  Transfer learning improves the performance of the target task by leveraging
the data of a specific source task: the closer the relationship between the
source and the target tasks, the greater the performance improvement by
transfer learning. In neuroscience, the relationship between cognitive tasks is
usually represented by similarity of activated brain regions or neural
representation. However, no study has linked transfer learning and neuroscience
to reveal the relationship between cognitive tasks. In this study, we propose a
transfer learning framework to reflect the relationship between cognitive
tasks, and compare the task relations reflected by transfer learning and by the
overlaps of brain regions (e.g., neurosynth). Our results of transfer learning
create cognitive taskonomy to reflect the relationship between cognitive tasks
which is well in line with the task relations derived from neurosynth. Transfer
learning performs better in task decoding with fMRI data if the source and
target cognitive tasks activate similar brain regions. Our study uncovers the
relationship of multiple cognitive tasks and provides guidance for source task
selection in transfer learning for neural decoding based on small-sample data.
