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

ABSTRACT: Understanding how adult humans learn nonnative 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

SUBMITTER: Paulon G 

PROVIDER: S-EPMC8513775 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature