Both linguistic and genetic evolution involve copying and mutation of variants. The simplest copying processassumes that variants are reproduced at a rate equal to their current frequency, exemplified by Kimura’s steppingstone model of neutral evolution, and the voter model. In this case, spatial patterns are driven by noise. Inthe linguistic context, an alternative possibility is that speakers preferentially select variants which are alreadypopular, yielding patterns driven by surface tension, exemplified by the Ising model. In this paper, we modellanguage change using a spatial network of speakers, inspired by the Hopfield neural network. The model’suniversality class—Voter or Ising—is determined by speakers’ learning function. We view maps generated bythe Survey of English Dialects as samples from our network. Maximum likelihood analysis, and comparison ofspatial auto-correlations between real and simulated maps, indicates that the underlying copying processes ismore likely to belong to the conformity-driven Ising class.
|Number of pages||36|
|Journal||Physical Review Research|
|Publication status||Published - 14 Oct 2020|
- language acquisition
- Statistical physics and nonlinear systems