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.
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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:
- Adams, J. A. (1971). A closed-loop theory of motor learning. Journal of Motor Behavior, 3, 111–150.
- Anderson, J. R. (1976). Language, memory, and thought.Mahwah, NJ: Lawrence Erlbaum.
- Arbib, M., Conklin, E., & Hill, J. (1987). From schema theory to language. New York: Oxford University Press.
- French, K. E., & Thomas, J. R. (1987). The relation of knowledge development to children’s basketball performance. Journal of Sport Psychology, 9, 15–32
- 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.
- Rosenbaum, D. A., Meulenbroek, R. G., Vaughan, J., & Jansen, C. (2001). Posture-based motion planning: Applications to grasping. Psychological Review, 108,709–734.
- 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.
- Schack, T., & Mechsner, F. (2006). Representation of motor skills in human long-term-memory. Neuroscience Letters, 391, 77–81.
- Schmidt, R. A. (1975). A schema theory of discrete motor skill learning. Psychological Review, 82,225–260.
- 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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