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The neutral theory of genetic and linguistic evolution holds that therelative frequencies of variants evolve by random drift. Neutral evolutionremains a plausible null model of language change. In this paper we provideevidence against the neutral hypothesis by considering the geographicalpatterns observed in language surveys. We model speakers as neurons in aHopfield network embedded in space, analogous to one of the classical twodimensional lattice models of statistical physics. The universality class ofthe model depends on the form of the activation function of the neurons, whichencodes learning behaviour of speakers. We view maps generated by the Survey ofEnglish Dialects as samples from our network. Maximum likelihood analysis, andcomparison of spatial auto-correlations between real and simulated maps,indicates that the maps are more likely to belong to the conformity-drivenIsing class, where interfaces are driven by surface tension, rather than theneutral Voter class, where they are driven by noise.

Spatial evidence that language change is not neutral