Studies of Goal Directed Movements by Emanuel V. Todorov
The main idea [of the discussion on a Bayesian model of sensory-motor processing] was that the sensory-motor system uses a casual model to predict the outcomes of hypothetical actions, and a generative model to predict sensory stimuli arising from hypothetical states of the world. These two models are then "inverted" via some powerful computation. [The Kalman filtering algorithm] does exactly that: the first line is the casual model describing the hand dynamics, and the second line is the generative model describing how sensory inputs arise. (p. 29)
Motor templates and learning
Memory decay and target resampling
Imagine that for some reason the controller is using inaccurate estimates of the noise terms in the system. In particular it is too confident in the sensory input (G is smaller than the actual amount of sensory noise) and too skeptical about stability of the world (D is larger than the actual amount of additive system noise). Such a controller will use a Kalman gain K larger than the optimal value i.e. it will overcorrect based on sensory inputs; in particular, in the absence of inputs the gain will not become exactly 0 and thus the state estimate (memory) will gradually degrade. Furthermore, the estimated variance of the state will be larger than its true value, thus from the point of view of the controller it will be advantageous to sample the sensory input as often as possible, and in particular look at the targets even if they have been presented before the movement. (pp. 38-9)
Sensory adaptations
Using a default control law L that corrects for systematic perturbations detected on previous trials can have effects quite similar to sensory adaptations. (p. 39)
It is possible that the primary objective of the adaptation process is restoring performance of the task (i.e. acquire the target withing the time limit) rather than restoring the shape of the baseline trajectory -- the latter being an epiphenomenon. (p. 93)