Skill Acquisition

Acquisition of skill is a type of learning in which repetition  results  in  enduring  changes  in  an  individual’s capability to perform a specific task. With enough repetition, performance of the task eventually  may  become  automatic,  with  little  need  for conscious  oversight.  Any  behavior  that  needs  to be learned and that is improved by practice can be considered to be a skill. Making a cup of tea can be  considered  a  skill,  just  as  is  drinking  it.  Skill acquisition in sport generally can be thought of as either  learning  to  coordinate  the  body  appropriately  to  achieve  an  intended  movement  outcome or  as  learning  any  of  the  myriad  mental  aspects associated with effective movement, such as where to  move  and  when.  A  highly  skilled  goalkeeper who saves a penalty kick has learned not only how to fling his body most effectively around the goalmouth to keep the ball out but also when and in which direction to fling his body. In most sports, these  different  facets  of  skill  cannot  be  viewed in  isolation,  as  it  is  the  combined  synchrony  of perceptual,  cognitive,  and  motor  processes  that culminates   in   highest   levels   of   performance. Performance of the skill will improve over time as a consequence of repeated engagement in the task (i.e., perhaps deliberate, perhaps not) and will be accompanied by increasingly consistent attainment of  the  desired  outcome,  which  eventually  can  be achieved  in  a  variety  of  conditions,  contexts,  or environments.  For  example,  a  skilled  golfer  may consistently hit the green from 150 yards and have a higher likelihood of doing so in the pouring rain or when it really matters than an unskilled golfer. A skilled performer can thus be defined as a person with  the  ability  to  achieve  an  intended  outcome repeatedly  in  a  variety  of  conditions  and  usually with  less  physical  or  cognitive  effort  than  a  less skilled performer.

The  study  of  skill  acquisition  dates  to  work that  investigated  how  field  operators  acquired telegraphic language skills. At the turn of the 20th century,  the  effectiveness  of  telegraphy  as  a  form of  rapid  long-distance  communication  relied  on operators  to  be  highly  skilled  at  the  perceptual, cognitive,  and  motor  aspects  involved  in  transmitting,  receiving,  and  translating  Morse  code. The work, which examined skill level as a consequence  of  experience,  showed  that  variability  of performance decreased as operators became more skilled,  and  that  their  performance  was  less  easily disrupted by outside distractions. Although the work showed that it generally took between 2 and 3  years  for  operators  to  become  skilled,  the  progression of learning was not continuous. Plateaus occurred   during   which   performance   barely changed, only progressing if the operator did not become despondent and discontinue practice. The researchers  argued  that  performance  could  not progress to a higher level until attention was freed by  the  automatization  of  lower  level  responses within a hierarchy of habits.

Although plateaus in performance level are not always  observed  over  the  course  of  skill  acquisition, one characteristic that is observed more often than  not  is  a  high  rate  of  change  in  performance level early in practice, with gradual reductions in rate  of  change  as  practice  continues.  This  power law of practice has the function log C = log B + n log x, where C refers to performance level, x refers to amount of practice, and B and n are constants.

There  is  little  doubt  that  the  level  of  performance that can be achieved when acquiring skills is  closely  related  to  the  extent  to  which  a  person practices  the  skill  and  the  way  in  which  the  person  practices.  Researchers  often  use  retention  or transfer tests to determine whether the changes in performance caused by practice are temporary or permanent.  By  assessing  performance  sometime after practice has been completed, perhaps a day or a week, it is possible to observe true performance level without temporary practice effects related to factors such as motivation, fatigue, or boredom.

