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A motor program is an abstract representations of movement that centrally organizes and controls the many degrees of freedom involved in performing an action (Schmidt and Lee, 2005 p. 182). Signals transmitted through efferent and afferent pathways allow the central nervous system to anticipate, plan, or guide movement. Evidence for the concept of motor programs include the following (Schmidt and Lee, 2005 p. 182):

  1. Processing of afferent information (feedback) is too slow for on-going regulation of rapid movements.
  2. Reaction time (time between “go” signal and movement initiation) increases with movement complexity, suggesting that movements are planned in advance.
  3. Movement is possible even without feedback from the moving limb.

This is not meant to underestimate the importance of feedback information, merely that another level of control beyond feedback is used (Schmidt and Lee, 2005):

  1. Before the movement as information about initial position, or perhaps to tune the spinal apparatus.
  2. During the movement, when it is either “monitored” for the presence of error or used directly in the modulation of movements reflexively.
  3. After the movement to determine the success of the response and contribute to motor learning.

Central organization[edit | edit source]

Open and closed-loop theories[edit | edit source]

Response-chaining hypothesis[edit | edit source]

The response-chaining, or reflex-chaining hypothesis, proposed by William James (1890), was one of the earliest descriptions of movement control. This open-loop hypothesis postulated that movements required attention only for initiation of the first action (Schmidt and Lee, 2005 p. 165). As such, each subsequent movement was thought be automatically triggered by response-produced afferent information from the muscles. Although feedback is involved in this process, ongoing movements cannot be modified if there are unexpected changes in the environment; feedback is not compared to some internally generated reference value for error checking. However, research involving deafferented animals (Taub et al., 1975) and humans (Rothwell et al., 1982) suggests feedback is not necessary for movement, thus the response-chaining hypothesis provides an incomplete account of movement control.

Adams’ closed-loop theory[edit | edit source]

In contrast to the open-loop response-chaining hypothesis, Adam’s closed-loop theory suggested that processing of afferent information was central in human motor control (Adams, 1971). Adams’ closed-loop theory is based on basic motor learning research that focused on slow, graded, linear positioning tasks, which involved error detection and correction to meet goal demands. To learn a movement, a “motor program” consisting of two states of memory (i.e. memory trace and perceptual trace), is required. The memory trace (equivalent to recall memory in verbal learning) initiates the motor movement, chooses its initial direction and determines the earliest portions of the movement. Strengthening of the memory trace results from practice and feedback about movement outcome (see motor learning). In addition, the perceptual trace (similar to recognition memory in verbal tasks) is involved in guidance of the limb to the correct position along a trajectory. This is accomplished by comparing incoming feedback to the perceptual trace, which is formed from the sensory consequences of the limb being at the correct/incorrect endpoint in past experience. In the event of an error, the limb is adjusted until the movement is appropriate to the goal of the action. Importantly, the more accurate the movement, the more useful the perceptual trace that is collected and retained.

Though this theory represented an important leap forward in motor learning research (see Schmidt 1975 for reasoning), one weakness in Adams’ closed-loop theory was the requirement of 1-to-1 mapping between stored states (motor programs) and movements to be made. This presented an issue related to the storage capacity of the central nervous system; a vast array of movements would require equally large repository of motor programs. Additionally, this theory could not be used to explain how motor programs for novel movements were formed.

Schmidt’s schema theory[edit | edit source]

Early motor program theories did not adequately account for evidence illustrating the influence of feedback for the modification of ongoing movement while providing a suitable explanation of motor programs storage or application in novel movement. Consequently, the notion of the generalized motor program (GMP) was developed (Schmidt and Lee, 2005, p. 205). The GMP is thought to contain an abstract representation for a class of movements with invariant features pertaining to the order of events, the relative timing of events and the relative force with which events are produced. In order to determine how a particular movement should be performed, parameters such as overall movement duration, overall force of contractions and the muscles involved are specified to the GMP. This revision of the motor program concept allows many different movements to be produced with the same motor program as well as the production of novel movements by specifying new parameters.

