Sentence examples for motor learning using from inspiring English sources

Designs based on this principle are likely to prove useful in studies of motor learning using fMRI.

Decorrelation control is thus a candidate algorithm for the basic cerebellar microcircuit, indicating how it could achieve motor learning using only signals available to the system.

This pattern has been demonstrated in a number of recent studies of motor learning using the A1-B-A2 paradigm [7] [13], and interference in motor learning has been widely reported not only in the case of force field learning, but also in the case of learning perturbations of visual feedback [6], [8], [10], [13].

We use the terms "learning" and "non-learning" hemispheres simply to reflect the known lateralized, offline plastic changes observe across a night of sleep using this task [14]; although it should be noted that practice-dependent motor learning using the non-dominant hand often involves bilateral motor cortex activation (e.g. [16]).

Fenobam (Sigma-Aldrich, St . Louis MO, USA) was injected intraperitoneally at a dose of 30 mg/kg 30 min before each associative motor learning session, using methyl cellulose (MC) as dissolvent.

Usually, the behavioral readout of motor learning tasks used for this purpose is restricted to a binary measure of performance (i.e. "successful" movement vs. "failure").

For example, numerous studies of motor learning have used objects that, when moved in a horizontal plane, generate forces proportional to hand speed and directed orthogonal to the current direction of hand movement.

To describe this new phenomena we borrowed the term "after-effects" from the well known motor learning paradigm that uses force fields to alter arm dynamics [ 35].

We seek to understand the principles of motor learning at this interface using a multidisciplinary approach.

Finally, motor practice may induce simultaneous signal increases (due to increased neural recruitment) and signal decreases (due to more efficient encoding), making motor learning difficult to detect using traditional fMRI paradigms (Steele and Penhune, 2010).

Recent studies of motor learning indeed suggest that bimanual movements are learned using nonlinear basis functions.