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Abstract:
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Understanding how adult humans learn non-native speech categories (e.g., 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 inverse Gaussian drift-diffusion mixed model for multi-alternative decision making processes in longitudinal settings. Our methodology builds on a novel Bayesian semiparametric framework for longitudinal data in the presence of a categorical covariate that allows automated assessment of the predictor's local time-varying influences. 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.
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