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• W2164610040 abstract "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." @default.
• W2164610040 created "2016-06-24" @default.
• W2164610040 creator A5047873616 @default.
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• W2164610040 date "2009-06-01" @default.
• W2164610040 modified "2023-09-24" @default.
• W2164610040 title "The Iterated Classification Game: A New Model of the Cultural Transmission of Language" @default.
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• W2164610040 doi "https://doi.org/10.1177/1059712309105818" @default.
• W2164610040 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/2828955" @default.
• W2164610040 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/20190877" @default.
• W2164610040 hasPublicationYear "2009" @default.
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