联合注意命名游戏中的 Metropolis-Hastings 算法：实验符号学研究
Metropolis-Hastings algorithm in joint-attention naming game: Experimental semiotics study
AI总结：
本文通过联合注意命名游戏实验验证人类符号涌现行为是否符合 Metropolis-Hastings 命名游戏理论，结果表明人类接受决策显著遵循 MH 接受概率，且 MH 模型预测准确率优于其他四个基线模型。
摘要：
在本研究中，我们通过一项实验符号学研究探讨了个体互动过程中符号的涌现。以往研究通过人工设计的主观实验考察人类如何通过交流组织符号系统。在本研究中，我们聚焦于一种联合注意命名游戏（JA-NG），参与者在假定共享联合注意的情况下，独立地对物体进行分类并赋予名称。在 Metropolis-Hastings 命名游戏（MHNG）理论中，听者根据使用 Metropolis-Hastings（MH）算法计算出的接受概率来接受所提供的名称。MHNG 理论表明，如果满足 MHNG 的条件，符号会作为对符号的近似去中心化贝叶斯推断而涌现，该推断被表示为一个共享的先验变量。本研究考察人类参与者在进行 JA-NG 时是否表现出与 MHNG 理论一致的行为。通过将人类对伙伴命名结果的接受决策与 MHNG 中计算出的接受概率进行比较，我们检验了人类行为是否与 MHNG 理论一致。本研究的主要贡献有两点。第一，我们拒绝了零假设，即人类以恒定概率做出接受判断，而不受 MH 算法计算出的接受概率影响。这一结果表明，人们在某种程度上遵循了 MH 算法计算出的接受概率。第二，基于 MH 的模型比其他四个模型——Constant、Numerator、Subtraction 和 Binary——更准确地预测了人类的接受/拒绝行为。这一结果表明，JA-NG 中的符号涌现可以用 MHNG 来解释，并可被视为一种近似的去中心化贝叶斯推断。
英文摘要：
In this study, we explore the emergence of symbols during interactions between individuals through an experimental semiotic study. Previous studies investigate how humans organize symbol systems through communication using artificially designed subjective experiments. In this study, we have focused on a joint attention-naming game (JA-NG) in which participants independently categorize objects and assign names while assuming their joint attention. In the theory of the Metropolis-Hastings naming game (MHNG), listeners accept provided names according to the acceptance probability computed using the Metropolis-Hastings (MH) algorithm. The theory of MHNG suggests that symbols emerge as an approximate decentralized Bayesian inference of signs, which is represented as a shared prior variable if the conditions of MHNG are satisfied. This study examines whether human participants exhibit behavior consistent with MHNG theory when playing JA-NG. By comparing human acceptance decisions of a partner's naming with acceptance probabilities computed in the MHNG, we tested whether human behavior is consistent with the MHNG theory. The main contributions of this study are twofold. First, we reject the null hypothesis that humans make acceptance judgments with a constant probability, regardless of the acceptance probability calculated by the MH algorithm. This result suggests that people followed the acceptance probability computed by the MH algorithm to some extent. Second, the MH-based model predicted human acceptance/rejection behavior more accurately than the other four models: Constant, Numerator, Subtraction, and Binary. This result indicates that symbol emergence in JA-NG can be explained using MHNG and is considered an approximate decentralized Bayesian inference.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
