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.

摘要-了解成年人如何学习非母语语音类别（例如语气信息），已经对依赖于经验的大脑可塑性的潜在机制有了新的见解。传统上，科学家们在多类别决策范式下使用纵向学习实验来研究这些问题。漂移扩散过程在这种情况下很受欢迎，因为它们能够模仿潜在的神经机制。受这些问题的启发，我们开发了一种新的贝叶斯半参数逆高斯漂移扩散混合模型，用于纵向环境中的多替代决策。我们设计了一个用于后验计算的马尔可夫链蒙特卡罗算法。我们通过综合实验评估该方法的经验性能。应用于我们的激励纵向音调学习研究，该方法提供了新的见解，了解生物学上可解释的模型参数如何随着学习而演变，输入-响应音调组合之间的差异，以及表现良好和表现不佳的成年人之间的差异。本文的补充材料，包括对可用于复制作品的材料的标准化描述，可作为在线补充材料获得。