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Synchronizing Minds through Collective Predictive Coding: A Computational Model of Parent-Infant Homeostatic Co-Regulation

Yushi Tsubamoto, Takato Horii

Inter-brain synchrony (IBS) observed in real-time dyadic interactions, including parent--infant exchanges, suggests that two agents come to share aligned latent 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 observes the infant only through an exteroceptive signal while the infant directly senses its own interoceptive state; the two agents agree on regulatory actions through a shared communicative variable whose acceptance is determined by a locally computable Metropolis--Hastings probability. The agents are further endowed with asymmetric generative-model knowledge: the parent knows how actions transform visceral states but must learn what the infant's body is communicating, whereas the infant perceives its visceral state directly but must learn how actions affect it. In a $6 \times 6$ visceral-state grid world, MHNG-mediated interaction regulated the infant's visceral state more adaptively than one-sided control conditions, and the two posteriors became rapidly aligned. Notably, this latent-state alignment emerged far earlier than the convergence of the learned generative matrices, indicating that representational synchrony does not presuppose fully shared world models. These results offer a minimal constructive account of latent-state alignment compatible with IBS reported in hyperscanning studies and support CPC as a candidate computational basis for inter-brain alignment.

（英語のみ）

Emergence of Social Reality of Emotion through a Social Allostasis Model with Dynamic Interpretants

Kentaro Nomura, Yushi Tsubamoto, Takato Horii

The theory of constructed emotion defines social reality as the community-level consensus on emotion concepts assigned to interoceptive sensations arising from bodily allostasis and social interaction. In this study, we simulate this emergence process using a computational model that integrates symbol emergence with degrees of freedom in symbol interpretation and active inference. Two agents receive interoceptive signals, exchange inferred symbols, and simultaneously adapt their bodily control goals and symbol interpretations to each other. Experimental results show that the interoceptive prior preferences and symbol probability distributions of the two agents converge, confirming the emergence of social reality grounded in social consensus.

（英語のみ）

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

Zehang Zhang, Nguyen Le Hoang, Tadahiro Taniguchi, et al.

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.

（英語のみ）

Decentralized Collective World Model for Emergent Communication

Ebara, T., Inoue, R., Taniguchi, T.

複数エージェントが Emergent Communication を介して潜在空間を共有する集合的 世界モデルを提案。中央制御なしで安定した協調行動が出現することを示す。

Collective Predictive Coding Hypothesis: Symbol Emergence as Decentralized Bayesian Inference

Taniguchi, T., Yoshida, M., Matsui, Y., et al.

記号創発を分散ベイズ推論として定式化する CPC 仮説を提示。MHNG による実装と 自由エネルギー原理との関係を論じ、個体の発達と歴史的進化を統一的に扱える枠組みを示す。

Metropolis-Hastings Naming Game: Symbol Emergence as Probabilistic Generative Models

Hagiwara, Y., Kobayashi, H., Taniguchi, A., et al.

記号創発のダイナミクスをベイズ推論として導出する Metropolis-Hastings Naming Game を提案。発話の受容・棄却を MH ステップとして定式化する。

Active Inference: A Process Theory

Friston, K., FitzGerald, T., Rigoli, F., et al.

This article presents active inference as a unifying process theory for perception, action, and learning under the free-energy principle. Behaviour is cast as inference over policies that minimise expected free energy, combining epistemic (information-seeking) and pragmatic (goal-directed) drives in a single objective.

（英語のみ）

Emergent Communication in Multi-Agent Reinforcement Learning: A Survey

Lazaridou, A., Baroni, M.

We survey approaches to emergent communication in multi-agent reinforcement learning, organising them around the dimensions of language input, learning signal, and population structure. We argue that scaling toward human-language-like systems requires bridging emergent and natural-language research traditions.

（英語のみ）

Google DeepMind — Multi-Agent Research

PI: (Multiple PIs)

Multi-agent learning and emergent communication research at DeepMind, spanning cooperation, competition, language emergence, and societal-scale simulations of intelligent agents.

（英語のみ）

Theoretical Neurobiology Group (Friston Lab)

PI: Karl Friston

The Wellcome Trust Centre for Human Neuroimaging hosts the theoretical neurobiology group, which develops the free-energy principle and active inference as a unifying account of perception, action, and learning.

（英語のみ）

Human & Machine Learning Lab

PI: Brenden Lake

The lab develops computational models of how humans learn rich, structured concepts from limited experience, and how machines can do the same. Recent work has focused on systematic generalisation in neural networks.

（英語のみ）

Improbable AI Lab

PI: Pulkit Agrawal

Research at the intersection of robot learning, multi-agent systems, and emergent behaviour. The lab studies how communication, coordination, and generalisation arise in embodied agents trained at scale.

（英語のみ）

谷口研究室 — 記号創発システム

PI: Tadahiro Taniguchi

記号創発、マルチモーダルロボティクス、CPC 理論を中心に、感覚運動経験から 概念と言語がどのように立ち上がるかを研究している。

記号創発ロボティクス国際シンポジウム 2026

2026年9月12日 – 2026年9月14日 · Kyoto, Japan

認知ロボティクスにおける記号創発システムの年次シンポジウム。MHNG、マルチ モーダル概念形成、身体性言語獲得、マルチエージェント通信などのトラックを設置。

CPC 輪読会 — 夏期セッション

2026年7月1日 – 2026年7月29日 · Online

CPC と Active Inference の最近の論文を扱う 4 週間のオンライン輪読会。各回は 英語・日本語のファシリテーションを交互に行い、資料は可能な限りバイリンガル。

Active Inference & Free Energy Principle Workshop

2026年6月15日 – 2026年6月17日 · London, UK

Three-day workshop on the latest theoretical and applied developments in active inference and the free-energy principle. Mixed-format programme with invited talks, posters, and small-group tutorials.

（英語のみ）

Emergent Communication ワークショップ @ NeurIPS 2025

2025年12月12日 · San Diego, USA

Emergent Communication に関する NeurIPS の年次ワークショップ。マルチエージェント 強化学習、言語創発、創発言語と自然言語の関係に関する研究を集約する。

CogSci 2025 — Annual Meeting of the Cognitive Science Society

2025年7月23日 – 2025年7月26日 · San Francisco, USA

The 47th annual meeting of the Cognitive Science Society. Sessions include symbol grounding, computational modelling of cognition, and language emergence in humans and machines.

（英語のみ）