A Mathematical Model of Prediction-Driven Instability: How Social Structure Can Drive Language Change

Authors:
W. Garrett Mitchener
Affiliations:
Department of Mathematics, College of Charleston, Charleston, USA 29401
Venue:
Journal of Logic, Language and Information
Year:
2011

Citing 2
Cited 0

The Informational Complexity of Learning: Perspectives on Neural Networks and Generative Grammar

The Informational Complexity of Learning: Perspectives on Neural Networks and Generative Grammar
Spontaneous evolution of linguistic structure-an iterated learningmodel of the emergence of regularity and irregularity

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.01

Visualization

Abstract

I discuss a stochastic model of language learning and change. During a syntactic change, each speaker makes use of constructions from two different idealized grammars at variable rates. The model incorporates regularization in that speakers have a slight preference for using the dominant idealized grammar. It also includes incrementation: The population is divided into two interacting generations. Children can detect correlations between age and speech. They then predict where the population's language is moving and speak according to that prediction, which represents a social force encouraging children not to sound out-dated. Both regularization and incrementation turn out to be necessary for spontaneous language change to occur on a reasonable time scale and run to completion monotonically. Chance correlation between age and speech may be amplified by these social forces, eventually leading to a syntactic change through prediction-driven instability.