ArticleJournal of the American Statistical Association2021
Bayesian Semiparametric Longitudinal Drift-Diffusion Mixed Models for Tone Learning in Adults.
Article in Journal of the American Statistical Association, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Reverse Hierarchical Processing of Speech in Talker Identification.The European journal of neuroscience · 2026Article
- Dual learning systems in talker identification: the effects of language, accent, and feedback.Attention, perception & psychophysics · 2025Article
- The Effect of Concurrent Auditory Working Memory Task in Auditory Category Learning.Behavioral sciences (Basel, Switzerland) · 2025Article
- Bayesian Semiparametric Longitudinal Inverse-Probit Mixed Models for Category Learning.Psychometrika · 2024Article
- Individual differences in working memory impact the trajectory of non-native speech category learning.PloS one · 2024Article
- Auditory and visual category learning in children and adults.Developmental psychology · 2023Article
- Comparing perceptual category learning across modalities in the same individuals.Psychonomic bulletin & review · 2021Article
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Authors and funding
4 authors.
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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.
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Registered trials
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