Richard Schmidt (1975) proposed the schema theory for motor control, suggesting in opposition to closed-loop theories, that a motor program containing general rules can be applied to different environmental or situational contexts via the involvement of open-loop control process and GMPs (Shummway Cook & Woollacott 2001, p 32). In Schmidt’s theory, the schema (psychology) contains the generalized rules that generate the spatial and temporal muscle patterns to produce a specified movement (Shummway-Cook and Woollacott 2001, p. 32). Therefore, when learning novel movements an individual may generate a new GMP based on the selection of parameters (reducing the novel movement problem), or refine an existing GMP (reducing the storage problem), depending on prior experience with movement and task context.

According to Schmidt, four things are stored in memory after an individual generates a movement (Schmidt, 1975):

  1. The initial conditions of the movement, such as the proprioceptive information of the limbs and body.
  2. The response specifications for the motor programs, which are the parameters used in the generalized motor program, such as speed and force.
  3. The sensory consequences of the response, which contain information about how the movement felt, looked and sounded.
  4. The outcome of that movement, which contains information of the actual outcome of the movement with knowledge of results (KR).

This information is stored in components of the motor response schema, which include the recall schema and recognition schema. The recall and recognition schema are strongly associated, as they use the relationship between the initial condition and actual outcomes; however, they are not isomorphic (Schmidt, 1975). They differ in that recall schema is used to select a specific response with the use of response specifications, whereas the recognition schema is used to evaluate the response with the sensory consequences. Throughout a movement, the recognition schema is compared to the expected sensory information (e.g., proprioceptive and extroceptive) from the ongoing movement to evaluate the efficiency of the response (Shumway-Cook and Woollacott, 2001, p. 32). An error signal is sent upon finalizing the movement, where the schema is then modified based on the sensory feedback and knowledge of results (see motor learning).

The schema theory illustrates that motor learning consists of continuous processes that update the recall and recognition schemas with each movement that is made (Shumway-Cook and Woollacott, 2001, p. 33).

Multiple paired forward and inverse models[edit | edit source]

An alternate viewpoint on the organization and control of motor programs may be considered a computational process of selecting a motor command (i.e., the input) to achieve a desired sensory feedback (i.e., the output; Wolpert and Kawato, 1998). Selection of the motor command depends on many internal and external variables, such as the current state of the limb(s), orientation of the body and properties of the items in the environment with which the body will interact. Given the vast number of possible combinations of these variables, the motor control system must be able to provide an appropriate command for any given context. One strategy for selecting appropriate commands involves a modular approach; multiple controllers exist such that each controller is suitable for one or a small set of contexts. Based on an estimate of the current context, a controller is chosen to generate the appropriate motor command.

This modular system can be used to describe both motor control and motor learning and requires adaptable internal forward and inverse models. Forward models describe the forward or causal relationship between system inputs, predicting sensory feedback that will occur. Inverse models (controllers) generate the motor command that will cause a desired change in state, given an environmental context. During motor learning, the forward and inverse models are paired and tightly coupled by a responsibility signal within modules. Using the forward model’s predictions and sensory contextual cues, responsibility signals indicate the degree to which each pair should be responsible for controlling current behavior.

Impairment of motor programs[edit | edit source]

Cerebellar degeneration[edit | edit source]

Errors in reaching are commonly found in patients with cerebellar degeneration. This suggests their motor commands do not predicatively compensate for interaction torques inherent in multi-joint motion (Bastian et al. 1996/2000; Goodkin et al. 1993; Topka et al. 1998). Several lines of research have been conducted to understand this, with evidence being provided that this impairment may be due to a malfunctioning inverse model:

  • the cerebellum plays a dominant role in representing the inverse model (Kawato and Gomi 1992)
  • the cerebellum is active during learning of arm movements in force fields (Nezafat et al. 2001).

With this knowledge, an experiment conducted by Smith and Shadmehr (2005) illustrated an impaired ability for cerebellar subjects to alter motor commands to compensate for applied force fields within a trial (i.e. modify an ongoing movement) as well as to use this error to update the following trial (i.e. changes in a following trial were unrelated to prior trial error). This agreed with prior work by Mascheke et al. (2004) who illustrated those with cerebellar degeneration had difficulty adapting motor commands when limb dynamics were altered.

