The sequence-in/sequence-out cerebellar machinery is considered from the computational point of view. We outline a learning framework which discriminates short-term from long-term learning and is able to explain single-trial adaptation to unexpected loads.

Fitts' law and the [Delta][Lambda] model are theories of motor control because they are limited to the kinematic aspects of movement and do not capture its essential dynamic nature. The internal source of that determines the speed/accuracy trade-off can be associated with the partial compensation of movement-generated forces.