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Metropolis-Hastings algorithm in joint-attention naming game: experimental semiotics study.
Okumura, Ryota; Taniguchi, Tadahiro; Hagiwara, Yoshinobu; Taniguchi, Akira.
Afiliación
  • Okumura R; Graduate School of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan.
  • Taniguchi T; College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan.
  • Hagiwara Y; Research Organization of Science and Technology, Ritsumeikan University, Kusatsu, Japan.
  • Taniguchi A; College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan.
Front Artif Intell ; 6: 1235231, 2023.
Article en En | MEDLINE | ID: mdl-38116389
ABSTRACT
We explore the emergence of symbols during interactions between individuals through an experimental semiotic study. Previous studies have investigated how humans organize symbol systems through communication using artificially designed subjective experiments. In this study, we focused on a joint-attention-naming game (JA-NG) in which participants independently categorized objects and assigned names while assuming their joint attention. In the Metropolis-Hastings naming game (MHNG) theory, listeners accept provided names according to the acceptance probability computed using the Metropolis-Hastings (MH) algorithm. The MHNG theory suggests that symbols emerge as an approximate decentralized Bayesian inference of signs, which is represented as a shared prior variable if the conditions of the MHNG are satisfied. This study examines whether human participants exhibit behavior consistent with the MHNG theory when playing the 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. The results of this study show that the model with acceptance probability computed by the MH algorithm predicts human behavior significantly better than the model with a constant probability of acceptance. Second, the MH-based model predicted human acceptance/rejection behavior more accurately than four other models (i.e., Constant, Numerator, Subtraction, Binary). Among the models compared, the model using the MH algorithm, which is the only model with the mathematical support of decentralized Bayesian inference, predicted human behavior most accurately, suggesting that symbol emergence in the JA-NG can be explained by the MHNG.
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Texto completo: 1 Base de datos: MEDLINE Idioma: En Revista: Front Artif Intell Año: 2023 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Idioma: En Revista: Front Artif Intell Año: 2023 Tipo del documento: Article