato Horii
Authors on Pith no claims yet

Pith reviewed 2026-05-13 07:46 UTC · model grok-4.3

classification 💻 cs.MA
keywords inter-brain synchronyparent-infant interactioncollective predictive codingPOMDPhomeostatic co-regulationlatent belief alignmentMetropolis-Hastings Naming Game
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The pith

Parent and infant agents align their latent representations through interaction before their generative models fully converge.

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

The paper constructs a model where a parent and an infant regulate the infant's internal state using local sensory information and a shared communication protocol. The parent sees external cues from the infant's body while the infant senses its own internal state directly, creating asymmetric knowledge. Through a naming game based on collective predictive coding, the agents develop synchronized beliefs about the situation. This synchrony appears quickly in simulations and holds steady even as the agents keep learning and the situation changes. If accurate, it suggests that brain-to-brain alignment in real interactions can happen without both parties having identical understandings of the world.

Core claim

In the proposed POMDP model integrated with the Metropolis-Hastings Naming Game, the Jensen-Shannon divergence between the parent and infant's latent belief distributions decreases rapidly during interactions, reaching low values well before the agents' generative models converge on accurate parameters, and this low divergence is maintained across multiple transitions in the infant's visceral state.

What carries the argument

The Metropolis-Hastings Naming Game as the communicative mechanism that generates a shared variable for agreeing on regulatory actions under asymmetric generative model knowledge.

If this is right

  • Latent synchrony can emerge from local computations without requiring identical world models.
  • The alignment persists through changing internal states during ongoing interactions.
  • This provides a minimal account consistent with observed inter-brain synchrony in parent-infant exchanges.
  • Collective predictive coding offers a basis for such alignment via predictive inference and communication.

Where Pith is reading between the lines

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

  • If the model holds, similar mechanisms could explain alignment in other asymmetric social contexts like teacher-student or therapist-client interactions.
  • Future simulations could test whether removing the naming game component eliminates the early synchrony.
  • Real-world validation might involve comparing model-predicted timing of synchrony with hyperscanning data from actual parent-infant pairs.

Load-bearing premise

The assumption that the Metropolis-Hastings Naming Game combined with the specific knowledge asymmetry between agents is sufficient to produce the observed early and persistent latent alignment.

What would settle it

Running the simulation with the same parameters but without the communicative naming game component and observing whether the Jensen-Shannon divergence still decreases early and stays low across state transitions.

Figures

Figures reproduced from arXiv: 2605.07524 by Takato Horii, Yushi Tsubamoto.

Figure 1
Figure 1. Figure 1: Graphical model of the proposed method. 𝑧𝑡−1 𝐴 𝑧𝑡 𝐴 𝑧𝑡+1 𝐴 𝑧𝑡−1 𝐵 𝑧𝑡 𝐵 𝑖𝑡 𝐵 𝑖𝑡−1 𝐵 𝐷 Α Α Α 𝐴 𝐵 Α Α 𝐵 Α 𝐴 𝐵 𝐵 𝐵 𝐷 𝐵 𝑤𝑡−1 Imprecise initial value 𝐷 Α Parent (Agent A) Infant (Agent B) 𝑖𝑡 𝐵𝐴 𝑖𝑡−1 𝐵𝐴 𝑎𝑡−1 𝑧𝑡+1 𝐵 𝑖𝑡+1 𝐵 Α Α 𝐴 𝐵 𝐵 Α 𝐵 𝐵 𝑤𝑡 𝑖𝑡+1 𝐵𝐴 𝑎𝑡 𝐶 𝐶 𝐶 E E [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Graphical model expanded along the time axis. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The setting of C and the result of C norm under each condition. Mental representation similarity JSDz . We measure the similarity between the parent’s and infant’s internal representations P(z A t ) and P(z B t ) using the Jensen–Shannon distance: JSDz t = 1 2DKL P(z A t ) ∥ Mt  + 1 2DKL P(z B t ) ∥ Mt  , (17) Mt = 1 2 [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 3
Figure 3. Figure 3: The setting of C and the result of C norm under each condition. For the same trial as in Figure 4a, the temporal evolution of the latent representation similarity JSDz t is shown in Figure 4b. Note that the scale of the x-axis differs from that of Figure 4a. After approximately 20 iterations, JSDz t approached 0, suggesting that the parent’s and infant’s latent representations had become synchronized. The … view at source ↗
Figure 4
Figure 4. Figure 4: Representative trends of generative-model learning. [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 4
Figure 4. Figure 4: Generative-model learning and transition of latent representations under the MHNG [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
read the original abstract

