I have felt that there’s circular reasoning going on with fMRI results, where the parts of the brain light up for something, showing that the process is in that part of the brain, and then they light up again with something else, which shows that thing is the same process.
A brain region can calculate more than function; so that part isn't problematic. But interpretation of BOLD signal is inherently tricky because its like measuring heat off of a processor; it might tell you how hard its working, but not its working on; but if you can modulate the temporal dynamics of the processing, then maybe you have something work with.
One approach is to have a generative normative model of a mechanism (e.g. temporal-difference learning) verified by lower-level research (unit recordings in animal models), fit parameters to the model based on the task behavior (e.g. learning rate) and then find the correlates to that (e.g. to the subjective reward prediction errors as they occur in the task at time of decision feedback). The benefit here is you have already a plausible mechanism that can recover the behavior, and you are finding changes in BOLD signal that track those. Doesn't solve the problem entirely, but its better than just correlation with whatever.
We also have information about what parts of the brain are responsible for what from examining functional loss resulting from injuries and tumors. It's all a bit messy because the boundaries are loose and things can develop differently for different people.
