Title :
Spontaneous evolution of linguistic structure-an iterated learning model of the emergence of regularity and irregularity
Author_Institution :
Dept. of Linguistics, Edinburgh Univ., UK
fDate :
4/1/2001 12:00:00 AM
Abstract :
A computationally implemented model of the transmission of linguistic behavior over time is presented. In this 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
Keywords :
digital simulation; grammars; iterative methods; learning (artificial intelligence); linguistics; multi-agent systems; ILM; fully compositional syntactic meaning-string mapping; irregularity emergence; iterated learning model; linguistic behavior transmission; linguistic structure evolution; meaning space; nonuniform frequency distribution; production mechanism; protolanguage; regularity emergence; spontaneous evolution; Biological system modeling; Biology computing; Computational modeling; Counting circuits; Evolution (biology); Humans; Induction generators; Natural languages; Signal generators; Signal mapping;
Journal_Title :
Evolutionary Computation, IEEE Transactions on
DOI :
10.1109/4235.918430