Coincidentally, today I was reading an interesting paper about forward and inverse models in the cerebellum. Here's a quote:

Humans demonstrate a remarkable ability to generate accurate and appropriate motor behaviour under many different and often uncertain environmental conditions. Considering the number of objects and environments, and their possible combinations, that can influence the dynamics of the motor system, the controller must be capable of providing approximate motor commands for a multitude of distinct contexts, such as different tasks and interactions with objects, that are likely to be experienced. Given this multitude of contexts, there are two qualitatively distinct strategies to motor control and learning. The first is to use a single controller that uses all the contextual information in an attempt to produce an appropriate control signal. However, such a controller would require enormous complexity to allow for all possible scenarios. If this controller were unable to encapsulate all the contexts, it would need to adapt every time the context of the movement changed before it could produce approximate motor commands - this would produce transient and possibly large performance errors. Alternatively, a modular approach can be used in which multiple controllers co-exist, with each controller suitable for one or a small set of contexts. Depending on the current context, only those appropriate controllers would be active to generate the motor command. While forward and inverse models could be learned by a single module, there are three potential benefits to employing a modular approach. First, the world is essentially modular, in that we interact with multiple qualitatively different objects and environments. By using multiple inverse models, each of which might capture the motor commands necessary when acting with a particular object or within a particular environment, we could achieve an efficient coding of the world. In other words, the large set of environmental conditions in which we are required to generate movement requires multiple behaviours or sets of motor commands, each embodied within a module. Secondly, the use of a modular system allows individual modules to adapt through motor learning without affecting the motor behaviours already learned by other modules. Thirdly, many situations that we encounter are derived from combinations of previously experienced contexts, such as novel conjoints of previously manipulated objects and environments. By modulating the contribution to the final motor command of of the outputs of the inverse modules, an enormous repertoire of behaviours can be generated. With as few as 32 inverse models, in which the output of each model either contributes or does not contribute to the final motor command, we have 2^32 or 10^10 behaviours - sufficient for a new behaviour for every second of one's life. Therefore, multiple internal models can be regarded conceptually as motor primitives, which are the building blocks used to construct intricate motor behaviours with an enormous vocabulary.