Inter-brain synchrony (IBS) observed in real-time dyadic interactions, including parent--infant exchanges, suggests that two agents can align their internal representations through interaction. Yet computational accounts of how such alignment can arise between agents that have only local sensory access and asymmetric internal knowledge remain underdeveloped. We propose a constructive model of parent--infant homeostatic co-regulation that integrates a POMDP formulation of active interoceptive inference with the Metropolis--Hastings Naming Game (MHNG) derived from the Collective Predictive Coding (CPC) hypothesis. In our model, the parent and infant agents agree on homeostatic regulatory actions for the infant's visceral state through a shared communicative variable generated by a locally computable Metropolis--Hastings probability. The parent observes the infant through body-generated exteroceptive cues, whereas the infant directly senses its own visceral state through interoception. This difference in access modality is implemented as asymmetric generative-model knowledge: the parent knows how actions transform visceral states but must learn what the infant's bodily cues indicate, whereas the infant perceives its visceral state directly but must learn how actions affect it. We operationalize representational alignment as the Jensen--Shannon divergence between the two agents' latent representations. Notably, this alignment emerged far earlier than the convergence of the generative-model learning and was maintained across successive state transitions during social interactions, indicating that latent representational synchrony does not presuppose fully shared world models. These findings offer a minimal constructive account of internal state synchrony compatible with IBS reported in hyperscanning studies and support CPC as a candidate computational basis for inter-brain alignment.

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

2 major / 1 minor

Summary. The paper proposes a constructive computational model of parent-infant homeostatic co-regulation that combines a POMDP formulation of active interoceptive inference with the Metropolis-Hastings Naming Game drawn from the Collective Predictive Coding hypothesis. Agents with asymmetric generative-model knowledge (parent knows action-to-visceral mappings but learns cues; infant knows visceral state but learns action effects) communicate via a locally computable MHNG probability to agree on regulatory actions. Representational alignment is measured by Jensen-Shannon divergence between the agents' latent beliefs; the model reports that this divergence drops early, well before generative-model convergence, and remains low across state transitions.

Significance. If the reported early and persistent JSD reduction holds under the stated conditions, the work supplies a minimal mechanistic account of inter-brain synchrony compatible with hyperscanning observations and supplies concrete support for CPC as a candidate substrate for alignment between agents that possess only local access and non-identical world models.

major comments (2)
  1. [Model description and Results on JSD trajectories] The central claim that latent synchrony 'does not presuppose fully shared world models' rests on the specific choice of Metropolis-Hastings Naming Game and the fixed parent-infant knowledge asymmetry. The manuscript provides no ablations, alternative communication protocols, or symmetric-knowledge controls, leaving open the possibility that the early JSD drop is an artifact of these modeling decisions rather than a general consequence of collective predictive coding (see the sections describing the communicative mechanism and the asymmetry implementation).
  2. [Operationalization of alignment] The Jensen-Shannon divergence is computed directly on the latent representations that the agents generate under the MHNG rule. The manuscript should explicitly demonstrate that the reported alignment is not partly tautological with the chosen communication protocol (see the paragraph operationalizing representational alignment and any equations defining the latent beliefs).
minor comments (1)
  1. [Simulation details] Clarify whether the reported early alignment is robust to the precise parameter values of the MHNG acceptance probability or to the exact form of the interoceptive and exteroceptive observation models.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major point below, providing clarifications on our modeling decisions and indicating where revisions will be incorporated.

read point-by-point responses

  1. Referee: [Model description and Results on JSD trajectories] The central claim that latent synchrony 'does not presuppose fully shared world models' rests on the specific choice of Metropolis-Hastings Naming Game and the fixed parent-infant knowledge asymmetry. The manuscript provides no ablations, alternative communication protocols, or symmetric-knowledge controls, leaving open the possibility that the early JSD drop is an artifact of these modeling decisions rather than a general consequence of collective predictive coding (see the sections describing the communicative mechanism and the asymmetry implementation).

