Automata networks for memory loss effects in the formation of linguistic conventions

TL;DR

This paper uses automata networks to model how populations rapidly develop linguistic conventions, highlighting sharp transition phenomena influenced by memory loss and local interactions.

Contribution

It introduces a simple automata network model with memory loss to explain the abrupt formation of linguistic conventions in populations.

Findings

Sharp transitions depend on local neighborhood size

Memory loss influences the speed of convention formation

Simulations show critical points in language agreement emergence

Abstract

This work attempts to give new theoretical insights to the absence of intermediate stages in the evolution of language. In particular, it is developed an automata networks approach to a crucial question: how a population of language users can reach agreement on a linguistic convention? To describe the appearance of sharp transitions in the self-organization of language, it is adopted an extremely simple model of (working) memory. At each time step, language users simply loss part of their word-memories. Through computer simulations of low-dimensional lattices, it appear sharp transitions at critical values that depend on the size of the vicinities of the individuals.