  To study information processing in the brain, neuroscientists manipulate
experimental stimuli while recording participant brain activity. They can then
use encoding models to find out which brain "zone" (e.g. which region of
interest, volume pixel or electrophysiology sensor) is predicted from the
stimulus properties. Given the assumptions underlying this setup, when stimulus
properties are predictive of the activity in a zone, these properties are
understood to cause activity in that zone.
  In recent years, researchers have used neural networks to construct
representations that capture the diverse properties of complex stimuli, such as
natural language or natural images. Encoding models built using these
high-dimensional representations are often able to significantly predict the
activity in large swathes of cortex, suggesting that the activity in all these
brain zones is caused by stimulus properties captured in the representation. It
is then natural to ask: "Is the activity in these different brain zones caused
by the stimulus properties in the same way?" In neuroscientific terms, this
corresponds to asking if these different zones process the stimulus properties
in the same way.
  Here, we propose a new framework that enables researchers to ask if the
properties of a stimulus affect two brain zones in the same way. We use
simulated data and two real fMRI datasets with complex naturalistic stimuli to
show that our framework enables us to make such inferences. Our inferences are
strikingly consistent between the two datasets, indicating that the proposed
framework is a promising new tool for neuroscientists to understand how
information is processed in the brain.

Neurons and Cognition;Artificial Intelligence;Machine Learning;Neural and Evolutionary Computing;Applications