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

TL;DR

This paper presents a computational model explaining how parent and infant agents achieve internal state synchrony through collective predictive coding, despite having asymmetric sensory access and knowledge.

Contribution

It introduces a novel model combining active interoceptive inference with collective predictive coding to explain inter-brain synchrony in parent-infant interactions.

Findings

Latent representational alignment occurs before full model convergence.

Representational synchrony persists across social interactions.

The model aligns with hyperscanning studies of inter-brain synchrony.

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.