  In recent years, neuroscience has made significant progress in building
large-scale artificial neural network (ANN) models of brain activity and
behavior. However, there is no consensus on the most efficient ways to collect
data and design experiments to develop the next generation of models. This
article explores the controversial opinions that have emerged on this topic in
the domain of vision and language. Specifically, we address two critical
points. First, we weigh the pros and cons of using qualitative insights from
empirical results versus raw experimental data to train models. Second, we
consider model-free (intuition-based) versus model-based approaches for data
collection, specifically experimental design and stimulus selection, for
optimal model development. Finally, we consider the challenges of developing a
synergistic approach to experimental design and model building, including
encouraging data and model sharing and the implications of iterative additions
to existing models. The goal of the paper is to discuss decision points and
propose directions for both experimenters and model developers in the quest to
understand the brain.
