Computational modeling plays an essential role in the study of language emergence. It aims to simulate the conditions and learning processes that could trigger the emergence of a structured language within a simulated controlled environment. Several methods have been used to investigate the origin of our language, including agent-based systems, Bayesian agents, genetic algorithms, and rule-based systems. This chapter explores another class of computational models that have recently revolutionized the field of machine learning: deep learning models. The chapter introduces the basic concepts of deep and reinforcement learning methods and summarizes their helpfulness for simulating language emergence. It also discusses the key findings, limitations, and recent attempts to build realistic simulations. This chapter targets linguists and cognitive scientists seeking an introduction to deep learning as a tool to investigate language evolution.

翻译：计算建模在语言涌现研究中扮演着至关重要的角色。其目标是在模拟可控环境中，模拟可能触发结构化语言产生的条件与学习过程。研究者已采用多种方法探究人类语言的起源，包括基于智能体的系统、贝叶斯智能体、遗传算法及基于规则的系统。本章探讨了近年来革新机器学习领域的另一类计算模型——深度学习模型。本章首先介绍深度学习和强化学习方法的基本概念，并总结其在模拟语言涌现中的实用价值。此外，本章还将讨论关键研究发现、现存局限性以及构建逼真模拟的最新尝试。本章面向希望了解如何运用深度学习工具研究语言演化的语言学家和认知科学家。