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 in Paulon, et al. (2020). Fitting drift-diffusion models however require data on both category responses and associated response times. Category response accuracies are, however, often the only reliable measure recorded by behavioral scientists to describe human learning. To our knowledge, however, drift-diffusion models for such scenarios have never been considered in the literature before. To address this gap, in this article we build carefully on Paulon, et al. (2020), but now with latent response times integrated out, to derive a novel biologically interpretable class of `inverse-probit' categorical probability models for observed categories. This marginal model, however, presents significant identifiability and inferential challenges not encountered originally for the joint model in Paulon, et al. (2020). We address these new 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.

翻译：理解成人大脑如何学习新类别是神经科学中的一个重要问题。漂移扩散模型因其能够模拟底层神经机制而在此类情境中广受欢迎。Paulon等人（2020）近期开发了一种适用于渐进纵向学习的此类模型。然而，拟合漂移扩散模型需要同时获取类别响应和相应响应时间的数据。但类别响应准确率通常是行为科学家描述人类学习时唯一可靠记录的测量指标。据我们所知，关于此类场景的漂移扩散模型此前在文献中从未被探讨过。为填补这一空白，本文在Paulon等人（2020）研究的基础上精心构建，通过积分消去潜在响应时间，推导出一类新颖的、具有生物学可解释性的"逆Probit"类别概率模型，用于观测到的类别。然而，这一边际模型存在显著的识别性和推断性挑战，这些挑战在Paulon等人（2020）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来应对这些新挑战，该方法采用对称性保持的识别约束，使我们能够在无约束空间中使用共轭先验。我们对该模型进行了调整，适用于纵向设置中的群体和个体水平推断。再次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用于后验计算。通过模拟实验评估了该方法的经验性能，并在纵向音调学习研究的应用中展示了其实用效果。