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

Giorgio Paulon, Fernando Llanos, Bharath Chandrasekaran, Abhra Sarkar*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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 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. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Original languageEnglish (US)
Pages (from-to)1114-1127
Number of pages14
JournalJournal of the American Statistical Association
Issue number535
StatePublished - 2021


  • Auditory category/tone learning
  • Drift-diffusion models
  • Inverse Gaussian distributions
  • Local clustering
  • Longitudinal mixed models
  • Perceptual decision making

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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