  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.