Important considerations and further reading[edit | edit source]

Sensory contributions to motor control[edit | edit source]

Vision/visual perception

  1. Kandel E.R., Schwartz J.H., & Jessell T.M. (Eds.). (2000). Principles of neural Science (4th ed.). New York: McGraw-Hill.
  2. Chapman G.J. & Hollands M.A. (2006). Age-related differences in stepping performance during step cycle-related removal of vision. Experimental Brain Research, 174(4), 613-621.
  3. Reynolds R.F. & Day B.L. (2005). Visual guidance of the human foot during a step. The Journal of Physiology, 569(Pt 2), 677-684.
  4. Elliott D. (1992). Intermittent versus continuous control of manual aiming movements. In L. Proteau, & D. Elliott (Eds.), Advances in Psychology 85: Vision and Motor Control (pp. 33-48). New York, New York: Elsevier Science Publishing Company Inc.



  1. Kandel E.R., Schwartz J.H., & Jessell T.M. (Eds.). (2000). Principles of neural Science (4th ed.). New York: McGraw-Hill.
  2. Latash M.L. (1998). In Wright J. P., Schutter C., Sprague E. and Sexton J. (Eds.), Neurophysiological Basis of Movement. Windsor, Ontario: Human Kinetics.
  3. Perry S.D., McIlroy W.E., & Maki B.E. (2000). The role of plantar cutaneous mechanoreceptors in the control of compensatory stepping reactions evoked by unpredictable, multi-directional perturbation. Brain Research, 877(2), 401-406.

Vestibular system

  1. Kandel E.R., Schwartz J.H., & Jessell T.M. (Eds.). (2000). Principles of neural Science (4th ed.). New York: McGraw-Hill.
  2. Bent L.R., McFayden B.J., and Inglis J.T. (2005). Vestibular contributions during human locomotor tasks. Exercise Sport Science Reviews, 33(3),107-113

Individual Differences[edit | edit source]

Challenge Point Framework

Control of Movement[edit | edit source]

Central Pattern Generator

  1. Grillner S. (1975). Locomotion in vertebrates: central mechanisms and reflex interaction. Physiol Rev., 55(2), 247-304.
  2. Grillner S., Wallén P. (1985). Central pattern generators for locomotion, with special reference to vertebrates. Annu Rev Neurosci., 8, 233-261.
  3. Marder E. and Calabrese R.L. (1996). Principles of rhythmic motor pattern generation. Physiol Rev., 76(3), 687-717.
  4. Shik M.L., Orlovskiĭ G.N., Severin F.V. (1968) Locomotion of the mesencephalic cat evoked by pyramidal stimulation. Biofizika, 13(1), 127-135.

Reflexive, Triggered, and Voluntary Movement

  1. Kandel E.R., Schwartz J.H., & Jessell T.M. (Eds.). (2000). Principles of neural Science (4th ed.). New York: McGraw-Hill.
  2. Latash M.L. (1998). In Wright J. P., Schutter C., Sprague E. and Sexton J. (Eds.), Neurophysiological Basis of Movement. Windsor, Ontario: Human Kinetics.
  3. Schmidt R.A. & Lee T.D. (1999). Motor Control and Learning: A Behavioral Emphasis (4th ed.). Champaign, IL: Human Kinetics.
  4. Rothwell J. (1994). Control of human voluntary movement (2nd ed.) Chapman and Hall.

Speed, Accuracy, Movement Complexity

  1. Fitts P.M. (1954). The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental Psychology, 47(6), 381-391.
  2. Hick W.E. (1952). On the rate of gain of information. The Quarterly Journal of Experimental Psychology, 4(1), 11-26.
  3. Dassonville P., Lewis S.M., Foster H.E., & Ashe J. (1999). Choice and stimulus-response compatibility affect duration of response selection. Brain Research: Cognitive Brain Research, 7(3), 235-240.
  4. Favilla M. (1996). Reaching movements: Programming time course is independent of choice number. Neuroreport, 7(15-17), 2629-2634.

Parameters of generalized motor programs[edit | edit source]


  1. Armstrong 1970 as cited in Schmidt R.A., Lee T.D. (2005). Motor control and learning: A behavioural emphasis. Champaign, IL: Human Kinetics.
  2. Summers 1975 as cited in Schmidt R.A., Lee T.D. (2005). Motor control and learning: A behavioural emphasis. Champaign, IL: Human Kinetics.
  3. Shaprio 1977 as cited in Schmidt R.A., Lee T.D. (2005). Motor control and learning: A behavioural emphasis. Champaign, IL: Human Kinetics.


