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Gross motor coordination addresses the gross motor skills: walking, running, climbing, jumping, crawling, lifting one's head, sitting up, etc.

Fine motor coordination addresses the fine motor skills, such as the abilities to manipulate small objects using small muscle movements of the fingers, usually in coordination with vision. A notable type of fine coordination is involved in the usage of vocal cords and other organs for producing speech or singing.

Elements of coordination include:

  • Spatial awareness
  • Timing
  • Balance
  • Combining several movements into a sequence (including motion learning, planning and memorizing)

Elements of nervous system involved in motor coordination

Types of coordination

  • Sensory–motor coordination
    • Visual–motor copordination
  • Left–right coordination
  • Flexor-extensor alternation and balance

Sensory–motor coordination

Integration of the sensory perception and motor output occurs in the cerebellum. The cerebellum is linked by many neural pathways with the motor cortex—which sends information to the muscles causing them to move—and the spinocerebellar tract—which provides feedback on the position of the body in space (proprioception). The cerebellum integrates these pathways, using the constant feedback on body position to fine-tune motor movements.

Left–right coordination

The term left–right coordination has two major meanings.

The first one refers to the rhythmic alternating left and right limb movement during, e.g., the locomotion in mammals or swimming of aquatic vertebrates. The basic neuronal circuits that generate this type of coordinated activity is located in the spinal cord. [1]

The second one one refers to various coordinated activities with left and right hands, e.g., in playing the piano, drumming, semaphore flag signalling, etc.

Motor coordination is among the most fundamental aspects of everyday life, seen in reaching for the morning cup of coffee to hitting the buttons on a clock to set your morning alarm. These tasks seem rudimentary in nature, however they are deceptively complex, as they arise from a complex coordination between:

  • muscles
  • limbs
  • complicated neural circuitry[2].

Motor coordination can be thought of as each physiological 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.

Original Theories

Pioneering theories in motor coordination focused on the division between the central nervous system (CNS) and peripheral nervous system (PNS) in volitional and reflexive movements. Theories divided the CNS and PNS functions between sensory acquisition via the PNS, and the integration and control developed in the CNS. The CNS then relayed information to the musculoskeletal system and the appropriate effectors.

Once the paradigm of division of roles between the CNS and PNS was established, it long survived as the sole interpretation of motor coordination. This theory was further developed to assign certain areas of the nervous system to specific muscles of the body.[3]

As neuroscience and cellular biology developed, it became clear not all movements followed the same biological series of events. Research with injured patients granted a greater understanding of how certain areas of the nervous system were vital for specific types of brain function, but also gave important evidence required to abolish the notion of localization in the brain. Experiments with the famous patient HM opened various questions regarding memory, which allowed for similar questions in formation of motor coordination memories. Non-human experimentation gave valuable insight into the variety of mechanisms by which motor coordination is achieved.

Originally, scientists thought all movements were sent through a communication loop involving the CNS. However this theory was disproved after experimentation showed evidence for central pattern generators (CPG) in the spinal cord. These CPGs allowed from repetitive or reflexive movements to bypass the CNS circuitry and work exclusively within the spinal cord. Eventually, the multiple avenues of information coalesced into several modern theories of motor coordination.

Feed Forward

In the mid 1990’s, motor coordination theories adopted the feed forward control system. Inspired by the physiological significance of feed forward systems, scientists crafted a theory in which sensory feedback from the actual limb movement is ignored during the movement. Instead, feed forward systems predict the necessary correction factor in order to account for some perturbance in the optimal state, before the actual perturbance occurs. A common example in physiology is the feed forward regulation in maintaining homeostatitic heart rhythm. In feed forward systems, tasks can be completed with greater speed than that of feedback systems, however a major issue lies in the lack of communication between the input variable and output variable. Feed forward systems rely on a predetermined response to a disturbance and therefore lack the ability to deal with a wide variety of disturbances. This issue forced scientists to question if motor coordination could solely be monitored by feed forward mechanisms.[4]

