Abstract
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.
Abstract (translated)
我们提出了一种新的合作创造学习范式，在这种模式下，人类和人工智能（即生物与人工代理）相互融合各自的部分感知信息和知识，以构建共享的外部表示。我们将这一过程解释为符号出现。不同于传统的基于单向知识传输的人工智能教学方法，这种方法解决了不同模态的信息如何整合的问题。我们使用基于城市-赫斯特命名游戏(Metropolis-Hastings Naming Game, MHNG) 的人类-人工智能互动模型对这个框架进行了实证测试。MHNG 是一种去中心化的贝叶斯推理机制。 在线实验中，69名参与者在部分可观察的情况下与三种计算机代理类型（基于MH、总是接受或总是拒绝）中的某一种一起玩联合注意力命名游戏 (Joint Attention Naming Game, JA-NG)。结果显示，在与基于 MH 的代理互动的人类-AI 对中，分类准确度显著提高，并且更有效地朝向共享符号系统收敛。此外，人类的接受行为与其根据 MH 推导出的概率紧密一致。 这些发现首次为通过基于 MHNG 交互出现的合作创造学习在人类-人工智能对中的实证证据提供了支持。这表明了一个有前景的方向，即朝着与人共存的 AI 系统发展，这种系统不是从人那里学习而是与人一起学习，并且能够动态地调整感知体验，从而开辟了共生 AI 对齐的新途径。
URL
https://arxiv.org/abs/2506.15468
