  Stimulus decoding of functional Magnetic Resonance Imaging (fMRI) data with
machine learning models has provided new insights about neural representational
spaces and task-related dynamics. However, the scarcity of labelled
(task-related) fMRI data is a persistent obstacle, resulting in
model-underfitting and poor generalization. In this work, we mitigated data
poverty by extending a recent pattern-encoding strategy from the visual memory
domain to our own domain of auditory pitch tasks, which to our knowledge had
not been done. Specifically, extracting preliminary information about
participants' neural activation dynamics from the unlabelled fMRI data resulted
in improved downstream classifier performance when decoding heard and imagined
pitch. Our results demonstrate the benefits of leveraging unlabelled fMRI data
against data poverty for decoding pitch based tasks, and yields novel
significant evidence for both separate and overlapping pathways of heard and
imagined pitch processing, deepening our understanding of auditory cognitive
neuroscience.
