Motor Learning and Sport

Motor  learning  is  the  shaping  of  individual  sensorimotor  capabilities  by  the  physical  and  social environment. It is based on changes of neural networks of the brain that enable a relatively permanent  improvement  of  performance,  even  though this may not always be manifest. Motor learning is induced by experience or practice and can take place with the intention to learn but also without; indeed,  even  without  conscious  awareness  that something  has  been  learned.  In  this  entry,  three issues  are  addressed:  (1)  the  behavioral  changes that give evidence of motor learning, (2) the internal  representations  and  mechanisms  involved, and  (3)  the  practice  conditions  that  affect  motor learning.

Behavioral Changes

Motor  learning  is  associated  with  a  number  of behavioral  changes.  Prominent  among  them  are improvements  of  speed  and  accuracy,  increasing movement consistency, economy, and automatization.

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For many motor skills, higher speed means better  performance.  Exceptions  are  skills  for  which correct temporal patterns are defined, as in playing a musical instrument, or where movements have to be synchronized with environmental events, as in catching  balls.  The  improvement  of  speed  is  captured by the power law of practice, which is a good approximation  for  a  variety  of  skills.  According to  this  empirical  law,  the  time  needed  to  perform a particular action declines with the number of  repetitions  in  a  way  that  can  be  described by  a  power  function,  kN-α.  Across  the  first repetitions  the  decline  of  movement  duration  is rapid.  Later  on  it  becomes  progressively  smaller, and  finally,  it  can  hardly  be  detected  against  the background of random variations of performance. Diminishing returns in terms of improvements are a  fairly  universal  characteristic  of  practice  curves not  only  with  temporal  measures  of  performance but also with many other dependent variables. As a consequence, the early stages of practice are generally more rewarding than later stages.

The  increase  of  accuracy  in  space  and/or  time is  a  second  major  behavioral  change.  For  many tasks,  there  is  a  trade-off  between  speed  and accuracy—for  example,  in  movement  aimed  at a  target,  known  as  Fitts’s  law.  However,  motor learning  overcomes  this  trade-off.  Typically  accuracy increases in concert with speed. Nevertheless, there is an important difference between learning related changes of these two performance characteristics.  Accuracy  is  always  defined  with  respect to a specific target, but speed is mostly “the faster the  better”—that  is,  the  goal  is  maximization  (of course, maximization can also be a goal for spatial characteristics in tasks such as throwing a ball as far as possible).

With  increasing  number  of  repetitions  of  a motor  skill,  performance  tends  to  become  more consistent—that  is,  less  variable.  This  can  also represent an increase of accuracy. As an example, consider the task of throwing a ball over a distance of  10  m.  The  throws  will  have  a  mean  deviation from  the  target  (called  constant  error),  and  they will  also  vary  around  the  mean  (called  variable error). A reduction of the constant error typically requires  that  the  learner  has  information  on  the error, whereas a reduction of the variable error can result from simple repetitions.

The example of throwing a ball over a certain distance  does  also  illustrate  that  movement  strategies  can  be  adapted  to  increase  accuracy  of  the outcome, the constant and variable error of throwing  distance.  The  distance  covered  by  a  thrown object depends on its initial velocity and the angle of its initial flight path with the horizontal plane. For a certain distance, the required initial velocity is smallest for an initial angle of 45 degrees and for deviations from this angle it increases. Deviations from  the  strict  relation  between  the  initial  flight angle and velocity required for the target distance result  in  different  throwing  errors  depending  on the range of flight angles and velocities where they occur.  Thus,  performance  improvement  can  also result from a proper choice of strategy. More generally, for many motor skills there may be strategies for which variability in movement production has comparatively small effects on the outcome.

