Wikipedia:Sandbox

Simple tasks, such as reaching for a cup of coffee, are actually surprisingly complex, requiring the successful coordination of sensory input (seeing the cup of coffee, sensing one's own movement towards it, feeling one's fingers touch it, sensing its weight when moving it. etc.) and motor output (moving the eyes, extending one's arm, grasping the cup and lifting it, adjusting one's muscle tone to compensate for the added weight, etc.). Motor control are information processing related activities carried out by the central nervous system that organize the musculoskeletal system to create coordinated movements and skilled actions. Thus the study of motor control involves studying perception and cognition, feedback processes, and biomechanics, to name a few.

Motor control is also the name of a thriving field within Neuroscience that analyzes how people, animals and their nervous system controls movement.

Aspects of motor control

Motor control can be thought to concern two types of movements: volitional and reflexive.

Beyond anatomical divisions, motor coordination studies often seek to explore one of the following questions:

• What physics and mathematical modeling of the limb movement may be involved?
• How complicated and coordinated is the limb movement? How are movements of several joints coordinated?

Fortunately for researchers, multi-limb movements can often be modeled by simple mathematical models. A single limb can be broken down into components such as muscles, tendons, bones, and nerves. The physics are then derived with the aid of modern computers. The study of multi-limb movement is then only slightly more complicated. The development of elementary models of intelligence, along with a gambit of built-in reflexive reactions, is suited to the modeling of this system.

Theoretical frameworks of motor control

• Coordination Dynamics framework emphasizes the dynamical and time-continuous interplay between brain, body, and environment as a holistic system.
• Equilibrium point approaches emphasize that biomechanics and in particular the elastic properties of muscles and reflexes in the spinal cord can render many movement problems easy.
• Reinforcement learning based approaches emphasize the learning of movement from motor errors.
• Optimal control and estimation frameworks (see Bayesian brain) start from the computational problems that need to be solved and ask which solutions would be optimal. Many internal model studies fall into this framework.

Motor control in athletes

• Improving in Motor control for specific sports that the athletes participate.

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• The cellular level of neurotransmitter (faster fired speed to action potential, or shorter refractory period) difference between athletes and non-athletes.

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• Neuron structure difference between athletes and non-athletes.

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• Brain (motor cortex) difference between athletes and non-athletes.

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• Example of athletes that have better motor control in compare with non-athletes.

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Suggested Reading

Shadmehr, R. (2004). The Computational Neurobiology of Reaching and Pointing: A Foundation for Motor Learning. MIT Press.

See also

• Motor learning
• Sensory integration
• Motor skill
• Motor system
• Motor coordination
• Two-alternative forced choice