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

2026-05-08

AI Summary

This study investigates how dyads—such as parent–infant pairs—achieve alignment of hidden states and co-regulation under conditions of partial perceptual access and asymmetric internal knowledge. To this end, we propose the first computational framework that integrates collective predictive coding with active interoceptive inference, combining a partially observable Markov decision process (POMDP) with a Metropolis–Hastings naming game to model bidirectional learning and action negotiation through both exteroceptive signals and interoceptive states. Experiments in a 6×6 interoceptive grid world demonstrate that our approach significantly outperforms unidirectional control strategies, rapidly aligning the agents’ posterior distributions. Notably, synchronization of hidden states precedes convergence of the generative models, offering a minimal yet viable computational account of interpersonal neural synchrony.

Abstract

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.

Problem

• inter-brain synchrony
• latent representation alignment
• asymmetric knowledge
• parent-infant interaction
• collective predictive coding

Innovation

• Collective Predictive Coding
• Inter-brain Synchrony
• Metropolis–Hastings Naming Game
• Homeostatic Co-regulation
• Asymmetric Generative Models