Knowledge Structure and Sport

Knowledge  about  tasks  and  the  environment  is organized  hierarchically  in  the  human  cognitive system,  involving  the  diverse  long-term  memory (LTM) systems and working memory. Knowledge structures  underlying  task  performance  are  fundamental  elements  of  action  control  in  sports. Experts’  skills  are  often  based  on  the  efficient access  to  relevant  task  knowledge  as  well  as  on enhanced  physical  abilities.  In  the  history  of research in general psychology, motor control, and sport psychology (SP), knowledge structures have been addressed in different ways, focusing on their significance  for  task  performance  from  different perspectives.

Structured Motor Programs, Schemata, and Events

In the field of motor control, Richard A. Schmidt’s generalized motor program (GMP) theory has been one of the most prevalent theories used to explain how we control coordinated movements and how we therefore develop structured plans and knowledge.  GMP  theory  is  based  on  feedback-based models, such as James A. Adams’s closed-loop theory of motor learning, and adopts the concept of schema,  making  it  more  adaptable  and  thereby  a more realistic explanation of motor learning, especially  as  it  occurs  in  sporting  contexts.  Schmidt describes GMPs as templates that serve as abstract plans for basic movements. GMPs can be adapted to individual situations by modifying variant and invariant parameters. Parameters such as absolute timing,  absolute  force,  and  building  blocks  (individual  muscle  actions)  within  the  GMP  can  vary (variant  parameters).  In  contrast,  the  sequence, relative timing, and relative force of these building blocks are thought to remain invariant.

Schmidt uses the term schemata (recall schema, recognition  schema)  to  explain  how  movements can be learned on the basis of GMPs. Support for a  structured  organization  comes  from  research investigating  the  underlying  neuronal  structures of  movement.  According  to  these  studies,  the basic  definition  describes  organization  principles in  action  and  perception  that  are  based  on  neuronal  structures  but  include  functional  levels  of behavior  and  cognition  up  to  and  including  language.  According  to  Michael  A.  Arbib’s  theory, motor schemata and perceptual schemata interact in  complex  ways  but  are  coordinated  by  control programs that generate and control action performance on a higher level.

More   recently,   event   segmentation   theory, developed  by  Jeffrey  M.  Zacks,  has  provided  an interesting  approach  to  studying  structure  and modularity in perception and knowledge memory, showing  that  the  way  in  which  a  person  spontaneously  segments  an  observed  action  determines the  understanding  and  later  memorizing  of  this action. This approach can be well applied to sporting contexts. When watching a basketball match, we  spontaneously  segment  the  observed  stream of movements performed by the different athletes into meaningful actions. Our understanding of the scenario is based on the way we store an event in memory and segment what we see, the number of segmentation  points,  the  choice  of  cues,  and  so on. Naturally, the quality of our understanding is strongly influenced by our own experience and our structured knowledge about the type of action.

Declarative and Procedural Knowledge Structures

A  very  interesting  and  often-cited  distinction  of knowledge  quality  and  structure  has  been  provided  in  the  adaptive  components  of  thought (ACT)  theory  by  John  Anderson,  a  psychologist at Carnegie Mellon University. ACT and later versions  such  as  ACT-R  have  been  inspired  by  the idea of Allen Newell to develop a cognitive architecture of knowledge. Such a cognitive architecture is computationally implemented in a technical system and is able to simulate a wide range of tasks. ACT is based on production systems. As do other psychological  theories,  it  differentiates  between declarative knowledge and procedural knowledge. Knowledge about facts (e.g., a BMW is a German car)  has  been  called  declarative  knowledge.  The relevant knowledge elements are facts and events. This knowledge has been characterized as explicit knowledge   and   as   knowing   that.   Procedural knowledge  about,  for  instance,  bicycle  riding  is implicit knowledge about how to perform a task. It has been therefore characterized as knowing how. Both  declarative  and  procedural  knowledge  are stored in LTM and are related to each other especially  in  the  learning  process.  Neurophysiological research has shown that their storage and retrieval are based on different brain structures.

Anderson  has  described  the  development  of knowledge structures within three phases of motor skill learning. The first phase is characterized as a cognitive phase. In this phase, the learning agents are developing a declarative encoding of the motor skill. They store a lot of facts and explicit elements of  the  learning  process  in  memory.  The  second phase is an associative phase. In this phase, single elements  of  procedural  and  declarative  knowledge  become  more  strongly  interconnected,  and motor production rules are created. In this phase, it is possible to perform some motor acts step by step and to develop a declarative frame for motor activities. The last phase is the autonomous phase. In this phase, the motor procedures become faster and more and more automated.

