Momentum in Language Change

Abstract

Like other socially transmitted traits, human languages undergo cultural evolution. While humans can replicate linguistic conventions to a high degree of fidelity, sometimes established conventions get replaced by new variants, with the gradual replacement following the trajectory of an s-shaped curve. Although previous modelling work suggests that only a bias favoring the replication of new linguistic variants can reliably reproduce the dynamics observed in language change, the source of this bias is still debated. In this paper we compare previous accounts with a momentum-based selection account of language change, a replicator-neutral model where the popularity of a variant is modulated by its momentum, i.e. its change in frequency of use in the recent past. We present results from a multi-agent model that are characteristic of language change, in particular by exhibiting spontaneously generated s-shaped transitions that do not require externally triggered actuation. We discuss several empirical questions raised by our model, pertaining to both momentum-based selection as well as other biases and pressures which have been suggested to influence language change.

Brownian dynamics for the vowel sounds of human language

“…Our model appears unrealistically stable when compared to real languages, generating changes too infrequently. We will show below how sensitivity to an age vector [40][41][42] in the linguistic community can generate more realistic change processes.…”

“…This direction is observable from differences in language use between older and younger speakers, often referred to as the age vector of a linguistic feature, or its momentum. A number of studies have sought to model such effects [40][41][42] in terms of the relative frequency of a linguistic feature. Although their mathematical details differ, the essential idea is that when (new) speakers select their linguistic state, they are biased in the direction of the age vector.…”

“…This is the shift in the average sound learned by a new speaker, based on their tendency to emphasize "younger" forms of speech. An analogous response function appears in the frequency-based models [40,41], where it is termed a prediction function [40] or generates a perceived frequency [41]. The parameter b in (79) is the momentum sensitivity and θ is the cutoff, giving the maximum possible magnitude of the momentum driven rate of change.…”

“…Its simplicity also allows a number of extensions to describe a range of different phenomena in one unified model. These include self-actuating sound change [40][41][42], allophonic sound variations, the effect of nonlinearities in the relationship between articulatory and acoustic parameters [11,43], and word-frequency effects which have recently been observed in a purely computational exemplar model [24].…”

Dynamical preferences and self-actuated language changes

“…This model is able to simulate different selection mechanisms and provides some insights on what properties are needed for self-actuated changes to occur. The results of our model are compared with the models of Stadler, Blythe, Smith, and Kirby (2016) and of Mitchener (2009).…”