Abstract

Language variation and change are driven both by individuals’ internal cognitive processes and by the social structures through which language propagates. A wide range of computational frameworks have been proposed to connect these drivers. We compare the strengths and weaknesses of existing approaches and propose a new analytic framework which combines previous network models’ ability to capture realistic social structure with practically and more elegant computational properties. The framework privileges the process of language acquisition and embeds learners in a social network but is modular so that population structure can be combined with different acquisition models. We demonstrate two applications for the framework: a test of practical concerns that arise when modeling acquisition in a population setting and an application of the framework to recent work on phonological mergers in progress.

Highlights

  • The process of language change should be thought of as a two-step cycle in which 1) individuals acquire their native languages from their predecessors 2) pass them on to their successors

  • The influence of communitylevel social factors on the path of language change is a major focus of sociolinguistics (Labov, 2001; Milroy and Milroy, 1985; Rogers Everett, 1995)

  • This is the primary source of empirical data in the field and the only way to look at language change in a naturalistic setting, but it is limited in that it cannot test cause and effect directly

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Summary

Introduction

The process of language change should be thought of as a two-step cycle in which 1) individuals acquire their native languages from their predecessors 2) pass them on to their successors. Niyogi and Berwick (2009) prove formally that even perfectly-mixed (i.e., uniform and homogeneous social network) populations admit phase transitions in the path of change unavailable to chains of single learners commonly implemented in iterated learning (Kirby et al, 2000) This suggests that small-population experimental studies in sociolinguistics and in child language acquisition do not paint the full picture of language change. It has an outer loop to represent generational progression, but it replaces the inner loop which calculates randomized interactions between agents with a single formula that is defined generally enough to allow the simulation of a wide range of scenarios It builds upon the principled formalism described by Niyogi and Berwick (1996, et seq.), privileging the acquisition model and separating it from the population model.

Related Work
Framework for Transmission in Social Networks
Representing the Network
Propagation in the Network
Learning in the Network
Application
Background
Model Setup
Results
Discussion
Full Text
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