Recent work (Xu et al., 2020) has suggested that numeral systems in different languages are shaped by a functional need for efficient communication in an information-theoretic sense. Here we take a learning-theoretic approach and show how efficient communication emerges via reinforcement learning. In our framework, two artificial agents play a Lewis signaling game where the goal is to convey a numeral concept. The agents gradually learn to communicate using reinforcement learning and the resulting numeral systems are shown to be efficient in the information-theoretic framework of Regier et al. (2015); Gibson et al. (2017). They are also shown to be similar to human numeral systems of same type. Our results thus provide a mechanistic explanation via reinforcement learning of the recent results in Xu et al. (2020) and can potentially be generalized to other semantic domains.
@article{arxiv.2105.13857,
title = {Learning Approximate and Exact Numeral Systems via Reinforcement Learning},
author = {Emil Carlsson and Devdatt Dubhashi and Fredrik D. Johansson},
journal= {arXiv preprint arXiv:2105.13857},
year = {2024}
}