Researchers  have  examined  many  methods designed  to  maximize  the  enduring  changes  in behavior  that  practice  must  produce  for  skill acquisition  to  occur.  For  example,  early  work examined  whether  it  was  better  to  practice  the different parts of a task independently or together as  a  whole  task.  Findings  were  mixed,  and  it  is probably  fair  to  say  that  no  consensus  emerged. The effects of massed and distributed practice have been examined at length. Massed practice involves continuous  practice,  whereas  distributed  practice involves  the  spacing  of  practice  over  time.  One meta-analysis  of  116  studies  suggested  that  distributed practice is preferable for continuous tasks but massed practice is preferable for discrete tasks. A  well-known  phenomenon  is  the  contextual interference  effect,  in  which  interference  appears to  cause  poor  performance  during  practice  but good  performance  during  retention  and  transfer. In particular, the effect is seen when interference is caused by switching randomly between skills when practicing, as opposed to practicing each skill one after  the  other  in  a  blocked  pattern.  Two  explanations have been proposed for the phenomenon. The  elaboration  hypothesis  suggests  that  random practice  of  different  skills  requires  performers  to actively  retrieve  movement  solutions  each  time that they perform, which results in a more distinct representation of each skill in memory. The reconstruction  hypothesis  suggests  that  switching  from one skill to another during random learning causes performers  to  forget  the  previous  movement  pattern.  Consequently,  performers  must  continually reconstruct the movement as they practice. A fascinating  adjunct  to  the  contextual  interference effect has been added in recent years by the concept  of  differential  learning,  which  suggests  that a  significant  learning  advantage  is  gained  from maximizing  interference  during  practice  by  introducing  stochastic  perturbations  of  performance. In essence, it is argued that a person learns more by  trying  to  achieve  the  same  skill  outcome  with maximum differences in the manner of execution.

Many  factors  influence  skill  acquisition.  The difficulty of the skill to be acquired, and the availability  of  information  or  the  form  in  which  the information  is  available,  play  a  significant  role  in the  process.  Information  about  potential  movement solutions can be acquired by instructions or guidance from an external agent or by observation or  modeling.  Much  research  has  examined  the effects  of  feedback  on  skill  acquisition.  Intrinsic feedback  provides  sensory  information  that  arises from executing a skill. Visual feedback often seems the most salient, but feedback from the other senses also  plays  a  part  in  skill  acquisition.  Extrinsic feedback  provides  augmented  information  about performance  of  a  skill,  often  supplementing  the information that is available via intrinsic feedback. In particular, knowledge of results and knowledge of performance are crucial types of extrinsic feedback during skill acquisition. Knowledge of results is   extrinsic   information   about   the   movement outcome,  whereas  knowledge  of  performance  is extrinsic information about the movements themselves.  The  information  in  both  instances  is  usually  provided  to  the  performer  verbally  (e.g.,  you missed left or keep your head still) or perhaps by video replay or even diagrams. Most people accrue much information by a process of hypothesis testing in which they devise and test strategies to correct  performance  errors.  There  are  few  cases  in which learning takes place in the absence of at least some intrinsic or extrinsic feedback. The challenge point framework proposes that what is learned is a function of the information that becomes available during  performance  and  that  the  crucial  factor  is the interaction between the difficulty of the skill to be learned and the level of ability of the performer.

The Cognitive Approach to Skill Acquisition

Cognitive characterizations of the human brain as a processor of information argue that information received by the senses contributes to the development  of  centralized  representations  in  memory, which  are  used  to  control  performance  using preprogrammed  commands  that  run  without  the need  for  modification  from  feedback  (i.e.,  open-loop  control)  or  from  continuous  comparison  of ongoing  feedback  with  representations  generated by previous movements (i.e., closed-loop control). A  significant  problem  for  these  theories  was  that a representation needed to be stored for each different  movement,  creating  a  storage  problem. Subsequently,  schema  theory  was  proposed  as  a solution  to  the  storage  problem.  Schema  theory proposed that a general motor program represents a class of movements (e.g., throwing), but that different  submovements  are  produced  by  specifying the  necessary  parameters  at  the  time  (e.g.,  speed or force).

The  cognitive  approach  suggests  that  during practice movement representations can be updated and  improved—using  knowledge  of  results  or knowledge  of  performance,  for  instance.  As  a consequence,  performance  efficiency  improves. An  outcome  of  this  process  is  that  skill  learning progresses  through  a  sequence  of  stages  characterized  by  fairly  distinct  cognitive  characteristics. At  first,  when  the  skill  has  been  little  practiced, performance  tends  to  be  cognitively  controlled as the learner consciously tries to select strategies to  use  and  the  relevant  information  to  attend  to. Performance  tends,  therefore,  to  be  explicit,  inefficient,  and  variable.  A  second  stage  follows  in which  the  intense  need  for  conscious,  cognitive involvement begins to ease. Strategies are cemented and  stimulus–response  (S–R)  associations  begin to  strengthen.  A  final  stage  is  characterized  by performance  that  requires  very  little  cognitive engagement.  Performance  is  consistent,  efficient, and  can  occur  automatically  without  conscious intervention. These characteristics are common in many  psychological  theories  of  skill  acquisition, although sometimes the theories differ with respect to why practice improves performance.