Based  on  perspectives  coming  mostly  from the  ACT  theory,  a  number  of  researchers  have addressed the question of how declarative knowledge is structured and networked in sport actions. The  major  issue  of  relevance  in  the  SP  domain is  whether  we  can  confirm  that  improved  performance  is  accompanied  by  a  higher  degree  of order  formation  in  the  sense  of  knowledge  structuring  and  hierarchies.  Research  has  therefore been  undertaken  to  confirm  expertise-dependent differences  in  the  classification  and  representation  of  context-specific  problem  states  in,  for instance,  springboard  divers,  judokas,  triathletes, and  weight  lifters.  Research  on  springboard  diving has revealed that the nodes of the representation structures in experts possess far more features than  those  of  novices.  This  result  replicates  findings  in  the  problem-solving  domain.  Likewise, expert springboard divers show a greater number of  connections  between  nodes,  just  as  do  experts in  problem  solving.  An  interesting  study  about the  development  of  declarative  knowledge  structures  and  performance  in  sports  was  conducted by  Karen  E.  French  and  Jerry  R.  Thomas.  They assessed various components of basketball performance (e.g., control of the basketball and cognitive decisions, dribbling and shooting skills) along with declarative  knowledge  in  children  ages  8  to  12. Declarative knowledge was measured via a paper and-pencil  test.  Results  confirmed  relationships between  knowledge  and  the  decision  component of  performance,  suggesting  that  knowledge  plays an important role in skilled sport performance.

Studies  using  categorization  tasks  have  shown that   experts   classify   problems   according   to underlying  functional  principles,  whereas  novices operate  more  strongly  with  superficial  features. Furthermore,  questionnaire  methods  and  interviews have revealed the structure and organization of movement knowledge in sports.

Mental Knowledge Representation and Hierarchies in Memory

The  idea  that  actions  are  mentally  represented in  functional  terms  as  a  combination  of  action execution  and  the  intended  or  observed  effects  is well  established  in  cognitive  psychology  and  has received growing acceptance in the fields of motor control  and  SP.  Perceptual-cognitive  approaches, such as the ideomotor approach to action control, propose that motor actions are formed by mental knowledge   representations   of   target   objects, movement  characteristics,  movement  goals,  and the  anticipation  of  potential  disturbances.  David A.  Rosenbaum  and  coworkers  demonstrated  that movements  can  be  understood  as  a  serial  and functional order of goal-related body postures, or goal  postures,  and  their  transitional  states.  The link  between  movements  and  perceptual  effects is  bidirectional  and  based  on  information  that  is typically stored in a hierarchical fashion in LTM. Complex  movements  can  be  conceptualized  as  a network  of  sensorimotor  information.  The  better the  order  formation  in  memory,  the  more  easily information  can  be  accessed  and  retrieved.  This leads  to  increased  motor  execution  performance, which  reduces  the  amount  of  attention  and  concentration  required  for  successful  performance. The  nodes  within  this  knowledge  network  contain  functional  subunits  or  building  blocks  that relate to motor actions and associated perceptual (including related semantic) content. These building  blocks  can  be  understood  as  representational units  in  memory  that  are  functionally  connected to perceptual events or as functional units for the control  of  actions,  linking  goals  to  perceptual effects of movements.

Research  in  complex  actions  in  sports,  dance, rehabilitation,  and  manual  action  has  demonstrated that basic action concepts (BACs) are fundamental building blocks at the cognitive level of representation. BACs are based on the chunking of body postures related to common functions in the realization of action goals and are conceptualized as  representational  units  in  LTM  that  are  functionally connected to perceptual events. From this point  of  view,  action  control  is  organized  as  perceptible event through a structures representation of anticipated characteristic (e.g., sensory) effects, with  the  corresponding  motor  activity  automatically and flexibly tuned to serve these effects.

