期刊
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
卷 5, 期 2, 页码 102-110出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/4235.918430
关键词
cultural selection; evolution; grammar induction; iterated learning; language
A computationally implemented model of the transmission of linguistic behavior over time is presented. In this model [the iterated learning model (ILM)], there is no biological evolution, natural selection, nor any measurement of the success of the agents at communicating (except for results-gathering purposes). Nevertheless, counter to intuition, significant evolution of linguistic behavior Is observed, From an initially unstructured communication system (a protolanguage), a fully compositional syntactic meaning-string mapping emerges. Furthermore, given a nonuniform: frequency distribution over a meaning space and a production mechanism that prefers short strings, a realistic distribution of string lengths and patterns of stable irregularity emerges, suggesting that the ILM is a good model for the evolution of some of the fundamental features of human language.
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