Bayesian Semiparametric Longitudinal Drift-Diffusion Mixed Models for Tone Learning in Adults

TL;DR

This paper introduces a Bayesian semiparametric drift-diffusion model for analyzing longitudinal tone learning in adults, providing insights into neural mechanisms and individual differences in non-native speech acquisition.

Contribution

It develops a novel Bayesian semiparametric inverse Gaussian drift-diffusion mixed model with MCMC for longitudinal decision-making analysis in tone learning, advancing understanding of brain plasticity.

Findings

Model captures evolution of learning parameters

Reveals differences between tone input-response pairs

Identifies variation between well and poorly performing adults

Abstract

Understanding how adult humans learn non-native speech categories such as tone information has shed novel insights into the mechanisms underlying experience-dependent brain plasticity. Scientists have traditionally examined these questions using longitudinal learning experiments under a multi-category decision making paradigm. Drift-diffusion processes are popular in such contexts for their ability to mimic underlying neural mechanisms. Motivated by these problems, we develop a novel Bayesian semiparametric inverse Gaussian drift-diffusion mixed model for multi-alternative decision making in longitudinal settings. We design a Markov chain Monte Carlo algorithm for posterior computation. We evaluate the method's empirical performances through synthetic experiments. Applied to our motivating longitudinal tone learning study, the method provides novel insights into how the biologically interpretable model parameters evolve with learning, differ between input-response tone combinations, and differ between well and poorly performing adults.