HCAILGJun 18, 2025

Co-Creative Learning via Metropolis-Hastings Interaction between Humans and AI

arXiv:2506.15468v12 citationsh-index: 30
Originality Highly original
AI Analysis

This addresses the challenge of building symbiotic AI systems that learn with humans by dynamically aligning experiences, though it is incremental as it builds on existing Bayesian inference mechanisms.

The paper tackled the problem of integrating partial perceptual information between humans and AI by proposing co-creative learning, and results showed that human-AI pairs with a Metropolis-Hastings-based agent significantly improved categorization accuracy and achieved stronger convergence toward a shared sign system in an online experiment with 69 participants.

We propose co-creative learning as a novel paradigm where humans and AI, i.e., biological and artificial agents, mutually integrate their partial perceptual information and knowledge to construct shared external representations, a process we interpret as symbol emergence. Unlike traditional AI teaching based on unilateral knowledge transfer, this addresses the challenge of integrating information from inherently different modalities. We empirically test this framework using a human-AI interaction model based on the Metropolis-Hastings naming game (MHNG), a decentralized Bayesian inference mechanism. In an online experiment, 69 participants played a joint attention naming game (JA-NG) with one of three computer agent types (MH-based, always-accept, or always-reject) under partial observability. Results show that human-AI pairs with an MH-based agent significantly improved categorization accuracy through interaction and achieved stronger convergence toward a shared sign system. Furthermore, human acceptance behavior aligned closely with the MH-derived acceptance probability. These findings provide the first empirical evidence for co-creative learning emerging in human-AI dyads via MHNG-based interaction. This suggests a promising path toward symbiotic AI systems that learn with humans, rather than from them, by dynamically aligning perceptual experiences, opening a new venue for symbiotic AI alignment.

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