  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.
