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The Iterated Classification Game: A New Model of the Cultural Transmission of Language

Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, swarup{at}vbi.vt.edu

Les Gasser

Graduate School for Library and Information Science, and Department of Computer Science, University of Illinois at Urbana-Champaign, gasser{at}illinois.edu

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.

Key Words: language evolution • cultural transmission • universal grammar • iterated learning model • iterated Bayesian learner • classification game • iterated classification game

Adaptive Behavior, Vol. 17, No. 3, 213-235 (2009)
DOI: 10.1177/1059712309105818


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