  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.
