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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. Motor control is also the name of a thriving field within Neuroscience that analyzes how people, animals and their nervous system controls movement.
Simple tasks such as reaching for a cup of coffee are actually surprisingly complex. They arise from a complex coordination between:
- muscles
- limbs
- neural circuitry
The involved processes are complex and may be roughly divided as follows:
- perception
- motor planning
- motor execution
- feedback
- biomechanics
Motor control is the process that must be performed in order to achieve movement. In other words, motor coordination is essentially the complex set of interactions between neural processes involved in moving a limb, and the actual limb in 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 are the physics and mathematical modeling of the limb movement involved?
- How complicated and coordinated is the limb movement? How are movements of several joints coordinated?
Unfortunately, common research methods are often unable to find a happy median between multi-limb movement and accurate mathematical models. On one side of research are those scientists who choose to study entire limbs. These scientists have the most accurate representation of what a coordinated multi-limb movement is, however they lack the ability to mathematically model the complex physics involved in a full limb movement. On the other side, are scientists who seek an accurate mathematical model the of a limb’s mechanics, and sacrifice multi-joint movement for single joint movements. These scientists lack the ability to claim substantial understanding of limb coordination.
Theoretical frameworks about motor control
- Dynamical systems frameworks emphasize the dynamical interactions between different parts of a 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.
See also
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