Bayesian Semiparametric Longitudinal Inverse-Probit Mixed Models for Category Learning
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Understanding how adult humans learn to categorize can shed novel insights into the mechanisms underlying experience-dependent brain plasticity. Drift-diffusion processes are popular in such contexts for their ability to mimic underlying neural mechanisms but require data on both category responses and associated response times for inference. Category response accuracies are, however, often the only reliable measure recorded by behavioral scientists to describe human learning. Building carefully on drift-diffusion models with latent response times, we derive a biologically interpretable inverse-probit categorical probability model for such data. The model, however, presents significant identifiability and inference challenges. We address these challenges via a novel projection-based approach with a symmetry preserving identifiability constraint that allows us to work with conjugate priors in an unconstrained space. We adapt the model for group and individual level inference in longitudinal settings. Building again on the model's latent variable representation, we design an efficient Markov chain Monte Carlo algorithm for posterior computation. We evaluate the method's empirical performances through simulation experiments. The method's practical efficacy is illustrated in applications to longitudinal tone learning studies.
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