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.

研究人员通过实验符号学研究探索个体之间交互中符号的出现。该研究对主动注意并称呼游戏进行了研究，并比较了人类接受概率和通过 Metropolis-Hastings 算法计算的接受概率的差异。研究表明，在 JA-NG 下进行符号出现的 MHNG 模型可以解释人类的行为，并认为这是一种近似的分散式贝叶斯推断。