During practice of many motor skills, increasing economy  of  movement  production  can  be  experienced.  A  good  example,  which  unfortunately  is somewhat  outdated,  is  the  operation  of  a  scythe. This  is  an  exhausting  exercise  for  a  beginner, whereas  an  expert  can  do  it  smoothly  for  a  long time. Operation of the scythe requires exact guidance  of  the  instrument.  In  particular,  skills  that require  high  accuracy  are  initially  accompanied by  cocontractions.  These  are  concurrent  activations  of  opposing  muscles.  They  do  not  result  in net torques but serve to increase the impedance of the  limb  and  thus  make  it  more  resistant  against perturbations.  In  many  motor  skills,  cocontractions  decline  in  the  course  of  practice  so  that movements  become  more  economical  in  terms of  muscle  activity  involved  and  thus  in  terms  of energy consumption.

Finally, motor skills tend to become automatic after sufficient amounts of practice. The “autonomous”  phase  represents  the  final  stage  of  a  quite popular  conceptualization  of  skill  acquisition  by Paul Fitts. It follows the initial “cognitive” phase, in which motor patterns are produced with strong cognitive involvement, and the subsequent “associative” phase, in which the components are gradually  bound  together  or  associated.  Subjectively automaticity comes close to the motor skill running off by itself once it has been started. Functionally, automaticity is inferred from the absence of interference  by  concurrent  cognitive  activity.  Absence of  dual-task  interference,  however,  is  not  a  universal  phenomenon  with  highly  practiced  motor skills. First, the perturbation of a practiced motor skill by a concurrent cognitive activity depends on the nature of the activity. Second, in the literature, one can find examples that even apparently quite separated cognitive activities such as mental arithmetic interfere with motor skills such as standing (in older persons in particular) or filing of skilled precision mechanics—that is, with skills that seem fully automatic at first glance.

Representations and Mechanisms

In  the  course  of  motor  learning,  the  control  of movements is changed, which results in better performance.  There  are  underlying  changes  of  internal representations, and there are mechanisms that bring these changes about.

Types  of  representation,  which  are  acquired or  modified  during  motor  learning,  are  representations  of  correct  movements,  of  environmental characteristics,  and  of  transformations  the  motor system  has  to  master.  Representations  of  correct movements  have  been  posited  by  a  number  of theories.  For  example,  a  core  construct  in  the influential theory of Jack Adams is the perceptual trace.  This  is  a  representation  of  the  reafference of  the  correct  movement  that  is  claimed  to  serve as  a  reference  for  closed-loop  control.  A  number of theorists have posited (generalized) motor programs, which are representations of the motor outflow. These have been conceived as prestructured motor  commands  but  also  as  prototypical  force time profiles that can be varied in certain ways so that  different  variants  of  movements  of  a  certain type can be produced.

Representations   of   environmental   characteristics  have  received  only  little  attention  in  the motor-learning literature. For example, in playing a  musical  instrument,  one  can  learn  which  notes are to be played next, or in playing soccer, one can learn  what  a  certain  opponent  player  is  likely  to do next in a certain situation. Such representations are  involved  in  anticipation,  and  they  can  contribute  to  pretty  extensive  changes  of  perception. They  also  allow  to  prepare  movement  sequences in advance and to form chunks of the elements of a  sequence,  such  as  in  typing,  which  can  then  be produced  in  rapid  succession.  Sometimes  representations  of  environmental  regularities  are  hard to distinguish from representations of movements. For example, skilled typing involves chunks of finger movements that are produced in rapid succession, but at the same time, these movements reflect the environmental regularity of the letter sequence.

Representations  of  transformations  are  generally referred to as internal models. To clarify what is  meant  by  transformations,  consider  again  the example of throwing a ball. Muscles are activated, forces of various muscles are generated and combined, joints are rotated, the hand is moved along a  certain  path  with  a  certain  velocity,  and  finally the  ball  flies  a  certain  distance.  To  produce  the proper hand movement and to release the ball at the  right  time  requires  a  fairly  complex  internal model  of  the  transformations  on  the  way  from muscle  activation  to  ball  flight.  More  precisely, what  is  required  is  an  inverse  model  that  specifies the proper input to the transformation (muscle activity) that results in the desired output (flight of the ball over 10 m). However, the forward model is also useful in that it allows rapid predictions of the outcome of a movement. In the example, it allows prediction  of  the  success  of  the  throw  before  the ball has even left the hand.

