Metropolis-Hastings algorithm in joint-attention naming game: Experimental semiotics study

TL;DR

This study investigates whether human behavior in a joint attention-naming game aligns with the Metropolis-Hastings naming game theory, showing that humans' acceptance decisions are consistent with MHNG and that the MH-based model predicts behavior better than alternatives.

Contribution

The paper demonstrates that human acceptance behavior in a semiotic game aligns with MHNG theory and introduces a model that outperforms other models in predicting this behavior.

Findings

Humans follow acceptance probabilities computed by MH algorithm.

MH-based model predicts human decisions more accurately than other models.

Symbol emergence can be explained as decentralized Bayesian inference.

Abstract

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.