  1. Hollerbach 1978 as cited in Schmidt R.A., Lee T.D. (2005). Motor control and learning: A behavioural emphasis. Champaign, IL: Human Kinetics.
  2. Denier van der Gon and Thuring 1965 as cited in Schmidt R.A., Lee T.D. (2005). Motor control and learning: A behavioural emphasis. Champaign, IL: Human Kinetics.
  3. Schmidt et al. 1979 as cited in Schmidt R.A., Lee T.D. (2005). Motor control and learning: A behavioural emphasis. Champaign, IL: Human Kinetics.

Muscle selection

  1. Shapiro 1977 as cited in Schmidt R.A., Lee T.D. (2005). Motor control and learning: A behavioural emphasis. Champaign, IL: Human Kinetics.
  2. Keele, Jennings, Jones, Caulton, and Cohen (1995) as cited in Schmidt R.A., Lee T.D. (2005). Motor control and learning: A behavioural emphasis. Champaign, IL: Human Kinetics.

See also[edit | edit source]

References[edit | edit source]

  1. Adams J.A. (1971). A closed-loop theory of motor learning. Journal of Motor Behavior, 3, 111-150.
  2. Bastian A.J. Martin T.A., Keating J.G., and Thach W.T. (1996). Cerebellar ataxia: Abnormal control of interaction torques across multiple joints. Journal of Neurophysiology, 76, 492-509.
  3. Bastian A.J., Zackowski K.M., and Thach W.T. (2000). Cerebellar ataxia: Torque deficiency or torque mismatch between joints? Journal of Neurophysiology, 83, 3019-3030.
  4. Goodkin H.P., Keating J.G., Martin T.A., and Thach W.T. (1993). Preserved simple and impaired compound movement after infarction in the territory of the superior cerebellar artery. Canadian Journal of Neurological Science 20, Supplement, 3, S93-@104.
  5. James W. (1890). The principles of psychology (Vol. 1), New York: Holt.
  6. Kawato M. and Gomi H. (1992). A computational model of four regions of the cerebellum based on feedback-error learning. Biological Cybernetics, 68, 95-103.
  7. Mascheke M., Gomez C.M., Ebner T.J., and Konczak J. (2004). Hereditary cerebellar ataxia progressively impairs force adaptation during goal-directed arm movements. Journal of Neurophysiology, 91, 230-238.
  8. Nezafat R., Shadmehr R., Holcomb H.H. (2001). Long-term adaptation to dynamics of reaching movements: a PET study. Experimental Brain Research, 140, 66-76.
  9. Rothwell J.C., Traub M.M., Day B.L., Obeso J.A., Thomas P.K., and Marsden C.D. (1982). Manual motor performance in a deafferented man. Brain, 105, 515-542.
  10. Schmidt R.A. (1975). A schema theory of discrete motor skill learning. Psychological Review, 82, 225-260
  11. Schmidt R.A., Lee T.D. (2005). Motor control and learning: A behavioural emphasis. Champaign, IL: Human Kinetics.
  12. Shumway-Cook A., Woollacott M. (2000) Motor Control: Theory and Practical Applications, Second Edition. Baltimore, MD: Lippincott Williams & Wilkins.
  13. Smith M.A. and Shadmehr R. (2005). Intact a bility to learn internal models of arm dynamics in Huntington's disease but not cerebellar degeneration. Journal of Neurophysiology, 93, 2809-2821.
  14. Taub E., Goldberg I.A., and Taub P. (1975). Deafferentation in monkeys: Pointing at a target without visual feedback. Experimental Neurobiology, 46, 178-186.
  15. Topka H., Konczak J., Schneider K., Boose A., and Dichgans J. (1998). Multijoint arm movements in cerebellar ataxia: Abnormal control of movement dynamics. Experimental Brain Research, 119, 493-503.
  16. Wolpert D. M., & Kawato M. (1998). Multiple paired forward and inverse models for motor control. Neural Networks, 11, 1317-1329.

External links[edit | edit source]

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