Understanding how the adult human brain learns novel categories is an important problem in neuroscience. Drift-diffusion models are popular in such contexts for their ability to mimic the underlying neural mechanisms. One such model for gradual longitudinal learning was recently developed by Paulon et al. (2021). Fitting conventional drift-diffusion models, however, requires data on both category responses and associated response times. In practice, category response accuracies are often the only reliable measure recorded by behavioral scientists to describe human learning. However, To our knowledge, drift-diffusion models for such scenarios have never been considered in the literature. To address this gap, in this article, we build carefully on Paulon et al. (2021), but now with latent response times integrated out, to derive a novel biologically interpretable class of `inverse-probit' categorical probability models for observed categories alone. However, this new marginal model presents significant identifiability and inferential challenges not encountered originally for the joint model by Paulon et al. (2021). We address these new challenges using 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 empirical performance of the method through simulation experiments. The practical efficacy of the method is illustrated in applications to longitudinal tone learning studies.

翻译：理解成年人大脑如何学习新类别是神经科学领域的一个重要问题。漂移扩散模型因其能够模拟底层神经机制而在此类研究中广受欢迎。Paulon等人（2021）最近开发了一种用于渐进式纵向学习的此类模型。然而，拟合传统漂移扩散模型需要同时获取类别反应数据及对应的反应时间数据。在实际研究中，行为科学家通常仅能记录类别反应准确度作为描述人类学习的可靠指标。但据我们所知，针对此类场景的漂移扩散模型在现有文献中尚未被探讨。为填补这一空白，本文在Paulon等人（2021）研究的基础上进行深入拓展，通过积分潜在反应时间变量，推导出一类新型的、具有生物学可解释性的“逆概率”分类概率模型，该模型仅依赖于观测到的类别数据。然而，这一新的边际模型带来了显著的识别性与推断挑战，这些挑战在Paulon等人（2021）的联合模型中并未出现。我们通过一种新颖的基于投影的方法应对这些新挑战，该方法采用保持对称性的识别约束，使我们能够在无约束空间中使用共轭先验。我们将该模型适配于纵向研究中的群体与个体水平推断。基于模型的潜变量表示，我们设计了一种高效的马尔可夫链蒙特卡洛算法进行后验计算。通过模拟实验评估了该方法的实证性能，并在纵向音调学习研究中展示了其实际应用效果。