The integration of representation units, like for instance  BACs,  into  structures  of  representation has  been  studied  with  a  wide  range  of  methods. Based  on  a  new  experimental  approach,  Thomas Schack  and  Franz  Mechsner  studied  the  tennis serve to investigate the nature and role of LTM in skilled athletic performance. In high-level experts, these representational frameworks were organized in a distinctive hierarchical treelike structure, were remarkably similar between individuals, and were well matched with the functional and biomechanical  demands  of  the  task.  In  comparison,  action representations in low-level players and nonplayers were organized less hierarchically, were more variable between persons, and were less well matched with functional and biomechanical demands.

The  results  of  different  studies  in  golf,  soccer, windsurfing,  volleyball,  gymnastics,  and  dancing have  shown  that  the  mental  representation  structures in place relate clearly to performance. As different  studies  have  shown,  these  representations are  also  position and  thereby  task-dependent. These  representation  structures  are  the  outcome of  an  increasing  and  effort-reducing  formation of  order  in  LTM.  This  order  formation  reveals  a clear  relation  to  the  structure  of  the  movement. With increasing expertise, the representation of the movement corresponds more and more exactly to its  topological  (spatiotemporal)  structure.  At  this level, the representation has nothing to do with a muscle-oriented  effector  code.  Evidently,  the  representation structures are formed through the sensory  movement  effects  of  distinctive  node  points (body  postures)  of  the  movement.  Therefore,  the representation structure itself possesses spatiotemporal properties, corresponding with the structure of the movement. Accordingly, movement control becomes  possible  by  representing  the  anticipated intermediate effects of the movement and comparing  them  with  incoming  effects.  Importantly,  this also means that no special translation mechanism is  required  between  perception,  representation, and movement.

Results   from   another   line   of   experimental research  have  showed  that  not  only  the  structure formation  of  mental  representations  in  LTM  but also chunk formation in working memory is built up  on  BACs  and  relates  systematically  to  movement  structures.  These  studies  have  revealed  a plausible  relation  between  chunking  and  priming processes in working memory and the structure of human movements, suggesting a movement-based chunking. Such findings provide experimental evidence  that  structures  in  movement  and  memory mutually overlap.

These  results  and  perspectives  are  of  central meaning for the understanding of cognitive learning and coaching processes. A disadvantage of traditional procedures in mental training (imagery) is that they try to optimize the performance through repeated  imagination  of  the  movement  without taking  the  athlete’s  mental  knowledge  representation  into  account  (i.e.,  they  are  representation blind). Problems arise if the movement’s cognitive reference  structure  has  structural  gaps  or  errors, as  these  will  tend  to  be  stabilized  rather  than overcome  by  repeated  practice.  The  alternative approach  is  to  measure  the  mental  representation of the movement before mental training and then integrate these results into the training. This mental training based on mental representations has now been  applied  successfully  for  several  years  in  professional sports such as golf, volleyball, gymnastics, and windsurfing and recently in the rehabilitation of hand functions in patients after stroke.

References:

  1. Adams, J. A. (1971). A closed-loop theory of motor learning. Journal of Motor Behavior, 3, 111–150.
  2. Anderson, J. R. (1976). Language, memory, and thought.Mahwah, NJ: Lawrence Erlbaum.
  3. Arbib, M., Conklin, E., & Hill, J. (1987). From schema theory to language. New York: Oxford University Press.
  4. French, K. E., & Thomas, J. R. (1987). The relation of knowledge development to children’s basketball performance. Journal of Sport Psychology, 9, 15–32
  5. Koch, I., Keller, P., & Prinz, W. (2004). The ideomotor approach to action control: Implications for skilled performance. International Journal of Sport and Exercise, 2, 362–375.
  6. Rosenbaum, D. A., Meulenbroek, R. G., Vaughan, J., & Jansen, C. (2001). Posture-based motion planning: Applications to grasping. Psychological Review, 108,709–734.
  7. Schack, T., & Hackfort, D. (2007). An action theory approach to applied sport psychology. In G. Tenenbaum & R. C. Eklund (Eds.), Handbook of sport psychology (3rd ed., pp. 332–351). Hoboken, NJ: Wiley.
  8. Schack, T., & Mechsner, F. (2006). Representation of motor skills in human long-term-memory. Neuroscience Letters, 391, 77–81.
  9. Schmidt, R. A. (1975). A schema theory of discrete motor skill learning. Psychological Review, 82,225–260.
  10. Zacks, J. M., Kumar, S., Abrams, R. A., & Mehta, R. (2009). Using movement and intentions to understand human activity. Cognition, 112, 201–216.

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