Motor Control in Sport

Motor  control,  in  reference  to  movements  of  an organism or motions of a robot, is often conceived of as a computational problem. How is something or someone able to move to achieve various environmental  goals?  For  human  movement,  in  particular, the question of how individuals are able to organize the motor system at multiple levels (e.g., joints, muscles, neurons) defines the study of motor control.  In  this  way,  motor  control  is  a  solution that is arrived at by the individual, which satisfies numerous and sometimes competing goals (e.g., to remain balanced, to reach a hot cup without getting  burned,  to  avoid  obstacles,  to  move  quickly and accurately, to minimize energy expenditure, to avoid  injury  or  uncomfortable  positioning).  The problem  of  control  is  typically  conceived  at  multiple levels and is often distinguished with respect to  the  voluntary  or  intentional  nature  of  control versus a more automatic or reflexive level. In the former  case,  movements  are  goal-directed  and  at least  part  of  an  unfolding  movement  designed  to reach  the  goal  is  under  voluntary  control  of  the actor. In the latter case, movements are not under intentional  control  and  take  place  without  perceived  control  or  conscious  attention.  This  issue concerning  what  is  controlled,  prepared,  or  programmed in advance of a movement and what is controlled as a movement unfolds dominates much of  the  discussion  in  the  motor  control  literature. For seemingly very simple movements like balance and walking, to more complex movements that are considered  to  define  many  sporting  accomplishments, the problem of motor control is enigmatic.

Hierarchical Control

Many  of  the  processes  underlying  human  movement take place without explicit awareness on the part  of  the  actor,  but  many  movements  are  still voluntary.  That  is,  an  actor  makes  a  conscious decision to act and this desire ultimately leads to movement. Yet the exact parameters of the movement  are  usually  unknown  and  not  directly  controlled  by  the  actor.  Therefore,  motor  control  is the  problem  of  which  transformations  intervene between  the  thought  of  attaining  a  goal  and  the muscle   activations   that   result   in   movement. Models  and  theories  of  motor  control  differ,  but most  if  not  all  of  the  models  are  hierarchical  in nature,  suggesting  that  (a)  the  goal  of  the  task  is loosely  set  at  a  high  level  (sometimes  referred  to as the task level); (b) a sequence of movements is specified at a lower level (also known as response programming);  and  (c)  at  the  lowest  level,  a  specific pattern of muscle activation allows for execution (execution level). Although for clarity we treat these various levels as independent, it will be clear from the following discussion that these processes are  neither  serial  nor  mutually  exclusive  and  our distinctions  across  levels  serve  primarily  to  aid thinking  about  the  various  problems  involved  in controlling movement.

Intending and Planning to Act (Response Identification and Selection)

At the task level, the goal of the movement is specified consciously by the actor, although there is evidence that actions are substantially influenced by purely perceptual stimuli without conscious intention on the part of the mover (e.g., different types of  handles  afford  different  grasping  postures). At the task level, the issue of movement preparation  or  planning  is  important,  and  considerable research has been conducted to explore how movements  are  planned  in  advance  of  execution.  We have  learned  a  lot  about  movement  preparation through  simple  measures  of  reaction  time  (RT) (i.e., the time between an environmental event and the actual action). Defining when an action actually begins is not always a simple procedure. For some  people,  action  onset  is  defined  with  respect to a change in displacement of a limb in comparison to a baseline resting value, whereas movement onset  has  also  been  defined  with  respect  to  muscle  activation  as  measured  by  electromyography (EMG).  One  of  the  most  robust  findings  is  that RT increases in a predictable, linear fashion as the number  of  stimulus–response  (S–R)  alternatives increases,  so  termed  Hick’s  law.  Hick’s  law  is  a problem of response selection. As choice increases, the time needed to make an appropriate movement response will also increase.

Further  inferences  about  movement  planning based on measures of RT have been verified with more sophisticated recording techniques that identify  brain  areas  involved  in  movement  preparation.  For  instance,  areas  in  the  prefrontal  cortex(PFC)  are  particularly  involved  in  the  task-level processes  of  motor  control.  Human  neuroimaging studies show decreased activity in the PFC as a result of motor learning, suggesting that as a skill is practiced there is less reliance on explicit (attention demanding) control of the movement. When people  learn  a  sequence  of  movements  implicitly (i.e., without awareness of learning and sequence regularities),  activity  in  PFC  decreases  relative  to explicit  learning  conditions  (i.e.,  with  verbalizable  knowledge  of  the  sequence).  Similarly,  when implicitly  learned  regularities  in  the  sequence  are explicitly revealed, PFC activity increases to baseline levels. Damage to PFC has also been shown to reduce  conscious  control  of  movement,  whereby brain-lesioned  patients  use  or  respond  to  objects in a stereotyped manner that is inappropriate for a given context.

