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The body is hierarchical in at least one obvious way. In order to make a voluntary movement, a particular muscle is targeted, but the specific muscle cell doesnt matter. What matters is the strength of the contraction. In assessing the result of the contraction, what matters is the change in position of the joint controlled by the muscle, not the change in position of specific muscle cells. Thus, an internal model needs only to work with intensity of effort, predicted outcome, and perceived outcome. This kind of model is something that computer neural networks can shed some light on. Certainly, there are more parameters, like anticipated resistance but there are probably not an overwhelming number of them.
The point of this is that, as Grush (2002) points out, the internal model has to be updateable in order to enable the organism to handle changes to its own capabilities over time. At least at this level, Hebbian learning (as if I knew exactly what that denoted) seems sufficient.