New! Sign up for our free email newsletter.
Science News
from research organizations

Motor learning for precise motor execution

Motor learning for explicit and implicit controls

Date:
September 25, 2018
Source:
Tokyo Metropolitan Institute of Medical Science
Summary:
Scientists have identified acquisition of two types of internal models for motor control, which are likely to be stored in the cerebellum. They show that motor control in human hand reaching movement relies on two types of motor learning: (i) acquisition of explicit motor control and (ii) acquisition of implicit motor control.
Share:
FULL STORY

It is generally accepted that precise motor control in our daily life depends on acquisition of an internal model. One motor learning for an internal model is necessary for execution of precise movements in our daily life. There are two hypothetical internal models for motor learning: where to move (i.e. calculation of destination for a given motor command) or how to move (i.e. calculation of motor command for a target). There has been long-standing controversy over "which internal model is actually working in our brain?," for more than 30 years.

Takeru Honda, PhD, a lead author of this study, and his coauthors thought that both internal models are necessary to execute precise movement. If we learn where to move, our brain has to update the mapping from the motor command to the destination of movement. If we learn how to move, our brain has to update the mapping from the target (destination) to the proper motor command. The subjects repeatedly made a reaching movement to touch a target on a touchscreen with the index finger. Then they wore a prism glasses to have their sights shifted rightward. During initial 10 trials after wearing the prism glasses, they were not able to touch the target precisely. Instead, they touched points shifted rightward from the target. After repetitive trials, they eventually learned to touch the target precisely. In this paradigm, the authors found learning elicited by correct touching on the targets and hidden learning elicited by error between the touch position and the target position. They also provided a theory and a simple empirical formulation. Their results show that learning where to move is necessary for explicit execution of correct movements, while learning how to move is necessary for implicit execution of correct movements. Furthermore, their theory predicts that cerebellar damages induce an impairment of "where to move" or an impairment of "how to move." Indeed, we found both types of deficits in cerebellar patients by evaluating them by clinical indexes which we developed.

Therefore, the applications of this finding may help to develop clinical tests to evaluate learning capabilities of different types of cerebellar patients. The test will help to measure effects of various rehabilitations or novel therapies for cerebellar ataxia. In sports field, the present results will also help to develop effective methods of training for top athletes.


Story Source:

Materials provided by Tokyo Metropolitan Institute of Medical Science. Note: Content may be edited for style and length.


Journal Reference:

  1. Takeru Honda, Soichi Nagao, Yuji Hashimoto, Kinya Ishikawa, Takanori Yokota, Hidehiro Mizusawa, Masao Ito. Tandem internal models execute motor learning in the cerebellum. Proceedings of the National Academy of Sciences, 2018; 115 (28): 7428 DOI: 10.1073/pnas.1716489115

Cite This Page:

Tokyo Metropolitan Institute of Medical Science. "Motor learning for precise motor execution." ScienceDaily. ScienceDaily, 25 September 2018. <www.sciencedaily.com/releases/2018/09/180925094018.htm>.
Tokyo Metropolitan Institute of Medical Science. (2018, September 25). Motor learning for precise motor execution. ScienceDaily. Retrieved April 19, 2024 from www.sciencedaily.com/releases/2018/09/180925094018.htm
Tokyo Metropolitan Institute of Medical Science. "Motor learning for precise motor execution." ScienceDaily. www.sciencedaily.com/releases/2018/09/180925094018.htm (accessed April 19, 2024).

Explore More

from ScienceDaily

RELATED STORIES