    Authors: We agree that the lack of ablations and controls leaves the generality of the early JSD reduction open to question. The MHNG and parent-infant asymmetry were selected to instantiate a minimal CPC mechanism with biologically motivated information access differences; however, we will revise the model description and results sections to include an explicit discussion of these design choices, their motivation from the CPC hypothesis, and a qualitative analysis of how the observed alignment might vary under symmetric knowledge or alternative protocols. A full set of quantitative ablations is beyond the scope of the current constructive model but will be noted as a direction for follow-up work. revision: partial

  2. Referee: [Operationalization of alignment] The Jensen-Shannon divergence is computed directly on the latent representations that the agents generate under the MHNG rule. The manuscript should explicitly demonstrate that the reported alignment is not partly tautological with the chosen communication protocol (see the paragraph operationalizing representational alignment and any equations defining the latent beliefs).

    Authors: The latent beliefs are the agents' posterior distributions over the hidden visceral state and action parameters, which are updated through standard Bayesian inference that incorporates (but is not identical to) the shared communicative variable produced by the MHNG rule. Alignment is measured by JSD between these posteriors after each interaction step; the protocol influences the likelihood term but does not directly prescribe the belief values themselves. We will expand the operationalization section with a short derivation clarifying the separation between the communication variable and the resulting belief distributions, showing that JSD reduction reflects convergence in state estimates rather than a direct consequence of the protocol output. revision: yes

Circularity Check

0 steps flagged

No circularity; emergent simulation outcome from explicit model dynamics

full rationale

The paper constructs an explicit POMDP + MHNG simulation with asymmetric knowledge and measures alignment via an independent JSD metric on generated latents. The reported early alignment before generative-model convergence is a dynamical outcome of the interaction rules, not a definitional reduction or fitted input renamed as prediction. No self-citation load-bearing steps, uniqueness theorems, or ansatz smuggling appear in the abstract or described chain; the result remains falsifiable by altering communication protocol or symmetry.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The model rests on the assumption that the Metropolis-Hastings Naming Game provides a locally computable shared communicative variable and that the parent and infant have complementary but incomplete generative models; no free parameters are named in the abstract.

axioms (2)
  • domain assumption Agents can compute a shared communicative variable via Metropolis-Hastings sampling from local information only.
    Invoked to generate the naming-game signal that enables alignment.
  • domain assumption Jensen-Shannon divergence between latent beliefs is a valid measure of representational synchrony.
    Used to operationalize the central claim of alignment.

pith-pipeline@v0.9.0 · 5598 in / 1298 out tokens · 28131 ms · 2026-05-13T07:46:59.023560+00:00 · methodology

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Reference graph

Works this paper leans on

18 extracted references · 18 canonical work pages

  1. [1]

    Growing a social brain.Nature human behaviour, 2(9):624–636, 2018

    Shir Atzil, Wei Gao, Isaac Fradkin, and Lisa Feldman Barrett. Growing a social brain.Nature human behaviour, 2(9):624–636, 2018

    work page 2018

  2. [2]

    Active inference on discrete state-spaces: A synthesis.Journal of Mathematical Psychology, 99:102447, 2020

    Lancelot Da Costa, Thomas Parr, Noor Sajid, Sebastijan Veselic, Victorita Neacsu, and Karl Friston. Active inference on discrete state-spaces: A synthesis.Journal of Mathematical Psychology, 99:102447, 2020

    work page 2020

  3. [3]

    Multi-agent rein- forcement learning with emergent communication using discrete and indifferentiable message

    Hiroto Ebara, Tomoaki Nakamura, Akira Taniguchi, and Tadahiro Taniguchi. Multi-agent rein- forcement learning with emergent communication using discrete and indifferentiable message. In2023 15th international congress on advanced applied informatics winter (IIAI-AAI-Winter), pages 366–371. IEEE, 2023

    work page 2023

  4. [4]

    Ruth Feldman. Parent–infant synchrony: A biobehavioral model of mutual influences in the formation of affiliative bonds.Monographs of the Society for Research in Child Development, 77(2):42–51, 2012

    work page 2012

  5. [5]

    The free-energy principle: a unified brain theory?Nature reviews neuroscience, 11(2):127–138, 2010

    Karl Friston. The free-energy principle: a unified brain theory?Nature reviews neuroscience, 11(2):127–138, 2010

    work page 2010

  6. [6]

    Hyperscanning: beyond the hype.Neuron, 109(3):404–407, 2021

    Antonia F de C Hamilton. Hyperscanning: beyond the hype.Neuron, 109(3):404–407, 2021

    work page 2021

  7. [7]

