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.
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.