Significant  attention  has  also  been  paid  to the  role  of  vision  in  the  planning  and  execution of  actions.  One  neurophysiologically  supported theory  is  that  there  are  two  main  processing routes  in  the  brain  that  are  activated,  with  selection  depending  on  whether  actions  are  required toward an object. When individuals interact with objects  or  move  to  targets,  processing  of  visual information  is  assumed  to  be  primarily  governed by  a  dorsal  route  in  the  brain  (i.e.,  a  route  that involves areas of the brain near the top of the cortex,  particularly  the  posterior  parietal  cortex).  In contrast,  when  individuals  make  decisions  about a  target’s  position  or  identity,  ventral  route  processing  in  the  brain  is  involved  (involving  lower level  cortical  areas,  primarily  occipital-temporal cortex). Patients with temporal cortex lesions are severely impaired when visually identifying objects but show unimpaired tool use and object manipulation.  Conversely,  patients  with  parietal  cortex lesions   have   unimpaired   recognition   abilities but  significantly  impaired  reaching  and  pointing accuracy  (suggesting  a  deficit  in  calculating  spatial  locations).  This  double  dissociation  between recognition  in  the  temporal  lobe  and  perceptual integration  in  the  parietal  lobe  suggests  that  the ventral  and  dorsal  streams  of  vision  might  be differentially  involved  in  response  selection  and response programming, respectively.

Response Programming

The  history  of  response  programming  in  motor control  dates  back  to  the  late  19th  and  early20th  century.  Programming  is  thought  to  be  the process   preceding   voluntary   actions   whereby action  plans  are  organized  and  potentially  stored in  cortical  or  subcortical  structures  in  the  brain, ready to be released when a response is required. Evidence  for  programming  is  that  people  are unable  to  inhibit  actions  when  they  are  close  to their  release  point,  studies  of  RT  show  that  as complexity  of  a  movement  increases  so  does  RT, stereotyped movement features are observed when an  action  is  repeatedly  executed,  suggesting  that at  least  part  of  the  response  is  programmed  in advance and is not influenced by feedback (termed open-loop   control),   and   features   of   a   movement  remain  when  the  movement  is  unexpectedly blocked or is initiated early due to a startling acoustic stimulus (i.e., the start-react effect).

The term motor program has had varied usage in theories of motor control, although there is little consensus  as  to  what  this  motor  program  might be  and  even  whether  such  terminology  helps  in our understanding of motor control. Some of the discussion has been with respect to the specific or general  nature  of  motor  programs  (i.e.,  are  programs  for  throwing  specific  to  certain  distances, ball sizes, limbs), the neuroanatomical location of such  a  motor  program  (cortical,  subcortical,  spinal),  as  well  as  the  necessity  of  motor  programs, if one considers actions to be assembled on the fly in  response  to  real-time  constraints  in  the  environment  (i.e.,  the  principle  of  self-organization in  dynamical  systems  theory).  More  recently,  a language of control has emerged that is based on the idea of internal models, rather than programs. This language and associated ideas were primarily a result of computational motor control theorists who  emphasized  predictive  processes  occurring as  an  action  unfolds  (i.e.,  forward,  anticipatory models)  as  well  as  the  idea  of  control  processes that  arguably  function  like  motor  programs  (i.e., responsible  for  programming  motor  commands). Despite the debate about the nature or even existence of motor programs, it is evident that actions are  at  least  in  part  assembled  prior  to  action execution. The nature of the programming that is required is discussed next.

One of the first processes that must occur at the programming level is the translation of objects in allocentric  space  (the  relation  of  objects  in  space to  each  other)  into  egocentric  space  (the  relation of  objects  in  space  to  the  actor).  This  translation is  tremendously  important  for  sport.  Consider  asoccer player who is trying to connect their body to volley an incoming ball. Making contact with the ball depends on an accurate calculation of where the  ball  is  in  space,  where  the  player’s  foot  is  in space, and calculation of the relationship between the moving ball and the moving leg. Neurological evidence  suggests  that  multiple  egocentric  spatial frames exist centered around the head, the arm, or the leg depending on the effector that is being used in the action. Other physiologically driven research has led to the suggestion that rather than the whole trajectory  of  a  moving  limb  being  computed  in advance, that only the endpoint of the movement is  programmed.  Rather  than  individual  joints  or muscles being represented in the cortex, many features of a movement arise from the peripheral nervous  system  (rather  than  the  brain)  and  intrinsic properties of muscles and joints (e.g., equilibrium point theory). However, there is evidence that the sequence of programming is hierarchical in nature, based on such parameters as the extent and direction of a movement as well as the choice of limb required. It is not possible to program movement amplitude if one does not know the limb required to make the movement.

Programming time is affected by the complexity of the response required. Movements that involve more components or that are of a longer duration than  others  take  longer  to  assemble  or  program. This programming time is however influenced by the skill level of the actor. After extended practice, action–response  sequences  become  grouped  or unitized (requiring less time to program), whereas for novices these responses exist as small(er) movement  “chunks”  requiring  more  time  to  prepare or  assemble.  A  skilled  tennis  player  is  therefore expected to take less time to prepare a return lob in tennis than a more novice player, if for the novice  player  the  lob  is  conceived  as  a  sequence  of components potentially involving a backswing and forward swing phase.

Also  at  the  programming  level,  the  correct sequence  of  movements  needs  to  be  specified  in order to generate the correct endpoint and movement  effect.  As  motor  sequences  are  learned,  the movement  becomes  more  accurate,  faster,  and more automatic. Neurophysiologic data show that the  PFC  and  presupplementary  motor  area  (preSMA)  are  important  for  learning  new  sequences. As  these  sequences  are  learned,  however,  there is  a  decrease  in  these  areas  and  a  corresponding increase  in  activity  in  the  supplementary  motor area  (SMA)  and  primary  motor  cortex.  The  connection of these cortical structures to lower levels of the brain, (i.e., the basal ganglia and the thalamus)  creates  feedback  loops  that  are  important for  the  programming  of  movement  sequences  in a  goal-directed  fashion  (i.e.,  learning  to  calculate the  appropriate  “next-step”  given  a  current  task goal).  Interestingly,  activity  in  the  SMA  is  found when individuals physically perform an action and when they mentally imagine performing an action. This suggests that mental imagery may be an effective  way  to  practice  certain  aspects  of  a  motor skill  because  mental  imagery,  to  a  limited  extent, requires programming of the motor sequence.

Making the Movement

At  the  execution  level,  there  is  little  or  no  access to  awareness  and  conscious  control  as  this  level consists  of  detailed  specification  of  motor  unit recruitment  in  both  time  and  space.  The  type  of representation  used  at  this  level  is  not  clear,  but there  is  research  to  suggest  that  rather  than  programming individual muscle contractions or joint torques,  the  execution  level  relies  on  stereotyped relationships   and   patterns   of   muscle   activity referred to as synergies or “motor primitives.” For simple  movements,  much  of  this  execution-level programming appears to occur at the level of the spinal  cord.  Electrical  stimulation  of  the  spinal interneurons in animals leads to relatively smooth movements  of  the  whole  effector  toward  a  consistent  endpoint.  In  contrast,  stimulation  of  the alpha-motor  neurons,  the  next  level  below  interneurons, leads to motor unit contractions within a single muscle at a constant force dependent on the magnitude of stimulation.

Reflex  loops  and  hardwired  neural  pathways exist  at  the  execution  level  that  differ  depending on  the  type  of  control  being  examined.  That  is, there  are  specific  neural  pathways  that  generate relatively  consistent  patterns  of  muscle  activity across individuals that maintain balance and posture,  stabilize  patterns  of  locomotion,  or  coordinate  movements  of  the  eyes  relative  to  the  head in  order  to  maintain  gaze.  These  fast,  low-level motor control processes operate outside of awareness  and  can  be  very  difficult  to  override  if  they conflict  with  voluntary  motor  actions.  However, there is evidence that these pathways can be modified through experience and can operate in a goal directed  manner  (i.e.,  the  result  of  a  reflex  loop can  change  depending  on  the  current  goal  of  the motor system).

Sensory  feedback,  as  well  as  the  prediction  of what  sensory  feedback  will  look,  feel,  and  sound like, is critical at the execution level so that actions are  controlled  appropriately  to  achieve  specific functions  and  goals.  The  motor  system  has  its own  internal  error  correction  mechanisms  that facilitate  success  in  movement  and  learn  from experience to progressively fine-tune goal-directed actions. The exact process by which these modifications are made to the motor system is not clear, but  several  variables  must  be  calculated  in  order to  detect  errors  and  correct  the  transformations from task level to execution level. These transformations might be conceived of as internal models or  dynamic  biological  processes  involving  both predictive and control processes. In order to tune these  processes,  the  motor  system  must  represent the actual state of the system, the desired state of the  system,  and  the  predicted  “next  state”  of  the system. By comparing these different states, error signals  can  be  generated  that  tune  both  the  predictors  and  the  controllers.  Predictors  are  tuned through the comparison of the predicted state and the  actual  state  (i.e.,  Did  that  do  what  I  thought it would?). Controllers (or motor commands) are tuned  through  comparison  of  the  desired  state with the actual state.

It  appears  that  these  processes  associated  with internal models take place at a mostly subconscious level. Neuroimaging data show that when a movement sequence is explicitly attended to, there is an increase in the activity of the PFC and a decrease in the SMA (i.e., a shift to the neural structures that were involved early in the learning process) and a corresponding  reduction  in  the  speed  at  which  a movement or sequence of movements is produced. Behavioral evidence also suggests that attending to the details of movement, compared to attending to the desired goal, leads to worse performance and less efficient motor control.

One  final  consideration  for  movement  execution is critical task variables that appear to affect how  the  movement  is  executed.  For  example,  it is  well  established  that  movement  time  (MT)  is (predictably)  dependent  on  the  size  and  extent of  a  movement  required  (so  called  index  of  difficulty).  Large  movements  to  small  objects  are slower  to  execute  than  small  movements  to  large objects,  arguably  because  more  time  is  needed  to process  visual  feedback  to  accurately  hone  in  on the  object.  Measurement  of  movement  kinematics  (or  movement  process  measures),  such  as  displacement,  velocity,  and  acceleration  of  a  limb, has  allowed  researchers  to  infer  how  movements are  controlled,  based  on  sensory  feedback,  as  an action is executed.

“Offline” Success Evaluation

This  general,  hierarchical  framework  provides  a basis  for  understanding  how  a  command  makes the   transformation   from   thought   to   action. However, in order to achieve the desired goal accurately  and  reliably,  the  motor  system  needs  to  be able to modify (“tune”) these transformation processes based on sensory feedback errors about the success  of  an  action.  This  evaluation  can  happen after the movement in what is termed an “offline” fashion.  Correcting  subsequent  movements  can be an onerous task for the motor system because there  is  considerable  ambiguity  in  what  sensory errors  mean  from  a  motor  control  standpoint. Errors convey information about the success of a movement (e.g., hits vs. misses) and some feedback about what needs to be corrected in the movement (e.g.  a  miss  to  the  left  versus  the  right),  but  the error  does  not  specify  which  aspect  of  the  movement needs to be modified (the credit assignment problem)  or  when  in  the  movement  a  modification  needs  to  occur  (temporal  credit  assignment). This is where a coach can play an important role, using his or her personal experience and expertise to  identify  where  and  when  an  error  occurs  and then  provide  additional  (augmented)  feedback  to an athlete to fix the error.


  1. Frith, C. D., Blakemore, S.-J., & Wolpert, D. M. (2000). Abnormalities in the awareness and control of action. Philosophical Transaction of the Royal Society, 355, 1771–1788.
  2. Latash, M. A. (2012). Fundamentals of motor control. Boston: Academic Press.
  3. Milner, A. D., & Goodale, M. A. (2006). The visual brain in action (2nd ed.). Oxford, UK: Oxford Psychology Series.
  4. Rosenbaum, D. (2009). Human motor control (2nd ed.). San Diego, CA: Academic Press.
  5. Schmidt R. A., & Lee T. D. (2011). Motor control and learning: A behavioural emphasis (5th ed.). Champaign, IL: Human Kinetics.
  6. Willingham, D. B. (1998). A neuropsychological theory of motor skill learning. Psychological Review, 105,558–584.
  7. Wolpert, D. M., & Kawato, M. (1998). Multiple paired forward and inverse models for motor control. Neural Networks, 11, 1317–1329.

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