    Couzin, and Alexander Tschantz

    Conor Heins, Beren Millidge, Daphne Demekas, Brennan Klein, Karl Friston, Iain D. Couzin, and Alexander Tschantz. pymdp: A python library for active inference in discrete state spaces. Journal of Open Source Software, 7(73):4098, 2022

    work page 2022

  8. [8]

    Emotion words, emotion concepts, and emotional development in children: A constructionist hypothesis.Developmental psychology, 55(9):1830, 2019

    Katie Hoemann, Fei Xu, and Lisa Feldman Barrett. Emotion words, emotion concepts, and emotional development in children: A constructionist hypothesis.Developmental psychology, 55(9):1830, 2019

    work page 2019

  9. [9]

    Homeostatic reinforcement learning for integrating reward collection and physiological stability.Elife, 3:e04811, 2014

    Mehdi Keramati and Boris Gutkin. Homeostatic reinforcement learning for integrating reward collection and physiological stability.Elife, 3:e04811, 2014

    work page 2014

  10. [10]

    An active-inference approach to second-person neuroscience.Perspectives on Psychological Science, 19(6):931–951, 2024

    Konrad Lehmann, Dimitris Bolis, Karl J Friston, Leonhard Schilbach, Maxwell JD Ramstead, and Philipp Kanske. An active-inference approach to second-person neuroscience.Perspectives on Psychological Science, 19(6):931–951, 2024

    work page 2024

  11. [11]

    Dynamic mutual predictions during social learning: a computational and interbrain model.Neuroscience & Biobehavioral Reviews, 157:105513, 2024

    Oded Mayo and Simone Shamay-Tsoory. Dynamic mutual predictions during social learning: a computational and interbrain model.Neuroscience & Biobehavioral Reviews, 157:105513, 2024

    work page 2024

  12. [12]

    Metropolis- hastings algorithm in joint-attention naming game: experimental semiotics study.Frontiers in Artificial Intelligence, 6:1235231, 2023

    Ryota Okumura, Tadahiro Taniguchi, Yoshinobu Hagiwara, and Akira Taniguchi. Metropolis- hastings algorithm in joint-attention naming game: experimental semiotics study.Frontiers in Artificial Intelligence, 6:1235231, 2023

    work page 2023

  13. [13]

    Co- creative learning via metropolis-hastings interaction between humans and ai.arXiv preprint arXiv:2506.15468, 2025

    Ryota Okumura, Tadahiro Taniguchi, Akira Taniguchi, and Yoshinobu Hagiwara. Co- creative learning via metropolis-hastings interaction between humans and ai.arXiv preprint arXiv:2506.15468, 2025

    work page arXiv 2025

  14. [14]

    Brain-to-brain synchrony in parent-child dyads and the relationship with emotion regulation revealed by fnirs-based hyperscanning.NeuroImage, 178:493–502, 2018

    Vanessa Reindl, Christian Gerloff, Wolfgang Scharke, and Kerstin Konrad. Brain-to-brain synchrony in parent-child dyads and the relationship with emotion regulation revealed by fnirs-based hyperscanning.NeuroImage, 178:493–502, 2018

    work page 2018

  15. [15]

    Synchrony across brains.Annual Review of Psychology, 76, 2024

    Leonhard Schilbach and Elizabeth Redcay. Synchrony across brains.Annual Review of Psychology, 76, 2024

    work page 2024

  16. [16]

    Active interoceptive inference and the emotional brain.Philo- sophical Transactions of the Royal Society B: Biological Sciences, 371(1708):20160007, 2016

    Anil K Seth and Karl J Friston. Active interoceptive inference and the emotional brain.Philo- sophical Transactions of the Royal Society B: Biological Sciences, 371(1708):20160007, 2016. 10

    work page 2016

  17. [17]

    Collective predictive coding hypothesis: Symbol emergence as decentral- ized bayesian inference.Frontiers in Robotics and AI, 11:1353870, 2024

    Tadahiro Taniguchi. Collective predictive coding hypothesis: Symbol emergence as decentral- ized bayesian inference.Frontiers in Robotics and AI, 11:1353870, 2024

    work page 2024

  18. [18]

    Emergent communication through metropolis-hastings naming game with deep generative models.Advanced Robotics, 37(19):1266–1282, 2023

    Tadahiro Taniguchi, Yuto Yoshida, Yuta Matsui, Nguyen Le Hoang, Akira Taniguchi, and Yoshinobu Hagiwara. Emergent communication through metropolis-hastings naming game with deep generative models.Advanced Robotics, 37(19):1266–1282, 2023. 11

    work page 2023