A  popular  variant  of  the  notion  of  internal models  is  provided  by  Richard  Schmidt’s  schema theory,  which  is  actually  several  years  older  than the  notion  of  an  internal  model.  According  to this theory, a representation called motor schema establishes a link between desired outcomes of an action (e.g., the desired amplitude of a throw) and the  parameters  of  a  generalized  motor  program (GMP) (e.g., the overall force level of a throwing movement or its total duration). One of the major predictions of schema theory is that variable practice should be more beneficial than practice under constant conditions. The reason for this prediction is  basically  that  to  learn  a  relation  between  variables  one  has  to  encounter  a  range  of  these  variables and not just single values.

Sufficiently accurate (inverse) internal models of the transformations involved in motor control are a prerequisite of open-loop control. In fact, motor learning  has  been  characterized  as  a  progression from closed-loop to open-loop control and also as a  progression  from  the  use  of  visual  feedback  to the use of proprioceptive feedback. Such progressions may indeed be found for certain motor skills, but there are also motor skills that remain dependent on visual feedback (as well as proprioceptive feedback) even after prolonged practice. Balancing a vertical rod on the tip of the index finger is an example.

Regarding  the  mechanisms  involved  in  motor learning, repetition effects, error-based corrections, reinforcement, motor resonance, and consolidation are  among  the  important  ones.  Historically,  the existence  of  repetition-based  (or  use-dependent) learning without any feedback about the outcome of the action has been doubted, but this is clearly unjustified.  Pure  repetition  can  serve  to  organize visual input (unsupervised learning), for example. Also movements will become faster and less variable. A basis for such changes might be facilitation within the neural networks that are involved in the production of a certain movement and in the perception of the relevant environmental conditions.

Error-based  learning  and  reinforcement  learning are sometimes hard to distinguish, and in the early 20th century, the distinction was uncommon. A concept that combines these two types of learning is knowledge of results (KR), which is typically provided after the end of a movement. It can take various formats. For example, it can be evaluative (“good,” “poor”), or it can be informative by way of indicating the precise error (“20 cm too short”).

In  the  first  case  there  is  likely  reinforcement,  an associated  neural  event  that  basically  strengthens what has been rewarded (“good”). What has not been reinforced is likely to be changed in the next attempt,  and  without  further  information,  the change  will  be  more  or  less  random.  In  the  case of  informative  KR,  there  is  error-based  learning in that the next attempt can be modified in a way that  compensates  the  error  indicated.  In  spite  of their similarity, these two mechanisms of learning invoke different neural structures.

A currently quite popular mechanism of motor learning  is  motor  resonance.  This  mechanism  is crucial  for  observational  learning.  Humans  can observe  movements  produced  by  other  people, and  often  they  can  reproduce  these  movements immediately. The popularity of this kind of learning mechanism has been boosted by the discovery of  mirror  neurons  in  monkeys.  These  neurons are  active  not  only  when  a  certain  movement  is produced  but  also  when  the  same  movement is  observed.  In  humans,  overlapping  patterns  of activated  brain  areas  have  been  observed  in  producing and observing movements and also in producing  and  imaging  them.  Thus,  observation  of a certain movement is likely to activate at least a subset  of  those  neurons  that  are  also  involved  in its  production—the  motor  system  “resonates”  in response to the visual input.

A   final   mechanism   is   consolidation.   Consolidation  refers  to  neural  changes  that  serve  to stabilize or even improve what has been practiced before. It is invoked when it comes to an improvement of motor performance after a break or after a night of sleep. It is also invoked when the question is addressed whether the acquisition of a new internal  model  of  a  transformation  overrides  an older internal model or is added to it. In any case, consolidation is the most comfortable mechanism of  motor  learning  in  that  it  does  not  require  any activity of the learner.

