A Bayesian nonparametric mixture model for studying universal patterns in color naming

Abstract

Variational Inference for the Beta-Bernoulli Dirichlet Process Mixture Model is employed to uncover universal patterns in color naming systems. The data used consist of 2552 participants from 106 World Color Survey languages. To study these languages collectively, the model is informed by universal biological, linguistic, and topological features of the task. We find that the majority of the naming systems are represented by eighteen clusters, each constituting a universal pattern. Novel mathematical techniques are developed to study the levels of similarity, underlying consensus, and diversity among these patterns. This implementation of nonparametric models demonstrates how machine learning methods can be tailored for behavioral science applications.