Swarup Samarth, Gasser Les
Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061,
Adapt Behav. 2009;17(3):213-235. doi: 10.1177/1059712309105818.
The Iterated Classification Game (ICG) combines the Classification Game with the Iterated Learning Model (ILM) to create a more realistic model of the cultural transmission of language through generations. It includes both learning from parents and learning from peers. Further, it eliminates some of the chief criticisms of the ILM: that it does not study grounded languages, that it does not include peer learning, and that it builds in a bias for compositional languages. We show that, over the span of a few generations, a stable linguistic system emerges that can be acquired very quickly by each generation, is compositional, and helps the agents to solve the classification problem with which they are faced. The ICG also leads to a different interpretation of the language acquisition process. It suggests that the role of parents is to initialize the linguistic system of the child in such a way that subsequent interaction with peers results in rapid convergence to the correct language.
迭代分类博弈（ICG）将分类博弈与迭代学习模型（ILM）相结合，以创建一个更现实的语言代际文化传播模型。它既包括向父母学习，也包括向同龄人学习。此外，它消除了对迭代学习模型的一些主要批评：即它不研究有根基的语言，不包括同龄人学习，以及它对组合语言存在偏见。我们表明，在几代人的时间跨度内，会出现一个稳定的语言系统，每一代人都能非常快速地习得该系统，它具有组合性，并且有助于智能体解决他们所面临的分类问题。迭代分类博弈还导致了对语言习得过程的不同解释。它表明父母的作用是以这样一种方式初始化孩子的语言系统，即随后与同龄人的互动会导致快速收敛到正确的语言。
