Motor control
Motor control is the regulation of movements in organisms that possess a nervous system. Motor control includes conscious voluntary movements, subconscious muscle memory and involuntary reflexes,[1] as well as instinctual taxis.
This article is about motor control by humans and other animals. For motor control by machines and robots, see Motor controller.
To control movement, the nervous system must integrate multimodal sensory information (both from the external world as well as proprioception) and elicit the necessary signals to recruit muscles to carry out a goal. This pathway spans many disciplines, including multisensory integration, signal processing, coordination, biomechanics, and cognition,[2][3] and the computational challenges are often discussed under the term sensorimotor control.[4] Successful motor control is crucial to interacting with the world to carry out goals as well as for posture, balance, and stability.
Some researchers (mostly neuroscientists studying movement, such as Daniel Wolpert and Randy Flanagan) argue that motor control is the reason brains exist at all.[5]
Model systems for motor control[edit]
All organisms face the computational challenges above, so neural circuits for motor control have been studied in humans, monkeys,[10] horses, cats,[11] mice,[12] fish[13] lamprey,[14] flies,[15] locusts,[16] and nematodes,[17] among many others. Mammalian model systems like mice and monkeys offer the most straightforward comparative models for human health and disease. They are widely used to study the role of higher brain regions common to vertebrates, including the cerebral cortex, thalamus, basal ganglia and deep brain medullary and reticular circuits for motor control.[18] The genetics and neurophysiology of motor circuits in the spine have also been studied in mammalian model organisms, but protective vertebrae make it difficult to study the functional role of spinal circuits in behaving animals. Here, larval and adult fish have been useful in discovering the functional logic of the local spinal circuits that coordinate motor neuron activity. Invertebrate model organisms do not have the same brain regions as vertebrates, but their brains must solve similar computational issues and thus are thought to have brain regions homologous to those involved in motor control in the vertebrate nervous system,[19] The organization of arthropod nervous systems into ganglia that control each leg as allowed researchers to record from neurons dedicated to moving a specific leg during behavior.
Model systems have also demonstrated the role of central pattern generators in driving rhythmic movements.[14] A central pattern generator is a neural network that can generate rhythmic activity in the absence of an external control signal, such as a signal descending from the brain or feedback signals from sensors in the limbs (e.g. proprioceptors). Evidence suggests that real CPGs exist in several key motor control regions, such as the stomachs of arthropods or the pre-Boetzinger complex that control breathing in humans. Furthermore, as a theoretical concept, CPGs have been useful to frame the possible role of sensory feedback in motor control.
Sensorimotor feedback[edit]
Response to stimuli[edit]
The process of becoming aware of a sensory stimulus and using that information to influence an action occurs in stages. Reaction time of simple tasks can be used to reveal information about these stages. Reaction time refers to the period of time between when the stimulus is presented, and the end of the response. Movement time is the time it takes to complete the movement. Some of the first reaction time experiments were carried out by Franciscus Donders, who used the difference in response times to a choice task to determine the length of time needed to process the stimuli and choose the correct response.[20] While this approach is ultimately flawed, it gave rise to the idea that reaction time was made up of a stimulus identification, followed by a response selection, and ultimately culminates in carrying out the correct movement. Further research has provided evidence that these stages do exist, but that the response selection period of any reaction time increases as the number of available choices grows, a relationship known as Hick's law.[21]
Closed loop control[edit]
The classical definition of a closed loop system for human movement comes from Jack A. Adams (1971).[22][23] A reference of the desired output is compared to the actual output via error detection mechanisms; using feedback, the error is corrected for. Most movements that are carried out during day-to-day activity are formed using a continual process of accessing sensory information and using it to more accurately continue the motion. This type of motor control is called feedback control, as it relies on sensory feedback to control movements. Feedback control is a situated form of motor control, relying on sensory information about performance and specific sensory input from the environment in which the movement is carried out. This sensory input, while processed, does not necessarily cause conscious awareness of the action. Closed loop control[24]: 186 is a feedback based mechanism of motor control, where any act on the environment creates some sort of change that affects future performance through feedback. Closed loop motor control is best suited to continuously controlled actions, but does not work quickly enough for ballistic actions. Ballistic actions are actions that continue to the end without thinking about it, even when they no longer are appropriate. Because feedback control relies on sensory information, it is as slow as sensory processing. These movements are subject to a speed-accuracy trade-off, because sensory processing is being used to control the movement, the faster the movement is carried out, the less accurate it becomes.