Servomechanism Theory

The most recent theory to gain popularity utilized a servomechanism scheme, which was initially promoted in the 1970’s, but grew out of favor as a result of the inability to explain the delays inherent in sensory feedback. Servomechanisms accomplish tasks by utilizing a feedback loop to adjust incoming information. These feedback signals interpret error signals, and update the output in order to achieve the task in an optimal way. When applied to motor coordination, a servomechanism would use sensory information received during movement to update the efferent movement signals in order to maintain an optimal state and accomplish the objective most efficiently. Although a useful theory in finally incorporating feedback principles to motor coordination, the theory of servomechanisms for motor coordination experiences difficulties in explaining how quickly movement is achieved. If the feedback incorporation results in extremely high gains, resulting delays in signals would be too large for real-time coordination between the neural circuitry and limb movements. Thus, signal delays cannot be present in organisms, such as humans, whose movements are highly coordinated and also quickly executed. Scientists soon began searching for a mechanism to utilize the feedback mechanisms that could also offer little to no signal delay.[5]

Optimal Feedback Control Theory

The optimal feedback control system is an extension of the servomechanism theory of motor coordination, in which efferent copy signals within the system solve feedback delays issues. The name optimal feedback comes from the continual interpretation of sensory input, and the constant readjusting of performance indices in order to adjust the system towards its optimal state.

  • The optimal state is defined as the estimated best series of events in order to achieve the desired task.
  • Efferent copy signals are identical signals sent back to the CNS for reevaluation, which constantly evaluate the system in relation to its optimal state. By incorporating efferent copy signals into the neural loop, the optimal feedback control theory circumvents the delay issue found in the servomechanism.

Optimal Feedback control has several important components:

  • Accurate estimate of State Variables: The optimal feedback system must have the ability to accurately estimate the state variables of the limb, or the position of the limb and forces acting upon or in the limb. This is done by:
    • Accurate sensory information interpretation: The concept of the feedback mechanism in the system relies heavily upon afferent information regarding the movement of the limb. The movement of the limb affects the way in which it actually moves, as the sensory information tells the CNS to modify its actions.
    • Efferent Copy Signals: Efferent copy signals are another key concept, which allow for lower gains from the feedback mechanism. In servomechanisms, the body relies solely upon sensory information to modify the actions of the limb. However, optimal feedback control uses efferent copy signals to cut down the time necessary to adjust the limb’s movement because these signals reach the CNS before the afferent sensory information can be evaluated.
  • The Minimal Intervention Principle: In order to optimize the system, the correction errors found in the feedback system are selectively amplified based upon the specific behavioral task. Because the human system is particularly noisy, this selective modification allows for only feedback signals, which are detrimental to the task to be inhibited, while all others are ignored.
  • Variability in controller and Feedback Gains: Optimizing the system to best execute its goal requires feedback gain settings to vary depending on the task at hand. In addition, modeling the system controller with flexible properties allows for various features of motor performance to be defined without creating additional variables.Liu D and Todorov E. (2007). Evidence for the flexible sensorimotor strategies predicted by optimal feedback control. Journal of Neuroscience 27: 9354–9368.</ref>

Feedback Loop System

File:Optimal feedback control.jpg

Figure 1: Optimal Feedback Model diagram. The schematic shows the inputs and feedback loop combination of efferent copy signals and afferent sensory data.

Main article: Negative feedback

The feedback loop system is explained in the figure to the right. In the diagram, volitional movement begins with a task to be performed selected. Next, the optimal feedback controller interprets the signal and initiates an efferent motor command, along with a copy of this signal. The efferent copy signal travels to the optimal state estimator and relays information to the optimal feedback controller to define the state variables of the limb. The efferent signal encounters noise on its way to the effector, which creates movement of the limb. The movement within the limb creates sensory feedback, which travels through afferent pathways to the optimal feedback estimator. The estimator relays the afferent sensory information of the limb to further redefine the state variables of the limb and move the system closer to optimal state.[6]

Aspects of Motor Coordination

Motor coordination can be thought to concern two types of movements: volitional and reflexive. Each of these movements use similar regions of the nervous system, however certain anatomical areas are associated with specific tasks.

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 many joints are involved?

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.

Pioneering Research

Recently, a movement towards finding a research paradigm for motor coordination involving multi-limb movements that can be accurately modeled using mathematics. The median between the two divisions may be able to answer some substantial answers in motor coordination[7] .


The Kinesiological Instrument for Normal and Altered Reaching Movements KINARM is a device created by Dr. Stephen Scott of Queen’s Land University used to study limb movement in humans. The KINARM is a two-jointed robotic arm that allows for computer analysis of movements of these two joints. Humans can attach their arms to the KINARM and move the machine. This motion is then analyzed by the computer system. The machine has a high degree of variability, as patients can be asked to reach for a target at any point in 3-dimensional space, and any load can be applied to the system. The key feature of the KINARM is amount of joints in the system: two. By limiting the number of joints to two, Dr. Scott is able to accurately analyze the system while still being able to claim a complex multi-joint system.[8]

The benefits of such a system are expected to be many. Currently, the machine helps diagnose stroke victims by accurately quantifying the amount of motor function lost from injury. Other clinical applications are expected to arise for traumatic brain injury patients and Parkinson’s patients. The KINARM offers a repeatable way to standardize loss of motor coordination, which is currently unavailable to clinicians.

Anatomical Divisions of Movement

Musculoskeletal System

File:Motor coordination division.jpg

Figure 2: Diagram showing the different levels of motor coordination control.

The human musculoskeletal system is a complex array of mutli-articulated joints and limbs being managed by a complex series of muscles and connective tissue. The degrees of freedom that exist at each joint surpass the required freedoms humans need to complete a given task. This is an interesting engineering question in evolution, and confounds research attempts to study limb mechanics. The musculoskeletal system is the primary effector in motor coordination and movement.

Peripheral Nervous System

The peripheral nervous system refers to the neurons that synapse outside the central nervous system. These neurons provide vital sensory information to the CNS, while also connecting the CNS to the effectors in the body (typically glands or muscles). The role of the PNS in motor coordination is two fold: sensory data required for feedback mechanisms and relay of data to the muscle to perform the movement.

Sensory Acquisition

Sensory data plays a vital role in the feedback mechanisms of the optimal feedback theory. Sensory data acquired and relayed by the PNS provides the CNS with a framework of what the limb is doing at a given moment, and allows the CNS to use an optimal controller to adjust the movement closer towards the optimal state. This is a vital aspect of the optimal feedback system, and our current understanding of motor coordination in general.

Relay of Efferent Data

The distinguishing feature of the optimal feedback theory is the use of efferent copy signals to circumvent the signal delay issues of past theories. These efferent copies provide the CNS with immediate information regarding the movement of the limb, reaching the CNS before the sensory data can be interpreted. The PNS is responsible for both the movement of the limb, along with the transmission of the efferent copy to the CNS.

Central Nervous System

The central nervous system includes the brain and spinal cord. The CNS is typically viewed as the integration center of the vast amount of afferent data acquired by the body. It then analyzes the afferent data and performs a given task by creating efferent signals. These signals travel to whatever effector may be desired to activate in order to accomplish the given task. In motor coordination, the CNS plays a vital role in integrating the afferent feedback data and efferent copy signals, comparing the state variables of a given limb to the optimal state, then adjusting the limb to perform closer to the optimal state. The CNS is generally divided into three hierarchal divisions:

  • The spinal cord
  • The brainstem
  • The cerebral cortex.

Each of these formations function in different manners to achieve the complex series of neural function required for motor coordination.

