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:理解成年人类大脑如何学习新类别是神经科学中的一个重要问题。 在这样的背景下,漂移-扩散模型因其模仿内在神经机制的能力而很受欢迎。 Paulon等人(2020年)最近开发了这种渐进纵向学习模型。 适应漂移-扩散模型需要关于类别反应和相关反应时间的数据。 但是,分类反应理解往往是行为科学家记录的唯一可靠尺度来描述人类学习。 但是,对于我们的知识来说,在文献中从未考虑过这种情景的流动-传播模型。 为了弥补这一差距,我们在本篇文章中谨慎地在Paulon等人(202020年)上构建了这种模型,但现在有潜在的反应时间被整合出来。 适应漂移-扩散模型需要关于分类反应和相关反应时间的数据。 然而,这种边缘模型提供了重要的识别性和推断性挑战,最初在Paulon, et al. (2020年) 。 我们通过基于新颖的预测-流传流传-流传模型的方法来应对这些新的挑战。 我们通过在结构前的变现-变现-变现-演算方法, 使得我们通过在设计阶段的变现- 的变现-演算方法中学会的演化的演化方法, 能够在结构结构中学习一个不同的演进- 。