Open loop control[edit]
The classical definition from Jack A. Adams is:[22][23] “An open loop system has no feedback or mechanisms for error regulation. The input events for a system exert their influence, the system effects its transformation on the input and the system has an output...... A traffic light with fixed timing snarls traffic when the load is heavy and impedes the flow when the traffic is light. The system has no compensatory capability.”
Some movements, however, occur too quickly to integrate sensory information, and instead must rely on feed forward control. Open loop control is a feed forward form of motor control, and is used to control rapid, ballistic movements that end before any sensory information can be processed. To best study this type of control, most research focuses on deafferentation studies, often involving cats or monkeys whose sensory nerves have been disconnected from their spinal cords. Monkeys who lost all sensory information from their arms resumed normal behavior after recovering from the deafferentation procedure. Most skills were relearned, but fine motor control became very difficult.[25] It has been shown that the open loop control can be adapted to different disease conditions and can therefore be used to extract signatures of different motor disorders by varying the cost functional governing the system.[26]
Planning in motor control[edit]
Individual movement optimization[edit]
There are several mathematical models that describe how the central nervous system (CNS) derives reaching movements of limbs and eyes. The minimum jerk model states that the CNS minimizes jerk of a limb endpoint trajectory over the time of reaching, which results in a smooth trajectory.[53] However, this model is based solely on the kinematics of movement and does not consider the underlying dynamics of the musculoskeletal system. Hence, the minimum torque-change model was introduced as an alternative, where the CNS minimizes the joint torque change over the time of reaching.[54]
Later it was argued that there is no clear explanation about how could the CNS actually estimate complex quantities such as jerk or torque change and then integrate them over the duration of a trajectory. In response, model based on signal-dependent noise was proposed instead, which states that the CNS selects a trajectory by minimizing the variance of the final position of the limb endpoint. Since there is a motor noise in the neural system that is proportional to the activation of the muscles, the faster movements induce more motor noise and are thus less precise.[55] This is also in line with the Fitts' Law and speed-accuracy trade-off.[56] Optimal control theory was used to further extend the model based on signal-dependent noise, where the CNS optimizes an objective function that consists of a term related to accuracy and additionally a term related to metabolic cost of movement.[57]
Another type of models is based on cost-benefit trade-off, where the objective function includes metabolic cost of movement and a subjective reward related to reaching the target accurately. In this case the reward for a successful reach within the desired target is discounted by the duration of reaching, since the gained reward is perceived less valuable when spending more time on it.[58][59] However, these models were deterministic and did not account for motor noise, which is an essential property of stochastic motor control that results in speed-accuracy trade-off. To address that, a new model was later proposed to incorporate the motor noise and to unify cost-benefit and speed-accuracy trade-offs.[60]
Multi-component movements[edit]
Some studies observed that the CNS can split a complex movement into sub-movements. The initial sub-movement tends to be fast and imprecise in order to bring the limb endpoint into vicinity of the target as soon as possible. Then, the final sub-movement tends to be slow and precise in order to correct for accumulated error by the first initial sub-movement and to successfully reach the target.[61][62] A later study further explored how the CNS selects a temporary target of the initial sub-movement in different conditions. For example, when the actual target size decreases and thus complexity increases, the temporary target of the initial sub-movement moves away from the actual target in order to give more space for the final corrective action. Longer reaching distances have a similar effect, since more error is accumulated in the initial sub-movement and thus requiring more complex final correction. In less complex conditions, when the final actual target is large and the movement is short, the CNS tends to use a single movement, without splitting it into multiple competents.[63]