Some theories (e.g., instance theory) are predicated  on  the  belief  that  practice  increases  the number   of   available   memory   representations that  can  be  accessed  to  support  performance, whereas other theories (e.g., adaptive components of  thought  [ACT]  theory)  are  predicated  on  the belief  that  practice  refines  strategies  and  procedures  that  support  performance.  These  theories often  make  a  distinction  between  declarative  and procedural knowledge that is associated with performance.  Declarative  knowledge  can  be  thought of  as  explicit  facts  or  propositions  that  describe information   related   to   performance   and   that can  be  described  by  the  performer,  whereas  procedural  knowledge  can  be  thought  of  as  implicit representations of how to perform, which cannot be  described  by  the  performer.  Use  of  declarative knowledge  to  support  skill  performance  tends  to require  processing  resources  and  is  effortful,  but eventually practice allows skills to be executed as procedures, with little or no reference to declarative knowledge. In such cases, far fewer processing resources  are  used,  and  performance  tends  to  be more  effortless  than  effortful.  Researchers  who have  used  implicit  motor  learning  techniques  to inhibit the accumulation of declarative knowledge during  skill  acquisition  claim  that  this  promotes the  benefits  associated  with  use  of  procedural knowledge to support performance.

An Ecological–Dynamical Approach to Skill Acquisition

The  ecological  approach  proposes  that  information  that  resides  in  the  environment  specifies  the options  that  are  available  for  behavior  by  an organism  (i.e.,  affordances).  Actions  and  perceptions are coupled, and movements can be initiated without  reference  to  memory  representations  or motor programs, using only information that specifies the constraints on movement. Practice is seen as a process of educating attention to pick up the specifying  information  rather  than  the  nonspecifying  information  in  the  environment.  In  tandem with the ecological approach, a dynamical systems theory  of  movement  coordination  has  evolved  in which  biological  organisms  are  seen  as  systems that can self-organize in response to existing constraints  or  parameters,  and  that  are  attracted  to stable patterns of coordination. This view emerged as a way to understand the complexities presented by a human motor apparatus that has evolved with countless ways in which to move. Nicolai Bernstein called this the degrees of freedom (df) problem and proposed that skill acquisition is a matter of solving the df problem. He argued that initially movement control is simplified by locking various joints in  order  to  reduce  many  of  the  df.  Gradually  the df are released during practice and the performer becomes able to control or exploit energetic forces, such  as  momentum  or  inertia,  during  movement. This  approach  provides  a  perspective  to  skill acquisition that has yet to be fully reconciled with traditional cognitive approaches in sport.


  1. Bernstein, N. A. (1967). The coordination and regulation of movement. London: Pergamon.
  2. Davids, K., Button, C., & Bennett, S. (2008). Dynamics of skill acquisition: A constraints-led approach. Champaign, IL: Human Kinetics.
  3. Fitts, P. M., & Posner, M. I. (1967). Human performance. Belmont, CA: Brooks/Cole.
  4. Gibson, J. J. (1979). The ecological approach to visual perception. Boston: Houghton Mifflin.
  5. Kelso, J. A. (1984). Phase transitions and critical behavior in human bimanual coordination. American Journal of Physiology Regulatory Integrative and Comparative Physiology, 15, 1000–1004.
  6. Kugler, P. N., Kelso, J. A. S., & Turvey, M. T. (1980). On the concept of coordinative structures as dissipative structures: I. Theoretical lines of convergence. In G. E. Stelmach & J. Requin (Eds.), Tutorials in motor behavior (pp. 3–47). New York: North-Holland.
  7. Masters, R. S. W., & Poolton, J. (in press). Advances in implicit motor learning. In A. M. Williams & N. J. Hodges (Eds.), Skill acquisition in sport: Research, theory and practice (2nd ed.). London: Routledge.
  8. Newell, A., & Rosenbloom, P. (1981). Mechanisms of skill acquisition and the law of practice. In J. R. Anderson (Ed.), Cognitive skills and their acquisition. Hillsdale, NJ: Lawrence Erlbaum.
  9. Schmidt, R. A. (1975). A schema theory of discrete motor skill learning. Psychological Review, 82, 225–260.

See also: