A Mathematical Model of Prediction-Driven Instability: How Social Structure Can Drive Language Change [Book Review]
David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jonathan Jenkins Ichikawa
Jack Alan Reynolds
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Journal of Logic, Language and Information 20 (3):385-396 (2011)
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
|Keywords||Language variation Language change Incrementation Mathematical model Social structure Prediction-driven instability|
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References found in this work BETA
David Adger (2003). Core Syntax: A Minimalist Approach. Oxford University Press.
Partha Niyogi & Robert C. Berwick (1996). A Language Learning Model for Finite Parameter Spaces. Cognition 61 (1-2):161-193.
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