  Neuroimaging studies produce gigabytes of spatio-temporal data for a small
number of participants and stimuli. Rarely do researchers attempt to model and
examine how individual participants vary from each other -- a question that
should be addressable even in small samples given the right statistical tools.
We propose Neural Topographic Factor Analysis (NTFA), a probabilistic factor
analysis model that infers embeddings for participants and stimuli. These
embeddings allow us to reason about differences between participants and
stimuli as signal rather than noise. We evaluate NTFA on data from an in-house
pilot experiment, as well as two publicly available datasets. We demonstrate
that inferring representations for participants and stimuli improves predictive
generalization to unseen data when compared to previous topographic methods. We
also demonstrate that the inferred latent factor representations are useful for
downstream tasks such as multivoxel pattern analysis and functional
connectivity.