Practice Conditions

From the various representations and mechanisms involved in motor learning, a number of principles for the design of practice conditions are obvious. Motor  learning  requires  information  to  enable error-based  learning  and  evaluative  feedback  to enable  reinforcement  learning,  variability  should facilitate  the  acquisition  of  internal  models  of motor transformations, and sufficient spacing can bring  in  consolidation  and  serve  to  prevent  muscle  fatigue  as  well  as  mental  fatigue.  Among  the less obvious design principles are the avoidance of over-optimization, the use of imagery and observation of the motor skill, and the proper direction of the focus of attention.

In  order  to  optimize  practice  conditions  and to  obtain  a  rapid  improvement  of  performance several  measures  can  be  taken.  Some  of  them can  produce  immediate  unintended  (and  perhaps counterintuitive) effects. For example, error-based learning can be facilitated by sufficiently accurate error information. When errors are presented visually, for example, they can be amplified; when they are  presented  numerically,  they  can  be  given  in smaller  rather  than  larger  units.  The  risk  of  such measures  is  that  the  scale  of  error  information exceeds the precision of movement production. In the  extreme  case,  when  movements  are  basically accurate  and  vary  only  randomly,  the  random errors  are  fed  back  and  learners  try  to  correct them, which is doomed to failure. It only has the effect of increasing variability.

Unintended  effects  of  optimized  practice  conditions  can  also  become  visible  after  a  delay;  for example, when the motor skill has to be performed under  real-life  conditions  after  augmented  feedback has been removed. Performance can become dependent  on  augmented  feedback  and  break down  when  augmented  feedback  is  no  longer available. In addition, optimization of continuous visual feedback for closed-loop control can impede the acquisition of internal models that are needed for  open-loop  control.  In  recent  years  more  and more robots have been designed to support motor (re-)learning, in particular in neuro-rehabilitation. Haptic guidance provided by them results in high levels  of  performance.  It  also  demonstrates  how the correct movement feels. But it prevents active generation  and  shaping  of  motor  commands  as well  as  active  error  corrections.  Therefore,  it  can impede rather than facilitate motor learning.

Motor imagery and movement observation can serve to improve motor performance. The reason is that both imagery and observation share neural structures  with  actual  movement  production.  In a  certain  way,  they  are  similar—and  functionally equivalent—to  central  processes  of  overt  motor behavior.  In  general,  observational  and  mental practice  are  less  efficient  than  physical  practice, but  combinations  of  the  different  types  of  practice  can  be  superior  to  physical  practice  alone.

Observation  can  offer  the  opportunity  to  notice aspects  of  performance  that  remain  unnoticed when  one  moves  oneself.  Imagery  offers  possibilities of slow motion and the exploration of skill variants that might even be physically impossible. Alternation of periods of observational or mental practice  with  periods  of  physical  practice  allows the  combination  of  the  respective  advantages  of the different procedures.

Finally,  during  the  last  several  years,  a  critical role  of  the  distribution  of  attention  for  motor learning has been documented. Basically an external focus of attention results in superior learning as compared with an internal focus. The external focus  is  on  movement  outcomes—for  example, the  swing  of  the  golf  club—whereas  the  internal focus  is  on  the  moving  limbs;  that  is,  the  movements of the arms. Most likely the benefits of an external focus of attention are related to the fact that motor control typically starts with a desired outcome  (e.g.,  a  throw  of  a  certain  distance), and  motor  commands  are  selected  according  to an  internal  model  that  has  been  acquired  during  motor  learning.  In  the  performance  of  many motor skills awareness of the details of the movements  is  quite  limited,  and  an  internal  focus  of attention  (and  thus  the  attempt  to  voluntarily control  details  of  muscular  contractions)  may actually interfere with the required delicate timing of  motor  commands  or  other  aspects  of  proper motor outflow.


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