Modeling self-organization of vocabularies under phonological similarity effects

TL;DR

This paper presents a computational model of how phonological similarity influences vocabulary formation and consensus in artificial populations, revealing critical parameters that cause sudden shifts in collective agreement.

Contribution

It introduces a novel automata network model to simulate phonological effects on word-meaning associations and analyzes phase transitions in collective vocabulary consensus.

Findings

Identification of critical parameters causing abrupt changes in consensus

Proofs of convergence for specific model cases

Simulation results showing phase transitions in vocabulary agreement

Abstract

This work develops a computational model (by Automata Networks) of phonological similarity effects involved in the formation of word-meaning associations on artificial populations of speakers. Classical studies show that in recalling experiments memory performance was impaired for phonologically similar words versus dissimilar ones. Here, the individuals confound phonologically similar words according to a predefined parameter. The main hypothesis is that there is a critical range of the parameter, and with this, of working-memory mechanisms, which implies drastic changes in the final consensus of the entire population. Theoretical results present proofs of convergence for a particular case of the model within a worst-case complexity framework. Computer simulations describe the evolution of an energy function that measures the amount of local agreement between individuals. The main finding is the appearance of sudden changes in the energy function at critical parameters.