Spatial evidence that language change is not neutral

TL;DR

This paper challenges the neutral theory of language change by analyzing geographical patterns in dialect surveys, showing that language evolution aligns more with conformity-driven models than neutral drift.

Contribution

It introduces a spatial neural network model to analyze language change, providing evidence that language evolution is influenced by social conformity rather than neutrality.

Findings

Language maps resemble the Ising class with surface tension dynamics.

Neutral Voter model is less consistent with observed dialect patterns.

Conformity-driven processes better explain language change patterns.

Abstract

The neutral theory of genetic and linguistic evolution holds that the relative frequencies of variants evolve by random drift. Neutral evolution remains a plausible null model of language change. In this paper we provide evidence against the neutral hypothesis by considering the geographical patterns observed in language surveys. We model speakers as neurons in a Hopfield network embedded in space, analogous to one of the classical two dimensional lattice models of statistical physics. The universality class of the model depends on the form of the activation function of the neurons, which encodes learning behaviour of speakers. We view maps generated by the Survey of English Dialects as samples from our network. Maximum likelihood analysis, and comparison of spatial auto-correlations between real and simulated maps, indicates that the maps are more likely to belong to the conformity-driven Ising class, where interfaces are driven by surface tension, rather than the neutral Voter class, where they are driven by noise.