The Spinal Cord

The spinal cord is the long tube of neurons, which connect the periphery to the brain. The spinal cord contains motor neurons responsible for creating movement in the body, and is vital in this sense. It also contains interneurons and afferent sensory neurons that synapse to relay sensory data acquired from the periphery to the brain. The spinal cord was discovered to contain low levels of motor coordination, such that the brain circuitry is bypassed for simple reflexive, or repetitive movements via central patter generators[9].


The next degree of hierarchical control after the spinal cord is the brainstem. The brainstem refers to the lower portion of the brain that is continuous with the spinal cord. The brainstem controls many vital aspects of bodily function, including regulation of much of the required subconscious aspects living (heart beat, breathing, posture, ect.) In motor coordination, the brainstem acts as the next level convergence for afferent sensory data. Two portions in specific have been identified as particularly important in motor coordination:

The vestibular nuclei relay balance and posture information from the vestibular system, which allow for posture control during motor coordination. The reticular formation can discriminate spinal cord data to amplify or decrease certain signals, based upon their value to a given task. In motor coordination, this allows the RF to regulate locomotive patterns established in the spinal cord.


The cerebral cortex is the highest level in the hierarchical control of motor coordination. The cortex is highly convoluted outer layer of the brain, responsible for many higher-level tasks including: consciousness, thought, speech, memory, and attention. In motor coordination, several different areas of the cerebral cortex aid in movement.

Primary Motor Cortex (M1)

The primary motor cortex (M1) is the most often associated region of the brain with movement. It synapses the largest amount of axons with the corticospinal tracts involved with movement. The M1 is thought to be the main area responsible for planning of motor function. [10]

Somatosensory Cortex (S1), Parietal Cortext (P1) and Cerebellar Pathways

The somatosensory information acquired from the periphery is integrated in the somatosensory cortex, parietal cortex, and cerebellar pathways. Somatosensory data provides the feedback system relevant data necessary to adjust the current state closer toward the optimal state.

Basal Ganglia (BG) and Cerebellum (C)

The basal ganglia and cerebellum are also important in motor coordination due to their high level of connection with the aforementioned structures. These areas aid in connecting much of the structures necessary for movement.[11]

See also


  1. Butt S.J., Lebret J.M., Kiehn O. "Organization of left-right coordination in the mammalian locomotor network", Brain Res. Brain. Res. Rev. 2002 Oct;40(1-3):107-17 PMID 12589910
  2. Berthier NE, Rosenstein MT, and Barto AG. (2005). Approximate optimal control as a model for motor learning. Psychological Review 112: 329–346.
  3. Liu D and Todorov E. (2007). Evidence for the flexible sensorimotor strategies predicted by optimal feedback control. Journal of Neuroscience 27: 9354–9368.
  4. Seidler RD, Noll DC, and Thiers G. (2004). Feedforward and feedback processes in motor control. NeuroImage 22: 1775–1783.
  5. Shabbott BA and Sainburg RL (December 6, 2008). Differentiating between two models of motor lateralization. Journal of Neurophysiology 100: 565–575.
  6. Scott SH (2004). Optimal feedback control and the neural basis of volitional motor control. Nature Reviews Neuroscience 5: 534–546.
  7. Scott SH and Norman Kathleen E. (2003). Computational approaches to motor control and their potential for interpreting motor dysfunction. Trauma and Rehabilitation 16: 693–698.
  8. Nancy Dorrance (December 6, 2004). Kudos for KINARM invention. Queen’s Gazette XXXV (20): 1775–1783.
  9. Ijspeert AJ. (2008). Central pattern generators for locomotion control in animals and robots: A review. Neural Networks 21: 642–653.
  10. Scott SH (2008). Inconvenient Truths about neural processing in primary motor cortex. Journal of Physiology-London 586: 1217–1224.
  11. Todorov E and Jordan MI (2002). . Optimal feedback control as a theory of motor coordination. Nature Neuroscience 5: 1226–1235.
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